habibeh Abbasi; Mohammad Taghi Aalami; Mohammad faraji
Abstract
This article aims to analyze the trend of monthly, seasonal and annual changes in the flow and sediment of the Mordaghchai. located in East-Azerbaijan province. In this regard, using non-parametric methods, discharge and sediment data of Gheshlagh-Amir hydrometric station have been analyzed in three ...
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This article aims to analyze the trend of monthly, seasonal and annual changes in the flow and sediment of the Mordaghchai. located in East-Azerbaijan province. In this regard, using non-parametric methods, discharge and sediment data of Gheshlagh-Amir hydrometric station have been analyzed in three time scales: annual, seasonal and monthly. The modified Mann-Kendall test was used to analyze the trend of gradual changes in discharge and sediment data. Also, the Sen's slope estimator was used to estimate the slope of the trend line and the non-parametric Pettitt test was used to investigate the abrupt changes in the discharge and sediment time series. The modified Mann-Kendall test was used to analyze the trend of gradual changes in discharge and sediment, and the Sen' slope estimator test was used to estimate the slope of trend line. Also, Pettit test was used to investigate abrupt changes in the river discharge and sediment time series. The results show that annual, monthly and spring, summer and winter discharges significantly decrease at the level of 5%. The annual and all-season sediment load data significantly decreased by 5%. There is a significant decrease in sediment load in all months except March, April and October. The results of the Pettitt test show that the average annual discharge in the period after the breaking point (1998) has decreased by 45% compared to the period before the breaking point. Also, the average annual sediment load after the breaking point (1996) has decreased by about 52% compared to the previous period.
hydrogeology
hamzeh saeediyan; Hamid reza Moradi; abdal salehpoor
Abstract
1-IntroductionSoil infiltration situation indicates soil behavior against water reaching the soil surface. This phenomenon determines the amount of both the water reaching the soil surface and rainfall losses. Soil infiltration of a basin has unique parameters based on its climate, soil conditions, and ...
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1-IntroductionSoil infiltration situation indicates soil behavior against water reaching the soil surface. This phenomenon determines the amount of both the water reaching the soil surface and rainfall losses. Soil infiltration of a basin has unique parameters based on its climate, soil conditions, and buildings. Soils are a set of discontinuous particles among which pores exist so that water can move from a point with more energy to a point with less energy; this property is called the passage of water through continuous pores. Gachsaran marl formation has a thickness of about 1600 m and consists of salt, anhydrite, colorful lime marl, and some shale from a lithology point of view. The age of this formation is lower Miocene (Ahmadi, 1999: 714). Estimation of soil infiltration using various erosion components can be a useful method to determine soil infiltration in the shortest time and at the lowest cost.2-MethodologyIn this study, soil infiltration was estimated using erosion different components in different land uses in deposits of Gachsaran formation by selecting a part of the Kuhe Gach watershed of Izeh city with an area of 1202 hectares. The relationship between soil infiltration and erosion different components, such as sediment rate, runoff rate, and runoff and erosion threshold, in different land uses of Gachsaran formation was determined by the multivariate regression. Then, different erosion components were sampled at six points with three replicates and different rainfall intensities of 0.75, 1, and 1.25 mm/min in three land uses of rangeland, residential area, and agricultural land using a rainfall simulator. SPSS and Excel software was used for statistical analysis. A portable Kamphorst rainfall simulator used in this study has a plot size of 625 cm2, which determines the characteristics of soil, erosion, and water infiltration, and is suitable for soil research. It is used as a standard method to determine the soil infiltration of surface deposits in the field. The experimental plot area was selected 625 cm2 with a smooth gradient. The preparation of the testing area was followed by installing and setting the rainfall simulator and then starting a chronometer upon observing the precipitation on the screen. The amount of plot infiltration was determined at 10-min intervals (Kamphorst, 1987: 407).3-Results and DiscussionThe estimation of soil infiltration was acceptable and appropriate in some models in this study, which have a lower regression coefficient. Therefore, it is not possible to make appropriate comments about the estimation of the models only using regression coefficients and other statistical coefficients nor the significance levels of observational and estimated data as well as the minimum square mean of errors (MMSEs); in some cases, the MMSEs are not sufficient and require more studies (Jain and Kumar, 2006: 272). Despite scientific advances and improvement of measuring equipment, regression models are still used by researchers in different fields due to simplicity.4-Conclusions The results showed that the most positive and negative effects of different erosion components on estimating soil infiltration were related to sediment rate, runoff, and erosion threshold in all three mentioned land uses in three precipitation intensities (0.75, 1, and 1.25 mm min). Meanwhile, the role of sediment rate in estimating soil infiltration was slightly higher than runoff, and erosion threshold and runoff rate had no role in estimating soil infiltration in this method due to a high correlation of data.
