Linear parametric models (LPM) applied to time series were used in hydrological applications for monthly and annual series analyses. Recently the extending of these models to daily series has allowed the developing of new applications. One of them could be to use the synthetic series generated by LPMs to improve the estimation of the BFI index. The Base Flow Index (BFI) of a river basin is an expression of water volume released by aquifer formation, usually in percentage terms. The index is derived using a hydrograph base flow separation procedure applied to mean daily flow time series. The BFI is defined by the ratio of the annual base flow volume to the corresponding total flow volume. Having a longer time series to define the annual BFI clearly allows a more accurate estimation of the final index. Therefore in order to improve the BFI index estimation, the LPMs are applied. Generating daily synthetic flow series, new scenarios are available. The BFI index can be estimated from these scenarios, thus obtaining a statistical representation of this parameter. A case study developed on the Tiber River series is shown.
A BFI estimation procedure based on linear parametric models
CASADEI, Stefano;MANCIOLA, Piergiorgio
2002
Abstract
Linear parametric models (LPM) applied to time series were used in hydrological applications for monthly and annual series analyses. Recently the extending of these models to daily series has allowed the developing of new applications. One of them could be to use the synthetic series generated by LPMs to improve the estimation of the BFI index. The Base Flow Index (BFI) of a river basin is an expression of water volume released by aquifer formation, usually in percentage terms. The index is derived using a hydrograph base flow separation procedure applied to mean daily flow time series. The BFI is defined by the ratio of the annual base flow volume to the corresponding total flow volume. Having a longer time series to define the annual BFI clearly allows a more accurate estimation of the final index. Therefore in order to improve the BFI index estimation, the LPMs are applied. Generating daily synthetic flow series, new scenarios are available. The BFI index can be estimated from these scenarios, thus obtaining a statistical representation of this parameter. A case study developed on the Tiber River series is shown.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
