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| Titel |
Spatial uncertainty assessment in modelling reference evapotranspiration at regional scale |
| VerfasserIn |
G. Buttafuoco, T. Caloiero, R. Coscarelli |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 14, no. 11 ; Nr. 14, no. 11 (2010-11-19), S.2319-2327 |
| Datensatznummer |
250012486
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| Publikation (Nr.) |
 copernicus.org/hess-14-2319-2010.pdf |
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| Zusammenfassung |
Evapotranspiration is one of the major components of the water balance and
has been identified as a key factor in hydrological modelling. For this
reason, several methods have been developed to calculate the reference
evapotranspiration (ET0). In modelling reference evapotranspiration it
is inevitable that both model and data input will present some uncertainty.
Whatever model is used, the errors in the input will propagate towards the output
of the calculated ET0. Neglecting the information about estimation
uncertainty, however, may lead to improper decision-making and water
resources management. One geostatistical approach to spatial analysis is
stochastic simulation, which draws alternative and equally probable,
realizations of a regionalized variable. Differences between the
realizations provide a measure of spatial uncertainty and allows to carry out
an error propagation analysis.
The aim of this paper is to assess spatial uncertainty of a monthly
reference evapotranspiration model resulting from the uncertainties in the
input attributes (mainly temperature) at a regional scale. A case study was
presented for the Calabria region (southern Italy). Temperature data were
jointly simulated by a conditional turning bands simulation with elevation as
external drift and 500 realizations were generated. Among the
evapotranspiration models, the Hargreaves-Samani model was used.
The ET0 was then estimated for each set of the 500 realizations of the
input variables, and the ensemble of the model outputs was used to infer the
reference evapotranspiration probability distribution function. This
approach allowed for the delineation of the areas characterised by greater
uncertainty, to improve supplementary sampling strategies and ET0 value
predictions. |
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