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David Penot - One of the best experts on this subject based on the ideXlab platform.
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Space-time simulation of precipitation based on Weather Pattern sub-sampling and meta-Gaussian model
Journal of Hydrology, 2020Co-Authors: Pradeebane Vaittinada Ayar, Juliette Blanchet, Emmanuel Paquet, David PenotAbstract:Simulation methods for design flood estimations in dam safety studies require fine scale precipitation data to provide quality input for hydrological models, especially for extrapolation to extreme events. This leads to use statistical models such as stochastic Weather generators. The aim here is to develop a stochastic model adaptable on mountainous catchments in France and accounting for spatial and temporal dependencies in daily precipitation fields. To achieve this goal, the framework of spatial random processes is adopted here. The novelty of the model developed in this study resides in the combination of an autoregressive meta-Gaussian process accounting for the spatio-temporal dependencies and Weather Pattern sub-sampling discriminating the different rainfall intensity classes. The model is tested from rain gauges in the Ardèche catchment located in South of France. The model estimation is performed in four steps, dealing respectively with: (i) the at-site marginal distribution, (ii) the mapping of the marginal distribution parameters at the target resolution, (iii) the at-site temporal correlation and (iv) the spatial covariance function. The model simulations are evaluated in terms of marginal distribution, inter-site dependence and areal rainfall properties and compared to the observations at calibration stations and also on a set of independent validation stations. Regarding all these aspects, the model shows good abilities to reproduce the observed statistics and presents really small discrepancies compared to the stations data. The sub-sampling is particularly efficient to reproduce the seasonal variations and the marginal mapping procedure induces very small differences in terms of daily rain amounts and daily occurrence probabilities.
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space time simulation of precipitation based on Weather Pattern sub sampling and meta gaussian model
Journal of Hydrology, 2020Co-Authors: Pradeebane Vaittinada Ayar, Juliette Blanchet, Emmanuel Paquet, David PenotAbstract:Abstract Simulation methods for design flood estimations in dam safety studies require fine scale precipitation data to provide quality input for hydrological models, especially for extrapolation to extreme events. This leads to use statistical models such as stochastic Weather generators. The aim here is to develop a stochastic model adaptable on mountainous catchments in France and accounting for spatial and temporal dependencies in daily precipitation fields. To achieve this goal, the framework of spatial random processes is adopted here. The novelty of the model developed in this study resides in the combination of an autoregressive meta-Gaussian process accounting for the spatio-temporal dependencies and Weather Pattern sub-sampling discriminating the different rainfall intensity classes. The model is tested from rain gauges in the Ardeche catchment located in South of France. The model estimation is performed in four steps, dealing respectively with: (i) the at-site marginal distribution, (ii) the mapping of the marginal distribution parameters at the target resolution, (iii) the at-site temporal correlation and (iv) the spatial covariance function. The model simulations are evaluated in terms of marginal distribution, inter-site dependence and areal rainfall properties and compared to the observations at calibration stations and also on a set of independent validation stations. Regarding all these aspects, the model shows good abilities to reproduce the observed statistics and presents really small discrepancies compared to the stations data. The sub-sampling is particularly efficient to reproduce the seasonal variations and the marginal mapping procedure induces very small differences in terms of daily rain amounts and daily occurrence probabilities.
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A regional model for extreme rainfall based on Weather Patterns subsampling
Journal of Hydrology, 2016Co-Authors: Guillaume Evin, Juliette Blanchet, Federico Garavaglia, Emmanuel Paquet, David PenotAbstract:Summary Many rainfall generators rely on the assumption that statistical properties of rainfall observations can be related to physical processes via Weather Patterns. The MEWP (Multi-Exponential Weather Pattern) model belongs to this class. In this daily rainfall model, extremes above a threshold are distributed exponentially, for each season and atmospheric circulation Pattern. A wide range of applications of this rainfall compound distribution has demonstrated its robustness and reliability. However, recent investigations showed that MEWP tends to underestimate the most extreme rainfall events in specific regions (e.g. the South-East of France). In this paper, we apply different versions of a generalized MEWP model: the MDWP (Multi-Distribution Weather Pattern) model. In the MDWP model, the exponential distribution is replaced by distributions with a heavier tail, such as the Generalized Pareto Distribution (GPD). Unfortunately, local applications of the MDWP model reveal a lack of robustness and overfitting issues. To solve this issue, a regional version of the MDWP model is proposed. Different options of a regionalization approach for excesses are scrutinized (e.g. choice of the scale factor, testing of homogeneous regions based on neighborhoods around each site, choice of the distribution modelling extreme rainfall). We compare the performances of local and regional models on long daily rainfall series covering the southern half of France. These applications show that the local models with heavy-tailed distributions exhibit a lack of robustness. In comparison, an impressive improvement of model robustness is obtained with the regional version, without a loss of reliability.
