Search

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]

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]

Selecting the appropriate climate models and determining their strengths and weaknesses are the key steps in examining climate change impacts on water resources. In this paper, the efficiency of monthly, seasonal, and annual precipitation estimations by 21 Global Climate Models (GCMs) provided by the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX-GDDP) was evaluated against the Observational Precipitation (OP) of 50 synoptic stations located in eight Precipitation Zones (PZs) of Iran. First, the average of GCMs precipitation for the period 1976–2005 was evaluated vs the OP data at both PZ and national levels. Then the efficiency of the GCMs and their Ensemble Average (EA) in estimating monthly, seasonal, and annual precipitation were determined using NSE, NRMSE, KGE, and PBIAS statistics, and subsequently, the GCMs were ranked according to their statistics. The results show that the time variability of the GCMs' average annual precipitation agrees with that of the OP, but it underestimates the extreme values. The agreement of the models' estimations with observation is generally lowest in summer and the highest in winter and spring. Most of the GCMs show a lower ability in precipitation estimation in dry seasons than in wet seasons. EA of the models for all individual months shows the highest errors in the coastal areas of the Caspian Sea, especially for the spring and winter seasons. The examination of the GCMs' efficiency at the national level shows that the EA and GFDL-ESM2G models have the least errors at monthly and annual time scales, respectively. [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]

The present study aims to investigate Iran's agrometeorological drought history and its properties using Multivariate Standardized Drought Index (MSDI). For this purpose, precipitation and soil moisture recorded at 99 synoptic stations in Iran during 1985 to 2018 were selected. Based on MSDI time series, drought properties including duration and severity were calculated and trend analysis was carried on using Trend Free Prewhitening Mann Kendall test. Also, the standard normal homogeneity test was applied to investigate chronological drought change-point detection. From agrometeorological perspective, results showed that longer and more severe droughts are more dominant in southern, southeastern, northeastern, and some western regions of the country. Trend analysis revealed that most study stations have rising frequency and significant drying trend especially in winter at central plateau and western regions. Also, we recognized two main periods 1991–2000 and 2006–2010 that many stations experienced change-point in their MSDI time series, mainly because of climatic and anthropogenic causes. Drought properties assessment shows 6 stations with serious upward trend in drought duration and severity in northeast and western regions. Sens's Slope statistics in these stations was calculated around 1.5 degree per event for drought severity and 1 month per event for drought duration. It is also observed that the 75th quantile of drought severity for most parts of the country is between 13 and 18 and the 75th quantile for drought duration varies between 8 and 24 months in different regions. The probability analysis revealed that most stations have a ratio around 2–5 times increase in drought occurrence probability after change point, leading to longer and more severe droughts in these stations. The paper indicates a serious situation related to agrometeorological drought occurrence, persistence, and severity in Iran because of many climatic and anthropogenic factors over the last 30 years. [ABSTRACT FROM AUTHOR]

Drought is a natural, global and recurring phenomenon caused by climatic anomalies and inevitable meteorological changes. Lake Urmia in northwestern Iran has been drying up in recent years mainly because of its management and operations. On the other hand, with decreasing rainfall, the process of the lake drying up has intensified and affected different aspects of people's lives, especially farmers. In this paper, regional drought was studied in the Urmia Lake Basin from an agricultural perspective in the period of 2001 to 2021 using different vegetation indices such as the Enhanced Vegetation Index (EVI), the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI) and the Temperature Vegetation Dryness Index (TVDI) as well as by applying the temperature product and vegetation cover product of the MODIS sensor of the Terra satellite. The efficiency of the methods was evaluated. The results of the linear correlation between drought indices and cumulative precipitation values during 1 month, 2 months, 3 months, 5 months and 7 months before the acquisition of the image showed that the correlation of indices, especially of the TVDINDVI index with the cumulative rainfall of 4 months, showed that the precipitation is more frequent than with other time scales due to the delayed response of vegetation to rainfall. From 2001 to 2020, it was observed that the severity of annual drought decreased according to TVDI values. The area with heavy drought decreased from 4.45 to 1.8% and medium drought decreased from 43.49 to 33%. Also, the correlation coefficient between other vegetation indices and temperature vegetation drought indices (TVDINDVI) showed that TCI and TVDI values were negatively (r = −0.73) correlated. Generally, by observing the calculated TVDI maps, it was seen that this basin has not encountered heavy drought so far. The results showed that the Urmia Lake Basin experienced long-term light and medium drought conditions. On the other hand, by calculating the NDWI index, changes in the area of Lake Urmia were monitored and it was found that the lake surface started to decrease beginning in 2005 and reached its lowest level in 2015, which corresponds to the other droughts that occurred. Finally, by conducting a trend analysis of TVDI, it was observed that there is no specific trend in the drought time series so it can be seen that drought may be one of the causes of a sharp decline in the lake's water level, but not the only factor. [ABSTRACT FROM AUTHOR]

This paper develops and applies algorithms for optimally merging satellite precipitation products with rain-gauge precipitation for accurate rainfall estimation. The satellite-based precipitation products (SBPs) PERSIANN-CDR, TMPA-3B42, GPM-IMERG, and GSMaP MKV are combined and evaluated to generate accurate rainfall estimates. Daily satellite precipitation data are compared with corresponding station data to calculate the bias for the period 2014–2019. Three different algorithms are proposed whose adjustable parameters are optimally determined by solving constrained optimization algorithms to produce combinations of satellite-based precipitation products. The optimal combination is named optimally merged satellite-based precipitation (OMSBP). The root mean square error (RMSE), coefficient correlation (CC), and the Nash–Sutcliffe error (NSE) are employed to test the proposed method with precipitation data for the Tehran urban region, Iran. The spatially resolved results over the studied urban area establish that TMPA-3B42, with an average value MAE, MBE, and RMSE equal to 0.68 mm, − 0.31 mm, and 2.94 mm, leads to better estimates of precipitation than those of PERSIANN-CDR, IMERG, and GSMaP MKV. Moreover, algorithms alg7 and alg8 yielded better results with respect to the MAE and MBE, respectively. Lastly, algorithm alg3 produced better results than alg7 and alg8 based on the RMSE, NSE, and CC corresponding to precipitation predictions. [ABSTRACT FROM AUTHOR]

