A comprehensive study of the effect of GIS data on hydrology and non-point source pollution modeling

• Published in: Agricultural water management 2013-02, Vol.118, p.93-102
• Main Authors: Shen, Z.Y., Chen, L., Liao, Q., Liu, R.M., Huang, Q.
• Format: Article
• Language: English
• Subjects: Agricultural and forest climatology and meteorology. Irrigation. Drainage; Agronomy. Soil science and plant productions; Biological and medical sciences; Computer simulation; Digital elevation model (DEM); Fundamental and applied biological sciences. Psychology; General agronomy. Plant production; Geographic information systems; Hydrology; Land use map; Mathematical models; nitrogen; nonpoint source pollution; phosphorus; Pollution sources; prediction; runoff; Satellite navigation systems; sediments; Soil and Water Assessment Tool (SWAT); Soil and Water Assessment Tool model; Three Gorges Reservoir Area; Uncertainty; Water pollution; watersheds
• ISSN: 0378-3774; 1873-2283
• DOI: 10.1016/j.agwat.2012.12.005

► The effect of the GIS combinations on watershed modeling is evaluated. ► This paper indicates sediment and P outputs are sensitively biased by GIS data. ► Threshold resolutions exist within which more precise data show no advantage. ► The digital elevation model determines the optimal combination of GIS inputs. The Geographic Information System (GIS) has been extensively integrated into hydrology and non-point source (H/NPS) pollution modeling. However, few studies have been carried out on the effect of GIS inputs on NPS modeling and the prediction uncertainty related to the combination of different GIS inputs. In this paper, the effect of GIS data on H/NPS simulation was investigated by re-sampling the initial GIS data into new grid sizes and grouping the available GIS maps in all possible ways. The effect of GIS inputs was then quantified by the Soil and Water Assessment Tool (SWAT) in the Daning watershed, China. Based on the results, the GIS inputs had a limited impact on runoff and nitrogen (N) predictions, while sediment and phosphorus (P) simulations were sensitively biased by GIS data. This study further demonstrated that a level (threshold) of GIS resolution existed, within which more precise data would not be of benefit to SWAT prediction. To reduce prediction uncertainty, the digital elevation model (DEM) should be tackled to determine at which resolution the model would perform optimally.