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- W3197287523 abstract "Abstract Precipitation is typically measured locally at a limited number of stations, though most environmental modeling and engineering applications require fine resolution spatial precipitation fields to produce reliable results. To obtain such spatial datasets, the use of spatial interpolation techniques that rely on assumptions about the spatial statistics of precipitation is recommended. Here a comparative study of the interpolation of point precipitation measurements using inverse distance weighting, multilinear regression, kriging, and cokriging with elevation demonstrates the advantages and disadvantages of each method in adequately depicting precipitation fields. Examples are given using open source code in R. Methods are applied on an annual precipitation dataset from the Island of Crete, Greece, where complex terrain causes strong orography effects that affect the spatial variability precipitation. Results show that in this case, cokriging of point precipitation with elevation provides an improved understanding of the underlying statistics and a more reliable spatial precipitation assessment." @default.
- W3197287523 created "2021-09-13" @default.
- W3197287523 creator A5019812825 @default.
- W3197287523 date "2021-01-01" @default.
- W3197287523 modified "2023-09-23" @default.
- W3197287523 title "Precipitation mapping and spatial analysis using R" @default.
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- W3197287523 doi "https://doi.org/10.1016/b978-0-12-822699-5.00008-2" @default.
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