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Background Ensuring public health is crucial in any policy debate on climate change. Paris Agreement on climate change is a global contract, through which countries have committed themselves to a public health treaty. The agreement has laid the foundation for mitigation and adaptation. This study was conducted to provide an evidence-based framework for policy-making in the health system of Iran in order to reduce the adverse effects of climate change on public health and to increase the adaptation of the health system as a result. Methods This is a qualitative study. We first used Delphi method to extract the components of Paris Agreement on climate change that were related to the functions and policymaking of health system in Iran. Twenty-three experts in health and climate change were identified purposefully and through snowball sampling as participants in Delphi. Data collection instrument was a structured questionnaire. We used SPSS software version 25 for data analysis based on the descriptive indices including the mean, the percentage of consensus above 75%, and the Kendall coordination coefficient. Results Seventy-nine components classified within nine categories were extracted. The most important examples of the implementation of Paris Agreement on climate change in the health system of Iran were: participation in the formulation of strategies for mitigation and adaptation, identifying vulnerable groups, assessing vulnerability, increasing the capacity of health services delivery during extreme events, using early warning systems, using new technologies to increase the adaptation, evaluation of interventions, financial support, increasing the number of researches, increasing the knowledge and skills of staff, and finally public awareness. Conclusions Evidence-based policy-making is pivotal to develop effective programs to control the health effects of climate change. This research provided policy translation and customization of micro and macro provisions of Paris Agreement on climate change, in line with the political context of health system in Iran. Our finding will pave the ground, we envisage, for further steps towards capacity building and enhancement of resiliency of the health system, adaptation interventions, and evaluation, identification of barriers and facilitators for adaptation and decreasing the adverse health effects caused by the climate change, in Iran and perhaps beyond.

The projection of climate change impacts can be very crucial for water resources planning and management. Hashtgerd plain is an immigrant destination due to socio-economic development and its proximity to the Tehran metropolis. The population growth has led to more water demand, resulting in excessive utilization of groundwater resources and water shortages in the agricultural sector. The water shortage and climate change in Hashtgerd plain could lead to more severe water crisis in the future. Therefore, this study aimed to provide adaptation strategies for minimizing the negative effects of climate change by taking into account the interaction between different sub-systems in Hashtgerd plain during 2020–2049 period. In order to assess the climate change impacts, the output of 19 AOGCMs models was used under RCP2.6, RCP4.5 and RCP8.5 emission scenarios. A comprehensive model was developed based on the system dynamics theory to investigate the interactions of water resources, agriculture, and socio-economic sub-systems and were used to investigate the impacts of climate change and evaluate various adaptation strategies on different sub-systems of the region. The simulation results showed the negative effects of climate change will intensify the water shortage in Hashtgerd Plain. The agricultural sector will be more vulnerable to climate change than the domestic and industrial sectors. The results of various policies showed that improving water use efficiency coupled with reducing the cultivation of high-consumption crops and changing the cropping pattern towards lower water requirement plants can effectively minimize the adverse impacts of climate change on water shortages in agriculture.

BACKGROUND: The consequences of climate change are highly impeding the achievement of Sustainable Development Goals (SDGs) anywhere, especially in low and middle-income countries. While climate change scales up, its health-related risks increase, which in turn leads to cause new challenges for public health. As a second largest country of the Eastern Mediterranean Region of World Health Organization, Iran is highly vulnerable to the effects of climate change. PURPOSE: This study seeks the notion of health risks and challenges of climate change in Iran and provide potential evidence-based remedies to prevent and diminish such destructive effects. METHODS: A comprehensive literature in various computerized databases was conducted, and numerous published original research and review articles about climate change status and evidences of adverse health consequences of climate change in Iran were reviewed. RESULTS: The evidence suggests that the expected health challenges related to climate change in Iran are: rising temperatures; frequent extreme weather events; reduction of air quality; food-borne, water-borne, and vector-borne diseases; mental health and well-being consequences; and the increasing trend of natural disasters and deaths associated with climatological hazards. CONCLUSIONS: By considering the growing burden of diseases associated with climate variability in Iran as well as the interdisciplinary nature of climate change and health issues, an integrated, multi-sectoral, and comprehensive approach for identification, prioritization, and implementation of adaptation options is required by Ministry of Health and Medical Education as a custodian of public health in order to enhance the resiliency and adaption against adverse health effects of climate change.

In many parts of the world, especially in metropolitan areas with dry climates, shortages of freshwater resources have become a significant challenge in water resources management. Rapid population growth and climate change in these areas impose additional burdens on water consumption, especially on surface water resources, which are increasingly becoming scarce. In this study, a change from the Standard Operating Policy (SOP) to the Modified Linear Decision Rule (MLDR) policy is considered as a solution to mitigate the impacts of climate change on dams and reservoir operation. The two policies were compared by using five reservoir system performance indices (reliability, resiliency, vulnerability, sustainability and meeting demands) and three hydropower energy generation indices. This study pertained to the Karaj hydropower dam reservoir in Iran and speculated on 2020–2039. Changing the operation policy from current SOP to the MLDR policy can mitigate the adverse effects brought by climate change. According to the results, the SOP policy works best for reservoir operation if there is enough water (optimistic scenario). In the case of pessimistic scenarios (shortage of water), operating the reservoir system with SOP policy would turn into a crisis in supplying drinking water. On the other hand, the MLDR policy prevents the complete draining of the reservoir. In all water-demand scenarios, the MLDR policy reduces the amount of time (by over 90%) in which the reservoir remains empty, compared to the SOP policy. The final results demonstrate how the MLDR policy can mitigate the undesirable effects of climate change.