maryam bayatikhatibi
Abstract
1-IntroductionIn the drainage basins of arid and semi-arid areas where the ecosystem is not able to recover quickly, extreme care should be taken with land use. The hydrological effects of changes in land use are manifested in the form of changes in runoff depth, minimum flow, maximum flow, soil moisture, ...
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1-IntroductionIn the drainage basins of arid and semi-arid areas where the ecosystem is not able to recover quickly, extreme care should be taken with land use. The hydrological effects of changes in land use are manifested in the form of changes in runoff depth, minimum flow, maximum flow, soil moisture, and evapotranspiration. Increasing runoff production in a particular area, in addition to increasing the potential for flooding, has other effects. Due to the type of soil and the topographic and climatic characteristics, the hydrogeomorphological changes caused by human encroachment on slopes and land use changes have been significant in Ojan Chai area (from the sub-basins located on the eastern slopes of Sahand Mountain). Due to erosion in the slopes of Ojan Chay area, it seems that the changes in the amount of runoff are very significant due to land use changes in the area. The study area is one of the rangelands of the country and unfortunately, cultivation is done in an unprincipled manner in the slopes that are not suitable for cultivation. In the coming days, the turbulence of the slopes will be intensified, the amount of runoff will increase, and the number of destructive floods will increase. Often, the soil of the slopes is severely eroded by runoff due to the extreme cultivation in the rangelands.2-MethodologyTo simulate the effects of land use change in a region or watershed, there are many hydrological models, one of which is the L-THIA. This model is a way to evaluate the long-term hydrological effects in a basin by which relative changes that occurred due to a change of use in the runoff can be simulated.The above model is a good tool to help measure the potential effects of land use change on surface runoff. This model is based on the Curve Number (CN) method of the US Soil Conservation Organization (SCS). Expresses CN values range between 0-100, where high values are for urban uses and low values are for areas with high permeability, such as wetlands and pastures with high vegetation density. One of the benefits of L-THIA is that it does not require calibrating the model with real area data. Model calibration is performed automatically using various default CN combinations available in L-THIA GIS. In this paper, to use the L-THIA model, station precipitation was prepared and Landsat satellite images (TM and ETM sensors) and specialized L-THIA software and Arc Map were used. In addition, the probability of a pixel being placed in a particular class is calculated, then the probability of its placement in other classes is estimated and classified according to the maximum similarity (maximum probability) in one of the classes. The above process is expressed based on Equation 1. (Eq.1). Where P (X) is the probability of the presence of the class wᵢ in the image, / x) wᵢ P (probability of each pixel with the spectral characteristic x belonging to the class wᵢ and p (wᵢ / x) the probability of belonging of each pixel with the spectral characteristic x appearing in the image Class wᵢ and p (X) is the probability of the presence of a pixel with a spectral characteristic. The error matrix, kappa coefficient and overall accuracy are used to evaluate the classification accuracy of the images using Equation 2.(Eq. 2). Where OA is overall accuracy, N is the number of experimental pixels, Pii∑ is the sum of the elements of the original diameter of the error matrix.The kappa index is calculated from Equation 3.(Eq. 3). Where po correctly observed, pc shows the expected agreement. The error matrix shows the interference or conversion of uses to each other. Land use maps have been prepared for two periods (1988, 2018) as well as land use change maps. 3-Results and DiscussionIn this research, using THIA L- model, the type of soil was determined according to the available soil maps, prepared samples, soil reports of studies of other organizations and field experiences, soil hydrological group in the study area as the basis of the model used. In the prepared map, it is clear that the range of hydrological group A is observed in the southern and southwestern parts. The area related to hydrological group B is mostly scattered in the northern, northeastern, and