Journal of New Approaches in Water Engineering and Environment

Abstract Objective: Field investigations in Khorasan Razavi Province revealed that in recent years, energy imbalances and power outages have led to changes in irrigation scheduling, resulting in reduced production of major crops, losses of poultry and aquatic animals, and damage to mechanized irrigation facilities and systems. Therefore, examining feasible methods for renewable energy generation in the agricultural sector, including in Khorasan Razavi Province, is essential. This study investigated renewable energy generation methods, the barriers to implementing renewable power plants, and strategies to overcome these barriers in Khorasan Razavi Province.
Methods: Data and statistical information were collected through both library and field approaches. In the library method, published articles, documents, and books were reviewed to identify the types of renewable energy, their applications in the agricultural sector, and the challenges faced by developing and developed countries that have experience in using these energy sources. The statistical population of the study consisted of farmers, experienced experts, and relevant organizations. Finally, based on international experiences, along with the suggestions of stakeholders and experienced experts, practical solutions were proposed to overcome barriers to the implementation of renewable power plants nationwide, including in Khorasan Razavi Province.
Results: The findings revealed that 44% of the electricity consumed in Khorasan Razavi Province is attributable to the agricultural sector. Moreover, approximately 40% of the electricity generated from renewable energy sources in the province is used in agriculture, mainly to ensure energy stability for agricultural wells supplying irrigation water. Financial constraints, social issues, the lack of reputable companies for solar panel installation, and insufficient government commitment were identified as major barriers to the adoption of renewable energy in the province.
Conclusions: Solutions such as developing a comprehensive and targeted strategy for the expansion of renewable energy, coherent interdepartmental planning among responsible institutions, adopting policies tailored to the conditions of farmers in each county, smartening the electricity systems of agricultural wells, and implementing extension and social activities can help reduce barriers to renewable energy production in the province.

Abstract Climate change is currently a major topic in scientific circles and in publications related to environmental and atmospheric sciences.The aim of this study is to investigate the impact of climate change on climatic variables and hydrological drought in the Urmia Lake basin, Iran.For this purpose, the SDI index was calculated, and the Ar(p) time series model was generated with the synthetic data generation technique 1,000 times. Possible changes in temperature and precipitation were then examined using climate scenarios.In this study, the NORESM1-M model was evaluated using 30-year temperature and precipitation data from the Urmia Lake basin for the base period(1980–2010) and for the near(2011–2040),middle(2040–2070), and far(2070–2100) future horizons under three scenarios: RCP4.5, RCP6, and RCP8.5. In the next step, a rainfall-runoff model calibrated with historical precipitation and discharge data was used to generate discharge data based on climate change scenarios for two periods(2020–2059) and(2060–2100), after which hydrological drought in the Urmia Lake basin was analyzed.The findings showed that, based on the time series models and climate scenarios studied, more severe droughts than those in historical periods are possible.The severity of droughts at stations east of Lake Urmia is greater than in the west, with the number of dry periods in the studied horizons projected to be 17 to 20 percent higher than current conditions.Additionally,based on the correlation coefficients obtained for different scenarios, the best scenario for precipitation studies in this region was identified as RCP6,with a correlation coefficient of 0.763, and for temperature, RCP4.5, with a correlation coefficient of 0.994

Abstract Objective: Corrosion can damage pipelines and release harmful substances into drinking water, thereby reducing water quality. Various factors influence water corrosivity, including water chemistry (pH, alkalinity, dissolved oxygen, and total dissolved solids), hydraulic conditions (such as temperature and flow velocity), surrounding environmental conditions (soil), climate, and pipe material. This study aimed to investigate the effects of water type and ionic speciation on the corrosion and scaling potential of drinking water in rural distribution networks across different climatic zones of Kermanshah Province, Iran, during 1999–2018.
Methods:Water quality data from rural areas in five climatic zones—cold semi-dry, warm semi-dry, warm and dry, cold Mediterranean, and moderate humid—were used to calculate the most common corrosion and scaling indices. These included the Langelier Saturation Index (LSI), Ryznar Stability Index (RSI), Aggressive Index (AI), Puckorius Scaling Index (PSI), and Larson–Skold Index (L-SI). Water type and ionic speciation were determined using AqQA and Visual MINTEQ software, respectively.
Results:Although the Piper diagram showed that the water type in all climatic zones was similar (calcium–bicarbonate, Ca–HCO₃), the corrosion and scaling indices, as well as ionic speciation, showed significant differences among the studied climatic zones (p < 0.001).In the warm semi-dry climate, LSI and AI values were higher, whereas RSI and PSI values were lower than in the other climatic zones. In the moderate humid climate, the lowest LSI and AI values and the highest RSI and PSI values were observed.
Ionic speciation analysis showed that calcium and magnesium were predominantly present as free ionic species, followed by complexes with sulfate and bicarbonate.
Conclusions: Lower corrosion potential and higher scaling tendency in warm and dry and warm semi-dry climates were attributed to the higher proportion of free calcium and magnesium ions in these climates.Consequently, water in the moderate humid climate exhibited greater corrosion potential than water in the other climatic zones.