Access to spatio-temporally consistent precipitation data is a key prerequisite for hydrological studies, especially in data-scarce regions. Different global precipitation products offer an alterna...

Simulations of soil organic carbon (SOC) stocks under climate change are partly subject to uncertainties deriving from the Global Climate Models (GCMs) used for weather projections of future climates. For this reason, SOC simulations are generally performed with a set of GCMs to avoid misleading results. SOC stocks were measured in different land covers in a forest area of Northern Iran (maple, alder, oak, cypress and a mixed natural forest), and the model RothC was used to predict SOC changes under climate change scenarios. We studied the effects on SOC stock changes deriving from an ensemble of nine GCMs and two Representative Concentration Pathways (RCPs), projected on four future periods (FPs) of 20 years between 2020 and 2099. The frequency analysis of SOC stock changes indicated a different effect of the GCMs/RCPs ensemble in the four future periods, with patterns showing more evident nonlinear probabilities of SOC changes in the 2030s and 2050s compared to the almost linear probabilities in the remaining future periods. This would imply that the effect of the GCMs/RCPs ensemble is more important in very close and intermediate future scenarios (the 2030s and 2050s) compared to fully realized climate change scenarios (the 2070s and 2090s). Results indicated the need to provide assistance in the management strategies in the reforestation sector in close and intermediate climate change scenarios. In fact, maple and quercus plantations had the highest probability of cumulated SOC change proving more sensitive to climate change. Conversely, cypress and alder plantations could limit the decrease of SOC stocks representing a good option for GHG mitigation in close and intermediate climate change scenarios and should be preferred in the afforestation of the degraded natural forest, and in mixed plantation systems within maple and oak stands.

Land-use changes can alter soil carbon (C) contents, and in particular deforestation has been responsible for a large part of the cumulative human-induced greenhouse gas (GHG) emissions. This study aimed to determine the influence of land-use and land cover change on soil organic carbon (SOC) content, microbial biomass C (MBC) and microbial respiration (MR) in the Hyrcanian forests, north of Iran. We compared an agricultural field (AF), plantations of Alnus subcordata (AS), Acer velutinum (AV), Quercus castaneifolia (QC) and Cupressus sempervirens (CS), and a natural forest (NF). Soil samples were collected at three different depths (0–20, 20–40 and 40–60 cm). Results showed that different land covers significantly affected soil characteristics, and SOC increased by 25% and 1.11% after the conversion of NF to CS and AS plantations respectively, and decreased by 4%, 12.11% and 53% when NF was converted to QC, AV and AF respectively. In all treatments, MBC and MR were significantly higher (p

In recent decades, climate change has influenced the quantity and quality of water resources, affecting water supply for various demands. In this case study, the effects of climate change on the quality of the Ardak River in the northeast of Iran are discussed. The Qual2kW model was used to simulate water quality parameters, by sampling dissolved oxygen (DO), pH, chemical oxygen demand (COD), and NO3. The rainfall-streamflow model IHACRES was used for simulating monthly streamflow. Monthly general circulation model (GCM) temperature and rainfall data from representative concentration pathways (RCP) RCP2.6 and RCP8.5 were downloaded for 1986 to 2005 and 2020 to 2039. The previously verified model LARS-WG was used to predict future temperatures and rainfall. By importing this data into IHACRES, stream flows were simulated, enabling Qual2kW to predict future effects on water quality. Although changes in temperature of 0.5 to 1.2 °C were predicted, maximum changes in temperature and rainfall will occur in winter and summer in series. Therefore, water quality was predicted to decrease only on the Abghad branch, due to increased temperature and lower flow rates. The highest percentage variations in DO and NO3 are −12.19 and 31.25 in RCP8.5 and in COD and PH, −35.4 and 0.29 in RCP2.6.

The AquaCrop model, calibrated for 2002 and validated for 2003, is used to simulate sugar beet root dry yield, dry biomass, water productivity based on irrigation (WPi), and water productivity based on total water input (WPi+p) in an experimental field of the Karaj Sugar Beet Seed Institute (Karaj, Iran). Three irrigation treatments including full irrigation, 75% deficit irrigation, and 50% deficit irrigation were carried out in the main plots. The results of statistical comparison between the model output and observed data in the calibration (2002) and validation (2003) years showed that the AquaCrop model reliably simulated sugar beet yield and the biomass under different genotypes and irrigation levels. AquaCrop did a better job of simulating dry biomass than root dry yield. The findings show that by decreasing water input, including irrigation and precipitation, WPi, and WPi+p will increase. In total, statistical indicators and scatter plots indicated that the AquaCrop model had enough fitness to predict yield, biomass, and water productivity for the future. Keywords: AquaCrop, Yield; Biomass, WPi, WPi+p.

