همایون مطیعی

Surface soil moisture is a key variable in hydrological, meteorological and environmental studies and one of the important and effective parameters in the occurrence of dust. The purpose of this paper is in two folds, the first to validate the soil moisture of ECV database and the second investigate the effect of soil moisture on occurrence dust. The first purpose of this study was to evaluate the relationship between estimated surface soil moisture of remote sensing ECV databases and observed surface soil moisture of agricultural meteorological stations located in the south and west of Iran in Khuzestan, Ilam, Kohgiluyeh and Boyer-Ahmad, Lorestan and Chaharmahal-Bakhtiari provinces.  For this purpose, statistical indicators such as Pearson correlation coefficient, mean absolute error, mean oblique error and root mean difference of squares were used to validate the data of this database with data measured in meteorological stations of Farkashhar, Sarablah and Silkhor in the south of the country.The results of soil moisture validation at selected stations showed that these data are able to measure the behavior and amount of soil moisture with relatively good accuracy.  The best result is obtained at Sarablah station, which shows a very good correlation coefficient (0.82), therefore ECV database data can be used to determine the behavior and amount of soil surface moisture on a large scale to compensate for the lack of terrestrial measurements inside and outside the country. The second purpose of this study is to find the effect and relationship between surface soil moisture in countries located in western Iran on the occurrence of dust in the southern and western provinces.  Regarding the relationship between the effect and the amount of surface soil moisture with the occurrence of dust, the results show that the when surface soil moisture decrease, the occurrence of dust in the interior of the same province has increased, but the decrease in soil moisture in foreign countries (western Iran), cause increasing the occurrence of dust in the entire southwestern region and even central of Iran. It was also found that the number of dust events during the cold periods of the year has increased in recent years.

Climate change impacts on climate variables are among the challenges of large cities. In this regard, General Circulation Models (GCMs) are among the most reliable tools for assessment of the future climate variables. In 5th assessment report on climate change, the Intergovernmental Panel on Climate Change (IPCC) has applied the Coupled Model Intercomparison Project, Phase 5 (CMIP5) models. These models use scenarios called Representative Concentration Pathway (RCP). In current study, the assessment of the climate variables of Tehran, Iran, under climate change impacts was addressed. The preceding studies for Tehran show they need to be updated with newer scenarios and more downscaling models. Furthermore, there is merit in using more synoptic stations due to the vastness of Tehran. Given these findings, the current study focuses on two main objectives. The first objective is to assess the climate variables under the RCP scenarios in Tehran for 2021-2040. To this end, the eight CMIP5 models under RCP2.6, RCP4.5 and RCP8.5 were used. Accordingly, seven climate variables including mean Temperature (Tmean), maximum Temperature (Tmax), minimum Temperature (Tmin), precipitation, relative humidity, mean Wind speed (Wmean) and the sunshine hours were used and simulated for baseline period (1989-2018) and then assessed for future period. In the Second objective, for downscaling the CMIP5s, in addition to use of the Statistical DownScaling Model (SDSM), Fuzzy logic was also applied for downscaling. Accordingly, the Fuzzy DownScaling Model (FDSM) was generated and the performances of FDSM and SDSM were analyzed.

In this study, to assess the Tehran climate variables under RCP scenarios, the multi-model ensemble were applied to reduce the CMIP5’s uncertainties. Accordingly, the eight CMIP5s including CanESM2, CNRM-CM5, CSIRO-Mk3.6, FGOALS-g2, GFDL-CM3, HadGEM2-ES, MIROC-ESM-CHEM and MPI-ESM-MR were used. Given the uncertainty caused by the different outputs of the eight CMIP5s, the weighted means of the models’ outputs were used to calculate the daily climate variables for future (according to the ability of the models in simulating the baseline period). For this purpose, first, the CMIP5s were downscaled. In this context, the SDSM software (version 5.3.5) was used and also FDSM was generated. Then the performances of FDSM and SDSM were analyzed. On this basis, the superior downscaling models were selected using the comparison of simulation results and the statistical indicators of R2, RMSE, NSE and MAE. Accordingly, the CMIP5’s outputs were downscaled using the superior downscaling models and then the daily values of each climate variable were calculated. In the calibration and validation of the downscaling models at baseline period, the predictors were selected from the daily data of the National Center for Environmental Prediction (NCEP) using correlation test in SDSM software. Furthermore, in developing the FDSM, the Fuzzy C-Means Clustering process was applied, to determine the Fuzzy Membership Functions and the relevant Fuzzy Rules. By using the structure obtained by clustering, the FDSM was built as a Mamdani Fuzzy Inference System. In this context, the FDSM was developed in MATLAB software using the trial and error process.

