Leaf area index (LAI) is an important agrometeorological index and a key variable which is influenced by many physical, biological and physiological processes in plant communities. This study evaluates the ability of RegCM model in simulating LAI using CLM land surface model in two states of static and dynamic vegetation cover for the period of 1991–2005. Two GCM models (CanESM2 and EC-Earth) are applied to determine the boundary conditions. The temperature at 2 m above ground and precipitation data were collected from 106 synoptic stations (Iran). Also, the ERA5 reanalysis data were implemented as the reference to evaluate the simulated LAI data. To identify the climate types of the selected sites, De Martonne climate classification was used. The results show the highest correlation coefficient (0.78) between simulated LAI of CLM (by CanESM2-RegCM-CLM model) versus reference LAI of ERA5. For the sites located in arid and Mediterranean climate regions, the LAI simulated by CanESM2-RegCM-CLM model presented the least deviations compared to the reference ERA5 data. Model results showed that for both CanESM2 and EC-Earth models, the static vegetation cover tends to overestimate the simulated LAI; but, the activation of carbon-nitrogen feedback within the models leads to underestimation of vegetation cover in the region. Future work is suggested for evaluating other agrometeorological indices (e.g. NDVI, EVI) to detect the dynamic feedback of carbon-nitrogen cycle.

Introduction: It is possible to guide the agricultural experts to achieve a suitable genotype and adapt to climatic conditions in proportion to the length of the modified growing season by identifying the impact of climate change in recent years on the cumulative rate of degree-days of plant growth. This will prevent the waste of capital and agricultural inputs and ultimately prevent the reduction of the final crop due to the mismatch of genotype-crop with the current climate. In the present study, an attempt has been made to study and compare the trend in the start and end of the growing season, the growing season length (GSL), and growing degree-days(GDD) during 1959-2018 in the elevated and coastal areas of Iran.Materials and Methods: For this study, the daily temperature of 27 synoptic stations were used including 19 stations in elevated areas and 8 stations in coastal areas during 1959-2018. The first day with a minimum daily temperature equal to or greater than 0, 5, and 10 °C was considered as the start of the growing season (SGS). Moreover, the first day after the start of the growing season which has a minimum daily temperature of less than 0, 5, and 10 °C was considered as the end of the growing season (EGS). Trend analysis was performed in time series of GSL and GDD based on thresholds of 0, 5, and 10 °C using the Mann-Kendall test. To compare the results, the statistical period of 60 years was divided into two periods of 30 years (1959-1988 and 1989-2018). In both periods, the statistical characteristics of the GSL and GDD based on the three thresholds mentioned in coastal and elevated areas were surveyed and compared. In this study, deviation from the mean was used to complete the study of changes in the GSL. This index shows the scatter of data around the mean.Results and Discussion: The GSL extension came from both the advance in SGS and the delay in EGS. Comparison results of the two 30-year periods (1959-1988 and 1989-2018) showed that during 1989-2018, in most stations the GSL has increased. During this period, based on 0 °C, the earliest and latest SGS were on February 24 and April 30 in Yazd and Shahrekord, respectively. Accordingly, the