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Inefficient and non-environmentally friendly absorbent production can lead to much resource waste and go against low carbon and sustainable development. A novel and efficient Mg-Fe-Ce (MFC) complex metal oxide absorbent of fluoride ion (F−) removal was proposed for safe, environmentally friendly, and sustainable drinking water management. A series of optimization and preparation processes for the adsorbent and batch experiments (e.g., effects of solution pH, adsorption kinetics, adsorption isotherms, effects of coexisting anions, as well as surface properties tests) were carried out to analyze the characteristics of the adsorbent. The results indicated that optimum removal of F− occurred in a pH range of 4–5.5, and higher adsorption performances also happened under neutral pH conditions. The kinetic data under 10 and 50 mg·g−1 were found to be suitable for the pseudo-second-order adsorption rate model, and the two-site Langmuir model was ideal for adsorption isotherm data as compared to the one-site Langmuir model. According to the two-site Langmuir model, the maximum adsorption capacity calculated at pH 7.0 ± 0.2 was 204 mg·g−1. The adsorption of F− was not affected by the presence of sulfate (SO42−), nitrate (NO3−), and chloride (Cl−), which was suitable for practical applications in drinking water with high F− concentration. The MFC adsorbent has an amorphous structure, and there was an exchange reaction between OH− and F−. The novel MFC adsorbent was proven to have higher efficiency, better economy, and environmental sustainability, and be more environmentally friendly.
Changjuan Dong; Xiaomei Wu; Zhanyi Gao; Peiling Yang; Mohd Khan. A Novel and Efficient Metal Oxide Fluoride Absorbent for Drinking Water Safety and Sustainable Development. Sustainability 2021, 13, 883 .
AMA StyleChangjuan Dong, Xiaomei Wu, Zhanyi Gao, Peiling Yang, Mohd Khan. A Novel and Efficient Metal Oxide Fluoride Absorbent for Drinking Water Safety and Sustainable Development. Sustainability. 2021; 13 (2):883.
Chicago/Turabian StyleChangjuan Dong; Xiaomei Wu; Zhanyi Gao; Peiling Yang; Mohd Khan. 2021. "A Novel and Efficient Metal Oxide Fluoride Absorbent for Drinking Water Safety and Sustainable Development." Sustainability 13, no. 2: 883.
The ecohydrological-separation (ES) hypothesis is that the water used for plant transpiration and the water used for streams and groundwater recharge comes from distinct subsurface compartmentalized pools. The ES hypothesis was first proposed in a study conducted in the Mediterranean climate region, based on the stable isotope method in 2010. To date, the ES hypothesis has proven to be widespread around the world. The ES hypothesis is a new understanding of the soil water movement process, which is different from the assumption that only one soil reservoir in the traditional hydrology. It is helpful to clear the water sources of plants and establish a new model of the ecohydrological process. However, the theoretical basis and mechanism of the ES hypothesis are still unclear. Therefore, we analyzed the characteristics of ES phenomenon in different climatic regions, summarized the research methods used for the ES hypothesis, concluded the definitions of tightly bound water and mobile water, discussed the mechanism of isotopic differences of different reservoirs and their impacts on ES evaluation and pointed out the existing problems of the ES hypothesis. Future research should focus on the following three aspects: (a) detailed analysis of ES phenomenon characteristics of different plant species in different climatic regions; (b) further understanding of the ES phenomenon mechanism; (c) improvement of the experimental methods.
Yaping Liu; Yongchen Fang; Hongchang Hu; Fuqiang Tian; Zhiqiang Dong; Mohd Yawar Ali Khan. Ecohydrological Separation Hypothesis: Review and Prospect. Water 2020, 12, 2077 .
AMA StyleYaping Liu, Yongchen Fang, Hongchang Hu, Fuqiang Tian, Zhiqiang Dong, Mohd Yawar Ali Khan. Ecohydrological Separation Hypothesis: Review and Prospect. Water. 2020; 12 (8):2077.
Chicago/Turabian StyleYaping Liu; Yongchen Fang; Hongchang Hu; Fuqiang Tian; Zhiqiang Dong; Mohd Yawar Ali Khan. 2020. "Ecohydrological Separation Hypothesis: Review and Prospect." Water 12, no. 8: 2077.
