Comparative suitability of the global Hydrological Model Glofas against a catchment-based model to simulate and predict floods in Uganda.

Abstract

This study aims to assess the comparative suitability of a global hydrological forecasting system, the Copernicus-EMS Global Flood Awareness System (GloFAS), and a catchment-based model (GR4J) as possible alternative or complementary flood forecasting tools in Uganda. This would help local relevant authorities understand whether global flood forecasts can be relied on as one of the tools to inform flood-preparedness actions in Uganda, or whether other ready-to-use models that can be set up more easily at the catchment scale provide advantages in particular areas. While GloFAS provides probabilistic extended-range forecasts, it has not been calibrated at any location in Uganda and other African countries as well. However, efforts are on to have GloFAS calibrated for several locations in Uganda and Africa. A simpler catchment-based model could be calibrated more easily using observed hydrological data.

Results are presented for four catchments (Akokorio [12646 km2], Manafwa [462 km2], Mitano [2102 km2] and Muzizi [2223 km2]) across Uganda with different morphological and hydrological characteristics (areas between 500-13000 km2). An evaluation of both GloFAS reanalysis (GloFAS-ERA5) and extended-range forecast has been carried out against observed streamflow data, analysing performance statistics including the Kling-Gupta Efficiency (KGE) for the reanalysis, and the False Alarm Ratio and Probability of Detection for forecasts at different lead times. The GR4J model simulations were run using the ERA5 meteorological reanalysis as input. In both calibration and validation mode, the calibrated GR4J model provides better KGE scores than GloFAS, especially for the smaller catchments. However, GloFAS performance is relatively good for the two largest basins (Akokorio and Muzizi) [>2200 km2] and is acceptable with respect to a mean flow benchmark for all catchments, except the smallest (500 km2). Our results suggest that in small- to medium-size basins in Uganda, a simple lumped catchment-based model may outperform GloFAS, but even without calibration GloFAS performs satisfactorily in larger basins. Thus, GloFAS can be relied on as interim solution for flood forecasting in Uganda, especially for larger river catchments and at longer lead times.