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Assessing the vulnerability of different sectors to climate change has great importance in determining the appropriate adaptation measures to deal with climate change impacts on a river basin scale. In this research, using a framework for modeling the agronomic system vulnerability to climate change, vulnerability assessment under different scenarios was conducted for the Gorganrud River Basin located in the agro-ecological zone of the Caspian coastal plain of Iran. Considering exposure, susceptibility, and lack of resilience components, 12 indicators were chosen and quantified for both agronomic-environmental and socio-economic aspects. The SSM-iCrop2 model was used to simulate crop yield under current and climate change scenarios across the basin. The analysis indicates that in the current condition, the vulnerability level is different across the watersheds of the Gorganrud River Basin. By applying the climate change scenarios, agronomic system vulnerability would increase in the basin to some extent, particularly in Madarsu and Tilabad watersheds attributed with high vulnerability (0.63 and 0.61, respectively). This justifies the need to implement adaptation plans for encountering water shortage in the future. The analysis also suggests that the vulnerability of the agronomic system for adaptation scenarios characterized by less water consumption under climate change conditions is going to be slightly different from the vulnerability under the climate change scenarios. Due to an increase in agronomic system vulnerability under climate change scenarios, coupled with the fact that most watersheds (except Chehelchai, Nardin, and Narmab) are moderately vulnerable even under current conditions, policymakers and planners should promote crop and livelihood diversification programs aiming to prevent an increase in agronomic vulnerability., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Allocation of water in the situation of climate change presents various uncertainties. Consequently, decisions must be made to ensure stability and functionality across different climatic scenarios. This study aims to examine the effectiveness of adaptation strategies in the agricultural sector, including a 5% increase in irrigation efficiency (S1) and a shift in irrigation method to Dry-DSR (direct seeded rice) under conditions of climatic uncertainty using a decision-making approach. The study focuses on the basin downstream of the Sefidroud dam, encompassing the Sefidroud irrigation and drainage network. Initially, basin modeling was conducted using the WEAP integrated management software for the period 2006-2020. Subsequently, the impact of climate change was assessed, considering RCP2.6, RCP4.5, and RCP8.5 emission scenarios on surface water resources from 2021 to 2050. Runoff and cultivated area, both subject to uncertainty, were identified as key parameters. To evaluate strategy performance under different uncertainties and determine the efficacy of each strategy, regret and satisfaction approaches were employed. Results indicate a projected decrease in future rainfall by 3.5-11.8% compared to the base period, accompanied by an increase in maximum and minimum temperatures (0.83-1.62 °C and 1.15-1.33 °C, respectively). Inflow to the Sefidroud dam is expected to decrease by 13-28%. Presently, the Sefidroud irrigation and drainage network faces an annual deficit of 505.4 MCM, and if current trends persist with the impact of climate change, this shortfall may increase to 932.7 MCM annually. Furthermore, satisfaction indices for strategy (S2) are 0.77 in an optimistic scenario and 0.70 in strategy (S1). In a pessimistic scenario, these indices are 0.67 and 0.56, respectively. Notably, changing the irrigation method with Dry-DSR is recommended as a robust strategy, demonstrating the ability to maintain basin stability under a broad range of uncertainties and climate change scenarios. It is crucial to note that the results solely highlight the effects of climate change on water sources entering the Sefidroud dam. Considering anthropogenic activities upstream of the Sefidroud basin, water resource shortages are expected to increase. Therefore, reallocating water resources and implementing practical and appropriate measures in this area are imperative., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This study evaluates the potential of gridded AgMERRA (the Modern-Era Retrospective Analysis for Research and Applications) to estimate aridity index (AI), growing degree days (GDD), and temperature seasonality (TS) for six land stations across northeast Iran. The researcher investigated the spatiotemporal variation of the AgMERRA-derived agro-climatic indices for the entire period 1981-2010 and three 10-year sub-periods for the 347 wheat harvested grid cells (0.25° × 0.25°) and their utility for agro-climate zoning in northeast Iran. Results indicated a good agreement between AgMERRA daily solar radiation, maximum and minimum temperatures, and annual total precipitation with corresponding land observations for the six studied sites. AgMERRA-derived evapotranspiration (ET o ), AI, GDD, and TS also exhibited good agreement (R 2 and d > 0.7) with the land station-derived indices for most of the locations. Annual analysis of the AI indicated a negative trend for all of the wheat harvested grid cells, but the decrease was significant (p < 0.05) only for 14.70% of grid cells, which were located in the southwest part of the studied region. The magnitude of the significant decreasing trends in annual AI was (-)0.0011 per year. The increase in aridity was due to the concurrent occurrences of positive ET o trends and negative precipitation trends. All of the wheat harvested grid cells showed a significant increasing trend (p < 0.05) for GDD at the rate of 24.10 °C d year -1 . The TS series demonstrated an apparent increasing trend for 99.2% of wheat harvested grid cells; however, only 16.9% of them had the significant positive trend (p < 0.05) with the average rate of 0.023 °C year -1 . The wheat harvested grid cells with increasing trend for TS were mainly distributed in the arid mountainous southern part of the study area. The 10 years sub-periods revealed that the best conditions in terms of most of the studied agro-climatic indices were found in sub-period 1981-1990 and the north Khorasan had better conditions in all three sub-periods. Based on AI, GDD, and TS, 13 major gridded agro-climatic zones were recognized in northeast Iran., (© 2021. ISB.)

This review paper investigates unprecedented recent investment and capital spending in cross-provincial water management and transfer infrastructure in Iran. Although numerous cross-provincial water transfer plans have been implemented in the country, the scale, approach, and stakeholders involved in this recent national plan are unprecedented. This notable national plan includes long water transfer pipelines that pass through seven provinces, aiming at transferring desalinated water from the Persian Gulf to the interior. Regarding the scale, there have been a few cases of transferring water at the cross-provincial level, but mostly across two provinces. The approach has also changed in this plan. Long-term efforts to construct dams for electricity or farming with limited geographical impact have been shifted to desalinating water, long-distance pipe-laying, large-scale electric-powered water pumping, and long-distance underground tunnels. Stakeholders have become more diverse, from the council members of a small village to high-level decision makers at the national level. This paper elaborates on these extraordinary alterations by exemplifying a few other contemporary case studies of water transfer plans in Iran. It also examines the fundamental logic, bottlenecks, and future scenarios of this large-scale plan. [ABSTRACT FROM AUTHOR]

