Articles | Volume 26, issue 11
https://doi.org/10.5194/hess-26-2939-2022
© Author(s) 2022. 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-26-2939-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jun 2022
Research article |
| 15 Jun 2022
| 15 Jun 2022
Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System
Gwyneth Matthews
CORRESPONDING AUTHOR
Department of Meteorology, University of Reading, Reading, United Kingdom
Christopher Barnard
Forecast Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Hannah Cloke
Department of Meteorology, University of Reading, Reading, United Kingdom
Forecast Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Department of Geography and Environmental Science, University of Reading, Reading, United Kingdom
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Centre of Natural Hazards and Disaster Science, CNDS, Uppsala, Sweden
Sarah L. Dance
Department of Meteorology, University of Reading, Reading, United Kingdom
Department of Mathematics and Statistics, University of Reading, Reading, United Kingdom
Toni Jurlina
Forecast Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Cinzia Mazzetti
Forecast Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Christel Prudhomme
Forecast Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Department of Geography, University of Loughborough, Loughborough, United Kingdom
UK Centre for Ecology and Hydrology, Wallingford, United Kingdom
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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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Jessica L. Neumann, Louise Arnal, Rebecca E. Emerton, Helen Griffith, Stuart Hyslop, Sofia Theofanidi, and Hannah L. Cloke
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Lila Collet, Shaun Harrigan, Christel Prudhomme, Giuseppe Formetta, and Lindsay Beevers
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Rebecca Emerton, Ervin Zsoter, Louise Arnal, Hannah L. Cloke, Davide Muraro, Christel Prudhomme, Elisabeth M. Stephens, Peter Salamon, and Florian Pappenberger
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Joanne A. Waller, Javier García-Pintado, David C. Mason, Sarah L. Dance, and Nancy K. Nichols
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Maliko Tanguy, Christel Prudhomme, Katie Smith, and Jamie Hannaford
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Louise Arnal, Hannah L. Cloke, Elisabeth Stephens, Fredrik Wetterhall, Christel Prudhomme, Jessica Neumann, Blazej Krzeminski, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 22, 2057–2072, https://doi.org/10.5194/hess-22-2057-2018, https://doi.org/10.5194/hess-22-2057-2018, 2018
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Shaun Harrigan, Christel Prudhomme, Simon Parry, Katie Smith, and Maliko Tanguy
Hydrol. Earth Syst. Sci., 22, 2023–2039, https://doi.org/10.5194/hess-22-2023-2018, https://doi.org/10.5194/hess-22-2023-2018, 2018
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Gregor Laaha, Tobias Gauster, Lena M. Tallaksen, Jean-Philippe Vidal, Kerstin Stahl, Christel Prudhomme, Benedikt Heudorfer, Radek Vlnas, Monica Ionita, Henny A. J. Van Lanen, Mary-Jeanne Adler, Laurie Caillouet, Claire Delus, Miriam Fendekova, Sebastien Gailliez, Jamie Hannaford, Daniel Kingston, Anne F. Van Loon, Luis Mediero, Marzena Osuch, Renata Romanowicz, Eric Sauquet, James H. Stagge, and Wai K. Wong
Hydrol. Earth Syst. Sci., 21, 3001–3024, https://doi.org/10.5194/hess-21-3001-2017, https://doi.org/10.5194/hess-21-3001-2017, 2017
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Simon Parry, Robert L. Wilby, Christel Prudhomme, and Paul J. Wood
Hydrol. Earth Syst. Sci., 20, 4265–4281, https://doi.org/10.5194/hess-20-4265-2016, https://doi.org/10.5194/hess-20-4265-2016, 2016
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This paper identifies periods of recovery from drought in 52 river flow records from the UK between 1883 and 2013. The approach detects 459 events that vary in space and time. This large dataset allows individual events to be compared with others in the historical record. The ability to objectively appraise contemporary events against the historical record has not previously been possible, and may allow water managers to prepare for a range of outcomes at the end of a drought.
Louise Arnal, Maria-Helena Ramos, Erin Coughlan de Perez, Hannah Louise Cloke, Elisabeth Stephens, Fredrik Wetterhall, Schalk Jan van Andel, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 20, 3109–3128, https://doi.org/10.5194/hess-20-3109-2016, https://doi.org/10.5194/hess-20-3109-2016, 2016
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Forecasts are produced as probabilities of occurrence of specific events, which is both an added value and a challenge for users. This paper presents a game on flood protection, "How much are you prepared to pay for a forecast?", which investigated how users perceive the value of forecasts and are willing to pay for them when making decisions. It shows that users are mainly influenced by the perceived quality of the forecasts, their need for the information and their degree of risk tolerance.
