Research Article
Irrigation
Z. Alizadeh; M. Shahsavandi; M. Masoudi-Moghaddam; S. Talebi; J. Yazdi
Abstract
IntroductionFloods rank among the most devastating natural disasters, causing significant loss of life and property each year. Floods are among the most destructive natural disasters, causing extensive loss of life and property annually. The Gorganrood watershed in northeastern Iran is particularly vulnerable ...
Read More
IntroductionFloods rank among the most devastating natural disasters, causing significant loss of life and property each year. Floods are among the most destructive natural disasters, causing extensive loss of life and property annually. The Gorganrood watershed in northeastern Iran is particularly vulnerable to frequent and severe flooding due to its unique geographical and climatic characteristics. Factors contributing to this high flood risk include steep topographical gradients, impermeable soil, and degraded vegetation cover, which lead to rapid and devastating flash floods. The region has experienced a rise in both the frequency and intensity of floods in recent years, resulting in significant damages, such as the catastrophic flood of March 2019. Consequently, the development of effective flood forecasting and early warning systems (FFEWS) has become a critical priority for crisis management. Recent advancements in numerical modeling offer powerful tools for more accurate and timely flood prediction. Weather Research and Forecasting (WRF) models are widely used for their precision in simulating regional precipitation. Hydrological models like the Hydrologic Modeling System (HEC-HMS) are essential for converting predicted rainfall into surface runoff, while hydraulic models such as HEC-RAS excel at simulating river flow and mapping inundation zones. This research aims to develop and evaluate a sophisticated, integrated online system specifically for the Gorganrood watershed. The primary innovation of this study is the creation of a unified online platform that couples the WRF, HEC-HMS, and a 2D HEC-RAS model to forecast flood inundation up to 48 hours in advance. A further novelty lies in its software architecture, which utilizes React for the front-end and a Python-based Django framework for the back-end, a combination not previously applied in similar research for real-time visualization of flood forecasts. Materials and Methods The research focused on the Gorganrood watershed, a major basin in northeastern Iran covering approximately 11,380 km². The system developed was an integrated Web-GIS software platform designed for end-to-end flood forecasting. The system's workflow began with the automated retrieval of meteorological data from the Global Forecast System (GFS). This data served as input for the regional WRF model to generate high-resolution precipitation forecasts. The rainfall predictions were then fed into a calibrated HEC-HMS model to simulate the rainfall-runoff process and generate flood hydrographs. In the final stage, a 2D HEC-RAS hydraulic model was executed for critical, populated river reaches to produce detailed flood inundation maps. The technological framework was built on modern software tools. The user interface (Front-End) was developed using React to create a dynamic user experience. The server-side logic (Back-End) was implemented in Python using the Django web framework. For data management, a PostgreSQL database with the PostGIS spatial extension was employed. GeoServer was used as the map server. The chosen models include:WRF Model: Selected for precipitation forecasting due to its open-source nature, flexibility, and widespread use.HEC-HMS Model: Chosen as the rainfall-runoff model for its suitability in a large basin with limited data. It was configured using the SCS-CN method for loss calculations, the SCS unit hydrograph method for runoff transformation, and the Muskingum method for channel routing.HEC-RAS Model: The 2D version was selected for hydraulic modeling due to its ability to simulate complex, two-dimensional flow dynamics when floods overtop riverbanks and its free availability. Results and Discussion The performance of each model component was rigorously evaluated. The WRF model was assessed using five historical storm events, comparing forecasts across five different lead times (6, 12, 18, 24, and 48 hours). Statistical analysis revealed that the 6-hour forecast horizon provided the optimal balance of accuracy and lead time, exhibiting the best performance metrics (R²=0.69, RMSE=12.25, NSE=0.0). Thus, a 6-hour lead time was adopted for the operational system. The HEC-HMS model was calibrated and validated against observed data from several hydrometric stations (Nodeh, Arazkuseh, etc.). The results demonstrated a good agreement between the simulated and observed hydrographs, particularly in capturing the peak discharge and timing of floods. Observed discrepancies in total flood volume were attributed to uncertainties in spatial rainfall data and potential