Characterizing Monsoonal Variation on Water Quality Index of River Mahi in India using Geographical Information System

Srivastava, P. K.; Mukherjee, Saumitra; Gupta, Manika; Singh, Sudhir Kumar

doi:10.1007/s12403-011-0038-7

Cited by 94 publications

(43 citation statements)

References 35 publications

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“…In addition, as the current calendar-based method is proposed to visualize the water quality change and make the interpretation of water condition intuitive, the proposed method can also be applied to the water resources community so as to evaluate how effective it is in communicating water quality with the public. Social surveys could be conducted to compare our method with other communicating methods, such as statistical charts [14,17], heat maps [20], dynamic maps [22,24], and information graphs [36], from the aesthetics, efficiency, and accuracy aspects.…”

Section: Discussionmentioning

confidence: 99%

A Novel Calendar-Based Method for Visualizing Water Quality Change: The Case of the Yangtze River 2006–2015

Huang

Zhong

Gan

et al. 2017

Water

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Abstract:Mapping water quality change is helpful in the study of the water environment of a region. Using visual methods, interpretation of water condition and pollution issues can be intuitive and easy to understand. In this paper, we present a map to represent the spatial and temporal variation of water quality change in the Yangtze River during the period from 2006 to 2015. The calendar view was developed to explore the water quality condition and water pollutants for sections of the Yangtze River. A "W" construction was proposed to arrange the weekly water quality data in a continuous logic, and qualitative colors were designed to identify the change in major pollutants throughout the period. This map provides a promising visual analytical approach to investigate the water quality status and reveal the spatial and temporal patterns of water quality change in the Yangtze River.

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Section: Discussionmentioning

confidence: 99%

A Novel Calendar-Based Method for Visualizing Water Quality Change: The Case of the Yangtze River 2006–2015

Huang

Zhong

Gan

et al. 2017

Water

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“…Many studies have proved the use of EO data namely groundwater [27,46,48,51,52], river water quality [55], coastal water [22], lake and wetlands [5,47,53,59,60], land use/land cover mapping [45,46,48,50,51], land use change trajectories [56], land use/land cover modeling [28,49], urban land use dynamics [4], hydrological modeling [31], forest mapping [44], cyclone tracking [16], soil characterization [37], climate change [54], slope estimation [57], landscape ecology [47,53], ocean studies [35,36] and watershed management [67], watershed prioritization [68]. GIS processing has become a critical step in hydrologic modeling since it contributes to generate model parameters in a spatially distributed manner.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Surface Runoff from Semi-arid Ungauged Agricultural Watershed Using SCS-CN Method and Earth Observation Data Sets

Rawat

Singh

2017

Water Conserv Sci Eng

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In the present study, Soil Conservation ServiceCurve Number (SCS-CN) method, Earth Observation (EO) data sets and Geographic Information System (GIS) have been used in order to estimate the surface runoff from Jhagrabaria an agricultural watershed of Allahabad district, Uttar Pradesh (India). LANDSAT-7ETM+, NOAA data and hydrologic soil groups have been used to prepare land use/land cover, rainfall and soil map. The traditional SCS-CN method for calculating the composite CN is very tedious and time demanding process of the hydrologic modeling. Therefore, GIS is now being used in combination with the SCS-CN method. The outcome of work showed 79.35 (CN II ) of normal condition, of dry condition 61.76 (CN I ) and of wet condition 89.84 (CN III ), respectively. This investigation outline that ungauged watershed exhibits an annual average of 14 years runoff volume as 3.58 × 10 6 m 3 from an average annual rainfall of 14 years 110.77 cm and the average annual surface runoff of 14 years was 23.83 cm and annual average runoff coefficient of 14 years was 0.22. The correlation analysis suggests that the strong correlation as R 2 (0.91) was observed between satellite drive rainfall and runoff from SCS-CN method. The developed rainfall-runoff model for the region will be useful to understand the watershed and its runoff flow characteristics.

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“…The clustering methods have been widely used in the water quality studies. For example: 1) clustering for chemical classification of water in Salado River [13]; 2) Hierarchical agglomerative cluster analysis for delineating and grouping pollution causing areas [14]; and 3) Fuzzy clustering of water quality parameters for Ulansuhai Lake [15]. In addition to classification of water quality, it is also important to understand the impact of causative factors on the surface water quality of rivers in Alberta.…”

Section: Introductionmentioning

confidence: 99%

Clusterization of Surface Water Quality and Its Relation to Climate and Land Use/Cover

Akbar¹,

Hassan²,

Achari³

2013

JEP

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The quality of surface water is rapidly changing due to climatic variations, natural processes, and anthropogenic activities. The objectives of this study were to classify and analyze the surface water quality of 12 major rivers of Alberta on the basis of 17 parameters during the period of five years (i.e., 2004-2008) using principal component analysis (PCA), total exceedance model and clustering technique. Seven major principal components (PCs) with variability of about 89% were identified. These PCs were the indicators of watershed geology, mineralization and anthropogenic activities related to land use/cover. The seven dominant parameters revealed from the seven PCs were total dissolved solids (TDS), true color (TC), pH, iron (Fe), fecal coliform (FC), dissolved oxygen (DO), and turbidity (TUR). The normalized data of dominant parameters were used to develop a model for obtaining total exceedance. The exceedance values acquired from the total exceedance model were used to determine the patterns for the development of five clusters. The performance of the clusters was compared with the classes obtained in Canadian Water Quality Index (CWQI). Cluster 1, cluster 2, cluster 3, cluster 4 and cluster 5 showed agreements of 85.71%, 83.54%, 90.22%, 80.74%, and 83.40% with their respective CWQI classes on the basis of the data for all rivers during [2004][2005][2006][2007][2008]. The water quality was deteriorated in growing season due to snow melting. This methodology could be applied to classify the raw surface water quality, analyze the spatio-temporal trends and study the impacts of the factors affecting the water quality anywhere in the world.

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Characterizing Monsoonal Variation on Water Quality Index of River Mahi in India using Geographical Information System

Cited by 94 publications

References 35 publications

A Novel Calendar-Based Method for Visualizing Water Quality Change: The Case of the Yangtze River 2006–2015

A Novel Calendar-Based Method for Visualizing Water Quality Change: The Case of the Yangtze River 2006–2015

Estimation of Surface Runoff from Semi-arid Ungauged Agricultural Watershed Using SCS-CN Method and Earth Observation Data Sets

Clusterization of Surface Water Quality and Its Relation to Climate and Land Use/Cover

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