Emmanuel Paquet - One of the best experts on this subject based on the ideXlab platform.
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Space-time simulation of precipitation based on Weather Pattern sub-sampling and meta-Gaussian model
Journal of Hydrology, 2020Co-Authors: Pradeebane Vaittinada Ayar, Juliette Blanchet, Emmanuel Paquet, David PenotAbstract:Simulation methods for design flood estimations in dam safety studies require fine scale precipitation data to provide quality input for hydrological models, especially for extrapolation to extreme events. This leads to use statistical models such as stochastic Weather generators. The aim here is to develop a stochastic model adaptable on mountainous catchments in France and accounting for spatial and temporal dependencies in daily precipitation fields. To achieve this goal, the framework of spatial random processes is adopted here. The novelty of the model developed in this study resides in the combination of an autoregressive meta-Gaussian process accounting for the spatio-temporal dependencies and Weather Pattern sub-sampling discriminating the different rainfall intensity classes. The model is tested from rain gauges in the Ardèche catchment located in South of France. The model estimation is performed in four steps, dealing respectively with: (i) the at-site marginal distribution, (ii) the mapping of the marginal distribution parameters at the target resolution, (iii) the at-site temporal correlation and (iv) the spatial covariance function. The model simulations are evaluated in terms of marginal distribution, inter-site dependence and areal rainfall properties and compared to the observations at calibration stations and also on a set of independent validation stations. Regarding all these aspects, the model shows good abilities to reproduce the observed statistics and presents really small discrepancies compared to the stations data. The sub-sampling is particularly efficient to reproduce the seasonal variations and the marginal mapping procedure induces very small differences in terms of daily rain amounts and daily occurrence probabilities.
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space time simulation of precipitation based on Weather Pattern sub sampling and meta gaussian model
Journal of Hydrology, 2020Co-Authors: Pradeebane Vaittinada Ayar, Juliette Blanchet, Emmanuel Paquet, David PenotAbstract:Abstract Simulation methods for design flood estimations in dam safety studies require fine scale precipitation data to provide quality input for hydrological models, especially for extrapolation to extreme events. This leads to use statistical models such as stochastic Weather generators. The aim here is to develop a stochastic model adaptable on mountainous catchments in France and accounting for spatial and temporal dependencies in daily precipitation fields. To achieve this goal, the framework of spatial random processes is adopted here. The novelty of the model developed in this study resides in the combination of an autoregressive meta-Gaussian process accounting for the spatio-temporal dependencies and Weather Pattern sub-sampling discriminating the different rainfall intensity classes. The model is tested from rain gauges in the Ardeche catchment located in South of France. The model estimation is performed in four steps, dealing respectively with: (i) the at-site marginal distribution, (ii) the mapping of the marginal distribution parameters at the target resolution, (iii) the at-site temporal correlation and (iv) the spatial covariance function. The model simulations are evaluated in terms of marginal distribution, inter-site dependence and areal rainfall properties and compared to the observations at calibration stations and also on a set of independent validation stations. Regarding all these aspects, the model shows good abilities to reproduce the observed statistics and presents really small discrepancies compared to the stations data. The sub-sampling is particularly efficient to reproduce the seasonal variations and the marginal mapping procedure induces very small differences in terms of daily rain amounts and daily occurrence probabilities.