Dave MacLeod, Hannah Cloke, Florian Pappenberger, and Antje Weisheimer
Hydrol. Earth Syst. Sci., 20, 2737–2743, https://doi.org/10.5194/hess-20-2737-2016, https://doi.org/10.5194/hess-20-2737-2016, 2016
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Soil moisture memory is a key aspect of seasonal climate predictions, through feedback between the land surface and the atmosphere. Estimates have been made of the length of soil moisture memory; however, we show here how estimates of memory show large variation with uncertain model parameters. Explicit representation of model uncertainty may then improve the realism of simulations and seasonal climate forecasts.
A. Chiverton, J. Hannaford, I. P. Holman, R. Corstanje, C. Prudhomme, T. M. Hess, and J. P. Bloomfield
Hydrol. Earth Syst. Sci., 19, 2395–2408, https://doi.org/10.5194/hess-19-2395-2015, https://doi.org/10.5194/hess-19-2395-2015, 2015
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Current hydrological change detection methods are subject to a host of limitations. This paper develops a new method, temporally shifting variograms (TSVs), which characterises variability in the river flow regime using several parameters, changes in which can then be attributed to precipitation characteristics. We demonstrate the use of the method through application to 94 UK catchments, showing that periods of extremes as well as more subtle changes can be detected.
I. Giuntoli, J.-P. Vidal, C. Prudhomme, and D. M. Hannah
Earth Syst. Dynam., 6, 267–285, https://doi.org/10.5194/esd-6-267-2015, https://doi.org/10.5194/esd-6-267-2015, 2015
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We assessed future changes in high and low flows globally using runoff projections from global hydrological models (GHMs) driven by global climate models (GCMs) under the RCP8.5 scenario. Further, we quantified the relative size of uncertainty from GHMs and from GCMs using ANOVA. We show that GCMs are the major contributors to uncertainty overall, but GHMs increase their contribution for low flows and can equal or outweigh GCM uncertainty in snow-dominated areas for both high and low flows.
G. Balsamo, C. Albergel, A. Beljaars, S. Boussetta, E. Brun, H. Cloke, D. Dee, E. Dutra, J. Muñoz-Sabater, F. Pappenberger, P. de Rosnay, T. Stockdale, and F. Vitart
Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, https://doi.org/10.5194/hess-19-389-2015, 2015
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ERA-Interim/Land is a global land surface reanalysis covering the period 1979–2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land includes a number of parameterization improvements in the land surface scheme with respect to the original ERA-Interim and a precipitation bias correction based on GPCP. A selection of verification results show the added value in representing the terrestrial water cycle and its main land surface storages and fluxes.
C. C. Sampson, T. J. Fewtrell, F. O'Loughlin, F. Pappenberger, P. B. Bates, J. E. Freer, and H. L. Cloke
Hydrol. Earth Syst. Sci., 18, 2305–2324, https://doi.org/10.5194/hess-18-2305-2014, https://doi.org/10.5194/hess-18-2305-2014, 2014
F. Wetterhall, F. Pappenberger, L. Alfieri, H. L. Cloke, J. Thielen-del Pozo, S. Balabanova, J. Daňhelka, A. Vogelbacher, P. Salamon, I. Carrasco, A. J. Cabrera-Tordera, M. Corzo-Toscano, M. Garcia-Padilla, R. J. Garcia-Sanchez, C. Ardilouze, S. Jurela, B. Terek, A. Csik, J. Casey, G. Stankūnavičius, V. Ceres, E. Sprokkereef, J. Stam, E. Anghel, D. Vladikovic, C. Alionte Eklund, N. Hjerdt, H. Djerv, F. Holmberg, J. Nilsson, K. Nyström, M. Sušnik, M. Hazlinger, and M. Holubecka
Hydrol. Earth Syst. Sci., 17, 4389–4399, https://doi.org/10.5194/hess-17-4389-2013, https://doi.org/10.5194/hess-17-4389-2013, 2013
C. Prudhomme and J. Williamson
Hydrol. Earth Syst. Sci., 17, 1365–1377, https://doi.org/10.5194/hess-17-1365-2013, https://doi.org/10.5194/hess-17-1365-2013, 2013
C. Prudhomme, T. Haxton, S. Crooks, C. Jackson, A. Barkwith, J. Williamson, J. Kelvin, J. Mackay, L. Wang, A. Young, and G. Watts
Earth Syst. Sci. Data, 5, 101–107, https://doi.org/10.5194/essd-5-101-2013, https://doi.org/10.5194/essd-5-101-2013, 2013
Related subject area
Subject: Water Resources Management | Techniques and Approaches: Uncertainty analysis
Actionable human-water systems modeling under uncertainty
Robust multi-objective optimization under multiple-uncertainties using CM-ROPAR approach: case study of the water resources allocation in the Huaihe River Basin
Coupled effects of observation and parameter uncertainty on urban groundwater infrastructure decisions
Disentangling sources of future uncertainties for water management in sub-Saharan river basins
Possibilistic response surfaces: incorporating fuzzy thresholds into bottom-up flood vulnerability analysis
Future hot-spots for hydro-hazards in Great Britain: a probabilistic assessment
Evaluation of impacts of future climate change and water use scenarios on regional hydrology
Planning for climate change impacts on hydropower in the Far North
