Age of Water - User contributions [en]

File:Revision3.docx

2015-09-29T01:23:23Z

Xuan: Xuan uploaded a new version of "File:Revision3.docx"

Outline

2015-09-29T01:16:22Z

Xuan: Undo revision 4073 by Xuan (talk)

[[Category:Task]]

1. Introduction and Motivation

2. Historical Research at Shale Hills

3. Current Model-Data Integration

A. Data monitoring network

B. Model implementation

C. Cyberinfrastructure framework

4. Data Sharing Prototype

5. Interdisciplinary Research Implications

A. Isotope Hydrology

B. Hyporheic zone hydrological processes

C. Climate Change Impacts

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}}

Versions of word draft

2015-06-12T15:10:20Z

Xuan:

The paper is in response to the call for paper of IEEE System Journal [http://www.ieeesystemsjournal.org/call-for-papers/special-issue-esm/]
Final version [http://www.organicdatascience.org/ageofwater/images/1/17/Revision3.docx]
[[Category:Task]]


{{#set:|
Owner=Xuan_Yu}}

File:Revision3.docx

2015-06-12T15:09:32Z

Xuan:

Versions of word draft

2015-06-12T15:03:46Z

Xuan:

The paper is in response to the call for paper of IEEE System Journal [http://www.ieeesystemsjournal.org/call-for-papers/special-issue-esm/]
Final version
[[Category:Task]]


{{#set:|
Owner=Xuan_Yu}}

Implement PIHM for North Temperate Lakes

2015-02-21T16:23:18Z

Xuan: Added PropertyValue: Participants = User3

[[Category:Task]]

== Context ==

We have selected [[Document_the_PIHM_catchment_model|PIHM]] as our catchment model.

Implementing a catchment model using the PIHM software has three basic steps that utilize specific tools:

1) Collect the geospatial and time series data for that covers the site: The site [www.hydroterre.psu.edu] includes the necessary geospatial data and climate forcing to carry out this task for HUC12 level catchments anywhere in the continental US.

2) Define the domain of the catchment boundaries and the stream network: PIHM_gis tool is a desktop tool for delineating the catchment domain, defining the stream network (user defined support) and delineating the stream network from a digital elevation model.

3) Create a numerical grid that satisfies the project goals or hypotheses and make initial estimates of the model parameters: PIHM_gis tool also has tools to make initial estimates of the model parameters for the soil, groundwater, surface flow and vegetation, These parameters are automatically assigned to each mesh element, and can be refined later by calibration constrained by stream gauging, groundwater levels, energy and soil observations.

In this research we will concentrate on 3 field sites. The north temperate lakes region in Wisconsin, the Shaver Creek watershed and Lake Perez in central PA and......tbd.



{{#set:
Owner=Chris_Duffy|
Participants=User3|
Participants=User1|
Participants=User2|
StartDate=2014-11-01|
SubTask=Set_up_the_PIHM_catchment_model_for_North_Temperate_Lakes|
SubTask=Run_the_PIHM_catchment_model_for_North_Temperate_Lakes|
SubTask=Calibrate_the_PIHM_catchment_model_for_North_Temperate_Lakes|
SubTask=Verify_and_validate_the_PIHM_catchment_model_for_North_Temperate_Lakes|
SubTask=Make_predictions_and_projections_to_test_the_PIHM_catchment_model_for_North_Temperate_Lakes}}

Implement PIHM for North Temperate Lakes

2015-02-21T16:23:15Z

Xuan: Added PropertyValue: Participants = User2

[[Category:Task]]

== Context ==

We have selected [[Document_the_PIHM_catchment_model|PIHM]] as our catchment model.

Implementing a catchment model using the PIHM software has three basic steps that utilize specific tools:

1) Collect the geospatial and time series data for that covers the site: The site [www.hydroterre.psu.edu] includes the necessary geospatial data and climate forcing to carry out this task for HUC12 level catchments anywhere in the continental US.

