https://www.waterjournal.ir/ Irrigation and Water Engineering en daily 1 Mon, 03 Jun 2019 00:00:00 +0430 Mon, 03 Jun 2019 00:00:00 +0430 https://www.waterjournal.ir/article_88367.html More attention to increase water productivity in water crisis and recent drought condition is needed. More attention to increase water productivity in water crisis and recent drought conditions is required. In this way, the use of pressurized irrigation systems as alternative instead of surface irrigation systems is recommended. Therefore, in this study, in order to assess the system performance and water productivity determination nine and ten potato fields in Isfahan and Hamedan Provinces respectively were evaluated for 2 years. Uniformity coefficients, water productivity, water use and potential and actual application efficiency of low quarter (PELQ، AELQ، CU and DU) were determined. Average of PELQ, AELQ, CU and DU in Isfahan were equal 61.5%, 58%, 66.8% and 75.8% and in Hamedan were equal 53.9%, 44.8%, 71.10%, 81.1% respectively. By determining the amount of water consumed and the yield of the product, the average water productivity of potato in Hamadan and Isfahan was estimated about 6.6 and 6.3 kg.m-3 respectively. The results of this study showed that in most of the studied farms, deficit irrigation used. The main issues on the results of these assessments can include factors such as non-compliance of design rules with the implementation phase and Unsuitable implementation, operational problems, and inadequate information on the operation and preservation of the system, and the mismatch of water consumption with the plant's water requirement. In addition, concurrent use of many sprinklers and using more than one sprinkler on laterals considered as the most important reason for low water distribution. https://www.waterjournal.ir/article_88368.html Direct estimation of some soil characteristics is time consuming, costly and sometimes not possible. In recent years, indirect methods have been used to estimate these properties. In the present study, to predict the soil texture fractions, 115 profiles were identified based on the Hypercube technique, and the horizons were sampled and the percentage of sand, clay and silt of soil samples were measured. Environmental variables used in this study include the terrain attributes (derived from a digital elevation model), Landsat 8 image data (acquired in 2015), geomorphological map, and spectrometric data (laboratory data). Artificial neural network, regression tree and neuro-fuzzy models were used to make a correlation between soil data (clay, sand and silt) and environmental variables. The results of this study showed that the neuro-fuzzy model was more accurate in prediction of the three parameters of clay, sand and silt than artificial neural network and tree regression . The RMSE value in the neuro fuzzy model was compared to regression tree model. The neuro fuzzy model results were, for clay surface 1.43 %, for sand surface 1.98% and for silt surface 2.1% that reduced by 6.71%, 8.49% and 5.42% for clay, sand and silt respectively, compared to regression tree model. The results also showed that the most important auxiliary variables are spectrometric data followed by MrVBF and wetness index. https://www.waterjournal.ir/article_245651.html Climatic transformations, through changes in temperature and precipitation regimes, threaten hydrological balance and the overall performance of water resource systems. This study investigates the impacts of climate change on groundwater resources in the Northern Ahvaz Plain aquifer, located in Khuzestan Province, Iran. To this end, outputs from coupled atmosphere–ocean general circulation models under the SSP2.6, SSP4.5, and SSP8.5 scenarios were downscaled for the Ahvaz synoptic station using the LARS‑WG stochastic weather generator (version 7.0). Variations in minimum temperature, maximum temperature, and precipitation were then evaluated for the baseline period (2000–2025) and the future period (2026–2045). The results showed that the annual mean minimum and maximum temperatures will increase under all scenarios compared with the baseline period. The highest increases in maximum and minimum temperatures, 2.29°C and 1.65°C, respectively, were projected under SSP8.5, whereas the lowest increases, 2.05°C and 1.56°C, were associated with SSP2.6 and SSP4.5. In contrast, annual mean precipitation is projected to decrease by 3.91%, 8.6%, and 11% under SSP4.5, SSP2.6, and SSP8.5, respectively. To assess the implications of these climatic variations for groundwater resources, a numerical groundwater flow model was developed using MODFLOW within the GMS environment (version 10.8). Simulation results indicated a general decline in groundwater levels during the future period under all scenarios. The largest decline, approximately 7.5 m, occurred under SSP8.5, reflecting a more critical aquifer condition. The proposed integrated framework can support quantitative assessment of aquifer sensitivity to climate change in similar basins. https://www.waterjournal.ir/article_236388.html Estimating