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PAST AND FUTURE OF ANALYSIS OF WATER RESOURCES TIME SERIES 1
Author(s) -
Yevjevich Vujica,
Harmancioglu Nilgun Bayraktar
Publication year - 1985
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.1752-1688.1985.tb05377.x
Subject(s) - series (stratigraphy) , water resources , time series , intermittency , variable (mathematics) , computer science , econometrics , inference , multivariate statistics , operations research , hydrology (agriculture) , mathematics , meteorology , engineering , geology , geography , machine learning , paleontology , ecology , mathematical analysis , geotechnical engineering , artificial intelligence , turbulence , biology
water resources supply and demand time series consist of several or all of the four basic characteristics: tendency, intermittency, periodicity and stochasticity. Their importance changes from one type of variables to another. Historic developments of analysis of time series in hydrology have varied significantly over the past, from the stress on search for periodicities and persistence in annual series to the emphasis on the series stochastic properties. Supply and demand series are often highly interrelated, which fact is most often neglected in planning water resources systems in general, and water storage capacities in particular. The future of series analysis in water resources will likely be by a joint use of physically‐based structural analysis and the use of advanced methods of treating data by stochastic processes, statistical estimation and inference techniques. The most intriguing challenge of the future of this analysis may be the treatment of nonnormal, nonlinear and in general nonstationary hydrologic and water use time series. The proper treatment of complex multivariate processes will also challenge the specialists, especially for the purposes of transfer of information between data on variables at given points, or between data at several points of a given variable, or both.