The aim of this study is to build a flood simulation model for the city of Hafr Al-Batin catchment area. The model consists of a hydrologic model, a hydraulic model and pre-and post-processing tools. A geographic information system-based modelling interface was used to pre-process the terrain and rainfall data and generate input files for hydrologic and hydraulic models. Soil map data, land cover map, land use map and digital elevation model was used to delineate the physical watershed's characteristics. The runoff estimation was based on the widely known Soil Conservation Service curve number approach. The hydrologic/hydraulic model simulations simulated the runoff hydrograph corresponding to different design storms and helped to delineate the resulting flood inundation maps. The results indicate good agreement between the delineated inundation map and the hazard map developed by the municipality. They also show that the location of the city complicates the runoff response for small storms creating two distinct peaks. The results of this study can be utilized for planning purposes and in the design of flood control structures as the study has estimated the runoff corresponding to different design storms and used hydraulics and geospatial data in delineating the flood zones.

Seawater intrusion (SWI) is the main threat to fresh groundwater (GW) resources in coastal regions worldwide. Early identification and delineation of such threats can help decision-makers plan for suitable management measures to protect water resources for coastal communities. This study assesses seawater intrusion (SWI) and GW salinization of the shallow and deep coastal aquifers in the Al-Qatif area, in the eastern region of Saudi Arabia. Field hydrogeological and hydrochemical investigations coupled with laboratory-based hydrochemical and isotopic analyses (18O and 2H) were used in this integrated study. Hydrochemical facies diagrams, ionic ratio diagrams, and spatial distribution maps of GW physical and chemical parameters (EC, TDS, Cl−, Br−), and seawater fraction (fsw) were generated to depict the lateral extent of SWI. Hydrochemical facies diagrams were mainly used for GW salinization source identification. The results show that the shallow GW is of brackish and saline types with EC, TDS, Cl−, Br− concentration, and an increasing fsw trend seaward, indicating more influence of SWI on shallow GW wells located close to the shoreline. On the contrary, deep GW shows low fsw and EC, TDS, Cl−, and Br−, indicating less influence of SWI on GW chemistry. Moreover, the shallow GW is enriched in 18O and 2H isotopes compared with the deep GW, which reveals mixing with recent water. In conclusion, the reduction in GW abstraction in the central part of the study area raised the average GW level by three meters. Therefore, to protect the deep GW from SWI and salinity pollution, it is recommended to implement such management practices in the entire region. In addition, continuous monitoring of deep GW is recommended to provide decision-makers with sufficient data to plan for the protection of coastal freshwater resources.

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Makkah region is one of the most flash flood-prone areas of Saudi Arabia due to terrain characteristics and the synoptic-scale weather conditions that intensify through interaction with the local topography causing high convective short-lived rainfall events, although these conditions are quite infrequent. Most of these events last for less than two hours. This study aims to assess the performance of five satellite precipitation products over a 1725 km2 sparsely gauged, arid basin. A fully distributed, physically based hydrologic model was forced by the five satellite precipitation products, and the evaluation included the hydrographs and runoff maps predicted by the model. Moreover, the propagation of the satellite rainfall errors into runoff predictions was quantified. Large variations and significant biases were found in satellites precipitation estimates compared to the available ground rainfall measurements. The Early IMERG product showed the best agreement with the reported total rainfall accumulations followed by Late IMERG while the other products significantly underestimated precipitation accumulations. Comparison with estimated runoff peaks showed that the Early IMERG product has the lowest errors in runoff peaks. Therefore, the hydrographs produced by the Early IMERG product were used as a reference to quantify the propagation of satellite precipitation errors into runoff predictions over the Makkah watershed. The results clearly indicated that both systematic and random rainfall errors were significantly amplified in runoff predictions.

Floods, one of the most common natural hazards globally, are challenging to anticipate and estimate accurately. This study aims to demonstrate the predictive ability of four ensemble algorithms for assessing flood risk. Bagging ensemble (BE), logistic model tree (LT), kernel support vector machine (k-SVM), and k-nearest neighbour (KNN) are the four algorithms used in this study for flood zoning in Jeddah City, Saudi Arabia. The 141 flood locations have been identified in the research area based on the interpretation of aerial photos, historical data, Google Earth, and field surveys. For this purpose, 14 continuous factors and different categorical are identified to examine their effect on flooding in the study area. The dependency analysis (DA) was used to analyse the strength of the predictors. The study comprises two different input variables combination (C1 and C2) based on the features sensitivity selection. The under-the-receiver operating characteristic curve (AUC) and root mean square error (RMSE) were utilised to determine the accuracy of a good forecast. The validation findings showed that BE-C1 performed best in terms of precision, accuracy, AUC, and specificity, as well as the lowest error (RMSE). The performance skills of the overall models proved reliable with a range of AUC (89–97%). The study can also be beneficial in flash flood forecasts and warning activity developed by the Jeddah flood disaster in Saudi Arabia.

Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil.

Abstract. Hafr Al-Batin is a Saudi Arabian city located in the northeastern part of the kingdom. The city lies in the dry valley of Wadi Al-Batin, part of Wadi Al-Rummah, which leads inland towards Medina and formerly emptied into the Arabian Gulf. Hafr Al-Batin is located in an area where three valleys meet, which makes the city under high risk of flooding, especially when intense rain occurs during short duration as in the case of arid and semi-arid regions. The yearly average rainfall intensity of Hafr Al-Baten is estimated to be 125 mm. Recently, extreme rainfall events occurred, generating flood water to flow from all valleys towards the city, causing serious damage to public and private properties. In this study, HEC-HMS and HEC-RAS models are used to simulate flood occurrence in the city. The results indicate that the average flow depths within the part of the main channel passing through Hafr Al-Batin city were 3.02 m, 3.26 m, 3.45 m, 3.76 m, 4.04 m and 4.34 m for the simulated 2, 5, 10, 25, 50 and 100-year design floods, respectively. Flood hazard maps are also generated to identify the areas within the city with high risk of flooding.

Population growth and land use modification in urban areas require the use of accurate tools for rainfall-runoff modeling, especially where the topography is complex. The recent improvement in the quality and resolution of remotely sensed precipitation satisfies a major need for such tools. A physically-based, fully distributed hydrologic model and a conceptual semi-distributed model, forced by satellite rainfall estimates, were used to simulate flooding events in a very arid, rapidly urbanizing watershed in Saudi Arabia. Observed peak discharge for two flood events was used to compare hydrographs simulated by the two models, one for calibration and one for validation. To further explore the effect of watershed heterogeneity, the hydrographs produced by three implementations of the conceptual were compared against each other and against the output of the physically-based model. The results showed the ability of the distributed models to capture the effect of the complex topography and variability of land use and soils of the watershed. In general, the GSSHA model required less calibration and performed better than HEC-HMS. This study confirms that the semi-distributed HEC-HMS model cannot be used without calibration, while the GSSHA model can be the best option in the case of a lack of data. Although the two models showed good agreement at the calibration point, there were significant differences in the runoff, discharge, and infiltration values at interior points of the watershed.