Recharge is considered a key parameter in groundwater models for sustainable management of aquifers, which is influenced by factors such as land use, soil and weather. The present study was conducted to couple WetSpass-M and MODFLOW models in the Neyshabur–Rokh Basin. To this aim, the simulated recharge by the WetSpass-M model was applied as an input into MODFLOW to assess the groundwater balance. The hydrodynamic coefficients were determined by calibrating the model, and the model sensitivity to the hydraulic conductivity coefficient, specific yield and recharge was evaluated. The results indicated that the annual average of surface runoff, actual evapotranspiration, interception and recharge during 1991–2017 equalled 18, 36, 7.6 and 42.6% of the average annual precipitation in the basin, respectively. The accurate alignment of simulated and observed water levels, along with the achievement of suitable evaluation criteria values in both steady and transient states, demonstrates the WetSpass-M model’s precision in estimating recharge and successfully integrating the two models. The groundwater balance assessment revealed a significant deficit in the aquifer, with the model demonstrating greater sensitivity to the hydraulic conductivity coefficient and providing valuable insights for the sustainable management of the Neyshabur aquifer.

The sustainable availability of water resources and the qualitative and quantitative status of these resources are threatened by many natural and antropogenic factors, among which climate change plays an important role. Climate change can have profound effects on the hydrological cycle through changes in the amount and intensity of precipitation, evapotranspiration, soil moisture, and increasing temperature. On the other hand, the distribution of rainfall in different parts of the world will be uneven. So that some parts of the world may face a significant decrease in the amount and intensity of precipitation, as well as major changes in the timing of wet and dry seasons. Therefore, sufficient knowledge about the effects of climate change on hydrological processes and water resources will be of particular importance. In this research, as the first comprehensive study, the effect of future climate change on the water resources components of Neyshabur-Rookh watershed was investigated by a set of one hydrological model and six General Circulation Models under the RCP4.5 scenario.

Climate change, as one of the global challenges of the present century, has profound impacts on water resources and agriculture. Increase in temperature and decrease in rainfall in arid and semi-arid regions have made the optimal water resource management a top priority. In countries facing climate change and drought, accurate estimation of evapotranspiration plays a vital role in water resource management and ensuring food security. One of the key factors affecting evapotranspiration is the vapor pressure deficit (VPD), which significantly impacts the curacy of related calculations. This study focuses on predicting the vapor pressure deficit using advanced machine learning techniques. The methods employed include Linear Regression ),Generalized Additive Model (GAM), Random Subspace (RSS), Random Forest (RF), and M5 Pruned model (M5P). In this study, monthly average data, including temperature, humidity, precipitation, and vapor pressure deficit, were extracted from the Japanese 55-year Reanalysis (JRA-55) database for the period from 1958 to 2023. The analysis on the vapor pressure it ta in Birjand, Sarayan, Qaen, and Tabas showed that the annual average VPD increased by 6 Pa, 10 Pa, 4 Pa, and 5 Pa, respectively. In the next step, the extracted data for temperature, cipitation, and humidity were used as input variables, and VPD was used as the target variable in machine learning algorithms. Model performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), Pearson correlation coefficient (CC), and Kling-Gupta efficiency (KGE). Results showed that the GAM model outperformed other models in all regions. The evaluation indices for each region were as follows: Birjand [RMSE=0.308, MAE=0.247, KGE=0.914, and CC=0.920], Sarayan [RMSE=0.401, MAE=0.303, KGE=0.937, and CC=0.951], Qaen [RMSE=0.072, MAE=0.055, KGE=0.987, and CC=0.997] and Tabas [RMSE=0.230, MAE=0.184, KGE=0.920, and CC=0.942]. Predictions showed that, over the next 10 rs, the annual average VPD in the studied regions will significantly increase. This increment is as follows: Birjand 9 Pa, Sarayan 10 Pa, Qaen 7 Pa, and Tabas 5 Pa. This increase signifies serious challenges for water resources and an increase in water consumption. Eventually, this study suggests the GAM model as an effective tool for future research, especially for use in the development of smart irrigation systems, which play a crucial role in sustainable water resource management.

بهره‌برداری بیش از حد از منابع آب زیرزمینی موجب کاهش تراز سطح آب در آبخوان دشت بیرجند شده است. در این مطالعه، تغییرات ذخیره آب زیرزمینی با استفاده از داده‌های ماهواره‌ای GRACE و GRACE-FO و بارندگی سالانه CHIRPS، در بستر سامانه Google Earth Engine طی دوره زمانی ۲۰۰۳ تا ۲۰۲۴ مورد بررسی قرار گرفت. تغذیه آب زیرزمینی از طریق سری‌های زمانی مربوط به ذخیره آب، با بهره‌گیری از روش نوسانات تراز سطح آب، برآورد شد. نتایج حاصل از تحلیل داده‌های GRACE و GRACE-FO نشان داد که بیشترین افزایش تراز آب زیرزمینی نسبت به میانگین بلندمدت، حدود ۷ سانتی‌متر در فوریه ۲۰۰۵ رخ داده است و بیشترین افت سطح آب، با کاهش حدود ۲۵ سانتی‌متر، در دسامبر ۲۰۲۳ به ثبت رسید. نرخ تغذیه خالص آب زیرزمینی در بازه ۲۱ ساله مورد مطالعه، بین ۳ تا ۱۲ سانتی‌متر در ماه متغیر بوده و میانگین آن برابر با ۶/۴ سانتی‌متر در سال محاسبه شد. این مطالعه نشان می‌دهد که تخمین تغییرات ذخیره آب زیرزمینی بر پایه داده‌های ماهواره‌ای GRACE از دقت قابل قبولی برخوردار است و می‌تواند در مناطقی که با کمبود داده‌های مشاهداتی چاه‌ها مواجه هستند، روند ماهانه تغییرات ذخیره آب زیرزمینی را نشان داده و به تصمیم‌گیری منابع آب کمک کند.