SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Estimation of long-term soil moisture using a distributed parameter hydrologic model and verification using remotely sensed data 
Authors:Narasimhan, B., R. Srinivasan, J.G. Arnold, and M. Di Luzio 
Journal:Transactions of the ASAE 
Article ID: 
URL (non-DOI journals):http://ddr.nal.usda.gov/handle/10113/5510 
Broad Application Category:hydrologic only 
Primary Application Category:hydrologic assessment 
Secondary Application Category:calibration, sensitivity, and/or uncertainty analysis 
Watershed Description:29,664 km^2 Upper Trinity; 15,200 km^2 Lower Trinity; 11,632 km^2 Red River; 14,736 km^2 Guadalope; 10,320 km^2; 26,656 km^2 Colorado (Texas) 
Calibration Summary:overall annual average r2/E = 0.75/0.75 (across 24 gauges within six watersheds for differing time periods); range of annual average r2 values = .54 - .99 (20 > .70); correspond E value range = .52 - .99 (21 > .68) 
Validation Summary:overall annual average r2/E = 0.70/0.70 (across 24 gauges within six watersheds for differing time periods); range of annual average r2 values = .63 - 1.00 (17 > .70); correspond E value range = .55 - .97 (22 > .60) 
General Comments:Initial streamflow calibration & validation was performed with a non-linear auto-calibration algorithm for stream gauges that were not affected by reservoirs, diversions, or return flows, and the calibrations were performed for 5-year periods with a "fair distributions of high and low flows". Soil moisture was simulated with SWAT at a spatial resolution of 16 km^2 and a temproal resolution of one week. The simulated soil moisture was evaluated on the basis of vegetation response, by using 16 years of normalized difference vegetation index (NDVI) data derived from NOAA-AVHRR satellite data. The predicted soil moistures were well correlated with agriculture and pasture NDVI values (approaching ~.8 in some years). The authors state that the study provides an alternative framework for estimating soil moisture on the basis of NDVI values (in the absence of ET and soil moisture data), as opposed to just traditional stream flow calibration & validation. 
Abstract:Soil moisture is an important hydrologic variable that controls various land surface processes. In spite of its importance to agriculture and drought monitoring, soil moisture information is not widely available on a regional scale. However, long-term soil moisture information is essential for agricultural drought monitoring and crop yield prediction. The hydrologic model Soil and Water Assessment Tool (SWAT) was used to develop a long-term record of soil water at a fine spatial (16 km^2) and temporal (weekly) resolution from historical weather data. The model was calibrated and validated using stream flow data. However, stream flow accounts for only a small fraction of the hydrologic water balance. Due to the lack of measured evapotranspiration or soil moisture data, the simulated soil water was evaluated in terms of vegetation response, using 16 years of normalized difference vegetation index (NDVI) derived from NOAA-AVHRR satellite data. The simulated soil water was well-correlated with NDVI (r as high as 0.8 during certain years) for agriculture and pasture land use types, during the active growing season April-September, indicating that the model performed well in simulating the soil water. The study provides a framework for using remotely sensed NDVI to verify the soil moisture simulated by hydrologic models in the absence of auxiliary measured data on ET and soil moisture, as opposed to just the traditional stream flow calibration and validation.