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A regional model for extreme rainfall based on Weather Patterns subsampling
Journal of Hydrology, 2016Co-Authors: Guillaume Evin, Juliette Blanchet, Federico Garavaglia, Emmanuel Paquet, David PenotAbstract:Summary Many rainfall generators rely on the assumption that statistical properties of rainfall observations can be related to physical processes via Weather Patterns. The MEWP (Multi-Exponential Weather Pattern) model belongs to this class. In this daily rainfall model, extremes above a threshold are distributed exponentially, for each season and atmospheric circulation Pattern. A wide range of applications of this rainfall compound distribution has demonstrated its robustness and reliability. However, recent investigations showed that MEWP tends to underestimate the most extreme rainfall events in specific regions (e.g. the South-East of France). In this paper, we apply different versions of a generalized MEWP model: the MDWP (Multi-Distribution Weather Pattern) model. In the MDWP model, the exponential distribution is replaced by distributions with a heavier tail, such as the Generalized Pareto Distribution (GPD). Unfortunately, local applications of the MDWP model reveal a lack of robustness and overfitting issues. To solve this issue, a regional version of the MDWP model is proposed. Different options of a regionalization approach for excesses are scrutinized (e.g. choice of the scale factor, testing of homogeneous regions based on neighborhoods around each site, choice of the distribution modelling extreme rainfall). We compare the performances of local and regional models on long daily rainfall series covering the southern half of France. These applications show that the local models with heavy-tailed distributions exhibit a lack of robustness. In comparison, an impressive improvement of model robustness is obtained with the regional version, without a loss of reliability.
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Evaluation of a compound distribution based on Weather Pattern subsampling for extreme rainfall in Norway
Natural Hazards and Earth System Sciences, 2015Co-Authors: Juliette Blanchet, J. Touati, Deborah Lawrence, Federico Garavaglia, Emmanuel PaquetAbstract: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 heavy-tailed distributions, in this case the Multi Generalized Pareto Weather Pattern (MGPWP) distribution. The analysis shows the clear benefit obtained from seasonal and Weather Pattern-based 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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reliability and robustness of rainfall compound distribution model based on Weather Pattern sub sampling
Hydrology and Earth System Sciences, 2010Co-Authors: Federico Garavaglia, J. Gailhard, Emmanuel Paquet, Michel Lang, Remy Garcon, Benjamin RenardAbstract:A new probabilistic model for daily rainfall, named MEWP (Multi Exponential Weather Pattern) distri- bution, has been introduced in Garavaglia et al. (2010). This model provides estimates of extreme rainfall quantiles using a mixture of exponential distributions. Each exponential dis- tribution applies to a specific sub-sample of rainfall obser- vations, corresponding to one of eight typical atmospheric circulation Patterns that are relevant for France and the sur- rounding area. The aim of this paper is to validate the MEWP model by assessing its reliability and robustness with rainfall data from France, Spain and Switzerland. Data include 37 long se- ries for the period 1904-2003, and a regional data set of 478 rain gauges for the period 1954-2005. Two complementary properties are investigated: (i) the reliability of estimates, i.e. the agreement between the estimated probabilities of ex- ceedance and the actual exceedances observed on the dataset; (ii) the robustness of extreme quantiles and associated con- fidence intervals, assessed using various sub-samples of the long data series. New specific criteria are proposed to quan- tify reliability and robustness. The MEWP model is com- pared to standard models (seasonalised Generalised Extreme Value and Generalised Pareto distributions). In order to eval- uate the suitability of the exponential model used for each Weather Pattern (WP), a general case of the MEWP distribu- tion, using Generalized Pareto distributions for each WP, is also considered. Concerning the considered dataset, the exponential hy- pothesis of asymptotic behaviour of each seasonal and Weather Pattern rainfall records, appears to be reasonable. The results highlight : (i) the interest of WP sub-sampling that lead to significant improvement in reliability models performances; (ii) the low level of robustness of the mod- els based on at-site estimation of shape parameter; (iii) the MEWP distribution proved to be robust and reliable, demon- strating the interest of the proposed approach.
Justin T. Schoof - One of the best experts on this subject based on the ideXlab platform.