The present paper intends to reconstruct temporal-spatial patterns of below − 15 °C cold waves in the northwest of Iran and carry out a synoptic analysis of the patterns of these cold waves. Carrying out research on this topic can assist researchers and the state authorities in solving problems of different spheres of society, such as agriculture, economics, public health, etc., since cold waves can affect health problems in society and frosts can inflict severe damages to the agricultural crops and thereby lead to serious economic losses. For the purpose of this paper, a data corpus related to a period of 142 years from 1871 to 2012 was obtained from the relevant twentieth-century data banks of NOAA. Having sorted out the relevant data, cold waves of − 15 °C as well as cold waves of below − 15 °C were identified in the first place. Then, the identified cold wave patterns were classified and synoptically analyzed. The results revealed that, at the sea level, high-pressure waves from Siberia, west and northern regions coupled with the atmospheric blockings and the northern airflow in the upper levels of the atmosphere, led to transition of cold waves from northern latitudes to the northwest regions of Iran, resulting in very intense and long-lasting cold waves. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) governs the exchange of energy and water between the atmosphere and land surface. In situ measurements of SM are uneven in Iran. This knowledge gap can be filled using satellite- and model-based products. This study assessed the performance of SM products, including Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer (AMSR2), and Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model (CLSM) against in situ observations considering the influence of soil texture, climate, and land cover over Lake Urmia Basin, which is the largest salt lake in Iran and the Middle East. In situ SM was measured over Lake Urmia Basin in the morning and afternoon using the time domain reflectometry (TDR) and oven drying and weighing techniques. Five statistical indicators, including correlation (R), absolute correlation (R(abs)), bias, root mean square error (RMSE), and unbiased root mean square error (ubRMSE), were applied. C-band AMSR2 products showed the best performance in grassland and croplands with the highest absolute correlation (0.63) and lowest average bias (−0.01). Among soil textures, SM products performed better in clay soils with the highest absolute correlation between C-band AMSR2 products and in situ observations (0.64) and low average bias and RMSE. Analyzing data based on climate, AMSR2 C1, and GLDAS products with the lowest average RMSE (0.08 m3m−3) and bias (0.01) and AMSR2 C2 with the absolute correlation of 0.6 showed the best performance in both temperate (Csa) and cold (Dsa) climate classes. For all classifications (land cover, soil texture, climate divisions), SMAP products reported the lowest average value of ubRMSE (0.03 m3m−3). The major contribution of the paper is finding the best SM products that can fill the gap in SM measurements data in Lake Urmia. In this analysis, the impacts of land cover, climate, and soil texture on the performance of products were considered. [ABSTRACT FROM AUTHOR]

Groundwater level observations due to the lack of synoptic water-level measurements are often available with irregular time intervals. The results lead to an inaccurate understanding of the parameters affecting groundwater level changes and their time delays. This paper presents a simple and low-cost approach to interpolate the irregular, periodic intervals of recorded groundwater level data for the Najafabad aquifer located in central Iran. Forty-six monitoring wells with an average of 20 years of data were used for this study. Piecewise cubic Hermite interpolating polynomial (PCHIP), cubic spline, linear, and nearest neighbor are the selected interpolation methods. Cross-validation with leave-one-out technique was also applied to evaluate the accuracy of the methods under three conditions, including low, average, and high groundwater level decline. The analysis proved that (1) PCHIP interpolation showed a high performance in all of the groundwater level decline conditions (0.22 < RMSE < 0.59 and 0.72 < R2 < 0.98). (2) Under slight and without abrupt fluctuations of groundwater level conditions, cubic spline showed a better performance than the linear interpolation (R2 = 0.98, RMSE = 0.49 for cubic spline and R2 = 0.98, RMSE = 0.51 for linear). The selected approach reduces the uncertainty in the study of issues such as changes in groundwater recharge and storage, groundwater flow directions, surface–groundwater interaction, and groundwater modeling. [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]

Agricultural activities are considered the most important source of income for ~ 23% of the population in Mazandaran Province, Iran. However, farming activities are overwhelmingly exposed to the detrimental impacts of drought in this region. The purpose of this paper is to establish differences in farmers' perceptions on the effects of drought on social, economic, and environmental activities between coastal lowland and inland mountain foreland regions. Our research focuses on rural farming communities residing in the towns of Chalus (coastal plain adjacent to the Caspian Sea) and Kelardasht (Alborz mountain foreland). In 2019, questionnaires were distributed to villagers, and subsequently completed by household heads. Sample size was determined using Cochran, with 384 responses per sub-region. Results obtained by statistical analyses using SPSS indicate that farmers in the coastal region perceived stronger drought impacts on food security, agriculture, economy, education, immigration, and the natural environment than those in the adjoining mountainous region. The study demonstrates how location over relatively small distance, but different in topography, distance from the coast, climate, infrastructural development, and societal factors, has profound impact of people's perceptions to, and experiences of, drought in northern Iran. [ABSTRACT FROM AUTHOR]

Accurate estimation of the spatial distribution of precipitation is crucial for hydrologic modeling. To achieve the realistic estimation of precipitation, developing a ground-based observatory system is a costly and time-consuming strategy compared with other solutions such as using a combination of satellite- and ground-based observations. In this paper, to improve the estimation accuracy of spatial precipitation variation, various linear regression methods were used that combine digital elevation model (DEM) data, rain gauge observations, and Tropical Rainfall Measuring Mission (TRMM) products. Specifically, fuzzy cluster-based linear regression (FCLR), local multiple linear regression using historical similarity (LMLR-HS), model tree (MT), and moving least squares (MLS) were used in the proposed methodology based on local data behavior. The results were compared with those obtained from multiple linear regression (MLR) methods including simple multiple linear regression (SMLR), robust multiple linear regression (RMLR), and generalized linear model (GLM) for monthly precipitation estimation. The study area was Namak Lake watershed, one of the largest watersheds in Iran. The results, estimated for wet and dry years (years 1999 and 2003, respectively), show superiority of local linear regression methods over the other linear methods. Based on the statistical metrics used for assessing the quality the results, FCLR and MLS outperformed other tested methods. [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]

The aim of this paper is to investigate the ability of artificial neural network (ANN) models for post-processing the monthly precipitation forecasts under North American multi-model ensemble (NMME) project and proposing a new multi-model ensemble neural network (MME-NN) model. Monthly precipitation hindcasts of eight models from NMME project are considered in this study. Multi-layer perceptron neural networks are employed for post-processing the output of the models in comparison with PERSIANN-CDR climatology data. Also, utilizing a multi-criteria decision-making approach, NMME models are ranked for each month. The study is implemented over Iran and detailed discussions are provided. The results indicate that the skill of NMME models is different for each month and for each region of the country. Also, it is shown that the neural network outperforms all NMME models for all months. By using the ranking of the models, for each month, the NMME models are ordered based on their skill and a monthly rank is devoted for each model. [ABSTRACT FROM AUTHOR]

Investigation of precipitation characteristics on daily, monthly, and annual time scales can contribute to gaining important information related to temporal and spatial distribution of precipitation or even flow rate challenges (e.g., hydrological droughts). The low levels of long-term precipitation and high variability in different time scales are considered the main inherent characteristics of climate in Iran. Due to the direct effects of precipitation on water resources, especially on the river flow rate, it is necessary to assess the efficient indices to visualize the variations in the components of water resources. One of the main indices is the precipitation concentration index (PCI) which is known as a strong indicator of the precipitation distribution generally used on annual and seasonal scales. In this study, drought analysis in the Lake Urmia Basin (LUB) located in northwest of Iran was performed with the daily river flow rate and monthly precipitation values within the period of 1984–2013. The results of changes in precipitation indicated that the irregularity of precipitation distribution had grown in spring months. Also, due to the diminishing precipitation trend on the annual time scale, PCI index also increased. It is concluded that LUB detected a significant descending trend on the annual, spring, and winter time scales in the last 30 years. The PCI values were proved high irregularity in summer with PCI amount of 20.1 and most regularity in winter with PCI amount of 10.4. This paper also aims to assess the effects of PCI on the river flow rate along with the flow shortness volume values using hydrometric and rain gauge stations within LUB. The results obtained from the changes in river flow rate and flow shortness volume revealed that the river flow rate has mostly a falling trend. Finally, it was observed that the time when the river flow rate data changed happened after beginning of changes in the precipitation data. A decrease in inflow from 900 million cubic meters up to 14 billion cubic meters with high flow shortness volume may happen in worst conditions. These results highlighted the importance of applying water resources management in LUB. [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]

