scholarly journals Incorporating climate change in flood estimation guidance

2021 ◽  
Vol 379 (2195) ◽  
pp. 20190548 ◽  
Author(s):  
Conrad Wasko ◽  
Seth Westra ◽  
Rory Nathan ◽  
Harriet G. Orr ◽  
Gabriele Villarini ◽  
...  
Keyword(s):  
Climate Change ◽  
Flash Flood ◽  
Flood Hazard ◽  
Research Effort ◽  
Estimation Methods ◽  
Future Research ◽  
Significant Progress ◽  
Design Flood ◽  
Deep Uncertainty ◽  
Flood Estimation

Research into potential implications of climate change on flood hazard has made significant progress over the past decade, yet efforts to translate this research into practical guidance for flood estimation remain in their infancy. In this commentary, we address the question: how best can practical flood guidance be modified to incorporate the additional uncertainty due to climate change? We begin by summarizing the physical causes of changes in flooding and then discuss common methods of design flood estimation in the context of uncertainty. We find that although climate science operates across aleatory, epistemic and deep uncertainty, engineering practitioners generally only address aleatory uncertainty associated with natural variability through standards-based approaches. A review of existing literature and flood guidance reveals that although research efforts in hydrology do not always reflect the methods used in flood estimation, significant progress has been made with many jurisdictions around the world now incorporating climate change in their flood guidance. We conclude that the deep uncertainty that climate change brings signals a need to shift towards more flexible design and planning approaches, and future research effort should focus on providing information that supports the range of flood estimation methods used in practice. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks'.

Revisiting design flood estimation of Nam River Dam basin considering climate change

2016 ◽  
Vol 49 (8) ◽  
pp. 719-729
Author(s):  
Hyunseung Lee ◽  
Taesam Lee ◽  
Taewoong Park ◽  
Chanyoung Son
Keyword(s):  
Climate Change ◽  
Design Flood ◽  
Flood Estimation

scholarly journals Nonstationary Flood Hazard Analysis in Response to Climate Change and Population Growth

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1811 ◽  
Author(s):  
Lei Yan ◽  
Lingqi Li ◽  
Pengtao Yan ◽  
Hongmou He ◽  
Jing Li ◽  
...  
Keyword(s):  
Climate Change ◽  
Population Growth ◽  
Physical Meaning ◽  
Flood Hazard ◽  
Hazard Analysis ◽  
Explanatory Power ◽  
Design Flood ◽  
Statistical Parameters ◽  
Xijiang River ◽  
Design Life

The predictions of flood hazard over the design life of a hydrological project are of great importance for hydrological engineering design under the changing environment. The concept of a nonstationary flood hazard has been formulated by extending the geometric distribution to account for time-varying exceedance probabilities over the design life of a project. However, to our knowledge, only time covariate is used to estimate the nonstationary flood hazard over the lifespan of a project, which lacks physical meaning and may lead to unreasonable results. In this study, we aim to strengthen the physical meaning of nonstationary flood hazard analysis by investigating the impacts of climate change and population growth. For this purpose, two physical covariates, i.e., rainfall and population, are introduced to improve the characterization of nonstationary frequency over a given design lifespan. The annual maximum flood series of Xijiang River (increasing trend) and Weihe River (decreasing trend) are chosen as illustrations, respectively. The results indicated that: (1) the explanatory power of population and rainfall is better than time covariate in the study areas; (2) the nonstationary models with physical covariates possess more appropriate statistical parameters and thus are able to provide more reasonable estimates of a nonstationary flood hazard; and (3) the confidences intervals of nonstationary design flood can be greatly reduced by employing physical covariates. Therefore, nonstationary flood design and hazard analysis with physical covariates are recommended in changing environments.

scholarly journals Design flood estimation for global river networks based on machine learning models

2020 ◽  
Author(s):  
Gang Zhao ◽  
Paul Bates ◽  
Jeffrey Neal ◽  
Bo Pang
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Flood Hazard ◽  
Model Development ◽  
Flood Frequency ◽  
Support Vector ◽  
Design Flood ◽  
Flood Estimation ◽  
Design Floods ◽  
Anderson Darling

Abstract. Design flood estimation is a fundamental task in hydrology. In this research, we propose a machine learning based approach to estimate design floods globally. This approach mainly involves three stages: (i) estimating at-site flood frequency curve for global gauging stations by the Anderson-Darling test and Bayesian MCMC method; (ii) clustering these stations into subgroups by a K-means model based on twelve globally available catchment descriptors, and (iii) developing a regression model in each subgroup for regional design flood estimation using the same descriptors. A total of 11793 stations globally were selected for model development and three widely used regression models were compared for design flood estimation. The results showed that: (1) the proposed approach achieved the highest accuracy for design flood estimation when using all twelve descriptors for clustering; and the performance of regression was improved by considering more descriptors during the training and validation; (2) a support vector machine regression provide the highest prediction performance among all regression models tested, with root mean square normalised error of 0.708 for 100-year return period flood estimation; (3) 100-year design flood in tropical, arid, temperate, cold and polar climate zones could be reliably estimated with the relative mean relative biases (RBIAS) of −0.199, −0.233, −0.169, 0.179 and −0.091 respectively; (4) This machine learning based approach shows considerable improvement over the index-flood based method introduced by Smith et al. (2015, https://doi.org/10.1002/2014WR015814) for the design flood estimation at global scales; and the average RBIAS in estimation is less than 18 % for 10, 20, 50 and 100-year design floods. We conclude that the proposed approach is a valid method to estimate design floods anywhere on the global river network, improving our prediction of the flood hazard, especially in ungauged areas.

