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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This study introduces a hybrid data assimilation scheme for precise streamflow predictions during intense rainfall and hurricanes. Tested in real events, it outperforms traditional methods by up to 50 %, utilizing ensemble and climatological background covariances. The adaptive algorithm ensures reliability with a small ensemble, offering improved forecasts up to 18 hours in advance, marking a significant advancement in flood prediction capabilities.
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We derive a new dual ensemble Kalman filter (EnKF) for state-parameter estimation. The derivation is based on the one-step-ahead smoothing formulation, and unlike the standard dual EnKF, it is consistent with the Bayesian formulation of the state-parameter estimation problem and uses the observations in both state smoothing and forecast. This is shown to enhance the performance and robustness of the dual EnKF in experiments conducted with a two-dimensional synthetic groundwater aquifer model.
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Aaron Heldmyer, Ben Livneh, James McCreight, Laura Read, Joseph Kasprzyk, and Toby Minear
Hydrol. Earth Syst. Sci., 26, 6121–6136, https://doi.org/10.5194/hess-26-6121-2022, https://doi.org/10.5194/hess-26-6121-2022, 2022
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Measurements of channel characteristics are important for accurate forecasting in the NOAA National Water Model (NWM) but are scarcely available. We seek to improve channel representativeness in the NWM by updating channel geometry and roughness parameters using a large, previously unpublished, dataset of approximately 48 000 gauges. We find that the updated channel parameterization from this new dataset leads to improvements in simulated streamflow performance and channel representation.
Erin Towler and James L. McCreight
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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Sea ice models suffer from large uncertainties arising from multiple sources, among which parametric uncertainty is highly under-investigated. We select a key ice albedo parameter and update it by assimilating either sea ice concentration or thickness observations. We found that the sea ice albedo parameter is improved by data assimilation, especially by assimilating sea ice thickness observations. The improved parameter can further benefit the forecast of sea ice after data assimilation stops.
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Katherine R. O'Brien, Tony R. Weber, Catherine Leigh, and Michele A. Burford
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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
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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
Hydrol. Earth Syst. Sci., 16, 4517–4530, https://doi.org/10.5194/hess-16-4517-2012, https://doi.org/10.5194/hess-16-4517-2012, 2012
M. B. Kalinowska and P. M. Rowiński
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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...
