Introducing a rainfall compound distribution model based on weather patterns sub-sampling

Garavaglia, Federico; Gailhard, Joël; Paquet, Emmanuel; Lang, Michel; Garçon, Rémy; Bernardara, Pietro

doi:10.5194/hess-14-951-2010

Cited by 84 publications

(103 citation statements)

References 27 publications

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“…The central rainfall values occurring in season s and WP k can be assumed to be identically distributed (Garavaglia et al, 2010). Thus the extreme value theory described in Sect.…”

Section: Mewp and Mgpwp Modelsmentioning

confidence: 99%

“…Furthermore, rainfall occurrence and intensities often vary with season, reflecting both variations in temperature and in storm tracks, for example. For this reason, Garavaglia et al (2010) proposed the use of subsampling based on seasons and weather patterns (WP). Each day of the record period is assigned to a WP.…”

Section: Mewp and Mgpwp Modelsmentioning

confidence: 99%

“…This article aims to make the first step towards such an evaluation. More specifically, we evaluate the rainfall probabilistic component of SCHADEX: the socalled multi-exponential weather pattern (MEWP) distribution (Garavaglia et al, 2010), a compound distribution based on season and weather pattern subsampling, for the whole of Norway. This approach is in contrast with the recent analyses of extreme precipitation in Norway based on annual maximum series and the application of a generalized extreme value distribution undertaken by Dyrrdal et al (2014).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of a compound distribution based on weather pattern subsampling for extreme rainfall in Norway

Blanchet

Touati

Lawrence

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. Simulation methods for design flood analyses require estimates of extreme precipitation for simulating maximum discharges. This article evaluates the multi-exponential weather pattern (MEWP) model, a compound model based on weather pattern classification, seasonal splitting and exponential distributions, for its suitability for use in Norway. The MEWP model is the probabilistic rainfall model used in the SCHADEX method for extreme flood estimation. Regional scores of evaluation are used in a split sample framework to compare the MEWP distribution with more general heavytailed distributions, in this case the Multi Generalized Pareto Weather Pattern (MGPWP) distribution. The analysis shows the clear benefit obtained from seasonal and weather patternbased subsampling for extreme value estimation. The MEWP distribution is found to have an overall better performance as compared with the MGPWP, which tends to overfit the data and lacks robustness. Finally, we take advantage of the split sample framework to present evidence for an increase in extreme rainfall in the southwestern part of Norway during the period 1979-2009, relative to 1948-1978.

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“…The central rainfall values occurring in season s and WP k can be assumed to be identically distributed (Garavaglia et al, 2010). Thus the extreme value theory described in Sect.…”

Section: Mewp and Mgpwp Modelsmentioning

confidence: 99%

Section: Mewp and Mgpwp Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of a compound distribution based on weather pattern subsampling for extreme rainfall in Norway

Blanchet

Touati

Lawrence

et al. 2015

Nat. Hazards Earth Syst. Sci.

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“…Fig. 3 of Garavaglia et al, 2010). The preferential selection of a regressive structure can also be obtained for rather small and specific regions.…”

Section: Discussionmentioning

confidence: 92%

“…patterns (WP, defined in Table 3, Garavaglia et al, 2010) from the selection frequency obtained for the all-days situation.…”

Section: Discussionmentioning

confidence: 99%

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

Chardon

Hingray

Favre

2018

Hydrol. Earth Syst. Sci.

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Abstract. Statistical downscaling models (SDMs) are often used to produce local weather scenarios from large-scale atmospheric information. SDMs include transfer functions which are based on a statistical link identified from observations between local weather and a set of large-scale predictors. As physical processes driving surface weather vary in time, the most relevant predictors and the regression link are likely to vary in time too. This is well known for precipitation for instance and the link is thus often estimated after some seasonal stratification of the data. In this study, we present a two-stage analog/regression model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are identified from fields of geopotential heights at 1000 and 500 hPa. For the regression stage, two generalized linear models are further used to model the probability of precipitation occurrence and the distribution of non-zero precipitation amounts, respectively. The two-stage model is evaluated for the probabilistic prediction of small-scale precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and amount. As the analog days vary from one prediction day to another, the atmospheric predictors selected in the regression stage and the value of the corresponding regression coefficients can vary from one prediction day to another. The model allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations.

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Sensitivity analysis of SCHADEX extreme flood estimations to observed hydrometeorological variability

et al. 2014

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[1] Stochastic flood simulation methods are typically based on a rainfall probabilistic model (used for simulating continuous rainfall series or for estimating probabilities of random rainfall events) and on a rainfall-runoff model. Usually, both of these models are calibrated over observed hydrometeorological series, which may be subject to significant variability and/or nonstationarity over time. The general aim of this study is thus to propose and test a methodology for performing a sensitivity analysis of extreme flood estimations to observed hydrometeorological variability. The methodology consists of performing a set of blockbootstrap experiments: for each experiment, the data used for calibration of a particular model (e.g., the rainfall probabilistic model) is bootstrapped while the model structure and the calibration process are held constant. The SCHADEX extreme flood estimation method has been applied over six catchments located in different regions of the world. The results show first that the variability of observed rainfall hazard has the most significant impact on the extreme flood estimates. Then, consideration of different rainfall-runoff calibration periods generates a significant spread of extreme flood estimated values. Finally, the variability of the catchment saturation hazard has a nonsignificant impact on the extreme flood estimates. An important point raised by this study is the dominating role played by outliers within the observed records for extreme flood estimation.

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Introducing a rainfall compound distribution model based on weather patterns sub-sampling

Cited by 84 publications

References 27 publications

Evaluation of a compound distribution based on weather pattern subsampling for extreme rainfall in Norway

Evaluation of a compound distribution based on weather pattern subsampling for extreme rainfall in Norway

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

Sensitivity analysis of SCHADEX extreme flood estimations to observed hydrometeorological variability

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