پژوهشی
Mehdi Hayatzadeh; Sahar Amini; Ali Fathzadeh; Maryam Asadi
پژوهشی
Gholam hassan jafari; Mina Avaji
Abstract
1-IntroductionEarth's climate is one of the most important structural factors. More natural and human trappings are affected by the weather. The coefficient of variation of less precipitation is reagent stability and steady time distribution (Fatahi, & Rezaei, 2009). The quaternary climatic changes ...
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1-IntroductionEarth's climate is one of the most important structural factors. More natural and human trappings are affected by the weather. The coefficient of variation of less precipitation is reagent stability and steady time distribution (Fatahi, & Rezaei, 2009). The quaternary climatic changes have created different landscapes, such as glacial circus, glaciers, and especially erratic rocks, according to the first topography of Iran that found in high regions of Iran. Geomorphologists' permanent snowline altitude is determined to help the circus of mountain effects. The continuous snow line, altitude above which or latitude beyond which snow does not melt in summer (Ramesht, & Shah Zidi, 2011). The temperature conditions quaternary reconstruct based on a permanent snowline altitude temperature difference compared to today. They also estimate the maximum expansion of tabs by ice moraines, the erratic rocks, valleys of glacial sediments and granulometry, and its height to consider as ice and water equilibrium line altitude. The water and ice equilibrium line is where ice flows entirely replace the water ice flows wholly replacing the water. The quaternary climatic changes, according to particular topography of Iran, have inherited different figures and landforms such as glacial circus, glaciers, and especially wanderer rocks. We cannot analyze by changing one element changes made; a complex mix of elements change been led to changes in the process and enduring numerous landforms. Any anomalies in each component s will cause defects and commotion in the whole system. 2-MethodologyAccording to the geomorphological landform, the remaining lake last is one of the methods of forecasting and estimating their condition. The Climatic factors role has particularly essential in the current situation of the Iranian domestic water hole. We used to examine the relationship between climatic factors and its effect on local lakes, dewatering of the temperature and precipitation data of the 50-year-old Asfazari database in cells 15 x 15 km (Masoudian, 2012). In addition to measurements of temperature and precipitation of central tendency, indexes used of dispersion indexes in statistical processes (Standard deviation and Coefficient of variation). Since the standard deviation is not used to compare the distribution of both characteristic varies with different units, the coefficient of variation (CV) used. Since most time, the Earth's surface has a temperature higher than the surrounding air, in this study were rainfall receiving below two degrees Celsius on the Basin. The coefficient of snow by reducing the temperature was estimated. With put, the factors in its relations appropriate amount of heat and precipitation determined in the Quaternary cold periods. We expect the coefficient of snow and temperature changes and precipitation decrease of 3, 6, and 9°C temperature for sub-study briefly. We cannot continuously study the effect of all elements and factors related to Quaternary climate changes. Still, we are trying to interpret the lake volume fluctuation due to climate change as a system through changes in temperature, precipitation, the coefficient of rain below 2°C, the ratio of variation coefficient of variation of temperature and precipitation.3-Results and DiscussionThresholds obtained show that the basin con ensures the exit of the lake that to be the average temperature of less than 15.78, average annual rainfall more than 215 millimeters, snowy coefficient more than 13 percent, the coefficient of variation of precipitation less than 40 percent and the ratio of difference of temperature more than 7.43. All basins inside Iran changed to a temperature not fit in the Quaternary. Status and evidence there are of lakes dewatering in the Quaternary do not match whit precipitation double and decline of temperature 6-12 degrees compared to the current conditions. So that line equilibrium of water and land could be the effects of the Quaternary terrace lacks by reducing three, nine, and 12-degree temperatures and increased precipitation. Change the line equilibrium of water and land cannot interpret with a temperature and precipitation changes alone, and causality of these changes in the line equilibrium of lakes water and soil must search in changes of precipitation regime and geomorphology of the region4- ConclusionsTo investigate the impact of climate parameters on the dewatering amount of water, we used primarily average of them. Accordingly, they are receiving basins Maharloo (375), Urmia (372), and Meighan (314 mm) maximum and basins Yazd (92), Bafgh (95), and Ardestān (114 mm) minimum of basin precipitation average. The basin has a higher temperature water demand more. If they receive Precipitation equal, drought intensity increases, the average temperature of pond, and their condition are such that allocated the lowest temperature to the basins of Urmia, Meighan, Gavkhoni, and the highest temperature to the lakes Qom and basins Jazmurian, Lute, the Bafgh and Qatruyeh. Basins of Urmia and Meighan have the best conditions. The basins Bafgh and Yazd have the worst conditions dewatering in terms of combining two elements of climate, temperature, and precipitation. These parameters alone will not be able to estimate the dewatering basins' performance reliable be due to the difference in average temperature and precipitation in the basin. Therefore, we used other vectors such as the coefficient of variation of climate (temperature and precipitation) and the coefficient of rain below 2°C. Investigation and compared the ratio of precipitation below 2°C on precipitation full in fourteen basin studies represents that the basins Meighan 21%, Urmia 20.8% and Qom 20.4% allocated to the most extensive and basins Jazmurian 1% and Qatruyeh 0.6% accounted to the lowest percentage of precipitation in below 2°C. To estimate sufficient rainfall in dewatering lakes, we can put number 40 in equation (2) instead of CVp, and we expect threshold precipitation of the basins. Number 40 is a threshold effect coefficient of variation Precipitation in dewatering lakes.Keywords: Quaternary, Climate, line equilibrium of water and land, Interior Lakes of Iran. 5-ReferencesFatahi, E., & Rezaei, T. (2009). Pattern of Daily Climate Circulation on Iran, Journal of Geographical Research, Isfahan University, No. 93: 45-74Masoudian, A. (2012). Iranian Climate, Sharia Toos Publications, Isfahan.Ramesht, M., Shah Zidi, S. (2011). Geomorphology Application in National, Regional, Economic, Tourism Planning, Second Edition, Isfahan University Press.
