An hourly rainfall generation model applicable to all types of climate

Arnaud, Patrick; Fine, Jean-Alain; Lavabre, Jacques

doi:10.1016/j.atmosres.2007.01.002

Cited by 33 publications

(26 citation statements)

References 28 publications

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“…It consists either in refining the analysis to homogeneous climatic zones, in which the shape parameter is considered to be constant (Madsen et al, 1995;Ribatet et al, 2007;Pujol et al, 2008), or in using indirect methods, i.e. methods based on stochastic simulation of rainfall events, such as the SHYPRE method (Arnaud et al, 2007), in which the parameters are estimated using a regional approach (SHYREG method, Arnaud et al, 2006a).…”

Section: Introductionmentioning

confidence: 99%

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

Garavaglia¹,

Gailhard²,

Paquet³

et al. 2010

Hydrol. Earth Syst. Sci.

100

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Abstract. This paper presents a probabilistic model for daily rainfall, using sub-sampling based on meteorological circulation. We classified eight typical but contrasted synoptic situations (weather patterns) for France and surrounding areas, using a "bottom-up" approach, i.e. from the shape of the rain field to the synoptic situations described by geopotential fields. These weather patterns (WP) provide a discriminating variable that is consistent with French climatology, and allows seasonal rainfall records to be split into more homogeneous sub-samples, in term of meteorological genesis.First results show how the combination of seasonal and WP sub-sampling strongly influences the identification of the asymptotic behaviour of rainfall probabilistic models. Furthermore, with this level of stratification, an asymptotic exponential behaviour of each sub-sample appears as a reasonable hypothesis. This first part is illustrated with two daily rainfall records from SE of France.The distribution of the multi-exponential weather patterns (MEWP) is then defined as the composition, for a given season, of all WP sub-sample marginal distributions, weighted by the relative frequency of occurrence of each WP. This model is finally compared to Exponential and Generalized Pareto distributions, showing good features in terms of robustness and accuracy. These final statistical results are computed from a wide dataset of 478 rainfall chronicles spread on the southern half of France. All these data cover the 1953-2005 period.

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Section: Introductionmentioning

confidence: 99%

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

Garavaglia¹,

Gailhard²,

Paquet³

et al. 2010

Hydrol. Earth Syst. Sci.

100

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“…Dans ce contexte, la méthode SHYREG, basée sur une régionalisation de la méthode SHYPRE (ARNAUD et LAVABRE, 1999;ARNAUD et LAVABRE, 2001;ARNAUD et LAVABRE, 2002;ARNAUD et al, 2006;ARNAUD et al, 2007), a la particularité de synthétiser l'ensemble de l'information pluie et de l'information débit d'une région, pour proposer des estimations de débits de crue en tout point du réseau hydrographique de cette région.…”

Section: Introductionunclassified

Régionalisation d'une méthode de prédétermination de crue sur l'ensemble du territoire français : la méthode SHYREG

