Flood inundation assessment in the data-scarce region using hydrodynamic models and google earth engine -A case of Ghed region, Ozat River basin, Gujarat, India

Trambadia, Nevil K.; Patel, Deep; Patel, Vinodkumar M.; Gundalia, Manoj

doi:10.21203/rs.3.rs-2321703/v1

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…The HEC-RAS hydraulic model has wide use in Southern African catchments of Manyame (Muvuti, 2021), Runde and Save catchments. The model has been successfully applied in other international basins and regions in India such as Ghed (Trambadia et al, 2023), Purna River (Pathan et al, 2022a,b), Vishwamitiri River basin (Shah et al, 2022), Ozat River basin in Gujarat (Trambadia et al, 2023).…”

Section: Extreme Value Analysismentioning

confidence: 99%

Urban flash floods modeling in Mzuzu City, Malawi based on Sentinel and MODIS data

Gumindoga,

Liwonde,

Rwasoka

et al. 2024

Front. Clim.

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Floods are major hazard in Mzuzu City, Malawi. This study applied geospatial and hydrological modeling techniques to map flood incidences and hazard in the city. Multi-sensor [Sentinel 1, Sentinel 2, and Moderate Resolution Imaging Spectroradiometer (MODIS)] Normalized Difference Vegetation Index (NDVI) datasets were used to determine the spatio-temporal variation of flood inundation. Ground control points collected using a participatory GIS mapping approach were used to validate the identified flood hazard areas. A Binary Logistic Regression (BLR) model was used to determine and predict the spatial variation of flood hazard as a function of selected environmental factors. The Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) was used to quantify the peak flow and runoff contribution needed for flood in the city. The runoff and peak flow from the HEC-HMS model were subjected to extreme value frequency analysis using the Gumbel Distribution approach before input into the Hydrologic Engineering Center River Analysis System (RAS) (HEC-RAS). The HEC-RAS model was then applied to map flood inundated areas producing flood extents maps for 100, 50, 20, and 10-year return periods, with rain-gauge and Climate Prediction Center MORPHed precipitation (CMORPH) satellite-based rainfall inputs. Results revealed that selected MODIS and Sentinel datasets were effective in delineating the spatial distribution of flood events. Distance from the river network and urban drainage are the most significant factors (p < 0.05) influencing flooding. Consequently, a relatively higher flood hazard probability and/susceptibility was noted in the south-eastern and western-most regions of the study area. The HEC-HMS model calibration (validation) showed satisfactory performance metrics of 0.7 (0.6) and similarly, the HEC-RAS model significantly performed satisfactorily as well (p < 0.05). We conclude that bias corrected satellite rainfall estimates and hydrological modeling tools can be used for flood inundation simulation especially in areas with scarce or poorly designed rain gauges such as Mzuzu City as well as those affected by climate change. These findings have important implications in informing and/updating designs of flood early warning systems and impacts mitigation plans and strategies in developing cities such as Mzuzu.

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Section: Extreme Value Analysismentioning

confidence: 99%

Urban flash floods modeling in Mzuzu City, Malawi based on Sentinel and MODIS data

Gumindoga,

Liwonde,

Rwasoka

et al. 2024

Front. Clim.

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Google Earth Engine (GEE) for Modeling and Monitoring Hydrometeorological Events Using Remote Sensing Data

Hazaymeh,

Zeitoun

2023

Modeling and Monitoring Extreme Hydrometeorological Events

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Google Earth Engine (GEE) has emerged as a powerful platform for modeling and monitoring extreme hydrometeorological events. In recent years, GEE has been used extensively for studying floods, droughts, and other natural disasters. It offers a comprehensive suite of tools that can help researchers and practitioners better understand the complex interactions between weather, climate, and water resources. By providing access to a wealth of satellite imagery, climate data, and geospatial datasets, GEE enables users to model and monitor these events with unprecedented accuracy and efficiency. This book chapter explores the various ways in which GEE can be used for modeling and monitoring extreme hydrometeorological events, understanding hydrometeorological events and their monitoring needs, including case studies and practical examples. It's worth noting that this chapter mainly focuses on using GEE for remote sensing and geospatial data analysis into hydrometeorological modeling and monitoring.

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Flood inundation assessment in the data-scarce region using hydrodynamic models and google earth engine -A case of Ghed region, Ozat River basin, Gujarat, India

Cited by 2 publications

References 47 publications

Urban flash floods modeling in Mzuzu City, Malawi based on Sentinel and MODIS data

Urban flash floods modeling in Mzuzu City, Malawi based on Sentinel and MODIS data

Google Earth Engine (GEE) for Modeling and Monitoring Hydrometeorological Events Using Remote Sensing Data

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