جستجوی مقالات مرتبط با کلیدواژه « climate change » در نشریات گروه « عمران »

The Earth’s climate has been changing since began drastically. Global warming and change in precipitation pattern are consequence of climate change. Climate change and over-withdrawal of water for industrial purposes has increase water stresses around the world. Long-term water stresses may cause disasters. Hence it is necessary to control water stress. This paper proposes a model to manage water stress of different catchments by proper locating of large industries considering climate change.

In recent decades, population growth, increasing urban development, and polluting industries have led to an increase in the production of greenhouse gases and the phenomenon of global warming in various regions of the world. Asphalt pavements, as the main infrastructure of the road transportation system, may suffer failures with high repair costs due to their rheological properties under the influence of this phenomenon. In this study, the Prophet model was used to predict the temperature for the years 1400 to 1421 in three 7-year periods under three optimistic, intermediate, and pessimistic scenarios. The prediction accuracy of this model based on the fitting coefficient was more than R2=0.8 on average. Based on the prediction, the country's demand for bitumen with different functional classifications is then presented in comparison with the available quantities. By predicting and calculating the functional classification of bitumen in 1440 and presenting the results at two confidence levels of 50% and 98% for 34 meteorological stations in the 31 provinces of Iran, a long-term overview of the temperature situation and the required functional classification of bitumen based on the meteorological information of each station is also provided. The increase in the average temperature of the studied stations at the end of the 21-year period was 0.73 and 1.094 degrees of centigrade at a confidence level of 50% and 98%, respectively. According to these results, most of the studied areas will experience a significant temperature increase in the coming years, and this phenomenon will increase the high and low temperatures of bitumen performance, which will require the use of PG76-10 and PG82-10 bitumen. The increase in high and low performance temperatures will require the use of alternative bitumens and the use of appropriate bitumen additives for all climatic conditions.

The main objective of this research is to compare the performance of artificial neural network (ANN) and deep neural network (DNN) models in rainfall-runoff modeling of Kashafrood and to predict the effects of climate change on meteorological parameters and river discharge.

With the architecture, Engineering and Construction (AEC) industry representing a significant share of global energy consumption and greenhouse gas (GHG) emissions, developing sustainable design and reducing buildings environmental impacts has become a priority over the past decades. Adopting building information modelling (BIM) tools and implementing them into life cycle analysis (LCA) techniques at early stages of design has proven to be one of the most effective methods for a buildings sustainability evaluation. It’s quite often that some of these attributes are overlooked due to their insignificance or in order to facilitate the analysis. With our environment constantly going through changes it is reasonable that the construction industry should also aim to adapt to these changes and make use of them. However, the role of local climate features and its effects on a buildings energy output is often so neglected. This research aims to consider the role of climatic attributes and local weather characteristics of a building by using BIM-LCA integration techniques, and see how it affects that buildings energy performance. It is witnessed that by using the proper equipment and construction materials, that matches the respective climate, up to 28% of the buildings energy consumption during the operational phase, can be saved. Admitting changes to the model however, can cause up to 3% increase in the models GHG emissions. Moreover, this work develops a prototype to validate the results.

Climate change has caused changes in the frequency and magnitude of heavy rainfall, which affects the design standards of some hydrological structures (Yousef and Taha, 2015). Rainfall intensity-duration-frequency (IDF) curves are a standard tool for hydrological risk analysis and design (Mohymont, 2004; Veneziano, 2002). IDF curves show useful information about the occurrence of floods in an area and that in the future a certain amount of rainfall or a certain volume of flow will return again (Basumatary and Sil, 2018). Because the trend of severe rainfall events is expected to change in the future, this affects IDF curves, so these curves need to be updated (Srivastav et al. 2014). The need for such an understanding stems from the fact that existing drainage systems are designed to deal with past rainfall events and may not be sufficient to compensate for future rainfall characteristics (Shrestha et al. 2017). In developing countries such as Iran, the large country area as well as the shortage of rain gauge stations and/or the statistical period of the recorded data, makes it difficult to estimate IDF curves (Zamani Nouri, 2011).

