Well-Observed Time Series Does Speak for Itself!
Our research focus on extracting the inherent characteristics of hydro-climatologic system from the time-series itself. This is particularly significant considering the complexity of the hydro-climate system and the inadequate information of system sub-components. Lack of holistic knowledge about such complex hydro-climatic systems steered us to hypothesise that "A well-observed time-series does speak for itself!" Concepts of chaos theory and phase-space re-construction are applied to unveil the nonlinear physical linkages between global-climatic causative factors and hydrologic-extreme episodes. Novel algorithms are developed to reduce predictive uncertainty by minimizing the infamous butterfly-effect and improving the model perfection. Further, we also focus on mapping and enhancing the predictability of different hydro-climatic variables to improve the hydrological predictions.