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Statistical downscaling of General Circulation Models (GCM) simulations is widely used for projecting precipitation at different spatiotemporal scales. However, the downscaling process is linked with different source of uncertainty including structural/parametric uncertainty of the model and output uncertainty. This research proposes a novel framework to assess the parametric uncertainty of downscaling model, and used this framework to assess the performance of different bias correction methods linked to the regression-based statistical downscaling model. The used downscaling framework in the current paper is Statistical Downscaling Model (SDSM). The conventional bias correction method linked with SDSM is the Variance InFlation method (VIF), this paper substitutes this method with three different bias correction methods including Local Intensity Scaling (LOCI), Power Transformation (PT), and Quantile Mapping (QM) to assess the associated parametric and global uncertainty of each method in different climate by using a new approach. The proposed method is applied to six different stations located in Iran and United States with different climate status. Average Relative Interval Length (ARIL), P-level, and Normalized Uncertainty Efficiency (NUE) are used as uncertainty indicators to evaluate the results. Results represent that in every assessed climate class, LOCI, and PT, work better than conventional VIF in both amount and occurrence modules of SDSM framework. More precisely, LOCI works better in station that has wet summer, while PT performs well in the stations where there is no or very limited precipitation in summer. Substituting LOCI with VIF, result in increasing the value of NUE by at least factor of 3 in occurrence and amount model which means the significant reduction in structural uncertainty. Also applying PT in arid regions improves the NUE indicator at least by factor 2 in occurrence and amount model and by factor 3 in output uncertainty assessment, and results in less parametric and output uncertainty. Results illustrate the important role of bias correction approaches in reducing structural, and output uncertainty and improving the statistical efficiency of the downscaling model. [ABSTRACT FROM AUTHOR]

Assessing the effects of climate change is a key component of the sustainable management of water resources and food security. In this paper, general circulation models (GCM) were evaluated using historical information for Birjand synoptic station, Iran. Modeling was performed using 35 models of the Fifth Climate Change Report for 27 historical periods. The results showed that longer annual periods are the most suitable periods for hydrological simulation when data are available. Therefore, the periods of 1960-1990 may be the most appropriate periods due to the adaptation to the observation data. To estimate rainfall, periods with more years showed a more accurate forecast of the future. Moreover, the results showed more changes in the RCP 8.5 scenario than in the RCP 4.5 scenario. According to the comparison of models, the NorESM1-M model with a root mean square error (RMSE) of 0.091 and the GISS-E2-R model with a low percent bias (PBIAS) can be an appropriate model for estimating rainfall. [ABSTRACT FROM AUTHOR]

This paper presents the results of studies on water resources management strategies, under the influence of climate change, in the Zarrineh River basin, providing some 41% of the total inflow into the Lake Urmia. Climate change simulation over the period 1992–2050 was performed by downscaling of seven global circulation models. The best model was then selected for modelling the hydrological behavior of the basin using a rainfall-runoff model while an allocation model was used to evaluate future supply and demands. A system dynamics model was further utilized to investigate the sustainability of the dynamic behavior of the basin. Five suggested water resources management strategies for two future climate scenarios were investigated. Results indicated that implementation of the strategy of 40% reduction in agricultural water demand would provide higher degrees of sustainability. Based on this, the average annual inflow to Lake Urmia in the years 2021 to 2050 would be 1029.75 MCM, which is equal to 81.02% of the Lake Urmia's environmental requirement from the river's share. [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]

Selecting appropriate climate change scenarios is crucial, as it influences the outcomes of climate change impact studies. Several storylines could be used to investigate the sensitivity of water resource schemes to weather variability and improve policymakers' adaptation strategies. This study proposes a comprehensive and generic methodology for assessing the future climate change impact on semi-arid and arid zones at the basin scale by comparing delta perturbation scenarios to the outcomes of seven collections of GCMs (general circulation models). The findings indicate that the two scenarios predicted nearly identical declines in average reservoir discharges over a monthly timescale. Consequently, their maximum values are almost similar. The projected decrease in the streamflow for the period 2080–2099 is approximately 48%—the same as the ratio from the delta perturbation scenario of Future16 (a 30% precipitation decrease and a 30% potential evapotranspiration increase). Furthermore, delta perturbation scenarios allow the impacts of model sensitivity to climate change to be clearly identified in relation to GCM scenarios. Delta perturbation scenarios allow for an extensive collection of possible climate changes at the regional scale. In addition, delta perturbation scenarios are simpler to create and use; therefore, they might complement GCM scenarios. [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]

The present study attempts to investigate potential impacts of climate change on floods frequency in Bazoft Basin which is located in central part of Iran. A combination of four general circulation models is used through a weighting approach to assess uncertainty in the climate projections. LARS-WG model is applied to downscale large scale atmospheric data to local stations. The resulting data is in turn used as input of the hydrological model Water and Energy Transfer between Soil, plants and atmosphere (WetSpa) to simulate runoff for present (1971-2000), near future (2020-2049) and far future (2071-2100) conditions. Results demonstrate good performance of both WetSpa and LARS-WG models. In addition in this paper instantaneous peak flow (IPF) is estimated using some empirical equations including Fuller, Sangal and Fill-Steiner methods. Comparison of estimated and observed IPF shows that Fill-Steiner is better than other methods. Then different probability distribution functions are fit to IPF series. Results of flood frequency analysis indicate that Pearson III is the best distribution fitted to IPF data. It is also indicated that flood magnitude will decrease in future for all return periods. [ABSTRACT FROM AUTHOR]

The planning of future supplies of agricultural water is beset by uncertainties stemming from inaccuracies in general circulation models, assumed greenhouse gases emissions scenarios (GHGESs), imperfect models employed for estimating reservoir inflows and approximate methods for estimating agricultural water demand. The uncertainty of providing agricultural water under climate change was assessed, relying on simulations involving baseline (1971–2000) and future periods (2040–2069 and 2070–2099). Climatic variables were simulated using six atmosphere–ocean general circulation models (AOGCMs) driven by GHGESs A2 and B2 in the Aidoghmoush basin, Iran. Projection of reservoir inflow was performed using the IHACRES model and artificial neural network (ANNs). Agricultural water demand was calculated using the FAO–Penman–Monteith and Hargreaves-Samani (HS) methods. Eight modelling scenarios were considered based on combinations of AOGCMs, GHGESs, reservoir inflow and agricultural water demand projections. Reservoir operation rules were calculated with a particle swarm optimisation algorithm. The results show that agricultural water demand will increase in future periods compared with the baseline period. The operation rule derived from the combination of the HS and ANN models (under GHGES A2) showed the best performance in 2040–2069 by achieving the highest reliability (93%) of water supply. The operation rule derived from the combination of HS and ANN models (under GHGES B2) achieved the highest reliability (95%) of water supply in 2070–2099. The results provide adjusted reservoir operation rules under uncertainty caused by climate change and related impacts on water resources management. [ABSTRACT FROM AUTHOR]

