Desert
Volume:29 Issue: 2, Summer -Autumn 2024

Climate change, an escalating global phenomenon, presents significant challenges with diverse impacts, particularly in developing regions. This study conducts a detailed statistical analysis of long-term rainfall trends and climatic disruptions at six meteorological stations in Kermanshah Province, Iran, spanning 1951 to 2023. The analysis identifies significant trends and breakpoints in rainfall patterns using the Mann-Kendall test, Sen's slope estimator, and Pettitt’s test. Results reveal a consistent decline in annual rainfall at five stations—Kermanshah, Ravansar, Songhor, Sahneh, and Bisotun—while Harsin exhibits a slight increase. Bisotun records the sharpest decline, with a 26.86% reduction in annual rainfall, equivalent to −5.38 mm per year, followed by Songhor with a 28.45% decline, reflecting heightened vulnerability in these areas.Conversely, Harsin demonstrates a 20.53% increase in annual rainfall after 1967, showcasing variable climatic responses within the region. Pettitt’s test identifies the 1990s as the predominant period for abrupt rainfall shifts, coinciding with global phenomena such as El Niño and La Niña. These shifts significantly reduced mean annual rainfall in critical locations, including Bisotun, where the mean declined from 540.9 mm to 395.61 mm after 1995. The findings emphasize the profound impact of climate change on regional hydrological dynamics, threatening water resources, agriculture, and livelihoods. The study underscores the urgency of adaptive water management strategies to address rainfall variability and recommends further research on the interaction between global atmospheric phenomena and local climatic shifts to inform effective mitigation and adaptation policies.

Halophytes and other salt-tolerant plants in rangeland ecosystems play an important role in environmental protection and food security. The spatial point pattern analyses of these plants in arid and semi-arid areas provide valuable information about how they affect each other and how we can preserve plant species as well as restore and manage the rangeland ecosystems. This advanced tool to interpret ecological processes can provide valuable information on plant coexistence patterns and biodiversity maintenance in arid and semi-arid regions. This research focuses on the investigation of spatial patterns and interactions occurring with two crucial species (Halocnemum strobilaceum (Pall.) M.Bieb and Climacoptera turcomanica (Litv.) Botsch) characterizing the Inchehboroun rangelands, in Golestan province, Iran. These two salty species dominate the rangeland and influence plant diversity and the relative conservation of rangeland productivity. We used the RTK GPS (Real Time Kinematic Global Positioning System) to record the position of each species in the study area during the field survey. Then, the spatial distribution and interactions between H. strobilaceum and Cl. turcomanica were determined by summary statistics methods including univariate and bivariate functions; g(r) and O-ring. The results showed that: 1) both investigated species have aggregated spatial patterns. 2) H. strobilaceum has a significantly positive correlation with Cl. turcomanica. Furthermore, according to the results of this study and the fact that most rangelands in the country are in moderate to poor condition, spatial pattern analyses using statistical functions can be useful in the development of restoration programs and ecological management of rangelands.

Climate changes have a significant effect on dust extremes. Dust extremes in humid ambient air can simultaneously or successively form wet mud deposition on the surface of urban areas. The mud deposition on the power network systems and devices causes irreversible damage and significantly influences system performance and efficiency in southwest Iran. This often results in blackouts that cause problems in the operation of urban infrastructure and people's daily activities for up to several days. Khuzestan province was chosen as the case study in this study, and the climatic conditions and risk assessment of mud formation in this area were investigated. Data on a diurnal and monthly timescale of dust and humidity level was used for assessing extreme dust and wet conditions. The data was taken from Khuzestan synoptic station 8 over 11 years (2009-2019). The multivariate copula-based framework is used to calculate univariate and bivariate return periods of mud deposition hazard. The results imply that dust anomalies increase the probability of dust extreme coincidence with wet extreme and occurrence of wet mud hazards in the cold seasons of the year. In addition, limited adaptive capacity, shortage of information, and poor coordination and cooperation by the authorities caused the large-scale impact of the wet mud hazard in Khuzestan. Considering only relative humidity data, the return period of 2017 Khuzestan mud adhesion hazard is approximately 12 to 43 years. If we consider only dust, the return period of 2017 Khuzestan mud adhesion hazard is estimated at 80 to 700 years. However, for both dust and relative humidity extremes, the joint return periods for TDR (Dust and Relative humidity) and T'DR (Dust or Relative humidity) are respectively estimated greater than 200 and lower than 20 years.

