On the Applicability of the Expected Waiting Time Method in Nonstationary Flood Design

Yan, Lei; Xiong, Lihua; Luan, Qinghua; Jiang, Cong; Yu, Kunxia; Xu, Chong‐Yu

doi:10.1007/s11269-020-02581-w

Cited by 17 publications

(8 citation statements)

References 37 publications

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“…The phenomenon is highly generated by the ENE method because of its calculative restriction relying on covariate length. Hence, the computation will definitely bring about higher levels of uncertainties [22]. However, the abovementioned results indicate that the occurrence periods of AMFP estimated by a climate-informed model are longer than return periods under a stationary model, which will be conservative for decision makers in considering the significant climatic factors in current conditions.…”

Section: Return Period and Associated Uncertainty Analysismentioning

confidence: 97%

“…The results indicated that ENE, DLL and ER yielded very similar design flood values for both increasing and decreasing trends. Yan et al (2020) focused on applying the EWT method to estimate the design flood quantiles and proposed the extrapolation time to guarantee the convergence of the EWT [22]. Given the advantages and disadvantages of these methods [23,24], we choose the ENE method to calculate design floods under a changing climate and the estimated stationary design floods serve as a benchmark for comparison.…”

Section: Introductionmentioning

confidence: 99%

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Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China

Zeng

Huang

Li³

et al. 2021

Water

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The accurate design flood of hydraulic engineering is an important precondition to ensure the safety of residents, and the high precision estimation of flood frequency is a vital perquisite. The Xiangjiang River basin, which is the largest river in Hunan Province of China, is highly inclined to floods. This paper aims to investigate the annual maximum flood peak (AMFP) risk of Xiangjiang River basin under the climate context employing the Bayesian nonstationary time-varying moment models. Two climate covariates, i.e., the average June-July-August Artic Oscillation and sea level pressure in the Northwest Pacific Ocean, are selected and found to exhibit significant positive correlation with AMFP through a rigorous statistical analysis. The proposed models are tested with three cases, namely, stationary, linear-temporal and climate-based conditions. The results both indicate that the climate-informed model demonstrates the best performance as well as sufficiently explain the variability of extreme flood risk. The nonstationary return periods estimated by the expected number of exceedances method are larger than traditional ones built on the stationary assumption. In addition, the design flood could vary with the climate drivers which has great implication when applied in the context of climate change. This study suggests that nonstationary Bayesian modelling with climatic covariates could provide useful information for flood risk management.

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Section: Return Period and Associated Uncertainty Analysismentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China

Zeng

Huang

Li³

et al. 2021

Water

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“…Water resources are important for social and economic development and the ecological environment, and accurate runoff forecasting can provide a reasonable decision-making basis for the optimal allocation and utilization of water resources (Huang et al, 2014;Xiong et al, 2019;Feng et al, 2020a;Yan et al, 2021a;Jian et al, 2022). However, under changing environments, the runoff process and associated hydrological system have been altered by human activities and climate change (Song et al, 2018;Sun et al, 2018Sun et al, , 2022Jiang et al, 2019;Lu et al, 2020;Yan et al, 2020;Hu et al, 2022), and the runoff series becomes nonlinear and nonstationary, which makes it challenging to capture the variation characteristics of runoff (Sun et al, 2014;Lin et al, 2020;Yan et al, 2021b;Samantaray et al, 2022a;Samantaray et al, 2022b;Samantaray et al, 2022c;Zhou et al, 2022). Therefore, there is an urgent need to develop a runoff prediction model with robustness and high forecasting accuracy under a changing environment (Sit et al, 2020;Niu et al, 2021;Zhao et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Climate-informed monthly runoff prediction model using machine learning and feature importance analysis

Yan

Lei

Jiang

et al. 2022

Front. Environ. Sci.

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Accurate runoff prediction can provide a reliable decision-making basis for flood and drought disaster prevention and scientific allocation of water resources. Selecting appropriate predictors is an effective way to improve the accuracy of runoff prediction. However, the runoff process is influenced by numerous local and global hydrometeorological factors, and there is still no universal approach about the selection of suitable predictors from these factors. To address this problem, we proposed a runoff prediction model by combining machine learning (ML) and feature importance analysis (FIA-ML). Specifically, take the monthly runoff prediction of Yingluoxia, China as an example, the FIA-ML model uses mutual information (MI) and feature importance ranking method based on random forest (RF) to screen suitable predictors, from 130 global climate factors and several local hydrometeorological information, as the input of ML models, namely the hybrid kernel support vector machine (HKSVM), extreme learning machine (ELM), generalized regression neural network (GRNN), and multiple linear regression (MLR). An improved particle swarm optimization (IPSO) is used to estimate model parameters of ML. The results indicated that the performance of the FIA-ML is better than widely-used long short-term memory neural network (LSTM) and seasonal autoregressive integrated moving average (SARIMA). Particularly, the Nash-Sutcliffe Efficiency coefficients of the FIA-ML models with HKSVM and ELM were both greater than 0.9. More importantly, the FIA-ML models can explicitly explain which physical factors have significant impacts on runoff, thus strengthening the physical meaning of the runoff prediction model.

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“…Under the framework of NFFA, if we still follow the design method under stationarity, we can obtain annual design flood for a given return period, which is impractical for engineering design since the relationship between design flood and return period is no longer one-to-one. To address the problem of design flood estimation in NFFA, researchers have carried out many studies and developed several design flood methods in recent years (Olsen et al 1998;Parey et al 2007Parey et al , 2010Cooley 2013;Rootzén & Katz 2013;Acero et al 2017Acero et al , 2018Hu et al 2018;Wang et al 2019;Byun & Hamlet 2020;Lu et al 2020;Yan et al 2020). Yan et al (2019a) comprehensively compared different methods and recommended the use of average design life level (ADLL) and equivalent reliability (Hu et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Design flood estimation with varying record lengths in Norway under stationarity and nonstationarity scenarios

et al. 2021

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In traditional flood frequency analysis, a minimum of 30 observations is required to guarantee the accuracy of design results with an allowable uncertainty; however, there has not been a recommendation for the requirement on the length of data in NFFA (nonstationary flood frequency analysis). Therefore, this study has been carried out with three aims: (i) to evaluate the predictive capabilities of nonstationary (NS) and stationary (ST) models with varying flood record lengths; (ii) to examine the impacts of flood record lengths on the NS and ST design floods and associated uncertainties; and (iii) to recommend the probable requirements of flood record length in NFFA. To achieve these objectives, 20 stations with record length longer than 100 years in Norway were selected and investigated by using both GEV (generalized extreme value)-ST and GEV-NS models with linearly varying location parameter (denoted by GEV-NS0). The results indicate that the fitting quality and predictive capabilities of GEV-NS0 outperform those of GEV-ST models when record length is approximately larger than 60 years for most stations, and the stability of the GEV-ST and GEV-NS0 is improved as record lengths increase. Therefore, a minimum of 60 years of flood observations is recommended for NFFA for the selected basins in Norway.

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On the Applicability of the Expected Waiting Time Method in Nonstationary Flood Design

Cited by 17 publications

References 37 publications

Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China

Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China

Climate-informed monthly runoff prediction model using machine learning and feature importance analysis

Design flood estimation with varying record lengths in Norway under stationarity and nonstationarity scenarios

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