groundwater recharge is one of the important challenges in groundwater resource management. Various techniques have been developed in recent years to estimate aquifer recharge. The water level fluctuation (WTF) method is one of the simplest and most practical of these techniques. In this study, the recharge rate of the Jiroft Plain aquifer was estimated by examining groundwater level fluctuations over a 24-year period. The results of this study showed that the minimum, average, and maximum annual aquifer recharge during this period were 0.97, 51.18, and 186.58 mm, respectively. The lowest recharge rate occurred in the southern part of the aquifer, while the highest recharge rate occurred in its northeastern margin. Factors such as surface geomorphological features, soil texture, and characteristics of the unsaturated zone have influenced the spatial distribution of aquifer recharge. Hydrological drought has a significant impact on aquifer recharge. Additionally, increased aquifer pumping due to the expansion of agricultural fields has also contributed to an increase in aquifer recharge. However, this increased recharge has not compensated for the water table drawdown resulting from aquifer pumping, and the water level continues to decline. https://www.waterjournal.ir/article_245659.html Drinking water quality is one of the most important components of public health and sustainable water resource management, and its continuous assessment, especially in urban areas, is of particular importance. In this study, the quality of drinking water wells in Mahmoudabad County was evaluated using the Water Quality Index (WQI) and the National Sanitation Foundation Water Quality Index (NSFWQI). The dataset included physical and chemical parameters such as pH, nitrate, total hardness, chloride, sulfate, total dissolved solids, turbidity, and temperature during the period 2020–2025. To achieve a more comprehensive evaluation, in addition to calculating water quality indices, Pearson correlation analysis was applied to examine the relationships among parameters, and the k-means clustering method was used to classify samples into groups with similar quality characteristics. The results indicated that the general water quality in most urban wells of Mahmoudabad ranged from good to excellent; however, in some samples higher index values reflected a relative decline in water quality and the sensitivity of groundwater resources to land-use changes and agricultural activities. Correlation analysis revealed consistent behavior among parameters such as total hardness, sulfate, and total dissolved solids, as well as the significant role of nitrate in reducing water quality. Furthermore, clustering analysis highlighted the spatial variability of water quality and the influence of hydrogeological conditions and human activities. The findings of this study demonstrate the effectiveness of a multidimensional approach for monitoring, management, and targeted planning of urban drinking water resources. https://www.waterjournal.ir/article_245072.html The decrease in precipitation and excessive exploitation of groundwater resources have led to a significant decline in groundwater levels in many regions worldwide. One of the aquifers experiencing groundwater level depletion is the Nahavand aquifer, located in Hamadan Province, western Iran. To model the groundwater level, the Long Short-Term Memory (LSTM) model and the Group Method of Data Handling (GMDH) were employed. In order to enhance model accuracy, the Complete Ensemble Empirical Mode Decomposition (CEEMD) preprocessing technique was applied, resulting in the development of four models: LSTM, GMDH, CEEMD-LSTM, and CEEMD-GMDH. The results indicated that the GMDH model performed better than the LSTM model. Moreover, integrating these models with the CEEMD preprocessing technique improved their performance. Specifically, the coefficient of determination (R²) for the LSTM model increased from 0.867 to 0.950 in the CEEMD-LSTM model. Similarly, the R² value for the GMDH model improved from 0.885 to 0.945 in the CEEMD-GMDH model. Additionally, the analysis of Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC) also demonstrated that the use of CEEMD preprocessing reduced model errors. Based on these findings, the CEEMD-LSTM hybrid model was identified as the most accurate and was therefore used to forecast groundwater levels for the next six months (the first half of the 2024–2025 water year). Overall, the CEEMD-LSTM hybrid model showed excellent performance in modeling groundwater levels in the Nahavand aquifer and has the potential to be applied to other aquifers as well. https://www.waterjournal.ir/article_246196.html The main purpose of irrigation is to compensate for the lack of usable water for the plant and the purpose of deficit irrigation is to save water consumption. Partial root zone drying is one of the low irrigation methods. One of the soil additives to improve its fertility is biochar. This study was conducted to investigate the effect of different partial root zone drying treatments at full irrigation, two levels of 65 and 