Spatiotemporal Characteristics Analysis and Driving Forces Assessment of Flash Floods in Altay

被引:6
作者
Ahemaitihali, Abudumanan [1 ,2 ]
Dong, Zuoji [1 ]
机构
[1] Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
[2] Altay Reg Comm Communist Youth League, Altay 836500, Peoples R China
关键词
flash flood; spatiotemporal change; driving factor; Altay; RISK-ASSESSMENT; CHINA; DISASTERS; INUNDATION; HAZARD; MODEL;
D O I
10.3390/w14030331
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Flash floods are devastating natural disasters worldwide. Understanding their spatiotemporal distributions and driving factors is essential for identifying high risk areas and predicting hydrological conditions. In this study, several methods were used to analyze the changing patterns and driving factors of flash floods in the Altay region. Results indicate that the number of flash floods each year increased in 1980-2015, with two sudden change points (1996 and 2008), and April, June, and July presented the highest frequency of events. Habahe and Jeminay were known to have high flash flood incidences; however, currently, Altay City, Fuhai, Fuyun, and Qinghe are most affected. In terms of driving force analysis, precipitation and altitude performance have a key impact on flash flood occurrence in this settlement compared to other subregions, with a high percentage increase in the mean squared error value of 39, 37, 37, 37, and 33 for 10 min precipitation in a 20-year return period, elevation, 60 min precipitation in a 20-year return period, 6 h precipitation in a 20-year return period, and 24 h precipitation in a 20-year return period, respectively. The study results provide insights into spatial-temporal dynamics of flash floods and a scientific basis for policymakers to set improvement targets in specific areas.
引用
收藏
页数:18
相关论文
共 52 条
  • [1] Bai S., 2015, DESERT OASIS METEORO, V9, P7
  • [2] Major flood disasters in Europe: 1950-2005
    Barredo, Jose I.
    [J]. NATURAL HAZARDS, 2007, 42 (01) : 125 - 148
  • [3] Benito G, 2004, NAT HAZARDS, V31, P623
  • [4] Flash floods Observations and analysis of hydro-meteorological controls Preface
    Borga, M.
    Anagnostou, E. N.
    Bloschl, G.
    Creutin, J-D
    [J]. JOURNAL OF HYDROLOGY, 2010, 394 (1-2) : 1 - 3
  • [5] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [6] Flood risk assessment for delta mega-cities: a case study of Jakarta
    Budiyono, Yus
    Aerts, Jeroen
    Brinkman, JanJaap
    Marfai, Muh Aris
    Ward, Philip
    [J]. NATURAL HAZARDS, 2015, 75 (01) : 389 - 413
  • [7] Analysis for spatial-temporal changes of grain production and farmland resource: Evidence from Hubei Province, central China
    Chai, Ji
    Wang, Zhanqi
    Yang, Jun
    Zhang, Liguo
    [J]. JOURNAL OF CLEANER PRODUCTION, 2019, 207 : 474 - 482
  • [8] Flood vulnerability assessment using the triangular fuzzy number-based analytic hierarchy process and support vector machine model for the Belt and Road region
    Duan, Yu
    Xiong, Junnan
    Cheng, Weiming
    Wang, Nan
    Li, Yi
    He, Yufeng
    Liu, Jun
    He, Wen
    Yang, Gang
    [J]. NATURAL HAZARDS, 2022, 110 (01) : 269 - 294
  • [9] [方建 Fang Jian], 2015, [自然灾害学报, Journal of Natural Disasters], V24, P1
  • [10] Flood mapping in the lower Mekong River Basin using daily MODIS observations
    Fayne, Jessica V.
    Bolten, John D.
    Doyle, Colin S.
    Fuhrmann, Sven
    Rice, Matthew T.
    Houser, Paul R.
    Lakshmi, Venkat
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2017, 38 (06) : 1737 - 1757