پژوهشی
Ali Nasiri Khiavi; Ali Faraji; Raoof Mostafazadeh
Volume 6, Issue 21 , March 2020, Pages 1-22
Abstract
1- IntroductionDetermining the sensitivity of streamflow to climate is necessary to make informed decisions to manage water resources and environmental systems for predicting hydro-climatic variability and climate change. Climate variability is considered as a key driver of hydrological processes. The ...
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1- IntroductionDetermining the sensitivity of streamflow to climate is necessary to make informed decisions to manage water resources and environmental systems for predicting hydro-climatic variability and climate change. Climate variability is considered as a key driver of hydrological processes. The sensitivity of streamflow to climate variables is predicted using a hydrological modeling procedure. In this regard, the results of streamflow modeling are comparing through the present and projected climate scenarios. Climate change in the last century has largely affected the processes of the water cycle and its components on different spatial scales. In recent years, the identification of effective factors and their impact on regional runoff changes has been widely explored by researchers in the field of hydrology. In the context of exploring water resources due to climate change, it is easy to estimate the impact of climate on political decision making and planning. Precipitation elasticity is defined as a tool to determine the rate of streamflow sensitivity regarding the precipitation variability. This study aims to calculate rainfall elasticity and variation of discharge in 20 watersheds using nonparametric elasticity estimation in the monthly timescale. 2- MethodologyIn this study, the sensitivity of rainfall to precipitation has been calculated using nonparametric estimation and a set of monthly data and precipitation data for Ardabil province. Climatic elasticity can be calculated by dividing the climatic variables such as rainfall, relative humidity, temperature, evapotranspiration, wind speed, specific radiation, etc. To estimate the Elasticity of precipitation (Ep), a non-parametric estimation of a set of average monthly discharge and rainfall data is required. At first, the monthly precipitation elasticity was calculated for 20 river gauge stations in the study area, and the median of these values was estimated as precipitation elasticity for the entire province in 12 months of the year. Then the Triple Diagram Model was used to assess the changes in the precipitation elasticity index with precipitation and discharge values. Also, based on the range of changes in the elasticity index, the hydrometric stations studied were classified into 3 categories and presented through a spatial map.3- ResultsThe results showed that the range of the elasticity index was between -2.21 to 3.96, which is related to Arbabkandi and Shamsabad stations, respectively. Based on the results of the Triple diagram model, the variability of the elasticity index is higher in the low discharges. Also, the value of the elasticity index is higher in the dry months, than the other months, which proves the greater impacts of precipitation on the river flow rising in dry months. There is also an inverse relationship between the elasticity index and the upland watershed area of each river gauge station. In watersheds located in upland parts of the area, the discharge shows fewer changes than precipitation, while in downstream watersheds, the discharge is changing more with precipitation variations. According to had the monthly elasticity-precipitation diagram, the calculated elasticity values had a higher amount in the range of medium values of precipitation (0-20 mm and 15-15 mm) in dry and wet months, respectively. 4- Discussion and conclusionThe results showed that the sensitivity of the elasticity index is higher at low discharge values, while in the higher values of the discharge, the elasticity index is less sensitive. According to the results, in the dry months, the value of elasticity index is higher than other months; in this case, it is possible to refer to the sensitivity of the change in rainfall to dry rainfall during the dry months. Changes in the values of the elasticity index in different rainfall indicate that the value of the low elasticity index was attributed to the precipitation occurs in the cold months of the year as a snowfall, which related to the delayed response of snow melting. In particular, due to snowmelt in upstream watersheds, this time delay reduces the elasticity index. It is also very difficult to distinguish the effects of human activities and changes using the employed approach. On the other hand, the sensitivity of the river flow varies over the study area, and it is always different considering the changes of climatic components, human exploitation, land use, geological characteristics, etc. In particular, calculating the elasticity index allows comparing the behavior of different rivers in terms of response to climate change changes. 5- References Chiew, F.H.S, Peel, M.C, Mcmohon, T.A, Siriwardena, L.W. (2006). Precipitation elasticity of streamflow in catchments across the world, Climate Variability and Change-Hydrological Impacts (Proceedings of the Fifth FRIEND World Conference held at Havana, Cuba, November 2006). IAHS Public. 308: 1-7.Mehri, S & Mostafazadeh, R. (2019). Comparing the variations in hydrologic response of Ardabil Province watersheds using precipitation-runoff polygons. Watershed Engineering and Management, 11(2), 381-391.Nasiri Khiavi, A & Mostafazadeh, R. (2018). Spatio-Temporal Assessment of River Flow Discharge Variability Indices in some Watersheds of Ardabil Province. Hydrogeomorphology, 17, 23-44.Nazari-Pouya, H., Kardovani, P & Farajirad, A.R. (2016). Investigation and Evaluation of Climate Change Impacts on Hydro-Climatic Parameters of Ekbatan Dam Basin (Hamadan Province). Ecohydrology, 3(2), 181-194.
