Groundwater
Sana Maleki; Vahid Nourani; Hessam Najafi
Abstract
Systems for assessing groundwater vulnerability are designed to protect groundwater resources from pollution. The DRASTIC method is a well-known approach for determining groundwater susceptibility. One drawback of the DRASTIC method is that it relies on expert judgment to rank parameters, which introduces ...
Read More
Systems for assessing groundwater vulnerability are designed to protect groundwater resources from pollution. The DRASTIC method is a well-known approach for determining groundwater susceptibility. One drawback of the DRASTIC method is that it relies on expert judgment to rank parameters, which introduces uncertainty. This study used a new generation of Fuzzy Logic (FL), called the Z-number theory, to estimate the specific vulnerability of aquifers and address this uncertainty. The specific vulnerability of the Ardabil and Qorveh-Dehgolan aquifers was estimated using two scenarios: the DRASTIC parameters as inputs and nitrate concentration values as output. The vulnerability of the aquifer was also evaluated by comparing the results of the proposed models with those of the DRASTIC model, which served as a benchmark. The analysis showed that the Z-number Based Modeling (ZBM), which considered data reliability and weighted the rules appropriately, produced higher-quality results than the classic FL. In the Ardabil plain, the ZBM yielded results that were 53% better (using seven inputs) and 184% better (using four inputs) compared to the classic FL. In the Qorveh-Dehgolan Plain (QDP), the ZBM produced results that were 127% better (using seven inputs) and 311% better (using four inputs) than the classic FL. The irregularity and non-linearity of the data, such as the high coefficient of variation (CV) in the Ardabil plain compared to the QDP, may contribute to the high CV value in the plains. Therefore, in plains with high CV, the quality of the extracted Z-number-based rules may be lower.
soghra andaryani; Vahid Nourani
Abstract
1-IntroductionMining industry has almost negative and destructive effects on the environment and ecosystems of regions and can affect the health of humans and other living organisms including animals, plants, soil, water, and the entire biosystem of the region on a local and regional scale.2-MethodologyThe ...
Read More
1-IntroductionMining industry has almost negative and destructive effects on the environment and ecosystems of regions and can affect the health of humans and other living organisms including animals, plants, soil, water, and the entire biosystem of the region on a local and regional scale.2-MethodologyThe study area is located in East Azerbaijan and it belongs to the sub-basins of the western part of the Aras Basin.2.1-DataLandsat 5, 8 data available in July (1984-2019), monthly and annually value of temperature and precipitation data (1998-2017) in Varzeqan station.2.2-Method In the present study, land-cover density using Normalized Differential vegetation Index (NDVI) was extracted from satellite imagery of Landsat (Thematic Mapper and Operational Land Imager) as time series for the period of June 1984-2019. 27 images were analyzed after correction and NDVI extraction. See question 1: (1)Where, NIR is reflectance value of near infrared and RED is reflectance value of red band.To determine the trend in vegetation, first, the land-cover density extracted from satellite images were pre-whited in time series and then the trend analysis was done by Mann-Kendall (MK) method in each of the pixels, and also beginning of the trends were analyzed by Mann-Kendall sequence (SMK) in per case studies. SMK was used in MATLAB software (Ye et al., 2013; Moraes et al., 1998). 3-Results and DiscussionThe average values of land-cover in all three case studies (i.e., case 1, case 2, and case 3) have increased in the period 1984-2019 and have almost had the same fluctuations over the period under study. Therefore, that linear regression was derived between the land-cover of cases 1 and 2, 1 and 3, and finally 2 and 3 with the average correlation coefficient of 96%, 96%, and 98%, respectively. The highest vegetation peak was in 1992, 2004, 2013, and 2018 to 2019, however, such an increase in the average occurred in all three study units. The peak of average land-cover density in different years is consistent with the peak of precipitation and decrease in temperature on an annual scale. According to the results, in studying the trend of vegetation changes, it is not possible to generalize the numerical average of vegetation for the whole region or analyze the trend. By emphasizing this result in each of the pixels as a time series, trend analysis was performed by MK method. Case 1 experienced the most fluctuation and case 3 (downstream of the mined area) experienced the least fluctuation trends. The significant decreasing trend in both levels of reliability, 95% and 99% has the highest level of the mining area (Fig. 1). Fig. (1): Classification of trend analysis at 95% and 99% confidence levels, (a) case 1, (b) case 2 and (c) case 3. Figures 1-3, which have