نشریه پژوهشهای تغییرات آب و هوایی
پیاپی 16 (زمستان 1402)

Intensity-Duration-Frequency (IDF) curves are crucial in the design of water and hydraulic infrastructures. These curves are estimated based on rainfall data collected by rain gauge stations. However, due to global climate change, the intensity and frequency of extreme events can be altered. Hence, the existing IDF curves may not be reliable for the design of infrastructures and should be revised based on more recent data. Climate models can be used to investigate the impact of climate change on IDF curves. Typically, after developing the IDF curves from each climate model, the final curve used is extracted based on the median of the data. This study explores the use of five CMIP6 climate models to estimate the intensity-frequency curves of 24-hour rainfall at 12 stations in Iran. In addition to evaluating each model individually, this study also assesses the combined output of the models and compares its results with the curve obtained from the median of the data. Evaluation metrics such as mean error (ME), root mean square error (RMSE) and relative error (RE) indicated that among the studied models, the CMCC-CM2-SR5 model provides a better estimate of the 24-hour rainfall intensity at most stations, likely due to its finer resolution. Furthermore, pooling the models and estimating the IDF curve with the pooled data yielded better results than the median method and most of the individual models, particularly for longer return periods. This suggests that using pooled data from multiple models could improve the accuracy of IDF curve estimates

One of the two-way consequences of global warming and climate change is its effects on water and air pollution, which can affect rural livelihoods or the destruction of rural areas. The current research examines the effect of human presence and his heat-generating devices and activities on the Land Surface Temperature (LST) of the summer residences of Taft city, Yazd province. For this purpose, the images of two comparative periods of April (April 10 with April 2) and summer (Friday with Thursday and Saturday) of Landsat 8 and 9 satellites were used. Then the surface temperature map and hot-spot analysis (G-i-star) of the area were prepared and the changes were evaluated. The results showed that in April period, 64% and in summer period, 60.1% of rural areas experienced an increase in LST. Also, 1.43% to 43.5% of the rural area of the region had experienced an increase in temperature in April and 31.2% to 31.8% in summer. An increase in temperature variance was also observed in these areas, which shows an increase in temperature variation in these areas. The number of hot spots in these areas also increased by 111.4% in April and 48% and 21.1% in summer. The results also showed that 65.1% of rural vegetation in April and 49.8% in summer faced an increase in LST, of which there was a 19% increase in April and 49.9 and 8.6% in summer in temperature variance and 3. 118 percent of April and 9.5 and 0.2 percent of summer in the number of hot spots, the share of areas with vegetation was. According to the results, all the villages had experienced an increase in LST, and the fluctuations of this increase were greater in the villages with less vegetation and less area. The current research can be considered as a warning for the creation of rural thermal islands (like cities) on holidays with many tourists and serious damage to vegetation and rural climate.

Examining temporal and spatial changes of snow melting is very important in various fields including water resource management. Therefore, the current research was conducted with the aim of investigating the temporal and spatial changes as well as the spatial autocorrelation of the amount of snow melting in the northwest of Iran for the months of the cold seasons. For this purpose, snowmelt data from the European Center for Medium-Range Weather Forecasts (ECMWF), version (ERA5) with a spatial resolution of 0.25 x 0.25 degrees for the cold season months during the statistical period from 1982 to 2022 received and then divided into four periods of ten years. In order to analyze spatial autocorrelation changes, global Moran indices and hot spot analysis (Gettis-ORDJ) were used at the significance level of 90, 95 and 99%. Also, in order to determine the effect of temperature on the amount of snow melting, the trend of changes in the average minimum monthly temperature of 20 synoptic stations in the northwest region was investigated. The results of the present research showed that in the studied area, snow melting has spatial autocorrelation and a strong cluster pattern. During the first decade to the end of the fourth decade, the amount of snow melting in the months of October, November and December was between 0  and 5.27 mm per day, and especially in the month of December, which is accompanied by a negative minimum temperature anomaly the area (number of pixels) and the amount of snowmelt in the northwest have been reduced. The results of the analysis of the amount of snow melting changes in the winter months also showed that both the amount and the area of snow melting have increased during the study period. Thus, the range of snow melting threshold changes in January, February and March has increased between 0.95 and 19.27 mm per day from the first decade to the end of the fourth decade. Among the months of the winter season, February is associated with a strong positive minimum temperature anomaly (significant increase in minimum temperature), and accordingly, the area (number of pixels) and the amount of snow melting in the northwest have been increased.

This study evaluates the performance of an ensemble framework using the Weather Research and Forecasting (WRF) model for probabilistic seasonal precipitation forecasts. In this study, two types of data were used: a) The meteorological initial and boundary conditions come from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) data. b) Precipitation data from the Global Precipitation Climatology Centre (GPCC) dataset used as observational data over Iran. The ensemble model was designed based on a one-way double-nested (60-parent domain and 20-nested Km resolutions) modeling system using Weather Research and Forecasting (WRF) version 4.2 customized over Iran to downscale the second version of the NCEP Climate Forecast System (CFSv2). The results showed that precipitation forecast at seasonal time scale in Iran has high uncertainty. Although probabilistic forecasts can increase the efficacy of seasonal forecasts more than deterministic, the uncertainty of these forecasts is still high. Additionally, the downscaling of the CFS.v2 model by WRF and using multiple initial conditions and model physics can increase the accuracy of seasonal forecasts. The spatial distribution of the forecast accuracy of the ensemble model is dependent on the spatial distribution of precipitation over Iran. Another factor that affects the model's accuracy is the forecast lead time dependent especially at 2-month and 3-month forecast lead times. The results showed that the model has high uncertainty in the east and southeast of Iran. The implementation of this model for an operational period showed that although the model can forecast the spatial variation of rainfall over Iran up to a three-month lead time, probabilistic forecasting cannot significantly reduce the uncertainty of the model in a seasonal time scale. The ensemble model tends to overestimate precipitation in the third lead time.

A comprehensive index definition that provides a more complete interpretation of meteorological and hydrological drought is essential. Based on the multivariate drought index (MSDI) was defined in this study based on rainfall and runoff to diagnose meteorological and hydrological droughts. In this regard, two indices, SPI and SSI, were calculated seasonally in the Galikesh and Arazkouse regions in Golestan province. 50-year rainfall and runoff statistics were used for the calculations. The MSDI index was also calculated using joint functions based on flow and precipitation variables. To test the compatibility of the MSDI index with SPI and SSI, the degree of correlation, trend, and change points were checked using Mann-Kendall and Pettit tests. The results showed that while there was no perfect correlation between the SPI and SSI indices, the MSDI index had a high correlation with both of them. The degree of correlation varied in different seasons and stations. Even in the worst case where SPI and SSI had no significant correlation, the MSDI index created a 62% correlation with SPI in Galikesh. The trend and mutation results were in good agreement in the two stations. At Arazkouse station, SPI had no trend, SSI only in spring, and MSDI had a trend in spring and summer. At Galikesh station, MSDI didn’t have a trend in any season, SPI had a trend in autumn, and SSI in spring. The MSDI index was similar to SPI and SSI indices in cases where they showed the same dry conditions. Otherwise, it was consistent with the index that showed a drier condition. Additionally, the MSDI index had a tendency to show drier seasons. In conclusion, the MSDI index can be considered a suitable index for the simultaneous analysis of meteorology and hydrology drought.

Climate change has a significant impact on evapotranspiration (ET) as the one of the main components of the hydrological cycle. The main purpose of this research is to project the amount of potential evapotranspiration (ETp), under RCP scenario comparing to the baseline period using the WEAP model in the selected stations of Karkhe basin, Iran. The 1999-2019 and 2020-2100 years were considered as the baseline and future periods. The projected climatic variables by KIOST-ESM and MPI-ESM1-2-LR climate models under two climate change scenarios of SSP2-4.5 and SSP5-8.5 and the observed data of the selected stations in Karkheh basin were used to estimate the  for the future and. According to the outputs of the WEAP model, the potential evapotranspiration will increase by 2100. The highest increase was projected under SSP5-8.5 scenario by the MPI-ESM1-2-LR model, with amount of 89 mm in June and by the KIOST-ESM model, 73 mm in May. The lowest values were equal to 26 and -0.5 mm in the months of December and September, respectively. Correspondingly, these values under SSP2-4.5 scenario were estimated as 85.4, 64.3, 23.3 and -4.6 during June, May, December and September compared to the base period, which the latter indicated a decrease for September comparing to the baseline. Also, the amount of potential evapotranspiration in regions with temperate Mediterranean climates will experience more variations comparing to those with arid temperate climates.