Describing the interannual variability of precipitation with the derived distribution approach: effects of record length and resolution
Dissolved oxygen prediction using a possibility theory based fuzzy neural network
Projected changes in US rainfall erosivity
Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data
Assessment of precipitation and temperature data from CMIP3 global climate models for hydrologic simulation
Robust global sensitivity analysis of a river management model to assess nonlinear and interaction effects
Sensitivity and uncertainty in crop water footprint accounting: a case study for the Yellow River basin
Irrigation efficiency and water-policy implications for river basin resilience
On an improved sub-regional water resources management representation for integration into earth system models
Statistical analysis of error propagation from radar rainfall to hydrological models
The implications of climate change scenario selection for future streamflow projection in the Upper Colorado River Basin
Prioritization of water management under climate change and urbanization using multi-criteria decision making methods
Crop yields response to water pressures in the Ebro basin in Spain: risk and water policy implications
Laura Gil-García, Nazaret M. Montilla-López, Carlos Gutiérrez-Martín, Ángel Sánchez-Daniel, Pablo Saiz-Santiago, Josué M. Polanco-Martínez, Julio Pindado, and C. Dionisio Pérez-Blanco
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-61, https://doi.org/10.5194/hess-2024-61, 2024
Revised manuscript accepted for HESS
Short summary
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This paper presents an interdisciplinary model for quantifying uncertainties in water allocation under climate change. It combines climate, hydrological, and microeconomic experiments with a decision support system. Multi-model analyses reveal potential futures for water management policies, emphasizing nonlinear climate responses. As illustrated in the Douro River Basin, minor water allocation changes have significant economic impacts, stresssing the need for adaptation strategies.
Jitao Zhang, Dimitri Solomatine, and Zengchuan Dong
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-57, https://doi.org/10.5194/hess-2023-57, 2023
Revised manuscript accepted for HESS
Short summary
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This paper proposes a coupled multi-objective robust optimisation and probabilistic analysis of robustness algorithm. The algorithm is applied to the Huaihe River basin in China. It is shown that the robustness of the solution obtained by the algorithm is significantly better than that of the traditional deterministic model solution, demonstrating the usefulness of the algorithm. In addition to this, robustness between multiple objectives is presented for the first time in this paper.
Marina R. L. Mautner, Laura Foglia, and Jonathan D. Herman
Hydrol. Earth Syst. Sci., 26, 1319–1340, https://doi.org/10.5194/hess-26-1319-2022, https://doi.org/10.5194/hess-26-1319-2022, 2022
Short summary
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Sensitivity analysis can be harnessed to evaluate effects of model uncertainties on planning outcomes. This study explores how observation and parameter uncertainty propagate through a hydrogeologic model to influence the ranking of decision alternatives. Using global sensitivity analysis and evaluation of aquifer management objectives, we evaluate how physical properties of the model and choice of observations for calibration can lead to variations in decision-relevant model outputs.
Alessandro Amaranto, Dinis Juizo, and Andrea Castelletti
Hydrol. Earth Syst. Sci., 26, 245–263, https://doi.org/10.5194/hess-26-245-2022, https://doi.org/10.5194/hess-26-245-2022, 2022
Short summary
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This study aims at designing water supply strategies that are robust against climate, social, and land use changes in a sub-Saharan river basin. We found that robustness analysis supports the discovery of policies enhancing the resilience of water resources systems, benefiting the agricultural, energy, and urban sectors. We show how energy sustainability is affected by water availability, while urban and irrigation resilience also depends on infrastructural interventions and land use changes.
Thibaut Lachaut and Amaury Tilmant
Hydrol. Earth Syst. Sci., 25, 6421–6435, https://doi.org/10.5194/hess-25-6421-2021, https://doi.org/10.5194/hess-25-6421-2021, 2021
Short summary
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Response surfaces are increasingly used to identify the hydroclimatic conditions leading to a water resources system's failure. Partitioning the surface usually requires performance thresholds that are not necessarily crisp. We propose a methodology that combines the inherent uncertainty of response surfaces with the ambiguity of performance thresholds. The proposed methodology is illustrated with a multireservoir system in Canada for which some performance thresholds are imprecise.