پژوهشی
GholamReza Maghami Moghim; AliAkbar Taghipour; Houshang Khairy
Volume 6, Issue 21 , March 2020, Pages 23-42
Abstract
1-IntroductionThe excessive usage of groundwater has led to some problems in recent years such as the salinization of wells water, drying of aqueducts and springs. Due to the importance of groundwater, some important studies have been conducted accordingly; the oldest belongs to the Greek philosophers. ...
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1-IntroductionThe excessive usage of groundwater has led to some problems in recent years such as the salinization of wells water, drying of aqueducts and springs. Due to the importance of groundwater, some important studies have been conducted accordingly; the oldest belongs to the Greek philosophers. There are also some articles in this field of Iranian scholars such as Abourihan and Hamdollah Mostofi. Pierre Perrault and Edme Mariotte were the first who wrote about the origin of groundwater in France in a scientific way. Moreover, significant studies have been carried out in this regard including Nayak (2006) investigated the Godavari Basin in Maharashtra province (India), Zhang (2009) studied the Pear Basin in China, Potop (2011) explored a study in the Czech Republic, Jang (2012) conducted a study in the Pingtung Plain in Taiwan, Sinha (2014) investigated Valapattnam Basin (India), and Yang (2012) carried out a study in the plain of Beijing. In Iran, as well, Khosravi (2014) in the Garmsar plain and Javdanian (2016) in the plains around the city of Isfahan, have conducted important studies in this regard. Although groundwater issues are attributed to humans in most of these studies, but it seems that besides the negative effects of some human activities, they have somewhat moderated the decline in groundwater levels in recent years. Despite the fact that the usage of groundwater in the Safiabad plain dates back to a few hundred years, there has not been any significant studies conducted in terms of its groundwater. In this study carried out through field and library methods, it was attempted to investigate the changes of groundwater level in this plain, so that the human positive effects could be identified in this field whose results could be utilized in better controlling and managing groundwater.2-MethodologyTo do the research, firstly, the studied region was determined using field studies as well as topographic and geological maps. Statistics related to the groundwater levels of 27 years (1992 -2016) were obtained from the North Khorasan Regional Water Authority, and the data on agricultural activities was collected from the Esfarayen Agricultural Jihad Department and the climatic statistics were sourced from the Safiabad Synoptic Meteorological Station. The data regarding smart meters was obtained by interviewing with North Khorasan Regional Water Authority experts. That part of this study, which was related to cultural issues and cash penalties, was conducted through interviews with farmers. The statistical analysis, the preparation of hydrographs and figures were processed using Minitab, Excel, and SPSS software.3-Results and DiscussionSafiabad plain is one of the northeastern plains of Iran, where the usage of groundwater is common there. The hydrograph consideration of this plain indicated that the its groundwater level has been decreased by about 6.9 m since 1989. In the study of changes regarding the groundwater levels, the first hypothesis which comes to the mind, is the effects of precipitation on these changes. Based on meteorological statistics, regression coefficient and histogram precipitation of this plain follows the normal distribution. Therefore, in recent years, the main cause of water table decline in this plain is the over-extraction of groundwater. The reduction of groundwater levels has led the Regional Water Authority administrations and farmers do some policies. Making concrete channels and water pipelines was the first step in this issue which causes saving 6 percent of the usage of groundwater in this plain.Also, according to the statistics, 42% of the farm lands of Safiabad plain are irrigated by modern systems, which would save 8.8% water consumption in this plain per year. The change of cultivation type was another policy that was applied since 2006. In 2017, pistachio gardens were replaced with the cultivation of grains and saffron replaced sugar beet; through these changes, about 40% were saved in the water resources of this plain. The imposition of cash penalties was another Regional Water Authority policy that saved 5% of the groundwater in the plain. Cultural issues are also other important cases that if implemented appropriately, can have a sustainable impact on the exploitation of water resources. However, no considerable action has been taken so far in this regard. The use of smart meters is one of the newest methods of groundwater control that was firstly used in 2003 in Safiabad plain. Although the effect of using this method was not significant until 2011, however, since this year, for the first time, the hydrograph of the plain showed the positive reflections to the use of this controlling method and groundwater level was matched with the data fitting line and sometimes reached to the levels higher than it.4- ConclusionSafiabad plain is one of the northern Khorasan plains, which is faced with a sharp decrease in the groundwater aquifers. Farmers and government agencies sought to control the level of water consumption in the plain by creating concrete channels and pipes for transferring water through the channels, using new methods of irrigation, changing in cultivation, cash fines, cultural actions and the installation of smart meters. The results indicated that by transferring water through the use of pipes and concrete channels, new irrigation methods, changing the cultivation type, cash penalties, cultural issues, using smart meters, respectively, 6%, 8.4%, 40%, 5%, 2% and 45% of water consumption was saved and overall, 105.4% of the water consumption has been saved in this plain. However, the hydrograph consideration of this plain showed that the groundwater level is still decreasing. More detailed studies indicated that some causes of this decrease were due to the high thickness of the unsaturated soil, causing a large amount of water not to reach into the saturation zone; however, the main reason for this issue is the increase of cultivation area in this plain. In other words, farmers in this plain have been saving water by taking some activities, but having increased the area under cultivation, the amount of groundwater consumption has continued as much as the past. Furthermore, the results of the research showed that among human actions, only the installation of smart meters has had a significant effect on the hydrograph of this plain, so that since 2011, for the first time, the level of groundwater has passed the fitting line.
