SWAT Literature Database for Peer-Reviewed Journal Articles

Title:A comparison of SWAT and ANN Models for daily runoff simulation in different climatic zones of peninsular Spain 
Authors:Jimeno-Sáez, P., J. Senent-Aparicio, J. Pérez-Sánchez and D. Pulido-Velazquez 
Year:2018 
Journal:Water 
Volume (Issue):10(2) 
Pages: 
Article ID:192 
DOI:10.3390/w10020192 
URL (non-DOI journals): 
Model:SWAT 
Broad Application Category:hydrologic only 
Primary Application Category:model and/or data comparison 
Secondary Application Category:hydrologic assessment 
Watershed Description:843 km^2 Ladra River (a tributary of the Miño-Sil River) and the 253 km^2 Segura River headwaters, which are located respectively in northwest and southeast Spain. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Streamflow data are of prime importance to water-resources planning and management, and the accuracy of their estimation is very important for decision making. The Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) models have been evaluated and compared to find a method to improve streamflow estimation. For a more complete evaluation, the accuracy and ability of these streamflow estimation models was also established separately based on their performance during different periods of flows using regional flow duration curves (FDCs). Specifically, the FDCs were divided into five sectors: very low, low, medium, high and very high flow. This segmentation of flow allows analysis of the model performance for every important discharge event precisely. In this study, the models were applied in two catchments in Peninsular Spain with contrasting climatic conditions: Atlantic and Mediterranean climates. The results indicate that SWAT and ANNs were generally good tools in daily streamflow modelling. However, SWAT was found to be more successful in relation to better simulation of lower flows, while ANNs were superior at estimating higher flows in all cases. 
Language:English 
Keywords:Soil and Water Assessment Tool (SWAT); Artificial Neural Network (ANN); data imputation; runoff simulation; hydrologic modelling