JWSS - Journal of Water and Soil Science

Climate change significantly affects the water use efficiency (WUE) and yield of field crops. This study evaluates the impacts of climate change on biological yield, grain yield, water consumption, and WUE of two barley genotypes, Goharan and Reyhan 03, under autumn and spring planting regimes using the CERES-Barley model within the Decision Support System for Agrotechnology Transfer (DSSAT) software. Data provided for model calibration and validation were sourced from the field experiments conducted at the Isfahan University of Technology research farm located in Najafabad, Iran. Meteorological data for the period of 2003 to 2016 were obtained from the Najafabad weather station, while future climate projections for 2020–2050 were generated using the MarkSim weather generator under the Representative Concentration Pathway (RCP) 8.5 scenario. Planting dates were analyzed within a ±35-day window relative to baseline dates of October 22 for autumn and March 3 for spring. The model demonstrated high accuracy in calibrating key traits, including days to anthesis, days to maturity, leaf area index, grain yield, and biological yield. Elevated temperatures associated with climate change reduced grain and biological yields across both planting seasons, with biological yield exhibiting a more pronounced decline, particularly under spring planting. During the 2040–2050 period, water consumption peaked at 387.5 mm for Goharan in autumn planting, while spring planting recorded a minimum of 239 mm for Reyhan 03. Delaying autumn planting by 20–25 days enhanced WUE, while planting earlier in the spring )10–20 days (improved WUE by exploiting cooler temperatures. Evapotranspiration increased by 399 mm in autumn but decreased by 267 mm in spring. The earlier-maturing Reyhan 03 genotype demonstrated smaller yield losses in spring planting due to climate change. The findings of this study suggest that programmed adjustments to planting dates may mitigate the adverse impacts of climate change on barley production, thereby enhancing sustainability.

Rapid urbanization and the expansion of impervious surfaces in urban areas can cause a reduction in infiltration rate, which consequently increases the flash floods and surface runoff in cities. In recent years, the use of bio-infiltration systems has been considered as one of the most effective approaches based on low-impact development (LID) for sustainable urban runoff management. In this study, the performance of six types of biological infiltration basins was investigated to reduce the volume of runoff and improve surface water management in the eastern region of Qazvin city. First, 40 years of rainfall data (1983–2023) were collected from the Qazvin meteorological station, and Intensity–Duration–Frequency (IDF) relationships were developed for various return periods. Six design scenarios were modeled: bioretention basins with and without a drainage system; tree boxes with and without a drainage system; infiltration trenches; and permeable pavements. The dimensions of all systems were kept constant to focus solely on hydrological performance without the influence of size or shape. Overall, using HEC-GeoHMS, SWMM, and MIDS models together offered a detailed and accurate framework for analyzing the hydrological behavior of bioretention systems in urban runoff management. Results showed that the runoff coefficients for the sub-basins averaged 0.79, highlighting the dominance of impervious surfaces in the area. These values were used as inputs for the MIDS model to simulate the six different bioretention scenarios. The results indicated that the permeable pavement scenario had the greatest effect on annual runoff reduction (about 728,555 m ³), while the bioretention cell with a drainage system had the lowest performance. SWMM results, based on DEM-derived sub-catchment data, showed low soil infiltration and high impervious surface coverage. These conditions highlighted the importance of bioretention systems in reducing urban flooding. Overall, the study demonstrates that well-planned bio-retention and other green infrastructure can decrease peak flows, increase time of concentration, and improve urban hydrological and environmental conditions.

Proper estimation of soil water infiltration parameters and Manning roughness coefficient is one of the influential factors in the correct design and evaluation of surface irrigation systems. The EDOSIM model, as a surface irrigation simulation-optimization model, uses a combination of simulation with the Volume Balance model and meta-heuristic optimization. In the evaluation part of this model, the Elliott-Walker two-point method is used to estimate the parameters of the Kostiakov-Lewis infiltration equation. In this study, the Manning coefficient and parameters of the Kostiakov-Lewis infiltration equation were calibrated in furrow and border irrigation using observational advance data. This was done through three methods: Volume Balance model with constant shape coefficients (VB-CC), a combination of Volume Balance and Zero Inertia models (VB-ZI), and Volume Balance model with variable shape coefficients (VB-VC), using a total of 10 data series of border and furrow irrigation evaluation. Then, the ability to calibrate the infiltration parameters and roughness coefficient using a superior method was added to the EDOSIM model. The results showed that the VB-CC model had the best calibration accuracy and precision in 10 farms, according to the average statistical indices R2=0.998, NRMSE=2.4%, and MBE=-0.06. The VB-ZI and VB-VC methods underestimated the advance length even with calibrated values. The use of the VB-CC model instead of the Elliott-Walker two-point method in the EDOSIM model increased the accuracy of simulation and optimization by reducing (improving) the objective function from an average of 0.34 to 0.13 in border irrigation and from an average of 0.86 to 0.36 in furrow irrigation. Therefore, it is recommended to use the VB-CC calibration method in the EDOSIM model as a powerful tool for optimal operation of surface irrigation systems.