55% and different levels of biochar consumption (zero, 6 and 12 tons per hectare) on the growth characteristics on height, diameter and wood diameter was calculated. The highest height was achieved in the 12-ton application treatment in the biochar soil in the second year, at 17.2 centimeters. This showed a 13% increase compared to the treatment without biochar in the first year, which had the lowest ear height. The treatment using 6 tons per hectare of biochar in the second year had the highest ear diameter of 2.4 cm. The highest ear diameter was observed in the treatment of 12 tons per hectare of biochar and full irrigation, at 4.45 cm. Which was significantly different from the treatment without biochar. Therefore, the effect of year on ear height and ear stalk diameter was highly significant. The interaction effect of planting year and irrigation had a highly significant effect on ear height.The effect of biochar on ear height and ear diameter was also significant. The effect of irrigation treatments on ear diameter was highly significant. https://www.waterjournal.ir/article_246193.html This study evaluates the AquaCrop model for simulating grain yield and biomass of barley in the Sistan region, considering water resource limitations and varying climatic conditions. Data from four farms in the Zahak and Hirmand counties (Research Center, Khwaja Ahmad, Zurabad, and Deh-Laghari) were used over two cropping seasons (2023–2024 and 2024–2025). Three planting date scenarios—early (November 6), mid-season (December 6), and late (January 5)—and three irrigation levels (full irrigation, 25% deficit irrigation, and 50% deficit irrigation) were considered. Simulation results showed that the AquaCrop model can predict grain yield and biomass with high accuracy. During calibration, the R² values for grain yield and biomass ranged from 0.96 to 0.97, and during validation, R² ranged from 0.94 to 0.98 for grain yield and from 0.90 to 0.92 for biomass. The high coefficients of determination and low error statistics indicated that the model successfully simulated grain yield and biomass with strong accuracy. The sensitivity analysis indicated that canopy transpiration coefficient (Kc), normalized water productivity (WP*), and reference harvest index (HIo) were the most influential parameters in the model. Furthermore, the irrigation and planting date scenarios showed that in wet years, a 25% reduction in irrigation decreased yield by 5–7%. In normal years, reducing irrigation by 25% and 50% led to a 13–20% reduction in yield, and in dry years, a 25–50% reduction in irrigation caused a 45–48% decrease in grain yield. https://www.waterjournal.ir/article_244935.html Water scarcity is one of the most critical challenges facing agriculture in arid and semi-arid regions. This study designed and implemented a fuzzy logic-based smart irrigation system to accurately predict the water requirements of Indian ginseng (Ashwagandha). Fuzzy logic was selected due to its exceptional ability to handle uncertainty and model complex, nonlinear relationships among input variables, making it an ideal approach for irrigation optimization. Field experiments were conducted on a 400 m² plot in Saravan (Sistan and Baluchestan Province, Iran) during the 2025 growing season. Utilizing five environmental inputs (soil moisture, air temperature, relative humidity, wind speed, and plant age) and 243 Mamdani-type fuzzy rules, the system achieved a **41.9% reduction in water consumption** (from 336 to 195 L per season), a **16.3% increase in yield** (from 46 to 53.5 kg), and a **98.2% prediction accuracy** (R² = 0.982). Compared to traditional irrigation practices, the proposed system improved water use efficiency by **71.4%**. https://www.waterjournal.ir/article_246221.html Drought is one of the most significant challenges facing Iran's agricultural sector, making the enhancement of resilience to drought a vital strategy for mitigating vulnerability. Therefore, this study was conducted to identify the critical success and failure factors for enhancing resilience to drought through water management, employing a futures studies approach. The research is descriptive-exploratory in nature and utilizes a sequential exploratory mixed-methods design (qualitative-quantitative). In the qualitative phase, success and failure factors for enhancing drought resilience were identified through a meta-synthesis . In the quantitative phase, a descriptive-survey method was employed to conduct a futures studies analysis using Structural Analysis of Cross-Impacts. The findings revealed that the success factors for enhancing resilience to drought comprise five dimensions (Governance Requirements, Infrastructural Requirements, Communication and Collaboration, Socio-Cultural Requirements, and Technical Requirements) and 12 components. Conversely, the failure factors consist of four dimensions (Managerial, Socio-Cultural, Economic, and Technical Challenges) and 8 components. The analysis of key drivers indicated that "Governance Requirements" was the most influential factor (key driver) in terms of both direct and indirect