Assessment of Suitability of Global Reanalysis for Hydrological Applications by Coupling Performance Statistics and Sensitivity Analysis in Kenya

Information about monitoring of hydrological extremes, agricultural yields and irrigation may be informed by early warning, forecasts and flood management advice through appropriate modelling skills. However hydrological modelling is a challenging task in poorly gauged catchments, especially in developing countries like Kenya. Open access global precipitation and temperature reanalysis datasets with different spatial and temporal resolutions provide alternative sources in data-scarce regions but, individual reanalysis precipitation datasets have significant uncertainties. Inspired by data scarcity issues, significant spatial and temporal gaps in gauge observations, and poor performance of individual reanalysis in hydrological models, this study assess the performance of five new-era reanalysis datasets (ERA5, ERA-Interim, Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA2), Climate Forecast System Reanalysis (CFSR) and Japanese 55-year Reanalysis Project(JRA55)) to simulate daily streamflow using the GR4J model across the 20 catchments in Kenya. Deviating from the modelling normality of calculating the model performance statistics for the calibration and validation periods to investigate whether a model serves as satisfactory representations of the natural hydrologic phenomenon, we couple with sensitivity analysis (SA) to unveil model structural uncertainty and suitability when forced with the different reanalysis products. In this study we use the reanalysis precipitation, maximum (T max) and minimum (T min) temperatures against the observations from the Climate Hazards group Precipitation (CHIRPS) for 1981–2016 to calculate performance statistics, streamflow simulations and sensitivity analysis. In addition, we develop model suitability index (MSI) by coupling the performance statistics with the sensitivity results across the different reanalysis products for our study catchments. Our results show that ERA5 performs better than other reanalysis products in terms of performance statistics and streamflow simulations at catchments scale. MSI results were suitable with ERA5 and lower in JRA55 across most of the Kenyan catchments, with 0.8 and 0.4 MSI respectively. MSI developed in this study is a quantitative measure that can be used for the comparison of reanalysis products for different catchments, thus useful for application to modelling to assess the suitability of both the modelling tools and catchment response to alternative forcings for early warning and inform early action.