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In recent years, extreme events (droughts, floods, and extreme heat waves), as indicators of climate change, have received heightened attention. Increased concentration of greenhouse gases has led to global warming and significant changes in climatic extreme events pattern. In this study, 28 extreme indices proposed by a joint project between the World Meteorological Organization, the Climatic Group, the Climate Variability and Predictability Group, and the Global Research Program for Climate Change Monitoring and Detection, have been calculated from daily temperature and precipitation series of 12 meteorological stations in South Khorasan province during the period of 1992-2016. The non-parametric Mann-Kendall (MK) statistical test has been used to assess the significance of trend in extreme indices time series. The results showed a significant increasing trend in warm extreme indices and the decreasing trend for cold extreme events in most stations. Such that, the number of summer days with mean temperature above 25 degrees Celsius (SU 25) has an increasing trend in all stations. A decreasing trend was observed in cool nights (TN10P) series in most stations. There exists a high spatial variation for rainfall in the study region and no significant pattern could be observed, but in general, the trend in most stations is decreasing, for instance, the rainfall events greater than 1mm in north and central regions of the province has significantly decreased.

Global warming has substantial impacts on the hydrologic cycle and precipitation characteristics. This study aimed to explore variability in the form of precipitation by analysis of trends in the snowfall-day (SD)/total precipitation-day (PD) ratio across Iran. To this end, the PD and SD records were acquired from 33 synoptic stations during 1965–2014. The Mann-Kendall (MK) test was the main method used for trend detection. Analyzing data exposed that the annual SD/PD ratio was less than 25% in a plethora of the stations. The MK test findings indicated downward tendencies in the SD/PD ratio over most regions of the country; although in many cases, the observed trends were not statistically significant. The changes in the annual SD/PD ratio were estimated down to − 2% per decade over ~ 70% of Iran’s total area. Further studies are required to ascertain the linkages between the large-scale climate oscillations and the form of precipitation across this region. Also, using remotely sensed data (e.g., obtained from the MODIS) and fine-resolution gridded data (e.g., global and regional reanalysis data) in future research could improve the findings of such studies and dwindle the uncertainties caused by the scanty site-based observations.

Unsustainable groundwater withdrawal is a major challenge facing semi-arid and arid regions of the world, where groundwater is the primary source of irrigation water. Conversion of some agricultural land to fallow land is one of the possible solutions to reduce the groundwater withdrawal to bring it to safe yield. Such a solution has a negative economic impact. In this study, we have developed an optimization approach that can use used to identify the size (and type) of the agricultural area to be fallowed to ensure sustainable groundwater use for irrigation with as little economic impact as possible. This approach is based on the concept of profit productivity, which relies on Landsat imageries to calculate crop yield and irrigation water withdrawal, and available in-situ data to calculate energy cost. We have applied this approach to the Neyshabour basin in Iran that has been experiencing unsustainable groundwater withdrawal for the last 30 years. Our sample results indicate that in order to ensure sustainable groundwater withdrawal with minimum economic impact, 4% of the agricultural land needs to be converted to fallow land by fallowing most of the agricultural land used for growing tomatoes.

By combining long-term ground-based data on water withdrawal with climate model projections, this study quantifies the compounding effects of human activities and climate change on surface water availability in Iran over the twenty-first century. Our findings show that increasing water withdrawal in Iran, due to population growth and increased agricultural activities, has been the main source of historical water stress. Increased levels of water stress across Iran are expected to continue or even worsen over the next decades due to projected variability and change in precipitation combined with heightened water withdrawals due to increasing population and socio-economic activities. The greatest rate of decreased water storage is expected in the Urmia Basin, northwest of Iran, (varying from ~ − 8.3 mm/year in 2010–2039 to ~ − 61.6 mm/year in 2070–2099 compared with an observed rate of 4 mm/year in 1976–2005). Human activities, however, strongly dominate the effects of precipitation variability and change. Major shifts toward sustainable land and water management are needed to reduce the impacts of water scarcity in the future, particularly in Iran’s heavily stressed basins like Urmia Basin, which feeds the shrinking Lake Urmia.