Climate change is among the most pressing challenges for human advancement in the twenty-first century. Climate change is having a significant impact on the agricultural sector. The main objective of this study is to evaluate the food security status and sustainability of crop production under different climate change scenarios in the arid and semi-arid zones of Iran. To this end, the Statistical Down-Scaling Model (SDSM) and the outputs of the General Circulation Model (GCM) are employed to project future climate parameters under three climate scenarios. The study then employs the Just and Pope approach by using a panel dataset to evaluate the impact of climate change on crop production. Also, the multi-criteria decision-making (MCDM) technique is used to assess the sustainability of crop production from an economic, social, and environmental perspective in the context of climate change and baseline conditions. The results of the study show that precipitation has a significant favourable effect on crop yield in both zones. Maximum temperature is positively and significantly related to crop yield in the semi-arid area, while the relationship is negative in the arid region. Future projections reveal that under different climate scenarios, the production of irrigated wheat, dryland wheat, irrigated barley, dryland barley, and potato crops will change by a maximum of approximately 7.57%, 35.70%, 62.86%, 15.90%, and 26.88% in the semi-arid zone and − 24.07%, 29.73%, − 31.33%, − 4.80%, and 25.80% in the arid zone, respectively. The findings imply that climate change will decrease the food security index for all crops in the arid zone, while improving the situation for crops in the semi-arid region. The results also indicate that future climate change can have a significant adverse effect on economic water productivity, economic benefits, and the sustainability of strategic crop production in the arid zone compared to the semi-arid zone. Given that a large part of Iran is covered by an arid climate, designing coherent adaptation actions and mitigation policies need to be prioritized to tackle the negative impact of climate change on food production systems. [ABSTRACT FROM AUTHOR]

The limitation in approachability to rainfall data sources with an appropriate spatial-temporal distribution is a significant challenge in different parts of the world. The development of general circulation models and mathematical algorithms has led to the generation of various rainfall products as new sources with the potential to overcome the shortage in data-scarce basins. In this study, the performance of the PERSIANN-CCS and CMORPH satellite-based rainfall product, as well as the ERA5 and ERA-Interim reanalysis, was evaluated based on detection skill and quantitative metrics in a daily, monthly and seasonal time scales in the Dez basin located in the southwest of Iran. The basin has a wide topographic variation and scattered rain gauge stations. Overall results denote that the ERA5 dataset has the best performance in all statistic verification than other rainfall products. Based on the daily evaluation of all rainfall products, the false alarm rate (FAR) is higher than 0.5, so none of the datasets could capture the temporal variability of rainfall occurrence. This study has covered the western parts of the Zagros steep slopes in which the topographic conditions have a significant effect on the activity of rainfall systems. On a monthly scale, the mean value of the correlation coefficient (CC) for ERA5, ERA-Interim, PERSIANN-CCS, and CMORPH was equal to 0.86, 0.85, 0.51, 0.39, respectively. The results of seasonal evaluation suggested that all datasets have better rainfall estimation in autumn and winter, and the capability of all datasets dramatically decreased in the spring. The current paper argues that the ERA5 reanalysis typically outperforms ERA-Interim and can be considered as a reliable rainfall source in the future hydrological investigation in the southwest of Iran. [ABSTRACT FROM AUTHOR]

This study analyses the impact of climate change on the inflows, sediment loads, and nutrient inputs to the Sabalan dam reservoir, a warm monomictic lake located northwest of Iran. For this purpose, the Soil and Water Assessment Tool (SWAT) was calibrated (2005–2018) and validated (2001–2004). Future climate-based data under the AR5 emission scenarios were obtained from the HadGEM2–ES general circulation model and then downscaled using the LARSWG 6.0. The tuned SWAT model was used to investigate the climate change impact on the hydrological processes and pollution loads to the Sabalan dam reservoir. Our findings based on the Nash–Sutcliffe efficiency coefficient and the coefficient of determination indicated an acceptable performance of the SWAT model in the simulation of inflows, sediment loads, and nutrient inputs to the reservoir. Inflow and sediment load to the reservoir will increase during the period of 2030–2070 compared to the base period (1998–2018). The annual total nitrogen (phosphorus) load to the reservoir will increase by 8.5% (9.4%), 7.3% (8.2%), and 5% (3.4%) under the emission scenarios of RCP2.6, RCP4.5, and RCP8.5, respectively. An increase in sediment loads and nutrient inputs to the Sabalan dam reservoir will significantly exacerbate the reservoir eutrophic condition, leading to water quality deterioration with acute consequences for the positive functions of the dam. [ABSTRACT FROM AUTHOR]

This study investigated the uncertainty involved in statistical downscaling of hydroclimatic time series obtained by artificial neural networks (ANNs). Phase 6 of the Coupled Model Intercomparison Project (CMIP6) general circulation model (GCM) Canadian Earth System Model, version 5 (CanESM5), was used as large-scale predictor data for downscaling temperature and precipitation parameters. Two ANNs, feed forward and long short-term memory (LSTM), were utilized for statistical downscaling. To quantify the uncertainty of downscaling, prediction intervals (PIs) were estimated via the lower upper bound estimation (LUBE) method. To assess performance of proposed models in different climate regimes, data from the Tabriz and Rasht stations in Iran were employed. The calibrated models via historical GCM data were used for future projections via the high-forcing and fossil fuel–driven development scenario shared socioeconomic pathway (SSP) 5-8.5. Projections were compared with the Canadian Regional Climate Model 4 (Can-RCM4) projections via the same scenario. Results indicated that both LSTM-based point predictions and PIs are more accurate than the feedforward neural network (FFNN)-based predictions, with an average of 55% higher Nash–Sutcliffe efficiency (NSE) for point predictions and 25% lower coverage width criterion (CWC) for PIs. Projections suggested that Tabriz is going to experience a warmer climate with an increase in average temperature of 2° and 5°C for near and far futures, respectively, and a drier climate with a 20% decrease in precipitation until 2100. Future projections for the Rasht station, however, suggested a more uniform climate with less seasonal variability. Average precipitation will increase by up to 25% and 70% until near and far future periods, respectively. Ultimately, point predictions show that the average temperature in Rasht will increase by 1°C until the near future and then be a constant average temperature until the far future. Significance Statement: The downscaling of hydroclimatic parameters is subject to uncertainty. The best way is to provide an area with the highest contingency of them as a prediction interval. The reduction width of such an interval leads to increased confidence in explaining and predicting these processes. We proposed and applied a deep learning–based machine learning method for both point prediction and prediction interval estimation of temperature and precipitation parameters for the future over two different climatic regions. The results show the superiority of such a machine learning–based prediction interval estimation for quantification of the downscaling uncertainty. [ABSTRACT FROM AUTHOR]

