Premium
EFFICIENCY OF HYDROLOGIC DATA COLLECTION SYSTEMS ROLE OF TYPE I AND II ERRORS 1
Author(s) -
Duckstein Lucien,
Kisiel Chester C.
Publication year - 1971
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1971.tb05792.x
Subject(s) - data collection , computer science , sample (material) , simple (philosophy) , process (computing) , hydrological modelling , groundwater recharge , mathematical model , statistical hypothesis testing , econometrics , operations research , statistics , mathematics , engineering , philosophy , chemistry , geotechnical engineering , epistemology , chromatography , climatology , groundwater , geology , operating system , aquifer
The efficiency of hydrologic data collection systems is relevant to solution of environmental problems, scientific understanding of hydrologic processes, model‐building and management of water resources. Because these goals may be overlapping and non‐commensurate, design of data networks is not simple. Identified are four elements of error or risk in such networks: (a) choice of variables and mathematical model for the same process, (b) accuracy of model parameter estimates, (c) acceptance of wrong hypothesis or rejection of correct hypothesis and (d) economic losses associated with error. Of these four, the classical hypothesis testing problem is specifically evaluated in terms of costs of type I and II errors for simple and composite hypotheses; mathematical models for these economic analyses also include costs of sample data and costs of waiting while new data is obtained. An illustrative computational example focuses on the hypothesis that natural recharge might be augmented by a system of pumping wells along an ephemeral channel. The relationship of the hypothesis testing problem to Bayesian decision theory is discussed; it is felt that the latter theory offers a more comprehensive framework for design and use of hydrologic data networks.