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Meccanica

, Volume 31, Issue 1, pp 87–101 | Cite as

Modelling hydrological data with and without long memory

  • Paolo Burlando
  • Alberto Montanari
  • Renzo Rosso
Article

Abstract

The paper focuses on the problem of long-range dependence when analysing time series of hydrological data. Three time series are analysed: the monthly rainfall in the town of Florence, Italy; the daily minimum temperatures in the same town; and, finally, the daily water inflow to Lake Maggiore, Italy. Heuristic methods and maximum likelihood estimation of a parametric model are used to investigate the Hurst phenomena and to detect whether long-range dependence is present in any of the time series. We found that long-range dependence is not present in the first series but it is present in the last two. The daily water inflow to Lake Maggiore was modelled by a fractionally differenced arima model (farima) which contains a long-range dependence component. It is shown that the fit is much better than the one provided by more traditional arima models that do not have such a component.

Key words

Time series Hurst phenomenon Long-range dependence farima Hydrometeorology 

Sommario

Lo studio considera il problema dell'identificazione dei fenomeni di dipendenza a lungo termine (long range dependence) nelle serie temporali di dati idrologici. Allo scopo sono state analizzate tre serie temporali, rispettivamente quella dei totali mensili di precipitazione rilevati alla stazione dell'Osservatorio Ximeniano de Firenze, quella delle temperature minime giornaliere per la stessa stazione e quella degli afflussi giornalieri al Lago Maggiore. Per identificare la presenza di fenomeni di dipendenza long range, attraverso la valutazione della consistenza del fenomeno di Hurst, sono stati utilizzati sia metodi euristici, sia la stima a massima verosimiglianza di un modello parametrico. Due delle tre serie analizzate sono risultate caratterizzate da tale dipendenza. Per la serie degli afflussi al Lago Maggiore, si è inoltre proceduto alla simulazione attraverso un modello arima a differenziazione frazionaria (farima), la cui struttura contiene una componente long-range. I risultati ottenuti, mostrano che tale modello fornisce risultati significativamente migliori dei tradizionali modelli arima, privi di tale componente.

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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Paolo Burlando
    • 1
  • Alberto Montanari
    • 1
  • Renzo Rosso
    • 1
  1. 1.Politecnico di MilanoD.I.I.A.R.Milano(Italy)

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