پژوهشی
Mohammad Saeidi; Mehdi Komasi; Shahab Hasanpor
Abstract
Over the past few decades, as a result, population growth, industrialization, urbanization, etc., demand for water has increased, most of these requirements have provided by exploiting groundwater resources. Therefore, the uncertainty in the demand and supply of water should be minimized by proper groundwater ...
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Over the past few decades, as a result, population growth, industrialization, urbanization, etc., demand for water has increased, most of these requirements have provided by exploiting groundwater resources. Therefore, the uncertainty in the demand and supply of water should be minimized by proper groundwater management, by identifying areas with groundwater potential. In this study, it has been attempted to find the potential groundwater resources in Silakhor plain using combined Analytical Hierarchy Process (AHP) and fuzzy TOPSIS method in GIS environment. In this regard, eleven thematic layers including layers of lithology, rainfall, vegetation cover, lineament density and distance, elevation, slope, land surface temperature, land use and drainage density and distance were prepared based on satellite image processing and statistical data, used to create a groundwater resource potential mapping. Groundwater resource potential map was classified into five categories including high, good, medium, low and very low potential. Accordingly, the high to moderate potential sites are located more in the center and southwest of the plain and correspond to quaternary alluvial and carbonate hard rocks zones. Validation was done by the number of wells in the area and the results indicate that the use of an integrated approach AHP and Fuzzy TOPSIS methods in groundwater potential mapping with the location of the wells is in good agreement, about 87% of the wells are located in areas with moderate to high groundwater potential.
پژوهشی
Hamid Amounia; Siavosh Shayan; Mojtaba Yamani
Abstract
1-IntroductionBeaches, due to environmental (natural-human) conditions, have many changes in the spatial-temporal dimension. Due to this fact, coastal areas are really important. Beaches are part of the complex system of the planet Earth, which occupies only 10% of the total area of the universe ...
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1-IntroductionBeaches, due to environmental (natural-human) conditions, have many changes in the spatial-temporal dimension. Due to this fact, coastal areas are really important. Beaches are part of the complex system of the planet Earth, which occupies only 10% of the total area of the universe (Cai et al., 2009). The majority of human beings choose beaches as their habitat so that about 60% of communities are located in coastal areas (Cracknell, 1999). Shoreline is an extension where exactly seawater intersects with land (Bird, 2008: 2). In international waters, the shoreline is defined as the line that connects the mean points between the maximum tide and the minimum tide. Regarding the dynamic nature of water and land, the coastline situation is not always stable in the short or long term. These shoreline changes can have adverse effects on the environment, natural resources, ecosystems, socio-economic, cultural, and ultimately defense security (Thoai, et al., 2019). A change in coastal land-use patterns can directly affect changes in coastal position (Griffiths, 1988). The coastline can change due to erosion and sedimentation (Rio et al., 2013) and by changing the pattern of land use near the coast, erosion or sedimentation occurs which leads to a change in the coastline (Erickson, 2006; Ahmed, 2011). This study aims to compare the shoreline changes in the digital shoreline analysis system and land use maps for 42 years, between the shoreline changes with the development of human activities and land uses, and then to analyze the relationship between the changes. Coastal land use during periods of the impact of Caspian Sea level fluctuations on coastline changes.2-MethodologyThe data used in this study are in two parts:Sea level data that have been used to draw the Caspian Sea level chart of and its basis is Anzali.Data including Landsat satellite images on TM and OLI sensors have been used to map historic coastlines and map coastal land uses. After registration, these images were downloaded from https://earthexplorer.usgs.gov. Sea level data was processed and analyzed with the help of Microsoft Office Excel software, and data related to satellite images were pre-processed and processed by Envi5.3 software. Coastline analyzes have been performed in GIS software (ArcGIS10.7) as well as the Coastal Line Digital Analysis System (DSAS) plugin. The present research method is analytical - comparison between data and sea-level information, shoreline changes, and land use maps. The land is 42 years old. After receiving the data from the Caspian Sea Research Center (CASPCOM), sea level data have been used to show the trend of changes in the Caspian Sea level at Anzali station.3-Results and DiscussionIn this study, first, the findings related to shoreline changes extracted through a digital analysis system; were analyzed and interpreted, and then the findings related to coastal land uses were presented and these findings were also interpreted.To study the changes in the Babolrood coastline, regarding the trend of fluctuations in the Caspian Sea water level, the periods 1976 to 1995 have been selected as the period of increasing the level and 1995 to 2017 as the period of decreasing the level of water. In the first period, according to the Net Shoreline Movement (NSM) statistics in the shoreline digital analysis system, all transects along with the shoreline show negative numbers. This means that in this period, the coastline has retreated to the mainland, and in this way, in this period, the coastal lands have been associated with a decrease in area. In the second period, when the trend has been decreasing, the majority of the Net Shoreline Movement (NSM) statistics are positive. In the map of these two periods, which shows the trend of changes during the interval; In the first period, in the whole range of the level trend, the level increase was the same as the shoreline movement process, which varies from -139 meters to -33 meters. But in the second period, it is observed that due to the decreasing trend of sea level, it has been receded. In the map from 1976 to 