Geo-spatial grid-based transformations of precipitation estimates using spatial interpolation methods

Teegavarapu, Ramesh S. V.; Meskele, T.; Pathak, Chandra S.

doi:10.1016/j.cageo.2011.07.004

Cited by 54 publications

(29 citation statements)

References 29 publications

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“…Many techniques are available to interpolate precipitation data set. The inverse distance weighting (IDW) and Kriging method have been widely applied for the spatial predication of precipitation (Watkins et al 2005;Szolgay et al 2009;Hussain et al 2010;Teegavarapu et al 2012). We use the Kriging method because it can provide more accurate estimates of precipitation during the growing season than the IDW method.…”

Section: Methodsmentioning

confidence: 99%

Interannual variability and correlation of vegetation cover and precipitation in Eastern China

Jiang

Zhang

et al. 2013

Theor Appl Climatol

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Based on the SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) data and daily precipitation data of 357 meteorological stations, the spatial and temporal variability of vegetation cover, measured by NDVI, and precipitation as well as their relationships are investigated in Eastern China, which is portioned into three subregions (regions I, II, and III), for the period 1998-2010. The results show that high NDVI values appear mainly in Northeastern China and in August while high precipitation (PRETOT) occurs in Southeastern China and in July (June for Southern China). Extreme precipitation days (RD95p) and amount (EPRETOT) coincide well with PRETOT. Extreme precipitation intensity (RINTEN) has a similar spatial variability to PRETOT but with a smaller seasonal variation than PRETOT. Growing season NDVI is positively correlated with PRETOT in 11.7 % of the study area (mostly in arid to subhumid regions of Northern China), where precipitation is a limiting factor for vegetation growth. In contrast, a negative correlation between growing season NDVI and PRETOT is found in 4.8 % of the study area, mostly in areas around the Yangtze River and deep Northeastern China. No significant correlations between these two variables are found for the other regions because vegetation response to precipitation is affected by other factors such as temperature, radiation, and human disturbance. On a monthly scale, there is a positive correlation between NDVI and PRETOT in May (for region II) and September (all subregions except region I). NDVI variations lag 1 month behind PRETOT in June (for region I) and October. Correlations between NDVI and RD95p, EPRETOT are similar to that with PRETOT, but the relationships between NDVI and RINTEN are relatively weaker than with PRETOT. This study provides the technical basis for agriculture development and ecological construction in Eastern China.

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Section: Methodsmentioning

confidence: 99%

Interannual variability and correlation of vegetation cover and precipitation in Eastern China

Jiang

Zhang

et al. 2013

Theor Appl Climatol

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“…Improvements were achieved in the estimation of precipitation data when the stations with lowest measurement uncertainty were selected in the interpolation process. Teegavarapu et al (2011) evaluated spatial interpolation methods for transformation of precipitation estimates from one grid to another. These methods use extents of spatial overlays as weights in nearest neighbour interpolation.…”

Section: Introductionmentioning

confidence: 99%

Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records

Teegavarapu

2012

Hydrological Sciences Journal

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Editor D. Koutsoyiannis; Associate editor S. Grimaldi Citation Teegavarapu, R.S.V., 2012. Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records. Hydrological Sciences Journal, 57 (3), 383-406. Abstract New mathematical programming models are proposed, developed and evaluated in this study for estimating missing precipitation data. These models use nonlinear and mixed integer nonlinear mathematical programming (MINLP) formulations with binary variables. They overcome the limitations associated with spatial interpolation methods relevant to the arbitrary selection of weighting parameters, the number of control points within a neighbourhood, and the size of the neighbourhood itself. The formulations are solved using genetic algorithms. Daily precipitation data obtained from 15 rain gauging stations in a temperate climatic region are used to test and derive conclusions about the efficacy of these methods. The developed methods are compared with some naïve approaches, multiple linear regression, nonlinear least-square optimization, kriging, and global and local trend surface and thin-plate spline models. The results suggest that the proposed new mathematical programming formulations are superior to those obtained from all the other spatial interpolation methods tested in this study.Key words missing precipitation data; spatial interpolation; genetic algorithms; nonlinear optimization methods; mixed integer mathematical programming (MINLP), multiple linear regression, nonlinear least squares approach; thin-plate splines, trend surface models, Kentucky, USA Interpolation spatiale à l'aide de modèles non linéaires de programmation mathématique pour l'estimation de précipitations manquantes Résumé Nous proposons ici des modèles de programmation mathématique innovants et originaux, développés et évalués pour estimer des données de précipitations manquantes. Ces modèles utilisent les formulations de la programmation mathématique non linéaire et non linéaire mixte en nombres entiers binaires, qui surmontent les limites des méthodes d'interpolation spatiale où le choix des paramètres de pondération est arbitraire, de même que le nombre de points de contrôle au sein d'un voisinage et que le voisinage lui-même. Les formulations sont établies en utilisant les algorithmes génétiques. Les données de précipitations quotidiennes provenant de 15 stations pluviométriques d'une région climatique tempérée ont été utilisées pour tester et évaluer l'efficacité de ces méthodes. Elles ont été comparées à quelques approches classiques : la régression linéaire multiple, l'optimisation non linéaire selon les moindres carrés, le krigeage, la surface tendancielle globale ou locale et les fonctions splines. Les résultats suggèrent que la proposition de nouvelles formulations de programmation mathématique fournit de meilleurs résultats que ceux obtenus à partir de toutes les autres méthodes d'interpolation spatiale testées dans cette étude.Mots clefs données de précipitations manq...

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“…It is most often applied as a precursor to creating contour or isoline plots, the drawing of equal value lines to produce a realistic surface between measurement points [16,17].…”

Section: Interpolation Methodsmentioning

confidence: 99%

“…The cross-validation was used to verify the accuracy of the different interpolation methods. To evaluate the goodness-of-fi t, we calculated the mean absolute error (MAE), the mean relative error (MRE), and the root mean square error (RMSE) [3,16,28]. The MRE represents the percentage of error between the observed and predicted values, while the RMSE and MAE summarize the mean difference in the units of observed and predicted values [3].…”

Section: Validationmentioning

confidence: 99%

Comparison of Spatial Interpolation Methods Based on Rain Gauges for Annual Precipitation on the Tibetan Plateau

Zhang¹,

Lü²,

Wang³

2016

Pol. J. Environ. Stud.

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Geo-spatial grid-based transformations of precipitation estimates using spatial interpolation methods

Cited by 54 publications

References 29 publications

Interannual variability and correlation of vegetation cover and precipitation in Eastern China

Interannual variability and correlation of vegetation cover and precipitation in Eastern China

Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records

Comparison of Spatial Interpolation Methods Based on Rain Gauges for Annual Precipitation on the Tibetan Plateau

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