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. 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 capability to accurately predict such decadal changes is, therefore, of utmost environmental and social importance. 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. 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. The obtained 25-year forecasts robustly indicate a prominent dry period in the late 2020s/early 2030s. Our prediction provides information of great value for hydrological management, and a target for current and future near-term numerical hydrological predictions.

Robust decadal hydroclimate predictions for northern Italy based on a twofold statistical approach

Rubinetti S.;Rubino A.;Zanchettin D.
2020

Abstract

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. 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 capability to accurately predict such decadal changes is, therefore, of utmost environmental and social importance. 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. 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. The obtained 25-year forecasts robustly indicate a prominent dry period in the late 2020s/early 2030s. Our prediction provides information of great value for hydrological management, and a target for current and future near-term numerical hydrological predictions.
File in questo prodotto:
File Dimensione Formato  
2020_Rubinetti_etal_atmosphere.pdf

accesso aperto

Tipologia: Documento in Post-print
Licenza: Accesso libero (no vincoli)
Dimensione 1.02 MB
Formato Adobe PDF
1.02 MB Adobe PDF Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3730042
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 3
social impact