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000003597 0247_ $$2DOI$$a10.1016/j.advwatres.2008.01.002
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000003597 084__ $$2WoS$$aWater Resources
000003597 1001_ $$0P:(DE-HGF)0$$aBormann, H.$$b0
000003597 245__ $$aAssessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) IV: Model sensitivity on data aggregation and spatial (re-)distribution
000003597 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2009
000003597 300__ $$a171 - 192
000003597 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article
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000003597 440_0 $$020263$$aAdvances in Water Resources$$v32$$x0309-1708$$y2
000003597 500__ $$aThe authors thank the "Deutsche Forschungsgemeinschaft" for the funding the collaborative research centre 299 "Land use options for peripheral regions".
000003597 520__ $$aThis paper analyses the effect of spatial resolution and distribution of model input data on the results of regional-scale land use scenarios using three different hydrological catchment models. A 25 m resolution data set of a mesoscale catchment and three land use scenarios are used. Data are systematically aggregated to resolutions up to 2 kill. Land use scenarios are spatially redistributed, both randomly and topography based. Using these data, water fluxes are calculated on a daily time step for a 16 year time period without further calibration. Simulation results are used to identify grid size, distribution and model dependent scenario effects. In the case of data aggregation, all applied models react sensitively to grid size. WASIM and TOPLATS simulate constant water balances for grid sizes from 50 m to 300-500 m, SWAT is more sensitive to input data aggregation, simulating constant water balances between 50 m and 200 m grid size. The calculation of scenario effects is less robust to data aggregation. The maximum acceptable grid size reduces to 200-300 m for TOPLATS and WASIM. In case of spatial distribution, SWAT and TOPLATS are slightly sensitive to a redistribution of land use (below 1.5% for water balance terms), whereas WASIM shows almost no reaction. Because the aggregation effects were stronger than the redistribution effects, it is concluded that spatial discretisation is more important than spatial distribution. As the aggregation effect was mainly associated with a change in land use fraction, it is concluded that accuracy of data sets is much more important than a high spatial resolution. (C) 2008 Elsevier Ltd. All rights reserved.
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000003597 65320 $$2Author$$aHydrological catchment models
000003597 65320 $$2Author$$aModel comparison
000003597 65320 $$2Author$$aLand use change
000003597 65320 $$2Author$$aSpatial distribution
000003597 65320 $$2Author$$aInput data aggregation
000003597 650_7 $$2WoSType$$aJ
000003597 7001_ $$0P:(DE-HGF)0$$aBreuer, L.$$b1
000003597 7001_ $$0P:(DE-HGF)0$$aGräff, T.$$b2
000003597 7001_ $$0P:(DE-Juel1)129472$$aHuisman, J. A.$$b3$$uFZJ
000003597 7001_ $$0P:(DE-HGF)0$$aCroke, B.$$b4
000003597 773__ $$0PERI:(DE-600)2023320-6$$a10.1016/j.advwatres.2008.01.002$$gVol. 32, p. 171 - 192$$p171 - 192$$q32<171 - 192$$tAdvances in water resources$$v32$$x0309-1708$$y2009
000003597 8567_ $$uhttp://dx.doi.org/10.1016/j.advwatres.2008.01.002
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