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Gil: Gil moved page PIHM calibration using evolutionary algorithms to Document PIHM calibration using evolutionary algorithms

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Gil moved page PIHM calibration using evolutionary algorithms to Document PIHM calibration using evolutionary algorithms

Chris at 18:14, 22 January 2014

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Chris at 17:59, 22 January 2014

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   Yu, X., G. Bhatt, C. Duffy, Y. Shi, 2013, Parameterization for Distributed Watershed Modeling
   Using National Data and Evolutionary Algorithm, Computers in Geoscience, Vol (58), p. 80-90.
 = Evolutionary Algorithm =
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   Hansen, N., 2006. The CMA Evolution Strategy: A Comparing Review. Towards a New Evolutionary computation,
   pp. 75-102. http://dx.doi:10.1007/3-540- 32494-1_4.
 = Basic Steps =
 # First assign a-priori model parameters to each element in the unstructured mesh in PIHM model. Typically we recommend that this would be done from National data, such as the data offered on-line as HUC-12 watersheds for Continental USA. The web service [[HydroTerre]] provides the essential geospatial/temporal data necessary for modeling at the HUC-12 scale anywhere in CONUS. The necessary geospatial tools are provided in PIHMgis for build the unstructured mesh and assign a-priori parameters. Model building from a-priori data is the subject of a separate Model Development Tutorial.
 # Next perform a parameter sensitivity using a Monte Carlo Sensitivity Analysis with the catchment model to establish characteristic time scales of important the processes operating in the catchment. The result of this step allows parameters to be partitioned with respect to characteristic time scales. Past experience tells us that we typically find 2 categories or partition groups (Yu, et al, 2013). “Event-scale” parameters, or those sensitive to rapid hydrologic change, and we refer to this group as the EG partition. A second group of parameters are less sensitive to short term events, but respond to seasonal or longer period hydro-climatic or biophysical processes. This group is defined as the seasonal-scale partition SG. Table 1 illustrates a typical grouping that works well in the northeast and mid-Atlantic region.  Additional groups are possible but we will confine our analysis to EG-SG partitions. The purpose of this partition is to reduce the computational requirements of the parameter estimation problem by carrying out the optimization w/re to each partition separately.
 # The third step involves applying the CMA-ES to optimize the parameters for the EG partition while holding the SG partition parameters constant (e.g. a-priori parameters). The time period selected for the EG parameters is typically a flooding after low streamflow months when the initial condition of the watershed is dry and can be resolved by spin-up.
 # Finally, using the new set of EG parameters (step 3) to estimate for the seasonal low-streamflow and storage conditions. We found near linear responses of SG partition parameters that are dominated by seasonal and long-term changes. The calibration of SG partition parameters amounts to optimizing seasonal parameters such as those associated with ET. Our experience suggests that sequence of estimating the EG then the SG partition parameters works the best in many watersheds.

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