基于集合与水文模型混合方法的达洛伊大坝洪水预报预警系统的开发

IF 1.6 Q3 WATER RESOURCES
Anant Patel, S. M. Yadav
{"title":"基于集合与水文模型混合方法的达洛伊大坝洪水预报预警系统的开发","authors":"Anant Patel, S. M. Yadav","doi":"10.2166/wpt.2023.178","DOIUrl":null,"url":null,"abstract":"Abstract The most frequent natural disaster is flooding. Advanced forecasting systems are lacking in developing countries. The majority of urban areas are located close to flood plains for rivers. Accurate flood forecasting is necessary for reservoir planning and flood management. The Sabarmati River's atmospheric-hydrologic ensemble flood forecasting model has been developed using TIGGE data. Precipitation can be reliably predicted by TIGGE's global ensemble numerical weather prediction (NWP) systems. By using NWP data, flood forecasting systems may be extended from hours to days. Ensemble weather forecasts are produced using the European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction together with 5-day lead times from TIGGE. The flood occurrences from 2015, 2017, and 2020 were used for the calibration and validation of the ensemble flood forecasting model. Bias was corrected using Bayesian model averaging (BMA), heterogeneous extended linear regression, censored non-homogeneous linear regression (cNLR), and other statistical downscaling techniques. Forecasted and downscaled precipitation data were checked using the Brier score and rank likelihood score. For cNLR, Brier's score performed admirably. The specificity vs. sensitivity performance of the cNLR and BMA approaches is 91.87 and 91.82%, respectively, according to receiver operating characteristic and area under the curve diagrams. Models with the hybrid hydrologic coupling approach accurately predict floods. Users may predict peak time and peak discharge hazard likelihood with reliability using peak time and flood warning probability distributions.","PeriodicalId":23794,"journal":{"name":"Water Practice and Technology","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for Dharoi Dam\",\"authors\":\"Anant Patel, S. M. Yadav\",\"doi\":\"10.2166/wpt.2023.178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The most frequent natural disaster is flooding. Advanced forecasting systems are lacking in developing countries. The majority of urban areas are located close to flood plains for rivers. Accurate flood forecasting is necessary for reservoir planning and flood management. The Sabarmati River's atmospheric-hydrologic ensemble flood forecasting model has been developed using TIGGE data. Precipitation can be reliably predicted by TIGGE's global ensemble numerical weather prediction (NWP) systems. By using NWP data, flood forecasting systems may be extended from hours to days. Ensemble weather forecasts are produced using the European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction together with 5-day lead times from TIGGE. The flood occurrences from 2015, 2017, and 2020 were used for the calibration and validation of the ensemble flood forecasting model. Bias was corrected using Bayesian model averaging (BMA), heterogeneous extended linear regression, censored non-homogeneous linear regression (cNLR), and other statistical downscaling techniques. Forecasted and downscaled precipitation data were checked using the Brier score and rank likelihood score. For cNLR, Brier's score performed admirably. The specificity vs. sensitivity performance of the cNLR and BMA approaches is 91.87 and 91.82%, respectively, according to receiver operating characteristic and area under the curve diagrams. Models with the hybrid hydrologic coupling approach accurately predict floods. Users may predict peak time and peak discharge hazard likelihood with reliability using peak time and flood warning probability distributions.\",\"PeriodicalId\":23794,\"journal\":{\"name\":\"Water Practice and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Water Practice and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2166/wpt.2023.178\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"WATER RESOURCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Practice and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2166/wpt.2023.178","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"WATER RESOURCES","Score":null,"Total":0}
引用次数: 0

摘要

洪水是最常见的自然灾害。发展中国家缺乏先进的预报系统。大多数城市地区靠近河流泛滥的平原。准确的洪水预报是水库规划和洪水管理的必要条件。利用TIGGE数据建立了萨巴尔马蒂河大气-水文集合洪水预报模型。TIGGE的全球集合数值天气预报系统可以可靠地预报降水。通过使用NWP数据,洪水预报系统可以从几小时延长到几天。综合天气预报是利用欧洲中期天气预报中心和国家环境预报中心,结合TIGGE的5天提前期编制的。利用2015年、2017年和2020年的洪水发生量对集合洪水预报模型进行了定标和验证。使用贝叶斯模型平均(BMA)、异质扩展线性回归、删减非齐次线性回归(cNLR)和其他统计降尺度技术校正偏差。使用Brier评分和秩似然评分对预测和缩减的降水数据进行检查。对于cNLR, Brier的分数表现得令人钦佩。根据受者工作特征和曲线图下面积,cNLR和BMA方法的特异性和敏感性分别为91.87和91.82%。采用混合水文耦合方法的模型能准确预测洪水。利用洪峰时间和洪峰预警概率分布,用户可以较为可靠地预测洪峰时间和洪峰排放危害可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for Dharoi Dam
Abstract The most frequent natural disaster is flooding. Advanced forecasting systems are lacking in developing countries. The majority of urban areas are located close to flood plains for rivers. Accurate flood forecasting is necessary for reservoir planning and flood management. The Sabarmati River's atmospheric-hydrologic ensemble flood forecasting model has been developed using TIGGE data. Precipitation can be reliably predicted by TIGGE's global ensemble numerical weather prediction (NWP) systems. By using NWP data, flood forecasting systems may be extended from hours to days. Ensemble weather forecasts are produced using the European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction together with 5-day lead times from TIGGE. The flood occurrences from 2015, 2017, and 2020 were used for the calibration and validation of the ensemble flood forecasting model. Bias was corrected using Bayesian model averaging (BMA), heterogeneous extended linear regression, censored non-homogeneous linear regression (cNLR), and other statistical downscaling techniques. Forecasted and downscaled precipitation data were checked using the Brier score and rank likelihood score. For cNLR, Brier's score performed admirably. The specificity vs. sensitivity performance of the cNLR and BMA approaches is 91.87 and 91.82%, respectively, according to receiver operating characteristic and area under the curve diagrams. Models with the hybrid hydrologic coupling approach accurately predict floods. Users may predict peak time and peak discharge hazard likelihood with reliability using peak time and flood warning probability distributions.
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
CiteScore
2.30
自引率
6.20%
发文量
136
审稿时长
14 weeks
×
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