Among climatic elements, temperature is of paramount importance, hence highlighting the necessity of studying the effects of temperature on the growth of organisms, especially plants. The main purpose of this paper is the analysis and zoning of degree days during the growing season at base temperatures of 5 and 10 °C using heat index at 31 synoptic stations in Iran during a 23-year common statistical period (from 1988–1989 to 2009–2010). For calculation of heat units, first, the growing season length at temperature thresholds of 5 and 10 °C was obtained and after calculating heat units during the growing season, the zoning map of heat units and growing season length was prepared in GIS software. Accordingly, heat units of the growing season at base temperatures of 5 and 10 °C could be divided into five categories. The heat units of the growing season at a base temperature of 5 °C showed a rising trend from the south to the north and from the west to the east of the country. At a base temperature of 10 °C, heat units declined from the south to the north and augmented from the west to the east. [ABSTRACT FROM AUTHOR]

In this paper, artificial neural network (ANN) was used for statistically downscale the outputs of general circulation models (GCMs) to assess future changes of precipitation and mean temperature in Tabriz synoptic station at north-west of Iran. Since one of the significant subjects in statistical downscaling of GCMs is to select the most dominant large scale climate variables (predictors) among huge number of potential predictors, the predictors screening methods including decision tree, mutual information (MI) and correlation coefficient (CC) were used to statistically downscale mean monthly precipitation and temperature. Three GCMs were used, including Can-ESM2 and BNU-ESM from IPCC AR5 models and CGCM3 from IPCC AR4 models. The results of downscaling in the base period (1951–2000) indicated that among feature extraction methods decision tree had superiority to MI and CC techniques. Therefore, the future projection of precipitation and mean temperature during 2020–2060 was implemented using ANN-based simulation according to the most efficient downscaling model (i.e., decision tree-based calibration). Different results according to different GCMs and scenarios were obtained for precipitation projection. In this way, the Can-ESM2 model under RCP8.5 showed 29.78% decrease in annual precipitation and CGCM3 model under B1 indicated 1.06% increase of annual precipitation. Temperature projection outcomes denoted that annual mean temperature will increase over the region and the most increase in mean temperature was determined by BNU-ESM model under RCP8.5. [ABSTRACT FROM AUTHOR]

This paper investigates the linkage between large-scale atmospheric circulation and monthly precipitation during November to April over Iran. Canonical correlation analysis (CCA) is used to set up the statistical linkage between the 850 hPa geopotential height large-scale circulation and monthly precipitation over Iran for the period 1968–2010. The monthly precipitation dataset for 50 synoptic stations distributed in different climate regions of Iran is considered as the response variable in the CCA. The monthly geopotential height reanalysis dataset over an area between 10° N and 60° N and from 20° E to 80° E is utilized as the explanatory variable in the CCA. Principal component analysis (PCA) as a pre-filter is used for data reduction for both explanatory and response variables before applying CCA. The optimal number of principal components and canonical variables to be retained in the CCA equations is determined using the highest average cross-validated Kendall's tau value. The 850 hPa geopotential height pattern over the Red Sea, Saudi Arabia, and Persian Gulf is found to be the major pattern related to Iranian monthly precipitation. The Pearson correlation between the area averaged of the observed and predicted precipitation over the study area for Jan, Feb, March, April, November, and December months are statistically significant at the 5% significance level and are 0.78, 0.80, 0.82, 0.74, 0.79, and 0.61, respectively. The relative operating characteristic (ROC) indicates that the highest scores for the above- and below-normal precipitation categories are, respectively, for February and April and the lowest scores found for December. [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]

Landslides are identified as one of the most important natural hazards in many areas throughout the world. The essential purpose of this study is to compare general linear model (GLM), general additive model (GAM), multivariate adaptive regression spline (MARS), and modified analytical hierarchy process (M-AHP) models and assessment of their performances for landslide susceptibility modeling in the west of Mazandaran Province, Iran. First, landslides were identified by interpreting aerial photographs, and extensive field works. In total, 153 landslides were identified in the study area. Among these, 105 landslides were randomly selected as training data (i.e. used in the models training) and the remaining 48 (30 %) cases were used for the validation (i.e. used in the models validation). Afterward, based on a deep literature review on 220 scientific papers (period between 2005 and 2012), eleven conditioning factors including lithology, land use, distance from rivers, distance from roads, distance from faults, slope angle, slope aspect, altitude, topographic wetness index (TWI), plan curvature, and profile curvature were selected. The Certainty Factor (CF) model was used for managing uncertainty in rule-based systems and evaluation of the correlation between the dependent (landslides) and independent variables. Finally, the landslide susceptibility zonation was produced using GLM, GAM, MARS, and M-AHP models. For evaluation of the models, the area under the curve (AUC) method was used and both success and prediction rate curves were calculated. The evaluation of models for GLM, GAM, and MARS showed 90.50, 88.90, and 82.10 % for training data and 77.52, 70.49, and 78.17 % for validation data, respectively. Furthermore, The AUC value of the produced landslide susceptibility map using M-AHP showed a training value of 77.82 % and validation value of 82.77 % accuracy. Based on the overall assessments, the proposed approaches showed reasonable results for landslide susceptibility mapping in the study area. Moreover, results obtained showed that the M-AHP model performed slightly better than the MARS, GLM, and GAM models in prediction. These algorithms can be very useful for landslide susceptibility and hazard mapping and land use planning in regional scale. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate characteristics of meteorological and agricultural droughts and their trends in Iran, as well as several subregions with different climate conditions from 1980 to 2013. The Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI) are used as the primary indicators of meteorological and agricultural droughts, respectively. This study assesses historical droughts using the Multivariate Standardized Drought Index (MSDI), which provides a composite model of meteorological-agricultural drought. Furthermore, this study discusses the behavior of MSDI relative to the other indices (SPI and SSI) over different climatic conditions ranging from humid, semiarid, and hyperarid regions. The Mann-Kendall trend test shows that the northern, northwestern, and central parts of Iran have experienced significant drying trends at a 95 % confidence level. However, no statistically significant drying trend was observed in the eastern part of Iran. The most severe drought across the country occurred between 1998 and 2001, with approximately 80 % of the country experiencing an exceptional drought (<2 % probability of occurrence). This event coincided with a prolonged cold phase El Niño-Southern Oscillation (La Niña) that led to persistently cold sea surface temperatures in the eastern Pacific and warm sea surface temperatures in the Indian and western Pacific. [ABSTRACT FROM AUTHOR]

