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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-46
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Feb 2018

Research article | 26 Feb 2018

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

The effect of input data complexity on the uncertainty in simulated streamflow in a humid, mountainous watershed

Linh Hoang1,2, Rajith Mukundan2, Karen E. B. Moore2, Emmet M. Owens2, and Tammo S. Steenhuis3 Linh Hoang et al.
  • 1Hunter College, City University of New York, 695 Park Avenue, New York, NY 10065, USA
  • 2New York City Department of Environmental Protection, 71 Smith Avenue, Kingston, NY 12401, USA
  • 3Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA

Abstract. Uncertainty in hydrological and water quality modelling is of significant concern due to its effects on prediction and subsequent application in watershed management. Similar to other distributed hydrological models, model uncertainty is an issue in applying the Soil and Water Assessment Tool (SWAT). Previous research has shown how SWAT predictions are affected by uncertainty in parameter estimation and input data resolution. Nevertheless, little information is available on how parameter uncertainty and output uncertainty are affected by input data of varying complexity. In this study, SWAT-Hillslope (SWAT-HS), a modified version of SWAT capable of predicting saturation excess runoff was applied to assess the effects of input data with varying degrees of complexity on parameter uncertainty and output uncertainty. Four digital elevation model (DEM) resolutions (1, 3, 10 and 30m) were tested for their ability to predict streamflow and saturated areas. In a second analysis, three soil maps and three land use maps were used to build nine SWAT-HS setups from simple to complex (fewer to more soil types/ land use classes), which were then compared to study the effect of input data complexity on model prediction/output uncertainty. The case study was the Town Brook watershed in the upper reaches of the West Branch Delaware River in the Catskill Region, New York, USA. Results show that DEM resolution did not impact parameter uncertainty or affect the simulation of streamflow at the watershed outlet but significantly affected the spatial pattern of saturated areas, with 10m being the most appropriate grid size to use for our application. The comparison of nine model setups revealed that input data complexity did not affect parameter uncertainty. Model setups using intermediate soil/land use specifications were slightly better than the ones using simple information, while the most complex setup did not show any improvement from the intermediate ones. We conclude that increasing spatial input details may not necessarily improve model performance or reduce parameter and output uncertainty, but using multiple temporal and spatial observations can aid in finding the appropriate parameter sets and in reducing prediction/output uncertainty.

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Short summary
The paper analyzes the effect of two input data (DEMs and the combination of soil and land use data) with different complexity on the uncertainty of model outputs (the predictions of streamflow and saturated areas) and the uncertainty of parameter estimation using SWAT-HS. Results showed that DEM resolution has significant effect on the spatial pattern of saturated areas and using complex soil and land use data may not necessarily improve model performance or reduce model uncertainty.
The paper analyzes the effect of two input data (DEMs and the combination of soil and land use...
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