Spatial Variability of Rainfall and Classification of Peninsular Indian Catchments
M Ravi Naik1, MVSS Giridhar2

1M Ravi Naik, Centre for Water Resources, UCEST, Jawaharlal Nehru Technological University, Hyderabad (Telangana), India.

2Dr. MVSS Giridhar, Centre for Water Resources, UCEST, Jawaharlal Nehru Technological University, Hyderabad (Telangana), India.

Manuscript received on 02 June 2023 | Revised Manuscript received on 05 October 2023 | Manuscript Accepted on 15 December 2023 | Manuscript published on 30 December 2023 | PP: 9-15 | Volume-10 Issue-12, December 2023 | Retrieval Number: 100.1/ijaent.F42140812623 | DOI: 10.35940/ijaent.F4214.12101223

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Abstract: The strength and success of hydrological analysis depend upon the quantity and quality of observed data. In the recent past, the availability of advanced computing facilities and measurement techniques had a great impact on the field of hydrology, especially in hydrologic analysis and hydrologic modeling. In spite of such growth, the present hydrologic modeling has certain challenges: complexity (involving a large number of parameters), applicability to a specific region (difficult to generalize for other regions), and lack of understanding of the connection between model theories and the actual system. The general solution of simplifying the models in terms of developing a classification framework has been discussed and focused on in the present study. It will greatly help to overcome the hydrologic modeling challenges and provides a better understanding of the hydrologic process. In general, classification is a way of grouping entities which has similar characteristics. The importance of applying nonlinear dynamics and chaos methods for classification has been realized in the recent past; since such studies provide exclusive information on hidden characteristics such as complexity, nonlinearity, dimensionality, etc. Of hydrological processes. The hydrologic processes are complex. In this study, information regarding the complexity is extracted by statistical analysis and linear methods such as Autocorrelation Function, and Average Mutual Information. 367 gridded rainfall stations over Peninsular Indian basins are used to investigate the applicability of different methods used in the study.

Keywords: Peninsular India, Hydrology, Rainfall, Nonlinear dynamics, Autocorrelation, Average Mutual Information.
Scope of the Article: Classification