Abstract Objective: Wastewater treatment is vital for addressing quantitative and qualitative water shortages, which are crucial for urban development. In this context, maintaining and improving the efficiency of wastewater treatment plants is essential for recovering these water resources. Therefore, accurately modeling the performance of wastewater treatment plants at different stages is of great importance.
Methods: This study used an artificial neural network (ANN) to model the effluent quality of the Bushehr wastewater treatment plant. The input and output data included the parameters BOD, COD, EC, TSS, and TDS. The results demonstrated that the ANN algorithm performed well in modeling the wastewater quality parameters, though with varying success at different treatment stages.
Results: The modeling results showed that accuracy was highest for the EC parameter in the first stage, with a coef-ficient of determination (R²) of 0.84 during the test period. The best accuracy for the TDS parameter was achieved in the third stage. For the COD parameter, the network's accuracy was very favorable in the first stage. The highest accuracy for the TSS parameter was in the third stage during training (R² = 0.79), while the second stage yielded the best result during testing (R² = 0.65). Finally, for the BOD parameter, the highest accuracy was in the third stage.
The best performance for EC and COD was observed in the first stage, with an accuracy exceeding 80%, while for TDS, the highest accuracy was in the second stage.

Abstract Objective: In mountainous regions of the Zagros, including the Maroon Basin in southwestern Iran, snow is the primary source of annual runoff. However, recent climatic changes, particularly rising temperatures and shifts in precipitation patterns, have altered the temporal and spatial distribution of snow accumulation and snowmelt. These changes have important implications for river hydrological regimes. Accordingly, this study investigates the temporal and spatial trends of snow cover and analyzes its relationship with Maroon River discharge over the period 2001–2022 to clarify the role of snow-cover changes in surface-flow dynamics. Methods: River discharge data from the Idenak hydrometric station (upstream of Maroon Dam) were also used, and missing data were reconstructed using regression methods. To investigate the relationship between snow cover and river discharge, Pearson correlation analysis was performed for both simultaneous and lagged relationships. Since snowmelt in the Maroon Basin typically occurs from late winter to mid-spring, lag effects of up to two months were considered to capture delayed runoff responses associated with snowmelt. All analyses were conducted using ArcGIS, Excel, and SPSS software. Results: The results showed a significant declining trend in snow cover during most months, particularly from January to March. Seasonal analysis also indicated a decreasing trend in snow-covered area during the snow season (October–May), with Z = −1.58 (p < 0.05). Sen’s slope estimates showed an average reduction of approximately 7.28 km² in snow-covered area per year during winter. Correlation analysis revealed the strongest positive relationship between NDSI and Maroon River discharge during the snowmelt period (March–May), both simultaneously and with time lags of one to two months, with correlation coefficients reaching 0.75. Conclusions: The significant decline in snow cover and its strong correlation with river discharge, both simultaneously and with 1–2-month delays, indicate a reduction in snow persistence and earlier snowmelt in the Maroon Basin during the past two decades. These changes have altered the hydrological regime of the Maroon River. This study, by combining long-term MODIS-based snow-cover trend analysis with simultaneous and lagged evaluation of the relationship between NDSI and river discharge at multiple temporal scales, provides a robust understanding of the dynamic relationship between snow-cover changes and surface-flow behavior in this critical basin.

Abstract V
Objective: Vegetation cover is a critical indicator of regional ecosystem health and a key component of global climate regulation models. In vulnerable mountainous environments, characterized by high sensitivity to climatic and anthropogenic pressures, monitoring vegetation dynamics over time is essential for effective resource management.
Method: This study focuses on the spatio-temporal variations of vegetation cover in East Azerbaijan Province, a crucial mountainous region in northwestern Iran, over a 22-year period (2000–2022). To achieve this objective, statistical and geospatial methods, including the Normalized Difference Vegetation Index (NDVI), Kolmogorov–Smirnov test (KST), Geographically Weighted Regression (GWR), and Principal Component Analysis (PCA), were applied to MODIS satellite products.
Result: The findings reveal sustained but unstable vegetation dynamics in the region. The KST indicated that NDVI distributions were non-normal across all months, suggesting considerable ecological instability. Notably, the mean annual rate of NDVI change increased from 0.166% in the first decade (2000–2009) to 0.192% in the second period (2010–2022). The high NDVI variance (36.78%) confirms pronounced spatial heterogeneity across the province. Furthermore, a moderate positive correlation (45%) was observed between precipitation and vegetation cover, highlighting the dominant role of moisture availability. Finally, PCA identified three high-density vegetation groups, collectively explaining 93.49% of the total variance, while the GWR model demonstrated strong predictive capability for localized vegetation changes.
Conclusion: These results provide valuable quantitative evidence of ongoing ecological changes in this important mountainous region and offer essential insights for regional land-use planning and conservation strategies.

