University of BirjandWater Harvesting Research2476-69768220250901A Risk-Based Reservoir Health Indicator for Long-Term Water Quality Assessment: Application to Dissolved Oxygen in Minab Dam177186361110.22077/jwhr.2025.10001.1186ENHamidAbdolabadiDepartment of Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.0000-0003-2589-2773Journal Article20250815Dissolved oxygen (DO) fluctuations directly affect biological processes and water quality in a reservoir. It can occur gradually or rapidly as a result of a large input load of pollution. This paper proposes the Reservoir Health Indicator (RHI) as a weighted combination of reliability, resiliency, and vulnerability indices. The one-dimensional First-Order Reliability Method (FORM) and the empirical framework are applied to estimate these indices. The analysis uses 50 years of daily DO simulation results, acquired from molding a Minab dam divided into five non-overlapping 10-year periods. An indicator value greater than 0.5 reveals that the dam is healthy and sufficiently reliable in meeting the DO standard. Three weighting scenarios are applied to explore the RHI sensitivity. Results showed that in the first scenario, the approximate range of RHI variation is between 0.6 and 0.2. This indicates that after 20 years, the dam has lost its ability to improve its condition. In the second scenario, the variation is between 0.63 and 0.4, and the dam almost loses its health at 25 years. The third scenario indicates successful performance of the dam such that system has almost ability to recover itself by the end of its life. Therefore, developing such an indicator can effectively help understand the variation of a reservoir water quality by integrating three vital aspects of reliability, resiliency, and vulnerability.https://jwhr.birjand.ac.ir/article_3611_4cf33e18ede11b79827bc78b7f2075ae.pdfUniversity of BirjandWater Harvesting Research2476-69768220250901Comparative Analysis of Hybrid Deep Learning Models for Dam Inflow Prediction: LSTM-GRU, CNN-LSTM, Attention-LSTM, and Transformer Approaches187206362210.22077/jwhr.2025.9938.1185ENMaryamSafaviPh.D Student, Department of Water Engineering, University of Birjand, Birjand, IranAbbasKhashei-SiukiProfessor, Department of Water Engineering, University of Birjand, Birjand, Iran0009-0001-2643-6721RezaHashemiAssociate Professor, Department of Water Engineering, University of Birjand, Birjand, IranMohammadEhteramPostdoctoral, Department of Water Engineering, University of Semnan, Semnan, IranJamshidPiriAssociate Professor, Department of Water Engineering, University of Zabol, Zabol, Iran0000-0002-3732-6288Journal Article20250815This study offers the first comprehensive comparison among four hybrid deep learning architectures—LSTM-GRU, CNN-LSTM, Attention-LSTM, and Transformer—for multipurpose dam inflow forecasting under severe hydrological variability. The study employed a 14-year dataset (168 observations, 2010-2023) obtained from Jiroft Dam in Iran and framed with hydrological and operational parameters including precipitation, reservoir capacity, agricultural discharge, and turbine functions. The LSTM-GRU architecture yielded the best performance by attaining 0.873 R² and 29.73 m³/s root mean square error (RMSE) during the validation procedure and demonstrating the best balance among accuracy and generalizability. The model robustness was confirmed by advanced validation methods including Taylor diagrams, violin diagrams, and statistical testing (Kolmogorov-Smirnov, Ljung-Box, and Breusch-Pagan tests). Seasonal analysis revealed a seven times change in flow rates ranging across winter maxima of 391.5 m³/s and autumn minima of 56.2 m³/s. The models showed a widespread tendency to predict lower peak flows (percentage bias, PBIAS: -14.34% to -20.86%), suggesting the presence of operational safety buffers. Precipitation–agricultural interactions were identified as the key forecasting variable (importance = 0.999). The model provides real-time support for decision-making on reservoir management, flood protection, and potable water supply under changing environmental circumstances and provides a validated model for AI-accelerated water resource management.https://jwhr.birjand.ac.ir/article_3622_856b503e276cc491e7e6e0ac1b9f4b17.pdfUniversity of BirjandWater Harvesting Research2476-69768220250901Groundwater Depletion in an Arid Aquifer: Insights from Long-Term MODFLOW Modeling of the Kerman Plain207227361510.22077/jwhr.2025.9858.1184ENBaharehMirkamandarPh.D Candidate, Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.0000-0003-2897-9348Mohammad BagherRahnamaAssociate Professor, Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.0000-0003-3569-8704MohammadZounemat-KermaniProfessor, Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.0000-0002-1421-8671Journal Article20250801The significant