Precipitation use efficiency (PUE) is defined as crop yield per unit of annual precipitation in a region. Proper management of rainfed lands requires investigation of the spatial variation of PUE. In this study, two hundred ninety-eight rainfed lands were investigated in the Khodabandeh region in the south of Zanjan province. PUE was calculated as the ratio of wheat grain yield to precipitation. In addition, climatic characteristics and soil physical properties (particle size distribution and water retention) were measured, along with soil chemical properties (pH, EC, organic matter, calcium carbonate, and macro- and micronutrients) in rainfed soils. Results indicated that the mean PUE in rainfed lands is 4.43 kg mm-1. Higher values were found in the northeast, south, and west regions of the area, although these parts had lower wheat grain yields. Geostatistical analysis showed moderate spatial variation in wheat grain yield and PUE, with patterns associated with climatic variables (precipitation and temperature) and land conditions such as soil properties (particle size distribution, organic matter, and nitrogen). Rainfed lands with fine-textured soils and higher amounts of organic matter and nitrogen had higher PUE. Adjusting tillage direction and using conservation tillage are important strategies to improve organic matter content and soil productivity in rainfed lands.

One of the key factors in agricultural production is the availability of sufficient and usable nutrients for plant growth, and among these, nitrogen plays a particularly important role. The waste of nitrogen fertilizers due to the low efficiency of using fertilizers has caused environmental problems such as pollution of surface and groundwater by nitrate or ammonium. In this study, the influence of fertigation as a combination of urea, ammonium nitrate, natural zeolite of Semnan (CP), and modified zeolite by surfactant (SMZ) in the reduction of waste and manure fertilizer was evaluated. Two separate experiments with a completely randomized design with three replications were used for loam soil columns. Treatments consisted of four levels of zeolite application (0, 4, 8, and 16 g/Kg) and a fertilization level with a concentration of 60 mg per liter nitrate applied at three fertigations during six irrigations. In the first part of the study, the role of nitrogen fertilizers in fertigation as a combination of urea and ammonium nitrate (UAN) for ordinary soil was examined to reduce the concentration of nitrate and ammonium in the drainage water, and the results were compared with one of the fertigation applications. The results showed that the loss of fertilizer in the form of nitrate was reduced by 40 percent for the UAN treatment during the first period of fertigation. Since the results of the first part of the experiment showed that the concentration of ammonium nitrate does not reduce to the same level as drinking water, in the second part of the experiment, the soil amendments were used. The measured nitrate removal showed that the highest nitrate removal belonged to the soil mixed with 16 g/kg of modified zeolite in the surface layer of soil (SM16) treatment, and it was equivalent to 90 percent. The highest ammonium removal belonged to the soil mixed with 16 g/kg of natural zeolite in the surface layer of soil (CP16) treatment, and it was 85 percent. Therefore, the use of fertigation as a combination of nitrogen fertilizers with soil amendments for conditions where there is a potential for groundwater pollution by nitrate leaching is recommended.

