The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa

Evapotranspiration (ET) is pivotal in the terrestrial water cycle in subhumid and tropical regions. In the water cycle, the contribution of plant transpiration can be distinctively more significant than soil evaporation. The seasonal dynamics of plant phenology, commonly represented as the vegetation attribute leaf area index (LAI), closely correlates with actual ET (AET). Hence, addressing the reciprocal LAI-AET interaction is essential for practitioners and researchers to comprehensively quantify the hydrological processes in water resources management, particularly in the perennially vegetated regions of West Africa. However, due to a lack of field measurements, evaluation of the LAI-AET interaction still remains challenging. Hence, our study aims to improve the understanding of the role of the LAI in AET estimation by investigating characteristic regions of West Africa. We set up ecohydrological models (using the Soil and Water Assessment Tool for the tropics - SWAT-T) for two homogeneous land cover types (forest and grassland) to guarantee the representativeness of field measurements for the LAI and AET. We apply different potential ET methods (the Hargreaves; Penman-Monteith - PET-PM; and Priestley-Taylor methods) to evaluate the LAI-AET interaction in SWAT-T. Further, the elementary effects method quantifies the parameter sensitivity for 27 relevant LAI-AET parameters. The comprehensive parameter set is then optimized using the shuffled complex evolution algorithm. Finally, we apply a benchmarking test to assess the performance of SWAT-T with respect to the simulation of AET and to determine the relevance of detailed LAI modeling. The results show that SWAT-T is capable of accurately predicting the LAI and AET at the footprint scale. While all three PET methods facilitate adequate modeling of the LAI and AET, the PET-PM technique outperforms the other methods for AET, independent of the land cover type. Moreover, the benchmarking highlights that, if it only accounts for the LAI but disregards AET data, an optimization process's 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 detailed LAI modeling for the AET estimation is more pronounced in the forested than in the grassland region.