impact on success. Similarly, for failure factors, "Economic Challenges" was identified as the top key driver. The findings of this study can serve as a practical guide for policymakers and planners in the field of drought management. https://www.waterjournal.ir/article_230970.html Background and Objective: this study investigates vegetation drought in East Azerbaijan Province using infrared imagery from the S-NPP JPSS sensor, as vegetation conditions are highly sensitive to rainfall fluctuations and drought is a recurring global phenomenon.Materials and Methods: visible and infrared images from April 1st to the end of July (weeks 13-26 AD) between 2013 and 2021 were analyzed to assess vegetation status and varying drought levels. Weekly averages of vegetation indices were used to detect changes and fluctuations. The monthly average Standard Precipitation Index (SPI) for East Azerbaijan Province was calculated from monthly precipitation data collected at six synoptic meteorological stations: Ahar, Kalibar, Maragheh, Mianeh, Sarab, and Tabriz. Correlations between average NDVI, VCI, TCI, VHI, and SPI were estimated to determine the relationship between precipitation and vegetation indices.Results: the correlation between the Standardized Precipitation Index (SPI) and NDVI, VCI, TCI, and VHI was 0.0037, 0.0048, 0.174, and 0.150, respectively. TCI exhibited the strongest correlation with SPI, making it a suitable method for combining remote sensing and meteorological data to assess vegetation conditions in East Azerbaijan province. The most intense vegetation droughts occurred in the western and central regions in 2013 and 2015, and province-wide in 2021. SPI calculations indicated below-normal precipitation during July, August, and September. Overall, the VHI was determined to be the most effective satellite-based index for monitoring vegetation drought in the study area. https://www.waterjournal.ir/article_246195.html Reduced rainfall, drought, and lack of surface water resources in desert areas have led to excessive extraction of aquifers and threatened groundwater quality. The Kerman study area, one of these areas, is facing a decline in water quality due to population growth, urbanization, and industry. This research evaluates and zones the groundwater quality of the plain using kriging in GIS and Hydrochemical analysis (Piper, Schoeller, and Wilcox diagrams) over a 22-year period (2002-2024). Data for 10 parameters (Ca, Mg, pH, TDS, Th, EC, SO₄, Cl, Na, SAR) were analyzed at 27 sampling points. The results showed that the dominant groundwater type was sodium bicarbonate and there was no significant change during the study period. In terms of quality, the eastern, southeastern and southern regions (Mahan and Joupar) have good to acceptable quality for drinking and agriculture. In contrast, the northwestern areas (Ekhtiar-Abad and Naghshineh) have unsuitable drinking water and a large part of the north and west of the plain is unsuitable to temporarily drinkable. For industrial uses, the water of the eastern, southern and western areas is suitable for industrial processes of the first and second groups (sensitive industries), while the northern and northwestern areas with the maximum chlorine concentration are suitable for industries of the third and fourth groups (relatively sensitive industries). For sustainable management, it is recommended that: transferring water from high-quality areas, improving the quality of polluted sources, cultivating salt-tolerant plants in the north, and eliminating unauthorized wells be on the agenda. https://www.waterjournal.ir/article_246152.html Flooding stands as one of the most destructive hydro-climatic hazards, inflicting substantial annual losses on human communities and economic infrastructure. Identifying flood-prone zones and creating sensitivity maps are crucial for effective risk management and natural resource planning. This study focused on assessing the performance of three machine learning algorithms—logistic regression (LR), support vector machine (SVM), and gradient boosting (GB)—for flood risk mapping in the Nekarood watershed of Mazandaran province. Thirteen environmental and hydrological factors, including rainfall, slope, elevation, proximity to waterways, land use, and morphometric indices, were analyzed alongside 152 recorded flood occurrence points. The models were evaluated using metrics such as AUC, overall accuracy, and the kappa coefficient. Results indicated that the GB model achieved the highest performance, with an AUC of 0.896, an overall accuracy of 87%, and a kappa coefficient of 0.84. The SVM model followed with an AUC of 0.872 and an accuracy of 83%, while the LR model, scoring an AUC of 0.853 and an accuracy of 80%, showed the weakest performance among the three. Analysis of variable importance revealed that rainfall, slope, distance from waterways, and elevation are the most significant factors influencing flood occurrence. Consequently, reinforcement learning-based algorithms could serve as effective tools for enhancing predictive accuracy and minimizing uncertainty in flood