earliest and latest EGS were on October 15 and December 11 in Shahrekord and Gorgan, respectively. Based on 5 °C, the earliest and latest SGS were on February 10 and June 2 in Abadan and Gorgan, respectively. Accordingly, the earliest and latest EGS on September 17 and December 6 were at Shahrekord, Bam, and Abadan, respectively. Based on 10 °C, the earliest and latest SGS was on February 11 and June 20 at stations, respectively. Accordingly, the earliest and latest EGS were on August 27 and December 8 in Shahrekord and Bushehr, respectively. The shortest and longest GSLs based on all three thresholds of 0, 5, and 10 °C were Shahrekord and Bandar Abbas, respectively. The highest and lowest coefficient of variation of GSL were 20.8% in Zanjan and 4.9% in Bandar Abbas, respectively. Based on 0, 5, and 10 °C, the lowest GDDs in GSL are 3233, 1767, and 880 °C.d, respectively, and all of them were obtained at Shahrekord. On the other hand, the highest GDD0, GDD5, and GDD10 in GSL were 6783, 7372, and 5761 °C.d, respectively, in Yazd, Abadan, and Bandar Abbas. The most significant trend in GSL was in Zanjan, Zahedan, and Khorramabad.Conclusion: Examination of changes in the GSL indicates the existence of a significant trend in a limited number of stations. Also, with increasing the threshold from 0 to 5 and from 5 to 10 °C, there is a significant decreasing trend in more stations. At the threshold of 10 °C a significant and decreasing trend of GSL was observed in Urmia, Sanandaj, Khorramabad, Birjand, and Bandar Abbas stations, In following, a significant increasing trend was observed in Tabriz, Tehran, Kermanshah, Isfahan, Yazd, and Bushehr stations. The results of the studies showed fewer changes in the time series of the GSL based on thresholds of 0 and 5 °C in the statistical period of 1989-2018. On the other hand, the results showed that the GSL trend is significant in more stations in the recent period based on the threshold of 10 °C. Deviation from the average GSL in coastal areas was greater than the elevated areas so that the GSL based on 10 °C in both areas increased with greater slope and continuity. This increasing trend of deviation from the average in the coastal areas from the early '70s and the elevated areas from the early '90s and continues until now. In this regard, Bandar Abbas station and then Bushehr station had the longest GSL, and Shahrekord station had the shortest GSL among other stations which has been studied. Comparison of GDDs of the GSL during 1989-2018 showed the decrease of GDDs from south to north and from west to east of the country. Accordingly, in the southern stations of the country, the conditions for tropical plants (threshold of 10 °C) have become more suitable than the cold stations of the west and northwest, Time series analysis of the average annual GDDs based on the three thresholds during 1989-2018 showed a significant increasing (positive) trend in 93% of the stations. During the second 30-years period, Shahrekord and Shiraz stations did not show a significant trend in all three mentioned thresholds. However, the analysis of the annual average of GDDs during 1959-1988 showed the trend in 41% of the stations. According to the results of this study, it can be concluded that in cold regions, due to the increase in GDDs, the supply of cooling units for plants with certain cooling needs is more difficult. In the south of the country, as the total required GDD is achieved earlier, the GSL gets shorter, and therefore less dry biomass will accumulate in the product. This likely leads to a reduction in crop yields in this part of the country.

