جستجوی مقالات مرتبط با کلیدواژه « Watershed » در نشریات گروه « آب و خاک »

The locals' health in mining sites can significantly benefit from the protection of water and soil resources. These heavy metal sources can negatively impact human health and the amount and quality of goods produced in the area. For this reason, the concentration of two elements, cadmium, and arsenic, in the Savad Kouh mining area of the province was calculated using three indices: pollution factor, degree of pollution or modified pollution factor, and accumulation of land. Fifty-three samples from the area, including 45 samples of agricultural topsoil and eight samples of riverside sediment, were collected to analyze the site. The samples prepared in a dry environment were brought into the lab and subjected to ICP-MS analysis using a 75-micron sample size. According to the pollution factor, cadmium and arsenic levels in agricultural soil have low pollution, which has a CF value of 1. Cadmium has a low pollution status, and arsenic with a CF = 6 has a low to medium status, according to the concentrations of elements in the riverside sediment. According to the modified pollution degree index (mCd), the region's agricultural soil and sediment have extremely low or low pollution levels. In other words, the mCd values for the two elements cadmium and arsenic are in the very low degree of pollution (0≤mCd≤1.5). According to the findings of the soil accumulation index for two elements, agricultural soil has a non-polluted or low-pollution status. In other words, the index value is between 0 and 1. The area falls into the low to medium pollution category in terms of two elements, according to the principal component analysis (PCA) results.

Introducing reliable regional models to predict the maximum discharge of floods using characteristics of sub-basins has special importance in terms of flood management and designing hydraulic structures in basins that have no hydrometric station. The present study has tried to provide appropriate regional flood models using generalized linear models (GLMs) to estimate 2-, 10-, 50-, and 100-year maximum daily discharges of 62 sub-basins in Great-Karoon and Karkhe basins. According to the results, the sub-basins were categorized into four sub-regions based on some physiographic and climatic characteristics of the study sub-basins. The results showed that regional flood modeling was successful in all sub-regions except sub-region II, which includes very large basins (A̅≈17300 km2). The adjusted R2 of the best models in sub-regions I, III, and IV were estimated at around 82.4, 91.3, and 90.6 percent, and these models have a relative error (RRMSE) of around 9.5, 9.23, and 6.7 percent, respectively. Also, it was found that more frequent floods with 2- and 10-year return periods are influenced by properties such as basin’s length, perimeter, and area, while rare floods with 50- and 100-year return periods are mostly influenced by the river systems characteristics such as the main river length, total lengths of the river system, and slope of the main river. According to the research, it can be stated that the behavior of maximum daily discharges in the study area is extremely influenced by the different climatic and physiographic characteristics of the watersheds. Therefore, the maximum daily discharges can be estimated accurately at ungauged sites by appropriate modeling in gauged catchments.

 order to study the spatial patterns and prepare spatial distribution maps, point data are generalized to the surface by moving away from the site in the interpolation process. The values ​​of indefinite points are estimated by using the measured values at definite points. There are different methods for spatial data interpolation and any major defect in the spatial distribution of climatic factors and slight inattention to the appropriate method of interpolation method can lead to errors in the design estimates. So far, many researches have been done in the field of climate data interpolation in the world. The aim of this paper is determining the most appropriate method of rainfall extension pattern and fitting the best distribution function to the annual average rainfall data. In addition to the performances of different interpolation methods, the choice of the optimal model for zoning rainfall amounts in different time periods is the goal of paper.

Iran is located in West Asia and borders the Caspian Sea, Persian Gulf, and Oman. With an area of 1,648,000 square kilometers (636,000 sq. mi), Iran ranks seventeenth in size among the countries of the world. The study area is located between 32° 25' 14.67" N and 53° 40' 58.86" E. Iran has a variable and continental climate in which most of the relatively scant annual precipitation falls from October through April. The average elevation of this plateau is about 900 meters (2,953 ft.), but several of the mountains that tower over the plateau exceed 3,000 meters (9,843 ft.). Volcanic Mount Damavand, 5,610 meters (18,406 ft.), located in the center of the Alborz, is not only the country's highest peak but also the highest mountain on the Eurasian landmass west of the Hindu Kush .The monthly precipitation data from 79 synoptic stations with at least 30 years during the period from 1988 to 2017 were used. Very severe drought years and SPI values identified in the intervals of 3, 5 and 8 months until the end of May were extracted from monthly rainfall data in each year and in three ten-year steps. In order to determine the distribution of rainfall in 10-year time periods in the lack of data points, the indices of mean absolute value of relative error (MARE), mean bias error (MBE), mean absolute percent error (MAPE) and the root mean squared error (RMSE) has been used.

