Groundwater is an important factor in sustaining water supply in semi-arid coastal basins, where surface water resources are limited and climatic variability greatly affects availability. Rainfall events translated to groundwater recharge are of paramount importance for planning as well as for sustainable resource management in the Mediterranean catchment. The interaction between rainfall and groundwater level is particularly complex within areas of geological heterogeneity and seasonal climatic regimes. As valuable as this relationship is, it continues to be poorly understood in the most vulnerable areas, including western Syria. This research examines the dynamic interaction between rainfall and groundwater levels in the Al-Hsain Basin, a semi-arid coastal area in western Syria. A transient groundwater flow model was built with MODFLOW and calibrated against 4 y (2020–2024) of monthly data from 35 observation wells and local precipitation measurements. The model effectively replicated seasonal groundwater variations controlled largely by rain, and spatial variations related to geological heterogeneity. A (0–1) month time lag between rainfall maxima and groundwater response suggests delayed infiltration in the unsaturated zone. Model performance was tested with statistical and hydrograph analyses, illustrating excellent agreement against over 95 % of observed data. The results confirmed the model as it gave a hydraulic head distribution very similar to the monitoring wells data (variations of less than 0.10–0.25 m). Spatial maps and water balance overviews under wet and dry conditions proved the model's robustness under hydrological conditions. Despite some data limitations, this study offers helpful data on groundwater recharge processes and practical recommendations for improving water resource management in similar Mediterranean coastal settings.

Climate change and irregular precipitation patterns have increasingly threatened groundwater sustainability in semi-arid regions like the Eastern Mediterranean. Specifically, in coastal Syria, the lack of quantitative understanding regarding aquifer recharge mechanisms hinders effective water resource management. To address this, this study investigates the dynamic relationship between rainfall and groundwater levels in the Al-Hsain Basin coastal plain using 48 months of monitoring data (2020–2024) from 35 wells. We employed a unified analytical framework combining statistical methods (correlation, regression) with advanced time–frequency techniques (Wavelet Coherence) to capture recharge behavior across diverse Quaternary, Neogene, and Cretaceous strata. The results indicate strong climatic control on groundwater dynamics, particularly in shallow Quaternary wells, which exhibit rapid recharge responses (lag < 1 month). In contrast, deeper aquifers showed delayed and buffered responses. A dual-variable model incorporating temperature significantly improved prediction accuracy (R² = 0.97), highlighting the role of evapotranspiration. These findings provide a transferable diagnostic framework for identifying recharge zones and supporting adaptive groundwater governance in data-scarce semi-arid environments.

Groundwater modeling in semi-arid regions presents significant challenges due to complex aquifer dynamics, limited data availability, and heterogeneous hydrogeological conditions. This study presents a comprehensive comparative analysis of three fuzzy expert system strategies for monthly groundwater level forecasting in the Al-Hsain Basin, Syria: localized models based on hydrogeographical grouping, a unified basin-wide approach, and an innovative behavioral clustering methodology. Using synchronized rainfall and temperature data from 35 monitoring wells over four years (2020–2024), we developed and evaluated fuzzy inference systems’ directional classification accuracy as the primary performance metric, categorizing groundwater level changes into rise, stable, and decline states rather than predicting continuous values. This choice reflects the qualitative nature of fuzzy expert systems and their suitability for groundwater management under data-limited conditions. The behavioral clustering approach achieved excellent overall performance with a mean accuracy of 0.74, outperforming localized models (0.71) and unified models (0.67). Behavioral clustering demonstrated effectiveness in 66% of wells, with individual accuracy improvements reaching up to 0.23, while reducing model complexity from five group-specific systems to three behaviorally coherent clusters. Localized models achieved optimal performance in 29% of wells where hydrogeological conditions aligned with spatial assumptions, whereas unified models provided consistent moderate performance across 89% of locations. The incorporation of lagged variables and seasonal indices in behavioral clustering models proved essential for capturing temporal complexity in semi-arid groundwater responses. Statistical analysis revealed lower intra-group variability in behavioral clusters (standard deviation 0.06–0.09) than in geographical groupings (0.08–0.14), confirming improved functional homogeneity through response-based organization. These findings indicate that fuzzy modeling strategy selection should be context-dependent, with behavioral clustering offering an effective balance between accuracy, interpretability, and generalization for regional groundwater management applications. The novelty of this work lies in isolating the effect of fuzzy system organization logic (localized, unified, and behavioral) on forecasting performance, robustness, and transferability, evaluated under an identical inference and time-series validation framework.