SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Watershed modeling with remotely sensed big data: MODIS Leaf Area Index improves hydrology and water quality predictions 
Authors:Rajib, A., I.L. Kim, H.E. Golden, C.R. Lane, S.V. Kumar, Z. Yu and S. Jeyalakshmi 
Journal:Remote Sensing 
Volume (Issue):12(13) 
Article ID:2148 
URL (non-DOI journals): 
Model:SWAT (modified) 
Broad Application Category:hydrologic and pollutant 
Primary Application Category:crop, forest and/or vegetation growth/yield and/or parameters 
Secondary Application Category:model and/or data comparison 
Watershed Description:16,860 km^2 Cedar River, which drains much of eastern Iowa, U.S. 
Calibration Summary: 
Validation Summary: 
General Comments: 
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 Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions. 
Keywords:data assimilation; ecohydrology; hydrologic modeling; remote sensing; SWAT; LAI