impact of climate change, reduced precipitation, and drought in recent decades has led to a marked decline in both surface and groundwater levels. This crisis has particularly affected regions with arid and semi-arid climates, resulting in excessive abstraction from various aquifers. The Kerman Plain aquifer, which is located in an arid to semi-arid region, has also experienced a severe drop in groundwater levels over these years. Therefore, quantitative assessment of groundwater levels in this plain is of great importance for improving management and decision-making during serious water crises. In the present study, a 22-year simulation of groundwater levels in the plain was conducted using the GMS software and the MODFLOW numerical model. Future aquifer conditions up to the point of reaching the bedrock were predicted based on groundwater levels in the Bodaghabad well, located in the northern part of the aquifer within the Kerman urban area. According to the modeling results for the period 2002–2024, the greatest annual decline was observed in the western and eastern regions, averaging 1.23 meters, while the minimum decline 0.27 meters per year occurred in the central and southern parts of the aquifer. Furthermore, the findings showed that the diversion of Kerman’s urban wastewater for use by adjacent industries, and the consequent lack of aquifer recharge from the city’s natural wastewater drainage, have increased the groundwater level decline rate by 15.85% and shortened the time to reach bedrock in the Kerman urban area by 14 years.https://jwhr.birjand.ac.ir/article_3615_d8c9d05ec6e86d5bbad7a2f88a1701d0.pdfUniversity of BirjandWater Harvesting Research2476-69768220250901Predicting the Gray Water Footprint and Water Use Efficiency in Farms Using ML Models (Case Study: Lorestan Province)228237364210.22077/jwhr.2025.10040.1187ENAthareKhakshourMSc Student, Department of Water Engineering, Lorestan University, Khorramabad, Iran.0000-0002-7813-2367MasoudShakaramiAssistant Professor, Department of Water Engineering, Lorestan University, Khorramabad, Iran.0000-0002-7813-2367MohammadNazeri TahroudiAssistant Professor, Department of Water Engineering, Lorestan University, Khorramabad, Iran.0000-0002-6871-2771Seyed YaghoubKarimiAssistant Professor, Department of Water Engineering, Lorestan University, Khorramabad, Iran.0000-0003-4389-4713Journal Article20250805This study aims to (1) evaluate the Crop Water Productivity (CWP) and gray Water Footprint (WFGray) for key agricultural systems in Lorestan province, Iran, to identify hotspots of inefficiency and pollution, and (2) develop and compare Machine Learning (ML) models for predicting these metrics to aid in management and forecasting. We calculated CWP and WFGray for major crops (including forage corn, wheat, beans, potatoes and vegetables) across multiple meteorological stations in Lorestan province. Furthermore, we employed two ML algorithms including Random Forest (RF) and Support Vector Machine (SVM) to model and predict these indices. Model performance was evaluated using the Mean Absolute Error (MAE). The assessment revealed significant regional and crop-specific disparities. Forage corn was the most efficient and sustainable system (CWP: 2.173 kg/m³, WFGray: 0.05 m³/kg), whereas bean production was the least efficient (CWP: 0.064 kg/m³). Spatially, stations like Azna (potato) demonstrated best practices, while Kuhdasht was identified as a critical area of concern due to low efficiency and high fertilizer pollution. In modeling, the optimal algorithm was target-dependent: RF was superior for predicting CWP (MAE: 0.236), while SVM performed relatively better for the more complex WFGray. This study concludes that addressing water security and agricultural pollution in the region requires tailored, crop-specific interventions and improved farm management practices. Furthermore, while ML model (particularly RF) proves to be a powerful tool for forecasting water productivity, accurately modeling the environmental impact (WFGray) remains a challenge, highlighting the need for more robust data and further research in this domain.https://jwhr.birjand.ac.ir/article_3642_300d1539c3b6aa1793b5678b857732cf.pdfUniversity of BirjandWater Harvesting Research2476-69768220250901Assessing Household Water Availability, Accessibility, and Harnessing Strategies for Sustainable Water Security in Kwahu East Plateau, Ghana238257375110.22077/jwhr.2025.9082.1171ENEnockAninakwahDepartment of Geography and Resource Development, University of Ghana, Legon, Accra, Ghana.0009-0001-6941-3730IsaacAninakwahDepartment of Social Sciences, St. Monica’s College of Education, Ashanti Mampong, Ghana.0000-0003-3782-5930EricKoomsonDepartment of Social Sciences, Abetifi Presbyterian College of Education, Abetifi, Ghana.0009-0008-0592-9835Journal