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Conditional Distributions of Neyman‐Scott Models for Storm Arrivals and Their Use in Irrigation Scheduling
Author(s) -
Ramirez Jorge A.,
Bras Rafael L.
Publication year - 1985
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/wr021i003p00317
Subject(s) - irrigation scheduling , storm , irrigation , hydrology (agriculture) , scheduling (production processes) , conditional probability , environmental science , meteorology , soil water , mathematics , mathematical optimization , geology , statistics , soil science , geography , geotechnical engineering , biology , ecology
This paper solves the deficit irrigation scheduling problem assuming that rainfall arrivals obey a Neyman‐Scott cluster model. The implied dependence between storms is represented by derived conditional distributions of the occurrence of rainfall based on the history of past storm arrivals. This new result is used on a physicostochastic model of the soil moisture history which in turn leads to an optimal control algorithm for making irrigation decisions. The decision of when and how much to irrigate now depends on measured soil moisture, available irrigation water, and time since the last rainfall occurrence.