A simple technique for prediction of suspended sediment concentration (SSC) or total suspended matter in the Himalayan Bhagirathi river is presented. Artificial neural network models have been developed using short time period data of discharge (Q) and SSC during the high activity monsoon period of June to October 2004, when variations are maximum. Two modeling approaches have been employed, a daily approach and a three hourly approach. Although the time period considered is the same in both the approaches, the modeling performance is marginally better in the three hourly approaches where there is a sixfold increase in the data set. The Levenberg–Marquardt optimization algorithm has been improved with NARX [nonlinear autoregressive with exogenous input] architecture and high values of coefficient of determination have been obtained [0.89–0.97]. This study shows that short duration time series data can be used for successfully predicting geo-hydrological variables in the highly complex Himalayan river scenario.
Nandita Singh; Mohd Yawar Ali Khan. ANN modeling of the complex discharge-sediment concentration relationship in Bhagirathi river basin of the Himalaya. Sustainable Water Resources Management 2020, 6, 1 -8.
AMA StyleNandita Singh, Mohd Yawar Ali Khan. ANN modeling of the complex discharge-sediment concentration relationship in Bhagirathi river basin of the Himalaya. Sustainable Water Resources Management. 2020; 6 (3):1-8.
Chicago/Turabian StyleNandita Singh; Mohd Yawar Ali Khan. 2020. "ANN modeling of the complex discharge-sediment concentration relationship in Bhagirathi river basin of the Himalaya." Sustainable Water Resources Management 6, no. 3: 1-8.
The Ramganga River is the first major tributary of Ganga River, which flows from the hilly region of Kumaon Himalayas throughout the forests of Jim Corbett National Park in Uttarakhand district and the Ganga floodplains in Uttar Pradesh. Due to high amount of sediment load and water discharge carrying by Ramganga River, causing numerous floods in major cities of Uttar Pradesh. Evaluation of grain size and sediment yield is important for studying sediment erosion rate, for dams and reservoir engineering, for predicting the response of climate and anthropogenic activities on river system, and for understanding the trace and heavy metal micropollutants and pathogens. In the present study, the characteristics of suspended and bank sediments were assessed pertaining to the grain size. Twenty samples of water and bank sediments were collected during July 2014 from River Ramganga and its tributaries to identify with their individual influence. The result for the spatial variation corresponding to the size of bank and suspended sediment of the river varies from 85.8 to 370.3 μm and 6.3 to 222.6 μm, respectively, in case of mainstream, whereas 68.3–338.8 μm and 9.6–116.9 μm, respectively, in case of tributaries. The result also shows that suspended sediments are mostly very fine sand to coarse silt in nature. In the case of bank sediments, grain size mostly varies from fine sand to very fine sand in nature.
Mohd Yawar Ali Khan. Spatial Variation in the Grain Size Characteristics of Sediments in Ramganga River, Ganga Basin, India. Handbook of Environmental Materials Management 2019, 2485 -2495.
AMA StyleMohd Yawar Ali Khan. Spatial Variation in the Grain Size Characteristics of Sediments in Ramganga River, Ganga Basin, India. Handbook of Environmental Materials Management. 2019; ():2485-2495.
Chicago/Turabian StyleMohd Yawar Ali Khan. 2019. "Spatial Variation in the Grain Size Characteristics of Sediments in Ramganga River, Ganga Basin, India." Handbook of Environmental Materials Management , no. : 2485-2495.
Rapid urbanization and global warming have caused a sequence of ecological issues in China including degradation of lake water environments which is one of the many consequences. Lakes are an important part of a biological system where a plethora of amphibian plants and animals reside. Other than this, they have a noteworthy impact in providing water for landscape irrigation, for domestic utilization, and most importantly sustaining a healthy ecosystem. Poyang Lake is the largest freshwater lake of China, with its rich water and biological resources for irrigation, water supply, shipping, and regulation of the flow; additionally, this lake can relieve the impact of droughts and floods by storing huge quantities of water and discharging it during shortages. However, the water environment is a standout among the most critical issues in Poyang Lake. This paper proposes two classification algorithms, i.e., classification and regression trees algorithm and particle swarm optimization + k-nearest neighbors algorithm to build up a connection between the water level and the primary water quality parameters of Poyang Lake. Two models have been trained with 8 years of data (2002~2008) and verified with 1 year of data (2009). Water quality forecasts from the particle swarm optimization + k-nearest neighbors algorithm was observed to be better when compared with the results obtained from the classification and regression trees algorithm. Finally, the category of the water quality was evaluated using 3 years of water level data (2010~2012) as an input to the particle swarm optimization + k-nearest neighbors algorithm.