In today's world, climate change and its unprecedented impact on food and nutrition security have emerged as a significant and formidable global challenge. This study aimed to investigate the effect of adaptation strategies on the food security of small-scale farmers in Khorramabad city, Iran, using a survey-based analysis. To assess the food security level, the Household Food Security Survey Module (HFSSM) was employed. Additionally, Structural Equation Modeling was utilized to examine the relationship between adaptation dimensions and food security. The findings revealed that households without children exhibited a higher level of food security (48%) compared to households with children (6%). Moreover, the results demonstrated a positive and significant correlation between the food security of households, regardless of whether they had children, and the different adaptation components. Despite a satisfactory level of knowledge regarding climate change adaptation in the study area, the actual implementation of such measures remained low. While weed management, agricultural crop insurance, and access to meteorological information were identified as the top three strategies employed by farmers, the results suggested that sustainable-oriented operations and organic-oriented practices could serve as more effective strategies for both climate change resilience and enhancing food security status., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mohammad Reza Pakravan-Charvadeh reports a relationship with Lorestan University that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Introduction: Climate change may affect human health due to various mechanisms including overexposure to environmental pollution or dispersed particles. Lake Urmia (LU) drying in recent years has turned into a crisis with particle distribution as its main manifest. It is told that this crisis may affect the health of neighbouring residents. In this paper, we elaborate on a research protocol developed to assess the potential health consequences of LU drying (LUD) by investigating the distribution of physical and mental health conditions/disorders among residents of LU's surrounding provinces with different exposure levels to LUD outcomes., Methods and Analysis: The target population of this study is the residents of the LU basin from East Azerbaijan and West Azerbaijan provinces of Iran. A total of 803 households and 2783 people are studied in 86 clusters. Cluster sampling is applied. The weighting of the samples is based on a satellite map of the density of suspended particles such that people living in areas with higher risk have a higher chance of selection. Various types of measurements are done in three major groups including self-reported health, clinical interview/examination and paraclinical assays., Ethics and Dissemination: In this project, all procedures are in accordance with the ethical standards of the Ethics Committee of Tabriz University of Medical Sciences. Moreover, an informed consent letter is obtained from all participants included in the study. The results from this study will be disseminated in international journals and implemented in the primary care guidelines and national policy documents on managing the potential health consequences of LUD., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Cultural landscapes, particularly cities with rich historical and cultural heritage, play a crucial role in bolstering the resilience of local communities. The occurrence of climate change-induced phenomena jeopardizes cultural landscapes, resulting in the deterioration of historical structures, natural landscapes, cultural heritage, the economy, and the livelihoods of residents in these areas. Therefore, adopting a resilient approach is essential for the integrated management of cultural landscapes. This study develops a model for enhancing cultural landscape resilience to climate change in Nishapur, a historical and cultural city in Iran. Through desk studies, factors affecting the resilience of cultural landscapes to climate change were extracted in the context of developing countries. Subsequently, a model was developed based on the frequency of the occurrence of dimensions and indicators. Snowball sampling was used to distribute questionnaires to 310 members of the academic and professional communities in the field. Next, confirmatory factor analysis (CFA) was conducted using IBM SEM-AMOS to analyze the data and measure the reliability and validity of the model. The findings indicate that the driving factors of change, such as changes in livelihood and social issues, historical fabric and physical environment, natural hazards, biodiversity patterns, and management patterns, can significantly affect the resilience of cultural landscapes to climate change. The developed model can contribute to policymaking in various fields, including urban design and planning, economics, sociology, and cultural heritage conservation. This can play a vital role in creating cultural landscapes that are resilient to the increasing impacts of climate change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The article reports on the calculated values of columnar aerosol size distribution function for atmosphere of Zanjan, Iran. It mentions that the Zanjan city in northwest Iran frequently experiences dust storms wherein dust sources like the Tigris and Euphrates basin have a major role in the aerosol content of the region. It notes that aerosol particles affect the climate system wherein the scatter and absorb both solar and thermal radiations and change cloud characteristics in many ways.

To evaluate the long-term climate change impacts on groundwater fluctuations of the Ardabil plain, Iran, a groundwater level (GWL) modeling was proposed in this study. Accordingly, the outputs of Global Climate Models (GCMs) under the sixth report of Coupled Model Intercomparison Project (CMIP6) and future scenario of the Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5), were used as climate change forcing to the Machine learning (ML) models. The GCM data were first downscaled and projected for the future via Artificial Neural Networks (ANNs). Based on the results, compared to 2014 (the last year of the base period), the mean annual temperature may increase by 0.8 °C per decade until 2100. On the other hand, the mean precipitation may decrease by about 8% compared to the base period. Then, the centroid wells of clusters were modeled by Feedforward Neural Network (FFNN), examining different input combination sets to simulate both autoregressive and non-autoregressive models. Since each of the ML models can extract different kinds of information from a dataset, after finding the dominant input set via FFNN, GWL time series were modeled via various ML methods. The modeling results indicated that the ensemble of shallow ML models could lead to a 6% more accurate outcome than the individual shallow ML models, and 4% than the deep learning models. Also, the simulation results for future GWLs illustrated that temperature can impact groundwater oscillations directly, whereas precipitation may not have uniform impacts on the GWLs. The uncertainty evolving in the modeling process was quantified and observed to be in acceptable range. Modeling results showed that the main reason for the declining GWL in the Ardabil plain could be primarily linked to the excessive exploitation of the water table, while climate change impact could be also notable., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

In this paper, the effects of climate change and human activities on the groundwater level and the concentration of pollutants, such as total dissolved solids (TDS), chloride, and sodium, were investigated in the western part of the Varamin Plain. The groundwater flow and pollutant transport were simulated with the two models of MODFLOW and MT3D, respectively. To investigate the impacts of climate change, the two parameters of temperature and precipitation were downscaled under the three scenarios of RCP 2.6, RCP 4.5, and RCP 8.5. Four scenarios, including the current condition (Scenario 0), a 25% increase in the extraction from pumping wells (Scenario 1), the climate change effects (Scenario 2), and an increase in the incoming effluent (TDS) to the Shoor River due to industrial activities (Scenario 3), were investigated for a future period of 30 years (2025–2055). The results show that the highest groundwater decline and chloride and sodium concentrations occur under Scenario 1, while Scenario 3 leads to the maximum TDS concentration (milligrams per liter). In Scenario 1, the average and maximum groundwater decline at the end of the simulation period will be 2.5 m and 7.3 m, and the chloride and sodium concentrations will increase by 7 and 5 milligrams per liter, respectively. [ABSTRACT FROM AUTHOR]

Growing agricultural and domestic demand exacerbates water shortages globally, creating substantial environmental challenges, especially in lakes and wetlands ecosystems. This paper presents a stationary policy to restore and sustain the water level in natural lakes using a new version of hedging rule that accounts for time-dependent environmental demand and the water allocation to the agricultural and municipal sectors under different climate change projections. The proposed framework is demonstrated via operational policy derived to rehabilitate Lake Urmia in Iran. To simplify the modeling process, all reservoirs in the basin are replaced with an equivalent reservoir (ER) to allocate the available water between potential users. The ER is then operated using the set of hedging rules derived for staged restoration and sustainability of the lake level while meeting other stakeholder objectives. A system dynamics-based model is used to simulate the multi-sectoral system of the basin while using a built-in optimization algorithm to develop the most desirable multi-period operational policy. The lake-level condition is investigated by producing lake-level duration curves, while the reservoir performance indices (reliability, resiliency, and vulnerability) are used for assessing water supply in the basin. The results indicate that the proposed framework is highly effective in restoring lake level while meeting agricultural and municipal water demand in the basin. The proposed model provides a stationary policy for the lake restoration accounting for the dynamic variation of the lake level and fluctuations of the reservoirs inflow due to climate variability and change. [ABSTRACT FROM AUTHOR]

Agricultural sectors worldwide are under direct threat from climate change conditions. In Iran, agricultural production has decreased due to droughts originating in an increase in annual maximum temperatures—with the corresponding increase in crop respiration and evapotranspiration—and a decrease in accumulated precipitation. Based on a static computable general equilibrium approach, this paper reports implication for the Iranian economy from the effects of climate change on agriculture––as modeled through three scenarios relying on assumptions about the magnitude of continued reduction in total agricultural production. Reductions of 6%, 12%, and 18% in total agricultural production reasonably cover the range of impacts that climate change is expected to impose on the Iranian agricultural sector––under the assumption that no behavioral adaptations or policy interventions are in place. Our simulations suggest that effects on the Iranian economy imply a reduction in GDP ranging between 3.7 and 6.3%. In addition, 5–17% of labor moves away from the agriculture sector––this labor relocation occurs due to declining agriculture incomes. Findings illustrate that climate change will reduce households' consumption and income in all economic sectors, particularly among rural households. We suggest that policies in Iran should focus on improving cultivation methods to save water resources and alleviate the expected effects of climate change. The current study's outcomes are helpful for policymakers, especially in countries with water scarcity. [ABSTRACT FROM AUTHOR]

