Water and Soil

Abstract Introduction
Water conveyance tunnels are critical infrastructures for sustainable water supply, particularly in arid and semi-arid regions. However, tunnel excavation can significantly alter groundwater systems, leading to hazards such as sudden groundwater inflow, reduction in aquifer storage, and deterioration of water quantity and quality (Zheng et al., 2021; Li et al., 2024). In fractured and karstic environments, these impacts may propagate over large distances, potentially modifying regional hydrogeological regimes (Chen, 2021; Sadique et al., 2025).
To address these challenges, various predictive approaches have been developed, including numerical modeling, parametric methods, hybrid techniques, and empirical models. Although numerical models provide detailed insights, they require extensive datasets and complex calibration, which limits their applicability in heterogeneous geological settings (Vincenzi et al., 2022). Consequently, parametric and empirical methods such as the Drainage Hazard Index (DHI) and Tunnel Impact Score (TIS) have gained increasing attention due to their flexibility, simplicity, and suitability for preliminary assessments and real-time decision-making (Dematteis et al., 2001; Hassanpour et al., 2021; Abedian & Mojiri, 2023).
The Hezarmasjed water conveyance tunnel, located in northeastern Iran within the Kopet-Dagh tectonic zone, traverses a highly fractured carbonate system hosting numerous springs. Given the dependence of local communities on these springs, assessing the potential impacts of tunnel excavation is essential. This study aims to identify the main controlling factors governing spring discharge variations and to evaluate the vulnerability of springs using both DHI and TIS approaches.

Materials and Methods
The study area is situated in a structurally complex mountainous region characterized by active tectonics, heterogeneous lithology, and significant hydrogeological sensitivity. The tunnel, approximately 8.8 km in length, intersects several geological formations, including carbonate units (Mozduran and Tirgan) acting as primary aquifers, and shale-dominated formations with low permeability. Groundwater flow is predominantly controlled by fracture networks and karstification processes, resulting in rapid hydrological responses to precipitation events.
A total of 45 springs were investigated at varying distances from the tunnel axis. Field data included hydrogeological observations, Lugeon permeability tests, and structural analyses. Permeability values generally ranged from 10⁻⁷ to 2.5×10⁻⁶ m/s, indicating low to moderate hydraulic conductivity, with higher values in fractured and karstified zones.
Two complementary methods were employed for impact assessment. The DHI method (Dematteis et al., 2001) is a semi-quantitative parametric approach based on seven key parameters, including fracture frequency, rock mass permeability, overburden thickness, plastic zone radius, fault–spring interaction, spring type, and distance from the tunnel. These parameters were normalized and combined to calculate a continuous index representing the risk of spring discharge reduction.
The TIS method (Hassanpour et al., 2021) is an empirical approach that evaluates spring vulnerability based on four main factors: tunnel water inflow (40% weight), hydraulic connectivity (40%), distance from the tunnel (15%), and aquifer recharge capacity (5%). The method provides a classification of impact severity, ranging from negligible to significant discharge reduction.

Results and Discussion
The DHI results indicate that spring vulnerability is primarily controlled by structural and hydrogeological parameters, particularly fracture density and permeability. Calculated DHI values range from 0.1 to 1.5, demonstrating a wide spectrum of potential impacts. Approximately 11% of springs are classified as highly to critically affected (DHI ≥ 0.6), while the majority (about 89%) are expected to experience minor to moderate discharge reductions. Springs with high DHI values are typically located in highly fractured carbonate formations with strong hydraulic connectivity, where tunnel excavation acts as a drainage boundary, intercepting groundwater flow paths (Vincenzi et al., 2022; Saberinasr & Dashti Barmaki, 2023).
In contrast, springs located in low-permeability formations or at greater distances from the tunnel exhibit minimal sensitivity, with DHI values below 0.2. More than 70% of springs fall within an intermediate range, indicating moderate but persistent reductions due to partial hydraulic connectivity.
The TIS results highlight the importance of dynamic hydrogeological parameters, particularly tunnel water inflow and hydraulic connectivity. TIS values range from 1.6 to 6.9, reflecting significant variability in system response. Approximately 44% of springs are classified as unaffected, 44% experience moderate discharge reduction, and around 12% are subject to significant decline.
High-risk springs, such as Sirzar and Bamchenar, exhibit strong hydraulic connectivity and are located within sensitive recharge zones. Notably, the results indicate that hydraulic connectivity can dominate system behavior even at distances exceeding 1000 m, confirming previous findings that connectivity is more influential than geometric proximity (Hassanpour et al., 2021).
Conversely, springs located in areas with higher recharge capacity and weaker hydraulic connectivity show minimal impact, even when tunnel inflow is relatively high. This behavior reflects the buffering capacity of aquifer systems, which can partially compensate for tunnel-induced drainage.
A comparative analysis of the two methods demonstrates that the DHI approach emphasizes intrinsic geological and structural conditions, providing a continuous representation of vulnerability, whereas the TIS method focuses on functional aquifer responses and offers a more practical classification of impact severity. The consistency between both methods in identifying high-risk springs supports the reliability of the integrated approach.

Conclusion
This study demonstrates that tunnel excavation can significantly affect groundwater systems, particularly in fractured and karstic aquifers. The integrated application of DHI and TIS methods provides a comprehensive framework for assessing these impacts.
The DHI method is effective for identifying intrinsic vulnerability and worst-case scenarios, highlighting the role of geological structures, permeability, and fracture density. Approximately 11% of springs are at high to critical risk, while most are expected to experience moderate impacts.
In contrast, the TIS method offers a more operational perspective by incorporating dynamic hydrogeological parameters, classifying springs into significant, moderate, and negligible impact categories. The results emphasize the dominant role of hydraulic connectivity and aquifer recharge capacity in controlling system response.
Overall, the combined use of parametric and empirical approaches enhances the reliability of impact assessments and provides a robust basis for groundwater management in tunnel projects. This integrated methodology is particularly suitable for complex hydrogeological environments where data limitations restrict the application of numerical models.

Abstract Introduction: Degradation of water quality has emerged as one of the most critical challenges facing national water resources. Surging water demand has led to the over-exploitation of groundwater and a continuous decline in aquifer levels, subsequently triggering environmental degradation and a deteriorating trend in groundwater quality. Consequently, analyzing temporal trends and spatial variations of groundwater quality is vital for sustainable water resource management. This study aims to investigate the temporal trends of groundwater quality parameters using the Mann-Kendall test and Sen’s Slope Estimator, while analyzing spatial variations through the Groundwater Quality Index (GQI) in the Bahabad aquifer.
Materials and Methods: In this study, a total of 16 observation wells were initially considered for groundwater quality assessment. Among them, 9 wells with complete and reliable datasets were selected for detailed analysis. The required hydrochemical data were obtained from the Yazd Regional Water Authority and included major groundwater quality parameters: calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺), chloride (Cl⁻), sulfate (SO₄²⁻), and total dissolved solids (TDS). A uniform temporal period from 2002 to 2023 was considered for all selected wells to ensure consistency in trend analysis.
To evaluate temporal variations in groundwater quality, non-parametric statistical methods, including the Mann–Kendall test and Sen’s slope estimator, were employed. These methods are widely used in hydrochemical studies due to their robustness against non-normal data distributions and their ability to detect both the direction and magnitude of trends over time.
Spatial variations in groundwater quality parameters were analyzed using a range of interpolation techniques, including both deterministic and geostatistical approaches. The applied methods consisted of Inverse Distance Weighting (IDW), Radial Basis Functions (RBF), Local Polynomial Interpolation (LPI), Global Polynomial Interpolation (GPI), and Kriging. All spatial analyses were performed in a GIS environment. The optimal interpolation method for each parameter was selected based on statistical performance indicators, primarily the lowest Root Mean Square Error (RMSE) and the highest coefficient of determination (R²).
Prior to spatial modeling, the normality of the dataset was evaluated using the Kolmogorov–Smirnov and Shapiro–Wilk tests, which are commonly applied to assess data distribution characteristics. The results of these tests were used to determine whether data transformation was required before applying interpolation and statistical analyses.
Finally, the Groundwater Quality Index (GQI) was calculated to provide an integrated assessment of groundwater quality conditions. By aggregating multiple hydrochemical parameters into a single index, GQI enables a comprehensive evaluation of groundwater quality and facilitates spatial comparison across the aquifer. This index was used as a key tool for interpreting overall water quality status and supporting groundwater management decisions.
Results and Discussion: The results indicate that while the groundwater quality of the Bahabad aquifer remained within the “Acceptable” category during the study period, a slight but statistically significant downward tendency in quality was observed. Spatial distribution analysis revealed that salinity patterns and ionic concentrations are influenced more by anthropogenic pressures such as over-pumping, water table drawdown, and inter-basin water transfer than by natural geological factors.
Temporal analysis highlighted significant spatial heterogeneity; for instance, Mg²⁺ exhibited a significant increasing trend across all stations (Sen’s slope ranging from 0.05 to 0.23), whereas Na⁺ and SO₄²⁻ showed decreasing or insignificant trends in certain wells. TDS displayed variable behavior, showing generally stable or slightly decreasing trends, while a sharp increase was observed at one specific station.
Furthermore, a comparison of interpolation techniques showed that under conditions of limited sampling density and weak spatial correlation, deterministic methods often yielded higher predictive accuracy than geostatistical models.
Conclusion: Despite only minor overall fluctuations in the GQI (ranging approximately between −0.5% and +0.1%), the index maps did not fully capture the intensity of localized quality degradation in critical parameters such as TDS, Na⁺, and SO₄²⁻. This indicates that composite indices like GQI may partially neutralize opposing trends among individual parameters, thereby providing a more generalized representation of groundwater quality conditions.
Trend analysis of individual parameters revealed considerable spatial heterogeneity across the aquifer. Magnesium and chloride exhibited consistent increasing trends in most monitoring wells, indicating progressive salinization, whereas sodium and sulfate showed mixed or insignificant trends in several locations. Total dissolved solids demonstrated spatially variable behavior, with localized sharp increases despite generally stable or declining trends elsewhere. This variability reflects the complex interaction between local hydrogeological conditions and human-induced pressures and confirms that groundwater quality evolution cannot be adequately described using a single parameter or uniform trend assumption.
The comparison of interpolation techniques further highlighted an important methodological implication: under conditions of limited sampling density and weak spatial autocorrelation, deterministic methods can provide more reliable estimates than geostatistical approaches. This emphasizes the need to adapt modeling strategies to data availability and hydrogeological settings, particularly in arid and semi-arid regions where monitoring networks are often sparse.
Although the Groundwater Quality Index proved effective in providing an overall assessment of groundwater status, it was less sensitive to localized deterioration in key parameters such as TDS, sodium, and sulfate. This confirms that composite indices, while useful for communication and management purposes, may mask emerging local-scale risks if used in isolation. Therefore, they should be applied alongside parameter-specific analyses rather than as standalone decision-making tools.
Overall, the findings highlight the urgent need for improved groundwater management in the Bahabad aquifer. Controlling excessive abstraction, regulating industrial withdrawals, and strengthening long-term monitoring programs are essential to prevent further degradation. Integrating spatial analysis with trend-based assessments can provide a more reliable framework for sustainable groundwater management, particularly in arid regions experiencing increasing water stress.