Yilu Li; Mohd Yawar Ali Khan; Yunzhong Jiang; Fuqiang Tian; Weihong Liao; Shasha Fu; Changgao He. CART and PSO+KNN algorithms to estimate the impact of water level change on water quality in Poyang Lake, China. Arabian Journal of Geosciences 2019, 12, 287 .
AMA StyleYilu Li, Mohd Yawar Ali Khan, Yunzhong Jiang, Fuqiang Tian, Weihong Liao, Shasha Fu, Changgao He. CART and PSO+KNN algorithms to estimate the impact of water level change on water quality in Poyang Lake, China. Arabian Journal of Geosciences. 2019; 12 (9):287.
Chicago/Turabian StyleYilu Li; Mohd Yawar Ali Khan; Yunzhong Jiang; Fuqiang Tian; Weihong Liao; Shasha Fu; Changgao He. 2019. "CART and PSO+KNN algorithms to estimate the impact of water level change on water quality in Poyang Lake, China." Arabian Journal of Geosciences 12, no. 9: 287.
The Yarlung Tsangpo-Brahmaputra River (YBR) originating from the Tibetan Plateau (TP), is an important water source for many domestic and agricultural practices in countries including China, India, Bhutan and Bangladesh. To date, only a few studies have investigated the impacts of climate change on water resources in this river basin with dispersed results. In this study, we provide a comprehensive and updated assessment of the impacts of climate change on YBR streamflow by integrating a physically based hydrological model, regional climate integrations from CORDEX (Coordinated Regional Climate Downscaling Experiment), different bias correction methods, and Bayesian model averaging method. We find that (i) bias correction is able to reduce systematic biases in regional climate integrations and thus benefits hydrological projections over YBR Basin; (ii) Bayesian model averaging, which optimally combines individual hydrological simulations obtained from different bias correction methods, tends to provide hydrological time series superior over individual ones. We show that by the year 2035, the annual mean streamflow is projected to change respectively by 6.8%, −0.4%, and − 4.1% under RCP4.5 relative to the historical period (1980–2001) at the Bahadurabad in Bangladesh, the upper Brahmaputra outlet, and Nuxia in China. Under RCP8.5, these percentage changes will substantially increase to 12.9%, 13.1%, and 19.9%. Therefore, the change rate of streamflow shows strong spatial variability along the YBR from downstream to upstream. The increasing rate of streamflow shows an augmented trend from downstream to upstream under RCP8.5 compared to an attenuated pattern under RCP4.5.
Ran Xu; Hongchang Hu; Fuqiang Tian; Chao Li; Mohd Yawar Ali Khan. Projected climate change impacts on future streamflow of the Yarlung Tsangpo-Brahmaputra River. Global and Planetary Change 2019, 175, 144 -159.
AMA StyleRan Xu, Hongchang Hu, Fuqiang Tian, Chao Li, Mohd Yawar Ali Khan. Projected climate change impacts on future streamflow of the Yarlung Tsangpo-Brahmaputra River. Global and Planetary Change. 2019; 175 ():144-159.
Chicago/Turabian StyleRan Xu; Hongchang Hu; Fuqiang Tian; Chao Li; Mohd Yawar Ali Khan. 2019. "Projected climate change impacts on future streamflow of the Yarlung Tsangpo-Brahmaputra River." Global and Planetary Change 175, no. : 144-159.
