Flash flood hazard zonation based on basin morphometry using remote sensing and GIS techniques: A case study of Wadi Qena basin, Eastern Desert, Egypt

Taha, Maysa M.N.; Elbarbary, Samah; Naguib, Doaa M.; El-Shamy, I.Z.

doi:10.1016/j.rsase.2017.08.007

Cited by 38 publications

(23 citation statements)

References 22 publications

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“…Basin length is an indicator of surface runoff characteristic, where longer streams indicate flatter gradients (Christopher, Idowu, & Olugbenga, 2010; Taha et al, 2017). This relation is depicted in Figure 5b, where a strong negative correlation ( r = −.69) between stream length and basin slope was found.…”

Section: Resultsmentioning

confidence: 99%

“…Development of land and water conservation measures necessitates morphometric analysis and prioritisation of sub‐watersheds within a basin (Aher, Adinarayana, & Gorantiwar, 2014). Morphometric analysis has been extensively used for the purpose of prioritisation and assessment of watersheds susceptibility to natural hazards such as flash floods and erosion (Abuzied, Yuan, Ibrahim, Kaiser, & Saleem, 2016; Alam, Ahmed, & Sammonds, 2020; Ameri, Pourghasemi, & Cerda, 2018; Asfaw & Workineh, 2019; Charizopoulos, Mourtzios, Psilovikos, Psilovikos, & Karamotsou, 2019; Hussein, Abdelkareem, Hussein, & Askalany, 2019; Kannan, Venkateswaran, Vijay Prabhum, & Sankar, 2018; Shivhare et al, 2018; Taha et al, 2017). These studies have used the classic works of Horton (1945; 1932), Smith (1950), Strahler (1952), Miller (1953), and Schumm (1956) as a guidance.…”

Section: Introductionmentioning

confidence: 99%

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Morphometric analysis and prioritisation of watersheds for flood risk management in Wadi Easal Basin (WEB), Jordan, using geospatial technologies

Obeidat

Awawdeh

Al‐Hantouli

2021

J Flood Risk Management

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Morphometric analysis and sub‐watersheds prioritisation were carried out for the Wadi Easal Basin, Jordan, which is characterised by a high topographic diversity. The total ranking method was applied to prioritise the sub‐watersheds in terms of susceptibility to flash flood. Results of morphometric analysis revealed that the study area is a fifth order drainage system with a dendritic drainage pattern and elongated shape. Prioritisation results showed that about 71% (15 out of 21 sub‐watersheds) of sub‐watersheds have high‐very high susceptibility to flooding, which forms about 64% of the total area of the basin. The main underlying morphometric parameters behind this are the high drainage density, stream frequency, high basin relief, basin slope, ruggedness number, and circulatory ratio, and the low value of basin shape. Overall, the basin has a rugged topography with steep slopes and high relief. Since the basin is ungauged, and no information about its past hydrological behaviour is present, the results of this study can be used as guidance for competent authorities to initialize flood mitigation or artificial groundwater recharge measures.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Morphometric analysis and prioritisation of watersheds for flood risk management in Wadi Easal Basin (WEB), Jordan, using geospatial technologies

Obeidat

Awawdeh

Al‐Hantouli

2021

J Flood Risk Management

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“…It works based on Bayes Theorem, which is known as Naive Bayes. The NB uses a decision tree (DT) for its structure and organizes an NB model on every leaf node of the constructed DT [66]. The NBT exhibits a significant classification performance and accuracy [67,68].…”

