Articles | Volume 23, issue 6
https://doi.org/10.5194/hess-23-2541-2019
© Author(s) 2019. 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-23-2541-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jun 2019
Research article |
| 05 Jun 2019
| 05 Jun 2019
Observation operators for assimilation of satellite observations in fluvial inundation forecasting
Department of Meteorology, University of Reading, Reading, UK
Sarah L. Dance
Department of Meteorology, University of Reading, Reading, UK
Department of Mathematics and Statistics, University of Reading, Reading, UK
Javier García-Pintado
MARUM Center for Marine Environmental Sciences, Department of Geosciences, University of Bremen, Bremen, Germany
Nancy K. Nichols
Department of Meteorology, University of Reading, Reading, UK
Department of Mathematics and Statistics, University of Reading, Reading, UK
Polly J. Smith
Department of Meteorology, University of Reading, Reading, UK
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-179, https://doi.org/10.5194/hess-2024-179, 2024
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Our research compares two techniques, Bias-Correction (BC) and Data Assimilation (DA), for improving river flow forecasts across 316 UK catchments. BC, which corrects errors post-simulation, showed broad improvements, while DA, adjusting model states pre-forecast, excelled in specific conditions like snowmelt and high base flows. Each method's unique strengths suit different scenarios. These insights can enhance forecasting systems, offering reliable and user-friendly hydrological predictions.
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We have tested a different way of simulating soil moisture and river flow. Instead of dividing the land up into over 10,000 squares to run our numerical model, we cluster the land into fewer, irregular areas with similar landscape characteristics. We show that different ways of clustering the landscape produce different patterns of soil moisture. We also show that with this method we can we match observations as well as our usual gridded approach for ten times less computational resource.
Elizabeth Cooper, Eleanor Blyth, Hollie Cooper, Rich Ellis, Ewan Pinnington, and Simon J. Dadson
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Soil moisture estimates from land surface models are important for forecasting floods, droughts, weather, and climate trends. We show that by combining model estimates of soil moisture with measurements from field-scale, ground-based sensors, we can improve the performance of the land surface model in predicting soil moisture values.
Hollie M. Cooper, Emma Bennett, James Blake, Eleanor Blyth, David Boorman, Elizabeth Cooper, Jonathan Evans, Matthew Fry, Alan Jenkins, Ross Morrison, Daniel Rylett, Simon Stanley, Magdalena Szczykulska, Emily Trill, Vasileios Antoniou, Anne Askquith-Ellis, Lucy Ball, Milo Brooks, Michael A. Clarke, Nicholas Cowan, Alexander Cumming, Philip Farrand, Olivia Hitt, William Lord, Peter Scarlett, Oliver Swain, Jenna Thornton, Alan Warwick, and Ben Winterbourn
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COSMOS-UK is a UK network of environmental monitoring sites, with a focus on measuring field-scale soil moisture. Each site includes soil and hydrometeorological sensors providing data including air temperature, humidity, net radiation, neutron counts, snow water equivalent, and potential evaporation. These data can provide information for science, industry, and agriculture by improving existing understanding and data products in fields such as water resources, space sciences, and biodiversity.
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Land surface models are important tools for translating meteorological forecasts and reanalyses into real-world impacts at the Earth's surface. We show that the hydrological predictions, in particular soil moisture, of these models can be improved by combining them with satellite observations from the NASA SMAP mission to update uncertain parameters. We find a 22 % reduction in error at a network of in situ soil moisture sensors after combining model predictions with satellite observations.
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Bertrand Bonan, Nancy K. Nichols, Michael J. Baines, and Dale Partridge
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Hydrol. Earth Syst. Sci., 28, 2617–2633, https://doi.org/10.5194/hess-28-2617-2024, https://doi.org/10.5194/hess-28-2617-2024, 2024
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Conrad Wasko, Seth Westra, Rory Nathan, Acacia Pepler, Timothy H. Raupach, Andrew Dowdy, Fiona Johnson, Michelle Ho, Kathleen L. McInnes, Doerte Jakob, Jason Evans, Gabriele Villarini, and Hayley J. Fowler
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Manuela Irene Brunner
Hydrol. Earth Syst. Sci., 27, 2479–2497, https://doi.org/10.5194/hess-27-2479-2023, https://doi.org/10.5194/hess-27-2479-2023, 2023
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I discuss different types of multivariate hydrological extremes and their dependencies, including regional extremes affecting multiple locations, such as spatially connected flood events; consecutive extremes occurring in close temporal succession, such as successive droughts; extremes characterized by multiple characteristics, such as floods with jointly high peak discharge and flood volume; and transitions between different types of extremes, such as drought-to-flood transitions.
