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Journal ArticleDOI

Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall

Pierre Goovaerts
- 21 Feb 2000 - 
- Vol. 228, Iss: 1, pp 113-129
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TLDR
In this article, three multivariate geostatistical interpolation algorithms for incorporating a digital elevation model into the spatial prediction of rainfall are presented, i.e., simple kriging with varying local means, krigging with an external drift, and colocated cokriging.
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This article is published in Journal of Hydrology.The article was published on 2000-02-21. It has received 1419 citations till now. The article focuses on the topics: Spatial dependence & Multivariate interpolation.

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Citations
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A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006

TL;DR: In this paper, the authors presented a European land-only daily high-resolution gridded data set for precipitation and minimum, maximum, and mean surface temperature for the period 1950-2006.
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An adaptive inverse-distance weighting spatial interpolation technique

TL;DR: Adaptive IDW performs better than the constant parameter method in most cases, and better than ordinary kriging in one of the authors' empirical studies when the spatial structure in the data could not be modeled effectively by typical variogram functions.
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A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors

TL;DR: Comparison studies in environmental sciences are used to assess the performance and to quantify the impacts of data properties on the performance of spatial interpolation methods, finding data variation is a dominant impact factor and has significant effects on theperformance of the methods.
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The generation of monthly gridded datasets for a range of climatic variables over the UK

TL;DR: In this article, the analysis process uses geographical information system capabilities to combine multiple regression with inverse-distance-weighted interpolation and local variations are then incorporated through the spatial interpolation of regression residuals.
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Review: Spatial interpolation methods applied in the environmental sciences: A review

TL;DR: In this paper, the authors provide guidelines and suggestions regarding application of spatial interpolation methods to environmental data by comparing the features of the commonly applied methods which fall into three categories, namely: non-geostatistical interpolation, geostatistic interpolation and combined methods.
References
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Journal ArticleDOI

An Introduction to Applied Geostatistics

Richard A. Bilonick
- 01 Nov 1991 - 
TL;DR: In this paper, an Introduction to Applied Geostatistics is presented, with a focus on the application of applied geometrics in the area of geostatistic applications.
Book

An Introduction to Applied Geostatistics

TL;DR: In this paper, Krigeage and continuite spatiale were used for interpolation of a variogramme with anisotropic interpolation reference record created on 2005-06-20, modified on 2011-09-01.
Book

GSLIB: Geostatistical Software Library and User's Guide

TL;DR: In this paper, the authors present a set of programs that summarize data with histograms and other graphics, calculate measures of spatial continuity, provide smooth least-squares-type maps, and perform stochastic spatial simulation.
Book

Geostatistics for natural resources evaluation

TL;DR: In this article, an advanced-level introduction to geostatistics and Geostatistical methodology is provided, including tools for description, quantitative modeling of spatial continuity, spatial prediction, and assessment of local uncertainty and stochastic simulation.
Book

Geostatistics: Modeling Spatial Uncertainty

TL;DR: In this article, the Intrinsic Model of Order (IMO) is used for structural analysis and nonlinear methods are used for nonlinear models of scale effects and inverse problems.