Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

Slater, Louise; Anderson, Bailey; Buechel, Marcus; Dadson, Simon; Han, Shasha; Harrigan, Shaun; Kelder, Timo; Kowal, Katie; Lees, Thomas; Matthews, Tom; Murphy, Conor; Wilby, Robert L.

doi:10.5194/hess-25-3897-2021

Cited by 145 publications

(82 citation statements)

References 414 publications

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“…We have summarized compound flooding events that include storm surge and high rainfall, storm surge and sea level rise, storm surge and riverine flooding, and coastal and riverine flooding. Looking ahead, there is a rising risk of compound flooding in the future because of changes in sea level rise, storm intensity and precipitation, land-use-land-cover change in the future (Slater et al, 2021).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods

et al. 2021

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Compound extremes pose immense challenges and hazards to communities, and this is particularly true for compound hydrometeorological extremes associated with deadly floods, surges, droughts, and heat waves. To mitigate and better adapt to compound hydrometeorological extremes, we need to better understand the state of knowledge of such extremes. Here we review the current advances in understanding compound hydrometeorological extremes: compound heat wave and drought (hot-dry), compound heat stress and extreme precipitation (hot-wet), cold-wet, cold-dry and compound flooding. We focus on the drivers of these extremes and methods used to investigate and quantify their associated risk. Overall, hot-dry compound extremes are tied to subtropical highs, blocking highs, atmospheric stagnation events, and planetary wave patterns, which are modulated by atmosphere-land feedbacks. Compared with hot-dry compound extremes, hot-wet events are less examined in the literature with most works focusing on case studies. The cold-wet compound events are commonly associated with snowfall and cold frontal systems. Although cold-dry events have been found to decrease, their underlying mechanisms require further investigation. Compound flooding encompasses storm surge and high rainfall, storm surge and sea level rise, storm surge and riverine flooding, and coastal and riverine flooding. Overall, there is a growing risk of compound flooding in the future due to changes in sea level rise, storm intensity, storm precipitation, and land-use-land-cover change. To understand processes and interactions underlying compound extremes, numerical models have been used to complement statistical modeling of the dependence between the components of compound extremes. While global climate models can simulate certain types of compound extremes, high-resolution regional models coupled with land and hydrological models are required to simulate the variability of compound extremes and to project changes in the risk of such extremes. In terms of statistical modeling of compound extremes, previous studies have used empirical approach, event coincidence analysis, multivariate distribution, the indicator approach, quantile regression and the Markov Chain method to understand the dependence, greatly advancing the state of science of compound extremes. Overall, the selection of methods depends on the type of compound extremes of interests and relevant variables.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods

et al. 2021

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“…The least-square method in the traditional linear regression provides the trend in the time series, e.g., annual average precipitation, number of days above 95th percentile, and number of days above 99th percentile. We also focus on trend seasonality and changes in extreme precipitation magnitudes using the non-parametric Theil-Sen (TS) slope estimator (Theil, 1950;Sen, 1968), used in several studies (Chandniha et al, 2017;Slater et al, 2021). TS produces more accurate trend magnitude predictions when applied on skewed datasets with several extremes (Arora et al, 2017).…”

Section: Trend Magnitudementioning

confidence: 99%

Investigating Temporal and Spatial Precipitation Patterns in the Southern Mid-Atlantic United States

2022

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The investigation of regional vulnerability to extreme hydroclimatic events (e.g., floods and hurricanes) is quite challenging due to its dependence on reliable precipitation estimates. Better understanding of past precipitation trends is crucial to examine changing precipitation extremes, optimize future water demands, stormwater infrastructure, extreme event measures, irrigation management, etc., especially if combined with future climate and population projections. The objective of the study is to investigate the spatial-temporal variability of average and extreme precipitation at a sub-regional scale, specifically in the Southern Mid-Atlantic United States, a region characterized by diverse topography and is among the fastest-growing areas in North America. Particularly, this work investigates past precipitation trends and patterns using the North American Land Data Assimilation System, Version 2 (NLDAS-2, 12 km/1 h resolution) reanalysis dataset during 1980–2018. Both parametric (linear regression) and non-parametric (e.g., Theil-Sen) robust statistical tools are employed in the study to analyze trend magnitudes, which are tested for statistical significance using the Mann-Kendall test. Standard precipitation indices from ETCCDI are also used to characterize trends in the relative contribution of extreme events to precipitation in the area. In the region an increasing trend (4.3 mm/year) is identified in annual average precipitation with ~34% of the domain showing a significant increase (at the 0.1 significance level) of +3 to +5 mm/year. Seasonal and sub-regional trends are also investigated, with the most pronounced increasing trends identified during summers along the Virginia and Maryland border. The study also finds a statistically significant positive trend (at a 0.05 significance level) in the annual maximum precipitation. Furthermore, the number of daily extremes (daily total precipitation higher than the 95th and 99th percentiles) also depicts statistically significant increases, indicating the increased frequency of extreme precipitation events. Investigations into the proportion of annual precipitation occurring on wet days and extremely wet days (95th and 99th percentile) also indicate a significant increase in their relative contribution. The findings of this study have the potential to improve local-scale decision-making in terms of river basin management, flood control, irrigation scheme scheduling, and stormwater infrastructure planning to address urban resilience to hydrometeorological hazards.

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“…Nevertheless, these stationary features may not be valid in the future due to the increasing effects of climate change. It is widely accepted that assuming stationarity in the hydrologic variables used for long-lived engineering designs is no longer tenable [34,35]. For the Thames Basin, it may hardly introduce covariates into the estimated parameters in the established copula model based on stationary historical records.…”

Section: Suggestions For Future Researchmentioning

confidence: 99%

Multivariate Hydrologic Risk Analysis for River Thames

Gabriel

Fan

2022

Water

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This study analyzed the multivariate flood risk for the river Thames at Kingston based on historical flood data from the National River Flow Archive (NRFA) website. The bivariate risk analysis framework was prepared from the joint return periods of the peak flow (m3/s) and 3-day annual maximum flow (m3/s) flood pair. A total of 137 samples of flood pairs from 1883 to 2019 were adopted for risk analysis. The multivariate return periods were characterized depending on the quantification of the bivariate flood frequency analysis of the pair through copulas methods. The unknown parameter of each copula was estimated using the method-of-moment (MOM) estimator based on Kendall’s tau inversion, in which the Clayton copula performed best to model the dependence of the two flood variables. Then, the bivariate hydrologic risk was characterized based on the joint return period in AND, established from the Clayton copula method. The results reveal that the flood pair would keep a constant hydrologic risk value for some time then moderately decrease as the 3-day AMAX flow increases from 700 m3/s. This hydrologic risk indicator was analyzed under four service time scenarios and three peak flows whose return periods were positioned at 50, 100, and 150 years. The outcomes from the bivariate risk analysis of the flood pairs can be used as decision support during the design of flood defenses and hydraulic facilities.

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Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

Cited by 145 publications

References 414 publications

Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods

Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods

Investigating Temporal and Spatial Precipitation Patterns in the Southern Mid-Atlantic United States

Multivariate Hydrologic Risk Analysis for River Thames

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