One of the main challenges regarding the prediction of groundwater resource changes is the climate change phenomenon and its impacts on quantitative variations of such resources. Groundwater resources are treated as one of the main strategic resources of any region. Given the climate change phenomenon and its impacts on hydrological parameters, it is necessary to evaluate and predict future changes to achieve an appropriate plan to maintain and preserve water resources. In this regard, the present study is put forward by utilizing the Statistical Down-Scaling Model (SDSM) to forecast the main climate variables (i.e., temperature and precipitation) based on new Rcp scenarios for greenhouse gas emissions within a period from 2020 to 2060. The results obtained from the prediction of climate parameters indicate different values in each emission scenario, so the limit, minimum and maximum values occur in the Rcp8.5, Rcp2.6 and Rcp4.5 scenarios, respectively. Also, a model is developed by utilizing the GMDH artificial neural network technique. The developed model predicts the average groundwater level based on the climate variables in such a way that by implementing the climate parameters forecasted by the SDSM model, the groundwater level within a time period from 2020 to 2060 is predicted. The results obtained from the verification and validation of the model imply its proper performance and reasonable accuracy in predicating groundwater level based on the climate variables. The findings derived from the present paper indicate that compared to the years prior to the prediction period, the groundwater level of the Sahneh Plain has dramatically dropped so that based on the Rcp scenarios, the groundwater level values are in their lowest state within the period from 2046 to 2056. The findings of this paper can be used by managers and decision makers as a layout for evaluating climate change effects in the Sahneh Plain.

地下水资源变化预测的主要挑战之一是气候变化现象及其对此类资源数量变化的影响。地下水资源被视为任何地区的主要战略资源之一。鉴于气候变化现象及其对水文参数的影响，有必要评估和预测未来的变化，以制定适当的计划来维护和保护水资源。对此，本研究提出利用统计降尺度模型（SDSM）来预测2020年至2060年期间基于新的Rcp情景温室气体排放的主要气候变量（即温度和降水）气候参数预测结果表明，每种排放情景下的值不同，因此极限值、最小值和最大值分别出现在Rcp8.5、Rcp2.6和Rcp4.5情景中。此外，还利用 GMDH 人工神经网络技术开发了一个模型。该模型根据气候变量预测平均地下水位，通过执行SDSM模型预测的气候参数，预测2020年至2060年期间的地下水位。模型的验证和验证所获得的结果表明该模型在根据气候变量预测地下水位方面具有良好的性能和合理的准确性。本论文的研究结果表明，与预测期之前的年份相比，Sahneh 平原的地下水位急剧下降，因此根据 Rcp 情景，地下水位值处于预测期之前的最低状态。 2046年至2056年。管理者和决策者可以将本文的研究结果用作评估萨尼赫平原气候变化影响的布局。