Determination of chilling and heat requirements is respected in the selection of the suitable cultivars for producers to avoid damages caused by an inadequate winter chilling in a certain area. It also play an important role within a breeding program, when selecting parents to get late or early flowering cultivars. The present study aimed to estimate the chilling and heat requirements of breaking bud dormancy for flowering and vegetative growth in three Iranian almond cultivars in an orchard located in the suburbs of Garmdarreh (32.45°N; 50.91°E; altitude 1960 m). The three almond cultivars had different flowering times, i.e. early: ʻMohebʼ, medium: ʻMamaieʼ and late: ʻRabiʼ. To provide adequate chilling, fourteen-year-old trees were exposed to low temperature treatments (4 ± 1 °C) to simulate 0–1500 h of chilling at 250 h intervals. The cultivars varied in their chilling and heat requirements in terms of chilling hours and growing degree hours, GDH, to achieve bud break for flowering, i.e. 'Moheb' (250 h and 2132 GDH), 'Mamaie' (500 h and 2460 GDH) and 'Rabi' (500 h and 2624 GDH). To achieve bud break for leaf growth, the late-leafing cultivar ('Rabi') required 750 chilling hours and a heat requirement of 3608 GDH. The earlier-leafing cultivars ('Moheb' and 'Mamaie') had lower heat requirements for leaf unfolding. Although 'Rabi' and 'Mamaie' had similar chilling requirements of 500 h, 'Rabi' required almost a week (168 h) of warm weather after chilling to achieve bud break. This implies that spring frost is rarely a problem for 'Rabi' in Iran. 'Moheb' was not suitable because of its low chilling and heat requirements, hence its inability to escape early spring frosts and late winter cold. [ABSTRACT FROM AUTHOR]

It is necessary to evaluate the impacts of climate change and human activities on vegetation dynamics. The purpose of this study is to assess the linkage of vegetation cover and climate fluctuations and distinguish the contribution of climate fluctuations and human activities on vegetation and its vulnerability in Namak Lake basin, Iran. For this purpose, changes in the enhanced vegetation index (EVI) in association with standardized precipitation evapotranspiration index (SPEI) were analyzed. The roles of climate fluctuations and human activities on vegetation vulnerability were then assessed in various land use land cover (LULC) classes using the residual analysis and probability of vegetation vulnerability index (PVVI). The EVI and land surface temperature (LST) maps for the period 2001–2019 were obtained from MOD13A2 and MOD11A2 products of MODIS, respectively. The results indicated that vegetation cover was mainly dependent on short-term climatic changes and their correlation decreased with increasing time scale of SPEI. It reflected that short-term water availability was vital for vegetation growth. Also, the sparse vegetation cover was mainly more vulnerable to climate fluctuations. Residual analysis showed that the vegetation dynamics was intensively attributed to the climate fluctuations, so that climate fluctuations affected vegetation cover in 78.94% of the basin, while 15.58% was affected by human activities and 5.48% was affected by both factors. The value of PVVI in the regions affected by climatic change was the highest (55.99); in the regions affected by human activities, it was lower (50.40); and in the regions affected by both factors, it was between the other two numbers (50.94). Therefore, climate fluctuations and human activities were both driving factors affecting vegetation covers over time in Namak Lake basin. [ABSTRACT FROM AUTHOR]

Hydrological modelling, especially in urban catchments, relies heavily on accurate rainfall data collection. Therefore, rainfall estimation and establishing a network of raingauge stations is essential, especially for regions with a limited number of stations. To simulate rainfall, the Weather Research and Forecasting (WRF) model was used in this study based on the six schemes including Lin, WSM3, WSM5, WSM6, WDM5, and WDM6. Furthermore, optimal spatial design of raingauge networks has been achieved using geostatistical and deterministic interpolation methods of Radial Basis Function (RBF), Local Polynomial Interpolation (LPI), Co-Kriging (COK), Inverse Distance Weighting (IDW), Global Polynomial Interpolation (GPI), and Empirical Bayesian Kriging (EBK). Hence, the error reduction in estimating rainfall on non-station points was considered as an indicator to determine the optimal location of stations. This study was conducted in the Sabzevar urban catchment in northeastern Iran, which faces significant flood damages in a few raingauge stations. Initially, the 24-h rainfall data on the five events (from winter to spring 2019 ~ 2020), covering all types of rainfall associated with the seasons, were selected for analysis. The results revealed that among the WRF model schemes, the Lin was chosen as the most desirable scheme to simulate the rainfall in the catchment. A positive verification criterion result between 0.65 and 1 also shows that rainfall values can be estimated efficiently by this scheme over a distance of 18.85 km from the observational raingauge station. Furthermore, based on the interpolation results, the RBF method with the highest R2 (98%) was the most accurate method for the optimal location of the stations in non-station points, i.e., the WRF model outputs with observational stations. Overally, it can be concluded that using the WRF meteorological model combined with the RBF interpolation method could be appropriate for simulating the rainfall, designing the raingauge stations, and forecasting highly accurate 24-h rainfall, especially for the urban catchment without stations. The proposed approach used in this study is also recommended to develop the optimal design of raingauge networks of non-station points in large catchments. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ETo) is a key factor in the hydrologic cycle and quantifying ETo for future periods is essential for the efficient management of water resources. The objective of this research was to project the possible changes in ETo in future periods in Iran. For this purpose, observed climate data in 40 stations across Iran were collected from 1984 to 2014 and ETo was calculated using these data and the FAO56–PM method. A multi-model ensemble of 27 CMIP6 models under two scenarios, SSP1-2.6 and SSP5-8.5, for the periods 2031–2060 and 2061–2090, was used. A linear regression (LR) model was applied for downscaling and bias correction of CMIP6 temperature using the observed temperature data. The output of the LR model was entered into an artificial neural network (ANN) model with an optimized structure, trained by the observed temperature and the calculated ETo, to project the future ETo based on the downscaled and bias-corrected CMIP6 future temperature. Results showed that ETo will increase in both 2031–2060 and 2061–2090 periods under SSP1–2.6 and SSP5–8.5 scenarios, although the rate of increment was higher under SSP5-8.5 in all stations. Considering all stations, the average of ETo changes was ~ 0.9 mm/day for 2061–2090 under SSP5-8.5. It was found that the ETo rising rate was higher in arid environments, and therefore, shifting to drier conditions with higher water demands will be expectable in such climates in the future. This could cause severe water crises in these environments. [ABSTRACT FROM AUTHOR]

Prediction of response values is a primary goal in many applications. The standard approach to this problem is kriging which is essentially a linear prediction using optimal least squares interpolation of the random field. However, the optimal predictor is not necessarily a linear one unless the geostatistical data support the Gaussian model. As data often exhibit non-normality, some of the most effective spatial processes are reviewed in the current study. The usefulness of the presented models is demonstrated based on the prediction of rainfall levels in Fars Province, Iran. The measurements were taken from 100 stations. To assess the predictive performance of the evaluated models, 15 stations were randomly withheld. Subsequently, the predicted values at these locations were evaluated against the measured ones. The results of the study indicated that, comparing to some well-known models, the skew Gaussian model introduced in this article demonstrated a better performance in the prediction. [ABSTRACT FROM AUTHOR]

