Assessing the Hazard Degree of Wadi Malham Basin in Saudi Arabia and Its Impact on North Train Railway Infrastructure

• Published in: ISPRS international journal of geo-information Vol. 12; no. 9; p. 380
• Main Authors: Alqreai, Fatmah Nassir, Altuwaijri, Hamad Ahmed
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Arid environments; Arid zones; Aridity; Artificial intelligence; Basins; Classification; Decision making; Disasters; Drainage; Drainage basins; Floods; geographic information system; Geomorphology; hazard degree; Human error; Hydrology; Hydrometric stations; Infrastructure; Infrastructure (Economics); Land use; Land use classification; Loam soils; morphometric analysis; North Train Railway; Permeability; Plateaus; Railroads; Railway stations; rain; Rainfall; Residential areas; risk; River basins; Runoff; sand; Saudi Arabia; Semi arid environments; Semiarid environments; Semiarid zones; Soil analysis; Soil permeability; Storm damage; Surface runoff; Wadi Malham; watersheds; Saudi Arabia; Egypt; Riyadh Saudi Arabia
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi12090380

The North Train Railway in the Kingdom of Saudi Arabia (KSA) extends over vast areas, crossing various terrains, including valleys, sand veins, plateaus, and hills. Therefore, the railway was designed and implemented to suit this environmental diversity under the highest safety standards. However, the railway may be subject to hazards for various reasons. In general, the possibility of direct surface runoff disasters increases if there are residential areas and facilities within the boundaries of drainage basins. Therefore, these areas should be studied, and the degree of hazard in drainage basins should be accurately determined. Hence, this study analyzed the degree of risk of 14 drainage basins affecting the North Train Railway within the Wadi Malham drainage basin. The risk degree model was used with eight parameters that have hydrological indications to give an idea of the behavior of direct surface runoff and alter the risk of direct surface runoff. We found that 28.57% of the total basins in the study area have overall score values indicating they are high-risk basins, namely basins 6, 7, 13, and 14. It is recommended to estimate the rainfall depth during different return periods, analyze soil permeability and land use classification in the study area, and apply hydrological modeling of drainage basins, which contributes to estimating the volume and peak of direct surface runoff in such arid and semi-arid environments that do not contain hydrometric stations to monitor the runoff.