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This textbook covers the main applications of statistical methods
in hydrology. It is written for upper undergraduate and graduate
students but can be used as a helpful guide for hydrologists,
geographers, meteorologists and engineers. The book is very useful
for teaching, as it covers the main topics of the subject and
contains many worked out examples and proposed exercises. Starting
from simple notions of the essential graphical examination of
hydrological data, the book gives a complete account of the role
that probability considerations must play during modelling,
diagnosis of model fit, prediction and evaluating the uncertainty
in model predictions, including the essence of Bayesian application
in hydrology and statistical methods under nonstationarity.The book
also offers a comprehensive and useful discussion on subjective
topics, such as the selection of probability distributions suitable
for hydrological variables. On a practical level, it explains MS
Excel charting and computing capabilities, demonstrates the use of
Winbugs free software to solve Monte Carlo Markov Chain (MCMC)
simulations, and gives examples of free R code to solve
nonstationary models with nonlinear link functions with climate
covariates.
This textbook covers the main applications of statistical methods
in hydrology. It is written for upper undergraduate and graduate
students but can be used as a helpful guide for hydrologists,
geographers, meteorologists and engineers. The book is very useful
for teaching, as it covers the main topics of the subject and
contains many worked out examples and proposed exercises. Starting
from simple notions of the essential graphical examination of
hydrological data, the book gives a complete account of the role
that probability considerations must play during modelling,
diagnosis of model fit, prediction and evaluating the uncertainty
in model predictions, including the essence of Bayesian application
in hydrology and statistical methods under nonstationarity.The book
also offers a comprehensive and useful discussion on subjective
topics, such as the selection of probability distributions suitable
for hydrological variables. On a practical level, it explains MS
Excel charting and computing capabilities, demonstrates the use of
Winbugs free software to solve Monte Carlo Markov Chain (MCMC)
simulations, and gives examples of free R code to solve
nonstationary models with nonlinear link functions with climate
covariates.
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