Get Chaos in Hydrology: Bridging Determinism and Stochasticity PDF

By Bellie Sivakumar

ISBN-10: 9048125510

ISBN-13: 9789048125517

ISBN-10: 9048125529

ISBN-13: 9789048125524

This authoritative ebook offers a entire 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 platforms and at the latest theories for his or her modeling; (2) the basics of nonlinear dynamic and chaos theories, tools for chaos id and prediction, and linked concerns; (3) a assessment of the purposes of chaos conception in hydrology; and (4) the scope and strength instructions for the future.

This ebook bridges the divide among the deterministic and the stochastic faculties in hydrology, and is easily appropriate as a textbook for hydrology courses.

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Additional info for Chaos in Hydrology: Bridging Determinism and Stochasticity

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G. double the amount of rainfall producing double the amount of flow), and ‘nonlinear’ means output is not proportional to the input. Looking at the general non-proportionality between hydrologic inputs and outputs, it is fair to say that most, if not all, hydrologic processes are nonlinear in nature. g. Minshall 1960; Jacoby 1966; Amorocho 1967; Dooge 1967b; Amorocho and Brandstetter 1971). However, much of early hydrologic analysis (during the 1960s– 1980s), especially based on time series methods (see Sect.

Let us assume that precipitation (P) over a river basin produces some flow (Q) at the outlet of the basin. It can then be said that the river basin system performs a transformation of precipitation (P) into flow (Q), which can be represented by: 12 1 Q ¼ f ðPÞ Introduction ð1:1Þ where f is the transformation function, or simply transfer function. Generally speaking, f is a transfer function between the input (cause) and the output (effect). The cause and effect can be either internal to the system or external to the system or a combination, depending on the ‘boundaries’ of the system.

S. Geological Survey Water Supply Paper 1591-D, U. S. Department of Interior, Washington, D. , D1-D18 Dawdy DR (2007) Prediction versus understanding (The 2007 Ven Te Chow Lecture). ASCE J Hydrol Eng 12:1–3 Dawdy DR, Kalinin GP (1969) Mathematical modeling in hydrology. International Association of Scientific Hydrology Report, Mid-Decade Conference of the International Hydrological Decade, held in August in Surány, Hungary DeCoursey DG (1971) The stochastic approach to watershed modeling. Nordic Hydrol 11:186– 216 Dibike YB, Velickov S, Solomatine DP, Abbott M (2001) Model induction with support vector machines: introduction and applications.

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Chaos in Hydrology: Bridging Determinism and Stochasticity by Bellie Sivakumar

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