Soil temperature is a fundamental agrometeorological variable which has a measureable influence on the development of plants. A comprehensive analysis of daily soil temperature trends at predetermined depths can provide a quantitative understanding for the sustainable management of agricultural systems, such as mitigating the adverse effects of anticipated temperature stress, supporting confidence levels of ideal seed sowing dates, as well as irrigation treatment durations and intervals. In this paper, daily soil temperature trends were analyzed (using the Mann-Kendall test) at depths of 5, 10, 20, 30, 50 and 100 cm at seven discrete meteorological stations located throughout northeastern Iran during a consecutive period from 1993 to 2016. The results of this research indicate that in most of the study stations there exists a non-significant negative trend in soil temperature in the first half of January and also in the second half of December; these trends become significant as the soil gets deeper. For the warmer months of the year, especially from April to the end of August, soil temperature trends are positive and significant (or near significant) and these positive trends are of greater significance with a corresponding increase in soil depth. Agri-environmental agencies and practitioners may benefit from the approach used in this research (i.e. using the Mann-Kendall test to analyze trends in daily soil temperature time series) since it is an easily transferable approach that can provide a comprehensive geospatial and temporal analysis of crop and plant patterns in different regions and environmental conditions. Additionally, farmers may use daily soil temperature trends as a means to encourage the protection and mitigation of a variety of projected environmental stresses, such as water scarcity, irrigation demands, seasonal temperature fluctuations, and redundant crop rotation.

Soil temperature is a very important variable in agricultural meteorology and strongly influences agricultural activities and planning (e.g. the date and depth of sowing crops, frost protection). There are many physically based studies in the literature which model soil temperature, but few are easily applicable for use in the field. Simple and precise short-term forecasting of soil temperature with minimum data requirements is the main goal of this study. The soil temperature at 0300, 0900 and 1500 GMT was forecast based only on surface air temperatures using artificial neural network (ANN) and wavelet transform artificial neural network (WANN) models. The hourly data were collected from the Mashhad synoptic station in Khorasan Razavi province in Iran between 2010 and 2013. The results of this study showed that using a wavelet transform for preprocessing improved the accuracy of soil temperature forecasting. It was also found that changing the temporal increment in forecasting time did not have a noticeable effect on errors in the WANN models. WANN models can be used as accurate tools to forecast soil temperature 1–7 days ahead at depths of 5–30 cm.

Air pollution is one of the biggest global environmental problems in urban regions. This study aimed to investigate the validity of the relationship between air pollution and respiratory and cardiovascular hospitalization using time series methods. This time series study was conducted in Isfahan, Iran. We used data of hospitalized cases in three main university hospitals (Alzahra, Noor, Chamran) and air pollution data from 2014 to 2016. We applied the autoregressive distributed lag (ARDL) bounds testing approach of cointegration to examine the relationship between the air pollution and hospitalizations of respiratory and cardiovascular diseases. The results of air quality assessment on the number of respiratory and cardiovascular hospitalization demonstrate that in the case of cardiovascular disease, both in the long run and in the short run, the air quality index has a significant impact on men and women with a bigger impact in the long run compared to the short run. The value of the long-run coefficient indicates the relationship between air pollution index and cardiovascular hospitalization is stronger than respiratory hospitalizations. In the long term, the effect of the air quality index (AQI) on the number of hospitalizations is more than that in the short term. Based on the results, a 10-unit increase in AQI leads to 5.3% increase in the number of respiratory hospitalization. Accordingly, a 10-unit increase in AQI will result in 7.3% increase in the number of cardiovascular hospitalizations.

