Stationary and nonstationary generalized extreme value modelling of extreme precipitation over a mountainous area under climate change

Panagoulia, Dionysia; Economou, Polychronis; Caroni, C.

doi:10.1002/env.2252

Cited by 70 publications

(52 citation statements)

References 57 publications

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“…This assumption is often difficult to be satisfied. Similar studies have also been conducted in regions out of California [27,28]. To our knowledge, no studies have been conducted to assess the changes in both climatic and hydrologic extremes in California, (1) at the spatial scale directly relevant to real-time water management operations; (2) using operational datasets; and (3) via a trend analysis approach other than the traditional linear regression method.…”

Section: Introductionmentioning

confidence: 97%

Changes in Extremes of Temperature, Precipitation, and Runoff in California’s Central Valley During 1949–2010

He¹,

Russo²,

Anderson³

et al. 2017

Hydrology

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This study presents a comprehensive trend analysis of precipitation, temperature, and runoff extremes in the Central Valley of California from an operational perspective. California is prone to those extremes of which any changes could have long-lasting adverse impacts on the society, economy, and environment of the State. Available long-term operational datasets of 176 forecasting basins in six forecasting groups and inflow to 12 major water supply reservoirs are employed. A suite of nine precipitation indices and nine temperature indices derived from historical (water year 1949-2010) six-hourly precipitation and temperature data for these basins are investigated, along with nine indices based on daily unimpaired inflow to those 12 reservoirs in a slightly shorter period. Those indices include daily maximum precipitation, temperature, runoff, snowmelt, and others that are critical in informing decision making in water resources management. The non-parametric Mann-Kendall trend test is applied with a trend-free pre-whitening procedure in identifying trends in these indices. Changes in empirical probability distributions of individual study indices in two equal sub-periods are also investigated. The results show decreasing number of cold nights, increasing number of warm nights, increasing maximum temperature, and increasing annual mean minimum temperature at about 60% of the study area. Changes in cold extremes are generally more pronounced than their counterparts in warm extremes, contributing to decreasing diurnal temperature ranges. In general, the driest and coldest Tulare forecasting group observes the most consistent changes among all six groups. Analysis of probability distributions of temperature indices in two sub-periods yields similar results. In contrast, changes in precipitation extremes are less consistent spatially and less significant in terms of change rate. Only four indices exhibit statistically significant changes in less than 10% of the study area. On the regional scale, only the American forecasting group shows significant decreasing trends in two indices including maximum six-hourly precipitation and simple daily intensity index. On the other hand, runoff exhibits strong resilience to the changes noticed in temperature and precipitation extremes. Only the most southern reservoir (Lake Isabella) shows significant earlier peak timing of snowmelt. Additional analysis on runoff indices using different trend analysis methods and different analysis periods also indicates limited changes in these runoff indices. Overall, these findings are meaningful in guiding reservoir operations and water resources planning and management practices. IntroductionClimatic and weather-induced hazards including excessive heat, flooding, and drought are often economically, environmentally, and societally disruptive [1][2][3]. Previous studies have suggested that such hazards are typically caused by changes in the frequency and intensity rather than the mean of hydro-climatic variables including precipita...

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Section: Introductionmentioning

confidence: 97%

Changes in Extremes of Temperature, Precipitation, and Runoff in California’s Central Valley During 1949–2010

He¹,

Russo²,

Anderson³

et al. 2017

Hydrology

View full text Add to dashboard Cite

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“…CI construction by the bootstrap methods has been examined for a GEV-based frequency model first by Kysely [52] in the stationary case and subsequently by Panagoulia et al (2014) [40] in the NS case. Bootstrapping models with a regression structure is never easy.…”

Section: The Delta Methodsmentioning

confidence: 99%

“…Bootstrapping models with a regression structure is never easy. Indeed, three general approaches are proposed in the literature (e.g., [40]):…”

Section: The Delta Methodsmentioning

confidence: 99%

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Temperature Extremes: Estimation of Non-Stationary Return Levels and Associated Uncertainties