risk mapping efforts. The findings from this study offer valuable insights for strengthening early warning systems, restricting development in high-risk zones, and reducing both human and economic damages caused by flooding in similar regions. https://www.waterjournal.ir/article_238950.html Accurate measurement of flow discharge in open channels is essential for efficient water resources management and optimal operation of irrigation and drainage systems. This study evaluated the hydraulic performance of portable SMBF flumes under free-flow conditions through controlled laboratory experiments and numerical modeling using FLOW-3D with the RNG turbulence model. Four contraction ratios (r = 0.342, 0.464, 0.561, and 0.726) were tested to examine the effect of contraction on discharge prediction accuracy. Model performance was assessed using statistical indicators including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Nash–Sutcliffe Efficiency (NSE), Agreement Index (AI), and Kling–Gupta Efficiency (KGE). The results demonstrated excellent agreement between numerical and experimental data, particularly at the mild contraction ratio of r = 0.342, where the relative error was approximately 3% and KGE reached 0.93. Increasing the contraction ratio intensified flow separation and turbulence, resulting in higher prediction errors up to 16.5% at r = 0.726. Overall, the FLOW-3D model showed stable and accurate performance under mild contractions (r < 0.5). The contraction ratio of r = 0.342 was identified as optimal, providing a balance between hydraulic stability and measurement precision. It is therefore recommended that mild contraction ratios (0.342–0.464) be used in practical SMBF flume designs. https://www.waterjournal.ir/article_244930.html This study develops and evaluates a river flow prediction model in the Kashkan River Basin. The main objective is to improve streamflow prediction accuracy at the Kashkan-Afrine and Kashkan-Poldokhtar hydrometric stations using the Random Forest model, combined with effective feature selection via a Genetic Algorithm and k-fold cross validation. The dataset includes observations from three hydrometric and nine meteorological stations.The RF model, by averaging predictions from an ensemble of decision trees, can identify complex patterns, capture nonlinear relationships, and prevent overfitting. Results showed that some input variables, such as the discharge at the Kakareza station, had a very high correlation (0.97) with the target variable, playing a key role in improving model accuracy. Removing these inputs caused a notable decline in performance. When all features were used, the coefficient of determination (R²) for the Kashkan-Poldokhtar and Kashkan-Afrine stations was 0.91 and 0.95, respectively, indicating strong generalization capability. Prediction errors had means close to zero, low standard deviations, and residuals distributed around zero. Excluding the Kakareza discharge data reduced the input correlation to about 0.64 and led to a noticeable performance drop, with R² decreasing to 0.72 for Kashkan-Poldokhtar and 0.73 for Kashkan-Afrine, while RMSE and MSE increased significantly. These findings highlight the importance of accurate feature selection and the inclusion of key hydrometric stations. Overall, integrating the RF model with effective feature selection and k-fold cross-validation provides an efficient approach for streamflow prediction in data-scarce basins. https://www.waterjournal.ir/article_237234.html AbstractThe development of the rainfall–runoff relationship constitutes a fundamental aspect of hydrological modeling. Given the inherent complexity of this relationship, accurate runoff prediction plays a pivotal role in water resources planning and management. This study investigates the rainfall–runoff relationship within the Khorramabad River Basin, employing simultaneous data from the Khorramabad Synoptic Station and the Cham-Anjir Hydrometric Station. Rainfall–runoff modeling was conducted using two conceptual hydrological models WEAP and IHACRES as well as three artificial intelligence (AI) approaches, namely Artificial ANN, ANFIS and SVM, to estimate runoff. The modeling period for all models extended from October 1956 to September 2024, except for the WEAP model, for which the period October 2010 to September 2023 was selected due to the large number of input parameters required. For the AI-based models, 80% of the data were used for training and 20% for testing. The performance of the models was evaluated using standard statistical indicators, including the R², NSE and RMSE. The results indicated that, among the hydrological models, WEAP outperformed IHACRES, and among the AI models, ANFIS exhibited superior performance compared to ANN and SVM. Overall, the ANFIS model demonstrated the best performance among all models employed, with R² = 0.96, NSE = 0.98, and RMSE = 2.08 during the training phase, and R² = 0.94, NSE = 0.87, and RMSE = 1.93 during the testing phase. Consequently, the findings suggest that artificial intelligence models generally outperform conceptual hydrological