Quantitative Assessment of Aquifer Depletion and Managed Recharge Scenarios: A Hybrid SARIMA-Wavelet Approach in the Galedar Aquifer, Southern Iran

Groundwater depletion in semi‑arid regions requires accurate forecasting and proactive management. This study integrates wavelet analysis, diagnostic SARIMA modeling, sensitivity analysis, comparative physical/machine learning models, and managed aquifer recharge (MAR) scenario evaluation to forecast groundwater levels in the Galedar aquifer, Iran (2006–2024). Wavelet decomposition reveals decadal variability (48.5% variance) in regional flow zones and annual dominance (45%) in the high‑recovery zone, with coherence to precipitation (1–3 month lag) and ENSO (r=0.35–0.50). Manual SARIMA achieves out‑of‑sample RMSE of 0.062–0.162 m. Sensitivity identifies specific yield as dominant (Sobol’ ST=0.58–0.71). The hybrid SARIMA‑wavelet model reduces RMSE by 16–31%. Under business‑as‑usual, the mean water level declines 2.6 m (0.26 m/year) by 2034, with cumulative storage loss (191.1 MCM) exceeding recoverable storage (150 MCM). MAR scenarios show: high‑recovery zone recharge (+1.41 m, 71% decline reduction); distributed recharge (1.8‑fold greater storage recovery); episodic recharge (+1.11 m, 40% lower efficiency). Cost‑benefit analysis confirms positive net present value (BCR 1.4–3.4). The framework supports risk‑based, spatially stratified management for stressed aquifers.