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

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Groundwater is one of the most important sources of water supply in arid and semi-arid regions, but its availability with sustainable quantity and quality is threatened by various factors, among which climate and land-use change have the most important role. Therefore, it is essential to achieve reliable methods for predicting changes in aquifer storage to plan for the sustainable use of groundwater resources. This study aimed to estimate the potential impact of climate change and land use on the groundwater level of Hashtgerd plain for sustainable use of groundwater resources. In this regard, groundwater supply and demand for 2020 as the base year was simulated to forecast the trends until 2050 by considering climate and land-use changes. Five General Circulation Models under four Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6, and RCP8.5) were used to project future rainfall and temperature. The LARS-WG model was used to downscale the climatic data. Classification of land-use was performed using Landsat satellite images of 1990, 2005, and 2020 in ENVI 5.3 software. Then, by these maps, the Markov chain method implemented in TerrSet software was used to model land-use change for 2050. The indexes used to evaluate the model were the overall accuracy of prediction and the kappa coefficient. The overall accuracy of 94.34% and kappa coefficient of 0.92 were in the calibration stage and 86.34 and 0.82 in the validation stage, respectively. Finally, the effect of climate and land-use change on the decrease of groundwater level was simulated using the MODFLOW model for the period 2020–2050. RMSE and MAE values of steady-state calibration were 0.91 and 0.75, respectively. These values were estimated to be 0.97 and 0.8 in the calibration step for the unsteady-state and 1.01 and 0.91, respectively, in the validation step. The results of climate change showed a decrease in the average annual precipitation and an increase in the average annual temperature. The annual temperature increased by 1.4 °C and 3.2 °C for RCP 4.5 and 8.5 respectively, by 2050. The annual precipitation decreased by 2.45% and 4.47% for both scenarios. The results of land-use change show an increase of residential, barren, and agricultural lands by 105, 41, and 8%, respectively, and a decrease of 94% in pastures lands. The future land-use change reduced aquifer reserve by up to 41%. The combined impacts of climate and land-use change in the most critical state reduced aquifer reserve by 61%. [ABSTRACT FROM AUTHOR]

The aim of this study was investigating the effect of drought on groundwater storage and measuring the impact of future drought events under climate change conditions on Najafabad aquifer in Iran. A Support Vector Machine (SVM) model was developed to simulate groundwater storage by using the Particle Swarm Optimization (PSO) to optimize SVM parameters. Various meteorological and hydrological drought indices were considered as model inputs and the best combination of drought indices was selected by the Gamma Test (GT) method. The drought analysis revealed that the Groundwater Resource Index (GRI) and the Reconnaissance Drought Index (RDI) were the most effective indices in modelling the aquifer. To examine future drought events under climate change, the outputs of five ISI-MIP5 (Inter-Sectoral Impact Model Inter-comparison Project) General Circulation Models (GCMs) were derived under two Representative Concentration Pathways (RCPs). The results indicated that the most severe droughts will occur during the years 2035–2039 and 2033–2041 under RCP4.5 and RCP8.5, respectively. Continuing the current aquifer withdrawal policy, the groundwater storage will be affected by future drought events and decrease by 8.8% and 9.5% compared to the base period (1990–2016) under RCP4.5 and RCP8.5, respectively. [ABSTRACT FROM AUTHOR]

Understanding the effects of climate change and anthropogenic activities on the hydrogeomorpholgical parameters in wetlands ecosystems is vital for designing effective environmental protection and control protocols for these natural capitals. This study develops methodological approach to model the streamflow and sediment inputs to wetlands under the combined effects of climate and land use / land cover (LULC) changes using the Soil and Water Assessment Tool (SWAT). The precipitation and temperature data from General Circulation Models (GCMs) for different Shared Socio-economic Pathway (SSP) scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) are downscaled and bias-corrected with Euclidean distance method and quantile delta mapping (QDM) for the case of the Anzali wetland watershed (AWW) in Iran. The Land Change Modeler (LCM) is adopted to project the future LULC at the AWW. The results indicate that the precipitation and air temperature across the AWW will decrease and increase, respectively, under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Streamflow and sediment loads will reduce under the sole influence of SSP2-4.5 and SSP5-8.5 climate scenarios. An increase in sediment load and inflow was observed under the combined effects of climate and LULC changes, this is mainly due to the projected increased deforestation and urbanization across the AWW. The findings suggest that the densely vegetated regions, mainly located in the zones with steep slope, significantly prevents large sediment load and high streamflow input to the AWW. Under the combined effects of the climate and LULC changes, by 2100, the projected total sediment input to the wetland will reach 22.66, 20.83, and 19.93 million tons under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. The results highlight that without any robust environmental interventions, the large sediment inputs will significantly degrade the Anzali wetland ecosystem and partly-fill the wetland basin, resulting in resigning the wetland from the Montreux record list and the Ramsar Convention on Wetlands of International Importance. [ABSTRACT FROM AUTHOR]

This study aims to investigate how changes in meteorological indicators affect groundwater resources, and hence to predict groundwater levels using these indicators, particularly in regions experiencing drought and subsidence. Precipitation, temperature, evapotranspiration and precipitable water vapor (PWV) are important meteorological parameters to predict groundwater levels. Two subsidence areas with different weather conditions were selected to conduct a comprehensive study on the effect of temperature and precipitation on groundwater level changes. The correct locations of the two subsidence areas were determined by analyzing Interferometric Synthetic Aperture Radar (InSAR) images of Sentinel-1A using the small baseline subset algorithm. The interferograms were processed to correct tropospheric effects using the advanced integration method. Specifying the exact locations of the two areas, the meteorological parameters were downscaled using the Statistical DownScaling Model (SDSM), synoptic observations, meteorological data, and the General Circulation Model (GCM). An Artificial Neural Network (ANN) was then employed to predict the groundwater level changes as a function of meteorological data, including Global Positioning System (GPS)-based PWV and the evapotranspiration index. The trained ANN, along with the downscaled meteorological indicators, was used to predict groundwater level changes over two time periods. In the first period, the prediction was performed over the current years to investigate the performance of the method using the available data, whereas in the second period, the prediction was performed for the coming years, up until 2030. The results confirmed the high performance of the prediction algorithm, and the importance of including PWV and evapotranspiration in groundwater level predictions. The Pearson correlation coefficient was used to check the relationship between groundwater level changes and meteorological variables. The statistical significance of these coefficients was tested at the significance level α = 0.05 . In more than 80% of the cases, the correlation coefficients were statistically significant, reaching more than 0.70 in some of the months. It is also observed that an increase in the depth of groundwater level has an obvious relationship with an increase in temperature and a decrease in rainfall. [ABSTRACT FROM AUTHOR]

