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 |