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dc.contributor.authorPujari, Manoj Kumar-
dc.contributor.authorSwain, Ratnakar-
dc.date.accessioned2024-03-22T09:59:33Z-
dc.date.available2024-03-22T09:59:33Z-
dc.date.issued2024-03-
dc.identifier.citation3rd Roorkee Water Conclave, Responsible Water management and Circular Economy (RWC), IIT Roorkee, 03-06 March 2024en_US
dc.identifier.urihttp://hdl.handle.net/2080/4502-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractThis study examined the effects of land use land cover (LULC)and climate change on streamflow, sediment yield, and nutrient loading in the Rengali catchment of the Brahmani basin, located in the Eastern part of India. The LULC was determined by using Landsat images. At the same time, climate data were obtained from eleven general circulation models (five models from CMIP5 and six models from CMIP6) for representative pathways (RCP) 4.5 (moderate emission) and 8.5 (high emission). An Artificial Neural Network (ANN) method was employed to forecast future LULC and its variations from 2000 to 2075. This was achieved by creating a Transition Probability Matrix that considers socioeconomic developments. The SWAT (Soil and Water Assessment Tool) model was used to evaluate the effects of LULC and climate changes on streamflow, sediment yield, and nutrient loading. The results showed that a reduction in forest area, water bodies, and barren land, along with an expansion of agricultural area and built-up areas between 2000 and 2075, would result in increased mean streamflow, sediment yield, and nutrient loading (nitrogen and phosphorus). Streamflow, sediment yield, nitrogen, and phosphorus loading show changes due to climate change. In RCP 4.5, these changes are 15.85%, 17.77%, 4.39%, and 4.83%, respectively, while in RCP 8.5, they are 16.27%, 18.59%, 2.36%, and 3.98% by 2075 compared to the baseline. Additionally, land use and climate changes result in variations of 16.02%, 17.32%, 5.32%, and 6.47% in RCP 4.5 and 17.97%, 20.53%, 7.36%, and 7.08% in RCP 8.5 by 2075 compared to the baseline condition. This research enhances the field of environmental modelling by offering a solid framework. It enables well informed decision making to protect and improve water quantity and quality in the ever-changing study area.en_US
dc.subjectNutrient loadingen_US
dc.subjectArtificial Neural Network (ANN)en_US
dc.subjectWater qualityen_US
dc.subjectClimate Changeen_US
dc.titleUnravelling the Intricacies of Hydrological System Dynamics: A Case Study on the Impact of Climate and Land Use Shiftsen_US
dc.typeArticleen_US
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