Atmospheric visibility (AV) is an indicator for assessing air quality and is measured in standard weather stations. The AV can change as a result of two main factors: air pollution and atmospheric humidity. This study aimed to investigate trends in the number of days with AV equal or less than 2 km (DAV2) in Iran during 1968–2013. Consequently, 43 weather stations with different climates were evaluated across the country, using the Mann–Kendall (MK) trend test. The results show that the number of stations with positive (i.e., significant or non-significant) MK z values was equal to, or greater than, those with negative MK z values, in all months and seasons of the year, as well as annually. Furthermore, summer and autumn had, respectively, the least and most stations with positive MK z values. Fewer trends in DAV2 were detected in the central, east, and northeast regions of the country. Analyzing the DAV2 and relative humidity together indicated that over 30% of stations had at-risk air quality in January, and that the largest number of stations with at-risk air quality was in the autumn and winter. These results are useful for better environmental planning to improve air quality, especially in developing countries such as Iran, where reduced air quality has been a major problem in recent decades.

The purpose of this research was to predict soil temperature profile using ‘panel-data’ models. Panel-data analysis endows regression analysis with both spatial and temporal dimensions. The spatial dimension pertains to a set of cross-sectional units of observation. The temporal dimension pertains to periodic observations of a set of variables characterizing these cross-sectional units over a particular time-span. This study was conducted in Khorasan-Razavi Province, Iran. Daily mean soil temperatures for 9 years (2001–2009), in 6 different depths (5, 10, 20, 30, 50 and 100 cm) under bare soil surface at 10 meteorological stations were used. The data were divided into two sub-sets for training (parameter training) over the period of 2001–2008, and validation over the period of the year 2009. The panel-data models were developed using the average air temperature and rainfall of the day before (Td−1 and Rt−1, respectively) and the average air temperature of the past 7 days (Tw) as inputs in order to predict the average soil temperature of the next day. The results showed that the two-way fixed effects models were superior. The performance indicators (R2 = 0.94 to 0.99, RMSE = 0.46 to 1.29 and MBE = −0.83 and 0.74) revealed the effectiveness of this model. In addition, these results were compared with the results of classic linear regression models using t-test, which showed the superiority of the panel-data models.

Large-scale climatic teleconnections have noticeable effects on meteorological events in different regions of the world. In this study, the linkages between three major climatic indices, Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and Southern Oscillation Index (SOI), and precipitation in Iran were assessed from 1960 to 2014, at 30 synoptic stations in a time-frequency space, using wavelet coherence (WCO). The results showed that the SOI is the most effective climatic teleconnection on precipitation in Iran, although the other studied climatic indices have noticeable effects as well. The predominant and effective period of AO on precipitation was equal to or greater than 32 months at most of the stations, while the major effective period of NAO was equal to or greater than 64 months. For the SOI, most parts of the country were affected by a period of less than 64 months, while the predominant period of SOI for the northwestern part of the country was greater than 64 months. A uniform phase difference was not observed between the three studied climatic indices and precipitation in the country; instead the phase differences were usually random. For long-term periods of SOI, an anti-phase situation was detected at most of the stations. The study suggested that the WCO is a very powerful and flexible method for studying the relationship between multiple time series in a time–frequency space, and its application in hydrological and meteorological research is expected to increase in the near future.

