Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 22, issue 7
Articles | Volume 22, issue 7
https://doi.org/10.5194/hess-22-3983-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-3983-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 7
Technical note
 | 
23 Jul 2018
Technical note |  | 23 Jul 2018

Technical note: Assessment of observation quality for data assimilation in flood models

Joanne A. Waller, Javier García-Pintado, David C. Mason, Sarah L. Dance, and Nancy K. Nichols

Related authors

Assimilation of satellite flood likelihood data improves inundation mapping from a simulation library system
Helen Hooker, Sarah Dance, David Mason, John Bevington, and Kay Shelton
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-178,https://doi.org/10.5194/hess-2024-178, 2024
Revised manuscript not accepted
Short summary
Assessing the spatial spread–skill of ensemble flood maps with remote-sensing observations
Helen Hooker, Sarah L. Dance, David C. Mason, John Bevington, and Kay Shelton
Nat. Hazards Earth Syst. Sci., 23, 2769–2785, https://doi.org/10.5194/nhess-23-2769-2023,https://doi.org/10.5194/nhess-23-2769-2023, 2023
Short summary
Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System
Gwyneth Matthews, Christopher Barnard, Hannah Cloke, Sarah L. Dance, Toni Jurlina, Cinzia Mazzetti, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 26, 2939–2968, https://doi.org/10.5194/hess-26-2939-2022,https://doi.org/10.5194/hess-26-2939-2022, 2022
Short summary
Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning
Remy Vandaele, Sarah L. Dance, and Varun Ojha
Hydrol. Earth Syst. Sci., 25, 4435–4453, https://doi.org/10.5194/hess-25-4435-2021,https://doi.org/10.5194/hess-25-4435-2021, 2021
Short summary
Assimilation of probabilistic flood maps from SAR data into a coupled hydrologic–hydraulic forecasting model: a proof of concept
Concetta Di Mauro​​​​​​​, Renaud Hostache, Patrick Matgen, Ramona Pelich, Marco Chini, Peter Jan van Leeuwen, Nancy K. Nichols, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 4081–4097, https://doi.org/10.5194/hess-25-4081-2021,https://doi.org/10.5194/hess-25-4081-2021, 2021
Short summary
More articles (5)

Related subject area

Subject: Engineering Hydrology | Techniques and Approaches: Uncertainty analysis
A comprehensive uncertainty framework for historical flood frequency analysis: a 500-year-long case study
Mathieu Lucas, Michel Lang, Benjamin Renard, and Jérôme Le Coz
Hydrol. Earth Syst. Sci., 28, 5031–5047, https://doi.org/10.5194/hess-28-5031-2024,https://doi.org/10.5194/hess-28-5031-2024, 2024
Short summary
Bayesian calibration of a flood simulator using binary flood extent observations
Mariano Balbi and David Charles Bonaventure Lallemant
Hydrol. Earth Syst. Sci., 27, 1089–1108, https://doi.org/10.5194/hess-27-1089-2023,https://doi.org/10.5194/hess-27-1089-2023, 2023
Short summary
Intercomparison of global reanalysis precipitation for flood risk modelling
Fergus McClean, Richard Dawson, and Chris Kilsby
Hydrol. Earth Syst. Sci., 27, 331–347, https://doi.org/10.5194/hess-27-331-2023,https://doi.org/10.5194/hess-27-331-2023, 2023
Short summary
Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too
David McInerney, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Fitsum Woldemeskel, Narendra Tuteja, and George Kuczera
Hydrol. Earth Syst. Sci., 26, 5669–5683, https://doi.org/10.5194/hess-26-5669-2022,https://doi.org/10.5194/hess-26-5669-2022, 2022
Short summary
An uncertainty partition approach for inferring interactive hydrologic risks
Yurui Fan, Kai Huang, Guohe Huang, Yongping Li, and Feng Wang
Hydrol. Earth Syst. Sci., 24, 4601–4624, https://doi.org/10.5194/hess-24-4601-2020,https://doi.org/10.5194/hess-24-4601-2020, 2020
More articles (10)

Cited articles

Andreadis, K. M., Clark, E. A., Lettenmaier, D. P., and Alsdorf, D. E.: Prospects for river discharge and depth estimation through assimilation of swath-altimetry into a raster-based hydrodynamics model, Geophys. Res. Lett., 34, L10403, https://doi.org/10.1029/2007GL029721, 2007. a
Bates, P. and Roo, A. D.: A simple raster-based model for flood inundation simulation, J. Hydrol., 236, 54–77, https://doi.org/10.1016/S0022-1694(00)00278-X, 2000. a
Bormann, N., Bonavita, M., Dragani, R., Eresmaa, R., Matricardi, M., and McNally, A.: Enhancing the impact of IASI observations through an updated observation-error covariance matrix, Q. J. Roy. Meteorol. Soc., 142, 1767–1780, https://doi.org/10.1002/qj.2774, 2016. a
Campbell, W. F., Satterfield, E. A., Ruston, B., and Baker, N. L.: Accounting for Correlated Observation Error in a Dual-Formulation 4D Variational Data Assimilation System, Mon. Weather Rev., 145, 1019–1032, https://doi.org/10.1175/MWR-D-16-0240.1, 2017. a
Cordoba, M., Dance, S., Kelly, G., Nichols, N., and Waller, J.: Diagnosing Atmospheric Motion Vector observation errors for an operational high resolution data assimilation system, Q. J. Roy. Meteorol. Soc., 143, 333–341, https://doi.org/10.1002/qj.2925, 2017. a
More articles (44)