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Hydrol. Earth Syst. Sci., 26, 4345–4378, https://doi.org/10.5194/hess-26-4345-2022, https://doi.org/10.5194/hess-26-4345-2022, 2022
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Deep learning methods have been increasingly used in flood management to improve traditional techniques. While promising results have been obtained, our review shows significant challenges in building deep learning models that can (i) generalize across multiple scenarios, (ii) account for complex interactions, and (iii) perform probabilistic predictions. We argue that these shortcomings could be addressed by transferring recent fundamental advancements in deep learning to flood mapping.
Manuela I. Brunner and Louise J. Slater
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Thorsten Wagener, Dragan Savic, David Butler, Reza Ahmadian, Tom Arnot, Jonathan Dawes, Slobodan Djordjevic, Roger Falconer, Raziyeh Farmani, Debbie Ford, Jan Hofman, Zoran Kapelan, Shunqi Pan, and Ross Woods
Hydrol. Earth Syst. Sci., 25, 2721–2738, https://doi.org/10.5194/hess-25-2721-2021, https://doi.org/10.5194/hess-25-2721-2021, 2021
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How can we effectively train PhD candidates both (i) across different knowledge domains in water science and engineering and (ii) in computer science? To address this issue, the Water Informatics in Science and Engineering Centre for Doctoral Training (WISE CDT) offers a postgraduate programme that fosters enhanced levels of innovation and collaboration by training a cohort of engineers and scientists at the boundary of water informatics, science and engineering.
Onno Bokhove, Tiffany Hicks, Wout Zweers, and Thomas Kent
Hydrol. Earth Syst. Sci., 24, 2483–2503, https://doi.org/10.5194/hess-24-2483-2020, https://doi.org/10.5194/hess-24-2483-2020, 2020
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Wetropolis is a
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Gianfranco Urciuoli, Luca Comegna, Marianna Pirone, and Luciano Picarelli
Hydrol. Earth Syst. Sci., 24, 1669–1676, https://doi.org/10.5194/hess-24-1669-2020, https://doi.org/10.5194/hess-24-1669-2020, 2020
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The aim of this paper is to demonstrate, through a numerical approach, that the presence of soil layers of higher permeability, a not unlikely condition in some deep landslides in clay, may be exploited to improve the efficiency of systems of drainage trenches for slope stabilization. The problem has been examined for the case that a unique pervious layer, parallel to the ground surface, is present at an elevation higher than the bottom of the trenches.
Bin Xiong, Lihua Xiong, Jun Xia, Chong-Yu Xu, Cong Jiang, and Tao Du
Hydrol. Earth Syst. Sci., 23, 4453–4470, https://doi.org/10.5194/hess-23-4453-2019, https://doi.org/10.5194/hess-23-4453-2019, 2019
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We develop a new indicator of reservoir effects, called the rainfall–reservoir composite index (RRCI). RRCI, coupled with the effects of static reservoir capacity and scheduling-related multivariate rainfall, has a better performance than the previous indicator in terms of explaining the variation in the downstream floods affected by reservoir operation. A covariate-based flood frequency analysis using RRCI can provide more reliable downstream flood risk estimation.
Bart van Osnabrugge, Remko Uijlenhoet, and Albrecht Weerts
Hydrol. Earth Syst. Sci., 23, 1453–1467, https://doi.org/10.5194/hess-23-1453-2019, https://doi.org/10.5194/hess-23-1453-2019, 2019
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A correct estimate of the amount of future precipitation is the most important factor in making a good streamflow forecast, but evaporation is also an important component that determines the discharge of a river. However, in this study for the Rhine River we found that evaporation forecasts only give an almost negligible improvement compared to methods that use statistical information on climatology for a 10-day streamflow forecast. This is important to guide research on low flow forecasts.
Cédric Rebolho, Vazken Andréassian, and Nicolas Le Moine
Hydrol. Earth Syst. Sci., 22, 5967–5985, https://doi.org/10.5194/hess-22-5967-2018, https://doi.org/10.5194/hess-22-5967-2018, 2018
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Inundation models are useful for hazard management and prevention. They are traditionally based on hydraulic partial differential equations (with satisfying results but large data and computational requirements). This study presents a simplified approach combining reach-scale geometric properties with steady uniform flow equations. The model shows promising results overall, although difficulties persist in the most complex urbanised reaches.