Lila Collet, Shaun Harrigan, Christel Prudhomme, Giuseppe Formetta, and Lindsay Beevers
Hydrol. Earth Syst. Sci., 22, 5387–5401, https://doi.org/10.5194/hess-22-5387-2018, https://doi.org/10.5194/hess-22-5387-2018, 2018
Short summary
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Floods and droughts cause significant damages and pose risks to lives worldwide. In a climate change context this work identifies hotspots across Great Britain, i.e. places expected to be impacted by an increase in floods and droughts. By the 2080s the western coast of England and Wales and northeastern Scotland would experience more floods in winter and droughts in autumn, with a higher increase in drought hazard, showing a need to adapt water management policies in light of climate change.
Seungwoo Chang, Wendy Graham, Jeffrey Geurink, Nisai Wanakule, and Tirusew Asefa
Hydrol. Earth Syst. Sci., 22, 4793–4813, https://doi.org/10.5194/hess-22-4793-2018, https://doi.org/10.5194/hess-22-4793-2018, 2018
Short summary
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It is important to understand potential impacts of climate change and human water use on streamflow and groundwater levels. This study used climate models with an integrated hydrologic model to project future streamflow and groundwater level in Tampa Bay for a variety of future water use scenarios. Impacts of different climate projections on streamflow were found to be much stronger than the impacts of different human water use scenarios, but both were significant for groundwater projection.
Jessica E. Cherry, Corrie Knapp, Sarah Trainor, Andrea J. Ray, Molly Tedesche, and Susan Walker
Hydrol. Earth Syst. Sci., 21, 133–151, https://doi.org/10.5194/hess-21-133-2017, https://doi.org/10.5194/hess-21-133-2017, 2017
Short summary
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We know that climate is changing quickly in the Far North (the Arctic and sub-Arctic). Hydropower continues to grow in this region because water resources are perceived to be plentiful. However, with changes in glacier extent and permafrost, and more extreme events, will those resources prove reliable into the future? This study amasses the evidence that quantitative hydrology modeling and uncertainty assessment have matured to the point where they should be used in water resource planning.
Claudio I. Meier, Jorge Sebastián Moraga, Geri Pranzini, and Peter Molnar
Hydrol. Earth Syst. Sci., 20, 4177–4190, https://doi.org/10.5194/hess-20-4177-2016, https://doi.org/10.5194/hess-20-4177-2016, 2016
Short summary
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We show that the derived distribution approach is able to characterize the interannual variability of precipitation much better than fitting a probabilistic model to annual rainfall totals, as long as continuously gauged data are available. The method is a useful tool for describing temporal changes in the distribution of annual rainfall, as it works for records as short as 5 years, and therefore does not require any stationarity assumption over long periods.
Usman T. Khan and Caterina Valeo
Hydrol. Earth Syst. Sci., 20, 2267–2293, https://doi.org/10.5194/hess-20-2267-2016, https://doi.org/10.5194/hess-20-2267-2016, 2016
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This paper contains a new two-step method to construct fuzzy numbers using observational data. In addition an existing fuzzy neural network is modified to account for fuzzy number inputs. This is combined with possibility-theory based intervals to train the network. Furthermore, model output and a defuzzification technique is used to estimate the risk of low Dissolved Oxygen so that water resource managers can implement strategies to prevent the occurrence of low Dissolved Oxygen.
M. Biasutti and R. Seager
Hydrol. Earth Syst. Sci., 19, 2945–2961, https://doi.org/10.5194/hess-19-2945-2015, https://doi.org/10.5194/hess-19-2945-2015, 2015
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We estimate future changes in US erosivity from the most recent ensemble projections of daily and monthly rainfall accumulation. The expectation of overall increase in erosivity is confirmed by these calculations, but a quantitative assessment is marred by large uncertainties. Specifically, the uncertainty in the method of estimation of erosivity is more consequential than that deriving from the spread in climate simulations, and leads to changes of uncertain sign in parts of the south.
M. C. Peel, R. Srikanthan, T. A. McMahon, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 1615–1639, https://doi.org/10.5194/hess-19-1615-2015, https://doi.org/10.5194/hess-19-1615-2015, 2015
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We present a proof-of-concept approximation of within-GCM uncertainty using non-stationary stochastic replicates of monthly precipitation and temperature projections and investigate the impact of within-GCM uncertainty on projected runoff and reservoir yield. Amplification of within-GCM variability from precipitation to runoff to reservoir yield suggests climate change impact assessments ignoring within-GCM uncertainty would provide water resources managers with an unjustified sense of certainty
T. A. McMahon, M. C. Peel, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 361–377, https://doi.org/10.5194/hess-19-361-2015, https://doi.org/10.5194/hess-19-361-2015, 2015
Short summary
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Here we assess GCM performance from a hydrologic perspective. We identify five better performing CMIP3 GCMs that reproduce grid-scale climatological statistics of observed precipitation and temperature over global land regions for future hydrologic simulation. GCM performance in reproducing observed mean and standard deviation of annual precipitation, mean annual temperature and mean monthly precipitation and temperature was assessed and ranked, and five better performing GCMs were identified.