Abstract : Earth-fill dams with clay cores are pivotal hydraulic structures for potable water supply, agriculture, and flood management; however, over 35% of their historical failures are attributed to seepage and internal erosion. This study focuses on the Nahand Earth-fill Dam (East Azerbaijan Province) and numerically investigates the impact of a 10% increase in the geometric dimensions of the vertical-horizontal chimney drain specifically increasing length from 15 m to 16.5 m and thickness from 1.5 m to 1.65 m using the Finite Element Method (FEM) in GeoStudio 2024. The 10% increment step was selected based on initial sensitivity analysis and reference benchmarks. The numerical model was developed using a mesh of 12,800 quadratic elements, incorporating realistic boundary conditions (upstream hydraulic head of 35 m and downstream head of 5 m). Comprehensive validation was performed using 18 years of instrumentation data from 22 piezometers and 6 observation wells, achieving a coefficient of determination (R2) of 0.982 and a Root Mean Square Error (RMSE) of 0.68 m. The results indicated that this geometric optimization led to: a 22.3% increase in controlled seepage discharge (from 0.008 to 0.0098 m3/sm^3/sm3/s per unit width), a 31.7% reduction in average pore water pressure within the clay core (from 2.180 to 1.123 kPa), and a 9.3% decrease in total hydraulic head loss. Furthermore, the exit gradient remained below the safety threshold of 0.8 in 94.2% of cases, while the Factor of Safety (FOS) for downstream slope stability improved by 25.5% (from 1.45 to 1.82). Additionally, 3D arching effects resulted in an 18.4% reduction in total discharge compared to 2D analysis. Monte Carlo simulation with 10,000 iterations demonstrated a reduction in failure probability to less than 0.3%. Model calibration via the Levenberg-Marquardt algorithm confirmed high precision in replicating the dam’s long-term hydraulic behavior. This cost-effective optimization significantly enhances drainage efficiency and overall structural safety, providing a practical framework with 94.5% generalizability for the design of similar embankment dams.
Keywords: Numerical Modeling, Hydraulic Conductivity, Alluvial Layer, Seepage Flow, Dam Stability








Introduction: Clay-core earth dams are indispensable hydraulic structures for water supply, irrigation, and flood control, yet more than 35% of their historical failures stem from excessive seepage and internal erosion. The hydraulic behavior of alluvial foundation layers, characterized by high permeability and variable thickness, exacerbates these risks by promoting uncontrolled seepage, pore-water pressure buildup, and slope instability. At Nahand Dam (East Azerbaijan Province, Iran), situated on a narrow U-shaped valley with a 16 m thick alluvial layer over Eocene marls, the existing horizontal chimney drain (15 m length, 1.5 m thickness) inadequately mitigates these issues under a 35 m upstream head and 5 m downstream head. Despite advances in numerical modeling, the synergistic effects of drain geometry on seepage control, hydraulic gradients, and stability particularly in heterogeneous 3D geometries remain underexplored. This study addresses this gap by numerically evaluating a targeted 10% dimensional increase in the chimney drain (length to 16.5 m, thickness to 1.65 m) using the finite element method, with the aim of optimizing drainage efficiency, reducing seepage-related hazards, and enhancing overall dam safety for similar structures.
Materials and Methods: The numerical analysis was conducted using GeoStudio 2024 (SEEP/W and SLOPE/W modules) based on site-specific geotechnical and hydrogeological data from Nahand Dam. The 2D and 3D models incorporated realistic geometry (35.5 m dam height, 730 m crest length, central vertical clay core), material properties (Table 3: saturated hydraulic conductivities from 1×10⁻⁹ m/s for the clay core to 5×10⁻² m/s for filters/drains; Table 4: mechanical parameters calibrated via 124 triaxial tests and inverse analysis), and boundary conditions (fixed upstream head of 35 m at normal reservoir level 1607 m asl; downstream head of 5 m). A mesh of 12,800 quadratic triangular elements (2D) and 48,600 tetrahedral elements (3D) was employed, with adaptive refinement ensuring convergence (grid sensitivity: <0.3% change in seepage and pressure). Five scenarios were simulated: baseline (S0: 15 m × 1.5 m) and incremental 10–50% increases in drain dimensions, under steady-state, transient (e.g., 45-day rapid filling, 7-day sudden drawdown), and coupled hydro-mechanical conditions. Validation against 18 years of instrumentation data (22 piezometers, 6 observation wells; R² = 0.982, RMSE = 0.68 m) confirmed model fidelity (98.2% agreement). Uncertainty was quantified via global Sobol sensitivity analysis (10,000 samples) and Monte Carlo simulation (10,000 runs) on key parameters (core permeability, drain dimensions).
Results and Discussion: The 10% increase in chimney drain dimensions yielded substantial hydraulic and stability enhancements. Controlled seepage discharge rose 22.3% (0.008 to 0.0098 m³/s per unit width), facilitating efficient pore-water pressure dissipation, with average core pressures declining 31.7% (2.180 to 1.123 kPa) and total hydraulic head loss reduced by 9.3%. Exit gradients remained below the safe threshold of 0.8 in 94.2% of cases, minimizing internal erosion risk. Downstream slope factor of safety improved 25.5% (1.45 to 1.82) under steady-state conditions, with greater gains (up to 31.5%) in transient scenarios (e.g., rapid filling post-earthquake, PGA = 0.45 g). Three-dimensional arching effects reduced overall discharge by 18.4% compared to 2D models, redistributing flow toward abutments in the narrow valley. Permeability dominated over thickness (745.92% vs. 16.85% seepage impact), as confirmed by Sobol indices (S₁ core k = 0.88–0.91). Monte Carlo results indicated failure probability <0.3%, underscoring the cost-effective nature of this optimization. These outcomes align with and extend prior studies (e.g., Salmasi & Abraham, 2022; Djehiche et al., 2023), providing a validated framework for drain design in alluvial foundations.
Conclusion: This study demonstrates that a modest 10% enlargement of the horizontal chimney drain at Nahand Dam markedly improves seepage management, reduces pore pressures and hydraulic gradients, and bolsters slope stability across steady and transient regimes. The resulting enhancements 22.3% higher controlled discharge, 31.7% lower core pressures, and 25.5% greater factor of safety mitigate internal erosion and uplift risks while accounting for 3D effects. Monte Carlo analysis affirms high reliability (<0.3% failure probability), offering a practical, generalizable (94.5% applicability) blueprint for optimizing drainage in similar clay-core earth dams. These findings advocate for precise, data-driven drain modifications to ensure long-term structural integrity amid evolving hydrological demands.

Abstract Introduction
A large amount of precipitation is lost annually. Therefore, studying the precipitation-runoff process and predicting runoff are essential in water resources studies and plans and water engineering discussions. Therefore, choosing a model that can predict runoff from rainfall with acceptable accuracy using effective factors seems essential. The aim of this research is to estimate the components of unit hydrographs resulting from several precipitation-runoff events in the Samian watershed located in Ardabil province using a nonlinear optimization method along with the application of probability distribution functions.

Materials and Methods
In order to determine the optimal values of the distribution function parameters, the objective function is considered as minimizing the sum of the squares of the deviation between the predicted and actual direct runoff hydrographs. The values of the probability distribution functions parameters are optimized using Mathematica software and nonlinear programming method. Finally, the direct runoff hydrographs are calculated using the effective rainfall hydrographs and the corresponding unit hydrographs and compared with the observed direct runoff hydrographs. The performance criteria of mean square error, mean absolute error and correlation coefficient are used to examine the ability of the functions in the calibration and testing stages. The classical least squares method is also used to extract the unit hydrographs and its efficiency is compared with the results of the nonlinear optimization model.

Results and Discussion
In the calibration period, for the first event, the RMSE and MAE errors of the Gumbel (0.0048 and 0.0042) and Nakagami (0.0134 and 0.0095) functions are higher and their correlation coefficients (0.935 and 0.237, respectively) are also lower than those of the other functions. The lowest errors (0.0021 and 0.0016) and the highest correlation coefficient (0.978) are related to the lognormal distribution. The weakest distribution is the Nakagami distribution with RMSE equal to 0.0134, MAE equal to 0.0095, and CC equal to 0.237. In the case of the second event, the RMSE and MAE errors of the Gumbel (0.0110 and 0.0070) and Nakagami (0.0223 and 0.0293) functions are higher and their correlation coefficients (0.682 and -0.087) are also lower than the other functions. The lowest errors (0.0068 and 0.0056) and the highest correlation coefficient (0.874) are related to the lognormal distribution. The weakest distribution is the Nakagami distribution with RMSE of 0.0223, MAE of 0.0293, and CC of -0.087. In the case of the third event, the performance of all functions is satisfactory due to very low RMSE and MAE values and very high correlation coefficient. However, the errors of the gamma and lognormal functions are slightly higher than the others. Also, Weibull and normal distributions show better performance than other distributions based on the values of performance criteria because these distributions showed high ability in predicting the ascending and descending branches of the unit hydrograph.
In the test period, considering the average values of the performance functions of the distributions in each event, it can be said that the lognormal distributions (with average RMSE and MAE errors of 0.0134 and 0.01115 and average correlation coefficient of 0.823) and the normal distribution (with average RMSE and MAE errors of 0.0142 and 0.01275 and average correlation coefficient of 0.739) have higher potential in estimating the unit hydrographs of the basin, respectively. The errors of Pearson and Nakagami distributions in both events were higher than the other distributions. The least squares method also had better performance criteria values than all distributions in both events.

Conclusion
In general, according to the results obtained in the calibration and testing period, it can be concluded that the lognormal distribution is the best distribution for estimating and calculating unit hydrographs (especially in estimating the ascending branch and peak time and discharge) in the Samian basin. Also, the Pearson and Nakagami functions are among the weakest distributions in estimating the hydrographs of that basin. The least squares method, regardless of its shortcomings, is among the appropriate methods in estimating the unit hydrograph.
For future studies, it is suggested to investigate the performance of other statistical distributions such as beta and logistic distributions in extracting the unit hydrograph of the Samian basin. Also, the performance of statistical distributions in determining the unit hydrograph from combined precipitation-runoff events and from precipitation-runoff events related to the warm months of the year in which the basin does not have snow storage should be investigated. Finally, similar research should be conducted in other watersheds of Ardabil province and a suitable model should be proposed for the entire province.

Keywords: Nonlinear optimization, least squares, precipitation-runoff event, Mathematica software, Unit Hydrograph

Abstract Reference evapotranspiration (ETo) constitutes a fundamental component in water resource management and irrigation scheduling, playing an important role in agricultural planning, hydrological modeling, and climate change impact assessment. Accurate estimation of ETo primarily depends upon the availability of comprehensive meteorological parameters, including maximum and minimum temperature, relative humidity, wind speed, and solar radiation, which are required for the implementation of the FAO-56 Penman-Monteith method, the internationally recognized standard for ETo calculation. However, ground-based meteorological data often encounter significant limitations in many regions due to sparse and heterogeneously distributed station networks, high maintenance costs, and limited accessibility, particularly in developing countries and areas with complex topography. These constraints lead to considerable uncertainty in ETo estimation and consequently create difficulties in sustainable water resource management, irrigation scheduling, and crop water requirement assessment. In response to these challenges, remote sensing technology has emerged as a promising alternative, offering spatially continuous, temporally consistent, and cost-effective data coverage at regional and global scales.
This study was conducted with the primary objective of assessing the feasibility of replacing ground-based meteorological data with remote sensing parameters for daily ETo estimation using the FAO-56 Penman-Monteith method. The research employed data from 15 weather stations in East Azerbaijan Province in northwestern Iran, covering the period from 2000 to 2023. This region was selected due to its semi-arid to cold semi-arid climate, significant topographical heterogeneity ranging from approximately 700 meters to over 1800 meters above sea level, and its agricultural importance, making it an ideal testbed for evaluating the potential of remote sensing data in complex environmental conditions. The ground-based meteorological data, obtained from the Islamic Republic of Iran Meteorological Organization (IRIMO), included daily maximum temperature (Tmax), minimum temperature (Tmin), mean relative humidity (RH), wind speed at 2-meter height (Ws), incoming solar radiation (Rs), and extraterrestrial radiation (Ra). These data were complemented by MODIS satellite products, including land surface temperature (LST) from both daytime and nighttime observations, solar radiation (DSR), albedo, Normalized Difference Vegetation Index (NDVI), and solar zenith angle (SZA), as well as soil moisture data obtained from the GLDAS satellite product, all extracted through the Google Earth Engine platform.
Four machine learning models were developed and evaluated with six distinct input scenarios, ranging from minimal ground-based variables to a full combination of satellite and ground-based data. The models included Gaussian Process Regression (GPR), a Bayesian non-parametric method capable of estimating prediction uncertainty; LightGBM, an efficient gradient boosting decision tree algorithm suitable for large-scale regression problems; Temporal Convolutional Network (TCN), a deep learning architecture based on causal and dilated convolutions with residual connections; and a hybrid TCN-GRU architecture designed to combine the multi-scale feature extraction capabilities of TCN with the long-term dependency modeling strengths of Gated Recurrent Units (GRU). The input scenarios were carefully designed to evaluate the incremental contribution of different data sources: IC1 used only ground-based variables (Tmax, Tmin, RH); IC2 and IC3 employed limited satellite variables; IC4 incorporated complete satellite-derived variables; IC5 combined complete satellite data with Tmax and RH; and IC6 combined satellite data with Tmin and Ws.
The results demonstrated that the TCN-GRU model provided the most accurate performance under the hybrid scenario IC5, which incorporated all satellite-derived variables alongside maximum temperature and relative humidity. This configuration yielded a coefficient of determination (NSE) of 0.975, a mean absolute error (MAE) of 0.29 mm/day, and a normalized root-mean-square error (NRMSE) of 7.6%, indicating excellent agreement between estimated and observed ETo values. In contrast, the exclusively satellite-based scenarios (IC2–IC4) exhibited a substantial decline in estimation accuracy, with the TCN-GRU model achieving NSE values below 0.79 in IC2 and NRMSE values exceeding 10.6% even in the most comprehensive satellite-only scenario (IC4), highlighting the inherent limitations of remote sensing data when used in isolation for ETo estimation.
The Diebold–Mariano test confirmed that the superiority of the TCN-GRU model over GPR and LightGBM was statistically significant across all scenarios at the 0.01 significance level, while its advantage over the standalone TCN was only significant under the IC5 scenario at the 0.05 level. This finding underscores the value of the hybrid architecture in effectively modeling temporal dependencies and spatial patterns in ETo estimation. Pearson correlation analysis further revealed that maximum temperature (r = 0.93) and solar radiation (r = 0.91) showed the strongest positive correlations with ETo, which is consistent with the physical principles of evapotranspiration, where temperature and energy availability are primary driving factors. Conversely, solar zenith angle (r = -0.88) and relative humidity (r = -0.73) showed the strongest negative correlations with ETo, indicating their role as limiting factors through their influence on atmospheric demand and energy receipt at the surface.
Overall, although remote sensing data cannot fully substitute for ground-based meteorological measurements, their integration with a minimal set of ground variables, specifically maximum temperature and relative humidity, can yield acceptable accuracy (NRMSE < 8%) for operational applications. The proposed TCN-GRU model, with its hybrid convolutional-recurrent architecture, effectively captures both short-term and long-term dependencies in meteorological time series and is recommended as an efficient tool for ETo estimation in regions with limited ground-based data availability.

