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RISS 인기검색어
- 검색키워드
- 저자 : K. V. Jayakumar (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Assessment of environmental flows using hydrological methods for Krishna River, India
Uday Kumar, A.,Jayakumar, K.V. Techno-Press 2018 Advances in environmental research Vol.7 No.3
Krishna River is significantly affected due to Srisailam dam from past 30 years. The impact of this hydraulic structure drastically reduced the minimum flow regime on the downstream, which made the river nearing to decaying stage. In the present paper, Environmental Flow called minimum flow values released for the dam are estimated with the help of three hydrological methods viz., Range of variability Approach (RVA), Desktop Reserve Model (DRM), and Global Environmental Flow Calculator (GEFC). DRM method suggested considering the intermediate values obtained from among the three methods to preserve the ecosystem on the downstream of the river, which amounts to an average annual allocation of 9378 Million Cubic Meter (MCM) which is equal to 23.11% of mean annual flow (MAF). In this regard GEFC and RVA methods accounted for 22% and 31.04% of MAF respectively. The results indicate that current reservoir operation policy is causing a severe hydrological alteration in the high flow season especially in the month of July. The study concluded that in the case of non-availability of environmental information, hydrological indicators can be used to provide the basic assessment of environmental flow requirements. It is inferred from the results obtained from the study, that the new reservoir operations can fulfil human water needs without disturbing Environmental Flow Requirements.
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Visualisation of urban flood inundation using SWMM and 4D GIS
P. Z. Seenu,E. Venkata Rathnam,K. V. Jayakumar 대한공간정보학회 2020 Spatial Information Research Vol.28 No.4
The areas vulnerable to flood disaster can be identified by using appropriate technologies for flood vulnerability mapping and visualisation. This paper describes the study taken up for flood inundation mapping using storm water management model (SWMM) and four-dimensional geographic information system for a part of Hyderabad city. Visualising the results from the SWMM simulation with respect to the spatial and temporal variation using Geographic Information System provides a better view of flood extension in the complex urban area. Thirty years of hourly rainfall data were available for the study from India Meteorological Department. Using this, the intensity–duration–frequency relationship has been developed and peak rainfall events have been identified. Variation of discharge with respect to time is simulated using SWMM. ArcScene is used for spatial and temporal variation of flood inundation map. These tools, combined with the fundamental concepts of hydrology were used to study the flood-prone areas along drainage network in study area to determine the spatial extent affected by the flood for varying degrees of rainfall. The results indicate that the major areas of inundation lie between Falaknuma and Malakpet, located in the downstream of the study area. The outcomes of the study will help in the management and mitigation of floods and flood-proofing of vulnerable areas.
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Exploring LULC changes in Pakhal Lake area, Telangana, India using QGIS MOLUSCE plugin
Ashok Amgoth,Hari Ponnamma Rani,K. V. Jayakumar 대한공간정보학회 2023 Spatial Information Research Vol.31 No.4
Dynamic processes such as environmental, economic, and social factors influence land use and land cover (LULC) changes, with temporal and spatial variations. This study aims to identify changes in LULC and predict future trends in the Pakhal Lake area in Peninsular India. Satellite images for the years from 2016 to 2022 were used for LULC classification using deep learning with Sentinel − 2 imagery in Google Earth Engine (GEE). Dynamic World dataset is used to classify the LULC changes of the study area with a 10 m near-real-time dataset. Images were classified based on six different LULC classes, namely water, vegetation, flooded vegetation, agriculture, built-up area, and bare land. The Cellular Automata–Artificial Neural Network (CA − ANN) technique was used to predict LULC changes. QGIS plugin MOLUSCE with Multi-Layer Perception (MLP), was used to predict and determine potential LULC changes for 2025 and 2028. The overall Kappa coefficient value of 0.78, and an accuracy of 82% indicated good results for LULC changes and projected maps for 2025. Prediction of LULC changes using MLP − ANN for the years 2025 and 2028 showed increase in agriculture, built-up areas, and barren land. The results of the study will be useful to develop better management techniques of natural resources.
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