By correlation test in SDSM software, the predictors were selected for the SDSM and FDSM models. Accordingly, the SDSM and FDSM were developed using the daily climate variable and the selected predictors. The performance analysis of both downscaling models (based on the statistical indicators of R2, RMSE, NSE and MAE and the comparison of simulation results in baseline period) demonstrate very good quality and performance for all the daily Tehran climate variables. Therefore, the Fuzzy approach has an appropriate capability in simulating and downscaling the climate variables. In addition, neither model has absolute superiority over the other in downscaling. However, it appears that with a slight margin, the FDSM had a better performance for Tmean, Tmax and Tmin, and SDSM had a better performance for precipitation, relative humidity, Wmean and the sunshine. Accordingly these models were chosen as the superior downscaling models. The results of future period show the increasing trend of annual changes in Tmean and Tmax, precipitation and the Wmean. The maximum increase of annual average in Tmean and Tmax and the Wmean among all scenarios will be in the order of 1.29oC, 1.57oC and 0.8m/s (for RCP8.5) and also the maximum increases of annual average precipitation will be 10mm (for RCP2.6). Furthermore, the month long-term averages of Tmean and Tmax in all three scenarios show significant increases in summer. For precipitation, relative stability in summer, and increases in winter and early spring are projected, but the changes in Tmin, relative humidity and sunshine indicate relative stability.

In this study, two main objectives including the assessment of the climate variables under the RCP scenarios in Tehran for 2021-2040 and also the performance analysis of Fuzzy logic in downscaling were addressed. The performance analysis of FDSM and SDSM demonstrated the high performance of both models and the appropriate ability of the Fuzzy approach in downscaling the Tehran climate variables. Therefore, taking the Fuzzy approach for downscaling has a technical justification. In this context, the application of the Mamdani Fuzzy Inference System and the Fuzzy C-Means Clustering increases the accuracy and quality of the results at different conditions. According to the results, the annual changes in Tmean, Tmax and the Wmean at all the three RCP scenarios will have an increasing trend, while precipitation will also (marginally) increase. However, the other variables will have a relative stability. From the point of view of monthly changes, there were noticeable increases in the long-term means of Tmean and Tmax in the future period during the months of July, August and September (i.e. summer season). As regards to precipitation, a relatively stable trend was observed in comparison with the baseline during the warm months of future period, but during winter and in particular at the beginning of the spring of the 2021–2040 period, there will be more precipitation at different months of the year than the 1989–2018 period.

Glaciers are one of the most important water resources in the world, which are heavily affected by global warming and climate change. This paper investigates the effects of global warming on the changes in the snow cover level of the Takht Suleiman region located in Mazandaran province during the warm months of the year through the past three decades using remote sensing. For this purpose, the images from June to August of the Landsat-5 and 8 satellites in the period of 1990 to 2021, as well as the data of the air temperature product of the ERA5 sensor were processed on the Google Earth Engine. In this research, NDSI index (Normalized Snow Cover Surface Index) was used to detect snow covered surfaces and the Mann-Kendall test was used to evaluate the trend of the data. The results of the overall accuracy and Kappa coefficient in the Google Earth Engine system show an overall accuracy of 94% and a Kappa coefficient of 89% in 2021, which shows the high compatibility of this method with real data.
The results obtained during the investigated period show an increase of about 1.5 degrees in temperature during the last three decades at a significant level of 95%. The snow and ice cover of the Takht Suleiman region in June month decreased from 127 square kilometers( in 1990) with a decrease of 82% to 22 square kilometers( in 2021). The trend of changes in the level of snow cover in June was analyzed with the Mann-Kendall test, which shows a decreasing trend at a significance level between 80 and 90%. In general, these results indicate an increase in temperature and a decrease in the level of this glacier during the statistical period studied, and the continuation of the gradual depletion of the glaciers of this region in the future is a serious threat to the downstream water source and the surrounding environment.

