simulation

Drought and heat stress as the most important limiting factors in the production of crops and finally food security, have resulted from changing climate due to human interventions in increasing greenhouse gas emissions. It is also simulated that each 1 ◦C increase in temperature caused a 6% decline in global wheat production. The increase in temperature can further decline grain yield when occurred during the reproductive stage. The simultaneous effects of drought and heat during the growth stages, especially the flowering and grain filling stages, which are the most sensitive, can be more harmful than the individual effects and lead to a significant reduction in yield. The current study aimed at determining the optimal cultivar × sowing date in different climates to coincide the flowering date with optimum climaticconditions (temperature and rainfall) using a simulation modeling approach.

The current study was conducted in eight locations with different climates according to the GYGA (Global Yield Gap Atlas) methods. Dezful and Shushtar had warm and dry climates with high fluctuations in average seasonal temperature, Hamedan and Nahavand had mild and dry climates with medium fluctuations in average seasonal temperature, Khorramabad and Aligodarz had mild and dry climates with high fluctuations in average seasonal temperature and Zanjan and Khodabendeh had cold and dry climates with medium fluctuations in average seasonal temperature. Choosing the study locations was based on both areas under wheat cultivation and the diversity in their climates. In this research, several management methods including 4 sowing dates, 4 initial soil water​​, and 3 cultivars in 8 locations with different climates for 37 years (1980-2016) were investigated. A modified version of the APSIM-Wheat model was used in which a heat stress module could capture the impacts of heat stress on grain number and weight. The simulations were conducted under drought stress alone (rainfed) as well as the simultaneous drought and heat stress.

The average grain yield under drought conditions was simulated at 2.99 tons per hectare, while the yield under simultaneous drought and heat stress was 2.44 tons per hectare, indicating a reduction of approximately 0.5 tons per hectare due to the combined effect of heat and drought stress. The mid-maturity cultivar had the highest yield reduction of 52% while the very early-maturity cultivar showed the lowest yield reduction of 0.16%. The wheat flowering time in cold regions occurred after the completion of flowering in warmer locations. In temperate climates, the occurrence of flowering dates was more extensive than in other locations. Overall, any delay in emergence under rainfed resulted in further yield reduction in all cultivars and locations.

Under warm climates, an early-maturity cultivar along with early sowing could provide better environmental conditions for photosynthesis and plants could escape from terminal drought and heat stresses. In contrast, in cold climates, any delay in flowering date increases yield. In mild regions, using a mid-maturity cultivar resulted in late flowering dates and higher yields in spite of coinciding wheat with heat and drought for a short period of time.

Roads as main infrastructure of development in spite of their social and economic advantages would lead to increase of air pollutants due to vehicles traffic. The main of this study is to estimate of suburban road networks impacts based on air pollution modeling approach. For this purpose, dispersion limits of CO, NO2, PM10 and SO2 pollutants emitted from Lorestan road network’s traffic have been investigated. The CALPUFF model were used to simulate pollutants in four seasons. Traffic volume and velocity, pollutants emission rate, synoptic meteorological data, land cover map, digital elevation model and slope map were used as input data. The result of modeling for 2461 km of road showed that maximum concentration of pollutants is in autumn and located in distance about 2 km from the roads. Based on the modeling outputs, SO2 gas has most distribution than the rest of pollutants. The most rate of rural population exposed to the air pollution of road networks are 11/5 percentage of all population of Lorestan province. This occurs in autumn. Although the mainly studies on air pollution have been focused in urban areas, but the results of this study in a province with natural regions showed even these regions are not in safe from the air pollution. Therefore, it is essential to attention of environmental consideration in road design and routing phase as well as decrease of vehicle’s age of suburban road transportation.

Today, due to increasing population growth and land use changes and its impact on the environment, monitoring and modeling land use changes is one of the main prerequisites for optimum use of land and achieving sustainable development. The purpose of this study was to evaluate and predict the spatial and temporal dynamics of land use in the county of Sardasht, in order to obtain basic information for planning in line with sustainable forest management.