Previous studies depicted that the global gridded hydroclimatic products mostly lack precision and are inconsistent throughout the real-world water cycle. The current study evaluates the efficiency of the eight streamflow datasets (FLDAS, GLDAS 2.1, GLDAS 2.0, ERA5, TERRA, ERA5_Land, MERRA2, and GRUN) in two large-scale watersheds with different climate conditions (Karkhe River basin in Iran and Rio Itapecuru River basin in Brazil). To correct the products via different statistical metrics, two tuning procedures (including scale factors and the network-based Muskingum method) have been used. Based on the results showing the paramount impacts on the correction procedures of the products, GRUN, ERA_land, and GLDAS 2 perform the best accuracy in the Karkhe River basin; however, the worst product in the watershed is GLDAS 2.1. In the Rio Itapecuru River basin, MERRA2 and TERRA have the best and worst performance, respectively. In the first watershed, the KGE and TaylorS in GLDAS 2.1 improved by 0.85 and 0.23 in the case of correction using scale factor, and these statistics also significantly increased using coupled scale factor and Muskingum routing methods by 0.84 and 0.21, respectively. In the second watershed, these statistics increased by 9.16 and 0.56 in the worst case using scale factors; these metrics also levelled up by 9.32 and 0.62 via coupled scale factor and Muskingum routing methods. In addition, it appeared that the corrected products could better simulate the streamflow in terms of oscillation time series and the extreme temporal values of the watersheds. [ABSTRACT FROM AUTHOR]

In any given region, climate change can be manifested in the form of various characteristics of climatic elements. In the present study, the frequency of different duration of rainy days was examined to investigate the precipitation variations as a sign of climate change. To this end, gridded precipitation data were used from 1971 through 2016, and days with more than 1 mm of precipitation were considered rainy days. According to the rainy days' frequency, it was revealed that during the study period, 1 to 36 days of duration occurred in the country. The 1-day duration had the highest frequencies and covered the vastest areas, while 36-day duration had the lowest frequencies and covered the minor areas. Accordingly, the 1-day duration played the most significant role in annual precipitation. The proportion of these rainfall events in some country's dry and semi-dry parts was more than 80%. The findings also revealed an increased frequency of short-term, especially 2-day duration, in large parts of the country and a decrease in the long-term duration. The results showed that latitude and longitude had the most significant impact on the frequency distribution of the duration of rainy days. Latitude had a direct effect (excluding the 1-day duration of annual precipitation), and longitude had an inverse effect (except the 1-day duration of annual precipitation) on the precipitation duration. [ABSTRACT FROM AUTHOR]

Landuse and climate change are the two main dynamics significantly affecting watershed hydrology. Adequate knowledge about how these changes will alter the hydrologic regime provides valuable information for future water resources planning and management. The current study attempts to analyze the joint consequences of these dynamics on the future hydrological response of the Gorganroud watershed in northern Iran. For landuse, the integrated Markov Chain analysis and Multi-Layer Perceptron Neural Network (MC-MLPNN) algorithm were used to obtain landuse for 2030 and 2050. For climate, we developed future scenarios based on the downscaled data from MPI-ESM-MR for 2021–2040 and 2041–2060. Soil and Water Assessment Tool (SWAT) was used to simulate watershed hydrology. We found that (1) from 1986 to 2050, agriculture and rangeland are likely to expand by 18.6% and 10.7% of the total watershed area, respectively, at the expense of forest covers. (2) Temperature and precipitation are expected to increase by 1.3 °C and 2.5 °C and by 31.7% and 27.1% for Representative Concentration Pathway 8.5 (RCP8.5) during 2021–2040 and 2041–2060, respectively, compared to the baseline of 1976–1995. (3) We found that the integrated landuse and climate change will likely increase annual evapotranspiration during 2021–2040 and 2041–2060 by 113.9% and 11.4%, lateral flow by 14.6% and 7%, baseflow by 166.7% and 77.2%, surface runoff by 54.2% and 41%, water yield by 50.5% and 35.9%, and streamflow by 48.6% and 32.1% for RCP8.5 compared to the baseline of 1976–1995. [ABSTRACT FROM AUTHOR]

In this study, Vector Autoregressive-Generalized Autoregressive Conditional Heteroskedasticity (VAR-GARCH), copula, and copula-GARCH models were used for joint frequency analysis of storms in the Aras river basin in northwestern Iran in period of 1998–2018. The heteroskedasticity in the series was considered using the vector autoregressive model. Two-dimensional copulas were also used for bivariate analysis. After confirming the correlation between the pair variables of storm with one event lag (S1) and storm with no lag (S0), bivariate frequency analysis was performed. In the simulation step, the residual series of the VAR model was extracted and fitted to the GARCH model. Then, the residual series of the GARCH model was modeled using the copula model. Finally, storm with no lag (S0) affected by storm with one event lag (S1) was simulated by VAR-GARCH, copula, and copula-GARCH models. According to the coefficient of determination (R2) and Nash–Sutcliffe Efficiency coefficient (NSE) and root mean square error (RMSE), the VAR-GARCH model had higher accuracy than copula and copula-GARCH models. The RMSE in the simulation of storm height using the VAR-GARCH model was estimated to be 18% and 11% less than copula and copula-GARCH models, respectively. The VAR-GARCH model provided higher accuracy in the simulations due to the consideration of different lags in the simulations and modeling the variance of the residual series. According to the Taylor diagram, the certainty of all three models in simulating storm height are acceptable. Finally, by two-dimensional analysis of pair variables of storm height and storm duration, typical curve was produced that can estimate the storm duration with different probabilities. In fact, having the storm information that has happened in the present can accurately predict the next storm information. It can be very useful in flood management and the generated curves can be used as a flood warning system in the basin. [ABSTRACT FROM AUTHOR]

Drought is an intangible natural disaster that can occur in any climate. The present study assessed drought vulnerability in Iran based on fuzzy logic and hierarchical analysis and weighted averaging process. Then, the weight and map of different components and values of ORness, GIS-OWA method were used to prepare a set of drought vulnerability maps based on effective drought index (EDI). The study of vulnerable classes in terms of adaptation capacity showed that the northern regions of the country enjoy the highest adaptation capacity. The lowest level of compatibility is related to the cities located in the south of Kerman province, west of Khorasan Razavi province, and Ilam province. As far as the exposure component is concerned, vulnerability extends across the country. Also, cities in the provinces of Sistan and Baluchestan and south of Kerman are in a very vulnerable category regarding the sensitivity component. In other words, according to the type of component, different regions of the country are in the vulnerable category. Also, according to all computational ORness results, drought risk in Sistan-Baluchestan, Kerman, and Khuzestan provinces are in very high vulnerability category. Also, the risk of drought in the western and eastern regions of the country is moderate and high, and only are the central and desert regions of the country and parts of the north-western regions of the country in a state of vulnerability and, accordingly, enjoy lower risk than other parts of the country. [ABSTRACT FROM AUTHOR]

The goal of this simulation study was to explore how rice yield for different water supply levels will respond to climate change at a field scale in northern Iran. The CSM-CERES-Rice model was used in combination with downscaled outputs of a General Circulation Model. Three representative concentration pathways (RCP2.6, RCP4.5, RCP8.5) and seven irrigation treatments (FI (full irrigation), PRD10, PRD30, PRD60 (partial root drying in different rates), RDI10, RDI30, RDI60 (regulated deficit irrigation in different rates)) were used in this study. Moreover, three adaptation strategies were evaluated to mitigate the vulnerability of yield to climate change. The results showed that irrigated rice yield will decrease for climate change projections for 2026–2047, but the reduction was insignificant for all RCPs. Our findings confirm the hypothesis that adaptations can significantly increase the irrigated rice yield under climate change. Shifting transplanting date 2 weeks earlier with FI, RDI10, PRD10, RDI30, and PRD30 showed a higher average yield between 4.67 and 5.03 ton/ha relative to RDI60 and PRD60 reference irrigation treatments for all RCPs. Shifting nitrogen fertilizer application date 1 week earlier under RCP2.6 and RCP8.5 and 2 weeks earlier under RCP4.5 with FI resulted in the highest yield ranging from 3.13 and 4.33 ton/ha. By adjusting the amount of nitrogen fertilizer applied, the highest yield was obtained for 2.5 times the application of the current application amount with FI for all RCPs. The evaluation of these adaptation scenarios suggests that shifting transplanting date is the best strategy compared to the other two adaptations, which resulted in a higher yield with the same amount of water for all RCPs. [ABSTRACT FROM AUTHOR]

