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COMPARISON OF GEOSTATISTICAL PROCEDURES FOR SPATIAL ANALYSIS OF PRECIPITATION IN MOUTAINOUS TERRAIN
Citation:
Phillips, D., J. Dolph, AND D. Marks. COMPARISON OF GEOSTATISTICAL PROCEDURES FOR SPATIAL ANALYSIS OF PRECIPITATION IN MOUTAINOUS TERRAIN. U.S. Environmental Protection Agency, Washington, D.C., EPA/600/J-93/491 (NTIS PB94130028), 1992.
Description:
Application of simulation models to assessment of global climate change effects often requires spatially distributed estimates of precipitation, both under current and future climate scenarios. imple interpolation methods fail to consider the effects of topography on precipitation and may be in considerable error in mountainous regions. he perf ormence of three geostatistical methods for making mean annual precipitation estimates on a regular grid of points in mountainous terrain was evaluated. he methods were (1) kriging, (2) kriging elevation-detrended data, and (3) cokriging with elevation as an auxiliary variable. he study area was the Willamette River Basin, a 2.9 million hectare region spanning the area between the Coast Range and the Cascade Range in western Oregon. ompared to kriging, detrended kriging and cokriging both exhibited better precision and accuracy in the estimation of mean annual precipitation. ontour diagrams for kriging and detrended kriging exhibited smooth zonation following general elevation trends, while cokriging showed a patchier pattern more closely corresponding to local topographic features. etrended kriging and cokriging offer improved spatially distributed precipitation estimates in mountainous terrain on the scale of a few million hectares. pplication of these methods for a larger region, the Columbia River drainage in the U.S. (57 million hectares) , was unsuccessful due to the lack of a consistent precipitationelevation relationship at this scale. recipitation estimation incorporating the effects of topography at larger scales will require either piecewise estimation using the methods described here or development of a physically-based orographic model.