Abstract Introduction The agricultural sector’s strong dependence on water and energy resources for ensuring food security for the growing global population amplifies the necessity of enhancing water and energy efficiency in agricultural products, while simultaneously maintaining the health of society and the environment. Sustainable provision of water and energy is one of the main challenges to development in all countries. Since the agricultural sector is one of the largest consumers of water and energy, any disruption in their supply can significantly impact agricultural production levels. Increasing efficiency and optimizing energy consumption management are key strategies for mitigating the environmental effects caused by food production processes. This approach not only creates economic benefits but also plays a crucial role in achieving the long-term sustainability of production systems through the conservation of fossil resources and reduction of air pollution. Consequently, extensive research has been focused on energy management. On the other hand, unsustainable use of agricultural inputs such as chemical fertilizers, pesticides, and fossil fuels leads to serious consequences, including global warming, biodiversity loss, and degradation of soil and air quality. Therefore, sustainable management of these inputs is essential and inevitable for maintaining environmental balance and achieving sustainable development. Thus, the challenges of food security and the need for sustainability of energy resources in modern agriculture have heightened the importance of assessing energy consumption efficiency. Energy footprint analysis serves as an effective tool for identifying high-consumption areas and providing managerial and technological solutions to improve energy efficiency in agricultural production.   Materials and Methods This research was conducted with the aim of examining the personal characteristics of farmers, analyzing energy consumption, and assessing energy indicators in the cultivation of three major crops: rice, forage corn, and potatoes, in the districts of Dorud, Kuhdasht, and Azna, respectively, in Lorestan Province. Data were collected through questionnaires and face-to-face interviews with farmers and analyzed using statistical methods. The input energy indicators included chemical fertilizers, fuel, irrigation water, seeds, and human labor. The main sections of the questionnaires included: (I) general information about the farmer and the farm, such as age, education, work experience, and land area; (II) information regarding the amount of input consumption, including human labor, machinery, chemical inputs, fuel, irrigation water, and seeds; and (III) information regarding crop yield. The statistical population consisted of one thousand active farmers involved in rice production (in Dorud), forage corn (in Kuhdasht), and potatoes (in Azna). To determine the sample size, the Cochran formula was used with a confidence level of 95% and a margin of error of 7%. To calculate the energy equivalent of inputs and outputs, all production inputs and outputs were converted to energy equivalents in terms of megajoules per hectare (MJ/ha). In this research, indicators such as Energy Ratio, Net Energy Yield, Energy Efficiency, and Water Use Efficiency were employed.   Results and Discussion The findings indicated that the majority of farmers were in the age range of 40-50 years, with primary or high school education, and had a minimum of five years of farming experience. Their predominant crop was forage corn. The findings indicated that the highest input energy consumption was observed for potatoes, with 123048 MJ ha⁻¹, of which fuel energy accounted for 58.17%. It was followed by rice with 121117.09 MJ ha⁻¹ (fuel energy share: 50%), and forage maize with 98644 MJ ha⁻¹, where 60.85% of the total energy input was derived from fuel consumption. After fossil fuels, agricultural machinery, with an average share of 30%, and chemical fertilizers, with an average of 10%, ranked nex. The energy share of irrigation water in rice was 11236.39 MJ ha⁻¹ (9.28%), which was significantly higher compared to the other two crops.   Conclusion Overall, the results revealed that potato had the highest total input energy (123048 MJ ha⁻¹) and output energy (151200 MJ ha⁻¹), while forage maize had the lowest input energy (98644 MJ ha⁻¹).

Abstract Introduction
The common bean is an important legume cultivated by farmers in various regions using low-input practices. It originated in South and Central America and is now cultivated in all tropical and temperate regions worldwide. Asia accounts for 43% of global bean production, while the Americas (North, Central, and South America) account for 29%, and Africa for 26%. Bean seeds, containing approximately 22% protein, are a valuable alternative to animal protein sources. Iran is an arid and semi-arid country that faces the problem of resource shortages, especially water. Undoubtedly, modifying consumption patterns as well as optimizing the use of agricultural inputs will lead to increased food security.
 
Materials and Methods
The experiments were conducted at the research farm of the Campus of Agricultural and Natural Resources (34°, 19´ N, 47°, 50´ E and altitude 1320 m) of Razi University in the Kermanshah region, located in western Iran, over three years from 2021 to 2023. In terms of climatic divisions, this region is located in the temperate mountainous regions with a Mediterranean climate. The experiments were conducted in a split plot in a randomized complete block design with three replications. The treatments included irrigation water amount as the main factor (providing 100% of the water requirement, equivalent to 7300 m³ ha^-1 (IR100%), providing 80% of the water requirement, equivalent to 5840 m³ ha^-1 (IR80%), and providing 60% of the water requirement, equivalent to 4380 m³ ha^-1 (IR60%) for 2021. Because the amount of water required by the plant is determined according to climatic factors, the amounts of irrigation water in each treatment were determined as 6800, 5440, and 4080 m³ ha^-1 for 2022 and 7200, 5760, and 4320 m³ ha^-1 for 2023, respectively. Three common red bean cultivars, including Ofogh, Yaghoot, and Derakhshan, were also considered as secondary factors. The soil water status, leaf area index (LAI), light absorption, total dry weight (TDW), light use efficiency (LUE), and grain yield (GY) were measured.
 
Results and Discussion
The soil volumetric water content fluctuated as a function of the amount of water entering the soil (irrigation) and the amount of water leaving the soil (evaporation from the soil surface and transpiration by the plant). Soil volumetric water content varied in different irrigation treatments. Regardless of the red bean cultivars, the highest and lowest soil water content were observed in the IR100% and IR60% treatments during 2021-2023, respectively. The volumetric soil water content also varied for the studied red bean cultivars during the experimental period. Regardless of irrigation treatments, the highest and lowest soil water content were observed for the Ofogh and Derakhshan cultivars, respectively. The Ofogh cultivar had a shorter growth period than the other cultivars, so it required less irrigation water. It seems that the Ofogh cultivar absorbed water from the soil more quickly in the early stages of growth, which led to a sharp decrease in soil moisture content. But at the end of the growing season, as the air gradually cooled, soil moisture content increased again due to a decrease in water absorption by the Ofogh cultivar. The Yaghoot cultivar required more irrigation water than the Ofogh cultivar due to its unlimited growth, and in the middle of its growth period, due to the greater development of leaves in the canopy, it absorbed more water from the soil, which led to a more severe decrease in soil moisture. The changes in soil water content for the Derakhshan cultivar were almost similar to the Yaghoot cultivar. Due to the longer growth period of Derakhshan cultivar compared to other cultivars, more irrigation water was absorbed by this cultivar, which led to a sharp decrease in soil volumetric water content at the end of the growth period. The results showed that soil moisture content varied significantly among irrigation treatments and cultivars. Severe water deficit stress reduced LAI, TDW, and GY. The highest LUE in the vegetative and reproductive stages for the Yaghoot cultivar compared to other cultivars were 1.36 and 0.54 g MJ^-1, respectively. In 2022, the evaluated traits were higher than in 2021 and 2023, due to the lower average temperature (27°C) during the growing season. Regardless of year, the highest grain yield (192.5 g m^-2) belonged to Ofogh cultivar under optimum irrigation conditions, and the lowest grain yield (80.9 g m^-2) belonged to Derakhshan cultivar in severe water stress conditions.
 
Conclusion
The results of this study showed a positive effect of irrigation on the light absorption and LUE of common bean cultivars. Severe water stress reduced the ability of the crop to maintain leaf area during the growth period. Finally, the reduction in light absorption, LUE, and the length of the bean growth period due to drought stress led to lower TDW and GY.
 

Abstract Introduction
Water scarcity, as one of the main challenges in the agricultural sector, especially in arid and semi-arid regions, has doubled the necessity of optimizing water use in the production of strategic crops such as sugar beet. Determining an optimal irrigation pattern that can simultaneously meet performance, technical, and economic objectives requires a comprehensive and multi-dimensional approach. Traditional evaluation methods, which often focus on one or a limited number of indicators, are unable to provide a complete picture of the trade-offs between different criteria. In this regard, Multi-Criteria Decision-Making (MCDM) methods, with their capability to simultaneously and compromisingly evaluate quantitative and qualitative indicators, are considered effective tools for prioritizing management options. This research aimed to evaluate the effects of different levels of management allowable depletion (MAD) on functional, technical, and economic indicators in a sugar beet irrigation system. Given water resource limitations and the need to optimize agricultural water use, determining the appropriate MAD level can significantly enhance crop productivity and profitability. The study employed the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) multi-criteria decision-making method to select the best irrigation treatment. This investigation was conducted within the context of increasing global water scarcity and the critical need for sustainable agricultural practices. Efficient irrigation management is paramount for ensuring food security and the economic viability of farming operations. By systematically analyzing the trade-offs between water conservation, crop yield, and economic returns under varying soil moisture regimes, this study provides a comprehensive framework for informed decision-making. The application of the VIKOR method is particularly suited to this problem, as it facilitates the identification of a balanced compromise solution that reconciles potentially conflicting objectives inherent in agricultural water management, thereby offering a robust scientific basis for optimizing sugar beet irrigation strategies in water-limited environments.
 
Materials and Methods
Integrated Methodology Section (Final Version): This experiment employed a randomized complete block design (RCBD) to systematically evaluate the impact of irrigation treatments. The study was conducted at the experimental field of the Soil and Water Research Institute in Karaj, Iran. Three distinct levels of management allowable depletion (MAD), specifically 40%, 60%, and 80%, were applied as the main treatments, each replicated four times to ensure statistical reliability and account for field variability. A comprehensive dataset was collected, encompassing key agronomic and economic indicators: crop evapotranspiration (ETc), root yield, water productivity (WP), energy productivity, and relevant economic metrics. For the statistical analysis of the data derived from the randomized complete block design, analysis of variance (ANOVA) was utilized. This analysis was conducted to examine the statistical significance of observed differences among the various irrigation treatments for each measured trait. In cases where the treatment effect was significant, Duncan's multiple range test at the 5% probability level was used to separate the means. To assess the reliability of the results and investigate the sensitivity of the final ranking to potential variations in criterion weights or data fluctuations, a sensitivity analysis based on Monte Carlo simulation was also performed. In this analysis, by introducing controlled random variations to the input parameters of the VIKOR model, the stability and robustness of the final ranking of the options were evaluated. The VIKOR multi-criteria decision-making method was subsequently applied to this integrated dataset. This method was chosen for its proven efficacy in handling complex decisions involving conflicting and non-commensurable criteria, allowing for the identification of a compromise ranking that best satisfies all evaluation parameters under the given experimental conditions. The integration of results from the analysis of variance, mean comparison tests, and Monte Carlo sensitivity analysis with the output of the VIKOR method provided a robust and multidimensional analytical framework for deriving valid and applicable conclusions.
 
Results and Discussion
The results demonstrated that the MAD=60% (T2) treatment optimally balanced root yield, WP, energy productivity, and benefit-cost ratio, emerging as the superior option. In the first year, this treatment achieved a VIKOR index (Q=0.2971), a root yield of 36.35 t/ha, and a WP of 5.07 kg/m³. In the second year, it recorded a VIKOR index (Q=0.1463), a root yield of 45.4 t/ha, and a WP of 5.8 kg/m³, confirming its superiority. To ensure the reliability of the results, a Monte Carlo sensitivity analysis was performed. This analysis confirmed the stability of the MAD=60% treatment, with probabilities of 85.94% (first year) and 100% (second year). These findings indicate that MAD=60% performs consistently under varying conditions.
 
Conclusion
The study concludes that MAD=60% is the optimal irrigation strategy for sugar beet cultivation under normal water resource conditions, excelling in functional, technical, and economic performance. These findings can guide farmers and policymakers in improving water management and crop efficiency.
 
Acknowledgments
The authors would like to thank the staff of the Soil and Water Research Institute, Karaj, Iran, for their assistance in fieldwork and data collection.
 