Accurate monitoring of water surface dynamics in semi-arid regions poses challenges due to uncertainties regarding the optimal spectral index and sensor selection for effective water resource management. This study assessed the comparative performance of nine spectral water indices across the Landsat-8 and Sentinel-2 platforms to identify the best index-sensor combinations for monitoring semi-arid reservoirs. Utilizing the Google Earth Engine cloud computing platform, 181 satellite images were processed for Golestan Dam in northeastern Iran, comprising 107 Landsat-8 scenes from 2013 to 2023 and 74 Sentinel-2 scenes from 2018 to 2024. After applying atmospheric corrections using the LEDAPS and Sen2Cor algorithms, nine spectral indices (NDWI, MNDWI, ANDWI, AWEI, WI2015, WI1, WI2, LSWI, and NDTI) were calculated and evaluated against the WI2 reference through RMSE, R², and Nash-Sutcliffe efficiency metrics. MNDWI showed superior performance for Sentinel-2 (RMSE=21.42 ha, R²=0.998, NS=0.996), while ANDWI was optimal for Landsat-8 (RMSE=54.54 ha, R²=0.977, NS=0.976). Time-series analysis revealed a 35% reduction in mean annual reservoir area, decreasing from 7.28 km² in 2014 to 4.72 km² in 2021. Consistent seasonal patterns were observed, with spring maxima (9.33 km² in March) and autumn minima (3.35 km² in September) evident across both sensors. A high inter-sensor correlation (r = 0.933) supports the potential for multi-sensor integration in comprehensive monitoring efforts. The LSWI and NDTI indices displayed systematic overestimation due to interference from soil moisture and vegetation, making them unsuitable for quantifying water area. These findings highlight the sensor-dependent nature of optimal index selection, recommending MNDWI-Sentinel-2 pairing for short-term monitoring and ANDWI-Landsat-8 for long-term trend analysis in the management of semi-arid reservoirs.

The water crisis and climatic conditions in Iran have necessitated prioritizing low water-demand crops in agricultural investment policies. Quinoa, as a pseudo-cereal with relatively low water requirements, can be considered a suitable alternative to water-intensive crops. This study was conducted to investigate the comparative advantage of quinoa production compared to water-intensive crops under water-scarce conditions in Isfahan Province. To assess comparative advantage, two indicators—Domestic Resource Cost (DRC) and Social Cost-Benefit Ratio (SCB)—were employed. Data were collected through questionnaires and governmental sources, and shadow prices of inputs and outputs were utilized in the analyses. The statistical population consisted of quinoa producers. The results indicated that quinoa possesses a comparative advantage in Isfahan Province. The breakeven yield of this crop was estimated at 653 kg/ha, meaning that production above this level indicates the existence of a comparative advantage. Furthermore, the maximum allowable water consumption to maintain this advantage was estimated at 15,792 m³/ha; water consumption exceeding this amount eliminates the comparative advantage of quinoa cultivation. Additionally, the economic water productivity of quinoa was evaluated to be higher than that of other common crops in the region. Overall, considering comparative advantage of quinoa and its higher water productivity compared to common water-intensive crops, this product has the necessary potential to be included in the cropping pattern of Isfahan Province. However, recommendations for complete substitution require targeted and multidimensional policy interventions at technological, marketing, and policy-making levels, as well as conducting complementary research on the analysis and development of the quinoa value chain and market, investigating consumer behavior, designing targeted supportive policies for the transition period, and conducting a more comprehensive analysis of the comparative advantage of other products.

Water erosion is a great issue in Iran, and due to a lack of reliable and sufficient data for the recognition of vulnerable areas, using models is inevitable. This study was designed for the evaluation of the MPSIAC model in the subbasin of Goharbaran in Chaharmahal va Bakhtiari province. The sediments of the reservoir were surveyed and measured after exhausting the water. Landsat-8 data were applied to study land use changes on soil erosion. During intensive fieldwork, 37 surficial (0-20 cm) soil samples were collected and, after pretreatment, were used for some soil physical and chemical analyses. For the determination of 9 MPSIAC factors, we used library, field, and laboratory data. Multitemporal analysis showed that the quality of rangelands has increased, and the surface area of orchards was doubled within 20 years (1994-2014). Sensitivity analysis indicated that the MPSIAC model was highly sensitive to the river erosion factor, while the least sensitive factor was soil. The sensitivity of the model to the runoff specific peak and soil silt percentage was also very high. Application of the MPSIAC model showed that 48% of soils were in moderate and 36% of soils were in severe sediment yield rate classes. Results of sediment measurements indicated that the specific sediment yield was 11.76 t.ha-1.y-1 during 26 years (1988-2014), while the forecasted value by the MPSIAC model was 5.97 t.ha-1.y-1 Despite others' findings, a lower estimation of the model revealed the need for MPSIAC model calibration before using it in similar environments.