1994, shoreline movements show the same trend as sea level data. But in the map of 1994 to 2017, in some parts, such as the estuary of the Babolrood River, where people have made changes in the coastline by constructing piers, the coastline has receded at a high level. This can show the relationship between land use and shoreline changes.In the present study, land use maps for the three years 1976, 1995, and 2018 have been prepared. After preparing the land use map and evaluating its accuracy, the area of six land use classes for each of the years in the study area was calculated. The results of the changes show that in the period from 1976 to 2018, the man-made use area and water compared to other uses has been increasing during this time, and in the meantime, the area of use of the rangeland has reached zero. To elucidate the type and percentage of changes from one use to another and to keep the same uses constant during the periods 1976 to 1995 and 1995 to 2018, diagrams of these changes have been drawn.The percentage change graph between 1997 and 1995; reveals that most of the area in this period includes the same man-made land uses. In the second period, i.e. from 1995 to 2018, this trend continued, although in this period, man-made lands had the largest area in the total area; but other uses (which had a smaller percentage of the area); have been transformed into man-made uses, with barren lands showing the greatest value during this period.A Diagram of the trend of land use changes reveals that man-made land uses have been increasing in both periods (first and second). Most other uses have become the same man-made uses at this time. This diagram also discloses the ineffectiveness of land uses from the fluctuations of the Caspian Sea water level fluctuations. Because, if this trend is affected, we should see a decrease in the area of man-made land uses, especially in the first period (when the shoreline progress conditions prevailed). The reason for this was a kind of shoreline management with the construction of dams and coastal walls.4-Conclusion(s)The findings of this study indicate the existence of a relationship between coastline changes and land-use changes and vice versa, they indicate no relationship, especially in the second period with the sea level elevation trend in the study area. In the study period, the water level of the Caspian Sea has an upward trend (1976 to 1995) and a downward trend (1995 to 2017). The trend of changes extracted from the drawing of coastlines in the same years and their digital analysis shows the lack of coordination between some of these trends with the way forward and backward coastline in the study area. From the combination of two diagrams of sea level and man-made use, it can be seen that this lack of coordination also exists in this field. More importantly, it has been determined that man was able to manage the coastline in his favor during these 42 years by creating constructions. In a way, man has been able to succeed against the advance of the sea towards the land.Keywords: Caspian Sea shoreline, land-use, DSAS, Babolrood, Babolsar.5- References Cai, F.; Liu, J.; Bing, L.;& Gang. L (2009) Coastal erosion in China under the condition of global climate change and measures for its prevention. Progress in Natural Science, 19(4), 415-426.Cracknell, A.P. (1999). Remote Sensing Techniques in Estuaries and Coastal Zones- an Update, International Journal of Remote Sensing, 19(3), 485-495.Thoai,D.T; Dang, A.N; & Oanh, N. T. K. (2019).Analysis of coastline change in relation to meteorological conditions and human activities in Ca mau cape, Viet Nam. Ocean & Coastal Management, 171(1), 56-65.Griffiths, C.J. (1988) The impact of Sand Extraction from Seasonal Streams on Erosion of Kunduchi Beach. In Beach Erosion along Kunduchi Beach, North of Dar es Salaam; A Report for NEMC by Beach Erosion Monitoring Committee, 55.Rio, L.D.; Gracia, F.J.; & Benaventae, J. (2013).Shoreline change patterns in sandy coasts. A case study in SW Spain. J Geomorphol., 196, 252–266.Ericson, J.P.; Vörösmarty, C.J.; Dingman, S.L.; Ward, L.G.;& Meybeck, M. (2006). Effective sea-level rise and deltas: Causes of change and human dimension implications. J Glob Planet Change, 50, 63–82.Ahmed, A. (2011). Some of the major environmental problems relating to land-use changes in the coastal areas of Bangladesh. J. Geogr. Reg. Plan, 4,1–8.
پژوهشی
Batool Zeynali; Ehsan Ghale; Shiva Safari
Abstract
1-IntroductionOne of the most important water sources in mountainous areas is snow cover, which significantly affects the amount of runoff on the ground. Moreover, seasonal snow cover influences biotic components and water quality in rivers. Snow cover is one of the most important sources of fresh water ...
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1-IntroductionOne of the most important water sources in mountainous areas is snow cover, which significantly affects the amount of runoff on the ground. Moreover, seasonal snow cover influences biotic components and water quality in rivers. Snow cover is one of the most important sources of fresh water and affects the hydrological system of different altitudes in mountainous areas. Climate change has a major impact on the diversity of snow cover, thereby having adverse effects on snowmelt runoff and glacier mass balance. Remote sensing, due to its advantages, can control large areas with high spatial and temporal resolution. This technology provides the ability to quantitatively measure the physical properties of snow and water in remote and inaccessible areas where ground surveying may be expensive and dangerous. Therefore, it can be said that in basins with no accurate information on snow cover, this technology can be used to extract snow cover.2-MethodologyThe study area is Sabalan Mountain located in Ardabil province and its surroundings. In this study, Landsat 8 satellite images for 2020 and Landsat 5 images for 2000 were used for February due to the presence of sufficient snow to extract the snow-covered area. It was tried to select images with minimal errors. The images were mosaicked after ensuring the absence of common errors and atmospheric correction using the FLAASH model in ENVI5.3 software, then a part of the image was cut based on the research. In the eCognition software, the images were classified into three classes of water, soil, and snow using NDSI and NDSINW, then the classification result was transferred to ArcGIS software and the snow