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SPACE‐TIME VALIDATION OF A THUNDERSTORM RAINFALL MODEL 1
Author(s) -
Fogel M. M.,
Duckstein L.,
Kisiel C. 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.tb05913.x
Subject(s) - thunderstorm , storm , environmental science , negative binomial distribution , meteorology , poisson distribution , probability distribution , event (particle physics) , surface runoff , statistics , conditional probability , rain gauge , hydrology (agriculture) , climatology , mathematics , geography , geology , precipitation , ecology , physics , geotechnical engineering , quantum mechanics , biology
A probability model for predicting the occurrence and magnitude of thunderstorm rainfall developed in the southwestern United States was tested in the metropolitan Chicago area with reasonable success, especially for the moderate to the extreme runoff‐producing events. The model requires the estimation of two parameters, the mean number of events per year and the conditional probability of rain given that an event has occurred. To tie in the data from more than one gage in an area, an event can be defined in several ways, such as the areal mean rainfall exceeding 0.50 inch and at least one gage receiving more than 1.0 inch. This type of definition allows both of the model parameters to be obtained from daily warm‐season rainfall records. Regardless of the definition used a Poisson distribution adequately described the number of events per season. A negative binomial distribution was derived as representing the frequency density function for rainfall where several gages are employed in defining a storm. Chicago data fit both distributions very well at events with relatively high return periods. The results indicate the possibility of using the model on a regional basis where limited amount of data may be used to estimate parameters for extensive areas.