Difference between revisions of "Alternate Run Modes:Efficient Local Search"
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Spatially explicit physics-based models such as GSSHA support a more realistic characterization of the physical aspects of the watershed system and a more transparent simulation and evaluation of project alternatives than is possible with traditional hydrologic simulation models (viz., lumped and semi-distributed model structures). And they have the potential to predict with greater reliability than lumped hydrologic model structures . But, they also have the potential to easily become highly parameterized, particularly when they are deployed to simulate on a continuous basis heterogeneous watershed systems. Moreover, their model run times are often far greater than lumped and semi-distributed hydrologic models. It is this combination of computationally intensive forward model run times and the potential for a highly dimensional specified adjustable model parameter space which present a unique challenge for the computer-based calibration of spatially explicit physics-based hydrologic models. In particular, their combination necessitates the use of a calibration method that is as efficient as possible. Moreover, highly parameterized model deployments can make calibration problematical in that the information content encapsulated in the available observation dataset may not support the unique estimation for each of the specified adjustable model parameters, resulting in poor fits between the observations and their model simulated counterparts and/or non-physical models (i.e., estimated parameter sets). | Spatially explicit physics-based models such as GSSHA support a more realistic characterization of the physical aspects of the watershed system and a more transparent simulation and evaluation of project alternatives than is possible with traditional hydrologic simulation models (viz., lumped and semi-distributed model structures). And they have the potential to predict with greater reliability than lumped hydrologic model structures . But, they also have the potential to easily become highly parameterized, particularly when they are deployed to simulate on a continuous basis heterogeneous watershed systems. Moreover, their model run times are often far greater than lumped and semi-distributed hydrologic models. It is this combination of computationally intensive forward model run times and the potential for a highly dimensional specified adjustable model parameter space which present a unique challenge for the computer-based calibration of spatially explicit physics-based hydrologic models. In particular, their combination necessitates the use of a calibration method that is as efficient as possible. Moreover, highly parameterized model deployments can make calibration problematical in that the information content encapsulated in the available observation dataset may not support the unique estimation for each of the specified adjustable model parameters, resulting in poor fits between the observations and their model simulated counterparts and/or non-physical models (i.e., estimated parameter sets). | ||
| − | This [media:FCSDR_Cal_WU_Use_PI_and_Tik_TN_draft_002.pdf|draft document]] describes how to use two separate but closely related methods of computer-based parameter estimation either of which can serve as an effective and efficient means to support the practical calibration of a GSSHA hydrologic model. The two methods are adaptations to the “efficient local search” alternate GSSHA run mode GSSHA model calibration methodology. The example problems files provided below relate to this draft document. | + | This [[media:FCSDR_Cal_WU_Use_PI_and_Tik_TN_draft_002.pdf|draft document]] describes how to use two separate but closely related methods of computer-based parameter estimation either of which can serve as an effective and efficient means to support the practical calibration of a GSSHA hydrologic model. The two methods are adaptations to the “efficient local search” alternate GSSHA run mode GSSHA model calibration methodology. The example problems files provided below relate to this draft document. |
===== Efficient local search with prior information ===== | ===== Efficient local search with prior information ===== | ||
Latest revision as of 18:36, 19 August 2013
Contents
Efficient Local Search
As previously mentioned, this GSSHA alternate run mode employs the independent ERDC implementations of the LM/SLM local search methods. There is no additional information that must be prepared for this specific GSSHA alternate run mode. The syntax required to use this alternate GSSHA run mode for GSSHA model calibration is:
gssha –slm case.pst
where case.pst is the name of the modified control file. The active user is referred to the technical report ERDC-CHL-TR-12-3, and its related appendix material and example problem files, for several examples which demonstrate in a clear and practical manner how to use various functionalities associated with the independent ERDC implementations of the LM/SLM local search methods, and also brief descriptions of the various output files that are associated with a LM/SLM supervised GSSHA model calibration run.
Example problem files prepared for a SLM supervised GSSHA alternate run mode model calibration run, which are supplied for use as a go by
Biased efficient local search
Spatially explicit physics-based models such as GSSHA support a more realistic characterization of the physical aspects of the watershed system and a more transparent simulation and evaluation of project alternatives than is possible with traditional hydrologic simulation models (viz., lumped and semi-distributed model structures). And they have the potential to predict with greater reliability than lumped hydrologic model structures . But, they also have the potential to easily become highly parameterized, particularly when they are deployed to simulate on a continuous basis heterogeneous watershed systems. Moreover, their model run times are often far greater than lumped and semi-distributed hydrologic models. It is this combination of computationally intensive forward model run times and the potential for a highly dimensional specified adjustable model parameter space which present a unique challenge for the computer-based calibration of spatially explicit physics-based hydrologic models. In particular, their combination necessitates the use of a calibration method that is as efficient as possible. Moreover, highly parameterized model deployments can make calibration problematical in that the information content encapsulated in the available observation dataset may not support the unique estimation for each of the specified adjustable model parameters, resulting in poor fits between the observations and their model simulated counterparts and/or non-physical models (i.e., estimated parameter sets).
This draft document describes how to use two separate but closely related methods of computer-based parameter estimation either of which can serve as an effective and efficient means to support the practical calibration of a GSSHA hydrologic model. The two methods are adaptations to the “efficient local search” alternate GSSHA run mode GSSHA model calibration methodology. The example problems files provided below relate to this draft document.
Efficient local search with prior information
Example 1 problem files prepared for a SLM with prior information supervised GSSHA alternate run mode model calibration run, which are supplied for use as a go by
Example 2 problem files prepared for a SLM with prior information supervised GSSHA alternate run mode model calibration run, which are supplied for use as a go by
Efficient local search version of the Tikhonov solution
Example 1 problem files prepared for a SLM version of the Tikhonov solution supervised GSSHA alternate run mode model calibration run, which are supplied for use as a go by
Example 2 problem files prepared for a SLM version of the Tikhonov solution supervised GSSHA alternate run mode model calibration run, which are supplied for use as a go by
GSSHA User's Manual
- 18 Alternate Run Modes
- 18.1 MPI and OpenMP Parallelization
- 18.2 Simulation Setup for Alternate Run Modes
- 18.3 Batch Mode Runs
- 18.4 Automated Calibration with Shuffled Complex Evolution
- 18.5 Monte Carlo Runs
- 18.6 ERDC Automated Model Calibration Software
- 18.6.1 Efficient Local Search
- 18.6.2 Multistart
- 18.6.3 Trajectory Repulsion
- 18.6.4 Effective and Efficient Stochastic Global Optimization
- 18.7 Inset Models