Vegetation exerts profound influences on evapotranspiration (ET) partitioning. Many studies have demonstrated the positive impact of vegetation cover on the ratio of transpiration (T) to ET. Whether it is universally true with regard to different vegetation types and different sites is understudied. In this study, five sites in Northern China with different vegetation types were selected for comparison study.ET partitioning is conducted using an approach based on the concept of the underlying water use efficiency with eddy covariance measurements. The results show various patterns of vegetation’s effects over ET partitioning and, when compared with existing studies, also reveal a new relationship between the T/ET ratio and Normalized Difference Vegetation Index (NDVI) at some of the sites. At the alpine meadow site, the T/ET ratio gradually increase when NDVI is low and rapidly increase as NDVI go beyond a certain value, whereas at the arid shrub site, the T/ET ratio rapidly increase when NDVI is low and plateaus at a certain value when NDVI reaches a relatively high value. In deciduous forest, the T/ET ratio becomes unresponsive to NDVI beyond a threshold value. This study also reveals that irrigation schemes play a major role in determining the correlation between the T/ET ratio and NDVI because the T/ET ratio becomes well correlated with NDVI in case of flood irrigation and irrelevant to NDVI in the case of mulch drip irrigation. Furthermore, this study helps us to understand ET partitioning under different sites and different human activities such as irrigation. These findings can help policymakers to better understand the connection between vegetation and climate change or human activities and provide significant information for water management policy.
Hongchang Hu; Lajiao Chen; Hui Liu; Mohd Yawar Ali Khan; Qiang Tie; Xuejun Zhang; Fuqiang Tian. Comparison of the Vegetation Effect on ET Partitioning Based on Eddy Covariance Method at Five Different Sites of Northern China. Remote Sensing 2018, 10, 1755 .
AMA StyleHongchang Hu, Lajiao Chen, Hui Liu, Mohd Yawar Ali Khan, Qiang Tie, Xuejun Zhang, Fuqiang Tian. Comparison of the Vegetation Effect on ET Partitioning Based on Eddy Covariance Method at Five Different Sites of Northern China. Remote Sensing. 2018; 10 (11):1755.
Chicago/Turabian StyleHongchang Hu; Lajiao Chen; Hui Liu; Mohd Yawar Ali Khan; Qiang Tie; Xuejun Zhang; Fuqiang Tian. 2018. "Comparison of the Vegetation Effect on ET Partitioning Based on Eddy Covariance Method at Five Different Sites of Northern China." Remote Sensing 10, no. 11: 1755.
The information on suspended sediments of river is considered to be crucial for issues concerning water management and the environment. The abrupt quantity and nature of sediment loads can be best studied by simultaneously considering the governing variables contributing towards this physical phenomenon. Artificial Neural Network (ANN) is one of the suitable data-mining technique which helps in carrying out the modelling of this phenomenon. In this study, ANNs are employed to approximate the monthly mean suspended sediment load for Ramganga River. Three simulations with rainfall and water discharge data were carried out to predict the suspended sediment load. In terms of the selected performance criteria, three algorithms were evaluated and the results so obtained are presented. It has been found that rainfall values were not sufficient to correctly predict the suspended sediment load. However, considering water discharge values as input improves the performance of all the three considered algorithms.
Mohd Yawar Ali Khan; Faisal Hasan; Fuqiang Tian. Estimation of suspended sediment load using three neural network algorithms in Ramganga River catchment of Ganga Basin, India. Sustainable Water Resources Management 2018, 5, 1115 -1131.
AMA StyleMohd Yawar Ali Khan, Faisal Hasan, Fuqiang Tian. Estimation of suspended sediment load using three neural network algorithms in Ramganga River catchment of Ganga Basin, India. Sustainable Water Resources Management. 2018; 5 (3):1115-1131.
Chicago/Turabian StyleMohd Yawar Ali Khan; Faisal Hasan; Fuqiang Tian. 2018. "Estimation of suspended sediment load using three neural network algorithms in Ramganga River catchment of Ganga Basin, India." Sustainable Water Resources Management 5, no. 3: 1115-1131.
The relation between the water discharge (Q) and suspended sediment concentration (SSC) of the River Ramganga at Bareilly, Uttar Pradesh, in the Himalayas, has been modeled using Artificial Neural Networks (ANNs). The current study validates the practical capability and usefulness of this tool for simulating complex nonlinear, real world, river system processes in the Himalayan scenario. The modeling approach is based on the time series data collected from January to December (2008–2010) for Q and SSC. Three ANNs (T1-T3) with different network configurations have been developed and trained using the Levenberg Marquardt Back Propagation Algorithm in the Matlab routines. Networks were optimized using the enumeration technique, and, finally, the best network is used to predict the SSC values for the year 2011. The values thus obtained through the ANN model are compared with the observed values of SSC. The coefficient of determination (R2), for the optimal network was found to be 0.99. The study not only provides insight into ANN modeling in the Himalayan river scenario, but it also focuses on the importance of understanding a river basin and the factors that affect the SSC, before attempting to model it. Despite the temporal variations in the study area, it is possible to model and successfully predict the SSC values with very simplistic ANN models.