Tarbat-e Jam is one of the major areas of melon production. Recent droughts and shortage of water have reduced melon yield. In this research, the role of nitroxin bio-fertilizer and potassium on mitigating the adverse effects of drought stress on melon yield was evaluated during the years of 2019 and 2020. This experiment was designed as a split-plot factorial with three replicates based on a randomized complete block design. The factors in the split-factorial design including: irrigation (I), bio-fertilizer (B) and Potassium (K). Irrigation levels (I100, I80, and I60) were kept at 60%, 80%, and 100% of crop evapotranspiration. The Nitroxin was used as seed coating (B1) and none seed coating (B0). The highest proline content and electrolyte leakage were obtained in I60 whit combination of B0 and K0. The highest and the lowest value of CAT activity were obtained in I60 + B0 and K2+B1 treatments respectively. The highest peroxidase activity was achieved in the I60. The seeds inoculated with nitroxin and Potassium application significantly decreased POD activity. Relative water content and total chlorophyll were decreased under drought stress but they increased significantly by using bio-fertilizer and potassium. The highest TSS was obtained in the K2 and in the I80+B1 treatment. Potassium application increased the yield significantly. The highest and lowest yields were recorded in the I100+B1 and I60+B0 respectively. In this research, the use of bio-fertilizer and potassium moderated the effect of drought stress and reduced its negative effects, and the yield improved under drought stress.

Germination is a critical stage of the growth of ornamental plants, which is susceptible to the adverse effects of salinity. Ornamental cabbage is one such important ornamental plants in green spaces, yet there is limited research on the effect of priming on its seed germination under saline conditions. This study investigated the effect of halopriming on the germination of this plant. The studied factors included four salinity levels with sodium chloride (0, 4, 8, and 12 dS/m) and four potassium nitrate priming levels (0, 50, 100, and 150 ppm), with three replications. Results indicated that the application of potassium nitrate ameliorated the negative effects of salinity on many of the studied traits. Germination percentage and rate at 4, 8, and 12 dS/m salinity levels with potassium nitrate priming at 50 and 100 ppm were significantly higher compared to the control (non-primed level), showing significant differences. The highest values for allometry coefficient and seedling tissue water content were obtained with the application of potassium nitrate at 100 ppm and 4 dS/m salinity level. Positive effects of potassium nitrate priming were also observed on root and shoot length, plant length, and root-to-shoot length ratio, mean daily germination, fresh and dry weight under most salinity levels. Based on the obtained results, the application of potassium nitrate at a concentration of 100 ppm had the greatest impact on mitigating the adverse effects of salinity stress on germination and seedling growth of ornamental cabbage.

Air pollution represents a significant challenge in Middle Eastern countries and has notably impacted the western regions of Iran, specifically the province of Khuzestan. The identification of pollution production centers and the spatial distribution and patterns of pollution are crucial for effective management. Limited synoptic ground stations and challenging data accessibility make it difficult to precisely monitor air pollution across various regions. Remote sensing, however, offers a viable solution for obtaining reliable air pollution information through time series analysis. This study utilized Sentinel-5P satellite image products and data from synoptic stations in Khuzestan province. Leveraging the Google Earth Engine system, the research identified atmospheric pollutants including NO2, CO, UV-Aerosol, and SO2 over a period of one year from January 2022 to January 2023. Subsequently, maps displaying the concentration of atmospheric pollutants were generated, visually representing pollutant concentrations through color-coded layers. Monthly fluctuations in NO2, CO, UV-Aerosol, and SO2 levels were graphed, revealing seasonal variations in pollutant concentration. The results indicated that NO and CO showed higher concentrations during spring and summer, while UV-Aerosol exhibited peak concentrations in spring. Additionally, the months of September and October highlighted the highest concentration of SO2 in the central and southwest regions of Khuzestan province. The research findings further illustrated an increase in pollutant levels from central areas to the south and southwest regions of the province. Finally, ground data from synoptic stations were utilized to validate the results.

Excessive dryness of arid regions has increased the intensity and spread of desertification. Investigating spatial and temporal patterns of desertification caused by climate change in the northwest of Yazd province using the Iranian model of desertification potential assessment (IMDPA) is one of the main objectives of this research. In this study, a twenty-year statistical period (2001-2020) was also selected as the base period to reveal climate change. And the precipitation and average temperature data collected from selected stations were downscaled with the BCC-CSM1-1 model from the CMIP5 series, under three radiative forcing scenarios RCP2.6, RCP4.5, and RCP8.5 using the LARS-WG6 simulator for the near future (period 2026-2055) and the far future (2071-2100). And the results of predicting climatic elements on the increase in the area of ​​areas prone to desertification in the studied region were evaluated. The results showed that the rainfall in the final decades of this century is lower than in the period 2026-2055 and in some places is increasing or decreasing compared to the average of the base period. Temperatures will increase relative to baseline for both future periods. Also, based on the IMDPA model, 80.54 percent of the area of ​​the region is in the severe desertification class in the base period. The intensity of climate-driven desertification in the distant future is more severe than in the near future and the base period. The largest changes in desertification classes in the near future are related to RCP2.6 and RCP4.5, and in the distant future are related to all scenarios. So that during this period, we will witness the transition and change of moderate and severe risk classes to severe and very severe classes, especially in RCP4.5. Therefore, at the end of this century, the intensity of desertification in the region will be more severe than in the base period and the near future.

Global warming, human activities, and increased water demand have led to a decrease in the resilience of the environment. Their effects in dry climates like Iran lead to the reduction of surface water and a water table drop. To evaluate the adaptation strategy for water resources with climate change, the Cham Anjir watershed  was selected in the west of Iran. The geostatistical techniques are applied here. In this study, to detect climate change in the Cham Anjir watershed , hydrological-climatic data from 1991 to 2020 were used, and to adapt to climate change, a researcher-constructed questionnaire was employed. The results showed that annual temperature has increased. Long-term droughts have led to a decrease of available water. The local community has a correct understanding of climate change and its effects. Weak financial resources, lack of proper agricultural insurance support, weak training and technical consulting activities, lack of access to new technologies, and administrative bureaucracy are the most important obstacles to adaptation to climate change. Climate change adaptation programs include measures to meet essential needs, provide financial resources (short-term), improve irrigation and increase productivity (mid-term), and diversify economic activities (long-term) emphasized and accepted by the local community. The findings showed that the difference between local communities and technical experts with government experts is the most important obstacle in adapting strategies to climate change. Therefore, correcting the views of farmers and farm technicians with public sector experts is crucial for the success of climate change adaptation measures.

This study evaluated the climatic suitability of several almond cultivars that flower at different times: Sefied, which flowers early, Mamaei and Rabie, which flowers in the middle, and Shahroud 7 and 12, which flowers late. The phenological stages of almond trees were first identified using prior research and discussions with provincial horticultural experts. After gathering long-term climate data, the climate requirements of almond plants were compared with the regional climate factors. Subsequently, the climatic suitability classes were identified, and a corresponding map was created. The evaluation of climatic suitability for early-flowering cultivars revealed that the province's climate presents severe to very severe limitations for growing and cultivating this crop, with a large percentage of the province (roughly 53.3%) experiencing very severe limitations, meaning that the climate is unsuitable for these cultivars.The climate of the province for mid-flowering almond cultivars varies,with regions categorized into severe limitations, very severe limitations (correctable), and non-correctable very severe limitations. Nonetheless, the majority of the province's counties are classified as S3 suitability class (severe limitation) for cultivating and developing mid-flowering cultivars. For late-flowering cultivars, the climate of the province across all counties falls within the S3 suitability class. Therefore, the climatic suitability of a region varies not only for each plant but also for different cultivars of the same plant. Additionally, the most limiting climatic parameter is the average minimum temperature during the flowering stage. In order to maximize production and lessen climate-related constraints, the study emphasizes how crucial it is to choose suitable almond cultivars based on blooming timings and local climate conditions, especially the average minimum temperature during the flowering stage.

Biocrusts are used as a protective cover to stabilize the soil in dry areas, where the soil is more affected by erosion. In this research, various characteristics of the soil under the biological and physical crust were investigated as indicators of soil performance in the the Incheh Borun region in the study area, from the subsoils of biological (moss, lichen and cyanobacteria) and physical crusts at the depth 0-2 cm were sampled Then, the influence of biological and physical shells on soil properties was analyzed with three replications. A one-way analysis was considered to discover significant contrast among treatments. Compare mean was done with using Duncan’s multiple range test. Notable distinction was reported at p< 0.05 between biocrust and physical crust. Carbon, nitrogen and phosphorus of microbial biomass, soil-based respiration, substrate-induced respiration, microbial metabolic quotient, mineralization quotient and other characteristics that have a direct impact on the functioning of the ecosystem (soil organic carbon, soil organic matter, mean weight diameter, geometric mean diameter, wind erosion soil stability) were higher in soil under biocrusts compared with the soil beneath the physical crust. Results showed that biocrusts increase the percentage of nutrients, structure and physical characteristics of soil. The biocrusts improved basal soil respiration, substrate-induced respiration, carbon, nitrogen and phosphorus of microbial biomass contents. Biocrusts improve the soil's biological characteristics, and have a crucial function on increasing the stability and resistance of the soil to erosion, which is confirmed by the results of the soil grain stability indicators.

Environmental planning and resource management necessitate an analysis of changes in land use and land cover (LULC). In recent years, climate change and human activities, notably the erection of the Ilisu dam, have adversely impacted the Tigris River Basin (TRB), one of the most vital natural resources in Western Asia, resulting in significant alterations in its LULC. Based on this, the present study developed multi-temporal (2003-2023) LULC maps for TRB through classifying Landsat images using the random forest (RF) and support vector machine (SVM) algorithms, and simulating future LULC states (2028) employing the cellular automata (CA)-Markov model. RF exhibited better performance than SVM in the classification of Landsat images, and its results were chosen for further investigation. The CA-Markov model simulated the landscape map of 2028 by considering LULC dynamics between 2018 and 2023. The model's performance was validated, confirming acceptable results with an accuracy rate of 0.798 and F1 score of 0.789. Notably, LULC changes in TRB were critical, including a reduction in water resources, wetlands and croplands. This could lead to several environmental challenges, highlighting the significance of quick LULC changes. The construction of the Ilisu Dam on the Tigris River in Turkey has worsened the situation by exacerbating water shortages, expanding bare ground, harming wetlands, reducing water quality, soil salinization, and damaging the aquatic ecosystems. The drying wetlands and expanding bare grounds will become potential dust sources in the future and affect surrounding countries. Accordingly, intergovernmental actions and special policies are needed to manage this environmental crisis.

Mangrove forests play a vital role in providing ecosystem services such as coastal protection and mitigating the impacts of climate change, necessitating mapping for assessment, monitoring, conservation, and management. Advances in remote sensing have enabled rapid and accurate mapping of these forests. This study aims to determine the best method for mapping Iran's mangrove forests (northern coasts of the Persian Gulf and the Gulf of Oman) by comparing the Mangrove Vegetation Index (MVI) and Random Forest (RF) classification using Landsat-9 and Sentinel-2 satellite data, as well as evaluating the accuracy of land cover products from the European Space Agency (ESA), the GLC_FCS30 land cover product, and the Global Mangrove Watch (GMW) product. The results show respective mangrove class accuracies of 95%, 84%, 91%, 86%, 83%, 80%, and 78% for MVI with Sentinel-2 data, MVI with Landsat-9 data, RF classification with Sentinel-2 data, RF classification with Landsat-9 data, ESA product, GLC_FCS30 product, and GMW product. The corresponding areas were 11,509 ha, 11,834.5 ha, 10,779.41 ha, 13,702.23 ha, 15,814 ha, 11,441.5 ha, and 11,117 ha, respectively. The findings indicate that Sentinel-2 data show higher potential than Landsat-9 data for mapping Iran's mangrove forests. Furthermore, the results demonstrate the higher accuracy of the generated maps compared to existing remote sensing products. These findings not only highlight the potential of modern remote sensing data for enhancing mangrove forest mapping but also pave the way for more precise and cost-effective monitoring strategies, which are crucial for conservation efforts in coastal ecosystems.