Article20250314The study evaluated household water supply, access, and harnessing methods in Abetifi, Kwahu East, Ghana, within a quantitative research framework, involving 400 respondents selected through stratified random sampling. It discussed seasonal changes in water supply, the primary factors affecting household access, and the efficiency of adaptation measures. During the dry season, 85 per cent of households lacked water, 70 per cent had no piped water, and people walking 3.5 km daily spent 4.8 hours a day collecting water. Contrastingly, the wet season had a minimum of 25 per cent scarcity, a 1.2 km travel distance, and an average collection time of 2.5 hours. More than 55 per cent of households spent more than 5 Ghana cedis per day on water, and 60 per cent of households experienced frequent breakdowns of the borehole or standpipe. During the wet season (70%), water was collected from rainwater; during the dry season (70%), came from boreholes, with the help of household storage systems (60%). The results of remote-sensing analysis indicated that the mean value of the NDWI (-0.582) during the wet season was larger than the mean value during the dry season (-0.461), and the results of the LULC analysis (2000-2025) demonstrated that vegetation cover and growth reduced by half and built-up areas expanded, which identified the fact of increased anthropogenic pressure on water resources. The study's originality lies in combining household survey information, NDWI, and LULC analysis within the Sustainable Livelihoods Framework to assess the interaction among seasonality in climatic conditions, human adaptation, and plateau hydrology. The results provide data-driven, practical recommendations for developing climate-resilient community-based water systems in Ghana's highlands.https://jwhr.birjand.ac.ir/article_3751_21ce689121e39821d07d04faab328370.pdfUniversity of BirjandWater Harvesting Research2476-69768220250901Prototype Development of a Two-Stage Hydrogel–CaCl₂ Absorption–Desorption Device for Atmospheric Water Harvesting258266375710.22077/jwhr.2025.10340.1189ENSajjadGhasemiM.Sc. Graduate, Department of Chemical and Polymer Engineering, Faculty of Engineering, Yazd University, Yazd, Iran.Mohammad HassanFazaelipoorProfessor, Department of Chemical and Polymer Engineering, Faculty of Engineering, Yazd University, Yazd, Iran0000-0002-6788-5531Journal Article20251008Freshwater scarcity has become a pressing global challenge, driving the need for innovative and sustainable water production technologies. Atmospheric water harvesting offers a promising solution by exploiting the vast reservoir of water vapor in the air, particularly for arid and remote regions. In this study, we developed a novel two-stage moisture absorption–desorption system using a highly hygroscopic hydrogel–CaCl₂ composite (7.4% hydrogel, 92.6% CaCl₂). The device comprises an absorption compartment equipped with 10 trays (0.675 kg of the composite per tray) for capturing atmospheric moisture and a condensation–recovery compartment integrated with a refrigeration system for efficient desorbed vapor condensation. The system operates in cyclic absorption and thermally driven desorption phases, with each phase offering fully programmable and controllable duration. The desorbed vapor is subsequently directed into a condensation chamber, where it is recovered through an integrated refrigeration unit, and discharged from the system. Experimental results demonstrated a freshwater production capacity of approximately 1 L per day, under air relative humidity of approximately 33%. This integrated approach highlights the potential of hydrogel–salt composites coupled with active condensation for atmospheric water harvesting applications.https://jwhr.birjand.ac.ir/article_3757_b4affa4f6b27df047d63d66fe4ac5600.pdfUniversity of BirjandWater Harvesting Research2476-69768220250901Climate Variability and Industrial Groundwater Abstraction in a Water-Stressed Petroleum Basin: Water Demand Dynamics in Lokichar Oil Production Wells, Kenya267283378410.22077/jwhr.2025.10366.1190ENMaryMakokhaDepartment of Geography, Kenyatta University, Nairobi, Kenya.0000-0003-1755-8089MartinKalibbalaNational Water and Sewerage Corporation, Kampala, Uganda.0009-0004-1399-4531Journal Article20251020The Lokichar Basin in Turkana, northern Kenya, is a semi-arid environment marked by pronounced rainfall variability and intense competition for water resources among domestic, livestock, and oil-driven industrial demands. This study assesses long-term rainfall trends (1981–2024), aquifer characteristics, groundwater levels, and demand dynamics to evaluate resource sustainability under oilfield development. Rainfall analyses using the Rainfall Anomaly Index, Standardized Precipitation Index, and Mann-Kendall tests show marked interannual variability, with