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- W2182409550 abstract "Runoff related variables (runoff, runoff statistics, temperature, concentrations) are often modelled through conceptually or physically based models. The methods are usually data intensive, and require calibration. Geostatistical methods can be regarded as more data driven methods, with a more limited need for data. However, such methods have rarely been applied for hydrological modelling, as the methods were traditionally based on point observations or observations with a regular support. There has recently been a development of geostatistical methods for non-point support, particularly within health modelling (Goovaerts, 2006) and hydrology (Gottschalk, et al., 2006; Skoien, et al., 2006). In this paper we will demonstrate an R package (rtop) that implements the methods presented by Skoien et al. (2006), and extended with suggestions from Gottschalk et al. (2006). Taking advantage of the existing methods in R for manipulating spatially referenced objects (points, lines, polygons, grids), and the extensive possibilities for visualizing the results, rtop makes it considerably easier to apply geostatistical interpolation methods to observations with a non-point support, in comparison to former implementations of the method. Variogram fitting differs strongly from that in traditional geostatistics, but we will present methods for automatic and manual variogram fitting, and for visualizing the model fits. Another feature of the kriging method is that it can handle observation uncertainty, either as a result of measurement uncertainty, or when interpolating statistics, such as annual mean or floods with a certain return period." @default.
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- W2182409550 date "2012-01-01" @default.
- W2182409550 modified "2023-09-27" @default.
- W2182409550 title "Interpolating runoff-related variables with rtop" @default.
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