Satellite-derived digital elevation model accuracy: Hydrological modelling requirements

T. A. Endreny, Eric F. Wood, D. P. Lettenmaier

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Hydrological models can benefit from satellite-derived digital elevation models (DEMs) only after determining the hydrological model sensitivity to DEM inaccuracies. This study examined how vertical errors within a SPOT satellite-derived DEM of the 532 km2 Little Washita River, OK, watershed affected hydrological predictions in the TOPLATS (topographically based land-atmosphere transfer scheme) water and energy balance model. Model predictions based on SPOT-derived DEM inputs were compared with US Geological Survey (USGS) 7.5-minute level 1 and level 2 DEM-based predictions to determine model sensitivity. Ten-year simulation runs using a statistical formulation of TOPLATS indicated that while DEM inaccuracies had little effect on basin average output, they had a significant effect on the upper and lower quartiles of predicted water table depth. In 12-day simulation runs using a spatially explicit formulation of TOPLATS, which used 30-m grid cells across a 600 000 pixel model domain, elevation errors propagated into model predictions of soil moisture, runoff, evapotranspiration, incoming solar radiation and surface skin temperature. Aggregation of the 30-m pixel model output to scales of 0.25 km2, however, reduced differences between model-predicted vadose zone hydrology. Agreement between model-predicted water table hydrology was achieved at much larger scales of 5 km2, indicating that topography and its associated error structure may have a greater influence on saturated rather than unsaturated hydrological modelling. Copyright (C) 2000 John Wiley and Sons, Ltd.

Original languageEnglish (US)
Pages (from-to)177-194
Number of pages18
JournalHydrological Processes
Issue number2
StatePublished - Feb 15 2000

All Science Journal Classification (ASJC) codes

  • Water Science and Technology


  • Digital elevation model uncertainty
  • Remote sensing
  • Water resource modelling


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