Andreas Paul Zischg, Guido Felder, Rolf Weingartner, Niall Quinn, Gemma Coxon, Jeffrey Neal, Jim Freer, and Paul Bates
Hydrol. Earth Syst. Sci., 22, 2759–2773, https://doi.org/10.5194/hess-22-2759-2018, https://doi.org/10.5194/hess-22-2759-2018, 2018
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We developed a model experiment and distributed different rainfall patterns over a mountain river basin. For each rainfall scenario, we computed the flood losses with a model chain. The experiment shows that flood losses vary considerably within the river basin and depend on the timing of the flood peaks from the basin's sub-catchments. Basin-specific characteristics such as the location of the main settlements within the floodplains play an additional important role in determining flood losses.
Guillaume Le Bihan, Olivier Payrastre, Eric Gaume, David Moncoulon, and Frédéric Pons
Hydrol. Earth Syst. Sci., 21, 5911–5928, https://doi.org/10.5194/hess-21-5911-2017, https://doi.org/10.5194/hess-21-5911-2017, 2017
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This paper illustrates how an integrated flash flood monitoring (or forecasting) system may be designed to directly provide information on possibly flooded areas and associated impacts on a very detailed river network and over large territories. The approach is extensively tested in the regions of Alès and Draguignan, located in south-eastern France. Validation results are presented in terms of accuracy of the estimated flood extents and related impacts (based on insurance claim data).
Ashley Wright, Jeffrey P. Walker, David E. Robertson, and Valentijn R. N. Pauwels
Hydrol. Earth Syst. Sci., 21, 3827–3838, https://doi.org/10.5194/hess-21-3827-2017, https://doi.org/10.5194/hess-21-3827-2017, 2017
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The accurate reduction of hydrologic model input data is an impediment towards understanding input uncertainty and model structural errors. This paper compares the ability of two transforms to reduce rainfall input data. The resultant transforms are compressed to varying extents and reconstructed before being evaluated with standard simulation performance summary metrics and descriptive statistics. It is concluded the discrete wavelet transform is most capable of preserving rainfall time series.
Ricardo Zubieta, Augusto Getirana, Jhan Carlo Espinoza, Waldo Lavado-Casimiro, and Luis Aragon
Hydrol. Earth Syst. Sci., 21, 3543–3555, https://doi.org/10.5194/hess-21-3543-2017, https://doi.org/10.5194/hess-21-3543-2017, 2017
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This paper indicates that precipitation data derived from GPM-IMERG correspond more closely to TMPA V7 than TMPA RT datasets, but both GPM-IMERG and TMPA V7 precipitation data tend to overestimate, in comparison to observed rainfall (by 11.1 % and 15.7 %, respectively). Statistical analysis indicates that GPM-IMERG is as useful as TMPA V7 or TMPA RT datasets for estimating observed streamflows in Andean–Amazonian regions (Ucayali Basin, southern regions of the Amazon Basin of Peru and Ecuador).
Anna Botto, Daniele Ganora, Pierluigi Claps, and Francesco Laio
Hydrol. Earth Syst. Sci., 21, 3353–3358, https://doi.org/10.5194/hess-21-3353-2017, https://doi.org/10.5194/hess-21-3353-2017, 2017
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The paper provides an easy-to-use implementation of the UNCODE framework, which allows one to estimate the design flood value by directly accounting for sample uncertainty. Other than a design tool, this methodology is also a practical way to quantify the value of data in the design process.
Amin Elshorbagy, Raja Bharath, Anchit Lakhanpal, Serena Ceola, Alberto Montanari, and Karl-Erich Lindenschmidt
Hydrol. Earth Syst. Sci., 21, 2219–2232, https://doi.org/10.5194/hess-21-2219-2017, https://doi.org/10.5194/hess-21-2219-2017, 2017
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Flood mapping is one of Canada's major national interests. This work presents a simple and effective method for large-scale flood hazard and risk mapping, applied in this study to Canada. Readily available data, such as remote sensing night-light data, topography, and stream network were used to create the maps.
D. C. Verdon-Kidd and A. S. Kiem
Hydrol. Earth Syst. Sci., 19, 4735–4746, https://doi.org/10.5194/hess-19-4735-2015, https://doi.org/10.5194/hess-19-4735-2015, 2015
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Rainfall intensity-frequency-duration (IFD) relationships are required for the design and planning of water supply and management systems around the world. Currently IFD information is based on the "stationary climate assumption". However, this paper provides evidence of regime shifts in annual maxima rainfall time series using 96 daily rainfall stations and 66 sub-daily rainfall stations across Australia. Importantly, current IFD relationships may under- or overestimate the design rainfall.
P. A. Marker, N. Foged, X. He, A. V. Christiansen, J. C. Refsgaard, E. Auken, and P. Bauer-Gottwein
Hydrol. Earth Syst. Sci., 19, 3875–3890, https://doi.org/10.5194/hess-19-3875-2015, https://doi.org/10.5194/hess-19-3875-2015, 2015
H. Vernieuwe, S. Vandenberghe, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 19, 2685–2699, https://doi.org/10.5194/hess-19-2685-2015, https://doi.org/10.5194/hess-19-2685-2015, 2015
H. Vuollekoski, M. Vogt, V. A. Sinclair, J. Duplissy, H. Järvinen, E.-M. Kyrö, R. Makkonen, T. Petäjä, N. L. Prisle, P. Räisänen, M. Sipilä, J. Ylhäisi, and M. Kulmala
Hydrol. Earth Syst. Sci., 19, 601–613, https://doi.org/10.5194/hess-19-601-2015, https://doi.org/10.5194/hess-19-601-2015, 2015
Short summary
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The global potential for collecting usable water from dew on an
artificial collector sheet was investigated by utilising 34 years of
meteorological reanalysis data as input to a dew formation model. Continental dew formation was found to be frequent and common, but daily yields were
mostly below 0.1mm.
D. E. Mora, L. Campozano, F. Cisneros, G. Wyseure, and P. Willems
Hydrol. Earth Syst. Sci., 18, 631–648, https://doi.org/10.5194/hess-18-631-2014, https://doi.org/10.5194/hess-18-631-2014, 2014
M. T. Pham, W. J. Vanhaute, S. Vandenberghe, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 17, 5167–5183, https://doi.org/10.5194/hess-17-5167-2013, https://doi.org/10.5194/hess-17-5167-2013, 2013
M. Schwarz, F. Giadrossich, and D. Cohen
Hydrol. Earth Syst. Sci., 17, 4367–4377, https://doi.org/10.5194/hess-17-4367-2013, https://doi.org/10.5194/hess-17-4367-2013, 2013
G. Di Baldassarre, A. Viglione, G. Carr, L. Kuil, J. L. Salinas, and G. Blöschl
Hydrol. Earth Syst. Sci., 17, 3295–3303, https://doi.org/10.5194/hess-17-3295-2013, https://doi.org/10.5194/hess-17-3295-2013, 2013
L. Brocca, S. Liersch, F. Melone, T. Moramarco, and M. Volk
Hydrol. Earth Syst. Sci., 17, 3159–3169, https://doi.org/10.5194/hess-17-3159-2013, https://doi.org/10.5194/hess-17-3159-2013, 2013
T. A. McMahon, M. C. Peel, L. Lowe, R. Srikanthan, and T. R. McVicar
Hydrol. Earth Syst. Sci., 17, 1331–1363, https://doi.org/10.5194/hess-17-1331-2013, https://doi.org/10.5194/hess-17-1331-2013, 2013
E. Habib, Y. Ma, D. Williams, H. O. Sharif, and F. Hossain
Hydrol. Earth Syst. Sci., 16, 3767–3781, https://doi.org/10.5194/hess-16-3767-2012, https://doi.org/10.5194/hess-16-3767-2012, 2012
A. Pathirana, B. Gersonius, and M. Radhakrishnan
Hydrol. Earth Syst. Sci., 16, 2499–2509, https://doi.org/10.5194/hess-16-2499-2012, https://doi.org/10.5194/hess-16-2499-2012, 2012
K. X. Soulis and J. D. Valiantzas
Hydrol. Earth Syst. Sci., 16, 1001–1015, https://doi.org/10.5194/hess-16-1001-2012, https://doi.org/10.5194/hess-16-1001-2012, 2012
G. Corato, T. Moramarco, and T. Tucciarelli
Hydrol. Earth Syst. Sci., 15, 2979–2994, https://doi.org/10.5194/hess-15-2979-2011, https://doi.org/10.5194/hess-15-2979-2011, 2011
A. Elshorbagy, G. Corzo, S. Srinivasulu, and D. P. Solomatine
Hydrol. Earth Syst. Sci., 14, 1943–1961, https://doi.org/10.5194/hess-14-1943-2010, https://doi.org/10.5194/hess-14-1943-2010, 2010
A. D. Koussis
Hydrol. Earth Syst. Sci., 14, 1093–1097, https://doi.org/10.5194/hess-14-1093-2010, https://doi.org/10.5194/hess-14-1093-2010, 2010
J. A. Velázquez, T. Petit, A. Lavoie, M.-A. Boucher, R. Turcotte, V. Fortin, and F. Anctil
Hydrol. Earth Syst. Sci., 13, 2221–2231, https://doi.org/10.5194/hess-13-2221-2009, https://doi.org/10.5194/hess-13-2221-2009, 2009
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Short summary
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.
Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to...