central parts. Hydrological group C is spread around the flood plains in the central part of the basin, and finally hydrological group D, which is the largest part of the basin surrounding Ojan largely.According to the land use map of 1988, the largest area is related to rangeland use with an area of 544.6575181 square kilometers and the smallest area is related to water use equal to 0.189899975 square kilometers. According to the land use map of the year 2018, the largest area is related to agricultural use with an area of 510.5889519 square kilometers and the smallest area is related to road use equal to 0.5715 square kilometers. Examination of runoff depth maps for 1988 and 2018 shows that significant changes have been made in terms of quantity and location. Examining the height of runoffs and comparing two different periods in a specific use in relation to changing the rainfall parameter shows that a change in the rainfall parameter can significantly increase runoff in agricultural areas. This situation in relation to the range of the gardens is different, especially in recent years, showing a complex situation. In the case of pastures between 2018 and 1988, there is no significant difference in the height of runoff. Runoff depth in different land uses and rainfall shows that in areas with low rainfall, the highest runoff height is seen in lands under agricultural use. With increasing rainfall, pastures produce the most runoff and again with increasing rainfall, the highest runoff production is related to agricultural lands. In agricultural lands, the amount of runoff has increased in three decades and decreased in pastures.4-ConclusionThe results show that over the past three decades, many rangelands have been cultivated. The area of agricultural lands has increased from 368.4917957 square kilometers in 1988 to 510.5889519 square kilometers in 2018. The results of calculations in such lands show that the height and volume of runoff has doubled from 1988 to 2018. In fact, increasing the area of cultivated land and land use changes from pasture to agricultural land has increased the amount of runoff. The results of studies on soils located on slopes show that the hydrological group of soils in this area is impermeable and with maximum daily rainfall that has increased in recent years, they can produce high-volume deep surface runoff in a short time. These slopes were considered pastures in 1988 (about 90 square kilometers of pastures have been converted into agricultural land). This has caused row crops to produce more runoff in these areas. The results of the studies with the model used and the result of this research in the area of Ojan Chay basin show that the main reason for the increase in height and volume of runoff was land use changes.Keywords: Land use changes, Runoff, Erosion, Flood, L-THIA model, Ojan Chay basin5-ReferencesKhaligi, B., Mahdavi, M., Sagafiyan, B. (2005). Investigating the effect of land use change on flooding using NRCS model, Natural Resources of Iran,vol,58,No,4,p 41-58.Razvizadeh, S., Salajegehe, A., Khaligi, S., Gafari, M. (2014). Investigating the effect of land use change on flooding using, HEC-HMS model (case study: Taleghan watershed) Journal of Rangeland and Watershed Management, Vol. 66, No.3, pp 373-386.Sadati, H, Golami, S., Sharifi, F., Ayobzadeh, A. (2008). Investigating the effect of land use change on runoff, Journal of Rangeland and Watershed Management, Vol. 4. No. 3, pp 301-315.-Hentati, A., Akira Kawamura, Hideo Amaguchi, Yoshihiko Iseri. (2010).Evaluation of sedimentation vulnerability at small hillside reservoirs in the semi-arid region of Tunisia using the Self-Organizing Map, Geomorphology, No. 122, 56–64-Kakembo,V., W.W. Xanga, K. Rowntree.(2009).Topographic thresholds in gully development on the hillslopes of communal areas in Ngqushwa Local Municipality, Eastern Cape, South Africa, Geomorphology, No. 110.188–194-Khairulmaini Osman Salleh and Fatemeh Mousazadeh.(2011).Gully erosion in semiarid regions,Procedia Social and Behavioral Sciences No.19, 651–661.Vahidi, Mohammadjavad; Rasoul Mirabbasi Najafabadi; Mohsen Ahmadi. (2020). Analysis and ranking of soil erosion prevention methods using multi-criteria decision-making methods in rural areas of Darmian County, South Khorasan, Hydrogeomorphology, Vol. 6, No, 23.209-233.Yamani, Mojtaba, Hamid Ganjaeian; Lila Garoso; Mahnaz Javedan. (2020). Identification of susceptible areas for the development of agricultural lands based on parameters Hydro geomorphology (Case study: Sanandaj city), Hydrogeomorphology, Vol. 6, No, 23.1-20.
