A novel approach to precipitation series completion in climatological datasets: application to Andalusia

Ramos-Calzado, Petra; Gómez‐Camacho, J.; Pérez‐Bernal, F.; López, María Fernanda Pita

doi:10.1002/joc.1657

Cited by 58 publications

(46 citation statements)

References 17 publications

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“…Hutchinson (1995) used thin-splines for interpolation of precipitation data. Ramos-Calzado et al (2008) proposed a new approach for estimating missing precipitation data considering the rainfall measurement uncertainty. Improvements were achieved in the estimation of precipitation data when the stations with lowest measurement uncertainty were selected in the interpolation process.…”

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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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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“…The efficacy of these methods for the interpolation of precipitation data has been investigated by a number of comparative studies, both on a monthly and daily basis (e.g. Ashraf et al, 1997;Teegavarapu and Chandramouli, 2005;Ramos et al, 2008), and generally found to be greater than that of weighting approaches. In particular, Eischeid et al (2000) demonstrated that multi-linear regression (MLR) outperforms most of the commonly used techniques concerning missing data handling in daily resolution precipitation series.…”

Section: Introductionmentioning

confidence: 99%

Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach

Simolo

Brunetti

Maugeri

et al. 2010

Intl Journal of Climatology

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This work presents a novel method for estimating missing values in daily precipitation series. It is aimed at identifying the event time location with good accuracy and reconstructing the correct amount of daily rainfall. In addition, the statistical properties of the time series, i.e. both probability distribution and long-term statistics, are preserved. The completion method is based on a two-step algorithm that uses information from a cluster of neighboring stations. First, wet and dry days are tagged, and subsequently, the full precipitation amount for wet-classified days is estimated by a modified multi-linear regression approach. This method avoids overestimation of the number of wet days and underestimation of intense precipitation events, which are typical side effects of common regression-based approaches.

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“…On the other hand, a great deal of other research has been carried on sub-regional and local scales (e.g. Romero et al 1998, Ceballos et al 2004, Saladié et al 2004, del Río et al 2005, Caramelo & Manso-Orgaz 2007, Ramos-Calzado et al 2008, which results are difficult to compare as station densities and the period under examination vary greatly (see the recent review by Gonzalez-Hidalgo et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Precipitation trends in Spanish hydrological divisions, 1946–2005

González‐Hidalgo¹,

Brunetti²,

Luis³

2010

Clim. Res.

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This paper presents an analysis of precipitation changes in the main Hydrological Divisions (HDs; 'Cuencas Hidrográficas', Water Planning Divisions) in Spain from . Trend analysis revealed several monthly long-term variations and important spatial differences that should be taken into account for any national water-planning scheme. The most spatially coherent signals were a negative trend observed in March and June, affecting all catchments (HDs), and a positive trend in October, which was particularly evident in the northwestern catchments. These caused a redistribution of precipitation throughout the year, with a reduction in the length of the wet season (due to the negative trend in March) and a concentration of precipitation at the beginning of the wet season in October. A running trend analysis revealed variability in the long-term tendency which, although quite persistent, changed in strength between sub-periods.

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A novel approach to precipitation series completion in climatological datasets: application to Andalusia

Cited by 58 publications

References 17 publications

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

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

Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach

Precipitation trends in Spanish hydrological divisions, 1946–2005

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