Study region: Data from 28 streamflow gauging stations located in the Campania Region, Southern Italy, were analysed. Study focus: The study was aimed at recommend regional methodologies for environmental flow (EF) and EF variability estimation for a climatological environment particularly affected by strong climate variability. Starting from an at-site statistical analysis of discharge data, a preliminary step where the quantification of EF average value, μ(Q95), and inter-annual variability, CV(Q95), was illustrated. A regional regression approach was then presented for the prediction of μ(Q95) and CV(Q95). New hydrological insights for the region: A step wise procedure highlighted the dominant hydrological variables and catchment attributes for EF prediction. Catchment area and mean annual daily discharge μ(Q) appeared strongly related to μ(Q95) whereas CV(Q95) was found to be dependent on the baseflow index and on precipitation variability. Regional predictions were evaluated on the base of the correlation coefficient and absolute average percentage errors. Prediction errors amounted to about 30 % and 17 % respectively in the case of μ(Q95) and CV(Q95). In the end, an implication for a fully regional approach, simply based on catchment attributes, also embedding the impact of hydrological variables, was presented. It showed clearly different performance capacity compared to the prediction based on the observed hydrological variables but not significantly lower.

From at-site to regional assessment of environmental flows and environmental flows variability in a Mediterranean environment

Longobardi A.;Villani P.
2020

Abstract

Study region: Data from 28 streamflow gauging stations located in the Campania Region, Southern Italy, were analysed. Study focus: The study was aimed at recommend regional methodologies for environmental flow (EF) and EF variability estimation for a climatological environment particularly affected by strong climate variability. Starting from an at-site statistical analysis of discharge data, a preliminary step where the quantification of EF average value, μ(Q95), and inter-annual variability, CV(Q95), was illustrated. A regional regression approach was then presented for the prediction of μ(Q95) and CV(Q95). New hydrological insights for the region: A step wise procedure highlighted the dominant hydrological variables and catchment attributes for EF prediction. Catchment area and mean annual daily discharge μ(Q) appeared strongly related to μ(Q95) whereas CV(Q95) was found to be dependent on the baseflow index and on precipitation variability. Regional predictions were evaluated on the base of the correlation coefficient and absolute average percentage errors. Prediction errors amounted to about 30 % and 17 % respectively in the case of μ(Q95) and CV(Q95). In the end, an implication for a fully regional approach, simply based on catchment attributes, also embedding the impact of hydrological variables, was presented. It showed clearly different performance capacity compared to the prediction based on the observed hydrological variables but not significantly lower.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4756079
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