Climate change impacts on intensity duration frequency curves of precipitation: A case study of Shiraz synoptic station, Iran

Intensity-duration-frequency (IDF) curves are among the standard design tools for many engineering applications such as urban drainage management. Since climate change may considerably affect precipitation, updating of IDF curves is highly necessary. The present study aims to examine the impacts of climate change on IDF curves of Shiraz synoptic station using downscaled outputs of Hadcm3 AOGCM under various emission scenarios (A1B, A2,B1) applying larswg-5 model for the period of 2046 to 2065. The fitted Gumbel distribution was used to estimate the maximum short-term precipitation quantiles in the base period (1968-2000) and the verified empirical Bell type equation was used for the future period. The results show that the mean of maximum daily precipitation and annual precipitation will decrease in the future. Also, the maximum precipitation intensities up to 60 min duration will reducefrom 0.15 mm hr-1 to about 10.79 mm hr-1 compared to the observed period in all returns periods and various scenarios. Overall, there were no tangible changes in intensities for durations higher than 60 min. The highest reduction in precipitation intensity would be at the 20 min duration with 100-year return period in the A2 scenario.

Investigating the Feasibility of Implementing a Cloud Seeding Project in Sistan and Baluchestan Province, South East of Iran

The water shortage is a challenge in many countries around the world. Today, the latest scientific and practical technologies are used to solve the problem of water shortage in arid and semi-arid regions. The optimal use of water resources as well as the use of novel methods of water extraction plays a significant role in alleviating the effects of this crisis. One of the methods used for increasing rainfall and water harvesting from the atmosphere is cloud seeding technology. The first step of this technique involves studying the target area and selecting the appropriate time and place for cloud seeding. The purpose of this study is to investigate the feasibility of cloud seeding in Sistan and Baluchestan province, south east of Iran, for rainmaking. Therefore, using the parameters of precipitation, minimum temperature, relative humidity and cloudy parameter, the status and feasibility for rainmaking in the province were evaluated and suitable months for cloud seeding were determined. Accordingly, December, January, February and March were found to provide suitable conditions for seeding. In order to select suitable places for cloud seeding, zoning maps of precipitation, temperature and relative humidity in selected months as well as the topographic map of the province were prepared by GIS After fuzzyization and integration of these maps, the zoning map of suitable areas for cloud seeding in Sistan and Baluchestan province was drawn to select the most susceptible areas. The area surrounding Khash synoptic station and the southern areas of the province were found to be suitable for cloud seeding.

Forecasting Daily Global Solar Radiation in Hot Semi-Arid Climate Using a Novel Hybrid Machine Learning Paradigm

Solar energy is one of the most important renewable energy sources. Assessing the solar potential of area needs analyzed information about the dataset of the measured global solar radiation (GSR). Recently, researches detected the high potential of state-of-the-art artificial intelligence (AI) methods in estimating the GSR successfully. In this study, a novel hybrid AI-based tool consisting of least square support vector machine (LSSVM) integrated with improved simulated annealing (ISA) is proposed to predict the GSR over the Ahvaz synoptic station located in the South-West of Iran. The potential of the proposed hybrid paradigm so-called LSSVM-ISA was evaluated by using multivariate adaptive regression spline (MARS), generalization regression neural network (GRNN), and multivariate linear regression with interactions (MLRI). For precise assessment of efficiency of the AI models, various statistical metrics and validation methods were used to assess the precision of the developed models. A comparison of the obtained results indicated that the LSSVM-ISA method performed better than the MARS, GRNN, and MLRI models. The achieved RMSE values of the MARS, GRNN, and MLRI models were decreased by 9%, 16%, and 30% using the LSSVM-ISA model. Finally, the results demonstrated that the LSSVM-ISA model could be successfully employed for accurately predicting GSR.

Climate change effects Management with the approach of the uncertainty of Atmosphere-Ocean General Circulation Models in Hamadan Province, Iran

INTRODUCTION: Since Iran is located in the semi-arid belt, it has faced such issues as drought, dust crisis, and intensified migration. The assessment of the effects of climate change includes identifying some key aspects of uncertainties used to estimate its impacts, such as uncertainties in the context of Atmosphere-Ocean General Circulation Models (AOGCMs): in regional-scale climatology, in statistical or dynamic downscaling methods, and parametric and structural uncertainties in different models. One of the most important sources of uncertainty in climate change is the use of different AOGCMs that produce different outputs for climate variables. METHODS: In this study, to investigate the uncertainty of AOGCM models, the downscaled data of the NASA Earth Exchange Global Daily Downscaled Projections dataset obtained from 21 AOGCMs with medium Representative Concentration Pathway4.5 scenario were downloaded from the NASA site for 81 cells in Hamadan Province, Iran. After the validation of the models, they were evaluated against the criteria of the coefficient of determination and model efficiency coefficient in comparison with the data of the Hamedan synoptic station in the statistical period of 1976-2005. To reduce the uncertainty of AOGCMs, the ensemble performance (EP) of models was used in Climate Data Operators software. FINDINGS: It was revealed that MRI-CGCM3, MPI-ESM-LR, BNU-ESM, ACCESS1-0, MIROC-ESM, MIROC-ESM-CHEM, and MPI-ESM-MR models had better performance than similar models. It was also found that IPSL-CM5A-LR, CNRM-CM5, CSIRO-Mk3-6-0, CESM1-BGC, and GFDL-ESM2M had the lowest correlation between observational and simulation data of mean monthly precipitation. CONCLUSION: According to the results, this method could provide a good estimate in the base period (1976-2005), compared to the data of the Hamedan synoptic station, and was more accurate compared to the single implementation method of each AOGCM model. The results of EP of models in the future period (2020-2049) showed that precipitation will not change considerably in the future and will increase by 0.23 mm. In addition, the average, maximum, and minimum annual temperatures will increase by 1.54°C, 1.7°C, and 1.40°C, respectively.

USING SATELLITE OBSERVATION FOR EARLY WARNING OF CONVECTIVE STORM IN TEHRAN

Severe convective storms are responsible for large amount of damage each year around the world. They form an important part of the climate system by redistributing heat, moisture, and trace gases, as well as producing large quantities of precipitation. As these extreme and rare events are in mesoscale there is many uncertainty in predicting them and we can’t rely on just models. On the other hand, remote sensing has a large application in Meteorology and near real time weather forecasting, especially in rare and extreme events like convective storms that might be difficult to predict with atmospheric models. On second of June 2014, near 12UTC a sudden and strong convective storm occurred in Tehran province that was not predicted, and caused economic and human losses. In This research we used satellite observations along with synoptic station measurements to predict and monitor this storm. Results from MODIS data show an increase in the amount of cloudiness and also aerosol optical depth and sudden decrease in cloud top temperature few hours before the storm occurs. EUMETSAT images show the governing of convection before the storm occurs. With combining the observation data that shows Lake of humidity and high temperature in low levels with satellite data that reveals instability in high levels that together caused this convective, we could track the storm and decrease the large amount of damage.