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Statistical Downscaling in Climatology
Geography Compass, 2013Co-Authors: Justin T. SchoofAbstract:Downscaling is a term that has been used to describe the range of methods that are used to infer regional-scale or local-scale climate information from coarsely resolved climate models. The use of statistical methods for this purpose is rooted in both operational Weather forecasting and synoptic climatology and has become a widely applied method for development of regional climate change scenarios. This article provides an overview of statistical downscaling with a focus on assumptions, common predictors and predictands, and methodological approaches ranging from interpolation and scaling to regression-based methods, Weather Pattern-based techniques, and stochastic Weather generators. Suggestions are made for improved assessment of the fundamental downscaling assumptions as well as reduction of uncertainty associated with application of downscaled climate information across models and greenhouse gas emission scenarios.
Pietro Rubino - One of the best experts on this subject based on the ideXlab platform.
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spatial and temporal variability of wheat grain yield and quality in a mediterranean environment a multivariate geostatistical approach
Field Crops Research, 2012Co-Authors: Mariangela Diacono, A Castrignano, Antonio Troccoli, Daniela De Benedetto, Bruno Basso, Pietro RubinoAbstract:Abstract In Mediterranean countries, on top of the erratic Weather Pattern, rainfed wheat grain yield and protein content in a field are spatially variable due to inherent variability of soil properties and position in the landscape. The objectives of this three-year field study were: (i) to assess the spatial and temporal variability of attributes related to the yield and quality of durum wheat production; and, (ii) to examine the temporal stability of sub-field management classes derived from (i). A Geostatistical approach was used to analyze data collected in each year from 100 georeferenced locations. In particular, block-kriging was used to produce maps of gluten and protein content, test weight, biomass weight and Harvest Index. The multivariate spatial data sets were then analyzed by Factorial co-Kriging Analysis (FKA). The classes obtained from the FKA output were compared with the yield maps in order to assess their production potential. The first factors relating to each year were also compared by using contingency matrices, to estimate the temporal consistency of field delineation. In the first two seasons, at most, about 50% of the total spatial variance of the crop attributes was ascribed to production potential. In the third season the variation was more erratic, equally influenced by all variables. The contingency matrices have showed that only 26% on average of the spatial variation of the attributes of wheat production was characterized by temporal stability. The present study highlighted the influence of climatic conditions over the persistence of wheat crop responses.
Juliette Blanchet - One of the best experts on this subject based on the ideXlab platform.
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space time simulation of precipitation based on Weather Pattern sub sampling and meta gaussian model
Journal of Hydrology, 2020Co-Authors: Pradeebane Vaittinada Ayar, Juliette Blanchet, Emmanuel Paquet, David PenotAbstract:Abstract Simulation methods for design flood estimations in dam safety studies require fine scale precipitation data to provide quality input for hydrological models, especially for extrapolation to extreme events. This leads to use statistical models such as stochastic Weather generators. The aim here is to develop a stochastic model adaptable on mountainous catchments in France and accounting for spatial and temporal dependencies in daily precipitation fields. To achieve this goal, the framework of spatial random processes is adopted here. The novelty of the model developed in this study resides in the combination of an autoregressive meta-Gaussian process accounting for the spatio-temporal dependencies and Weather Pattern sub-sampling discriminating the different rainfall intensity classes. The model is tested from rain gauges in the Ardeche catchment located in South of France. The model estimation is performed in four steps, dealing respectively with: (i) the at-site marginal distribution, (ii) the mapping of the marginal distribution parameters at the target resolution, (iii) the at-site temporal correlation and (iv) the spatial covariance function. The model simulations are evaluated in terms of marginal distribution, inter-site dependence and areal rainfall properties and compared to the observations at calibration stations and also on a set of independent validation stations. Regarding all these aspects, the model shows good abilities to reproduce the observed statistics and presents really small discrepancies compared to the stations data. The sub-sampling is particularly efficient to reproduce the seasonal variations and the marginal mapping procedure induces very small differences in terms of daily rain amounts and daily occurrence probabilities.