Formulation of sustainable development plans in the water sector requires reliable estimates of future hydrological conditions. The general circulation models (GCM) are usually used in the prediction of runoff in future periods but the predictions have large uncertainty. This study aimed to propose an approach for reducing the uncertainty of the results when using GCM models. To that end, the IHACRES hydrological model was first calibrated for the baseline period (1981–2005) in the Gharesu basin, Iran. The runoff corresponding to the GCM outputs of temperature and precipitation was then calculated in the historical period using the IHACRES. Twelve top GCMs suitable to the case study for the estimation of runoff were selected by the TOPSIS algorithm. The selected twelve GCMs were combined by the runoff hybrid approach (RHM), as an ensemble model, to reduce the uncertainty of hydrological modeling. The RHM model is formed by weighted integration of the calculated runoff based on the temperature and precipitation achieved from the selected GCMs. Results showed that the mean coefficient of variation (CV) of RHM was 0.76 and the uncertainty of runoff estimation by ensemble modeling was less than that of any single GCM. The RHM model under the RCP4.5 and RCP8.5 scenarios was used to predict runoff in the near future period P1 (2006–2030), mid-future period P2 (2031–2055), and far future period P3 (2056–2080). The annual runoff prediction for the Gharesu basin under scenario RCP4.5 showed an increase of about 1% in the near future period (P1), a decrease of -2.4% in the period P2, and a decrease of -10% in the period P3 relative to the baseline period. Runoff decreased by -7.8, -6.9, and − 1.9%, respectively, in periods P1, P2, and P3 under scenario RCP8.5. The results picture that the studied sub-basin of Karkheh will face more heavy rains and floods in the winter and will be drier than the past. That is an important alarm for water managers to adapte their management strategies. [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]

Climate change has significant effects on forest ecosystems around the world. Since tree diameter increment determines forest volume increment and ultimately forest production, an accurate estimate of this variable under future climate change is of great importance for sustainable forest management. In this study, we modeled tree diameter increment under the effects of current and expected future climate change, using multilayer perceptron (MLP) artificial neural networks and linear mixed-effect model in two sites of the Hyrcanian Forest, northern Iran. Using 573 monitoring fixed-area (0.1 ha) plots, we measured and calculated biotic and abiotic factors (i.e., diameter at breast height (DBH), basal area in the largest trees (BAL), basal area (BA), elevation, aspect, slope, precipitation, and temperature). We investigated the effect of climate change in the year 2070 under two reference scenarios; RCP 4.5 (an intermediate scenario) and RCP 8.5 (an extreme scenario) due to the uncertainty caused by the general circulation models. According to the scenarios of climate change, the amount of annual precipitation and temperature during the study period will increase by 12.18 mm and 1.77 °C, respectively. Further, the results showed that the impact of predicted climate change was not very noticeable and the growth at the end of the period decreased by only about 7% annually. The effect of precipitation and temperature on the growth rate, in fact, neutralize each other, and therefore, the growth rate does not change significantly at the end of the period compared to the beginning. Based on the models' predictions, the MLP model performed better compared to the linear mixed-effect model in predicting tree diameter increment. [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]

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]

In this study, the Hydrologic Evaluation of Landfill Performance (HELP3.8D) model was developed to evaluate the spatiotemporal distribution of potential groundwater recharge (GWR) in Tasuj aquifer, northwestern Iran. High-resolution future climatic data from second-generation Canadian Earth System Model (CanESM2) general circulation models (GCMs) was produced under different scenarios of Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5). The analysis of climate parameters demonstrated that under RCP2.6, climatic variation will be substantially similar to that of the observed period (1961–2005), while moderate and severe droughts are anticipated under scenarios RCP4.5 and RCP8.5, respectively, over 2017–2030. The projection results showed that GWR will be altered by climate change, on average, from 31 mm/year at baseline to 32 (+3%), 28.5 (−8%) and 11.5 (−63%) mm/year under the RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively. This approach can be easily replicated by other researchers and could be beneficial for monitoring water security and managing groundwater resources in other catchment areas. [ABSTRACT FROM AUTHOR]

Temperature and precipitation are among the most important climatic elements in the study of climate change due to significant temporal and spatial changes, and the projection of their changes is very important in environmental hazards and planning. Therefore, in this study, the future of temperature and precipitation changes in the Middle East and North Africa (MENA) region was projected. For this purpose, the data of 23 global circulation models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) were used as networks under the influence of two scenarios SSP3–7.0 and SSP5–8.5 for temperature and precipitation changes in the two future periods (2020–2049 and 2050–2079) were investigated comparing to the base period (1985–2014). The results showed that the temperature will increase in both periods, which will be between 0.8 and 3.3 °C in the period 2020–2079 compared to the base period. The highest and lowest temperature changes are related to the eastern and northern regions of the study area, respectively. Projection of precipitation changes also showed that the precipitation in most of the study area will decrease in the next two periods compared to the base period, which will be between 5 and 133 mm on average. Most of its changes are related to the northern regions and in the form of a strip from Morocco to the northwest of Iran. In both studied periods, the SSP5–8.5 scenario shows the highest temperature and precipitation changes in the study area. [ABSTRACT FROM AUTHOR]

