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Since climate change is a major challenge that affects the environment, especially the hydrological flow, this research has used the Soil and Water Assessment Tool (SWAT) to study the climate change effects on the runoff in Alvand mountain basin in Iran. The SWAT model calibration was done based on the SUFI-2 algorithm and the validation results for a 20-year prediction period (2020–2040) showed that the model had high accuracy in simulating the runoffs. CMIP5 models were used to predict the basin's temperature and rainfall (the best scenario was Miroc5) and the results showed that the basin's future climate conditions increase in temperature and decrease in rainfall were appropriate. According to Miroc5 (RCP8.5), the annual runoff in the mentioned period would decrease by 8.36% compared to the past. In a seasonal scale, the largest runoff reduction would be 75% in summer (under the Miroc5 (RCP4.5)), and in a monthly scale, it would be 79% in April increasing dramatically to reach 335% in September (under Miroc5 (RCP8.5)). The trend of the future runoff was investigated using the Mann–Kendall model and the Gresson estimation method. Results showed that there would be no significant trend in the mentioned period and the trend of the future drought intensity would be increasing under all studied scenarios. [ABSTRACT FROM AUTHOR]

In this study, we assessed the impacts of climate variations on streamflow in 28 near-natural catchments in Iran. To this end, we analysed the trend of annual streamflow, precipitation, and temperatures using the Mann-Kendall test and Sen's slope between water years 1982 and 2011. We evaluated the frequency of precipitation in different classes, i.e. below 5, 5–10, 10–15, and above 15 mm/day. Our results indicate a decline in streamflow at 25 catchments, at a rate of −5.66 to −0.19 mm/year. The annual precipitation amount did not decrease significantly, while the frequency of light precipitation events (<5 mm/day) increased. About 60% of the upward trend was significant. Mean temperature increased at all studied catchments, at an average rate of 0.055°C/year. In short, our results indicate that increases in temperature and the frequency of light precipitation events are two leading factors in streamflow reduction across studied catchments. [ABSTRACT FROM AUTHOR]

In recent years, gridded precipitation products have been widely used in hydrology studies and other fields of water sciences. This study evaluated the potential of several gridded precipitation products, including GPCC, TRMM, CRU, ERA-Interim, and ERA5, in trend analysis of precipitation depth and the number of rainy days in various regions of Iran. Moreover, the observational precipitation data of the daily time series were collected from 68 Iranian synoptic stations. The Mann–Kendall test was conducted to determine gridded and observed precipitation trends in the period of 1997 to 2017. The probability of detection (POD) and false alarm ratio (FAR) indices were utilized to compare gridded and observed precipitation trends. Results showed that the best consistency (POD: 52% ~ 80%, FAR: 60% ~ 88%) was observed between the observed trends of the number of rainy days and those obtained by TRMM product over different regions of Iran. Moreover, ERA-Interim offered a better performance (POD: 50% ~ 100%, FAR: 58% ~ 72%) in the trend analysis of precipitation depth in Iran. The consistency between observational and gridded precipitation trends has never been analyzed in Iran at this level; therefore, this is considered a unique analysis. Besides, the generated maps of precipitation products' performance provide a comprehensive view of better water resources management over different regions of Iran. [ABSTRACT FROM AUTHOR]

