Age of Water - User contributions [en]

2014 first Steering Committee Meeting

2014-12-01T13:23:27Z

Chris: /* Meeting Summary */

[[Category:Task]]

==Date and Venue==

The date for the first advisory committee meeting is Monday November 17, 2014. Rick Hooper and David Tarboton will meet with the team on Sunday from 11:30 noon to 4 pm. They both have prior commitments and will not make the main meeting on Monday.
The meeting will be held near the airport in Denver at:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary 
• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov agenda) 

'''Agenda: Monday 17 Nov, 2014'''

{| class="wikitable"
! Time
! Topic
|-
| {{nowrap|09:00am - 10:00am}}
|
'''Introductions''' 
• Introduce the steering committee and their background (15 min) 
• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)
|-
| {{nowrap|9:30am - 10:30am}}
|
'''Research Overview in Lake-Catchment Data-Models-Collaboration''' 
• Overview of catchment and age research (30 min) (Chris) 
• Overview of lake research (30) (Paul, Jordan) 
• Collaboration concept and overview of organic data science (30) (Yolanda)
|-
| {{nowrap|10:30am - 11:00am}}
|
''Coffee Break'' 
|-
| {{nowrap|11:00am - 12:00pm}}
|
'''Demonstrating the strategy''' 
• Example: Writing a research paper using the Organic Data Science Framework (online) (30) (Yolanda) 
• Training newcomers using the Organic Data Science Framework (online) (15) (Hilary)
• Example: Organizing a workshop using the Organic Data Science Framework (online) (15) (Hilary) 
|-
| {{nowrap|12:00pm - 1:15pm}}
|
''Lunch (~12 people)'' 
|-
| {{nowrap|1:15pm - 2:45pm}}
|
'''Demonstrating the strategy cont’d''' 
• Future work on the Organic Data Science Framework (online) (30) (Yolanda) 
• Jumpstarting new collaboration groups and new communities (30) (Hilary and Yolanda) 
• Creating cross-disciplinary models and data (30)
|-
| {{nowrap|2:45pm - 3:00pm}}
|
''Coffee Break'' 
|-
| {{nowrap|3:00pm - 4:00pm}}
|
'''Open discussion around questions:''' 
• What the INSPIRE Team would like from the Collaborators? 
• What the Collaborators would like from the Team? 
• Strategy for expanded participation?
|-
| {{nowrap|4:00pm - 4:30pm}}
|
''Feedback from Collaborators'' 
|-

| {{nowrap|4:30pm}}
| '''To airport for most, self organized dinner for those staying on'''
|}

==Invitation Letter==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==List of Collaborators==

* Supporting Regional Community DATA Infrastructure: '''Gordon Grant''', geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

* National Data Infrastructure and Social Networking: '''David Tarboton''', lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

* Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: '''Anthony Aufdenkampe''', lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Inspire Team on CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

* National Geospatial Data Infrastructure and the CUAHSI Water Data Center: '''Rick Hooper & Alva Couch''', Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

* National Data and Interoperability Standards: '''Brian Wee''', scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

* Data Publication, Discovery and Access Infrastructure: '''David Vieglais''' DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

* Towards a Shared Data Infrastructure across NSF Science Domains: '''Emily Stanley''' will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

* ETV infrastructure for high resolution precipitation isoscape (CONUS): '''Kei Yoshimura''': climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

* Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: '''Jordan Read''', USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.

==Meeting Summary==

Attending: Brian Wee, David Vieglas, Rick Hooper, Kei Yoshimura, Hilary Dugan, Jordan Read, David Tarboton,
Christopher Duffy, Yolanda Gil, and Paul Hanson



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
Progress=100|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Low}}

2014 first Steering Committee Meeting

2014-12-01T13:22:46Z

Chris: /* Meeting Summary */

[[Category:Task]]

==Date and Venue==

The date for the first advisory committee meeting is Monday November 17, 2014. Rick Hooper and David Tarboton will meet with the team on Sunday from 11:30 noon to 4 pm. They both have prior commitments and will not make the main meeting on Monday.
The meeting will be held near the airport in Denver at:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary 
• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov agenda) 

'''Agenda: Monday 17 Nov, 2014'''

{| class="wikitable"
! Time
! Topic
|-
| {{nowrap|09:00am - 10:00am}}
|
'''Introductions''' 
• Introduce the steering committee and their background (15 min) 
• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)
|-
| {{nowrap|9:30am - 10:30am}}
|
'''Research Overview in Lake-Catchment Data-Models-Collaboration''' 
• Overview of catchment and age research (30 min) (Chris) 
• Overview of lake research (30) (Paul, Jordan) 
• Collaboration concept and overview of organic data science (30) (Yolanda)
|-
| {{nowrap|10:30am - 11:00am}}
|
''Coffee Break'' 
|-
| {{nowrap|11:00am - 12:00pm}}
|
'''Demonstrating the strategy''' 
• Example: Writing a research paper using the Organic Data Science Framework (online) (30) (Yolanda) 
• Training newcomers using the Organic Data Science Framework (online) (15) (Hilary)
• Example: Organizing a workshop using the Organic Data Science Framework (online) (15) (Hilary) 
|-
| {{nowrap|12:00pm - 1:15pm}}
|
''Lunch (~12 people)'' 
|-
| {{nowrap|1:15pm - 2:45pm}}
|
'''Demonstrating the strategy cont’d''' 
• Future work on the Organic Data Science Framework (online) (30) (Yolanda) 
• Jumpstarting new collaboration groups and new communities (30) (Hilary and Yolanda) 
• Creating cross-disciplinary models and data (30)
|-
| {{nowrap|2:45pm - 3:00pm}}
|
''Coffee Break'' 
|-
| {{nowrap|3:00pm - 4:00pm}}
|
'''Open discussion around questions:''' 
• What the INSPIRE Team would like from the Collaborators? 
• What the Collaborators would like from the Team? 
• Strategy for expanded participation?
|-
| {{nowrap|4:00pm - 4:30pm}}
|
''Feedback from Collaborators'' 
|-

| {{nowrap|4:30pm}}
| '''To airport for most, self organized dinner for those staying on'''
|}

==Invitation Letter==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==List of Collaborators==

* Supporting Regional Community DATA Infrastructure: '''Gordon Grant''', geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

* National Data Infrastructure and Social Networking: '''David Tarboton''', lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

* Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: '''Anthony Aufdenkampe''', lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Inspire Team on CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

* National Geospatial Data Infrastructure and the CUAHSI Water Data Center: '''Rick Hooper & Alva Couch''', Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

* National Data and Interoperability Standards: '''Brian Wee''', scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

* Data Publication, Discovery and Access Infrastructure: '''David Vieglais''' DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

* Towards a Shared Data Infrastructure across NSF Science Domains: '''Emily Stanley''' will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

* ETV infrastructure for high resolution precipitation isoscape (CONUS): '''Kei Yoshimura''': climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

* Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: '''Jordan Read''', USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.

==Meeting Summary==

Attending: Brian Wee, David Vieglas, Rick Hooper, Kei Yoshimura, Hilary Dugan, Jordan Read, David Tarboton, Christopher Duffy, Yolanda Gil, and Paul Hanson



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
Progress=100|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Low}}

2014 first Steering Committee Meeting

2014-12-01T13:15:01Z

Chris:

[[Category:Task]]

==Date and Venue==

The date for the first advisory committee meeting is Monday November 17, 2014. Rick Hooper and David Tarboton will meet with the team on Sunday from 11:30 noon to 4 pm. They both have prior commitments and will not make the main meeting on Monday.
The meeting will be held near the airport in Denver at:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary 
• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov agenda) 

'''Agenda: Monday 17 Nov, 2014'''

{| class="wikitable"
! Time
! Topic
|-
| {{nowrap|09:00am - 10:00am}}
|
'''Introductions''' 
• Introduce the steering committee and their background (15 min) 
• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)
|-
| {{nowrap|9:30am - 10:30am}}
|
'''Research Overview in Lake-Catchment Data-Models-Collaboration''' 
• Overview of catchment and age research (30 min) (Chris) 
• Overview of lake research (30) (Paul, Jordan) 
• Collaboration concept and overview of organic data science (30) (Yolanda)
|-
| {{nowrap|10:30am - 11:00am}}
|
''Coffee Break'' 
|-
| {{nowrap|11:00am - 12:00pm}}
|
'''Demonstrating the strategy''' 
• Example: Writing a research paper using the Organic Data Science Framework (online) (30) (Yolanda) 
• Training newcomers using the Organic Data Science Framework (online) (15) (Hilary)
• Example: Organizing a workshop using the Organic Data Science Framework (online) (15) (Hilary) 
|-
| {{nowrap|12:00pm - 1:15pm}}
|
''Lunch (~12 people)'' 
|-
| {{nowrap|1:15pm - 2:45pm}}
|
'''Demonstrating the strategy cont’d''' 
• Future work on the Organic Data Science Framework (online) (30) (Yolanda) 
• Jumpstarting new collaboration groups and new communities (30) (Hilary and Yolanda) 
• Creating cross-disciplinary models and data (30)
|-
| {{nowrap|2:45pm - 3:00pm}}
|
''Coffee Break'' 
|-
| {{nowrap|3:00pm - 4:00pm}}
|
'''Open discussion around questions:''' 
• What the INSPIRE Team would like from the Collaborators? 
• What the Collaborators would like from the Team? 
• Strategy for expanded participation?
|-
| {{nowrap|4:00pm - 4:30pm}}
|
''Feedback from Collaborators'' 
|-

| {{nowrap|4:30pm}}
| '''To airport for most, self organized dinner for those staying on'''
|}

==Invitation Letter==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==List of Collaborators==

* Supporting Regional Community DATA Infrastructure: '''Gordon Grant''', geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

* National Data Infrastructure and Social Networking: '''David Tarboton''', lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

* Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: '''Anthony Aufdenkampe''', lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Inspire Team on CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

* National Geospatial Data Infrastructure and the CUAHSI Water Data Center: '''Rick Hooper & Alva Couch''', Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

* National Data and Interoperability Standards: '''Brian Wee''', scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

* Data Publication, Discovery and Access Infrastructure: '''David Vieglais''' DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

* Towards a Shared Data Infrastructure across NSF Science Domains: '''Emily Stanley''' will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

* ETV infrastructure for high resolution precipitation isoscape (CONUS): '''Kei Yoshimura''': climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

* Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: '''Jordan Read''', USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.

==Meeting Summary==



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
Progress=100|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Low}}

2014 first Steering Committee Meeting

2014-11-17T11:50:36Z

Chris: /* List of Collaborators */

[[Category:Task]]

==Date and Venue==

The date for the first advisory committee meeting is Monday November 17, 2014. Rick Hooper and David Tarboton will meet with the team on Sunday from 11:30 noon to 4 pm. They both have prior commitments and will not make the main meeting on Monday.
The meeting will be held near the airport in Denver at:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary 
• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov agenda) 

'''Agenda: Monday 17 Nov, 2014'''

{| class="wikitable"
! Time
! Topic
|-
| {{nowrap|09:00am - 10:00am}}
|
'''Introductions''' 
• Introduce the steering committee and their background (15 min) 
• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)
|-
| {{nowrap|9:30am - 10:30am}}
|
'''Research Overview in Lake-Catchment Data-Models-Collaboration''' 
• Overview of catchment and age research (30 min) (Chris) 
• Overview of lake research (30) (Paul, Jordan) 
• Collaboration concept and overview of organic data science (30) (Yolanda)
|-
| {{nowrap|10:30am - 11:00am}}
|
''Coffee Break'' 
|-
| {{nowrap|11:00am - 12:00pm}}
|
'''Demonstrating the strategy''' 
• Example: Writing a research paper using the Organic Data Science Framework (online) (30) (Yolanda) 
• Training newcomers using the Organic Data Science Framework (online) (30) (Hilary)
|-
| {{nowrap|12:00pm - 1:15pm}}
|
''Lunch (~12 people)'' 
|-
| {{nowrap|1:15pm - 2:45pm}}
|
'''Demonstrating the strategy cont’d''' 
• Example: Organizing a workshop using the Organic Data Science Framework (online) (30) (Hilary) 
• Collaborative research using the Organic Data Science Framework (online) (30) (Yolanda) 
• Creating cross-disciplinary models and data (30)
|-
| {{nowrap|2:45pm - 3:00pm}}
|
''Coffee Break'' 
|-
| {{nowrap|3:00pm - 4:00pm}}
|
'''Open discussion around questions:''' 
• What the INSPIRE Team would like from the Collaborators? 
• What the Collaborators would like from the Team? 
• Strategy for expanded participation?
|-
| {{nowrap|4:00pm - 4:30pm}}
|
''Feedback from Collaborators'' 
|-

| {{nowrap|4:30pm}}
| '''To airport for most, self organized dinner for those staying on'''
|}

==Invitation Letter==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==List of Collaborators==

* Supporting Regional Community DATA Infrastructure: '''Gordon Grant''', geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

* National Data Infrastructure and Social Networking: '''David Tarboton''', lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

* Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: '''Anthony Aufdenkampe''', lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Inspire Team on CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

* National Geospatial Data Infrastructure and the CUAHSI Water Data Center: '''Rick Hooper & Alva Couch''', Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

* National Data and Interoperability Standards: '''Brian Wee''', scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

* Data Publication, Discovery and Access Infrastructure: '''David Vieglais''' DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

* Towards a Shared Data Infrastructure across NSF Science Domains: '''Emily Stanley''' will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

* ETV infrastructure for high resolution precipitation isoscape (CONUS): '''Kei Yoshimura''': climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

* Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: '''Jordan Read''', USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

2014 first Steering Committee Meeting

2014-11-17T11:48:58Z

Chris: /* List of Collaborators */

[[Category:Task]]

==Date and Venue==

The date for the first advisory committee meeting is Monday November 17, 2014. Rick Hooper and David Tarboton will meet with the team on Sunday from 11:30 noon to 4 pm. They both have prior commitments and will not make the main meeting on Monday.
The meeting will be held near the airport in Denver at:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary 
• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov agenda) 

'''Agenda: Monday 17 Nov, 2014'''

{| class="wikitable"
! Time
! Topic
|-
| {{nowrap|09:00am - 10:00am}}
|
'''Introductions''' 
• Introduce the steering committee and their background (15 min) 
• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)
|-
| {{nowrap|9:30am - 10:30am}}
|
'''Research Overview in Lake-Catchment Data-Models-Collaboration''' 
• Overview of catchment and age research (30 min) (Chris) 
• Overview of lake research (30) (Paul, Jordan) 
• Collaboration concept and overview of organic data science (30) (Yolanda)
|-
| {{nowrap|10:30am - 11:00am}}
|
''Coffee Break'' 
|-
| {{nowrap|11:00am - 12:00pm}}
|
'''Demonstrating the strategy''' 
• Example: Writing a research paper using the Organic Data Science Framework (online) (30) (Yolanda) 
• Training newcomers using the Organic Data Science Framework (online) (30) (Hilary)
|-
| {{nowrap|12:00pm - 1:15pm}}
|
''Lunch (~12 people)'' 
|-
| {{nowrap|1:15pm - 2:45pm}}
|
'''Demonstrating the strategy cont’d''' 
• Example: Organizing a workshop using the Organic Data Science Framework (online) (30) (Hilary) 
• Collaborative research using the Organic Data Science Framework (online) (30) (Yolanda) 
• Creating cross-disciplinary models and data (30)
|-
| {{nowrap|2:45pm - 3:00pm}}
|
''Coffee Break'' 
|-
| {{nowrap|3:00pm - 4:00pm}}
|
'''Open discussion around questions:''' 
• What the INSPIRE Team would like from the Collaborators? 
• What the Collaborators would like from the Team? 
• Strategy for expanded participation?
|-
| {{nowrap|4:00pm - 4:30pm}}
|
''Feedback from Collaborators'' 
|-

| {{nowrap|4:30pm}}
| '''To airport for most, self organized dinner for those staying on'''
|}

==Invitation Letter==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==List of Collaborators==

* Supporting Regional Community DATA Infrastructure: '''Gordon Grant''', geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

* National Data Infrastructure and Social Networking: '''David Tarboton''', lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

* Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: '''Anthony Aufdenkampe''', lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

* National Geospatial Data Infrastructure and the CUAHSI Water Data Center: '''Rick Hooper & Alva Couch''', Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

* National Data and Interoperability Standards: '''Brian Wee''', scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

* Data Publication, Discovery and Access Infrastructure: '''David Vieglais''' DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

* Towards a Shared Data Infrastructure across NSF Science Domains: '''Emily Stanley''' will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

* ETV infrastructure for high resolution precipitation isoscape (CONUS): '''Kei Yoshimura''': climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

* Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: '''Jordan Read''', USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

2014 first Steering Committee Meeting

2014-11-17T11:46:20Z

Chris: Chris moved page 2014 first Advisory Committee Meeting to 2014 first Steering Committee Meeting without leaving a redirect: Moving page

[[Category:Task]]

==Date and Venue==

The date for the first advisory committee meeting is Monday November 17, 2014. Rick Hooper and David Tarboton will meet with the team on Sunday from 11:30 noon to 4 pm. They both have prior commitments and will not make the main meeting on Monday.
The meeting will be held near the airport in Denver at:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary 
• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov agenda) 

'''Agenda: Monday 17 Nov, 2014'''

{| class="wikitable"
! Time
! Topic
|-
| {{nowrap|09:00am - 10:00am}}
|
'''Introductions''' 
• Introduce the steering committee and their background (15 min) 
• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)
|-
| {{nowrap|9:30am - 10:30am}}
|
'''Research Overview in Lake-Catchment Data-Models-Collaboration''' 
• Overview of catchment and age research (30 min) (Chris) 
• Overview of lake research (30) (Paul, Jordan) 
• Collaboration concept and overview of organic data science (30) (Yolanda)
|-
| {{nowrap|10:30am - 11:00am}}
|
''Coffee Break'' 
|-
| {{nowrap|11:00am - 12:00pm}}
|
'''Demonstrating the strategy''' 
• Example: Writing a research paper using the Organic Data Science Framework (online) (30) (Yolanda) 
• Training newcomers using the Organic Data Science Framework (online) (30) (Hilary)
|-
| {{nowrap|12:00pm - 1:15pm}}
|
''Lunch (~12 people)'' 
|-
| {{nowrap|1:15pm - 2:45pm}}
|
'''Demonstrating the strategy cont’d''' 
• Example: Organizing a workshop using the Organic Data Science Framework (online) (30) (Hilary) 
• Collaborative research using the Organic Data Science Framework (online) (30) (Yolanda) 
• Creating cross-disciplinary models and data (30)
|-
| {{nowrap|2:45pm - 3:00pm}}
|
''Coffee Break'' 
|-
| {{nowrap|3:00pm - 4:00pm}}
|
'''Open discussion around questions:''' 
• What the INSPIRE Team would like from the Collaborators? 
• What the Collaborators would like from the Team? 
• Strategy for expanded participation?
|-
| {{nowrap|4:00pm - 4:30pm}}
|
''Feedback from Collaborators'' 
|-

| {{nowrap|4:30pm}}
| '''To airport for most, self organized dinner for those staying on'''
|}

==Invitation Letter==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==List of Collaborators==

* Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

* National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

* Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

* National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

* National Data and Interoperability Standards: Brian Wee, scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

* Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

* Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

* ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

* Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

Engage broader community

2014-11-17T11:46:20Z

Chris: Deleted PropertyValue: SubTask = 2014_first_Advisory_Committee_Meeting

[[Category:Task]]


{{#set:
Owner=Hilary_Dugan|
StartDate=2014-07-01|
SubTask=G16_Workshop_on_Modeling_the_Age_of_Water|
SubTask=Present_framework_at_Science_in_the_Northwoods|
TargetDate=2016-12-31|
Type=Medium}}

Engage broader community

2014-11-17T11:46:20Z

Chris: Added PropertyValue: SubTask = 2014_first_Steering_Committee_Meeting

2014 first Steering Committee Meeting

2014-11-04T16:22:42Z

Chris:

[[Category:Task]]

==Date and Venue==

The date for the first advisory committee meeting is Monday November 17, 2014. Rick Hooper and David Tarboton will meet with the team on Sunday from 11:30 noon to 4 pm. They both have prior commitments and will not make the main meeting on Monday.
The meeting will be held near the airport in Denver at:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Tentative Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary 
• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov aagenda) 

'''Agenda: Monday 17 Nov, 2014'''

9:00 am Introductions

• Introduce the steering committee and their background (15 min) 
• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)

9:30-10:30 Research Overview in Lake-Catchment Data-Models-Collaboration

• Collaboration concept and overview of organic data science (30) (Yolanda) 
• Overview of catchment and age research (30 min) (Chris) 
• Overview of lake research (30) Paul, Jordan)

10:30-11:00 Coffee break

11:00-12:00 Demonstrating the strategy

• Navigating/using the Organic Data Science website (online) (30) (Yolanda) 
• A “Task-Centered” example (30) (Hilary)

12:00-1:15 Lunch (~12 people)

1:15-2:45 Demonstrating the strategy cont’d

• Training users (30) 
• Writing a research paper (30) 
• Creating cross-disciplinary models and data (30)

2:45-3:00 Coffee break

3:00-4:00 Open discussion around questions:

• What the INSPIRE Team would like from the Collaborators? 
• What the Collaborators would like from the Team? 
• Strategy for expanded participation?

4:00-4:30 Feedback from Collaborators

To airport for most, self organized dinner for those staying on.

