The health and sustainability of a watershed are complex issues that must be evaluated from social, economic, and environmental perspectives using a variety of indicators. The objective of this study is to assess the sustainability and health status of the Khansar watershed in Yazd Province based on the modified WHSI model. This model, developed in accordance with local conditions in Iran and the available variables, includes 34 key variables, comprising 13 social variables, 5 hydrological variables, 10 water quality variables, and 6 land-use–related variables. In this study, ten-year data were collected for each variable and scored using quantitative methods. According to the results, 11 variables were in good condition, one variable was moderate, three variables were weak, and 19 variables were in a critical state. The WHSI model analysis showed that the social indicators were in a relatively better condition, whereas the hydrological, water quality, and land-use variables were predominantly in a critical state. The findings of this study also indicated that the health status of the Khansar watershed in Yazd, with a score of 74, falls within the intermediate health category, while its sustainability, with a score of 12, is classified as unsustainable. The results of this research provide a precise depiction of the critical variables and can serve as a foundation for formulating targeted management policies, improving the quality and quantity of water resources, restoring vegetation cover, controlling unsustainable exploitation, and strengthening climate adaptation programs. In doing so, it can play a significant role in enhancing resilience and improving the health and sustainability of the watershed.
This study aimed to analyze the spatial–temporal correlation between the Vegetation Health Index (VHI) and climatic variables, including precipitation, potential evapotranspiration (PET), and mean temperature, in Golestan Province during the period 2000–2024. MODIS satellite products were used for vegetation and land surface temperature data, while the TerraClimate dataset provided precipitation and PET variables. After spatial–temporal alignment, the Cross-Correlation Function (CCF) was applied to identify optimal time lags, and the Random Forest model was employed to assess the relative importance of the climatic drivers. Turning to the results, increasing trends in mean temperature and PET were observed, alongside a significant decrease in precipitation, which led to intensified climatic stress and reduced VHI across the province, especially during summer in croplands and rangelands. The relationship between VHI and precipitation was positive (maximum correlation of 0.299 in croplands), negative with PET (−0.287), and non-linear with temperature (0.275). Notably, VHI responded to precipitation with short-term lags (0–1 month), whereas PET and temperature effects emerged with longer lags (2–4 months). The Random Forest analysis highlighted precipitation as the most influential factor on VHI, followed by PET and temperature, achieving strong predictive performance (R² = 0.78, RMSE = 0.09). Overall, these findings emphasize precipitation as the immediate driver of vegetation health, while PET and temperature act as secondary, cumulative stressors. The results provide valuable insights for developing climate adaptation and sustainable resource management strategies in agriculture and natural ecosystems of Golestan Province
Splash erosion is the initial stage of soil erosion by water, which can be significantly influenced by soil properties. The rate of this type of soil erosion in drylands of semi-arid regions is high due to sparse vegetation cover, particularly during the early stages of plant growth. This study was conducted to investigate the soil properties determining splash erosion in semi-arid drylands. Soil aggregates with a diameter of 6 to 8 mm were taken from the soil surface (0-30 cm depth) in thirty dryland farms at three replications. Soil aggregates were purred into splash bowls and exposed to simulated rainfalls with an intensity of 60 mm h-1 for 30 minutes. Different soil properties were determined in ninety soil samples. Based on the results, the highest splash erosion occurred in clay loam (0.0021 gm⁻²s⁻¹), while the lowest value was in loamy sand texture (0.0008 gm⁻²s⁻¹). Splash erosion was significantly affected by grain size distribution; so that positive correlations were found with silt (r= 0.43), clay (r= 0.44), and dispersible clay (r= 0.47), whereas negative correlations existed with sand (r= -0.46) and gravel (r= -0.53). Furthermore, splash erosion was considerably influenced by organic matter (r= -0.23), calcium carbonate (r= -0.22), bulk density (r= -0.60), aggregate stability (r= -0.44), and hydraulic conductivity (r= -0.44). This study revealed that the drylands with fine-textured soils and having a lower amount of organic matter as well as calcium carbonate, have a higher susceptibility to splash erosion in semi-arid regions.