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.
Behrooz Sari sarraf; Tahereh Jalali Ansaroodi
Volume 6, Issue 19 , September 2019, , Pages 163-185
Abstract
Introduction In the recent decades, the growth of the industrial activities and the increase in greenhouse gases have imbalanced the Earth's climate which is called the phenomenon of the climate change. This phenomenon directly affects the hydrological parameters. While climate change directly affects ...
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Introduction In the recent decades, the growth of the industrial activities and the increase in greenhouse gases have imbalanced the Earth's climate which is called the phenomenon of the climate change. This phenomenon directly affects the hydrological parameters. While climate change directly affects surface water resources through changes in the major long-term climate variables such as air temperature, precipitation, and evapotranspiration, the relationship between the changing climate variables and groundwater is more complicated and difficult to quantify. The large amount of water is needed in different parts of arid and semi-arid regions provided through groundwater resources. In recent decades, the quantity and quality of water resources have been reduced by unprotected exploitation. In addition, climate change and global warming increase the severity of the problem. Therefore, the predicted effects of climate change on groundwater recharge play an important role in the management of these resources in the future. In this study, Global circulation models, HadCM3 under A2 and B2 scenarios, were used for investigating the impact of climate change on groundwater recharge rates between 2017 and 2030, in the Tasouj aquifer. Methodology In this study, to investigate the climate change in Tasouj basin, the required data were obtained from two sources including Global model output AOGCM which was based on the HadCM3 model and the observed data of the precipitation and temperature of Tabriz synoptic station with the statistical length of 1961 to 2016. To downscale the general circulation modal, the statistical method of SDSM was used. The Hydrologic Evaluation of Landfill Performance model (HELP) simulates all of the important processes in the hydrological cycle including surface runoff, evapotranspiration, vegetative growth, soil moisture storage, and vertical unsaturated drainage for each discrete layered soil column. In general, the modeled hydrologic processes by the program can be divided into two categories of surface and subsurface processes. The modeled surface processes are snowmelt, interception of rainfall by vegetation, surface runoff, and evaporation of water. The modeled subsurface processes are evaporation of water from the soil, plant transpiration, vertical unsaturated drainage. Vegetative growth and frozen soil models were also included in the program to aid modeling of the water routing processes. The required general data included growing season, average annual wind speed, average quarterly relative humidity, monthly normal mean temperatures, maximum leaf area index, evaporative zone depth and latitude. Result According to the simulation of Hadcm3 model, during the period of 2017-2030, the average monthly temperature in all months of the year will increase in the studied area. The highest amount of heating in the average temperature will happen in July about 2 degree Celsius. The highest decrease in precipitation will occur in April and May about 9 mm than the base period. The highest percentage of precipitation in Tasouj basin is used for evaporation. During 14 years of the prediction, the year 2020 has the highest and the year 2029 has the lowest amount of evaporation. In terms of runoff caused by precipitation, the year 2023 with 9.69 percent of precipitation will have the highest runoff. The lowest and highest amount of recharge will respectively happen in 2021 and 2027. The depth of water precipitation is significantly affected by soil moisture and with increasing soil moisture; the depth of water percolation to soil will decrease. The soil moisture content is negative in 2027. Consequently, the highest amount of recharge due to precipitation will happen in Tasouj basin. In the base period, the year 1990 had lowest precipitation and the year 