==Invitation==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==List of Collaborators==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

Engage broader community

2014-11-04T16:13:41Z

Chris: Added PropertyValue: SubTask = 2014_first_Advisory_Committee_Meeting

[[Category:Task]]


{{#set:
Owner=Hilary_Dugan|
StartDate=2014-07-01|
SubTask=G16_Workshop_on_Modeling_the_Age_of_Water|
SubTask=2014_first_Advisory_Committee_Meeting|
TargetDate=2016-12-31|
Type=Medium}}

2014 first Steering Committee Meeting

2014-11-04T16:13:40Z

Chris: Chris moved page Set up first advisory committee meeting to 2014 first Advisory Committee Meeting without leaving a redirect: Moving page

[[Category:Task]]

==Date and Venue==

The date for the first advisory committee meeting is Monday November 17, 2014. Rick Hooper and David Tarboton will meet with the team on Sunday from 11:30 noon to 4 pm. They both have prior commitments and will not make the main meeting on Monday.
The meeting will be held near the airport in Denver at:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Tentative Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary.

• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov aagenda).

'''Agenda: Monday 17 Nov, 2014'''

9:00 am Introductions

• Introduce the steering committee and their background (15 min)

• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)

9:30-10:30 Research Overview in Lake-Catchment Data-Models-Collaboration

• Collaboration concept and overview of organic data science (30) (Yolanda)

• Overview of catchment and age research (30 min) (Chris)

• Overview of lake research (30) Paul, Jordan)

10:30-11:00 Coffee break

11:00-12:00 Demonstrating the strategy

• Navigating/using the Organic Data Science website (online) (30) (Yolanda)

• A “Task-Centered” example (30) (Hilary)

12:00-1:15 Lunch (~12 people)

1:15-2:45 Demonstrating the strategy cont’d

• Training users (30)

• Writing a research paper (30)

• Creating cross-disciplinary models and data (30)

2:45-3:00 Coffee break

3:00-4:00 Open discussion around questions:

• What the INSPIRE Team would like from the Collaborators?

• What the Collaborators would like from the Team?

• Strategy for expanded participation?

4:00-4:30 Feedback from Collaborators

To airport for most, self organized dinner for those staying on.

==Invitation==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==List of Collaborators==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

Engage broader community

2014-11-04T16:13:40Z

Chris: Deleted PropertyValue: SubTask = Set_up_first_advisory_committee_meeting

2014 first Steering Committee Meeting

2014-11-04T16:12:26Z

Chris: /* Tentative Agenda */

[[Category:Task]]

==Date and Venue==

The date for the first advisory committee meeting is Monday November 17, 2014. Rick Hooper and David Tarboton will meet with the team on Sunday from 11:30 noon to 4 pm. They both have prior commitments and will not make the main meeting on Monday.
The meeting will be held near the airport in Denver at:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Tentative Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary.

• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov aagenda).

'''Agenda: Monday 17 Nov, 2014'''

9:00 am Introductions

• Introduce the steering committee and their background (15 min)

• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)

9:30-10:30 Research Overview in Lake-Catchment Data-Models-Collaboration

• Collaboration concept and overview of organic data science (30) (Yolanda)

• Overview of catchment and age research (30 min) (Chris)

• Overview of lake research (30) Paul, Jordan)

10:30-11:00 Coffee break

11:00-12:00 Demonstrating the strategy

• Navigating/using the Organic Data Science website (online) (30) (Yolanda)

• A “Task-Centered” example (30) (Hilary)

12:00-1:15 Lunch (~12 people)

1:15-2:45 Demonstrating the strategy cont’d

• Training users (30)

• Writing a research paper (30)

• Creating cross-disciplinary models and data (30)

2:45-3:00 Coffee break

3:00-4:00 Open discussion around questions:

• What the INSPIRE Team would like from the Collaborators?

• What the Collaborators would like from the Team?

• Strategy for expanded participation?

4:00-4:30 Feedback from Collaborators

To airport for most, self organized dinner for those staying on.

==Invitation==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==List of Collaborators==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

2014 first Steering Committee Meeting

2014-11-04T16:11:07Z

Chris: /* INVITATION */

[[Category:Task]]

==Date and Venue==

The date for the first advisory committee meeting is Monday November 17, 2014. Rick Hooper and David Tarboton will meet with the team on Sunday from 11:30 noon to 4 pm. They both have prior commitments and will not make the main meeting on Monday.
The meeting will be held near the airport in Denver at:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Tentative Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary.
• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov aagenda).

'''Agenda: Monday 17 Nov, 2014'''

9:00 am Introductions

• Introduce the steering committee and their background (15 min)
• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)

9:30-10:30 Research Overview in Lake-Catchment Data-Models-Collaboration

• Collaboration concept and overview of organic data science (30) (Yolanda)
• Overview of catchment and age research (30 min) (Chris)
• Overview of lake research (30) Paul, Jordan)

10:30-11:00 Coffee break

11:00-12:00 Demonstrating the strategy
• Navigating/using the Organic Data Science website (online) (30) (Yolanda)
• A “Task-Centered” example (30) (Hilary)

12:00-1:15 Lunch (~12 people)

1:15-2:45 Demonstrating the strategy cont’d

• Training users (30)
• Writing a research paper (30)
• Creating cross-disciplinary models and data (30)

2:45-3:00 Coffee break

3:00-4:00 Open discussion around questions:

• What the INSPIRE Team would like from the Collaborators?
• What the Collaborators would like from the Team?
• Strategy for expanded participation?

4:00-4:30 Feedback from Collaborators

To airport for most, self organized dinner for those staying on.

==Invitation==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==List of Collaborators==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

2014 first Steering Committee Meeting

2014-11-04T16:10:18Z

Chris: /* Tentative Agenda */

[[Category:Task]]

==Date and Venue==

The date for the first advisory committee meeting is Monday November 17, 2014. Rick Hooper and David Tarboton will meet with the team on Sunday from 11:30 noon to 4 pm. They both have prior commitments and will not make the main meeting on Monday.
The meeting will be held near the airport in Denver at:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Tentative Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary.
• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov aagenda).

'''Agenda: Monday 17 Nov, 2014'''

9:00 am Introductions

• Introduce the steering committee and their background (15 min)
• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)

9:30-10:30 Research Overview in Lake-Catchment Data-Models-Collaboration

• Collaboration concept and overview of organic data science (30) (Yolanda)
• Overview of catchment and age research (30 min) (Chris)
• Overview of lake research (30) Paul, Jordan)

10:30-11:00 Coffee break

11:00-12:00 Demonstrating the strategy
• Navigating/using the Organic Data Science website (online) (30) (Yolanda)
• A “Task-Centered” example (30) (Hilary)

12:00-1:15 Lunch (~12 people)

1:15-2:45 Demonstrating the strategy cont’d

• Training users (30)
• Writing a research paper (30)
• Creating cross-disciplinary models and data (30)

2:45-3:00 Coffee break

3:00-4:00 Open discussion around questions:

• What the INSPIRE Team would like from the Collaborators?
• What the Collaborators would like from the Team?
• Strategy for expanded participation?

4:00-4:30 Feedback from Collaborators

To airport for most, self organized dinner for those staying on.

==INVITATION==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==List of Collaborators==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

2014 first Steering Committee Meeting

2014-11-04T16:09:27Z

Chris: /* DATE and VENUE */

[[Category:Task]]

==Date and Venue==

The date for the first advisory committee meeting is Monday November 17, 2014. Rick Hooper and David Tarboton will meet with the team on Sunday from 11:30 noon to 4 pm. They both have prior commitments and will not make the main meeting on Monday.
The meeting will be held near the airport in Denver at:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Tentative Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary
• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov aagenda).

'''Agenda: Monday 17 Nov, 2014'''

9:00 am Introductions

• Introduce the steering committee and their background (15 min)
• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)

9:30-10:30 Research Overview in Lake-Catchment Data-Models-Collaboration

• Collaboration concept and overview of organic data science (30) (Yolanda)
• Overview of catchment and age research (30 min) (Chris)
• Overview of lake research (30) Paul, Jordan)

10:30-11:00 Coffee break

11:00-12:00 Demonstrating the strategy
• Navigating/using the Organic Data Science website (online) (30) (Yolanda)
• A “Task-Centered” example (30) (Hilary)

12:00-1:15 Lunch (~12 people)

1:15-2:45 Demonstrating the strategy cont’d

• Training users (30)
• Writing a research paper (30)
• Creating cross-disciplinary models and data (30)

2:45-3:00 Coffee break

3:00-4:00 Open discussion around questions:

• What the INSPIRE Team would like from the Collaborators?
• What the Collaborators would like from the Team?
• Strategy for expanded participation?

4:00-4:30 Feedback from Collaborators

To airport for most, self organized dinner for those staying on.

==INVITATION==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==List of Collaborators==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

2014 first Steering Committee Meeting

2014-11-04T16:06:23Z

Chris: /* LIST OF COLLABORATORS */

[[Category:Task]]

==DATE and VENUE==

The date for the first advisory committee meeting is Monday November 17, 2014. The meeting will be held in Denver CO.

The meeting will be held:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Tentative Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary
• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov aagenda).

'''Agenda: Monday 17 Nov, 2014'''

9:00 am Introductions

• Introduce the steering committee and their background (15 min)
• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)

9:30-10:30 Research Overview in Lake-Catchment Data-Models-Collaboration

• Collaboration concept and overview of organic data science (30) (Yolanda)
• Overview of catchment and age research (30 min) (Chris)
• Overview of lake research (30) Paul, Jordan)

10:30-11:00 Coffee break

11:00-12:00 Demonstrating the strategy
• Navigating/using the Organic Data Science website (online) (30) (Yolanda)
• A “Task-Centered” example (30) (Hilary)

12:00-1:15 Lunch (~12 people)

1:15-2:45 Demonstrating the strategy cont’d

• Training users (30)
• Writing a research paper (30)
• Creating cross-disciplinary models and data (30)

2:45-3:00 Coffee break

3:00-4:00 Open discussion around questions:

• What the INSPIRE Team would like from the Collaborators?
• What the Collaborators would like from the Team?
• Strategy for expanded participation?

4:00-4:30 Feedback from Collaborators

To airport for most, self organized dinner for those staying on.

==INVITATION==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==List of Collaborators==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

2014 first Steering Committee Meeting

2014-11-04T16:04:57Z

Chris: /* Tentative Agenda */

[[Category:Task]]

==DATE and VENUE==

The date for the first advisory committee meeting is Monday November 17, 2014. The meeting will be held in Denver CO.

The meeting will be held:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Tentative Agenda==

'''Agenda: Sunday 16 Nov, 2014'''

• 12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary
• 1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov aagenda).

'''Agenda: Monday 17 Nov, 2014'''

9:00 am Introductions

• Introduce the steering committee and their background (15 min)
• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)

9:30-10:30 Research Overview in Lake-Catchment Data-Models-Collaboration

• Collaboration concept and overview of organic data science (30) (Yolanda)
• Overview of catchment and age research (30 min) (Chris)
• Overview of lake research (30) Paul, Jordan)

10:30-11:00 Coffee break

11:00-12:00 Demonstrating the strategy
• Navigating/using the Organic Data Science website (online) (30) (Yolanda)
• A “Task-Centered” example (30) (Hilary)

12:00-1:15 Lunch (~12 people)

1:15-2:45 Demonstrating the strategy cont’d

• Training users (30)
• Writing a research paper (30)
• Creating cross-disciplinary models and data (30)

2:45-3:00 Coffee break

3:00-4:00 Open discussion around questions:

• What the INSPIRE Team would like from the Collaborators?
• What the Collaborators would like from the Team?
• Strategy for expanded participation?

4:00-4:30 Feedback from Collaborators

To airport for most, self organized dinner for those staying on.

==INVITATION==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==LIST OF COLLABORATORS==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

2014 first Steering Committee Meeting

2014-11-04T16:02:00Z

Chris: /* Tentative Agenda */

[[Category:Task]]

==DATE and VENUE==

The date for the first advisory committee meeting is Monday November 17, 2014. The meeting will be held in Denver CO.

The meeting will be held:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Tentative Agenda==

Agenda: Sunday 16 Nov, 2014

12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary

1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov aagenda).

Agenda: Monday 16 Nov, 2014

9:00 am Introductions

• Introduce the steering committee and their background (15 min)
• Introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)

9:30-10:30 Research Overview in Lake-Catchment Data-Models-Collaboration

• Overview of catchment and age research (30 min) (Chris)
• Overview of lake research (30) Paul, Jordan)
• Collaboration concept and overview of organic data science (30) (Yolanda)

10:30-11:00 Coffee break

11:00-12:00 Demonstrating the strategy
• Navigating/using the Organic Data Science website (online) (30) (Yolanda)
• A “Task-Centered” example (30) (Hilary)

12:00-1:15 Lunch (~12 people)

1:15-2:45 Demonstrating the strategy cont’d

• Training users (30)
• Writing a research paper (30)
• Creating cross-disciplinary models and data (30)

2:45-3:00 Coffee break

3:00-4:00 Open discussion around questions:

• What the INSPIRE Team would like from the Collaborators?
• What the Collaborators would like from the Team?
• Strategy for expanded participation?

4:00-4:30 Feedback from Collaborators

To airport for most, self organized dinner for those staying on.

==INVITATION==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==LIST OF COLLABORATORS==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

2014 first Steering Committee Meeting

2014-11-04T15:57:04Z

Chris:

[[Category:Task]]

==DATE and VENUE==

The date for the first advisory committee meeting is Monday November 17, 2014. The meeting will be held in Denver CO.

The meeting will be held:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==Tentative Agenda==

Agenda: Sunday 16 Nov, 2014

12 noon lunch reservations at hotel restaurant for Rick, David, Chris, Paul and Hilary

1-4 pm Briefing on INSPIRE Age of Water project and Organic Data Science Collaboration software (abbreviated but following 17 Nov aagenda).

Agenda: Monday 16 Nov, 2014

9am Introductions
• Introduce the steering committee and their background (15 min)
• Paul and Chris introduce the PI’s and the motivation for the research and describe what we intend to accomplish (15)

9:30-10:30 Research Overview in Lake-Catchment Data-Models-Collaboration
• Chris overview of catchment and age research (30 min)
• Paul and Jordan overview of lake research (30)
• Collaboration concept and overview of organic data science (30)

10:30-11:00 Coffee break

11:00-12:00 Demonstrating the strategy
• Navigating/using the Organic Data Science website (online) (30)
• A “Task-Centered” example (30)

12:00-1:15 Lunch (~12 people)

1:15-2:45 Demonstrating the strategy cont’d
• Training users (30)
• Writing a research paper (30)
• Creating cross-disciplinary models and data (30)

2:45-3:00 Coffee break

3:00-4:00 Open discussion around questions:
• What the INSPIRE Team would like from the Collaborators?
• What the Collaborators would like from the Team?
• Strategy for expanded participation?

4:00-4:30 Feedback from Collaborators

To airport for most, self organized dinner for those staying on.

==INVITATION==

October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==LIST OF COLLABORATORS==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

2014 first Steering Committee Meeting

2014-11-04T15:54:48Z

Chris: /* DATE */

[[Category:Task]]

==DATE and VENUE==

The date for the first advisory committee meeting is Monday November 17, 2014. The meeting will be held in Denver CO.

The meeting will be held:

DoubleTree by Hilton Hotel Denver
3203 Quebec Street, Denver, Colorado, 80207, USA
TEL: +1-303-321-3333
FAX: 1-303-329-5233
http://doubletree3.hilton.com/en/hotels/colorado/doubletree-by-hilton-hotel-denver-RLDV-DT/index.html

==INVITATION==
October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==LIST OF COLLABORATORS==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

2014 first Steering Committee Meeting

2014-11-04T15:51:40Z

Chris: /* LIST OF COLLABORATORS */

[[Category:Task]]

==DATE==

The date for the first advisory committee meeting is Monday November 17, 2014. The meeting will be held in Denver CO.

==INVITATION==
October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==LIST OF COLLABORATORS==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, scientist with NEON (the National Ecological Observing Network) will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
StartDate=2014-10-01|
TargetDate=2014-11-24|
Type=Medium}}

Versions of word draft

2014-10-07T15:59:38Z

Chris: Set PropertyValue: Owner = Xuan Yu

The paper is in response to the call for paper of IEEE System Journal [http://www.ieeesystemsjournal.org/call-for-papers/special-issue-esm/]
The first version of the draft is here.[http://www.organicdatascience.org/images/6/6f/Reanalysis_XY_v1.doc (v1)][[Category:Task]]


{{#set:|
Owner=Xuan_Yu}}

Outline

2014-10-07T15:21:01Z

Chris:

[[Category:Task]]

1. Introduction and Motivation

2. Historical Research at Shale Hills

3. Current Model-Data Integration

4. Data Sharing Prototype

5. Interdisciplinary Research Implications

6. Collaborative data sharing facilitating watershed reanalysis

7. Summary and Conclusions

8. References



{{#set:
Owner=Chris_Duffy|
Participants=Chris_Duffy|
Participants=Gopal_Bhatt|
Participants=Yolanda_Gil}}

IEEE paper application of catchment reanalysis

2014-10-07T15:10:02Z

Chris: Set PropertyValue: Progress = 10

[[Category:Task]]


{{#set:
Owner=Xuan_Yu|
Participants=Chris_Duffy|
Progress=10|
StartDate=2014-10-01|
SubTask=Outline|
SubTask=Abstract|
TargetDate=2014-11-10}}

IEEE paper application of catchment reanalysis

2014-10-07T15:09:50Z

Chris: Set PropertyValue: TargetDate = 2014-11-10

[[Category:Task]]


{{#set:
Owner=Xuan_Yu|
Participants=Chris_Duffy|
StartDate=2014-10-01|
SubTask=Outline|
SubTask=Abstract|
TargetDate=2014-11-10}}

IEEE paper application of catchment reanalysis

2014-10-07T15:09:32Z

Chris: Set PropertyValue: StartDate = 2014-10-01

IEEE paper application of catchment reanalysis

2014-10-07T15:09:06Z

Chris: Added PropertyValue: Participants = Chris Duffy

[[Category:Task]]


{{#set:
Owner=Xuan_Yu|
Participants=Chris_Duffy|
SubTask=Outline|
SubTask=Abstract}}

IEEE paper application of catchment reanalysis

2014-10-07T15:08:21Z

Chris: Set PropertyValue: Owner = Xuan Yu

[[Category:Task]]


{{#set:
Owner=Xuan_Yu|
SubTask=Outline}}

Document the PIHM catchment model

2014-10-07T15:02:59Z

Chris:

[[Category:Task]]

= Overview =
:The Penn State Integrated Hydrologic Model (PIHM) is a multiprocess, multi-scale hydrologic model where the major hydrological processes are fully coupled using the semi-discrete finite volume method. PIHM represents our strategy for the synthesis of multi-state, multiscale distributed hydrologic models using the integral representation of the underlying physical process equations and state variables. Our interest is in devising a concise representation of watershed and/or river basin hydrodynamics, which allows interactions among major physical processes operating simultaneously, but with the flexibility to add or eliminate states/processes/constitutive relations depending on the objective of the numerical experiment or purpose of the scientific or operational application.

:The PIHM Modeling System was initially developed under research grants to The Pennsylvania State University (Penn State) from NSF (EAR 9876800, 1999-2007; EAR 03-10122, 2003-2007), NOAA (NA040AR4310085, 2003-2007), NASA (NAG5-12611, 2002-2005), with continuing grants from NSF (0725019) Critical Zone Observatory and EPA for community model development.

:Penn State University makes no proprietary claims, either statutory or otherwise, to this version and release of PIHM and considers PIHM to be in the public domain for use by any person or entity for any purpose without any fee or charge. We request that any PIHM user include a credit to Penn State in any publications that result from the use of PIHM. The names Penn State shall not be used or referenced in any advertising or publicity which endorses or promotes any products or commercial entity associated with or using PIHM, or any derivative works thereof, without the written authorization of Penn State University. 

:PIHM is provided on an "AS IS" basis and any warranties, either express or implied, including but not limited to implied warranties of noninfringement, originality, merchantability and fitness for a particular purpose, are disclaimed. Penn State will not be obligated to provide the user with any support, consulting, training or assistance of any kind with regard to the use, operation and performance of PIHM nor to provide the user with any updates, revisions, new versions, error corrections or "bug" fixes. In no event will Penn State be liable for any damages, whatsoever, whether direct, indirect, consequential or special, which may result from an action in contract, negligence or other claim that arises out of or in connection with the access, use or performance of PIHM, including infringement actions.

= Concept =

:The Penn State Integrated Hydrologic Model (PIHM) is a fully coupled multiprocess hydrologic model. Instead of coupling through artificial boundary conditions, major hydrological processes are fully coupled by the semi-discrete finite volume approach. For those processes whose governing equations are partial differential equations (PDE), we first discretize in space via the finite volume method. This results in a system of ordinary differential equations (ODE) representing those procesess within the control volume. Within the same control volume, combining other processes whose governing equations are ODE’s, (e.g. the snow accumulation and melt process), a local ODE system is formed for the complete dynamics of the finite volume. After assembling the local ODE system throughout the entire domain, the global ODE system is formed and solved by a state-of-art ODE solver.

:The approach is based on the semi-discrete finite-volume method (FVM) which represents a system of coupled partial differential equations (e.g. groundwater-surface water, overland flow-infiltration, etc.) in integral form, as a spatially-discrete system of ordinary differential equations. Domain discretization is fundamental to the approach and an unstructured triangular irregular network (e.g. Delaunay triangles) is generated with constraints (geometric, and parametric) using TRIANGLE. A local prismatic control volume is formed by vertical projection of the Delauney triangles forming each layer of the model. Given a set of constraints (e.g. river network support, watershed boundary, altitude zones, ecological regions, hydraulic properties, climate zones, etc), an “optimal” mesh is generated. River volume elements are also prismatic, with trapezoidal or rectangular cross-section, and are generated along edges of river triangles. The local control volume contains all equations to be solved and is referred to as the model kernel. The global ODE system is assembled by combining all local ODE systems throughout the domain and then solved by a state-of-the-art parallel ODE solver known as CVODE developed at the Lawrence- Livermore National Laboratory.