Understanding the complexity and feedbacks among food, energy, and water (FEW) systems is key to making informed decisions about sustainable development. This paper presents qualitative representation and quantitative system dynamics simulation of the water resources system in the Qazvin Plain, Iran, taking into account the energy intensity of water supply and interconnected water use sectors (e.g., urban, industrial, and agricultural). Qazvin Plain faces water resources challenges that are common to arid/semi-arid areas, including frequent droughts, declining surface water and groundwater, and increased urban and agricultural water demand. A system dynamics model is developed using historical data (2006–2016) to investigate the effects of anticipated dynamics of integrated water and energy sectors in the next two decades. The results of policy scenarios (2020–2039) demonstrate that the continuation of the existing management policies will cause severe damage to the water and energy sectors, pushing the system towards water resources limits to growth. An annual groundwater table decline of nearly 1 m is anticipated, indicating significant overshoot of the plain's natural recharge capacity, which may lead to the depletion of recoverable groundwater in the plain within the next three decades. The groundwater table decline will cause energy consumption of water supply to increase by about 32% (i.e., 380 GWh) to maintain irrigated agriculture. It is critical to implement a combination of water demand and supply management policies (e.g., net agricultural water savings and recycling treated wastewater) to delay the problem of water limits to growth in the region.

Additional file 1: Supplementary file. Questionnaire. Researcher-made questionnaire: First round of Delphi. As a part of research at Tehran University of Medical Sciences, we conducting a survey that extracts the components of functions and policymaking to provide an evidence-based framework for health policy-making towards reducing greenhouse gases emissions and adapting to the health effects of climate change in Iran.

Hydrological changes due to climate change will impact the sustainability of social-ecological systems (SES). It is essential to develop methodologies, capable of addressing economic as well as the environmental aspects, to take care of the water-associated system through improving the system adaptive capacity, particularly in arid and semi-arid areas. Through the application of the Post-Modern Portfolio Theory (PMPT), we propose a new approach to water allocation problem in the face of climate change, as a case of Lake Tashk-Bakhtegan basin, Southern Iran. The economic model based on PMPT is adapted for designing water allocation schemes to maximize local economic return, minimize its risk and minimize the Gini coefficient indicative of social inequities. The climatic variables were generated by three Atmosphere-Ocean General Circulation Models. The temperature and precipitation will increase and decrease by about +2.4% and −10% on average in the future compared to those in the historical period, respectively. In order to identify hydrological and socio-economic implications of climate change in the basin, an integrated system dynamic model was developed. Finally, the decision space of equally effective solutions has been determined in the form of Pareto points at the efficient portfolio plane. The findings showed a decrease in the economic return (on average by 5.7% in the both RCPs) and the ratio of employment to unemployment (average 10% in most of sub-basin), owing to the reduction of water resources, especially in agriculture. Base on Portfolio results, the ratio of economic return to risk doubled and tripled in optimized condition compared to the normal condition in the future. The efficient plane of the portfolio can be used to allocate water to economic activities according to a risk-taking attitude of a decision-maker. Following the model results, services and industrial sectors would need to be developed in order to sustain local water resources with possible additional allocations for environmental requirements, with simultaneous improvement of economic return on water resources.

This study examined the separate and combined impacts of future changes in climate and land use on streamflow, nitrate and ammonium in the Kor River Basin, southwest of Iran, using the representative concentration pathway 2.6 and 8.5 scenarios of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). Different land use and climate change scenarios were used and the streamflow, nitrate and ammonium in the future period (2020–2049) under these scenarios were simulated by Integrated Catchment Model for Nitrogen (INCA–N). Results indicated that climate change will increase streamflows and decrease nitrate and ammonium concentrations in summer and autumn. Land use changes were found to have a little impact on streamflows but a significant impact on water quality, particularly under an urban development scenario. Under combined scenarios, larger seasonal changes in streamflows and mixed changes of nitrate and ammonium concentrations were predicted.

Earth is faced with dramatic changes in the weather systems, which leads to climate change. Climate change affects water resources and crop production. In this study, five and seven general circulation models (GCMs) were respectively collected via the IPCC Fourth and Fifth Assessment Reports. Emission scenarios including B1, A1B, and A2 for AR4 and RCP2.6 and RCP8.5 for AR5 were applied to predict future climate change. The weighting method of mean observed temperature-precipitation (MOTP) was utilized to compute uncertainty related to different climate models. The scenario files made by ΔT and ΔP were applied to the downscaled model of LARS-WG to generate weighted multi-model ensemble means of temperature and precipitation for the period 2020–2039 centered on 2030s. These ensemble means were incorporated into the calibrated AquaCrop model to predict final yield and biomass. In this study, soybean data were applied for four different varieties under three irrigation treatments in field experiments carried out at Karaj Seed and Plant Improvement Institute in two successive years. However, the results of statistical analysis between the model output and observed data for all varieties and irrigation treatments in the calibration year (2010) and validation year (2011) were the same at the 95% confidence level. It is suggested that AquaCrop is a valid model to predict yield and biomass for the study area in the future. Furthermore, comparing future climatic variables to the historical period during the soybean growing season showed enhancement of these variables by the 2030s. The amplitude change of temperature was larger in AR5, whereas the amplitude change of precipitation and CO2 were larger in AR4. The soybean yield and biomass increased for all treatments in the 2030 s with positive correlation with the climatic variables. The maximum temperature represented the most significant correlation with yield and biomass for almost all treatments. Finally, soybeans might achieve an optimal threshold temperature in the future, leading to yield increases in the 2030s.