Holding a lasting balance between the water resources and water demands has become a challenging task for water resources managers, especially in recent years with the looming global warming crisis and its resulting climatic change effects. This paper focuses on modeling the optimized operation of the Zayandehrud Reservoir, located in west-central Iran, under two fifth-generation climate change scenarios called RCP4.5 and RCP8.5. A novel variant of the gravitational search algorithm (GSA), named the adaptive accelerated GSA (AAGSA) is proposed and adopted as the optimizer of the reservoir operation in this paper. The major advancement of the AAGSA against the original GSA is its high exploration capability, allowing the proposal to effectively tackle a variety of difficulties any complex optimization problem can face. The goal of the optimization process is the maximization of the sustainability of supplying the downstream water demands by the reservoir. The optimal results obtained by the original GSA and the proposed AAGSA algorithms suggest that the AAGSA can achieve much more accurate results with much less computational runtime, such that the proposed AAGSA is able to achieve the reservoir operation sustainability index of 98.53% and 99.46%, under RCP4.5 and RCP8.5 scenarios, respectively. These figures are higher than those obtained by the original GSA by 23.5% and 16% under RCP4.5 and RCP8.5, respectively, while the runtime of the proposal is reduced by over 80% in both scenarios, as compared to the GSA, suggesting the high competence of the proposed AAGSA to solve such a high-dimensional and complex real-world engineering problem. [ABSTRACT FROM AUTHOR]

Due to the severe effects of climate change on the agricultural sector, urgent action is required on the part of farmers and is, indeed, critical to reducing climate change impacts. However, reports globally revealed farmers' engagement in climate change adaptation is still insufficient, ambivalent, and inconsistent and farmers do not consider adaptation to be urgent. Researchers have argued that this issue is rooted in psychological biases beside other factors. Therefore, the aim of this study is to evaluate how psychological distance determines climate change beliefs, risk perception and adaptation strategies among Iranian farmers. A cross-sectional paper-based survey was conducted in the Dasht-e Azadegan county of Khuzestan province in southwest Iran. The study sample consisted of 250 farmers selected through a multi-stage random sampling process. An expert panel review and a pilot study were conducted to confirm convergent validity and reliability of the scales. The results confirm that all four dimensions of psychological distance influence water management adaptation strategies and non-farm activities. Moreover, all psychological dimensions, except the temporal dimension, affect adaptation in farming management. Thus, making climate change more proximal to decision makers could be a strategic way of encouraging individuals to take adaptive actions. This study emphasizes that concepts of psychological distance can be applied to help organizations (e.g., agriculture extension services) to understand farmers' risk perceptions and responses to climate change impacts and improve risk communication to better engage farmers in climate action. [ABSTRACT FROM AUTHOR]

For an effective reservoir operation during drought, the variations of both water supply and water demand which depend on hydrological and meteorological conditions need to be dealt with. This paper aimed to consider these variations in the Aharchay basin (Iran) by coupling a hedging rule (HR)-based reservoir operation model (HRROM) with a climate-based irrigation scheduling model (CBISM) at the farm level. Through the HRROM, optimal long-term decisions for Sattarkhan reservoir were made by considering the probable streamflow scenarios in the system. Given the variable agricultural demands (VAD) in the CBISM, the irrigation water was optimally allocated to the crops using several evapotranspiration (ET) scenarios. The CBISM employs three sub-models including linear programming (LP), nonlinear programming (NLP), and particle swarm optimization (PSO) to maximize the total income of the Aharchay agricultural network as a function of the climate factors and the supplied water. To this end, the daily weather and discharge data from 1990 to 2015 were used in this study. The standardized precipitation-evapotranspiration index (SPEI) and the streamflow drought index (SDI) were used to detect the meteorological and hydrological droughts, respectively. The SPEI was calculated based on the high-resolution-gridded datasets of the Climatic Research Unit (CRU). The findings demonstrated that the HRROM-CBISM generally managed to increase the time-based (αt) and volume-based (αv) reliability indices by 20% and 44%, respectively, compared with the conventional standard operation policy (SOP). For more investigations, the three major droughts of 2000–2002, 2004–2006, and 2008–2014 were separately analyzed. The average values of αt, αv, and vulnerability (V) for SOP were 0.33, 0.51, and 0.48, respectively. With the HRROM-CBISM, these values were about 0.5, 0.55, and 0.45, respectively. Among these indices, αt had the highest variations, while αv had the lowest variations in both the SOP and HRROM-CBISM approaches. The average water shortage for the mentioned droughts was significantly decreased from 89 (SOP) to 75 MCM (HRROM-CBISM). [ABSTRACT FROM AUTHOR]

The distribution of stations network is not uniform in Iran. The number of stations is more in densely populated areas and less around a large part of the central areas of Iran. Also, these stations have been operating for various periods. Therefore, in situ observation cannot lead to robust results and conclusions for the climate model evaluation and climate change studies. In recent years, reanalysis products have been considered as pseudo-observations, especially for climate studies and weather forecasting. The evaluation of reanalysis products is fundamental and critical because of the uncertainty due to the interpolation and to the data assimilation and forecasting models. In this paper, the reanalysis data of CFS-v2, MERRA-2, and ERA-5 climate databases are evaluated, using the data of 143 meteorological stations over the period 1991-2020. Reanalysis data are compared using Pearson's correlation coefficient (R), root mean square error (RMSE), normalized mean square error (NRMSE), Nash-Sutcliffe efficiency coefficient (NSC), bias index (BIAS), mean absolute error (MAE) and Kling-Gupta efficient (KGE), in the 6 regions representative of climate diversity in Iran. Also, the seasonal accuracy, annual trend, and probability distribution of the reanalyzed data were analyzed in comparison with the observations. The results show that CFS-v2, MERRA-2, and ERA-5 have high accuracy for estimating the temperature in Iran. In general, based on all statistical indicators and in all regions, ERA-5 is the most accurate; MERRA-2 and CFS-v2 were ranked second and third, with MAE values of 1.39, 1.71, and 2.077, respectively. In the spring, for the hot cluster (southwest), CFS-v2 data; in the summer, for the hot (southwest) and very cold (northwest) clusters, MERAA-2 data; and in autumn and winter, for the warm coastal cluster (coasts of Oman Sea and Persian Gulf), MERAA-2 data; are most accurate. In terms of annual trends, in the very cold cluster (northwest), CFS-v2 data; in the hot cluster (southwest), MERRA-2 data; and in other places, the data of ERA-5 have the closest trend to the trend of observations. [ABSTRACT FROM AUTHOR]

An increase in energy demand and consumption is one of the significant challenges of the world community. Global climate change and temperature rise can significantly affect energy demand, especially in the building sector. Green passive design strategies (GPDS) such as green roof and green wall are considered a passive energy-saving technology which can deal with further climate change in near future. This paper compares the energy demand and CO2 emissions of a building with different structural scenarios during the current (2000–2019) and future climatic conditions (the 2050s) in two hot-dry (Kerman) and hot-humid (BandarAbbas) climate samples in Iran. The base case, green roof, and green wall modeling of the selected building have been developed by DesignBuilder software. Results revealed that 61% of the annual energy consumption of Kerman is related to the heating sector, while it will be changed to 47% under the effect of climate change and based on RCP2.6. However, 99% of the annual energy consumption of BandarAbbas belongs to cooling demand and it will not change by 2050s. Also, the maximum heating and cooling energy demand were calculated for the base building. Based on the results, green wall has more efficiency in optimizing total energy consumption compared to green roof in both climate types. On the other hand, GPDS are more efficient to optimize heating energy demand in comparison with cooling energy demand. Furthermore, the green wall strategy has better performance in reducing CO2 emissions as well. Accordingly, CO2 emissions reduce in Kerman by 2.73% and 2.93% by the implementation of the green wall during the observation period and 2050s, respectively. Meanwhile, this strategy can reduce CO2 emissions by only 1% per year in BandarAbbas during all studied periods. [ABSTRACT FROM AUTHOR]