significant increases in annual and October–December rainfall and evidence of non-stationarity in seasonal precipitation. Pumping tests from 23 boreholes reveal heterogeneous transmissivity (4.17 × 10⁻⁶–6.00 × 10⁻³ m²/s) and specific yield (0.036–0.500), with high-yield zones concentrated in Nakukulas. Continuous monitoring indicates daily groundwater fluctuations linked to domestic abstraction, while simulations project extreme midday drawdowns exceeding 60 m under full-scale oil production. Estimated natural recharge (21.8–43.6 Mm³ yr⁻¹) falls short of 2022 industrial demand (~53.0 Mm³ yr⁻¹), exposing a recharge–demand deficit. These findings highlight the vulnerability of Lokichar aquifer to over-abstraction and emphasize the need for integrated management, regulated abstraction, enhanced monitoring, and climate-responsive planning to secure water resources and support sustainable petroleum development in this fragile semi-arid basin. https://jwhr.birjand.ac.ir/article_3784_e16e74a63567ecb44ade5c87002bb1d9.pdfUniversity of BirjandWater Harvesting Research2476-69768220250901Groundwater Flow Modeling and Adopting a Different Approach in Dealing Pumping Wells284298384210.22077/jwhr.2025.10542.1195ENMohammad JavadZeynaliAssistant Professor, Department of Water Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran.0009-0006-9615-5702MohammadNazeri TahroudiAsistant Professor, Department of Water Engineering, Lorestan University, Khorramabad, Iran.0000-0002-6871-2771OmolbaniMohammadrezapourAssociate Professor, Department of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.0000-0002-3815-6356Journal Article20251121In groundwater flow modeling, as in any modeling problem, a certain amount of error is inevitable. Recharge or discharge wells, acting as point sources or sinks, play a key role in modeling accuracy, and the way they are treated can either reduce or increase errors. In this study, two approaches were investigated: first, transferring the well to the nearest node in its neighborhood, and second, distributing the pumping rate of the well among the closest nodes. A hypothetical aquifer was examined under two conditions-unconfined and confined-and using both triangular and square meshes. The results indicated that simplifying the model by moving the pumping well to the nearest node is justified only for unconfined aquifers with triangular meshes. For other cases-including unconfined aquifers with square meshes and confined aquifers with either mesh-the second approach is recommended, as it significantly reduces errors in groundwater flow modeling. These findings can also be generalized to real aquifer studies. Quantitative results show that Approach 2 consistently reduces modeling errors: for unconfined aquifers, MAE values are below 0.03 for both mesh types, whereas confined aquifers exhibit larger reductions, particularly with triangular meshes, where MAE reaches 0.38 and maximum errors up to 1.17. These results highlight the robustness of Approach 2 across different mesh configurations and aquifer conditions, providing an effective and reliable numerical tool for groundwater modeling.https://jwhr.birjand.ac.ir/article_3842_7fa215c9efebb3811a7ef58409907899.pdfUniversity of BirjandWater Harvesting Research2476-69768220250901Investigation of Sand Trap and Bar Screen Mechanisms on Urban Drains (Case Study: Shahjalal Upashahar, Sylhet, Bangladesh)299309385810.22077/jwhr.2026.10501.1192ENAshutusSinghaDepartment of Irrigation and Water Management, Sylhet Agricultural University, Sylhet-3100, Bangladesh.0000-0002-1946-3860RabiaSultanaDepartment of Irrigation and Water Management, Sylhet Agricultural University, Sylhet-3100, Bangladesh.0009-0007-3244-1352SumiyaAkterDepartment of Irrigation and Water Management, Sylhet Agricultural University, Sylhet-3100, Bangladesh.0009-0003-6853-5546A. K. MohibulIslamDepartment of Irrigation and Water Management, Sylhet Agricultural University, Sylhet-3100, Bangladesh.0009-0003-4526-6064Journal Article20251116Improper maintenance, inadequate drainage system design, and increasing non-porous surfaces due to urbanization result in waterlogging in urban areas. Shahjalal Upashahar is a prominent urban area in Sylhet city which faces waterlogging conditions due to sediment deposition, leading to the loss of original drainage channel capacity. This study focused on reducing the sediment accumulation in the drainage channel by implementing a sand trap and bar screen mechanism. To design the sand trap for a 100-year return period, the study area was divided into five sub-watersheds using the ArcGIS tool. Rainfall data (2000-2023) were collected from the nearby meteorological station. Based on the determined slope and length of all sub-catchments, the concentration times range from 10.995 to 13.8707 minutes. Using the concentration time, the