Conversion of forest land to farmland in the Hyrcanian forest of northern Iran increases the nutrient input, especially the phosphorus (P) nutrient, thus impacting the water quality. Modeling the effect of forest loss on surface water quality provides valuable information for forest management. This study predicts the future impacts of forest loss between 2010 and 2040 on P loading in the Tajan River watershed at the sub-watershed level. To understand drivers of the land cover, we used Land Change Modeler (LCM) combining with the Soil Water Assessment Tool (SWAT) model to simulate the impacts of land use change on P loading. We characterized priority management areas for locating comprehensive and cost-effective management practices at the sub-watershed level. Results show that agricultural expansion has led to an intense deforestation. During the future period 2010–2040, forest area is expected to decrease by 34,739 hm2. And the areas of pasture and agriculture are expected to increase by 7668 and 27,071 hm2, respectively. In most sub-watersheds, P pollution will be intensified with the increase in deforestation by the year 2040. And the P concentration is expected to increase from 0.08 to 2.30 mg/L in all of sub-watersheds by the year 2040. It should be noted that the phosphorous concentration exceeds the American Public Health Association′s water quality standard of 0.2 mg/L for P in drinking water in both current and future scenarios in the Tajan River watershed. Only 30% of sub-watersheds will comply with the water quality standards by the year 2040. The finding of the present study highlights the importance of conserving forest area to maintain a stable water quality.

Soil organic carbon (SOC) contains a considerable portion of the world's terrestrial carbon stock, and is affected by changes in land cover and climate. SOC modeling is a useful approach to assess the impact of land use, land use change and climate change on carbon (C) sequestration. This study aimed to: (i) test the performance of RothC model using data measured from different land covers in Hyrcanian forests (northern Iran); and (ii) predict changes in SOC under different climate change scenarios that may occur in the future. The following land covers were considered: Quercus castaneifolia (QC), Acer velutinum (AV), Alnus subcordata (AS), Cupressus sempervirens (CS) plantations and a natural forest (NF). For assessment of future climate change projections the Fifth Assessment IPCC report was used. These projections were generated with nine Global Climate Models (GCMs), for two Representative Concentration Pathways (RCPs) leading to very low and high greenhouse gases concentration levels (RCP 2.6 and RCP 8.5 respectively), and for four 20year-periods up to 2099 (2030s, 2050s, 2070s and 2090s). Simulated values of SOC correlated well with measured data (R2=0.64 to 0.91) indicating a good efficiency of the RothC model. Our results showed an overall decrease in SOC stocks by 2099 under all land covers and climate change scenarios, but the extent of the decrease varied with the climate models, the emissions scenarios, time periods and land covers. Acer velutinum plantation was the most sensitive land cover to future climate change (range of decrease 8.34-21.83tCha-1). Results suggest that modeling techniques can be effectively applied for evaluating SOC stocks, allowing the identification of current patterns in the soil and the prediction of future conditions.

Leaf Area Index is a vegetation characteristic which affects water cycle in watersheds by intercepting water. Hence, we decided to simulate LAI with high spatial resolution for different land covers in Kasilian watershed located in Iran from 2004 to 2013. For this purpose, the gridded BIOME-BGC was developed with 500 m spatial resolution which includes 319 grids within Kasilian watershed domain. In this study, The particle swarm optimization (PSO) algorithm was applied to find a combination of 3 parameters including maximum stomatal conductance (Gs_max), new fine root C: new leaf C (FRC: LC), and canopy average specific leaf area which contribute to the best fit between BIOME-BGC LAI and MODIS LAI in each pixels. According to the PSO results, this algorithm indicated an appropriate performance during the calibration and the root mean square error (RMSE) between BIOME-BGC LAI and MODIS LAI converged to an optimum point during the primary iterations in all pixels. Moreover, the computed per cent err...

This study assessed the impact of climate change on flood frequency and flood source area at basin scale considering Coupled Model Intercomparison Project phase 5 General Circulation Models (CMIP5 GCMs) under two Representative Concentration Pathways (RCP) scenarios (2.6 and 8.5). For this purpose, the Soil and Water Assessment Tool (SWAT) hydrological model was calibrated and validated for the Talar River Basin in northern Iran. Four empirical approaches including the Sangal, Fill&ndash, Steiner, Fuller, and Slope-based methods were used to estimate the Instantaneous Peak Flow (IPF) on a daily basis. The calibrated SWAT model was run under the two RCP scenarios using a combination of twenty GCMs from CMIP5 for the near future (2020&ndash, 40). To assess the impact of climate change on flood frequency pattern and to quantify the contribution of each subbasin on the total discharge from the Talar River Basin, Flood Frequency Index (FFI) and Subbasin Flood Source Area Index (SFSAI) were used. Results revealed that the projected climate change will likely lead to an average discharge decrease in January, February, and March for both RCPs and an increase in September and October for RCP 8.5. The maximum and minimum temperature will likely increase for all months in the near future. The annual precipitation could increase by more than 20% in the near future. This is likely to lead to an increase of IPF. The results can help managers and policy makers to better define mitigation and adaptation strategies for basins in similar climates.

