By Bellie Sivakumar
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
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.
Chaos in Hydrology: Bridging Determinism and Stochasticity by Bellie Sivakumar