The Significance of the Leaf-Area-Index on the Evapotranspiration Estimation in SWAT-T for Characteristic Land Cover Types of Western Africa

Abstract. Evapotranspiration (ET) plays a pivotal role in the terrestrial water cycle in sub-humid and tropical regions. Thereby, the contribution of plant transpiration can be distinctively greater than the soil evaporation. The seasonal dynamics of plant phenology, e.g., commonly represented as the vegetation attribute leaf-area-index (LAI), closely correlates with actual ET (AET). Addressing the reciprocal LAI-AET interaction is hence essential for practitioners and researchers to comprehensively quantify the hydrological processes in water resources management, particularly in the perennially vegetated regions of Western Africa. However due to the lack of field measurements, the evaluation of the LAI-AET interaction still remains challenging. Hence, our study aims to improve the understanding of the role of LAI on the AET estimation with the investigation of characteristic regions of Western Africa. We setup eco-hydrological models (SWAT-T) for two homogeneous land cover types (forest and grassland) to guarantee the representativeness of field measurements for LAI and AET. To evaluate the LAI-AET interaction in SWAT-T, we apply different potential ET methods (Hargreaves, Penman-Monteith (PET-PM), Priestley-Taylor). Further, the parameter sensitivity for 27 relevant LAI-AET parameters is quantified with the elementary effects method. The comprehensive parameter set is then optimized using the Shuffled-Complex-Evolution algorithm. Finally, we apply a benchmark test to assess the performance of SWAT-T to simulate AET and to determine the relevance of a detailed LAI modelling. The results show that SWAT-T is capable to accurately predict LAI and AET on the footprint scale. While all three PET methods facilitate an adequate modelling of LAI and AET, PET-PM outperforms the methods for AET independent of the land cover type. Moreover, the benchmarking highlights that if an optimization process only accounts for LAI but disregards AET data, its prediction of AET still yields an adequate performance with SWAT-T for all PET methods and land cover types. Our findings demonstrate that the significance of a detailed LAI modelling on the AET estimation is more pronounced in the forested than in the grassland region.