پژوهشی
Somaiyeh Khaleghi; MohammadMahdi Hosseinzadeh; Payam Fathollah Atikandi
Volume 6, Issue 21 , March 2020, Pages 43-64
Abstract
1-IntroductionOne of the methods used in river surveys is river classification. The main aim of the classification of the river is simplify the processes of hydrology and sedimentation, and ultimately predict river behavior. So far, rivers have been categorized from different perspectives and the basics ...
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1-IntroductionOne of the methods used in river surveys is river classification. The main aim of the classification of the river is simplify the processes of hydrology and sedimentation, and ultimately predict river behavior. So far, rivers have been categorized from different perspectives and the basics of these categories are including topography, slope, flow discharge, river age, and pattern in the plan. The first classification Recognized by Davis in 1899. Davis classified the rivers according to their evolution and modification into three groups of young, mature, and old. Leopold and Welman (1957) divided the form of alluvial rivers based on the sinuosity coefficient and the ratio of width to Depth into three straight, meandering and braided groups. A descriptive classification by Shumm (1963) presented based on two factors of river stability and sediment transport. The objectives of this research are to identify the factors affecting the bank erosion of the Kaleybarchai River, identifying the damages incurred in the construction and banks of the river, runoff and preventing possible floods. In this research, the river classification system is based on the Rosgen method, which is presented by the American researcher Rosgen (1994) to the river engineering community. The Rosgen method is the most complete and comprehensive method provided so far and includes many of the features of previous systems. Rivers are living beings that constantly change their beds and banks, and this causes the river to undergo major changes over time. In addition, human activities, such as the utilization of riverine material and river modification, will cause the river to be moved.2-MethodologyTo evaluate the classification of the flow pattern in the Kaleybarchai River, the Rosgen model has been used at levels I, II, III. A reach of 3 km between the two villages of Pazhagh and Gheshlag was determined, and then 8 cross sections were selected in this reach. To simulate the river and extract the required parameters from geological maps, topography, land use and ARC GIS software was used. After determining the river reaches, based on field observations and topographic maps, classification in level I and level II were carried out in 8 cross-sections at the Kaleybarchai River, which are based on the slope, curvature coefficient, bankfull width, mean flood plain depth, flood plain width and bed material.3-ResultsAfter crossing the river route with field observations and then analyzing data and general calculations, 8 cross sections from the entire river course were extracted in all of the studied river and all the parameters required for classification and geometrical identification of the channel wrer calculated.In order to obtain the average size of channel material, 16 samples were taken at river in different reaches and were analzed in the laboratory (Table 2). According to the obtained data, the highest percentage of particles along the river were average sand with 26.6% and cobble up to 14.7%, which were evaluated for the Rosgen classification, according to the results, the total of river is in groups B and C.To determine the channel type at level I, after obtaining the slope of the Kaleybarchai River in the study area, four sections of the river were in type B and four sections in type C.4-Discussion and conclusionBased on morphological indices, sediment content and flow conditions, two different types of channels including B and C were identified in the study area and evaluated level according to the Rosgen in level I, II and III.Morphological study of type B in relation to the evaluation of the correspondence and efficiency of the Rosgen model showed that their dominant morphology consisted of narrow valleys with relatively low widths and moderate slopes and relatively stable banks. Type C has meandering and high sinuosity, valleys with floodplain and point bars in low slope.The high instability of the river bed in the reaches of 3, 5, 7, is a threat to the agriculture land land and surrounding buildings. Due to the fact that the braided rivers are not stable and the flow and position of the sedimentary islands and the width of this rivers are constantly changing, it is necessary to manage and organize the operations in this section with regard to the morphological variables and Flow conditions. The results of the Kaleybarchai River assessment based on the Rasgen classification system at level I, II and III showed that the Rosgen system present good the patterns of the channel in the Kaleybarchai River and, consequently, the effective parameters in the classification and separation of the channels. In this way, there are differences in the quantities and the parameters due to the specific conditions of the factors affecting in the locality.
پژوهشی
Sodabeh Behyan Motlagh; Afshin Honarbakhsh; Khodayar Abdolahi; Mehdi Pajouhesh
Volume 6, Issue 21 , March 2020, Pages 65-86
Abstract
1-IntroductionStreamflow modeling is an important attitude generally considered for planning and management of water resources as well as watershed management practices. Thus it is highlighted as one of the fundamental issues in applied hydrology. It is also one of the methods used for modelling streamflow ...