Section: Classification and Regression Trees (Cart)mentioning

confidence: 99%

Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach

2020

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In an arid region, flash floods (FF), as a response to climate changes, are the most hazardous causing massive destruction and losses to farms, human lives and infrastructure. A first step towards securing lives and infrastructure is the susceptibility mapping and predicting of occurrence sites of FF. Several studies have been applied using an ensemble machine learning model (EMLM) but measuring FF magnitude using a hybrid approach that integrates machine learning (MCL) and geohydrological models have not been widely applied. This study aims to modify a hybrid approach by testing three machine learning models. These are boosted regression tree (BRT), classification and regression trees (CART), and naive Bayes tree (NBT) for FF susceptibility mapping at the northern part of the United Arab Emirates (NUAE). This is followed by applying a group of accuracy metrics (precision, recall and F1 score) and the receiving operating characteristics (ROC) curve. The result demonstrated that the BRT has the highest performance for FF susceptibility mapping followed by the CART and NBT. After that, the produced FF map using the BRT was then modified by dividing it into seven basins, and a set of new FF conditioning parameters namely alluvial plain width, basin gradient and mean slope for each basin was calculated for measuring FF magnitude. The results showed that the mountainous and narrower basins (e.g., RAK, Masafi, Fujairah, and Rol Dadnah) have the highest probability occurrence of FF and FF magnitude, while the wider alluvial plains (e.g., Al Dhaid) have the lowest probability occurrence of FF and FF magnitude. The proposed approach is an effective approach to improve the susceptibility mapping of FF, landslides, land subsidence, and groundwater potentiality obtained using ensemble machine learning, which is used widely in the literature.

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“…This is led us to study the vulnerability of the Ibex protectorate to flash flood via the determination of the watershed boundary and the basins of the region. Using the Hydrological tools in ArcMap, the DEM was first filled for any gaps and the flow direction, flow accumulation, stream order and basin boundaries were identified and obtained from the DEM (Dawod et al 2012, Farahat et al 2017, El Bastawesy 2014, Taha et al 2017.…”

Section: The Flash Flood Evaluationmentioning

confidence: 99%

Physiographic Characteristics Along the Ibex Protectorate: Remote Sensing Application, Hotet Bani Tamim, Central Saudi Arabia

2019

JEES

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Tuwiaq plateau is one of the most characteristic physiographic components of central Saudi Arabia. The Middle Jurassic limestone was the result of the natural harmony between the Tethys sedimentation, tectonics, and climate. The plateau was studied within the Ibex Protectorate of Hotet Bani Tamim area. Remote sensing applications on satellite images of 2019 were able to measure the geomorphology, distinguish the sedimentary units, and emphasized the role of tectonics in dividing the plateau to step-like escarpment, elevated from 1080 m at Al-Onouk the extreme northwest to 600m at Al-Hulwah with an average slope of 0.8cmm -1 . The slope at catchment reaches up to 70° while at the downstream not more than 16°. Two basins are detected at the Ibex protectorate, these are Wadi Meta'am basin and Wadi Bowydan-Nokhailan basin who down flow into Wadi Meta'am and Wadi Berek respectively). Wadi Meta'am derives its water through three tributaries Al-Ghaba, Ghafar, and Al-Agmaa, while Wadi Bowydan, on the other hand, cuts deeper and drain water with Wadi Nokhailan downstream to Wadi Berek. The high-density dendritic drainage pattern with 5 th and 6 th order tributaries support the vulnerability of the area to flash flood hazards. The heavy rains were able to sculpt such an amazing geomorphosite and leave its finger prints in the form of deep streams (about 140m depth), channel lags of cobbles and pebbles, Quaternary thick colluvium, water-saturated soils, and karstic features such as the small pool, calcretes and travertines. The present study seeks the importance to schedule the area within the program of development according to the vision 2030 and to add among the geoheritage sites

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Flash flood hazard zonation based on basin morphometry using remote sensing and GIS techniques: A case study of Wadi Qena basin, Eastern Desert, Egypt

Cited by 38 publications

References 22 publications

Morphometric analysis and prioritisation of watersheds for flood risk management in Wadi Easal Basin (WEB), Jordan, using geospatial technologies

Morphometric analysis and prioritisation of watersheds for flood risk management in Wadi Easal Basin (WEB), Jordan, using geospatial technologies

Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach

Physiographic Characteristics Along the Ibex Protectorate: Remote Sensing Application, Hotet Bani Tamim, Central Saudi Arabia

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