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Understanding Mathematical and Statistical Techniques in Hydrology: An Examples-based Approach [Kõva köide]

(University of Oxford), (Hydro-GIS Ltd, Oxfordshire)
  • Formaat: Hardback, 104 pages, kõrgus x laius x paksus: 252x175x10 mm, kaal: 367 g
  • Ilmumisaeg: 01-Jan-2016
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 1444335499
  • ISBN-13: 9781444335491
Teised raamatud teemal:
Understanding Mathematical and Statistical Techniques in Hydrology: An Examples-based ApproachSuurem pilt
  • Formaat: Hardback, 104 pages, kõrgus x laius x paksus: 252x175x10 mm, kaal: 367 g
  • Ilmumisaeg: 01-Jan-2016
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 1444335499
  • ISBN-13: 9781444335491
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781444335491.html
Märksõnad:
Pick up any hydrology textbook and it will not be long before you encounter pages listing sequences of equations representing complex mathematical concepts. Students and practitioners of hydrology will not find this very helpful, as their aim, generally, is to study and understand hydrology, and not to find themselves confronted with material that even students of mathematics would find challenging. Often, equations appear to be copied and pasted into hydrological texts in an attempt to give a more rigorous scientific basis to the narrative. However, they are commonly wrong, poorly explained, without context or background, and more likely to confuse and distance the reader than to enlighten and engage them in the topic.

Understanding Mathematical and Statistical Techniques in Hydrology provides full and detailed expositions of such equations and mathematical concepts, commonly used in hydrology. In contrast to other hydrological texts, instead of presenting abstract mathematical hydrology, the essential mathematics is explained with the help of real-world hydrological examples.
Preface, vii
How to use this book, x
1 Fundamentals, 1(18)
1.1 Motivation for this book,
1(1)
1.2 Mathematical preliminaries,
2(17)
2 Statistical modelling, 19(15)
2.1 The Central European Floods, August 2002,
19(3)
2.2 Extreme value analysis,
22(1)
2.3 Simple methods of return period estimation,
22(3)
2.4 Return periods based on distribution fitting,
25(5)
2.5 Techniques for parameter estimation,
30(1)
2.6 Bayesian parameter estimation,
30(1)
2.7 Resampling methods: bootstrapping,
31(3)
3 Mathematics of hydrological processes, 34(11)
3.1 Introduction,
34(1)
3.2 Algebraic and difference equation methods,
34(2)
3.3 Methods involving exponentiation,
36(1)
3.4 Rearranging model equations,
36(2)
3.5 Equations with iterated summations and products,
38(3)
3.6 Methods involving differential equations,
41(2)
3.7 Methods involving integrals,
43(2)
4 Techniques based on data fitting, 45(10)
4.1 Experimental and observed data,
45(1)
4.2 Rating curves,
46(3)
4.3 Regression with two or more independent variables,
49(2)
4.4 Demonstration of decaying quantities,
51(1)
4.5 Analysis based on harmonic functions,
52(3)
5 Time series data, 55(16)
5.1 Introduction,
55(1)
5.2 Characteristics of time series data,
55(2)
5.3 Testing for time dependence,
57(1)
5.4 Testing for trends,
58(1)
5.5 Frequency analysis,
59(1)
5.6 Other analysis methods,
60(1)
5.7 Smoothing and filtering,
60(1)
5.8 Linear smoothing and filtering methods,
61(3)
5.9 Nonlinear filtering methods,
64(2)
5.10 Time series modelling,
66(1)
5.11 Hybrid time series/process-based models,
67(2)
5.12 Detecting non-stationarity,
69(2)
6 Measures of model performance, uncertainty and stochastic modelling, 71(15)
6.1 Introduction,
71(1)
6.2 Quantitative measures of performance,
71(2)
6.3 Comparing measures,
73(2)
6.4 The Nash—Sutcliffe method,
75(1)
6.5 Stochastic modelling,
76(1)
6.6 Monte Carlo simulations,
77(2)
6.7 Non-uniform Monte Carlo sampling,
79(2)
6.8 Uncertainty in hydrological modelling,
81(1)
6.9 Uncertainty in combined models,
82(1)
6.10 Assessing uncertainty given observed data: Bayesian methods,
83(3)
Glossary, 86(2)
Index, 88
Dr Harvey J. E. Rodda graduated in Environmental Science from Lancaster University and completed his PhD in the Department of Geography, Exeter University in 1993 in the field of hydrological modelling. He is currently a director of Hydro-GIS Ltd, a consultancy company providing specialist services in hydrology and GIS mostly within the private sector. Since 2005 he has been a visiting lecturer at University College London, Department of Earth Sciences, teaching a hydrology module as part of the Geophysical Hazards MSc course.

Professor Max A. Little began his career writing software, signal processing algorithms and music for video games, then moved on by way of a degree in mathematics to the University of Oxford. After postdoc positions in Oxford investigating rainfall and biophysical time series data, he won a Wellcome Trust fellowship at MIT to follow up on his doctoral research work in behavioural and biomedical signal processing. He is currently an associate professor of mathematics at Aston University and a visiting professor at MIT's Media Lab.