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1-IntroductionStreamflow modeling is an important attitude generally considered for planning and management of water resources as well as watershed management practices. Thus it is highlighted as one of the fundamental issues in applied hydrology. It is also one of the methods used for modelling streamflow processing methods, which is considered as one of the common black–box methods that correlates input and output data. Regression techniques and time series models have been derived from data processing methods.Since 1962, use have been made of hydrologic as well as stochastic methodsfor flow discharge modeling asAutoregression (AR), Moving Average (MA), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) statistical models were introduced.Application of both fuzzy and time series models in discharge modeling of conducted studies, have shown the appropriate results of discharge estimation. The purpose of this study wasto evaluate the performance of two time series models (ARIMA) and fuzzy least squares regression with symmetric triangular membership function in streamflow modeling. Consideringthe background of the research, the least squares fuzzy regression method had not been used and no comparisons have been made between this model and time series model. Since both models arefrom black-boxed family, the present study have used both models and their efficacy have been evaluated.2-MethodologyThis study aimed atsimulating the average monthly discharge of KoheSokhteh Watershed. This region has been located inShahrekord, Boroujen and Kiar in Chaharmahal and Bakhtiari province.The monthly data and an average of 25 yearshave been used. The ARIMA model investigates the modelling of auto-correlated and random components shown as .It has to be noted that the values cannot be negative.-Identification of the type and rank of model for investigating timeseries modelsThe autocorrelation function (ACF) and the partial autocorrelation function (PACF) were used in order to determine the type and rank of the time series model. Having diagnosed model`s ranks, a modal of model`s figure is identified and model`s parameters are appointed through obtained correlational function. ACF charts are obtained with k delay. The other way for expressing the time dependency of the time series data is to define the PACF function. If beconsidered as partial autocorrelation function with k delay,….-Parameters` estimation and model adequacyAfter choosing an appropriate model along with its order, the next step is to estimate the parameters of the selected model. We should calculate the remaining values that follow the normal distribution with a zero mean using multi-year data for simulated discharge values. Before starting simulation, data is transformed to a normal data set.-Calculating the relationship between fuzzy least squares` regression model In this section, the dependent variable of discharge is considered as a fuzzy dataset and observations related to independent variables (precipitation, evaporation, squer root, lag time) are consideredas non-fuzzy variables. Based on this type of data and taking the 20 percent as theallowance error for the measured data, a model with fuzzy coefficients is fitted to the data.-Data used in the modelThe data was provided based onobserved data of observation matrices as well as matrix A. The matrices s and y, and also the matrices a and σ, were calculated. If Rank (x)=n+1, then the matrix A would bea definite positive. If A wasa definite positive, it would havea reverse A-1, andthe relation between σ and α would haveunique answers.-Description of the performance of least squares fuzzy regression model in watershedsWith regards to the matrix X, the observation matrix (matrix X) was calculated for watersheds. Then the calculation proceeds using the matrix of observations and matrix A. Matrix A was calculated for three of the watersheds. After calculating the matrix A, the y vector (discharge observation values) was calculated for watersheds. Then the s vector wascalculated for each watershed. Finally, the optimal fitted model applied to the data as well as α and σ matrices were obtained.3-ResultsIn order to develop time series models, it was identified in the initial analysis that once applying differentials forconverting an unstable time series to a static time series was sufficient. Nextstep wasidentifying the order of the model. ACF as well asPACF graphshave been utilized. ACF suggested MA and PACF suggested AR. Subsequently a combination of MA and AR was proposed for modeling this series. The parameters of the selected model were calculated using the MINITAB software based on the information extracted from PACF and ACF. Standard errors of the parameters for the selected model were relatively small, indicatingthe applicability of the parameters in modeling.-Fuzzy least squares regression-Estimated dischargeThe values of estimated discharge of watersheds were calculated using fuzzy least squares regression. The results of the estimated discharge have been shown in Table 2.4-Discussion and conclusionTo investigate the efficiency of the ARIMA time series model and fuzzy model at monthly forecasting scale, ARIMA model as well as fuzzy least squares regression models were utilized. Both models properly predictedmonthly discharge. The predicted discharge values using ARIMA model were lower than the observed discharge values. In the fuzzy least-squares regression model, peak discharge was well modelled but discharge with low values had further differences with the observations.During the calibration phase, the fuzzy least squares regression model showed a Nash-Sutcliff coefficient of 88% and ARIMA (211)(111) model had a Nash-Sutcliff coefficient of 84%. The least squares fuzzy regression model with low difference showed a superior to ARIMA model. The fuzzy least squares regression model`s superiority showed less effect of seasonal changes in predicting discharge, which was in contrast to Moayeni et al. (18) research.
پژوهشی
yaser hoseini
Volume 6, Issue 21 , March 2020, Pages 87-107
Abstract
1-IntroductionFlood discharge is of high importance in studies regarding water resource exploitation, flood control, construction of dams, basin management, and hydrologic studies (Alzahrani et al, 2017). Therefore, to a large extent the accuracy of these studies and the safety of water constructions ...
