Development of flood forecasting and warning system using hybrid approach of ensemble and hydrological model for Dharoi Dam

IF 1.6 Q3 WATER RESOURCES Water Practice and Technology Pub Date : 2023-10-26 DOI:10.2166/wpt.2023.178
Anant Patel, S. M. Yadav
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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.
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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
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。
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来源期刊
CiteScore
2.30
自引率
6.20%
发文量
136
审稿时长
14 weeks
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