Biochar, a stable, economical, and environmentally friendly carbonaceous material, has garnered significant attention in the development of advanced adsorbents for the immobilization and removal of environmental pollutants from water and soil. This interest stems from its oxygen-containing functional groups, aromatic structure, notable porosity, high specific surface area, and suitable cation exchange capacity. However, the performance limitations of pristine biochar in pollutant removal underscore the necessity for its modification and engineering. In this regard, the production of biochar composites through the combination with minerals and iron-containing compounds has emerged as an effective strategy to enhance structural and chemical properties, as well as surface reactivity. This review paper examines the primary methods for synthesizing biochar composites, including post-pyrolysis modification and direct mixing with mineral phases. Synergistic and direct loading methods are introduced as novel approaches. Furthermore, the influence of biomass type, pyrolysis conditions, modifier type, and synthesis pathway on the final physicochemical characteristics of the composites is analyzed. Additionally, the governing mechanisms underlying the performance of these materials in immobilizing and removing heavy metals are discussed, encompassing adsorption, precipitation, complexation, ligand exchange, redox reactions, electron transfer, electrostatic interactions, and ion exchange. Study results indicate that the synergy between biochar and mineral/iron-bearing phases can significantly enhance the efficiency of immobilizing pollutants, including both anionic and cationic heavy metals. This review emphasizes the importance of targeted design of biochar composites based on a precise understanding of the relationship between synthesis methods and functional properties. Future challenges and prospects concerning the application of biochar-mineral composites in environmental remediation and the promising commercialization of this technology are also addressed.

Scour is a major challenge in river engineering, as it causes bridge failures during flood events and leads to significant economic losses. This study aims to estimate the normalized scour depth (Dse/Dp) around pile groups by examining relevant hydrodynamic and geometric parameters. A dataset comprising 299 laboratory measurements collected from various sources was assembled and divided into training and testing subsets. As machine learning inputs, several models were employed, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and a meta-ensemble learning model (Stacking). Hyperparameter tuning was performed using the Grid search method to achieve optimal regression performance. Model performance evaluation indicated that the ANN and SVR models achieved coefficients of determination of R² = 0.87 and R² = 0.91, respectively. The XGBoost model outperformed these approaches, yielding R² = 0.94 with an RMSE of approximately 0.28. Ultimately, the stacking ensemble model, by integrating the outputs of the base learners, demonstrated the highest predictive accuracy with R² = 0.96 and an RMSE of 0.11, representing an improvement of approximately 15% compared to ANN and 7% compared to XGBoost. Overall, the findings highlight that ensemble machine learning models—particularly the Stacking approach—provide a robust and efficient framework for predicting scour depth around pile groups and for capturing the complex flow behaviors in hydraulic systems.

This study evaluated the impact of four fertilization systems—no fertilizer, urea-based chemical fertilizer, cow manure, and combined urea and cow manure—on nitrogen mineralization and dry matter yield in sorghum cultivated on sandy loam soil with low fertility. Nitrogen mineralization potential (N₀) was assessed using a first-order kinetic model across various fertilizer treatments. Results indicated that urea-based treatments exhibited superior nitrogen mineralization potential compared to cow manure and combined treatments, with N₀ values ranging from 13.46 to 17.89 mg N/kg soil. Combined fertilization (urea + cow manure) consistently produced higher N₀k values, suggesting enhanced nitrogen availability and mineralization rates. Correspondence between nitrogen mineralization peaks and critical sorghum growth stages was observed, particularly during vegetative and reproductive phases. The highest dry matter yield was achieved with 240 kg N/ha from combined fertilizer, although 160 kg N/ha from the same source yielded comparably. These findings highlight the synergistic benefits of integrating organic and inorganic fertilizers to optimize nitrogen dynamics and crop productivity in low-fertility soils.
 