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1-IntroductionFlood discharge is of high importance in studies regarding water resource exploitation, flood control, construction of dams, basin management, and hydrologic studies (Alzahrani et al, 2017). Therefore, to a large extent the accuracy of these studies and the safety of water constructions depend on flood study methods. Flood is a natural phenomenon that threatens the life and properties of a large number of people all over the world, and it is impossible to manage water resources in basins without the accurate determination of the peak flood discharge (Badri et al, 2017). The advances in flood estimation techniques have made it possible to use rainfall-runoff models to assess the hydrographic properties of the flood in watersheds and decrease the risks of the flood. Therefore, this study was carried out to compare the SCS unit hydrograph and Uniform methods in determining the peak flood discharge with WMS model in Amughin basin of Ardabil province.2-MethodologyAmughin basin with an area of approximately 78 km2 is located in the northwest of Iran. The physiographic features were extracted using the basin map (scale: 1:25000) and WMS model. This study applied Arc GIS 9.2 and Idrisi32 software to obtain the properties of the studied basin using DEM (Digital Elevation Map) of the National Cartographic Center, NCC. Remote sensing methodology was utilized to study the geographical land use changes occurred during the study period. Landsat images of TM and ETM+ of Amughin basin area were collected from the USGS Earth Explorer web site. After image preprocessing, un-supervised and supervised image classification were performed to classify the images into different land use categories. In general, soil hydrologic groups were divided into three subgroups of B, C, and D and CN value of 78.7 was estimated for the Amughin basin based on the geological examination, permeability, vegetation, and hydrologic conditions of the basin soil.3-ResultsThe model calibration results showed that the simulated peak discharge and flow volume were in good correspondence with the observed values (RE%= 7.17, RMSE= 0.44). Thus, the calibration results were used for optimum values of parameters. The model was validated using two rainfall events and the model performance indices were acceptable in both cases (RE%= 2.51, RMSE= 0.0042) in SCS method. To evaluate and test model validation, two rainfall events, were used. That the model performance indices were acceptable. Distribution of CN amount in the area showed that the upstream flow had higher CN values and consequently increased flood volume in these areas. Based on the values of obtained CN, the amount of peak flood discharge was calculated for return periods of 25, 50, and 100 years.4-Discussion and conclusionAccording to the results, the SCS model has good agreement with experimental results among the different methods used for estimating flood discharge in the northwest of Iran. In fact, this model requires calibration in the study region. In small watersheds in the northwest of Iran, the SCS model yields better results than the Uniform method because the conditions required for using this model are satisfied in these basins. Moreover, the results obtained from this method can be closer to actual values provided that the watershed concentration time is calculated more accurately. Our results also showed that the SCS model has a high sensitivity to rainfall distribution across the region and that the rainfall across the region needs to be analyzed to obtain desirable results. Besides, the rainfall distribution and its time distribution should be close to the corresponding values in the region. A comparison between the obtained results of peak discharge from the SCS and Uniform methods in return periods of 25, 50, and 100 years revealed that the average estimates of the Uniform were approximately 5% higher than the SCS method. According to paired T-test, the difference between Uniform and SCS values were not significant at a confidence level of 0.01. Overall, the results obtained from this method can be closer to actual values if the watershed lag time is calculated more accurately using the floods occurred in the studied basin.
پژوهشی
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.
پژوهشی
Kazem Nosrati; Milad Rostami; Zahra Etminan
Volume 6, Issue 21 , March 2020, Pages 133-154
Abstract
1-IntroductionOne of the most elements in river geomorphology, engineering and management is the channel issue. Rivers are natural and complex systems that their classification offers a better knowledge regarding their processes and forms. Based on dynamic and channel forms, rivers are varied and find ...
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1-IntroductionOne of the most elements in river geomorphology, engineering and management is the channel issue. Rivers are natural and complex systems that their classification offers a better knowledge regarding their processes and forms. Based on dynamic and channel forms, rivers are varied and find natural changes over the years. However, nowadays anthropogenic effects comprising building structures such as bridges, and roads accelerate the destructive process. River classification is a suitable way to increase our knowledge about rivers. One important aspect of river is hydrogeomorphological understanding. Therefore the objectives of this study were to assess and analyze the Taleghan river hydrogeomorphological conditions using the geomorphological quality index method (MQI) in upstream of the Taleghan dam and to validate MQI in this river.2-MethodologySix reaches in upstream of the Taleghan River have been selected and MQI for each reach determined using satellite images and field survey. In order to determine SQI for each reach, three aspects have considered including river processes continuity (longitudinal and crosswise), channel morphological condition, cross-section form, sediment bed loads and vegetation. These aspects investigated in the form of three components consists of geomorphological functions, processes and river forms (F), artificial (A) and channel adjustments (CA).3-ResultsAfter measurement and field observation of the Taleghan River, effective variables on morphological quality index were obtained in reach. MAI and MQI values for each reach were calculated and classification was done based on the range of each class. The condition of each reach based on MQI was explained as follows:The first reach: took place in a mountain area and upstream of the river that on both sides connected to the river and is limited.Based on the NBS model erosion of bank steam is in a minor amount and the average bed slope in this reach is 0.028 with gravel bed.Channel width is variable between 10 to 18 m and has a single channel pattern. The obtained MQI values in this reach is 0.714 that grouped in a good class.Reaches comprising four reaches (two, three, five and six) have the same condition that in the point of human interfering, morphological condition and values of bed and bank erosion have a normal array and grouped in moderate class.In reach 4 the river is fairly limited and flood plain is present discontinuously on one side and the other side is attached to the hillside. The river has a sinuouse pattern and bed load sediment formed by fine and coarse sand. Furthermore, forth reach strongly influenced by human changes consists of gravel and sand quarring, the existence of bridge structure on the river, presence of built-up area and destruction of vegetation along the river.4-Discussion and conclusionThe results showed that reach one with score value of 0.714 due to low human interference and locating in upstream of the river categorized in a good class. Otherwise, reaches two, three, five and six have values of 0.58, 0.54, 0.59 and 0.61, respectively and they are categorized in moderate class; in these reaches in comparison to reach one the values of human interference have been increased. The reach four with MQI score value of 0.49, is categorized in a weak class because of human interference including gravel and sand exploitation and structures such as bridges on the river. The results reveal that the MQI model is approperiate for the classification of rivers in the Taleghan River in southern of Alborz Chain Mountain.