 Data from the Landsat 7 images (+ETM) 2003, and Landsat 8 (OLI) 2015 were analyzed. The Maximum Likelihood algorithm has been used to mapping the land use for the years. The analysis of the change dynamics using traditional Markov Chain and Cellular Automata was predicted for the next 25 years.

The overall accuracy of classified images in 2003 and 2015 was 89% and 94%, respectively, and the Kappa coefficient was 0.87 and 0.92. The results showed that during the period, bout 7% of the forest area has decreased and the agricultural lands has increased by 72%. According to the Markov transmission probability matrix the classes most affected by these changes is the forests and rangelands that changed to agricultural and residential.

 Changes in the extent of forests until 2040 show that the area of forests will decrease continuously. The results of current study could provide quantitative information, which represents a base for assessing the sustainability in the management of Zagros forest ecosystems and for taking actions to mitigate degradation.

Iran has a high potential of solar radiation and lots of sunny days (more than 300 days) in a year.  In this study the feasibility of establishment the photovoltaic powerhouse in several agricultural research stations of Markazi, Esfahan and Kerman Provinces was investigated. In this regard, after determining and analyzing the required meteorological information using Meteonorm software and system simulation by PVsys software, economic analysis of each station were performed by determining the breakeven point and the average cost of energy production for different discount rates. The study also examined the reduction of pollution due to the reduction of Cox, Nox and Sox emissions as a result of employing photovoltaic power plants. According to the results, the most solar electricity generation was occurred in Rudasht station of Esfahan province equaled to 303 kWh/m2 per a year. Photovoltaic powerhouse establishment in the research stations could eliminate pollution production up to 748 tons in a year which exhausted from fuel combustion in the power generation turbines. Results also illustrates considering electricity purchase tariff (6370 IRR/kWh), the construction of photovoltaic power plants with the usual interest rate (more than 8%) is not economically viable for investors. Therefore, the implementation of such projects cusses to produce clean energy and to bring environmental protection, a special banking facilities with interest rates below 8% is recommend to motivate private sector for investment.

In this study, assessed genomic prediction accuracies based on different selection methods (phenotypic and estimated breeding value), evaluation procedures (GBLUP and ssGBLUP),training population (TP) sizes, heritability (h2) levels, marker densities and pedigree error (PE) rates in a simulated population. QMSim software was used to create a reference database of 1000 and number of animals was reduced to 200 (100 males and 100 females) during 95 generations to create LD and mutation-drift equilibrium. The heritability of the trait was 0.1, 0.3 and 0.5 and the marker density was simulated for three strategies of 1, 5 and 10 K. The proportions of errors substituted were 10%, 20% and 30%, respectively.The results of this study showed that, compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection.With increasing reference population size and trait heritability, genomic prediction accuracy increased in all strategies.When errors were introduced into the pedigree dataset from 0 to 30%, the prediction accuracies were only minimally influenced across all scenarios.Our study suggests that the use of ssGBLUP, EBV sThe results of this study showed that, compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection.With increasing reference population size and trait heritability, genomic prediction accuracy increased in all strategies.When errors were introduced into the pedigree dataset from 0 to 30%, the prediction accuracies were only minimally influenced across all scenarios.Our study suggests that the use of ssGBLUP, EBV selection, and high marker density could help improve genetic gainseven in the case of pedigree error in cattle.election, and high marker density could help improve genetic gainseven in the case of pedigree error in cattle.The results of this study showed that, compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection.With increasing reference population size and trait heritability, genomic prediction accuracy increased in all strategies.When errors were introduced into the pedigree dataset from 0 to 30%, the prediction accuracies were only minimally influenced across all scenarios.Our study suggests that the use of ssGBLUP, EBV selection, and high marker density could help improve genetic gainseven in the case of pedigree error in cattle.

One of the obstacles to develop sustainable is the poor quality of water. The assessment of water quality is usually based on chemical decomposition and measurement of chemical parameters of water. Measuring these parameters in big area is costly and time-consuming, as result it required to estimating methods for prediction of those parameters. The purpose of this study is to model the groundwater quality parameters of Zeydoon plain using ANN+PSO and geostatistics models.