Physical evaluation of active soil layer can be a suitable indicator for detecting climate change trends, especially the temporal study of soil and air temperatures. Using the singular spectrum analysis (SSA), trends, oscillatory components, and the degree of the coincidence of the soil temperature (ST) versus air temperature (AT) and precipitation (Prc) time series were investigated in Iran for three thermal regime classes (mesic, thermic, and hyperthermic) during 1993–2017. The results showed that the highest and lowest increases in ST trend occurred in the mesic and thermic thermal regimes, respectively. The return period of approximately 2.1 to 2.6 years was detected in all three studied thermal classes likely as a result of quasi-biennial oscillation (QBO) variation. In the mesic regime, the annual ST was affected to an equal extent by almost every season. For the thermic regime, the annual ST was most influenced by the ST oscillations in autumn and spring and regarding the hyperthermic regime in winter and summer. Result demonstrated by the implementation of the coincidence which exists between the short- and long-term oscillations of ST and AT time series, one can generate and reconstruct ST data gaps based on AT.

Surface albedo is a key parameter in earth energy budget and global climate change studies. In this aspect, variation in vegetation covers is one of the most critical issues affecting global energy balance. The present study is conducted to examine the relationship between changes in vegetation cover (by using NDVI and EVI indices) and changes in surface albedo. Using qualified MODIS data, mean annual albedo variations (during the growing season) and their relation with the changes in NDVI and EVI values in 6 climate zones are investigated for the period of 2004 to 2016. The results show that the lowest variations of albedo are observed in the region with SH-C-VW climate and the highest in the SA-C-W climate. With the exception of A-C-W and SA-C-W climates, the absolute value of the Pearson correlation coefficient between albedo and EVI is stronger than NDVI. Hot spots of temperature and precipitation are detected by employing daily measurements of 17 weather stations to find the relationship with the changes in surface albedo. According to the results, surface albedo has increased during the study period in majority of the weather sites. Generally, increase in surface albedo was evident for 61% of the study area, while decreases were detected in 30% and no significant changes in the remaining areas. Also, except for SH-C-VW climate, albedo has experienced an increasing trend in all climates from April to September. The major albedo declines are found in the Hyrcanian forests, where higher vegetation cover is confirmed by Iran Forests, Range and Watershed Management National Organization. Examining the hot spots in temperature and precipitation reveals that the reduction of albedo (due to the increase in vegetation cover) in the areas coincides with more rainfall in the region. In contrast, higher temperatures were observed for areas with the decreased vegetation cover.

Soil moisture plays a crucial role in vegetation growth. However, the long-term influence of soil moisture on vegetation growth was not sufficiently understood in many regions, especially in developing countries, due to the lack of ground measurements. Remote sensing data provide a promising way to overcome this limitation. In this study, a long-term spatiotemporal variation in remote sensing surface soil moisture (SSM) and vegetation indices (VIs) and their relationship during 1988–2015 were analyzed over Iran. Trend-free pre-whitening Mann–Kendall test was applied for the detection of the trends in soil moisture and VIs time series. Also, the validity of the “dry gets drier, wet gets wetter” (DGDWGW) paradigm was examined throughout the country. Finally, the consistency between SSM and vegetation indices trends was investigated using homogeneity Chi-squared test. The results showed that the monthly average of the SSM over 45% of Iran is lower than 0.15 m3/m3. Seasonal average of SSM is 0.17 and 0.12 m3/m3 in spring and winter, respectively. On average, monthly SSM trends are downward in 70% of Iran, which 30% of those is statistically significant. Over the past 28 years, about 50% and 35% of Iran got drier with rates of 0.24 × 10−2 (spring) and 0.78 × 10−3 (summer) m3/m3 per year. According to DGDWGW paradigm examining, 14% of Iran follows the DGDWGW paradigm. Summer and spring normalized difference vegetation index (NDVI) and enhanced vegetation index values are less than 0.1 in about 45% and 65% of the areas, respectively. The NDVI values are decreased in 40% of Iran in the last 28 years, of which half of those are statistically significant. SSM trends were consistent with vegetation indices using a homogeneity test. About 45% of SSM trends agree in sign with NDVI values. Life zone in the Southeast of Iran is arid desert scrubs, and in this area with sparse vegetation cover, mixing the spectral of soil and vegetation causes a serious problem in vegetation indices representing therefore the prominent mismatch between SSM and vegetation indices trends which were observed in Southeast of Iran.

The current study aimed at investigating cycles and the spatial autocorrelation pattern of anomalies of thunderstorms in Iran during different periods from 1961 to 2010. In this analysis, 50-year periods (1961–2010) of thunderstorm codes have been collected from 283 synoptic stations of Meteorological Organization of Iran. The study period has been divided into five different decades (1961–1970, 1971–1980, 1981–1990, 1991–2000, and 2001–2010). Spectral analysis and Moran’s I were used to analyze cycles and the spatial autocorrelation pattern, respectively. Furthermore, in order to conduct the calculations, programming facilities of MATLAB have been explored. Finally, Surfer and GIS were employed to come up with the graphical depiction of the maps. The results showed that the maximum of positive anomalies mainly occurred in the northwestern and western parts of Iran due to their special topography, during all the five studied periods. On the other hand, the minimum of negative anomalies took place in central regions of the country because of lack of appropriate conditions (e.g., enough humidity). Moran’s I spatial analysis further confirmed these findings as Moran’s I depicts the positive and negative spatial autocorrelation patterns in line with negative and positive anomalies, respectively. However, in recent decades, this pattern has experienced a declining trend, especially in southern areas of Iran. The results of harmonic analysis indicated that mainly short-term and midterm cycles dominated Iran’s thunderstorms.

