Utilizing the MODIS-derived leaf area index to investigate the impact of vegetation processes on hydrological simulation of macroscale catchment

Integration of vegetation processes in rain-runoff (RR) models significantly affects runoff response by influencing evapotranspiration in mesoscale catchments. However, it is impossible to interpret the impacts of vegetation processes on runoff simulations in macroscale catchments using results from mesoscale catchments. Few studies involved vegetation process impacts on hydrological simulations by integrating daily vegetation information into conceptual RR models of macroscale catchments. In this study, we integrated the remotely sensed leaf area index (LAI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) into a daily Distributed Time-Variant Gain Model (DTVGM). Then, this study assessed the performances of two DTVGM versions, with and without vegetation processes, in the Wei River catchment, China. The results showed that: (1) Integration of MODIS-LAI into the DTVGM model improved the calibration and runoff simulation results of the initial DTVGM model. (2) Inclusion of vegetation processes in the DTVGM changed the simulated proportions of water balance components in the hydrological model and made the simulation of water balance components more accurate. (3) The fact that inclusion of vegetation processes could improve the hydrological simulation performance of the daily conceptual RR model in the macroscale catchment was consistent with studies in mesoscale catchment.