Giessener Elektronische Bibliothek

GEB - Giessener Elektronische Bibliothek

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

Camargos, Carla ; Julich, Stefan ; Houska, Tobias ; Bach, Martin ; Breuer, Lutz

Originalveröffentlichung: (2018) Water 10(5):621 doi: 10.3390/w10050621
Zum Volltext im pdf-Format: Dokument 1.pdf (1.930 KB)

Bitte beziehen Sie sich beim Zitieren dieses Dokumentes immer auf folgende
URN: urn:nbn:de:hebis:26-opus-147839

Bookmark bei

Freie Schlagwörter (Englisch): discharge , uncertainty analysis , input data , SPOTPY , Soil and Water Assessment Tool (SWAT)
Sammlung: Open Access - Publikationsfonds
Universität Justus-Liebig-Universität Gießen
Institut: Professur für Landschafts-, Wasser- und Stoffhaushalt
Fachgebiet: IFZ Interdisziplinäres Forschungszentrum für Umweltsicherung
DDC-Sachgruppe: Landwirtschaft
Dokumentart: Aufsatz
Sprache: Englisch
Erstellungsjahr: 2018
Publikationsdatum: 01.08.2019
Kurzfassung auf Englisch: 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.
Lizenz: Lizenz-Logo  Creative Commons - Namensnennung 4.0