For this purpose, the information of 42 observation wells in Zeidoon plain on a monthly basis for 7 years has been used. Neural network model inputs including qualitative parameters SO42- ، pH ، HCO32-، Na+، Mg2+، Ca2+، TDS، SAR and EC were considered.

The results of simulation of groundwater quality parameters using ANN + PSO model showed that in SAR simulator model the highest simulation accuracy is related to the model with sigmoid logarithm function, in EC simulator model the highest accuracy is similar. The construction is related to the model with the stimulus function of the sigmoid tangent. Also, in the TDS simulator model, the highest simulation accuracy of the model with the sigmoid tangent stimulus function was obtained. As RMSE and MAE have the lowest value and R2 index has the highest value. The results of simulation of groundwater quality parameters using the geostatistical model showed that the highest accuracy of the kriging model in the simulation is related to EC, SAR and TDS parameters, respectively.

Finally, comparing the results of comparing the results of ANN + PSO model and Kriging model showed that ANN + PSO model is more accurate in simulating groundwater quality parameters of Zidon plain than Kriging model. Also, the results of this research showed that the combination of intelligent models with optimization algorithms with correct architecture and complete model inputs are used as a useful tool for simulating groundwater quality parameters.

As a result of the rising awareness of environmental issues and the increase in the cost of energy, building professionals increasingly have to consider the sustainability and energy performance of their designs by using building energy performance simulation tools. The aim of the current study is to optimize energy consumption of office buildings in cold climate of Tabriz and providing optimal model of double skin façade (DSF) in this climate.

 The research method in this paper is based on causal research and simulation. The research tool in this paper is Design Builder software. The energy performance of DSF is studied in 36 scenarios under 4 types of DSFs in an office building in cold climate of Tabriz in three cavity depths of 0/7, 1 and 1/3m in three modes: 5,10 and 15-story to achieve minimum energy consumption.

The results indicate that, the 5-story box window type with a depth of 1/3m has a minimum electricity consumption and the 15-story multi-story type with a depth of 0/7m has a minimum gas consumption. In a fixed cavity depth, electricity consumption will increase and gas consumption will decrease by increasing the numbers of floors. Electricity consumption also increases with the increase of cavity depth. The 15-story multi-story DSF with a cavity depth of 1m has the lowest total energy consumption and the 5-story corridor DSF with a cavity depth of 0/7m has the highest total energy consumption.

Due to the high heating needs of the cold climate and energy efficiency of DSF facades, it is necessary to choose the appropriate type for this region.

Investigating the Rivers Role in the Gorgan Bay Possible Future After Separation From the Caspian Sea In this research, the probable future of the Gorgan Bay after separation from the Caspian Sea under five scenarios is examined using Mike 21 model. In the first scenario, the bay was studied under the influence of the prevailing climate on it. In the second scenario, the bay was studied under the influence of the climate and river inputs. In the next three scenarios, bay was assessed under the influence of climate and rivers environmental minimum requirements according to Tennant (1976) suggestions. The modeling results under the first scenario showed that, 551 days after the separation of the bay from the Sea, the bay would be dwarfed by the climate conditions. Modeling results under the second scenario showed that bay after 1110 days, it becomes a wetland with a minimum area of 51 km2. Modeling results under the third, fourth and fifth scenarios showed that the bay after 653, 665 and 1015 days into a wetland with a minimum area of 7, 12 and 32 km2 respectively. So long as the Gorgan Bay be connected to the mother sea determining the biological water right from rivers leading to it will not help to increasing the water level and prevent dryness of the bay. However, after separating the bay from the mother sea water supply through rivers will have a significant impact on the formation of wetland. The northern and western prevailing winds have a greatest impact on increasing of the bay area in the northeast part and temporary wetlands creating under presumed scenarios due to the formation of stormsurg. If the bay does not communicate with the sea and does not provide water right supplies of rivers, ecological successions in the Gorgan bay will be disclimax.