Snow is one of the most important climatic-hydrological variables that plays a vital role in watersheds. This importance is multiplied in Iran, as Iran is a warm country which is dependent on melting snow for the warm seasons. The purpose of this study is to monitor and project snowfall changes in Iran. To this end, we used two sources of data; the daily snow cover data of MODIS Terra satellite with horizontal resolution of 0.01 arc degree during the period of 2000–2019 for snow monitoring, and bias-corrected output of CMIP6 models under SSP scenarios for the period 2021–2100 with horizontal resolution of 0.5 arc degree for projection of snow cover. According to satellite data, spatial snow cover often occurs in the Alborz and Zagros altitudes with a snow boundary of 4000 m above sea level (MASL), and most snow cover occurs in January. The snowfall projection for the future period showed a significant downward trend at level of 0.05, the slope of which is steeper in the cold months of the year (December to February). Caspian Sea, Persian Gulf, and Oman Sea basins show a more decreasing trend of snowfall than other basins. Under a pessimistic scenario of SSP 5–8.5 in the period 2061–2100, snowfall will decrease by 6.8–7.4 cm in the Persian Gulf and Oman Sea basins for the high Zagros highlands in January. Due to the reduction of snowfall in the main basins of Iran, integrated watershed management is crucial in the field of water resources. [ABSTRACT FROM AUTHOR]

The present study aims to examine the economic impact of changing climate variables on two components of food security in Iran: availability and access to food. Wheat and rice, the two most important foods in the country, were considered representatives of food security. A CGE model was developed to achieve the research goals. In this context, a stochastic model based on Monte Carlo simulation was used to provide three scenarios (best, average, and worst) indicating probable changes in climate variables. It is important to model the problem of changing climatic variables for irrigated crops, as groundwater resource depletion and restrictions on extraction from Iranian aquifers reduce planted areas and yields. Therefore, this study applies this model to both rain-fed and irrigated crops, whereas studies in the literature only evaluate rain-fed crops. Food security will face serious challenges as food supplies, and consumption of goods and services are declining in average and worst scenarios, according to findings. Consequently, the negative impact of climate change on food security and people's livelihoods requires a review of the policies implemented within the country. Effective solutions include research and development to introduce drought-tolerant varieties and adopt appropriate strategies to adapt to climate change. Improving the incomes of farmers is one solution to mitigating the impacts of climate change. [ABSTRACT FROM AUTHOR]

Floods are one of the most frequent and destructive natural events which lead to lots of human and financial losses with damage to the houses, farms, roads, and other buildings. Intensity–duration–frequency (IDF) curves are the main and practical tools that have been used for flood control studies, including the design of the water structures. In many cases, there is no measuring device at the desired place, or their information is not helpful if there is any available. In this case, it is not possible to extract these curves through conventional methods. Regionalizing the IDF curves is a method that has solved the issues mentioned in the common methods. In this research, the regionalized IDF curves are extracted in Khuzestan province, Iran using 21 rain gauge stations through L-moments and neural gas networks. Clustering is one of the most effective steps and a prerequisite for regional frequency analysis (RFA) that divides the region and existing stations into hydrologically homogenous regions. In this study, clustering is done using two new models named neural gas (NG) and growing neural gas (GNG) network. Comparing the regional IDF curves with at-site curves, it was found that neural gas network models had a more accurate performance and higher efficiency, so they had the lowest estimate error amount among other models. Also, due to the acceptable difference between regional and at-site curves, the efficiency of L-moments in RFA was evaluated as appropriate. [ABSTRACT FROM AUTHOR]

Specifying seasons is one of the most significant and arduous parts of climatology studies. In the present study, seasons are defined to apparent temperature index. For determining changes in the length, start, and end dates of the season in Iran in line with global climate change, in this research we used daily data of apparent temperature for 32 synoptic stations with locations and topography over a 60-year period (1959–2018). Temperature indices used for determining the start of seasons are thresholds of apparent temperatures of 0 and 20 degrees, with a 10-day continuation and without reverting to the initial conditions during this period for winter and summer, respectively. In the same vein, transitional seasons were specified and changes were analyzed using linear regression. The findings revealed that, in various regions, the start of spring and summer occur earlier while the start of fall and winter are delayed. The biggest statistically significant changes are observed at the start of the spring and fall seasons, as the increase is 2.25 days for summer and 1.5 days for winter per decade. Therefore, the duration of cold seasons have decreased and the duration of warm seasons have increased in climatic regions. These changes are statistically significant in foothill and high-altitude regions; the hot and dry climatic range is expanding with the increase and decrease of the summer and winter length, respectively. However, considering the inhomogeneity of altitude and geographical phenomena in the Iranian plateau and its flat northern and southern regions, these changes are not uniform or equivalent. We can say that, overall, climate change, as a direct factor influencing season change and climatic belts, plays a more influential role in comparison with local factors. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the effects of climate change on the climate condition of outdoor tourists in Hormozgan province, Iran, through Outdoor Tourism Climate Index (OTCI). For this purpose, the data pertaining to 7 weather stations as well as 2 global climate models (GCMs) under 2.6 and 8.5 Representative Concentration Pathways (RCP) were applied. GCMs were statistically downscaled by the change factor (CF) approach. The findings illuminated that, based on OTCI, December, January, February, and March were regarded as the optimal months for the outdoor tourism activities. Nevertheless, for the concerned months, the range of OTCI score in twenty-first century is changing from 2 to − 12 regarding the base period (1980–2010). Mostly, the changes in OTCI score is predicted to occur at March, April, May, October, and November, whereas June would record the least. Moreover, Hajiabad and Kish stations in the North and South of the under-study area would encounter the most and least changes, respectively, in the future. [ABSTRACT FROM AUTHOR]