Pasture, forest, and farmland are the dominant land covers in the Tajan River watershed and this landscape status has a direct connection with nitrate pollution. Understanding the correlations between landscape variables and nitrate pollutant is a priority in order to assess pollutants loading and predicting the impact on surface water quality. The soil and water assessment tool was used to simulate nitrate loads in different land cover types in different years. The landscape pattern was calculated by FRAGSTATS. The contributing share of each land use/land cover shows nitrate pollutant produced by grassland (5.7%) and forest (29%) are less than those produced by agricultural land (64.2%). Agricultural land was identified as the main source of nitrate pollution. Paddy fields and orchards had the most intensive soluble nitrate loss especially in spring and summer. Statistical analysis indicated that nitrate was positively associated with patch density, edge density, patch number, total edge, effective mesh size, largest patch index, and landscape shape index (p ≤ 0.01). We then analyzed how nitrate was related to landscape attributes in six different sites. Also the regression analysis results suggested that landscape metrics could account for more than 94% of the variance of nitrate in the whole catchment. The regression models confirmed the great importance of the agriculture metrics and forest metric in predicting nitrate in watershed. Defining the generation and extent of pollution in this particular watershed which discharges into the Caspian Sea can constitute an important step toward protecting this ecosystem.

This research aims at providing a statistical framework for detection and attribution of climate variability and change at regional scale when at least 30 years of observation data are available. While extensive research has been done on detecting significant observed trends in hydroclimate variables and attribution to anthropogenic greenhouse gas emissions in large continents, less attention has been paid for regional scale analysis. The latter is mainly important for adaptation to climate change in different sectors including but not limited to energy, agriculture, and water resources planning and management, and it is still an open discussion in many countries including the West Asian ones. In the absence of regional climate models, an informative framework is suggested providing useful insights for policymakers. It benefits from general flexibility, not being computationally expensive, and applying several trend tests to analyze temporal variations in temperature and precipitation (gradual and step changes). The framework is implemented for a very important river basin in the west of Iran. In general, some increasing and decreasing trends of the interannual precipitation and temperature have been detected. For precipitation annual time series, a reducing step was seen around 1996 compared with the gradual change in most of the stations, which have not experience a dramatical change. The range of natural forcing is found to be ±76 % for precipitation and ±1.4 °C for temperature considering a two-dimensional diagram of precipitation and temperature anomalies from 1000-year control run of global climate model (GCM). Findings out of applying the proposed framework may provide useful insights into how to approach structural and non-structural climate change adaptation strategies from central governments.

Urban stormwater systems are of important infrastructures in urban planning, and any mitigation in their design and implementation can be problematic. Changes in the hydrologic regime due to the climate change lead to exacerbation of extreme events such as flood. In this study, firstly, the impacts of climate change in the extreme precipitations of a part of Tehran in Iran were investigated during the period 2020–2049 using 10 Atmospheric-Ocean General Circulation Models (AOGCMs) under two Representative Concentration Pathway scenarios (i.e., RCP2.6 and RCP8.5). Then, Using the Storm Water Management Model (SWMM), the urban stormwater system of the study area was modelled during the base period (1981–2010) along with the moderate, pessimistic and optimistic conditions of the AOGCM models during the future period (2020–2049). Finally, the network performance was evaluated based on reliability, resiliency, and vulnerability criteria, along with the sustainability index. The simulation results revealed that the sustainability of the urban stormwater system is 77.89% during the base period while its value will be 77.73% and 77.61% under RCP2.6 and RCP8.5 scenarios, respectively, during the future period based on the moderate conditions of the AOGCM models. Furthermore, the network sustainability was 73.10% and 81.98%, respectively, according to the results of CanESM2 model for pessimistic and CESM1-WACCM model for optimistic conditions under RCP2.6 scenario. It was found that during the future period, under the influence of both studied scenarios, there will be no significant increase in the number of network flooding points compared to the baseline period. The approach presented in this study can be used to evaluate the performance of stormwater systems in other urban areas.

Soybean is one of the most important oilseed crops in the world. Its economic value is based on the concentration of protein and oil produced in the seeds. However, in climate change studies, a crop simulation model like AquaCrop is unable to predict the qualitative yield of crops. Therefore, this study aims to simulate qualitative soybean yield based on regression models between observed dry grain yield (Yd) from 12 treatments as independent variables with their corresponding observed values for oil and protein contents as dependent variables. The P-value (