Abstract Objective: The absence of a systemic paradigm and the lack of attention to the compatibility and selection of irrigation technologies based on multi-criteria decision-making approaches have intensified the water crisis in the agricultural sector. The present study aimed to investigate the compatibility of irrigation technologies with the actual conditions of farmers, with a primary focus on optimal management of water resources.
Methods: The present research is applied in nature and employs a survey-based data collection method. The criteria were weighted using the CRITIC method, and the WASPAS multi-criteria decision-making method was employed to rank the irrigation technologies.
Results: The results indicated that the final weight for the technology’s compatibility with social conditions was determined to be 0.202, and for farmer proficiency, it was 0.167. Furthermore, the final weights for the technology’s compatibility with climatic conditions, system productivity, on-farm compatibility, technology level, and economic compatibility were 0.143, 0.125, 0.125, 0.120, and 0.116, respectively. Accordingly, the highest weight corresponds to the technology’s compatibility with social conditions, while the lowest weight pertains to its compatibility with economic conditions. Furthermore, WASPAS method analysis revealed that the pressurized sprinkler irrigation system
Conclusions: The development and application of appropriate irrigation technologies is the prerequisite for optimal water management, and it requires an optimal multi-attribute decision, taking into account ecological, technical, and especially socio-economic dimensions.

Abstract Population growth and droughts have led to water scarcity in arid and semi-arid regions. Optimal allocation of water resources is a key strategy for prioritizing water use across sectors. The aim of this study is to prioritize the allocation of agricultural water among sub-sectors in Zanjan Province. To this end, the main and sub-criteria, as well as options, were determined based on expert opinions, and the options were prioritized using the AHP. The five main criteria included food security, economic potential, technical and operational feasibility, resource and environmental sustainability, and governance and comparative advantage. The options consisted of crop farming, horticulture, livestock production, poultry farming, aquaculture, and greenhouse cultivation. The results showed that food security (weight=0.40), was the most important criterion, highlighting the need to focus on food-basket products. Economic potential (weight=0.08) ranked lowest in priority. Equitable access to food and drought resilience had the highest importance (each with a weight of 0.40), while the self-sufficiency ratio had the lowest importance (weight=0.20). Irrigated crop farming (weight=0.25) with the highest priority, plays a crucial role in ensuring food security and supporting employment. Sensitivity analysis showed that irrigated crop farming and poultry farming ranked highest, and only under extreme weighting of technical or economic criteria could greenhouse cultivation or poultry farming replace crop farming. The findings indicate that agricultural water allocation in Zanjan Province requires a comprehensive and multi-dimensional approach in which achieving sustainable food security is prioritized.

Abstract Laboratory evaluation was carried out at the Agricultural Engineering Research Institute (AERI), and field monitoring was conducted in three pilot sites: Khusf (1 km length), Gorgan and Qom (2 km length). In the laboratory, Khusf and Qom pilots, the concrete canvas lining was installed longitudinally with rolls averaging 12.5 m, while in Gorgan it was installed transversely with 2 m rolls, joined by bolts and nuts at 10-20 cm intervals for grade 3 and 4 canals. The laboratory results showed that Manning’s roughness coefficient for the concrete canvas was 0.01, which was more favorable compared to the brick-and-cement control (0.022) and conventional concrete linings (0.013–0.027). The values of shear velocity and shear stress were estimated at 0.032 m/s and 1.06 N/m2, respectively, which were 54% and 11% lower than the control. Seepage under laboratory conditions was estimated at about 1.29 lit/m2/day, which was 96% lower than conventional linings such as concrete and stone. In the field pilots, average seepage in the longitudinal installation method was about 21% lower than in the transverse method, indicating greater effectiveness of the longitudinal approach in reducing seepage. The results also showed that roughness coefficient and seepage in canals lined with concrete canvas were approximately 35% and 58% lower, respectively, than those of conventional concrete in the monitored areas.

Abstract Objective: This study aims to compare four different methods for estimating sediment volume behind check dams: the geometric, pyramidal, trapezoidal, and prismatic methods. The study area is the Rhein Shirabad watershed in the Maneh and Samalqan region of North Khorasan Province, Iran. The primary objective is to evaluate the accuracy and applicability of these methods in estimating sediment deposition and to determine the most reliable approach for sediment volume calculation.
Methods: The geometric, pyramidal, trapezoidal, and prismatic methods were applied to estimate sediment volume. To enhance the accuracy of the estimations, bias correction was implemented on the computed values. Statistical performance metrics, including the coefficient of determination (R²), root mean square error (RMSE), and mean bias error (MBE), were used to assess the accuracy of each method. The calculated values from each method were compared with actual field measurements to determine their reliability.
Results: The findings indicate that the geometric and prismatic methods provide higher accuracy in estimating sediment deposition compared to the pyramidal and trapezoidal methods. Before bias correction, the RMSE values for the prismatic and geometric methods were lower (728.06 and 736.16, respectively) than those of the trapezoidal and pyramidal methods. The MBE values suggested that the prismatic method had the least estimation error. After applying bias correction, the RMSE values for all methods decreased significantly, indicating improved estimation accuracy. The mean bias error (MBE) values for all methods approached zero after correction, demonstrating a closer alignment between estimated and actual sediment volumes.