ABSTRACT
Time series modeling is the analysis of a temporally distributed sequence of data or the synthesis of a model for prediction in which time is an independent variable. In many cases, time is not actually used to predict the magnitude of a random variable such as peak discharge, but the data are ordered by time. Time series are analyzed for a number of reasons. One might be to detect a trend due to another random variable. For example, an annual maximum flood series may be analyzed to detect an increasing trend due to urban development over all or part of the period of record. Second, time series may be analyzed to formulate and calibrate a model that would describe the time-dependent characteristics of a hydrologic variable. For example, time series of low-flow discharges might be analyzed in order to develop a model of the annual variation of base flow from agricultural watersheds. Third, time series models may be used to predict future values of a time-dependent variable. A continuous simulation model might be used to estimate total maximum daily loads from watersheds undergoing deforestation.
