![]() ![]() Moreover, if the components are characterized by multiscale periodicities, this strategy allows adapting the prediction algorithms to each of the relevant timescales. #Kspectra for windows seriesIn order to obtain a reliable prediction of the process underlying an observational record, the noise level can be reduced by extracting the deterministic components from the original time series (e.g., ). In fact, instrumental climatic series are usually characterized by a high level of noise, partly intrinsic to the underlying process and partly originated by the measurement procedure adopted to obtain the record. Such information can be meaningfully extrapolated to the forecast time, if the prediction is performed after the application of a denoising procedure. Statistical predictions, on the other hand, rely on the assumption that robust information about the dynamics of the process of interest can be extracted from the noisy observational series. Estimation of the probabilistic distribution of regional climate changes requires large ensemble simulations. Climate model predictions rely on a realistic numerical representation of key physical and chemical processes determining climate evolution, as well as on proper initialization and on assumptions about future external forcings (e.g., ). Our prediction provides information of great value for hydrological management, and a target for current and future near-term numerical hydrological predictions.ĭecadal hydroclimate predictions can be clustered into two main types: (i) predictions based on ensembles of initialized numerical simulations performed with coupled climate models and (ii) predictions based on statistical techniques. The obtained 25-year forecasts robustly indicate a prominent dry period in the late 2020s/early 2030s. Both methods are applied to each significant variability component extracted from the raw discharge time series using Singular Spectrum Analysis, and the final forecast is obtained by merging the predictions of the individual components. Here, we present a twofold decadal forecast of Po River (Northern Italy) discharge obtained with a statistical approach consisting of the separate application and cross-validation of autoregressive models and neural networks. ![]() The capability to accurately predict such decadal changes is, therefore, of utmost environmental and social importance. However, many climate records like, e.g., North Italian precipitation and river discharge records, indicate that significant decadal variability is often superposed or even dominates long-term hydrological trends. ![]() The Mediterranean area belongs to the regions most exposed to hydroclimatic changes, with a likely increase in frequency and duration of droughts in the last decades. ![]()
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