Abstract  Introduction Drought is one of the most complex and costly natural disasters, and its accurate monitoring is essential for water resources management and vulnerability reduction. The Standardized Precipitation-Evapotranspiration Index (SPEI), as a bivariate index, utilizes precipitation and temperature data (to estimate evapotranspiration) and has gained widespread application due to its multiscalar nature and sensitivity to climate change. However, limited access to long-term and uniform station-based data in a country with high climatic diversity and complex topography like Iran poses a significant challenge for accurate drought monitoring. The global gridded database SPEIbase, with a spatial resolution of 0.5 arc degrees, has been developed by integrating satellite data, climate models, and in-situ measurements. This study aims to evaluate the reliability of SPEIbase compared to station-based data across Iran's catchments to assess the feasibility of substituting this database due to its extensive spatial coverage and easier accessibility.   Materials and Methods Two datasets were used in this research: 1) Station data including monthly precipitation time series and monthly mean temperature from 43 meteorological stations in Iran for the statistical period 1986-2023, obtained from the Iran Meteorological Organization (IRIMO). 2) Gridded SPEIbase data, version 2.11, based on the CRU TS 4.09 dataset, extracted for the same period from the Climatic Research Unit (CRU) at the University of East Anglia. The station-based SPEI was calculated using potential evapotranspiration estimated via the Thornthwaite method at time scales of 1, 3, 6, 9, and 12 months. The SPEIbase index, based on potential evapotranspiration estimated via the FAO-56 Penman-Monteith method, was extracted for the corresponding period. The concordance and agreement between the two indices were evaluated using three statistical methods: 1) Pearson's correlation coefficient to assess linear relationship, 2) Weighted Cohen's Kappa statistic to measure agreement in drought classes (8 classes ranging from extreme drought to extremely wet), and 3) Bland-Altman plots to examine limits of agreement and systematic bias.   Results and Discussion Findings indicated that the Pearson correlation coefficient between SPEI and SPEIbase fell within the strong correlation range (0.70 to 0.90) for most stations and catchments in Iran. The highest correlations were observed at stations in mountainous areas, such as Khorramabad (0.92), Hamedan (0.87), and Urmia (0.86) at the 12-month scale. Conversely, coastal and lowland stations like Bandar Abbas, Ramsar, and Babolsar showed relatively weaker correlations (0.63 to 0.67) at shorter time scales. Results from the Weighted Cohen's Kappa statistic also demonstrated substantial agreement (0.60 to 0.80) in drought classification between the two indices in mountainous regions and moderate agreement (0.40 to 0.60) in lowland and coastal areas. Bland-Altman plots revealed narrow limits of agreement and random scatter of points around the mean line, indicating no significant systematic bias. A key finding was the increase in correlation and agreement with increasing time scale; the highest agreement was observed at 9 and 12-month scales (hydrological drought) and the lowest at the 1-month scale (meteorological drought). Bam station, with a mean annual precipitation of approximately 53.7 mm, exhibited the lowest correlation (0.64) and agreement (0.39) at the 12-month scale, possibly due to hyper-arid conditions and limitations of evapotranspiration estimation methods in such climates. Constructed violin plots confirmed the concentration of correlation and kappa coefficients at higher ranges with increasing time scale.   Conclusion The SPEIbase database demonstrates suitable accuracy for drought monitoring across Iran's catchments and shows high concordance and agreement with station-based data in many regions, particularly mountainous areas. As the time scale increases from 1 to 12 months, correlation and agreement improve, indicating higher reliability of this database for monitoring agricultural and hydrological droughts. In coastal and lowland regions, especially at shorter time scales, caution is recommended when using this database. Given its extensive spatial coverage, appropriate temporal resolution, and free accessibility, SPEIbase can serve as a reliable alternative to station-based data in data-sparse regions or areas with incomplete records. It is suggested that future research calculate station-based SPEI using the FAO-56 Penman-Monteith method to evaluate the impact of the evapotranspiration estimation method on the level of agreement with SPEIbase   Acknowledgement  The authors would like to thank the Iran Meteorological Organization (IRIMO) for providing the meteorological data used in this research free of charge.

Abstract Introduction Due to Iran's geographical location and climatic conditions, the quantity and quality of water resources are considered one of the limiting factors for agriculture in this country. Droughts in the last two decades, on the one hand, and lack of attention to the optimal use and proper exploitation of water, on the other hand, have exacerbated the water crisis in Iran. For this reason, the agricultural sector has witnessed serious developments and new perspectives on the rational exploitation of water resources, limited irrigation shortages, the use of saline water resources in agriculture, changing irrigation systems, and cultivating water-intensive plants over the same period. Quinoa has been introduced as a forage crop for ensuring food security in the world, which can tolerate drought stress conditions to some extent. However, the development of its cultivation under drought stress conditions in Khuzestan Province should be based on determining the limits of irrigation water quantities and determining its tolerance to salinity.   Material and Methods The present study was conducted to investigate the effect of different amounts of irrigation water and different levels of salinity on quantitative and qualitative parameters of quinoa. To carry out the work, quinoa was cultivated under drip and pulsed drip irrigation. The treatments studied included irrigation water quantity (I1: 60, I2: 80 and I3: 100% of field capacity), water quality (F (fresh): 0.5 and S (saline): 6 dS/m), and two irrigation managements: continuous drip (C) and pulsed drip (P). At the end of the growing season, sampling was performed to determine quantitative plant characteristics such as height, stem diameter, leaf area index, root length, root volume, fresh and dry root weight, and fresh and dry forage yield. Also, to investigate the effect of treatments on quinoa forage quality, qualitative parameters including leaf chlorophyll a and b content, leaf proline content, total digestible nutrients (TDN), crude protein (CP), Neutral detergent fiber (hemicellulose and lignin) (NDF), and Acid detergent fiber (hemicellulose-free cell wall) (ADF) percentage were measured. To investigate the distribution of salinity in the soil profile during and at the end of the growing season compared to its beginning, soil samples were taken from dug profiles in the center of all experimental plots at depths of 0-30 cm and 30-60 cm. These samples were transferred to the laboratory and saturated extraction was prepared from them. Then, the electrical conductivity of these samples was determined using an EC meter. All data collected from the experiment were entered into EXCEL 2019 software. After categorizing the data, SAS 9.1 software was used to analyze them. Data analysis of variance was performed at the 5% and 1% significance levels, and then means were compared using Duncan's multiple range test.   Result The results showed that the treatments of continuous drip irrigation with fresh water at 100% depth, and pulsed drip irrigation at 100% depth (first, second and, third pulses of fresh water) had the highest quantitative traits. Continuous drip irrigation with fresh water at 80% depth, and pulsed drip irrigation at 80% depth (first, second and, third pulses of fresh water) also had high quantitative traits. The highest values of plant height, stem diameter, leaf area index, fresh biomass, and dry biomass were 94 cm, 1.10 mm, 23.152 cm², 25.31420, and 14.8256 tons per hectare, respectively, which were obtained from the continuous drip irrigation treatment at 100% of the plant's water requirement using fresh water. In these treatments, the final soil salinity was close to the initial salinity at the time of the experiment, and the highest amounts of chlorophyll a and b and carotenoids were also observed. The TDN and ADF values were high in two treatments: continuous drip irrigation with fresh water and 100% depth, and pulsed drip irrigation and 100% depth (first, second and, third pulse of fresh water). However, these treatments had low soluble sugar, proline, and protein levels. Applying saline water alone or in combination with sweet pulses and water stress in treatments 2, 4, and 12- 30 increased the quality of the produced forage (due to high soluble sugar, proline, and protein). The highest water use efficiency was obtained from treatment 26, which included pulsed drip irrigation and a depth of 60% (first pulse of fresh water, second pulse of salin water, third pulse of fresh water).   Conclusion Therefore, considering that both water use efficiency and quality are important in forage production, it is recommended to use this treatment under conditions of saline water and pulsed drip irrigation.

Abstract Introduction Wheat is the principal crop in Iran in terms of both production and cultivated area. Enhancing its production is critically important for the economy and national food security. Assessing the water productivity of commercial wheat varieties is key to achieving more efficient water use. Previous studies have demonstrated that wheat water productivity is influenced by cultivar type and irrigation amount, with different varieties exhibiting varying grain yields under both water deficit and surplus conditions. Increasing yield and water productivity depend on several factors, most notably the selection of high-yielding, improved cultivars with lower water requirements, coupled with irrigation management strategies tailored to the characteristics of these varieties, especially in hot and arid climates.   Materials and Methods A two-year field experiment was conducted during the 2021-2022 and 2022-2023 growing seasons at the Safiabad Agricultural and Natural Resources Research and Education Center, using a randomized complete block design with three replications. The experimental treatments consisted of five irrigation levels (25%, 50%, 75%, 100%, and 125% of crop evapotranspiration) as the horizontal factor and six bread wheat cultivars (Azadegan, Ouj, Barat, Jalal, Darion, and Mehregan) as the vertical factor. Following land preparation, fertilization with macronutrients was applied before planting and during the vegetative growth stage, based on soil test recommendations. Sowing was performed using a Wintersteiger row planter, and irrigation was supplied via a T-tape drip irrigation system. Water was sourced from the Dez irrigation and drainage network. The designated irrigation treatments were implemented starting from the third irrigation event, with water volumes measured using volumetric water meters. Crop evapotranspiration was calculated using the evaporation pan method, and soil moisture balance was monitored by auger sampling and the gravimetric method. Weed control was achieved through appropriate herbicide applications.   Results and Discussion In the first and second years, total evapotranspiration (ET) was 310 mm and 300 mm, while total rainfall was 145 mm and 289 mm, respectively. Accounting for effective rainfall, the irrigation treatments supplied 42, 62, 82, 102, and 122% of the crop ET in the first year, and 60, 76, 89, 103, and 117% in the second year. A combined analysis of variance revealed a significant year effect. The grain yield of the cultivars in response to ET showed maximum variations of 10% and 30% in the first and second years, respectively. Water productivity, measured as grain yield per millimeter of water consumed, ranged from 18.43 to 22.38 kg ha⁻¹ mm⁻¹. By selecting an appropriate cultivar and managing deficit irrigation, the applied water productivity and crop water productivity indices increased by 63% and 93%, respectively. The relationship between plant water supply and grain yield followed a quadratic function. Based on this model, the optimal deficit irrigation strategy was to supply 70% of wheat ET, which resulted in an acceptable grain yield reduction of approximately 15%. Across irrigation treatments in both years, the Darion, Mehregan, and Jalal cultivars exhibited higher average grain yield and crop water productivity than the mean of all cultivars. A cluster analysis was performed using R software, based on the regression coefficients of the grain yield, relative water supply, and evapotranspiration data. The cultivars were grouped into distinct clusters, and practical, cluster-specific management recommendations are provided.   Conclusion Hot and dry climates are characterized by high variability in evaporation, precipitation patterns, and crop water requirements across different wheat growth stages. Consequently, to achieve sustainable production and enhance water productivity within the genetic potential of wheat varieties, breeding programs should target the development of cultivars capable of producing more than 24 kg of grain per hectare per millimeter of water consumed.

Abstract Introduction Water is a primary and fundamental need for survival, industrial development, and economic prosperity. In other words, the key to development depends on the expansion of water resources and their optimal use. To achieve this, the correct allocation of water resources to different uses based on priorities is of great importance (Loucks and Van Beek, 2017). Any management planning to control water consumption in the agricultural sector requires a correct understanding of the mutual influence of variables controlling water resources and consumption on each other in order to ensure optimal provision of agricultural water needs and also to ensure the improvement of the economic situation of farmers (Veysel, 2024). The purpose of this study is to examine and compare available water resources and uses, including agricultural, drinking, industrial, and environmental needs, for the Tajan River basin, including the two parts of the Tajan Plain and the river basin up to the entrance to the plain during the exploitation season, in order to determine the possible shortage of water supply in different months. It is worth mentioning that the results presented in the report on the study of water resources and needs of the Tajan River Basin (Pandam Company, 1402) were used to prepare this article.   Materials and Methods The Tajan River is one of the important rivers of Mazandaran Province, which originates from the Hezar Jarib Mountains and flows into the Caspian Sea after passing through the city of Sari in Farahabad. The enJulye catchment area of this river up to the entrance to the plain with an area of about 4050 square kilometers and the Tajan Plain with an area of about 800 square kilometers, has been considered as the study area to investigate water resources and uses. In this study, long-term statistical data from hydrometric stations of the Zaremroud (Garm Rud Station), Lajim (Vastan Station), Chahardangeh (Varand Station), and Tajan (Kordkheil Station) rivers, as well as the inflow and outflow statistics of Shahid Rajaee Dam (during the ten-year operation period of 1390-1400) as the main source of water supply for the Tajan basin, have been used. For groundwater resources (wells), information obtained from the Mazandaran Regional Water Company in 1400 and basic water resource studies have been used. Regarding water ponds, the results of the interpretation of new satellite images in the form of a land use map have been used.   Results The results of the surveys show that in the entire study area, including the Tajan River basin to the entrance to the plain and the lands of the Tajan Plain, there are about 119,800 hectares of pure agricultural land in the form of irrigated and dry-land cultivation, of which 74,500 hectares are irrigated and the rest are dry-land cultivation. There are fish farming ponds with a total area of 550 hectares in the Tajan Plain, which are generally related to warm-water fish. Also, some of the ponds located in the area are used for aquaculture. The total water requirement of the Tajan Plain area (agriculture and aquaculture) is 492 million cubic meters, and in the upstream lands (the Tajan basin to the entrance to the plain), this requirement is estimated to be 106 million cubic meters. The total drinking, industrial and tourism drinking needs of the region (Sari, Miandoroud and Kiasar counties) are 7.68 million cubic meters per year. The estimated environmental demand of the Tajan River is also equivalent to 100 million cubic meters per year. Available water resources for various uses include the regulated water flow of the Shahid Rajaee Dam, the seasonal flow of the rivers in the middle basin, dam water storage, and groundwater resources. To compare uses with available water resources, three comparison levels including normal, dry, and very dry water years have been used. The volume of water that can be extracted from the Shahid Rajaee Dam in the first six months of the year for normal, dry, and very dry water years is 231, 194, and 122 million cubic meters, respectively. The volume of water that can be extracted from ponds and wells located in the area are estimated to be 12.8 and 176 million cubic meters per year, respectively.   Discussion and Conclusion A comparison of water needs and available resources indicates that the total water shortage in the Tajan Basin is 166, 231, and 324 million cubic meters in normal, dry, and very dry years, respectively. Similarly, in the Tajan Plain area, the shortages are estimated at 147, 212, and 305 million cubic meters for the same conditions. In other words, in a normal or normal water year, there is a water shortage of about 150 million cubic meters in the irrigation network and the Tajan Plain area. The reduction in available water resources, including decreased outflow from the Shahid Rajaee Dam, leads to resource constraints and consequently causes water shortages in the lands of the Tajan Plain. In the dry water year, the water shortage was mainly due to the decrease in discharge from the Shahid Rajaee reservoir dam and the rivers of the middle basin. Therefore, in addition to the necessity of managing water distribution and scheduling, as well as improving the Tajan irrigation network, which will partially reduce the amount of damage caused by water shortage, the construction of the planned dams on the Zaremroud and Chahardangeh rivers is very important in regulating the monthly discharge.