L. J. M. Peeters, G. M. Podger, T. Smith, T. Pickett, R. H. Bark, and S. M. Cuddy
Hydrol. Earth Syst. Sci., 18, 3777–3785, https://doi.org/10.5194/hess-18-3777-2014, https://doi.org/10.5194/hess-18-3777-2014, 2014
L. Zhuo, M. M. Mekonnen, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 18, 2219–2234, https://doi.org/10.5194/hess-18-2219-2014, https://doi.org/10.5194/hess-18-2219-2014, 2014
C. A. Scott, S. Vicuña, I. Blanco-Gutiérrez, F. Meza, and C. Varela-Ortega
Hydrol. Earth Syst. Sci., 18, 1339–1348, https://doi.org/10.5194/hess-18-1339-2014, https://doi.org/10.5194/hess-18-1339-2014, 2014
N. Voisin, H. Li, D. Ward, M. Huang, M. Wigmosta, and L. R. Leung
Hydrol. Earth Syst. Sci., 17, 3605–3622, https://doi.org/10.5194/hess-17-3605-2013, https://doi.org/10.5194/hess-17-3605-2013, 2013
D. Zhu, D. Z. Peng, and I. D. Cluckie
Hydrol. Earth Syst. Sci., 17, 1445–1453, https://doi.org/10.5194/hess-17-1445-2013, https://doi.org/10.5194/hess-17-1445-2013, 2013
B. L. Harding, A. W. Wood, and J. R. Prairie
Hydrol. Earth Syst. Sci., 16, 3989–4007, https://doi.org/10.5194/hess-16-3989-2012, https://doi.org/10.5194/hess-16-3989-2012, 2012
J.-S. Yang, E.-S. Chung, S.-U. Kim, and T.-W. Kim
Hydrol. Earth Syst. Sci., 16, 801–814, https://doi.org/10.5194/hess-16-801-2012, https://doi.org/10.5194/hess-16-801-2012, 2012
S. Quiroga, Z. Fernández-Haddad, and A. Iglesias
Hydrol. Earth Syst. Sci., 15, 505–518, https://doi.org/10.5194/hess-15-505-2011, https://doi.org/10.5194/hess-15-505-2011, 2011
Cited articles
Abramowitz, M. and Stegun, I. A.: Handbook of mathematical functions with
formulas, graphs, and mathematical tables, Dover Publications, Inc., New
York, ISBN 9780486612720, 1972. a
Alizadeh, B., Limon, R. A., Seo, D.-J., Lee, H., and Brown, J.: Multiscale
postprocessor for ensemble streamflow prediction for short to long ranges,
J. Hydrometeorol., 21, 265–285, 2020. a
Arroyo, M. and Montoya-Manzano, G.: Real Time Quality Checks,
https://efascom.smhi.se/confluence/display/EHDCC/5.2 (last access: 30 April 2021), 2019. a
Barnard, C., Krzeminski, B., Mazzetti, C., Decremer, D., Carton de Wiart, C.,
Harrigan, S., Blick, M., Ferrario, I., Wetterhall, F., Thiemig, V., Salamon, P., Prudhomme, C.: Reforecasts of river discharge and related data by the European Flood Awareness System
version 4.0, ECMWF [data set], https://doi.org/10.24381/cds.c83f560f, 2020. a, b
Berghuijs, W. R., Harrigan, S., Molnar, P., Slater, L. J., and Kirchner, J. W.:
The relative importance of different flood-generating mechanisms across
Europe, Water Resour. Res., 55, 4582–4593, 2019. a
Bogner, K. and Kalas, M.: Error-correction methods and evaluation of an
ensemble based hydrological forecasting system for the Upper Danube
catchment, Atmos. Sci. Lett., 9, 95–102, 2008. a
Bogner, K., Pappenberger, F., and Cloke, H. L.: Technical Note: The normal quantile transformation and its application in a flood forecasting system, Hydrol. Earth Syst. Sci., 16, 1085–1094, https://doi.org/10.5194/hess-16-1085-2012, 2012. a, b
Boucher, M.-A., Perreault, L., Anctil, F., and Favre, A.-C.: Exploratory
analysis of statistical post-processing methods for hydrological ensemble
forecasts, Hydrol. Process., 29, 1141–1155, 2015. a
Brown, J., Ramos, M.-H., and Voisin, N.: Intercomparison of streamflow
postprocessing techniques: first results of a HEPEX community experiment, in:
EGU General Assembly Conference Abstracts, EGU2013–8221 pp., https://meetingorganizer.copernicus.org/EGU2013/EGU2013-8221.pdf (last access: 21 September 2021), 2013. a
Brown, J. D. and Seo, D.-J.: A nonparametric postprocessor for bias correction
of hydrometeorological and hydrologic ensemble forecasts, J. Hydrometeorol., 11, 642–665, 2010. a
Cloke, H. and Pappenberger, F.: Ensemble flood forecasting: A review, J. Hydrol., 375, 613–626, 2009. a
Coccia, G.: Analysis and developments of uncertainty processors for real time