rainfall intensity was calculated from the Intensity Duration Frequency curve (IDF). The highest runoff was calculated for all sub-watersheds using the rational equation. The peak discharge for catchments 1 through 5 were 1.263, 1.784, 0.254, 1.183 and 1.326 m3/sec, respectively. The required cross-section of the rectangular sand trap was determined using the equation of continuity Q = AV. The bar screen was designed based on the size of solid waste and the prevailing velocity of flow. In this study, the designed cross-sectional areas of sand traps ranging from 0.38 to 2.26 m2 for five sub-watersheds were expected to reduce sediment accumulation by maintaining full drainage channel capacity. https://jwhr.birjand.ac.ir/article_3858_50dd7100bcbd98c41b1179143a2325a4.pdfUniversity of BirjandWater Harvesting Research2476-69768220250901Investigation the Joint Probabilistic Behavior of River Flow Discharge-Suspended Sediment Load in Nazlochai Basin, Urmia310327387310.22077/jwhr.2026.10879.1199ENSorayaTavasoliM.Sc. Student, Department of Water Engineering, Saba Higher Education Institute, Urmia, Iran.KeivanKhaliliAssociate Professor, Urmia Lake Research Institute, Urmia University, Urmia, Iran.0009-0005-6478-8938MohammadNazeri TahroudiAssistant Professor, Department of Water Engineering, Lorestan University, Khorramabad, Iran.0000-0002-6871-2771Journal Article20251007Accurately determining the relationship between river flow discharge and suspended sediment load in watersheds is challenging due to the influence of natural and human-induced variables on river estimations. Therefore, it is essential to employ modern methods to improve existing models. In this context, multivariate methods and copula-based modeling and simulation, given their ability to analyze data distributions, can be suitable options. In this study, joint frequency analysis of river flow discharge and suspended sediment load was conducted at the Abajalo and Tapik stations in the Nazlochai sub-basin, Lake Urmia, Iran using copula functions and marginal distributions. First, the correlation between variables was examined using Kendall’s tau coefficient, indicating a strong positive relationship between river flow discharge and suspended sediment load. For modeling marginal distributions, the Log-Normal and GEV distributions with NSE=0.99 at the Abajalo station were selected as the best distributions for flow discharge and suspended sediment load, respectively, while the GEV and Generalized Pareto distributions with NSE=0.99 at the Tapik station were chosen for river flow discharge and suspended sediment load, respectively. In the joint analysis, the Galambos and Gumbel-Hougaard copula functions demonstrated the best performance based on evaluation criteria. Bivariate analysis revealed that at the Abajalo station, with a 90% probability and river flow discharge exceeding 40 m³/s, the suspended sediment load reaches over 3,000 tons/day, while at the Tapik station, the same probability with a river flow discharge of 25 m³/s indicates a suspended sediment load exceeding 500 tons/day. Finally, based on the conditional density of copula functions and considering various probabilities, equations were proposed for simulating suspended sediment load conditioned on river flow discharge at both stations. The proposed equations were suggested for probability levels of 80–90%, 90–95%, and 95–99% and evaluated using various statistical metrics. The proposed equations for conditional estimation of suspended sediment load demonstrated high performance at the Abajalo station (NSE>0.95 and 386.2<RMSE<642.8 tons/day) and the Tapik station (NSE>0.84 and 26.8<RMSE<836.8 tons/day). This study emphasizes that multivariate methods based on copula functions are effective tools for modeling nonlinear hydrological relationships.https://jwhr.birjand.ac.ir/article_3873_db346ccb62d491029b590bbbf0f5c412.pdfUniversity of BirjandWater Harvesting Research2476-69768220250901Integrative Evaluation of Urban Groundwater Wells Using Physicochemical Parameters, Water Quality Indices, and Trace Metal Indicators (Cr, As): Sarbisheh Plain, South Khorasan, Iran328338388210.22077/jwhr.2026.10730.1197ENAmenehFatahMSc Student, Department of Environmental Engineering, University of Birjand, Birjand, Iran.MohammadRezaRezaeiAssociate Professor, Department of Environmental Engineering, University of Birjand, Birjand, Iran.0009-0002-3591-2395MahmoodHajianiAssociate Professor, Department of Civil Engineering, University of Birjand, Birjand, Iran.0000-0002-3612-2339HodaMousazadehPhD, Department of Environment and Water Resources Quality, Regional Water Company of South Khorasan, Birjand, Iran.Journal Article20250925Groundwater quality is influenced by the region’s natural climatic–geological setting and anthropogenic practices such as agriculture, industry, and