Application of Meta-Models for Accurate Calibration of Hydrological Model Parameters - Ammara Nusrat , Hamza Farooq Gabriel , Sajjad Haider , Muhammad Shahid (International Journal of Advanced Science and Technology, 2020)
One of the impacts of climate change is an increase in the frequency of floods. The efficientand optimized flood analysis system needs to be used for the reliable flood forecasting. Thecredibility and the reliability of the flood forecasting system is depending upon the frameworkused for its parameter optimization. A comprehensive framework for optimizing the inputparameters of the computationally extensive distributed hydrological model has been presented.A large river basin has the high spatio-temporal heterogeneity of aquifer and surface properties.Estimating the parameters in fully distributed hydrological model is a challenging task. Theparameter optimization becomes computationally more demanding when the model inputparameters (30 to 100 even greater) have multi-dimensional parameter space, many outputparameters which make the optimization problem multi-objective and large number of modelsimulations requirement for the optimization. Aforementioned challenges are met by introducingthe methodology to optimize the input parameters of fully distributed hydrological model,following steps are included (1) screening of the parameters through Morris sensitivity analysismethod in different flow periods, so that optimization would be performed for sensitiveparameters, different scalar output functions are used in this regard (2) Surrogate models ormeta-models are used to simulate the hydrological response of a dynamic model (3) sampling ofparameters values using the optimized ranges obtained from the meta-models developed frommultivariate regression adaptive splines (MRAS); the results are evident that the parameteroptimization using the proposed framework is efficient can be effectively performed. Theefficiency and performance of the proposed framework has been demonstrated through theaccurate calibration of the model with fewer model runs. This study also demonstrates theimportance and use of scalar functions in calculating sensitivity indices, when the model outputis temporally variable.