%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Lin, S. P.
%A Jing, C. W.
%A Coles, N. A.
%A Chaplot, Vincent
%A Moore, N. J.
%A Wu, J. P.
%T Evaluating DEM source and resolution uncertainties in the Soil and Water Assessment Tool
%D 2013
%L fdi:010058818
%G ENG
%J Stochastic Environmental Research and Risk Assessment
%@ 1436-3240
%K DEM resolution ; SWAT ; Model uncertainty ; ASTER GDEM ; SRTM
%K CHINE
%M ISI:000312730500016
%N 1
%P 209-221
%R 10.1007/s00477-012-0577-x
%U https://www.documentation.ird.fr/hor/fdi:010058818
%> https://www.documentation.ird.fr/intranet/publi/2013/01/010058818.pdf
%V 27
%W Horizon (IRD)
%X DEMs as important input parameters of environmental risk assessment models are notable sources of uncertainties. To illustrate the effect of DEM grid size and source on model outputs, a widely used watershed management model, the Soil and Water Assessment Tool (SWAT), was applied with two newly available DEMs as inputs (i.e. ASTER GDEM Version 1, and SRTM Version 4.1). A DEM derived from 1:10,000 high resolution digital line graph (DLG) was used as a baseline for comparisons. Eleven resample resolutions, from 5 to 140 m, were considered to evaluate the impact of DEM resolution on SWAT outputs. Results from a case study in South-eastern China indicate that the SWAT predictions of total phosphorus and total nitrogen decreased substantially with coarser resample resolution. A slightly decreasing trend was found in the SWAT predicted sediment when DEMs were resampled to coarser resolutions. The SWAT predicted runoff was not sensitive to resample resolution. For different data sources, ASTER GDEM did not perform better than SRTM in SWAT simulations even it was provided with a smaller grid size and higher vertical accuracy. The predicted outputs based on ASTER GDEM and SRTM were similar, and much lower than the ones based on DLG. This study presents potential uncertainties introduced by DEM resolutions and data sources, and recommends strategies choosing DEMs based on research objects and maximum acceptable errors.
%$ 062 ; 021 ; 020