Nitrate loss is a major reason for non-point source contamination on agricultural lands. The objective of this study was to assess the Hydrus-1D model for simulation of soil nitrate in different irrigation regimes, including 100 (I1), 85 (I2), and 70 (I3) % of the water requirement for sugarcane and different urea fertilization rates with 150 (N1), 250 (N2), and 350 (N3) kg/ha. Van Genuchten (VG) parameters were estimated by the RETC [Retention Curve Program for Unsaturated Soils] program. According to the results, by reducing the amount of irrigation water, the soil nitrate accumulation from the soil surface to the deep soil increased by 17 and 35% under I2 and I3 treatments compared with I1. Results showed that the Hydrus-1D model had fair potential for predicting the NO3-N accumulation in the soil profile over the sampling period (AE: −1.25 to 0.99, RMSE: 0.96 to 2.50, d: 0.78 to 0.98). The coefficient of determination between the field measured and simulated values in the soil layers at depths of 30, 60, 90 and 120 cm were 0.87, 0.88, 0.74, and 0.52, respectively. To reduce nitrogen losses, fertilizer application rates must be considered based on sugarcane needs and soil hydraulic properties.

This study investigates the accuracy of the ISCCP FD and AIRS surface air and skin temperatures, as well as the ISCCP FD and MODIS surface albedos and aerosol optical depths. The comparisons showed that the AIRS air and skin temperatures were slightly more accurate compared to the surface measurements than those in ISCCP FD. The MODIS surface albedos differed from the ISCCP FD values by no more than 0.02–0.07, but because these differences are mostly at longer wavelengths, they did not change the net solar radiation very much. Therefore, to obtain the best estimate of surface net radiation with the best combination of spatial and temporal resolution, we developed a method to adjust the ISCCP FD surface longwave fluxes using the AIRS surface air and skin temperatures to obtain the higher spatial resolution of the latter, while retaining the 3-hr time intervals of the former.

In arid areas, the variation of air temperature can be considerable, so instantaneous air temperature Tai estimation is needed in different environmental researches. In this research, two different remote sensing data are used for estimating Tai for clear sky days in 2009 in Fars Province, Iran, including atmospheric temperature profile and land surface temperature LST data from Moderate Resolution Imaging Spectroradiometer. The Tai from a number of surface weather sites is used to judge the best Tai estimation. Stations’ elevation, latitude, and land cover type are considered to show their effect on Tai estimation. The estimated Tai evaluation focuses on daily and seasonal timescales in the daytime and night time separately. Both LST and vertical temperature profile data produced relatively high coefficient of determination values and small root mean square error value for Tai estimation, especially during the night time. Land cover and elevation vary the error values in Tai estimation more, when LST data is used. In comparison atmospheric temperature profile indicates a smaller error in Tai estimation in spring and summer and in urban land cover type, while using LST data presents a better result in fall and winter especially at night time.