Background: In 2015, it was estimated that the burden of disease in Iran comprised of 19 million disability-adjusted life years (DALYs), 74% of which were due to non-communicable diseases (NCDs). The observed leading causes of death were cardiovascular diseases (41.9%), neoplasms (14.9%), and road traffic injuries (7.4%). Even so, the health research investment in Iran continues to remain limited. This study aims to identify national health research priorities in Iran for the next five years to assist the efficient use of resources towards achieving the long-term health targets.Methods: Adapting the Child Health and Nutrition Research Initiative (CHNRI) method, this study engaged 48 prominent Iranian academic leaders in the areas related to Iran's long-term health targets, a group of research funders and policy makers, and 68 stakeholders from the wider society. 128 proposed research questions were scored independently using a set of five criteria: feasibility, impact on health, impact on economy, capacity building, and equity.Findings: The top-10 priorities were focused on the research questions relating to: health insurance system reforms to improve equity; integration of NCDs prevention strategy into primary health care; cost-effective population-level interventions for NCDs and road traffic injury prevention; tailoring medical qualifications; epidemiological assessment of NCDs by geographic areas; equality in the distribution of health resources and services; current and future common health problems in Iran's elderly and strategies to reduce their economic burden; the status of antibiotic resistance in Iran and strategies to promote rational use of antibiotics; the health impacts of water crisis; and research to replace the physician-centered health system with a team-based one.Conclusions: These findings highlight consensus amongst various prominent Iranian researchers and stakeholders over the research priorities that require investment to generate information and knowledge relevant to the national health targets and policies. The exercise should assist in addressing the knowledge gaps to support both the National General Health Policies by 2025 and the health targets of the United Nations' Sustainable Development Goals by 2030.

Due to higher temperatures and lower water availability, climate change is likely to have a major impact on walnut production in the near future. Climate change will alter the land suitability for walnut cultivation around the world, especially in arid and semi-arid regions like Iran. Here, land suitability for the cultivation of walnut (Juglans regia L.) in Iran was determined using the GIS for present and future conditions (2020–2049) with an approach to climate change. Accordingly, data from 375 synoptic stations throughout Iran were gathered for climatic factors including average, minimum and maximum temperatures, relative humidity and chilling requirement. Also, ASTER sensors (Advanced Spaceborne Thermal Emission and Reflection Radiometer) and their data provided this research with cells that make a precision of 150 m (5 s), and the data were used for gauging geological parameters such as altitude and land slope. The electrical conductivity (EC) of soil and water were informed by the data bank of the Iranian Water Resources Management. The results of temperature simulations for the future (2020–2049) were analyzed by 21 AOGCM-AR5 models under the RCP4.5 emission scenario. In the first phase of evaluations, the maps of land suitability were constructed for present conditions by considering a network of the above-mentioned parameters. By combining these layers of information, the final map of land suitability was illustrated for walnut cultivation. In the second phase, the NEX-GDDP was used in order to determine land suitability for the future (2020–2049). The results showed that Iran currently has 582844 km2 of land suitable for walnut cultivation. However, the future will see less suitable lands: the current area will be reduced by 6.19%, from 582844 km2 to 546710 km2. In general, the northern, northwestern and western margins of Iran are currently suitable for walnut cultivation. By approximation, these lands will also be major areas for prospective cultivations of walnut in the future (2020–2049), even though their current stretch will be reduced.

Background: Climate change and air pollution are linked. Both of them are expected to impact human health. Climate can increase health risks from poor air quality and lead to emergencies. Forecasting health consequences of air pollution episodes is a matter of great concern. Objectives: The current study mainly aimed at simulating the climate change impact on emergency medical services (EMS) clients caused by air pollution to estimate the future trend of EMS clients with cardiovascular and respiratory symptoms by 2050. Methods: Future climate pattern was projected using general circulation model outputs under the scenario of two representative concentration pathways (RCP2.6 and RCP8.5). Statistical downscaling was performed by LARS weather generator to produce high-resolution synthetic time series weather dataset. Simulation was performed using an artificial neural network (ANN). Observed climate and air pollutant variables were tagged as predictors in ANN, and EMS clients were considered as the target. Projected future (2020 - 2050) climate and air pollution were applied to estimate the future trend of EMS clients. Results: The climate pattern was predicted to become warmer and wetter in the study area (Tehran, Iran). Annual trend of EMS clients with cardiovascular and respiratory symptoms increases under both RCP scenarios. Further increase is under RCP8.5 for EMS clients with cardiovascular symptoms, and the least increase is under RCP2.6 for those with respiratory symptoms. Annual and monthly trends of EMS clients with cardiovascular and respiratory problems are more sensitive to different groups of climate and air pollution variables. Conclusions: ANN is an executive tool to simulate the impact of climate change and air pollution on public health to estimate the future trend of related morbidity and forecast short-term cases across the world.

Any change in characteristics of low flows under climate change may have important effects on various socioeconomic, environmental, water resource, and governmental planning aspects. The ai...

This research addressed the separate and combined impacts of climate and land use change on streamflow, suspended sediment and water quality in the Kor River Basin, Southwest of Iran, using (BASINS–WinHSPF) model. The model was calibrated and validated for hydrology, sediment and water quality for the period 2003–2012. The model was run under two climate changes, two land use changes and four combined change scenarios for near-future period (2020–2049). The results revealed that projected climate change impacts include an increase in streamflow (maximum increases of 52% under RCP 2.6 in December and 170% under RCP 8.5). Projected sediment concentrations under climate change scenarios showed a monthly average decrease of 10%. For land use change scenarios, agricultural development scenario indicated an opposite direction of changes in orthophosphate (increases in all months with an average increase of 6% under agricultural development scenario), leading to the conclusion that land use change is the dominant factor in nutrient concentration changes. Combined impacts results indicated that streamflows in late fall and winter months increased while in summer and early fall decreased. Suspended sediment and orthophosphate concentrations were decreased in all months except for increases in suspended sediment concentrations in September and October and orthophosphate concentrations in late winter and early spring due to the impact of land use change scenarios.

