Hydrodynamic modeling of dam breach floods for predicting downstream inundation scenarios using integrated approach of satellite data, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE)

IF 5.7 3区 环境科学与生态学 Q1 WATER RESOURCES Applied Water Science Pub Date : 2024-08-06 DOI:10.1007/s13201-024-02253-9
Kishanlal Darji, Dhruvesh Patel, Indra Prakash, Hamad Ahmed Altuwaijri
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Abstract

Dam breach floods pose significant threats to downstream areas, necessitating accurate prediction of inundation scenarios to mitigate potential damage. This paper presents a novel methodology for hydrodynamic modeling of dam breach floods, leveraging a comprehensive approach that integrates satellite imagery, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE) to forecast downstream inundation scenarios. Specifically, UAVs were utilized to generate high-resolution Digital Elevation Models (DEMs) of the flood-affected areas, ensuring precise representation of topography in the model. The approach incorporates Cartosat DEM data for catchment modeling, while NASA's Global Precipitation Measurement mission data, integrated with GEE, facilitated accurate estimation of rainfall in ungagged catchment areas. Furthermore, the Hydrological Engineering Center-Hydrological Modeling System was employed for rainfall-runoff simulation and flood hydrograph derivation, followed by application of the HEC River Analysis System (RAS) for hydrodynamic modeling under dam breach conditions. This integrated modeling approach was applied as a case study of Banaskantha district, Gujarat, India. The outcome was the generation of scenario maps based on HEC-RAS results, which include flood extent, water depth, and flow velocity, highlighting downstream areas affected by flooding. Validation of the hydrodynamic dam breach model performance was conducted using actual field measurements and simulated results, employing statistical analysis methods including Support Vector Regression (SVR) and linear regression to determine coefficient of determination (R2), Root-Mean-Square Error, and Mean Absolute Error of observed and simulated data. The coefficient of determination (R2) values for measured and simulated flow (0.91) and water level (0.86) calculated using SVR demonstrate strong correlation between observed and simulated values. This integrated study of hydrodynamic modeling in data-scarce areas aids in accurate estimation of future probable flooding in downstream areas in the event of a dam break, underscoring the potential of advanced surveying and modeling techniques in flood assessment and management. Ultimately, this integration of technologies aims to enhance community resilience and mitigate socioeconomic costs associated with dam breach floods.

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利用卫星数据、无人驾驶飞行器(UAV)和谷歌地球引擎(GEE)的综合方法,建立溃坝洪水的水动力模型,预测下游淹没情况
溃坝洪水对下游地区构成重大威胁,需要对淹没情况进行准确预测,以减轻潜在的破坏。本文介绍了一种新颖的溃坝洪水流体力学建模方法,该方法综合利用了卫星图像、无人机(UAV)和谷歌地球引擎(GEE)来预测下游淹没情况。具体而言,利用无人飞行器生成受洪水影响地区的高分辨率数字高程模型(DEM),确保在模型中精确呈现地形。该方法将 Cartosat DEM 数据用于集水建模,而 NASA 的全球降水测量任务数据与 GEE 相结合,有助于准确估算无标记集水区的降雨量。此外,还采用水文工程中心水文建模系统进行降雨-径流模拟和洪水水文图推导,然后应用水文工程中心河流分析系统(RAS)进行溃坝条件下的水动力建模。这种综合建模方法被用作印度古吉拉特邦 Banaskantha 地区的案例研究。结果是根据 HEC-RAS 的结果生成了情景图,其中包括洪水范围、水深和流速,并突出显示了受洪水影响的下游地区。利用实际现场测量和模拟结果对水力溃坝模型的性能进行了验证,并采用了统计分析方法,包括支持向量回归(SVR)和线性回归,以确定观测数据和模拟数据的判定系数(R2)、均方根误差和平均绝对误差。使用 SVR 计算出的测量和模拟流量的判定系数 (R2) 值(0.91)和水位的判定系数 (R2) 值(0.86)表明,观测值和模拟值之间具有很强的相关性。这项在数据稀缺地区进行的水动力建模综合研究有助于准确估算大坝决堤时下游地区未来可能发生的洪水,凸显了先进测量和建模技术在洪水评估和管理方面的潜力。最终,这种技术集成旨在提高社区的抗灾能力,降低与溃坝洪水相关的社会经济成本。
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来源期刊
Applied Water Science
Applied Water Science WATER RESOURCES-
CiteScore
9.90
自引率
3.60%
发文量
268
审稿时长
13 weeks
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