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Anna E. Sikorska
Researcher at Warsaw University of Life Sciences
Publications - 43
Citations - 789
Anna E. Sikorska is an academic researcher from Warsaw University of Life Sciences. The author has contributed to research in topics: Biology & Horticulture. The author has an hindex of 14, co-authored 26 publications receiving 586 citations. Previous affiliations of Anna E. Sikorska include Swiss Federal Institute of Aquatic Science and Technology & University of Zurich.
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Journal ArticleDOI
A Comparison of Methods for Streamflow Uncertainty Estimation
Julie E. Kiang,Christopher L. Gazoorian,H McMillan,Gemma Coxon,Jérôme Le Coz,Ida Westerberg,Arnaud Belleville,Damien Sevrez,Anna E. Sikorska,Asgeir Petersen-Øverleir,Trond Reitan,Jim Freer,Benjamin Renard,Valentin Mansanarez,Robert R. Mason +14 more
TL;DR: In this article, the authors compared uncertainty estimates and stage-discharge rating curves from seven methods at three different locations of varying hydraulic complexity and found that fullwidth 95% uncertainties for the different methods ranged from 3 to 17% for median flows.
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Flood-type classification in mountainous catchments using crisp and fuzzy decision trees
TL;DR: In this article, the authors present a flood classification for identifying flood patterns at a catchment scale by means of a fuzzy decision tree, where events are represented as a spectrum of six main possible flood types that are attributed with their degree of acceptance.
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Flood type specific construction of synthetic design hydrographs
Manuela I. Brunner,Daniel Viviroli,Anna E. Sikorska,Olivier Vannier,Anne-Catherine Favre,Jan Seibert +5 more
TL;DR: In this article, a statistical approach for the estimation of the design variables peak and volume by constructing synthetic design hydrographs for different flood types such as flash-floods, short-rain floods, longrain floods and rain-on-snow floods was proposed.
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Estimating the Uncertainty of Hydrological Predictions through Data-Driven Resampling Techniques
TL;DR: A nonparametric technique is proposed as an alternative to parametric error models to estimate the uncertainty of hydrological predictions, and it is proved that the results obtained are compared with those obtained using a formal statistical technique from the same case study.
Journal ArticleDOI
Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models
TL;DR: In this article, a procedure to explicitly account for input uncer- tainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model was proposed.