flood forecasting, PhD thesis, Alma Mater Studiorum University of Bologna, 41–44, https://doi.org/10.6092/unibo/amsdottorato/3423, 2011. a, b, c, d
Coccia, G. and Todini, E.: Recent developments in predictive uncertainty assessment based on the model conditional processor approach, Hydrol. Earth Syst. Sci., 15, 3253–3274, https://doi.org/10.5194/hess-15-3253-2011, 2011. a
Crochemore, L., Ramos, M.-H., and Pappenberger, F.: Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, 2016. a
Dance, S., Ballard, S., Bannister, R., Clark, P., Cloke, H., Darlington, T., Flack, D., Gray, S., Hawkness-Smith, L., Husnoo, N., Illingworth, A., Kelly, G., Lean, H., Li, D., Nichols, N., Nicol, J., Oxley, A., Plant, R., Roberts, N., Roulstone, I., Simonin, D., Thompson, R., and Waller, J.: Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall
Exploiting New Data Assimilation Techniques and Novel Observations of
Convection) Project, 10, 125, https://doi.org/10.3390/atmos10030125, 2019. a
De Roo, A., Wesseling, C., and Van Deursen, W.: Physically based river basin
modelling within a GIS: the LISFLOOD model, Hydrol. Process., 14,
1981–1992, 2000. a
de Zea Bermudez, P. and Kotz, S.: Parameter estimation of the generalized
Pareto distribution—Part I, J. Stat. Plan. Infer.,
140, 1353–1373, 2010. a
Dey, D. and Rao, C.: Handbook of Statistics, in: Volume 25: Bayesian Thinking, Modeling and Computation, Elsevier, Burlington, 2006. a
Ferro, C. A., Richardson, D. S., and Weigel, A. P.: On the effect of ensemble
size on the discrete and continuous ranked probability scores, Meteorol. Appl., 15, 19–24, 2008. a
Field, C. B., Barros, V., Stocker, T. F., and Dahe, Q.: Managing the risks of
extreme events and disasters to advance climate change adaptation: special
report of the intergovernmental panel on climate change, Cambridge University Press, ISBN 9781107607804, 2012. a
Flack, D., Skinner, C., Hawkness-Smith, L., O'Donnell, G., Thompson, R., Waller, J., Chen, A., Moloney, J., Largeron, C., Xia, X., Blenkinsop, S., Champion, A., Perks, M., Quinn, N., and Speight, L.: Recommendations for Improving Integration in National End-to-End Flood Forecasting Systems: An Overview of the FFIR (Flooding From Intense Rainfall) Programme, Water, 11, 725, https://doi.org/10.3390/w11040725, 2019. a
Georgakakos, K. P., Seo, D.-J., Gupta, H., Schaake, J., and Butts, M. B.:
Towards the characterization of streamflow simulation uncertainty through
multimodel ensembles, J. Hydrol., 298, 222–241, 2004. a
Gneiting, T.: Making and evaluating point forecasts, J. Am. Stat. Assoc., 106, 746–762, 2011. a
Gneiting, T.: Calibration of medium-range weather forecasts, ECMWF Technical Memoranda, 719, 1–28, 2014. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91, 2009. a
Haiden, T., Magnusson, L., Tsonevsky, I., Wetterhall, F., Alfieri, L., Pappenberger, F., de Rosnay, P., Muñoz-Sabater, J., Balsamo, G., Albergel, C., Forbes, R., Hewson, T., Malardel, S., and Richardson, D.: Medium-Range Weather Forecasts, Reading, United Kingdom, 34, 2014, in: Central Europe, European Centre for Medium-Range Weather Forecasts, Reading, MA, 1–32,
https://www.ecmwf.int/sites/default/files/elibrary/2014/9731-ecmwf-forecast-performance-during-june-2013-flood (last access: 21 September 2021), 2014. a
Haiden, T., Janousek, M., Vitart, F., Ben Bouallegue, Z., Ferranti, L., Prates, F., and Richardson, D.: Evaluation of ECMWF forecasts, including the 2020 upgrade, European Centre for Medium Range Weather Forecasts, https://www.ecmwf.int/en/elibrary/19879-evaluation-ecmwf-forecasts-including-2020-upgrade, last access: 21 September 2021. a, b
Hamill, T. M., Whitaker, J. S., and Mullen, S. L.: Reforecasts: An important
dataset for improving weather predictions, B. Am. Meteorol. Soc., 87, 33–46, 2006. a
Harrigan, S., Zoster, E., Cloke, H., Salamon, P., and Prudhomme, C.: Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2020-532, in review, 2020. a
Hemri, S.: Applications of postprocessing for hydrological forecasts,