The present study was conducted with the aim of quantitative and qualitative analysis of agricultural water consumption in Markazi Province, and calculated and examined water consumption at the level of 18 crops and 12 counties using the water footprint as a comprehensive indicator. A simultaneous study of the three components of the blue, green, and gray water footprint was conducted as an analytical tool to assess the amount and manner of water consumption. In this study, meteorological, agricultural, and input consumption data were used in the 2022-2023 crop year, and water footprint values were estimated in terms of units and totals by crop and county. The results showed that BWFU is strongly influenced by spatial factors (climate and precipitation) and plant characteristics (yield, crop type, and growth period). A difference of up to 98% in BWFU among different crops and a difference of more than 9 times in GWFU in rainfed compared to irrigated lands were observed. Also, GRWFU values exceeded BWFU for many crops, indicating a significant pollutant load from the use of chemical fertilizers. In addition to spatial factors and plant characteristics, the difference of 223 MCM between Saveh and Ashtian counties and the difference of 52.7 MCM between Shazand and Mahallat counties in BWF and GWF, respectively, indicate spatial differences in BWFU and cultivation area. Also, the difference in 1377 MCM between the GRWF of Arak and Ashtian counties is affected by the amount and type of fertilizer used, in addition to the cultivation area. In addition to improving performance, suggested management measures include reducing the cultivation area of high-consumption crops, expanding rainfed lands in high-rainfall areas, optimizing input consumption, and modifying the cultivation pattern in accordance with resources and climatic conditions in order to maintain the quantity and quality of water resources. Accordingly, the research results demonstrate the potential of the water footprint index in location-based and product-based analysis of water consumption and formulation of management responses.
Mathematical models are a suitable tool for surface irrigation design. The EDOSIM model, as a surface irrigation simulation-optimization model, utilizes simulation with the volume balance model and meta-heuristic optimization. In this study, with the aim of improving the simulation of the advanced phase in the EDOSIM model, the Full Hydrodynamic model was replaced by the Volume Balance model for furrow irrigation design, leading to the development of the EDOSIM-HD model. The Saint-Venant equations were discretized using the implicit Preissmann’s finite difference scheme and transformed into a set of nonlinear equations in the form of a system of equations. The resulting system of equations was linearized using the Newton-Raphson method and solved using the Sparse matrix method. The results were compared with the SIRMOD software to validate the simulation. Using the particleswarm solver of the MATLAB software optimization toolbox, the inflow rate as a decision variable was used to optimize the hydraulic objective function consisting of efficiency, adequacy, and uniformity. The results in the experimental field showed that in the initial simulation with an inflow rate of 1.4 lps, important irrigation times, infiltration volume, performance indicators, profiles, and hydrographs showed a deep percolation loss of about 50 percent of water. Also, the results of the EDOSIM-HD model were closer to the Hydrodynamic model of the SIRMOD software than the EDOSIM model. By optimizing and increasing the optimal flow rate (1.8 lps) compared to the initial inflow rate, the advance, cut-off, depletion, and recession times were reduced, and the required infiltration time remained unchanged. The reduction in infiltration volume was also achieved by applying higher inflow rates in less time. All performance indicators also moved closer to their optimal state. Except for Tail Water Ration (TWR), which showed a slight increase of 11 percent (due to higher inflow rate), was negligible compared to the sharp 22% reduction in Depth Percolation Ratio (DPR), and 10% increase in Application Efficiency (Ea). Totally, according to the performance indicators obtained in the validation with the SIRMOD, the simulation of the EDOSIM-HD model was better than in the EDOSIM model in the advanced phase of furrow irrigation design
Gully erosion is one of the most important types of water erosion. Since the amount of soil loss due to this erosion is directly related to environmental factors, the amount of soil loss due to each gully can be modeled based on environmental conditions. According to the high ability of machine learning models based on artificial intelligence to analyze environmental information, in addition to determining soil loss due to gully erosion, modeling has been carried out using two random forest models, and artificial neural networks and evaluating their efficiency in the Mahurmilati watershed located in the southwest of Fars province in this study. The dimensional parameters of 70 gullies were measured over four years (2021-2024), and the volume and weight of soil lost were calculated. 15 environmental factors were selected as predictive variables, and modeling was performed with a cross-validation approach using these two models, and the accuracy of the models was evaluated using quantitative criteria. The amount of soil loss in gullies during the study period was 15300.94 tons. The accuracy evaluation of the models showed that the random forest model had better performance based on the coefficient of determination (R2=0.66-0.73). Also, this model had the lowest value in terms of the RSR error index evaluation criterion (RSR=0.66-1.03) and the highest accuracy. In terms of the fit evaluation index (D), the random forest model also had the highest fit between the observational and forecast data and had the highest value of this index (D=0.83), and therefore, it was introduced as the superior model for predicting soil loss due to gully erosion in this watershed.