پژوهشی
Mehdi Teimouri; Omid Asadi Nalivan
Volume 6, Issue 21 , March 2020, Pages 155-179
Abstract
1-IntroductionThe main objective of this research is to prioritize the factors affecting the occurrence of landslide and its susceptibility zoning in Lorestan province using the maximum entropy and MaxEnt models. To do this research, 11 factors affecting the occurrence of landslide including height, ...
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1-IntroductionThe main objective of this research is to prioritize the factors affecting the occurrence of landslide and its susceptibility zoning in Lorestan province using the maximum entropy and MaxEnt models. To do this research, 11 factors affecting the occurrence of landslide including height, slope, aspect, surface curvature, distance from the stream, fault and road, lithology, land use, rainfall, and topographic humidity index have been used. In this research, 30, 40, 50, 60 and 70 percent of landslides were evaluated for validation to determine the sensitivity and accuracy of the model. For evaluation of the model, the relative recognition function curve (ROC) was used. From the total of 176 landslides, 70% of the data was used as the test data and 30% as the validation data using Mahalanobis distance method and the accuracy of the model in the testing and validation stages based on the curve level was reduced. The results showed that 35.5% of the province of Lorestan has a landslide sensitivity. Based on jackknife diagram, rainfall, distance from road, lithology and land use layers were the most important factors influencing the sensitivity of landslide. The AUC level based on the relative function recognition curve indicated a 90% accuracy (excellent) of the maximum entropy method at the training stage and 83% (very good) at the validation stage to determine the landslide susceptibility. The results of this study will be suitable for provincial administrators and managers in order to land planning and reduce the damage caused by landslide occurrence.Mass movements, including landslide, is one of the most important issues in natural hazards, because its occurrence can cause many human and economic losses, especially in mountainous areas (Symeonakis et al., 2016). Regarding the destructive effects of landslides on natural resources, as well as human habitats and erosion of significant volumes of valuable soils, the identification of susceptible areas and zoning of potential occurrence or landslide susceptibility is vital and very important (Zhang et al., 2019). In recent years, the use of GIS and remote sensing along with machine learning methods has created a new step in the zoning of landslide occurrences. Lorestan province is a vulnerable area to landslide hazard due to the mountainous and wetness conditions. Therefore, the main objective of this research was to prioritize the factors affecting the occurrence of landslide and its susceptibility zoning in Lorestan province using the maximum entropy and MaxEnt model.2-MethodologyLorestan province with an area of 2829612 hectares is one of the major provinces in the west of the country. To do this research, 11 factors affecting the occurrence of landslide including altitude, slope, aspect, surface curvature, distance from the stream, fault, and road, lithology, land use, rainfall, and topographic humidity index have been used. The required maps were prepared using GIS and RS techniques. In this research, 30, 40, 50, 60 and 70 percent of landslides` division were evaluated for validation to determine the sensitivity and accuracy of the model. For evaluation of the model, the relative recognition function curve (ROC) was used. Using Mahalanobis distance method, from the total of 176 landslides, 70% of the data was used as the test data and 30% were utilized as the validation data for having the best classification. The difference of the current research with other similar studies was that in this study, use was made of Mahalanobis distance method for classification of validation data and training instead of random classification. The Mahalanobis distance helps to classify data richness and prevents random selection of points for validation. Maximum entropy method (MaxEnt model) is one of the methods of machine learning and one of the main advantages of MaxEnt model is the ability of this model to identify the most important variables and sensitivity analysis of variables using Jackknife method, which has been investigated in the current study.3-ResultsThe results showed that 35.5% of the province of Lorestan had landslide susceptibility. Based on Jackknife diagram, rainfall, distance from road, lithology and land use were, respectively, the most important factors influencing the susceptibility of landslide. The AUC level, based on the relative function recognition curve, indicated 90% accuracy (excellent) of the maximum entropy method at the training stage and 83% (very good) at the validation stage to determine the susceptibility of landslide occurrence.4-Discussion and conclusionLandslide is considered as one of the most dangerous natural disasters in the world. In this study, taking into account the affective environmental and human factors, and using the maximum entropy method, the map of landslide susceptibility of Lorestan province was prepared. The results showed that factors such as rainfall, distance from the road, lithology, land use, distance from the fault and slope were the most important factors influencing landslide susceptibility with the participation of over 60%, regarding which, land use management and road construction principles need human activity interventions. The drawn ROC curve showed that the accuracy of the model in the estimation of landslide susceptibility regions both in the stage of the test and in the validation stage was excellent and very good, which meant the excellent performance of the model. According to the obtained results, it can be said that MaxEnt model had a high ability to determine areas with landslide susceptibility and due to the speed and accuracy of the model,it is suggested that in similar researches, especially in developing countries, due to the lack of facilities and financial resources, as well as the time consuming of identifying areas with landslide susceptibility, it can be used. In addition to natural factors, some human factors such as road construction, play an important role in the occurrence of landslide, which requires avoiding ecosystem change as a disaster risk factor to reduce relative risks. The results of this research can be applicable to the decision making and management of provincial lands as well as urban planning, and they can have a significant role in preventing and reducing the damage caused by landslide.