= Distributed Modeling with PIHM =

:PIHM has incorporated channel routing, surface overland flow, and subsurface flow together with interception, snow melt and evapotranspiration using the semi-discrete approach with FVM. Table 1 shows all these processes along with the original and reduced governing equations. For channel routing and overland flow which is governed by St. Venant equations, both kinematic wave and diffusion wave approximation are included. For saturated groundwater flow, the 2-D Dupuit approximation is applied. For unsaturated flow, either shallow groundwater assumption in which unsaturated soil moisture is dependent on groundwater level or 1-D vertical integrated form of Richards’s equation can be applied. From physical arguments, it is necessary to fully couple channel routing, overland flow and subsurface flow in the ODE solver. Snowmelt, vegetation and evapotranspiration are assumed to be weakly coupled. That is, these processes are calculated at end of each time step, which is automatically selected within a user specified range in the ODE solver.

PIHM_Processes

:The Penn State Integrated Hydrologic Model (PIHM) is a finite volume code that couples process-level equations for channel routing, surface overland flow, and subsurface flow together with interception storage and through fall, snow melt and evapotranspiration using the semi-discrete formulation and implicit solver. Table 1 shows all these processes along with the original and reduced governing equations. For channel routing and overland flow which is governed by St. Venant equations, both kinematic wave and diffusion wave approximation are included. For saturated groundwater flow, the 2-D Dupuit approximation is applied. For unsaturated flow, either shallow groundwater assumption in which unsaturated soil moisture is dependent on groundwater level or 1-D vertical integrated form of Richards’s equation can be applied. From physical arguments, it is necessary to fully couple channel routing, overland flow and subsurface flow in the ODE solver. Snowmelt, vegetation and evapotranspiration are assumed to be weakly coupled. That is, these processes are calculated at end of each time step, which is automatically selected within a user specified range in the ODE solver.

PIHMgis

:PIHMgis is an open source, “tightly-coupled” GIS interface to PIHM code. PIHMgis is platform independent and extensible. The tight coupling between GIS and the model is achieved by developing a shared data-model and hydrologic-model data structure for the deal-top. Details of PIHMgis are found by clicking on the link [[http://www.pihm.psu.edu]]

= Distributed Data System =

:The HydroTerre Data System [http://www.hydroterre.psu.edu] is data infrastructure that enables research on water model development on a national scale. It represents a robust, reusable, and extensible framework of data management building blocks, and demonstrate the utility of these infrastructure tools that scale over geo-spatial extent: rivers, river basins, and systems of rivers. HydroTerre aggregates and pre-processes essential terrestrial variable data from federal agencies at different geo-spatial resolutions and over varying temporal scales; it improve access to federal data; make community data resources available via federation; and can interface with other community activities (e.g CUAHSI Hydroshare) to provide registration of new community data sets and discovery and access. HTDS has specialized server architecture that utilizes 2U and 4U servers with 24-48 cpu’s and up to 100 TB of data per server. The configuration greater enhances model-data accessibility and scalability during larger river basin simulations. HydroTerre is a component of the Penn State Institute for CyberScience (ICS) and has been developed with support from ICS, the Penn State Institute for Energy and the Environment, the World Universities Network, NOAA, NASA and EPA. You can get to the HydroTerre site from here. [[http://www.hydroterre.psu.edu]]

= Model Applications=

The Shale Hills Critical Zone Observatory, PA 

Geography: The Shale Hills CZO is a small, forested, upland catchment in Central PA near the Penn State University Park Campus. The observatory is highly instrumented and serves real-time data to the National CZO Program. The observatory lies within the Valley and Ridge Physiographic Province of the central Appalachian Mountains in Huntingdon County, Pennsylvania (40º39’52. 39”N 77º54’24.23”W). It is a first order, V-shaped basin characterized by relatively steep slopes (25-35%) and narrow ridges. The stream is a tributary of Shavers Creek that eventually discharges into the Juniata River, a part of the Susquehanna River Basin. The SSHO basin is oriented in an east-west direction and the major side slopes have almost true north and south facing aspects. Elevation ranges from 256 meters at the outlet to 310 meters at the highest ridge. The relatively uniform side slopes are periodically interrupted by seven distinct topographic depressions.

Climate/Meteorology: Shale Hills is situated in a humid continental climate. Temperatures average 9.5°C with large seasonal differences: January temperature is –5.4°C, July is 19.0°C. The highest temperature recorded is 33.5°C (April 27, 2009) lowest –24.8°C (January 17, 2009). Annual average relative humidity is 70.2%.

Land Use: Historically, the region was logged for charcoal to support a 19th and 20th century iron industry. Today, Shale Hills is a relatively pristine forest and good wildlife habitat with little human impact. The basin is primarily available for recreation, education and research. The Penn State forest, of which the basin is a part, is managed for timber with set-asides for research. There are a number of active PSU research projects within the Penn State Forest.
[[File:CZO_Obs_12.png|thumb|Figure 1: Shale Hill CZO Field Observations, Sep 2012]]
Ecosystem Types: The Shale Hills forest ecosystem is dominated by oak (Quercus), hickory (Carya) and pine (Pinus) species. Hemlock (Tsuga canadensis), red maple (Acer rubrum), white oak (Quercus alba) and white pine (Pinus strobus) line the deep, moist soils of the stream banks, while on the drier, shallower north and south-facing slopes, red oak (Quercus rubra), chestnut oak (Quercus prinus), pignut hickory (Carya glabra) and mockernut hickory (Carya tomentosa) are dominant, with Virginia pine (Pinus virginiana) only appearing on the north-facing ridge tops.

Observations: The Shale Hills watershed has a comprehensive base of instrumentation for physical, chemical and biological characterization of water, energy, stable isotopes and geochemical conditions. This includes a dense network of soil moisture observations at multiple depths (120), a shallow observation well network (24 wells), soil lysimeters at multiple depths (+80), a COSMOS soil moisture instrument, a research weather station including eddy flux measurements for latent and sensible heat flux, CO2, and water vapor, radiation, barometric pressure, temperature, relative humidity, wind speed/direction, snow depth sensors, leaf wetness sensors, a load cell precipitation gauge. A laser precipitation monitor (LPM: rain, sleet, hail, snow, etc.) was installed in 2008, as were automated water samplers (daily) for precipitation, groundwater, and stream water for chemistry and stable isotopes with weekly sampling of lysimeters. Arrays of sapflow measurements are carried out over several years as a function of tree species (25 species in the watershed). A 25 node multi-hop wireless sensor network has been deployed for real-time observations of soil moisture, groundwater level, ground temperature. As of Sep 2012 the network is demonstrated in the figure.

Simulating the Water Balance: A model was calibrated for the Shale Hills Observatory and a simulation was carried out by Xuan Yu. The model was forced by National Land-Data-Assimilation [[File:reanalysisresponsetostorm.png|thumb|Figure 2: Shale Hills storm library from 1979-2012]]System hourly climate data (NLDAS-2) from NCEP-NOAA for the period Jan 1979-2012.

The results are presented in the following link as daily time series for the catchment water balance: [http://www.pihm.psu.edu/Shalehillsreanalysis/versionII/budget.html]. The data can be manipulated by selecting and dragging to zoom in on short term events such as the impact of tropical storms on the soil moisture or groundwater storage for example. The figure illustrates some of the extra-tropical storms that produced large rainfall in late summer and early fall.

Lysina Catchment, Czech Republic 

Developing the Lysina catchment model required an extensive data mining strategy to extract geospatial temporal data from paper documents, spreadsheets, agency archives and existing records from the Lysina research station [http://www.pihm.psu.edu/lysina/forest.html]. The model required geospatial- geotemporal data sufficient to support the physics-based numerical watershed simulator [http://www.tandfonline.com/doi/abs/10.1080/02626667.2014.897406]. The catchment model is now in a mature state and will be used for testing additional scenarios of climate change, and landuse change via soil degradation.

Through model scenario simulations we were able to show that sustainable tree harvesting practices can be compatible with sustainable water supply in a watershed where a forest of multiple-age trees are selectively harvested in small patches. The clearing of small patches of uniform age trees does not significantly change the overall water budget of the watershed or the potential for increased flooding or drought. Using the model to simulate the impact of removing the forest and changing the landuse to agricultural crops or pasture, indicates we should expect an increase in flooding potential in the spring but with a modest increased streamflow during the summer drought period.

==IEEE Paper Catchment Reanalysis==



{{#set:
Model software=PIHM_Software|
Owner=Chris_Duffy|
Participants=Xuan_Yu|
Participants=Gopal_Bhatt|
Progress=20|
SubTask=Document_calibration_approaches_for_the_PIHM_catchment_model}}

2014 first Steering Committee Meeting

2014-10-06T04:28:51Z

Chris: /* INVITATION */

[[Category:Task]]

==INVITATION==
October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==LIST OF COLLABORATORS==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, lead scientist for NEON and the national Ecological Observing Network will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
Progress=10|
StartDate=2014-10-01|
TargetDate=2014-11-24}}

2014 first Steering Committee Meeting

2014-10-06T04:28:19Z

Chris:

2014 first Steering Committee Meeting

2014-10-06T04:26:13Z

Chris:

[[Category:Task]]

==INVITATION==
October 1, 2014

To: CyberInfrastructure Collaborators: NSF INSPIRE (1344272)

The Age of Water and Carbon in Hydroecological Systems: A New Paradigm for Science Innovation and Collaboration through Organic Team Science.

Re: Update on activities and request for ½ day meeting in November in Denver

From: Christopher Duffy (PI), Yolanda Gil (Co-PI) and Paul Hanson (Co-PI)

Dear Collaborators,

We have been busily building up the core ideas for this effort over the first 10 months, and are now ready to engage you all as you expressed interest to collaborate on this project. As you may recall, we are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open, task-centered framework that facilitates ad-hoc participation and entices collaborations based on common science goals. We are integrating analytical frameworks from two communities – hydrology and stable water isotope modeling in Critical Zone Observatories (CZOs) and hydrodynamic water quality modeling from the Global Lake Ecological Observatory Network (GLEON) – to quantify water and material fluxes from two research sites, the Shales Hills CZO and the GLEON member site, North Temperate Lakes LTER.

We wanted to kickstart our collaboration activities with all of you with a one-day meeting in Denver this November. Our goal is to review the work we have done to date and plan possible collaboration activities with you for the coming year. We propose three possible dates for this meeting: 10, 17 or 24 November. The expenses for your travel will be covered by the grant. Details for convenient access to the meeting to follow.

Organic Data Science is an open task-centered hierarchical collaboration framework for developing and documenting research activities in the project. The Open Data Science framework is implemented on a semantic wiki, where each task that we are currently engaged in, the participants on that task, the progress to date, and other key information. The website is at www.organicdatascience.org, where you can see the tasks we have laid out and the first year’s activities and progress. The framework itself has been developed in collaboration with our computer science group at USC/ISI. The website outlines not only research activities on the science side, but also the activities concerning the development of the framework proper as well as outreach. Our first outreach activity is being planned to take advantage of the next GLEON annual meeting, where we will hold a workshop on Lake-Catchment Modeling in Jovance, Quebec 26 October, 2014. We will have ~30 invited participants at the workshop.

Please let us know which dates in November in Denver would work for your schedule and if you might like to participate in the GLEON Workshop contact us.

Sincerely,

Christopher J. Duffy, Yolanda Gil and Paul Hanson

==LIST OF COLLABORATORS==

Supporting Regional Community DATA Infrastructure: Gordon Grant, geomorphologist and chair of the NSF Critical Zone Steering Committee will work with the team to develop a strategy to share important ETV (Essential Terrestrial Variables) geospatial data resources and to consult on new ETV data resources for d18O and d2H in precipitation across the Willamette watershed and Cascade Mountains, an area rich in previous studies of isotopic chemistry and hydrology

National Data Infrastructure and Social Networking: David Tarboton, lead scientist on the CUAHSI HydroShare funded by NSF effort will work with the PI’s on the social computing aspects the project and in integrating modeling and data resources into the HydroShare community data system.

Serving a High Resolution Precipitation Isoscape for all Critical Zone Observatories: Anthony Aufdenkampe, lead scientist on NSF funded Integrated Data Management for Critical Zone Observatories and PI on the Christina River basin CZO, will collaborate with the Creativ Team for CZO Data and in building a new database for stable isotope data in precipitation (1979-Pres) for all CZO sites.

National Geospatial Data Infrastructure and the CUAHSI Water Data Center: Rick Hooper & Alva Couch, Executive Director and lead scientist for CUAHSI and the CUAHSI Water Data Center will collaborate by advising project personnel on standards and services interfaces used by CUAHSI Water Data Center to allow integration and sharing of geospatial services between WDC and Hydroterre.

National Data and Interoperability Standards: Brian Wee, lead scientist for NEON and the national Ecological Observing Network will collaborate by advising project personnel on standards and interoperability of NEON data with CZO’s, GLEON and other observatories.

Data Publication, Discovery and Access Infrastructure: David Vieglais, DataOne Director of Development and Operations, will support access to existing infrastructure to publish data with appropriate metadata annotations that support discovery and access. Dr. Gil participates in the DataOne Provenance Working Group, which is extending the W3C PROV standard to science workflows.

Towards a Shared Data Infrastructure across NSF Science Domains: Emily Stanley will support access to a vast array of data from lakes in northern Wisconsin, including limnological, hydrological, meteorological, and land use land cover. Stanley will help integrate the proposed work into the scientific agenda of NTL, providing additional collaboration opportunities and broader impact. Futhermore, hydrologists currently working within NTL LTER will provide invaluable insights into regional hydrology, greatly expediting the model calibration needed in the proposed work.

ETV infrastructure for high resolution precipitation isoscape (CONUS): Kei Yoshimura: climate scientist and lead scientists for global and regional scale simulation of stable isotopes in precipitation will contribute hourly, daily and weekly and gridded time series for North America which will serve to extend the measurement record at Shale Hills Trout lake to the climate reanalysis record 1979-present and provide an important new data resource for catchment isoscapes globally.

Sharing USGS Federal Data and Modeling Assets For Catchment-Lake Modeling: Jordan Read, USGS, physical limnologist and lake modeler who has extensive experience in developing and implementing numerical simulations for lakes. His recent implementation of GLM, lake numerical simulation software, models lake temperatures in hundreds of Wisconsin lakes.



{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
Progress=10|
StartDate=2014-10-01|
TargetDate=2014-11-24}}

2014 first Steering Committee Meeting

2014-10-06T04:23:15Z

Chris: Set PropertyValue: Progress = 10

[[Category:Task]]


{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
Participants=Paul_Hanson|
Progress=10|
StartDate=2014-10-01|
TargetDate=2014-11-24}}

2014 first Steering Committee Meeting

2014-10-06T04:22:51Z

Chris: Added PropertyValue: Participants = Paul Hanson

[[Category:Task]]


{{#set:
Owner=Chris_Duffy|
Participants=Paul_Hanson|
Participants=Yolanda_Gil|
StartDate=2014-10-01|
TargetDate=2014-11-24}}

2014 first Steering Committee Meeting

2014-10-06T04:22:15Z

Chris: Added PropertyValue: Participants = Yolanda Gil

[[Category:Task]]


{{#set:
Owner=Chris_Duffy|
Participants=Yolanda_Gil|
StartDate=2014-10-01|
TargetDate=2014-11-24}}

2014 first Steering Committee Meeting

2014-10-06T04:21:56Z

Chris: Set PropertyValue: Owner = Chris Duffy

[[Category:Task]]


{{#set:
Owner=Chris_Duffy|
StartDate=2014-10-01|
TargetDate=2014-11-24}}

2014 first Steering Committee Meeting

2014-10-06T04:21:24Z

Chris: Set PropertyValue: TargetDate = 2014-11-24

[[Category:Task]]


{{#set:
StartDate=2014-10-01|
TargetDate=2014-11-24}}

2014 first Steering Committee Meeting

2014-10-06T04:21:11Z

Chris: Set PropertyValue: TargetDate = 2014-11-10

[[Category:Task]]


{{#set:
StartDate=2014-10-01|
TargetDate=2014-11-10}}

2014 first Steering Committee Meeting

2014-10-06T04:20:58Z

Chris: Set PropertyValue: StartDate = 2014-10-01

[[Category:Task]]


{{#set:|
StartDate=2014-10-01}}

Develop mathematical model of age of water and carbon

2014-09-10T10:20:56Z

Chris:

[[Category:Task]]

==Concept==
[[File:ExampleAgeOfWater.jpg|thumb|right|Fig.1 The simulated catchment isoscape illustrating the 2009 annual mean spatial pattern for groundwater age at the Shale Hills CZO (Bhatt, 2012) based on the PIHM model and calibration with CZO data. The overall space-time mean age is 217 days. ]]
From the physical science perspective this research focuses on theoretical and experimental aspects of the isotopic “age” of water and carbon in lake-catchment systems. In this context, “age” is defined as the time since the water parcel and environmental tracer entered the system as precipitation. We note that each of our communities have developed an observing system for isotope ratios of carbon, oxygen and hydrogen but with very different science questions. In this research we will test a framework using models and data for defining a unified “isoscape” for the watershed-lake system, forming a richer and more collaborative shared research strategy. Our hypothesis is that the lake-catchment isoscape provides the experimental basis for predicting flow paths, residence times and the relative age of water in space and time, and that understanding these spatiotemporal patterns will provide a deeper understanding of fundamental biogeochemical processes including carbon and nitrogen cycling within the lake-catchment system. There is a wide literature on the use of residence time and relative age distribution of isotopes in environmental systems. Theories have been proposed using tracers for age modeling in ocean ventilation, atmospheric circulation, soil water, stream, groundwater flow, biophysics of vegetation photosynthesis as well as the circulation of blood. We begin this research with a simple model for the age of an environmental tracers in a ecohydrologic setting. Details of the approach can be found in Duffy (2010). The figure below is a simulation for the space-time distribution of isotopic age from the Shale Hills Critical Zone Observatory (Bhatt, 2012).

A useful analogy to understand the concept of “age” comes from population biology (Forester, 1959; Rotenberg, 1972). Consider a random population of individuals (species or particles) being born, dying and migrating. Given a long-term census of the population, the distribution of the size of the population through time and the distribution of ages of the population through time can be evaluated. Other moments may also be useful and can easily be determined from the census given enough data. The important concept to consider here is that there are actually two things we wish to evaluate in our long-term census: the number of individuals in our population through time, and the mean age of the population through time. For dissolved chemical species in water each component is characterized by physical time (clock time) and by the relative time or age since the species entered the system. The relative time for each component has it's own frame of reference particular to the dynamics of the flow system, the transport processes and the interaction with other components in the system.

In the broader context, our goal is to construct a predictive model for the fifth dimension of environmental tracers, the space-time-age distribution of the physical, chemical and biological pathways of the terrestrial environmental systems.

==Stable Isotopes as Tracers==

== A Well-Mixed Lake ==
We begin with a simple lake or reservoir model for a single input and output as shown in Fig. 2. We define the isotopic “age” as the elapsed time since the particular tracer or isotope has entered the system as input. In other words the tracer is assumed to have zero age upon entering the lake. We note that in general, the tracer age is a statistical quantity that depends on the sources of tracer, the particular transport and flow processes, the biochemical interactions, as well as the boundaries and initial conditions of the physical system. Over the course of this project we will demonstrate the concept of “age” for a fully-coupled, spatially-distributed, lake-catchment system. In this basic example to follow we demonstrate how the age of the tracer is a simple extension of traditional ecohydrologic models.

Figure 1 illustrates a simplified physical domain for this example. Typical observations for flow and tracer concentration would include sampling the lake inputs (precipitation, streamflow, groundwater, etc.), sampling the lake storage volume to determine the average values, and sampling the lake outputs (evapotranspiration, groundwater, surface outflow, etc.). From the introduction to this section recall that each tracer observation has two properties in this simple system: the physical or absolute time <math> t </math> of the observation (e.g clock time), and the relative time since the sample entered the system, which is the age of the individual tracer <math>\tau </math> . Assuming the tracer enters the system randomly, the joint distribution function describes the number of particles in the lake volume for time interval <math>dt </math> and age interval <math> d \tau </math>. [[image:Lake.png|200px|thumb|right|Fig 2 A well-mixed lake]]

In general, the functional form of c(t,<math>\tau</math>) is required to develop complete information on the joint age-time distribution for our experiment, and this might be accomplished by fitting a particular function to experimental data. However, it may be straight-forward to estimate the mass of our tracer in each sample volume, it is not generally feasible to determine the age of each sample. An alternative strategy is to relax the need for a colmplete description of the isotope population distribution and settle for a partial answer. That is, we assume c(t,<math>\tau</math>) exists but with an unknown functional form, and with certain constraints on the moments. The usual rules of probability apply and we can estimate the moments in t by integrating c(t,<math>\tau</math>) w/re to <math>\tau</math> (see Delhez, 1999 or Duffy, 2010):

<math>
\operatorname{\mu_n}(t) =
\int_0^{\infty} \tau^{n}c(t,\tau)\,d\tau,\quad n=0,1,2...\quad (1)
</math>

The 0th and 1st moment of (1) are given by:

<math>
\mathit{C}(t)=\operatorname{\mu_0}(t) =
\int_0^{\infty} \tau^{0}c(t,\tau)\,d\tau,\quad n=0;\quad (2)</math>

<math>
\mathit{M}(t)=\operatorname{\mu_1}(t) =\int_0^{\infty} \tau^{1}c(t,\tau)\,d\tau,\quad n=1;\quad (3)</math>

where we identify the 0th moment as the tracer concentration C(t) and M(t) the 1st moment of c(t,<math>\tau</math>). The key to our analysis is that the ratio of the 1st to 0th moment is the classical definition of the mean age of the system :

<math>
\mathit{Age}= {\alpha}(t) =
\frac{\mu_1}{\mu_0} \ =\frac{\mathit{M}(t)}{\mathit{C}(t)}\, \quad (4)
</math>

At this point we have defined the tracer as a dynamic variable that depends on the duration that the observation has spent in the lake, and the physical time describing the evolution of all tracer particles in the system. Equations (1-3) define the moment relations for our experiment, and the next step is to develop a physical model for the system.

