Abstract
Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK) and geographically weighted regression Kriging (GWRK) methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM), normalized difference vegetation index (NDVI), solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.
Highlights
Precipitation, one of the most important climatic factors, is a vital part of the hydrologic cycle, affecting energy transfer and maintaining biosphere functions [1,2,3]
Alternative distribution maps of precipitation were interpolated by multiple linear regression Kriging (MLRK) and geographically weighted regression Kriging (GWRK)
Kriging processes using different methods: the variance explanation of the GWRK regression model was higher than that of MLRK, but the contrary is true of the Kriging process
Summary
Precipitation, one of the most important climatic factors, is a vital part of the hydrologic cycle, affecting energy transfer and maintaining biosphere functions [1,2,3]. It is the focus of hydrology, agriculture, ecology, and environmental science, as well as other related disciplines [4,5,6]. The main methods for obtaining precipitation data include ground-based meteorological measurements and spaceborne radar observations [9,10]. Spaceborne radar data have low spatial resolution and large uncertainties, which could lead to significant errors in precipitation distribution prediction [11].
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