mining. Ongoing evaluation of groundwater quality is therefore vital for secure drinking supplies, agricultural production, industrial operations, public-health protection, and efficient treatment processes. This study aims to evaluate the quality of groundwater in Sarbisheh Plain, South Khorasan, Iran. Water-quality data for 2020 and 2021 were examined and analyzed for the 18 wells supplying Sarbisheh’s water demand. The status and concentrations of 12 physico-chemical parameters during the mentioned years were evaluated and statistically analyzed using SPSS software. The overall quality of the studied water resources was also evaluated using groundwater quality index. The results showed that the average EC in the water-supply wells of Sarbisheh is approximately 4513.5 μS/cm, which exceeds the standard limit. The TDS values also ranged from 596 to 8511 mg/l, with the mean for most wells falling outside the acceptable standard range. Among the studied ions, sodium and chloride exhibited the highest concentrations at 682.1 mg/l and 677.3 mg/l, respectively, while potassium and fluoride showed the lowest levels at 28 mg/l and 0.3 mg/l. Calculations of the water quality index for the 18 wells showed that 33.33% of the wells fell into the good category, while the remaining wells ranged from poor to very poor. The results demonstrated that assessment and monitoring of groundwater quality in study area are very important; moreover, for drinking purposes, treatment is required to improve water quality and meet the necessary standards.https://jwhr.birjand.ac.ir/article_3882_df42e2244c97a0d80d565ae8176d3351.pdfUniversity of BirjandWater Harvesting Research2476-69768220250901Impact of Climate Change on River Discharge and Groundwater Level Using Copula Functions (Case Study: Siminehrood River Basin, Iran)339355388510.22077/jwhr.2026.11000.1201ENFahimehSharifanPhD Graduate, Department of Water Engineering, University of Birjand, Birjand, Iran.YousefRamezaniAssociate Professor, Department of Water Engineering, University of Birjand, Birjand, Iran & Member of the Unconventional Water Resources Research Group, University of Birjand, Birjand, Iran.0000-0002-8085-9290MahdiAmirabadizadehAssociate Professor, Department of Water Engineering, University of Birjand, Birjand, Iran.0000-0003-2814-0948CarloDe MicheleProfessor, Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy.0000-0002-7098-4725Journal Article20251006The aim of this research is to simulate and predict the groundwater level in the Siminehrood River Basin, which is situated south of Lake Urmia, Iran. This simulation was conducted using copula functions while accounting for changes in river discharge influenced by climate change. A total of 26 large-scale CMIP6 models were utilized in this study. Precipitation data were downscaled and simulated using the LARS WG 7.0 model. Subsequently, precipitation data for both the baseline period (1988-2018) and the future period (2031-2050) were predicted for three scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5 through a weighted average method. Following the simulation and prediction of precipitation in the Siminehrood River Basin, copula functions were employed to simulate and predict both river discharge and groundwater levels. Prior to fitting the copula function, correlations between pair of parameters precipitation-river discharge and river discharge-groundwater level were examined using Kendall's tau coefficient; correlation values obtained were 0.43 for precipitation-river discharge and 0.44 for river discharge-groundwater level. After selecting marginal distributions and examining these correlations, ten different copula functions were fitted to each pair of parameters in order to identify the most suitable model among them. The results from predicting precipitation related to climate change indicated that annual precipitation under all three scenarios would decrease compared to the measured precipitation. Annual precipitation reductions were projected to be 5.1 mm, 31.5 mm, and 34.8 mm under the scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. Analysis through copula functions revealed that the Clayton copula provided optimal performance when creating a joint distribution for these pair of parameters during simulation phases concerning river discharge as well as groundwater levels and its accuracy was validated based on evaluation criteria including NSE (Nash-Sutcliffe Efficiency), RMSE (root mean square error), and R² (coefficient of determination). Furthermore, it was concluded that reductions in annual precipitation would lead to decreases in annual river discharge ranging from 2.9 m³/s to 6.6 m³/s alongside an annual drop in groundwater levels estimated between 0.3 m and 1.5 m.https://jwhr.birjand.ac.ir/article_3885_311abb492a69fe7da41be858fdd2aae8.pdf