FORECASTING OF SHORT-TERM RAINFALL USING ARMA MODELS

Flood forecasting depends essentially on forecasting of rainfall or snow melt. In this paper, rainfall forecasting is approached assuming that hourly rainfall follows an autoregressive moving average (ARMA) process. This assumption is based on the fact that the autocovariance structure of some point processes, such as hourly rainfall processes, is equivalent to the autocovariance structure of certain low-order ARMA processes. Two estimation and fitting procedures are investigated. The first takes all rainfall occurrences throughout the period of record as the basis for parameter estimation, and the second is an event-based adaptive procedure. These procedures are compared for rainfall data at a point and rainfall data averaged over a basin. Hourly rainfall from two gaging stations in Colorado, USA, and from several stations in Central Italy are used. Results show that the event-based estimation approach yields better forecasts.