Increase of the greenhouse gases has a considerable effect on climatic variables changes such as temperature and precipitation. Three-dimensional models of coupled ocean–atmosphere general circulation, AOGCMs, under all emissions scenarios, are able to simulate past and future climate periods. The present study investigates the possible attribution of climate change to the increase of the greenhouse gases in northwest Iran. Interannual variability range for the long-term (1000 years) of temperature and precipitation, resulting from control run (greenhouse gas constant) CGCM3 model used for the study area in northwest Iran. Then, the range of natural climate variables was determined from two-dimensional graphs of temperature and precipitation based on two-variant normal distribution. Ultimately, the detected trends of climate variables can be compared with the natural climate variability range of the region. The results showed that in different parts of the study area, the range of natural climate variables for temperature and precipitation changes (95% probability) in the west of the study area are less than 1.8 C° and 40%, respectively. These values are less than 4 C° and 40% respectively, in the east of the study area. However, the climate variables in the most regions of the west are almost outside the range of natural climate variables in the recent years (1998–2008). The result indicates the effect of climate changes on the climatic variables for the case study in recent years.

Floods are natural hazards with serious impact on many aspects of human life. The Intergovernmental Panel on Climate Change (IPCC) reported that climate change already has significant impact on magnitude and frequency of flood events worldwide. Thus, it is suggested to adopt strategies to manage damage impacts of climate change. For this, involving the local community in the decision-making process, as well as experts and decision-makers, is essential. We focused on assessing the social acceptability of flood management strategies under climate change through a socio-hydrological approach using the Contingent Valuation Method (CVM). For this purpose as well, hydro-climate modelling and the Analytical Network Process (ANP) were used. Among twelve investigated flood management strategies, &ldquo, river restoration&rdquo, &ldquo, agricultural management and planning&rdquo, and &ldquo, watershed management&rdquo, were the publicly most accepted strategies. Assessment of the social acceptability of these three strategies was carried out by use of the CVM and Willingness to Pay (WTP) methodology. Generally, 50%, 38%, and 18% were willing to pay and 44%, 48%, and 52% were willing to contribute flood management strategy in zones 1, 2, and 3, respectively. Overall, peoples&rsquo, WTP for flood management strategies decreased with increasing distance from the river. Among different investigated dependent variables, household income had the highest influence on WTP.

summary Zayandeh-Rud River Basin is one of the most important basins in central Iran, which has been continually challenged by water stress during the past 60 years. Traditionally, a supply-oriented management scheme has been prescribed as a reliable solution to water shortage problems in the basin, resulting in a number of water transfer projects that have more than doubled the natural flow of the river. The main objective of this study is to evaluate the reliability of inter-basin water transfer to meet the growing water demand in Zayandeh-Rud River Basin. A system dynamics model is developed to capture the interrelationships between different sub-systems of the river basin, namely the hydrologic, socioeconomic, and agricultural sub-systems. Results from simulating a range of possible policy options for resolving water shortage problems indicate that water is essentially the development engine of the system. Therefore, supplying more water to the basin without considering the dynamics of the interrelated problems will eventually lead to increased water demand. It is demonstrated that the Zayandeh-Rud River Basin management system has characteristics of the ‘‘Fixes that Backfire’’ system archetype, in which interbasin water transfer is an inadequate water management policy, causing significant unintended sideeffects. A comprehensive solution to the problem includes several policy options that simultaneously control the dynamics of the system, minimizing the risk of unintended consequences. In particular, policy makers should consider minimizing agricultural water demand through changing crop patterns as an effective policy solution for the basin’s water problems.

Urmia Lake, the largest lake in Iran, is an important water body and habitat for a variety of different species. Data of ten climate models among the atmosphere ocean general circulation models (AOGCMs) were assembled for the 2021–2050 period under A2 and B1 emission scenarios. First, both the 30-year historic (1961–1990) and 30-year future monthly temperature and precipitation in the region were generated and weighted by the beta function (β) that assigns weights to AOGCMs based on the model evaluations. Then, the cumulative density function (CDF) in each month was determined. Precipitation and temperature values at 25, 50, and 75% probability were extracted from the CDFs. Subsequently these values were introduced to the Long Ashton Research Station Weather Generator model (LARS-WG) to downscale and produce time series of temperature and precipitation in the future, subject to the uncertainty of climate models. Then, these monthly time series at different risk levels were introduced into the arti...

There are many factors of uncertainty regarding the impact of climate change on reference evapotranspiration ( ET o ). The accuracy of the results is strictly related to these factors and ignoring any one of them reduces the precision of the results, and reduces their applicability for decision makers. In this study, the uncertainty related to two ET o models, the Hargreaves-Samani (HGS) and Artificial Neural Network (ANN), and two Atmosphere-Ocean General Circulation Models (AOGCMs), Hadley Centre Coupled Model, version 3 (HadCM3) climatic model and the Canadian Global Climate Model, version 3 (CGCM3) climatic model under climate change, was evaluated. The models predicted average temperature increases by 2010 to 2039 of 0.95 °C by the HadCM3 model and 1.13°C by the CGCM3 model under the A2 scenario relative to observed temperature. Accordingly, the models predicted average ET o would increase of 0.48, 0.60, 0.50 and 0.60 (mm/day) by 2010 to 2039 projected by four methods (by introducing the temperature of the HadCM3-A2 model and the CGCM3-A2 to ANN and HGS) relative to ET o of the observed period. The results showed that uncertainty of the AOGCMs is more than that of the ET o models applied in this study.

