Geostatistical Approach for Spatial Interpolation of Meteorological Data

Öztürk, Derya; Kilic, Fatmagul

doi:10.1590/0001-3765201620150103

Cited by 39 publications

(21 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To determine the spatial distribution of each index over Khuzestan province, we used the ordinary Kriging estimator, which has been extensively employed in similar studies (Bhusal et al 2018;Ozturk and Kilic 2016). We used ArcGIS 10.5 software to produce the zoning maps over the province.…”

Section: Interpolation Methodsmentioning

confidence: 99%

New indices for assessing changes in seasons and in timing characteristics of air temperature

Hekmatzadeh

Kaboli

Haghighi

2020

Theor Appl Climatol

View full text Add to dashboard Cite

Previous studies examining climate change and changes in the timing of seasons have used a fixed temperature threshold for season onset. In this study, the timing of seasons was determined using non-fixed threshold methods. Twelve new timing indices were defined to account for shifts in seasons and season onset day, thermal centroid day, and length. The Mann-Kendall test, Theil-Sen's slope estimator, sequential Mann-Kendall test, and least square linear regression were used to assess trends. The timing indices were examined using data from two meteorological stations in Iran with 50 years of records. Spatio-temporal variations in each index over 30 years (1987-2016) were then determined for Khuzestan province in southwestern Iran. Trend analysis for several indices indicated that the timing of seasons had probably changed in the south and west of the study area, while mountainous regions showed non-significant trends. Based on the hottest and coldest 90-day periods (summer and winter, respectively), during the three decades studied, spring lengthened by 5-10 days/decade in the plain region of Khuzestan province and autumn shortened by about 5-8 days/decade. The centroid of winter occurred earlier, by 2-5 days/decade, in the plains area, while the thermal centroid of summer did not change significantly. Overall, the difference between the thermal centroid of winter and summer (C win-sum) in the plains area significantly decreased, by 6-8 days/decade, in the 30-year period.

show abstract

Section: Interpolation Methodsmentioning

confidence: 99%

New indices for assessing changes in seasons and in timing characteristics of air temperature

Hekmatzadeh

Kaboli

Haghighi

2020

Theor Appl Climatol

View full text Add to dashboard Cite

show abstract

“…The regionalized variable regarded as the most widely used realization Gaussian random field [10][11][12]. In this paper we predict unsampled location by interpolation [22,23]. The prediction requires assumption of isotropic stationary Gaussian process, that is achieved after transformation.…”

Section: Discussionmentioning

confidence: 99%

Kriging and Simulation in Gaussian Random Fields Applied to Soil Property Interpolation

Reshid¹

2019

AJTAS

View full text Add to dashboard Cite

Spatial modeling is increasingly prominent in many fields of science as statisticians attempt to characterize variability of the processes that are spatially indexed. This paper shows that the Gaussian random field framework is useful for characterizing spatial statistics for soil properties. A sample of soil properties in 94 spatial locations are taken from a field (186.35m×211.44m) wide in northern Ethiopia, Karsa-Malima. We use observations of organic carbon (OC) from the site in our study. Box-Cox transformation is used because of OC follows non-Gaussian distributions. We develop ordinary kriging which is universal kriging with unknown trend models which enables us to predict any point within the field even outside the field up to the "Range" of the model. In this thesis work we predict 100×100 grids (10000 points) using kriging interpolation models. More over in each of these 10000 locations 1000 conditional simulations are made. Interestingly prediction using universal kriging and mean of conditional simulations agree in expectation and kriging variance. For covariance and/or variogram modeling and for parameter estimation we used least square principle and maximum likelihood estimation method. The classical geostatistical approach known as kriging is used as a spatial model for spatial prediction with associated spatial variances. Moreover, conditional simulation is performed. From ordinary kriging model results, predictions are accurate when predictions are close to observation locations. Prediction variance in the observed locations is close to the nugget effect.

show abstract

“…This technique is well known for its robustness in allowing statistical values and uncertainties of predicted values in the formation of trend surfaces. The spline method predicts the spatial coverage of temperature accurately -especially for an agricultural region [26], while geostatistical tools of ArcGIS software were used to perform spatial interpolation over temperature data [27]. Regression and moving average methods were employed to investigate the temporal behavior of temperature [28,29].…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Variability of Temperature and Its Extremes Over an Agro-Ecological Region of Tamil Nadu, India

Ganeshkumar¹,

Krishna²

2020

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

Temperature data from 14 stations were used to explore the spatiotemporal variability of temperature and its extremes for the period 1960-2018 in the Cauvery River Delta (CRD) area, an agro-ecological region of Tamil Nadu State, India. This investigation was carried out by using spline interpolation method, linear regression and Mann-Kendall test with a Sen's slope estimator. Results indicated that the spatial patterns and temporal trends of annual and seasonal temperature showed an upward trend in most parts of the CRD region. The spatial changes of mean temperature at annual and seasonal scale were similar and showed an upward trend. Extreme temperature indices showed an increasing trend throughout the area. This investigation was carried out for a period of six decades from the year 1960. The CRD region exhibited a warming trend after the 1980s. Temperature variability over this region was witnessed by the global and regional level climate warming. These variability conditions would influence the growth and life cycle of agricultural crops. The results of this investigation could support decision makers and authorities to activate adequate irrigation and crop-changing strategies in order to protect vulnerable ecosystems and environmental conditions of the CRD region.

show abstract

Geostatistical Approach for Spatial Interpolation of Meteorological Data

Cited by 39 publications

References 3 publications

New indices for assessing changes in seasons and in timing characteristics of air temperature

New indices for assessing changes in seasons and in timing characteristics of air temperature

Kriging and Simulation in Gaussian Random Fields Applied to Soil Property Interpolation

Spatiotemporal Variability of Temperature and Its Extremes Over an Agro-Ecological Region of Tamil Nadu, India

Contact Info

Product

Resources

About