Towards a Hydrological Monitoring System for the West of South America: The Role of Vegetation Across Ecosystems

Abstract:

Vegetation alters water and energy surface fluxes, and, thereby, influences water budget terms, such as evapotranspiration, soil moisture, and streamflow. This study aims to quantify the sensitivity of the simulation of hydrological states and fluxes in an advanced land surface model to the manner in which vegetation is represented in the model. The sensitivity test is performed using a regionally customized Land Data Assimilation System (LDAS)—integrating satellite data, the Noah-MP 3.6 Land Surface Models (LSMs), and the Hydrological Modeling and Analysis Platform (HyMAP) river routing model—applied to western South America. Model sensitivity is tested using three sets of simulations, performed with Noah-MP in offline mode. In the first simulation experiment, we used the Green Vegetation Fraction (GVF) climatology derived from the Moderate-resolution Imaging Spectroradiometer (MODIS). In the second experiment, we used the MODIS time-varying GVF dataset, which considers the inter-annual variability of the vegetation. In our last experiment, both GVF and LAI are simulated using the Noah-MP dynamic leaf model. Results include the influences of the inter-annual vegetation dynamics on evaporation, runoff, and soil moisture variables and the accuracy of the dynamic vegetation model of Noah-MP in simulating GVF and LAI. Simulations that use climatological and time-varying MODIS-derived data are broadly similar, with some deviation in the case of extreme events. It was found that the Noah-MP dynamic leaf model overestimates vegetation variability and consistently underestimates both GVF and LAI in most of the ecoregions. Impacts of these biases on the simulation of hydrology were, however, modest in most ecosystems at most times. The results of this research quantify the vegetation sensitivity on the model design, which is relevant for the monitoring system because it provides a better understanding of water and energy surface fluxes, and it might help to improve future studies that apply advanced LSMs for monitoring, forecast, or climate change projection in the west of South America.