cover area was calculated. The NDSI was proposed based on the normalization of the green band difference and SWIR1 on MODIS images. NDSI and MNDWI are among the most widely used indices for implementing SCG maps. 3-Results and DiscussionIn this research, in order to obtain a snow cover map and its area, an object-oriented classification and NDSI and NDSINW have been used. The snow-covered areas extracted using the object-oriented method for the years 2000 and 2020 were calculated as 2500 and 1954 square kilometers, respectively. The values of 2557 and 1937 square kilometers were extracted as snow-covered area by applying NDSINW and 2610 and 2577 square kilometers were extracted by applying NDSI. The NDSI shows a larger snow and ice cover than it exists because it considers water as snow (Commission Error). Therefore, it is not suitable for distinguishing water from snow or extracting snow-covered area in areas where water exists. In contrast, the NDSINW is able to extract snow cover in areas with aquatic terrains because it uses near-infrared and middle-infrared bands and the difference between them in snow reflection to remove water-covered area. The classification maps were validated using samples taken from the satellite images and for both 2000 and 2020 images, overall accuracy coefficient and the kappa coefficient of the classification were estimated 0.99 and 99%, respectively.4-Conclusions In the present study, the object-oriented classification method was applied for detecting and extracting the snow-covered area based on the combination of optical bands on the Landsat 8 and Landsat 5 images of Sabalan region in Ardabil province. Then, the normalized difference snow index (NDSI) and the normalized difference snow index with no water information (NDSINW) were applied and the results of them were compared to identify the snow cover using the accurate object-oriented classification method. According to the results of the object-oriented classification map and the applied indices, it was found that both indices were able to extract snow cover compared to the object-oriented method in cold and winter area. However, the NDSI index had some error in extracting the snow-covered area due to not limiting aquatic terrains and water-covered areas and considering them same as the snow-covered areas, especially in areas where the presence of water is significant. Therefore, in areas with little or no water, it can be a very good index for extracting the snow-covered area.Keywords: Object Oriented Classification, Snow-covered Area, NDSI and NDSINW Spectral Indicators, Sabalan Mountain. 5-References Custodio, E., Cabrera, M.D.C., Poncela, R., Puga, L.O., Skupien, E., & Del Villar, A. (2016). Groundwater intensive exploitation and mining in Gran Canaria and Tenerife, Canary Islands, Spain. Hydrogeological, environmental, economic and social aspects, Science of the Total Environment, 557, 425–437.Donmez, C., Çiçekli, S.Y., Cilek, A., & Arslan, A. (2020). Mapping snow cover using landsat data: toward a fine-resolution water-resistant snow index. Meteorology and Atmospheric Physics. 10.1007/s00703-020-00749-y.Manickam, S., & Barros, A. (2020). Parsing Synthetic Aperture Radar Measurements of Snow in Complex Terrain: Scaling Behavior and Sensitivity to Snow Wetness and Land cover. Journal remote sensing, 12(483), 1-31.Parajka J., Holko, L., & Kostka, Z. (2001). Distributed modelling of snow water equivalent-Coupling a snow accumulation and melt model and GIS. Institute of Hydrology. Slovak Academy of Sciences, 14, 86-102.Sood, V., Singh, S., Taloor, A., Prashar, SH., & Kaur, R. (2020). Monitoring and mapping of snow cover variability using topographically derived NDSI model over north Indian Himalayas during the period 2008–19.Thomas, A.C., Reager, J.T., Famiglietti, J., & Rodell, M. (2014). A GRACE-based water storage deficit approach for hydrological drought characterization. Geophysical Research Letters, 41(5), 1537–1545.Voss K.A., Famiglietti, J., Lo, M., de Linage, C., Rodell, M., & Swenson, S. (2013). Groundwater depletion in the Middle East from GRACE with implications for transboundary water management in the Tigris-Euphrates-Western Iran region. Water Resource Research, 49: 27-39.Taylor, R.G., Scanlon, B., Doll, P., Rodell, M., van Beek, R., Wada, Y., Longuevergne, L., Leblanc, M., Famiglietti, J., Edmunds, M., Konikow, L., Green, T.R., Chen, J.Y., Taniguchi, M., Bierkens, M.F.B., MacDonald, A., Fan, Y., Maxwell, R.M., Yechieli, Y., Gurdak, J.H., Allen, D., Shamsudduha, M., Hiscock, K., Yeh, P.J.F., Holman, I., & Treidel, H. (2013). Ground water and climate change. Nature Climate Change, 3(4): 322–329.
پژوهشی
Farnaz Daneshvar Vousoughi; Rasoul Samadzadeh
Abstract
1-IntroductionNowadays water resources management is a vitally important task and is the optimum planning of irrigation projects, and the development and exploitation of water resources especially during drought and flood events are strictly dependent on the accuracy of the used rainfall-runoff modeling ...
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1-IntroductionNowadays water resources management is a vitally important task and is the optimum planning of irrigation projects, and the development and exploitation of water resources especially during drought and flood events are strictly dependent on the accuracy of the used rainfall-runoff modeling tool. Therefore, different models have been already developed and employed for modeling rainfall-runoff processes of the watersheds (Partovian et al., 2017).The wavelet-based pre-processing approach in the present study was used in the modeling of runoff time series via ANN. Furthermore, the impacts of denoising (smoothing) and wavelet transform have been simultaneously investigated in the accuracy of runoff prediction for one month ahead at the outlet of Ardabil plain.2-Methodology2-1-Case of the StudyThe plain of Ardabil (38 – 38 N and 47 – 48 E), locatedin north-western Iran, covers an area of about 990 km2 (see Fig. 1). In the present study, the trend analysis was carried out on the rainfall (P) and runoff (R) parameters for three stations including Samian (PS, RS), Gilandeh (PG, RG), and Kozatopraghi (PK, RK)) located in the Ardabil plain from 1977 to 2019. The data sampling has been reported in the one-month interval at all of the stations. Figure 2 shows the locations of the rainfall and runoff stations. In this study, five combinations of input data were consumed for runoff prediction as