Geostatistical Approach for Spatial Interpolation of Meteorological Data.

Derya Ozturk,Fatmagul Kilic

doi:10.1590/0001-3765201620150103

Derya Ozturk, Fatmagul Kilic

Open Access

https://doi.org/10.1590/0001-3765201620150103

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Abstract

Meteorological data are used in many studies, especially in planning, disaster management, water resources management, hydrology, agriculture and environment. Analyzing changes in meteorological variables is very important to understand a climate system and minimize the adverse effects of the climate changes. One of the main issues in meteorological analysis is the interpolation of spatial data. In recent years, with the developments in Geographical Information System (GIS) technology, the statistical methods have been integrated with GIS and geostatistical methods have constituted a strong alternative to deterministic methods in the interpolation and analysis of the spatial data. In this study; spatial distribution of precipitation and temperature of the Aegean Region in Turkey for years 1975, 1980, 1985, 1990, 1995, 2000, 2005 and 2010 were obtained by the Ordinary Kriging method which is one of the geostatistical interpolation methods, the changes realized in 5-year periods were determined and the results were statistically examined using cell and multivariate statistics. The results of this study show that it is necessary to pay attention to climate change in the precipitation regime of the Aegean Region. This study also demonstrates the usefulness of the geostatistical approach in meteorological studies.

Highlights

Measurement and evaluation of the spatially distributed meteorological data have become important in connection with climate-change impact studies, determination of water budgets at different temporal and spatial scales, as well as validation of atmospheric and hydrological models
Based on the multivariate statistics, spatial analyses were applied for the monthly precipitation and temperature layer series which calculated by the Ordinary Kriging
Meteorological data are required in many fields such as environment, agriculture and management of natural disasters where spatial data are used

Summary

Introduction

Measurement and evaluation of the spatially distributed meteorological data have become important in connection with climate-change impact studies, determination of water budgets at different temporal and spatial scales, as well as validation of atmospheric and hydrological models. Meteorological data are usually available from a limited number of meteorological stations (Hofierka et al 2002), mostly because it is not economically and technically possible to obtain meteorological data throughout the entire surface. For this reason, spatial interpolation of the meteorological variables obtained from the certain sample points is performed in order to create a model for the entire surface. Deterministic interpolation techniques calculate the values of unsampled points and create surfaces from measured points, based on either the extent of similarity or the degree of smoothing (Matthews 2002). Geostatistical interpolation techniques use the statistical properties of the measured points, quantify the spatial autocorrelation among the measured points and account for the spatial configuration of the sample points around the estimation location (Matthews 2002)

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