scholarly journals Flood and Flash Flood Hazard Mapping Using the Frequency Ratio, Multilayer Perceptron and Their Hybrid Ensemble

Proceedings ◽  
2019 ◽  
Vol 48 (1) ◽  
pp. 6
Author(s):  
Mihnea Cristian Popa ◽  
Daniel Constantin Diaconu
Keyword(s):  
Climate Change ◽  
Frequency Ratio ◽  
Flash Flood ◽  
Flood Hazard ◽  
Fuzzy Theory ◽  
Regional Scale ◽  
Hazard Mapping ◽  
Scientific World ◽  
Modelling Techniques ◽  
The Impact

The importance of identifying the areas vulnerable for both floods and flash-floods is an important component of risk management. The assessment of vulnerable areas is a major challenge in the scientific world. Adaptation and mitigation have generally been treated as two separate issues, both in public politics and in practice, in which mitigation is seen as the attenuation of the cause, and studies of adaption look into dealing with the consequences of climate change. Studies on the impact of climate change on flood risk are mostly conducted at the river basin or regional scale. Remote sensing and GIS technologies, together with the latest modelling techniques, can contribute to our ability to predict and manage floods. Various methods are commonly used to map flood sensitivity. Recent methods such as multicriteria evaluation, decision tree analysis (DT), fuzzy theory, weight of samples (WoE), artificial neural networks (ANN), frequency ratio (FR) and logistic regression (LR) approaches have been widely used by many researchers.

scholarly journals Design flood estimation for global river networks based on machine learning models

2021 ◽  
Vol 25 (11) ◽  
pp. 5981-5999
Author(s):  
Gang Zhao ◽  
Paul Bates ◽  
Jeffrey Neal ◽  
Bo Pang
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Flood Hazard ◽  
Model Development ◽  
Flood Frequency ◽  
Support Vector ◽  
Design Flood ◽  
Flood Estimation ◽  
Design Floods ◽  
Anderson Darling

Abstract. Design flood estimation is a fundamental task in hydrology. In this research, we propose a machine-learning-based approach to estimate design floods globally. This approach involves three stages: (i) estimating at-site flood frequency curves for global gauging stations using the Anderson–Darling test and a Bayesian Markov chain Monte Carlo (MCMC) method; (ii) clustering these stations into subgroups using a K-means model based on 12 globally available catchment descriptors; and (iii) developing a regression model in each subgroup for regional design flood estimation using the same descriptors. A total of 11 793 stations globally were selected for model development, and three widely used regression models were compared for design flood estimation. The results showed that (1) the proposed approach achieved the highest accuracy for design flood estimation when using all 12 descriptors for clustering; and the performance of the regression was improved by considering more descriptors during training and validation; (2) a support vector machine regression provided the highest prediction performance amongst all regression models tested, with a root mean square normalised error of 0.708 for 100-year return period flood estimation; (3) 100-year design floods in tropical, arid, temperate, cold and polar climate zones could be reliably estimated (i.e. <±25 % error), with relative mean bias (RBIAS) values of −0.199, −0.233, −0.169, 0.179 and −0.091 respectively; (4) the machine-learning-based approach developed in this paper showed considerable improvement over the index-flood-based method introduced by Smith et al. (2015, https://doi.org/10.1002/2014WR015814) for design flood estimation at global scales; and (5) the average RBIAS in estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. We conclude that the proposed approach is a valid method to estimate design floods anywhere on the global river network, improving our prediction of the flood hazard, especially in ungauged areas.

scholarly journals Nonstationary Design Flood Estimation in Response to Climate Change, Population Growth and Cascade Reservoir Regulation

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2687
Author(s):  
Yuzuo Xie ◽  
Shenglian Guo ◽  
Lihua Xiong ◽  
Jing Tian ◽  
Feng Xiong
Keyword(s):  
Climate Change ◽  
Population Growth ◽  
Change Point Detection ◽  
Flood Frequency Analysis ◽  
Data Series ◽  
Flood Events ◽  
Design Flood ◽  
Cascade Reservoirs ◽  
Han River ◽  
Flood Estimation

The hydrologic data series are nonstationary due to climate change and local anthropogenic activities. The existing nonstationary design flood estimation methods usually focus on the statistical nonstationarity of the flow data series in the catchment, which neglect the hydraulic approach, such as reservoir flood regulation. In this paper, a novel approach to comprehensively consider the driving factors of non-stationarities in design flood estimation is proposed, which involves three main steps: (1) implementation of the candidate predictors with trend tests and change point detection for preliminary analysis; (2) application of the nonstationary flood frequency analysis with the principle of Equivalent Reliability (ER) for design flood volumes; (3) development of a nonstationary most likely regional composition (NS-MLRC) method, and the estimation of a design flood hydrograph at downstream cascade reservoirs. The proposed framework is applied to the cascade reservoirs in the Han River, China. The results imply that: (1) the NS-MLRC method provides a much better explanation for the nonstationary spatial correlation of the flood events in Han River basin, and the multiple nonstationary driving forces can be precisely quantified by the proposed design flood estimation framework; (2) the impacts of climate change and population growth are long-lasting processes with significant risk of flood events compared with stationary distribution conditions; and (3) the swift effects of cascade reservoirs are reflected in design flood hydrographs with lower peaks and lesser volumes. This study can provide a more integrated template for downstream flood risk management under the impact of climate change and human activities.

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