to the following:Comb. 1: RS(t), RS(t-1), PS(t); Comb. 2: RS(t), RS(t-12), RS(t-24), PS(t); Comb. 3: RK(t), RG(t), RG(t-1), PG(t-12), RK(t-12) Fig. (1): Case of the study and the position of rainfall and runoff stations.2-2-Artificial Neural Network (ANN) Three-layered feed-forward backpropagation, which is usually used in forecasting hydrologic time series, provides a general framework for representing the nonlinear functional mapping between a set of input and output variables.2-3-Wavelet transform (WT)In hydrological problems, the time series are usually in the discrete but continuous format; therefore, the discrete WT was used in the following form (Mallat, 1998):(2) 2-4-Wavelet based de-noisingWavelet de-noising technique is operated as follows: (1) an applicable mother wavelet and several resolution level methods are selected. An approximation subseries at the resolution level L and detailed sub-series at different resolution levels are decomposed from main time series xi (2) The absolute amounts of detailed-sub-series, which exceed the values of the fixed threshold are changed by the difference between the values of threshold and detailed sub-series.2-5- Efficiency criteria in runoff predictionTwo different criteria were used to measure the efficiency of the proposed forecasting methods; the root means square error(RMSE) and the determination coefficient (DC). 3-Results and DiscussionSome temporal features may also exist in the runoff time series due to their highly non-stationary fluctuations. To handle such features, wavelet-based temporal pre-processed data were entered into the ANNs to improve the accuracy of runoff modeling. WT and wavelet-based de-noising approaches were used for modeling the rainfall-runoff process via the ANN model. The Daubechies-4(db4) mother wavelet, which is almost similar to the runoff signal could capture the features of the signal, especially peak values, thus, it was selected as the mother wavelet for the decomposition of the runoff time series in this study. The decomposition of runoff time series at level L yields L+1 sub-signals (one approximation sub-signal, Pa(t) and L detailed sub-signals, Pdi(t) (i=1, 2, …, L)). Decomposition level 3 was considered as the optimum decomposition level. Each of the decomposed sub-series of the runoff demonstrated a specific seasonal feature of the process. In WT-ANN (WANN) model, decomposed sub-series accompanied by the rainfall and runoff data of each compound were used in the FFNN to predict one-month-ahead runoff values at the outlet of Ardabil plain (Samian station). In the second stage, the runoff time series were denoised via WT, and the denoised runoff data were used to predict the runoff at Samian station for one month ahead. Finally, the ANN model was compared with ANN models using pre-processing inputs.The results of three models for one-step-ahead runoff forecasting at Samian station have been presented in Table 1. Results indicated that better accuracy was comprised with another model via the WANN model in the comb. 3. WANN models via comb. 3 used the runoff data of Gilandeh and Kozatopragi that lied in the upstream and showed accurate performance. These demonstrated Gilandeh and Kozatopragi runoff time series played an important role in Samian runoff modeling. Accuracy improvement in the WANN model was 17%, 3.5%, and 35% combs. 1, 2, and 3 of inputs. The ANN model with denoised inputs showed little improvement (1, 6, and 6.2 percent in combs. 1, 2, and 3 of data) in runoff modeling at the outlet of the plain. Table (1): The results of ANN and SVM models for one-step-ahead predictionsInput combinationOutputvariableModel Type DCRMSE (Normalized) CalibrationVerificationCalibrationVerification1RS(t+1)ANNANN with denoised dataWANN 0.5920.5940.7910.4340.4380.5870.0650.0650.0470.0510.0520.0442RS(t+1)ANNANN with denoised dataWANN 0.7950.8130.7910.5670.6010.5870.0430.0390.0470.04340.0420.0443RS(t+1)ANNANN with denoised dataWANN 0.9070.8310.8800.7300.7750.8540.0290.0400.0320.0350.0320.0264-ConclusionsIn this study, the wavelet-based denoised data and WT were employed in ANN for rainfall-runoff modeling at the outlet of Ardabil plain using data pre-processing techniques. Accordingly, first, it was sought to smooth the hydrological time series by eliminating the outliers and large noises of raw observed time series, which may be due to human or tool measurement error or systematic error. Then, different sub-series were generated by decomposing runoff time series and used to train the ANN model for rainfall-runoff modeling. Using processed and unprocessed data, the obtained results were compared; this comparison indicated the merit of applied data pre-processing approaches due to robust identification of hidden patterns in data so that the developed models could simulate and predict runoff values with lower margin of error and higher confidence and the best results were achieved by employing the decomposed runoff data via WT having different training time series with the same components of original time series. For future study, it is recommended to examine the efficiency of the proposed data pre-processing method in the rainfall-runoff modeling of other watersheds since it is expected that the merit of the method is more highlighted where the quality of the collected data is blurred due to the technical limitations. Furthermore, it is suggested to evaluate the efficiency of the proposed method in modeling the process at other time scales and for modeling other hydrological processes which may involve distinct noise levels and patterns regarding the type of process.Keywords: Runoff modeling, Wavelet Transform, Wavelet-based de-noising, Artificial Neural Network (ANN), Ardabil plain5-References Donoho, D.H., 1995. Denoising by soft-thresholding. IEEE Transactions on Information Theory. 41(3):613–617.Mallat, S.G., 1998. A Wavelet Tour of Signal Processing, second ed. Academic Press, San Diego. Partovian, A., Nourani, V., Aalami, M.T., 2016. Optimizing Neural Network for Monthly Rainfall-Runoff Modeling with Denoised-Jittered Data. Journal of Tethys. 4(4), 284–294.
پژوهشی
Maryam Ansari; Iraj Jabbari; Farhang Sargordi
Abstract
1-IntroductionIran is one of the arid and semi-arid regions of the world with an average annual rainfall of 240 mm. The country is such arid that the average annual rainfall is less than 130 mm (Jafari and Tavili, 2013:149) in 65% of its regions; therefore, it has been facing a water shortage for a long ...
