非洲南部干旱气候下的洪水灾害建模:以津巴布韦林波波盆地Beitbridge为例

IF 1 4区 环境科学与生态学 Q4 WATER RESOURCES
Lloyd Chikwiramakomo, Webster Gumindoga, Munyaradzi Davis Shekede, Tawanda Winmore Gara, Talent Chuma
{"title":"非洲南部干旱气候下的洪水灾害建模:以津巴布韦林波波盆地Beitbridge为例","authors":"Lloyd Chikwiramakomo, Webster Gumindoga, Munyaradzi Davis Shekede, Tawanda Winmore Gara, Talent Chuma","doi":"10.17159/wsa/2021.v47.i4.3787","DOIUrl":null,"url":null,"abstract":"Floods are among the natural hazards that have adverse effects on human lives, livelihoods, economies and infrastructure. Dry climates of southern Africa have, over the years, experienced an increase in the frequency of tropical cyclone induced floods. However, understanding the key factors that influence susceptibility to floods has remained largely unexplored in these dry climates. Therefore, this study sought to model flood hazards and determine key factors that significantly explain the probability of flood occurrence in the southern parts of Beitbridge District, Zimbabwe. To achieve these objectives, logistic regression was used to predict spatial variations in flood hazards following cyclone Dineo in 2017. Before spatial prediction of flood hazard, environmental variables were tested for multicollinearity using the Pearson correlation coefficient. Only two environmental variables, i.e., elevation and rainfall, were not significantly correlated and were thus used in the subsequent flood hazard modelling. Results demonstrate that two variables significantly (p < 0.05) predicted spatial variations in flood hazard in the southern parts of the Beitbridge District with relatively high accuracy defined by the area under the curve (AUC = 0.98). In addition, results indicate that ~56 % of the study area is regarded as highly susceptible to floods. Given the projected increase in extreme events such as intense rainfall as a result of climate change, floods will be expected to correspondingly increase in these semi-arid regions. Results presented in this study underscore the importance of geospatial techniques in flood-hazard modelling, which is the key input in sustainable land-use planning. It can thus be concluded that spatial analytical techniques play a key role in flood early warning systems aimed at supporting and building resilient communities in the face of climate change–induced floods.","PeriodicalId":23623,"journal":{"name":"Water SA","volume":"7 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Modelling flood hazard in dry climates of southern Africa: a case of Beitbridge, Limpopo Basin, Zimbabwe\",\"authors\":\"Lloyd Chikwiramakomo, Webster Gumindoga, Munyaradzi Davis Shekede, Tawanda Winmore Gara, Talent Chuma\",\"doi\":\"10.17159/wsa/2021.v47.i4.3787\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Floods are among the natural hazards that have adverse effects on human lives, livelihoods, economies and infrastructure. Dry climates of southern Africa have, over the years, experienced an increase in the frequency of tropical cyclone induced floods. However, understanding the key factors that influence susceptibility to floods has remained largely unexplored in these dry climates. Therefore, this study sought to model flood hazards and determine key factors that significantly explain the probability of flood occurrence in the southern parts of Beitbridge District, Zimbabwe. To achieve these objectives, logistic regression was used to predict spatial variations in flood hazards following cyclone Dineo in 2017. Before spatial prediction of flood hazard, environmental variables were tested for multicollinearity using the Pearson correlation coefficient. Only two environmental variables, i.e., elevation and rainfall, were not significantly correlated and were thus used in the subsequent flood hazard modelling. Results demonstrate that two variables significantly (p < 0.05) predicted spatial variations in flood hazard in the southern parts of the Beitbridge District with relatively high accuracy defined by the area under the curve (AUC = 0.98). In addition, results indicate that ~56 % of the study area is regarded as highly susceptible to floods. Given the projected increase in extreme events such as intense rainfall as a result of climate change, floods will be expected to correspondingly increase in these semi-arid regions. Results presented in this study underscore the importance of geospatial techniques in flood-hazard modelling, which is the key input in sustainable land-use planning. It can thus be concluded that spatial analytical techniques play a key role in flood early warning systems aimed at supporting and building resilient communities in the face of climate change–induced floods.\",\"PeriodicalId\":23623,\"journal\":{\"name\":\"Water SA\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2021-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Water SA\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.17159/wsa/2021.v47.i4.3787\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"WATER RESOURCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water SA","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.17159/wsa/2021.v47.i4.3787","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"WATER RESOURCES","Score":null,"Total":0}
引用次数: 3

