An event-oriented database of meteorological droughts in Europe based on spatio-temporal clustering

被引:0
|
作者
Carmelo Cammalleri
Juan Camilo Acosta Navarro
Davide Bavera
Vitali Diaz
Chiara Di Ciollo
Willem Maetens
Diego Magni
Dario Masante
Jonathan Spinoni
Andrea Toreti
机构
[1] European Commission, Dipartimento di Ingegneria Civile e Ambientale
[2] Joint Research Centre (JRC),undefined
[3] ARCADIA SIT,undefined
[4] Technische Universiteit Delft,undefined
[5] ARHS Developments,undefined
[6] Politecnico di Milano,undefined
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Droughts evolve in space and time without following borders or pre-determined temporal constraints. Here, we present a new database of drought events built with a three-dimensional density-based clustering algorithm. The chosen approach is able to identify and characterize the spatio-temporal evolution of drought events, and it was tuned with a supervised approach against a set of past global droughts characterized independently by multiple drought experts. About 200 events were detected over Europein the period 1981-2020 using SPI-3 (3-month cumulated Standardized Precipitation Index) maps derived from the ECMWF (European Centre for Medium-range Weather Forecasts) 5th generation reanalysis (ERA5) precipitation. The largest European meteorological droughts during this period occurred in 1996, 2003, 2002 and 2018. A general agreement between the major events identified by the algorithm and drought impact records was found, as well as with previous datasets based on pre-defined regions.
引用
收藏
相关论文
共 50 条
  • [41] Capability of Remotely Sensed Drought Indices for Representing the Spatio-Temporal Variations of the Meteorological Droughts in the Yellow River Basin
    Wang, Fei
    Wang, Zongmin
    Yang, Haibo
    Zhao, Yong
    Li, Zhenhong
    Wu, Jiapeng
    REMOTE SENSING, 2018, 10 (11):
  • [42] Determining the response of ecological drought to meteorological and groundwater droughts in Northwest China using a spatio-temporal matching method
    Jiang, Tianliang
    Su, Xiaoling
    Qu, Yanping
    Singh, Vijay P.
    Zhang, Te
    Chu, Jiangdong
    Hu, Xuexue
    JOURNAL OF HYDROLOGY, 2024, 633
  • [43] An enhancement of location estimation and disaster event prediction using density based SPATIO-temporal clustering with GPS
    Ravikumar, K.
    RajivKannan, A.
    MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (5-6) : 3929 - 3941
  • [44] Spatio-temporal characteristics of meteorological drought based on the MCI of Penman–Monteith
    Haixia Yu
    Dandan Yang
    Bingjun Liu
    Jianyu Fu
    Zhihao Liang
    Natural Hazards, 2023, 119 : 221 - 241
  • [45] An enhancement of location estimation and disaster event prediction using density based SPATIO-temporal clustering with GPS
    K. Ravikumar
    A. RajivKannan
    Multimedia Tools and Applications, 2020, 79 : 3929 - 3941
  • [46] Dynamic model-based clustering for spatio-temporal data
    Lucia Paci
    Francesco Finazzi
    Statistics and Computing, 2018, 28 : 359 - 374
  • [47] Dynamic model-based clustering for spatio-temporal data
    Paci, Lucia
    Finazzi, Francesco
    STATISTICS AND COMPUTING, 2018, 28 (02) : 359 - 374
  • [48] A spatio-temporal database system based on TimeDB and Oracle Spatial
    Carvalho, Alexandre
    Ribeiro, Cristina
    Sousa, A. Augusto
    RESEARCH AND PRACTICAL ISSUES OF ENTERPRISE INFORMATION SYSTEMS, 2006, : 11 - 20
  • [49] Dynamic operations for spatio-temporal database based on change mapping
    ZHOU Xiaoguang CHEN Jun School of InfoPhysics and Geomatics Engineering Central South University Hunan Changsha China National Geomatics Center of China Beijing China
    遥感学报, 2009, 13 (04) : 647 - 652
  • [50] The Spatio-Temporal Data Modeling and Application Based on Graph Database
    Zheng, Linjiang
    Zhou, Longhui
    Zhao, Xin
    Liao, Li
    Liu, Weining
    2017 4TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE), 2017, : 741 - 746