measurement errors. For the numerous sub-basins lacking gauging stations, model parameters were regionalized using a clustering technique based on physiographic similarity to the calibrated sub-basins. The integrated online system allows users to run the entire forecast chain through a web interface. To manage the significant computational requirements, the 2D HEC-RAS model was implemented for two high-priority areas: the region downstream of the Golestan Dam and the flood-prone city of Aq-Qala. A key challenge was the high computational demand of the models, which was addressed by leveraging the High-Performance Computing (HPC) cluster at Shahid Beheshti University. Another challenge is the potential for model instability and limitations imposed by data quality, which can be mitigated by more detailed calibration. ConclusionThis research successfully developed a comprehensive, integrated online system for flood forecasting in the Gorganrood watershed by coupling the WRF, HEC-HMS, and HEC-RAS models. The evaluation showed that the WRF model provided acceptable precipitation forecasts, the HEC-HMS model accurately simulated rainfall-runoff processes, and the 2D HEC-RAS model produced valuable, high-resolution flood inundation maps. The system's robust software architecture, utilizing React, Python/Django, and PostgreSQL/PostGIS, provides an efficient, scalable, and user-friendly platform for operational flood management. This work demonstrates that the integration of advanced numerical models into a single, automated platform is a highly effective approach to mitigating flood risk. The resulting system offers a powerful tool for crisis managers and serves as a replicable model for developing similar advanced warning systems in other flood-prone basins. AcknowledgementThe authors would like to acknowledge the use of the High-Performance Computing (HPC) system at Shahid Beheshti University for the execution of the numerical models in this research. Keywords: Django, Flood forecasting, Gorganrood watershed, HEC-HMS, HEC-RAS (2D), Integrated system, React, WRF
Research Article
Soil science
O. Rahmati; S.M. Soleimanpour; S. Shadfar; S. Zahedi
Abstract
IntroductionGully erosion is one of the most important factors affecting sediment production and land degradation, and predicting its occurrence is one of the practical solutions to prevent gully erosion. Since the occurrence of gully erosion is directly related to environmental factors and human activities, ...
Read More
IntroductionGully erosion is one of the most important factors affecting sediment production and land degradation, and predicting its occurrence is one of the practical solutions to prevent gully erosion. Since the occurrence of gully erosion is directly related to environmental factors and human activities, it is possible to identify areas prone to gully erosion using models based on artificial intelligence and data mining. Modeling helps to save time and cost of measuring gutters. Also, because artificial intelligence and data mining models have a high ability to analyze environmental information; they are able to identify nonlinear and complex relationships between variables, and for this reason, they have been widely accepted by researchers in various sciences worldwide. For this purpose, this study aimed to predict gully erosion susceptibility using random forest models and boosted regression trees in the Talwar watershed located in the southeast of Kurdistan province. Materials and MethodsInitially, 99 gullies were identified during field visits, the location of the gully head-cut was recorded, and a map of the spatial distribution of the gullies was prepared. The recorded gullies were randomly divided into two groups: training and validation in a ratio of 70:30, such that 70% of the gullies were in the training group and the rest in the validation group. In addition, maps of factors affecting gully erosion including elevation, slope gradient, slope aspect, lithology, distance from the stream, topographic wetness index, land use, plan curvature, profile curvature, average annual rainfall, relative slope position, stream power index, distance from the road, soil order, and soil texture were prepared in geographic information system. Subsequently, in the modeling process, environmental factors were considered as independent variables and the creation of gullies as a dependent variable. In order to model gully erosion, the training group gullies were used in this stage to calibrate the models. In this study, Random Forest (RF) and Boosted Regression Trees (BRT) machine learning models were used to predict gully erosion. In these models, raster layers related to environmental factors affecting gully erosion were introduced as independent variables to the model. Also, the layer of gully front points, which were previously named after the training group, was introduced as a dependent variable to the model. The process of running the models was carried out in the