The daily minimum air temperature data from 18 stations located in the northwest of Iran during the period 1986–2015 was used to analyse the inter-annual variations and trends of thermal growing season indices (tGSI) and their relations with different atmospheric teleconnection patterns (ATPs). To analyze the changes in tGSI, tGSS (thermal growing season start), tGSE (thermal growing season end), and tGSL, the time period between tGSS and tGSE were considered. Using non-parametric Mann-Kendall and Spearman tests, the existence of a significant trend for time series of the tGSI and the correlation between ATPs and tGSI was evaluated. For eliminating the effect of serial correlation on test results, the trend-free pre-whitening approach was applied. Furthermore, residual bootstrap method was used to estimate the standard deviation of the Spearman correlation coefficient between tGSI and ATPs. The climate-based results showed the maximum tGSL increase (13.3 days per decade) for SA-C-M climate. For SH-K-W climate, the maximum significant trends for tGSS and tGSE were 9.6 (earlier start) and 10.8 (delay) days per decade, respectively. In general, in all statistically significant cases, the main cause of the extended tGSL was both earlier tGSS and delayed tGSE. In regional scale, it was found that the most effective teleconnection pattern on tGSS and tGSE are MEI (positive correlation), occurring during late winter and spring, and PDO index (negative correlation) in the summer, respectively. Moreover, the tGSL demonstrated the highest correlation (negative) with PDO with 1-month delay. The findings highlight that the inter-annual variations of tGSI in northwest of Iran can be attributed to the influence of certain atmospheric teleconnection patterns such as MEI, PDO, NAO, AO, EA, and AMO.

The most important factor in determining crop water requirement is estimation of evapotranspiration (ET). Majority of the methodsestimate ET apply series of relatively complex formula,which is then used to determine crop evapotranspiration (ETc). The parameters used in aforesaid methods are: Solar radiation, wind speed, humidity, etc. Unfortunately, in Iran and many countries, long-term records of these parameters are not readily available. The purpose of this study is to calculate the Selianinov Hydrothermic Index that merely requires daily temperature and precipitation data in order to determine correlation coefficients (r) versus ET and Crop Water Requirement (CWR) of some agricultural crops of Iran. First, the Selianinov index is calculated from daily precipitation and temperature during the growth season. Further, the results are correlated against both ETc and CWR. The model results indicate inverse (negative) strong exponential and polynomial relations between the dependent and independent variables. Coefficient of determination (R2) for polynomial equations (on average 0.84) in all crops was better than exponential equations (on average 0.72). Correlation between Selianinov index and CWR indicates that coefficient of determination in both equations was close together (0.83 for polynomial equations and 0.82 for exponential equations).

The present study examined annually and seasonally trends in climate-based and location-based indices after detection of artificial change points and application of homogenization. Thirteen temperature and eight precipitation indices were generated at 27 meteorological stations over Iran during 1961–2012. The Mann–Kendall test and Sen’s slope estimator were applied for trend detection. Results revealed that almost all indices based on minimum temperature followed warmer conditions. Indicators based on minimum temperature showed less consistency with more cold and less warm events. Climate-based results for all extremes indicated semi-arid climate had the most warming events. Moreover, based on location-based results, inland areas showed the most signs of warming. Indices based on precipitation exhibited a negative trend in warm seasons, with the most changes in coastal areas and inland, respectively. Results provided evidence of warming and drying since the 1990s. Changes in precipitation indices were much weaker and less spatially coherent. Summer was found to be the most sensitive season, in comparison with winter. For arid and semi-arid regions, by increasing the latitude, less warm events occurred, while increasing the longitude led to more warming events. Overall, Iran is dominated by a significant increase in warm events, especially minimum temperature-based indices (nighttime). This result, in addition to fewer precipitation events, suggests a generally dryer regime for the future, which is more evident in the warm season of semi-arid sites. The results could provide beneficial references for water resources and eco-environmental policymakers.

This study was carried out to evaluate the use of the crop water stress index (CWSI) for irrigation scheduling of sugar beet for two years under the semi arid climate of Iran. Statistical relationships between CWSI and yield, quality parameters and irrigation water use efficiency (IWUE) were investigated. Irrigations were scheduled based on 100 (I100), 85 (I85), 70 (I50) and 0% (I0) of plant water requirement. CWSI values were calculated from the measurements of canopy temperatures by infrared thermometer, air temperatures and vapor pressure deficit values for all the irrigated treatments. The highest IWUE was found in I70 with 9.16 and 1.66 kg m−3 for the root and sugar yield, respectively, in 2013. A non-water stressed baseline (lower line) equation for sugar beet was measured from full irrigated plots as (Tc − Ta)ll = −0.832VPD + 2.1811; R2 = 0.6508. There was a high determination coefficient between CWSI with the root and sugar yield and IWUE. The CWSI could be used to determine the irrigation time of sugar beet, and 0.3 could be offered as a threshold value. Results indicated that the CWSI can be used to evaluate crop water stress and improve irrigation scheduling for sugar beet under semiarid conditions.

