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Climate as one of the key factors in water resources management affects the amount of water in the hydrological cycle, which subsequently impacts the level of water availability. Considering the challenges that the South Alborz Region, Iran is currently facing in supplying water for various consumers; in this study, the climate change adaptation scenarios are investigated for sustainable water supply and demand. This study uses a procedure in which five different adaptation approaches, under RCPs scenarios, were established using the WEAP model to assess the impacts of various adaptation strategies on increasing the balance between water supply and demand over current and 2020s accounts. The findings suggest an imbalance between supply and demand in the current situation with the greatest imbalance in domestic use while the minimum in the industrial sector. The results of assessing adaptive scenarios show that various scenarios have different effects on balancing the water supply and demand by different consumers; on the other hand, the scenarios that directly affect domestic water demand have the greatest effect on minimizing the gap between supply and demand in the region; therefore, the options for decreasing the population demand along with diminishing the losses in the domestic water distribution network are the most effective alternatives for balancing supply and demand under all of the climate scenarios. The findings of this research indicate that adaptive management with the focus on restricting demand helps actively management of water resources in the regions with scarce water resources. [ABSTRACT FROM AUTHOR]

Nowadays, rainwater harvesting systems, particularly on the roof of the buildings in residential areas could be considered as a managerial procedure to reduce water crisis. This efficient method is being investigated and implemented in different countries all around the world. This study was conducted on the estimation of rainfall, runoff coefficient, and the calculation of the optimized tank volume and the total harvested amount of rainwater on the roofs of the residential areas in Torbat-e Jam. It is noteworthy to mention that the roofs' surfaces are covered by waterproofing. This study aimed to estimate the amount of the harvested rainwater in the study area, and to apply this method in different areas of Torbat-e Jam, Razavi Khorasan Province, Iran. According to the results, the values of the roofs' runoff coefficient and average rainfall were 0.9 and 154 mm, respectively. The results of the tank volume sensitivity analysis showed that the average peak 24-hour rainfall is the best rainfall index to calculate the tank volume. The total harvested amount of rainwater was 5,606 m3 considering the total surface of the roofs and the annual rainfall in the study area. Applying this procedure in other areas of Torbat-e Jam, the harvested rainwater was estimated as 772,806 m3. This amount of harvest resulted in 15.5% water saving. [ABSTRACT FROM AUTHOR]

Hazardous flooding occurs across most climate zones. Owing to the lack of appropriate infrastructures and applicable predictive methods, flooding in arid and semi-arid regions may be especially damaging. Based on a study of Abarkuh County, Iran, we introduce an integrated approach for identifying high-priority flood risk areas using machine learning (ML) and multi-criteria decision-making (MCDM) methods, which is transferable to other (semi)arid regions. Results indicate that among the ML models we examined—including classification and regression tree (CART), mixture discriminant analysis (MDA), and support vector machine (SVM)—the SVM model performs best. We estimate that 75% of the study area is subject to high or very flood hazard. Our application of the Jackknife technique identifies precipitation, vegetation, and drainage density as the most important conditional factors for regional flood hazards. Our analytical network process (ANP)-decision making trial and evaluation laboratory (DEMATEL) results reveal that population density and agricultural area density have the greatest influence on flood vulnerability. Results integrating SVM and ANP-DEMATEL flood hazard and vulnerability maps indicate that 6% of the study area is at high or very high flood risk. Application of this approach can assist local authorities in identifying priority areas for flood management interventions. [Display omitted] • Introduces new, transferable approach to assess flood risk in (semi)arid areas. • Maps flood susceptibility using machine learning (ML) techniques. • Maps flood vulnerability using multi-criteria decision-making (MCDM) methods. • ML results show that 75% of the study area is highly susceptible to flooding. • Integrated ML and MCDM results show that 6% of the area is at high flood risk. [ABSTRACT FROM AUTHOR]

Agroforestry has been recognized as a sustainable strategy over conventional agriculture that can mitigate environmental impacts, enhance ecosystem services, maintain natural resources, and simultaneously improve smallholders' livelihoods in rural areas. Agroforestry will be most effective in agricultural lands that are more vulnerable in terms of environmental and socioeconomic aspects. Therefore, it is necessary to identify the priority areas that are more susceptible to agroforestry. The objective of this study was to evaluate where and to what extent Iran's farmlands were subjected to increased environmental and socioeconomic pressures that can be alleviated through the implementation of agroforestry practices. To do so, two climatic, four soil, and four socioeconomic indicators were selected, and their maps were generated as well. Then, pressure maps of these indicators were created by applying the critical threshold of each indicator to the corresponding map. Finally, all the pressure maps were accumulated on a map called the Agroforestry Suitability Map (ASM). The locations that have more than five pressures on the current map were designated as priority areas for the development of agroforestry. The main findings showed that rise in temperature and soil organic carbon (SOC) deficit were the dominant pressures that affected the study area. Furthermore, about 17% of the total farmlands were recognized as the priority areas. The priority areas were mostly located in arid and semi-arid regions, which indicates the greater vulnerability of these regions to climatic and socioeconomic conditions. Our results highlighted that the farmlands of Kermanshah, Khuzestan, and Lorestan provinces, located adjacent to the Zagros Mountains, are the most suited areas for agroforestry implementation, respectively. The study findings could assist decision makers in mitigating the negative effects of environmental pressures and in providing a wide range of other beneficial services through the establishment of agroforestry systems in the recognized priority areas. [ABSTRACT FROM AUTHOR]

