Groundwater
sayyad Asghari Saraskanrood; Maryam Riahinia
Abstract
Today, due to population increase, industrial development, excessive exploitation, droughts, exploitation of underground water has multiplied. Therefore, identifying areas with underground water as one of the important sources for providing drinking water, agriculture, and various industries is considered ...
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Today, due to population increase, industrial development, excessive exploitation, droughts, exploitation of underground water has multiplied. Therefore, identifying areas with underground water as one of the important sources for providing drinking water, agriculture, and various industries is considered to be one of the important and necessary issues in water resources management. The purpose of this research is to investigate and zonate the areas with underground water in Khorram Abad plain located in Lorestan province using convolutional neural network method. For this purpose, maps of nine factors affecting underground water were first prepared in the ArcGist environment. In the convolution method, the number of samples was determined as the ratio between the training set and the test set was 70:30, and the convolution neural network framework was used as 2 convolution layers and 2 integration layers, 2 complete connections. layers and finally the sigmoid layer was used for classification from the 3-3 convolution kernel, the Relu function as the activation function and the cross entropy function as the loss function. The obtained maps were classified into 5 classes: very good, good, average, low and very low. Confusion matrix was also used to validate the results of the model. 30% of the real data was used for evaluation, which resulted in an overall accuracy of 92%, that is, the model was able to correctly identify 92% of the data as underground water and 93% as the absence of underground water. The analysis of the groundwater potential map of the convolutional neural network model shows that about 57% of the area is in low groundwater conditions and 43% of the area is in good groundwater conditions.
Saeed Jahanbakhsk Asl; Hossein Asakereh Asakereh; Saeideh Ashrafi
Volume 6, Issue 21 , March 2020, , Pages 109-132
Abstract
1-IntroductionStudying and identifying the climate variabilities occurring in different regions, may give insight toward possible future climate variabilities. Using available climate models as well as downscaling, is a way to recognize the possible variabilities of climate components of future. ...
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1-IntroductionStudying and identifying the climate variabilities occurring in different regions, may give insight toward possible future climate variabilities. Using available climate models as well as downscaling, is a way to recognize the possible variabilities of climate components of future. In the present study, the precipitation and runoff of Rood Zard Basin were downscaled and simulated for the time period of 2006-2100. For this purpose, the RCP output scenarios of the CanESM2 model were utilized for 1975-2005. For downscaling the precipitation and runoff of Rood Zard Basin, the daily precipitation data of Baghmalek and the runoff data of the Mashin station and artificial neural network method were used. The mean sea-level pressure, the geopotential height at 500 hPa, and the mean temperature at ground level were all selected as the predictive variables, using correlation and partial correlation calculations, as well as the backward variable elimination method. The verification of the design was carried out by the RMSE and R2 indexes. Finally, the network architecture was selected through the Bayesian Regularization algorithm along with three hidden layers as the optimal network. The results show that annual precipitation have decrease trends in future 95 years. revealed that the precipitation increased in the hot months of the year and decreased in the cold months. In other words, the increase of local rainfalls due to the temperature rise is most probable in future periods. The runoff would decrease in the cold months and increase in the warm months regardless of the temperature and vegetation impact.Climate change is the main phenomenon affecting the climate and the human environment as well as environmental phenomena (such as droughts and wetness years, water resources, sea level changes, temperature alterations, changes in the behavior of climate elements, and many other phenomena). Investigating many phenomena of the past decades revealed that the planet earth's climate is changing. Compared to the previous time periods, the results of the previous studies indicated that the climate variablity trend has become faster in the past 150 years. To fully understand the climate, all the units involved in its formation should be evaluated simultaneously. For this purpose, models may be helpful to some extent. Modeling is the process of creating a model that can provide the structure and function of systems. One of these methods is GCM in which the climate is simulated. These models are developed based on different climate scenarios aiming to simulate the impact of greenhouse gases on the earth's climate. Moreover, they are able to simulate and predict the future climate of the earth.These models create various time series of climate variables with relatively large networking. However, they are not suitable for direct use in the studies relating to the local climate variability. Thus, researchers have designed suitable downscaling methods to gain the climate data on a local scale. One of these methods is the statistical downscaling. 