Kriging in the hydrosciences

Delhomme, J.P.

doi:10.1016/0309-1708(78)90039-8

Cited by 662 publications

(276 citation statements)

References 4 publications

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“…Rainfall quantities retained in previous studies were mainly taken in a deterministic way. Effectively, a single rainfall pattern was selected for which the average kriging variance was minimized to achieve the best new rain gauge locations (Delhomme, 1978;Pardo-Igúzquiza, 1998;Chebbi et al, 2011). In the present study, it is aimed to find out new observation locations using a collection of rainfall patterns or rainfall auxiliary variables, each characterized by its probability of occurrence.…”

Section: A Chebbi Et Al: Development Of a Methods Of Robust Rain Gaumentioning

confidence: 99%

Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

Chebbi

Bargaoui

Cunha

2013

Hydrol. Earth Syst. Sci.

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Abstract. Based on rainfall intensity-duration-frequency (IDF) curves, fitted in several locations of a given area, a robust optimization approach is proposed to identify the best locations to install new rain gauges. The advantage of robust optimization is that the resulting design solutions yield networks which behave acceptably under hydrological variability. Robust optimization can overcome the problem of selecting representative rainfall events when building the optimization process. This paper reports an original approach based on Montana IDF model parameters. The latter are assumed to be geostatistical variables, and their spatial interdependence is taken into account through the adoption of crossvariograms in the kriging process. The problem of optimally locating a fixed number of new monitoring stations based on an existing rain gauge network is addressed. The objective function is based on the mean spatial kriging variance and rainfall variogram structure using a variance-reduction method. Hydrological variability was taken into account by considering and implementing several return periods to define the robust objective function. Variance minimization is performed using a simulated annealing algorithm. In addition, knowledge of the time horizon is needed for the computation of the robust objective function. A short-and a longterm horizon were studied, and optimal networks are identified for each. The method developed is applied to north Tunisia (area = 21 000 km 2 ). Data inputs for the variogram analysis were IDF curves provided by the hydrological bureau and available for 14 tipping bucket type rain gauges. The recording period was from 1962 to 2001, depending on the station. The study concerns an imaginary network augmentation based on the network configuration in 1973, which is a very significant year in Tunisia because there was an exceptional regional flood event in March 1973. This network consisted of 13 stations and did not meet World Meteorological Organization (WMO) recommendations for the minimum spatial density. Therefore, it is proposed to augment it by 25, 50, 100 and 160 % virtually, which is the rate that would meet WMO requirements. Results suggest that for a given augmentation robust networks remain stable overall for the two time horizons.

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Section: A Chebbi Et Al: Development Of a Methods Of Robust Rain Gaumentioning

confidence: 99%

Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

Chebbi

Bargaoui

Cunha

2013

Hydrol. Earth Syst. Sci.

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“…According to Virdee and Kottegoda (1984), Delhomme (1978;1979) should be credited for the application of kriging and stochastic simulations to groundwater studies. A detailed background to stochastic techniques is described in de Marsily et al (2005), Noetinger et al (2005) and Renard (2007).…”

Section: Two-point Geostatisticsmentioning

confidence: 99%

Integrando dados geofísicos aéreos em simulações estatísticas multi-ponto para auxiliar modelos de fluxo de águas subterrâneas

et al. 2015

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The process of accounting for heterogeneity has made significant advances in statistical research, primarily in the framework of stochastic analysis and the development of multiple-point statistics (MPS). Among MPS techniques, the direct sampling (DS) method is tested to determine its ability to delineate heterogeneity from aerial magnetics data in a regional sandstone aquifer intruded by low-permeability volcanic dykes in Northern Ireland, UK. The use of two two-dimensional bivariate training images aids in creating spatial probability distributions of heterogeneities of hydrogeological interest, despite relatively 'noisy' magnetics data (i.e. including hydrogeologically irrelevant urban noise and regional geologic effects). These distributions are incorporated into a hierarchy system where previously published density function and upscaling methods are applied to derive regional distributions of equivalent hydraulic conductivity tensor K. Several K models, as determined by several stochastic realisations of MPS dyke locations, are computed within groundwater flow models and evaluated by comparing modelled heads with field observations. Results show a significant improvement in model calibration when compared to a simplistic homogeneous and isotropic aquifer model that does not account for the dyke occurrence evidenced by airborne magnetic data. The best model is obtained when normal and reverse polarity dykes are computed separately within MPS simulations and when a probability threshold of 0.7 is applied. The presented stochastic approach also provides improvement when compared to a previously published deterministic anisotropic model based on the unprocessed (i.e. noisy) airborne magnetics. This demonstrates the potential of coupling MPS to airborne geophysical data for regional groundwater modelling.

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“…La géostatistique a d'abord été utilisée dans le domaine minier [7] mais ses champs d'application se sont rapidement étendus à d'autres domaines tel celui des sciences de l'eau [8]. Plusieurs études ont montré que les outils géostatistiques s'avéraient efficaces pour caractériser la variabilité des pré-cipitations [9].…”

Section: Iii1 Présentationunclassified

Variabilité spatio-temporelle des précipitations aux échelles de l’hydrologie urbaine

Emmanuel¹,

Leblois²,

Andrieu³

et al. 2011

La Houille Blanche

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Kriging in the hydrosciences

Cited by 662 publications

References 4 publications

Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

Integrando dados geofísicos aéreos em simulações estatísticas multi-ponto para auxiliar modelos de fluxo de águas subterrâneas

Variabilité spatio-temporelle des précipitations aux échelles de l’hydrologie urbaine

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