In this study variation of six climatic indices including accumulated precipitation (P), accumulated potential evapotranspiration (PET), accumulated actual evapotranspiration (AET), accumulated crop evapotranspiration (ETC), accumulated water stress (S) and climatic water deficit (D), was investigated. Climatic indices and their variation were calculated during seven growth stages of wheat in five locations in the northeast of Iran from 1983 to 2008. Principal component analysis (PCA) technique was applied to explore major modes of variation in the regional climatic indices during different crop growth stages. The principle component obtained for each region was correlated to the regional winter wheat yield. Finally the regional amount of water and precipitation use efficiency (WUE and PUE) were analyzed in order to assess any possible association with wheat yield. The results showed that the highest precipitation occurred during the tillering stage and spatially decreased from north (Bojnord) to south (Birjand) and from east (Mashhad) to west (Sabzevar). The difference between the highest and the lowest precipitation across all locations was 2.5 of standard value. The variation pattern of AET, compared to other indices, showed more similarity to variation of precipitation at different growth stages and the highest AET (more than 2 of standard value in all locations) occurred during the tillering stage. The PCA indicated that effective components varied in different locations. The most positive and effective components were types of evapotranspiration that are associated with crop (ETC and AET) and precipitation. However none of these effective PCs showed a significant correlation with final yield. The PUE and WUE analysis indicated that PUE provides more information to interpret the relationship between total amounts of precipitation and the final yield.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission with the MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) L-band radiometer provides global soil moisture (SM) data. SM data and products from remote sensing are relatively new, but they are providing significant observations for weather forecasting, water resources management, agriculture, land surface, and climate models assessment, etc. However, the accuracy of satellite measurements is still subject to error from the retrieval algorithms and vegetation cover. Therefore, the validation of satellite measurements is crucial to understand the quality of retrieval products. The objectives of this study, precisely framed within this mission, are (i) validation of the SMOS Level 1C Brightness Temperature (TBSMOS) products in comparison with simulated products from the L-MEB model (TBL-MEB) and (ii) validation of the SMOS Level 2 SM (SMSMOS) products against ground-based measurements at 10 significant Iranian agrometeorological stations. The validations were performed for the period of January 2012 to May 2015 over the Southwest and West of Iran. The results of the validation analysis showed an RMSE ranging between 9 to 13 K and a strong correlation (R = 0.61–0.84) between TBSMOS and TBL-MEB at all stations. The bias values (0.1 to 7.5 K) showed a slight overestimation for TBSMOS at most of the stations. The results of SMSMOS validation indicated a high agreement (RMSE = 0.046–0.079 m3 m−3 and R = 0.65–0.84) between the satellite SM and in situ measurements over all the stations. The findings of this research indicated that SMSMOS shows high accuracy and agreement with in situ measurements which validate its potential. Due to the limitation of SM measurements in Iran, the SMOS products can be used in different scientific and practical applications at different Iranian study areas.

Agricultural vulnerability can be referred to the degree that agricultural systems may experience harm due to a stress. A simulation study was conducted to assess the vulnerability of wheat (irrigated and rainfed) and maize production due to drought and climate change in the Northeast of Iran. UNEP Aridity Index (AI U ) was calculated to measure drought situation in five agricultural centers including Birjand, Bojnourd, Mashhad, Sabzevar and Torbat Heydarieh. Projected changes in climate variables were simulated by two General Circulation Models: HadCM3 and IPCM4 under three scenarios (A1B, A2 and B1), simulated by LARS-WG. The Cropping System Model (CSM)-CERES-Wheat and (CSM)-CERES-Maize were used for crop growth simulation under projected climate conditions. In order to quantify the magnitude of vulnerability to varying drought conditions, vulnerability was considered as a function of sensitivity, well-being state relative to its damage threshold and exposure. Vulnerability was calculated considering severe droughts in the selected years and the expected vulnerability considering the expected frequency of drought. The results showed that in all the study locations the wheat and maize production have been affected extremely by severe droughts during the base period and both crops were extremely sensitive to drought. It was also projected that crop production will be extremely vulnerable to probable droughts during the projected years the same as the base period.

Human health and comfort, crop productivity, water resource availability, as well as other critical hydrological, climatological, and ecological parameters are heavily influenced by trends in daily temperature maxima and minima (T d max, T d min, respectively). Using Mann–Kendall and sequential Mann–Kendall tests, trends in the number of days when T d max ≥ 30 °C or T d min ≤ 0 °C, over the period of 1961 to 2010, were examined for 30 synoptic meteorological stations in Iran. For 67 % of stations, days when T d min ≤ 0 °C showed a significant negative trend, while only 40 % of stations showed a significant positive trend in days when T d max ≥ 30 °C. The upward trend in T d max became significant between 1967 and 1975, according to the station, while the downward trend in T d min became significant between 1962 and 1974 for the same stations. Changes in precipitation type across most parts of the country show a high correlation with these temperature trends, especially with the negative trend in T d min. This suggests that future climatological and hydrological alterations within the country, along with ensuing climatic issues (e.g., change in precipitation, drought, etc.) will require a great deal more attention.