Identifying agricultural disaster risk regions before the occurrence of climate-related disasters is critical for early mitigation planning. This paper aims to identify these regions based on data from the Food and Agriculture Organization of the United Nations (FAO), the bilateral and multilateral trade network data of the World Integrated Trade Solution(WITS) and the agent-based economic model Acclimate. By applying a uniform forcing across agricultural sectors of some breadbasket regions (US, EU and China), when single and simultaneous extreme weather events occur, such as the 2018 European heatwave, production and consumption value losses and gains are calculated at regional and global levels. Comparing the FAO data sets, WITS, and Acclimate's production value losses, the results show a strong dependence of agricultural production losses on a region's output and connectivity level in the global supply and trade network. While India, Brazil, Russia, Canada, Australia, and Iran are highly vulnerable, the imposition of export restrictions to compensate for demand shortfalls makes Sub-Saharan Africa the most vulnerable region, as it is heavily dependent on agricultural imports. In addition, simultaneous extreme weather events can exacerbate the loss of value of agricultural production relative to single extreme weather events. Agricultural practices to increase production such as smart farming, increased investment in plantation agriculture, and diversification of trading partners can help mitigate future food security risks in Sub-Saharan Africa and other agricultural import-dependent regions., Competing Interests: The authors have declared that no competing interests exist.

Under-five years old acute watery diarrhea (U5AWD) accounts for most diarrheal diseases' burden, but little is known about the adjusted effect of meteorological and socioeconomic determinants. A dataset containing the seasonal numbers of U5AWD cases at the district level of Iran is collected through MOHME. Accordingly, the district-level standardized incidence ratio and Moran's I values are calculated to detect the significant clusters of U5AWD over sixteen seasons from 2014 to 2018. Additionally, the author tested twelve Bayesian hierarchical models in order to determine which one was the most accurate at forecasting seasonal number of incidents. Iran features a number of U5AWD hotspots, particularly in the southeast. An extended spatiotemporal model with seasonally varying coefficients and space–time interaction outperformed other models, and so became the paper's proposal in modeling U5AWD. Temperature demonstrated a global positive connection with seasonal U5AWD in districts (IRR: 1.0497; 95% CrI: 1.0254–1.0748), owing to its varying effects during the winter ((IRR: 1.0877; 95% CrI: 1.0408–1.1375) and fall (IRR: 1.0866; 95% CrI: 1.0405–1.1357) seasons. Also, elevation (IRR: 0.9997; 95% CrI: 0.9996–0.9998), piped drinking water (IRR: 0.9948; 95% CrI: 0.9933–0.9964), public sewerage network (IRR: 0.9965; 95% CrI: 0.9938–0.9992), years of schooling (IRR: 0.9649; 95% CrI: 0.944–0.9862), infrastructure-to-household size ratio (IRR: 0.9903; 95% CrI: 0.986–0.9946), wealth index (IRR: 0.9502; 95% CrI: 0.9231–0.9781), and urbanization (IRR: 0.9919; 95% CrI: 0.9893–0.9944) of districts were negatively associated with seasonal U5AWD incidence. Strategically, developing geoinformation alarm systems based on meteorological data might help predict U5AWD high-risk areas. The study also anticipates increased rates of U5AWD in districts with poor sanitation and socioeconomic level. Therefore, governments should take appropriate preventative actions in these sectors. [ABSTRACT FROM AUTHOR]

Water and water management are no longer related to environmental studies. There is a very strong link between water and a state's security, as well as its survival and development. A decline in water availability may lead to a collapse or even the extinction of a whole civilization. Sometimes referred to as a "water‐bankrupt" state, Iran is a case that underlines the close relation between water and security, from the perspective of survival and opportunities for development. Without a doubt, the Islamic Republic of Iran is among those countries that are the most affected by climate change and low water levels. The main goal of this paper is to analyze challenges related to water in Iran, including the political, security, economic, and social dimensions. [ABSTRACT FROM AUTHOR]

Integrated water planning and management face multiple challenges, among which are the competing interests of several water-using sectors and changing climatic trends. This paper presents integrated and non-integrated climate-environment-water approaches for reservoir operation, illustrated with Karkhe reservoir, Iran. Reservoir operation objectives are meeting municipal, environmental, and agricultural water demands. Results show the integrated approach, which relies on multi-objective optimization of municipal, environmental, and agricultural water supply, improves the municipal, environmental, and agricultural objectives by 70, 32, and 65% compared with the objectives' values achieved with the non-integrated approach, which implements a standard operating policy. [ABSTRACT FROM AUTHOR]

Indigenous knowledge about climate change that makes adaptability necessary by coping strategies leads to the sustainability of nomadic livelihoods. This paper proposed a new adaptability approach, an association coping strategy (ACS), which is established based on the association rulesʼ recommendations. The adaptability was empirically analyzed using 12 coping strategies used by Kashkooli nomads in Sepidan plain, Fars province, Iran. Required data were gathered both in the form of time series from November 1988 to February 2020 and cross section during June–July 2019. Household perceptions of climatic and environmental variables, supported by meteorological data, revealed that climate change was negatively impacting the Kashkooli nomadic livelihood in Sepidan plain. The findings also indicated that taking simultaneous coping strategies resulted in selling livestock surplus, saving water consumption, livestock insurance, rangeland regeneration schemes, and immigration to more suitable rangelands. Therefore, Kashkooli nomadic livelihood survived climate changes using ACSs. The association rules were useful for recommending anticipatory coping strategies that were adopted based on indigenous knowledge about climate change by nomads to establish appropriate adaptability. [ABSTRACT FROM AUTHOR]

Snakebite envenoming is an important public health problem in Iran, despite its risk not being quantified. This study aims to use venomous snakes' habitat suitability as an indicator of snakebite risk, to identify high-priority areas for snakebite management across the country. Thus, an ensemble approach using five distribution modelling methods: Generalized Boosted Models, Generalized Additive Models, Maximum Entropy Modelling, Generalized Linear Models, and Random Forest was applied to produce a spatial snakebite risk model for Iran. To achieve this, four venomous snakes' habitat suitability (Macrovipera lebetinus, Echis carinatus, Pseudocerastes persicus and Naja oxiana) were modelled and then multiplied. These medically important snakes are responsible for the most snakebite incidents in Iran. Multiplying habitat suitability models of the four snakes showed that the northeast of Iran (west of Khorasan-e-Razavi province) has the highest snakebite risk in the country. In addition, villages that were at risk of envenoming from the four snakes were identified. Results revealed that 51,112 villages are at risk of envenoming from M. lebetinus, 30,339 from E. carinatus, 51,657 from P. persicus and 12,124 from N. oxiana. Precipitation seasonality was identified as the most important variable influencing distribution of the P. persicus, E. carinatus and M. lebetinus in Iran. Precipitation of the driest quarter was the most important predictor of suitable habitats of the N. oxiana. Since climatic variables play an important role in shaping the distribution of the four venomous snakes in Iran, thus their distribution may alter with changing climate. This paper demonstrates application of species distribution modelling in public health research and identified potential snakebite risk areas in Iran by using venomous snakes' habitat suitability models as an indicating factor. Results of this study can be used in snakebite and human-snake conflict management in Iran. We recommend increasing public awareness of snakebite envenoming and education of local people in areas which identified with the highest snakebite risk.

Integrated water planning and management face multiple challenges, among which are the competing interests of several water-using sectors and changing climatic trends. This paper presents integrated and non-integrated climate-environment-water approaches for reservoir operation, illustrated with Karkhe reservoir, Iran. Reservoir operation objectives are meeting municipal, environmental, and agricultural water demands. Results show the integrated approach, which relies on multi-objective optimization of municipal, environmental, and agricultural water supply, improves the municipal, environmental, and agricultural objectives by 70, 32, and 65% compared with the objectives' values achieved with the non-integrated approach, which implements a standard operating policy.

Changes in amount and frequency of runoff are caused by interaction of the climatic and anthropogenic factors in human-nature system. Those part of the runoff variations caused by anthropogenic loadings are more manageable compared with climate effects occurring over a long period. In this paper, we propose a framework to decompose the effect of anthropogenic and climatic time series on streamflow. To fulfill this task, anthropogenic time series attribution (ATSA) method is introduced. ATSA employs the output of climate elasticity (CE) method to extract human-affected time series and naturalizes the annual streamflow. In other words, ATSA makes it possible to exploit CE output in time series analysis. Furthermore, temporal downscaling process performed by a hybrid discrete wavelet transform and artificial neural network (DWT-ANN) enhances the temporal resolution of ATSA. We applied ATSA framework on Urmia Lake (northwest of Iran) inflow over the 1972–2010 period. CE application indicated that the contribution of climatic and anthropogenic factors in reduction of the mean inflow to the lake is about 60 and 40%, respectively. ATSA also revealed how human exploitation affected the time series of the lake inflow. Roughly, 14,000 MCM of the lake inflow reduction was attributed to the anthropogenic impacts between 1995 and 2010 such that the removal of anthropogenic loading could have led to rising water level up to 1275.5 meters above the sea level (i.e., 1.4 m above the ecological level) in 2010. Furthermore, the effect of de-trended inflow time series was investigated via DWT-ANN. Trend-free pre-whitening Mann-Kendall (TFPW-MK) test indicated significant increase in temperature, potential evapotranspiration, population, reservoir capacity, irrigated area, and the number of wells, over 39 years of the study period. [ABSTRACT FROM AUTHOR]

Current rainfall Intensity-Duration-Frequency (IDFs) curves generally were developed based on stationary extreme value assumption. However, climate change is expected to alter extreme rainfalls and invalidate the stationary assumption. So, it is crucial to develop future rainfall IDFs taking into account the impacts of climate change. Difficulty in preparing reliable rainfall time series with fine time step and capturing extremes for future climate scenarios has led to limited studies aimed at investigating the change of IDF curves due to climate change. In this paper, a robust stochastic rainfall model (NSRP) is employed to produce future rainfall IDFs for five stations across Karkheh and Karun basins in Iran. NSRP can generate long-term realistic rainfall series containing extremes. Moreover, it applies changes in different rainfall statistical characteristics, projected by GCMs, in the downscaling procedure. For each station, the model was calibrated using observed rainfalls series. Then, 3000-year daily rainfall series were generated, and historical IDFs were developed. Consequently, NSRP parameters were perturbed based on GCM projections. Then, 3000-year future rainfall series were generated, and future IDFs were developed. The climate change uncertainties were represented by employing two GCM (CanESM2 and HadGem2) and three emission scenarios (RCP2.6, RCP4.5, and RCP8.5). The results showed NSRP is able to reproduce observed extreme rainfalls in a wide range of time scales. Also, climate change will lead to a considerable increase in future extreme rainfall intensity in the study basins. As averages of all considered scenarios, rainfall intensities will increase between 22 and 206% in the future. [ABSTRACT FROM AUTHOR]

Amaranthus retroflexus L. and Chenopodium album L. are noxious weeds that have a cosmopolitan distribution. These species successfully invade and are adapted to a wide variety of diverse climates. In this paper, we evaluated the morphology and biochemistry of 16 populations of A. retroflexus L. and 17 populations of C. album L. Seeds from populations collected from Spain, France, and Iran were grown together at the experimental field of the agriculture research of University of Mohaghegh Ardabili, and a suite of morphological traits and biochemical traits were assessed. Among the populations of A. retroflexus L. and of C. album L. were observed significant differences for all the measured traits. The number of branches (BN) for A. retroflexus L. (12.22) and inflorescence length (FL; 14.34) for C. album L. were the two characteristics that exhibited the maximum coefficient of variation. Principal component analysis of these data identified four principal components for each species that explained 83.54 (A. retroflexus L.) and 88.98 (C. album L.) of the total variation. A dendrogram based on unweighted neighbor-joining method clustered all the A. retroflexus L. and C. album L. into two main clusters and four sub-clusters. Canonical correlation analysis (CCA) was used to evaluate relationships between climate classification of origin and traits. Similarly, the measured characteristics did not group along Köppen climate classification. Both analyses support the conclusion that A. retroflexus L. and C. album L. exhibit high levels of diversity despite similar environmental histories. Both species also exhibit a high diversity of the measured biochemical compounds indicating that they exhibit different metabolic profiles even when grown concurrently and sympatrically. Several of the biochemical constituents identified in our study could serve as effective indices for indirect selection of stresses resistance/tolerance of A. retroflexus L. and C. album L. The diversity of the morphological and biochemical traits observed among these populations illustrates how the unique selection pressures faced by each population can alter the biology of these plants. This understanding provides new insights to how these invasive plant species successfully colonize diverse ecosystems and suggests methods for their management under novel and changing environmental conditions. [ABSTRACT FROM AUTHOR]

Background: During the past 20 years, Iran has been experiencing a significant increase in the occurrence of disasters mainly due to the emergence of anthropogenic climate change. This paper aims at analyzing the trend of national budget allocation in Iran over the last 100 years to evaluate the focus of the Iranian state on the four phases of Preparedness, Mitigation, Response, and Recovery and propose modifications. Methods: It is used a critical policy analysis with what's the problem represented approach. In this approach is focused on problematization and policy gaps. The most important policy statement in any government is the budget. During the first screening, 1028 regulations and laws were found from 1910 to 2020. After full text screening, 494 regulations and laws related to budget allocation to disasters were analyzed. Results: The Iranian government has spent around 29 billion USD on disasters during the last 100 years. Droughts, earthquake and flood have costs the government more than other disasters, accounting for more than 14, 6.9, and 6.1 billion USD, respectively, in the allocated budget. Most of the Iranian government expenditure during the last 100 years on various disasters such as drought, flood, earthquake, and COVID-19 has been spent on involuntary costs including Response and Recovery. Mitigation and Preparedness are the two critical disaster management phases with very small shares of national budgeting. Conclusions: From policy audit and policy gaps it is concluded that Iranian governments during last 100 years, problematized the issue of "disasters strike" and not "disasters' risks". In time of disasters, governments tried to solve the issues or impacts of disasters with budgeting to response and recovery. Nevertheless, disasters' prevention or mitigation or preparedness was not a problem for Iranian governments from 1920 to 2020. [ABSTRACT FROM AUTHOR]

This paper analyzes climate variability and change in the Urmia Lake Basin, northwest of Iran. Annual average of the following data time series has been analyzed by statistical methods: dry bulb temperature, maximum and minimum temperature, precipitation, and number of rainy and snowy days. We have also used mean monthly temperature and precipitation data for analysis of drought spells for the period 1964-2005 to find out whether fluctuations in the lake level are attributable to natural drought. Our results indicate that mean precipitation has decreased by 9.2 % and the average maximum temperature has increased by 0.8°C over these four decades. The seasonal changes are particularly visible in winter and spring. Results of the Palmer Drought Severity Index show that on average, drought episodes have hit the Urmia Lake Basin every 5 years and most of them reached severe levels, but recent droughts have become more intense and last longer. [ABSTRACT FROM AUTHOR]

Regulating ecosystem services provided by urban forests are of great importance for the quality of life among city dwellers. To reach a maximum contribution to well-being in cities, the urban regulating ecosystem services (URES) must match with the demands in terms of space and time. If we understand the matches or mismatches between the current urban dwellers' desired quality conditions (demand) and the supply of URES by urban forests (UF) in the cities, this will facilitate integrating the concepts of ecosystem services in urban planning and management, but such an assessment has suffered from major knowledge limitations. Since it is complex and problematic to identify the direct demands for URES and the spatiotemporal patterns therein, improving the demand indicators can help to determine the actual requirements. In this paper, a methodological approach based on indicators is presented and demonstrated for two important URES: air quality improvement and global climate change mitigation provided by urban trees and shrubs. Four air quality standards and greenhouse gas reduction targets were used and compared to supplies of the URES in Tabriz, Iran. Our results show that the mean contribution of the URES supply to air quality standards and greenhouse gas reduction targets is modest. Hence, in Tabriz, there is a strong mismatch between demand and supply. Mismatches at the city scale will have to be reduced by both a reduction in pollutant emissions and an increased provisioning of URES supply through urban greenery. The presented assessment approach and the results for Tabriz make it explicit how different the demands and supplies of the two studied URES are, and we expect similar mismatches in many other cities. Therefore, our approach, relatively simple but still realistic and easy-to-apply, can raise awareness about, and the utility of, the ecosystem services concepts for urban planning and policymaking., Competing Interests: The authors have declared that no competing interests exist.

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Due to the spatial-temporal inadequacy of large-scale general circulation models (GCMs), linking large-scale GCM data with small-scale local climatic data has found great interest. In this paper, in order to downscale minimum and maximum temperatures and precipitation predictands, the performance of three statistical downscaling techniques including Long Ashton Research Station-Weather Generator (LARS-WG), statistical downscaling model (SDSM), and artificial neural network (ANN) was compared based on Intergovernmental Panel on Climate Change-Fifth Assessment Report (IPCC-AR5) in northwest Iran. For this purpose, a nonparametric test named Mann-Whitney test, Spearman correlation coefficient, and the root mean square error (RMSE) were utilized to assess the efficiency of downscaling models. To scrutinize the climate change impacts, periods of 1961–1990 and 1991–2005 were considered as the baseline and verification periods, respectively. The findings revealed the superior performance of the ANN model for minimum and maximum temperatures, while for precipitation predictand, the SDSM represented the best performance among the models. Simulation results for future temperature indicated an ascending trend as 0.1–1.3 °C, 0.3–1.7 °C, and 0.5–2.1 °C for LARS-WG, SDSM, and ANN techniques, respectively. On the other hand, simulation outputs for the precipitation indicated a descending trend of 10–30% in future precipitation of the region according to downscaling models under Representative Concentration Pathway 8.5 (RCP8.5) pessimistic scenario of Hadley Center Coupled Model version 3 (HadCM3) GCM model. [ABSTRACT FROM AUTHOR]

It is impractical to implement conservation efforts for all species due to complexity of natural systems, large scale of biodiversity issues, and budget limitations. Prioritizing species of conservation importance can alleviate this issue. Multiple interrelated criteria may be used for conservation prioritization of species. Therefore, the accurate evaluation of criteria is a multi-criteria decision-making problem. In the current paper, which is a complementary study to our previous work on identifying landscape species in central Iran, we aimed to provide more details about the integrated DEMATEL Analytical Network Process (DANP) for evaluating the criteria used for the process of selecting landscape species. The impact-relation map implied that home range size had the greatest effect on other criteria and the feasibility of monitoring received the most impact from other criteria. Also, conflict with human, vulnerability to climate change, and extent of occurrence have the highest priorities for selecting landscape species. Based on our findings, multi-criteria decision making systems effectively evaluate the interrelationships between the criteria affecting the selection process of species and rank the criteria according to their impacts. This framework can be used as a guideline for prioritizing species to increase the efficiency of site-based conservation efforts. [ABSTRACT FROM AUTHOR]

This paper addresses the genetic resources, domestication and breeding history of spinach as a comprehensive review of these crop aspects is currently unavailable. It is shown that the availability of genetic resources of wild relatives belonging to the primary gene pool is currently very limited, which hampers breeding and research activities. Therefore, new collecting expeditions are clearly warranted. The domestication of spinach is discussed on the basis of its presumed migration routes and the traits that were probably involved in the domestication syndrome. Spinach is thought to have domesticated in former Persia. Migration then occurred eastwards to China and westwards to Europe, but additional genetic data are needed to reveal the most likely migration routes. Morphological changes in pistillate flowers and loss of dormancy are identified as the main traits involved in the domestication syndrome of spinach. To a large extent we could re-construct the relationships between spinach cultivars that were developed until the 1950s, but this appeared difficult for the more recent cultivars due to intellectual property protection by breeding companies. Resistance against downy mildew has been the main breeding target in spinach. The introgression of NBS-LRR resistance genes from wild relatives is the major strategy to develop downy mildew resistant cultivars. However, the use of loss-of-function alleles of susceptibility genes may provide a more durable strategy to develop resistant cultivars. So far, abiotic resistance and quality traits have received minor attention in spinach research and breeding. This is expected to change considering the potential effects of climate change on these traits. [ABSTRACT FROM AUTHOR]

In this paper, trends of minimum and maximum temperatures in Iran were studied using time series of daily minimum and maximum temperatures of 45 meteorological stations from 1976 to 2005 (as the baseline period). Mann-Kendall test, for maximum and minimum temperature, was obtained 1.85 and 3.56, respectively, which was positive and significant. The slope of the trend line for maximum and minimum temperature was obtained 0.23 and 0.39 °C decade−1, respectively. In this study, the trend of extreme temperature indicators was also evaluated. According to the obtained results, in annual terms, TX10, FDO, TN10, and IDO indices have had a negative trend at most stations, but TX90, TR20, TNx, TNx, TXn, TN90, SDI, and SU25 indices showed a positive trend. In the seasonal scale, the indices of cold days (TX10) and cold nights (TN10) showed significant negative trends in most seasons. Warm days (TX90) and warm nights (TN90) showed significant positive trends at most stations. The results of future simulations using several general circulation models in different time periods showed that the highest increase in maximum and minimum temperature related to the RCP8.5 scenario in periods of 2071 to 2099. The results also showed that northwest of Iran would have the highest temperature rise. The results also showed that the probability density function of the minimum and maximum temperatures will shift to warmer temperatures. This could be an indication of climate change in the future decades in Iran. [ABSTRACT FROM AUTHOR]

In recent years, climate change as a consequence of global warming, has garnered significant attention. Therefore, serious measures must be considered in the development planning process to adapt to and reduce effects of climate change. This research evaluates six development plans in Isfahan, Iran in the face of climate change effects using a framework consisting of three components of the analytical Awareness-Analysis-Action (A-A-A) approach and using quantitative and qualitative content analysis methods. In fact, the evaluation of the level of attention of the development plans from the perspective of the level of reflection and attention to raising awareness about the issues of climate change, the ability to analyze the available climate information in a useful way to support planning decisions and the level of special actions to deal with these changes. Finally, out of the three elements of the approach, the highest amount of attention in the plans is related to the element of action with 67/70% and the lowest is related to the element of awareness with 39/10%. The overall evaluation of the development plans shows that their role in integrating climate change issues into urban planning in Iran is still limited and insufficient. On the other hand, air pollution and climate change are closely related, as chemicals that cause air quality deterioration often enter together with greenhouse gases. Therefore, it can be said that although indicators of the transportation dimension (A3-J) with 80.66%, resource management have been given special attention under the action component, but carbon sequestration dimension (A2-F) with 14.63%, under the analysis component that seeks to evaluate the amount of air pollutants, classification of the type and sources of emission of pollutants could not reflect more than half of the total percentage in all development plans. [ABSTRACT FROM AUTHOR]

In recent decades, the depletion of surface water resources within the Lake Urmia Basin (LUB), Iran, has emerged as a significant environmental concern. Both anthropogenic activities and climate change have influenced the availability and distribution of surface water resources in this area. This research endeavors to provide a comprehensive evaluation of the impacts of climate change and anthropogenic activities on surface water resources across the LUB. Various critical climatic and anthropogenic factors affecting surface water bodies, such as air temperature (AT), cropland (CL), potential evapotranspiration (PET), snow cover, precipitation, built-up areas, and groundwater salinity, were analyzed from 2000 to 2021 using the Google Earth Engine (GEE) cloud platform. The JRC-Global surface water mapping layers V1.4, with a spatial resolution of 30 m, were employed to monitor surface water patterns. Additionally, the Mann–Kendall (MK) non-parametric trend test was utilized to identify statistically significant trends in the time series data. The results reveal negative correlations of −0.56, −0.89, −0.09, −0.99, and −0.79 between AT, CL, snow cover, built-up areas, and groundwater salinity with surface water resources, respectively. Conversely, positive correlations of 0.07 and 0.12 were observed between precipitation and PET and surface water resources, respectively. Notably, the findings indicate that approximately 40% of the surface water bodies in the LUB have remained permanent over the past four decades. However, there has been a loss of around 30% of permanent water resources, transitioning into seasonal water bodies, which now account for nearly 13% of the total. The results of our research also indicate that December and January are the months with the most water presence over the LUB from 1984 to 2021. This is because these months align with winter in the LUB, during which there is no water consumption for the agriculture sector. The driest months in the study area are August, September, and October, with the presence of water almost at zero percent. These months coincide with the summer and autumn seasons in the study area. In summary, the results underscore the significant impact of human activities on surface water resources compared to climatic variables. [ABSTRACT FROM AUTHOR]

Lake Urmia, in northwest Iran, is the second most saline lake in the world. During the past two decades, the level of water has markedly decreased. In this paper, climate of the lake region is investigated by using data from four meteorological stations near the lake. The data include climatic parameters such as temperature, precipitation, humidity, wind speed, sunshine hours, number of rain days, and evaporation. Climate around the lake is examined by way of climate classification in the periods before and after the reduction in water level. Rainfall in the lake catchment is also evaluated using both gauge and satellite data. The results show a significant decreasing trend in mean annual precipitation and wind speed and an increasing trend in annual average temperature and sunshine hours at the four stations. Precipitation and wind speed have decreased by 37 mm and 2.7 m/s, respectively, and the mean annual temperature and sunshine hours have increased by 1.4 °C and 41.6 days, respectively, over these six decades. Only the climate of the Tabriz region is seen to have significantly changed, going from semiarid to arid. Gauge records and satellite data show a large-scale decreasing trend in rainfall since 1995. The correlation between rainfall and year-to-year changes in lake level is 0.69 over the period 1965 to 2010. The relationship is particularly strong from the early 1990s to 2005. This suggests that precipitation has played an important role in the documented decline of the lake. [ABSTRACT FROM AUTHOR]

Tamarisk witches' broom, yellowing, and little leaf symptoms were observed during 2018–2023 surveys of rural deserts in central regions of Iran with the highest disease incidence up to 72% in Chah Afzal (Yazd province). A verification of the presence and identity of phytoplasmas associated with these symptoms was then performed. Tamarisk tree branch cuttings obtained from symptomatic plants sprouted up to 90.3% but with 15–25 days' delay compared to the asymptomatic ones and showed internode shortening and witches' broom, while the branch cuttings from asymptomatic plants had normal growth and sprouted up to 97.8%. Phytoplasma transmission by dodder bridges to periwinkle did not succeed, while nested polymerase chain reaction on the phytoplasma ribosomal gene followed by RFLP and phylogenetic analyses revealed the presence of 'Candidatus Phytoplasma asteris', 'Ca. P. australasiae=australasiaticum', and 'Ca. P. trifolii' (ribosomal subgroups 16SrI-B, 16SrII-D, and 16SrVI-A, respectively) in the samples from symptomatic plants only. Further amplifications were performed on selected phytoplasma-positive samples on tuf and secA genes, and the produced sequences indicated the presence of mixed phytoplasma infection in some of the samples. In particular, in the tuf gene, a mixed infection of 'Ca. P. australasiae=australasiaticum' and 'Ca. P. trifolii' was detected, while in the secA gene, the presence of 'Ca. P. asteris' or 'Ca. P. tritici' strains was identified. The first-time detection of diverse phytoplasma strains in symptomatic T. aphylla suggests that this species represent a relevant source of infection for the agricultural crops and for landscape plants especially when temperature allows insect vector transmission, and therefore, it represents a risk in every environment especially in the frame of climatic changes. [ABSTRACT FROM AUTHOR]

The composition of food baskets can significantly influence the water consumption rate and society's environmental impact. This study evaluates the sustainability of Iran's food and nutrition security by evaluating five food baskets using water and carbon-footprint indices. These food baskets are the common Iranian food basket (CIFB), the vegetarian food basket (VFB), the Ministry of Health desired food basket (MHDFB), the Swiss Society for Nutrition food basket (SSNFB), and the Mediterranean food basket (MFB). This study also examines the role of Iran's agricultural sector under the Paris Climate Change Agreement (COP21). The TOPSIS multi-criteria decision-making method was used to identify the most desirable food basket. The results of this study show that the VFB could be the most desirable alternative to the CIFB. Replacing the CIFB with the VFB causes a 10% and 21% lower water and carbon footprint, respectively. From a 30-year perspective (around 2050), choosing the VFB would reduce the blue water footprint by 19.7 BCM and the gray water footprint by 3.6 BCM compared to the CIFB. According to the provisions of COP21 regarding Iran's voluntary program to reduce greenhouse gas emissions, the agricultural sector will be one of the sources of greenhouse gas emissions that will face an upward trend. Hence, to adhere to this voluntary commitment, these emissions should be reduced by changing the CIFB to the VFB or reducing emissions in other sectors, such as industry. Choosing the VFB can reduce 10.7 million tons of CO2 emissions, which can be a significant step for the agricultural sector in Iran in reducing greenhouse gas emissions. Overall, considering the characteristics of farm products (water and carbon footprints) in Iran, a country located in a semi-arid climate, the VFB is recommended. [ABSTRACT FROM AUTHOR]

This study aims to predict the yield of two strategic crops in Iran; wheat and rice, under climate scenarios that indicate probable changes in climate variables. It implemented by a stochastic model based on the Monte Carlo method. This model were estimated based on historical data from 1988 to 2019 for precipitation and temperature provided possible changes in the pattern of and their probability of occurrence. The results show that rain-fed wheat, irrigated wheat and rice yields decrease by 42%, 29% and 21% respectively in the average scenario. Therefore, policy makers should try to make the right decisions to increase the production of the country's strategic crops. R&D management to introduce drought-tolerant varieties, use of modern irrigation systems and use of low-volume irrigation methods are some of the proposed solutions to mitigate the effects of climate change. [ABSTRACT FROM AUTHOR]

Measures taken to cope with the possible effects of climate change on water resources management are key for the successful adaptation to such change. This work assesses the environmental water demand of the Karkheh river in the reach comprising Karkheh dam to the Hoor-al-Azim wetland, Iran, under climate change during the period 2010-2059. The assessment of the environmental demand applies (1) representative concentration pathways (RCPs) and (2) downscaling methods. The first phase of this work projects temperature and rainfall in the period 2010-2059 under three RCPs and with two downscaling methods. Thus, six climatic scenarios are generated. The results showed that temperature and rainfall average would increase in the range of 1.7-5.2 and 1.9-9.2%, respectively. Subsequently, flows corresponding to the six different climatic scenarios are simulated with the unit hydrographs and component flows from rainfall, evaporation, and stream flow data (IHACRES) rainfall-runoff model and are input to the Karkheh reservoir. The simulation results indicated increases of 0.9-7.7% in the average flow under the six simulation scenarios during the period of analysis. The second phase of this paper's methodology determines the monthly minimum environmental water demands of the Karkheh river associated with the six simulation scenarios using a hydrological method. The determined environmental demands are compared with historical ones. The results show that the temporal variation of monthly environmental demand would change under climate change conditions. Furthermore, some climatic scenarios project environmental water demand larger than and some of them project less than the baseline one. [ABSTRACT FROM AUTHOR]

