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Uncertainty in river discharge observations: a quantitative analysis

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Di Baldassarre, G., Montanari, A.·2009·Hydrology and Earth System Sciences

Abstract

Abstract. This study proposes a framework for analysing and quantifying the uncertainty of river flow data. Such uncertainty is often considered to be negligible with respect to other approximations affecting hydrological studies. Actually, given that river discharge data are usually obtained by means of the so-called rating curve method, a number of different sources of error affect the derived observations. These include: errors in measurements of river stage and discharge utilised to parameterise the rating curve, interpolation and extrapolation error of the rating curve, presence of unsteady flow conditions, and seasonal variations of the state of the vegetation (i.e. roughness). This study aims at analysing these sources of uncertainty using an original methodology. The novelty of the proposed framework lies in the estimation of rating curve uncertainty, which is based on hydraulic simulations. These latter are carried out on a reach of the Po River (Italy) by means of a one-dimensional (1-D) hydraulic model code (HEC-RAS). The results of the study show that errors in river flow data are indeed far from negligible.

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Metadata

Authors
Di Baldassarre, G., & Montanari, A.
Venue
Hydrology and Earth System Sciences
Published
2009-06-25
Volume
13
Issue
6
Pages
913-921
ISSN
  • 1027-5606 (ISSN-L)
  • 1607-7938
OpenAlex
W2099812305
Cited by
656
References
0
FWCI
35.62
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Uncertainty in river discharge observations: a quantitative analysis. https://app.cochat.ai/papers/10.5194/hess-13-913-2009
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Di Baldassarre, G., & Montanari, A. (2009). Uncertainty in river discharge observations: a quantitative analysis. Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-13-913-2009

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.