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.
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.
the Creative Commons Attribution 4.0 License.
Technical note: Assessment of observation quality for data assimilation in flood models
Joanne A. Waller
CORRESPONDING AUTHOR
School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, UK
Javier García-Pintado
School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, UK
MARUM – Center for Marine Environmental Sciences and Department of Geosciences, University of Bremen, Bremen, Germany
David C. Mason
School of Archaeology, Geography and Environmental Science, University of Reading, Reading, UK
Sarah L. Dance
School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, UK
Nancy K. Nichols
School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, UK
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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 under review for HESS
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This study introduces a method that uses satellite data to enhance flood map selection for forecast-based financing applications. Tested on the 2022 Pakistan floods, it successfully triggered flood maps in four out of five regions, including those with urban areas. The approach ensures timely humanitarian aid by updating flood maps, even when initial triggers are missed, aiding in better disaster preparedness and risk management.
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
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Ensemble forecasts of flood inundation produce maps indicating the probability of flooding. A new approach is presented to evaluate the spatial performance of an ensemble flood map forecast by comparison against remotely observed flooding extents. This is important for understanding forecast uncertainties and improving flood forecasting systems.
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
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The European Flood Awareness System creates flood forecasts for up to 15 d in the future for the whole of Europe which are made available to local authorities. These forecasts can be erroneous because the weather forecasts include errors or because the hydrological model used does not represent the flow in the rivers correctly. We found that, by using recent observations and a model trained with past observations and forecasts, the real-time forecast can be corrected, thus becoming more useful.
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
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The acquisition of river-level data is a critical task for the understanding of flood events but is often complicated by the difficulty to install and maintain gauges able to provide such information. This study proposes applying deep learning techniques on river-camera images in order to automatically extract the corresponding water levels. This approach could allow for a new flexible way to observe flood events, especially at ungauged locations.
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
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This study evaluates how the sequential assimilation of flood extent derived from synthetic aperture radar data can help improve flood forecasting. In particular, we carried out twin experiments based on a synthetically generated dataset with controlled uncertainty. Our empirical results demonstrate the efficiency of the proposed data assimilation framework, as forecasting errors are substantially reduced as a result of the assimilation.
Sean F. Cleator, Sandy P. Harrison, Nancy K. Nichols, I. Colin Prentice, and Ian Roulstone
Clim. Past, 16, 699–712, https://doi.org/10.5194/cp-16-699-2020, https://doi.org/10.5194/cp-16-699-2020, 2020
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We present geographically explicit reconstructions of seasonal temperature and annual moisture variables at the Last Glacial Maximum (LGM), 21 000 years ago. The reconstructions use existing site-based estimates of climate, interpolated in space and time in a physically consistent way using climate model simulations. The reconstructions give a much better picture of the LGM climate and will provide a robust evaluation of how well state-of-the-art climate models simulate large climate changes.
Elizabeth S. Cooper, Sarah L. Dance, Javier García-Pintado, Nancy K. Nichols, and Polly J. Smith
Hydrol. Earth Syst. Sci., 23, 2541–2559, https://doi.org/10.5194/hess-23-2541-2019, https://doi.org/10.5194/hess-23-2541-2019, 2019
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Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to warn people if and when they are likely to be affected by river floodwater, but such predictions are not always accurate or reliable. Information about flood extent from satellites can help to keep these forecasts on track. Here we investigate different ways of using information from satellite images and look at the effect on computer predictions. This will help to develop flood warning systems.
Bertrand Bonan, Nancy K. Nichols, Michael J. Baines, and Dale Partridge
Nonlin. Processes Geophys., 24, 515–534, https://doi.org/10.5194/npg-24-515-2017, https://doi.org/10.5194/npg-24-515-2017, 2017
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We develop data assimilation techniques for numerical models using moving mesh methods. Moving meshes are valuable for explicitly tracking interfaces and boundaries in evolving systems. The application of the techniques is demonstrated on a one-dimensional
model of an ice sheet. It is shown, using various types of observations, that
the techniques predict the evolution of the edges of the ice sheet and its height accurately and efficiently.
