Abstract
Abstract Accurate and reliable measurement and prediction of the spatial and temporal distribution of rain field over a wide range of scales are important topics in hydrologic investigations. In this study, a geostatistical approach was adopted. To estimate the rainfall intensity over a study domain with the sample values and the spatial structure from the radar data, the cumulative distribution functions (CDFs) at all unsampled locations were estimated. Indicator kriging (IK) was used to estimate the exceedance probabilities for different preselected threshold levels, and a procedure was implemented for interpolating CDF values between the thresholds that were derived from the IK. Different probability distribution functions of the CDF were tested and their influences on the performance were also investigated. The performance measures and visual comparison between the observed rain field and the IK-based estimation suggested that the proposed method can provide good results of the estimation of indicator variables and is capable of producing a realistic image.
Highlights
As rainfall constitutes the main source of freshwater for our planet and all living things on earth, accurate and reliable measurement and prediction of its spatial and temporal distribution over a wide range of scales is an important subject for hydrologic investigations
The results indicated that geostatistical framework could provide more accurate representations of the spatial distribution of rainfall than those found in the traditional analyses
The data are indicator-transformed and their resulting indicator variograms are used to describe the changes in spatial variability along with the cumulative distribution functions (CDFs)
Summary
As rainfall constitutes the main source of freshwater for our planet and all living things on earth, accurate and reliable measurement and prediction of its spatial and temporal distribution over a wide range of scales is an important subject for hydrologic investigations. The estimation of the rainfall amount over the spatial domain from the data taken at measurement stations is a crucial stage in many hydrological applications. The problem of estimating spatial distribution of rainfall may be described as follows. Obtain an estimate of the unknown rainfall amount, r(x0), at an arbitrary location, x0, within the estimation domain, A, using nearby measurements in and around A, r(x1), r(x2), .
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