Climate change significantly affects the water use efficiency (WUE) and yield of field crops. This study evaluates the impacts of climate change on biological yield, grain yield, water consumption, and WUE of two barley genotypes, Goharan and Reyhan 03, under autumn and spring planting regimes using the CERES-Barley model within the Decision Support System for Agrotechnology Transfer (DSSAT) software. Data provided for model calibration and validation were sourced from the field experiments conducted at the Isfahan University of Technology research farm located in Najafabad, Iran. Meteorological data for the period of 2003 to 2016 were obtained from the Najafabad weather station, while future climate projections for 2020–2050 were generated using the MarkSim weather generator under the Representative Concentration Pathway (RCP) 8.5 scenario. Planting dates were analyzed within a ±35-day window relative to baseline dates of October 22 for autumn and March 3 for spring. The model demonstrated high accuracy in calibrating key traits, including days to anthesis, days to maturity, leaf area index, grain yield, and biological yield. Elevated temperatures associated with climate change reduced grain and biological yields across both planting seasons, with biological yield exhibiting a more pronounced decline, particularly under spring planting. During the 2040–2050 period, water consumption peaked at 387.5 mm for Goharan in autumn planting, while spring planting recorded a minimum of 239 mm for Reyhan 03. Delaying autumn planting by 20–25 days enhanced WUE, while planting earlier in the spring )10–20 days (improved WUE by exploiting cooler temperatures. Evapotranspiration increased by 399 mm in autumn but decreased by 267 mm in spring. The earlier-maturing Reyhan 03 genotype demonstrated smaller yield losses in spring planting due to climate change. The findings of this study suggest that programmed adjustments to planting dates may mitigate the adverse impacts of climate change on barley production, thereby enhancing sustainability.
Groundwater is a vital resource for meeting drinking, agricultural, and industrial needs in arid and semi-arid regions of Iran. In this study, quantitative and qualitative changes in groundwater in the Garmsar Plain were modeled using GIS, MODFLOW, and MT3DMS software during the period 2011-2013. Spatial and climatic data were comprehensively processed and prepared in the GIS environment, and groundwater flow was simulated using the MODFLOW model, and water quality changes were analyzed using the MT3DMS model. After validation with field data from 2012 to 2013, the model showed acceptable accuracy with statistical indicators of mean absolute error (MAE) in the range of 0.4 to 0.5 meters and root mean square error (RMSE) between 0.5 and 0.6 meters. The modeling results showed that a 15% increase in water withdrawal led to a decrease in the water table of up to 8 meters, a constant withdrawal led to a decrease of 7 meters, and a 15% decrease in withdrawal led to a decrease of 5 meters in the water table. From a quality perspective, the decrease in withdrawal improved the quality of irrigation water but increased the concentration of some pollutants, which requires the development of effective management strategies to protect groundwater resources. The findings of this study illustrate the importance of sustainable exploitation and smart management of groundwater resources in the Garmsar Plain.