Despite being located in a semi-arid and arid part of the world, Iran enjoys a very diverse climate. Our objective is to regionalize the country into homogeneous precipitation regions and determine the monthly and annual precipitation water volume and depth in each region, required in hydro-climatological studies and applications, from simple water budget calculation to infrastructure design. We investigate the spatiotemporal diversity of precipitation over the country by analyzing the 33-year-long monthly precipitation time series (1983–2016) at 461 rain-gauge stations. We employed cluster analysis (CA) both hierarchical and non-hierarchical clustering approaches and principal component analysis (PCA) to determine the homogeneous precipitation zones at three macro-, meso-, and micro-scales (resolutions). First, the country is divided into six macro-precipitation regions (MPRs) using CA each showing a mean annual hyetograph of unique pattern and depth. The Siberian cold continental air mass enters the country from the north, the Sudan air mass from the south and southwest, the Mediterranean air mass from the west, the North Atlantic and the Black Sea cyclones from the northwest, and the Maritime air mass from the southeast create these six precipitation regions. Then, the six regions were divided into ten zones of meso-resolution through hierarchical clustering (HC) and k-means clustering. The occasional collision of the air masses causes the division of the six macro-regions into ten zones at meso-resolution. Finally, we subjected the precipitation time series of ten meso-zones to PCA, HC, and k-means clustering and established an optimal number of 24 micro-zones for the first time that reflects a comprehensive precipitation map over the country. The annual hyetograph of each zone shows a unique pattern and distribution with a varying magnitude of monthly precipitation compared to others as the result of varied physio-geographical characteristics of the country prevailing in each micro-zone. The result shows that hierarchical clustering (Ward's method-Pearson correlation) and PCA have the same classification performance and strength in meso- and micro-climatological zoning. The long-term (i.e., 33 years) mean annual hyetograph in each region and zone is also calculated, and the monthly and annual-precipitation water volume and depth in the country are estimated. The findings provide the researchers, practitioners, and decision-makers with an accurate baseline reference for future research and water resource management and will advance the understanding of precipitation dynamics in different regions of the country. [ABSTRACT FROM AUTHOR]

The recent droughts in Iran have contributed to declining runoff, diminishing lake water levels, and rising salt levels due to lower runoff. This study aims to quantify how the recent changes affected to investigate the trend of land surface albedo in Iran between 2000 and 2018. Accessing field data from the Iran is difficult, and thus the common understanding of climate change in the region is strongly based on satellite data. We use remotely sensed data of land surface albedo, land surface temperature (LST), number of snow-covered days (SCDs), normalized difference vegetation index (NDVI), and the land cover type, obtained from Moderate Resolution Imaging Spectroradiometer (MODIS). The results show decreasing trends in albedo and SCDs by − 0.02 and − 0.52, respectively, and upward trends in the LST and NDVI data by 0.07 °C and 0.009, respectively. Due to the recent drought condition in Iran, SCDs decreased significantly, which might explain the reason behind the albedo decreases in winter time in Iran. [ABSTRACT FROM AUTHOR]

An accurate forecast of precipitation can significantly enhance the management of water resources. While the data originated from ground-based synoptic stations are known to be the most accurate inputs of hydrological models, it is mostly unavailable in developing countries. So, the other approaches, such as numerical weather predictions (NWPs), are considered proper alternatives. This study utilized the precipitation data of the UK Meteorological Office (UKMO) model over eight different regions of Iran. The eleven ensemble data of UKMO at 47 ground-based synoptic stations from 2007 and 2017 were chosen as the input variables, while the ground-based precipitation was considered the output variable. The long short-term memory (LSTM) model was used as the predictive model, and the three proposed input strategies were evaluated using correlation coefficient (CC) and normalized-root mean squared error (NRMSE). The results showed that the combination of LSTM and the principal component analysis (PCA) approaches in post-processing of the UKMO data (PPUKMOD) enhances CC and NRMSE by 9% compared to the raw UKMO dataset. Besides, the most performance in PPUKMOD is found in the G7 (Zagros Highlands) region. Moreover, the Zagros mountain and the northern-eastern part of Iran showed better performance in PPUKMOD based on the evaluation of longitude, latitude, and elevation ranges. The temporal assessment also revealed that the highest performance in PPUKMOD was observed in the cold and rainy months (CCaverage = 0.59 and NRMSEaverage = 0.74) where November was the first rank. The proposed methodology for post-processing the UKMO ensemble sources aligns well with Iran's observed precipitations. Subsequently, it can be used as the input of hydrological models. [ABSTRACT FROM AUTHOR]

In many parts of the world, teleconnection patterns are one of the climate phenomena that significantly change the causative climate anomalies, especially temperature and precipitation. Thus, statistical analysis and modeling of their effects are of great importance in order to understand the fluctuations and climate variability in a region. In this study, 52 teleconnection indices were utilized to perform statistical analysis and conceptual modeling as well as simulation of temperature and precipitation fluctuations in Iran. For this purpose, temperature and precipitation data of 36 synoptic stations in Iran and 52 teleconnection indices for the period 1951–2019 were used. For the analysis, the relevant data were classified into four groups of global (GLB), regional (RGN), El Niño–Southern Oscillation (ENSO), and all indices (ALL). Then, the correlation of the aforementioned indices with temperature and precipitation was calculated using the teleconnectional–statistical model (TSM). Afterward, 5 years with the highest correlation coefficient was selected and considered as the forecasting parameters. The results revealed that the local temperature forecasting using RGN indices and the precipitation forecasting using GLB and ALL indices were more accurate than those using other indices. Our findings highlighted the prominent role of the indices with broader geographical regions that mainly were evolved across the oceans. Moreover, the effect of ENSO teleconnection on Iran climate was explained by the dynamic mechanism of the atmospheric bridge. Comparing TSM outputs and the Climate Forecast System Version 2 (CFSV2), TSM outperformed CFSV2 in the conducting experiment. Overall, the findings of this study emphasized on the existence of coincided synergy in the fluctuation or trend within almost all indices and atmospheric parameters, and most of the fluctuations in the indices occurred simultaneously or right after the ENSO events, including specific super El Niño in 1997–1998 (turning point). Also, the dramatic effect of sunspots on the average temperature of Iran is shown in inverse harmony, which is not easily revealed by correlation analysis. Also, the influence of global warming and especially synchronization with El Niño event on climatic change was briefly discussed in the Middle East. In this work, for the first time in the Middle East, physical mechanisms of how linkages affect Iran's climate and rainfall were proposed in a synoptic conceptual model. [ABSTRACT FROM AUTHOR]

Occurrence of extreme climatic phenomena such as frost will cause significant risks and costs to many sectors, especially agriculture, horticulture, and forests. Frost will cause the worst damage when it occurs at the critical stages of crops, especially in spring. The frost phenomena are one of the important climatic and environmental hazards that cause a lot of damage to the agricultural sector of Iran every year. In this respect, the present study intends to highlight the projection of late spring rost by global circulation models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6). For this purpose, minimum temperature data of 17 synoptic stations were used in the period 1985–2014 in cold regions of Iran. For projecting the changes of LSF, the ACCESS-ESM1-5 and Nor-ESM2-LM Models were used under three (Shared Socioeconomic Pathway (SSP)) scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5 for the next three periods (i.e., 2020–2049, 2050–2079, and 2080–2099). Then, the changes were compared to the historical period (1985–2014). The root mean square error (RMSE), mean bias error (MBE), correlation coefficient (CC), and Nash-Sutcliff efficiency (NSE) indices evaluated the models' performances. The results revealed that the latest and earliest dates of LSF during the base period occurred in the western and central parts of Iran, respectively. The model evaluation indicated that the performance of ACCESS-ESM1-5 (MBE = 0.3, CC = 0.87, and NSE = 0.68) exhibited a higher accuracy than the NorESM2-LM model. Based on both GCM under all three SSP scenarios, the projection of changes in future periods (compared to the base period) indicated that the date of occurrence of LSF would be earlier than the base period, with the highest and lowest changes projected based on NorESM2-SSP5-8.5 and ACCESS-ESM1-5-SSP1-2.6 in Arak, Isfahan, Khorramabad, Sabzevar, Shahrekord, and Shahroud stations. In general, depending on the model and climate scenario, the LSF phenomenon occurs earlier or later in cold regions of Iran, and its changes would be between − 76 and + 19 days in the future period. [ABSTRACT FROM AUTHOR]