The extreme temperature indices (ETI) are an essential indicator of climate change. The detection of their changes over the next years can play an essential role in the climate action plan (CAP). In this study, four temperature indices (mean of daily minimum temperature (TN), mean of daily maximum temperature (TX), cold-spell duration index (CSDI), and warm-spell duration index (WSDI)) were defined by ETCCDI and two new indices,the maximum number of consecutive frost days (CFD) and the maximum number of consecutive summer days (CSU), were used to examine ETIs in Iran under climate change conditions. We used minimum and maximum daily temperatures of five general circulation models (GCMs), including HadGEM2-ES, IPSL-CM5A-LR, GFDL-ESM2M, MIROC-ESM-CHEM, and NorESM1-M, from the set of CMIP5 bias-correction models. We investigated two representative concentration pathway (RCP) scenarios of RCP4.5 and RCP8.5 during the historical (1965–2005) and future (2021–2060 and 2061–2100) periods. The performance of each model evaluated using the Taylor diagram on a seasonal scale. Among models, GFDL-ESM2M and HadGEM2-ES showed the highest, and NorESM1-M and IPSL-CM5A-LR showed the lowest performance in Iran. Then, an ensemble model was generated using independence weighted mean (IWM) method. The results of multi-model ensembles (MME) showed a higher performance compared to individual CMIP5 models in all seasons. Also, the uncertainty value significantly reduced, and the correlation value of the MME model reached 0.95 in all seasons. Additionally, it is found that WSDI and CSU indices showed positive anomalies in future periods, and CSDI and CFD showed negative anomalies throughout Iran. Also, at the end of the twenty-first century, no cold spells are projected in almost every part of Iran. The CSU index showed that summer days are increasing sharply; according to the results of the RCP8.5 scenario in spring (MAM) and autumn (SON), the CSU will increase by 18.79 and 20.51 days, respectively, at the end of the twenty-first century. It projected that in the future, the spring and autumn seasons will be shorter and summers will be much longer than before. [ABSTRACT FROM AUTHOR]

Ecological restoration plays a vital role in the management of degraded ecosystems; nevertheless, the success of restoration plans depends to a large extent on determining optimal habitats for target species' growth and survival. Ecological niche modeling can be used to predict where climate is presently suitable for a particular species used in restoration, and where suitable climates will be located in the future. Here, we used ensemble ecological modeling to identify areas suitable for restoration of the arid shrub Haloxylon persicum in southeast Iran. We generated environmental suitability predictions for H. persicum restorations from eight different niche modeling algorithms and averaged these predictions to generate an ensemble model prediction. We present projections for the climate suitability of H. persicum for the present climate and the years 2050 and 2070 under the RCP4.5 and RCP8.5 emissions scenarios and for three different global circulation models. Our model projected the optimum mean annual temperature for this species to be between 35.5 and 37°C, the optimum temperature of the coldest quarter to be between 3.5 and 13°C and the optimum precipitation of the coldest quarter to be between 65 to 90 mm. Based on our results, H. persicum is predicted to persist north of 27°N latitude in southeast Iran under current and future conditions; however, the availability of habitat suitable for H. persicum restoration is expected to dramatically diminish under future climate change. This study will help identify and prioritize locations for ecological restoration of this species in the face of climate change. [ABSTRACT FROM AUTHOR]

The main objective of this research was to evaluate the possible impact of climate change on groundwater levels in the Tasuj Plain, Iran. To accomplish this, the values of precipitation for a near future period was projected through four general circulation models (GCM). Then, the groundwater level variations through the genetic expression programming (GEP) model were evaluated. The projection results indicated that the average annual precipitation with 33.55 mm in the base period would decline to 20.51, 20.11, and 19.14 mm under three representative concentration pathway (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP8.5, respectively. The values of the determination coefficient range from 0.92 to 0.99; the root mean square error between 0.12 and 0.61, and mean absolute error from 0.08 to 0.54 showed that in the forecasting of groundwater level through the GEP model, all models have acceptable results. The evaluation of groundwater level simulation demonstrated that the developed model through the precipitation and previous groundwater level performs better. Prediction of groundwater level for a future period based on climate change scenarios indicated that the average groundwater level in Tasuj Plain at the beginning of the period would experience a gradual reduction and would then increase very slightly to the end of the period. Overall, it was found that the declining precipitation under climate change has no significant impacts on the groundwater level in the Tasuj Plain, and other parameters like anthropogenic activities could be the primary reason for groundwater depletion. [ABSTRACT FROM AUTHOR]

To turn General Circulation Models (GCMs) projection toward better assessment, it is crucial to employ a downscaling process to get more reliability of their outputs. The data-driven based downscaling techniques recently have been used widely, and predictor selection is usually considered as the main challenge in these methods. Hence, this study aims to examine the most common approaches of feature selection in the downscaling of daily rainfall in two different climates in Iran. So, the measured daily rainfall and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) predictors were collected, and Support Vector Machine (SVM) was considered as downscaling methods. Also, a complete set of comparative tests considering all dimensions was employed to identify the best subset of predictors. Results indicated that the skill of various selection methods in different tests is significantly different. Despite a few partial superiorities viewed between selection models, they not presented an obvious distinction. However, regarding all related factors, it may be deduced that the Stepwise Regression Analysis (SRA) and Bayesian Model Averaging (BMA) are better than others. Also, the finding of this study showed that there are some weaknesses in the interpretation of SRA, so concerning this issue, it may be concluded that BMA has more reliable performance. Furthermore, results indicated that generally, the downscaling procedure has more accuracy in arid climate than cold-semi arid climate. [ABSTRACT FROM AUTHOR]

Climate change, as an emerging phenomenon, has led to changes in the distribution, movement, and even risk of extinction of various wildlife species and this has raised concerns among conservation biologists. Different species have two options in the face of climate change, either to adopt or follow their climatic niche to new places through the connectivity of habitats. The modeling of interpatch landscape communications can serve as an effective decision support tool for wildlife managers. This study was conducted to assess the effects of climate change on the distribution and habitat connectivity of the endangered subspecies of Asian black bear (Ursus thibetanus gedrosianus) in the southern and southeastern Iran. The presence points of the species were collected in Provinces of Kerman, Hormozgan, and Sistan-Baluchestan. Habitat modeling was done by the Generalized Linear Model, and 3 machine learning models including Maximum Entropy, Back Propagation based artificial Neural Network, and Support Vector Machine. In order to achieve the ensemble model, the results of the mentioned models were merged based on the method of "accuracy rate as weight" derived from their validation. To construct pseudo-absence points for the use in the mentioned models, the Ensemble model of presence-only models was used. The modeling was performed using 15 habitat variables related to climatic, vegetation, topographic, and anthropogenic parameters. The three general circulation models of BCC-CSM1, CCSM4, and MRI-CGCM3 were selected under the two scenarios of RCP2.6 and RCP8.5 by 2070. To investigate the effect of climate change on the habitat connections, the protected areas of 3 provinces were considered as focal nodes and the connections between them were established based on electrical circuit theory and Pairwise method. The true skill statistic was employed to convert the continuous suitability layers to binary suitable/unsuitable range maps to assess the effectiveness of the protected areas in the coverage of suitable habitats for the species. Due to the high power of the stochastic forest model in determining the importance of variables, this method was used. The results showed that presence/absence models were successful in the implementation and well distinguished the points of presence and pseudo-absence from each other. Based on the random forests model, the variables of Precipitation of Driest Quarter, Precipitation of Coldest Quarter, and Temperature Annual Range have the greatest impact on the habitat suitability. Comparing the modeling findings to the realities of the species distribution range indicated that the suitable habitats are located in areas with high humidity and rainfall, which are mostly in the northern areas of Bandar Abbas, south of Kerman, and west and south of Sistan-Baluchestan. The area of suitable habitats, in the MRI-CGCM3 (189731 Km2) and CCSM4 (179007 Km2) models under the RCP2.6 scenario, is larger than the current distribution (174001 Km2). However, in terms of the performance of protected areas, the optimal coverage of the species by the boundary of the protected areas, under each of the RCP2.6 and RCP8.5 scenarios, is less than the present time. According to the electric circuit theory, connecting the populations in the protected areas of Sistan-Baluchestan province to those in the northern Hormozgan and the southern Kerman would be based on the crossing through the heights of Sistan-Baluchestan and Hormozgan provinces and the plains between these heights would be the movement pinch points under the current and future scenarios. Populations in the protected areas of Kerman have higher quality patch connections than that of the other two provinces. The areas such as Sang-e_Mes, Kouh_Shir, Zaryab, and Bahr_Aseman in Kerman Province and Kouhbaz and Geno in Hormozgan Province can provide suitable habitats for the species in the distribution models. The findings revealed that the conservation of the heights along with the caves inside them could be a protective priority to counteract the effects of climate change on the species. [ABSTRACT FROM AUTHOR]

