Multi-site Multi-variable Hydrologic Model Development for Spatially Heterogeneous River Basins to Achieve Realistic Basin Modeling

Abstract:

Hydrologic modeling is an indispensable tool for simulation of river basin processes. A large catchment with high spatial heterogeneity is modeled realistically when calibration is done at multiple locations, for multiple hydrologic variables like streamflow, soil moisture, evapotranspiration, etc. This ensures maximum utilization of field measurements, reduces the uncertainty in parameter identification and highlights the areas that need greater calibration effort. In this study, hydrologic model simulations are run for the Mahanadi river basin in India using SWAT (Soil and Water Assessment Tool) and calibration, uncertainty analysis, sensitivity analysis and validation are performed using SUFI-2 algorithm in SWAT-CUP (SWAT Calibration and Uncertainty Programs). Mahanadi river basin is calibrated for several variables like streamflow, soil moisture and evapotranspiration at various locations. The spatial heterogeneity of the catchment is taken into account by choosing appropriate ranges of different parameters for each sub basin based on the soil types, slope classes and land use land cover. In multi-site multi-variable calibration, serial calibration for individual variables and locations gives different result when compared with the simultaneous calibration for all variables and locations. This work compares serial calibration for individual hydrologic variables and locations with simultaneous calibration for all hydrologic variables and locations. Various performance measures like Nash-Sutcliffe efficiency (NSE), percent bias, coefficient of determination, etc. are used to assess model performance. This work talks about the suitability of the performance measure to different hydrologic variables. The model simulation results for the Mahanadi river basin are compared with the observed values using different performance measures for calibration and validation of the model. The results show that model performance improves when it is calibrated at multiple locations, for multiple variables, by taking the spatial variability of parameters into account. This study explores the suitability of different performance measures for different hydrologic variables and compares the serial and simultaneous calibration for multiple variables at multiple locations.