Despite the specific capabilities of the ARMA model to assess the impacts of climate change on an annual scale, this model has been rarely considered in previous climate change studies. Because it is not clear that how skewed series can be downscaled using this model. Precipitation series on the daily and monthly scales is often skewed, but annual rainfall in many areas is has a normal distribution. In this paper, the performance of the annual ARMA model and the LARS-WG daily model for generating annual rainfall and temperature series is compared. The results show that the ARMA model well reproduces the various statistics of annual observed rainfall and temperature, as well as the frequency distribution of these variables. But the LARS-WG model, which has a good performance in reproduction of daily statistics, does not have an acceptable performance in reproducing the annual distribution frequencies. It is because of the inability of the LARS-WG model in reproducing of inter-annual variabilities (specially annual standard deviation). Climate change impact on annual precipitation and temperature of the Zanjan station is assessed using ARMA model and HADGEM2 outputs under RCP4.5 scenario. The results indicate that for various return periods, the temperature will increase and rainfall will decrease. based on results, by considering climate variability and change, 2-year return period precipitation will decrease between 7% to 23% and 2-year return period temperature will increase between 2.2 to 3.8 ºC respect to corresponding observed values.

Precipitation affects quantity and quality of water resources and agricultural production. Therefore, the estimation and analysis of its spatial-temporal variations is of great importance. In many regions of Iran, limited spatial-temporal information is available due to sparse distribution of monitoring stations and short observational records. On the other hand, dependency of rain-fed and irrigated production systems on precipitation increases the importance of the analysis of spatiotemporal variations of this weather variable. One way to address this limitation is to use regional/global gridded datasets. In this study, monthly precipitation data were obtained from the CRU dataset (developed principally by the UKchr('39')s Natural Environment Research Council (NERC) and the US Department of Energy) and used to investigate temporal trends in annual, seasonal and monthly precipitations in 675 grid cells (0.5°×0.5°) across Iran over two periods, 1957-1986 and 1987-2016. The results of the previous studies showed that the CRU gridded dataset offers quality data in Iran, especially for trend analysis. Also, the accuracy of the CRU dataset was validated in 14 selected stations regarding monthly precipitations and temporal trends over two different periods, pre-1987 and post-1987. The significance of temporal trends was assessed using a modified version of the rank-based nonparametric Mann-Kendall (MK) test. Trend magnitudes (i.e. slope) were estimated with the Theil-Sen approach and the Trend Free Pre-whitening (TFPW) procedure was applied to remove the effect of serial correlation. The results confirm the acceptable accuracy of the CRU dataset for trend analysis purposes, especially over the last three decades, except in the northern strip of the country (RMSE=10.71mm, R2=0.84). Two 30-year periods (1957-1986 and 1987-2016) were compared in terms of spatial patterns and temporal trends. Annual precipitation over the last three decades (1987-2016) has decreased as compare to the previous 30-year period (1957-1986) in most parts of the country. Over the last three decades, around 42% and 50% of the country’s total area experienced significant and insignificant decreasing trends in annual precipitation, respectively. National average annual precipitation has decreased by 15.78 mm/decade over the same period. Three important regions regarding agricultural production experienced the most significant reductions in annual precipitation: (1) Ardebil, East Azerbaijan, Kurdistan, Kermanshah, Ilam, Lorestan, Zanjan, Hamadan, and parts of West Azerbaijan, Markazi and Gilan (in the west and northwest), (2) Sistan and Baluchestan, Kerman, and southern parts of South Khorasan (in the south and south east), and (3) North Khorasan, northern parts of Razavi Khorasan and east of Golestan (in the east and north east). Reduced annual precipitation was mainly attributed to the reduction in seasonal precipitations in winter and spring, which have critical role in agricultural production and domestic water supply. Temporal trends were also analysed at the monthly scale. January, February, March and December revealed the largest number of grid cells with significant decreasing trends over 1987-2016 while November is the only month with significant number of grid cells experiencing significant increasing trends. The results of this study show that the monthly time series of the CRU TS 4.01 dataset, which has an almost complete spatial and temporal coverage in Iran over the last 60 years, are promising alternatives to weather station observations especially in data-scarce regions of Iran. Analysis of variations and the seasonal and monthly scales help understand the recent climate change and target the most crucial features of it when it comes to formulating adaptation strategies.