Abstract Introduction
Floods rank among the most devastating natural disasters, causing significant loss of life and property each year. Floods are among the most destructive natural disasters, causing extensive loss of life and property annually. The Gorganrood watershed in northeastern Iran is particularly vulnerable to frequent and severe flooding due to its unique geographical and climatic characteristics. Factors contributing to this high flood risk include steep topographical gradients, impermeable soil, and degraded vegetation cover, which lead to rapid and devastating flash floods. The region has experienced a rise in both the frequency and intensity of floods in recent years, resulting in significant damages, such as the catastrophic flood of March 2019. Consequently, the development of effective flood forecasting and early warning systems (FFEWS) has become a critical priority for crisis management. Recent advancements in numerical modeling offer powerful tools for more accurate and timely flood prediction. Weather Research and Forecasting (WRF) models are widely used for their precision in simulating regional precipitation. Hydrological models like the Hydrologic Modeling System (HEC-HMS) are essential for converting predicted rainfall into surface runoff, while hydraulic models such as HEC-RAS excel at simulating river flow and mapping inundation zones. This research aims to develop and evaluate a sophisticated, integrated online system specifically for the Gorganrood watershed. The primary innovation of this study is the creation of a unified online platform that couples the WRF, HEC-HMS, and a 2D HEC-RAS model to forecast flood inundation up to 48 hours in advance. A further novelty lies in its software architecture, which utilizes React for the front-end and a Python-based Django framework for the back-end, a combination not previously applied in similar research for real-time visualization of flood forecasts.
 
Materials and Methods
 The research focused on the Gorganrood watershed, a major basin in northeastern Iran covering approximately 11,380 km². The system developed was an integrated Web-GIS software platform designed for end-to-end flood forecasting. The system's workflow began with the automated retrieval of meteorological data from the Global Forecast System (GFS). This data served as input for the regional WRF model to generate high-resolution precipitation forecasts. The rainfall predictions were then fed into a calibrated HEC-HMS model to simulate the rainfall-runoff process and generate flood hydrographs. In the final stage, a 2D HEC-RAS hydraulic model was executed for critical, populated river reaches to produce detailed flood inundation maps. The technological framework was built on modern software tools. The user interface (Front-End) was developed using React to create a dynamic user experience. The server-side logic (Back-End) was implemented in Python using the Django web framework. For data management, a PostgreSQL database with the PostGIS spatial extension was employed. GeoServer was used as the map server. The chosen models include:

WRF Model: Selected for precipitation forecasting due to its open-source nature, flexibility, and widespread use.
HEC-HMS Model: Chosen as the rainfall-runoff model for its suitability in a large basin with limited data. It was configured using the SCS-CN method for loss calculations, the SCS unit hydrograph method for runoff transformation, and the Muskingum method for channel routing.
HEC-RAS Model: The 2D version was selected for hydraulic modeling due to its ability to simulate complex, two-dimensional flow dynamics when floods overtop riverbanks and its free availability.

 
Results and Discussion
 The performance of each model component was rigorously evaluated. The WRF model was assessed using five historical storm events, comparing forecasts across five different lead times (6, 12, 18, 24, and 48 hours). Statistical analysis revealed that the 6-hour forecast horizon provided the optimal balance of accuracy and lead time, exhibiting the best performance metrics (R²=0.69, RMSE=12.25, NSE=0.0). Thus, a 6-hour lead time was adopted for the operational system. The HEC-HMS model was calibrated and validated against observed data from several hydrometric stations (Nodeh, Arazkuseh, etc.). The results demonstrated a good agreement between the simulated and observed hydrographs, particularly in capturing the peak discharge and timing of floods. Observed discrepancies in total flood volume were attributed to uncertainties in spatial rainfall data and potential measurement errors. For the numerous sub-basins lacking gauging stations, model parameters were regionalized using a clustering technique based on physiographic similarity to the calibrated sub-basins. The integrated online system allows users to run the entire forecast chain through a web interface. To manage the significant computational requirements, the 2D HEC-RAS model was implemented for two high-priority areas: the region downstream of the Golestan Dam and the flood-prone city of Aq-Qala. A key challenge was the high computational demand of the models, which was addressed by leveraging the High-Performance Computing (HPC) cluster at Shahid Beheshti University. Another challenge is the potential for model instability and limitations imposed by data quality, which can be mitigated by more detailed calibration.
 
Conclusion
This research successfully developed a comprehensive, integrated online system for flood forecasting in the Gorganrood watershed by coupling the WRF, HEC-HMS, and HEC-RAS models. The evaluation showed that the WRF model provided acceptable precipitation forecasts, the HEC-HMS model accurately simulated rainfall-runoff processes, and the 2D HEC-RAS model produced valuable, high-resolution flood inundation maps. The system's robust software architecture, utilizing React, Python/Django, and PostgreSQL/PostGIS, provides an efficient, scalable, and user-friendly platform for operational flood management. This work demonstrates that the integration of advanced numerical models into a single, automated platform is a highly effective approach to mitigating flood risk. The resulting system offers a powerful tool for crisis managers and serves as a replicable model for developing similar advanced warning systems in other flood-prone basins.
 
Acknowledgement
The authors would like to acknowledge the use of the High-Performance Computing (HPC) system at Shahid Beheshti University for the execution of the numerical models in this research.
 
Keywords: Django, Flood forecasting, Gorganrood watershed, HEC-HMS, HEC-RAS (2D), Integrated system, React, WRF
 
 
 

Abstract Introduction
Drought, as an environmental crisis, not only impacts ecosystems but also poses risks to human activities and has significant negative effects. The occurrence of intermittent and prolonged droughts, along with significant fluctuations in climate, exacerbates water scarcity, particularly in surface water resources; thus, groundwater resources play a key role as a vital source for supplying water for various consumption needs.
Groundwater drought is one of the serious and increasing challenges that has been acutely felt in recent years. Climate change and increasing water demand in agricultural and industrial sectors has led to increase dextraction from groundwater sources, significantly affecting many plains and groundwater resources in the country, resulting in severe depletion. This has consequently led to water crises and recurrent droughts. Therefore, understanding the relationship between drought and the status of groundwater resources is crucial. This issue not only impacts agriculture and food security but also has negative effects on public health, the economy, and the environment. For this reason, proper and sustainable management of these resources in the face of drought challenges is essential.
 
Materials and Methods
The examination of hydrogeological droughts and the monitoring of groundwater levels is essential for providing appropriate solutions for the protection and management of water resources.
In the present study, the Groundwater Resource Index (GRI) was used to assess groundwater drought in the Qazvin Plain. Additionally, to explore the relationship between the GRI and the Standardized Precipitation Index (SPI) across different time scales, the correlation coefficient between the two indices was calculated. Subsequently, the GRI was localized within the plain by analyzing its values across various monitoring wells.
 
Results and Discussion
The high correlation between the GRI index and the SPI drought index over a 48-month timeframe indicated that groundwater resources in the Qazvin plain were influenced by both wet and dry weather phenomena, with a time lag of approximately three to six months before meteorological drought translated into groundwater drought. Eslamian et al. (2009) also reported a three-month time lag for the effects of drought on the groundwater resources of the Qazvin, Buin Zahra, and Hamadan plains in their research.
 
Conclusion
The localization study of the GRI index in the Qazvin Plain region concluded that the index is highly responsive for assessing and evaluating groundwater drought. It effectively identified wet and dry years and showed a strong alignment with the behavior of the groundwater table. The analysis of drought during the years from 1996-2001 also illustrated that the impacts of drought continued into subsequent years on groundwater resources, and according to the GRI index, the decline in groundwater levels persisted in later years. This was evident even with increased precipitation in 2002 and thereafter, where we continued to witness declines and the ongoing trend of groundwater drought.
 
Acknowledgments
We would like to thank the University of Tehran and the Water Resources Management Company of Iran for providing the necessary facilities to conduct this research study and prepare relevant papers.

Abstract Introduction The basic strategy to mitigate water crisis is to save agricultural water consumption by increasing productivity, which will result in more income for farmers and sustainable production. Due to the economic importance of barley production in the country, it is necessary to study the volume of irrigation water and water productivity to produce this strategic product. Based on extensive field research on irrigation water management and application of different irrigation methods in barley farms, the innovations of this research were: a) measuring water consumed and determining water use efficiency in barley production, b) the up-to-date of the measurements and research findings, c) findings applicability for application in agricultural planning at the national and regional levels, d) the ability to development the findings in barley farms at the national level to improve water use efficiency. The hypotheses of this research are: a) barley irrigation water is various in different regions, b) water applied in barley farms is more than the required one, c) the water use efficiency of barley is different in the main production areas, and d) The applied water of barley is not the same in different irrigation methods. Therefore, the main objective of this study is to determine the water consumed and water use efficiency in barley production; to measure the water applied to barley farms in the main production areas; to compare the water measured in the production areas with the net irrigation requirement; and finally to determine water use efficiency of the barley in the main production areas in the Iran.   Materials and Methods  For this purpose, the volume of irrigation water and barley yield in 296 selected farms in 12 provinces (about 75% of the area under cultivation and production of barley in Iran) including Khuzestan, East Azerbaijan, Ardabil, North Khorasan, Fars, Khorasan Razavi, Tehran, Semnan, Markazi, Isfahan, Hamedan and Qazvin were measured directly. Farms in the mentioned provinces were selected to cover various factors such as irrigation method, level of ownership, proper distribution and quality of irrigation water. By carefully monitoring the irrigation program of selected farms during the growing season, the amount of irrigation water for barley during one year was measured. At the end of the season and after determining the average yield of barley during the 2020-2021 year, the values of irrigation water productivity and total water productivity (irrigation+effective rainfall) were determined in selected barley farms in each region. The volume of water supplied was compared with the gross irrigation requirements estimated by the Penman-Monteith method using meteorological data from the last ten years, and compared with the values of the National Water Document. Analysis of variance was used to investigate the possible differences in yield, irrigation water and water productivity in barley production.   Results and Discussion To assess the reliability of statistical analysis, we evaluated the sufficiency of the number of measurements needed for both the quantity of irrigation water and the ley yield on the farms. Subsequently, we computed statistical indices, such as the mean and standard deviation. The results showed that the number of measurements of irrigation water and barley yield was to be 296 and 283, respectively, which was more than the number of measurements required for irrigation water (41 dataset) and yield (50 dataset). Therefore, the sufficiency of the data for the statistical analysis was reliable. The results showed that the difference in yield, volume of irrigation water and water productivity indices were significant in the mentioned provinces. The volume of barley irrigation water in the studied areas varied from 1900 to 9300 cubic meters per hectare and its average weight was 4875 cubic meters per hectare. The average barley yield in selected farms varied from 1630 to 7050 kg ha^-1 and the average was 3985 kg ha^-1. Irrigation water productivity in selected provinces ranged from 0.22 to 1.53 and its weight average was 0.90 kg m^-3. Average gross irrigation water requirement in the study areas by the Penman-Monteith method using meteorological data of the last ten years and the national water document were 4710 and 4950 cubic meters per hectare, respectively. Irrigation efficiency of barley fields in the country is estimated at 62-65% without deficit irrigation.   Conclusion In order to reduce water consumption and improve water productivity, it is suggested to manage water delivery to farms during the season and deliver water rights to them according to crops water requirements. To reduce water losses and enhance productivity in the barley farms, it is suggested the application of modern irrigation systems according to the farms conditions with the suitable operation; and modification and improvement of surface and traditional irrigation methods. Note that, water is only one of several necessary and effective inputs in the optimal and economic production of barley. On the other hand, attention should be paid to the optimal application of other inputs including: seeds, fertilizers, equipment and tools etc.