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1-IntroductionIran is one of the arid and semi-arid regions of the world with an average annual rainfall of 240 mm. The country is such arid that the average annual rainfall is less than 130 mm (Jafari and Tavili, 2013:149) in 65% of its regions; therefore, it has been facing a water shortage for a long time. Thus, due to the limitation of surface and groundwater resources in the country, particularly arid and semi-arid regions, it is necessary to identify the factors affecting the quality of water resources for protection to reduce the vulnerability of these resources. Among the various factors that cause water quality degradation, the type and material of rocks or geology are crucial in changing groundwater quality (Jehbez, 1994: 1). Accordingly, in this research, the efficiency of the GWR model was measured to determine the sources of water pollution by selecting the Izdakhvat basin as a sample of inland Zagros basin that has good but saline water resources; these areas received the most impact from a particular formation.2-MethodologyIzadkhaast catchment, code 2647, is one of the closed basins of the Mond River catchment located in Fars province. The area of this basin is 1371.3 square kilometers the height and plain of which is respectively, 879.6 and 491.7 square kilometers of the total area of the basin. The maximum and minimum height in the basin are, respectively, 2182 and 1029 meters.In this study, the geographical weight regression (GWR) model has been used to investigate the relationship between geological formations, water quality parameters, and spatial modeling. This method is based on processing the hydrological information (water quality data) and geology using the GIS technique. The required parameters were considered as model inputs; moreover, geological map 1:100000 sheets of ZarrinDasht, Jahrom, and Bezenjan Geological Survey was used to extract geological data as well as the obtained data of observation wells, Fars Regional Water Joint Stock Organization. As the water quality data is related to 14 observation wells in 2010 (due to the more complete data), which is among 16 quality parameter data, after examining the relationship between the parameters together, those who had the highest correlation and significant relationship with the EC parameter, were selected for statistical analysis. They were also selected to quantify the geological formations. For each well, Polygon Thyssen was drawn. The area of the formations in each of the polygons was extracted and added as an independent variable to the descriptive table of the desired file shape, and then they were analyzed for modeling in ARC GIS environments in the following steps:1- First, to enter the best model for execution in the GWR method, independent variables related to trial and error in the OLS method were analyzed so that the best model with a significant relationship between variables, i.e., P value less than 0.05, R2 more and lower AICc coefficient was selected.2- After selecting the best model, the Moran index was used to evaluate the spatial autocorrelation of the OLS model residues. This index measures the degree of clustering or dispersion of standard residues. The residues were used to test the reliability of the model in predicting local conditions by experimenting with spatial correlation.3- Finally, the variables selected from the OLS model were entered into the GWR model to achieve higher precision in spatial relationship analysis. The GWR recorded local changes by weighing more close observations than farther ones (Pratt and Chang, 2012:52).GWR outputs include local residuals as well as the results of R2 or the coefficient of determination, where R2 is the standard for determining the performance of multivariate regression models.3-Results and DiscussionAccording to the results of the OLS model, the sign of beta coefficients for Aghajari Formation (MPLa), alluvial deposits Qc, and QScg were negative. They indicated their inverse relationship with qualitative parameters. However, most of the qualitative parameters were directly and remarkably related to seasonal lakes, salt dome (Pc CHD), Champe member (Mchm), and mole member (Mmo) in the area, which indicated surface erosion and leaching of salt and gypsum from the surface by surface currents and their transfer to the low points of the basin, i.e., seasonal lakes. These formations have also shown themselves as Mahour and Badland hills due to their instability against further erosion.After selecting the best models, all the standard residues of the selected OLS models were examined to ensure the normal distribution of the data and to evaluate the spatial autocorrelation using the Moran index. All residuals in the selected OLS models were within the standard range, indicating a normal data distribution.Finally, to better understand the correlation between geological formations and water quality parameters in different parts of the basin, the variables selected from the OLS model were entered into the GWR model. The results of this model have been presented as spatial model maps for each parameter based on the results of coefficients of determination (R2).According to the maps, the highest correlation was related to the potassium parameter, and the lowest value was related to the chlorine parameter, while the other parameters also showed a very high correlation with independent variables. In most qualitative parameters such as sodium, potassium, chlorine, and electrical conductivity, the highest correlation was related to the west of the basin, which indicated the high impact of the salt diaper in the west of the basin on water resources and wells that are close to the points of lower quality than wells in higher and farther points. Low resistance and erosion of evaporative sediments were also contributed to this issue, as water sources in contact with evaporative sediments may contain large amounts of potassium, sodium, chlorine, and sulfate in an insoluble form.4-ConclusionsThe results of this study revealed that this model with high spatial variability determined the impact of different formations on water resources in various places and critical areas with the most negative effects. This significant model was a simple and enriched method for managing and planning in basins that do not have enough data.The results of this model also showed that evaporative sediments in the basin, including the salt dome in the west of the basin, were the most important formations of water quality degradation. Also, the significant relationship between water quality parameters and low points of the basin or seasonal lakes indicated the leaching and transport of these sediments to these points by running water. These formations have shown the faces of mounds and hills in the region due to their weakness.Keywords: Water Quality, Geology, GWR, Izadkhast basin 5-References Jafari, M., & Tavili, A. (2013). Reclamation of Arid lands, Tehran, Tehran University press, 4, 396 p.Jehbez, O. (1994). Hydrochemical evaluation of Sarvestan basin with an emphasis on the role of geological formations, MSc in Hydrology, University of Shiraz, 436 p.Pratt, B., & Changa, H. (2012). Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales, Journal of Hazardous Materials, 209–210, 48-58.
پژوهشی
Mohamad Sharifi Paichoon; Kourosh Shirani; Mayedeh Shirani
Abstract
1-Introduction Landslide as a process of change in the stress-strain state of a slope occurs under the influence of natural and human parameters leading mass separation and its movement to down slopes. However, the relationship between the sliding mass and the slope remains constant. Accordingly, the ...
