Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice

Jiang, Cong; Xiong, Lihua; Liu, Yan; Dong, Jianfan; Xu, Chong‐Yu

doi:10.5194/hess-2018-291

Cited by 8 publications

(14 citation statements)

References 47 publications

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“…According to the above nonstationary diagnosis, the trend‐caused nonstationarity did exist in winter, spring, and summer flood extremes both in univariate and multivariate cases. It is important to do a cause‐effect analysis to objectively verify the nonstationarities of hydrological time series (Jiang et al, 2019; Xiong et al, 2015). In order to distinguish nonstationarities from the slowly varying climate variations or detect the potential relation between seasonal flood extremes and NAO, SOI, NINO3.4 precipitation, reservoir index (RI), and the extreme temperature (23 covariates in Table 1), the dynamic nonstationary marginal and copula models incorporating climate indexes as covariates were investigated.…”

Section: Resultsmentioning

confidence: 99%

Multivariate Hazard Assessment for Nonstationary Seasonal Flood Extremes Considering Climate Change

Wang

Singh

et al. 2020

JGR Atmospheres

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In recent years copulas have been widely employed in multivariate modeling of hydrological extremes. However, anthropogenic and climate changes have greatly impacted the probabilistic behavior of these extremes and have challenged the stationarity assumption of the marginal distributions of individual characteristics of the extremes as well as their dependence structure inherent in copula-based modeling. This study developed a dynamic copula-based multivariate risk analysis model (DCM) to analyze seasonal flood extremes (MWL and AMS), observed at Yichang Station in Yangtze River basin, China. The model entailed three parts: (1) Time-varying moment models, combined with log-likelihood ratio tests, were employed to explore whether the trend-caused nonstationarity existed in the marginal distributions or the dependence structure of the seasonal flood extremes; (2) a nonstationary multivariate probability distribution was developed using a dynamic marginal and copula-based model, incorporating different large-scale climate forcings (NAO, SOI, and NINO3.4), meteorological factor (rainfall and temperature) and reservoir index as covariates; and (3) flood hazard was quantified using the multivariate hazard model. The climate-related nonstationarity-based multivariate frequency model through the incorporation of climatic indices would help predict the hazard level of a certain quantile pair for the next year of the observed period.

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Section: Resultsmentioning

confidence: 99%

Multivariate Hazard Assessment for Nonstationary Seasonal Flood Extremes Considering Climate Change

Wang

Singh

et al. 2020

JGR Atmospheres

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“…The non‐stationarity is an important issue and deserves further exploration in future studies, which is beyond the scope of this research. Finally, a flood is a multi‐faceted natural hazard, because either high flood peak, huge total volume, or long persistence can lead to severe consequences to the socio‐economy and environment (McAneney et al ., 2017; Yin et al ., 2018b, b; Jiang et al ., 2019). This study only employs the annual maximum series to characterize floods.…”

Section: Discussionmentioning

confidence: 99%

“…The runoff routine module transforms excess water from the soil moisture routine to discharge, which characterizes the concentration process by upper and lower reservoirs with five parameters. Finally, the released runoff is filtered by a triangular weighting function (Jiang et al ., 2019).…”

Section: Methodsmentioning

confidence: 99%

On future flood magnitudes and estimation uncertainty across 151 catchments in mainland China

Yin

Wang

et al. 2020

Intl Journal of Climatology

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As atmospheric moisture holding capacity is positively dependent on temperatures, a large intensification of precipitation extremes is projected under foreseeable climate warming. Flooding that is mainly attributed to extreme storms usually accounts for an ambitious target in weather-related hazard mitigation over China. Previous works seldom focused on flooding evolution patterns under climate change at a national scale, and fewer flooding projections considered the estimation uncertainty sourced from limited samples. This study systematically projected changes in flood quantiles based on annual maximum series and seasonality and also evaluated the variations of sampling uncertainty for 151 catchments over mainland China under the emission scenario of representative concentration pathway (RCP) 8.5. In order to project future streamflow series, the bias-corrected outputs of six global climate models (GCMs) were input into a best-performing hydrological model, which was selected from four calibrated hydrological models based on the KGE criteria. The Pearson type-III (P-III) distribution and L-moments (L-M) method were employed to derive the flood quantiles for different return periods during historical (1961-2005) and future (2056-2100) periods, and the bootstrapping method was applied to estimate the sampling uncertainty. A regression trend method was used to track the variations of flood seasonality in the context of climate warming. Our results project earlier flood timing and larger flood quantiles for most catchments in future period than those in the historical period, despite being accompanied by substantial spatial variations. We also project that the sampling uncertainty in estimating flood quantiles is increased in a warming future. Many catchments are exposed to dramatic changes in both flood quantile and estimation uncertainty by over 50%, while only a few catchments are projected to have decreasing flood risks. These results suggest an urgent need to improve the functionality of early warning systems and increase societal resilience to warming climates over China.

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“…The statistical parameters of GEV‐NS models are described as a function of time or other physical covariates. Thus, how to estimate the NS design precipitation with a prescribed return period under NS condition is one of the core questions (Acero et al, 2017; Acero, Parey, García, & Dacunha‐Castelle, 2018; Jiang, Xiong, Yan, Dong, & Xu, 2019; Salas & Obeysekera, 2014; Yan, Xiong, Guo, et al, 2017). According to the design concepts under ST condition, the annual design precipitation associated with a given return period varies over time.…”

Section: Covariate‐based Ns Idf Curvesmentioning

confidence: 99%

Updating intensity–duration–frequency curves for urban infrastructure design under a changing environment

et al. 2021

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The intensity and frequency of extreme precipitation have increased in many regions in the past century due to climate change. Many studies have revealed that short‐duration extreme precipitations are likely to become more and more severe in many areas, thus raising a question on whether our urban infrastructures have been designed adequately to cope with these changes. Currently, Intensity–duration–frequency (IDF) curves, which summarize the relationships between the intensity and frequency of extreme precipitation for different durations, are recommended as a criterion for urban infrastructure design and stormwater management. However, climate change is thought to have invalidated the stationary assumption in deriving IDF curves, that is, current IDF curves could misevaluate future extreme precipitation in many cases. Therefore, it is necessary to update the current IDF curves by considering possible changes of extreme precipitation. In this review, we first summarize observed changes in urban short‐duration extreme precipitation and explore the physical mechanisms associated with changes. Then, we introduce two major approaches for updating IDF curves, namely the covariate‐based nonstationary IDF curves and climate‐model‐based IDF curves. Advances in these two updating approaches for IDF curves are the focus of this review. These include the investigation of physically‐based covariates with nonstationary modeling of extreme precipitation; nonstationary precipitation design strategies; and the statistical downscaling and dynamic downscaling methods for projecting future short‐duration precipitation. Finally, we summarize some future research challenges and opportunities on providing reliable projections of future short‐duration extreme precipitation and better characterize the probabilistic behavior of short‐duration extreme precipitation for IDF design. This article is categorized under: Engineering Water > Engineering Water

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Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice

Cited by 8 publications

References 47 publications

Multivariate Hazard Assessment for Nonstationary Seasonal Flood Extremes Considering Climate Change

Multivariate Hazard Assessment for Nonstationary Seasonal Flood Extremes Considering Climate Change

On future flood magnitudes and estimation uncertainty across 151 catchments in mainland China

Updating intensity–duration–frequency curves for urban infrastructure design under a changing environment

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