In this paper, the  effects  of the El Niño-Southern Oscillation (ENSO) on the annual maximum flood (AMF) and volume over threshold (VOT) in two major neighbouring river basins in southwest Iran are investigated. The basins are located upstream of the Dez and Karun-I dams and cover over 40 000 km2in total area. The effects of ENSO on the frequency, magnitude and severity (frequency times magnitude) of flood characteristics over the March–April period were analysed. ENSO indices were also correlated with both AMF and VOT. The results indicate that, in the Dez and Karun basins, the El Niño phenomenon intensifies March–April floods compared with neutral conditions. The opposite is true in La Niña conditions. The degree of the effect is more intense in the El Niño period. [ABSTRACT FROM AUTHOR]

The southern coast of the Caspian Sea in northern Iran is bordered by a mountain range with forested catchments which are susceptible to droughts and floods. This paper examines possible changes to runoff patterns from one of these catchments in response to climate change scenarios. The HEC-HMS rainfall–runoff model was used with downscaled future rainfall and temperature data from 13 global circulation models, and meteorological and hydrometrical data from the Casilian (or “Kassilian”) Catchment. Annual and seasonal predictions of runoff change for three future emissions scenarios were obtained, which suggest significantly higher spring rainfall with increased risk of flooding and significantly lower summer rainfall leading to a higher probability of drought. Flash floods arising from extreme rainfall may become more frequent, occurring at any time of year. These findings indicate a need for strategic planning of water resource management and mitigation measures for increasing flood hazards.EDITOR M.C. Acreman ASSOCIATE EDITOR not assigned [ABSTRACT FROM PUBLISHER]

Coastal areas are of paramount importance due to their pivotal role in facilitating a wide range of socio-economic activities and providing vital environmental services. These areas, as the meeting points of land and sea, face significant risks of flooding due to the ongoing rise in sea levels caused by climate change. Additionally, they are susceptible to extreme events like king tides and large waves in the future. This paper introduces a framework for estimating the extreme total water level (TWL) by considering the effects of regional sea level rise (RSLR) resulting from a warming climate under RCP 8.5. It also incorporates the contributions of high tides, 100-year storm surge, and 100-year wave setup and run-up. The proposed framework is utilized to evaluate the occurrence of extreme coastal flooding along the Persian Gulf coast of Iran, an area that is home to significant industries in the country. The results offer an estimated increase of RSLR by 0.23 m from 2020 to 2050 considering an ensemble of climate model projections. Extreme wave setup values are estimated to range between 0.19 and 0.66 m, while storm surge is projected to vary from 0.4 to 1.44 m across the studied coastline. These together yield in a projected extreme TWL along the coastline within the range of 3.18 and 3.90 m above the current sea level. This significant increase in sea level could lead to the inundation of approximately 513 km2 of low-lying coastal land, which accounts for about 16% of the studied domain and could pose serious flooding threat to the people and their assets in this region. Finally, relative ranking of flooded zones (i.e., 6 zones) helps determine the areas with higher chance of flood exposure, at which investment in flood mitigation measures should be prioritized. [Display omitted] • Regional sea level rise is quantified using GCMs under RCP 8.5. • Flooding maps are shown under regional SLR, tides, storm surge, wave setup and run-up. • Extreme total water level is expected to inundate important ports in Persian Gulf coast of Iran by 2050. • Research projects that 513 km of low-lying coastal areas will be inundated due to extreme total water level. [ABSTRACT FROM AUTHOR]

Excessive concentration of greenhouse gases in atmosphere emitted from human activities has been considerably changing the world's climate, especially in the last 50 years. Agriculture, as humans' food production system, has undoubtedly interrelated with climate change (CC). During current decades, the impacts of CC on agriculture have been properly investigated; however, the impacts of agriculture on CC have received lower attention. This may be due to the scarcity of long-term spatiotemporal climatic and agricultural data to analyze coupling trends and interactions. Benefiting from a comprehensive database and using structural equation modeling, this study seeks to investigate the contribution of agriculture to CC in Iran for more than half a century. For this, two indicators were developed to evaluate structural characteristics of agricultural expansion (AEI) and CC at the province level. Then, the effect of AEI on CC was investigated using the structural equation modeling technique. The results showed that AEI has not had a positive contribution to raising the long-term average surface temperature. Precisely, the provinces with a higher level of surface temperature have had a lower AEI, indicating that other sectors outweigh agriculture in exacerbating long-term CC in the country. Nevertheless, Iran still needs to improve and sustain its agricultural practices and technologies. The main conclusion of this study is that if the government and policymakers aspire to manage CC, they should have a more holistic and systematic view. In other words, not only do they need to consider all drivers of CC, but they also have to pay close attention to the network of relationships among the drivers. [ABSTRACT FROM AUTHOR]

Rising temperatures of 1.5–4 °C by the end of the twenty-first century and continuing after 2100 have caused climate change to threaten food security. Indeed, the rainfed production of strategic crops such as wheat in countries such as Iran, which are likely to be highly affected by climate change, is very important. This study aimed to identify the suitable areas for rainfed wheat cultivation and the yield gap estimation in current and future climatic conditions using geographical analysis, a crop simulation model, and the GYGA protocol in Iran. For this, environmental variables (including average, minimum, and maximum temperatures, rainfall) along with topographic (slope, elevation) and edaphic components (such as organic carbon, pH, EC, [CaCO3], exchangeable sodium percentage, cation exchange capacity, and soil erosion) were involved. The results revealed that around 30% of the total areas (6.62 million ha) are not suitable at all, while just 13% of total lands (2.97 million ha) were highly suitable and suitable. These areas are mainly located in the northern part of Iran. The study of the climate change scenarios indicated that the northern, northwestern, and western parts of the country have great potential for rainfed wheat production and future climate change will benefit wheat production in these areas, but in other parts of the country, more unfavorable conditions are expected. The mean water-limited potential yield (Yw) difference between marginally suitable to not suitable classes was narrow (< 0.3 t ha−1), while for highly suitable to moderately suitable classes was about 2.5 t ha−1. [ABSTRACT FROM AUTHOR]

Climate change is among the most pressing challenges for human advancement in the twenty-first century. Climate change is having a significant impact on the agricultural sector. The main objective of this study is to evaluate the food security status and sustainability of crop production under different climate change scenarios in the arid and semi-arid zones of Iran. To this end, the Statistical Down-Scaling Model (SDSM) and the outputs of the General Circulation Model (GCM) are employed to project future climate parameters under three climate scenarios. The study then employs the Just and Pope approach by using a panel dataset to evaluate the impact of climate change on crop production. Also, the multi-criteria decision-making (MCDM) technique is used to assess the sustainability of crop production from an economic, social, and environmental perspective in the context of climate change and baseline conditions. The results of the study show that precipitation has a significant favourable effect on crop yield in both zones. Maximum temperature is positively and significantly related to crop yield in the semi-arid area, while the relationship is negative in the arid region. Future projections reveal that under different climate scenarios, the production of irrigated wheat, dryland wheat, irrigated barley, dryland barley, and potato crops will change by a maximum of approximately 7.57%, 35.70%, 62.86%, 15.90%, and 26.88% in the semi-arid zone and − 24.07%, 29.73%, − 31.33%, − 4.80%, and 25.80% in the arid zone, respectively. The findings imply that climate change will decrease the food security index for all crops in the arid zone, while improving the situation for crops in the semi-arid region. The results also indicate that future climate change can have a significant adverse effect on economic water productivity, economic benefits, and the sustainability of strategic crop production in the arid zone compared to the semi-arid zone. Given that a large part of Iran is covered by an arid climate, designing coherent adaptation actions and mitigation policies need to be prioritized to tackle the negative impact of climate change on food production systems. [ABSTRACT FROM AUTHOR]

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