Net radiation (Rn) is one of the effective inputs for controlling soil heat flux, thermal convection, moisture flux exchange, and reference crop evapotranspiration (ET0) rate. In this research, the accuracy of some empirical and semiempirical Rn models is examined for different climates of Iran versus the recommended net radiation model as proposed in the Penman-Monteith Food and Agricultural Organization of the United Nations 56standard (FAO 56) model for the period 1980-2007. For estimating daily net radiation, various net radiation models [Wright, basic regression model (BRM), Linacre, Berliand, Irmak, and Monteith] were examined. Model evaluations were implemented for four climate types. On regional averages, the linear BRM had the superior performance in generating the most accurate daily ET0. Results showed that for 70% of the study sites, the linear Rn models can be reliable candidates instead of sophisticated nonlinear Rn models, which are proposed in the reference FAO 56model. For some sites, with low altitude and high relative humidity (e.g.,coastal humid sites), the Irmak model suggested the minimum deviations from the reference FAO 56model. Using the best-performing Rn models is recommended for the agricultural sites where comprehensive weather data are not available. (C) 2016 American Society of Civil Engineers.

Introduction :Higher temperature as the result of climate change are likely to affect horticultural production. Deciduous fruit trees need winter chilling to break winter dormancy. Climate plays an important role in the successful production of deciduous fruit. Winter dormancy is one of the key factors of the annual cycle of deciduous fruit and nut trees along with the following breaking of the dormant state. This state is maintained through the winter period each year to protect against damaging cold temperatures. To be released from dormancy, trees require exposure to a predetermined quantity of cold temperatures in a process known as winter chilling or vernalization. Insufficient chilling can lead to sporadic and light bud break, poor fruit development, small fruit size and uneven ripening times. The main objective of this study is to investigate climate change effect on the winter chilling requirement (WCR) in Hamadan. Materials and Methods:This research was performed based on the General Circulation Models (BCM2, HADCM3,GFCM2 and IPCM4) and different emission scenarios (A2, B1, A1B), as recommended by the Forth Report of the IPCC. The output of the GCMs was downscaled by LARS-WG model. The hourly weather data were generated as the inputs of three different Chilling Requirement Models (CRMs), and the winter chilling trend of deciduous fruit trees were predicted for Hamadan. The projected daily temperature time series were then converted into hourly temperatures. The projected hourly temperature data were run through each of the three chill models for all four GCMs in different scenarios. Three chill models [the 0.0–7.2°C (CH), the Utah (UT), and the Utah Positive (UTPos) models] were used to investigate changes in chill accumulation in Hamadan, according to localized temperature change related to increases in global average temperatures. In addition, the winter chilling requirement time series were divided into two periods: baseline and future time. Historical daily minimum and maximum data from 1980 to 2010 were used from the Hamadan airport synoptic station. Future time horizon splittedinto early (2011-2030) and late (2031-2050) periods. For evaluating the long-term future changes in the chilling requirement, we used parametric and non-parametric tests. Results and Discussion: The model results showed a decreasing WCR trend during the recent decade. In general, the outputs of downscaled climate models predicted a decreasing WCR trend for the study site. For the time horizon of 2031-2050, this dramatic reduction in the WCR varied rom 25 percent to 40 percent. Future chill profiles differentiated between the WCR models as demonstrated through Hamadan global average temperature, causing a small decline in accumulated chill unit, with further warming causing greater decreases. This decrease in the UT models can be due to the negative effect of high temperature during this period. The study result which showed the WCR mean during early time horizon 2011-2030, was not significant but further time horizon 2031-2050 had a very significant change, as compared to the baseline. The aim of this study was to assess changes in the WCR rather than completing a model skill analysis. Through using previous climate model performance studies a justification of the addition of GCMs was described. Such defenses for model selection are recommended in all climate change impact studies. Test of model output in other scenarios and different GCMs showed an insignificant versatility between them. Conclusions: This research represents a significant update to the previous climate impact analysis of chill in cold semi-arid climate of Hamadan. It also highlights that sensitivity studies as a useful method for impact assessments. The severity and rate of decline of winter chilling requirement, depends on which chill model was used. The general trend showed decreasing of the winter chilling requirement against the winter temperature trend. Therefore, in the context of global warming, the earlier flowering dates of many deciduous tree species is likely leads to increased risk of damage during the late spring frost. For future fruit farm management, decisions can be implemented with deliberation of the likely changes in the winter chilling requirement reported here. There might be some adaptation, at least to some degree, being essential for most production areas in Hamadan and other similar climate conditions within the next 40 years. Reduction in winter chilling, prevents breaking winter dormancy, which finally may lead to serious damage to deciduous fruits.