R software environment. The prediction accuracy of the models was also evaluated using the area under the receiver operating characteristic curve (AUC) method. Results and DiscussionThe spatial pattern of gully erosion by these two models showed that in this basin, generally the middle, eastern and northern parts, which are adjacent to waterways and rivers, had a higher tendency to cause gully erosion. Since the prediction interval of gully erosion in artificial intelligence models varied between zero and one, it can be considered as the probability of gully erosion. The lowest and highest values of the probability of gully erosion by the random forest model were obtained as 0.006 and 0.996, respectively. The median value of the prediction of gully erosion in the prediction of the random forest model was also calculated as 0.322. Therefore, 50% of the pixels in this basin had a tendency to cause gully erosion greater than 0.322 and the other half of its tendency was less than 0.322. The spatial pattern of gully erosion prediction from the boosted regression tree model also varied from 0.011 to 0.799. This model generally introduced the adjacent sections of the drainage network in the middle, eastern, and northern parts as the most favorable lands for the creation and formation of gully erosion. The median predicted value from this model was 0.387. Prediction accuracy, measured by the area under the receiver operating characteristic curve, was 0.952 for the random forest model and 0.891 for the boosted regression tree model. ConclusionThe findings showed that the random forest model had more accuracy in spatial prediction of gully erosion in the Talwar watershed. Also, based on the AUC criterion, the random forest model was placed in the excellent group (AUC>0.9) and the boosted regression tree model was placed in the very good group (AUC<0.8). According to the findings of this study, executive agencies can use artificial intelligence and data mining models, such as the random forest model, to prepare a gully erosion map and plan and prioritize areas for implementing soil conservation measures. Certainly, focusing soil conservation executive measures and management programs in areas prone to gully erosion in the country's watersheds will improve the performance and optimize the financial resources of natural resources and watershed management departments.
Research Article
Soil science
S. Etminan; V. Jalali
Abstract
Introduction
Climate change and drought events over the past decades have led to a decrease in surface and groundwater resources, particularly fresh water sources. On the other hand, the global population growth rate is increasing, which has resulted in a rising demand for food. One of the essential ...
Read More
Introduction
Climate change and drought events over the past decades have led to a decrease in surface and groundwater resources, particularly fresh water sources. On the other hand, the global population growth rate is increasing, which has resulted in a rising demand for food. One of the essential pillars of food security is providing adequate water resources for agricultural use. In recent years, water resource management aimed at improving efficiency has emerged as both a management and research challenge. One proposed strategy is blending saline and fresh water with the application of modern irrigation techniques at the farm scale. This study examines the effects of center-pivot irrigation using saline water sources on alfalfa farm performance over four consecutive years. The effect of this management approach was analyzed using the HYDRUS-3D model, focusing on plant growth and yield, soil moisture variations, leaching rates, and nitrate accumulation within the farm.
Materials and Methods
In this study, a four-year-old alfalfa farm at Birjand University, covering an area of 3 hectares and irrigated using the center-pivot method, was selected. A one-meter-deep soil profile was excavated to examine changes in the soil's physical and chemical properties over time. Soil moisture was measured using Time Domain Reflectometry (TDR) at different depths and various irrigation intervals to assess root-zone moisture dynamics. Since the soil in the study area was deficient in organic matter, soil samples were collected before planting alfalfa. To ensure adequate phosphorus levels, diammonium phosphate fertilizer was applied during the tillering stage. Ammonium and nitrate concentrations were also analyzed by collecting soil samples at different depths over various periods and measuring them using a spectrophotometer. The Levenberg-Marquardt optimization algorithm was employed to estimate hydraulic parameters and solute transport characteristics. To model changes in ammonium and nitrate levels in the soil, the Freundlich adsorption coefficient was applied. For simulating variations in soil moisture, ammonium, nitrate, and plant growth trends in the second and fourth years, the HYDRUS-3D hydraulic model was utilized.
Results and Discussion
The accuracy and efficiency of the HYDRUS-3D model in analyzing soil variations in terms of water flow and solutes transport were assessed using two statistical indices: RMSE (Root Mean Square Error) and NSE (Nash-Sutcliffe Efficiency). The RMSE values of calibration for soil moisture variations at three studied depths (0-40 cm, 40-60 cm, and 60-100 cm) were 0.0057, 0.0049, and 0.0044 cm3.cm-3, respectively. The NSE values at these depths during the calibration phase were 0.91, 0.98, and 0.99, respectively. For the validation phase, the RMSE and NSE values at 0-40 cm were 0.0021 and 0.97 cm3.cm-3, at 40-60 cm were 0.0038 and 0.99, and at 60-100 cm were 0.0029 and 0.99, respectively. Based on the results, the efficiency of the Levenberg-Marquardt optimization algorithm and the capability of the HYDRUS-3D model in simulating soil moisture dynamics in the plant root zone under center-pivot irrigation were verified. The RMSE values for ammonium simulation at the three depths during validation were 0.0055, 0.0003, and 0.0008 mg.l, while the NSE values were 0.97, 0.99, and 0.99, respectively. For nitrate concentration analysis at the same depths, the RMSE values were 0.009, 0.009, and 0.008 mg.l, while the NSE values were 0.99, 0.98, and 0.99, respectively. These findings confirm the effectiveness of the HYDRUS-3D model in estimating solute variations in soil. However, accuracy decreased with depth due to soil heterogeneity and unsaturated conditions, as the model assumes a homogeneous environment. Nitrate accumulation in plants showed an increasing trend as the plant growth period increased. The measured nitrate concentration in two-year-old alfalfa was significantly lower than that in four-year-old alfalfa. Additionally, nitrate accumulation in the root zone of four-year-old plants was higher than the younger ones. This process is influenced by fertilization practices and the expansion of the root system in the fourth year, which enhances nutrient uptake efficiency.
Conclusion
Based on the statistical indices obtained from the simulation of soil variations using the HYDRUS-3D model compared to measured values, it can be concluded that the Levenberg-Marquardt optimization algorithm had provided an accurate and practical estimation of soil hydraulic parameters under the applied management conditions. Furthermore, the HYDRUS-3D model had effectively simulated long-term variations over a four-year period within this management framework. Therefore, both the optimization algorithm and the HYDRUS-3D model demonstrated sufficient capability for assessing soil moisture dynamics and solute variations under modern irrigation management techniques at the farm scale. These methods can serve as powerful tools for formulating management strategies and evaluating the outcomes of different irrigation practices.
Research Article
Soil science
A. Barvar; N. Boroomand; M. Hejazi-Mehrizi; M. Sadat Hosseini
Abstract
IntroductionPhosphorus as a vital nutrient for plant growth and development, contributes significantly to processes like photosynthesis, energy production, and root development. In soil, phosphorus mainly exists as phosphate, though much of it is not accessible to plants. There are several methods for ...
Read More
IntroductionPhosphorus as a vital nutrient for plant growth and development, contributes significantly to processes like photosynthesis, energy production, and root development. In soil, phosphorus mainly exists as phosphate, though much of it is not accessible to plants. There are several methods for providing phosphorus to plants: such as chemical, organic, and combination of chemical and organic phosphorus fertilizers. The chemical fertilizers rapidly supply plants with absorbable phosphorus. However, excessive use of the fertilizer can degrade soil quality and cause environmental pollution. Manure and compost as organic fertilizers release phosphorus slowly in the soil. In addition to providing phosphorus, they enhance the chemical and biological properties of the soil. Using combination of chemical and organic fertilizers can increase phosphorus availability, promote soil health, and improve the sustainability of agricultural production. Given the importance of understanding the various forms of phosphorus in the soil for better plant nutrient management, as well as evaluating the combined application of different levels of chemical fertilizers and animal manure on the different forms of phosphorus, this study was conducted to examine the impact of these two phosphorus sources on its dynamics in the soil. Materials and MethodsTo investigate the effects of mono-potassium phosphate (MKP) fertilizer on various forms of phosphorus in soil, a completely randomized factorial experimental design was conducted. The treatments included four levels of MKP fertilizer (0, 70, 140, and 210 kg ha-1) and two levels of cow manure (0 and 40 tons ha-1). The impacts of the treatments on soil electrical conductivity (EC), pH, organic carbon (OC), and different phosphorus (P) fractions (water-extractable, NaOH-extractable, HCl-extractable, NaHCO₃-extractable, Olsen-P, and residual P) were examined. After harvesting, soil samples were taken from a depth of 20 cm below the initial fertilizer application site to examine the different fractions of soil phosphorus. A composite soil sample was taken from each treatment and after being transported to the laboratory, air-dried and passed through a 2 mm sieve. Results and DiscussionOverall, the results indicated that the addition of MKP and cow manure increased the soil EC and organic matter content. MKP fertilizer and cow manure significantly influenced various P fractions in the soil. Organic carbon content notably increased in the presence of cow manure. However, the interaction of high levels of phosphorus and cow manure resulted in a decrease in soil electrical conductivity (EC). The highest and lowest P concentrations were observed in the water-extractable fraction and residual P fraction, respectively. Organic matter predominantly enhanced the concentration of various P fractions, particularly water-soluble P. Organic matter exhibited a positive and significant correlation with water-extractable P (0.57), NaHCO₃-extractable P (0.44), NaOH-extractable P (0.44), and HCl-extractable P (0.6). The most pronounced effect of organic matter was on the water-extractable fraction, where its interaction with the MKP levels of 0, 70, 140, and 210 kg ha-1 resulted in respective increases of 35%, 51%, 36%, and 62% compared to the control. The inclusion of manure in the soil boosts the levels of available, water-extractable phosphorus, as well as phosphorus extractable with bicarbonate and sodium, and also increases residual phosphorus. Furthermore, higher levels of monopotassium phosphate fertilizer enhance soil electrical conductivity and extractable phosphorus. However, in some instances, such as with acid-soluble phosphorus, they can lead to a decrease in the concentration. In general, the combined application of manure and monopotassium phosphate improved soil phosphorus content; however, their impact on other soil properties, such as pH and organic carbon may vary. ConclusionThe simultaneous use of chemical and organic fertilizers can had a significant positive effect on the availability of phosphorus in the soil. As highlighted in the text, animal manure enhanced phosphorus availability to plants due to its phosphorus content and its influence on soil phosphorus solubility. Additionally, the observed sequence of phosphorus concentrations in different soil fractions (water > sodium > bicarbonate > acid > residue) reflected the distinct effects of these phosphorus sources, which, when combined, can improve the availability of this nutrient. Another important consideration was the need for further research to determine the optimal levels of these fertilizers. This would help identify the most effective combination and application rates for improving soil fertility and boosting agricultural productivity. Overall, such studies enable farmers and researchers to develop more effective strategies for managing phosphorus in soil, thereby contributing to the maintenance of soil health and higher agricultural yields.
Research Article
Soil science
S. Masoumi; H.R. Matinfar; S.R. Mousavi
Abstract
IntroductionSoil organic carbon (SOC), as one of the most important components of the global carbon cycle, plays a vital role in maintaining soil quality, enhancing fertility, and moderating climate change. The spatio-temporal variations of SOC are influenced by various factors, including land use, climatic ...
Read More
IntroductionSoil organic carbon (SOC), as one of the most important components of the global carbon cycle, plays a vital role in maintaining soil quality, enhancing fertility, and moderating climate change. The spatio-temporal variations of SOC are influenced by various factors, including land use, climatic conditions, topography, and human activities. Additionally, SOC contributes to diverse functions in natural and agricultural ecosystems, such as increasing soil fertility, controlling erosion, enhancing water permeability in soil, and reducing the effects of greenhouse gases.Materials and MethodsGiven the critical role of SOC in enhancing soil quality, this study aims to investigate the spatio-temporal variability of SOC using a reverse modeling approach based on a spatial model developed in 2024. The model is extended to analyze data from the years prior to 2015, 2010, and 2000, incorporating environmental variables derived from remote sensing (RS), topographic attributes, climatic data, land use, and geological information within the Zayandeh Rud watershed. For the environmental covariates, RS data, land use, and climatic information were obtained from Google Earth Engine's open-source spatial database for the relevant years from 2000 to 2024. In total, 76 auxiliary variables were prepared, including vegetation indices derived from band ratios of RS data, as well as the digital elevation model (DEM), geology, land use, and climatic factors, which were used as representatives of soil-forming factors. A relative importance feature selection method was employed to finalize the dataset of environmental covariates. Furthermore, three machine learning models—Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) tree—were utilized to explore the relationships between environmental factors and SOC. Four common statistical indices, including the coefficient of determination (R²), concordance correlation coefficient (CCC), root mean square error (RMSE), and percentage of normalized root mean square error (nRMSE), were used to evaluate the performance of the machine learning models Two uncertainty quantification approaches for prediction, namely bootstrap and k-fold cross-validation, were also applied. Results and DiscussionThe evaluation of machine learning models for predicting SOC revealed a notable decline in the performance of all models from the year 2024 back to 2000, as indicated by the R² statistic. Among the models assessed, the RF model exhibited superior performance, achieving the highest R² values for the years 2024 and 2015, thereby indicating its effectiveness in capturing the complexities of SOC dynamics. The SVR model demonstrated intermediate performance, while the XGBoost model showed relatively weaker results compared to the other two model. Despite these variances in performance, all three machine learning models effectively established a robust connection between SOC and the predictive environmental variables, affirming their suitability for this analysis. Furthermore, the uncertainty quantification of SOC predictions highlighted that the bootstrap method outperformed the k-fold method, yielding lower values for both standard deviation (SD) and mean uncertainty, which suggests that the bootstrap approach provides a more reliable prediction of SOC variability. In terms of the relative importance of environmental variables in predicting SOC, the analysis across all time periods indicated that climatic factors played the most significant role, closely followed by topographical attributes. Other environmental variables, including land use, geology, and RS data, had a lesser impact on explaining spatial variations in SOC. The spatial analysis indicated alarming increases in areas with very low SOC content, suggesting soil degradation risks. Furthermore, higher rainfall and lower temperatures were associated with highest SOC levels, emphasizing the need for effective soil management strategies. ConclusionThis study emphasizes the necessity of ongoing monitoring and management of Soil Organic Carbon (SOC) to combat soil degradation and promote sustainable agriculture. The findings also provide a framework for creating soil property maps in data-scarce regions, enhancing decision-making for effective soil management strategies.Overall, the findings from this study highlight the need for continuous monitoring and management of SOC to mitigate risks related to soil degradation and to promote sustainable agricultural practices. Also, the methodology and framework that applied in this research can be used as useful guideline for land manager, young pedometrisians, expert in digital soil mapping, and decision makers for preparing the high detailed maps of other key soil properties such as (soil texture component: Sand, Silt, Clay, Gypsum, calcium carbonate equivalent, soil EC, available phosphorus and potassium that directly affecting on soil quality index in the similar zones with study area.
Research Article
Agricultural Meteorology
M. Asadi
Abstract
IntroductionHuman activities and the substantial increase in greenhouse gas concentrations-particularly carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) have exacerbated global warming and triggered significant alterations in climatic patterns. Consequently, climate change has emerged ...