Statistical Postprocessing of Ensemble Forecasts, 1, 219–240, https://doi.org/10.1016/C2016-0-03244-8, 2018. a
Hemri, S., Lisniak, D., and Klein, B.: Multivariate postprocessing techniques
for probabilistic hydrological forecasting, Water Resour. Res., 51,
7436–7451, 2015a. a
Hemri, S., Lisniak, D., and Klein, B.: Multivariate postprocessing techniques
for probabilistic hydrological forecasting, Water Resour. Res., 51,
7436–7451, https://doi.org/10.1002/2014WR016473, 2015b. a
Hersbach, H.: Decomposition of the continuous ranked probability score for
ensemble prediction systems, Weather Forecast., 15, 559–570, 2000. a
Hofmann, H., Wickham, H., and Kafadar, K.: Value plots: Boxplots for large
data, J. Comput. Graph. Stat., 26, 469–477, 2017. a
Jordan, A., Krüger, F., and Lerch, S.: Evaluating Probabilistic Forecasts
with scoringRules, J. Stat. Softw., 90, 1–37, 2019. a
Kalman, R. E.: A New Approach to Linear Filtering and Prediction Problems,
J. Basic Eng.-T. ASME, 82, 35–45, https://doi.org/10.1115/1.3662552, 1960. a
Kan, G., He, X., Li, J., Ding, L., Hong, Y., Zhang, H., Liang, K., and Zhang,
M.: Computer aided numerical methods for hydrological model calibration: An
overview and recent development, Arch. Comput. Methods E., 26, 35–59, 2019. a
Kleiber, C. and Kotz, S.: Statistical size distributions in economics and
actuarial sciences, vol. 470, John Wiley & Sons, ISBN 978-0-471-15064-0, 2003. a
Klein, B., Pechlivanidis, I., Arnal, L., Crochemore, L., Meissner, D., and
Frielingsdorf, B.: Does the application of multiple hydrological models
improve seasonal streamflow forecasting skill?, in: EGU General Assembly
Conference Abstracts, 20187, https://doi.org/10.5194/egusphere-egu2020-20187, 2020. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol.,
424, 264–277, 2012. a
Krzysztofowicz, R. and Herr, H. D.: Hydrologic uncertainty processor for
probabilistic river stage forecasting: precipitation-dependent model, J. Hydrol., 249, 46–68, 2001. a
Krzysztofowicz, R. and Kelly, K. S.: Hydrologic uncertainty processor for
probabilistic river stage forecasting, Water Resour. Res., 36,
3265–3277, 2000. a
Krzysztofowicz, R. and Maranzano, C. J.: Hydrologic uncertainty processor for
probabilistic stage transition forecasting, J. Hydrol., 293,
57–73, 2004. a
Lavers, D. A., Harrigan, S., and Prudhomme, C.: Precipitation biases in the
ECMWF integrated forecasting system, J. Hydrometeorol., 22,
1187–1198, 2021. a
Li, W., Duan, Q., Miao, C., Ye, A., Gong, W., and Di, Z.: A review on
statistical postprocessing methods for hydrometeorological ensemble
forecasting, Wiley Interdisciplinary Reviews: Water, 4, e1246, https://doi.org/10.1002/wat2.1246, 2017. a
Liu, Y., Weerts, A. H., Clark, M., Hendricks Franssen, H.-J., Kumar, S., Moradkhani, H., Seo, D.-J., Schwanenberg, D., Smith, P., van Dijk, A. I. J. M., van Velzen, N., He, M., Lee, H., Noh, S. J., Rakovec, O., and Restrepo, P.: Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities, Hydrol. Earth Syst. Sci., 16, 3863–3887, https://doi.org/10.5194/hess-16-3863-2012, 2012. a
Mason, D., Garcia Pintado, J., Cloke, H. L., Dance, S., and Munoz-Sabater, J.: Assimilating high resolution remotely sensed soil moisture into a distributed hydrologic model to improve runoff prediction, ECMWF Technical Memorandum, https://doi.org/10.21957/5isuz4a91, 2020. a
Mason, S. J. and Graham, N. E.: Conditional probabilities, relative operating
characteristics, and relative operating levels, Weather Forecast., 14,
713–725, 1999. a
Matthews, G. and Barnard, C.: Post-processed reforecasts of the European Flood Awareness System and related evaluation data, University of Reading [data set], https://doi.org/10.17864/1947.333, 2022. a
Mazzetti, C. and Harrigan, S.: What's new in EFAS 4.0? Model improvements,
6-hourly calibration, new evaluation layers & reporting points, presented at
EFAS Annual Meeting [Online],