Abstract Climatic changes and human activities are among the important factors that affect the flow of rivers and it is very important to determine the contribution of these factors in order to better manage water resources. In recent years, there have been major changes in the watersheds, and the amount of runoff and river flow has decreased, or in some cases, the flow has increased due to the occurrence of floods. The issue of reducing the amount of runoff, especially in the arid and semi-arid regions of Iran, is one of the basic challenges related to the management of water resources. Hydrological changes primarily result from a combination of natural or climatic factors, including precipitation levels, air temperature, and overall warming of the Earth. Additionally, human activities, such as the construction of dams, creation of reservoirs, urbanization expansion, and indiscriminate harvesting, play a significant role. It is important to note that these factors are interconnected, and alterations in one can impact the others. The increase of greenhouse gases and climate change has caused a change in the hydrological cycle and the amount of runoff in the watersheds and has increased the number of climatic extreme events. The main purpose of this study is to determine the contribution of each of these factors on the discharge changes of the Gharehsoo River, one of the most important rivers of Ardabil province, using elasticity-based methods (non-parametric and Bodiko-based methods).   Materials and Methods In this research, firstly, in order to determine the point of change in the amount of river runoff and to divide the base and change period, Petit's test was used during the statistical period of 1984-2019. This test was done using Xlstat software. According to the results of this test, there was a change in the annual flow time series in 1997, which was considered as the base period from 1984 to 1997 and from 1998 to 2019 as the period of changes. Then, the contribution of each of these factors was determined using elasticity-based methods.   Results and Discussion In the elasticity-oriented method, the non-parametric method and the methods based on Bodiko's assumptions were used to calculate the elasticity coefficient.The results showed that in Samyan station, in the non-parametric method, the contribution of human activities is 88.26% and the contribution of climate change is 11.74%. The contribution of human activities and the contribution of climate change for the methods of Schreiber, Aldekap, Bodiko, Peek and Zhang, respectively 91.98 and 8.02, 90.02 and 9.97, 91.98 and 8.02, 90.80 and 9.20, 92.37 and 7.62 are estimated. In general, in the elasticity method, the contribution of human activities is 88.26 to 92.37 percent and the contribution of climate change is from 7.63 to 11.74 percent, depending on the non-parametric and Bodiko method. At the Dost-Beiglo station, employing the non-parametric method reveals that human activities account for 96.13% of the observed changes, while the remaining 3.87% is attributed to climate change. The contribution of human activities and the contribution of climate change for the methods of Schreiber, Eldekap, Bodiko, Pick and Zhang are 97.71 and 2.29, 97.42 and 2.58, 97.56 and 2.44, 97.48 and 2.52, 97.71 and 2.29 are estimated. In general, in the elasticity-oriented method, the contribution of human activities between 96.13 and 97.71 percent and the contribution of climate change from 2.29 to 3.87 percent, depending on the non-parametric and Boudico-oriented method, have been met.   Conclusion In this research, different hydrometeorological data such as precipitation, evaporation and transpiration and monthly discharge from the Samyan and Dost Beiglo stations were used for the statistical period of 1982-2019. First, by using Pettitt's test, it was determined that the river flow rate has changed abruptly since 2016. Therefore, the entire statistical period was divided into two natural and change periods, and then, using elasticity-based methods, the contribution of human activities and the contribution of climate change were determined. According to the results obtained in both stations, the impact of human activities (more than 88%) on the basin's runoff is far more than climate change (less than 11%). Therefore, it seems necessary to prevent the effective human activities on reducing the river flow in solving and managing water problems in the basin.

Abstract Introduction The rapid growth and development of urban communities, coupled with the increased industrial and economic activities in recent years, have led to the production and release of various pollutants into the environment. These pollutants have adverse effects on human health, living organisms, and the overall environment. With limitations in water resources, insufficient rainfall, the looming risk of water crises in many countries, and the escalating pollution of surface and underground water, there is a pressing need for environmental solutions to mitigate these issues. It is important to acknowledge that wastewater often contains pollutants that may render it unsuitable for certain applications. The utilization of biochar derived from cost-effective materials and innovative technologies such as ultrasonics is one avenue that warrants exploration for enhancing water quality. In this approach, a nitrate solution is exposed to both an adsorbent and ultrasonic waves. This dual treatment induces changes in the physical and chemical properties of water, thereby offering potential improvements in water quality.   Materials and Methods This study aimed to explore the impact of utilizing biochar derived from rice straw, which was coated with iron(III) and zinc cations, and subjected to ultrasonication, on the nitrate adsorption process from aqueous solutions. In order to produce biochar, cheap materials of rice straw were used. The chopped straw was placed in the electric furnace and heated for one hour to reach the desired temperature. Then it was kept at that temperature for 2 hours. After that, the obtained biochar was washed three times with distilled water at a ratio of 1:20 and dried in an oven at 70°C for 24 hours. In this study, two temperature levels, 350 °C and 650 °C, were used for biochar production. Based on the results from pre-tests, it was found that biochars produced at 650 °C exhibited higher nitrate removal efficiency. These biochars were then used for the continuation of the experiments. To optimize the adsorbent dose, pre-tests were conducted using doses of 0.1, 0.3, 0.5, 0.8, and 1 gram of the adsorbent with 40 ml of nitrate solution. The concentrations of nitrate solution tested were 20, 45, 80, 100, 150, and 200 mg L^-1. The research involved conducting experiments to determine the optimal parameters for each treatment, with three repetitions conducted in the water quality laboratory of Sari agricultural sciences and Natural Resources University during the years 2021 and 2022. The treatments comprised biochar (B), biochar and ultrasonic (BU), biochar with iron(III) coating (BF), biochar with iron(III) coating and ultrasonic (BFU), biochar with zinc coating (BZ), and biochar with zinc coating and ultrasonic (BZU). In this investigation, Langmuir and Freundlich adsorption isotherms were examined.   Results and Discussion The results indicated that the BF and BFU treatments exhibited a higher maximum adsorption capacity. The Freundlich isotherm demonstrated higher correlation coefficients for BF, BFU, BZ, and B, suggesting a superior fit of the Freundlich model in these treatments. The better fit of the Freundlich adsorption isotherm indicates the heterogeneity of biochar surface adsorption sites, which means that the adsorption process is not confined to a single constituent layer. Nitrate adsorption on biochar surface is probably influenced by electrostatic adsorption and ion exchange. Conversely, the BZU and BU treatments showed a better fit with the Langmuir model. In the analysis of the Freundlich isotherm, n_f values revealed that BF, BFU, and BZ treatments exhibited a favorable adsorption state with a desirable curve shape. The B treatment displayed a normal adsorption state with a linear curve shape, while BU and BZU treatments showed a weak adsorption state with an unfavorable curve shape. The elevated values of adsorption capacity (K_F) obtained for BF, BFU, and BZ, namely 1909.414, 1484.22, and 386.63 ((mg g^-1)(L mg^-1)^1/n), respectively, underscore the high nitrate adsorption capacity of these treatments. Also, biochars coated with iron(III) and with iron solution concentration of 10000 mg L^-1 had a very good performance in removing nitrate from aqueous solutions. The new ultrasonic technology was able to improve the performance of the tested adsorbents in a period of 5 minutes without the need to stir the mixture of biochar and nitrate solution in the obtained equilibrium times, which were between 60 and 120 minutes. Application of this technology can be effective and useful in increasing the economic benefits of using limited water resources and increasing the efficiency of water consumption.   Conclusion The utilization of cost-effective biochars derived from rice straw, along with the application of ultrasonic technology, can substantially decrease nitrate levels in aqueous solutions. In the case of biochar with iron(III) coating, biochar with iron(III) coating combined with ultrasonic treatment, and biochar combined with ultrasonic treatment, there is a notable affinity for nitrate to be adsorbed onto the surface of the adsorbent.  

Abstract Introduction
Reservoirs are crucial for water supply to human societies, making their proper and planned management essential. Dams serve multiple purposes, including urban water supply, agricultural irrigation, flood control, and hydroelectric power generation. In order to properly manage and monitor the consumption of these important reserves, it is inevitable to know their capacities. Using water stage and the reservoir's initial volume-area-elevation curve, which is prepared with the hydrographic operations, is a common method for estimating the storage capacity of reservoirs at different water levels. Over time, the occurrence of numerous sedimentations, often due to factors such as floods, can alter the initial volume-area-elevation curve of a reservoir, requiring it to be updated. Hydrographic operations, using tools like eco-sounders, are conventional methods for updating this curve; however, these methods are both expensive and time-consuming. In recent years, various studies have focused on remote sensing techniques aimed at estimating the volume of water stored in reservoirs, using water levels to establish the surface area-elevation curve. The basis of these studies is the separation of water-land masks using spectral indices, the calculation of water levels, and the development of reservoir surface area-elevation curves through linear or polynomial relationships. However, the main limitation of these methods is the inaccuracy of linear or polynomial relationships in fitting the surface area-elevation curves at the beginning and end points of the water stage change interval, which correspond to the empty or full states of the reservoir. This inaccuracy arises due to factors such as drought or flood events. In this research, the limitation of linear and polynomial relationships in accurately predicting the points of the reservoir surface area-elevation curves, where observational data are unavailable due to non-occurrence, was addressed by using the Modified Strahler method to draw the hypsometric curve. This method allows for the calculation of the storage capacity of the reservoir between successive water levels and the determination of the final volume of water stored in the reservoir. By comparing the volumes of water stored at the present and initial reservoir capacities, the sedimentation rate and the useful life of the Negarestan Dam reservoir were estimated.
 
Material and Methods
Negarestan Dam (Kabudval) is located on the Qarasu (Zarin Gol) river, 45 km east of Gorgan in the Golestan Province. This dam is used for purposes such as supplying urban water to Aliabad city and supplying water needed for the agricultural irrigation network of Qarasu. In this study, landsat8 satellite images were used to estimate the useful life of the Negarestan reservoir. The required images of the ROI were downloaded through the USGS database and pre-processed in Envi5.3 software. Using visible and infrared spectral bands, water indices NDWIMCFeeters, NDWIGao, MNDWI, AWEISh and TCWet were calculated to separate land-water masks. After evaluating the accuracy of the obtained water level results by comparing it with the initial volume-area-elevation curve of Negarestan reservoir, the MNDWI index was used as the most accurate index to calculate water levels. In this study, the modified Strahler method was used to obtain the hypsometric curve of the surface area-elevation of the reservoir, which has high accuracy in extrapolating the beginning and end points of the curve. By using the hypsometric curve, water levels were extracted for arbitrary water levels, and with the help of the prismoidal method, the volume between consecutive water levels was calculated. The sum of these volumes equaled the current storage capacity of the reservoir. To estimate the sedimentation rate of the Negarestan Dam reservoir, the current storage capacity was compared with the initial storage capacity in 2015. Based on this comparison, the useful life of the reservoir was accurately predicted.
 
Results and Discussion
Validation results for calculating water surface areas using NDWIMCFeeters, NDWIGao, MNDWI, AWEISh and TCWet water indices showed that the MNDWI index with an average water surface areas calculation error equal to 5% is more accurate than other indices. Therefore, the MNDWI index was used in this study. Additionally, the comparison of the volume of water stored in the Negarestan reservoir with its initial storage capacity at the time of operation revealed that, over a period of 9 years, the storage capacity of the reservoir (at a water level of approximately 189.5 meters), which is close to the overflow crest level, had significantly decreased. It has decreased from about 24 to 20 million cubic meters, based on which the average annual sedimentation rate of the reservoir was estimated, to about 1.6%. The results showed that in a period of 9 years, the average level of the bathymetry of Negarestan reservoir has increased by 10 meters due to the accumulation of sediments, and the minimum level of the batymetry has reached from 160 to about 170 meters. According to the statistics of the International Commission on Large Reservoirs (ICOLD), the average annual sedimentation rate of the world's reservoirs is reported to be about 0.95%, and the results show that this amount in the Nagaristan Dam reservoir is almost 2 times the average rate. It is universal. According to the results obtained from this research and assuming constant climatic conditions, the useful life of the Nagarestan dam reservoir was estimated to be about 53 years from the beginning of 2024.
 
Conclusion
Considering the increasing importance of water resources management, including dam reservoirs, this study employed a fast and cost-effective method based on remote sensing to calculate the volume of water stored in dam reservoirs and estimate their useful life. In addition to providing appropriate accuracy, this method was able to overcome the limitations of previous approaches in estimating the volume of accumulated sediment in the deeper parts of the reservoir. As a result, it offers a reliable tool for the effective management of water resources.

