Automatic calibration of a global hydrological model using satellite data as a proxy for stream flow data

Abstract:

Model calibration and validation are commonly restricted due to the limited availability of historical in situ observational data. Several studies have demonstrated that using complementary remotely sensed datasets such as soil moisture for model calibration have led to improvements. The aim of this study was to evaluate the use of remotely sensed signal of the Global Flood Detection System (GFDS) as a proxy for stream flow data to calibrate a global hydrological model used in operational flood forecasting. This is done in different river basins located in Africa, South and North America for the time period 1998-2010 by comparing model calibration using the raw satellite signal as a proxy for river discharge with a model calibration using in situ stream flow observations. River flow is simulated using the LISFLOOD hydrological model for the flow routing in the river network and the groundwater mass balance. The model is set up on global coverage with horizontal grid resolution of 0.1 degree and daily time step for input/output data. Based on prior tests, a set of seven model parameters was used for calibration. The parameter space was defined by specifying lower and upper limits on each parameter. The objective functions considered were Pearson correlation (R), Nash-Sutcliffe Efficiency log (NSlog) and Kling-Gupta Efficiency (KGE’) where both single- and multi-objective functions were employed. After multiple iterations, for each catchment, the algorithm generated a set of Pareto-optimal front of solutions. A single parameter set was selected which had the lowest distance to R=1 for the single-objective and NSlog=1 and KGE’=1 for the multi-objective function. The results of the different test river basins are compared against the performance obtained using the same objective functions by in situ discharge observations. Automatic calibration strategies of the global hydrological model using satellite data as a proxy for stream flow data are outlined and discussed.