Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hessd-12-11421-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
03 Nov 2015
Review status
This discussion paper has been under review for the journal Hydrology and Earth System Sciences (HESS). The revised manuscript was not accepted.
A comprehensive evaluation of input data-induced uncertainty in nonpoint source pollution modeling
L. Chen1, Y. Gong2, and Z. Shen1 1State Key Laboratory of Water Environment, School of Environment, Beijing Normal University, Beijing 100875, China
2Key Laboratory of Urban Stormwater System and Water Environment, Ministry of Education, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Abstract. Watershed models have been used extensively for quantifying nonpoint source (NPS) pollution, but few studies have been conducted on the error-transitivity from different input data sets to NPS modeling. In this paper, the effects of four input data, including rainfall, digital elevation models (DEMs), land use maps, and the amount of fertilizer, on NPS simulation were quantified and compared. A systematic input-induced uncertainty was investigated using watershed model for phosphorus load prediction. Based on the results, the rain gauge density resulted in the largest model uncertainty, followed by DEMs, whereas land use and fertilizer amount exhibited limited impacts. The mean coefficient of variation for errors in single rain gauges-, multiple gauges-, ASTER GDEM-, NFGIS DEM-, land use-, and fertilizer amount information was 0.390, 0.274, 0.186, 0.073, 0.033 and 0.005, respectively. The use of specific input information, such as key gauges, is also highlighted to achieve the required model accuracy. In this sense, these results provide valuable information to other model-based studies for the control of prediction uncertainty.

Citation: Chen, L., Gong, Y., and Shen, Z.: A comprehensive evaluation of input data-induced uncertainty in nonpoint source pollution modeling, Hydrol. Earth Syst. Sci. Discuss., 12, 11421-11447, doi:10.5194/hessd-12-11421-2015, 2015.
L. Chen et al.
L. Chen et al.
L. Chen et al.

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Short summary
Quantifying model uncertainty at the large basin scale is notably complex because of the complexity and interaction of input data, which complicate the application of watershed models. In this paper, an evaluation of the error-transitivity from input data to nonpoint source pollution modeling was conducted by quantifying the effects of four input data.These results provide valuable information for developing watershed model, and can be extrapolated to other model-based research.
Quantifying model uncertainty at the large basin scale is notably complex because of the...
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