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 efficient and optimized flood analysis system needs to be used for the reliable flood forecasting. The credibility and the reliability of the flood forecasting system is depending upon the framework used for its parameter optimization. A comprehensive framework for optimizing the input parameters 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. The parameter optimization becomes computationally more demanding when the model input parameters (30 to 100 even greater) have multi-dimensional parameter space, many output parameters which make the optimization problem multi-objective and large number of model simulations requirement for the optimization. Aforementioned challenges are met by introducing the methodology to optimize the input parameters of fully distributed hydrological model, following steps are included (1) screening of the parameters through Morris sensitivity analysis method in different flow periods, so that optimization would be performed for sensitive parameters, different scalar output functions are used in this regard (2) Surrogate models or meta-models are used to simulate the hydrological response of a dynamic model (3) sampling of parameters values using the optimized ranges obtained from the meta-models developed from multivariate regression adaptive splines (MRAS); the results are evident that the parameter optimization using the proposed framework is efficient can be effectively performed. The efficiency and performance of the proposed framework has been demonstrated through the accurate calibration of the model with fewer model runs. This study also demonstrates the importance and use of scalar functions in calculating sensitivity indices, when the model output is temporally variable.