This study compares the precipitation regime by using harmonic analysis during the last four decades (1965-2004).We used interpolated precipitation data from different weather stations distributed across Iran by applying 15x15 km spatial grids for the interpolated data. Data validations were employed by statistical tests. In this study three harmonies of precipitation variances were evaluated. Variability of precipitation regime was explored by using three-harmonic analysis method. In addition, the effect of geographical factors (altitude, latitude, longitude) affecting to the precipitation regime was verified. Analysis of the first harmonic method proved that the main precipitation regime in Iran occurs in winter season as result of large scale Mediterranean systems passing over Iran in the mentioned season. Moreover, the fluctuations of the seasonal precipitation regime were found to be different, so that in one region led to the appearance of the new regimes and in other region caused change or disappearance of the regimes. In all three harmonics, variances of precipitation were mainly a function of the geographical factors. This effect was more evident in the third harmonic, in such a way that the increases in latitudes (moving to northern region) caused higher precipitation variance. This means that precipitation regime in northern sites are more exposed to local factors and seasonal precipitation than those of southern sites. The results of this research can be used in land–use projects, environmental plans and water resources management.

In this study, changes in the spatial and temporal patterns of climate extreme indices were analyzed. Daily maximum and minimum air temperature, precipitation, and their association with climate change were used as the basis for tracking changes at 50 meteorological stations in Iran over the period 1975–2010. Sixteen indices of extreme temperature and 11 indices of extreme precipitation, which have been quality controlled and tested for homogeneity and missing data, are examined. Temperature extremes show a warming trend, with a large proportion of stations having statistically significant trends for all temperature indices. Over the last 15 years (1995–2010), the annual frequency of warm days and nights has increased by 12 and 14 days/decade, respectively. The number of cold days and nights has decreased by 4 and 3 days/decade, respectively. The annual mean maximum and minimum temperatures averaged across Iran both increased by 0.031 and 0.059 °C/decade. The probability of cold nights has gradually decreased from more than 20 % in 1975–1986 to less than 15 % in 1999–2010, whereas the mean frequency of warm days has increased abruptly between the first 12-year period (1975–1986) and the recent 12-year period (1999–2010) from 18 to 40 %, respectively. There are no systematic regional trends over the study period in total precipitation or in the frequency and duration of extreme precipitation events. Statistically significant trends in extreme precipitation events are observed at less than 15 % of all weather stations, with no spatially coherent pattern of change, whereas statistically significant changes in extreme temperature events have occurred at more than 85 % of all weather stations, forming strongly coherent spatial patterns.