Sensitivity Analysis of Hydraulic Parameters in the Nowshahr-Nur Aquifer Using Numerical Methods

Coastal aquifers undergo multifaceted influences affecting their water quality. Among these factors are climate change, excessive exploitation, and agricultural practices, leading to escalated salinity due to both seawater intrusion and pollution. The primary aim of this study is to identify the principal vulnerabilities within the aquifer by executing a sensitivity analysis of the model. This analysis involves the adjustment of various model parameters-like hydraulic conductivity, specific drainage coefficients, recharge rates, layer thickness, well extraction rates, as well as boundary locations and types-to evaluate their impact on the model's outcomes. Typically, sensitivity analysis involves altering one input parameter independently. Notably, a substantial alteration in the model's output resulting from a change in a specific input parameter signifies the model's sensitivity to that parameter. Our investigation focused on the model's sensitivity to alterations in surface recharge, hydraulic conductivity, and specific drainage coefficients. The findings of the sensitivity analysis emphasized that the model is more responsive to increased recharge than to reduced recharge. For instance, a 10% decrease in recharge caused a root-mean-square error (RMSE) of 0.770 m, while a 20% decrease resulted in an RMSE of 0.732 m. Conversely, a 10% and 20% increase in recharge led to RMSE values of 0.806 and 0.850, respectively. The southern segments of the aquifer, where groundwater fronts are situated, exhibit higher hydraulic conductivity due to coarser soil particles and greater permeability. Upon assessing this parameter's sensitivity, it was noted that a 20% decrease in hydraulic conductivity led to an RMSE of 0.833, while a 20% increase produced an RMSE of 0.769. Additionally, a sensitivity analysis on specific drainage coefficients and well extraction rates revealed that while the model is not notably sensitive to specific drainage coefficients, an increase in extraction rates led to a decrease in the model's error rate. Therefore, within the sensitivity analysis of this aquifer, the recharge rate and well extraction rate emerge as the most influential parameters.