Sylvain Delahaies, Ian Roulstone, and Nancy Nichols
Geosci. Model Dev., 10, 2635–2650, https://doi.org/10.5194/gmd-10-2635-2017, https://doi.org/10.5194/gmd-10-2635-2017, 2017
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Carbon is a fundamental constituent of life and understanding its global cycle is a key challenge for the modelling of the Earth system. We use a variational method to estimate parameters and initial conditions for the carbon cycle model DALECv2 using multiple sources of observations. We develop a methodology that helps understanding the nature of the inverse problem and evaluating solution strategies, then we demonstrate the efficiency of the variational method in an experiment using real data.
B. Bonan, M. J. Baines, N. K. Nichols, and D. Partridge
The Cryosphere, 10, 1–14, https://doi.org/10.5194/tc-10-1-2016, https://doi.org/10.5194/tc-10-1-2016, 2016
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This paper introduce a moving-point approach to model the flow of ice sheets. This particular moving-grid numerical approach is based on the conservation of local masses. This allows the ice sheet margins to be tracked explicitly. A finite-difference moving-point scheme is derived and applied in a simplified context (1-D). The conservation method is also suitable for 2-D scenarios. This paper is a first step towards applications of the conservation method to realistic 2-D cases.
Related subject area
Subject: Engineering Hydrology | Techniques and Approaches: Uncertainty analysis
A more comprehensive uncertainty framework for historical flood frequency analysis: a 500-year long case study
Bayesian calibration of a flood simulator using binary flood extent observations
Intercomparison of global reanalysis precipitation for flood risk modelling
Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too
An uncertainty partition approach for inferring interactive hydrologic risks
Predicting discharge capacity of vegetated compound channels: uncertainty and identifiability of one-dimensional process-based models
Uncertainty quantification of floodplain friction in hydrodynamic models
Developing a drought-monitoring index for the contiguous US using SMAP
Sedimentation monitoring including uncertainty analysis in complex floodplains: a case study in the Mekong Delta
Assessing the impact of uncertainty on flood risk estimates with reliability analysis using 1-D and 2-D hydraulic models
The transferability of hydrological models under nonstationary climatic conditions
Why hydrological predictions should be evaluated using information theory
Spatial uncertainty assessment in modelling reference evapotranspiration at regional scale
Confidence intervals for the coefficient of L-variation in hydrological applications
Possibilistic uncertainty analysis of a conceptual model of snowmelt runoff
Mathieu Lucas, Michel Lang, Benjamin Renard, and Jérôme Le Coz
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-50, https://doi.org/10.5194/hess-2024-50, 2024
Revised manuscript accepted for HESS
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This paper proposes Binomial models for the inclusion of historical data into flood frequency analysis, which recognize the uncertain nature of the perception threshold and the starting date of the historical period. The procedure is applied to a long systematic series 1816–2020 and 13 historical floods over 1500–1815 (Rhône River, Beaucaire, France). Inclusion of historical floods reduces the uncertainty flood quantiles, even when only the number of perception threshold exceedances is known.
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
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We proposed a methodology to obtain useful and robust probabilistic predictions from computational flood simulators using satellite-borne flood extent observations. We developed a Bayesian framework to obtain the uncertainty in roughness parameters, in observations errors, and in simulator structural deficiencies. We found that it can yield improvements in predictions relative to current methodologies and can potentially lead to consistent ways of combining data from different sources.
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
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Reanalysis datasets are increasingly used to drive flood models, especially for continental and global analysis. We investigate the impact of using four reanalysis products on simulations of past flood events. All reanalysis products underestimated the number of buildings inundated, compared to a benchmark national dataset. These findings show that while global reanalyses provide a useful resource for flood modelling where no other data are available, they may underestimate impact in some cases.
