Spatio-temporal compounding of connected extreme events: Projection and hotspot identification

被引:9
|
作者
Velpuri, Manikanta [1 ]
Das, Jew [1 ]
Umamahesh, N. V. [1 ]
机构
[1] Natl Inst Technol, Warangal, India
关键词
CMIP6; Connected extremes; Hotspot; India; Spatio-temporal compounding; CLIMATE-CHANGE; PRECIPITATION EVENTS; HEAT WAVES; DATA SET; TEMPERATURE; MODEL; INDIA; UNCERTAINTY; RAINFALL; RISK;
D O I
10.1016/j.envres.2023.116615
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In general, the impact of two different connected extreme events is noticed on the same duration and spatial area. However, the connected extreme events can have footprint over different temporal and spatial scales. Thus, this article analyses the connected extreme events over India using the spatio-temporal compounding technique to understand the impact at different temporal and spatial scales. This approach is applied to analyse the historical and future connected extreme events. In the present study, coincident heat waves and droughts (Event C1), coincident heat waves and extreme precipitation (Event C2) are considered as connected extreme events. The future events are investigated using the suitable global climate models (GCMs) projections under three climate change scenarios (Shared Socioeconomic Pathways (SSP) 2-4.5, SSP3-7.0, and SSP5-8.5). The suitable GCMs are identified with the help of compromise programming. Subsequently, the hotspot regions are identified applying the Regional Climate Change Index (RCCI) method. The outcomes from the study suggest that with increasing temporal compounding, the mean duration of extreme events also increases. Highest increase in mean duration is observed for Event C1 over PI (Peninsular India), WCI (West Central India), and some parts of CNI (Central Northeast India) regions. The regions with high magnitude of duration have low magnitude of occurrence. The duration of Event C1 is likely to increase with respect to climate change scenarios and temporal compounding, especially in the PI region and some parts of WCI. However, there is insignificant change in the duration of Event C2. The PI region identified as the most vulnerable region followed by WCI and HR regions. The highest per-centage of area under the emerging hotspot category is noticed under SSP5-8.5 climate change scenario.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Spatio-temporal compounding of connected extreme events: Projection and hotspot identification (vol 235, 116615, 2023)
    Velpuri, Manikanta
    Das, Jew
    Umamahesh, N. V.
    ENVIRONMENTAL RESEARCH, 2023, 236
  • [2] Spatio-temporal Graphical Models for Extreme Events
    Yu, Hang
    Zhang, Liaofan
    Dauwels, Justin
    2014 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT), 2014, : 2032 - 2036
  • [3] Spatio-temporal response of extreme events on Bornean rainforests
    Boyd, DS
    Phipps, PC
    Foody, GM
    IGARSS 2003: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS I - VII, PROCEEDINGS: LEARNING FROM EARTH'S SHAPES AND SIZES, 2003, : 2547 - 2549
  • [4] Spatio-Temporal Distribution of Extreme Weather Events in India
    Singh, Ajay
    Patwardhan, Anand
    INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND DEVELOPMENT (ICESD 2012), 2012, 1 : 258 - 262
  • [5] Spatio-temporal extreme events in a laser with saturable absorber
    Rimoldi, Cristina
    Barland, Stephane
    Prati, Franco
    Tissoni, Giovanna
    2015 SPATIOTEMPORAL COMPLEXITY IN NONLINEAR OPTICS (SCNO), 2015,
  • [6] Spatio-temporal assessment of extreme precipitation events in southeastern Brazil
    José Victor Orlandi Simões
    Lincoln Muniz Alves
    George Ulguim Pedra
    Nauriente de Jesus da Costa Barreto
    Modeling Earth Systems and Environment, 2025, 11 (3)
  • [7] A hierarchical Bayesian spatio-temporal model for extreme precipitation events
    Ghosh, Souparno
    Mallick, Bani K.
    ENVIRONMETRICS, 2011, 22 (02) : 192 - 204
  • [8] Spatio-Temporal Variation of Extreme Heat Events in Southeastern Europe
    Malcheva, Krastina
    Bocheva, Lilia
    Chervenkov, Hristo
    ATMOSPHERE, 2022, 13 (08)
  • [9] Controlling spatio-temporal extreme events by decreasing the localized energy
    Du, Lin
    Xu, Wei
    Li, Zhanguo
    Zhou, Bingchang
    PHYSICS LETTERS A, 2011, 375 (18) : 1870 - 1876
  • [10] Modeling Spatio-Temporal Extreme Events Using Graphical Models
    Yu, Hang
    Dauwels, Justin
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2016, 64 (05) : 1101 - 1116