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1-Introduction Landslide as a process of change in the stress-strain state of a slope occurs under the influence of natural and human parameters leading mass separation and its movement to down slopes. However, the relationship between the sliding mass and the slope remains constant. Accordingly, the mechanism of formation and development of a landslide is a systematic sequence of changes in the stress-tension state of a slope influenced by natural and anthropogenic parameters. This event is destroying human settlements and infrastructures and causing financial losses and many deaths around the world annually. The rapid population growth in the last half-century, the expansion of settlements towards steep mountainous areas on the one hand, and the false human being intervention in the destruction and changes of slopes, on the other hand, increased the frequency of landslides and this has led to an increase in damages. Iran has favorable natural conditions for a wide range of landslides with mainly mountainous topography, high tectonic and seismic activity as well as diverse climatic and geological conditions. Therefore, landslide studies on understanding factors and parameters affecting it, and identifying high risk and vulnerable areas in the world as well as in Iran have received serious attention. This research mainly aims to investigate the parameters affecting the landslide in the Vahregan catchment which located in the Sanandaj-Sirjan construction zone. Where metamorphic rocks, marl and shale, as well as wide area of quaternary sediments, have provided very favorable conditions for landslide occurrence. 2-Methodology Multiple linear regression method was used to perform this research. Thus, the scatter map of the landslides of the region as dependent variable and twelve factors includes elevation, slope, slope direction, lithology, fault, precipitation, drainage network, road, land use, vegetation, TWI and SPI as independent variables were considered. To prepare landslide distribution map of the study area, aerial photographs of 1994 with a scale of 1: 40,000 were used and interpreted. Accordingly, the landslides area and their location in Google Earth software were determined. Then, 138 landslides occurred in the Vahregan catchment were determined with field studies, with the help of available maps and information, and the use of GPS system. It was then mapped using GIS software. After converting all the factors to information layers in GIS, these layers were adapted to the scattering map of the landslides of the region and were calculated the percentage of region located within the landslide area for all factors. 3-Results and Discussion The results showed that the most effective factors in Vahregan catchment landslides based on multivariate regression method are distance from road, lithology, precipitation, land use, slope direction, distance from drainages, distance from faults, SPI drought, elevation, slope and TWI, with coefficient of 0.851, respectively. Their coefficient of R is 0.851 which is acceptable. The results showed that although natural factors can alone cause landslides, human factors are currently the most important parameters in causing landslides in the study area. Accordingly, most new landslides occur in close proximity to roads. In other words, it can be said that the downstream cutting of slopes by human being has increased the frequency and magnitude of landslides. Therefore, results showed that the road with 0.411 standard coefficient was the most important factor in creating landslide so that much of the landslide has occurred within less than 3 km of roads. Then, the natural factors includes lithology and precipitation with a standard coefficient of 0.362 and 0.299 and land use with a standard coefficient of 0.286 played the most role. However, vegetation factor and the TWI index with a standard coefficient of 0.103 and 0.127, played the lowest role in the landslides of the Vahregan catchment. According to the final landslide zoning map, more than 50% of the area has located in a high risk area. 4-Conclusion The study area has great potential for landslides in terms of natural features such as lithology, precipitation, elevation, Permanent River, and slope. The landslide map with 382 landslides indicates this. However, in the last two to three decades, environmental changes such as drought and consequently changes in vegetation covers on the one hand, and false human intervention, including the construction of multiple roads and the geometrical change of slope, on the other hand, have increased the frequency and magnitude of landslides in the studied area. The results of the final mapping showed that more than 50% of the basin is in high and very high risk areas. Accordingly, special attention should be paid to the extent of landslide risk and its threat in all human activities, especially environmental planning and management.
پژوهشی
babak shahinejad; zohreh izadi; behzad javadi
Abstract
1-IntroductionRivers are the most important sources of drinking, agricultural, and industrial water supply. In recent decades, however, these resources have become the main receivers of sewer pipelines due to rapid population growth. To evaluate the effects of pollutant discharge on the self-purification ...
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1-IntroductionRivers are the most important sources of drinking, agricultural, and industrial water supply. In recent decades, however, these resources have become the main receivers of sewer pipelines due to rapid population growth. To evaluate the effects of pollutant discharge on the self-purification of rivers, it is necessary to use numerical simulations of water quality. Today, various softwares have been designed for this purpose. One of the most important of these softwares used in this research is the one-dimensional QUAL2Kw model that simulates water quality variables in a steady and non-uniform flow mode. In the present study, the water quality of the Khorramabad River was simulated with the help of this model over a distance of 35 km from the river.2-MethodologyThe range studied in this research is about 35 km along the Khorramabad River from the source of Khorramrud upstream of Robat Namaki village to Chamjangir hydrometric station, which in the geographical coordinates of 33°36'54" to 33°26'37" north latitude; it is located at 48°17'39" to 48°14'38" longitude. Khorramabad River pollution sources are divided into three main parts: urban, industrial, and agricultural. Due to the location of pollutant sources in the river, 5 points along the river were considered as sampling sites, two stations including the beginning and end of the study area, one station in the center of Khorramabad, and the other two stations were selected before the river entered the city and after leaving the city, respectively.In this research, the QUAL2Kw model version 5.1 was used. The required data of the model is divided into three parts: geometric-hydraulic data, qualitative data, and meteorological data. The river was divided into 11 sections and simulated using the hydraulic Manning equation. In this study, important water quality parameters such as DO, CBODf, COD, NO3, EC, and pH and temperature parameters in July and September of 2019 for calibration and validation, respectively, the model was used. Finally, RMSE, NRMSE, and MAE indices were used to evaluate the model in the simulation.3-Results and DiscussionThe results showed that the number of parameters including COD, CBODf, and NO3 increased after the Karganeh tributary joined the river and also the inflow of pollutant sources such as slaughterhouses, municipal treatment plant, milk factory, and alcohol production unit into the river. However, the pH (in both months) and EC (in July) parameters did not change much along the river; in other words, the river can self-purifying these parameters. In the research of Hashemi et al. (2019), for the simulation of the Talar River, the same result was obtained for these two parameters. Babakhani et al. (2019) in a study conducted on the Diwandara River reported a strong correlation between the measured and simulated values of the pH parameter because in surface water the pH value along the path with carbonate and bicarbonate in the path there reaches the equilibrium concentration. According to the results of the research and the fact that the Khorramabad River is used for agricultural and industrial purposes and is not a source of drinking water, at present, there is no limiting factor to achieve this purpose in the study route. Then, the calculation of statistical indices showed that the value of the NRMSE index in the calibration and validation stage of the model is the lowest for pH and equal to 8.83 and 9.22 percent and for EC is 11.05 and 13.86 percent, respectively. The simulation of DO parameter also had fluctuations along the river, while the statistical indices of NRMSE, RMSE, and MAE for this parameter in both calibration and validation stages were obtained at an acceptable level; thus, the above indices in the calibration stage of the model 12.49, 0.917 and 0.72, respectively, and in the validation stage of the model were calculated 24.65, 1.78, 1.55, respectively. In addition, the model was able to simulate the temperature parameter with high accuracy in July (RMSE = 1.92 and MAE = 1.57) and September (RMSE = 2.77 and MAE = 2.5709). Finally, the results of this study indicate the considerable accuracy of the QUAL2Kw model in simulating the above parameters in the Khorramabad River.4-ConclusionsThe results showed that the amount of chemical oxygen demand, biochemical oxygen demand, and nitrate parameters increased due to the entry of effluents from industrial pollutants. Besides, the evaluation index indicates that the QUAL2Kw model has shown good performance in estimating the acidity parameter compared to other parameters. It is suggested that in addition to the low water season, modeling be done in high water seasons and use two-dimensional quality models to simulate rivers. Keywords: Qualitative Parameters, Simulation, QUAL2Kw Model, Khorramabad River, Lorestan Province5-References Babakhani, Z., Saraee Tabrizi, M., & Babazadeh, H. (2019). Determining the Self-Purification capacity of Diwandara River using model qual2kw. Journal of Echo Hydrology, 6(3), 673-684.Hashemi, Z., Gholami Sefidkouhi, M. A., & Ahmadi, K. (2019). Evaluation and Simulation of Talar River Quality by using QUAL2KW Model. Iranian Journal of Irrigation & Drainage, 12(6), 1500-1510.
