Understanding the dynamics of floods in dry environments and predicting an accurate flood hazard map considering multiple standards and conflicting objectives is of great political and planning importance in the Kingdom of Saudi Arabia’s vision for the year 2030, in order to reduce losses in lives, property, and infrastructure. The objectives of this study are (1) to develop a flood vulnerability map identifying flood-prone areas along the Al-Shamal train railway pathway; (2) to forecast the vulnerability of urban areas, agricultural land, and infrastructure to possible future floods hazard; and (3) to introduce strategic solutions and recommendations to mitigate and protect such areas from the negative impacts of floods. In order to achieve these objectives, multicriteria decision analysis based on geographic information systems (GIS-MCDA) is used to build a flood hazard map of the study area. The analytic hierarchy process (AHP) is applied to extract the weights of eight criteria which affect the areas which are prone to flooding hazards, including flow accumulation, distance from the wadi network, slope, rainfall density, drainage density, and rainfall speed. Furthermore, the receiver operating characteristic (ROC Curve) method is used to validate the presented flood hazard model. The results of the study reveal that there are five degrees of flooding hazard along the Al-Shamal train path, ranging from very high to very low. The high and very high hazard zones comprise 19.2 km along the path, which constitutes about 26.45% of the total path length, and are concentrated at the intersections of the Al-Shamal train pathway with the Bayer and Al-Makhrouk wadis. Moderate, low, and very low flood severity areas constitute nearly 53.39 km, representing 73.55% of the total length (72.59 km) of the track. These areas are concentrated at the intersection of the Al-Shamal train track with the Haseidah Al-Gharbiyeh and Hsaidah Umm Al-Nakhleh wadis. Urban and agricultural areas that are vulnerable to high and very high flooding hazards are shown to have areas of 29.23 km2 (22.12%) and 59.87 km2 (46.39%), respectively.

Drainage basins in dry and semiarid environments are exposed to sudden, irregular flooding that poses a threat to urban areas and infrastructure. The associated risk is exacerbated by land use changes. Geomorphometric analyses of drainage basins based on geographic information systems (GIS) are essential tools for assessing conceptual flood hazards. Geomorphological data extracted from high-precision digital elevation models (DEMs) provide valuable information for modeling the geomorphic, surface classifications of the earth, and for flood hazard mapping. This study aimed to develop an integrative approach to the mapping of flood hazards along the Al-Shamal train pathway in the city of Qurayyat in the Kingdom of Saudi Arabia (KSA) using GIS and hazard modeling for geomorphological ranking. Furthermore, we propose strategic solutions to provide mitigation and protection from negative impacts with the aim of improving the level of awareness of flood geomorphology. The hazard model of geomorphological ranking was used in mapping and calculating the degree of hazards using 24 geomorphometric criteria. These criteria were divided into formal criteria, terrain criteria, and criteria related to the drainage network. The results of the study revealed that the drainage sub-basins are exposed to flood hazards along the Al-Shamal train pathway in the city of Qurayyat. The very high flood hazard constituted 4228.3 km2, accounting for 70.3% and 65.7%, respectively, of the drainage basins of the wadis of Makhrouq and Bayer. The high flood hazard represented 61% (4712.4 km2) of the basin of the wadis of Sarmadaa. The medium flood hazard was concentrated in the drainage basin of the wadi of Hasidah, accounting for nearly 57.7% (1271.3 km2). The very low flood hazard was present in 46.5% of the drainage basin of the wadis of Hasidah Umm Nakhla, accounting for an area of 799.4 km2. The methodology applied in this study can be used in the estimation of flood hazards in different drainage basins throughout Saudi Arabia and in similar arid regions.

No abstract

Saudi Arabia has experienced substantial urban growth over the last few decades, transforming from rural to urban communities due to rapid economic growth. Saudi Arabia is ranked as one of the most urbanized countries, with more than 80% of its population existing in urban centers. Four Landsat imagery datasets acquired in 1989, 2002, 2013, and 2021 were used to estimate the dynamics of land cover and urban growth in Al-Qurayyat City and investigate the relationship between the construction of Al-Shamal train in 2011 and the land dynamics. The results emphasize a strong intercorrelation between the construction of the Al-Shamal train pathway and the land development and the rapid urbanization in Al-Qurayyat City. The results show that the urban and built-up area expanded from 1.96% to 7.25% between 1989 and 2021. Future prediction of land cover dynamics and urban growth in 2030 were estimated using the Markov chain and CA-Markov models. The findings of future prediction show that more than 60% of the total area of Al-Qurayyat City will transform into urban and built-up areas by 2030. The dramatic increase in urban and built-up areas and the subsequent reduction in other land cover types will impact the environmental sustainability of Al-Qurayyat City. The findings in this paper recommend smart growth, which guarantees environmentally friendly development for future land use/land cover planning in Al-Qurayyat City. This study will be beneficial to the urban planner and policymakers for proper sustainable development decisions by exploring the land cover changing pattern and the trends of urban expansion.

Al-Shamal train pathway, which is extended between Saudi Arabia and Jordan, is prone to geo-hazards due to the geological features, proximity to faults, earthquake epicenter, and the human activities along the pathway. The objectives of this study are to shed light on the ground subsidence susceptibility along Al-Shamal train pathway in Qarrayat city in Saudi Arabia and develop a ground subsidence susceptibility model to determine the prone areas to the impacts of ground subsidence to mitigate and avoid the loss of life and property. This study integrated the various data types to map the subsidence susceptibility along Al-Shamal train pathway. Nine ground subsidence causative parameters were selected as subsidence controlling factors in the study area including lithology, land cover/land use, elevation, slope, aspect, annual average rainfall, distance to faults, distance to earthquake epicenter, and distance to streams. The analytical hierarchy process is applied to obtain accurate weight to each criterion through the distribution of online Google form questionnaire to experts in different expertise and get their judgments on the weights of ground subsidence causative parameters in the study area. A subsidence susceptibility index was derived by classifying susceptible maps into five classes, namely, very low, low, moderate, high, and very high using the statistical distribution analysis. The results revealed that the study area is subjected to moderate susceptibility with about 32.56. A total of 29.8 and 11.52% of the study area had very low and low susceptibilities, respectively, and 8.44 and 17.68% had very high and high susceptibilities, respectively. The results were validated using the receiver operating characteristic using previous ground subsidence locations. The area under the curve showed 0.971, which is equivalent to 97.1%. Consequently, the findings of the study are thought to be beneficial to managers and decision makers for future planning, mitigating, and preventing subsidence in the study area.