This study investigated the effect of climate change on future precipitation and temperature from 2021 to 2050. Three general circulation models (GCMs), namely GFDL-ESM2M, HadGEM2-ES, and IPSL-CM5A-LR, and two greenhouse emission scenarios, RCP2.6 and RCP8.5, were analyzed for this purpose. The CCT model, precipitation data, and minimum and maximum daily temperatures (from 1986 to 2019) were used for downscaling and correcting precipitation and daily temperature bias. According to the results, the weighted annual precipitation recorded in rain-gauge stations was ascending in all scenarios, except for RCP8.5 in the IPSL-CM5A-LR model. The mean weighted precipitation rate of rain-gauge stations in winter did not descend under any climate change conditions, but the precipitation rate decreased or increased in other seasons. The highest increase of 23 mm in the weighted mean precipitation in winter was calculated under the RCP2.6 scenario in the GFDL-ESM2M model. The highest decrease of 10.5 mm in the weighted mean precipitation was observed in autumn. No temperature decline occurred in meteorological stations. The highest increase of 3.1 °C in the weighted mean temperature and the highest seasonal temperature rise of 8.5 °C were observed in summer under the RCP8.5 scenario in the HadGEM2-ES model. According to the standardized precipitation index (SPI), almost 70% of the future 30-year period are dry years, and drought occurs in almost all scenarios in all Iranian watersheds from 2030 to 2040. Given severe long droughts (14 years), Iran needs a comprehensive management plan and a long-term vision of managers and authorities for water resources. [ABSTRACT FROM AUTHOR]

Coastal plains are prone to various wind erosion due to their characteristics. This study aimed to investigate the effect of climatic factors in the western region of Makran coastal plain in a southeastern region of Iran on the wind erosion potential during 1993–2018. First, the data related to wind velocities, relative humidity and precipitation, and the granulometric data of plain surface sediments were obtained. Then, the wind erosion threshold velocity in humid air conditions was determined. Finally, the Mann–Kendall test was applied to analyze the probability of wind erosion and its temporal variability. The results indicated that the wind erosion threshold, in terms of humidity changes, varied from 7.21 to 12.31 m/s during the study period. The highest probability of wind erosion was in February, March, and April, with 24.69%, 21.51%, and 20.41%, respectively. The lowest probability of wind erosion was in October, November, and September with 4.00%, 4.12%, and 6.00%, respectively. Finally, the erosion trend analysis indicated that wind erosion was a temporal phenomenon that increased significantly in January (winter solstice) and July (summer solstice). These months were characterized by an increase in the wind blowing at speed above the threshold. However, the months were not different from other months of the year in terms of precipitation or the number of dry days. Therefore, wind erosion is expected to maximize in the early winter and the early summer. [ABSTRACT FROM AUTHOR]

Crop yield is one of the most critical factors in the food security chain. Climate plays a crucial role in crop water productivity in rainfed and irrigated crop productions. Climate changes would significantly impact crop characteristics, especially in Iran, where water is the major constraint of crop production. This study assessed the impact of climate change on crop water productivity with related uncertainty. The global climate model simulations of rainfall and temperature were statistically downscaled using LARS-WG6 for climate projection. The projected climate was used in the FAO AquaCrop model to simulate the variability of crop characteristics (crop cycle length, crop yield, and water productivity) for the assessment of climate change effect on major crops for three future horizons (2021–2040, 2041–2060, 2061–2080). Results revealed an increase in wheat yield by 14 − 54% and a decrease of growth duration by 1 − 12%, leading to an increase in water productivity by 9 − 96% in the future compared to the base period (1985–2016). In contrast, reduction in corn and soybean yield by 1 − 5% and 2 − 6% and growth period by 1 − 5% and 3 − 12%, and thus, an increase in water productivity by 1 − 9% and 2 − 24%, respectively, were projected. The growth duration of all the major crops was projected to decrease due to a rise in temperature and an increase in crop water productivity in the study area. The results indicate a more favorable condition for crop agriculture in the study area under the projected climate. [ABSTRACT FROM AUTHOR]

High spatiotemporal climate variability in the northeast Iran has led to increased production risk for rainfed crops. Hence, to explore potential opportunities for reducing the probable risk, it is necessary to characterize the main rainfed growing regions based on drought patterns. The CSM-CERES-Wheat and -Barley models were applied to simulate yield and to achieve input for estimating water supply demand ratio (WSDR) in 10 reference weather stations (RWSs) for the period 1980–2009. Simulated WSDR was averaged for every 100°Cd to and from flowering for each location (each RWS and each season). The environmental classes and their optimum number for each RWSs and the region were derived by k-means clustering. The soil diversity combined with spatiotemporal variations in rainfall caused rainfed wheat and barley to be exposed to drought stresses, which were very different across locations and among seasons. Nevertheless, four main environmental classes could represent the variability at local and regional scales. The first environmental class corresponded to environments with low drought stress with a frequency of 1.10 and 12.01% for wheat and barley, respectively. For wheat, environmental classes 2, 3, and 4 with a frequency of 22.62, 51.46, and 24.82% begin from 1100, 900, and 700°Cd (degree days) before flowering, respectively, and lasted until maturity. For barley, these three environmental classes with the frequency of 26.50, 32.86, and 28.62%, respectively, corresponded to sever drought stress levels with different degrees that begin from 500°Cd before flowering and was maximized at about 300°Cd after flowering which coincided with the grain filling. The occurrence frequency of environmental classes differed temporally and spatially. Over time from 1980 to 2009, environmental class 4 increased and environmental class 1 decreased for wheat and barley, respectively. In most locations, wheat and barley yield tended to decline from environmental classes 1 to 4. Such characterization of production environment can be used to help breeders focus on genes and traits adapted to target production environments. [ABSTRACT FROM AUTHOR]

In this study, statistical and soft-computing methods are compared in forecasting groundwater levels under Shared Socioeconomic Pathways (SSPs) SSP1-2.6, SSP2-4.5, and SSP5-8.5 from Coupled Model Intercomparison Project Phase 6 (CMIP6) in Tasuj Plain, Iran, for a near future period (2022–2027). A combination of general circulation models (GCMs) was used in the projection of precipitation and temperature in the future period. The estimation of climate variables for 2020–2044 period indicated that the temperature will increase, while the precipitation will decline. In simulation temporal groundwater level, wavelet-nonlinear autoregressive network with exogenous inputs (NARX), autoregressive integrated moving average (ARIMA), and wavelet-adaptive neuro-fuzzy inference system (ANFIS) models were used. The comparison of performance criteria for these models in the simulation of groundwater level demonstrated that the wavelet-NARX model with the R2 of 0.99 has shown better efficiency. Finally, the simulation of groundwater level through the wavelet-NARX model was carried out for different scenarios. The results indicated that future groundwater levels in Tasuj Plain would continue to decline by 3.12 m, 3.96 m, and 4.79 m, for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. [ABSTRACT FROM AUTHOR]