| dc.description.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. | en_US |