For a single input and single output, we take the volumetric inflow rate to be <math> Qi [L^3/T])</math>, the outflow as <math>Q [L^3/T]</math>, and for simplicity the flow is initially assumed to be at steady-state (<math>Qi =Q</math>). The input tracer Ci can be isotopes of water (<math>\delta^{18} O</math> or <math>\delta^{2} H</math>), carbon or other solutes. As was the case in the population example given earlier, we expect the mass balance for our system to be conserved w/re to both time and age, and this is represented in the model as:

<math>
\frac{\partial c}{\partial t}+\frac{\partial c}{\partial \tau}=\Gamma_c-\mathit{L}(c),\quad (5)
</math>

where the left hand side is the total derivative for time and age of the isotope, and the right hand side represents tracer inputs, transformations and outputs. Integrating (4) w/re to as in (1), and applying the moment equations (2) yields the following dynamical system for the first 2 moments {n=0,1}:

<math>
\frac{\partial C}{\partial t}=k\left ( \mathit{C_i} - \mathit{C} \right )
</math>

<math>
\frac{\partial M}{\partial t}=\left ( \mathit{C}- k\mathit{M} \right )\qquad (6)
</math>

<math>
\mathit{Age}= {\alpha}(t) =\frac{\mathit{M}(t)}{\mathit{C}(t)},\quad
</math>

<math>
{\alpha}(\infty) =\frac{V}{Q} \ =\frac{\mathit{M}(\infty)}{\mathit{C}(\infty)}\,
</math>

where C(t) is tracer concentration, M(t) is the 1st moment, and their ratio is the the age <math>\alpha (t)</math> and <math>\alpha (\infty)</math> is the steady-state age of the lake, with input Ci . It should be noted that for a well-mixed reservoir, the steady-state age of the tracer is identical to the mean age of the tracer leaving the system (e.g. the steady-state residence time). Equation (6) shows that the age and residence time are actually dynamic quantities, with a constant asymptotic- or long-term average value. The asymptotic value is the traditional steady-state residence time.

The system (6) admits a closed form solution for steady flow conditions (Qi =Q), with constant input (Ci) and uniform initial conditions (C(0)=C0):

<math>
\mathit{C}(t)=e^{-kt}\left ( \ \mathit{C_0}- \mathit{C_i}+ \mathit{C_i}e^{kt}\right )\,
</math>[[image:Fig_2.png|200px|thumb|right|Fig 3 Solutions for various initial conditions and input concentrations]]

<math>
\mathit{M}(t)=\frac{e^{-kt}}{k}\left ( \ \mathit{C_i}e^{kt}- \mathit{C_i}+ \mathit{C_0}kt-\mathit{C_i}kt\right )\, \quad (7)
</math>

<math>
\alpha(t)=\frac{\left ( \ \mathit{C_i}e^{kt}- \mathit{C_i}+ \mathit{C_0}kt- \mathit{C_i}kt\right )}{k\left ( \mathit{C_0}- \mathit{C_i}+ \mathit{C_i}e^{kt}\right )}\,
</math>

For purpose of discussion, Fig. 3 illustrates the tracer concentration solution trajectories for a range of initial conditions and inputs. Figure 4 illustrates the solution for the mean age of the tracer in the lake. It is important to realize that tracer age in the dynamical model is a relative quantity that also depends on the initial conditions and asymptotic “age” of the system at steady-state. In this case we assume the initial tracer age in the lake is α(t)=0 and the inputs Ci are zero age as they enter the lake (e.g. a birth in the population context). Of course other initial and input conditions are possible. Note that as

<math>Q\Rightarrow 0\quad</math> and <math>C_i\Rightarrow 0\quad</math>, the system simply ages in clock time or the solution (7) is <math>\alpha= t</math>.

[[image:Fig_3.png|200px|thumb|right|Fig 4 Case 1:Variable V/Q]]
===Case 1: ''Variable V/Q''===
The solutions for variable in Fig. 4 shows the impact of varying the rate constant k (or steady-state age α( )=k-1) when the input concentration exceeds the initial condition, Ci. ≥ C0. The dynamic age of the tracer evolves from zero age to the asymptotic steady-state value. We see that the average age of the tracer in the lake and, by the well-mixed assumption, the age of the tracer in the outflow, will reach a constant age.
[[image:Fig_4.png|200px|thumb|right|Fig 5 Case 2:Variable Initial Condition C(o)=Co]]

===Case 2: ''Variable Initial Condition Co''===
The solutions for in Fig. 5 demonstrates that age calculations in models must be thought of as “relative age”, or the age since the start of the experiment. The initial condition C0 in Fig. 5 is varied while holding the input value at Ci=1. Note that when C0 > Ci, the early time age (t<V/Q) grows until the older water is displaced. The greater the concentration difference (C0 - Ci), the greater the effect on early time age. At late time in the event, the age solution gradually approaches the asymptotic age <math>\alpha (\infty)</math>.

In the catchment hydrology literature there has been a long- standing discussion about how to interpret the age of runoff. Typically, experiments find that old water or pre-event water chemical signatures dominate the runoff response during and after rainfall events (e.g. the runoff event concentration is much different than the precipitation concentration. It would seem that this simple experiment (case 2) explains why field observations of old water following an input event can be explained as a simple function of two conditions: the difference between the initial and input concentrations (C0 - Ci), and the steady-state age V/Q of the system.
[[image:Fig_5.png|200px|thumb|right|Fig 6 Case 3:Variable Input Ci]]

===Case 3: ''Variable Input Ci''===
In case 3 (Fig. 6) the input for the tracer was varied, for a constant initial concentration (C0 =20). Again note that the early time age is depends on the difference (C0 - Ci) and the steady-state age is V/Q=10 years.
[[image:Fig_6.png|200px|thumb|right|Fig 7 Case 4:Variable Initial Age α(0)]]
===Case 4: ''Variable Initial Age α(0)''===
In cases 1-3, the initial condition for tracer age in the lake was assumed to be <math>\alpha (0)=0</math>. In Case 4 we examine the effect of varying the initial age of the lake water and the solutions are given in Fig. 7. In systems where the initial age is old relative to the characteristic time of our system, the initially old water evolves to the new steady-state age in approximately 3 characteristic time scales, t~3V/Q. This example could be applied to the problem of assessing the impact of management practices, where contaminant inputs have been reduced and the time it takes to see the impacts on the lake or lake outflow is of interest. Performance assessment from reduced nutrient loading from agricultural runoff, or reduced atmospheric pollutants from the energy production and atmospheric contaminants, could apply a dynamic ageing model to assess the time it might take to measure the value of management practices and time to ecosystem recovery.

=== Reactions, Sources and Sinks===

The case of solute interactions is examined next for a 2-component reactive system in series under unsteady flow conditions. Consider the transient 2-component model where the first component is a source term for the 2nd and the 2nd component is subject to first-order loss. [[image:reservoir_3.png|200px|thumb|right| A two-component reactive tracer input with transient flow]]

<math>
\frac{\partial \mathit{V}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \mathit{V C_1}}{\partial t}= \mathit{Q_i}\mathit{C_{1i}} -\mathit{k_1 VC_1} - \mathit{QC_1}
</math>

<math>
\frac{\partial \mathit{V C_2}}{\partial t}= \mathit{Q_i}\mathit{C_{2i}} +\mathit{k_1 VC_1} - \mathit{k_2 VC_2} - \mathit{QC_2}
</math>

where <math>Q_i </math> is the net lake input flux, <math>Q_{s} </math> is the lake input flux from streams, <math>Q</math> is lake outflow, <math>V</math> is the lake volume, <math>C_{1i}</math>and <math>C_{2i}</math> are the lake inputs, <math> k_1, k_2</math> are first order reaction rates and the subscripts refers to each reactive tracer. The observed net inputs are given by:

<math>
\mathit{Q_i}= Q_{s}+A_s P-A_s ET
</math>

<math>
\mathit{C_{ji}}= \left(\frac{Q_{in}}{ \mathit{Q_i}} \mathit{C_{in}}+ \frac{\mathit{A_s P}}{ \mathit{Q_i}} \mathit{C_p}- \frac{\mathit{A_s ET}}{ \mathit{Q_i}} \mathit{C_{ET}}\right )_j, j=1,2
</math>

The zero-moment of tracer concentration equations are rearranged to form a simplified dynamical system:

<math>
V \frac{\partial \mathit{C_1}}{\partial t}= Q_i\left (\mathit{C_{1i}} - \mathit{C_1} \right )-\mathit{k_1 VC_1}
</math>

<math>
V \frac{\partial \mathit{C_2}}{\partial t}= Q_i\left (\mathit{C_{2i}} - \mathit{C_2} \right )+ \mathit{k_1 VC_1} - \mathit{k_2 VC_2}
</math>

The first moment equations for the 2-component system are given by:

<math>
V \frac{\partial \mathit{M_1}}{\partial t}= \mathit{VC_1}- \mathit{Q_i M_1} - \mathit{k_1 VM_1}
</math>

<math>
V \frac{\partial \mathit{M_2}}{\partial t} =\mathit{VC_2} -\mathit{Q_i M_2} + \mathit{k_1 VM_1}-\mathit{k_2 VM_2}
</math>

and the ratio of the first moment to the zeroth moment, or Age for the surface and deep layer are:

<math>
\mathit{Age_1}= {\alpha_1}(t) =\frac{\mathit{M_1}(t)}{\mathit{C_1}(t)},\quad
</math>

<math>
\mathit{Age_2}= {\alpha_2}(t) =\frac{\mathit{M_2}(t)}{\mathit{C_2}(t)},\quad
</math>

To summarize, estimating the age for this 2 component serial reactive system requires the solution of 4 equations: <math>C_1, C_2, M_1, M_2 </math>, and the ratios <math>M_1/C_1, M_2/C_2 </math> provide the age of each component. Note that this formulation shows that the age of each tracer depends on transient flow as well as tracer concentration.

The sequential model can be generalized for n reactions, <math> \mathit{j=1,2,3,..,n} </math>

<math>
V \frac{\partial \mathit{C_1}}{\partial t}= Q_i\left (\mathit{C_{1i}} - \mathit{C_1} \right )-\mathit{k_1 VC_1}
</math>

<math>
V \frac{\partial \mathit{C_j}}{\partial t}= Q_i\left (\mathit{C_{ji}} - \mathit{C_j} \right )-\mathit{k_j VC_j}+\mathit{k_{j-1} VC_{j-1}}
</math>

and

<math>
V \frac{\partial \mathit{M_1}}{\partial t}= \mathit{VC_1}- \mathit{Q_i M_1} - \mathit{k_1 VM_1}
</math>

<math>
V \frac{\partial \mathit{M_j}}{\partial t}= \mathit{VC_{j}} - \mathit{Q_i M_j} -\mathit{k_j MA_j}+\mathit{k_{j-1} VM_{j-1}}
</math>

=== Well-Mixed Tracer and Transient Flow===

We can extend the lake model to include transient flow. Following the same strategy as before the equations for flow and concentration have the form: [[image:reservoir_1.png|200px|thumb|right|Well mixed tracer in transient flow]]

<math>
\frac{\partial \mathit{V}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \left ( \mathit{VC}\right )}{\partial t}= \mathit{Q_i}\mathit{C_i} - \mathit{Q}\mathit{C}
</math>

where <math>Q_i </math> is the lake input flux, <math>Q</math> is lake outflow. Note that it is straight forward to include multiple input loadings <math>Q_i C_i, i=1,2,...n\quad</math>. Expanding the 2nd equation and after some manipulation we arrive at our dynamical system for transient flow, tracer concentration and age for the lake assuming time variable storage-outflow:

<math>
\frac{\partial \mathit{V}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \mathit{C}}{\partial t}= \frac {1}{\tau_c} \left (\mathit{C_i} - \mathit{C} \right )
</math>

<math>
\frac{\partial M}{\partial t}=\mathit{C}- \frac {1}{\tau_c}\mathit{M} \qquad
</math>

<math>
\mathit{Age}= {\alpha}(t) =\frac{\mathit{M}(t)}{\mathit{C}(t)},\quad
</math>

<math>
{\tau_c}(t) =\frac{V(t)}{Q_i(t)}
</math>

where V(t) is the time variable lake volume, C(t) is concentration and M(t) is 1st moment. The characteristic time in this case is not constant <math> {\tau_c}(t) </math> and the equations are nonlinear which will require numerical solutions.

==Two-Zone Lake Model with Incomplete Mixing==

Next we develop a two-zone model with incomplete vertical exchange between the surface layer and a deeper stagnant layer. <math>V(t)= V_1 +V_2 </math> is the total lake volume.<math>C_1 </math> and <math>C_2 </math> are the concentrations for each layer and the layers exchange mass by diffusion, while advection occurs only in the surface layer. The equations for flow and concentration have the form: [[image:reservoir_2.png|200px|thumb|right| A two-zone lake with incomplete mixing and and flow-through in the surface layer]]

<math>
\frac{\partial \mathit{V_1}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \mathit{V_1 C_1}}{\partial t}= \mathit{Q_i}\mathit{C_i} - \mathit{Q}\mathit{C} +E'\left ( C_2 -C_1\right )
</math>

<math>
\frac{\partial \mathit{V_2 C_2}}{\partial t}= -E'\left ( C_2 -C_1\right )
</math>

where <math>Q_i </math> is the net lake input flux, <math>Q</math> is lake outflow, <math>V_1</math> and <math>V_2</math> are the volumes of the surface and deep layers and <math>E'=DA_s/l</math>is a bulk diffusion coefficient where <math>D</math> is molecular diffusion and <math>l</math> is the length scale. In this case we assume the system has multiple observed inputs:

<math>
\mathit{Q_i}= Q_{in}+A_s P+A_s ET
</math>

<math>
\mathit{C_{i}}= \frac{Q_{in}}{ \mathit{Q_i}} \mathit{C_{in}}+ \frac{\mathit{A_s P}}{ \mathit{Q_i}} \mathit{C_p}+ \frac{\mathit{A_s ET}}{ \mathit{Q_i}} \mathit{C_{ET}}
</math>

where <math>\mathit{Q_{in}C_{in}}</math> is the input mass flow rate, <math>\mathit{P C_p} </math> is the precipitation loading rate, <math>\mathit{ET C_{ET}}</math> is evapotranspiration mass flux and <math>A_s </math> is the area of the lake or the surface area between <math>V_1</math> and <math>V_2</math>.

As before the equations are rearranged to form the simplified form of the dynamical system:

<math>
V_1 \frac{\partial \mathit{C_1}}{\partial t}= Q_i\left (\mathit{C_i} - \mathit{C_1} \right )+E'\left (\mathit{C_2} - \mathit{C_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{C_2}}{\partial t}= -E'\left (\mathit{C_2} - \mathit{C_1} \right )
</math>

The first moment equations are given by:

<math>
V_1 \frac{\partial \mathit{M_1}}{\partial t}= \mathit{V_1 C_1}- \mathit{Q_i M_1} +E'\left (\mathit{M_2} - \mathit{M_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{M_2}}{\partial t}=\mathit{V_2 C_2} -E'\left (\mathit{M_2} - \mathit{M_1} \right )
</math>

The ratio of the first moment to the zeroth moment is the Age for the surface and deep layer.

<math>
\mathit{Age_1}= {\alpha_1}(t) =\frac{\mathit{M_1}(t)}{\mathit{C_1}(t)},\quad
</math>

<math>
\mathit{Age_2}= {\alpha_2}(t) =\frac{\mathit{M_2}(t)}{\mathit{C_2}(t)},\quad
</math>

To summarize, estimating the ages for this 2 component system requires the solution of 4 equations: <math>C_1, C_2, M_1, M_2 </math>, and ratios <math>M_1/C_1, M_2/C_2 </math> are calculated to give the ages for the surface and deep layer respectively. Note that this formulation shows that the evolution of tracer age depends on both flow and tracer dynamics.

==Lake-Catchment Dynamical System==

Next we examine the coupled dynamics for a lake-catchment system where groundwater inputs/ouput to/from the lake are unknown and where surface water inputs and outputs to the lake are observed. [[image:Lake-Catchment-model.png|200px|thumb|right| Interacting lake-catchment]]

<math>
\frac{\partial \mathit{V_1}}{\partial t}= \mathit{Q_{1i}} - \mathit{Q_1}+\mathit{Q_{12}}
</math>

<math>
\frac{\partial \mathit{V_2}}{\partial t}= \mathit{Q_{2i}} - \mathit{Q_2}-\mathit{Q_{12}}
</math>

<math>
\frac{\partial \mathit{V_1 C_1}}{\partial t}= \mathit{Q_{1i}}\mathit{C_{1i}} -\mathit{Q_1 C_1} + \mathit{Q_{12}C_{12}}
</math>

<math>
\frac{\partial \mathit{V_2 C_2}}{\partial t}= \mathit{Q_{2i}}\mathit{C_{2i}} -\mathit{Q_2 C_2} - \mathit{Q_{12}C_{12}}
</math>

where <math>Q_{ji} </math>(<math>j=1,2</math>) is the net flux to the lake, where (<math>j=1</math>) refers to the lake and (<math>j=2</math>) the catchment respectively, <math>i</math> indicates input, <math>Q_1</math> is the lake outflow, <math>Q_2</math> is the catchment outflow to the stream. <math>V_1</math> is the lake storage volume and <math>V_2</math> is the catchment storage volume. <math>C_{1i}</math>and <math>C_{2i}</math> are the lake and catchment isotopic inputs and <math>A_1</math>, <math>A_2</math> are surface areas of the lake and catchment respectively. The multiple observed inputs are given by:

<math>
\mathit{Q_{ji}}= \left (\mathit{A_j P}-\mathit{A_j ET}\right ), j=1,2
</math>

<math>
\mathit{C_{ji}}= \left (\frac{\mathit{A_j P}}{ \mathit{Q_{ji}}} \mathit{C_p}- \frac{\mathit{A_j ET}}{ \mathit{Q_{ji}}} \mathit{C_{ET}}\right ), j=1,2
</math>

The total flow at the outlet of the catchment is given by:

<math>
\mathit{Q}= \mathit{Q_1}+\mathit{Q_2}
</math>

The zero-moment or tracer concentration equations can be rearranged to form a simplified dynamical system:

<math>
V_1 \frac{\partial \mathit{C_1}}{\partial t}= Q_{1i}\left (\mathit{C_{1i}} - \mathit{C_1} \right )+Q_{12}\left (\mathit{C_{12}} - \mathit{C_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{C_2}}{\partial t}= Q_{2i}\left (\mathit{C_{2i}} - \mathit{C_2} \right )-Q_{12}\left (\mathit{C_{12}} - \mathit{C_2} \right )
</math>

Note that <math>C_{12} </math> depends on the direction of flow or the sign of <math> \pm Q_{12} </math>.

<math>
C_{12} =
\begin{cases}
C_1 & Q_{12} \le 0 \\
C_2 & \mbox{otherwise}
\end{cases}
</math>

The first moment equations for the lake-catchment system are given by:

<math>
V_1 \frac{\partial \mathit{M_1}}{\partial t}= \mathit{V_1 C_1}- \mathit{Q_{1i} M_1} + Q_{12}\left (\mathit{M_{12}} - \mathit{M_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{M_2}}{\partial t} =\mathit{V_2 C_2} -\mathit{Q_{2i} M_2} -Q_{12}\left (\mathit{M_{12}} - \mathit{M_2} \right )
</math>

where again <math>M_{12} </math> depends on the direction of flow or the sign of <math> \pm Q_{12} </math>.

<math>
M_{12} =
\begin{cases}
M_1 & Q_{12} \le 0 \\
M_2 & \mbox{otherwise}
\end{cases}
</math>

and the ratio of the first moment to the zeroth moment, or Age for the surface and deep layer are:

<math>
\mathit{Age_1}= {\alpha_1}(t) =\frac{\mathit{M_1}(t)}{\mathit{C_1}(t)},\quad
</math>

<math>
\mathit{Age_2}= {\alpha_2}(t) =\frac{\mathit{M_2}(t)}{\mathit{C_2}(t)},\quad
</math>

To summarize, estimating the age for this lake-catchment system requires the solution of 4 equations: <math>C_1, C_2, A_1, A_2 </math>, and the ratios <math>A_1/C_1, A_2/C_2 </math> provide the age of each component. Note that this formulation shows that the age of each tracer depends on transient flow as well as tracer concentration.

==A Distributed Model for Lake-Catchment Dynamics==

In this section we will develop field equations for the spatially-distributed lake-catchment system based on the formulation by G. Bhatt (2012).



