Journal of Range and Watershed Managment

Abstract The growing population exerts increasing pressure on agricultural systems, water resources, and energy supplies. Effective management of these systems is essential to ensuring their sustainability. One of the key characteristics of a sustainable system is its capacity to adapt to changes. This study aims to assess the institutional adaptive capacity of the agricultural sector within the water-energy-food nexus. Institutions refer to rule-making systems, decision-making procedures, and programs that shape social movements, define the roles of individuals in these movements, and determine how they interact. In this research, the adaptive capacity wheel model was employed to evaluate institutional adaptive capacity. This model consists of six key criteria: 1) Variety, 2) Learning Capacity, 3) Room for Autonomous Change, 4) Leadership, 5) Resources, and 6) Fair Governance. To carry out the assessment, the agricultural system was first analyzed, and 27 relevant organizational stakeholders were identified. A capacity assessment questionnaire was distributed among these organizational stakeholders, and interviews were conducted to collect supplementary information. The results indicated that all aspects of institutional adaptive capacity were generally assessed negatively. This implies that there are significant gaps that need to be addressed in order to effectively cope with emerging challenges and adapt to changing conditions. The financial resources indicator, showing the highest negative value, reflects the structural weaknesses of organizations in responding to new circumstances. According to the findings, the adaptive capacity wheel score of the agricultural system within the water-energy-food nexus was rated at -0.29. This result highlights the presence of barriers that hinder the effective adaptation of the system to the challenges posed by the nexus.

Abstract Land subsidence is a complex geomorphological phenomenon characterized by the vertical downward movement of the Earth's surface, with far-reaching implications for ecosystems, human infrastructure, and the environment. This study aims to analyze the relationship between groundwater level decline and land subsidence in the Tehran-Shahryar plain, using measured data. The research methodology is based on advanced remote sensing techniques, including Interferometric Synthetic Aperture Radar (InSAR) using Sentinel-1 satellite images, and analysis of groundwater level data using statistical methods. A key innovation of this research is the simultaneous use of all available piezometers in the study area and the calibration of radar data with accurate ground leveling measurements. The findings of the study indicate that groundwater level decline is the main trigger of subsidence in the region. Cross-correlation analysis of the data reveals a time lag of 0 to 3 years, with an average of 1 year, between groundwater level decline and the occurrence of subsidence across the entire area. This time lag is due to complex geotechnical processes in the water-bearing sedimentary layers, especially in areas with complex geological structures. Spatial-temporal analyses show that the Tehran-Shahryar plain is facing a high potential for subsidence, with average subsidence rates exceeding 23 mm per year in some areas. This trend can pose serious threats to critical infrastructure, engineering structures, and the region's ecosystem. The implications of this study highlight the importance of integrated groundwater management, controlled extraction, and continuous monitoring of geodynamic changes. Moreover, the results can provide a suitable scientific basis for making major decisions in water resources management and mitigating environmental hazards.

Abstract Natural restoration of forest after harvesting operations is one of the most important goals in forest sustainability. Forestry machine traffic by considering weight, speed, and intensity have significant impact on forest regeneration and consequently, biodiversity indices such as dominance, and equitability in the plant communities of forest floor drastically influenced. The current study has been carried out in the watershed no. 45 in the Hyrcanian forest and aim to study the skidding operation effect on diversity and tree species regeneration over a long-term period. Hence, four compartments with time since harvest of 7, 10, 15, 20 year in the Kheyrud forest (compartments no. 316, 317, 220, and 221) were selected and sampled. Also, the effect of soil physical properties (porosity and peneteration resistance), pH, litter (C, N, N/N), and light under different traffic intensity (landing, low, medium, high), and its influence on biodiversity indexes using dominance, Shannon, Simpson, Margalef, and equitability were assessed. Results revealed that skidding operations had drastic effect on forest regeneration and change species composition as well as plants quantitate. Velvet maple as light-demanding species increased by 8 percent and shade-tolerant species such as beech diminished by 68 percentages, which can be considered as ecological alarm in the Hyrcanian forest. Results demonstrated that traffic intensity and time since harvest significantly influenced on forest regeneration, and also all on the environmental factors were influenced by these parameters. PCA analyses showed that soil properties including soil penetration resistance and biodiversity indexes significantly influenced by skidding operations. There was a positive correlation between litter properties, light and porosity.

