Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy

Piazza, Ambra Di; Conti, Francesco Lo; Noto, Leonardo; Viola, Francesco; Loggia, Goffredo La

doi:10.1016/j.jag.2011.01.005

Cited by 214 publications

(164 citation statements)

References 23 publications

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“…Weights were based on the distance between the measured points, the prediction locations, and the overall spatial arrangement among the measured points. Kriging showed the best performance among the univariate methods [76,77].…”

Section: Temporal and Spatial Precipitation Distribution Over Slovakimentioning

confidence: 99%

Precipitation Trends over Slovakia in the Period 1981–2013

Zeleňáková

Vido

Portela

et al. 2017

Water

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Abstract:The objective of this paper was to analyze the temporal and spatial trends in annual and seasonal precipitation in Slovakia utilizing 487 gauging station data collected statewide in the period from 1981 to 2013. The nonparametric Mann-Kendall (MK) statistical test, which has been widely used to assess the significance of trends in hydrological time series, was applied to detect the significant trends. The Theil-Sen (TS) approach was applied to quantify the trend magnitude, and the Sequential Mann-Kendall (SQMK) test was used to detect abrupt temporal trend shifts. In general, precipitation data in the study area have not changed during the last 33 years, and there are no big gaps. However, predominantly increasing trends in precipitation time series were found at most of the gauging stations in Slovakia. The results corroborate previous climatic studies in the area of central Europe. In Slovakia, there is evidence of different rain distribution from the monthly point of view. Decreasing trends were detected in December in the northern part of Slovakia, while the central and southern parts revealed increasing trends. Most of the stations showed increasing summer precipitation trends, especially in July. It is expected that the findings of this comprehensive study of precipitation trends over Slovakia will produce more insights for the understanding of the regional hydrological behavior in Slovakia over the last several decades.

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Section: Temporal and Spatial Precipitation Distribution Over Slovakimentioning

confidence: 99%

Precipitation Trends over Slovakia in the Period 1981–2013

Zeleňáková

Vido

Portela

et al. 2017

Water

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“…However, it is no doubt that measured data are always unsatisfactory. This leads to unknown errors inherent in measured data (Zhou & Liu, 2002 (Hosseini et al, 1993;Gotway et al, 1996;Zimmerman et al 1999;Erxleben et al, 2002;Vicente-Serrano et al, 2003;Attorre et al, 2007;Piazza et al, 2011) found that Kriging is the best one among all the existing interpolation models. Another phenomenon should be mentioned is that the frequency of interpolation methods compared varies considerably among methods and different studies have compared a suite of different methods, which makes it difficult to draw general conclusions.…”

Section: Comparison Results: Most Commonly Used Methods For Evaluatiomentioning

confidence: 99%

Quantitative Evaluation of Spatial Interpolation Models Based on a Data-Independent Method

Liu¹,

Hu²,

Ma³

2011

Advances in Data, Methods, Models and Their Applications in Geoscience

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“…For example, compared with the neighboring places, more rainy days and shorter sunshine duration are experienced in Shijiazhuang and Xingtai, among others, on the plains at the base of Taihang Mountain, where low water requirement of winter wheat is observed because Taihang Mountain provides shade in the area. Generally, mean square error of prediction which measures the average square difference between the true crop water requirement and its estimate in the validation points (MSE), mean bias error (MBE), mean absolute error (MAE), scaled mean square error of prediction which measures the average square difference between the observed crop water requirement and its estimate crop water requirement divided by the observed crop water requirement in the Nv-validation points (s-MSE), and linear correlation coefficient (CC) are used to assess interpolation methods [29]. The comparison of the spatial processing of these two methods is shown in Table 7.…”

Section: Spatial Distribution Of the Water Requirement Of Winter Wheamentioning

confidence: 99%

Estimation of crop water requirement based on principal component analysis and geographically weighted regression

et al. 2013

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In this study the principal component analysis (PCA) and geographically weighted regression (GWR) are combined to estimate the spatial distribution of water requirement of the winter wheat in North China while the effect of the macro-and micro-topographic as well as the meteorological factors on the crop water requirement is taking into account. The spatial distribution characteristic of the water requirement of the winter wheat in North China and its formation are analyzed based on the spatial variation of the main affecting factors and the regression coefficients. The findings reveal that the collinearity can be effectively removed when PCA is applied to process all of the affecting factors. The regression coefficients of GWR displayed a strong variability in space, which can better explain the spatial differences of the effect of the affecting factors on the crop water requirement. The evaluation index of the proposed method in this study is more efficient than the widely used Kriging method. Besides, it could clearly show the effect of those affecting factors in different spatial locations on the crop water requirement and provide more detailed information on the region where those factors suddenly change. To sum up, it is of great reference significance for the estimation of the regional crop water requirement. Crop water requirement is an important parameter in the calculation of the equilibrium of water and soil resources and the design, operation, and management of the irrigation projects [1]. Many methods of measuring and estimating crop water requirement have been proposed in China and foreign countries [2][3][4][5]. Although these methods exhibit accuracy in their respective applications to some extent, what is obtained when they are used are mostly point data, which can neither be used directly for other points nor as the average data of a large area [6]. Therefore, how to make full use of the data obtained from the measured points has been the focus of most studies since it is impossible to lay out as many test sites as desired [7][8][9][10][11][12]. Spatial interpolation methods now commonly used, such as inverse distance method, are simple and easy to apply. However, only the effect of the geographic coordinates are considered in those methods and the change in the spatial structure of the crop water requirement is scarcely revealed. Although adjusted geological statistics is usually applied in the estimation of crop water requirements, the sample data should meet important prerequisites [13]. When regression analysis is adopted, both the effect of macro-terrain and that of the micro-terrain factors on crop water requirement are taken into account. However, those factors are complicated and a correlation among those factors is also found [14]. The correlation contradicts the hypothesis that all of these factors are independent from traditional global regression analysis. Moreover, crop water requirements are typically regionalized variables [8] and almost spatially dependent. In most traditional s...

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Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy

Cited by 214 publications

References 23 publications

Precipitation Trends over Slovakia in the Period 1981–2013

Precipitation Trends over Slovakia in the Period 1981–2013

Quantitative Evaluation of Spatial Interpolation Models Based on a Data-Independent Method

Estimation of crop water requirement based on principal component analysis and geographically weighted regression

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