Sensitivity of global hydrological models to potential evapotranspiration estimation methods in the Senegal River Basin (West Africa)

• DOI: 10.1016/j.ejrh.2024.101823
• Publication Date: 2024-05-18T11:09:01Z
• Authors: Papa Malick Ndiaye; Ansoumana Bodian; Alain Dezetter; Andrew Ogilvie; Omar Goudiaby
• Keywords: Physical geography; QE1-996.5; 550; 590; 0207 environmental engineering; Geology; 551; GB3-5030; Sensitivity; Sensitivity Analysis; Senegal River Basin; [SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology; [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology; Global Hydrological models; Evapotranspiration methods; Analysis; 0105 earth and related environmental sciences

Study region: Senegal River Basin in West Africa Study focus: This paper aims to evaluate the sensitivity of global hydrological models to potential evapotranspiration (PET) methods in the Senegal River Basin. Potential evapotranspiration is estimated using 21 methods and its influence on the performance of three GR models (GR4J, GR5J and GR6J) is investigated in five catchments. The data used are mean rainfall, discharge and observed and calculated PET over the period 1984–1995. PET is calculated based on observed climate data and those from NASA POWER reanalysis data. The methodology consists in: (i) comparing the consistency of reanalysis data with respect to the observed PET, (ii) assessing the robustness of GR models and their sensitivity to different PET estimation methods. The evaluation criteria used to assess the performance of the hydrological model are KGE and PBIAS. New hydrological insights for the region: Good consistency is obtained between PET calculated with observed and reanalysis data. The GR4J and GR5J are more efficient to simulate flows in the Senegal River sub-basins. The aerodynamic PET methods perform well with the three hydrological models. However, in this context where data are scarce, temperature methods such as Droogers and Allen are a good choice for hydrological modeling. The results also show that the GR models have the ability to adapt to poorly estimated PET.