Possibility Studies and Parameter Finding for Interlinking of Thamirabarani and Vaigai Rivers in Tamil Nadu, India

Venkatesan G, Raj Chandar Padmanaban

Abstract


Water availability is becoming dearer and dearer day by day in Tamil Nadu due to monsoon vagaries and increasing population propulsion. The spatial and temporal variations in the rainfall over Tamil Nadu has led to denotation of water ‘surplus’ and water scarce river basins in the state. This project is an attempt for possibility studies and finding parameters for interlinking the rivers in Tamil Nadu state aims at transferring water from water ‘surplus’ to the water scarce basins. This aims the prevailing reductionist concept of ‘surplus’ flows in some river basins irrespective of its diverse ecological needs and of its diversion to water scarce regions. The project touches on the fact that though the interlinking proposal has been made to reduce the water scarcity in the rain scarce areas of western and southern parts of Tamil Nadu, the choice of this gigantic project as the appropriate mechanism to achieve the goal is questioned. The project is focused on the justifiability of the assumption of an arithmetic expansion in irrigated land as the only possible solution towards maintaining Tamil Nadu food security. Based on the above observations, it identifies the need for a totally transparent techno-economic and environmental feasibility study and comparison with other possible options, before the interlinking project is given final approval. Using Remote Sensing and Geographic Information System the various parameters such as Soil, Geology, Geomorphology, Land use, Slope, Rainfall, Drainage, Basin, Relief were analyzed. All relevant data the transfer of equitable water could be distributed there by the water scarcity for drinking and irrigation purposes could resolve by linking various water channels.

 


Keywords


Parallelepiped, Minimum Distance, Mahalanobis Distance, Maximum Likelihood, Spectral Angle Mapper, Spectral Information Divergence, Binary Encoding, Neural Net, ISO data and K-means

Full Text: PDF

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

*2016 Journal Impact Factor was established by dividing the number of articles published in 2014 and 2015 with the number of times they are cited in 2016 based on Google Scholar, Google Search and the Microsoft Academic Search. If ‘A’ is the total number of articles published in 2014 and 2015, and ‘B’ is the number of times these articles were cited in indexed publications during 2016 then, journal impact factor = A/B. To know More: (http://en.wikipedia.org/wiki/Impact_factor)