Impacts of climate change on water resources will force decision-makers to adopt climate change adaptation policies in order to reduce social-economic problems and difficulties resulting from it and water resource sustainable development. One of the adaptation methods is to increase water use efficiency in agriculture that will adjust climate change impacts include decreasing runoff and increasing water demands. In this study, the impact of water use efficiency as a climate change adaptation approach is assessed in the optimal operation of JAREH dam. Fifteen climate change scenarios were generated by using downscaling technique on CMIP5 data for the near (2020-2044) and far (2070-2094) future. Based on these scenarios, time series of reservoir inflow and downstream water demand were projected for both future periods. An optimization model is developed considering the water efficiency coefficient parameter in order to define four water use efficiency scenarios (0-S1, 0.1-S2, 0.3-S3, 0.5-S4). Results show that reservoir inflow decreases up to 18.8% and water agriculture demand increases up to 29%. The amount of water allocation would increase up to 18.7% in the future periods than in the baseline period under S1 scenario to supply the increased water demand, which may decrease reliability of reservoir system for water allocation. Increasing water use efficiency coefficient up to 0.5 in the future periods would increase system reliability up to 20% that will reduce social-economic problems caused by climate change impact in this study area.

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.

Iran is located an arid and semi-arid region of the world, with an average annual rainfall of less than one-third of the global average. There is lack of water to meet the consumptions in many sectors. The construction of a reservoir on the rivers as an instrument for regulating flow in time and place is one of the ways to deal with the water crisis, , as more than one dam has been built on some rivers. Functional dependence and the impact of the operation of each reservoir on the other one leads to the complexity of the operation of multi reservoir systems. In reservoir operation models, various requirements, such as agriculture, drinking, environmental and hydropower is considered. Multi-objective optimization models are an appropriate tool for dealing with heterogeneous multi-objective problems. MOPSO and NSGA-II are effective multi-objective optimization models that have been considered by many researchers so far. In this research, the comparison of two widely used NSGA-II and MOPSO algorithms to extract the rule curves of Boostan and Golestan reservoirs located in Gorgan Rood watershed in drought conditions has been investigated.

Urmia lake due to the presence of various species of wildlife, species of vegetation on the islands, create a natural balance in the Azerbaijan region,  tourist, recreational and social value, medical value, reserve of the Bio sepehr and as well as a wetland of international importance is special. Over the last few decades , use remote sensing technology to detect trends such changes various researchers have drawn attention to themselves. Factors that have caused Urmia lake will be in such a situation  is varied. But in general, they can be divided into two categories :The factors that played a role in humans includes free use of water resources , agriculture unbridled development around the lake, and environmental factors like climate change , which according to the reduction of heavens and evaporation of Urmia lake water And reducing the flow volume and reduce annual temperature the lake ecosystem has been affected. Study of meteorological parameters of Urmia lake and investigation of its level changes in order to apply water resources management is important. Recent studies show which level and volume of lake water relatively decreasing. Urmia lake water level from 1992 to 1997 significantly increased and decreased from 1997 to 2009 and has remained almost constant since 2010. As a result, to rebuild the lake and managing the water resources of this lake is necessary, the role of effective parameters is determined. Therefore, neural network method was used in this research,meteorological parameters such as evaporation,temperature, precipitation, and annual amounts of groundwater abstraction of  wells around the Urmia lake and the amount of water entering the lake, between 1997 and 2011, as input parameters And the annual altitude and area of the lake water entered the neural network as output parameters. In this research, the Levenberg rules were used to train the network. After training model by meteorological parameters, it was determined that the neural network model approximates the data in a perfectly accurate and accurate manner. It can also be predicted that changes in height and area occur by changing each of the parameters. This network estimates the lake area of Urmia at 3% error and 97% accuracy and lake level of 0/8 m. The correlation coefficient of the removal was obtained with the height and the range of -0.4. The correlation coefficient of precipitation with 2 dependent parameters was obtained +0.15 Input flow rate of +0.4.  After reviewing the model, it was found that the removal parameter from underground wells and the Input water volume into the lake compared to other parameters have a more significant effect on altitude and area. The results indicate that water use for agriculture and harvesting of water resources have increased And also the crops that are grown are products with a high water consumption pattern And also the water stored behind the dams has reduced the inflow to the lake.