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Predicting recessions through convolution
Author(s) -
Yates Paul,
Snyder W. M.
Publication year - 1975
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr011i003p00418
Subject(s) - recession , streamflow , storm , convolution (computer science) , flow (mathematics) , environmental science , hydrology (agriculture) , meteorology , climatology , geology , mathematics , computer science , economics , geography , drainage basin , geotechnical engineering , cartography , keynesian economics , artificial neural network , geometry , machine learning
The recession of streamflow following storm periods has been studied for a long time. Flow during recessions is important as a dependable minimum supply of water. However, quantitative mathematical expressions are still lacking for accurately predicting flow during recession periods. A convolutional model of streamflow recession has been formulated and tested utilizing sequential values of mean daily discharge. Parameters of the model are determined by optimization with historical streamflow records. Preliminary relationships of parameters to rate of flow and size of drainage area are explored.