In this research, the impact of climate change on the snow and ice covering of Alamkuh glacier in Mazandaran province was assessed using LANDSAT-7, LANDSAT-8 satellite images as well as the regression trends of historical data of precipitation and temperature in the last two decades (2000-2020) as well as projection of GCM models with CMIP5 scenarios over the next two decades (2020-2040). Projected data were evaluated using GCM models including EC-EARTH, GFDL-CM3, HadGEM2-ES, MIRO-C5 and MPI-ESM-MR for the period of 2020-to 2040. The observation data were used with 35-year period (1984- 2014) and LARS-WG-6 was used for GCM models downscaling. The least error method (including RMSE and R2) was used to find the model in which the predicted data best fit the historical period data; the MIRO-C5 model had the lowest error among the 5 models. The results obtained for the last twenty years showed a gradual decrease in the surface of the snow and ice of Alamkuh region, especially in the summer, demonstrating the gradual decline of this glacier. Complete melting of the glacier in summer poses a serious threat to the downstream water resources of the area and the environment around the glacier.

The objective of this research was to investigate the effects of climate change on precipitation and temperature parameters of Karkheh Basin and inflow to Karkheh dam reservoir. This was conducted by applying 21 GCM models under CMIP5 scenarios. The error indices of R2, RMSE and MAE models with the observed precipitation and temperature data were examined to find the appropriate GCM model, MRI-CGCM3. The outputs of this model under RCP2.6 and RCP8.5 scenarios were used to predict precipitation and temperature in future periods (2020-2040 and 2040-2060). With change factor downscaling of MRI-CGCM3 outputs, the precipitation decrease and temperature rise was 2.8% and 1°C under the RCP scenario of 2.6, 6.8% and 1.5°C under the RCP8 scenario, respectively in the period of 2020-2040. IHACRES model was used to study the effect of climate change on basin runoff and inflow to Karkheh Dam reservoir. Runoff simulation results show that overall runoff reduction is 10.2% under RCP2.6 and 20.1% under RCP8.5 scenarios in 2020-2040 compared to baseline period. These results indicate the inappropriate future situation of Karkheh Dam reservoir, which will have an impact on the water supply required for agriculture and electricity of the region.

Pipes failure events in the water distribution networks provide leakage of water. Failures cause the loss of significant fresh water and investments losses. The most important parameters are: material, age, length, diameter and hydraulic pressure. In this paper four statistical methods have used for analyzing pipe incidents, with the goal of estimation of failure probability in future, with finding the most influences parameters in the incidents. The statistical regression models using in this research are linear regression model, exponential regression model, Poisson regression model , and Logistic regression model . For evaluation of the models, the data of a pilot in the first district of the Tehran’s Water and Wastewater Company with more than 48500 consumers, total pipe length of 582702 meter, different materials and diameters were used. The results demonstrated that the logistic model has a better performance than others to predict the future events with a higher probability.

Global warming and climate change has caused serious impacts on water resources of the world, including the glaciers with reducing their surfaces and volumes. Surface and volume decreasing of the glaciers, could have the serious impacts on the discharge of the glaciers rivers downstream as well the soil erosion of watersheds. The increasing amount depends on the local and regional conditions. Changing in the rainfall amount, land using, vegetation covers and erosionable potential could influence the rate of river discharge and watershed erosion. In this paper, the impact of climate change on the Sardabrood river in the west part of the Challoos city in the North of Iran has been studied with statistical methods for a fifty years period (1958-2008). In this regard, the Hodric-Prescott filter as well as regression method, for investigation the randomized and non-randomized trends of the discharge and sediment. The results showed that the discharge and sediment trends increase in this glacier watershed. The general conclusion is that sediment increase by increasing the discharge, and indicates further erosion in the basin.

Studying the role of drops as one of the important hydraulic structures for flood control is essential. In the present study, the effects of drops on inundation depth and extension have been investigated in a reach of the Kan River with 7 Km in length. The hydraulic characteristics of flow were computed for floods with 5 to 700 year return periods using the HEC-RAS computer model for both upstream and downstream sections of the structures. The results of this study verified a different role of drops as well as an incremental and reductive impact on inundation depth and extension in upstream and downstream, respectively.