پژوهشی
mehrdad hassanzadeh; mehdi momeni reghabadi; amir robati
Abstract
1-IntroductionGroundwater pollution is one of the most serious and important issues in urban and agricultural areas due to land use. For this purpose, in order to obtain methods and garbage water from the pollutants that removes them, the use of methods for garbage water vulnerability assessment ...
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1-IntroductionGroundwater pollution is one of the most serious and important issues in urban and agricultural areas due to land use. For this purpose, in order to obtain methods and garbage water from the pollutants that removes them, the use of methods for garbage water vulnerability assessment such as AVI, GODS, DRSTIC, SINTACS, etc. were developed. Intrinsic vulnerability is assessed according to the hydrological and hydrogeological characteristics of the region, such as the characteristics of the aquifer and the stresses imposed on it. Occurs with inherent vulnerability components. The most common methods of assessing vulnerability index include DRASTIC, GOD, SINTACS, SI and AVI rating methods. In this study, the vulnerability of the aquifer has been investigated using DRASTIC and SINTACS models, and in order to validate the results of the methods used, electrical conductivity concentration data were used. 2-MethodologyHajiabad plain is located 160 km north of Bandar Abbas and between 35, 55 to 00 and 56 longitudes and latitudes 17, 28 to 21 and 28 north, from the north to the heights of Bibi Dokhtaran mountain from the west to Sirjan-Bandar Abbas road from To the east to the heights of Anfuzeh mountain and from the south to the congomara hills and the average width is 4 km. The climate of the region is warm and the average temperature of the region is 19.8 degrees Celsius and the average annual evaporation of the plain is 2464.7 mm. In order to study the hydrochemical properties of groundwater in the region, 16 samples of water analyzed from groundwater study wells by the Regional Water Organization of West Azerbaijan Province for the water year 93 were used.3- Results and DiscussionVulnerability maps of Drastik and SINTACS models were prepared by applying weights related to each parameter and combining layers using the overlap function. According to the SINTACS map, the vulnerability of the plain is estimated from 115 to156, the plain is in the range of medium, medium to high and high vulnerability. According to the vulnerability classification with SINTACS model, it shows that parts of the center of the plain (near Aliabad and Hajiabad villages) are in the upper floor and the northern slope of the Hajiabad plain basin has the middle floor. Most of the plain area was in the range of moderate to high vulnerability. The results showed that the Syntax model has more flexibility than the Drastic model and the probability of vulnerability is slightly higher than the Drastic model. The final map of Drastik model estimated the vulnerability of the plain from 94 to 128. The highest vulnerability is in parts of the center of the plain (near Aliabad and Hajiabad villages) and the lowest in the northern slope of Hajiabad plain basin and according to the range of Drastic vulnerability index provided by Aller Et al, (1987), vulnerability of the region is divided into 3 categories between low to medium risk. In order to study more closely and also to compare the classical methods used in this study, the method of calculating the correlation index (CI) in the aquifer and electrical conductivity data were used. For this purpose, electrical conductivity values were divided into three categories of low, medium and high electrical conductivity. Adaptation of wells with three levels of EC pollution and vulnerability categories predicted by DRASTIC and SINTACS methods was brought for Hajiabad aquifer. Based on the value of the correlation coefficient between the map produced using the drastic model with the electrical conductivity map, 39 and the same value was obtained for the Syntax model 35, which are slightly different from each other.4-Conclusions In this study, both drastic and syntactic methods predicted the potential risk in Hajiabad aquifer with almost equal accuracy. Having the correlation index between the electrical conduction point data and the vulnerability map, it showed that the Drastic model provided better vulnerability than the SINTACS model. Contamination potential in both studied models is low in the northern and southern regions. This can be due to high groundwater depth and low hydraulic conductivity. Comparing the models with the coefficient of determination between the electrical conductivity concentration and the vulnerability parameters showed that the highest correlation was in the slope layer, depth to the water table and the material of the unsaturated medium.Keywords:Aquifer vulnerability, SINTACS Method, Groundwater, Hormozgan5-References Aller, L., T. Bennet, J.H. Lehr, R.J. Petty, and G. Hackett. (1987). DRASTIC: a standardized system for evaluating groundwater pollution potential using hydrogeological settings. EPA/600/2–87/035. US Environmental Protection Agency, Ada, OK, USA.