A field experiment was conducted using a factorial in a randomized complete block design to evaluate the growth and yield of rice (Oryza sativa L.) cv. Hashemi in response to nitrogen (N) and phosphorus (P) fertilization. The first factor comprised four N levels (0, 46, 69, and 92 kg N ha⁻¹), and the second factor comprised three P₂O₅ levels (0, 23, and 46 kg P₂O₅ ha⁻¹), with three replications conducted over two cropping years. At the end of the growing season, growth indices, yield, and yield components of rice were measured. The results indicated that the main effects of N and P fertilization on several traits (aboveground dry matter, grain yield, and straw yield) were significant at the 1% probability level. The N × P interaction for grain yield and the main effect of year were significant for most traits at the 5% level. The highest grain yield (935.2 kg ha⁻¹) and straw yield (1455 kg ha⁻¹) occurred with 92 kg N ha⁻¹ and 46 kg P₂O₅ ha⁻¹, representing increases of 115% and 120%, respectively, relative to the control (no N or P applied). This treatment is recommended for rice production under ratoon cultivation in areas with similar climatic and soil conditions

Excessive groundwater abstraction is a major environmental and economic challenge in Iran’s arid and semi-arid regions. This study examined farmers’ perceptions of groundwater over-abstraction and its perceived socioeconomic consequences in the Siminehroud catchment. Data from 373 questionnaires were analyzed using descriptive statistics, Mann–Whitney U tests, and chi-square tests. Effect sizes were reported, and p-values were adjusted using the Benjamini–Hochberg procedure. Overall, 41.8% of respondents believed that groundwater over-abstraction was occurring in their locality. Respondents with this perception were younger (median 48.5 versus 53 years; p<0.001), and the proportion with this perception was lower among users of piped water conveyance than among users of traditional canals (24.2% versus 45.6%; p=0.001). Both associations remained significant after false-discovery-rate adjustment. The most frequently perceived consequences of groundwater-level decline were declining farm income, loss of irrigated land, changes in cropping patterns, and increased migration. The findings indicate that groundwater management in Siminehroud should incorporate aquifer monitoring, improved water conveyance, and social interventions tailored to users’ characteristics.
 

Conventional methods for desalination of water and wastewater are usually not economically viable for providing the water required for agricultural production due to high energy consumption and equipment costs. This study aimed to evaluate the efficiency of magnetic nanobiochar produced from sugarcane bagasse to remove sodium ions from agricultural wastewater under the influence of a magnetic field. Non-nanoactive, nanoactive, and magnetic nanoactive adsorbents were produced using a pyrolysis process in an electric furnace at 600 °C, chemical activation with potassium hydroxide, and magnetization with a combination of hexahydrate ferric chloride and heptahydrate ferric sulfate. The results showed that the highest removal rates of chloride ions (44.6%) and sodium ions (58.3%) were obtained in the magnetically activated nano-biochar under the influence of a magnetic field of 0.5 Tesla, for an activator-to-biochar ratio of 3, a microwave power of 400 W, and an activation time of 10 minutes. Surface analyses including FT-IR spectroscopy and scanning electron microscopy (SEM) confirmed that the hydroxyl, carboxyl, and carbonyl functional groups on the surface of the adsorbent play a key role in the adsorption of sodium and chloride ions. It was also observed that the application of a magnetic field improved the orientation of the ions towards the adsorbent and weakened the hydrogen bonds in water, which led to an increase in the efficiency of the adsorption process. This study shows that magnetic nano-biochar, under a magnetic field, could be an economical and sustainable solution for reducing the salinity of agricultural wastewater.
 

This study evaluated groundwater-level variations in the Isfahan-Borkhar aquifer and examined their relationship to land subsidence. Representative hydrographs, groundwater-level maps, depth-distribution maps for the wettest and driest months of the study period, and a 20-year groundwater-level variation map were prepared and compared with the land subsidence map within the aquifer area. The results indicated that subsidence zones were mainly located in areas experiencing considerable groundwater-level decline. The occurrence of critical subsidence zones in regions with a high density of pumping wells and agricultural lands further confirmed this relationship. In addition, the alluvial-thickness map of the study area was prepared and compared with the land subsidence map to investigate the relationship between the spatial distribution of subsidence zones and alluvial thickness. The findings indicated that land subsidence is associated not only with groundwater-level decline but also with other factors, including alluvial thickness and soil texture. In other words, even under substantial groundwater-level decline, significant land subsidence is unlikely where alluvial thickness and the proportion of compressible fine-grained sediments are low.