2-Methodology and methodsIn the present study, the precipitation and runoff of the Rood Zard Basin are downscaled based on the RCP climate scenarios. RCPs are new emission stimulant scenarios which are used as the input of CMIP5 climate models and are based on the fifth report of IPCC. Scenarios are important parts of climate simulations that allow the researchers to study the long-term outcomes of the current decisions. In the RCP scenarios, 26 atmospheric parameters were considered for future simulations. Each of these has a relatively high connection to environmental elements. The selection of the most optimal parameter for expressing the relationship between weather conditions and the environmental characteristics depends on the type of environmental parameters. To select the appropriate parameters, the correlation and partial correlation calculations and the Backward Variable Elimination methods were applied.For downscaling, the BOX_019X_44Y data were acquired from the Environment website of Canada. The data were analyzed through calculating the correlation coefficients, partial correlation and also the Backward Variable Elimination method. The results revealed that 3 variables including the Mean Sea Level Pressure, the geopotential height at 500 hPa, and the mean temperature at ground level had an acceptable correlation with the precipitation at the Baghmalek station and omitting other variables created a lower missing variance.Downscaling was carried out based on the artificial neural network model with the Bayesian Regularization algorithm. Artificial neural networks are the patterns for processing data which are produced by imitating the neural network of the human brain. In recent decades, this method has been recognized as a useful and reliable tool for modeling complex maps existing between different variables. Artificial neural networks are able to pick up a system’s hidden behavior through available data. Each network has three layers: the input layer, the hidden layer, and the output layer. The input layer is, in fact, a layer used for producing the data given to the network as an input. The output layer includes values that are simulated by the network. The hidden layer is the place of analyzing the data. Unusually, the number of chosen neurons in this layer is obtained through trial and error.3-Results and discussionIn order to downscale neural network using the output RCP scenarios of the CanESM2 model, the daily precipitation data in the Baghmalek station during a time period of 30 years (1975-2005) were chosen as the base statistical period. After the selection of atmospheric high-scale variables, these variables were introduced into the neural network as input. The precipitation was considered as the target and the network was designed using algorithms and numerous hidden layers. Finally, the network designed with the Bayesian regularization and 3 hidden layers were chosen as the optimal network.As mentioned earlier, the artificial neural network was used for downscaling. Moreover, the daily precipitation data were simulated for the statistical period of 2006-2100. Linear regression was applied for simulating the runoff for the aforementioned period. The daily runoff, as well, was estimated for this period. The results demonstrated that the estimated monthly precipitation rate from November to December in the future 95-year period has decreased. Likewise, the simulated precipitation rates from January to November were higher than the monthly precipitation rates in the base period. Therefore, it can be concluded that the precipitation decreased in the cold months and increased in the hot months. Additionally, the runoff in the base period from January to May was less than the observed runoff and it was more than the observed runoff from June to December. This was due to the fact that only precipitation was used as an independent variable for modeling; whilst, the runoff was affected by other factors such as springs water in addition to the rainfall. From November to May, the estimated monthly rates of runoff for the next 95 years were reduced.Moreover, from November to October, the simulated runoff rates were more than the monthly runoff rates in the base period. Accordingly, it can be concluded that the runoff decreased in the cold season and increased in the hot season, as well. The increase in the precipitation and runoff rates in the hot season could be due to the rise in the local rainfalls. In other words, an increase in the local rainfalls due to global warming was probable in future periods.
Mahnaz Karami Jozani; Alireza Ildoromi; Hamid Nouri; Abdollah Pernia
Abstract
IntroductionThe Climate forecasts show that climate change will change the hydrological cycle. The purpose of this study was to assess the effect of climate change on the Gorganrood- Ghareh Sou watershed in Golestan province using two generic oocytes of HadCM3 and ECHAM4 and the LARS-WG model according ...
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IntroductionThe Climate forecasts show that climate change will change the hydrological cycle. The purpose of this study was to assess the effect of climate change on the Gorganrood- Ghareh Sou watershed in Golestan province using two generic oocytes of HadCM3 and ECHAM4 and the LARS-WG model according to the three scenarios of A2, B1 and A1B for the period of 2011-2030. The results showed that discharge has insignificantly decreased in two stations of Tamar and Arazkooseh in the studied watershed. In addition, changes in the minimum temperature and rainfall have a more significant effect on river discharge changes in the watershed. The results also indicated a decrease in the discharge rate in all scenarios of two models of general circulation of the atmosphere in the future period relative to the base period.Many of the environmental problems of our age, including floods, storms, droughts, and the like are all rooted in global climate change. The study of the effects of climate change on water resources is an important issue that has been considered in recent years. For example, Kling et al. (2012) examined variations in runoff in the Danube watershed under the influence of changing scenarios. The results showed that most models predicted precipitation increase and runoff reduction for future years. Rajabi (2013) investigated the effect of changes on Ghareh Sou runoff in Kermanshah province in the coming decades and its results showed that in the coming periods, the average rainfall of the watershed reduced. Singh et al. (2013) evaluated the performance of artificial neural network in a small watershed in India based on RMSE and R criteria. The results showed that the neural network model had an acceptable performance in the study of climate change in the region.MethodologyGorganrood watershed-Ghareh Sou is in the southeastern part of the Caspian Sea with an area of 13061 km2. The average annual rainfall is about 300 mm to 1000 mm, and the annual average temperature varies from about 7.5 to 17 ° C. In this study, the seasonal and annual data series of minimum and maximum parameters of temperature, precipitation and annual discharge of the year and non-parametric tests were used to determine the trend direction and correlation of the studied parameters. In order to investigate the effect of variation on discharge, the data from B1, A2 and A1B scenarios of the HadCM3 model and B1, A2 and A1B scenarios of ECHAM4 model were used. In addition, Lars statistical model was used for calibration of the data, after calibrating and validating it for the simulation of rainfall-runoff, The output of the Lars statistical model was introduced into the neural network model and the changes in the discharge rate were investigated in the course of 2030-2011 (near future). In order to evaluate the performance of the model, the statistical index of the coefficient of explanation and the mean squared error were used.ResultsThe annual variations in discharge at two stations of Tamar and Arazkooseh showed that precipitation on both stations of Arazkooseh and Tamar was significant at 99% probability level. But it had less effect on rainfall than river discharge. The studies showed that during the last 30 years in the study area, the maximum temperatures and precipitation, had insignificantly increased. The minimum temperature had a significant increase in most of the studied time series. Also, the climatic parameters had a more significant effect on rainfall than the minimum temperature.The results of the climate simulation showed that the average temperature for the HadCM3 for 2011-2030 period would increase with all scenarios. The results of the HadCM3 model showed that precipitation is rising in all scenarios. But in the ECHAM4 model, the precipitation in the A2, B1 scenarios will decrease, but in A1B scenario it will increase. In HadCM3 and ECHAM4 models, the highest precipitation rates are respectively for A2 and A1B scenarios.Discussion and ConclusionThe results of the two HadCM3 and ECHAM4 models indicated an increase in precipitation (except for scenario A2 and B1 in the ECHAM4 model) and increase in temperature in the Gorganrood-Ghareh Sou watershed. Moreover, the changes in minimum temperature will be higher than maximum temperature. Discharge will decrease in both climatic models. The results showed that the greatest decrease in the amount of discharge in both climates models and in all three scenarios was in September. The results of the changes in the discharge rate at the two hydrometric stations of Tamar and Arazkooseh indicated that although the changes were not significant in any one, the decrease in discharge rate during the period at the Tamar station was more pronounced than that of the Arazkooseh station. The results showed that the LARS meteorological model had a high potential for generating daily data.
