Evaluation of the Parameter Sensitivities of a Coupled Land Surface Hydrologic Model at a Critical Zone Observatory

AbstractLand surface models (LSMs) and hydrologic models are parameterized models. The number of involved parameters is often large. Sensitivity analysis (SA) is a key step to understand the complex relationships between parameters and between state variables and parameters. SA is also critical to understand system dynamics and to examine the parameter identifiability. In this paper, parameter SA for a fully coupled, physically based, distributed land surface hydrologic model, namely, the Flux–Penn State Integrated Hydrologic Model (Flux–PIHM), is performed. Multiparameter and single-parameter tests are performed to examine the three dimensions of identifiability: distinguishability, observability, and simplicity. Results show that Flux–PIHM model predictions of discharge, water table depth, soil moisture, land surface temperature, and surface heat fluxes are very sensitive to the selection of parameter values. Parameter uncertainties produce large uncertainties in hydrologic and land surface variable pre...

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