Enhancing hydrological modeling with bias-corrected satellite weather data in data-scarce catchments: a comparative analysis of SWAT and GR4J models

Hydrological models are widely used to assess climate change effect on water resources at the catchment scale. However, data scarcity is one of the main challenges faced by hydrological modelers especially in developing countries. Remotely sensed and large-scale climatic datasets offer a viable alternative for hydrological modeling. Hence, this study evaluates CFSR-NCEP reanalysis data for discharge simulation using SWAT semi-distributed and GR4J conceptual lumped hydrological models. First, the CFSR-NCEP monthly reanalysis precipitation and temperature were compared to the observed data. Then, the performance of SWAT and GR4J models to simulate monthly discharge using both daily CFSR-NCEP reanalysis data with and without bias correction was compared across different climate conditions. Results indicated that the GR4J model performed well, with an average NSE of 0.89 across calibration and validation periods, indicating its ability to handle low-quality input data. A poor performance of the SWAT model was observed using CFSR-NCEP data without bias correction (NSE < 0.60). Primarily due to biases in meteorological data, and to low quality of spatial data. Bias correction improved both models’ performance, with NSEs exceeding 0.78 for SWAT model and 0.91 for GR4J model. Moreover, the stability of models’ performances under the three calibration periods shows that SWAT and GR4J models are, respectively, influenced and not much influenced by the climate of the calibration period. Consequently, GR4J remains valid for climate projection. Our research shows that despite their widespread use, complex physics-based hydrological models such as SWAT are often less performing in data-limited catchments. However, conceptual models prove more performing, providing valuable information for researchers and decision-makers to devise robust quantitative water resource management strategies under these challenging conditions.