Abstract Introduction Watermelon (Citrullus lanatus) is a widely recognized product with high demand, nutritional value, and export potential worldwide. Since the ultimate goal of agricultural production systems is to maximize plant yield, providing sufficient water to the plant is one of the most critical factors influencing yield. Therefore, investigating the effects of water limitation is an essential and undeniable necessity. On the other hand, deficit irrigation has been introduced as an approach to increase water productivity. Therefore, it is essential to consider the effects of this water-saving method on plant production, which highlights the need for further research. Deficit irrigation involves supplying only a portion of the plant's water requirements, while regulated deficit irrigation is a specific type of deficit irrigation that can be applied in various ways, such as irrigation based on growth stages, or allocating water to stages that are more sensitive to drought. It is important to recognize that plant response to water deficit depends on several factors, including climatic conditions, plant type, the intensity and method of deficit irrigation application, soil condition, and management practices.   Materials and Methods In order to determine the effect of deficit irrigation and regulated deficit irrigation on yield and water productivity of the watermelon, an experiment in the form of randomized complete blocks with 8 treatments including three irrigation levels of 100, 70 and 50 % of the plant's water requirement (evapotranspiration estimated by the FAO-Penman-Monteith method) and 5 regulated deficit irrigation levels including 50% of the water requirement in the stages of seedling, vine, flowering, fruit expansion and fruit maturity were carried out with three replications under black plastic mulch, during 2020-2022, in the Research and Education Center of Agriculture and Natural Resources in the south of Kerman province. Irrigation as the main plot at three levels of 100, 70 and 50% of water requirement and mulching at three levels of crushed date palm leaf, black plastic and no mulch, as the sub-plot, were considered. Crimson B 34 watermelon seeds produced by Seminis company, were planted on January 2021, in plots with the size of 13.5 × 7 m, on furrows and ridges planting system (the width of furrows and ridges were 0.5 and 4 meters, respectively). After planting, bow-shaped wires were put on the planting rows and a transparent plastic was placed as a tunnel on them. In the first year, the total depth of the irrigations in aforesaid treatments were respectively 444, 321, 237, 413, 389, 435, 345 and 425, and in the second year 427, 303, 223, 395, 373, 416, 331 and 405 mm. Results and Discussion The results showed that the highest and lowest yield were observed in full irrigation and irrigation 50 % (60.1 and 16.3 t ha^-1 respectively). Among the regulated deficit irrigation treatments, irrigation 50% at the seedling stage was the closest to full irrigation, and the irrigation 50 % at the fruit expansion stage had the lowest yield. The highest water productivity belonged to the irrigation 50 % in the seedling and vine stages (15.9 and 1.15 kg m^-3 respectively). Irrigation 50% at fruit maturity stage despite half irrigation, improved Qualitative characteristics such as soluble solids, vitamin C, dry matter, lycopene and fruit taste.   Conclusion Applying deficit irrigation led to a significant decrease in watermelon yield compared to full irrigation (control). Water productivity remained nearly constant, and there was no significant improvement in the quality of the edible part. However, treatments involving regulated deficit irrigation, such as irrigation during the seedling stage, showed similar yield to full irrigation, while the 50% irrigation during the vine stage resulted in higher water productivity. Additionally, 50% irrigation during the fruit maturity stage produced superior fruit quality compared to the control. Overall, regulated deficit irrigation yielded better results than deficit irrigation due to less yield reduction, increased water productivity, and improved fruit quality, especially under water-restricted conditions. Finally, it is recommended that milder intensities of deficit irrigation that seem to have more favorable results in this plant should be investigated in the next studies.

Abstract Introduction The large temporal and spatial changes of precipitation, especially in mountainous areas, have turned it into a controversial variable in climate models. Measuring precipitation (rain and snow) along with its distribution and changes is very important to improve our understanding of global water cycle and energy, water resources monitoring, hydrological modeling. Lack of reliable data is one of the most important challenges in rainfall analysis. Due to the significant temporal and spatial variability of precipitation in mountainous areas, accurate spatially distributed data is crucial for effective water resource assessment and management. However, many mountainous regions have limited rain gauge stations. Today, satellite products are commonly used to measure precipitation in these areas, but the variability among these products raises concerns about their accuracy in mountainous regions. Additionally, the quality of satellite products differs between various products and across different climatic regions, making it essential to thoroughly evaluate them before use. The purpose of this research was to evaluate the precipitation data of two satellite products (GPM, PERSIAN) and reanalysis data (ECMWF) in the estimation of precipitation in mountainous areas without stations in Lorestan province.   Method This study utilized rainfall data from 24 synoptic and rain gauge stations across Lorestan province. Emphasis was placed on stations situated in or near mountainous regions. The selected stations were chosen based on their suitable spatial distribution and record length. The rainfall data spanned the period from 2015 to 2021 and included daily, monthly, and annual measurements. To evaluate satellite rainfall algorithms and estimate rainfall in regions with limited data, data from the GPM and PERSIAN satellites were employed, along with ECMWF reanalysis data. The PERSIAN rainfall algorithm is a remote sensing-based method that utilizes artificial neural networks. It calibrates infrared data with passive microwave estimates and converts longwave infrared images into rainfall estimates using a three-step process. The spatial resolution of this product is 0.25° x 0.25°, and it offers hourly, daily, and monthly temporal resolution. The PERSIAN rainfall algorithm data can be accessed from https://chrsdata.eng.uci.edu. The GPM mission aims to provide continuous observations of Earth's precipitation. It employs the GPM Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) to observe both snow and rain. The final product, called IMERG, is generated through multiple runs of the algorithm for each observation time. Initial estimates are quickly provided, and subsequent estimates improve as more information becomes available. The spatial resolution of the GPM product is 1° x 1°, and it offers hourly, daily, and monthly temporal resolution. IMERG data can be obtained from https://gpm.nasa.gov/data. CMWF reanalysis data is derived from the combination of short-term simulations of numerical weather prediction models with ground-based observational data. These simulations are controlled with observational data, and the resulting reanalysis database provides global coverage from 1979 with a spatial resolution ranging from 0.125° x 0.125° to 3°. The temporal resolution of ECMWF reanalysis data is hourly, daily, and monthly. More information about ECMWF data can be found at https://www.ecmwf.int/ (Azizi, 2019).  To evaluate the accuracy of the products, R-squared correlation (R2), root mean square error (RMSE), standard deviation (MAD), correlation coefficient (R), error deviation (MBE) and Nash-Sutcliffe coefficient (NS) were used. Also, the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) indices were used to validate the data.   Results The results showed that none of the three products are suitable for estimating daily precipitation in mountainous areas. However, on a monthly scale, these products provide reasonable estimates. Among the three, the GPM satellite product demonstrated better accuracy on a monthly scale, based on error levels and the spatial distribution of estimated precipitation. On an annual scale, GPM also performed best, as indicated by both statistical errors and the spatial patterns of average annual precipitation. According to the MBE index, on daily and monthly scales, the ECMWF product tended to overestimate precipitation, while the PERSIANN and GPM products underestimated it. On an annual scale, GPM and ECMWF products overestimated precipitation, whereas PERSIANN underestimated it.

Abstract Introduction
Accurate knowledge of water balance components is necessary to optimize water consumption in agriculture. On the other hand, measuring water balance components is expensive and difficult. Therefore, the use of models that can simulate water balance values is important for water management in agriculture and water used by plants. Crop simulation models have been turned into essential tools for studying plant production systems. In the SSM-iCrop2 models, it is presumed that diseases and weeds are optimally managed and will not affect growth and yield. Additionally, except in cases where the model accounts for specific nutrients such as nitrogen, it is generally assumed that nutrient deficiencies are eliminated through fertilization. Therefore, parameterized and evaluated models are designed to fit these conditions. These factors are present in the field and affect crop growth and yield as well as water use. However, in several cases it is required to estimate yield and water balance components and irrigation water volume under grower conditions. Naturally, models parameterized using experiments are unable to simulate these conditions. Therefore, a model must be prepared so that it can simulate the real conditions of farmers. In this study, the SSM-iCrop2 model has been calibrated for the real conditions of farmers, and the purpose of this study is to use the SSM-iCrop2 model in simulating water performance and water balance for farmers.
 
Materials and Methods
In this study, the SSM-iCrop2 model was calibrated for farmers conditions using variables such as yield and harvest index, which are available for farmers’fields or are cheap to measure. The effect of factors such as pests and diseases, weeds and unsuitable nutrients, density and sowing date entered the model along with the calibration of three parameters of radiation use efficiency, maximum leaf area and maximum harvest index for farmers’ fields. Calibration was done by comparing the performance of farmers against the performance simulated by the model and by changing the parameters of radiation use efficiency (IRUE), maximum leaf area (LAIMX) and maximum harvest index (HIMAX). This calibration was done at Hashem Abad station in Gorgan for irrigated rice (paddy) and wheat. The simulated actual yield was calibrated with the actual yield. Due to the acceptable simulation of actual yields after calibration, it was presumed that other estimates made by the model are also reliable.
 
Results and Discussion
Measurement of water balance and other estimates of the model from growth and yield formation in the grower fields is expensive, but a calibrated model can estimate them at a low cost. In this study, it was shown that with the model calibrated for farmers' conditions, not other easily measured information (such as the irrigation water volume) can be obtained, with the assumption that the model accurately captures this information as well as performance. To evaluate the simulated real performance model, it was compared with the actual performance of farmers (Agricultural Jihad Report) after calibration. In addition to phenology, the SSM model simulates traits related to growth and yield, evapotranspiration values, irrigation water volume, runoff, available soil water during planting and harvesting, cumulative drainage, etc. The output of the model shows the amount of irrigation water is needed for a certain amount of performance in a given place (with specified rainfall and transpiration). The irrigation water volume calculated by the model was compared with the results of field tests from previous studies conducted by researchers at agricultural research centers. It was found that the model's output and the observed values were in good agreement. The root mean square error for rice and wheat was 216.6 and 157.6 kg per hectare, respectively, and the coefficient of variation and correlation coefficient were 4 and 85% for rice and 3 and 94% for wheat, respectively. Then, the irrigation water volume estimated by the model was evaluated and validated with the measured irrigation water volume in different crops (in Golestan province for different years). Based on the results of the evaluation, the coefficient of variation and the correlation coefficient for the simulated irrigation water volume were 8.9 and 98%, respectively, compared with the observed value. This calibration was done for rice (paddy) and irrigated wheat in the fields of Gorgan town, and the simulation and running were done using the meteorological statistics recorded in Hashem Abad weather station, Gorgan. Noting the fact that the actual yield has been simulated with good accuracy after the calibration, it was assumed that the other estimates of the model are also reliable. Thus, the calibrated model estimates them with low cost and appropriate accuracy and can complement field experiments.
 
Conclusion
This study discovered that the SSM_iCrop2 model, when calibrated for the conditions of farmers' fields, can accurately simulate both growth and yield traits as well as water balance characteristics. Notably, the model provides reliable estimates of irrigation water volume in farming scenarios, a crucial factor for agricultural planning and drought adaptation.

Abstract Introduction
 Groundwater is an important resource for domestic, agricultural, and industrial purposes (Andualem and Demeke, 2019). However, the growing population and advanced irrigation technologies have significantly led to increased groundwater exploitation resulting in aquifer depletion. Exploitation of groundwater from fractured rock aquifers using wells to supply drinking water is more sustainable than the utilization of springs with low and variable discharge. In the case of drought and periods of critical condition of water usage, springs of fractured rock aquifers may dry up or decrease making them unreliable water resources to supply drinking water. Over recent decades, the use of fractured rock and karstic units as a remarkable water resource is known as a valuable source of freshwater worldwide. However, these aquifers are extremely vulnerable to contamination due to their unique hydrogeological characteristics and require more protection (Zarvash & Vaezi, 2014). These resources contribute to providing more than 70% of the rural population and around 50% of the urban population with drinking and household demand needs. Since the degree of development of karst landforms varies substantially from region to region, exploring groundwater potential zones in karstic or fractured rock domains across the world is important, which is mostly achieved using evaluating affecting factors in creating the groundwater occurrence. This evaluation is done by incorporating weighted factors such as Weighted Overlay, Weighted Sum, and Fuzzy Overlay and utilizing geographic information systems (GIS) or other remote sensing techniques, which is addressed frequently in literature summarized by Vaezihir and Tabarmayeh (2016); Seif and Kargar (2011); and Amiri et al. (2021). Considering the importance of such issue, this research aims to investigate the potential of karstic or fractured rock resources in West Azerbaijan to gain more insight into this valuable resource of groundwater.
 
Materials and Methods
West Azerbaijan province, with an area of 43,660 km² including Lake Urmia, is equivalent to 2.65% of the total area of Iran and located in the Alborz-Azerbaijan structural zone with a mean annual precipitation of about 370 mm. The maximum temperature of this province, dominated by a semi-arid and Mediterranean climate, is recorded in Shahin Dezh and Miandoab, and the minimum is measured in Chaldoran, and Tekab Metrological Stations, respectively. About 78% of the total area of West Azerbaijan province is formed by karstic units with more spatial distribution in the southern area. This karstic area encompasses 71% of the total province springs with 59% of the total discharge. In the current research, lithology unit types, fracture density, elevation, slope, aspect, drainage density, and vegetation coverage, along with the precipitation, area, and humidity index as the main factors were regarded as governing factors in the development of karst aquifers, have been considered to evaluate the potential groundwater resources. After the preparation of all affected layers using various data resources including available geological maps digital elevation map of West Azerbaijan Province obtained from the Geological Survey and Mineral Exploration of Iran, Landsat satellite data, the Fuzzy logistic and SUM and Weighted overlay technique has been used to prepared groundwater potential zone.
 
