SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Deriving spatially-distributed precipitation using the artificial neural network and multi-linear regression models 
Authors:Sharma, S., S. Isik, P. Srivastava and L. Kalin 
Journal:Journal of Hydrologic Engineering 
Article ID: 
URL (non-DOI journals): 
Broad Application Category:hydrologic only 
Primary Application Category:climate data effects 
Secondary Application Category:none 
Watershed Description:1837 km^2 Saugahatchee Creek in southeast Alabama, U.S. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Precipitation is the primary driver for hydrologic modeling. Since hydrologic models often require long term, spatially-distributed precipitation datasets for calibration and validation, a novel approach was developed to generate spatially-distributed precipitation data using the Artificial Neural Network (ANN) for the periods when NEXRAD data are either unavailable or the quality of the NEXRAD data is not good. The Multi-Linear Regression (MLR) technique was also evaluated for completeness. The study’s focus was the Saugahatchee Creek watershed in southeast Alabama. In the study area, the wet seasons are dominated by frontal precipitations, while the dry seasons primarily contain patchy, convective thunderstorms. The basic approach was to train and validate the ANN and MLR models using recent NEXRAD and rain gauge precipitations, and then use the trained model with the rain gauge precipitation data to generate past, spatially-distributed precipitation estimates at the NEXRAD grid locations. Results showed that, for the testing period, the ANN-simulated wet season precipitations in all the NEXRAD grids had a Nash Sutcliff Efficiency (NSE) greater than or equal to 0.72 and a Mass Balance Error (MBE) less than or equal to 14%. The same model performance parameters were 0.65 and 17% for the dry season. The MLR model did not perform as well as the ANN model. For the MLR model, the wet season MBE ranged from 13 to 48%, while the dry season MBE ranged from 0.1 to 36% on the testing dataset. An uncalibrated Soil and Water Assessment Tool (SWAT) model was used to assess the improvements in stream flow simulations with the ANN-simulated spatially-distributed precipitation data. It was found that the stream flow simulations using ANN-generated, spatially-distributed precipitations were closer to the observed stream flows as compared to stream flows generated using the rain gauge precipitations. Overall, the results suggest that the method developed in this study can be used to generate past, spatially distributed precipitations at NEXRAD grids locations. 
Keywords:Precipitation, NEXRAD, Artificial Neural Network, Multi-Linear Regression, Hydrologic Modeling, SWAT