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Efficient methods of discharge measurements in rivers and streams based on the probability concept
Author(s) -
Chiu ChaoLin,
Hsu ShihMeng,
Tung NingChien
Publication year - 2005
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.5857
Subject(s) - flow (mathematics) , simplicity , computer science , sampling (signal processing) , basis (linear algebra) , streams , stability (learning theory) , set (abstract data type) , constant (computer programming) , simple (philosophy) , point (geometry) , flood myth , hydrology (agriculture) , environmental science , mathematics , geology , computer network , philosophy , geometry , theology , geotechnical engineering , filter (signal processing) , epistemology , machine learning , computer vision , programming language
Regularities exist in fluid flows and can be represented by a set of constants. These constants are functions of the parameter of a probability distribution that exhibits resilience and stability under various flow conditions. Together, these regularities form a network and interact with each other, such that if one is known then the others can be determined from it. The regularities and their network explain the various fluid‐flow phenomena and can be used in analysis of rivers and streams. For example, they can be used as the basis to develop simple and efficient methods of discharge measurements as presented herein, which only require velocity sampling at a single point on a water surface or a few points on a single vertical. Because of their simplicity and the short time requirement, these methods can be easily automated for collecting discharge data in unsteady, high flows that are badly needed for real‐time flow forecasting and design of flood control structures, and for advancing the fundamental, scientific knowledge in hydrology. Copyright © 2005 John Wiley & Sons, Ltd.