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Modelling of non-point source pollution in a watershed using remote sensing and gis
Authors:V M Chowdary  Yatindranath  S Kar  S Adiga
Institution:(1) Department of Water Resources Development and Management, IIT Roorkee, Roorkee, Uttarakhand, 247 667, India;(2) RRSSC Campus IIT Kharagpur, Kharagpur, 721 302, India;(3) Department of Agricultural and Food Engineering, IIT Kharagpur, Kharagpur, 721 302, India
Abstract:Assessment of the environmental impact of Non Point Source (NPS) pollutants on a global, regional and localized scale is the key component for achieving sustainability of agriculture as well as preserving the environment. The knowledge and information required to address the problem of assessing the impact of NPS pollutants like Nitrogen (N), Phosphorus (P), etc., on the environment crosses several sub-disciplines like remote sensing, Geographical Information System (GIS), hydrology and soil science. The remote sensing data, by virtue of its potential like synopticity, multi-spectral and multi-temporal capability, computer compatibility, besides providing almost real time information, has enhanced the scope of automation of mapping dynamic elements, such as land use/land cover, degradation profile and computing the priority categorisation of sub-watersheds. The present study demonstrates the application of remote sensing, GIS and distributed parameter model Agricultural Non-Point Source Pollution Model (AGNPS) in the assessment of hazardous non-point source pollution in a watershed. The ARC-INFO GIS and remote sensing provided the input data to support modelling, while the AGNPS model predicted runoff, sediment and pollutant (N and P) transport within a watershed. The integrated system is used to evaluate the sediment pollution in about 2700 ha Karso watershed located in Hazaribagh area of Jharkhand State, India. The predicted values of runoff and sediment yield copared reasonably well with the measured values. It is important to emphasize that this study is not intended to characterise, in an exhaustive manner. Instead, the goal is to illustrate the implications and potential advantages of GIS and remote sensing based Hydrology and Water quality (H/WQ) modelling framework.
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