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

被引:142
|
作者
Slater, Louise J. [1 ]
Anderson, Bailey [1 ]
Buechel, Marcus [1 ]
Dadson, Simon [1 ,5 ]
Han, Shasha [2 ]
Harrigan, Shaun [3 ]
Kelder, Timo [1 ]
Kowal, Katie [3 ]
Lees, Thomas [1 ]
Matthews, Tom [3 ]
Murphy, Conor [4 ]
Wilby, Robert L. [3 ]
机构
[1] Univ Oxford, Sch Geog & Environm, Oxford OX1 3QY, England
[2] European Ctr Medium Range Weather Forecasts ECMWF, Forecast Dept, Reading, Berks, England
[3] Loughborough Univ, Geog & Environm, Loughborough, Leics, England
[4] Maynooth Univ, Dept Geog, Irish Climate Anal & Res UnitS ICARUS, Maynooth, Kildare, Ireland
[5] UK Ctr Ecol & Hydrol, Maclean Bldg, Wallingford OX10 8BB, Oxon, England
基金
爱尔兰科学基金会;
关键词
HYDROMETEOROLOGICAL TIME-SERIES; DECISION-SUPPORT-SYSTEMS; FLOOD FREQUENCY-ANALYSIS; LAND-COVER CHANGE; CLIMATE-CHANGE; NORTH-ATLANTIC; NON-STATIONARITY; TREND ANALYSIS; WIND-SPEED; PRECIPITATION EXTREMES;
D O I
10.5194/hess-25-3897-2021
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Hydroclimatic extremes such as intense rainfall, floods, droughts, heatwaves, and wind or storms have devastating effects each year. One of the key challenges for society is understanding how these extremes are evolving and likely to unfold beyond their historical distributions under the influence of multiple drivers such as changes in climate, land cover, and other human factors. Methods for analysing hydroclimatic extremes have advanced considerably in recent decades. Here we provide a review of the drivers, metrics, and methods for the detection, attribution, management, and projection of nonstationary hydroclimatic extremes. We discuss issues and uncertainty associated with these approaches (e.g. arising from insufficient record length, spurious nonstationarities, or incomplete representation of nonstationary sources in modelling frameworks), examine empirical and simulation-based frameworks for analysis of nonstationary extremes, and identify gaps for future research.
引用
收藏
页码:3897 / 3935
页数:39
相关论文
共 50 条
  • [31] Extreme value modeling with errors-in-variables in detection and attribution of changes in climate extremes
    Yuen Tsz Abby Lau
    Tianying Wang
    Jun Yan
    Xuebin Zhang
    Statistics and Computing, 2023, 33
  • [32] Extreme value modeling with errors-in-variables in detection and attribution of changes in climate extremes
    Lau, Yuen Tsz Abby
    Wang, Tianying
    Yan, Jun
    Zhang, Xuebin
    STATISTICS AND COMPUTING, 2023, 33 (06)
  • [33] Drought Attribution Studies and Water Resources Management
    Olsen, J. R.
    Dettinger, M. D.
    Giovannettone, J. P.
    BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2023, 104 (02) : E435 - E441
  • [34] Methods for detection and enumeration of coliforms in drinking water: a review
    Tambi, Ashish
    Brighu, Urmila
    Gupta, A. B.
    WATER SUPPLY, 2023, 23 (10) : 4047 - 4058
  • [35] Continuous Chlorine Detection in Drinking Water and a Review of New Detection Methods
    Wilson, Robert Euan
    Stoianov, Ivan
    O'Hare, Danny
    JOHNSON MATTHEY TECHNOLOGY REVIEW, 2019, 63 (02): : 103 - 118
  • [36] Detection and attribution of climate change through econometric methods
    Francisco Estrada
    Pierre Perron
    Boletín de la Sociedad Matemática Mexicana, 2014, 20 (1) : 107 - 136
  • [37] Detection and attribution of climate change through econometric methods
    Estrada, Francisco
    Perron, Pierre
    BOLETIN DE LA SOCIEDAD MATEMATICA MEXICANA, 2014, 20 (01): : 107 - 136
  • [38] Resilience of Sundarban mangroves in South Asia to weather extremes and anthropogenic water pollution
    Chauhan, Tejasvi A.
    Bhadury, Punyasloke
    Rodda, Suraj Reddy
    Thumaty, Kiran Chand
    Jha, Chandra Shekhar
    Ghosh, Subimal
    ENVIRONMENTAL RESEARCH COMMUNICATIONS, 2025, 7 (02):
  • [39] Erodibility of synthetic water repellent granular materials: Adapting the ground to weather extremes
    Zheng, Shuang
    Lourenco, Sergio D. N.
    Cleall, Peter J.
    Ng, Angel K. Y.
    SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 689 : 398 - 412
  • [40] DADA: data assimilation for the detection and attribution of weather and climate-related events
    A. Hannart
    A. Carrassi
    M. Bocquet
    M. Ghil
    P. Naveau
    M. Pulido
    J. Ruiz
    P. Tandeo
    Climatic Change, 2016, 136 : 155 - 174