{{#set:
Expertise=Ecosystem_modeling|
Expertise=Computational_hydrology|
Owner=Chris_Duffy|
Participants=Gopal_Bhatt|
Participants=Jordan_Read|
Participants=Paul_Hanson|
StartDate=2014-05-11|
SubTask=Write_paper:_Dynamic_age_of_water_and_carbon|
TargetDate=2014-11-11|
Type=Medium}}

Develop mathematical model of age of water and carbon

2014-09-10T10:17:10Z

Chris: /* A Distributed Model for Lake-Catchment Dynamics */

[[Category:Task]]

==Concept==
[[File:ExampleAgeOfWater.jpg|thumb|right|Fig.1 The simulated catchment isoscape illustrating the 2009 annual mean spatial pattern for groundwater age at the Shale Hills CZO (Bhatt, 2012) based on the PIHM model and calibration with CZO data. The overall space-time mean age is 217 days. ]]
From the physical science perspective this research focuses on theoretical and experimental aspects of the isotopic “age” of water and carbon in lake-catchment systems. In this context, “age” is defined as the time since the water parcel and environmental tracer entered the system as precipitation. We note that each of our communities have developed an observing system for isotope ratios of carbon, oxygen and hydrogen but with very different science questions. In this research we will test a framework using models and data for defining a unified “isoscape” for the watershed-lake system, forming a richer and more collaborative shared research strategy. Our hypothesis is that the lake-catchment isoscape provides the experimental basis for predicting flow paths, residence times and the relative age of water in space and time, and that understanding these spatiotemporal patterns will provide a deeper understanding of fundamental biogeochemical processes including carbon and nitrogen cycling within the lake-catchment system. There is a wide literature on the use of residence time and relative age distribution of isotopes in environmental systems. Theories have been proposed using tracers for age modeling in ocean ventilation, atmospheric circulation, soil water, stream, groundwater flow, biophysics of vegetation photosynthesis as well as the circulation of blood. We begin this research with a simple model for the age of an environmental tracers in a ecohydrologic setting. Details of the approach can be found in Duffy (2010). The figure below is a simulation for the space-time distribution of isotopic age from the Shale Hills Critical Zone Observatory (Bhatt, 2012).

A useful analogy to understand the concept of “age” comes from population biology (Forester, 1959; Rotenberg, 1972). Consider a random population of individuals (species or particles) being born, dying and migrating. Given a long-term census of the population, the distribution of the size of the population through time and the distribution of ages of the population through time can be evaluated. Other moments may also be useful and can easily be determined from the census given enough data. The important concept to consider here is that there are actually two things we wish to evaluate in our long-term census: the number of individuals in our population through time, and the mean age of the population through time. For dissolved chemical species in water each component is characterized by physical time (clock time) and by the relative time or age since the species entered the system. The relative time for each component has it's own frame of reference particular to the dynamics of the flow system, the transport processes and the interaction with other components in the system.

In the broader context, our goal is to construct a predictive model for the fifth dimension of environmental tracers, the space-time-age distribution of the physical, chemical and biological pathways of the terrestrial environmental systems.

== A Well-Mixed Lake ==
We begin with a simple lake or reservoir model for a single input and output as shown in Fig. 2. We define the isotopic “age” as the elapsed time since the particular tracer or isotope has entered the system as input. In other words the tracer is assumed to have zero age upon entering the lake. We note that in general, the tracer age is a statistical quantity that depends on the sources of tracer, the particular transport and flow processes, the biochemical interactions, as well as the boundaries and initial conditions of the physical system. Over the course of this project we will demonstrate the concept of “age” for a fully-coupled, spatially-distributed, lake-catchment system. In this basic example to follow we demonstrate how the age of the tracer is a simple extension of traditional ecohydrologic models.

Figure 1 illustrates a simplified physical domain for this example. Typical observations for flow and tracer concentration would include sampling the lake inputs (precipitation, streamflow, groundwater, etc.), sampling the lake storage volume to determine the average values, and sampling the lake outputs (evapotranspiration, groundwater, surface outflow, etc.). From the introduction to this section recall that each tracer observation has two properties in this simple system: the physical or absolute time <math> t </math> of the observation (e.g clock time), and the relative time since the sample entered the system, which is the age of the individual tracer <math>\tau </math> . Assuming the tracer enters the system randomly, the joint distribution function describes the number of particles in the lake volume for time interval <math>dt </math> and age interval <math> d \tau </math>. [[image:Lake.png|200px|thumb|right|Fig 2 A well-mixed lake]]

In general, the functional form of c(t,<math>\tau</math>) is required to develop complete information on the joint age-time distribution for our experiment, and this might be accomplished by fitting a particular function to experimental data. However, it may be straight-forward to estimate the mass of our tracer in each sample volume, it is not generally feasible to determine the age of each sample. An alternative strategy is to relax the need for a colmplete description of the isotope population distribution and settle for a partial answer. That is, we assume c(t,<math>\tau</math>) exists but with an unknown functional form, and with certain constraints on the moments. The usual rules of probability apply and we can estimate the moments in t by integrating c(t,<math>\tau</math>) w/re to <math>\tau</math> (see Delhez, 1999 or Duffy, 2010):

<math>
\operatorname{\mu_n}(t) =
\int_0^{\infty} \tau^{n}c(t,\tau)\,d\tau,\quad n=0,1,2...\quad (1)
</math>

The 0th and 1st moment of (1) are given by:

<math>
\mathit{C}(t)=\operatorname{\mu_0}(t) =
\int_0^{\infty} \tau^{0}c(t,\tau)\,d\tau,\quad n=0;\quad (2)</math>

<math>
\mathit{M}(t)=\operatorname{\mu_1}(t) =\int_0^{\infty} \tau^{1}c(t,\tau)\,d\tau,\quad n=1;\quad (3)</math>

where we identify the 0th moment as the tracer concentration C(t) and M(t) the 1st moment of c(t,<math>\tau</math>). The key to our analysis is that the ratio of the 1st to 0th moment is the classical definition of the mean age of the system :

<math>
\mathit{Age}= {\alpha}(t) =
\frac{\mu_1}{\mu_0} \ =\frac{\mathit{M}(t)}{\mathit{C}(t)}\, \quad (4)
</math>

At this point we have defined the tracer as a dynamic variable that depends on the duration that the observation has spent in the lake, and the physical time describing the evolution of all tracer particles in the system. Equations (1-3) define the moment relations for our experiment, and the next step is to develop a physical model for the system.

For a single input and single output, we take the volumetric inflow rate to be <math> Qi [L^3/T])</math>, the outflow as <math>Q [L^3/T]</math>, and for simplicity the flow is initially assumed to be at steady-state (<math>Qi =Q</math>). The input tracer Ci can be isotopes of water (<math>\delta^{18} O</math> or <math>\delta^{2} H</math>), carbon or other solutes. As was the case in the population example given earlier, we expect the mass balance for our system to be conserved w/re to both time and age, and this is represented in the model as:

<math>
\frac{\partial c}{\partial t}+\frac{\partial c}{\partial \tau}=\Gamma_c-\mathit{L}(c),\quad (5)
</math>

where the left hand side is the total derivative for time and age of the isotope, and the right hand side represents tracer inputs, transformations and outputs. Integrating (4) w/re to as in (1), and applying the moment equations (2) yields the following dynamical system for the first 2 moments {n=0,1}:

<math>
\frac{\partial C}{\partial t}=k\left ( \mathit{C_i} - \mathit{C} \right )
</math>

<math>
\frac{\partial M}{\partial t}=\left ( \mathit{C}- k\mathit{M} \right )\qquad (6)
</math>

<math>
\mathit{Age}= {\alpha}(t) =\frac{\mathit{M}(t)}{\mathit{C}(t)},\quad
</math>

<math>
{\alpha}(\infty) =\frac{V}{Q} \ =\frac{\mathit{M}(\infty)}{\mathit{C}(\infty)}\,
</math>

where C(t) is tracer concentration, M(t) is the 1st moment, and their ratio is the the age <math>\alpha (t)</math> and <math>\alpha (\infty)</math> is the steady-state age of the lake, with input Ci . It should be noted that for a well-mixed reservoir, the steady-state age of the tracer is identical to the mean age of the tracer leaving the system (e.g. the steady-state residence time). Equation (6) shows that the age and residence time are actually dynamic quantities, with a constant asymptotic- or long-term average value. The asymptotic value is the traditional steady-state residence time.

The system (6) admits a closed form solution for steady flow conditions (Qi =Q), with constant input (Ci) and uniform initial conditions (C(0)=C0):

<math>
\mathit{C}(t)=e^{-kt}\left ( \ \mathit{C_0}- \mathit{C_i}+ \mathit{C_i}e^{kt}\right )\,
</math>[[image:Fig_2.png|200px|thumb|right|Fig 3 Solutions for various initial conditions and input concentrations]]

<math>
\mathit{M}(t)=\frac{e^{-kt}}{k}\left ( \ \mathit{C_i}e^{kt}- \mathit{C_i}+ \mathit{C_0}kt-\mathit{C_i}kt\right )\, \quad (7)
</math>

<math>
\alpha(t)=\frac{\left ( \ \mathit{C_i}e^{kt}- \mathit{C_i}+ \mathit{C_0}kt- \mathit{C_i}kt\right )}{k\left ( \mathit{C_0}- \mathit{C_i}+ \mathit{C_i}e^{kt}\right )}\,
</math>

For purpose of discussion, Fig. 3 illustrates the tracer concentration solution trajectories for a range of initial conditions and inputs. Figure 4 illustrates the solution for the mean age of the tracer in the lake. It is important to realize that tracer age in the dynamical model is a relative quantity that also depends on the initial conditions and asymptotic “age” of the system at steady-state. In this case we assume the initial tracer age in the lake is α(t)=0 and the inputs Ci are zero age as they enter the lake (e.g. a birth in the population context). Of course other initial and input conditions are possible. Note that as

<math>Q\Rightarrow 0\quad</math> and <math>C_i\Rightarrow 0\quad</math>, the system simply ages in clock time or the solution (7) is <math>\alpha= t</math>.

[[image:Fig_3.png|200px|thumb|right|Fig 4 Case 1:Variable V/Q]]
===Case 1: ''Variable V/Q''===
The solutions for variable in Fig. 4 shows the impact of varying the rate constant k (or steady-state age α( )=k-1) when the input concentration exceeds the initial condition, Ci. ≥ C0. The dynamic age of the tracer evolves from zero age to the asymptotic steady-state value. We see that the average age of the tracer in the lake and, by the well-mixed assumption, the age of the tracer in the outflow, will reach a constant age.
[[image:Fig_4.png|200px|thumb|right|Fig 5 Case 2:Variable Initial Condition C(o)=Co]]

===Case 2: ''Variable Initial Condition Co''===
The solutions for in Fig. 5 demonstrates that age calculations in models must be thought of as “relative age”, or the age since the start of the experiment. The initial condition C0 in Fig. 5 is varied while holding the input value at Ci=1. Note that when C0 > Ci, the early time age (t<V/Q) grows until the older water is displaced. The greater the concentration difference (C0 - Ci), the greater the effect on early time age. At late time in the event, the age solution gradually approaches the asymptotic age <math>\alpha (\infty)</math>.

In the catchment hydrology literature there has been a long- standing discussion about how to interpret the age of runoff. Typically, experiments find that old water or pre-event water chemical signatures dominate the runoff response during and after rainfall events (e.g. the runoff event concentration is much different than the precipitation concentration. It would seem that this simple experiment (case 2) explains why field observations of old water following an input event can be explained as a simple function of two conditions: the difference between the initial and input concentrations (C0 - Ci), and the steady-state age V/Q of the system.
[[image:Fig_5.png|200px|thumb|right|Fig 6 Case 3:Variable Input Ci]]

===Case 3: ''Variable Input Ci''===
In case 3 (Fig. 6) the input for the tracer was varied, for a constant initial concentration (C0 =20). Again note that the early time age is depends on the difference (C0 - Ci) and the steady-state age is V/Q=10 years.
[[image:Fig_6.png|200px|thumb|right|Fig 7 Case 4:Variable Initial Age α(0)]]
===Case 4: ''Variable Initial Age α(0)''===
In cases 1-3, the initial condition for tracer age in the lake was assumed to be <math>\alpha (0)=0</math>. In Case 4 we examine the effect of varying the initial age of the lake water and the solutions are given in Fig. 7. In systems where the initial age is old relative to the characteristic time of our system, the initially old water evolves to the new steady-state age in approximately 3 characteristic time scales, t~3V/Q. This example could be applied to the problem of assessing the impact of management practices, where contaminant inputs have been reduced and the time it takes to see the impacts on the lake or lake outflow is of interest. Performance assessment from reduced nutrient loading from agricultural runoff, or reduced atmospheric pollutants from the energy production and atmospheric contaminants, could apply a dynamic ageing model to assess the time it might take to measure the value of management practices and time to ecosystem recovery.

=== Reactions, Sources and Sinks===

The case of solute interactions is examined next for a 2-component reactive system in series under unsteady flow conditions. Consider the transient 2-component model where the first component is a source term for the 2nd and the 2nd component is subject to first-order loss. [[image:reservoir_3.png|200px|thumb|right| A two-component reactive tracer input with transient flow]]

<math>
\frac{\partial \mathit{V}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \mathit{V C_1}}{\partial t}= \mathit{Q_i}\mathit{C_{1i}} -\mathit{k_1 VC_1} - \mathit{QC_1}
</math>

<math>
\frac{\partial \mathit{V C_2}}{\partial t}= \mathit{Q_i}\mathit{C_{2i}} +\mathit{k_1 VC_1} - \mathit{k_2 VC_2} - \mathit{QC_2}
</math>

where <math>Q_i </math> is the net lake input flux, <math>Q_{s} </math> is the lake input flux from streams, <math>Q</math> is lake outflow, <math>V</math> is the lake volume, <math>C_{1i}</math>and <math>C_{2i}</math> are the lake inputs, <math> k_1, k_2</math> are first order reaction rates and the subscripts refers to each reactive tracer. The observed net inputs are given by:

<math>
\mathit{Q_i}= Q_{s}+A_s P-A_s ET
</math>

<math>
\mathit{C_{ji}}= \left(\frac{Q_{in}}{ \mathit{Q_i}} \mathit{C_{in}}+ \frac{\mathit{A_s P}}{ \mathit{Q_i}} \mathit{C_p}- \frac{\mathit{A_s ET}}{ \mathit{Q_i}} \mathit{C_{ET}}\right )_j, j=1,2
</math>

The zero-moment of tracer concentration equations are rearranged to form a simplified dynamical system:

<math>
V \frac{\partial \mathit{C_1}}{\partial t}= Q_i\left (\mathit{C_{1i}} - \mathit{C_1} \right )-\mathit{k_1 VC_1}
</math>

<math>
V \frac{\partial \mathit{C_2}}{\partial t}= Q_i\left (\mathit{C_{2i}} - \mathit{C_2} \right )+ \mathit{k_1 VC_1} - \mathit{k_2 VC_2}
</math>

The first moment equations for the 2-component system are given by:

<math>
V \frac{\partial \mathit{M_1}}{\partial t}= \mathit{VC_1}- \mathit{Q_i M_1} - \mathit{k_1 VM_1}
</math>

<math>
V \frac{\partial \mathit{M_2}}{\partial t} =\mathit{VC_2} -\mathit{Q_i M_2} + \mathit{k_1 VM_1}-\mathit{k_2 VM_2}
</math>

and the ratio of the first moment to the zeroth moment, or Age for the surface and deep layer are:

<math>
\mathit{Age_1}= {\alpha_1}(t) =\frac{\mathit{M_1}(t)}{\mathit{C_1}(t)},\quad
</math>

<math>
\mathit{Age_2}= {\alpha_2}(t) =\frac{\mathit{M_2}(t)}{\mathit{C_2}(t)},\quad
</math>

To summarize, estimating the age for this 2 component serial reactive system requires the solution of 4 equations: <math>C_1, C_2, M_1, M_2 </math>, and the ratios <math>M_1/C_1, M_2/C_2 </math> provide the age of each component. Note that this formulation shows that the age of each tracer depends on transient flow as well as tracer concentration.

The sequential model can be generalized for n reactions, <math> \mathit{j=1,2,3,..,n} </math>

<math>
V \frac{\partial \mathit{C_1}}{\partial t}= Q_i\left (\mathit{C_{1i}} - \mathit{C_1} \right )-\mathit{k_1 VC_1}
</math>

<math>
V \frac{\partial \mathit{C_j}}{\partial t}= Q_i\left (\mathit{C_{ji}} - \mathit{C_j} \right )-\mathit{k_j VC_j}+\mathit{k_{j-1} VC_{j-1}}
</math>

and

<math>
V \frac{\partial \mathit{M_1}}{\partial t}= \mathit{VC_1}- \mathit{Q_i M_1} - \mathit{k_1 VM_1}
</math>

<math>
V \frac{\partial \mathit{M_j}}{\partial t}= \mathit{VC_{j}} - \mathit{Q_i M_j} -\mathit{k_j MA_j}+\mathit{k_{j-1} VM_{j-1}}
</math>

=== Well-Mixed Tracer and Transient Flow===

We can extend the lake model to include transient flow. Following the same strategy as before the equations for flow and concentration have the form: [[image:reservoir_1.png|200px|thumb|right|Well mixed tracer in transient flow]]

<math>
\frac{\partial \mathit{V}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \left ( \mathit{VC}\right )}{\partial t}= \mathit{Q_i}\mathit{C_i} - \mathit{Q}\mathit{C}
</math>

where <math>Q_i </math> is the lake input flux, <math>Q</math> is lake outflow. Note that it is straight forward to include multiple input loadings <math>Q_i C_i, i=1,2,...n\quad</math>. Expanding the 2nd equation and after some manipulation we arrive at our dynamical system for transient flow, tracer concentration and age for the lake assuming time variable storage-outflow:

<math>
\frac{\partial \mathit{V}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \mathit{C}}{\partial t}= \frac {1}{\tau_c} \left (\mathit{C_i} - \mathit{C} \right )
</math>

<math>
\frac{\partial M}{\partial t}=\mathit{C}- \frac {1}{\tau_c}\mathit{M} \qquad
</math>

<math>
\mathit{Age}= {\alpha}(t) =\frac{\mathit{M}(t)}{\mathit{C}(t)},\quad
</math>

<math>
{\tau_c}(t) =\frac{V(t)}{Q_i(t)}
</math>

where V(t) is the time variable lake volume, C(t) is concentration and M(t) is 1st moment. The characteristic time in this case is not constant <math> {\tau_c}(t) </math> and the equations are nonlinear which will require numerical solutions.

==Two-Zone Lake Model with Incomplete Mixing==

Next we develop a two-zone model with incomplete vertical exchange between the surface layer and a deeper stagnant layer. <math>V(t)= V_1 +V_2 </math> is the total lake volume.<math>C_1 </math> and <math>C_2 </math> are the concentrations for each layer and the layers exchange mass by diffusion, while advection occurs only in the surface layer. The equations for flow and concentration have the form: [[image:reservoir_2.png|200px|thumb|right| A two-zone lake with incomplete mixing and and flow-through in the surface layer]]

<math>
\frac{\partial \mathit{V_1}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \mathit{V_1 C_1}}{\partial t}= \mathit{Q_i}\mathit{C_i} - \mathit{Q}\mathit{C} +E'\left ( C_2 -C_1\right )
</math>

<math>
\frac{\partial \mathit{V_2 C_2}}{\partial t}= -E'\left ( C_2 -C_1\right )
</math>

where <math>Q_i </math> is the net lake input flux, <math>Q</math> is lake outflow, <math>V_1</math> and <math>V_2</math> are the volumes of the surface and deep layers and <math>E'=DA_s/l</math>is a bulk diffusion coefficient where <math>D</math> is molecular diffusion and <math>l</math> is the length scale. In this case we assume the system has multiple observed inputs:

<math>
\mathit{Q_i}= Q_{in}+A_s P+A_s ET
</math>

<math>
\mathit{C_{i}}= \frac{Q_{in}}{ \mathit{Q_i}} \mathit{C_{in}}+ \frac{\mathit{A_s P}}{ \mathit{Q_i}} \mathit{C_p}+ \frac{\mathit{A_s ET}}{ \mathit{Q_i}} \mathit{C_{ET}}
</math>

where <math>\mathit{Q_{in}C_{in}}</math> is the input mass flow rate, <math>\mathit{P C_p} </math> is the precipitation loading rate, <math>\mathit{ET C_{ET}}</math> is evapotranspiration mass flux and <math>A_s </math> is the area of the lake or the surface area between <math>V_1</math> and <math>V_2</math>.

As before the equations are rearranged to form the simplified form of the dynamical system:

<math>
V_1 \frac{\partial \mathit{C_1}}{\partial t}= Q_i\left (\mathit{C_i} - \mathit{C_1} \right )+E'\left (\mathit{C_2} - \mathit{C_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{C_2}}{\partial t}= -E'\left (\mathit{C_2} - \mathit{C_1} \right )
</math>

The first moment equations are given by:

<math>
V_1 \frac{\partial \mathit{M_1}}{\partial t}= \mathit{V_1 C_1}- \mathit{Q_i M_1} +E'\left (\mathit{M_2} - \mathit{M_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{M_2}}{\partial t}=\mathit{V_2 C_2} -E'\left (\mathit{M_2} - \mathit{M_1} \right )
</math>

The ratio of the first moment to the zeroth moment is the Age for the surface and deep layer.

<math>
\mathit{Age_1}= {\alpha_1}(t) =\frac{\mathit{M_1}(t)}{\mathit{C_1}(t)},\quad
</math>

<math>
\mathit{Age_2}= {\alpha_2}(t) =\frac{\mathit{M_2}(t)}{\mathit{C_2}(t)},\quad
</math>

To summarize, estimating the ages for this 2 component system requires the solution of 4 equations: <math>C_1, C_2, M_1, M_2 </math>, and ratios <math>M_1/C_1, M_2/C_2 </math> are calculated to give the ages for the surface and deep layer respectively. Note that this formulation shows that the evolution of tracer age depends on both flow and tracer dynamics.