Abstract Detecting vegetation changes in arid and semi-arid regions, particularly for identifying dried patches, is a major challenge in remote sensing. This study was conducted to identify and analyze areas affected by dryness in the rangelands of Chaharmahal-va-Bakhtiari Province, using remote sensing data and analyzing vegetation changes in the years 2013 and 2023. After obtaining the image differencing for the years 2013 and 2023 for various indices such as NDVI, VHI, TCT, and SR, thresholding of the difference values for each index was performed to identify dried patches. To validate the results, after identifying the dried patches, field visits and sampling were carried out to assess the correspondence between the identified areas and the actual conditions. The results showed that the SR index had the best performance in identifying dried patches. The findings indicated that the SR index, with an overall accuracy of 75% and a Kappa coefficient of 0.75, had the best performance in identifying dried patches, demonstrating a strong agreement with the field data. Following SR, the VHI index—with 60% accuracy and a Kappa value of 0.40—was identified as the second most suitable index for detecting dried areas. The TCT index was also able to detect vegetation changes with an overall accuracy of 52% and a Kappa coefficient of 0.32. Among the indices, NDVI, with 40% accuracy and a Kappa coefficient of 0.28, showed lower-than-expected performance and was less effective in identifying vegetation changes in the dried areas. This study, demonstrates that the SR index, as an effective tool for detecting dried patches in rangeland areas, can significantly contribute to the early-warning, monitoring and optimal management of natural resources, i.e., rangelands.

Abstract Due to the increasing water stress in arid and semi-arid regions of Iran, the need for accurate, non-destructive, and cost-effective methods to estimate vegetation water consumption has become increasingly critical. The METRIC model (Mapping Evapotranspiration at High Resolution with Internalized Calibration), as an advanced remote sensing-based approach, has recently gained significant attention for estimating actual evapotranspiration (ETa) across diverse landscapes. This study aimed to evaluate the accuracy and reliability of the METRIC model in estimating ETa in the arid region of the Chahandab Plain, Varamin, using Landsat 8 Level-2 satellite imagery in combination with in-situ meteorological observations. After performing radiometric and atmospheric corrections, surface energy balance components were derived with careful consideration of local climatic conditions and land surface characteristics. The model outputs were compared with the standard FAO Penman-Monteith model due to the unavailability of data from weighing lysimeters. The results demonstrated high accuracy and strong applicability of the METRIC model under Iranian climatic conditions. The mean coefficient of determination (R²) exceeded 0.90, while the root mean square error (RMSE) remained below 0.52 mm/day, confirming the robustness of the model and its potential for use in operational water management. Furthermore, a strong correlation between ETo and ETa emphasized the critical role of vegetation density and health in regulating the evapotranspiration process, particularly in arid environments where water availability is highly variable.

Abstract Urban river-valleys, as mediating landscapes between natural systems and urban structures, play a critical role in enhancing social quality, collective experience, and urban identity. This study, employing a descriptive–analytical approach, evaluates the effectiveness of recent revitalization efforts in improving social quality and strengthening human connections in the eastern river-valleys of Tehran (Darabad and Darband). Theoretical data were collected through literature review and field observations, conceptually coded, and transformed into preliminary statements. After assessing conceptual adequacy, thematic frequency, and alignment with revitalization goals and SWOT dimensions, a final set of 12 statements was validated by academic experts. Instrument reliability was confirmed with a Cronbach’s alpha of 0/70. The statistical population included 30 specialists selected through purposive sampling: 20 university faculty members in watershed management, hydrology, geomorphology, landscape, and environmental studies (67%), and 10 senior managers and experts of Tehran Municipality in urban planning, sociocultural, and river-valley management (33%). Content validity was confirmed, and the final SWOT questionnaire, consisting of 12 items on a five-point Likert scale, was developed. Responses were weighted from 1 to 5 and classified into strengths, weaknesses, opportunities, and threats. The SWOT results indicate that despite the social dynamism, cultural background, and valuable ecosystem services of Darabad and Darband, spatial fragmentation, perceived insecurity, infrastructural discontinuities, and dominance of engineering-oriented interventions have hindered the full realization of social and identity-based goals. The prevailing strategy is the conservative (ST) strategy, emphasizing the strategic use of natural, managerial, and institutional capacities to mitigate social–environmental threats. Achieving sustainable social quality requires moving beyond physical interventions and simultaneously reinforcing monitoring mechanisms, spatial cohesion, institutional capacities, and social participation.

Abstract This study aimed to evaluate the impact of various ecotypes on the physicochemical indices and nutritional value of two medicinal-rangeland species, namely Bilhar (Dorema aucheri) and Bon-Sorkh (Allium jesdianum), across the habitats of the Central Zagros region. Sampling of the aerial parts of the plants was conducted during the flowering stage. Following the homogenization of leaves and stems, samples were analyzed in four replicates for ash content (ASH), neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), water-soluble carbohydrates (WSC), dry matter digestibility (DMD), moisture content (HUM), crude fat (FAT), titratable acidity (ACID), and pH.Results: Based on the mean comparisons, the "Bid-Sardareh" ecotype emerged as the superior habitat for both species, exhibiting the highest crude protein content (23.43% in Bilhar and 27.68% in Bon-Sorkh) and the lowest NDF (24.09% in Bilhar and 23.35% in Bon-Sorkh). Notably, this ecotype also yielded the highest dry matter digestibility, peaking at 72.58% in Bilhar. Furthermore, Bilhar in the "Zangva" habitat (20.91%) under drought stress, and Bon-Sorkh in "Kuhrang" under cold stress, responded by upregulating their water-soluble carbohydrate (WSC) levels. A highly significant negative correlation was observed between ADF and DMD, confirming the reliability and efficiency of using ADF for the rapid estimation of forage quality. Overall, the results underscore the superiority of the Bid-Sardareh ecotype and highlight the importance of conserving ecotypic diversity as a strategic approach for the sustainable management and conservation of the Zagros rangelands.

Abstract The aim of the present study is to investigate the trend of changes in the severity of soil erosion risk in the area of Yasouj city during the period 1992 to 2025 using the SL 190-2007 standard and remote sensing data in the geographic information system environment.
In this study, in order to assess the risk of soil erosion, a combination of slope angle, vegetation cover and land use parameters was used. The slope angle was extracted from the digital elevation model. Land use maps were prepared and validated using Landsat TM satellite images for 1992 and ETM+ for 2025 and using the supervised classification method using the maximum likelihood method. In order to quantify the vegetation cover, the normalized difference vegetation index (NDVI) was calculated and the vegetation fraction (VFC) was extracted using the dual pixel model (DPM). Finally, based on the SL 190-2007 standard, soil erosion risk maps were prepared in six classes: very low, low, moderate, severe, very severe, and extremely severe, and their spatial-temporal changes were analyzed.
The results showed that during the period 1992 to 2025, the soil erosion risk pattern in Yasouj city has undergone significant changes. The area of severe, very severe, and extremely severe risk classes has increased from a total of 36% in 1992 to 62% in 2025, while the share of very low and low risk classes, which included more than half of the area of the region at the beginning of the period, has decreased significantly.

Abstract This study compared of univariate outlier detection methods among vegetation data in a study of the effect of grazing intensity in the rangelands of arid regions. For this purpose, after measuring the vegetation cover in the rangeland and before the statistical analysis, the presence of outlier data was examined as the assumption of parametric comparison tests. In this study, eight methods including the boxplot and IQR (Tukey method), standard deviation of the mean (three-sigma rule), median absolute deviation (Hampel method), trimmed mean, 1st percentile and 99th percentile, The Chi Square test (χ²), the Grubbs Test (ESD) and the Rosner test (generalized ESD) were used. The results showed that the vegetation cover of rangelands with light and moderate grazing intensity was not normally distributed (Shapiro-Wilk test: p≤0.05). Even deletion of outliers did not lead to a normal distribution, but it resulted in the homogeneity of variances (Levene's test: p≥0.05). The modified Z-score and the Grubbs and Rosner tests (p≥0.05) did not identify outliers from the vegetation cover data. Among the methods evaluated, the boxplot and MAD method, which are not dependent on the mean, are more suitable for the vegetation cover. Therefore, before performing any comparison test, a combination of visual and statistical methods is recommended to evaluate the presence of outliers.

Abstract Rangelands, as one of the vital ecosystems in Iran's arid and semi-arid regions, play a crucial role in biodiversity conservation, soil erosion prevention, and forage supply. In recent years, anthropogenic pressures such as overgrazing and climate change have led to the decline in species diversity and the ecological degradation of rangelands. This study aimed to investigate the trends of plant species diversity and richness indices from 2021 to 2023 (corresponding to 1400–1402 in the Iranian calendar) in the Jishabad rangeland site of Tarom County. For this purpose, a systematic sampling design was applied, in which four main plots (each 10×10 m) were randomly selected within a one-hectare study site. In each main plot, four subplots of 1×1 m were established. Field measurements were conducted during the peak growth season (May–June) over three consecutive years. In each subplot, data on species composition, life form, percentage cover of vegetation, litter, moss, bare soil, gravels, and erosion intensity were recorded. Species identification was conducted using reliable floristic references for Iranian flora. Diversity indices including richness (Margalef and Menhinick), evenness (Pielou), and diversity (Shannon–Wiener and Simpson) were calculated and analyzed using appropriate parametric and non-parametric statistical methods. The results revealed that biodiversity indices varied significantly across the study years. Results showed significant increases in biodiversity indices over the study period (2021–2023): the mean number of species per subplot rose from 5.69 in 2021 to 7.75 in 2023 (P < 0.01); the Shannon–Wiener index increased from 1.62 to 1.96, and the Simpson index from 0.16 to 0.83 These changes, accompanied by a 10% reduction in bare soil cover and increased perennial plant frequency, were likely driven by grazing management, natural exclosure, and favorable climatic conditions.

Abstract The aim of this study was to model the subsidence of Abarkouh plain using inSAR and artificial intelligence techniques. At first, the subsidence map was prepared using the 46 Sentinel - 1 radar images (2014 – 2018) and radar interferometry techniques. Then, the Feedforward artificial neural network (ANN) algorithm was used to model the subsidence. In this algorithm, groundwater level changes (2014-2018), groundwater level, aquifer thickness, clay thickness in the aquifer and the clay thickness in the range of groundwater level changes (2014 - 2018) were introduced as input layers and the subsidence layer obtained from the radar interferometry method was introduced as an output layer to model training. These five parameters were obtained from the measured data set of 34 piezometer wells and 77 logs available in the archive of Regional Water Company of Yazd province. After initial checking of the data accuracy, the Kriging interpolation method was used to extend the five parameters to the whole region and the raster layers were prepared. The results of inSAR showed that maximum subsidence in parts of the studied area, i.e. in east, north - east and north, were 6, 2.7 and 1.6 cm/year respectively. Also, in order to verify the accuracy of the map resulting from using a neural network model, it was compared with the map with the radar imaging method. For this purpose, model evaluation criteria such as Nash-Sutcliffe (NS), RMSE,,MAE)and MARE were used, which 0.9524, 0.0018, 0.0012 and 0.1545 were obtained respectively.

Abstract Due to the ability of land use/cover changes monitoring and predicting to understand the performance and health of ecosystems, this purposed method can provide possibility of sustainable land use management and planning, especially in the rapid change areas without master/land use plan. The present study has aimed to introduce Google Earth Engine to evaluate the pattern of land changes during 2006- 2021 and predict the pattern of future changes by using an integrated model based on Cellular automata and Markov chain using Google Earth Engine system. Three Landsat images (2006, 2014 and 2021) were classified using the support vector machine classifier method, and were simulated using the integrated model of cellular automata and Markov chain. In order to evaluate the accuracy of the predicted map of 2021, the classified map of the same year was applied. The accuracy of classified and simulated maps was Kno=0.812, Klocation=0.816, Kstandard=0.786 respectively. Evaluation of the land use/cover changes shows that between 2006 and 2035, the buildup areas will reach from 4839.01 hectares to 7199.76 hectares with increasing of 2360.75 hectares. These results indicate the necessity of land use planning principles. Simulation models can reduce the risks of long-term decision-making in land use management and Google Earth Engine can reduce the time and cost for classification and satellite image processing.

Abstract Reliable estimation of precipitation is one of the most essential needs in water resources management. However, in many parts of the world, especially in Iran, the lack of time and place of rainfall data is very noticeable. Therefore, the use of satellite information is one of the ways to compensate for the lack of information. The purpose of this research is to compare the accuracy of rainfall information of TRMM-3B42 and PERSIANN-CDR products on a daily scale. The products of these two satellites are available daily for free in the pixel size of 0.25 degrees. The daily rainfall of 12 stations in the southern slopes of Alborz in a statistical period of 2000-2014 was used. The results show that these two satellite products are not the same in different statistical parameters, so that CDR and 3B42 have estimated 100% and 25% more rainfall events than the stations, respectively. Also, PERSIANN satellite is significantly superior to 3B42 in terms of RMSE, POD and CSI parameters, but on the other hand, it is weaker in terms of Bias and FAR parameters. Therefore, the selection of the desired satellite product should be based on the proper parameters.

Abstract In the last two decades, the concept of social capital has been emphasized through its connection with the basic social components, including awareness, participation, trust, cohesion and social network, in the direction of the sustainable development of societies, especially local societies; Therefore, to achieve sustainable local development, having social capital is essential. Considering the importance of this issue and relying on numerous problems and deficiencies at the level of villages in dry areas, this article analyzes the dynamics of social capital in the establishment of local sustainable development in the dry area of southern Kerman based on the method of social network analysis in two periods before and after. It has implemented the community-oriented project of empowering local communities in three villages of Gaubnan, Chahan and Chah Nasir of Qalaganj city. First, by completing the questionnaire and direct interview with the local stakeholders (the heads of the rural microcredit fund), the required data was collected. Then, outgroup social capital was investigated based on trust and participation links using quantitative and mathematical indicators of the macro level of the network (density index and E-I). The results show the increase of density index and improvement of out-group relations in the stage after the implementation of the project and strengthening the amount of out-gro, to realize rich outgroup social capital in this society, because the social dimension of development It is not possible to be sustainable without taking into account the social capital and the participation of that area.

Abstract The current study evaluates the effectiveness of Clark's Instantaneous Unit Hydrograph (IUH) model from the accuracy of different Digital Elevation Models (DEMs) including TOPO, ALOS PALSAR, ASTER, SRTM and GTOPO in the Amameh watershed. For this purpose, at first, 34 rainfall-runoff events were selected. Also, the drainage network, the length and slope of the main river in each of the five DEMs were calculated using Arc GIS software. Then, the 30-minute isochrone map of the watershed was extracted using the spatial distribution of travel time method. Finally, the dimensions of Clark's IUH were estimated for each rainfall-runoff event and DEM. The results showed that with the decrease in the length of the main river following the decrease in DEM spatial resolution, the number of isochrone has been decreased, so that TOPO DEM has estimated the largest number of isochrone with the largest estimate of the length of the main river. The average percentage of the Relative Error (RE) of the runoff volume was estimated as 22.92, 26.68, 27.7, 32.15 and 35.66% respectively for the aforementioned DEMs. Regarding peak flow estimation, there is a significant difference between the average RE values in different DEMs. So that the lowest average value of the RE is related to TOPO DEM with a value of 31.7%. On the other hand, the Root Mean Square Error (RMSE) values also show that TOPO DEM has the lowest RMSE value (3.39 m3) compared to other DEMs. In general, it can be said that the use of TOPO DEM in Clark's IUH model will provide acceptable results.