Mohd Yawar Ali Khan; Fuqiang Tian; Faisal Hasan; Govind Joseph Chakrapani. Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga, Ganges Basin, India. International Journal of Sediment Research 2018, 34, 95 -107.
AMA StyleMohd Yawar Ali Khan, Fuqiang Tian, Faisal Hasan, Govind Joseph Chakrapani. Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga, Ganges Basin, India. International Journal of Sediment Research. 2018; 34 (2):95-107.
Chicago/Turabian StyleMohd Yawar Ali Khan; Fuqiang Tian; Faisal Hasan; Govind Joseph Chakrapani. 2018. "Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga, Ganges Basin, India." International Journal of Sediment Research 34, no. 2: 95-107.
The Ramganga River is the first major tributary of Ganga River, which flows from the hilly region of Kumaon Himalayas throughout the forests of Jim Corbett National Park in Uttarakhand district and the Ganga floodplains in Uttar Pradesh. Due to high amount of sediment load and water discharge carrying by Ramganga River, causing numerous floods in major cities of Uttar Pradesh. Evaluation of grain size and sediment yield is important for studying sediment erosion rate, for dams and reservoir engineering, for predicting the response of climate and anthropogenic activities on river system, and for understanding the trace and heavy metal micropollutants and pathogens. In the present study, the characteristics of suspended and bank sediments were assessed pertaining to the grain size. Twenty samples of water and bank sediments were collected during July 2014 from River Ramganga and its tributaries to identify with their individual influence. The result for the spatial variation corresponding to the size of bank and suspended sediment of the river varies from 85.8 to 370.3 μm and 6.3 to 222.6 μm, respectively, in case of mainstream, whereas 68.3–338.8 μm and 9.6–116.9 μm, respectively, in case of tributaries. The result also shows that suspended sediments are mostly very fine sand to coarse silt in nature. In the case of bank sediments, grain size mostly varies from fine sand to very fine sand in nature.
Mohd Yawar Ali Khan. Spatial Variation in the Grain Size Characteristics of Sediments in Ramganga River, Ganga Basin, India. Handbook of Environmental Materials Management 2018, 1 -11.
AMA StyleMohd Yawar Ali Khan. Spatial Variation in the Grain Size Characteristics of Sediments in Ramganga River, Ganga Basin, India. Handbook of Environmental Materials Management. 2018; ():1-11.
Chicago/Turabian StyleMohd Yawar Ali Khan. 2018. "Spatial Variation in the Grain Size Characteristics of Sediments in Ramganga River, Ganga Basin, India." Handbook of Environmental Materials Management , no. : 1-11.
Development of infrastructure needs enormous natural Earth materials in the form of coarse and fine river aggregate materials. In India, flood plains of the Himalayan Rivers serve as an important source of river sand, leading to extensive sand mining. River Ramganga, the first major tributary of River Ganga, is one such river. In this study, 28 samples of river sediments across the stretch of the river were collected over two seasons: pre-monsoon and monsoon. The engineering properties of these sediments were studied with respect to the specifications of the Bureau of Indian Standards (BIS) and the American Society for Testing and Materials (ASTM) to understand the suitability of their use as fine aggregates in construction. An attempt has also been made in this study to correlate the variability of these properties with respect to the location and time of collection to the Ramganga River Dam at Kalagarh, the contribution of major tributaries, and the effect of monsoon. A pattern emerges from the variation of the physical properties that is explicable by these factors, whereas in general, the variation of the chemical properties does not follow a regular pattern.
Shaumik Daityari; Mohd Yawar Ali Khan. Temporal and spatial variations in the engineering properties of the sediments in Ramganga River, Ganga Basin, India. Arabian Journal of Geosciences 2017, 10, 134 .
AMA StyleShaumik Daityari, Mohd Yawar Ali Khan. Temporal and spatial variations in the engineering properties of the sediments in Ramganga River, Ganga Basin, India. Arabian Journal of Geosciences. 2017; 10 (6):134.
