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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]

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]

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]

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]

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]

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]

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]

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]

Tourism is a rapidly growing international sector and relies intrinsically on an amenable climate to attract visitors. Climate change is likely to influence the locations preferred by tourists and the time of year of peak travel. This study investigates the effect of climate change on the Tourism Climate Index (TCI) for Iran. The paper first calculates the monthly TCI for 40 cities across Iran for each year from 1961 to 2010. Changes in the TCI over the study period for each of the cities are then explored. Increases in TCI are observed for at least one station in each month, whilst for some months no decreases occurred. For October, the maximum of 45 % of stations demonstrated significant changes in TCI, whilst for December only 10 % of stations demonstrated change. The stations Kashan, Orumiyeh, Shahrekord, Tabriz, Torbat-e-Heidarieh and Zahedan experienced significant increases in TCI for over 6 months. The beginning of the change in TCI is calculated to have occurred from 1970 to 1980 for all stations. Given the economic dependence on oil exports, the development of sustainable tourism in Iran is of importance. This critically requires the identification of locations most suitable for tourism, now and in the future, to guide strategic investment. [ABSTRACT FROM AUTHOR]

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

In order to better understand the effect associated with global climate change on Iran's climate condition, it is important to quantify possible shifts in different climatic types in the future. To this end, monthly mean minimum and maximum temperature, and precipitation from 181 synoptic meteorological stations (average 1970-2005) have been collected from the meteorological organization of Iran. In this paper, to study spatial changes of Iran's climatic zones affected by climate changes, Extended De Martonne's classification (originally formulated by De Martonne and extended by Khalili ()) was used. Climate change scenarios were simulated in two future climates (average conditions during the 2050s and the 2080s) under each of the SRES A1B and A2, for the CSIRO-MK3, HadCM3, and CGCM3 climate models. Coarse outputs of GCMs were downscaled by delta method. We produced all maps for three time periods (one for the current and two for the future) according to Extended De Martonne's classification. Finally, for each climatic zone, changes between the current and the future were compared. As the main result, simulated changes indicate shifts to warmer and drier zones. For example, in the current, extra arid-cold ( A1.1m2) climate is covering the largest area of the country (21.4 %), whereas in both A1B and A2 scenarios in the 2050s and the 2080s, extra arid-moderate ( A1.1m3) and extra arid-warm ( A1.1m4) will be the climate and will occupy the largest area of the country, about 21 and 38 %, respectively. This analysis suggests that the global climate change will have a profound effect on the future distribution of severe aridity in Iran. [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]

Accurate use of precipitation can be considered as one of the best options to decrease the amount of underground water extraction for agriculture in arid and semi-arid areas such as northeast of Iran. For this reason, characteristics of the growing season such as onset, cessation, and length of the growing period should be analyzed. In this paper, we have calculated growing season characteristics of five locations in northeast of Iran using 45 years historical daily weather data and employed four approaches with different calculation methods. As temperature is one of the limiting factors in irrigation-based agriculture, the first approach has been based on this factor. The three remaining approaches were based on joint rainfall and temperature approach, rainfall, evapotranspiration, and temperature approach, and the final approach was based on availability of adequate water in 0.25 m of soil profile. The calculated onset dates using second and third approaches have been based on soil water balance model and relative evapotranspiration rate, and both were evaluated also to find whether the onset is a false start occurrence or not. The results showed that, when temperature was the only limiting factor, Bojnourd station with 197 days showed the longest growing season, however, when precipitation was used along with temperature, longest growing season (124 days) was obtained for Sabzevar station. The third approach which benefits from a water balance model and is similar to rainfed conditions showed the longest growing season with 147 days for Mashhad station. When adequate soil water approach was used, Bojnourd station with 255 days showed the longest growing season. Evaluation of false start of the growing season indicated the lowest probability of false start occurrence for Mashhad compared with other locations. [ABSTRACT FROM AUTHOR]