In general, using the above errors in the quarterly, five-month and eight-month periods between definitive interpolation methods, radial basis functions (RBF) and local polynomial interpolation (LPI) compared to the other two methods (IDW, GPI) has provided proper results that is consistent with the results of Misaghi (2006) and Eivazy (2012). Regarding the method of radial functions, the quadratic interpolation method had the lowest correlation for all variables except rainfall with a period of 3 months. Kriging methods have given better results compared to IDW method, which is consistent with the results of Soleimani et al. (2004) and Zabihi et al. (2011). The methods of kriging, simple kriging method (second-order logic) in the interpolation parameters of mean rainfall, (3 and 5 months), simple kriging method (exponential) in the interpolation variables precipitation with a period of 8 months were the best methods. Therefore, kriging method has presented relatively more accurate results, which is consistent with the results of Mirmousavi et al. (2010).

The results showed that the most droughts occurred in the second decade of the third decade, the trend is still less severe drought is occurring and the lowest is in the first decade. In order to study the effect of SOI and NAO on drought and wet years in the country, the correlation matrix was determined to investigate the drought and wet periods. Then, geostatistical modelings were performed to describe these effects. Finally, based on the study of stations and zoning models, it can be stated that the Lanina phenomenon has a little effect on drought and annual rainfall and the El Nino phenomenon has a relatively greater effect. The results show that GPI and IDW methods are not considered as good performance. These methods are based on the fit of polynomial functions on spatial data. According to the results obtained in every area and for different parameters, a single method cannot be selected as the most appropriate method and the most appropriate method can be selected. Based on the results, the most appropriate choice depends on the characteristics of the regions and a single method cannot be selected for all regions. The results show that GPI and IDW methods are not suitable and modeling in these two methods is based on fitting polynomial functions to spatial data. RBF and LPI methods have provided better output rather than other methods because the results of the 3 and 5 month periods were above 62% and the 8 months were above 72%.

The entry of pollutants from agricultural land into water bodies is one of the most environmental issues connected to the tile drainage systems. Also leaching of drain water may cause the required nutrients for crop growth becomes out of reach. Thus, applying Best Management practies (BMPs) to relief the side effects of agricultural tile drainage systems is crucial. Over the past few decades, several BMPs have been introduced and studied with the aim of manage and control the volume and pollutants amount of tile drain water in agricultural lands, of which Controlled Tile Drainage (CTD) is one of them. Due to difficulty and costly nature of long-term monitoring programs, which is often used to assess impacts of applying BMPs on non-point source pollution, the AnnAGNPS model developed by USDA is a reliable alternative to simulate an effect of applying CTD at the watershed scale. AnnAGNPS, a comprehensive computational model that evolved from AGNPS, was designed for evaluating non-point pollutant loadings in agricultural watersheds and river basins. Briefly, AnnAGNPS is a distributed parameter simulation model that subdivides a watershed into small, homogeneous subwatersheds called “cells.” Cells are interconnected by stream channels called “reaches” In this study, an impact assessment of applying CTD on Total Suspended Sediment (TSS), Total Nitrogen (N total), Dissolved Nitrogen (N dissolved) and Surface Runoff were evaluated by AnnAGNPS at the Qharesu Watershed.

Qharesu watershed with an area of about 1670 square kilometers is one of the watersheds of Golestan province, which is surrounded by the Gorganrud watershed area from the north and east, Nekarud watershed in the south and the Gorgan Bay from the west. Finally, it is discharged into this bay. An average meteorological data of 5 statistical years of 2016 to 2020, used as a model input data in order to further adapt to the climatic conditions of the region in future, when tile drainage system is operated. The surface runoff data and suspended sediment load were also obtained from the Siah-Ab hydrometric station (outlet point of Qharesu watershed). Calibration and validation of the model performed by runoff and TSS observation data, in which the degree of confirmity between observation data and model output data performed by using three indices of Nash-Sutcliffe, RSR, and PBIAS.Two tile drain depth scenarios were examined in detail to mimic tile drainage control: The depth of 0.2m (CTD0.2) as an extensive controlled drainage and the depth of 0.6m (CTD0.6) as the recommended depth in the growing season for controlled drainage. The depth of the drainage base considered 1.1m as a reference condition as well.