==Lake-Catchment Dynamical System==

Next we examine the coupled dynamics for a lake-catchment system where groundwater inputs/ouput to/from the lake are unknown and where surface water inputs and outputs to the lake are observed. [[image:Lake-Catchment-model.png|200px|thumb|right| Interacting lake-catchment]]

<math>
\frac{\partial \mathit{V_1}}{\partial t}= \mathit{Q_{1i}} - \mathit{Q_1}+\mathit{Q_{12}}
</math>

<math>
\frac{\partial \mathit{V_2}}{\partial t}= \mathit{Q_{2i}} - \mathit{Q_2}-\mathit{Q_{12}}
</math>

<math>
\frac{\partial \mathit{V_1 C_1}}{\partial t}= \mathit{Q_{1i}}\mathit{C_{1i}} -\mathit{Q_1 C_1} + \mathit{Q_{12}C_{12}}
</math>

<math>
\frac{\partial \mathit{V_2 C_2}}{\partial t}= \mathit{Q_{2i}}\mathit{C_{2i}} -\mathit{Q_2 C_2} - \mathit{Q_{12}C_{12}}
</math>

where <math>Q_{ji} </math>(<math>j=1,2</math>) is the net flux to the lake, where (<math>j=1</math>) refers to the lake and (<math>j=2</math>) the catchment respectively, <math>i</math> indicates input, <math>Q_1</math> is the lake outflow, <math>Q_2</math> is the catchment outflow to the stream. <math>V_1</math> is the lake storage volume and <math>V_2</math> is the catchment storage volume. <math>C_{1i}</math>and <math>C_{2i}</math> are the lake and catchment isotopic inputs and <math>A_1</math>, <math>A_2</math> are surface areas of the lake and catchment respectively. The multiple observed inputs are given by:

<math>
\mathit{Q_{ji}}= \left (\mathit{A_j P}-\mathit{A_j ET}\right ), j=1,2
</math>

<math>
\mathit{C_{ji}}= \left (\frac{\mathit{A_j P}}{ \mathit{Q_{ji}}} \mathit{C_p}- \frac{\mathit{A_j ET}}{ \mathit{Q_{ji}}} \mathit{C_{ET}}\right ), j=1,2
</math>

The total flow at the outlet of the catchment is given by:

<math>
\mathit{Q}= \mathit{Q_1}+\mathit{Q_2}
</math>

The zero-moment or tracer concentration equations can be rearranged to form a simplified dynamical system:

<math>
V_1 \frac{\partial \mathit{C_1}}{\partial t}= Q_{1i}\left (\mathit{C_{1i}} - \mathit{C_1} \right )+Q_{12}\left (\mathit{C_{12}} - \mathit{C_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{C_2}}{\partial t}= Q_{2i}\left (\mathit{C_{2i}} - \mathit{C_2} \right )-Q_{12}\left (\mathit{C_{12}} - \mathit{C_2} \right )
</math>

Note that <math>C_{12} </math> depends on the direction of flow or the sign of <math> \pm Q_{12} </math>.

<math>
C_{12} =
\begin{cases}
C_1 & Q_{12} \le 0 \\
C_2 & \mbox{otherwise}
\end{cases}
</math>

The first moment equations for the lake-catchment system are given by:

<math>
V_1 \frac{\partial \mathit{M_1}}{\partial t}= \mathit{V_1 C_1}- \mathit{Q_{1i} M_1} + Q_{12}\left (\mathit{M_{12}} - \mathit{M_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{M_2}}{\partial t} =\mathit{V_2 C_2} -\mathit{Q_{2i} M_2} -Q_{12}\left (\mathit{M_{12}} - \mathit{M_2} \right )
</math>

where again <math>M_{12} </math> depends on the direction of flow or the sign of <math> \pm Q_{12} </math>.

<math>
M_{12} =
\begin{cases}
M_1 & Q_{12} \le 0 \\
M_2 & \mbox{otherwise}
\end{cases}
</math>

and the ratio of the first moment to the zeroth moment, or Age for the surface and deep layer are:

<math>
\mathit{Age_1}= {\alpha_1}(t) =\frac{\mathit{M_1}(t)}{\mathit{C_1}(t)},\quad
</math>

<math>
\mathit{Age_2}= {\alpha_2}(t) =\frac{\mathit{M_2}(t)}{\mathit{C_2}(t)},\quad
</math>

To summarize, estimating the age for this lake-catchment system requires the solution of 4 equations: <math>C_1, C_2, A_1, A_2 </math>, and the ratios <math>A_1/C_1, A_2/C_2 </math> provide the age of each component. Note that this formulation shows that the age of each tracer depends on transient flow as well as tracer concentration.

==A Distributed Model for Lake-Catchment Dynamics==

In this section we will develop field equations for the spatially-distributed lake-catchment system based on the formulation by G. Bhatt (2012).



{{#set:
Expertise=Ecosystem_modeling|
Expertise=Computational_hydrology|
Owner=Chris_Duffy|
Participants=Gopal_Bhatt|
Participants=Jordan_Read|
Participants=Paul_Hanson|
StartDate=2014-05-11|
SubTask=Write_paper:_Dynamic_age_of_water_and_carbon|
TargetDate=2014-11-11|
Type=Medium}}

Develop mathematical model of age of water and carbon

2014-09-10T10:13:15Z

Chris:

[[Category:Task]]

==Concept==
[[File:ExampleAgeOfWater.jpg|thumb|right|Fig.1 The simulated catchment isoscape illustrating the 2009 annual mean spatial pattern for groundwater age at the Shale Hills CZO (Bhatt, 2012) based on the PIHM model and calibration with CZO data. The overall space-time mean age is 217 days. ]]
From the physical science perspective this research focuses on theoretical and experimental aspects of the isotopic “age” of water and carbon in lake-catchment systems. In this context, “age” is defined as the time since the water parcel and environmental tracer entered the system as precipitation. We note that each of our communities have developed an observing system for isotope ratios of carbon, oxygen and hydrogen but with very different science questions. In this research we will test a framework using models and data for defining a unified “isoscape” for the watershed-lake system, forming a richer and more collaborative shared research strategy. Our hypothesis is that the lake-catchment isoscape provides the experimental basis for predicting flow paths, residence times and the relative age of water in space and time, and that understanding these spatiotemporal patterns will provide a deeper understanding of fundamental biogeochemical processes including carbon and nitrogen cycling within the lake-catchment system. There is a wide literature on the use of residence time and relative age distribution of isotopes in environmental systems. Theories have been proposed using tracers for age modeling in ocean ventilation, atmospheric circulation, soil water, stream, groundwater flow, biophysics of vegetation photosynthesis as well as the circulation of blood. We begin this research with a simple model for the age of an environmental tracers in a ecohydrologic setting. Details of the approach can be found in Duffy (2010). The figure below is a simulation for the space-time distribution of isotopic age from the Shale Hills Critical Zone Observatory (Bhatt, 2012).

A useful analogy to understand the concept of “age” comes from population biology (Forester, 1959; Rotenberg, 1972). Consider a random population of individuals (species or particles) being born, dying and migrating. Given a long-term census of the population, the distribution of the size of the population through time and the distribution of ages of the population through time can be evaluated. Other moments may also be useful and can easily be determined from the census given enough data. The important concept to consider here is that there are actually two things we wish to evaluate in our long-term census: the number of individuals in our population through time, and the mean age of the population through time. For dissolved chemical species in water each component is characterized by physical time (clock time) and by the relative time or age since the species entered the system. The relative time for each component has it's own frame of reference particular to the dynamics of the flow system, the transport processes and the interaction with other components in the system.

In the broader context, our goal is to construct a predictive model for the fifth dimension of environmental tracers, the space-time-age distribution of the physical, chemical and biological pathways of the terrestrial environmental systems.

== A Well-Mixed Lake ==
We begin with a simple lake or reservoir model for a single input and output as shown in Fig. 2. We define the isotopic “age” as the elapsed time since the particular tracer or isotope has entered the system as input. In other words the tracer is assumed to have zero age upon entering the lake. We note that in general, the tracer age is a statistical quantity that depends on the sources of tracer, the particular transport and flow processes, the biochemical interactions, as well as the boundaries and initial conditions of the physical system. Over the course of this project we will demonstrate the concept of “age” for a fully-coupled, spatially-distributed, lake-catchment system. In this basic example to follow we demonstrate how the age of the tracer is a simple extension of traditional ecohydrologic models.

Figure 1 illustrates a simplified physical domain for this example. Typical observations for flow and tracer concentration would include sampling the lake inputs (precipitation, streamflow, groundwater, etc.), sampling the lake storage volume to determine the average values, and sampling the lake outputs (evapotranspiration, groundwater, surface outflow, etc.). From the introduction to this section recall that each tracer observation has two properties in this simple system: the physical or absolute time <math> t </math> of the observation (e.g clock time), and the relative time since the sample entered the system, which is the age of the individual tracer <math>\tau </math> . Assuming the tracer enters the system randomly, the joint distribution function describes the number of particles in the lake volume for time interval <math>dt </math> and age interval <math> d \tau </math>. [[image:Lake.png|200px|thumb|right|Fig 2 A well-mixed lake]]

In general, the functional form of c(t,<math>\tau</math>) is required to develop complete information on the joint age-time distribution for our experiment, and this might be accomplished by fitting a particular function to experimental data. However, it may be straight-forward to estimate the mass of our tracer in each sample volume, it is not generally feasible to determine the age of each sample. An alternative strategy is to relax the need for a colmplete description of the isotope population distribution and settle for a partial answer. That is, we assume c(t,<math>\tau</math>) exists but with an unknown functional form, and with certain constraints on the moments. The usual rules of probability apply and we can estimate the moments in t by integrating c(t,<math>\tau</math>) w/re to <math>\tau</math> (see Delhez, 1999 or Duffy, 2010):

<math>
\operatorname{\mu_n}(t) =
\int_0^{\infty} \tau^{n}c(t,\tau)\,d\tau,\quad n=0,1,2...\quad (1)
</math>

The 0th and 1st moment of (1) are given by:

<math>
\mathit{C}(t)=\operatorname{\mu_0}(t) =
\int_0^{\infty} \tau^{0}c(t,\tau)\,d\tau,\quad n=0;\quad (2)</math>

<math>
\mathit{M}(t)=\operatorname{\mu_1}(t) =\int_0^{\infty} \tau^{1}c(t,\tau)\,d\tau,\quad n=1;\quad (3)</math>

where we identify the 0th moment as the tracer concentration C(t) and M(t) the 1st moment of c(t,<math>\tau</math>). The key to our analysis is that the ratio of the 1st to 0th moment is the classical definition of the mean age of the system :

<math>
\mathit{Age}= {\alpha}(t) =
\frac{\mu_1}{\mu_0} \ =\frac{\mathit{M}(t)}{\mathit{C}(t)}\, \quad (4)
</math>

At this point we have defined the tracer as a dynamic variable that depends on the duration that the observation has spent in the lake, and the physical time describing the evolution of all tracer particles in the system. Equations (1-3) define the moment relations for our experiment, and the next step is to develop a physical model for the system.

For a single input and single output, we take the volumetric inflow rate to be <math> Qi [L^3/T])</math>, the outflow as <math>Q [L^3/T]</math>, and for simplicity the flow is initially assumed to be at steady-state (<math>Qi =Q</math>). The input tracer Ci can be isotopes of water (<math>\delta^{18} O</math> or <math>\delta^{2} H</math>), carbon or other solutes. As was the case in the population example given earlier, we expect the mass balance for our system to be conserved w/re to both time and age, and this is represented in the model as:

<math>
\frac{\partial c}{\partial t}+\frac{\partial c}{\partial \tau}=\Gamma_c-\mathit{L}(c),\quad (5)
</math>

where the left hand side is the total derivative for time and age of the isotope, and the right hand side represents tracer inputs, transformations and outputs. Integrating (4) w/re to as in (1), and applying the moment equations (2) yields the following dynamical system for the first 2 moments {n=0,1}:

<math>
\frac{\partial C}{\partial t}=k\left ( \mathit{C_i} - \mathit{C} \right )
</math>

<math>
\frac{\partial M}{\partial t}=\left ( \mathit{C}- k\mathit{M} \right )\qquad (6)
</math>

<math>
\mathit{Age}= {\alpha}(t) =\frac{\mathit{M}(t)}{\mathit{C}(t)},\quad
</math>

<math>
{\alpha}(\infty) =\frac{V}{Q} \ =\frac{\mathit{M}(\infty)}{\mathit{C}(\infty)}\,
</math>

where C(t) is tracer concentration, M(t) is the 1st moment, and their ratio is the the age <math>\alpha (t)</math> and <math>\alpha (\infty)</math> is the steady-state age of the lake, with input Ci . It should be noted that for a well-mixed reservoir, the steady-state age of the tracer is identical to the mean age of the tracer leaving the system (e.g. the steady-state residence time). Equation (6) shows that the age and residence time are actually dynamic quantities, with a constant asymptotic- or long-term average value. The asymptotic value is the traditional steady-state residence time.

The system (6) admits a closed form solution for steady flow conditions (Qi =Q), with constant input (Ci) and uniform initial conditions (C(0)=C0):

<math>
\mathit{C}(t)=e^{-kt}\left ( \ \mathit{C_0}- \mathit{C_i}+ \mathit{C_i}e^{kt}\right )\,
</math>[[image:Fig_2.png|200px|thumb|right|Fig 3 Solutions for various initial conditions and input concentrations]]

<math>
\mathit{M}(t)=\frac{e^{-kt}}{k}\left ( \ \mathit{C_i}e^{kt}- \mathit{C_i}+ \mathit{C_0}kt-\mathit{C_i}kt\right )\, \quad (7)
</math>

<math>
\alpha(t)=\frac{\left ( \ \mathit{C_i}e^{kt}- \mathit{C_i}+ \mathit{C_0}kt- \mathit{C_i}kt\right )}{k\left ( \mathit{C_0}- \mathit{C_i}+ \mathit{C_i}e^{kt}\right )}\,
</math>

For purpose of discussion, Fig. 3 illustrates the tracer concentration solution trajectories for a range of initial conditions and inputs. Figure 4 illustrates the solution for the mean age of the tracer in the lake. It is important to realize that tracer age in the dynamical model is a relative quantity that also depends on the initial conditions and asymptotic “age” of the system at steady-state. In this case we assume the initial tracer age in the lake is α(t)=0 and the inputs Ci are zero age as they enter the lake (e.g. a birth in the population context). Of course other initial and input conditions are possible. Note that as

<math>Q\Rightarrow 0\quad</math> and <math>C_i\Rightarrow 0\quad</math>, the system simply ages in clock time or the solution (7) is <math>\alpha= t</math>.

[[image:Fig_3.png|200px|thumb|right|Fig 4 Case 1:Variable V/Q]]
===Case 1: ''Variable V/Q''===
The solutions for variable in Fig. 4 shows the impact of varying the rate constant k (or steady-state age α( )=k-1) when the input concentration exceeds the initial condition, Ci. ≥ C0. The dynamic age of the tracer evolves from zero age to the asymptotic steady-state value. We see that the average age of the tracer in the lake and, by the well-mixed assumption, the age of the tracer in the outflow, will reach a constant age.
[[image:Fig_4.png|200px|thumb|right|Fig 5 Case 2:Variable Initial Condition C(o)=Co]]

===Case 2: ''Variable Initial Condition Co''===
The solutions for in Fig. 5 demonstrates that age calculations in models must be thought of as “relative age”, or the age since the start of the experiment. The initial condition C0 in Fig. 5 is varied while holding the input value at Ci=1. Note that when C0 > Ci, the early time age (t<V/Q) grows until the older water is displaced. The greater the concentration difference (C0 - Ci), the greater the effect on early time age. At late time in the event, the age solution gradually approaches the asymptotic age <math>\alpha (\infty)</math>.

In the catchment hydrology literature there has been a long- standing discussion about how to interpret the age of runoff. Typically, experiments find that old water or pre-event water chemical signatures dominate the runoff response during and after rainfall events (e.g. the runoff event concentration is much different than the precipitation concentration. It would seem that this simple experiment (case 2) explains why field observations of old water following an input event can be explained as a simple function of two conditions: the difference between the initial and input concentrations (C0 - Ci), and the steady-state age V/Q of the system.
[[image:Fig_5.png|200px|thumb|right|Fig 6 Case 3:Variable Input Ci]]

===Case 3: ''Variable Input Ci''===
In case 3 (Fig. 6) the input for the tracer was varied, for a constant initial concentration (C0 =20). Again note that the early time age is depends on the difference (C0 - Ci) and the steady-state age is V/Q=10 years.
[[image:Fig_6.png|200px|thumb|right|Fig 7 Case 4:Variable Initial Age α(0)]]
===Case 4: ''Variable Initial Age α(0)''===
In cases 1-3, the initial condition for tracer age in the lake was assumed to be <math>\alpha (0)=0</math>. In Case 4 we examine the effect of varying the initial age of the lake water and the solutions are given in Fig. 7. In systems where the initial age is old relative to the characteristic time of our system, the initially old water evolves to the new steady-state age in approximately 3 characteristic time scales, t~3V/Q. This example could be applied to the problem of assessing the impact of management practices, where contaminant inputs have been reduced and the time it takes to see the impacts on the lake or lake outflow is of interest. Performance assessment from reduced nutrient loading from agricultural runoff, or reduced atmospheric pollutants from the energy production and atmospheric contaminants, could apply a dynamic ageing model to assess the time it might take to measure the value of management practices and time to ecosystem recovery.

=== Reactions, Sources and Sinks===

The case of solute interactions is examined next for a 2-component reactive system in series under unsteady flow conditions. Consider the transient 2-component model where the first component is a source term for the 2nd and the 2nd component is subject to first-order loss. [[image:reservoir_3.png|200px|thumb|right| A two-component reactive tracer input with transient flow]]

<math>
\frac{\partial \mathit{V}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \mathit{V C_1}}{\partial t}= \mathit{Q_i}\mathit{C_{1i}} -\mathit{k_1 VC_1} - \mathit{QC_1}
</math>

<math>
\frac{\partial \mathit{V C_2}}{\partial t}= \mathit{Q_i}\mathit{C_{2i}} +\mathit{k_1 VC_1} - \mathit{k_2 VC_2} - \mathit{QC_2}
</math>

where <math>Q_i </math> is the net lake input flux, <math>Q_{s} </math> is the lake input flux from streams, <math>Q</math> is lake outflow, <math>V</math> is the lake volume, <math>C_{1i}</math>and <math>C_{2i}</math> are the lake inputs, <math> k_1, k_2</math> are first order reaction rates and the subscripts refers to each reactive tracer. The observed net inputs are given by:

<math>
\mathit{Q_i}= Q_{s}+A_s P-A_s ET
</math>

<math>
\mathit{C_{ji}}= \left(\frac{Q_{in}}{ \mathit{Q_i}} \mathit{C_{in}}+ \frac{\mathit{A_s P}}{ \mathit{Q_i}} \mathit{C_p}- \frac{\mathit{A_s ET}}{ \mathit{Q_i}} \mathit{C_{ET}}\right )_j, j=1,2
</math>

The zero-moment of tracer concentration equations are rearranged to form a simplified dynamical system:

<math>
V \frac{\partial \mathit{C_1}}{\partial t}= Q_i\left (\mathit{C_{1i}} - \mathit{C_1} \right )-\mathit{k_1 VC_1}
</math>

<math>
V \frac{\partial \mathit{C_2}}{\partial t}= Q_i\left (\mathit{C_{2i}} - \mathit{C_2} \right )+ \mathit{k_1 VC_1} - \mathit{k_2 VC_2}
</math>

The first moment equations for the 2-component system are given by:

<math>
V \frac{\partial \mathit{M_1}}{\partial t}= \mathit{VC_1}- \mathit{Q_i M_1} - \mathit{k_1 VM_1}
</math>

<math>
V \frac{\partial \mathit{M_2}}{\partial t} =\mathit{VC_2} -\mathit{Q_i M_2} + \mathit{k_1 VM_1}-\mathit{k_2 VM_2}
</math>

and the ratio of the first moment to the zeroth moment, or Age for the surface and deep layer are:

<math>
\mathit{Age_1}= {\alpha_1}(t) =\frac{\mathit{M_1}(t)}{\mathit{C_1}(t)},\quad
</math>

<math>
\mathit{Age_2}= {\alpha_2}(t) =\frac{\mathit{M_2}(t)}{\mathit{C_2}(t)},\quad
</math>

To summarize, estimating the age for this 2 component serial reactive system requires the solution of 4 equations: <math>C_1, C_2, M_1, M_2 </math>, and the ratios <math>M_1/C_1, M_2/C_2 </math> provide the age of each component. Note that this formulation shows that the age of each tracer depends on transient flow as well as tracer concentration.