2) Define the domain of the catchment boundaries and the stream network: PIHM_gis tool is a desktop tool for delineating the catchment domain, defining the stream network (user defined support) and delineating the stream network from a digital elevation model.

3) Create a numerical grid that satisfies the project goals or hypotheses and make initial estimates of the model parameters: PIHM_gis tool also has tools to make initial estimates of the model parameters for the soil, groundwater, surface flow and vegetation, These parameters are automatically assigned to each mesh element, and can be refined later by calibration constrained by stream gauging, groundwater levels, energy and soil observations.

In this research we will concentrate on 3 field sites. The north temperate lakes region in Wisconsin, the Shaver Creek watershed and Lake Perez in central PA and......tbd.



{{#set:
Owner=Chris_Duffy|
Participants=User2|
Participants=User1|
StartDate=2014-11-01|
SubTask=Set_up_the_PIHM_catchment_model_for_North_Temperate_Lakes|
SubTask=Run_the_PIHM_catchment_model_for_North_Temperate_Lakes|
SubTask=Calibrate_the_PIHM_catchment_model_for_North_Temperate_Lakes|
SubTask=Verify_and_validate_the_PIHM_catchment_model_for_North_Temperate_Lakes|
SubTask=Make_predictions_and_projections_to_test_the_PIHM_catchment_model_for_North_Temperate_Lakes}}

Implement PIHM for North Temperate Lakes

2015-02-21T16:23:11Z

Xuan: Added PropertyValue: Participants = User1

[[Category:Task]]

== Context ==

We have selected [[Document_the_PIHM_catchment_model|PIHM]] as our catchment model.

Implementing a catchment model using the PIHM software has three basic steps that utilize specific tools:

1) Collect the geospatial and time series data for that covers the site: The site [www.hydroterre.psu.edu] includes the necessary geospatial data and climate forcing to carry out this task for HUC12 level catchments anywhere in the continental US.

2) Define the domain of the catchment boundaries and the stream network: PIHM_gis tool is a desktop tool for delineating the catchment domain, defining the stream network (user defined support) and delineating the stream network from a digital elevation model.

3) Create a numerical grid that satisfies the project goals or hypotheses and make initial estimates of the model parameters: PIHM_gis tool also has tools to make initial estimates of the model parameters for the soil, groundwater, surface flow and vegetation, These parameters are automatically assigned to each mesh element, and can be refined later by calibration constrained by stream gauging, groundwater levels, energy and soil observations.

In this research we will concentrate on 3 field sites. The north temperate lakes region in Wisconsin, the Shaver Creek watershed and Lake Perez in central PA and......tbd.



{{#set:
Owner=Chris_Duffy|
Participants=User1|
StartDate=2014-11-01|
SubTask=Set_up_the_PIHM_catchment_model_for_North_Temperate_Lakes|
SubTask=Run_the_PIHM_catchment_model_for_North_Temperate_Lakes|
SubTask=Calibrate_the_PIHM_catchment_model_for_North_Temperate_Lakes|
SubTask=Verify_and_validate_the_PIHM_catchment_model_for_North_Temperate_Lakes|
SubTask=Make_predictions_and_projections_to_test_the_PIHM_catchment_model_for_North_Temperate_Lakes}}

Write paper: Dynamic age of water and carbon

2014-12-01T19:13:47Z

Xuan: Undo revision 9817 by Xuan (talk)

[[Category:Task]]


{{#set:
Participants=Chris_Duffy|
Participants=Paul_Hanson|
Participants=Jordan_Read|
Participants=Yolanda_Gil|
Participants=Gopal_Bhatt|
Participants=Lele_Shu|
Participants=Xuan_Yu|

StartDate=2014-08-25|
TargetDate=2014-11-01|
Type=Medium}}

==Title==

"Dynamic age of water and carbon isotopes in lake-catchment systems" (tentative)

==Authors==

Chris Duffy, Paul Hanson, Jordan Read, Yolanda Gill, Gopal Bhatt, Lele Shu, Xuan Yu, …...

