Conditional Distributions of Neyman‐Scott Models for Storm Arrivals and Their Use in Irrigation Scheduling

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Abstract
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.