پژوهشی
behrouz sobhani; Leyla Jafarzadehaliabad; Vahid Safarianzengir
Volume 6, Issue 21 , March 2020, Pages 181-202
Abstract
1-Introduction Drought is one of the most important natural disasters affecting agriculture and water resources, and its abundance is extremely high in arid and semi-arid regions (Shamsenya et ...
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1-Introduction Drought is one of the most important natural disasters affecting agriculture and water resources, and its abundance is extremely high in arid and semi-arid regions (Shamsenya et al., 2008: 165). Drought is a natural phenomenon that has a complex process due to the interactions of various meteorological factors and occurs in all climatic conditions and in all regions of the planet (Samandianfard & Asadi, 2017). According to the domestic and foreign studies, many researchers have conducted research on drought monitoring and prediction, but the research that can show the drought phenomenon more accurately with the future vision is not takenhas not been conducted if both do not cover the issue adequately. According to the researchers, this study was conducted to model, monitor and predict drought with the new method in Iran in this study.2-MethodologyIn this study, drought modelling in Iran was carried out using climatic data of rainfall, temperature, sunshine, relative humidity and wind speed monthly (for 6 and 12 months scale) for the period of 29 years (1990-2018). At 30 stations using the new TIBI architecture model, a fuzzy set of four indicators (SET, SPI, SEB, and MCZI) valid in the World Meteorological Organization was used. For modelling the new TIBI index, the climatic data were first normalized, then four indices (SET, SPI, SEB, and MCZI) were calculated separately and the fuzzy modelling of the four indices was performed in the Matlab software and eventually to prioritize the drought-affected areas, the multivariate decision-making model, TOPSIS was used.3-ResultsIn order to investigate the effect of drought fluctuations in drought conditions of stations, it is possible to determine the changes in the indicators (SET, SPI, SEB, and MCZI) in the TIBI index analysis. Considering the large number of stations studied, For better understanding, only the drought series diagrams were presented at Bojnourd station on two 6 and 12 month scales (Figures 7 and 8),, (in the mentioned figures, the red arrow shows the drought margin at a 6-month scale with a value of 0.44 and greater, and a value of 0.76 and greater within the 12-month scale. The analysis of these forms shows that at the 6-year and 12-month scale at Bojnourd station, the amount of evapotranspiration was similar in drought conditions, which decreased from April 1994 to February 1999, and after this month an increase was observed if the impact of rainfall on a 6-month scale is weaker than the 12-month scale. It means that from May 1993 to November 1997, an increasing trend followed by the same pattern, and the indicators (SET, SPI, SEB, and MCZI) affect the TIBI index and show some trends, indicating that the new TIBI fuzzy index reflects the four indicators well. The T.I.B.I index at the 6-month scale shows a sharper shape than the scale 12.Prioritization of the stations involved in drought in Iran was analyzed using the TOPSIS model. The results of the TOPSIS model implementation using the degree of importance of the criteria derived from the entropy method indicate that, in terms of drought, more and fewer places are involved with drought by combining the two 6 and 12-month scale. According to the TOPSIS multivariate decision-making model, it was determined that the three stations most affected by drought based on the TOPSIS model were Bandar Abbas, Ahvaz and Bushehr, respectively, in the south and southwest regions of Iran with priority points of score (1, 0.78, and 0.62 respectively), and the three stations of Gorgan, Shahrekord and Orumieh in the northern and western parts of Iran with the scores of 0.026, 0.033 and 0.035 had lower priorities for drought response, respectively (Table 6) and (Figure 11).4-Discussion and conclusionDrought is a natural disaster that is gradually evolving under the influence of climatic anomalies over a long period of time. In recent years, various parts of the Middle East have faced drought, including those regions of Iran in Southwest Asia. In this study, drought phenomenon was assessed at 6 and 12 months using the new fuzzy index T.I.B.I. The results of the research showed that the total frequency of droughts in the 12-month scale was more than 6 months but the severity of a 6-month-old drought is more than 12 months old. On a 12-month scale, the number of drought repetitions is more than 6 months. Drought persistence was higher at 12-month scale, droughts were shorter at short-term and affected by temperature parameter. However, the intensity of drought over a long period of time had a slower response to rainfall changes. The highest percentage of drought incidence in scale of 6 months; Bandar Abbas, Bushehr, Ahvaz and Zahedan stations in the southern half of the study area respectively with the of drought (16.62, 11.24, 14.13 and 62.6 and the lowest in the 6-month scale were Urmia and Ardebil stations, with the percentages of 1.10 and 1.88, Ilam and Yasuj with the drought frequency of 1.61 and 2.01, Rasht and Gorgan, with a high percentage of drought frequency (1.26 and 0.87) in the northern and western part of Iran. The highest percentage of drought occurrence in scale 12; Bandar Abbas and Bushehr stations respectively with drought frequency of 24.30 and 14.83, Ahvaz with drought severity of 18.47, Kerman with 6.74 percent of drought frequencies in the south and southwest of Iran and the lowest in the 6-month scale; stations of Birjand (1.70), Bojnurd (66.6), Urmia (1.17), and Tabriz (66.2) in the northwest of Iran, Rasht (0.58), Sari (0.78) in the northern part of Iran.
