Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions.

ABSTRACT: Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km² agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model's built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution

SUBMITTER: Rajib A 

PROVIDER: S-EPMC7788070 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature