University of Tehran PressJournal of Ecohydrology2423-609812320251023Evaluation of the Effects of Mechanical Watershed Management Measures on Reducing Sedimentation in the Zayandeh Rood Dam ReservoirEvaluation of the Effects of Mechanical Watershed Management Measures on Reducing Sedimentation in the Zayandeh Rood Dam Reservoir81283110438710.22059/ije.2025.397787.1876FAHamidNouriAssociate Professor of Climatology, Department of Natural Engineering, Faculty of Natural Resources and Environment, Malayer University, Natural Resources and Watershed Management Organization, International Center for Integrated Watershed Management and Biological Resources in Arid and Semi-arid Regions under the auspices of UNESCO (ICIMWB), Iran, climatologyHasanVahidPhD student in Rangeland Management, Gorgan University of Natural Resources and Agriculture, Natural Resources and Watershed Management OrganizationJournal Article20250713<strong>Research Topic:</strong> Estimation of river sediment load plays a fundamental role in understanding the ecohydrological relationships of the watershed and the useful life of dams. The structural complex in watershed management, including the management of the bed slope and banks of waterways, always has a significant impact on the sediments transported to rivers and dam reservoirs.<br /><strong>Method</strong>: In this paper, hydraulic and sedimentological data from 11 stations in the Zayandeh-Rood Dam basin were analyzed (1960 to 2019) to select the best methods for estimating suspended load and bed load, and to determine the cumulative effect of sediments stored by watershed management check dams that prevent the transport of these sediments downstream, by province and structure.<br /><strong>Results</strong>: Sediment gauge curve methods without correction factors estimate values lower than the actual sediment discharge, and among these methods, the single-line, bilinear, and median curve methods have estimated the lowest sediment amount, respectively. On the other hand, the results showed that the FAO correction factor in all gauge curve methods is overestimated compared to other methods. Measurement and calculation of sediment trapping show that the sediment storage volume by the total watershed dams was approximately equivalent to 4 years of sediment transport in the 2001s or 5 years of sediment transport in the 2011s in the Zayandeh-Roud Dam basin.<br /><strong>Conclusions</strong>: The volume of sediment storage behind of check dams of watershed managent in the Zayandeh-Roud Dam basin indicates the importance and role of these structures in managing river sediment and reducing inflow to the dam reservoir.<strong>Research Topic:</strong> Estimation of river sediment load plays a fundamental role in understanding the ecohydrological relationships of the watershed and the useful life of dams. The structural complex in watershed management, including the management of the bed slope and banks of waterways, always has a significant impact on the sediments transported to rivers and dam reservoirs.<br /><strong>Method</strong>: In this paper, hydraulic and sedimentological data from 11 stations in the Zayandeh-Rood Dam basin were analyzed (1960 to 2019) to select the best methods for estimating suspended load and bed load, and to determine the cumulative effect of sediments stored by watershed management check dams that prevent the transport of these sediments downstream, by province and structure.<br /><strong>Results</strong>: Sediment gauge curve methods without correction factors estimate values lower than the actual sediment discharge, and among these methods, the single-line, bilinear, and median curve methods have estimated the lowest sediment amount, respectively. On the other hand, the results showed that the FAO correction factor in all gauge curve methods is overestimated compared to other methods. Measurement and calculation of sediment trapping show that the sediment storage volume by the total watershed dams was approximately equivalent to 4 years of sediment transport in the 2001s or 5 years of sediment transport in the 2011s in the Zayandeh-Roud Dam basin.<br /><strong>Conclusions</strong>: The volume of sediment storage behind of check dams of watershed managent in the Zayandeh-Roud Dam basin indicates the importance and role of these structures in managing river sediment and reducing inflow to the dam reservoir.https://ije.ut.ac.ir/article_104387_e126e59dbf6fcb4733d81b3cb7004060.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609812320251023Trivariate Uncertainty Analysis of Hydro-Climatic Drought Risk Using Bootstrap Method (Case Study: Esteghlal Dam Basin, Minab)Trivariate Uncertainty Analysis of Hydro-Climatic Drought Risk Using Bootstrap Method (Case Study: Esteghlal Dam Basin, Minab)83285010325010.22059/ije.2025.395400.1871FAZohrehPakdamanAssistant Professor, Department of statistics, Faculty of science, University of Hormozgan, Bandar Abass, IranOmolbaninBazrasshanProfessor, Department of Natural Resources Engineering, Faculty of Agriculture, University of Hormozgan, Bandar Abass, IranJournal Article20250708<strong>Research Topic:</strong> Trivariate Uncertainty Analysis of Hydro-Climatic Drought Risk Using Bootstrap Method (Case Study: Esteghlal Dam Basin, Minab).<br /><strong>Objective:</strong> The main objective was to develop a trivariate uncertainty framework for analyzing hydro-climatic drought in the Esteghlal Dam watershed (Minab), by integrating meteorological and hydrological drought indices and predicting conditional probabilities of drought severity under varying conditions.<br /><strong>Method</strong>: Monthly precipitation and runoff data (1991–2021) were standardized using the Gamma distribution. A Joint Drought Index (JDI) was derived by combining the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI). Copula functions from Archimedean and elliptical families were applied to capture dependence among drought variables. Maximum Likelihood Estimation (MLE) was used for parameter estimation, and the Sn goodness-of-fit test was employed to identify optimal copula models. Finally, trivariate conditional probabilities of drought severity were estimated, and bootstrap resampling was used to quantify the associated uncertainty.<br /><strong>Results</strong>: The Gumbel copula provided the best fit for modeling the dependence structure between severity, duration, and magnitude. Results showed that increasing drought duration led to higher severity, while reducing conditional probability (longer return periods) decreased severity. For example, at a 10-year return period (CP = 0.1), when duration = 20 months and magnitude = 0.33, severity was 2.47; with magnitude increased to 2.87, severity rose to 17.1. Conversely, reducing conditional probability from 0.2 to 0.005 lowered severity from 5.1 to 0.7. The most severe recorded drought exhibited severity = 93, duration = 31 months, and magnitude = 2.87.<br /><strong>Conclusions</strong>: <br />The study confirms that extreme droughts become less frequent as return periods increase, yet they can be significantly more severe. The proposed trivariate uncertainty framework, combining copula modeling and bootstrap analysis, provides a robust tool for assessing drought risks under uncertainty. This approach enhances understanding of drought dynamics and offers valuable insights for water resource management and early-warning systems in arid and semi-arid regions.<strong>Research Topic:</strong> Trivariate Uncertainty Analysis of Hydro-Climatic Drought Risk Using Bootstrap Method (Case Study: Esteghlal Dam Basin, Minab).<br /><strong>Objective:</strong> The main objective was to develop a trivariate uncertainty framework for analyzing hydro-climatic drought in the Esteghlal Dam watershed (Minab), by integrating meteorological and hydrological drought indices and predicting conditional probabilities of drought severity under varying conditions.<br /><strong>Method</strong>: Monthly precipitation and runoff data (1991–2021) were standardized using the Gamma distribution. A Joint Drought Index (JDI) was derived by combining the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI). Copula functions from Archimedean and elliptical families were applied to capture dependence among drought variables. Maximum Likelihood Estimation (MLE) was used for parameter estimation, and the Sn goodness-of-fit test was employed to identify optimal copula models. Finally, trivariate conditional probabilities of drought severity were estimated, and bootstrap resampling was used to quantify the associated uncertainty.<br /><strong>Results</strong>: The Gumbel copula provided the best fit for modeling the dependence structure between severity, duration, and magnitude. Results showed that increasing drought duration led to higher severity, while reducing conditional probability (longer return periods) decreased severity. For example, at a 10-year return period (CP = 0.1), when duration = 20 months and magnitude = 0.33, severity was 2.47; with magnitude increased to 2.87, severity rose to 17.1. Conversely, reducing conditional probability from 0.2 to 0.005 lowered severity from 5.1 to 0.7. The most severe recorded drought exhibited severity = 93, duration = 31 months, and magnitude = 2.87.<br /><strong>Conclusions</strong>: <br />The study confirms that extreme droughts become less frequent as return periods increase, yet they can be significantly more severe. The proposed trivariate uncertainty framework, combining copula modeling and bootstrap analysis, provides a robust tool for assessing drought risks under uncertainty. This approach enhances understanding of drought dynamics and offers valuable insights for water resource management and early-warning systems in arid and semi-arid regions.https://ije.ut.ac.ir/article_103250_c320baf28930c9ea30564552196872ca.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609812320251023Quantification and Valuation of Climate Regulation Ecosystem Services Using the InVEST Blue Carbon Model in the Khorkhoran International Wetland ReserveQuantification and Valuation of Climate Regulation Ecosystem Services Using the InVEST Blue Carbon Model in the Khorkhoran International Wetland Reserve85186610416110.22059/ije.2025.399269.1878FAFatemehMohammadyariDepartment of Environmental Engineering, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, IranArdavanZarandianEnvironmental assessment and risks group, Research centre for environment and sustainable development, Department of Environment, Tehran, IranMajidRamezani MehrianDepartment of Environmental Studies, The Institute for Research and Development in the Humanities (SAMT), Tehran, IranRoyaMousazadehResearch Group of Environmental Economics, Research Center for Environment and Sustainable Development (RCESD), Department of Environment, Tehran, IranJournal Article20250703<strong>Research Topic:</strong> Assessing the potential of Khorkhoran International Mangrove Wetland Habitats in Providing Ecosystem Services.<br /><strong>Objective:</strong> Quantifies and values the blue carbon storage and sequestration ecosystem service as a climate change adaptation strategy.<br /><strong>Method:</strong> In this study, the InVEST Blue Carbon model was employed to simulate carbon storage and sequestration services within the coastal mangrove forests of Khorkhoran. Input data comprised land use/ land cover maps, carbon pool inventories, biomass carbon, soil carbon, and disturbance rates. The social cost of carbon method was also used to value this ecosystem service.<br /><strong>Results:</strong> Between 2000–2020, each hectare of mangrove forests in the study area sequestered an average of 10,510,000 tons of CO₂. Total carbon sequestration from 2000–2050 was estimated at 23,122,400.79 Mt, equating to an average release of 954,000 tons of CO₂ per hectare over this period mangrove forests in the study area. The annual economic value of blue carbon sequestration also, across the Khorkhoran International Wetland sub-basins was valued at 176,241,001,299,200 Rials (approx. USD 4.2 million*).<br /><strong>Conclusions:</strong> Decision-makers can leverage the results of this research to support protective measures that preserve mangroves while enhancing co-benefits like water quality and ecosystem health.<strong>Research Topic:</strong> Assessing the potential of Khorkhoran International Mangrove Wetland Habitats in Providing Ecosystem Services.<br /><strong>Objective:</strong> Quantifies and values the blue carbon storage and sequestration ecosystem service as a climate change adaptation strategy.<br /><strong>Method:</strong> In this study, the InVEST Blue Carbon model was employed to simulate carbon storage and sequestration services within the coastal mangrove forests of Khorkhoran. Input data comprised land use/ land cover maps, carbon pool inventories, biomass carbon, soil carbon, and disturbance rates. The social cost of carbon method was also used to value this ecosystem service.<br /><strong>Results:</strong> Between 2000–2020, each hectare of mangrove forests in the study area sequestered an average of 10,510,000 tons of CO₂. Total carbon sequestration from 2000–2050 was estimated at 23,122,400.79 Mt, equating to an average release of 954,000 tons of CO₂ per hectare over this period mangrove forests in the study area. The annual economic value of blue carbon sequestration also, across the Khorkhoran International Wetland sub-basins was valued at 176,241,001,299,200 Rials (approx. USD 4.2 million*).<br /><strong>Conclusions:</strong> Decision-makers can leverage the results of this research to support protective measures that preserve mangroves while enhancing co-benefits like water quality and ecosystem health.https://ije.ut.ac.ir/article_104161_51c2797147a8fb2c18868b14e6ebb202.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609812320251023Assessment of the effects of climate change on the exploitation of surface water resources and aquifers (Study area: Dehloran study area)Assessment of the effects of climate change on the exploitation of surface water resources and aquifers (Study area: Dehloran study area)86788210454810.22059/ije.2025.403140.1885FAAliPourahmadPhd student, Department of Water Sciences, Shushtar Branch, Islamic Azad UniversityMohsenSolimani BabarsadAssistant professor of hydraulic science, Department of Water Sciences, Shushtar Branch, Islamic Azad university0000-0002-6676-0323HosseinEslamiAssistant professor of hydraulic science, Department of Water Sciences, Shushtar Branch, Islamic Azad universitySaebKhoshnavaz KomelehAssistant professor of hydraulic science, Department of Water Sciences, Shushtar Branch, Islamic Azad universityMahmoudJazayeri MoghaddasAssistant professor of hydraulic science, Department of Water Sciences, Shushtar Branch, Islamic Azad universityJournal Article20250717<strong>Research Topic:</strong> Climate change and its effects on components influencing the groundwater balance are critically important for sustainable water resource management under conditions of water supply instability.<br /><strong>Objective:</strong> Investigating the exploitation of groundwater resources as a sustainable resource in climate change conditions.<br /><strong>Method</strong>: Among 14 climate models, the most suitable models for simulating precipitation and temperature were selected based on statistical indicators. Considering the importance of precipitation in groundwater recharge, surface water behavior was estimated through regression relationships.<br /><strong>Results</strong>: Climate simulation results identified the AWI-CM-1-1-MR model as the best for future precipitation projections and HadGEM3-GC31-MM for temperature projections. In contrast, temperature is expected to increase across all three scenarios. Based on regression relationships between precipitation and streamflow at three hydrometric stations, two components—recharge from surface runoff and aquifer drainage—were calculated for the groundwater balance. Groundwater inflow and outflow were estimated numerically in relation to aquifer water level changes.<br /><strong>Conclusions</strong>: Climate change significantly affects the quantitative dynamics of the aquifer, particularly due to the high sensitivity of groundwater outflow and water table levels. Moreover, the findings reveal that groundwater abstraction has a relatively smaller impact compared to climate change effects, suggesting that well-planned abstraction policies could lead to effective adaptation strategies under future climate scenarios.<strong>Research Topic:</strong> Climate change and its effects on components influencing the groundwater balance are critically important for sustainable water resource management under conditions of water supply instability.<br /><strong>Objective:</strong> Investigating the exploitation of groundwater resources as a sustainable resource in climate change conditions.<br /><strong>Method</strong>: Among 14 climate models, the most suitable models for simulating precipitation and temperature were selected based on statistical indicators. Considering the importance of precipitation in groundwater recharge, surface water behavior was estimated through regression relationships.<br /><strong>Results</strong>: Climate simulation results identified the AWI-CM-1-1-MR model as the best for future precipitation projections and HadGEM3-GC31-MM for temperature projections. In contrast, temperature is expected to increase across all three scenarios. Based on regression relationships between precipitation and streamflow at three hydrometric stations, two components—recharge from surface runoff and aquifer drainage—were calculated for the groundwater balance. Groundwater inflow and outflow were estimated numerically in relation to aquifer water level changes.<br /><strong>Conclusions</strong>: Climate change significantly affects the quantitative dynamics of the aquifer, particularly due to the high sensitivity of groundwater outflow and water table levels. Moreover, the findings reveal that groundwater abstraction has a relatively smaller impact compared to climate change effects, suggesting that well-planned abstraction policies could lead to effective adaptation strategies under future climate scenarios.https://ije.ut.ac.ir/article_104548_6668eeb045a27888abb7d809230c596a.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609812320251023Quantifying the impact of surface and climatic factors on changes in land surface temperature in the Kardeh watershedQuantifying the impact of surface and climatic factors on changes in land surface temperature in the Kardeh watershed88390010294910.22059/ije.2025.396399.1872FAMaryamAkbariPh.D Student in Watershed Sciences and Engineering, Faculty of Natural Resources and Desert Studies, Yazd University, Yazd, IranHosseinMalekinezhadAssociate Professor, Rangeland and Watershed Group, Faculty of Natural Resources and Desert Studies, Yazd University, Yazd, IranMahbobehHajibiglooPh.D in Watershed Sciences and Engineering, Faculty of Rangeland and Watershed, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, IranJournal Article20250618<strong>Research Topic:</strong> This study used surface and climate data in 2020 to analyze the relationships between LST and driving factors using correlation analysis and spatial regression models.<br /><strong>Objective:</strong> The objectives of the present study are: (1) to investigate the spatial variations of LST in the Kardeh River Basin, (2) to measure <sup>the</sup> explanatory power of natural and human factors and their interactions on LST changes, and (3) to determine the appropriate ranges of the main driving factors that can affect the LST of the region. In general, this study uses the Geodetector model to analyze the spatial heterogeneity of LST and investigate the driving factors on this heterogeneity and provides a ranking of the importance of the factors.<br /><strong>Method:</strong> In the present study, considering LST as the dependent variable, 9 driving forces including topography (elevation, slope, aspect), land use, vegetation, average annual precipitation, distance from residential area, distance from road and distance from river were considered as independent variables. Then, based on data classification, 21404 random points were generated using the Fishnet feature in ArcGIS and the LST layer and other classified environmental factors were spatially overlapped. Finally, the values of raster cells with the generated random points were extracted and a descriptive table was obtained to determine the correlation between LST and driving parameters and the outputs were implemented in the Geodetector model.<br /><strong>Results:</strong> The results show that the changes in LST of the basin are affected by natural conditions and human activities. The average LST in the Kardeh River basin is 33.37 degrees Celsius, which is the highest value in the plains and the lowest value in the highlands and mountainous areas. Therefore, the altitude parameter is the most effective factor on the spatial variability of LST in the study area. According to the results, in the first place, altitude, then land use and average annual precipitation have explanatory power of 43.17%, 31.29% and 21.23%, respectively. The results of the interactive detector analysis showed a two-factor increase for both factors, and the interaction between altitude and land use expressed the highest explanatory power (0.64). Also, the interaction between the altitude factor and other parameters with the highest q value ranged from 0.43 to 0.60. In addition, we determined the optimal range of specific variables that affect LST; Which showed that the low-lying, low-elevation, and shallow-slope areas of the basin are mainly dominated by the intensity of human activities and dryland agriculture and interact with terrestrial factors; as a result, they have the highest temperature.<br /><strong>Conclusions:</strong> These findings will help facilitate sustainable management of climate change, analyze surface environmental models and environmental protection, and also improve land management strategies in the Kardeh River Basin and other regions with arid and semi-arid climates.<strong>Research Topic:</strong> This study used surface and climate data in 2020 to analyze the relationships between LST and driving factors using correlation analysis and spatial regression models.<br /><strong>Objective:</strong> The objectives of the present study are: (1) to investigate the spatial variations of LST in the Kardeh River Basin, (2) to measure <sup>the</sup> explanatory power of natural and human factors and their interactions on LST changes, and (3) to determine the appropriate ranges of the main driving factors that can affect the LST of the region. In general, this study uses the Geodetector model to analyze the spatial heterogeneity of LST and investigate the driving factors on this heterogeneity and provides a ranking of the importance of the factors.<br /><strong>Method:</strong> In the present study, considering LST as the dependent variable, 9 driving forces including topography (elevation, slope, aspect), land use, vegetation, average annual precipitation, distance from residential area, distance from road and distance from river were considered as independent variables. Then, based on data classification, 21404 random points were generated using the Fishnet feature in ArcGIS and the LST layer and other classified environmental factors were spatially overlapped. Finally, the values of raster cells with the generated random points were extracted and a descriptive table was obtained to determine the correlation between LST and driving parameters and the outputs were implemented in the Geodetector model.<br /><strong>Results:</strong> The results show that the changes in LST of the basin are affected by natural conditions and human activities. The average LST in the Kardeh River basin is 33.37 degrees Celsius, which is the highest value in the plains and the lowest value in the highlands and mountainous areas. Therefore, the altitude parameter is the most effective factor on the spatial variability of LST in the study area. According to the results, in the first place, altitude, then land use and average annual precipitation have explanatory power of 43.17%, 31.29% and 21.23%, respectively. The results of the interactive detector analysis showed a two-factor increase for both factors, and the interaction between altitude and land use expressed the highest explanatory power (0.64). Also, the interaction between the altitude factor and other parameters with the highest q value ranged from 0.43 to 0.60. In addition, we determined the optimal range of specific variables that affect LST; Which showed that the low-lying, low-elevation, and shallow-slope areas of the basin are mainly dominated by the intensity of human activities and dryland agriculture and interact with terrestrial factors; as a result, they have the highest temperature.<br /><strong>Conclusions:</strong> These findings will help facilitate sustainable management of climate change, analyze surface environmental models and environmental protection, and also improve land management strategies in the Kardeh River Basin and other regions with arid and semi-arid climates.https://ije.ut.ac.ir/article_102949_811242fb06028a8fe86d22871120839c.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609812320250923Spatial assessment of desertification severity using climate, soil, vegetation, and water criteria and presenting a management plan (Case study: Isfahan Province)Spatial assessment of desertification severity using climate, soil, vegetation, and water criteria and presenting a management plan (Case study: Isfahan Province)90192610478410.22059/ije.2025.404049.1889FASeyyed AliMousaviAssistant Professor, Drylands Management Department, Faculty of Natural Resources and Earth Sciences, University of KashanJournal Article20250702<strong>Research Topic:</strong> Land degradation in arid and semi-arid regions due to human activities and climate change leads to desertification.
<strong>Objective:</strong> In this study, the IMDPA model was used to spatially assess the intensity of desertification in Isfahan Province with four main criteria: vegetation, soil, climate, and water.
<strong>Method:</strong> The criteria were scored based on field observations and regional conditions. The index map of each criterion was combined with the geometric mean method, and then a desertification severity map of each criterion and a final map of the province were prepared using ArcGIS software. Also, a management plan to reduce desertification was developed using the SWOT model.
<strong>Results:</strong> The results indicate that climate, vegetation, soil, and water criteria are the most important factors of desertification, respectively. The climate criterion with a geometric value of 3.08 was in the severe class, and vegetation (2.72), soil (1.98), and water (1.07) were in the moderate class. Drought indices, annual precipitation, and vegetation renewal were the most important, and groundwater table decline, vegetation exploitation, and percentage of rocks and gravel were the least important factors.
<strong>Conclusions:</strong> The final map showed that 5.52 percent of the area is in the low desertification class and the rest is in the medium desertification class. SWOT analysis showed a defensive pattern for Isfahan province. Among the internal strengths, appropriate indigenous knowledge shows the greatest impact and water transfer to neighboring provinces shows the least impact. In terms of external opportunities, the government's attention to desertification and the restoration of the Zayandeh Rood River have the greatest positive impact, and the number of sunny hours for solar panels have the least positive impact. Internal weaknesses include dry climate and water shortages, which have the greatest negative impact, and overgrazing of livestock, which has the least negative impact. Also, in terms of external threats, proximity to the desert and dust production show the greatest negative impact, and inefficient governance management shows the least negative impact.<strong>Research Topic:</strong> Land degradation in arid and semi-arid regions due to human activities and climate change leads to desertification.
<strong>Objective:</strong> In this study, the IMDPA model was used to spatially assess the intensity of desertification in Isfahan Province with four main criteria: vegetation, soil, climate, and water.
<strong>Method:</strong> The criteria were scored based on field observations and regional conditions. The index map of each criterion was combined with the geometric mean method, and then a desertification severity map of each criterion and a final map of the province were prepared using ArcGIS software. Also, a management plan to reduce desertification was developed using the SWOT model.
<strong>Results:</strong> The results indicate that climate, vegetation, soil, and water criteria are the most important factors of desertification, respectively. The climate criterion with a geometric value of 3.08 was in the severe class, and vegetation (2.72), soil (1.98), and water (1.07) were in the moderate class. Drought indices, annual precipitation, and vegetation renewal were the most important, and groundwater table decline, vegetation exploitation, and percentage of rocks and gravel were the least important factors.
<strong>Conclusions:</strong> The final map showed that 5.52 percent of the area is in the low desertification class and the rest is in the medium desertification class. SWOT analysis showed a defensive pattern for Isfahan province. Among the internal strengths, appropriate indigenous knowledge shows the greatest impact and water transfer to neighboring provinces shows the least impact. In terms of external opportunities, the government's attention to desertification and the restoration of the Zayandeh Rood River have the greatest positive impact, and the number of sunny hours for solar panels have the least positive impact. Internal weaknesses include dry climate and water shortages, which have the greatest negative impact, and overgrazing of livestock, which has the least negative impact. Also, in terms of external threats, proximity to the desert and dust production show the greatest negative impact, and inefficient governance management shows the least negative impact.https://ije.ut.ac.ir/article_104784_51ad571b6233690e40dcd86a3ef3edbc.pdfUniversity of Tehran PressJournal of Ecohydrology2423-609812320250923Assessment of the Impact of Centralized MPC-Based Operational Automation on Adequate and Reliable Distribution of Agricultural Water Rights under Surface-Water Allocation UncertaintyAssessment of the Impact of Centralized MPC-Based Operational Automation on Adequate and Reliable Distribution of Agricultural Water Rights under Surface-Water Allocation Uncertainty92794410478310.22059/ije.2025.405571.1894FARezaJamaliPhD. Candidate, Dept. of Water Engineering, Faculty of Agricultural Technology (Aburaihan), University College of Agriculture & Natural Resources, University of Tehran, Tehran, IranAburayhanMASHALWater Eng. Dept.