پژوهشی
Meysam Yari; Somayeh Soltani-Gerdefaramarzi; Mohsen Ghasemi; Rouhollah Taghizadeh
Volume 6, Issue 21 , March 2020, Pages 203-225
Abstract
1-Introduction Given the growing population and the increasing need for food, water and soil conservation are of great value. In the context of conservation of soil and water resources, information on the amount of runoff production and erosion to achieve sustainable development is the basis for planning ...
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1-Introduction Given the growing population and the increasing need for food, water and soil conservation are of great value. In the context of conservation of soil and water resources, information on the amount of runoff production and erosion to achieve sustainable development is the basis for planning and decision making. Therefore, careful investigation of surface runoff and floods is an important and key step in planning and managing optimal water resources. One of the factors affecting the characteristics of surface runoff is land-use changes at different basin levels (Melesse and Shih, 2002). Land use is influenced by two components of human needs and environmental processes. Inaccurate land-use changes will disrupt the water cycle from natural equilibrium, resulting in devastating floods, including economic damage, loss of life, loss of water, and consequently reduced water resources (Jakeman et al., 2005). During the last two decades, the Qhareh-su watershed, particularly its downstream, has been experiencing rapid growth in the construction and expansion of residential structures. Human activities and changes in the basin have affected the natural arrangement of stream processes that transmit water and sediment from upstream to downstream. Human interventions are one of the major hazardous issues in this basin that causes changes in the pattern of surface currents and natural conditions of the catchments and encroachment on rivers and streams. In this regard, the present study aimed to investigate the role of land-use change on runoff in a part of Qhareh-su watershed in Ardebil province over a period of almost 20 years due to the availability of information and access to satellite images of different time periods. 2-Methodology The study area consists of a part of Qhareh-su watershed located in Ardabil province with an area of 2162.6283 km2. The minimum and maximum elevation of the mentioned watershed are 1280 and 3829 m respectively, and its average slope is 11.57%. Land use in this area often includes dry and irrigated agriculture, pasture, forest, and residential areas. The aim of the current research is to study the effect of different land uses and its changes during the years 1992-2012 on the surface runoff in a part of Qhareh-su, Ardabil watershed. At first, the maps of land use and curve number in the mentioned years were gathered and the area of each of the units was extracted. In the following, the process of land-use changes in the cases of the study period and its effect on changing the specific retention (S) and curve number were calculated and the height of runoff was estimated using the SCS method. 3-Results The results showed that during the case of the study period, area of forest, water farming, and wasteland land uses were decreased by 2.54%, 16.69%, and 1.19% respectively and the area of the rangeland, dry farming, and urban land uses were increased by 5.74%, 12.39%, 2.29% respectively. These changes have caused the increase of curve number from 78.57 to 79.77 in the years 1992 and 2012, respectively and following the decrease of the specific retention (S) from 69.28 mm in the year 1992 to 64.42 mm in the year 2012. Also, runoff height has increased from 263.4 mm in the year 1992 to 297.07 mm in the year 2012 (11.33%). Calculation of correlation coefficient between different land uses and curve number and runoff height showed that these variables have a direct relationship with rangeland, dry farming, and urban land uses while they have an inverse relationship with the forest, water farming, and wasteland. 4-Discussion and conclusion In the present study, the results of the study showed that land-use change due to its effect on the curve number of the studied basin causes a change in the surface runoff. During this 20-year period, land use has changed and this land-use change has tended to decrease from 1992 to 2012 land use including residential, pasture and dryland areas increased by 2.29%, 12.39% and 5.74% respectively, as well as forest, water and wastewater land use decreased by 2.54%, 16.69% and 1.19%, respectively. As a result, its curve number has increased, followed by a runoff height of 11.33%. This shows that in a natural ecosystem, land use and environmental changes, especially vegetation and land use affect the hydrological responses such as flooding and erosion and sedimentation rate in the area. Ultimately, it will cause severe economic and social damages. Changes in the total volume of runoff and changes in hydrological balance are the most important effects of land-use change on watershed hydrology.
