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This paper considers analyzing and modeling a time series of mean monthly streamflows using the stochastic model ARIMA (autoregressive integrated moving average). This analysis aims to model and forecast the monthly streamflow in the Potiribu river basin, located in the northwest region of Rio Grande do Sul state, Brazil. We tested several models of the SARIMA type, or ARIMA models which take the seasonal characteristics of the data into account. Among all tested models we selected some competing ones that had the lowest Akaike information criteria

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The effects of land-use changes on hydrology have been widely discussed for relatively small basins. In large basins, the use of hydrological models to study land-use changes has been an alternative experiment. However, the models must be sensitive to hydrological changes due to land-use modifications in the basin. In this sense, the objective of this study was to assess the sensitivity of the distributed model for large basins (MGB-IPH) in simulating land-use changes. The simulations showed that MGB-IPH is able to perform scenario studies of land-use changes, such as deforestation or reforestation, being consistent with the experimental results available in the literature.

This work introduces the Rayleigh autoregressive moving average (RARMA) model, which is useful to interpret multiple different sets of remotely sensed data, from wind measurements to multitemporal synthetic aperture radar (SAR) sequences. The RARMA model is indeed suitable for continuous, asymmetric, and nonnegative signals observed over time. It describes the mean of Rayleigh-distributed discrete-time signals by a dynamic structure including autoregressive and moving average terms, a set of regressors, and a link function. After presenting the conditional likelihood inference for the model parameters and the detection theory, in this work a Monte Carlo simulation is performed to evaluate the finite signal length performance of the conditional likelihood inferences. Finally, the new model is applied first to sequences of wind speed measurements, and then to a multitemporal SAR image stack for land-use classification purposes. The results in these two test cases illustrate the usefulness of this novel dynamic model for remote sensing data interpretation.