Hamdi¹,

Duluc²,

Rebour³

2018

Atmosphere

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Estimating temperature extremes (TEs) and associated uncertainties under the non-stationary (NS) assumption is a key research question in several domains, including the nuclear safety field. Methods for estimating TEs and associated confidence intervals (CIs) have often been used in the literature but in a stationary context, separately and without detailed comparison. The extreme value theory is often used to assess risks in a context of climate change. It provides an accurate indication of distributions describing the frequency of occurrence of TEs. However, in an NS context, the notion of the return period is not easily interpretable. For instance, to predict a high return level (RL) in a future year, time-varying distributions must be used and compared. This study examines the performance of a new concept to predict RLs in an NS context and compares three methods for constructing the associated CIs (delta, profile likelihood, and parametric bootstrap). The present work takes up the concept of integrated return periods that define the T-year RL as the level for which the expected number of events in a T-year period is one and proposes a new method based on conditional predictions that is useful for predicting high RLs of extreme events in the near future (the 100-year RL in the year 2030, for instance). The daily maximum temperature (DMT) observed at the Orange Station in France was used as a case study. Several trend models were compared and a new likelihood-based method to detect breaks in TEs is proposed. The analyses were conducted assuming the time-varying Generalized Extreme Value (GEV) distribution. The concepts have been implemented in a software package (Non-Stationary Generalized Extreme Value (NSGEV)). The application demonstrates that the RL estimates for NS situations can be quite different from those corresponding to stationary conditions. Overall, the results suggest that the NS analysis can be helpful in making a more appropriate assessment of the risk for periodic safety reviews during the life of a nuclear power plant (NPP).

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“…From a mathematical point of view, the statistical theory of extreme values appears to be the suitable probabilistic framework for modeling their frequency and intensity. The extreme value theory (EVT) is well established for univariate stationary series [ Beirlant et al , ], and there is now a growing effort to develop EVT models able to reproduce spatial variability and consistency and to deal with nonstationary problems [e.g., Panagoulia et al , ; Jonathan et al , ]. Multivariate extreme distributions appear in climate and environmental applications in order to take into account the dependence between extremes [e.g., Coles and Tawn , ; De Haan and De Ronde , ; Schlather and Tawn , ; Fawcett and Walshaw , ].…”

Section: Introductionmentioning

confidence: 99%

A spatial hybrid approach for downscaling of extreme precipitation fields

2015

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For a few decades, climate models are used to provide future scenarios of precipitation with increasingly higher spatial resolution. However, this resolution is not yet sufficient to describe efficiently what happens at local scale. Dynamical and statistical methods of downscaling have been developed and allow us to make the link between two levels of resolution and enable us to get values at a local scale based on large-scale information from global or regional climate models. Nevertheless, both the extreme behavior and the spatial structures are not well described by these downscaling methods. We propose a two-step methodology, called spatial hybrid downscaling (SHD), to solve this problem. The first step consists in applying a univariate (i.e., one-dimensional) statistical downscaling to link the high-and low-resolution variables at some given locations. Once this 1d-link is performed, a conditional simulation algorithm of max-stable processes adapted to the extremal t process enables us to get conditional distributions of extreme precipitation at any point of the region. An application is performed on precipitation data in the south of France where extreme (Cevenol) events have major impacts (e.g., floods). Different versions of the SHD approach are tested. Most of them show particularly good results regarding univariate and multivariate criteria and overcome classical downscaling techniques tested in comparison. Furthermore, these conclusions are robust to the choice of the 1d-link functions tested and to the choice of the conditioning points to drive the conditional local-scale simulations performed by the SHD approach.

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Stationary and nonstationary generalized extreme value modelling of extreme precipitation over a mountainous area under climate change

Cited by 70 publications

References 57 publications

Changes in Extremes of Temperature, Precipitation, and Runoff in California’s Central Valley During 1949–2010

Changes in Extremes of Temperature, Precipitation, and Runoff in California’s Central Valley During 1949–2010

Temperature Extremes: Estimation of Non-Stationary Return Levels and Associated Uncertainties

A spatial hybrid approach for downscaling of extreme precipitation fields

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