https://www.efas.eu/sites/default/files/AM/AM2020/EFAS_AM_2020_2_What%20is%20new%20in%20EFAS4.pdf (last access: 1 October 2021), 2020. a
Mazzetti, C., Decremer, D., Barnard, C., Blick, M., Carton de Wiart, C., Wetterhall, F., Schweim, C., Ziese, M., Garcia, R., Garcia Padilla, M., Gomes, G., Thiemig, V., Salamon, P., Prudhomme, C.: River discharge and related historical data from the
European Flood Awareness System v4.0, ECMWF [data set], https://doi.org/10.24381/cds.e3458969, 2020. a, b
Mazzetti, C., Decremer, D., and Prudhomme, C.: Challenges of the European Flood Awareness System (EFAS) hydrological calibration, presented at Joint Virtual Workshop on “Connecting global to local hydrological modelling and
forecasting: scientific advances and challenges” [Online], https://events.ecmwf.int/event/222/contributions/2268/attachments/1256/2322/Hydrological-WS-Mazzetti.pdf (last access: 21 September 2021), 2021a. a
McMillan, H., Krueger, T., and Freer, J.: Benchmarking observational
uncertainties for hydrology: rainfall, river discharge and water quality,
Hydrol. Process., 26, 4078–4111, 2012. a
Pappenberger, F. and Beven, K. J.: Ignorance is bliss: Or seven reasons not to use uncertainty analysis, Water Resour. Res., 42, W05302, https://doi.org/10.1029/2005WR004820, 2006. a
Pappenberger, F., Cloke, H. L., Parker, D. J., Wetterhall, F., Richardson,
D. S., and Thielen, J.: The monetary benefit of early flood warnings in
Europe, Environ. Sci. Policy, 51, 278–291,
https://doi.org/10.1016/j.envsci.2015.04.016, 2015a. a
Pappenberger, F., Ramos, M.-H., Cloke, H. L., Wetterhall, F., Alfieri, L.,
Bogner, K., Mueller, A., and Salamon, P.: How do I know if my forecasts are
better? Using benchmarks in hydrological ensemble prediction, J. Hydrol., 522, 697–713, 2015b. a
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 20 May 2021), 2019. a
Reggiani, P., Renner, M., Weerts, A., and Van Gelder, P.: Uncertainty
assessment via Bayesian revision of ensemble streamflow predictions in the
operational river Rhine forecasting system, Water Resour. Res., 45, W02428, https://doi.org/10.1029/2007WR006758, 2009. a
Roundy, J., Duan, Q., and Schaake, J.: Hydrological predictability, scales, and uncertainty issues, Handbook of Hydrometeorological Ensemble Forecasting, 1, 3–31, 2019. a
Schaake, J. C., Hamill, T. M., Buizza, R., and Clark, M.: HEPEX: the
hydrological ensemble prediction experiment, B. Am. Meteorol. Soc., 88, 1541–1548, 2007. a
Schaeybroeck, B. V. and Vannitsem, S.: Post-processing through linear
regression, Nonlinear Proc. Geoph., 18, 147–160, 2011. a
Seo, D.-J., Herr, H. D., and Schaake, J. C.: A statistical post-processor for accounting of hydrologic uncertainty in short-range ensemble streamflow prediction, Hydrol. Earth Syst. Sci. Discuss., 3, 1987–2035, https://doi.org/10.5194/hessd-3-1987-2006, 2006. a
Shrestha, D. L., Pagano, T., Wang, Q., and Robertson, D.: Application of
Ensemble Dressing for Hydrological Applications, in: Geophysical Research Abstracts, vol. 13, EGU2011-5397, https://meetingorganizer.copernicus.org/EGU2011/EGU2011-5397.pdf (last access: 30 September 2021), 2011. a
Silverman, B. W.: Spline Smoothing: The Equivalent Variable Kernel Method, Ann. Statist., 12, 898–916, https://doi.org/10.1214/aos/1176346710, 1984. a
Siqueira, V. A., Weerts, A., Klein, B., Fan, F. M., de Paiva, R. C. D., and
Collischonn, W.: Postprocessing continental-scale, medium-range ensemble
streamflow forecasts in South America using Ensemble Model Output Statistics
and Ensemble Copula Coupling, J. Hydrol., 600, 126520, https://doi.org/10.1016/j.jhydrol.2021.126520, 2021. a
Škute, A., Gruberts, D., Soms, J., and Paidere, J.: Ecological and
hydrological functions of the biggest natural floodplain in Latvia,
Ecohydrology & Hydrobiology, 8, 291–306,
https://doi.org/10.2478/v10104-009-0023-y, 2008. a
Smith, P., Pappenberger, F., Wetterhall, F., Thielen del Pozo, J.,
Krzeminski, B., Salamon, P., