Seyedsaeid Nabavi; Arash Malekian; Naser Mashhadi; Khaled Ahmadaali; Raoof Mostafazadeh; Ali Shabazi
Abstract
The Baliqlu Chay River watershed in Ardabil Province is a key water source for regional demands. In this study, the role of climate change and human activities on streamflow is examined by using hydrological indices and trend analysis over the period 1991-2021. Monthly, seasonal, and annual climatic ...
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The Baliqlu Chay River watershed in Ardabil Province is a key water source for regional demands. In this study, the role of climate change and human activities on streamflow is examined by using hydrological indices and trend analysis over the period 1991-2021. Monthly, seasonal, and annual climatic and discharge variables were assessed for trends using the Mann-Kendall trend test and Sen's slope estimator. Results indicate streamflow is controlled by seasonal snowmelt, groundwater reliance, and the controlling impact of the Yamchi Dam. Moreover, the high flow variability observed at the Yamchi Station, as indicated by elevated standard deviation and coefficient of variation values, suggests an increased risk of flooding in this area. Trend analysis results over the 30-year statistical period reveal a decreasing trend in annual precipitation at the Ardabil and Nir stations, although these trends are not statistically significant at the 99% and 95% confidence levels. In contrast, a statistically significant increasing trend in annual precipitation was detected at the synoptic station in Sareyn at the 99% confidence level. Additionally, the annual temperature increase at all stations was found to be statistically significant at the 99% confidence level. The assessment of streamflow trends on an annual scale at the Nirchay and Yamchi Dam hydrometric stations indicates a statistically significant decreasing trend at the 1% level (p-value ≤ 0.01). These results emphasize the urgent need for sustainable water management strategies, including optimization of resource use, revised consumption patterns, and improved operation of the Yamchi Dam. Such interventions are essential to reduce the negative impacts of climate change and anthropogenic pressures on the river’s hydrological regime.
Kourosh Shirani; Morteza Khodagholi; Rostam Khalifehzadeh
Abstract
Awareness of organic carbon status of rangeland soil is important for erosion control and soil protection management. The aim of this study is to prioritize the effective factors, modelling and predicting organic carbon amount using Landsat 8 satellite imagery, accurate digital elevation model (DEM) ...
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Awareness of organic carbon status of rangeland soil is important for erosion control and soil protection management. The aim of this study is to prioritize the effective factors, modelling and predicting organic carbon amount using Landsat 8 satellite imagery, accurate digital elevation model (DEM) related to ALOS sensor and the combined application of factor analysis and multivariate regression model in Semirom watershed located in the south of Isfahan province. For this purpose, after determining the homogeneous units and Stratified Random Sampling of 218 soil samples from these units, the amount of organic carbon, percentages of sand, silt and clay were determined in the laboratory. The development of the combined method was performed using 15 spectral and non-spectral variables and two sets of training data (70%) and test data (30%) of soil samples in order to implement and validate the model, respectively. Then, effective factor prioritization, determination of main components and spatial soil organic carbon zonation map were prepared. Finally, using error measurement criteria, the model was validated and evaluated in the training and test stages. The results showed that fifteen independent variables in the form of six principal components namely vegetation, soil particle size, surface reflectance, soil surface shape, moisture storage and chemical properties have the largest contribution in soil organic carbon storage. Based on the error evaluation metrics (RMSE) and correlation coefficients (R2), the model implementation stage (Training Phase), with respective values of 0.23 and 0.84, demonstrates higher efficiency and captures greater variability in soil organic carbon, as compared to the prediction stage (Test Phase) characterized by a higher error (0.27) and a lower correlation coefficient (0.80). Also, the soil organic carbon content classes of 0.70-0.80 and 1.20-2.35 with an area of 24% and 6% have the highest and lowest area outcrops of soils in the study area, respectively.
Leila Mahmoudzadeh; Pejman Tahmasebi; Ataollah Ebrahimi; Samaneh Sadat Mahzooni Kachapi
Abstract
Considering the widespread changes in biodiversity and its vital importance in maintaining ecosystem stability and functionality, precise and continuous assessment of plant diversity indices is essential. Due to temporal, spatial, and economic constraints, field sampling is often difficult and costly ...
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Considering the widespread changes in biodiversity and its vital importance in maintaining ecosystem stability and functionality, precise and continuous assessment of plant diversity indices is essential. Due to temporal, spatial, and economic constraints, field sampling is often difficult and costly in many regions. Therefore, remote sensing data have increasingly gained attention as a reliable, efficient, and cost-effective source for biodiversity assessment and monitoring. This study aims to evaluate the capability of Sentinel-2 satellite data in estimating plant biodiversity indices in semi-steppe rangelands. To this end, eight sampling sites were selected based on management conditions, vegetation cover, and ecological characteristics, and three 30*30 m2 macroplots were established at each site. Vegetation cover sampling was performed using a systematic-random method with 2*2 m2 plots along three transects. After calculating plant diversity indices including alpha diversity, beta diversity, and functional diversity, the relationships between these indices and vegetation indices derived from Sentinel-2 data were examined and statistically analyzed. Data analysis was conducted using linear regression and correlation tests in the R software environment. The results clearly demonstrate that vegetation indices derived from Sentinel-2 satellite imagery are capable of predicting different components of biodiversity in semi-steppe rangelands. Among the indices, EVI showed the strongest correlation with alpha diversity (R²=0.20, P-value=0.02) and functional diversity (functional richness) (R²=0.34, P-value=0.001), whereas NDVI exhibited the highest correlation with beta diversity (Bray-Curtis Similarity and distance indices index) (R²=0.21, P-value=0.01). Other indices such as MSAVI2, AVI, and SAVI also revealed positive and significant correlations with various biodiversity components, although their correlation coefficients were lower than those of the primary indices.
Mohammad Reza Sayadi; Mehdi Ghorbani; Mohammad Jafari; Hamidreza Keshtkar; Leila Avazpour
Abstract
The objective of this paper is to identify the factors affecting the medicinal plant supply chain in the Nadushan region using a Glaser approach. The research method is applied in terms of purpose and qualitative in terms of method based on grounded theory and Glaser approach (emerging approach); and ...
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The objective of this paper is to identify the factors affecting the medicinal plant supply chain in the Nadushan region using a Glaser approach. The research method is applied in terms of purpose and qualitative in terms of method based on grounded theory and Glaser approach (emerging approach); and it is exploratory based on the nature of the data and the use of inductive philosophy. The study population consisted of experienced local people and managers and experts in the field of the medicinal plant supply chain with more than five years of experience. Participants were selected using purposeful sampling and theoretical judgment. The data collection method was fieldwork, and the data collection tool was in-depth and structured interviews with 30 participants, including native farmers (15), researchers and experts (10), and intermediaries (5) in the field. The grounded theory approach was used to analyze the data and identify the key factors affecting the supply chain. The results identified 9 selective codes and 41 core codes. The factors affecting the supply chain include climate and weather, the region's high potential for medicinal plant cultivation, initial budget and capital, storage conditions, institutional support, policy, medicinal plant production and harvesting management, medicinal plant processing management, and the use of healthy practices in productivity. Therefore, ensuring a sustainable and efficient supply chain is crucial for maintaining the quality, availability, and affordability of medicinal plants.
Khaled Haji Maleki; Alireza Vaezi; Fereydoon Sarmadian; Asghar Rahmani
Abstract
Soil moisture in the upper soil layers plays a vital role in water and soil resource management, directly influencing infiltration, runoff, agricultural productivity, and flood regulation. Its spatial variability is controlled by multiple factors, including climate conditions, topography, vegetation, ...
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Soil moisture in the upper soil layers plays a vital role in water and soil resource management, directly influencing infiltration, runoff, agricultural productivity, and flood regulation. Its spatial variability is controlled by multiple factors, including climate conditions, topography, vegetation, and soil characteristics. Neglecting these variations often leads to significant errors in hydrological and agricultural modeling. This study investigates the relationship between geomorphometric indices and surface soil moisture across five sub-basins of the Simineh and Zarrineh rivers in northwest Iran, using both field observations and satellite data. Soil moisture measurements from 287 points (2015–2017) were compared with Soil Moisture Active Passive (SMAP) satellite estimates to generate high-resolution spatial maps. Several geomorphometric indices were derived, including the Topographic Wetness Index (TWI), Topographic Position Index (TPI), Wind Exposure Index (WEI), flow direction (Flow_D), flow accumulation, and Analytical Hillshading (AH). The Random Forest (RF) model was applied to determine the importance of geomorphometric attributes. Validation results revealed a strong correspondence between SMAP data and field observations, with July showing the highest correlation (r = 0.77, soil moisture = 0.18 cm³·cm⁻³) and May the lowest (r = 0.50). The RF model achieved robust performance (R² > 0.7, RMSE = 0.04%). Among the indices, WEI and TWI exhibited the greatest importance (>16%), followed by AH (13%), while Flow_D had the lowest influence (8.9%). These findings confirm the significant role of topographic and hydrological features in controlling soil moisture distribution. The integration of SMAP data with machine learning and geomorphometric indices provides a reliable framework for soil moisture monitoring, offering valuable insights for agricultural management, hydrological modeling, and environmental planning in similar watersheds.
Zohreh Azizzadeh; Majid Mohammad Esmaili; Ali Sattarian; Seyed Ali Hosseini; Bahareh Bahmanesh
Abstract
Soil moisture in the upper soil layers plays a vital role in water and soil resource management, directly influencing infiltration, runoff, agricultural productivity, and flood regulation. Its spatial variability is controlled by multiple factors, including climate conditions, topography, vegetation, ...
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Soil moisture in the upper soil layers plays a vital role in water and soil resource management, directly influencing infiltration, runoff, agricultural productivity, and flood regulation. Its spatial variability is controlled by multiple factors, including climate conditions, topography, vegetation, and soil characteristics. Neglecting these variations often leads to significant errors in hydrological and agricultural modeling. This study investigates the relationship between geomorphometric indices as a proxy of topography and topsoil moisture across five sub-basins of the Simineh and Zarrineh rivers in northwest Iran, using both field observations and satellite data. Soil moisture measurements from 287 points (2015–2017) were compared with Soil Moisture Active Passive (SMAP) satellite estimates to produce high-resolution spatial maps. Several geomorphometric indices were derived, including the Topographic Wetness Index (TWI), Topographic Position Index (TPI), Wind Exposure Index (WEI), flow direction (Flow_D), flow accumulation, and Analytical Hillshading (AH). The Random Forest (RF) model was applied to determine the importance of geomorphometric attributes. Validation results revealed a strong correspondence between SMAP data and field observations, with July showing the highest correlation (r = 0.77, soil moisture = 0.18 cm³·cm⁻³), and May the lowest (r = 0.50). The RF model achieved robust performance (R² > 0.7, RMSE = 0.04%). Among the indices, WEI and TWI exhibited the greatest importance (>16%), followed by AH (13%), while Flow_D had the lowest influence (8.9%). These findings confirm the significant role of topographic and hydrological features in controlling soil moisture distribution. The integration of SMAP data with machine learning and geomorphometric indices provides a reliable framework for soil moisture monitoring, offering valuable insights for agricultural management, hydrological modeling, and environmental planning in similar watersheds.
Mohammaadreza Tatian; ٍElahe Shayesteh; Reza Tamartash; Mohammadreza Shooshtari; Nateq Lashkari
Abstract
Iran, owing to its diverse climatic conditions, is considered as one of the main centers of genetic diversity in grasses, particularly forage grasses, in the world. This research aimed to evaluate the variations in forage quality of Bromus tomentellus, Festuca ovina, and Stipa lessingiana in Gavanban ...
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Iran, owing to its diverse climatic conditions, is considered as one of the main centers of genetic diversity in grasses, particularly forage grasses, in the world. This research aimed to evaluate the variations in forage quality of Bromus tomentellus, Festuca ovina, and Stipa lessingiana in Gavanban rangelands of Harsin, Kermanshah Province, at vegetative growth, flowering, and seed maturity stages. To achieve this, each species was randomly sampled in three replicates at each phenological stage. Samples were dried, ground, and assessed using Near-Infrared Reflectance Spectroscopy (NIRS) for the determination of their nutritive value, including CP, DMD, WSC, ADF, NDF, ash, N, P, K, and ME. ANOVA followed by Duncan’s multiple range test for mean comparisons were performed in R software. The results indicated that forage quality traits differed significantly during the phenological stages. During the transition from vegetative to seed maturity stages, all three species showed a reduction in DMD, CP, ME, ash, and nitrogen content, while WSC, ADF, and NDF levels increased. At the seed maturity stage, CP decreased most significantly in B. tomentellus (68.02%), while DMD and ME showed their highest decreases in F. ovina, at 30.73% and 38.75%, respectively. In contrast, S. lessingiana revealed a 17.77% increase in WSC. The interspecific comparison demonstrated that B. tomentellus maintained higher nutritional value across phenological stages, which can be attributed to its elevated ME, DMD, and WSC levels and lower ADF content. These findings highlight that optimizing forage nutritive value and ensuring rangeland sustainability require grazing management to be aligned with the phenological stages of dominant species. According to the results, B. tomentellus has the greatest nutritional potential at the vegetative stage, whereas S. lessingiana, despite its lower forage quality, is valuable for rangeland stabilization and restoration due to its strong grazing tolerance and soil-protective role.
Behnaz Attaeian; Arash Samavati; Kamran Shayesteh; Hamidreza Saeidi Graghani
Abstract
Ecotourism, or nature-based tourism, is one of the most important forms of tourism that shows the highest compatibility with sustainable livelihoods. It plays a key role in rural sustainable development and livelihood sustainability. The aim of this study is to compare the views of nomads and experts ...
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Ecotourism, or nature-based tourism, is one of the most important forms of tourism that shows the highest compatibility with sustainable livelihoods. It plays a key role in rural sustainable development and livelihood sustainability. The aim of this study is to compare the views of nomads and experts regarding the impacts of ecotourism on the sustainable livelihood of the Turkashvand tribe in Hamadan Province. The statistical population consisted of 45 nomads, of whom 40 were selected using the Krejcie and Morgan table, and 10 experts from the General Office of Nomadic Affairs in Hamadan were selected through a census method. Data were collected using a questionnaire with acceptable reliability (Cronbach’s alpha: 0.81 for nomads and 0.84 for experts). The results indicated significant differences between the two groups in most of the sustainable livelihood indicators, especially in financial-economic and natural-ecological capitals. However, there was no significant difference regarding the “household labor force” in human capital, and the indicators of “unity and cohesion within customary structures,” “participation,” and “mutual trust among nomads” in social capital. The divergence in viewpoints is attributed to differing perceptions and expectations; for instance, nomads often have short-term and practical expectations, while experts tend to adopt a more strategic and long-term perspective. It is recommended that both groups participate simultaneously in planning processes to effectively utilize ecotourism potential for sustainable livelihood improvement.
