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MOESHA: A genetic algorithm for automatic calibration and estimation of parameter uncertainty and sensitivity of hydrologic models
Citation:
Barnhart, B., K. Sawicz, D. Ficklin, AND G. Whittaker. MOESHA: A genetic algorithm for automatic calibration and estimation of parameter uncertainty and sensitivity of hydrologic models. Transactions of the ASABE. AMERICAN SOCIETY OF AGRICULTURAL AND BIOLOGICAL ENGINEERS, ST. JOSEPH, MI, 60(4):1259-1269, (2017).
Impact/Purpose:
Many tools exist to calibrate hydrologic models and to determine model uncertainty due to variations in input parameters; however, few tools integrate the calibration process with an uncertainty estimator to dynamically search for optimal model solutions and produce robust model results as well as estimations of error in a single step. We introduce a tool called MOESHA that utilizes a genetic algorithm to calibrate a hydrologic model and calculate estimations of error due to variations in input parameters. We demonstrate that MOESHA is able to pinpoint non-impactful parameters while achieving excellent calibration results at a catchment surrounding the Dee River in Wales using the EXP-HYDRO hydrological model. The results agree well with Sobol’s global sensitivity analysis method, which is widely used for sensitivity analysis of hydrologic models. The tool is customizable to work with nearly any hydrologic model and is parallelized to work seamlessly with multi-processor laptops to large-scale superclusters with little required configuration. Overall, this manuscript will ultimately improve the use of hydrologic models and therefore will be important for watershed managers, city planners, and ecologists throughout the world. This work relates to RAP task SSWR501.A: Green infrastructure model research. This paper contributes to SSWR 5.01A
Description:
Characterization of uncertainty and sensitivity of model parameters is an essential and often overlooked facet of hydrological modeling. This paper introduces an algorithm called MOESHA that combines input parameter sensitivity analyses with a genetic algorithm calibration routine to dynamically sample parameter space as an alternative to traditional static space-sampling methods, such as stratified sampling or Latin hypercube sampling. In addition to calibrating input parameters to a hydrologic model, MOESHA determines the optimal distribution of input parameters that maximizes model robustness and minimizes error. Subsequently, the variance of the input parameter distributions are used to differentiate between impactful and non-impactful input parameters. In this way, an optimally calibrated model is produced, and estimations of model uncertainty as well as the relative impact of input parameters on model output (i.e., sensitivity) are determined. A case study using a single-cell hydrological model (EXP-HYDRO) is used to test the method using river discharge data from the Dee River catchment in Wales. We compare the results of MOESHA with Sobol’s global sensitivity analysis method and demonstrate that the algorithm is able to pinpoint non-impactful parameters while achieving excellent calibration results.