been resulted from Fig. 6 (a), indicate areas with significant decrease. Figs. 1 and 2 are the mining areas and Fig. 3 is the Andaryan village0.48% of the case 1 area is under the significant decreasing trend, which is 0.18% and 0.22% in case 2 and 3, respectively. Therefore, there is a significant decrease in all three case studies. Approximately, 5%, 2%, and 3% of the area of cases 1, 2, and 3 have a decreasing trend, respectively. The percentage of areas with a significant increasing trend at both 95% and 99% confidence levels are equal to 35.5%, 54%, and 36.5% for each of the 1-3 case studies, respectively. According to Varzeqan station data, these areas have received good rainfall in the last decade, so the area of vegetation has increased significantly. The existence of 88% correlation between the area where the mining took place and the area that is untouched in terms of exploration operations shows the insignificant impact of exploration operations and smelting services on the vegetation of the area. Although most of land-cover of about 51 hectares has been lost due to road construction on steep slopes for mining and smelting services, based on sustainable development goals, the affected vegetation can be restored to the original state and at the same time to make the best use of existing minerals and consider future generations (Thenepalli et al., 2019). 4- ConclusionThe results show fluctuations in land-cover density; however, in general, high dense areas in terms of vegetation are observed in all three areas. The case studies 1- 2, 1 - 3, and 2 -3 have a correlation of 96%, 96%, and 98% with each other, respectively. Therefore, using the Mann- Kendall statistical model, NDVI values were analyzed pixel by pixel as a time series. The results show a significant decrease in the vegetation of regions 1-3 equal to 0.48%, 0.19% of the area in all three regions, respectively. The results of the Mann-Kendall sequence and correlation in the areas with a significant reduction in vegetation and the considered various hypotheses showed no chemical leakage to downstream of the basin.Keywords: Impact of mining, Land-cover, NDVI, Mann-Kendall Test, Varzegan, Northwestern Iran 5-References Ye, X., Zhang, Q., Liu, J., Li, X., & Xu, C.Y. (2013). Distinguishing the relative impacts of climate change and human activities on variation of streamflow in the Poyang Lake catchment, China. Journal of Hydrology, 494, 83–95.Moraes, J.M., Pellegrino, H.Q., Ballester, M.V., Martinelli, L.A., Victoria, R.L., & Krusche, A.V. (1998). Trends in hydrological parameters of southern Brazilian watershed and its relation to human induced changes. Water Resources Management, 12, 295–311.Thenepalli, T., Chilakala, R., Habte, L., Quang Tuan, L., & Sik Kim, C. (2019). A Brief Note on the Heap Leaching Technologies for the Recovery of Valuable Metals. Sustainability, 11, 334.
Vahid Nourani; Saleh Mohsenzadeh
Volume 4, Issue 11 , September 2017, , Pages 83-103
Abstract
Introduction
In this study, the MPSIAC model was used to consider the effects of the dominant factors in sediment production in order to estimate the rate of the erosion and sediment load in sub-basins of the Aji Chay River. Since the sediment rate of this model is the annual average, the variations ...
Read More
Introduction
In this study, the MPSIAC model was used to consider the effects of the dominant factors in sediment production in order to estimate the rate of the erosion and sediment load in sub-basins of the Aji Chay River. Since the sediment rate of this model is the annual average, the variations of the nine fold factors of this model was examined in order to calculate the sediment for each year. Then, the annual and monthly sediment rates were quantified using a cascading method.
Methodology
In order to estimate the sediment production and the relationship between the degree of the sediment yield and the amount of production, equation (1) which was based on determining the scores of the factors considered in the PSIAC model and obtaining their total scores in each hydrological unit was used
38.77e0.0353R = Equation(1): QS
Qs=sediment yield (m3/km2/year) R= sedimentation rate
The PSIAC model specifies some variations for each factor, which is somewhat selective and requires an expert judgment. Johnson and Gombard (1982) have made the nine-fold factors for this method as numerical equations.
The estimated sediment rate using MPSIAC method is based on the annual average. Therefore, the variations of the factors of MPSIAC model were examined and compared to estimate the sediment for each year. Due to the fact that sediment is not the same throughout the year, it was not possible to equally consider annual sediment for all months of the year. Thus, for the purpose of the quantification of the monthly sediment, the cascading micro-scale was used through verifying the existing data and filling the deficiencies of the data. In the process of disintegration, the sediment, which was the annual sediment in the initial intervals, was sequentially broken into smaller surfaces with specific coefficients and calibrated.