Organde

Arnaud²,

Fine

et al. 2013

rseau

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La méthode SHYREG a été développée pour la connaissance régionale des quantiles de débits de crue (débit de pointe et lames d'eau maximales écoulées sur les durées de 1 h à 72 h) pour les périodes de retour de 2 à 100 ans suivant une approche spatialisée. Elle associe un simulateur de pluies horaires et une modélisation simple pluie-débit, mis en oeuvre à une résolution kilométrique. Les quantiles de débits se déduisent directement des distributions de fréquence empiriques des valeurs maximales extraites des très longues chroniques de débit simulées. On obtient alors une base de quantiles de crues que l'on peut agréger à l'échelle de n'importe quel bassin versant, moyennant une règle d'abattement avec la surface. La régionalisation de la méthode a été réalisée sur la France métropolitaine, à l'exclusion de la Corse, en exploitant les données hydrométriques de 1 359 stations de jaugeage et des caractéristiques hydro-climatiques et hydrogéologiques spatialisées permettant de décrire la variabilité du paramètre saisonnier du modèle. Au final, cette régionalisation permet la connaissance des quantiles de débits de crue en tout bassin versant de la France métropolitaine avec une bonne restitution des quantiles de débit de pointe et journalier, pour les périodes de retour comprises entre 2 et 10 ans : un critère de Nash minimum de 80 % est obtenu sur les quantiles de débit de pointe pseudo-spécifique et de débit journalier spécifique des bassins versants non utilisés pour la régionalisation.The SHYREG method was developed for regional flood frequency analysis to estimate peak flow and flood discharges for various durations (1 h to 72 h) and return periods (2 to 100 years), according to a spatialized approach. For each 1-km2 pixel, the method combines an hourly rainfall model with a simple rainfall-runoff model. The discharge flood frequency estimates are deduced directly from the empirical frequency distributions for the maximum values, which are extracted from very long simulated discharge time series. This gives a database of 1-km2 gridded flood quantiles that can be aggregated for any catchment by using an areal averaging method. The method was regionalized for metropolitan France, excluding Corsica, using flow data from 1,359 gauging stations and regional hydroclimatic and hydrogeological characteristics to describe the variability of the rainfall-runoff model parameter. Such regionalization provides flood discharge quantiles for any catchment in metropolitan France for various durations and return periods. Regarding the method performance, accurate estimates of flood quantiles were produced for peak discharge and mean daily discharge for return periods of two to 10 years for gauged basins in dependent validation and cross-validation. A minimum NASH criterion of 80% is obtained for peak flow and mean daily discharge for the catchments not used in the regionalization process

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“…The application of stochastically generated rainfall models can consider the stochastic nature of many of the mentioned variables. The Neyman-Scott and Bartlett-Lewis models based on rectangular pulses, which have been applied to the temporal simulation of precipitation at a single site, are the most widely used [32][33][34]. Other approaches show promising results, for example those based on the theory of copulas that correlate variables such as the storm duration, average intensity or rainfall volumes (e.g., [35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

The Influence of the Annual Number of Storms on the Derivation of the Flood Frequency Curve through Event-Based Simulation

Sordo‐Ward

Bianucci²,

Garrote

et al. 2016

Water

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This study addresses the question of how to select the minimum set of storms that should be simulated each year in order to estimate an accurate flood frequency curve for return periods ranging between 1 and 1000 years. The Manzanares basin (Spain) was used as a study case. A continuous 100,000-year hourly rainfall series was generated using the stochastic spatial-temporal model RanSimV3. Individual storms were extracted from the series by applying the exponential method. For each year, the extracted storms were transformed into hydrographs by applying an hourly time-step semi-distributed event-based rainfall-runoff model, and the maximum peak flow per year was determined to generate the reference flood frequency curve. Then, different flood frequency curves were obtained considering the N storms with maximum rainfall depth per year, with 1 ď N ď total number of storms. Main results show that: (a) the degree of alignment between the calculated flood frequency curves and the reference flood frequency curve depends on the return period considered, increasing the accuracy for higher return periods; (b) for the analyzed case studies, the flood frequency curve for medium and high return period (50 ď return period ď 1000 years) can be estimated with a difference lower than 3% (compared to the reference flood frequency curve) by considering the three storms with the maximum total rainfall depth each year; (c) when considering only the greatest storm of the year, for return periods higher than 10 years, the difference for the estimation of the flood frequency curve is lower than 10%; and (d) when considering the three greatest storms each year, for return periods higher than 100 years, the probability of achieving simultaneously a hydrograph with the annual maximum peak flow and the maximum volume is 94%.

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An hourly rainfall generation model applicable to all types of climate

Cited by 33 publications

References 28 publications

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

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

Régionalisation d'une méthode de prédétermination de crue sur l'ensemble du territoire français : la méthode SHYREG

The Influence of the Annual Number of Storms on the Derivation of the Flood Frequency Curve through Event-Based Simulation

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