The results showed that the model is reliable for simulating the studied parameters. The indices of E, RSR and PBIAS for runoff were 0.85, 0.48 and -2.6 for the calibration period and 0.76, 0.44 and -10.9 for the validation period respectively. These numbers were 0.73, 0.43 and -31.7 (calibration) and 0.66, 0.54 and -29.1 (validation) for sediment data.The AnnAGNPS model predicted a slight decrease in runoff and total nitrogen and a moderate decrease in dissolved nitrogen at the outlet of the Qharaesu River under the CTD0.6 scenario (during the growing season). Also, the increase in sediment load due to CTD0.6 was almost negligible. Based on the model results, Controlled drainage to a depth of approximately 0.6m below ground level is a suitable equilibrium point for reducing nitrogen and sediment loads at the watershed scale. Considering the importance of the outlet point of Gharesu, which is discharged to Gorgan bay, and since a significant part of tile drainage system in this watershed has not been impelemented yet, applying controlled tile drainage system is possible at a lower cost and more comfortable. Therefore, the possibility of applying CTD0.6 scenario should be on the agenda of authorities.

Integrated assessment of the watershed is critical in arid and semi-arid areas due to the severe water stress in these regions. Data and information are an essential part of decision making and water governance to obtain integrated water resources management at the watershed scale. Water accounting is a helpful tool to organize information and present them as the standard indicators to achieve this goal. Therefore, the objective of this study is to implement the Water Accounting Plus framework (WA+) in the Ferizi watershed located in the Khorasan-e Razavi Province. In this study, water accounting indicators of the Ferizi watershed for a period of 28 years (1990-2017) and wet (1990-1997) and dry (1998-2009) periods were calculated using the SWAT model. The calculated indicators showed that the amount of manageable water and usefulness of consumption (transpiration) is low in the watershed and a large part of the share of irrigation in the watershed is provided by groundwater resources. Generally, the results of this study showed that the use of the SWAT model, WA+ framework, and analysis of water accounting indicators play a significant role in assessing the agricultural and hydrological conditions of the watershed. The proposed approach in this study can help managers make enlightened decisions to keep the sustainability of the watershed.

Several issues impact the water sustainability of a river basin. Among them are the social, economic, and environmental aspects. However, they are often treated separately, and not as an integrated, dynamic process. In order to integrate the hydrologic, environmental, life and policy issues, as well as the existing pressures and policy responses in one quantitative, dynamic, and aggregated indicator, a watershed sustainability index (WSI), which uses a pressure–state–response function, was developed and is proposed in this paper. Applied to a 2,200 km2 Unesco–HELP demonstration basin in Brazil (SF Verdadeiro), the value obtained for WSI was 0.65, which represents an intermediate level of basin sustainability.

Surface energy balance algorithm for land (SEBAL) is one of the actual evapotranspiration predictor algorithms which are based on remote sensing. To determine outgoing long wave radiation from land surface in this algorithm, an index named soil adjusted vegetation index (SAVI) is used. In calculating SAVI, an equation consists an empirical coefficient (L) calibrated based on Idaho satellite images, USA, is used. In this study using MODIS satellite images of neyshabour watershed during 2009 to 2013, the mentioned coefficient was calibrated. The results showed the proposed value nearly L=0.5 for all areas may not evaluate adequately and it is better to calibrate this coefficient before using in SEBAL algorithm in each area. The results showed the trend of SAVI changes and thus determine the optimum value of L coefficient is sensitive to the time of satellite images and it is better to use the months with appropriate vegetation cover on the land surface. The results also showed the values of SAVI index are more sensitive to lower values of L coefficient. Results’ analysis showed the proposed value of 0.2 for L coefficient application in studies related to SEBAL algorithm in this area is properly assessed.