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.

This study deals with the use of remote sensing (RS), geographic information systems (GISs), hydrologic modeling (water modeling system, WMS), and hydraulic modeling (Hydrologic Engineering Center River Analysis System, HEC-RAS) to evaluate the impact of flash flood hazards on the sustainable urban development of Tabuk City, Kingdom of Saudi Arabia (KSA). Determining the impact of flood hazards on the urban area and developing alternatives for protection and prevention measures were the main aims of this work. Tabuk City is exposed to frequent flash flooding due to its location along the outlets of five major wadis. These wadis frequently carry flash floods, seriously impacting the urban areas of the city. WMS and HEC-HMS models and RS data were used to determine the paths and morphological characteristics of the wadis, the hydrographic flow of different drainage basins, flow rates and volumes, and the expansion of agricultural and urban areas from 1998 to 2018. Finally, hydraulic modeling of the HEC-RAS program was applied to delineate the urban areas that could be inundated with floodwater. Ultimately, the most suitable remedial measures are proposed to protect the future sustainable urban development of Tabuk City from flood hazards. This approach is rarely used in the KSA. We propose a novel method that could help decision-makers and planners in determining inundated flood zones before planning future urban and agricultural development in the KSA.

The present study was aimed at evaluating the quality of life in the districts of Buraidah city in the Kingdom of Saudi Arabia as part of the Kingdom’s Vision 2030, based on provided services in terms of distance and time parameters. This was achieved by integrating Geographic Information System (GIS)-based network analysis and multicriteria decision analysis (MCDA) techniques. The service area analysis technique of the GIS Network Analysis tools was applied to the available services in the districts of Buraidah city. Twelve services were investigated with regard to their coverage within 5, 10, and 15 minutes of travel time: universities, high schools, middle schools, elementary schools, hospitals, health centers, ambulance facilities, government services, religious services, security services, sports services, and recreational services. GIS-based multicriteria decision analysis (GIS-MCDA) was implemented in the study to obtain a spatial suitability map of the standard of living quality in the various districts in Buraidah city. The analytic hierarchy process (AHP) technique was used to determine the criteria weights. The location-allocation model was used in the study as well for suggestions of new service locations to improve the spatial distribution of services and enhance the quality of life in the districts. The results of the final suitability map to determine districts with the best standard of living according to the provided services show that the living standards in the city are very good, with very high and high levels in around 51 districts, representing 72.9% (449,235 people), and about 19 districts with moderate, low, and very low levels, representing 27.1% (56,765 people). Narrowing the quality of life gap and improving spatial planning requires correcting the current deficiency in services, which is estimated in this study to be 16 services: one high school, one elementary school, one middle school, two universities, one sports service, two security services, two courts, two hospitals, one health center, one park, and two ambulance facilities.

This study aims to assess the impact of flash floods in the Wadi Nu’man basin on urban areas, east of Mecca, which are subjected to frequent floods, during the period from 1988–2019. By producing and analyzing the maps of the regions, an integrated approach to geomatics and hydraulic modelling is employed. The following maps are used: Flood-prone urbanity from 1988–2019, a flood risk map of Wadi Nu’man based on a risk matrix map, and a map of the proposed protection measures and alternatives in the study area. In order to achieve these goals, changes in the land use in the Wadi Nu’man basin were monitored by analyzing successive satellite images, taken by the US satellite, Landsat, in 1988, 1998, 2013, and 2019. Using a supervised classification, with the maximum likelihood method of ERDAS IMAGINE 2016, GIS was used in the production and analysis of soil maps, and geological and hydrological groups of drainage basins, as well as the hydrological model (HEC-HMS), were applied in calculating the hydrograph curve of the Wadi Nu’man basin. The flood water volumes and flow rates were estimated based on the SCS unit hydrograph, and the rain depth was analyzed and estimated for different periods. The hydraulic modeling program (HEC-RAS) was employed, when developing a two-dimensional model to calculate the speed, depth, and spread of the flood, in order to apply the risk matrix method. The recommendations based on this study give priority to the implementation of a flood prevention plan and the protection of infrastructure by maintaining the existing flood drainage facilities and establishing new drainage facilities to protect lives, property, and infrastructure.

This study sought to integrate the Cellular Automata-Markov Model (CA-Markov) and the Cellular Automata (CA) using sensing data for land cover maps for the years: 1988, 2000, 2013 and 2020 to monitor, detect, and predict the spatial and temporal of Land Use/Land Cover (LULC) change in Al-Hassa Oasis, Saudi Arabia. The maximum likelihood classifier (MLC) method was used as a supervised classification algorithm to super control classification coefficients. Training areas were extracted to classify green and water from Landsat images using The Natural Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) indicators, respectively. The Markov form has been used Cellular Automata (CA)To create a Transition probability Matrix (TPM)), Transition Area Matrix (TAM) and Transition Suitability Maps (TSM), which is the main factor for obtaining to simulate the prediction of the future of land use/land cover in the years 2030 and 2050, while was used Cellular Automata-Markov Model (CA-Markov) To validate and Forecasting LULC. The results showed an expansion in urban areas by 63.5% equivalent to about 105.7 km2, and an increase in agricultural land by 6.6% equivalent to 10.7 km2 despite the loss of about 3.2% of the old agricultural land during the period 2000–2013 AD. Also, the area of marshes and water increased by about 41.7% equivalent to about 8.7 km, which led to the loss of 5.9% or the equivalent of about 139 km2 of arid lands during the past three decades. The projected LULC maps for 2030 and 2050 indicate that these patterns of arid lands that change into built-up areas and agricultural lands will continue over the next thirty years due to urban growth and the development of agricultural activities. The main strength of this study is the use of a relatively long period that allowed us to conduct a very good assessment of LULC in the study area, as well as to predict the different patterns for 2030 and 2050. Moreover, the results obtained in this study provide information to help decision makers to carry out effective practices for future planning and management of urban land growth and land use planning, especially in the Saudi Vision 2030.

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.