The sequential model can be generalized for n reactions, <math> \mathit{j=1,2,3,..,n} </math>

<math>
V \frac{\partial \mathit{C_1}}{\partial t}= Q_i\left (\mathit{C_{1i}} - \mathit{C_1} \right )-\mathit{k_1 VC_1}
</math>

<math>
V \frac{\partial \mathit{C_j}}{\partial t}= Q_i\left (\mathit{C_{ji}} - \mathit{C_j} \right )-\mathit{k_j VC_j}+\mathit{k_{j-1} VC_{j-1}}
</math>

and

<math>
V \frac{\partial \mathit{M_1}}{\partial t}= \mathit{VC_1}- \mathit{Q_i M_1} - \mathit{k_1 VM_1}
</math>

<math>
V \frac{\partial \mathit{M_j}}{\partial t}= \mathit{VC_{j}} - \mathit{Q_i M_j} -\mathit{k_j MA_j}+\mathit{k_{j-1} VM_{j-1}}
</math>

=== Well-Mixed Tracer and Transient Flow===

We can extend the lake model to include transient flow. Following the same strategy as before the equations for flow and concentration have the form: [[image:reservoir_1.png|200px|thumb|right|Well mixed tracer in transient flow]]

<math>
\frac{\partial \mathit{V}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \left ( \mathit{VC}\right )}{\partial t}= \mathit{Q_i}\mathit{C_i} - \mathit{Q}\mathit{C}
</math>

where <math>Q_i </math> is the lake input flux, <math>Q</math> is lake outflow. Note that it is straight forward to include multiple input loadings <math>Q_i C_i, i=1,2,...n\quad</math>. Expanding the 2nd equation and after some manipulation we arrive at our dynamical system for transient flow, tracer concentration and age for the lake assuming time variable storage-outflow:

<math>
\frac{\partial \mathit{V}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \mathit{C}}{\partial t}= \frac {1}{\tau_c} \left (\mathit{C_i} - \mathit{C} \right )
</math>

<math>
\frac{\partial M}{\partial t}=\mathit{C}- \frac {1}{\tau_c}\mathit{M} \qquad
</math>

<math>
\mathit{Age}= {\alpha}(t) =\frac{\mathit{M}(t)}{\mathit{C}(t)},\quad
</math>

<math>
{\tau_c}(t) =\frac{V(t)}{Q_i(t)}
</math>

where V(t) is the time variable lake volume, C(t) is concentration and M(t) is 1st moment. The characteristic time in this case is not constant <math> {\tau_c}(t) </math> and the equations are nonlinear which will require numerical solutions.

==Two-Zone Lake Model with Incomplete Mixing==

Next we develop a two-zone model with incomplete vertical exchange between the surface layer and a deeper stagnant layer. <math>V(t)= V_1 +V_2 </math> is the total lake volume.<math>C_1 </math> and <math>C_2 </math> are the concentrations for each layer and the layers exchange mass by diffusion, while advection occurs only in the surface layer. The equations for flow and concentration have the form: [[image:reservoir_2.png|200px|thumb|right| A two-zone lake with incomplete mixing and and flow-through in the surface layer]]

<math>
\frac{\partial \mathit{V_1}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \mathit{V_1 C_1}}{\partial t}= \mathit{Q_i}\mathit{C_i} - \mathit{Q}\mathit{C} +E'\left ( C_2 -C_1\right )
</math>

<math>
\frac{\partial \mathit{V_2 C_2}}{\partial t}= -E'\left ( C_2 -C_1\right )
</math>

where <math>Q_i </math> is the net lake input flux, <math>Q</math> is lake outflow, <math>V_1</math> and <math>V_2</math> are the volumes of the surface and deep layers and <math>E'=DA_s/l</math>is a bulk diffusion coefficient where <math>D</math> is molecular diffusion and <math>l</math> is the length scale. In this case we assume the system has multiple observed inputs:

<math>
\mathit{Q_i}= Q_{in}+A_s P+A_s ET
</math>

<math>
\mathit{C_{i}}= \frac{Q_{in}}{ \mathit{Q_i}} \mathit{C_{in}}+ \frac{\mathit{A_s P}}{ \mathit{Q_i}} \mathit{C_p}+ \frac{\mathit{A_s ET}}{ \mathit{Q_i}} \mathit{C_{ET}}
</math>

where <math>\mathit{Q_{in}C_{in}}</math> is the input mass flow rate, <math>\mathit{P C_p} </math> is the precipitation loading rate, <math>\mathit{ET C_{ET}}</math> is evapotranspiration mass flux and <math>A_s </math> is the area of the lake or the surface area between <math>V_1</math> and <math>V_2</math>.

As before the equations are rearranged to form the simplified form of the dynamical system:

<math>
V_1 \frac{\partial \mathit{C_1}}{\partial t}= Q_i\left (\mathit{C_i} - \mathit{C_1} \right )+E'\left (\mathit{C_2} - \mathit{C_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{C_2}}{\partial t}= -E'\left (\mathit{C_2} - \mathit{C_1} \right )
</math>

The first moment equations are given by:

<math>
V_1 \frac{\partial \mathit{M_1}}{\partial t}= \mathit{V_1 C_1}- \mathit{Q_i M_1} +E'\left (\mathit{M_2} - \mathit{M_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{M_2}}{\partial t}=\mathit{V_2 C_2} -E'\left (\mathit{M_2} - \mathit{M_1} \right )
</math>

The ratio of the first moment to the zeroth moment is the Age for the surface and deep layer.

<math>
\mathit{Age_1}= {\alpha_1}(t) =\frac{\mathit{M_1}(t)}{\mathit{C_1}(t)},\quad
</math>

<math>
\mathit{Age_2}= {\alpha_2}(t) =\frac{\mathit{M_2}(t)}{\mathit{C_2}(t)},\quad
</math>

To summarize, estimating the ages for this 2 component system requires the solution of 4 equations: <math>C_1, C_2, M_1, M_2 </math>, and ratios <math>M_1/C_1, M_2/C_2 </math> are calculated to give the ages for the surface and deep layer respectively. Note that this formulation shows that the evolution of tracer age depends on both flow and tracer dynamics.

==Lake-Catchment Dynamical System==

Next we examine the coupled dynamics for a lake-catchment system where groundwater inputs/ouput to/from the lake are unknown and where surface water inputs and outputs to the lake are observed. [[image:Lake-Catchment-model.png|200px|thumb|right| Interacting lake-catchment]]

<math>
\frac{\partial \mathit{V_1}}{\partial t}= \mathit{Q_{1i}} - \mathit{Q_1}+\mathit{Q_{12}}
</math>

<math>
\frac{\partial \mathit{V_2}}{\partial t}= \mathit{Q_{2i}} - \mathit{Q_2}-\mathit{Q_{12}}
</math>

<math>
\frac{\partial \mathit{V_1 C_1}}{\partial t}= \mathit{Q_{1i}}\mathit{C_{1i}} -\mathit{Q_1 C_1} + \mathit{Q_{12}C_{12}}
</math>

<math>
\frac{\partial \mathit{V_2 C_2}}{\partial t}= \mathit{Q_{2i}}\mathit{C_{2i}} -\mathit{Q_2 C_2} - \mathit{Q_{12}C_{12}}
</math>

where <math>Q_{ji} </math>(<math>j=1,2</math>) is the net flux to the lake, where (<math>j=1</math>) refers to the lake and (<math>j=2</math>) the catchment respectively, <math>i</math> indicates input, <math>Q_1</math> is the lake outflow, <math>Q_2</math> is the catchment outflow to the stream. <math>V_1</math> is the lake storage volume and <math>V_2</math> is the catchment storage volume. <math>C_{1i}</math>and <math>C_{2i}</math> are the lake and catchment isotopic inputs and <math>A_1</math>, <math>A_2</math> are surface areas of the lake and catchment respectively. The multiple observed inputs are given by:

<math>
\mathit{Q_{ji}}= \left (\mathit{A_j P}-\mathit{A_j ET}\right ), j=1,2
</math>

<math>
\mathit{C_{ji}}= \left (\frac{\mathit{A_j P}}{ \mathit{Q_{ji}}} \mathit{C_p}- \frac{\mathit{A_j ET}}{ \mathit{Q_{ji}}} \mathit{C_{ET}}\right ), j=1,2
</math>

The total flow at the outlet of the catchment is given by:

<math>
\mathit{Q}= \mathit{Q_1}+\mathit{Q_2}
</math>

The zero-moment or tracer concentration equations can be rearranged to form a simplified dynamical system:

<math>
V_1 \frac{\partial \mathit{C_1}}{\partial t}= Q_{1i}\left (\mathit{C_{1i}} - \mathit{C_1} \right )+Q_{12}\left (\mathit{C_{12}} - \mathit{C_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{C_2}}{\partial t}= Q_{2i}\left (\mathit{C_{2i}} - \mathit{C_2} \right )-Q_{12}\left (\mathit{C_{12}} - \mathit{C_2} \right )
</math>

Note that <math>C_{12} </math> depends on the direction of flow or the sign of <math> \pm Q_{12} </math>.

<math>
C_{12} =
\begin{cases}
C_1 & Q_{12} \le 0 \\
C_2 & \mbox{otherwise}
\end{cases}
</math>

The first moment equations for the lake-catchment system are given by:

<math>
V_1 \frac{\partial \mathit{M_1}}{\partial t}= \mathit{V_1 C_1}- \mathit{Q_{1i} M_1} + Q_{12}\left (\mathit{M_{12}} - \mathit{M_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{M_2}}{\partial t} =\mathit{V_2 C_2} -\mathit{Q_{2i} M_2} -Q_{12}\left (\mathit{M_{12}} - \mathit{M_2} \right )
</math>

where again <math>M_{12} </math> depends on the direction of flow or the sign of <math> \pm Q_{12} </math>.

<math>
M_{12} =
\begin{cases}
M_1 & Q_{12} \le 0 \\
M_2 & \mbox{otherwise}
\end{cases}
</math>

and the ratio of the first moment to the zeroth moment, or Age for the surface and deep layer are:

<math>
\mathit{Age_1}= {\alpha_1}(t) =\frac{\mathit{M_1}(t)}{\mathit{C_1}(t)},\quad
</math>

<math>
\mathit{Age_2}= {\alpha_2}(t) =\frac{\mathit{M_2}(t)}{\mathit{C_2}(t)},\quad
</math>

To summarize, estimating the age for this lake-catchment system requires the solution of 4 equations: <math>C_1, C_2, A_1, A_2 </math>, and the ratios <math>A_1/C_1, A_2/C_2 </math> provide the age of each component. Note that this formulation shows that the age of each tracer depends on transient flow as well as tracer concentration.

==A Distributed Model for Lake-Catchment Dynamics==



{{#set:
Expertise=Ecosystem_modeling|
Expertise=Computational_hydrology|
Owner=Chris_Duffy|
Participants=Gopal_Bhatt|
Participants=Jordan_Read|
Participants=Paul_Hanson|
StartDate=2014-05-11|
SubTask=Write_paper:_Dynamic_age_of_water_and_carbon|
TargetDate=2014-11-11|
Type=Medium}}

Develop mathematical model of age of water and carbon

2014-09-10T10:11:13Z

Chris: Added PropertyValue: Participants = Gopal Bhatt

[[Category:Task]]

==Concept==
[[File:ExampleAgeOfWater.jpg|thumb|right|Fig.1 The simulated catchment isoscape illustrating the 2009 annual mean spatial pattern for groundwater age at the Shale Hills CZO (Bhatt, 2012) based on the PIHM model and calibration with CZO data. The overall space-time mean age is 217 days. ]]
From the physical science perspective this research focuses on theoretical and experimental aspects of the isotopic “age” of water and carbon in lake-catchment systems. In this context, “age” is defined as the time since the water parcel and environmental tracer entered the system as precipitation. We note that each of our communities have developed an observing system for isotope ratios of carbon, oxygen and hydrogen but with very different science questions. In this research we will test a framework using models and data for defining a unified “isoscape” for the watershed-lake system, forming a richer and more collaborative shared research strategy. Our hypothesis is that the lake-catchment isoscape provides the experimental basis for predicting flow paths, residence times and the relative age of water in space and time, and that understanding these spatiotemporal patterns will provide a deeper understanding of fundamental biogeochemical processes including carbon and nitrogen cycling within the lake-catchment system. There is a wide literature on the use of residence time and relative age distribution of isotopes in environmental systems. Theories have been proposed using tracers for age modeling in ocean ventilation, atmospheric circulation, soil water, stream, groundwater flow, biophysics of vegetation photosynthesis as well as the circulation of blood. We begin this research with a simple model for the age of an environmental tracers in a ecohydrologic setting. Details of the approach can be found in Duffy (2010). The figure below is a simulation for the space-time distribution of isotopic age from the Shale Hills Critical Zone Observatory (Bhatt, 2012).

A useful analogy to understand the concept of “age” comes from population biology (Forester, 1959; Rotenberg, 1972). Consider a random population of individuals (species or particles) being born, dying and migrating. Given a long-term census of the population, the distribution of the size of the population through time and the distribution of ages of the population through time can be evaluated. Other moments may also be useful and can easily be determined from the census given enough data. The important concept to consider here is that there are actually two things we wish to evaluate in our long-term census: the number of individuals in our population through time, and the mean age of the population through time. For dissolved chemical species in water each component is characterized by physical time (clock time) and by the relative time or age since the species entered the system. The relative time for each component has it's own frame of reference particular to the dynamics of the flow system, the transport processes and the interaction with other components in the system.

In the broader context, our goal is to construct a predictive model for the fifth dimension of environmental tracers, the space-time-age distribution of the physical, chemical and biological pathways of the terrestrial environmental systems.

== A Well-Mixed Lake ==
We begin with a simple lake or reservoir model for a single input and output as shown in Fig. 2. We define the isotopic “age” as the elapsed time since the particular tracer or isotope has entered the system as input. In other words the tracer is assumed to have zero age upon entering the lake. We note that in general, the tracer age is a statistical quantity that depends on the sources of tracer, the particular transport and flow processes, the biochemical interactions, as well as the boundaries and initial conditions of the physical system. Over the course of this project we will demonstrate the concept of “age” for a fully-coupled, spatially-distributed, lake-catchment system. In this basic example to follow we demonstrate how the age of the tracer is a simple extension of traditional ecohydrologic models.

Figure 1 illustrates a simplified physical domain for this example. Typical observations for flow and tracer concentration would include sampling the lake inputs (precipitation, streamflow, groundwater, etc.), sampling the lake storage volume to determine the average values, and sampling the lake outputs (evapotranspiration, groundwater, surface outflow, etc.). From the introduction to this section recall that each tracer observation has two properties in this simple system: the physical or absolute time <math> t </math> of the observation (e.g clock time), and the relative time since the sample entered the system, which is the age of the individual tracer <math>\tau </math> . Assuming the tracer enters the system randomly, the joint distribution function describes the number of particles in the lake volume for time interval <math>dt </math> and age interval <math> d \tau </math>. [[image:Lake.png|200px|thumb|right|Fig 2 A well-mixed lake]]

In general, the functional form of c(t,<math>\tau</math>) is required to develop complete information on the joint age-time distribution for our experiment, and this might be accomplished by fitting a particular function to experimental data. However, it may be straight-forward to estimate the mass of our tracer in each sample volume, it is not generally feasible to determine the age of each sample. An alternative strategy is to relax the need for a colmplete description of the isotope population distribution and settle for a partial answer. That is, we assume c(t,<math>\tau</math>) exists but with an unknown functional form, and with certain constraints on the moments. The usual rules of probability apply and we can estimate the moments in t by integrating c(t,<math>\tau</math>) w/re to <math>\tau</math> (see Delhez, 1999 or Duffy, 2010):

<math>
\operatorname{\mu_n}(t) =
\int_0^{\infty} \tau^{n}c(t,\tau)\,d\tau,\quad n=0,1,2...\quad (1)
</math>

The 0th and 1st moment of (1) are given by:

<math>
\mathit{C}(t)=\operatorname{\mu_0}(t) =
\int_0^{\infty} \tau^{0}c(t,\tau)\,d\tau,\quad n=0;\quad (2)</math>

<math>
\mathit{M}(t)=\operatorname{\mu_1}(t) =\int_0^{\infty} \tau^{1}c(t,\tau)\,d\tau,\quad n=1;\quad (3)</math>

where we identify the 0th moment as the tracer concentration C(t) and M(t) the 1st moment of c(t,<math>\tau</math>). The key to our analysis is that the ratio of the 1st to 0th moment is the classical definition of the mean age of the system :

<math>
\mathit{Age}= {\alpha}(t) =
\frac{\mu_1}{\mu_0} \ =\frac{\mathit{M}(t)}{\mathit{C}(t)}\, \quad (4)
</math>

At this point we have defined the tracer as a dynamic variable that depends on the duration that the observation has spent in the lake, and the physical time describing the evolution of all tracer particles in the system. Equations (1-3) define the moment relations for our experiment, and the next step is to develop a physical model for the system.

For a single input and single output, we take the volumetric inflow rate to be <math> Qi [L^3/T])</math>, the outflow as <math>Q [L^3/T]</math>, and for simplicity the flow is initially assumed to be at steady-state (<math>Qi =Q</math>). The input tracer Ci can be isotopes of water (<math>\delta^{18} O</math> or <math>\delta^{2} H</math>), carbon or other solutes. As was the case in the population example given earlier, we expect the mass balance for our system to be conserved w/re to both time and age, and this is represented in the model as:

<math>
\frac{\partial c}{\partial t}+\frac{\partial c}{\partial \tau}=\Gamma_c-\mathit{L}(c),\quad (5)
</math>

where the left hand side is the total derivative for time and age of the isotope, and the right hand side represents tracer inputs, transformations and outputs. Integrating (4) w/re to as in (1), and applying the moment equations (2) yields the following dynamical system for the first 2 moments {n=0,1}:

<math>
\frac{\partial C}{\partial t}=k\left ( \mathit{C_i} - \mathit{C} \right )
</math>

<math>
\frac{\partial M}{\partial t}=\left ( \mathit{C}- k\mathit{M} \right )\qquad (6)
</math>

<math>
\mathit{Age}= {\alpha}(t) =\frac{\mathit{M}(t)}{\mathit{C}(t)},\quad
</math>

<math>
{\alpha}(\infty) =\frac{V}{Q} \ =\frac{\mathit{M}(\infty)}{\mathit{C}(\infty)}\,
</math>

where C(t) is tracer concentration, M(t) is the 1st moment, and their ratio is the the age <math>\alpha (t)</math> and <math>\alpha (\infty)</math> is the steady-state age of the lake, with input Ci . It should be noted that for a well-mixed reservoir, the steady-state age of the tracer is identical to the mean age of the tracer leaving the system (e.g. the steady-state residence time). Equation (6) shows that the age and residence time are actually dynamic quantities, with a constant asymptotic- or long-term average value. The asymptotic value is the traditional steady-state residence time.

The system (6) admits a closed form solution for steady flow conditions (Qi =Q), with constant input (Ci) and uniform initial conditions (C(0)=C0):

<math>
\mathit{C}(t)=e^{-kt}\left ( \ \mathit{C_0}- \mathit{C_i}+ \mathit{C_i}e^{kt}\right )\,
</math>[[image:Fig_2.png|200px|thumb|right|Fig 3 Solutions for various initial conditions and input concentrations]]

<math>
\mathit{M}(t)=\frac{e^{-kt}}{k}\left ( \ \mathit{C_i}e^{kt}- \mathit{C_i}+ \mathit{C_0}kt-\mathit{C_i}kt\right )\, \quad (7)
</math>

<math>
\alpha(t)=\frac{\left ( \ \mathit{C_i}e^{kt}- \mathit{C_i}+ \mathit{C_0}kt- \mathit{C_i}kt\right )}{k\left ( \mathit{C_0}- \mathit{C_i}+ \mathit{C_i}e^{kt}\right )}\,
</math>

For purpose of discussion, Fig. 3 illustrates the tracer concentration solution trajectories for a range of initial conditions and inputs. Figure 4 illustrates the solution for the mean age of the tracer in the lake. It is important to realize that tracer age in the dynamical model is a relative quantity that also depends on the initial conditions and asymptotic “age” of the system at steady-state. In this case we assume the initial tracer age in the lake is α(t)=0 and the inputs Ci are zero age as they enter the lake (e.g. a birth in the population context). Of course other initial and input conditions are possible. Note that as

<math>Q\Rightarrow 0\quad</math> and <math>C_i\Rightarrow 0\quad</math>, the system simply ages in clock time or the solution (7) is <math>\alpha= t</math>.

[[image:Fig_3.png|200px|thumb|right|Fig 4 Case 1:Variable V/Q]]
===Case 1: ''Variable V/Q''===
The solutions for variable in Fig. 4 shows the impact of varying the rate constant k (or steady-state age α( )=k-1) when the input concentration exceeds the initial condition, Ci. ≥ C0. The dynamic age of the tracer evolves from zero age to the asymptotic steady-state value. We see that the average age of the tracer in the lake and, by the well-mixed assumption, the age of the tracer in the outflow, will reach a constant age.
[[image:Fig_4.png|200px|thumb|right|Fig 5 Case 2:Variable Initial Condition C(o)=Co]]

===Case 2: ''Variable Initial Condition Co''===
The solutions for in Fig. 5 demonstrates that age calculations in models must be thought of as “relative age”, or the age since the start of the experiment. The initial condition C0 in Fig. 5 is varied while holding the input value at Ci=1. Note that when C0 > Ci, the early time age (t<V/Q) grows until the older water is displaced. The greater the concentration difference (C0 - Ci), the greater the effect on early time age. At late time in the event, the age solution gradually approaches the asymptotic age <math>\alpha (\infty)</math>.

In the catchment hydrology literature there has been a long- standing discussion about how to interpret the age of runoff. Typically, experiments find that old water or pre-event water chemical signatures dominate the runoff response during and after rainfall events (e.g. the runoff event concentration is much different than the precipitation concentration. It would seem that this simple experiment (case 2) explains why field observations of old water following an input event can be explained as a simple function of two conditions: the difference between the initial and input concentrations (C0 - Ci), and the steady-state age V/Q of the system.
[[image:Fig_5.png|200px|thumb|right|Fig 6 Case 3:Variable Input Ci]]

===Case 3: ''Variable Input Ci''===
In case 3 (Fig. 6) the input for the tracer was varied, for a constant initial concentration (C0 =20). Again note that the early time age is depends on the difference (C0 - Ci) and the steady-state age is V/Q=10 years.
[[image:Fig_6.png|200px|thumb|right|Fig 7 Case 4:Variable Initial Age α(0)]]
===Case 4: ''Variable Initial Age α(0)''===
In cases 1-3, the initial condition for tracer age in the lake was assumed to be <math>\alpha (0)=0</math>. In Case 4 we examine the effect of varying the initial age of the lake water and the solutions are given in Fig. 7. In systems where the initial age is old relative to the characteristic time of our system, the initially old water evolves to the new steady-state age in approximately 3 characteristic time scales, t~3V/Q. This example could be applied to the problem of assessing the impact of management practices, where contaminant inputs have been reduced and the time it takes to see the impacts on the lake or lake outflow is of interest. Performance assessment from reduced nutrient loading from agricultural runoff, or reduced atmospheric pollutants from the energy production and atmospheric contaminants, could apply a dynamic ageing model to assess the time it might take to measure the value of management practices and time to ecosystem recovery.