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
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Streamflow forecasts a day to a month ahead are highly valuable for water resources management. Current practice often develops forecasts for specific lead times and aggregation timescales. In contrast, a single, seamless forecast can serve multiple lead times/timescales. This study shows seamless forecasts can match the performance of forecasts developed specifically at the monthly scale, while maintaining quality at other lead times. Hence, users need not sacrifice capability for performance.
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
Adam Kiczko, Kaisa Västilä, Adam Kozioł, Janusz Kubrak, Elżbieta Kubrak, and Marcin Krukowski
Hydrol. Earth Syst. Sci., 24, 4135–4167, https://doi.org/10.5194/hess-24-4135-2020, https://doi.org/10.5194/hess-24-4135-2020, 2020
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The study compares the uncertainty of discharge curves for vegetated channels, calculated using several methods, including the simplest ones, based on the Manning formula and advanced approaches, providing a detailed physical representation of the channel flow processes. Parameters of each method were identified for the same data sets. The outcomes of the study include the widths of confidence intervals, showing which method was the most successful in explaining observations.
Guilherme Luiz Dalledonne, Rebekka Kopmann, and Thomas Brudy-Zippelius
Hydrol. Earth Syst. Sci., 23, 3373–3385, https://doi.org/10.5194/hess-23-3373-2019, https://doi.org/10.5194/hess-23-3373-2019, 2019
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This study presents how the concept of uncertainty quantification can be applied to river engineering problems and how important it is to understand the limitations of numerical models from a probabilistic point of view. We investigated floodplain friction formulations using different uncertainty quantification methods and estimated their contribution in the final results. The analysis of uncertainties is planned to be integrated in future projects and also extended to more complex scenarios.
Sara Sadri, Eric F. Wood, and Ming Pan
Hydrol. Earth Syst. Sci., 22, 6611–6626, https://doi.org/10.5194/hess-22-6611-2018, https://doi.org/10.5194/hess-22-6611-2018, 2018
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Of particular interest to NASA's SMAP-based agricultural applications is a monitoring product that assesses near-surface soil moisture in terms of probability percentiles for dry and wet conditions. However, the short SMAP record length poses a statistical challenge for the meaningful assessment of its indices. This study presents initial insights about using SMAP Level 3 and Level 4 for monitoring drought and pluvial regions with a first application over the contiguous United States (CONUS).
N. V. Manh, B. Merz, and H. Apel
Hydrol. Earth Syst. Sci., 17, 3039–3057, https://doi.org/10.5194/hess-17-3039-2013, https://doi.org/10.5194/hess-17-3039-2013, 2013
L. Altarejos-García, M. L. Martínez-Chenoll, I. Escuder-Bueno, and A. Serrano-Lombillo
Hydrol. Earth Syst. Sci., 16, 1895–1914, https://doi.org/10.5194/hess-16-1895-2012, https://doi.org/10.5194/hess-16-1895-2012, 2012
C. Z. Li, L. Zhang, H. Wang, Y. Q. Zhang, F. L. Yu, and D. H. Yan
Hydrol. Earth Syst. Sci., 16, 1239–1254, https://doi.org/10.5194/hess-16-1239-2012, https://doi.org/10.5194/hess-16-1239-2012, 2012
S. V. Weijs, G. Schoups, and N. van de Giesen
Hydrol. Earth Syst. Sci., 14, 2545–2558, https://doi.org/10.5194/hess-14-2545-2010, https://doi.org/10.5194/hess-14-2545-2010, 2010
G. Buttafuoco, T. Caloiero, and R. Coscarelli
Hydrol. Earth Syst. Sci., 14, 2319–2327, https://doi.org/10.5194/hess-14-2319-2010, https://doi.org/10.5194/hess-14-2319-2010, 2010
A. Viglione
Hydrol. Earth Syst. Sci., 14, 2229–2242, https://doi.org/10.5194/hess-14-2229-2010, https://doi.org/10.5194/hess-14-2229-2010, 2010
A. P. Jacquin
Hydrol. Earth Syst. Sci., 14, 1681–1695, https://doi.org/10.5194/hess-14-1681-2010, https://doi.org/10.5194/hess-14-1681-2010, 2010
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