Articles | Volume 25, issue 9
https://doi.org/10.5194/hess-25-5315-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-5315-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Sep 2021
Research article |
| 29 Sep 2021
| 29 Sep 2021
Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding
Mohamad El Gharamti
CORRESPONDING AUTHOR
NCAR, Computational and Information Systems Laboratory (CISL), Boulder CO, USA
James L. McCreight
NCAR, Research Application Laboratory (RAL), Boulder CO, USA
Seong Jin Noh
Civil Engineering, Kumoh National Institute of Technology, Gumi, South Korea
Timothy J. Hoar
NCAR, Computational and Information Systems Laboratory (CISL), Boulder CO, USA
Arezoo RafieeiNasab
NCAR, Research Application Laboratory (RAL), Boulder CO, USA
Benjamin K. Johnson
NCAR, Computational and Information Systems Laboratory (CISL), Boulder CO, USA
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Hydrol. Earth Syst. Sci., 25, 2599–2615, https://doi.org/10.5194/hess-25-2599-2021, https://doi.org/10.5194/hess-25-2599-2021, 2021
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Using the classical model for structured expert judgment to estimate extremes: a case study of discharges in the Meuse River
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Guus Rongen, Oswaldo Morales-Nápoles, and Matthijs Kok
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This study proposes a new method for predicting extreme events such as floods on the river Meuse. The current method was shown to be unreliable as it did not predict a recent flood. We developed a model that includes information from experts and combines this with measurements. We found that this approach gives more accurate predictions, particularly for extreme events. The research is important for predictions of extreme flood levels that are necessary for protecting communities against floods.
Somil Swarnkar, Anshu Malini, Shivam Tripathi, and Rajiv Sinha
Hydrol. Earth Syst. Sci., 22, 2471–2485, https://doi.org/10.5194/hess-22-2471-2018, https://doi.org/10.5194/hess-22-2471-2018, 2018
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Katherine R. O'Brien, Tony R. Weber, Catherine Leigh, and Michele A. Burford
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F. Hoss and P. S. Fischbeck
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This paper further develops the method of quantile regression (QR) to generate probabilistic river stage forecasts. Besides the forecast itself, this study uses the rate of rise of the river stage in the last 24 and 48h and the forecast error 24 and 48h before as predictors in QR configurations. When compared to just using the forecast as an independent variable, adding the latter four predictors significantly improved the forecasts, as measured by the Brier skill score and the CRPS.
R. A. Skeffington, S. J. Halliday, A. J. Wade, M. J. Bowes, and M. Loewenthal
Hydrol. Earth Syst. Sci., 19, 2491–2504, https://doi.org/10.5194/hess-19-2491-2015, https://doi.org/10.5194/hess-19-2491-2015, 2015
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The EU Water Framework Directive requires rivers to be of good chemical and ecological quality. Chemical quality is assessed by sampling and analysing the water. Normal sampling regimes might involve taking a sample monthly or weekly. This paper uses high-frequency data from rivers to assess how accurate these regimes are at assessing the true chemical quality. Weekly sampling was more accurate than monthly, but there were still large uncertainties. We suggest ways to improve sampling accuracy.
S. C. van Pelt, J. J. Beersma, T. A. Buishand, B. J. J. M. van den Hurk, and P. Kabat
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M. B. Kalinowska and P. M. Rowiński
Hydrol. Earth Syst. Sci., 16, 4177–4190, https://doi.org/10.5194/hess-16-4177-2012, https://doi.org/10.5194/hess-16-4177-2012, 2012
A. Domeneghetti, A. Castellarin, and A. Brath
Hydrol. Earth Syst. Sci., 16, 1191–1202, https://doi.org/10.5194/hess-16-1191-2012, https://doi.org/10.5194/hess-16-1191-2012, 2012
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Short summary
The article introduces novel ensemble data assimilation (DA) techniques for streamflow forecasting using WRF-Hydro and DART. Model-related biases are tackled through spatially and temporally varying adaptive prior and posterior inflation. Spurious and physically incorrect correlations, on the other hand, are mitigated using a topologically based along-the-stream localization. Hurricane Florence (2018) in the Carolinas, USA, is used as a test case to investigate the performance of DA techniques.
The article introduces novel ensemble data assimilation (DA) techniques for streamflow...