=== Reactions, Sources and Sinks===

The case of solute interactions is examined next for a 2-component reactive system in series under unsteady flow conditions. Consider the transient 2-component model where the first component is a source term for the 2nd and the 2nd component is subject to first-order loss. [[image:reservoir_3.png|200px|thumb|right| A two-component reactive tracer input with transient flow]]

<math>
\frac{\partial \mathit{V}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \mathit{V C_1}}{\partial t}= \mathit{Q_i}\mathit{C_{1i}} -\mathit{k_1 VC_1} - \mathit{QC_1}
</math>

<math>
\frac{\partial \mathit{V C_2}}{\partial t}= \mathit{Q_i}\mathit{C_{2i}} +\mathit{k_1 VC_1} - \mathit{k_2 VC_2} - \mathit{QC_2}
</math>

where <math>Q_i </math> is the net lake input flux, <math>Q_{s} </math> is the lake input flux from streams, <math>Q</math> is lake outflow, <math>V</math> is the lake volume, <math>C_{1i}</math>and <math>C_{2i}</math> are the lake inputs, <math> k_1, k_2</math> are first order reaction rates and the subscripts refers to each reactive tracer. The observed net inputs are given by:

<math>
\mathit{Q_i}= Q_{s}+A_s P-A_s ET
</math>

<math>
\mathit{C_{ji}}= \left(\frac{Q_{in}}{ \mathit{Q_i}} \mathit{C_{in}}+ \frac{\mathit{A_s P}}{ \mathit{Q_i}} \mathit{C_p}- \frac{\mathit{A_s ET}}{ \mathit{Q_i}} \mathit{C_{ET}}\right )_j, j=1,2
</math>

The zero-moment of tracer concentration equations are rearranged to form a simplified dynamical system:

<math>
V \frac{\partial \mathit{C_1}}{\partial t}= Q_i\left (\mathit{C_{1i}} - \mathit{C_1} \right )-\mathit{k_1 VC_1}
</math>

<math>
V \frac{\partial \mathit{C_2}}{\partial t}= Q_i\left (\mathit{C_{2i}} - \mathit{C_2} \right )+ \mathit{k_1 VC_1} - \mathit{k_2 VC_2}
</math>

The first moment equations for the 2-component system are given by:

<math>
V \frac{\partial \mathit{M_1}}{\partial t}= \mathit{VC_1}- \mathit{Q_i M_1} - \mathit{k_1 VM_1}
</math>

<math>
V \frac{\partial \mathit{M_2}}{\partial t} =\mathit{VC_2} -\mathit{Q_i M_2} + \mathit{k_1 VM_1}-\mathit{k_2 VM_2}
</math>

and the ratio of the first moment to the zeroth moment, or Age for the surface and deep layer are:

<math>
\mathit{Age_1}= {\alpha_1}(t) =\frac{\mathit{M_1}(t)}{\mathit{C_1}(t)},\quad
</math>

<math>
\mathit{Age_2}= {\alpha_2}(t) =\frac{\mathit{M_2}(t)}{\mathit{C_2}(t)},\quad
</math>

To summarize, estimating the age for this 2 component serial reactive system requires the solution of 4 equations: <math>C_1, C_2, M_1, M_2 </math>, and the ratios <math>M_1/C_1, M_2/C_2 </math> provide the age of each component. Note that this formulation shows that the age of each tracer depends on transient flow as well as tracer concentration.

The sequential model can be generalized for n reactions, <math> \mathit{j=1,2,3,..,n} </math>

<math>
V \frac{\partial \mathit{C_1}}{\partial t}= Q_i\left (\mathit{C_{1i}} - \mathit{C_1} \right )-\mathit{k_1 VC_1}
</math>

<math>
V \frac{\partial \mathit{C_j}}{\partial t}= Q_i\left (\mathit{C_{ji}} - \mathit{C_j} \right )-\mathit{k_j VC_j}+\mathit{k_{j-1} VC_{j-1}}
</math>

and

<math>
V \frac{\partial \mathit{M_1}}{\partial t}= \mathit{VC_1}- \mathit{Q_i M_1} - \mathit{k_1 VM_1}
</math>

<math>
V \frac{\partial \mathit{M_j}}{\partial t}= \mathit{VC_{j}} - \mathit{Q_i M_j} -\mathit{k_j MA_j}+\mathit{k_{j-1} VM_{j-1}}
</math>

=== Well-Mixed Tracer and Transient Flow===

We can extend the lake model to include transient flow. Following the same strategy as before the equations for flow and concentration have the form: [[image:reservoir_1.png|200px|thumb|right|Well mixed tracer in transient flow]]

<math>
\frac{\partial \mathit{V}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \left ( \mathit{VC}\right )}{\partial t}= \mathit{Q_i}\mathit{C_i} - \mathit{Q}\mathit{C}
</math>

where <math>Q_i </math> is the lake input flux, <math>Q</math> is lake outflow. Note that it is straight forward to include multiple input loadings <math>Q_i C_i, i=1,2,...n\quad</math>. Expanding the 2nd equation and after some manipulation we arrive at our dynamical system for transient flow, tracer concentration and age for the lake assuming time variable storage-outflow:

<math>
\frac{\partial \mathit{V}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \mathit{C}}{\partial t}= \frac {1}{\tau_c} \left (\mathit{C_i} - \mathit{C} \right )
</math>

<math>
\frac{\partial M}{\partial t}=\mathit{C}- \frac {1}{\tau_c}\mathit{M} \qquad
</math>

<math>
\mathit{Age}= {\alpha}(t) =\frac{\mathit{M}(t)}{\mathit{C}(t)},\quad
</math>

<math>
{\tau_c}(t) =\frac{V(t)}{Q_i(t)}
</math>

where V(t) is the time variable lake volume, C(t) is concentration and M(t) is 1st moment. The characteristic time in this case is not constant <math> {\tau_c}(t) </math> and the equations are nonlinear which will require numerical solutions.

==Two-Zone Lake Model with Incomplete Mixing==

Next we develop a two-zone model with incomplete vertical exchange between the surface layer and a deeper stagnant layer. <math>V(t)= V_1 +V_2 </math> is the total lake volume.<math>C_1 </math> and <math>C_2 </math> are the concentrations for each layer and the layers exchange mass by diffusion, while advection occurs only in the surface layer. The equations for flow and concentration have the form: [[image:reservoir_2.png|200px|thumb|right| A two-zone lake with incomplete mixing and and flow-through in the surface layer]]

<math>
\frac{\partial \mathit{V_1}}{\partial t}= \mathit{Q_i} - \mathit{Q}
</math>

<math>
\frac{\partial \mathit{V_1 C_1}}{\partial t}= \mathit{Q_i}\mathit{C_i} - \mathit{Q}\mathit{C} +E'\left ( C_2 -C_1\right )
</math>

<math>
\frac{\partial \mathit{V_2 C_2}}{\partial t}= -E'\left ( C_2 -C_1\right )
</math>

where <math>Q_i </math> is the net lake input flux, <math>Q</math> is lake outflow, <math>V_1</math> and <math>V_2</math> are the volumes of the surface and deep layers and <math>E'=DA_s/l</math>is a bulk diffusion coefficient where <math>D</math> is molecular diffusion and <math>l</math> is the length scale. In this case we assume the system has multiple observed inputs:

<math>
\mathit{Q_i}= Q_{in}+A_s P+A_s ET
</math>

<math>
\mathit{C_{i}}= \frac{Q_{in}}{ \mathit{Q_i}} \mathit{C_{in}}+ \frac{\mathit{A_s P}}{ \mathit{Q_i}} \mathit{C_p}+ \frac{\mathit{A_s ET}}{ \mathit{Q_i}} \mathit{C_{ET}}
</math>

where <math>\mathit{Q_{in}C_{in}}</math> is the input mass flow rate, <math>\mathit{P C_p} </math> is the precipitation loading rate, <math>\mathit{ET C_{ET}}</math> is evapotranspiration mass flux and <math>A_s </math> is the area of the lake or the surface area between <math>V_1</math> and <math>V_2</math>.

As before the equations are rearranged to form the simplified form of the dynamical system:

<math>
V_1 \frac{\partial \mathit{C_1}}{\partial t}= Q_i\left (\mathit{C_i} - \mathit{C_1} \right )+E'\left (\mathit{C_2} - \mathit{C_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{C_2}}{\partial t}= -E'\left (\mathit{C_2} - \mathit{C_1} \right )
</math>

The first moment equations are given by:

<math>
V_1 \frac{\partial \mathit{M_1}}{\partial t}= \mathit{V_1 C_1}- \mathit{Q_i M_1} +E'\left (\mathit{M_2} - \mathit{M_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{M_2}}{\partial t}=\mathit{V_2 C_2} -E'\left (\mathit{M_2} - \mathit{M_1} \right )
</math>

The ratio of the first moment to the zeroth moment is the Age for the surface and deep layer.

<math>
\mathit{Age_1}= {\alpha_1}(t) =\frac{\mathit{M_1}(t)}{\mathit{C_1}(t)},\quad
</math>

<math>
\mathit{Age_2}= {\alpha_2}(t) =\frac{\mathit{M_2}(t)}{\mathit{C_2}(t)},\quad
</math>

To summarize, estimating the ages for this 2 component system requires the solution of 4 equations: <math>C_1, C_2, M_1, M_2 </math>, and ratios <math>M_1/C_1, M_2/C_2 </math> are calculated to give the ages for the surface and deep layer respectively. Note that this formulation shows that the evolution of tracer age depends on both flow and tracer dynamics.

==Lake-Catchment Dynamical System==

Next we examine the coupled dynamics for a lake-catchment system where groundwater inputs/ouput to/from the lake are unknown and where surface water inputs and outputs to the lake are observed. [[image:Lake-Catchment-model.png|200px|thumb|right| Interacting lake-catchment]]

<math>
\frac{\partial \mathit{V_1}}{\partial t}= \mathit{Q_{1i}} - \mathit{Q_1}+\mathit{Q_{12}}
</math>

<math>
\frac{\partial \mathit{V_2}}{\partial t}= \mathit{Q_{2i}} - \mathit{Q_2}-\mathit{Q_{12}}
</math>

<math>
\frac{\partial \mathit{V_1 C_1}}{\partial t}= \mathit{Q_{1i}}\mathit{C_{1i}} -\mathit{Q_1 C_1} + \mathit{Q_{12}C_{12}}
</math>

<math>
\frac{\partial \mathit{V_2 C_2}}{\partial t}= \mathit{Q_{2i}}\mathit{C_{2i}} -\mathit{Q_2 C_2} - \mathit{Q_{12}C_{12}}
</math>

where <math>Q_{ji} </math>(<math>j=1,2</math>) is the net flux to the lake, where (<math>j=1</math>) refers to the lake and (<math>j=2</math>) the catchment respectively, <math>i</math> indicates input, <math>Q_1</math> is the lake outflow, <math>Q_2</math> is the catchment outflow to the stream. <math>V_1</math> is the lake storage volume and <math>V_2</math> is the catchment storage volume. <math>C_{1i}</math>and <math>C_{2i}</math> are the lake and catchment isotopic inputs and <math>A_1</math>, <math>A_2</math> are surface areas of the lake and catchment respectively. The multiple observed inputs are given by:

<math>
\mathit{Q_{ji}}= \left (\mathit{A_j P}-\mathit{A_j ET}\right ), j=1,2
</math>

<math>
\mathit{C_{ji}}= \left (\frac{\mathit{A_j P}}{ \mathit{Q_{ji}}} \mathit{C_p}- \frac{\mathit{A_j ET}}{ \mathit{Q_{ji}}} \mathit{C_{ET}}\right ), j=1,2
</math>

The total flow at the outlet of the catchment is given by:

<math>
\mathit{Q}= \mathit{Q_1}+\mathit{Q_2}
</math>

The zero-moment or tracer concentration equations can be rearranged to form a simplified dynamical system:

<math>
V_1 \frac{\partial \mathit{C_1}}{\partial t}= Q_{1i}\left (\mathit{C_{1i}} - \mathit{C_1} \right )+Q_{12}\left (\mathit{C_{12}} - \mathit{C_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{C_2}}{\partial t}= Q_{2i}\left (\mathit{C_{2i}} - \mathit{C_2} \right )-Q_{12}\left (\mathit{C_{12}} - \mathit{C_2} \right )
</math>

Note that <math>C_{12} </math> depends on the direction of flow or the sign of <math> \pm Q_{12} </math>.

<math>
C_{12} =
\begin{cases}
C_1 & Q_{12} \le 0 \\
C_2 & \mbox{otherwise}
\end{cases}
</math>

The first moment equations for the lake-catchment system are given by:

<math>
V_1 \frac{\partial \mathit{M_1}}{\partial t}= \mathit{V_1 C_1}- \mathit{Q_{1i} M_1} + Q_{12}\left (\mathit{M_{12}} - \mathit{M_1} \right )
</math>

<math>
V_2 \frac{\partial \mathit{M_2}}{\partial t} =\mathit{V_2 C_2} -\mathit{Q_{2i} M_2} -Q_{12}\left (\mathit{M_{12}} - \mathit{M_2} \right )
</math>

where again <math>M_{12} </math> depends on the direction of flow or the sign of <math> \pm Q_{12} </math>.

<math>
M_{12} =
\begin{cases}
M_1 & Q_{12} \le 0 \\
M_2 & \mbox{otherwise}
\end{cases}
</math>

and the ratio of the first moment to the zeroth moment, or Age for the surface and deep layer are:

<math>
\mathit{Age_1}= {\alpha_1}(t) =\frac{\mathit{M_1}(t)}{\mathit{C_1}(t)},\quad
</math>

<math>
\mathit{Age_2}= {\alpha_2}(t) =\frac{\mathit{M_2}(t)}{\mathit{C_2}(t)},\quad
</math>

To summarize, estimating the age for this lake-catchment system requires the solution of 4 equations: <math>C_1, C_2, A_1, A_2 </math>, and the ratios <math>A_1/C_1, A_2/C_2 </math> provide the age of each component. Note that this formulation shows that the age of each tracer depends on transient flow as well as tracer concentration.



{{#set:
Expertise=Ecosystem_modeling|
Expertise=Computational_hydrology|
Owner=Chris_Duffy|
Participants=Gopal_Bhatt|
Participants=Jordan_Read|
Participants=Paul_Hanson|
StartDate=2014-05-11|
SubTask=Write_paper:_Dynamic_age_of_water_and_carbon|
TargetDate=2014-11-11|
Type=Medium}}

G16 Workshop on Modeling the Age of Water

2014-09-09T08:43:02Z

Chris: Added PropertyValue: Participants = Chris Duffy

[[Category:Task]]

<center>
=== Workshop: Modeling the Age of Water (The first steps toward the age of carbon)===
Sunday, October 26th 2014
 
Jovance – The meeting venue for GLEON 16
</center>

The purpose of this workshop is to give participants first hand experience with lake catchment hydrology modeling, as well as lake and reservoir hydrodynamic modeling, while at the same time learning some of the principles underlying the use of stable isotopes to age water and identify its flowpaths. These are the initial steps toward using integrated catchment-lake models to identify sources and flowpaths of both water and carbon through lake and reservoir catchments. This work is conducted within the context of “organic team science”, which is an approach to science that pools knowledge, effort, and resources toward a common set of goals, using online resources. Participants will be invited to collaborate on lake and reservoir catchment modeling projects, with specific science questions being identified at the end of the workshop.

==== Abbreviated Agenda ====
{| class="wikitable"
! Time
! Topic
|-
| {{nowrap|09:00am - 10:00am}}
| Principles of catchment hydrology modeling
|-
| {{nowrap|10:00am - 11:00am}}
| Installing and configuring PIHM
|-
| {{nowrap|11:00am - 12:00pm}}
| Using PIHM to model a catchment
|-
| {{nowrap|01:00pm - 03:00pm}}
| Using GLM to model lake hydrodynamics and water quality
|-
| {{nowrap|03:00pm - 05:00pm}}
| Principles of organic team science implemented in The Age of Water and Carbon and discussion of science applications
|}

==Course materials==

Links to course materials will be provided in early October. Stay tuned.



{{#set:
Owner=Paul_Hanson|
Participants=Chris_Duffy|
StartDate=2014-07-18|
SubTask=Participant_list|
SubTask=Logistics|
SubTask=Leadership_list|
SubTask=Preparation_materials|
TargetDate=2014-10-24|
Type=High}}

G16 Workshop on Modeling the Age of Water

2014-09-09T08:42:18Z

Chris:

[[Category:Task]]

<center>
=== Workshop: Modeling the Age of Water (The first steps toward the age of carbon)===
Sunday, October 26th 2014
 
Jovance – The meeting venue for GLEON 16
</center>

The purpose of this workshop is to give participants first hand experience with lake catchment hydrology modeling, as well as lake and reservoir hydrodynamic modeling, while at the same time learning some of the principles underlying the use of stable isotopes to age water and identify its flowpaths. These are the initial steps toward using integrated catchment-lake models to identify sources and flowpaths of both water and carbon through lake and reservoir catchments. This work is conducted within the context of “organic team science”, which is an approach to science that pools knowledge, effort, and resources toward a common set of goals, using online resources. Participants will be invited to collaborate on lake and reservoir catchment modeling projects, with specific science questions being identified at the end of the workshop.

==== Abbreviated Agenda ====
{| class="wikitable"
! Time
! Topic
|-
| {{nowrap|09:00am - 10:00am}}
| Principles of catchment hydrology modeling
|-
| {{nowrap|10:00am - 11:00am}}
| Installing and configuring PIHM
|-
| {{nowrap|11:00am - 12:00pm}}
| Using PIHM to model a catchment
|-
| {{nowrap|01:00pm - 03:00pm}}
| Using GLM to model lake hydrodynamics and water quality
|-
| {{nowrap|03:00pm - 05:00pm}}
| Principles of organic team science implemented in The Age of Water and Carbon and discussion of science applications
|}

==Course materials==

Links to course materials will be provided in early October. Stay tuned.



{{#set:
Owner=Paul_Hanson|
StartDate=2014-07-18|
SubTask=Participant_list|
SubTask=Logistics|
SubTask=Leadership_list|
SubTask=Preparation_materials|
TargetDate=2014-10-24|
Type=High}}

Engage broader community

2014-09-08T20:06:08Z

Chris: Added PropertyValue: SubTask = Set up first advisory com meeting

[[Category:Task]]


{{#set:|
SubTask=Set_up_first_advisory_com_meeting}}

2014 first Steering Committee Meeting

2014-09-08T20:06:07Z

Chris: Creating new page with Category: Task

[[Category:Task]]

G16 Age of Water workshop preparation materials

2014-09-08T20:03:49Z

Chris:

[[Category:Task]]


The workshop material for downloading the geospatial data will be available by the first week of October. Gopal will notify Hillary as soon as available.


{{#set:
Owner=Hilary_Dugan|
Participants=Gopal_Bhatt|
StartDate=2014-09-20|
TargetDate=2014-10-10|
Type=Medium}}

G16 Age of Water workshop preparation materials

2014-09-08T20:00:24Z

Chris: Added PropertyValue: Participants = Gopal Bhatt

[[Category:Task]]


{{#set:
Owner=Hilary_Dugan|
Participants=Gopal_Bhatt|
Progress=10|
StartDate=2014-09-20|
TargetDate=2014-10-10}}

G16 Workshop on Modeling the Age of Water

2014-09-08T19:59:41Z

Chris: Deleted PropertyValue: Participants = Gopal Bhat

[[Category:Task]]

<center>
=== Workshop: Modeling the Age of Water (The first steps toward the age of carbon)===
Sunday, October 26th 2014
 
Jovance – The meeting venue for GLEON 16
</center>

The purpose of this workshop is to give participants first hand experience with lake catchment hydrology modeling, as well as lake and reservoir hydrodynamic modeling, while at the same time learning some of the principles underlying the use of stable isotopes to age water and identify its flowpaths. These are the initial steps toward using integrated catchment-lake models to identify sources and flowpaths of both water and carbon through lake and reservoir catchments. This work is conducted within the context of “organic team science”, which is an approach to science that pools knowledge, effort, and resources toward a common set of goals, using online resources. Participants will be invited to collaborate on lake and reservoir catchment modeling projects, with specific science questions being identified at the end of the workshop.

==== Abbreviated Agenda ====
{| class="wikitable"
! Time
! Topic
|-
| {{nowrap|09:00am - 10:00am}}
| Principles of catchment hydrology modeling
|-
| {{nowrap|10:00am - 11:00am}}
| Installing and configuring PIHM
|-
| {{nowrap|11:00am - 12:00pm}}
| Using PIHM to model a catchment
|-
| {{nowrap|01:00pm - 03:00pm}}
| Using GLM to model lake hydrodynamics and water quality
|-
| {{nowrap|03:00pm - 05:00pm}}
| Principles of organic team science implemented in The Age of Water and Carbon and discussion of science applications
|}



{{#set:
Owner=Paul_Hanson|
StartDate=2014-07-18|
SubTask=Participant_list|
SubTask=Logistics|
SubTask=Leadership_list|
SubTask=Preparation_materials|
TargetDate=2014-10-24|
Type=High}}