Residual diagnostics for variogram fitting

Glatzer, Ernst; Müller, Werner G.

doi:10.1016/j.cageo.2004.06.008

Cited by 12 publications

(4 citation statements)

References 23 publications

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“…Subsequently, the bias and standardized residuals are computed. Standardized residuals are obtained by dividing the residuals between the observed and krigged values by the standard deviation of the kriging error (Glatzer and Muller ). If the absolute standardized error is higher than two, outliers are suspected.…”

Section: Methodsmentioning

confidence: 99%

Seasonal precipitation variability in regional climate simulations over Northern basins of Tunisia

et al. 2013

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Northern Tunisia is the rainiest part of the country where most of the water management structures (dams, reservoirs, etc) are located. Its strategic situation with respect to surface water resources encourages the investigation of the climate change impacts projected by climate models. The goal of this study is first to compare the observed precipitation with climate model outputs, and then to evaluate the future changes projected by different climate models. The study area is subdivided into four regions: the upstream and downstream transboundary Medjerda basin, the northern coastal basins and the eastern coastal basins. A database provided by the Tunisian hydrological service includes 388 stations with complete monthly precipitation data over the period 1961-2000. An ensemble of Regional Climate Models (RCM) simulations provided by the European Union-funded project ENSEMBLES are used. Six RCM model runs (CNR-A, DMI-A, DMI-B, ICT-E, SMH-B and SMH-E) are analysed, for the control period 1961-2000 and two projection periods, 2011-2050 and 2051-2090. The models efficiency in reproducing seasonal precipitation amounts and variability is evaluated. A 1-km monthly precipitation reference grid is computed through the interpolation of rainfall observations during the period 1961-2000 with kriging techniques. Monthly precipitation series averaged over the four basins are built for comparison during the control period. The RCM outputs are evaluated with respect to the annual precipitation cycle and rainfall frequency distribution using robust statistics. For the control period, features of the seasonal regimes are well reproduced by all models. It is found that models underestimate seasonal precipitation on average by 20%. The discrepancy between model outputs and observations depends on the season. For the future, in summer and autumn the different models do not project major changes in the seasonal distributions. However, for winter and spring, all the models project a significant decrease of precipitations.

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Section: Methodsmentioning

confidence: 99%

Seasonal precipitation variability in regional climate simulations over Northern basins of Tunisia

et al. 2013

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“…Further work could investigate our models within an interactive visualisation environment. For example, the variographic methods of Glatzer and Müller (2004) are useful, or more generally, the methods demonstrated in Demšar et al (2008) in a non-stationary regression context would be transferable. As all our predictors are univariate in construction, we did not make use of available explanatory data.…”

Section: Discussionmentioning

confidence: 98%

The comap as a diagnostic tool for non-stationary kriging models

Harris

Brunsdon

Charlton

2013

International Journal of Geographical Information Science

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“…The standardized error and the coefficient of determination are adopted as criteria to evaluate the cross-validation results. The standardized error is equivalent to the value of the residuals between the observed values and the kriged Z* values, divided by the standard deviation of kriging errors (Glatzer and Muller, 2004). Standardized residuals which are more than 2 and less than -2 are usually considered too large and, consequently, the parameters of the model semivariogram are modified in order to insure an acceptable range for the standardized residuals.…”

Section: Interpolation Using Ordinary Krigingmentioning

confidence: 99%

Optimization of a hydrometric network extension using specific flow, kriging and simulated annealing

Chebbi

Bargaoui

Abid

et al. 2017

Journal of Hydrology

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In hydrometric stations, water levels are continuously observed and discharge rating curves are constantly updated to achieve accurate river levels and discharge observations. An adequate spatial distribution of hydrological gauging stations presents a lot of interest in linkage with the river regime characterization, water infrastructures design, water resources management and ecological survey. Due to the increase of riverside population and the associated flood risk, hydrological networks constantly need to be developed. This paper suggests taking advantage of kriging approaches to improve the design of a hydrometric network. The context deals with the application of an optimization approach using ordinary kriging and simulated annealing (SA) in order to identify the best locations to install new hydrometric gauges. The task at hand is to extend an existing hydrometric network in order to estimate, at ungauged sites, the average specific annual discharge which is a key basin descriptor. This methodology is developed for the hydrometric network of the transboundary Medjerda River in the North of Tunisia. A Geographic Information System (GIS) is adopted to delineate basin limits and centroids. The latter are adopted to assign the location of basins in kriging development. Scenarios where the size of an existing 12 stations network is alternatively increased by 1, 2, 3, 4 and 5 new station(s) are investigated using geo-regression and minimization of the variance of kriging errors. The analysis of the optimized locations

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Residual diagnostics for variogram fitting

Cited by 12 publications

References 23 publications

Seasonal precipitation variability in regional climate simulations over Northern basins of Tunisia

Seasonal precipitation variability in regional climate simulations over Northern basins of Tunisia

The comap as a diagnostic tool for non-stationary kriging models

Optimization of a hydrometric network extension using specific flow, kriging and simulated annealing

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