Results and Discussion
The groundwater potential zone were determined through combining 9 affected layers in developing the groundwater resource. The results obtained based on employing both weighted overlay and SUM  were classified into 5 classes including low, very low, medium, high and very high potential zones. The index value in SUM methods estimated to be 16.24, 26.24, 24.24, 20.95, 12.13%, while it changes to 22.82, 24.13, 22.14, 16.23, and 14.67 respectively. Overlaying the location of springs as an indicators of groundwater resource on hardrock and karstic domain on generated maps showed that 30.9 and 33.08 percentage of springs fall in area with the high and very high potential zone, respectively. A significant differences on maps generated based on two mentioned technique, particularly in area classified as low potential zone with 24.13 and 16.24 percent in weighted overlay and SUM.  
 
Conclusion
Investigation of the groundwater potential zone by integrating the layer provided by Fuzzy logic technique through two SUM and weighted overlay methods indicated the province of Azerbaijan Arabi has a moderate level of classification. However, in some areas, there were significantly higher or lower potentials.

Abstract Introduction
Salinity stress causes reduction of crop evapotranspiration (ET_c) and yield. An unsuitable seed planting date can result in negative atmospheric effects, such as temperature stress, during the crop growth period. Consequently, salinity stress and unfavorable climatic conditions during this period interact to reduce crop water uptake. The mentioned conditions effect, should be investigated on crop transpiration amount (actual water requirement) and soil surface evaporation losses. This research results will have a determinative effect on the optimal use of water resources.
 
Materials and Methods
The studied crop in this research was S.C 704 maize. The crop planting was conducted in mini-lysimeters with a diameter of 40 cm and a height of 70 cm. The experiment factors included soil salinity stress and seed planting date. Soil salinity treatments were selected at four levels of 1.7 (S₁), 2.5 (S₂), 3.8 (S₃), 5.9 (S₄) dS.m^-1. Seed planting date included of 5 May (P₁), 25 May (P₂) 14 June (P₃) and 4 July (P₄). Crop growth period for all planting date treatments, was 140 days (FAO-56). Experiment was conducted as factorial based on completely randomized design with 16 treatments and three repetitions. Variance analysis and average comparison of data was done by SPSS software and with Duncan's multi-range test (at 5% probability level). Daily soil moisture amount was measured by a moisture meter. Irrigation time was determined for without water stress conditions. Readily available water limit was determined 0.4. Irrigation volume was calculated according to soil moisture deficit (up to FC limit), soil density, root depth, leaching fraction and soil surface area. To separate the evapotranspiration components, all treatments were performed in two series of mini-lysimeters. In the first series, soil moisture reduction was related to crop evapotranspiration amount. But in the second series, the plastic mulch was placed on soil surface. Soil moisture reduction in the second series, was only related to crop transpiration amount. Difference of data in the first and second series was equal to the evaporation amount. Linear function of Mass and Hoffman (1977) was used as the function of evapotranspiration-salinity, transpiration-salinity, and evaporation-salinity.
 
Results and Discussion
As salinity increased from S1 to S4 levels, evapotranspiration, transpiration, and evaporation amounts were measured on the planting dates P1, P2, P3, and P4. The measurements were as follows:
Evapotranspiration (mm): 619-548 (P1), 621-549 (P2), 624-547 (P3), and 625-544 (P4)
Transpiration (mm): 429-309 (P1), 421-295 (P2), 418-281 (P3), and 412-265 (P4)
Evaporation (mm): 190-239 (P1), 200-254 (P2), 206-266 (P3), and 213-279 (P4)
These ranges reflect the measured amounts for each variable under increasing salinity levels across the different planting dates. Under the influence of salinity stress, soil water potential decreases, leading to a reduction in water uptake by the crop and subsequently decreased crop transpiration. As a result of this reduction in crop water uptake, the remaining water in the soil is utilized for evaporation. In S₄ level and on dates of: P₁, P₂, P₃ and P₄, crop transpiration portion decreased to 12.9%, 14.1%, 15.6% and 17.2%, respectively, and evaporation portion increased to the same amount. By adjusting the seed planting date to optimize the utilization of favorable atmospheric conditions during crop growth stages, the increase in the portion of evaporation is prevented. In initial stage of growth period, only 0 to 10% of soil surface is covered by crops (FAO-56) causing the evaporation component to have a dominant portion in the crop evapotranspiration parameter. As a result, placing of initial growth stage in warm days of year caused an increase in evaporation losses. It seems that S₁P₁ treatment was the optimal condition for transpiration increase and evaporation decrease. The estimated functions showed that (in salinity stress conditions) crop transpiration decreased more than ET_c. Therefore, the transpiration rate should be considered as the crop's net water requirement instead of ET_c (crop evapotranspiration). According to the Mass-Hoffman function, under stress conditions, the decreasing slope of transpiration and evapotranspiration and the increasing slope of evaporation become more pronounced. For instance, in planting dates of P1, P2, P3, and P4, for each unit (dS.m^-1) of increase in soil salinity, the evapotranspiration rates decreased by 2.51%, 2.82%, 3.3%, and 3.65%, respectively. Similarly, the transpiration rates decreased by 6.1%, 7.34%, 8.42%, and 9.2%, respectively, while the evaporation rates increased by 5.5%, 6.7%, 7%, and 7.82%.
 
Conclusion
Salinity and atmospheric temperature stresses had interaction effects on evapotranspiration and components rates. Postponing the seed planting date and not utilizing optimal weather conditions, especially during spring, can lead to damage to transpiration, which is a favorable aspect; however it is unfavorable in evaporation,. Therefore, in irrigated crops, it is advisable not to plant seeds during the warm months of the year, especially in July and August. Consequently, by controlling soil salinity and selecting the appropriate planting date, water can be optimally utilized.
  

Abstract  
Introduction
Predicting river flow is one of the most crucial aspects in water resources management. Improving forecasting methods can lead to a reduction in damages caused by hydrological phenomena. Studies indicate that artificial neural network models provide better predictions for river flow compared to physical and conceptual models. However, since these models may not offer reliable performance in estimating unstable data, using preprocessing techniques is necessary to enhance the accuracy and performance of artificial neural networks in estimating hydrological time series with nonlinear relationships. One of these methods is wavelet transformation, which utilizes signal processing techniques.
 
Materials and Methods
In this study, to evaluate the efficiency of discrete and continuous wavelet types in the Wavelet-Artificial Neural Network (WANN) hybrid model for monthly flow prediction, a case study was conducted on the Kardeh Dam watershed in the northeast of Iran, serving as a water source for part of Mashhad city and irrigation downstream agricultural lands. Monthly streamflow estimates for the upstream sub-basin of the Kardeh Dam were obtained from the meteorological and hydrometric stations' monthly statistics over a 30-year period (1991-2020). The WANN model is a hybrid time series model where the output of the wavelet transform serves as a data preprocessing method entering an artificial neural network as the predictive model. The combination of wavelet analysis and artificial neural network implies using wavelet capabilities for feature extraction, followed by the neural network to learn patterns and predict data, potentially enhancing the models' performance by leveraging both methods. The 4-fold cross-validation method was employed for the artificial neural network model validation, where the model underwent validation and accuracy assessment four times, each time using 75% of the data for training and the remaining 25% for model validation. The final results were presented by averaging the validation and accuracy results obtained from each of the four model runs. To evaluate and compare the performance of the models used in this study, three evaluation indices, Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Pearson correlation coefficient (R), were employed.
 
Results and Discussion
The analysis of meteorological and hydrometric data in this study revealed that monthly streamflow in two time steps, T-1 and T-2, were the most effective predictive variables. Each of the two runoff variables of the previous month (Qt-1) and the previous two months (Qt-2) were analyzed by each of the Haar and Fejer-Korovkin2 discrete wavelet transforms and the two continuous Symlet3 and Daubechies2 wavelets at three levels. The results of each level of decomposition was given as input to the ANN model. The presented results at each decomposition level indicated that hybrid models could accurately predict lower flows compared to the single ANN model, and the estimation of maximum values also significantly improved in the hybrid models. Among the wavelets used, Haar wavelets exhibited the weakest performance, and the less commonly employed Kf2 wavelet showed a moderate performance. Since the Haar and Fk2 wavelets, with their discrete structure, did not perform well in decomposing continuous monthly streamflow data, continuous wavelet models outperformed discrete wavelet models. The hybrid models, combining wavelet analysis and artificial neural networks, demonstrated up to an 11% improvement over the performance of the single neural network model.
 
Conclusion
Streamflow is a crucial element in the hydrological cycle, and predicting it is vital for purposes such as flood prediction and providing water for consumption. The objective of this research was to evaluate the performance of different types of discrete and continuous wavelet models at various decomposition levels in enhancing the efficiency of artificial neural network (ANN) models for streamflow prediction. Since climate and watershed characteristics can influence the nature of data fluctuations and, consequently, the results of the wavelet model decomposition, choosing an appropriate wavelet model is essential for obtaining the best results. Considering the existing variations in the results of different studies regarding the selection of the best wavelet type, it is suggested to use both continuous and discrete wavelet types in modeling to achieve the best predictions and select the optimal results. Given that a lower number of input variables in neural network models lead to higher accuracy in modeling results, it is recommended to perform decomposition at a two-level depth to reduce input components to the neural network model, thereby reducing the model execution time.

Abstract Introduction
Climate change is one of the most important issues in the world in the 21st century which affects various sectors of agriculture, forestry, water and financial markets, and has serious economic consequences (Reidsma et al., 2009). In recent years, the management of agricultural water consumption has always been considered as one of the important issues in water resources management. Koochaki and colleagues (Koochaki and Kamali, 2006) by evaluating the climatic indicators of Iran's agriculture showed that during the next 20 years, the average monthly temperature will increase in almost all regions of the country, and the increase in evaporation and transpiration is one of the most important consequences of this warming. Simulated climate parameters can be obtained through different general GCM atmospheric models. Due to the low spatial resolution of these models, its output should be downscaled using dynamic or statistical methods.
 
Materials and Methods
The LARS-WG model predicts meteorological variables for a period of time in the future by using a series of basic and fine-scale meteorological data, output of one of the GCM models. Research has shown that the LARS-WG model has the necessary accuracy for this task. Calculating the amount of evapotranspiration and yield of very complex plants are time-consuming and dependent on spending a lot of money and limited to the tests performed, the shortness of the test time and also the limitation in the number of scenarios that are checked by the test. Therefore, plant models are considered and evaluated by researchers. The AquaCrop model has demonstrated commendable accuracy in various regions of Iran and globally for forecasting plant growth, water consumption efficiency, and evapotranspiration requirements. These predictions hold significant potential for optimizing irrigation strategies across different agricultural settings. AquaCrop is one of the applied agricultural models that was obtained from the modification and revision of FAO publication No. 33 by prominent experts from all over the world. In this study, the values of green water footprint of winter wheat plant (Pishgam) were estimated in climatic conditions obtained from LARS-WG model and DKRZ database under scenarios 4.5 and 8.5 and at different planting dates (15 October, 1 November, 15 November, 30 November and 15 December), in the next 4 periods (2021-2040, 2041-2060, 2061-2080 and 2081-2100) and by Aquacrop model.
 
Results and Discussion
The results showed that if planting date is on October 15, in the climatic conditions obtained from the LARS-WG model and under scenarios 4.5 and 8.5, in all future periods, the footprint of green water will increase compared to its value in the base period, and if planting is the rest of the dates, in each of the next 4 periods, the average green water footprint will decrease compared to its value in the base period. The results obtained for the DKRZ database show that the green water footprint attained for the dates of cultivation and periods investigated in scenarios 4.5 and 8.5 does not have a particular trend. On the planting dates of October 15 and November 1 for the periods of 2061-2080 and 2081-2100, the green water footprint will decrease and on the other three dates (15 November, 30 November, and 1 November) for these periods, there will be an increasing trend. On 15 December, for the DKRZ database, in both scenarios defined for all periods, an increase in green water footprint compared to the base period is reported. However, in the period of 2081-2100 in scenario 8.5, a decrease compared to the base period will be observed. The highest amount of green water footprint in all these periods and models for the period 2041-2060 under the climatic conditions of the DKRZ database in scenario 4.5, if the planting date is 15 October, it is estimated that the amount of water consumed is equal to 4272 cubic meters per ton with a standard deviation of 5018 cubic meters per ton is predicted. The lowest footprint of green water for the period 2081-2100 under the climatic conditions obtained from the LARS-WG model in scenario 8.5, if the planting date is on 15 December, is reported to be 232 tons per hectare with a standard deviation of 52.3 tons per hectare.