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.

No abstract

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.

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.