Digital elevation model for flood hazards analysis in complex terrain: Case study from Jeddah, Saudi Arabia

Al-Areeq, Ahmed M.; Sharif, Hatim O.; Abba, S.I.; Chowdhury, Shakhawat; Al-Suwaiyan, Mohammed; Benaafi, Mohammed; Yassin, Mohamed A.; Aljundi, Isam H.

doi:10.1016/j.jag.2023.103330

Cited by 8 publications

(5 citation statements)

References 55 publications

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“…The data involved of flash flood disasters include real-time rainfall, soil water content, field investigation, disaster analysis and evaluation, prediction model, early warning model, document data, etc. (AL-Areeq et al, 2023). These data have strong multi-source heterogeneity and highly dynamic spatiotemporal characteristics, which challenges disaster data collection, storage, and calculation (Li et al, 2022).…”

Section: Uncertainty Of Multi-source Spatiotemporal Datamentioning

confidence: 99%

The Early Identification of Flash Flood Disasters: Mechanism, Model and Uncertainty

Yang,

Lu,

Song

et al. 2024

Preprint

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Abstract. Flash flood disasters are one of the major natural disasters in the world, and rapid and accurate early identification of flash flood disasters is the key to preventing and controlling them. In recent years, computer and spatial information technology development has promoted the advancement of early identification technology for flash floods. However, previous research has mainly focused on the impact of "water" and neglected the impact of "sediment" deposition on the rise of water levels. To gain a more comprehensive understanding of flash floods and improve the accuracy of early identification, this article first uses bibliometric methods to review the spatiotemporal distribution, internal relationships, and research hotspots of literature in this field over the past 42 years. Then, the research practice of considering the impact of sediment on the early identification of flash floods was introduced from three aspects: mechanism, model, and uncertainty. Finally, the existing problems in current research were discussed, and future research directions were proposed. The research results have shown that the number of publications in this field has been increasing yearly and will continue to increase, but the cooperation between authors is not close. The coupling effect between sediment replenishment and floods cannot be ignored. Taking into account multiple uncertainties can greatly improve recognition accuracy. This study can provide a panoramic literature perspective and practical research experience for relevant researchers and decision-makers and support further improving flash flood prevention and control capabilities.

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Section: Uncertainty Of Multi-source Spatiotemporal Datamentioning

confidence: 99%

The Early Identification of Flash Flood Disasters: Mechanism, Model and Uncertainty

Yang,

Lu,

Song

et al. 2024

Preprint

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“…By adopting a water body masking function from [50], we ensured that reliable laser points from the ground are used for interpolation. This function relies on two principles: (1) airborne LiDAR data has low point density over water bodies [51], and (2) water surfaces are locally flat. This integration also facilitates hydro-flattened DTM creation [52] in one step, removing the need for post-processing hydro-flattening.…”

Section: Creating the Final Dtmmentioning

confidence: 99%

“…A digital terrain model (DTM) represents a three-dimensional model of the ground surface excluding above-ground features such as buildings and trees. DTM is primarily used for serving as a basis terrain map for simulating and analyzing events occurring over large areas of the Earth, such as hydrological simulations [1], landslide monitoring [2,3], urban object mapping [4][5][6], glacier monitoring [7], and agricultural and forestry applications [8].…”

Section: Introductionmentioning

confidence: 99%

An Object-Based Ground Filtering of Airborne LiDAR Data for Large-Area DTM Generation

Song,

Jung

2023

Remote Sensing

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Digital terrain model (DTM) creation is a modeling process that represents the Earth’s surface. An aptly designed DTM generation method tailored for intended study can significantly streamline ensuing processes and assist in managing errors and uncertainties, particularly in large-area projects. However, existing methods often exhibit inconsistent and inexplicable results, struggle to clearly define what an object is, and often fail to filter large objects due to their locally confined operations. We introduce a new DTM generation method that performs object-based ground filtering, which is particularly beneficial for urban topography. This method defines objects as areas fully enclosed by steep slopes and grounds as smoothly connected areas, enabling reliable “object-based” segmentation and filtering, extending beyond the local context. Our primary operation, controlled by a slope threshold parameter, simplifies tuning and ensures predictable results, thereby reducing uncertainties in large-area modeling. Uniquely, our method considers surface water bodies in modeling and treats connected artificial terrains (e.g., overpasses) as ground. This contrasts with conventional methods, which often create noise near water bodies and behave inconsistently around overpasses and bridges, making our approach particularly beneficial for large-area 3D urban mapping. Examined on extensive and diverse datasets, our method offers unique features and high accuracy, and we have thoroughly assessed potential artifacts to guide potential users.

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“…According to the Koppen-Geiger Map of climate classification, Saudi Arabia has been classified into the hot semi-arid weather zone (BWh) [19][20][21], as shown in Figure 2. Figure 3 depicts the mean temperature distribution in Saudi Arabia for each season using data collected between the years 1991 and 2020 by the Climatic Research Unit (CRU) of the University of East Anglia.…”

Section: Climatic Classification Of the Kingdommentioning

confidence: 99%

Flood Risk and Vulnerability from a Changing Climate Perspective: An Overview Focusing on Flash Floods and Associated Hazards in Jeddah

Hussain Shah,

Yassin,

Abba

et al. 2023

Water

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Natural hazard threats have grown as a result of climate change, fast demographic development, and major urbanization. Devastating floods have occurred in several areas of the world recently, including the Kingdom of Saudi Arabia, which is located in a region with a dry environment. In arid or semi-arid regions, rapidly forming flash floods associated with debris flowing down over dry water courses leading to a potential threat to both lives and property. Being located at the coastal plain of western Saudi Arabia, Jeddah City has witnessed an unexpected amount of rainfall events in recent years. Such extreme rainfall events, integrated with other factors, namely topography, land use, surface runoff, etc., have led to flood generation, which is alarming indeed. Herein, this paper addresses the varying climatic classifications of the Kingdom, its risk and vulnerability, followed by reasoning about the impact of flash flood events and the associated casualties and property losses. Further, it reports about the existing strategies of the government and proposes a systematic way forward on how to alleviate such events in future. Thus, risk variables have been discovered and integrated in the context of climate change and rising anthropogenic strain on coastal communities to give planners and decision makers tools to assure effective and appropriate flood risk management.

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Digital elevation model for flood hazards analysis in complex terrain: Case study from Jeddah, Saudi Arabia

Cited by 8 publications

References 55 publications

The Early Identification of Flash Flood Disasters: Mechanism, Model and Uncertainty

The Early Identification of Flash Flood Disasters: Mechanism, Model and Uncertainty

An Object-Based Ground Filtering of Airborne LiDAR Data for Large-Area DTM Generation

Flood Risk and Vulnerability from a Changing Climate Perspective: An Overview Focusing on Flash Floods and Associated Hazards in Jeddah

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