The thermal behavior of underground buildings is different from that of above ground buildings. The approaching temperature of the inner layers of the earth to the average annual outdoor temperature reduces the heat exchange of these buildings compared to buildings on the ground. Reducing energy consumption reduces greenhouse gas emissions. The purpose of this study is to evaluate the feasibility of using geothermal energy in buildings to reduce carbon dioxide emissions in the city of Hamedan with cold climatic conditions in 2019.

The simulation was performed by using version 8-1-0 of Energy Plus software. In the model, a standard 900 Ashrae sample space with residential usage was considered. At first the building was located on the ground as default and its energy consumption was set to 0/424 MWh /m2. In later stages, the sample is gradually placed in depth in successive steps equal to 0.5, 1, 1.5, 2, 2.5 and 3 meters.

findings showed that in each depth, 14.51%, 28.38%, 44.56%, 57.25%, 74.70% and 79.07% of the annual energy requirements of the sample space has been reduced. On the other hand, in each scenario, the carbon dioxide pollutant emissions as the main greenhouse gas, has been declined to 0.65, 1.27, 1.99, 2.57, 3.35 and 3.55 percent, respectively.

Therefore, It was concluded that Placement of buildings under the ground reduces carbon dioxide pollutant emissions in environment.

Reducing fossil energy resources, the environmental impact of high energy consumption and increasing the energy consumption in the building section, has increased the importance of attention to building energy consumption in the country. Providing the standard of building energy consumption in our country is one of the important activities in this regard. This study aimed to determine the effect of different strategies to reduce energy consumption in the energy classification of office buildings as a case study by Design Builder software.

In this study, the total energy consumption of of an office building in Semnan climate was simulated by Design Builder software and the results obtained by the software, with the actual energy consumption of the building in 2016, which was obtained from the energy bills (electricity and gas) of the building, were validated. Then, using the energy rating of building, different strategies for reducing energy consumption were simulated and compared with the existing situation.

Using energy simulation software, various energy efficiency strategies such as using external horizontal shading and removing internal shading, replacing windows with low-emitting double-glazed windows instead of single-glass windows and installing a thermal insulation in the external wall of the building were investigated that 15.2, 18.4 and 8.2% reduction in energy consumption achived compared to normal case, respectively. Then, the intensity of building energy consumption for each of the proposed strategies were calculated and the energy rating was determined for each.

The results showed that the replacement of external shading instead of internal shading had the least amount of savings and had no effect on the change in energy rating and the use of a combination of double glazing instead of single-walled and thermal insulation in the outer wall of the building while upgrading to the classification, the optimal suggested mode. Finally, the results of this study showed that in the case of accurate simulation of existing buildings and then validation the effects of different strategies on energy rating and consumption can be predicted accurately.

Proper modeling of groundwater quality is an important tool for planning and decision making in water resources management. The present study was conducted to simulate the groundwater quality parameters of Behbahan Plain including SAR, EC, and TDS using ANN and ANN + PSO models and finally to compare their results with the measured data. Input information to the models gathered were for TDS quality parameter including electrical conductivity, absorption ratio of sodium, sulfate, calcium, magnesium and sodium, for SAR quality parameter including total dissolved salts, sodium, bicarbonate, and for EC quality parameter including sulfate, calcium, magnesium and ratio Sodium uptake from 2010 to 2017. The results indicated that the highest prediction accuracy of quality parameters of EC and TDS is related to the ANN + PSO model with the tangent sigmoid activation function and for the SAR parameter is related to the ANN + PSO model with the logarithm sigmoid activation function so that the MAE and RMSE statistics had the minimum and R2 had the maximum value for the model. In the test phase the values calculated were for EC parameter RMSE=14.61, MAE=9.27, NRMSE=0.41, EF=0.942, and R2=0.96 and for TDSparameter RMSE=22.21, MAE=18.32, NRMSE=0.398, EF=0.925, and R2=0.836 and for SARparameter RMSE=9.45, MAE=7.2, NRMSE=0.301, EF=9.27, and R2=0.974. In addition, the results of the mean comparison between measured and simulated data showed that the predicted values with models were not significantly different with the measured date.