==Abstract==

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.

==REFERENCES==

Duffy, C. J., 2010,

Bhatt, G. 2012

Forester, 1959

Rotenberg, 1972

==Outline==

Write paper: Dynamic age of water and carbon

2014-12-01T19:10:22Z

Xuan: Added PropertyValue: Participants = Chris Duffy

[[Category:Task]]


{{#set:
Participants=Chris_Duffy|
Participants=Lele_Shu|
Participants=Paul_Hanson|
Participants=Jordan_Read|
Participants=Yolanda_Gil|
Participants=Gopal_Bhatt|
StartDate=2014-08-25|
TargetDate=2014-11-01|
Type=Medium}}

==Title==

"Dynamic age of water and carbon isotopes in lake-catchment systems" (tentative)

==Authors==

Chris Duffy, Paul Hanson, Jordan Read, Yolanda Gill, Gopal Bhatt, Lele Shu, Xuan Yu, …...

==Abstract==

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.

==REFERENCES==

Duffy, C. J., 2010,

Bhatt, G. 2012

Forester, 1959

Rotenberg, 1972

==Outline==

Write paper: Dynamic age of water and carbon

2014-12-01T19:09:07Z

Xuan: Undo revision 9812 by Xuan (talk)

[[Category:Task]]


{{#set:
Participants=Lele_Shu|
Participants=Paul_Hanson|
Participants=Jordan_Read|
Participants=Yolanda_Gil|
Participants=Gopal_Bhatt|
StartDate=2014-08-25|
TargetDate=2014-11-01|
Type=Medium}}

==Title==

"Dynamic age of water and carbon isotopes in lake-catchment systems" (tentative)

==Authors==

Chris Duffy, Paul Hanson, Jordan Read, Yolanda Gill, Gopal Bhatt, Lele Shu, Xuan Yu, …...

==Abstract==

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.

==REFERENCES==

Duffy, C. J., 2010,

Bhatt, G. 2012

Forester, 1959

Rotenberg, 1972

==Outline==

Document the PIHM catchment model

2014-12-01T19:04:24Z

Xuan: /* Model Applications */

[[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 [https://dx.doi.org/10.1002/9781118872086.ch31].

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|
StartDate=2014-11-01|
SubTask=Document_calibration_approaches_for_the_PIHM_catchment_model|
SubTask=IEEE_paper_application_of_catchment_reanalysis}}

Write paper: Dynamic age of water and carbon

2014-12-01T18:47:30Z

Xuan: /* Authors */

[[Category:Task]]


{{#set:
Participants=Xuan_Yu}}

==Title==

"Dynamic age of water and carbon isotopes in lake-catchment systems" (tentative)

==Authors==

Chris Duffy, Paul Hanson, Jordan Read, Yolanda Gill, Gopal Bhatt, Lele Shu, Xuan Yu, …...

==Abstract==

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.