Chicago/Turabian StyleShaumik Daityari; Mohd Yawar Ali Khan. 2017. "Temporal and spatial variations in the engineering properties of the sediments in Ramganga River, Ganga Basin, India." Arabian Journal of Geosciences 10, no. 6: 134.
The River Ganges being the most sacred river and lifeline to millions of Indians in serving their water requirements is facing excessive threat of pollution. Under various river management and conservation strategies for its protection, the assessment of water quality of its main tributary Ramganga River is lacking. This study focuses on assessment of physicochemical and heavy metal pollution of the Ramganga River by application of multivariate statistical techniques. Sampling of Ramganga River at sixteen sampling sites was carried out in three seasons (summer, monsoon and winter) of 2014. The collected water samples were analyzed for physicochemical parameters and heavy metals. Results from cluster analysis (CA) of the data divided the whole stretch of the river into three clusters as elevation from 1304 to 259 m as less polluted, from 207 to 154 m as moderately polluted and from elevation 154 to 139 m as high-polluted stretches with anthropogenic as main sources of pollution in high-polluted stretch. Principal component analysis of the seasonal dataset resulted in three significant principal components (PC) in each season explaining 72–8% of total variance with strong loadings (>0.75) of PC1 on fluoride (F−), chloride (Cl−), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), bicarbonate (HCO3−), total dissolved solids and electrical conductivity. Temporal variation by one-way ANOVA (Analysis of Variance) showed significant seasonal variation was in the pH, chemical oxygen demand, biochemical oxygen demand, turbidity, HCO3−, F−, Zn, cadmium (Cd) and Mn (p < 0.05). Turbidity showed approximately a twofold increase in monsoon season due to rainfall in the catchment area and subsequent flow of runoff into the river. Concentration of HCO3−, F− and pH also showed similar increase in monsoon. The concentration of Zn, Cd and Mn showed an increasing trend in summers compared to monsoon and winter season due to dilution effect in the monsoon season and its lasting effect in winters.
Mohd Yawar Ali Khan; Khalid Gani; Govind Joseph Chakrapani. Spatial and temporal variations of physicochemical and heavy metal pollution in Ramganga River—a tributary of River Ganges, India. Environmental Earth Sciences 2017, 76, 231 .
AMA StyleMohd Yawar Ali Khan, Khalid Gani, Govind Joseph Chakrapani. Spatial and temporal variations of physicochemical and heavy metal pollution in Ramganga River—a tributary of River Ganges, India. Environmental Earth Sciences. 2017; 76 (5):231.
Chicago/Turabian StyleMohd Yawar Ali Khan; Khalid Gani; Govind Joseph Chakrapani. 2017. "Spatial and temporal variations of physicochemical and heavy metal pollution in Ramganga River—a tributary of River Ganges, India." Environmental Earth Sciences 76, no. 5: 231.
Discharges and water levels are essential components of river hydrodynamics. In unreachable terrains and ungauged locations, it is quite difficult to measure these parameters due to rugged topography. In the present study an artificial neural network model has been developed for the Ramganga River catchment of the Ganga Basin. The modelled network is trained, validated and tested using daily water flow and level data pertaining to 4 years (2010–2013). The network has been optimized using an enumeration technique and a network topology of 4-10-2 with a learning rate set at 0.06, which was found optimum for predicting discharge and water-level values for the considered river. The mean square error values obtained for discharge and water level for the tested data were found to be 0.046 and 0.012, respectively. Thus, monsoon flow patterns can be estimated with an accuracy of about 93.42%.
Mohd Yawar Ali Khan; F. Hasan; S. Panwar; G. J. Chakrapani. Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin, India. Hydrological Sciences Journal 2016, 61, 2084 -2095.
AMA StyleMohd Yawar Ali Khan, F. Hasan, S. Panwar, G. J. Chakrapani. Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin, India. Hydrological Sciences Journal. 2016; 61 (11):2084-2095.
Chicago/Turabian StyleMohd Yawar Ali Khan; F. Hasan; S. Panwar; G. J. Chakrapani. 2016. "Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin, India." Hydrological Sciences Journal 61, no. 11: 2084-2095.
Mohd Yawar Ali Khan; Babra Khan; Govind Joseph Chakrapani. Assessment of spatial variations in water quality of Garra River at Shahjahanpur, Ganga Basin, India. Arabian Journal of Geosciences 2016, 9, 1 .