A downscaling model capable of explaining the temporal and spatial variability of regional hydroclimatic variables is essential for reliable climate change studies and impact assessments. This study proposes a novel statistical approach based on generalized hierarchical linear model (GHLM) to downscale precipitation from the outputs of general circulation models (GCMs) at multiple sites. GHLM partitions the total variance of precipitation into within- and between-site variability allowing for transferring information between sites to develop a regional downscaling model. The methodology is demonstrated by downscaling precipitation using the outputs of eight GCMs in Lake Urmia basin in northwestern Iran. Multi-model ensemble simulations are merged and bias-corrected using Bayesian model averaging and equidistant quantile mapping, respectively. The results of this study show projected declining trends in precipitation resulting in approximately 11.2% and 15.3% decrease during 2060–2080 compared to the historical period of 1985–2005 considering representative concentration pathways (RCPs) 4.5 and 8.5, respectively. [ABSTRACT FROM AUTHOR]

Climate change represents a set of mechanisms that result in significant impacts toward hydrologic processes both at global and regional scales and usually they are developing countries where face the most challenging consequences. For instance, arid and semi-arid environments experience a high level of uncertainty in rainfall, which results in unforeseen periods of droughts, or high-intensity rainfall, which generates severe floods, increasing the water insecurity and flood damage in these areas. Climate pattern variations are specifically critical to forecast conditions for flood hazard management. General Circulation Models (GCMs) are useful tools to forecast future climate change scenarios. However, the application of GCM in flood hazard management is particularly challenging, given that they operate under high uncertainty and coarse scale. In this context, a five-class Flood Hazard Index (FHI) map was generated by using a multi criteria index approach, based on defining the greater influence of seven factors related to flood generation: runoff coefficient, elevation, slope, distance from the drainage network, rainfall intensity, soil erosion and land use. In this paper, we utilized monthly data for the baseline period (1980–2004), and then the output of 8 GCMs for surface air temperature (SAT) and precipitation were downscaled using a statistical model and evaluated in the baseline period. Consequently, the most suitable models were selected according to their average monthly performance. The Soil and Water Assessment Tool (SWAT) was then used to develop a runoff coefficient map, using the downscaled outputs of the selected GCMs. Accordingly, a flood hazard model was developed at a large basin in the northeast of Iran for baseline and future periods (2040–2064) under RCP 8.5 emission scenarios. Having validated the generated flood map for the baseline period, the future maps were generated and compared. Results show the increase in temperature and decrease in precipitation; however, it was found that rainfall intensities are to be increased. This supports the final flood hazard map for the future climate conditions scenario with an increased relative hazard level. This was particularly observed in flat, and moderate sloped areas, compared to the model results with historic climate data. Unlabelled Image • 8 GCMs were statistically evaluated, best models were used for future predictions. • An integrated framework assessing flood hazard under climate change was provided. • A developed methodology coupling SWAT, GIS, GCMs, and MCDA. • An adopted approach was validated using historical flood events. • The future flood hazard maps for the Mashhad basin were provided for the first time. [ABSTRACT FROM AUTHOR]

• Lower Upper Bound Estimation method used for constructing prediction intervals. • General circulation models applied for downscaling precipitation and temperature. • Results compared with the results of classic Bootstrap method. • Performance of methods was assessed by the criteria of coverage and width. • Prediction intervals for temperature were narrower than precipitation. In this paper, point prediction and prediction intervals (PIs) of artificial neural network (ANN) based downscaling for mean monthly precipitation and temperature of two stations (Tabriz and Ardabil in North West of Iran) were evaluated using general circulation models (GCMs). PIs were constructed by novel Upper Lower Bound Estimation (LUBE) method in which an ANN with two outputs was constructed for estimating the prediction bounds. Also, Bootstrap method as a classic technique for assessing uncertainty of ANN was used to further examine the proposed LUBE method. In this way, the accuracy of PIs was quantified by coverage and width criteria. Three GCMs, Can-ESM2, BNU-ESM, INM-CM4 and ensemble-GCM (ensemble of mentioned models) were used in four grid points around each of station for evaluating ANN-based downscaling of precipitation and temperature parameters. Comparison between the results of two methods indicated that LUBE method could lead to more reliable results than the Bootstrap method. PIs width and coverage probability were 10–40% lower and 2–10% higher than the Bootstrap method for different GCMs, respectively. Ensemble-GCM led to more accurate results so that computed PIs width and coverage probability were 10–60% lower and 2–20% higher than those for the single GCMs. [ABSTRACT FROM AUTHOR]