models in simulating the rainfall–runoff process. https://www.waterjournal.ir/article_177282.html Weirs located on rivers massively used to regulate the flow of water, facilitate flow diversion for acrigaltur porpuses. Given that the weirs site is often restricted in width, weirs with different shapes have been dveloped to increase weir length and discharge capacity.Accurate estimation of the maximum possible depth of scour at weirs is important in decision-making for the safe depth of burial of footings. Labyrinth weirs located on the rivers are often exposed to Flood, therefore one of the principal challenges of their use of them is scouring around them. In this study, the influence of water head ratio and sidewall angle was investigated on flow pattern at first and then scour mechanism. Then considering the close relationship between flow pattern and scour phenomenon, We studied the parameters effecting the scour at the upstream weir by focusing on the pattern flow. Observations showed that by changing the water head ratio, it is possible to have four different regimes and Each of these regimes has a different effect on the scour mechanism and maximum depth scour location. As the water head ratio is increased, in addition to the significant expansion of the scour hole dimensions, the location of hole formation is displaced from the vicinity Weir’s walls to the center of the cycle. In addition to the water head ratio, the sidewall angle also has a meaningful effect on the mechanism and amount of scouring on the weir. As the sidewall angle is increased, the amount of scouring is significantly decreased. https://www.waterjournal.ir/article_227668.html Assessing the effectiveness of watershed management projects and their influence on watershed hydrology is essential for achieving sustainable water and land management. This study aimed to evaluate the hydrological impacts of implemented watershed management interventions in the Fariman Dam watershed. Hydro-meteorological data, including streamflow and sediment records, were collected from monitoring stations for periods before and after the watershed management measures were executed. Statistical analyses were conducted to compare pre- and post-intervention data, employing the Mann-Kendall trend test and Sen’s slope estimator to detect trends and quantify changes, respectively. Flow duration curves were also generated to assess alterations in flow regimes. The results indicated that the mean streamflow decreased from 0.52 m³/s to 0.3 m³/s and the average sediment yield declined from 5.8 tons/day to 0.78 tons/day after the interventions; both reductions were statistically significant. Annual precipitation exhibited no significant trend over the study period, underscoring that observed changes stem from the management measures rather than climatic variability. Furthermore, the flow duration analysis revealed a reduction in the probability of both high-flow floods and hydrological droughts following the implemented projects. Analysis of maximum instantaneous discharge across various return periods showed an increased frequency of low discharges (short recurrence intervals) and a decreased frequency of high discharges (long recurrence intervals). Overall, watershed management measures in the Fariman Dam watershed have demonstrably and effectively reduced streamflow and sediment loads, improving hydrological stability in the region. https://www.waterjournal.ir/article_246800.html The present study aimed to map and analyze the spatiotemporal dynamics of flood hazard in the Kalarud watershed, Babol (northern Iran), covering an area of 216 km². This basin, with an elevation range of 45–2272 m and an average slope of 19.46%, is among the flood-prone zones of the Central Alborz Mountains. To achieve a comprehensive assessment, a hybrid approach combining Shannon Entropy (SE), Evidential Belief Function (EBF), and Markov Chain (MC) models was applied to evaluate both spatial and temporal aspects of flood risk. Nine conditioning factors—elevation, slope, slope aspect, drainage density, distance from rivers and roads, land use, soil, and Topographic Wetness Index (TWI)—were analyzed in a GIS environment. Results of the SE model indicated that elevation (0.1983), slope aspect (0.1517), and slope (0.1423) were the most influential factors. The EBF model confirmed the 48% cumulative weight of these variables, while the Markov Chain model was employed to forecast spatial changes in flood hazard from 2013 to 2030. Model performance evaluation showed that EBF (AUC = 0.83, RMSE = 0.219) outperformed the Shannon model (AUC = 0.71, RMSE = 0.293), whereas the Markov model (AUC = 0.79) exhibited stable temporal behavior. Overall, 26.4% of the basin area falls within high and very high hazard classes, mainly encompassing the central and southern sectors, including the villages of Shiyadeh, Anjilek, and Lamsukola. The integrated EBF–Markov–SE framework effectively addresses spatial uncertainty and temporal dynamics, providing a reliable tool for flood risk management, watershed planning, and sustainable development in northern Iran.