AMA StyleMohd Yawar Ali Khan, Babra Khan, Govind Joseph Chakrapani. Assessment of spatial variations in water quality of Garra River at Shahjahanpur, Ganga Basin, India. Arabian Journal of Geosciences. 2016; 9 (8):1.
Chicago/Turabian StyleMohd Yawar Ali Khan; Babra Khan; Govind Joseph Chakrapani. 2016. "Assessment of spatial variations in water quality of Garra River at Shahjahanpur, Ganga Basin, India." Arabian Journal of Geosciences 9, no. 8: 1.
The Ramganga River flows from the mountainous regions of Kumaon Himalayas, through the forests of Jim Corbett National Park and the Ganga flood plains. It is the first major tributary of the Ganga River, carrying high sediment load causing frequent floods in major cities of Uttar Pradesh. The water discharge of the river is controlled by glacial melt as well as precipitation, making it a perennial river. This study is on the temporal and spatial variation of water discharge and sediment flux of the Ramganga River and identifies the factors which control them. In this study, 84 samples were collected from different locations over the 642 km stretch of the river and its major tributaries to observe the temporal and spatial variation of suspended matter in river water. In addition, daily water flow and sediment concentration data of two locations, e.g. Bareilly and Dabri, for a duration of 10 years were used to understand the variation in those parameters over an extended time period. An attempt was also made to relate meandering to the change in water discharge and sediment flux in the Ganga flood plains. Human activities also contribute to the sediment concentration. The results of this study showed that a significant amount of water flow and sediment flux (>75 %) were attributed to the monsoon months. However, in 2009, the results were not similar to other years, probably because of low rainfall due to the occurrence of an El Niño.
M. Y. A. Khan; S. Daityari; G. J. Chakrapani. Factors responsible for temporal and spatial variations in water and sediment discharge in Ramganga River, Ganga Basin, India. Environmental Earth Sciences 2016, 75, 1 -18.
AMA StyleM. Y. A. Khan, S. Daityari, G. J. Chakrapani. Factors responsible for temporal and spatial variations in water and sediment discharge in Ramganga River, Ganga Basin, India. Environmental Earth Sciences. 2016; 75 (4):1-18.
Chicago/Turabian StyleM. Y. A. Khan; S. Daityari; G. J. Chakrapani. 2016. "Factors responsible for temporal and spatial variations in water and sediment discharge in Ramganga River, Ganga Basin, India." Environmental Earth Sciences 75, no. 4: 1-18.
Alaknanda River is a major tributary of river Ganga in its upper catchment area. The significant feature of the river is its high rates of sediment erosion and chemical denudation. The Alaknanda River catchment area has many major hydroelectric power projects and is always in the news because of environmental concerns. In the present study, both suspended and bank sediment characteristics of the river were evaluated for grain size and composition. The results show that suspended and bank sediment sizes vary from 8.79 to 56.34 µm and 23.73 to 563.24 µm, respectively. Suspended sediments are mostly finer to symmetrically skewed, whereas the tributaries are found to be negatively skewed. In case of bank sediments, grains are moderately sorted and mesokurtic. The provenance determination using rare earth elements and major oxide composition in suspended and bank sediments shows the Higher Himalayas as the dominant supplier of sediments. Along with the sediments, the source reservoir of dissolved load was determined using the water composition and an often used forward model. The results of forward model show silicate and carbonate weathering processes as a major contributor of dissolved load. Using this multi-approach of dealing with sediment and water, geochemical nature of water and sediments of the Alaknanda River has been better understood from the present study.
S. Panwar; M. Y. A. Khan; G. J. Chakrapani. Grain size characteristics and provenance determination of sediment and dissolved load of Alaknanda River, Garhwal Himalaya, India. Environmental Earth Sciences 2016, 75, 1 -15.
AMA StyleS. Panwar, M. Y. A. Khan, G. J. Chakrapani. Grain size characteristics and provenance determination of sediment and dissolved load of Alaknanda River, Garhwal Himalaya, India. Environmental Earth Sciences. 2016; 75 (2):1-15.
Chicago/Turabian StyleS. Panwar; M. Y. A. Khan; G. J. Chakrapani. 2016. "Grain size characteristics and provenance determination of sediment and dissolved load of Alaknanda River, Garhwal Himalaya, India." Environmental Earth Sciences 75, no. 2: 1-15.