==REFERENCES==

Duffy, C. J., 2010,

Bhatt, G. 2012

Forester, 1959

Rotenberg, 1972

==Outline==

Write paper: Dynamic age of water and carbon

IEEE paper application of catchment reanalysis

2014-10-07T15:11:18Z

Xuan: Added PropertyValue: SubTask = Outline

IEEE paper application of catchment reanalysis

2014-10-07T15:10:43Z

Xuan: Deleted PropertyValue: SubTask = Outline

Abstract

2014-10-07T15:09:57Z

Xuan: Set PropertyValue: Owner = Chris Duffy

[[Category:Task]]
Abstract—Cyberinfrastructure is enabling ever-more integrative and transformative science. Watershed reanalysis is an objective quantitative method of synthesizing all sources of hydrologic variables (historical, real-time, future scenarios, observed and modeled), which requires intensive data sharing and collaborations of interdisciplinary geosciences research. In this context, a task-centered collaboration strategy was implemented to carry out watershed-based historical climate simulations (reanalysis) at the Shale Hills Critical Zone Observatory.. The collaboration was carried out using cyberinfrastructure developed under the Organic Data Science project (www.organicdatascience.org) which involved setting up on-line team tasks for creating workflows that included: accessing national watershed data, setting up the model grid and initial parameters, conducting historical watershed simulations forced by climate reanalysis, calibrating the model, and finally applying the model to IPCC climate scenarios. Collaborative science is becoming a de-facto strategy for earth science research (ref CZO national, NEON, etc.). Earth and environmental sciences can benefit from advances in cyber-science through implementation of collaboration software that improves our ability to access and share data, allow collective development of science hypotheses, supports building models as via team participation, and offers simplified access to data analytics and visualization software.


{{#set:|
Owner=Chris_Duffy}}

Abstract

2014-10-07T15:09:46Z

Xuan:

[[Category:Task]]
Abstract—Cyberinfrastructure is enabling ever-more integrative and transformative science. Watershed reanalysis is an objective quantitative method of synthesizing all sources of hydrologic variables (historical, real-time, future scenarios, observed and modeled), which requires intensive data sharing and collaborations of interdisciplinary geosciences research. In this context, a task-centered collaboration strategy was implemented to carry out watershed-based historical climate simulations (reanalysis) at the Shale Hills Critical Zone Observatory.. The collaboration was carried out using cyberinfrastructure developed under the Organic Data Science project (www.organicdatascience.org) which involved setting up on-line team tasks for creating workflows that included: accessing national watershed data, setting up the model grid and initial parameters, conducting historical watershed simulations forced by climate reanalysis, calibrating the model, and finally applying the model to IPCC climate scenarios. Collaborative science is becoming a de-facto strategy for earth science research (ref CZO national, NEON, etc.). Earth and environmental sciences can benefit from advances in cyber-science through implementation of collaboration software that improves our ability to access and share data, allow collective development of science hypotheses, supports building models as via team participation, and offers simplified access to data analytics and visualization software.

Abstract

2014-10-07T15:08:59Z

Xuan: Creating new page with Category: Task

[[Category:Task]]

IEEE paper application of catchment reanalysis

2014-10-07T15:08:59Z

Xuan: Added PropertyValue: SubTask = Abstract

[[Category:Task]]


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

IEEE paper application of catchment reanalysis

2014-10-07T15:07:47Z

Xuan: Added PropertyValue: SubTask = Outline

[[Category:Task]]


{{#set:|
SubTask=Outline}}

IEEE paper application of catchment reanalysis

2014-10-07T15:05:30Z

Xuan: Creating new page with Category: Task

[[Category:Task]]

Document the PIHM catchment model

2014-10-07T15:05:30Z

Xuan: Added PropertyValue: SubTask = IEEE paper application of catchment reanalysis

[[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=IEEE_paper_application_of_catchment_reanalysis|
SubTask=Document_calibration_approaches_for_the_PIHM_catchment_model}}

Document the PIHM catchment model

2014-09-18T03:40:39Z

Xuan: /* Model Applications */

[[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.



{{#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}}

Document the PIHM catchment model

2014-09-18T03:39:17Z

Xuan: /* Model Applications */

[[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.



{{#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}}

Document the PIHM catchment model

2014-09-18T03:29:12Z

Xuan: Set PropertyValue: Progress = 20

[[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.



{{#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}}

Document the PIHM catchment model

2014-09-15T23:48:29Z

Xuan: /* Model Applications */

[[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.



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

Document the PIHM catchment model

2014-09-15T20:17:22Z

Xuan: /* Model Applications */

[[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. 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.



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

Document the PIHM catchment model

2014-09-15T20:01:42Z

Xuan: /* Model Applications */

[[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. The model required geospatial- geotemporal data sufficient to support the physics-based numerical watershed simulator (Yu & Lamacova, 2014). 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.



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