Understanding Catchment Scale Processes: Hydrological Modelling and Information-Theoretic Approaches

Abstract

Catchments are complex environmental systems, and they serve as the fundamental units for hydrological classification. They are self-organizing systems whose form, drainage network, ground and channel slopes, channel hydraulic geometries, soils and vegetation, are all a result of adaptive ecological, geomorphic and land-forming processes. The hydrological responses of a catchment are predominantly governed by complex interactions among processes occurring at various spatial and temporal scales. As hydrological processes exhibit non-linear behaviour at all scales, it is important to explore their intricate relationships and have a detailed understanding of the catchment behaviour. Quantification of morphometric indices and hydrological signatures provide vital information about the complex system properties and the functional behaviour of catchments. Evaluation of catchment characteristics can significantly improve the scientific understanding of the variability of hydrological processes at various scales and provide useful insights for the development of scaling relationships. Hydrological modelling serves as a powerful tool in assimilating the complex behaviour of hydrological systems. The performance and applicability of each hydrological model can differ between catchments due to several catchment characteristics and dominant hydrological processes. With a wide variety of model structures, it is important to evaluate how different hydrological models capture the process dynamics in various catchments. Many a time, the use of a single model can lead to simulation uncertainties, especially in catchments of poor input data availability and in large-scale modelling exercises. Hence, effective modelling strategies should be designed in such a way that the inclusion of more than one hydrological model is ensured, and an ensemble approach should be adopted, especially in highly heterogeneous catchments. The application of information-theoretic measures has been found to be extremely useful in tackling various problems related to hydrological modelling and understanding process relationships. Information theory serves as a powerful tool in computing the information content in a variable as well as the amount of information one variable provides about another. Also, such measures do not require any prior assumptions on the characteristics of the underlying distributions. Hence, they can be widely applied to address a variety of problems in the hydrological domain. The key focus of the research presented in this thesis is to evaluate catchment scale hydrological process relationships by adopting a model-oriented approach in a regionally complex catchment. A holistic study of the catchment scale processes is carried out by combining a model-based analysis and applying statistical evaluation methods and information-theoretic measures. The study area chosen for the analyses is the Cauvery River Basin, a major river basin in peninsular India. The thesis contributes towards providing an understanding of hydrological processes at the catchment scale by combining the knowledge gained through hydrological modelling with information-theoretic measures. Catchment characteristics are quantified by evaluating various geomorphologic indices which serve as a baseline for building better modelling strategies. Three hydrological models, namely, GWAVA (Global Water AVailability Assessment) model, SWAT (Soil Water Assessment Tool) and VIC (Variable Infiltration Capacity) model, are set up for the study region, and their individual performances along with an ensemble mean simulation are investigated. Additionally, to develop deeper insights into the long-term hydro-climatology and distribution of water resources within the study region, a synthesis of hydrological model evaluations and statistical methods is adopted. To further explore the relationships between various hydrological fluxes simulated using a physically-based hydrological model, a methodology is suggested through the application of information-theoretic measures such as Shannon Entropy and Mutual Information.