Agricultural sustainability under the warming climate highly depends on limiting the extreme use of the blue water resources within the irrigated lands, and instead, optimally applying green water resources within the expanded rainfed lands. In this research, potentials for expanding dry farming under climate change were analyzed in five climatic zones of Iran, based on the future projections of basic weather data and drought indices under 20 general circulation models (GCMs) and four emission scenarios, including RCP2.6, RCP4.5, RCP6.5, and RCP8.5. Drought indices included aridity index (AI), expressed as precipitation divided by evapotranspiration, and green water deficit (GWD), expressed as precipitation minus mines evapotranspiration. While annual predictions show a general increase in both precipitation and air temperature over the 2041–2070 period, given results at the monthly scale well revealed the spatial and temporal non-uniformity in the climate change projections. Such heterogeneities, together with those presented for different GCMs and RCPs, well indicated the importance of undertaking a wide range of uncertainties when implementing long-term planning. Based on GWD and AI, the arid and semi-arid regions, the backbone of the rainfed agriculture in Iran, are the most vulnerable to global warming. In these regions, green water availability will reduce by 0.8–167.5% especially within the wet seasons, and drought severity will increase by 0.1–33.2% through the rainfed cropping cycles. On the other hand, future climate may provide new opportunities for expanding dry farming within the hyper-arid region, which is now the most water-stressed region of the country. In this region, global warming projections show a positive increase of 0.3–146.2% in green water availability, and a reduction of 5.1–266.4% in drought severity. However, the humid region remained as the most suitable region for producing rainfed crops, although being widely allocated to irrigated-rice production under current condition. Based on the results, achieving sustainable dryland agriculture highly depends on regionally prioritizing susceptible area for dry farming, since the criteria indices showed an extreme spatial and temporal variability over the mid twenty-first century. [ABSTRACT FROM AUTHOR]

GCMs (general circulation models) are main tools for generating climate projections for climate change research in hydrology and water resources. Accordingly, evaluating the performance of these models in simulating future climate is very important for choice of proper models. In this study, performance of 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) model series was assessed using a technique for order performance by similarity to ideal solution (TOPSIS)-based approach together with normalized root mean square error (NRMSE), the Taylor skill score (STaylor), and two probability density function (PDF) skill scores. Precipitation and temperature data during 1976 to 2005 from three river basins including Zard River (ZR), Bakhtegan (BKH), and Ghareso (GH) in west and southwest Iran were used to select the best model. In general, models showed superiority in simulating temperature over precipitation. Based on the GCM ranking results for the ZR Basin, MIROC-ESM and IPSL-CM5A-LR were selected as the best and the weakest model, respectively. For the BKH Basin, the best model was BCC-CSM1.1 and the weakest IPSL-CM5A-MR and CCSM4. In other words, BCC-CSM1.1 had the maximum relative closeness to ideal solution. Based on the TOPSIS results, BCC-CSM1.1 and CanESM2 were the best models and IPSL-CM5A-MR the weakest model with a minimum relative closeness to the ideal solution in simulating temperature and precipitation for the GH basin. The approach presented in this study can be utilized to select appropriate climate models in other regions for future studies of climate change. [ABSTRACT FROM AUTHOR]

1- Introduction Assessing the climatic characteristics and identifying the climatic elements and parameters of a region, can have a major role in programming and the welfare of that region. Effective Precipitation, as one of the most important climatic elements in the regions of the northwest of Iran with considerable temporal-spatial variations, has a major role in the way environmental, energy, and economic potentials are used. Therefore, predicting natural phenomena like flood and drought and the necessity of correctly recognizing atmospheric conditions and consequently presenting an appropriate solution for fighting natural hazards, has made programmers draw the effective precipitation clusters of this region. 2-Methodology In this research, for comparing the basic period (past) with the simulated data, first the statistics of the daily effective precipitation of the six stations of Ardebil, Ghazvin, Hamedan, Kermanshah, Sanandaj, and Tabriz, have been provided for a 30-year period (1961-1990) and on the other hand, have been compared with the simulated data of 2021 to 2050 that have been generated using the general atmosphere circulation model HADCM3 and the A1 scenario and been downscaled using the LARS-WG model. The method for comparing effective precipitations in different clusters was done based on the clustering in the form of 5 clusters for all the stations and study periods. 3-Discussion One of the results of this research is the greater concentration of effective precipitation for the cold periods of the year (winter and fall) and the increase of annual precipitation by the amount of 20.62 millimeters for the average of the period 2021-2050 compared to the basic period. Also, the results indicate the increase in the intensity of effective precipitation thresholds for the stations of Ardebil, Kermanshah, and Tabriz in future decades compared to the basic period 1961-1990 and the decrease in the number of days in which rain has fallen for Kermanshah, Sanandaj, and Tabriz for future decades. Then, for evaluating the two coordinates of precipitation percentage and cluster frequency percentage compared to a normal line (on each spot of this line the intensity of effective precipitations and the frequency of days are equal) their diagram was drawn. 4-Conclusion The outputs of this section demonstrate that the effective precipitations of cluster 3 which have the features of light rain with average intensity, fall on this normal line for most of the stations and study periods. But the precipitations of cluster 1, are very heavy and intense, which in most cases have the most distance to the normal line. Therefore, the precipitations of the third cluster need optimal exploitation management and the precipitations of the first cluster need risk management and in some cases crisis management. [ABSTRACT FROM AUTHOR]

Global greenhouse gases increase could be a threat to sustainable agriculture since it might affect both green water and air temperature. Using the outputs of 15 general circulation models (GCMs) under three SRES scenarios of A1B, A2 and B1, the projected annual and seasonal precipitation (P) and cardinal temperatures (T) were analyzed for five climatic zones in Iran. In addition, the probable effects of climate change on cereal production were studied using AquaCrop model. Data obtained from the GCMs were downscaled using LARS-WG for 52 synoptic stations up to 2100. An uncertainty analysis was done for the projected P and T associated to GCMs and SRES scenarios. Based on station observations, LARS-WG was capable enough for simulating both P and T for all the climatic zones. The majority of GCMs as well as the median of the ensemble for each scenario project positive P and T changes. In all the climatic zones, wet seasons have a higher P increase than dry seasons, with the highest increase (27.9-83.3%) corresponding to hyper-arid and arid regions. A few GCMs project a P reduction mainly in Mediterranean and hyper-humid climatic regions. The highest increase (11.2-44.5%) in minimum T occurred in Mediterranean climatic regions followed by semi-arid regions in which a concurrent increase in maximum T (2.9-14.6%) occurred. The largest uncertainty in P and cardinal T projection occurred in rainy seasons as well as in hyper-humid regions. The AquaCrop simulation results revealed that the increased cardinal T under global warming will cause 0-28.5% increase in cereal water requirement as well as 0-15% reduction in crop yield leading to 0-30% reduction in water use efficiency in 95% of the country. [ABSTRACT FROM AUTHOR]