Read More
IntroductionHuman activities and the substantial increase in greenhouse gas concentrations-particularly carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) have exacerbated global warming and triggered significant alterations in climatic patterns. Consequently, climate change has emerged as a critical challenge for natural resource management and agricultural systems in recent decades. These changes, especially temperature and precipitation fluctuations, directly impact plant phenological and vegetative cycles and may even shift the suitable geographical ranges for cultivating certain plant species. Among these species is Ziziphus jujuba Mill. (Family: Rhamnaceae), a medicinally valuable plant that exhibits relative adaptability to arid and semi-arid climates, such as those in Iran. However, it remains vulnerable to climate change impacts. Historically cultivated in South Khorasan Province, this region now accounts for over 72% of Iran’s jujube production. Investigating climatic trends and their effects on the reproductive and vegetative thresholds of Ziziphus jujuba is both scientifically and practically significant. Such analyses enhance our understanding of regional climate change dynamics and facilitate predictive assessments of its agricultural consequences. Therefore, the objective of the present study was to identify the reproductive and vegetative thresholds of jujube throughout the year in the counties of South Khorasan Province and to spatially analyze these thresholds in terms of temperature and precipitation, both under baseline conditions and future scenarios influenced by trends in temperature and precipitation changes. Material and MethodsIn this study, the modified Mann-Kendall test, Sen's slope estimator, and linear regression analysis were employed to analyze trends in data related to determining the cultivation range of the jujube plant. The study data included monthly temperature and precipitation averages from seven synoptic stations within the study area, covering a statistical period of 25 years from 2000 to 2024. These data were extracted from the National Meteorological Organization and served as the foundation for the study. Station data were converted into z-scores using the modified Mann-Kendall test in Minitab software. Additionally, linear trends of variables such as minimum temperature, maximum temperature, mean temperature, precipitation, sunshine hours, and hot days, along with their corresponding slopes, were examined. Results and DiscussionJujube plants, like other plant species, require specific temperature ranges for optimal growth during different vegetative and reproductive stages. This study examined the thermal thresholds that impact the growth of jujube trees. It was found that 25°C was the threshold at which reproductive growth stops, while 40°C was the threshold for the cessation of vegetative growth. Additionally, the biological zero for jujube growth had been established at 11°C, and this plant can tolerate low temperatures down to -33°C. Some studies have even reported the plant's ability to withstand temperatures as low as -40°C. In this research, each of the seven studied stations in the region was individually analyzed in terms of maximum temperatures and critical points leading to the cessation of vegetative and reproductive growth. ConclusionThe findings revealed that the Zirkuh station, with an average annual precipitation of 182.8 mm, received the highest rainfall among the studied stations. Nevertheless, even at this station, jujube plants required supplementary irrigation of 267.2 mm. Fortunately, the region's climatic conditions were characterized by rare and minimal summer rainfall, a phenomenon that could otherwise cause fruit cracking, making this area particularly suitable for jujube cultivation. Analysis of climatic data from 2000 to 2024 demonstrated significant spatial heterogeneity in temperature trends. Modified Mann-Kendall test results indicated a warming trend across all stations, with the most pronounced increase observed in Nehbandan station (3.43°C) and the least in Zirkuh station (0.94°C). These spatial variations can be attributed to altitudinal differences, geographical positioning, and localized microclimatic conditions. Sen's slope estimator corroborated these findings, showing the steepest positive slope in Ferdows station (0.24) and the gentlest in Khosf station (0.03). Linear regression analysis revealed a decadal temperature increase ranging from 0.07°C in Birjand and Zirkuh stations to 2.48°C in Nehbandan station. Statistical analysis of p-values demonstrated significant spatial patterns in temperature changes. While northern and central stations (e.g., Birjand, Boshruyeh, and Ghaen; p ≤ 0.05) show no significant trend, southern stations, particularly Nehbandan (p ≤ 0.02), exhibited statistically significant warming. Regarding precipitation, all stations showed decreasing trends, with a maximum reduction in Nehbandan (-3.32 mm) and a minimum in Birjand (-0.63 mm). Sen's slope analysis indicated the steepest decline in Ferdows (-0.34) and the mildest in Zirkuh (-0.13). Regression analysis estimates an annual precipitation decreased ranging from 0.04 mm/decade in Zirkuh to 1.80 mm/decade in Ghaen. Statistically, northern and central stations (p ≤ 0.05) show significant drying trends, while southern stations like Nehbandan (p = 0.28) exhibited no statistically significant trend.
