By Bellie Sivakumar
This authoritative booklet provides a accomplished account of the basic roles of nonlinear dynamic and chaos theories in knowing, modeling, and forecasting hydrologic structures. this can be performed via a scientific presentation of: (1) details at the salient features of hydrologic structures and at the latest theories for his or her modeling; (2) the basics of nonlinear dynamic and chaos theories, equipment for chaos id and prediction, and linked matters; (3) a assessment of the purposes of chaos conception in hydrology; and (4) the scope and power instructions for the future.
This publication bridges the divide among the deterministic and the stochastic colleges in hydrology, and is definitely suitable as a textbook for hydrology courses.
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Extra resources for Chaos in Hydrology: Bridging Determinism and Stochasticity
Supercomputers, remote sensors, and geographic information systems) have facilitated the emergence of various nonlinear concepts and the development of a host of nonlinear time series methods as well as others. In addition to the nonlinear stochastic ones, these methods include: data-based mechanistic models, artiﬁcial neural networks, wavelets, entropy theory, support vector machines, genetic programming, fuzzy logic, and nonlinear dynamics and chaos. g. 3 Scientiﬁc Development of Hydrology 9 1996; Singh 1997; Govindaraju and Rao 2000; Sivakumar 2000; Dibike et al.
In fact, for most practical problems, only a few processes of the hydrologic cycle are considered at a time, and then only considering a small portion of the Earth’s surface. 4 Concept of Hydrologic System 11 the case in the ﬁeld of fluid mechanics. In this context, a hydrologic system can be deﬁned as a structure or volume in space, surrounded by a boundary, that accepts water and other inputs, operates on them internally, and produces them as outputs. The structure (for surface or sub-surface flow) or volume in space (for atmospheric moisture flow) is the totality of the flow paths through which the water may pass as throughput from the point it enters the system to the point it leaves.
G. successes, failures, limitations, concerns) in Chap. g. parameter identiﬁcation, multi-variable analysis, model simpliﬁcation and integration, reconstruction of system equations, linking data and physics), especially in light of future challenges (including the impacts of climate change), are highlighted in Chap. 14. Finally, Chap. 15 offers some thoughts on the philosophy and pragmatism in studying hydrology and argues in favor of chaos theory as a balanced and middle-ground approach to our dominant extreme views of determinism and stochasticity.