This study explores a general framework for quantifying anthropogenic influences on groundwater budget based on normalized human outflow (hout) and inflow (hin). The framework is useful for sustainability assessment of groundwater systems and allows investigating the effects of different human water abstraction scenarios on the overall aquifer regime (e.g., depleted, natural flow-dominated, and human flow-dominated). We apply this approach to selected regions in the USA, Germany and Iran to evaluate the current aquifer regime. We subsequently present two scenarios of changes in human water withdrawals and return flow to the system (individually and combined). Results show that approximately one-third of the selected aquifers in the USA, and half of the selected aquifers in Iran are dominated by human activities, while the selected aquifers in Germany are natural flow-dominated. The scenario analysis results also show that reduced human withdrawals could help with regime change in some aquifers. For instance, in two of the selected USA aquifers, a decrease in anthropogenic influences by ~20% may change the condition of depleted regime to natural flow-dominated regime. We specifically highlight a trending threat to the sustainability of groundwater in northwest Iran and California, and the need for more careful assessment and monitoring practices as well as strict regulations to mitigate the negative impacts of groundwater overexploitation.

The aim of this research is to study the spatial and temporal variability of aridity in Iran, through analysis of temperature and precipitation trends during the 48-year period of 1961–2008. In this study, four different aridity criteria have been used to investigate the aridity situation. These aridity indexes included Lang’s index or rain factor, Budyko index or radiational index of dryness, UNEP aridity index, and Thornthwaite moisture index. The results of the analysis indicated that the highest and lowest mean temperatures occurred in July and January respectively in all locations. Among the study locations, Ahvaz with 37.1 °C and Kermanshah with 20.2 °C has the highest and lowest in July. For January, the highest was 12.4 °C for Ahvaz and the lowest was −4.5 °C for Hamedan and Kermanshah together. The range of monthly mean temperature of study locations indicated that the maximum and minimum difference between day and night temperatures, almost in all study locations, occurred in September and January, respectively, and the highest and lowest fluctuation of temperature was observed in Kerman and Tehran. The temperature anomalies showed that the most significant increasing temperature occurred at the beginning of twenty-first century (2000–2008) in all locations. The long-term mean of monthly rainfall showed that, in most study locations, the maximum and minimum of mean precipitation occurred in winter and summer, respectively. Rasht with 1,355 mm had the highest and Yazd with 55 mm had the lowest of total precipitation compared with other locations. According to precipitation anomalies, all locations experienced dry and wet periods, but generally dry periods occurred more often especially in the beginning of twenty-first century. According to applied different aridity indexes, all the study locations often experienced semi-arid to arid climate, severe water deficit to desert climate, arid to hyperarid climate, and semi-arid climate during the study period.

In this paper, we have utilized ANN (artificial neural network) modeling for the prediction of monthly rainfall in Mashhad synoptic station which is located in Iran. To achieve this black-box model, we have used monthly rainfall data from 1953 to 2003 for this synoptic station. First, the Hurst rescaled range statistical (R/S) analysis is used to evaluate the predictability of the collected data. Then, to extract the rainfall dynamic of this station using ANN modeling, a three-layer feed-forward perceptron network with back propagation algorithm is utilized. Using this ANN structure as a black-box model, we have realized the complex dynamics of rainfall through the past information of the system. The approach employs the gradient decent algorithm to train the network. Trying different parameters, two structures, M531 and M741, have been selected which give the best estimation performance. The performance statistical analysis of the obtained models shows with the best tuning of the developed monthly prediction model the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are 0.93, 0.99, and 6.02 mm, respectively, which confirms the effectiveness of the developed models.