The most important impacts of climate change relate to temperature and precipitation. Precipitation is particularly important, because changes in precipitation patterns may lead to floods or droughts in different areas. Also, precipitation is a major factor in agriculture and in recent years interest has increased in learning about precipitation variability for periods of months to annual and s...

This study explores an information-theoretic/log-linear approach to multivariate time series analysis. The method is applied to daily rainfall data (4 sites, 9 years), originally quantitative but here treated as dichotomous. The analysis ascertains which lagged variables are most predictive of future rainfall and how season can be optimally de ned as an auxiliary predicting parameter. Call the ...

Several methods exist for the detection of nonlinearity in univariate time series. In the present work we consider riverflow time series to infer the dynamical characteristics of the rainfall-runoff transformation. It is shown that the non-Gaussian nature of the driving force (rainfall) can distort the results of such methods, in particular when surrogate data techniques are used. Deterministic...

The transformation of rainfall into runoff is one of the most important processes in hydrology. In the past few decades, a wide variety of automated or computer-based approaches have been applied to model this process. However, many such approaches have an important limitation in that they treat the rainfall-runoff process as a realization of only a few parameters of linear relationships rather...

Missing values are a common problem faced in the analysis of time-series data. Many SAS® time-series procedures (PROC ARIMA, PROC VARMAX, etc.) are intolerant of missing values, particularly when these missing values are embedded in the time-series, rather than occurring at the beginning or end of the series. PROC EXPAND is designed to convert time series from one sampling interval or frequency...

Accurate rainfall time series prediction is one of the important tasks in hydrological study. A conventional time series model such as autoregressive moving average or an intelligent model such as artificial neural network have been used efficiently to perform this task. However, such models are difficult to interpret by human analysts because their prediction mechanism is in the parametric for...