Abstract: The two most widely used modern soil classification schemes are Soil Taxonomy (ST) and World Reference Base for Soil Resources (WRB). The purpose of this paper is to clarify the differences and highlight the similarities between ST and WRB in their current state, with special emphasis on calcareous soils in arid and semi-arid regions of Iran. Four study sites were selected from central, northeast, and southeast Iran, and four representative pedons were described and classified for each site. Then, soil units from WRB were compared to those obtained by using ST at the family level. The results demonstrated that WRB shows more accessory soil properties compared to ST; moreover, through the method of combining qualifiers, the WRB system is a better option for naming calcareous soils. Defining new great groups, including Natrisalids, Gypsisalids, Calcisalids, Gypsicalcids, and Calcixeralfs, and new subgroups consisting of the Calcic Natrisalids and Calcic Natrargids for ST, and the “Paralithic” qualifier for the WRB system, could increase the usefulness and correlation between these two soil classification systems. Moreover, the definition of some reference soil groups (such as Vertisols), diagnostic horizons (such as salic), and/or qualifiers (such as Luvic prefix) should be revised to harmonize the classification systems. [Copyright &y& Elsevier]

Phosphorus (P) sorption by sediments may play a vital role in buffering P concentration in the overlying water column. To characterize P sorption-desorption in the river bed sediments, 17 bed sediment samples collected from Abshineh river, in a semi arid region, Hamedan, western Iran were studied through a batch experiment and related to sediment composition. The sorbed fraction ranged from 4.4% to 5.4% and from 38.5% to 86.0% of sorption maxima when 20 and 1,500 mg P kg, respectively, was added to the sediment samples. Phosphorus sorption curves were well fitted to the Langmuir model. Zero equilibrium P concentration ranged from 0.10 to 0.51 mg P l and varied with sediment characteristics. Phosphorus desorption differed strongly among the studied bed sediments and ranged from 10.8% to 80.2% when 1,500 mg P kg was added. The results of the geochemical modelling indicated that even under low P addition (2 mg l), the solutions are mainly saturated with respect to hydroxyapatite and ß-tricalcium phosphate minerals and undersaturated with respect to other Ca and Mg minerals, whereas under higher P addition (150 mg l), most Ca-P solid phases, except the most soluble mineral (brushite), will likely precipitate. A Langmuir sorption maximum was positively correlated with carbonate calcium. Estimated P retention capacity of the bed sediments are generally lower and zero equilibrium P concentration values higher in upstream sites than at the downstream sites, suggesting that sediments in upstream and downstream may act as source and sink of P, respectively. [ABSTRACT FROM AUTHOR]

Iran's Nomadic tribes represent part of its rich cultural heritage, and retain traditional knowledge in the sustainable management of the arid nation's rangelands. Mobile pastoralists constitute 2.1% of the national population and own one-third of the total livestock, using the country's otherwise unused rangelands, which cover about 963,000 km2 (59%) of Iran's land area. Iran's pastoral stockbreeders produce more than 200,000 tonnes of red meat, 470,000 tonnes of milk and other dairy products and 21,000 tonnes of wool annually. They also cultivate about 450,000 ha of land and produce a significant volume of handicrafts, such as carpets, ghelims and ghabbehs. Although the geo-climate characteristics of Iran are more suitable for pastoralism, rather than crop cultivation, particularly in the central plateau, successive governments since 1906 have tried to reduce nomadism and promote agriculture. [ABSTRACT FROM AUTHOR]

In this study, the spatio-temporal changes of urban heat island (UHI) in a mega city located in a semi-arid region and the relationships with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) are appraised using Landsat TM/OLI images with the help of ENVI and ArcGIS software. The results reveal that the relationships between NDBI, NDVI and land surface temperature (LST) varied by year in the study area and they are not suitable indices to study the land surface temperature in arid and semi-arid regions. The study also highlights the importance of weather conditions when appraising the relationship of these indices with land surface temperature. Overall, it can be concluded that LST in arid and steppe regions is most influenced by barren soil. As a result, built-up areas surrounded by soil or bituminous asphalt experience higher land surface temperatures compared to densely built-up areas. Therefore, apart from setting-up more green areas, an effective way to reduce the intensity of UHI in these regions is to develop the use of cool and smart pavements. The experiences from this paper may be of use to cities, many of which are struggling to adapt to a changing climate.