Evaluating the skill of the mesoscale Hydrologic Model (mHM) for simulating River Discharge in Sparsely-Gauged Basins in Nigeria

Predictive hydrologic modelling to understand and support agricultural water resources management and food security policies in Nigeria is a demanding task due to the paucity of hydro-meteorological measurements. This study assessed the skill of using different remotely-sensed products in a multi-calibration framework for evaluating the performance of the mesoscale Hydrologic Model (mHM) across four (4) different data-scarce basins within the Guinea-Sudano region of Nigeria. Satellite rainfall estimates (SFEs) obtained from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Climate Prediction Center (CPC), European Center for Medium-Range Weather Forecast (ECMWF) Reanalysis 5th Generation (ERA5), Global Precipitation Climatological Center (GPCC) and Multi-Source Weighted Ensemble Precipitation (MSWEP) models were used to drive the mHM for different basins across different climatic regions in Nigeria. The multiscale parameter regionalization (MPR) approach was implemented to overcome the problems of over-parameterization and equifinality of model parameters during model calibration. Model calibration was first performed using discharge (Q), and next calibrated by using a combination of discharge (Q) and actual evapotranspiration (AET) for each setup driven by a rainfall product. A multi-variable approach using both Q and AET was also used during model evaluation. The mHM model driven with CHIRPS dataset showed reasonable results (0.5 < KGE ≤ 0.85) during calibration with both Q and AET variables while KGE varied between 0.34 – 0.63 during model validation using the same variables across all basins under consideration. This study underscores the utility of the CHIRPS model for hydrologic modelling in sub-Saharan Africa as well as the spatial predictive skill of the mHM. Generally, this study draws special attention to the MPR approach as a good alternative to consider for distributed hydrologic modelling in poorly-gauged basins.