, Aburayhan Campus,, Emam Reza Blv.S. MehdiHashemy ShahdanyAssociate Professor, Dept. of Water Engineering, Faculty of Agricultural Technology (Aburaihan), University College of Agriculture & Natural Resources, University of Tehran, Tehran, IranJaberSoltaniAssociate Professor, Dept. of Water Engineering, Faculty of Agricultural Technology (Aburaihan), University College of Agriculture & Natural Resources, University of Tehran, Tehran, Iran
Email: jsoltani@ut.ac.irJournal Article20250629<strong>Research Topic:</strong> This research investigates the comparative performance of an automated operation system in improving the reliability of surface-water distribution among water-right holders in an irrigation network.
<strong>Objective:</strong><strong> </strong>The main objective of this study is to analyze the spatial and temporal effects of surface-water operation system modernization on the adequate and reliable distribution of agricultural water rights among water user cooperatives within the Nekoabad irrigation network. The principal analytical outcome is the precise determination of the contribution of surface-water resources-under water-scarcity conditions at the diversion dam-in meeting the water rights of 163 farmer cooperatives within the network.
<strong>Method: </strong>Operational simulations were performed for two modes: manual operation (baseline condition) and automated operation using a centralized Model Predictive Controller (MPC). The temporal analysis was based on categorizing surface-water availability at the diversion dam into seven scenarios ranging from normal to extreme shortage. Spatial analysis was conducted by simulating the surface-water distribution process among 163 water-right holders located along thirteen secondary canals of the Nekoabad irrigation district.
<strong>Results:</strong><strong> </strong>The results revealed that, compared with the manual system, the MPC-based operation exhibited significantly higher stability, uniformity, and reliability. Under shortage conditions, the manual system showed nonlinear degradation in adequacy and dependability indices, with a pronounced increase in spatial inequality of water delivery. In contrast, the MPC system maintained a higher mean adequacy, reduced temporal fluctuations, and achieved spatial homogenization, extending the shortage-tolerance threshold by up to 25 percentage points. Spatial correlation analysis of performance indices, statistical distribution of operational outcomes, and surface-water delivery shares all confirmed the superiority of the automated system in sustaining equity and resilience across the irrigation network.
<strong>Conclusion: </strong>Aligned with the main goal of this study, the practical outcome is the accurate quantification of the percentage and variation range of surface-water deliveries allocated to each of the 163 water outlets under different inflow-supply scenarios at the diversion dam.<strong>Research Topic:</strong> This research investigates the comparative performance of an automated operation system in improving the reliability of surface-water distribution among water-right holders in an irrigation network.
<strong>Objective:</strong><strong> </strong>The main objective of this study is to analyze the spatial and temporal effects of surface-water operation system modernization on the adequate and reliable distribution of agricultural water rights among water user cooperatives within the Nekoabad irrigation network. The principal analytical outcome is the precise determination of the contribution of surface-water resources-under water-scarcity conditions at the diversion dam-in meeting the water rights of 163 farmer cooperatives within the network.
<strong>Method: </strong>Operational simulations were performed for two modes: manual operation (baseline condition) and automated operation using a centralized Model Predictive Controller (MPC). The temporal analysis was based on categorizing surface-water availability at the diversion dam into seven scenarios ranging from normal to extreme shortage. Spatial analysis was conducted by simulating the surface-water distribution process among 163 water-right holders located along thirteen secondary canals of the Nekoabad irrigation district.
<strong>Results:</strong><strong> </strong>The results revealed that, compared with the manual system, the MPC-based operation exhibited significantly higher stability, uniformity, and reliability. Under shortage conditions, the manual system showed nonlinear degradation in adequacy and dependability indices, with a pronounced increase in spatial inequality of water delivery. In contrast, the MPC system maintained a higher mean adequacy, reduced temporal fluctuations, and achieved spatial homogenization, extending the shortage-tolerance threshold by up to 25 percentage points. Spatial correlation analysis of performance indices, statistical distribution of operational outcomes, and surface-water delivery shares all confirmed the superiority of the automated system in sustaining equity and resilience across the irrigation network.
<strong>Conclusion: </strong>Aligned with the main goal of this study, the practical outcome is the accurate quantification of the percentage and variation range of surface-water deliveries allocated to each of the 163 water outlets under different inflow-supply scenarios at the diversion dam.https://ije.ut.ac.ir/article_104783_d40600fd62a21f779a337195923902fc.pdf