Multivariate Hazard Assessment for Nonstationary Seasonal Flood Extremes Considering Climate Change

Xu, Pengcheng; Wang, Dong; Singh, Vijay P.; Lu, Huayu; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Liu, Jiufu; Zhang, Jianyun

doi:10.1029/2020jd032780

Cited by 10 publications

(2 citation statements)

References 59 publications

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“…As a result of anthropogenic greenhouse gas emissions, the climate is changing and the composition of Earth's atmosphere is being altered, which has not only increased the complexity of hydrometeorological simulations but also necessitates the incorporation of trend-caused nonstationarity in modeling hydrometeorological variables [33][34][35]. In this study, trend-caused nonstationarity was first incorporated into the optimal design of the rain gauge network.…”

Section: The Impact Of Time Variability In Precipitation On the Optim...mentioning

confidence: 99%

Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, China

Liu,

Li,

Wang

et al. 2023

Water

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The nonstationary characteristics caused by significant variation in hydrometeorological series in the context of climate change inevitably have a certain impact on the selection of an optimal gauging network. This study proposes an entropy-based, multi-objective, rain gauge network optimization method to facilitate the design of a 43 stations-based network in Huaihe River Basin (HRB), China. The first goal of this study is to improve the accuracy of gauge-related information estimation through the selection and comparison of discretization methods. The second goal of this study is to quantify the impact of trend-caused nonstationarity on optimal network design using the sliding window method. This study compares the divergence of three kinds of discretization methods, including the floor function-based approach, Scott’s equal bin width histogram (EWH-Sc) approach, and Sturges’s equal bin width histogram (EWH-St) approach. The matching degree of the variance and marginal entropy of the observed series is computed to select the most suitable of the above three discretization methods. The trend-caused nonstationarity in 75% of all stations in the HRB could definitely influence the final results of the optimal rain-gauge network design using the sliding window method. Therefore, in future studies of rain-gauge network optimization, it is necessary to carry out uncertainty research according to local conditions in view of climate change and human activities.

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Section: The Impact Of Time Variability In Precipitation On the Optim...mentioning

confidence: 99%

Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, China

Liu,

Li,

Wang

et al. 2023

Water

Self Cite

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“…Climate change leads to flood events characterized by greater frequency and more severity (Liang et al 2019), and therefore greatly impacts the probabilistic behavior of extreme annual streamflows (Xu et al 2020). Given that, one of the main roles of water systems is responding to natural disasters, such as floods, by controlling the outflow from the water system (Men et al 2019;Yang et al 2022).…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Assessment of water resources system resilience under hazardous events using system dynamic approach and artificial neural networks

Stojković¹,

Marjanović²,

Rakić

et al. 2023

Journal of Hydroinformatics

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The objective of this research is to propose a novel framework for assessing the consequences of hazardous events on a water resources system using dynamic resilience. Two types of hazardous events were considered: a severe flood event and an earthquake. Given that one or both hazards have occurred and considering the intensity of those events, the main characteristics of flood dynamic resilience were evaluated. The framework utilizes an artificial neural network (ANN) to estimate dynamic resilience. The ANN was trained using a large, generated dataset that included a wide range of situations, from relatively mild hazards to severe ones. A case study was performed on the Pirot water system (Serbia). Dynamic resilience was derived from the developed system dynamics model alongside the hazardous models implemented. The most extreme hazard combination results in the robustness of 0.04, indicating a combination of an earthquake with a significant magnitude and a flood hydrograph with a low frequency of occurrence. In the case of moderate hazards, the system robustness has a median value of 0.2 and the rapidity median value of 162 h. The ANN's efficacy was quantified using the average relative error metric which equals 2.14% and 1.77% for robustness and rapidity, respectively.

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Climate change increased the compound extreme precipitation-flood events in a representative watershed of the Yangtze River Delta, China

Wang

et al. 2022

Stoch Environ Res Risk Assess

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A compound perspective on hydrological extreme events is of paramount signi cance as it may lead to damages with larger losses. In this study, an integrated framework, based on downscaled climate variables and hydrological model, i.e. the Soil and Water Assessment Tool, was applied to generate extreme precipitation (Rx1day) and extreme stream ow (Sx1day) series under historical and future climate conditions. Then the potential impacts of climate change for univariate and bivariate joint frequency of extreme precipitation and ood in Xitiaoxi River Basin (XRB), a representative watershed of the Yangtze River Delta, is detected. The compound risk of extreme precipitation and ood under different levels of joint return period for historical and projected periods are estimated by copula-based two-dimensional approaches. Major ndings can be summarized: (1) The Rx1day and Sx1day under future scenarios increased by -0.4 ~ 11.7% and 0.7 ~ 20.4%, respectively, compared to historical period based on univariate frequency analysis, indicating the increasing magnitude of the ood in the future. (2) Climate change with different emission scenarios all have a driving effect on the rising coactivity of extreme precipitation and ood under compound ooding frequency analysis. In addition, the enhancement of climate change to extreme events is more apparent for extremes with higher return period and under the periods of 2080s. (3) Moreover, the ood frequency designs are deduced by bivariate joint distribution are safer than that by univariate distribution. This study may provide actionable insights to formulate the planning scheme of ood control and disaster reduction under the changing environment. IntroductionGlobal precipitation characteristics and ood situations are expected to undergo signi cant changes as a consequence of global climate change including increasing temperatures and varying precipitation patterns projected for the future climate (IPCC, 2014). The incessant increase in magnitude and frequency of weather extremes (

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Multivariate Hazard Assessment for Nonstationary Seasonal Flood Extremes Considering Climate Change

Cited by 10 publications

References 59 publications

Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, China

Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, China

Assessment of water resources system resilience under hazardous events using system dynamic approach and artificial neural networks

Climate change increased the compound extreme precipitation-flood events in a representative watershed of the Yangtze River Delta, China

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