 

Nickel (Ni) is an essential element for living organisms and plays important biological roles in plants; however, its accumulation at high concentrations can cause toxicity and adverse effects. This study aimed to evaluate nickel accumulation using the leaves of Albizia lebbeck, Prosopis cineraria, and Eucalyptus globulus as biological indicators in Ahvaz City during the summer of 2025. A total of 126 leaf samples and 63 soil samples were collected from seven stations across Ahvaz City. Chemical digestion of the leaf and soil samples was performed using the Johnson and EPA 3050 methods, respectively. Nickel concentrations were determined using atomic absorption spectrometry. Significant differences were observed in Ni concentrations among the leaves of Albizia lebbeck, Prosopis cineraria, and Eucalyptus globulus (P < 0.05), whereas no significant difference was observed in Ni concentrations among the soil samples (P > 0.05). The highest and lowest Ni concentrations in the unwashed and washed leaves of Albizia lebbeck were 36.2 and 10.3 mg kg⁻¹, respectively. The highest and lowest Ni concentrations in the soil samples collected from the tree rhizospheres were 57.36 and 44.1 mg kg⁻¹, respectively. The highest Ni transfer factor was observed in Albizia lebbeck leaves (0.446), whereas the lowest was observed in Eucalyptus globulus leaves (0.203). The Ni transfer factors for the leaves of Albizia lebbeck, Prosopis cineraria, and Eucalyptus globulus, together with the linear regression analysis of Ni concentrations in washed and unwashed leaves, indicate that atmospheric deposition may be an important source of Ni accumulation in the leaves. In contrast, the contribution of soil-derived Ni to leaf accumulation appears to be relatively low.

Rainfall erosivity is a key factor in soil erosion models. Although Iran’s climate is predominantly hot and dry, the occurrence of intense and heavy rainfall events, although infrequent, can cause significant damage to soil resources. In the Universal Soil Loss Equation, rainfall erosivity is calculated based on rainfall intensity. In this study, 45 years of hourly rainfall data from the ERA5 reanalysis dataset were used to estimate rainfall erosivity across Iran. The results revealed that the spatial distribution of rainfall events in Iran is highly heterogeneous. Central Iran, with fewer than 20 rainfall events per year, and the Caspian Sea coast, with nearly 450 events per year, represent two contrasting rainfall regimes. Both the Caspian coast and the Zagros highlands receive slightly less than 10 mm of rainfall per event, while the mean duration of rainfall events in these regions is approximately 10 hours. On the Caspian coast, moisture originating from the Caspian Sea, and over the Zagros Mountains, moisture originating from the Arabian Sea, contribute to the observed rainfall duration and amount per event. Rainfall event intensity in the western Zagros and along the entire southern coast of Iran is considerably higher than in other parts of the country. The transport of moisture from the Arabian Sea into Iran contributes to the high intensity of rainfall events in these regions. Rainfall erosivity is very low across most parts of Iran; however, the Zagros Mountains, the Caspian coast, and some mountainous areas exhibit relatively high levels of erosivity. The mean annual rainfall erosivity over Iran is approximately 80  MJ.mm.ha^-1.h^-1.y^-1.
 

Managed Aquifer Recharge (MAR) using treated wastewater is a viable strategy for water-resource sustainability in arid regions; however, the simultaneous assessment of its hydrogeological and economic aspects remains challenging. The objective of this study was to hydro-economically evaluate the implementation of an MAR project in the Qotb-Abad Plain by integrating numerical modeling and cost-benefit analysis (CBA). The quantitative and qualitative responses of the aquifer were simulated using the MODFLOW and MT3DMS models to estimate changes in groundwater levels, nitrate concentrations, and total dissolved solids (TDS). Economic benefits, including pumping energy savings, the value of stored water, and improvements in water quality, were evaluated alongside capital and operational costs under lower- and upper-bound scenarios. Subsequently, economic indicators, including net present value (NPV), benefit-cost ratio (BCR), internal rate of return (IRR), and payback period, were calculated. The results indicated that implementing MAR with an annual treated wastewater volume of 1.898 million m³ resulted in an approximately 4.5 m rise in groundwater level, accompanied by reductions in TDS from 1859 to 1752 mg/L and in nitrate concentration from 22.2 to 15.4 mg/L by the end of the five-year simulation period. Under the upper-bound scenario, the NPV, BCR, and payback period were 43.7 billion Rials, 1.36, and 3.23 years, respectively. In contrast, the lower-bound scenario was not economically justified, as its BCR was less than 1. Overall, integrating numerical modeling with cost-benefit analysis provides a comprehensive framework for supporting groundwater-management decision-making.