Flood risk mapping is vital in watershed management and planning, especially in reducing flood damages. In this study, a flood risk map was developed for the Wadi Al-Lith watershed (Saudi Arabia) by combining geographic information system techniques with a multi-criteria decision-making method known as the Analytical Hierarchy Process (AHP). Several factors were investigated in the study, including elevation, slope, topographic wetness index, drainage density, rainfall, soil and land use, and land cover. The watershed was divided into five regions: very high, high, moderate, low, and very low flooding danger areas. The results showed that 35.86% of the total watershed area is under high and very high flood risks, while 26.85% of the total area is under a moderate flood risk. Less than 38% of the total watershed area was under a low flooding risk. The results of the developed model were validated with the flooding event that occurred on 23 November 2018 in the study area. The model was also compared with the flood mapping of the 100-year return period generated by the HEC-RAS software. Both the developed model and the HEC-RAS software showed similar results. The findings demonstrated that the developed model could be used to develop flood risk maps, especially in watersheds that experience scarcity and shortages in the short-interval rainfall measurements and the stream flow gauges (e.g., Wadi Al-Lith watershed and other watersheds in Saudi Arabia). Additionally, the obtained results can provide helpful knowledge for the policy- and decision-makers to make the right decisions regarding the effectiveness of the protective structures of the study area against the risk of flash flooding in the future.

Rising temperature and shifting precipitation patterns due to climate change are likely to intensify droughts throughout the world. Understanding the drought characteristics of possible future scenarios under climate change requires verification of past drought events using appropriate drought indices. Consequently, this study investigates the application of two widely used drought indices, the standardized precipitation index (SPI) and standardized streamflow index (SSI), to characterize historical droughts, drought trends, and their impact on water quality and stream integrity for a selected study basin in New Jersey. Results indicated that both SPI and SSI were able to identify historical drought events, including three drought emergency periods and the most recent drought-watch periods. A significant positive meteorological drought was observed at the western side of the basin, whilst a significant positive hydrological drought was found in the eastern side. The average pollutant concentration of drought periods were lesser than non-drought periods due to reduction of different processes, such as erosion and transport of sediment and nutrients into rivers and streams, during drought periods as opposed to non-drought periods. The findings from this study will serve to bolster the ongoing efforts to formulate better drought management strategies for future climate change in the Raritan Basin.

The management of groundwater resources is crucial in arid and semi-arid regions such as Al-Lith Basin, and therefore, the identification of suitable areas for groundwater recharge is important in solving the problem of water scarcity and ensuring the long-term sustainability of aquifers. In this study, the Analytic Hierarchy Process (AHP) technique, in conjunction with GIS, was applied to locate potential zones for groundwater recharge in the Al-Lith basin. The research methodology includes collecting the essential data, including lineament density, slope, rainfall, drainage density, LU/LC, soil, elevation, and TWI. AHP is used to assign relative weights to different qualities, considering their significance in influencing groundwater recharge. The ArcGIS was employed to process and analyze the weighted layers, which resulted in the creation of a comprehensive map illustrating the potential for groundwater recharge. The study results suggested that the Al-Lith Subbasin has several zones that exhibit different levels of groundwater recharge capability. The results obtained from this study indicated that 12.83% of the basin area has significant groundwater recharge potential zones (GWPZ), ranging from high to very high levels. The groundwater recharge potential zones in the basin are categorized as very poor to poor, covering 19.4% of the area, while the moderate groundwater recharge potential zones represent 67.77% of the basin area. The groundwater protection zones were validated using 19 wells distributed across the basin. The validation findings indicate there is an overlap between the GWPZ classes and the wells in the specified location. Overall, the findings of this study can enhance groundwater resource management and inform policy development for targeted interventions aimed at enhancing groundwater recharge.

Understanding the historical patterns of drought changes is important to effectively manage and mitigate drought. This paper aims to provide a quantitative assessment of the spatiotemporal drought patterns in Saudi Arabia from 1985 to 2022. The study used the Standardized Precipitation Index (SPI) to examine drought patterns on both monthly and yearly timescales. The findings indicate a significant trend of increasing drought conditions in certain regions of the Kingdom from 1985 to 2022. The average rates of change for SPI-03, SPI-06, and SPI-12 were found to be −0.003 yr⁻¹, −0.0034 yr⁻¹, and −0.0099 yr⁻¹, respectively. Droughts were more frequent and persistent in the northern regions of the country, while the western region experienced severe and intense droughts. There were fewer drought occurrences before 2000, but droughts became more frequent after 2000, with large-scale impacts occurring during 2007–2008 and 2013–2014. These findings have important implications for water management strategies and can help mitigate the effects of drought, as they identify hotspot regions across Saudi Arabia at different timescales. Overall, it is important to implement province-specific efforts to reduce environmental vulnerabilities to droughts.

Accurate rainfall data with good spatial–temporal distribution remain a challenge worldwide, particularly in arid regions such as western Saudi Arabia, where variability critically influences water resource management and flood mitigation. This study evaluates five satellite-based rainfall products—GPM, GPCP, CHIRPS, PERSIANN-CDR and PERSIANN—against observed monthly rainfall at 28-gauge stations, using the correlation coefficient (CC), root mean square error (RMSE), relative bias (RB) and mean absolute error (MAE). Among uncorrected products, GPM achieved the highest mean CC (0.52), and lowest RMSE (17.0 mm) and MAE (9.18 mm) compared with CC = 0.39 (RMSE 19.9 mm) for GPCP, CC = 0.20 (RMSE 21.6 mm) for CHIRPS, CC = 0.43 (RMSE 19.2 mm) for PERSIANN-CDR and CC = 0.26 (RMSE 57.3 mm) for PERSIANN. Four bias correction methods—linear scaling, nonlinear adjustment, quantile mapping and artificial neural networks (ANN)—were applied. The ANN reduced GPM’s RMSE by 19% to 13.8 mm, increased CC to 0.59, lowered RB to 2.5% and achieved an MAE of 6.89 mm. These results demonstrate that GPM, particularly when bias-corrected via ANN, provides a dependable rainfall dataset for hydrological modeling and flood risk assessment in arid environments.