Rivers constitute the lifeline for any country. Both natural and anthropogenic processes influence the river processes. Recently, erosion processes and fluvial transport of materials have become a focus of reviving attention owing to their significance in land use and environmental aspects. To understand the erosion process and sediment geochemistry, more than 25 samples were collected from river Ramganga. Ramganga River originates from a Namik glacier in Gairsain village of Chamoli district in Uttarakhand lying at an elevation of 2926metres. Ramganga River flows in the Kumaon Himalayas and is the first major tributary of river Ganga in the Indo-Gangetic plains. The study area includes the entire catchment of river Ramganga covering a stretch of approx. 350 km from the Chamoli district in Uttarakhand to Farrukhabad district in Uttar Pradesh before joining with the Ganga River on its left bank at an elevation of 124 meters. The river bank sediments were evaluated for their size determination by using the particle size analyzer. All the sediments were treated with H2O2 to remove organic matter; the computed results were interpreted using the statistical approach mentioned by [2]. Most of the sediment were found to be of sand grade and are well sorted to poorly sorted in nature. Population of sediments varies from being coarse skewed to fine skewed. An attempt has been made to find out the factors controlling the sediment size. Multivariate analysis was performed to find out the relation of sediment size with discharge and climatic conditions such as rainfall. The results point out the role of topographical, lithological, and climatic factors in determining the grain size of sediments.
M. Y. A. Khan; G. J. Chakrapani. Particle Size Characteristics of Ramganga Catchment Area of Ganga River. Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment 2016, 307 -312.
AMA StyleM. Y. A. Khan, G. J. Chakrapani. Particle Size Characteristics of Ramganga Catchment Area of Ganga River. Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment. 2016; ():307-312.
Chicago/Turabian StyleM. Y. A. Khan; G. J. Chakrapani. 2016. "Particle Size Characteristics of Ramganga Catchment Area of Ganga River." Geostatistical and Geospatial Approaches for the Characterization of Natural Resources in the Environment , no. : 307-312.
Ramganga River is the main tributary of the Ganges River which is the most sacred and largest river basin of India. For effective management of Ganges, assessment of water quality in its tributaries is must, and this river lacks it so far. The present study focuses on the evaluation of water quality of this river and its adjoining tributaries. Organic pollution indicators, chemical oxygen demand (COD) and biological oxygen demand (BOD5) of river water ranges from 15.2 to 55.5 mg/L and 7.1 to 29 mg/L, respectively. Nutrient parameters nitrate (NO3−-N) and phosphate (PO42−-P) of river water ranges from 0.2 to 12.7 mg/L and 0.02 to 0.76 mg/L, respectively. While in tributaries, these parameters range from 0.2 to 9.9 mg/L and 0.03 to 1.47 mg/L, respectively. The most polluted stretches of river were from Moradabad to Farrukhabad via Bareilly especially in terms of organic pollution. Pair sample t test applied to compare the water quality of river and its tributaries revealed no significant difference in COD, NO3−-N, PO42—P, and fluoride (F−) while sulfate (SO42−) was significantly large (25.1 mg/L) in tributaries. The spatial variation in water quality of river was addressed by cluster analysis (CA) which grouped the 16 sampling points into three significant clusters corresponding to lower pollution, moderate pollution, and severe pollution regions. The results from CA restructure the entire sampling campaign to a cheaper and less-effort sampling program that will be helpful in water quality assessment and management of the river.
Mohd Yawar Ali Khan; Khalid Muzamil Gani; Govind Joseph Chakrapani. Assessment of surface water quality and its spatial variation. A case study of Ramganga River, Ganga Basin, India. Arabian Journal of Geosciences 2015, 9, 1 -9.
AMA StyleMohd Yawar Ali Khan, Khalid Muzamil Gani, Govind Joseph Chakrapani. Assessment of surface water quality and its spatial variation. A case study of Ramganga River, Ganga Basin, India. Arabian Journal of Geosciences. 2015; 9 (1):1-9.
Chicago/Turabian StyleMohd Yawar Ali Khan; Khalid Muzamil Gani; Govind Joseph Chakrapani. 2015. "Assessment of surface water quality and its spatial variation. A case study of Ramganga River, Ganga Basin, India." Arabian Journal of Geosciences 9, no. 1: 1-9.