Next Article in Journal
Indirect Damage of Urban Flooding: Investigation of Flood-Induced Traffic Congestion Using Dynamic Modeling
Next Article in Special Issue
EmiStatR: A Simplified and Scalable Urban Water Quality Model for Simulation of Combined Sewer Overflows
Previous Article in Journal
Potential of Biofilters for Treatment of De-Icing Chemicals
Previous Article in Special Issue
A Heuristic Method for Measurement Site Selection in Sewer Systems
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessFeature PaperArticle
Water 2018, 10(5), 621; https://doi.org/10.3390/w10050621

Effects of Input Data Content on the Uncertainty of Simulating Water Resources

1
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35392 Giessen, Germany
2
Institute of Soil Science and Site Ecology, TU Dresden, Pienner Str. 19, 01737 Tharandt, Germany
3
Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, Senckenbergstraße 3, 35390 Giessen, Germany
*
Author to whom correspondence should be addressed.
Received: 1 March 2018 / Revised: 4 May 2018 / Accepted: 8 May 2018 / Published: 10 May 2018
(This article belongs to the Special Issue Quantifying Uncertainty in Integrated Catchment Studies)
View Full-Text   |   Download PDF [1930 KB, uploaded 18 May 2018]   |  

Abstract

The widely used, partly-deterministic Soil and Water Assessment Tool (SWAT) requires a large amount of spatial input data, such as a digital elevation model (DEM), land use, and soil maps. Modelers make an effort to apply the most specific data possible for the study area to reflect the heterogeneous characteristics of landscapes. Regional data, especially with fine resolution, is often preferred. However, such data is not always available and can be computationally demanding. Despite being coarser, global data are usually free and available to the public. Previous studies revealed the importance for single investigations of different input maps. However, it remains unknown whether higher-resolution data can lead to reliable results. This study investigates how global and regional input datasets affect parameter uncertainty when estimating river discharges. We analyze eight different setups for the SWAT model for a catchment in Luxembourg, combining different land-use, elevation, and soil input data. The Metropolis–Hasting Markov Chain Monte Carlo (MCMC) algorithm is used to infer posterior model parameter uncertainty. We conclude that our higher resolved DEM improves the general model performance in reproducing low flows by 10%. The less detailed soil-map improved the fit of low flows by 25%. In addition, more detailed land-use maps reduce the bias of the model discharge simulations by 50%. Also, despite presenting similar parameter uncertainty (P-factor ranging from 0.34 to 0.41 and R-factor from 0.41 to 0.45) for all setups, the results show a disparate parameter posterior distribution. This indicates that no assessment of all sources of uncertainty simultaneously is compensated by the fitted parameter values. We conclude that our result can give some guidance for future SWAT applications in the selection of the degree of detail for input data. View Full-Text
Keywords: discharge; uncertainty analysis; input data; SPOTPY; Soil and Water Assessment Tool (SWAT) discharge; uncertainty analysis; input data; SPOTPY; Soil and Water Assessment Tool (SWAT)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Camargos, C.; Julich, S.; Houska, T.; Bach, M.; Breuer, L. Effects of Input Data Content on the Uncertainty of Simulating Water Resources. Water 2018, 10, 621.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top