This study is conducted to investigate the regional effects of climate change on Zayandeh-Rud River Basin located in the central part of Iran for the both near and far future scenarios. A combination of various general circulation models (GCMs) is used through a weighting approach to generate different climate change patterns including the ideal, medium, and critical patterns. Each of the GCMs has different ability to simulate the baseline climatic parameters in various months and regions of the basin. A new method, namely 'modified weighting method based on the actual values' (MWM-AV), also is applied to convert the local effects of climate change to the regional effects. The results showed that the annual temperature of Zayandeh-Rud River Basin would increase by 0.59-1.34 and 1.02-2.53 °C, respectively, in the near and far futures in which maximum increase in seasonal temperature is expected to happen in the summer. Annual precipitation would change by +1.78 to −20.78 % in the near future and −14.35 to −32.82 % in the far future. The maximum decrease in precipitation is observed to be in the winter. The results of temperature and precipitation regionalization showed that the applied method has a good precision in estimating temperature and precipitation in different regions of the desired basin. The eastern part of the basin would have the maximum increase in temperature, while the western part would experience the maximum decrease in precipitation. Overall, the results demonstrated that due to the centralization of the main water uses in the east and the water resources in the west, the Zayandeh-Rud River Basin will face an intensive water shortage under climate change. [ABSTRACT FROM AUTHOR]

Wheat is the main food for the majority of Iran's population. Precise estimation of wheat yield change in future is essential for any possible revision of management strategies. The main objective of this study was to evaluate the effects of climate change, CO concentration, technology development and their integrated effects on wheat production under future climate change. This study was performed under two scenarios of the IPCC Special Report on Emission Scenarios (SRES): regional economic (A2) and global environmental (B1). Crop production was projected for three future time periods (2020, 2050 and 2080) in comparison with a baseline year (2005) for Khorasan province located in the northeast of Iran. Four study locations in the study area included Mashhad, Birjand, Bojnourd and Sabzevar. The effect of technology development was calculated by fitting a regression equation between the observed wheat yields against historical years considering yield potential increase and yield gap reduction as technology development. Yield relative increase per unit change of CO concentration (1 ppm) was considered 0.05 % and was used to implement the effect of elevated CO. The HadCM3 general circulation model along with the CSM-CERES-Wheat crop model were used to project climate change effects on wheat crop yield. Our results illustrate that, among all the factors considered, technology development provided the highest impact on wheat yield change. Highest wheat yield increase across all locations and time periods was obtained under the A2 scenario. Among study locations, Mashhad showed the highest change in wheat yield. Yield change compared to baseline ranged from −28 % to 56 % when the integration of all factors was considered across all locations. It seems that achieving higher yield of wheat in future may be expected in northeast Iran assuming stable improvements in production technology. [ABSTRACT FROM AUTHOR]

Future projection of drought vulnerability is vital for northern provinces of Iran, including North Khorasan, Khorasan-Razavi, and South Khorasan, due to the highly dependent of their economy on agriculture. The study is motivated by the fact that no research has been conducted to project the future Drought Vulnerability Index (DVI). DVI consist of three components of exposure, sensitivity, and adaptation capacity. More exposure levels of drought, higher sensitivity value, and lower adaptation capacity lead to a higher amount of vulnerability. Combined ERA-Interim-observation meteorological data, CMIP5 models under RCP4.5 and RCP8.5 scenarios, and national census data are used to estimate DVI in the past and future periods. CanESM2, GFDL-ESM2M, and CNRM-CM5 General Circulation Model (GCM) are selected from CMIP5 based on Taylor diagram results. The delta-change technique was selected for statistical downscaling of GCM outputs because it is most widely used. The study period is regarded as 1986–2005 as observation and four future 20-years periods during 2021–2100. Results indicated that the dissipation of the class of "very low" vulnerability is eminent in the near future period of 2021–2040 under the RCP4.5 scenario, and all provinces would experience a new worse class of "very high" vulnerability at 2081–2100, both under RCP4.5 and RCP8.5 scenarios. [ABSTRACT FROM AUTHOR]

Development and evaluation of mitigation strategies are fundamental to manage climate change risks. This study was built on (1) quantifying the response of maize ( Zea mays L.) grain yield to potential impacts of climate change and (2) investigating the effectiveness of changing sowing date of maize as a mitigation option for Khorasan Province which is located in northeast of Iran. Two types of General Circulation Models (GCM: (United Kingdom Met Office Hadley Center :HadCM3) and (Institute Pierre Simon Laplace: IPCM4)) and three scenarios (A1B, A2 and B1) at four locations (Mashhad, Birjand, Bojnourd and Sabzevar) employed in this study. Long Ashton Research Station-Weather Generator (LARS-WG) was employed for generating the future climate. The Cropping System Model (CSM)-CERES-Maize was used for crop growth simulation under projected climate conditions. The results showed the simulated grain yields of maize gradually would decrease (from −1% to −39%) during future 100 years compared to baseline under different scenarios and two GCM at all study locations. The simulation results suggested that delayed sowing date from May to June at all study locations, except Sabzevar location is the most effective mitigation option for avoiding thermal stress at end of growth period. In addition, shifting in sowing date to March or April will be beneficial in terms of obtaining higher yields in Sabzevar. Grain yield did not show special trend from north to south of Khorasan Province in the future climate. In general, change of sowing date may be quite beneficial to mitigate climate change impacts on grain yield of maize in northeast of Iran. [ABSTRACT FROM AUTHOR]

The complexity of understanding the groundwater resources in relation to climate change is caused by direct and indirect effects of climate change on hydrological processes. The study herein aims at implementing a physically based groundwater model to investigate the effects of climate change on groundwater system under 15 general circulation models (GCMs) in a semi-arid region from 2020 to 2044. A non-parametric probability density function estimator was used to quantify the level of uncertainties in the simulations. The method was applied to an area of 2073 km 2 in southwest Iran, consisting of five plains: western Dez, eastern Dez, Sabili, Deymche and Lor. The results indicate that there is a decline in the recharge in April, May, June and October. The range of changes in the recharge was determined to be between - 10 % and + 13 % in the Sabili plain, - 6 % and + 10 % in the Deymche plain, - 4 % and + 10 % in the western-Dez plain, - 6 % and + 26 % in the eastern-Dez plain, and - 40 % and + 100 % in the Lor plain. The most significant decline in the groundwater level occurred in the Sabili plain in September. The largest uncertainty in the simulation of recharge under GCM scenarios was determined to be in August, September and December. This study highlights that climate change can have a significant effect on groundwater resources in the region that reinforces the need for groundwater management plans and a long-term perspective. [ABSTRACT FROM AUTHOR]