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Space-time simulation of precipitation based on Weather Pattern sub-sampling and meta-Gaussian model
Journal of Hydrology, 2020Co-Authors: Pradeebane Vaittinada Ayar, Juliette Blanchet, Emmanuel Paquet, David PenotAbstract:Simulation methods for design flood estimations in dam safety studies require fine scale precipitation data to provide quality input for hydrological models, especially for extrapolation to extreme events. This leads to use statistical models such as stochastic Weather generators. The aim here is to develop a stochastic model adaptable on mountainous catchments in France and accounting for spatial and temporal dependencies in daily precipitation fields. To achieve this goal, the framework of spatial random processes is adopted here. The novelty of the model developed in this study resides in the combination of an autoregressive meta-Gaussian process accounting for the spatio-temporal dependencies and Weather Pattern sub-sampling discriminating the different rainfall intensity classes. The model is tested from rain gauges in the Ardèche catchment located in South of France. The model estimation is performed in four steps, dealing respectively with: (i) the at-site marginal distribution, (ii) the mapping of the marginal distribution parameters at the target resolution, (iii) the at-site temporal correlation and (iv) the spatial covariance function. The model simulations are evaluated in terms of marginal distribution, inter-site dependence and areal rainfall properties and compared to the observations at calibration stations and also on a set of independent validation stations. Regarding all these aspects, the model shows good abilities to reproduce the observed statistics and presents really small discrepancies compared to the stations data. The sub-sampling is particularly efficient to reproduce the seasonal variations and the marginal mapping procedure induces very small differences in terms of daily rain amounts and daily occurrence probabilities.
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A regional model for extreme rainfall based on Weather Patterns subsampling
Journal of Hydrology, 2016Co-Authors: Guillaume Evin, Juliette Blanchet, Federico Garavaglia, Emmanuel Paquet, David PenotAbstract:Summary Many rainfall generators rely on the assumption that statistical properties of rainfall observations can be related to physical processes via Weather Patterns. The MEWP (Multi-Exponential Weather Pattern) model belongs to this class. In this daily rainfall model, extremes above a threshold are distributed exponentially, for each season and atmospheric circulation Pattern. A wide range of applications of this rainfall compound distribution has demonstrated its robustness and reliability. However, recent investigations showed that MEWP tends to underestimate the most extreme rainfall events in specific regions (e.g. the South-East of France). In this paper, we apply different versions of a generalized MEWP model: the MDWP (Multi-Distribution Weather Pattern) model. In the MDWP model, the exponential distribution is replaced by distributions with a heavier tail, such as the Generalized Pareto Distribution (GPD). Unfortunately, local applications of the MDWP model reveal a lack of robustness and overfitting issues. To solve this issue, a regional version of the MDWP model is proposed. Different options of a regionalization approach for excesses are scrutinized (e.g. choice of the scale factor, testing of homogeneous regions based on neighborhoods around each site, choice of the distribution modelling extreme rainfall). We compare the performances of local and regional models on long daily rainfall series covering the southern half of France. These applications show that the local models with heavy-tailed distributions exhibit a lack of robustness. In comparison, an impressive improvement of model robustness is obtained with the regional version, without a loss of reliability.
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Evaluation of a compound distribution based on Weather Pattern subsampling for extreme rainfall in Norway
Natural Hazards and Earth System Sciences, 2015Co-Authors: Juliette Blanchet, J. Touati, Deborah Lawrence, Federico Garavaglia, Emmanuel PaquetAbstract: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 heavy-tailed distributions, in this case the Multi Generalized Pareto Weather Pattern (MGPWP) distribution. The analysis shows the clear benefit obtained from seasonal and Weather Pattern-based 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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Evaluation of a compound distribution based on Weather Patterns subsampling for extreme rainfall in Norway
2015Co-Authors: Juliette Blanchet, F. Garavaglia, J. Touati, Deborah Lawrence, E. PaquetAbstract:Abstract. Simulation methods for design flood analyses require estimates of extreme precipitation for simulating maximum discharges. This article evaluates the 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 heavy-tailed distributions, in this case the Multi Generalized Pareto Weather Pattern (MGPWP) distribution. The analysis shows the clear benefit obtained from seasonal and Weather Pattern-based 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 south-western part of Norway during the period 1979–2009, relative to 1948–1978.
