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| Titel |
{Baseflow index regionalization analysis in a Mediterranean area and data scarcity context} |
| VerfasserIn |
A. Longobardi, P. Villani |
| Konferenz |
EGU General Assembly 2009
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 11 (2009) |
| Datensatznummer |
250027722
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| Zusammenfassung |
| Low flow characteristics are affected by different physiographic factors such as climate,
topography, geology and soils, and regional regression prediction models, to estimate low
flow indexes at ungauged sites, mainly rely on these factors. The present study focuses on the
baseflow index, one of the most important low flow characteristics for a catchment, and
presents: i) the analysis of baseflow separation algorithms for BFI evaluation and ii) a
regional approach to predict the BFI at ungauged sites in a Mediterranean region, for which
only very poor data are available. As showed in Longobardi and Villani (2008), the
prediction of baseflow contribution to total streamflow is based on the introduction of a
permeability index at the catchment scale, and regional linear regression equations simply
relate the latter to the BFI. Initially defined on the base of a hydro-geomorhological
classification, successfully used for flood prediction in ungauged sites, the permeability
index is computed on the base of an apparently over-simplified scheme which only
account for lithological and hydrogeological characteristics of the studied region. Its
computation does not require extensive soil surveys, being thus particularly suited for very
poorly gauged sites. The case study here presented is represented by 29 stations,
ranging in area from 13 to 5500 km2, located within a region of about 20.000 kmq,
in Southern Italy. Catchment lithology appeared to be the major factor affecting
baseflow in the studied area, and it is shown that an accurate catchment geology spatial
variability description reduces the average long term BFI index prediction error from
23% to 14% and above all increases the explained variance from 23% to 68%. |
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