This study aims to evaluate climate change adaptation strategies for the ZayandehRud water system located in central Iran. A probabilistic multi-model ensemble scenario is used to characterize uncertainties in climate change projections for the study period (2015–2044). The Zayandeh-Rud Watershed Management and Sustainability Model (ZRW-MSM) is run under an ensemble scenario with different uncertainty levels to evaluate the effects of climate change on the Zayandeh-Rud water system and to identify effective adaptation strategies to minimize these effects. ZRW-MSM is a system dynamics model that captures the interrelations between the basin’s hydrologic, socioeconomic, and agricultural sub-systems. This model can provide insights about the behavior trends of the basin’s sub-systems under climate change impacts. If current water management policies hold into the future, GavKhouni Marsh, an important ecosystem will severely degrade because of the lack of environmental flows, which will likely aggravate with climate change. Results indicate that supply oriented strategies (water transfer) alone are not effective in mitigating climate change impacts on different use sectors. Nevertheless, when combined with effective water demand management, these measures can alleviate climate change-related anthropogenic water stress in the basin.

It is essential to assess the adaptation of reservoir operation to climate change in arid regions. The main objective of this research is to propose a framework for assessment of reservoir rule-curve (RRC) adaptation for climate change scenarios. The framework is applied to an arid zone in Iran and consists of the three models: downscaling, rainfall-runoff and reservoir optimisation models. LARS-WG is tested in 99% confidence level before to using it as downscaling model. Seven artificial neural network models are proposed, examined and compared with IHACRES to find proper rainfall-runoff model for arid zone. Current and adapted reservoir rule curves are derived by dynamic programming optimisation. The results demonstrate capability of proposed framework in assessment of adaptation and show that global warming negatively influences proposed index (water supply index) in normal and wet years, but has positive influence for dry years. It also improves reservoir reliability, but it cannot restore current reliability.

This study evaluates climate change impacts on crop production and water productivity of four major crops (wheat, barley, rice, and corn) in Iran's Zayandeh-Rud River Basin. Multi-model ensemble scenarios are used to deal with uncertainties in climate change projections for the study period (2015–2044). On average, monthly temperature will increase by 1.1 to 1.5 °C under climate change. Monthly precipitation changes may be positive or negative in different months of the year. Nevertheless, on the annual basis, precipitation will decrease by 11 to 31% with climate change. While warming can potentially shorten the crop growth period, crop production and water productivity of all crops are expected to decrease due to lower precipitation and higher water requirements under higher temperature. Out of the four studied crops, rice and corn are more vulnerable to climate change due to their high irrigation water demand. So, their continued production can be compromised under climate change. This finding is of particular importance, given the locally high economic and food value of these crops in central Iran.

Potential negative impacts of climate change provide compelling arguments for action and adaptive measures to help mitigate pressure on water resources, agricultural production and environmental quality. To explore the possible impacts and evaluate the effectiveness of different adaptation strategies, an integrated modelling framework that allows linkage and integration of climate models, basin- and field-scale hydrological models is of great importance. This paper describes an assessment of climate change impacts on water resources, agriculture and the environment in the Zayandeh Rud irrigation system of Esfahan (Iran) using simulation models. The paper also focuses on alternative adaptation strategies to mitigate the negative impacts. Results of the simulations show that potential impact of climate change could cause the Zayandeh Rud irrigation system to face more frequent and severe water shortage problems. Among the evaluated adaptation strategies, change in cropping patterns was found to be the best possible measure to mitigate the negative impacts of climate change. Results also show the capability of the modelling system to deliver the required simulations using readily available data. Copyright © 2008 John Wiley & Sons, Ltd. Les impacts negatifs potentiels du changement climatique conduisent a chercher des actions et des mesures d'adaptation pour contribuer a attenuer la pression sur les ressources en eau, la production agricole et la qualite de l'environnement. Pour explorer les effets possibles et evaluer l'efficacite des differentes strategies d'adaptation, il est tres important de disposer d'un cadre de modelisation integree qui permette de relier et d'integrer les modeles climatiques avec des modeles hydrologiques au niveau du bassin et de la parcelle. Cet article apporte une evaluation des effets du changement climatique sur les ressources en eau, l'agriculture et l'environnement dans le systeme d'irrigation du Zayandeh Rud, pres d'Ispahan (Iran), a l'aide de modeles de simulation. Il met egalement l'accent sur les differentes strategies d'adaptation pour attenuer les impacts negatifs. Les resultats des simulations montrent que l'impact potentiel du changement climatique sur le systeme d'irrigation du Zayandeh Rud pourrait se traduire par des penuries d'eau plus frequentes et plus graves. Parmi les strategies d'adaptation evaluees, le changement de mode de cultures est ressorti comme la meilleure mesure possible pour attenuer les impacts negatifs des changements climatiques. Les resultats montrent egalement la capacite du systeme de modelisation a traiter le probleme du changement climatique a l'aide des donnees disponibles. Copyright © 2008 John Wiley & Sons, Ltd.