Using a rainfall stochastic generator to detect trends in extreme rainfall

Cantet, Philippe; Bacro, Jean-Noel; Arnaud, Patrick

doi:10.1007/s00477-010-0440-x

Cited by 19 publications

(9 citation statements)

References 35 publications

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“…Nevertheless, it has been used in France to map the parameters of a rainfall generator (Carreau et al 2013) with about 2000 rain gauge stations. Observations were split into four climatic zones (Cantet et al 2011) in which the method was performed independently with a reasonable CPU-time. For regions with a (relatively) complex topography such as "Mediterranean" and "Highland", EDK clearly provides the most accurate prediction according to a repeated random subsampling validation (as in Section 4.3 above) whereas, in flatter regions, the gain of EDK is less pronounced.…”

Section: Discussionmentioning

confidence: 99%

Mapping the mean monthly precipitation of a small island using kriging with external drifts

Cantet

2015

Theor Appl Climatol

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This study focuses on the spatial distribution of mean annual and monthly precipitation in a small island (1128 km 2 ) named Martinique, located in the Lesser Antilles. Only 35 meteorological stations are available on the territory, which has a complex topography. With a digital elevation model (DEM), 17 covariates that are likely to explain precipitation were built. Several interpolation methods, such as regression-kriging (MLRK, PCRK, and PLSK) and external drift kriging (EDK) were tested using a cross-validation procedure. For the regression methods, predictors were chosen by established techniques whereas a new approach is proposed to select external drifts in a kriging which is based on a stepwise model selection by the Akaike Information Criterion (AIC). The prediction accuracy was assessed at validation sites with three different skill scores. Results show that using methods with no predictors such as inverse distance weighting (IDW) or universal kriging (UK) is inappropriate in such a territory. EDK appears to outperform regression methods for any criteria, and selecting predictors by our approach improves the prediction of mean annual precipitation compared to kriging with only elevation as drift. Finally, the predicting performance was also studied by varying the size of the training set leading to less conclusive results for EDK and its performance. Nevertheless, the proposed method seems to be a good way to improve the mapping of climatic variables in a small island.

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Section: Discussionmentioning

confidence: 99%

Mapping the mean monthly precipitation of a small island using kriging with external drifts

Cantet

2015

Theor Appl Climatol

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“…Stochastic methods have been widely used in the fields of hydrology (Cantet et al 2011;Haan 1994;Lee and Yoo 2001), hydraulics (Chen et al 2009;Erik 2011;Kim et al 2004) and agriculture (Perreault et al 2000;Wu and Wang 1998;Young et al 2011). The nonlinear nature of dynamic pressure and dissipation rate is confirmed from Figs.…”

Section: Wall Shear Stressmentioning

confidence: 65%

Numerical and multivariate stochastic approaches to characterize flow in a constructed wetland basin

Wei

Plappally

Soboyejo

et al. 2011

Stoch Environ Res Risk Assess

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Reduction in specific and viscous dissipation rate in surface waters by flow control and contaminant removal are the goals of constructed wetlands. A twodimensional simulation study on surface flow through a constructed wetland in Guilin, China is performed. The flow through the wetland is modeled and dynamically simulated by distinct case studies by varying both inlet width and inflow rate. Nonlinear increase in peak dynamic pressure and specific dissipation rates as a function of increasing inflow rate is reported for the different cases studied. The results of the numerical models confirm an increase in viscous dissipation, shear stress and dynamic pressure within the wetland with increase in inflow rate. These modeling results are used as inputs for performing a statistical data analysis. Further, a multivariate stochastic statistical framework has been proposed for the prediction of dissipation as a function of variables including inflow rate, inlet geometry and wall shear stress. Multivariate and variance analysis is performed to validate the appropriateness of the theoretical models proposed. Results provide simplified meaningful suggestions to constructed wetland design and related applications.

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“…SHYPRE was first developed to simulate catchment flood scenarios. It couples a stochastic hourly rainfall generator (Arnaud and Lavabre 1999, Cernesson et al 1996, Arnaud et al 2006, Cantet et al 2010, Cantet and Arnaud 2014) with a rainfall-runoff model. In this way the model generates a set of flood hydrographs, which can then be used to empirically deduce the frequency distribution of peak and maximum mean flows over different durations.…”

Section: The Shyreg Methods 21 the Principlementioning

confidence: 99%

Relevance of an at-site flood frequency analysis method for extreme events based on stochastic simulation of hourly rainfall

Arnaud

Cantet

Aubert³

2015

Hydrological Sciences Journal

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Extreme events are rarely observed, so their analysis is generally based on observations of more frequent values. The relevance of flood frequency analysis (FFA) method depends on its capability to estimate the frequency of extreme values with reasonable accuracy using extrapolation. A FFA method based on stochastic simulation of flood event is assessed based on its reliability and stability. For such an assessment, different training/testing decompositions are performed for a set of data from more than 1,000 gauging stations. We showed that the method enables relevant "predictive" estimates, e.g. by assigning correct return periods to the record values that are systematically absent in calibration data sets. The model is also highly stable visa -vis the sampling. This characteristic is linked to the use of regional statistical rainfall data and a simple rainfall-runoff model that requires calibrating only one parameter. Résumé: Les événements extrêmes sont par nature rarement observés, c'est pourquoi leur estimation est généralement basée sur l'observation de valeurs plus courantes. La pertinence d'une méthode de prédétermination des événements extrêmes dépend donc de sa capacité à raisonnablement extrapoler les distributions de fréquences vers les valeurs extrêmes. Dans cette étude, une méthode de prédétermination de crues basée sur la simulation de scénarios de pluies horaires, est évaluée sur sa capacité à produire des estimations justes et stables. Cette évaluation s'appuie sur différents tests d'échantillonnage sur les périodes de calage et de validation, appliqués sur un jeu de données conséquent (plus de 1000 stations). Nous montrons que la méthode est capable de fournir une estimation pertinente sur les événements extrêmes bien que ceux-ci soit systématiquement ôtés de la période de calage. La méthode montre aussi une grande stabilité face à l'échantillonnage. Cette caractéristique est liée à l'utilisation d'une information statistique régionale sur la pluie et à la simplicité de la modélisation hydrologique paramétrée par un seul paramètre.

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Using a rainfall stochastic generator to detect trends in extreme rainfall

Cited by 19 publications

References 35 publications

Mapping the mean monthly precipitation of a small island using kriging with external drifts

Mapping the mean monthly precipitation of a small island using kriging with external drifts

Numerical and multivariate stochastic approaches to characterize flow in a constructed wetland basin

Relevance of an at-site flood frequency analysis method for extreme events based on stochastic simulation of hourly rainfall

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