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Snow melt is one of the sources of freshwater supply in the late spring and summer in the mountainous regions of Iran, especially in the Urmia Lake Basin (ULB). In this study, past and future droughts of Urmia Lake Basin (ULB) have been studied by analyzing three types of droughts: (i) precipitation-deficit based characterized by the Standardized Precipitation Index (SPI), (ii) precipitation-evapotranspiration based droughts characterized by the Standardized Precipitation and Evapotranspiration Index (SPEI) and (iii) those droughts forced additionally by snow melt using the Snowmelt and Rain Index (SMRI). While reanalysis data ERA5-land describes the past climate, bias-corrected CMIP6 ensemble serves as the data for the future climate. Contrary to the SPI drought index, an increasing trend has been projected both in snowmelt-based (SMRI trend -0.068 units/year) and evapotranspiration-based (SPEI trend -0.079 units/year) drought indices, both for the period 1995–2014 and significant at the 5 % level. This indicates that summer droughts in the ULB will increase in the future, particularly because of increasing evapotranspiration and less snowmelt, while precipitation changes play a minor role. Drought severity will generally increase from the near future (2021–2040) to the far future (2081–2100), particularly forced by snowmelt deficit under the SSP5-8.5 scenario for the far future. Under the present climate, the extent of drought-affected areas is similar for all three types of droughts. However, under future climate drought-affected areas forced by snowmelt deficit will increase from about 20 % in the near future (2021–2040) to 60 % in the far future (2081–2100), showing that snow melt plays a vital role in aggravating the drought over the Basin. A decrease in the Basin's drought trend in the 2080s and later can be seen both for SMRI and SPEI indices under SSP1-2.6, which may be due to the temperature effect on snowmelt and evapotranspiration from the reduction of greenhouse gas emissions in SSP1-2.6 scenario at the end of 21st century. Such a decrease in SMRI and SPEI drought indices can also be seen around the 2090s under the SSP2-4.5 scenario. Results also reveal that the mountainous areas of the Basin will experience much less drought compared to the lowlands (including the lake) and foothills. [ABSTRACT FROM AUTHOR]

Multi-model projections in climate studies are performed to quantify and narrow uncertainty and improve reliability in climate projections. The challenging issue is that there is no unique way to obtain performance metrics, nor is there any consensus about which method would be exactly the best method for combining models. The goal of this study was to investigate whether combining climate model projections using an artificial neural network approach could improve climate projections and therefore reduce the range of uncertainty. The equally-weighted model averaging (the mean model) and single climate model projections (the best model) were also considered as a reference of comparison for our artificial neural network combination approach. Simulations of historical climate and future projections from 15 General Circulation Models for temperature and precipitation were employed. Our results indicate that based on calculated performance indices combining General Circulation Models projections by using the artificial neural network approach significantly improves the simulations of temperature and precipitation for the historical period compared to the best model approach and the mean model approach. Our results also indicate that based on the calculated performance indices for the three approaches, projections based on single model simulation might not yield reliable results because the best model changed between temperature and precipitation, and also among stations that were studied. Therefore, there was no a unique model which could represent the best model for all climate variables and/or stations in the study region. The mean model was also not skillful enough in giving an accurate projection of historical climate compared to the other two approaches. Therefore, the ANN approach was used to estimate projections of future temperature and precipitation for the study region based on three different emission scenarios. Simulation of temperature indicated that the artificial neural network approach had the best skill at simulating monthly means of the historical period compared to other approaches in all stations. Simulation of precipitation in the historical period, however, indicated that the artificial neural network approach was not the best approach in all stations, although this modeling approach performed better than the mean model approach. Multi-model projections of future climate variables for this study region performed by the artificial neural network approach projected an increase in temperature and reduction in precipitation in all stations and for all scenarios. The artificial neural network approach can benefit projections of the climate variables and has the potential to reduce the uncertainty aspects in constructing and combining metrics used for weighting the models. However, this approach is subject to some limitations which exist in similar skill-based performance studies of models and should be considered in future similar studies. [ABSTRACT FROM AUTHOR]

In order to investigate the scope of uncertainty in projections of GCMs for Tehran province, a multi-model projection composed of 15 models is employed. The projected changes in minimum temperature, maximum temperature, precipitation, and solar radiation under the A1B scenario equivalent to RCP4.5 for Tehran province are investigated for 2011-2030, 2046-2065, and 2080-2099. GCM projections for the study region are downscaled by the LARS-WG5 model. In climate change impact assessment studies, due to the influence of different sources of uncertainty on the output of the predicting system, projections do not have sufficient confidence. Therefore, it is recommended that for quantifying the range of uncertainty in the projections, the maximum number of available GCM models be used in simulations. In this regard, 15 GCMs used in this study are a subset of the CMIP4 models used in the IPCC 4th assessment report published in 2007. All these models are the coupled Atmospheric-Oceanic models and have been run for the 1960-2100 period. Uncertainty among the projections is evaluated from three perspectives: large-scale climate scenarios, downscaled values, and mean decadal changes. 15 GCMs unanimously project an increasing trend in the temperature for the study region. Also, uncertainty in the projections for the summer months is greater than projection uncertainty for other months. The mean absolute surface temperature increase for the three periods is projected to be about 0.8°C, 2.4°C, and 3.8°C in the summers, respectively. The uncertainty of the multi-model projections for precipitation in summer seasons and the radiation in the springs and falls is higher than in other seasons for the study region. Model projections indicate that for the three future periods and relative to their baseline period, springtime precipitation will decrease about 5%, 10%, and 20%, and springtime radiation will increase about 0.5%, 1.5%, and 3%, respectively. The projected mean decadal changes indicate an increase in temperature and radiation and a decrease in precipitation. Furthermore, the performance of the GCMs in simulating the baseline climate by the MOTP method does not indicate any distinct pattern among the GCMs for the study region. The future projection of temperature confirms that Tehran will experience hotter summers in the future compared to the base period. This, together with the increased sunshine in the springs and summers, can increase the frequency of temperature- and radiation-related phenomena such as photochemical pollution and may degrade the future summertime air quality in the study region. Moreover, the projected reduction in winter and spring precipitation, together with increased temperature, may increase the demands in the region. [ABSTRACT FROM AUTHOR]