Equation(2): SNij = Sij
Equation(3): SijNky = Sk
Results and discussion
In this paper, the annual sediment rate was estimated using remote sensing, GIS techniques, and the application of the experimental model of MPSIAC in hydrological units and its zoning in the area. Then, by inserting the DEM into the GIS environment and by modifying the ups and downs, the flow direction, the network of waterways, and the primary and secondary sub-basins were produced. As a result, the production rate of the sediment and the scores of the each of the factors in the sub-basins were calculated using the equations presented in the MPSIAC model. The results showed that there was a high correlation between the estimated sediment load with the MPSIAC model and the observed and recorded results.
The results of the MPSIAC model for the estimated sediment rate were based on the annual average, so the existing data and nine-fold factors of MPSIAC model, which were time-consuming, were used for the monthly sedimentation. To measure the amount of the precipitation and runoff for different months of each statistical year and to study the amount and manner of changes in vegetation and land use in the studied area, the annual precipitation and annual erosion were calculated for each statistical year. Then, sub-scaling was done through the calculation of the sub-scale coefficients of annual to monthly sediment.
Conclusion
The estimated sediment rate using MPSIAC model and observational and measured data of the sediment in the hydrometric stations of the Aji Chay basin has high accuracy and acceptable correlation. In addition, by comparing and verifying the available and measured data in the hydrometric stations of the AjiChay basin at low scales with extractive data of this method, it turns out that the sediment values can be estimated at low scales by specifying the sub-scale coefficients and calculating the sediment for each year.
Mehdi Komasi; Soroush Sharghi; Vahid Nourani
Volume 3, Issue 9 , March 2017, , Pages 63-86
Abstract
Time series analysis of hydrological processes plays an important role in accurate recognition of this process. Wavelet-entropy index is a new indicator to assess the fluctuations of time series. In this paper, the effective factor in groundwater level declining in the Silakhor plain is examined using ...
Read More
Time series analysis of hydrological processes plays an important role in accurate recognition of this process. Wavelet-entropy index is a new indicator to assess the fluctuations of time series. In this paper, the effective factor in groundwater level declining in the Silakhor plain is examined using wavelet-entropy index. Generally, wavelet-entropy index reduction or time series complexity reduction of a phenomenon, indicates the reduction in time series natural fluctuations and thus the occurrence of an unfavorable trend in time series. In this way, to identify the main cause of declining aquifer water table, firstly, monthly time series of precipitation, temperature and rivers flow of this plain divided into shorter time periods and then, each of these time series were decomposed to multiple frequent time series by wavelet transform and then, normalized wavelet energies were calculated for these decomposed time series and finally, wavelet-entropy index was calculated for each three different time periods. The results of wavelet-entropy index analysis reflect the fact that, the complexity reduction of the flow time series about 71% is more effective on groundwater time series complexity reducing compared to the complexity reduction of the precipitation and temperature time series about 13% and 10.5% respectively. This result indicates the primacy of the human factors compared with the climate change factors impacts in declining the groundwater level in this plain.
Vahid Nourani; Narges Azad; Mahsa Ghasemzade; Elnaz Sharghi
Volume 3, Issue 7 , October 2016, , Pages 141-159
Abstract
Vahid Nourani[1] Narges Azad*[2] Mahsa Ghasemzade[3] Elnaz Sharghi[4] Abstract This paper aims to detect trends and investigate the relationship between long-term time series of the Urmia lake water level and other hydro-climatologic parameters, including precipitation, runoff, temperature and relative ...
Read More
Vahid Nourani[1] Narges Azad*[2] Mahsa Ghasemzade[3] Elnaz Sharghi[4] Abstract This paper aims to detect trends and investigate the relationship between long-term time series of the Urmia lake water level and other hydro-climatologic parameters, including precipitation, runoff, temperature and relative humidity, in monthly, seasonal and annual scales using Mann-Kendall (MK) and discrete wavelet transform (DWT). The MK test and sequential MK analysis were applied to different combinations of DWT to calculate components responsible for trend of time series.The results showed that 8-month period was important in the involved trend at the original monthly time series. Also there is a significant negative trend in different scales of lake water levels and runoff time series. In general, rainfall, relative humidity and temperature time series did not show significant trends.The results of this research indicate that downward trend in the rainfall time series has more effective role in Urmia lake drying. In addition, the sequential MK test was used to find the start points of changes in monthly time series. The results showed a significant decreasing trend from 1377 in the lake water level and runoff time series. Finally, the results of Sen’s trend analysis, also confirmed the results of the proposed wavelet-based MK test. [1]- Professor of Water Resources Engineering. [2]- Master Degree Student of Water Resources Engineering (Corresponding Author), Email:narges.azad1991@gamil.com [3]- Master Degree Student of Water Resources Engineering. [4]- Assistant Professor of Water Resources Engineering Department of Water Resources Engineering Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran.