摘要

洪水是对人类生活、生计、经济和基础设施产生不利影响的自然灾害之一。多年来,在南部非洲气候干燥的地区,热带气旋引发洪水的频率有所增加。然而,在这些干旱气候中,了解影响洪水易感性的关键因素在很大程度上仍未被探索。因此,本研究试图建立洪水灾害模型,并确定能够显著解释津巴布韦Beitbridge地区南部洪水发生概率的关键因素。为了实现这些目标,使用逻辑回归预测了2017年飓风“迪诺”后洪水灾害的空间变化。在洪水灾害空间预测之前,利用Pearson相关系数对环境变量进行多重共线性检验。只有两个环境变量,即海拔和降雨量,没有显著的相关性,因此被用于随后的洪水灾害建模。结果表明,两个变量对北桥地区南部洪涝灾害的预测具有显著性(p < 0.05),曲线下面积定义的准确度较高(AUC = 0.98)。此外,结果表明,研究区约56%的区域被认为是洪水的高度易感区。鉴于气候变化导致的强降雨等极端事件预计会增加,预计这些半干旱地区的洪水也会相应增加。本研究的结果强调了地理空间技术在洪水灾害建模中的重要性,这是可持续土地利用规划的关键投入。因此,可以得出结论,空间分析技术在洪水预警系统中发挥关键作用,该系统旨在支持和建设应对气候变化引发的洪水的弹性社区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling flood hazard in dry climates of southern Africa: a case of Beitbridge, Limpopo Basin, Zimbabwe
Floods are among the natural hazards that have adverse effects on human lives, livelihoods, economies and infrastructure. Dry climates of southern Africa have, over the years, experienced an increase in the frequency of tropical cyclone induced floods. However, understanding the key factors that influence susceptibility to floods has remained largely unexplored in these dry climates. Therefore, this study sought to model flood hazards and determine key factors that significantly explain the probability of flood occurrence in the southern parts of Beitbridge District, Zimbabwe. To achieve these objectives, logistic regression was used to predict spatial variations in flood hazards following cyclone Dineo in 2017. Before spatial prediction of flood hazard, environmental variables were tested for multicollinearity using the Pearson correlation coefficient. Only two environmental variables, i.e., elevation and rainfall, were not significantly correlated and were thus used in the subsequent flood hazard modelling. Results demonstrate that two variables significantly (p < 0.05) predicted spatial variations in flood hazard in the southern parts of the Beitbridge District with relatively high accuracy defined by the area under the curve (AUC = 0.98). In addition, results indicate that ~56 % of the study area is regarded as highly susceptible to floods. Given the projected increase in extreme events such as intense rainfall as a result of climate change, floods will be expected to correspondingly increase in these semi-arid regions. Results presented in this study underscore the importance of geospatial techniques in flood-hazard modelling, which is the key input in sustainable land-use planning. It can thus be concluded that spatial analytical techniques play a key role in flood early warning systems aimed at supporting and building resilient communities in the face of climate change–induced floods.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Water SA
Water SA 环境科学-水资源
CiteScore
2.80
自引率
6.70%
发文量
46
审稿时长
18-36 weeks
期刊介绍: WaterSA publishes refereed, original work in all branches of water science, technology and engineering. This includes water resources development; the hydrological cycle; surface hydrology; geohydrology and hydrometeorology; limnology; salinisation; treatment and management of municipal and industrial water and wastewater; treatment and disposal of sewage sludge; environmental pollution control; water quality and treatment; aquaculture in terms of its impact on the water resource; agricultural water science; etc. Water SA is the WRC’s accredited scientific journal which contains original research articles and review articles on all aspects of water science, technology, engineering and policy. Water SA has been in publication since 1975 and includes articles from both local and international authors. The journal is issued quarterly (4 editions per year).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信