Comparative Study of Two State-of-the-Art Semi-Distributed Hydrological Models

Abstract

Performance of a newly developed semi-distributed (grid-based) hydrological model (satellite-based hydrological model (SHM)) has been compared with another semi-distributed soil and water assessment tool (SWAT)—a widely used hydrological response unit (HRU)-based hydrological model at a large scale (12,900 km2) river basin for monthly streamflow simulation. The grid-based model has a grid cell size of 25 km2, and the HRU-based model was set with an average HRU area of 25.2 km2 to keep a balance between the discretization of the two models. Both the model setups are calibrated against the observed streamflow over the period 1977 to 1990 (with 1976 as the warm-up period) and validated over the period 1991 to 2004 by comparing simulated and observed hydrographs as well as using coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) as statistical indices. Result of SHM simulation (NSE: 0.92 for calibration period; NSE: 0.92 for validation period) appears to be superior in comparison to SWAT simulation (NSE: 0.72 for calibration period; NSE: 0.50 for validation period) for both calibration and validation periods. The models’ performances are also analyzed for annual peak flow, monthly flow variability, and for different flow percentiles. SHM has performed better in simulating annual peak flows and has reproduced the annual variability of observed streamflow for every month of the year. In addition, SHM estimates normal, moderately high, and high flows better than SWAT. Furthermore, total uncertainties of models’ simulation have been analyzed using quantile regression technique and eventually quantified with scatter plots between P (measured data bracketed by the 95 percent predictive uncertainty (PPU) band) and R (the relative length of the 95PPU band with respect to the model simulated values)-values, for calibration and validation periods, for both the model simulations. The analysis confirms the superiority of SHM over its counterpart. Differences in data interpolation techniques and physical processes of the models are identified as the probable reasons behind the differences among the models’ outputs.