Estimation of evapotranspiration, representing a highly sensitive variable to global warming, is often required in water resources and agricultural studies. This study explores the possible effects of climate change on evapotranspiration (ETo) across Iran. The ETo was computed using the Penman‐Monteith Temperature (PMT) method that requires only minimum and maximum temperature (Tmin and Tmax), provided by the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX‐GDDP) with a 0.25° × 0.25° spatial resolution. Accuracy of the NEX‐GDDP Tmin and Tmax were evaluated against ground truth observations at 41 synoptic weather stations distributed across the country using a set of statistical measures, including the coefficient of determination (R2) and Nash‐Sutcliffe Efficiency (NSE). Similarly, the PMT estimated evapotranspiration based on the NEX‐GDDP Tmin and Tmax (ETo‐GDDP) were statistically evaluated against the ETo computed with observed variables at selected stations (ETo‐obs). Then, at each grid cell, the 45‐years ETo‐GDDP time series was partitioned into three 15‐years subperiods such that the differences between three subperiods were computed and inter‐compared. Furthermore, annual, seasonal, and monthly ETo time series trends of each cell were evaluated using the Mann–Kendall trend test (MK) while the corresponding change rates were estimated using Theil–Sen's slope estimator (TSSE). The results demonstrated a good agreement between ETo‐GDDP and ETo‐obs with R2 greater than 0.8 and NSE greater than 0.7 in 90.2% and 65.8% of the stations, respectively. The maps of differences between the three subperiods showed negligible changes in ETo between 1975–1990 and 1950–1975 subperiods. The spatial patterns of annual and seasonal MK statistics showed a significant increasing trend in most parts of Iran. On seasonal scale, the highest and lowest ETo changes were observed in spring (0.00035–0.0045) and in autumn (0.0005–0.0015), respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Urmia lake in north-west Iran has dried up to perilously low levels in the past two decades. In this study, we investigate the drivers behind the decline in lake water level with the help of in-situ and remote sensing data. We use total water storage (TWS) changes from the gravity recovery and climate experiment (GRACE) satellite mission. TWS from GRACE includes all the water storage compartments in a column and is the only remote sensing product that can help in estimating groundwater storage (GWS) changes. The coarse spatial (approx. 300 km) resolution of GRACE does not allow us to identify local changes that may have led to the Urmia lake disaster. In this study, we tackle the poor resolution of the GRACE data by employing three machine learning (ML) methods including random forest (RF), support vector regression (SVR) and multi-layer perceptron (MLP). The methods predict the groundwater storage anomaly (GWSA), derived from GRACE, as a function of hydro-climatic variables such as precipitation, evapotranspiration, land surface temperature (LST) and normalized difference vegetation index (NDVI) on a finer scale of 0.25° × 0.25°. We found that i) The RF model exhibited highest R (0.98), highest NSE (0.96) and lowest RMSE (18.36 mm) values. ii) The RF downscaled data indicated that the exploitation of groundwater resources in the aquifers is the main driver of groundwater storage and changes in the regional ecosystem, which has been corroborated by few other studies as well. The impact of precipitation and evapotranspiration on the GWSA was found to be rather weak, indicating that the anthropogenic derivers had the most significant impact on the GWSA changes. iii) We generally observed a significant negative trend in GWSA, having also significant positive correlations with the well data. However, over regions with dam construction significant negative correlations were found. [Display omitted] • Anthropogenic drivers and climate change on the drought evolution were investigated. • RF provides the best performance to downscale the groundwater storage anomalies. • No significant trend in precipitation, ET and NDVI time series was detected. • Drying up of the Urmia Lake is due to the development of agriculture and irrigation. • Flooding irrigation was expanded by dam construction and groundwater pumping. [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]

Abstract: Trend analysis of climatic variables has received a great deal of attention from researchers recently. The main aim of this study was to investigate trends in maximum (Tmax) and minimum (Tmin) air temperatures in the annual, seasonal and monthly time-scales for 19 synoptic stations in the arid and semi-arid regions of Iran during 1966–2005. From the results it is clear that the majority of the trends in the annual, seasonal and monthly Tmax and Tmin time series showed increasing tendency during the last decades, while the increasing trends in the Tmin series were stronger than those in the Tmax series. The trend in annual Tmax and Tmin averaged over all 19 stations was 0.090 and 0.444°C per decade, respectively. The Tmax and Tmin warming trends were more obvious in summer and winter than in autumn and spring. On the monthly basis, the highest Tmax and Tmin increases of 0.827 and 1.585°C per decade were found at the stations located in the northeastern corner of the region in January and September, respectively. [Copyright &y& Elsevier]

The spatial and temporal changes in annual and seasonal aridity, the shift of land from one arid class to another and the effect of this shift on different landuses in Iran during 1951–2016 have been assessed in this study. The monthly rainfall data of global precipitation climatology center (GPCC), and the monthly mean temperature and potential evapotranspiration (PET) data of climate research unit (CRU) having a spatial resolution of 0.5° were used for this purpose. The novelty of the study is the assessment of the significance in the shift of arid land between 1951–1980 and 1987–2016. Besides, the association of rainfall and temperature with aridity in different arid zones were assessed to understand the driving factors of the shift of arid lands. The results revealed an increase in annual and seasonal aridity in Iran, which caused expansion of arid land. The most remarkable changes include conversion of 4.84% semi-arid land to arid land due to an increase in annual aridity, shift of 4.84% arid land to hyper-arid during summer and 6.45% semi-arid land to arid during winter. However, only the expansion of semi-arid land to dry-subhumid land was found statistically significant. Analysis of results revealed different contributions of rainfall and temperature in the expansion of different classes of arid lands. The decrease in rainfall was the cause of the increasing aridity in the arid and semi-arid region, while the increasing temperature was found to play a major role in increasing aridity in the humid region. The overlapping of landuse map on aridity shift map revealed that the rangelands and farmlands in the north and the northwest were more affected by the expansion of aridity which might have severe consequences on agricultural production and food security of the country. • The shift of dry lands from one arid class to another in Iran during 1951–2016 was investigated • The spatial and temporal trends in aridity and the significance in the shift of arid lands were evaluated. • The association of rainfall and temperature with aridity was assessed to understand the driving factors of aridity shift. • About 4.84% semi-arid land was found to become arid due to increase in annual aridity. [ABSTRACT FROM AUTHOR]