1963 had the highest precipitation. Due to having a negative soil moisture storage in 1990, of 148 mm of annual precipitation, about 76.28 mm was spent for recharge. The amount of runoff is almost zero in this year and the rest of precipitation is evaporated. Despite the high annual precipitation in 1963, due to the high moisture content of the soil, the amount of recharge is only 4 percent of precipitation and most of the precipitation changes to runoff and evaporation. The status of evaporation, runoff and recharge in 2022, as the forecasted most precipitation year, is similar to 1963. Discussion and conclusion In recent years, the climate change has led to significant changes in the weather and the condition of surface and underground water resources in different locations. The response of the groundwater resources to drought and climate change is not as rapid as that of the surface water, but considering that the renewability of these resources takes much longer than that of the surface water, the impact of long-term drought on groundwater resources is much more serious than that of the surface water resources. Therefore, the monitoring of the condition and maintenance of the sustainability of these resources is important. In this way, by using a step by step approach, the impact of climate change on recharge, evaporate, and runoff for the 2017-2030 period was investigated and the simulation result showed that with increasing temperature and decreasing precipitation, of three parameters of evaporation, recharge, and runoff, the evaporation dominated the other parameters. But the high consumption of basin and the increase of temperature and precipitation decrease prevented Tasouj aquifer from returning to its balance. Therefore, a principle planning to control the harvest and treatment of aquifer by underground dam and artificial nourishment is necessary
Hadi Nayyeri; Khabat Amani; Hamid Ganjaeian
Volume 3, Issue 7 , October 2016, , Pages 19-38
Abstract
Hadi Nayyeri[1]* Khabat Amani[2] Hamid Ganjaeian[3] Abstract The rivers physical and morphological properties survey and study is one of the first and most important actions in hydrological plans design and implementation. The aims of this research is Tarval drainage basin physical, hydrological, hydrographic ...
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Hadi Nayyeri[1]* Khabat Amani[2] Hamid Ganjaeian[3] Abstract The rivers physical and morphological properties survey and study is one of the first and most important actions in hydrological plans design and implementation. The aims of this research is Tarval drainage basin physical, hydrological, hydrographic and hydro geomorphology's traits surveying using software and statistical methods in order to access the appropriate information's to planning and implementing the constructions and watershed management plans. Tarval stream is the Caspian Sea sub basin that its drainage basin area from confluence location with Ghezel Owzan is 6955 km2. According to present statistics from 1971 to 2011 years the annual average of meteorology and synoptic basin temperature are 12.5 centigrade degree and annual precipitation is 352 mm that shows semi-arid situation of basin climatologically. The result shown that the drainage net densities in this basin is low and the number of streams per unit area is few. By considering the study area dispersal coverage and is some cases are high-density, the runoff coefficient is 0.35 percent and the basin delay time is 1.65 hours, and its time of concentration is 2.75 hours. The results shows that by considering the factors such as precipitation rate, basin low slope, little discontinuous seed sediment, the basin runoff amount is very low and precipitation of this area speedily drops down. For this reason the soil erosion percentage in this basin is so little and be controllable. In addition, the flood debit curvy changes with time passing have a slight curve that represent the basin immunity against flooding. [1]- Assistant Professor in Dept. of Geomorphology, Faculty of Natural Resources, University of Kurdistan, (Corresponding Autor), Email:nayyerihadi@yahoo.com. [2]- Student Hydregeomorphlogy, University of Tehran. Graduate Student Hydregeomorphlogy, [3]- Student Hydregeomorphlogy, University of Tehran. Graduate Student Hydregeomorphlogy,
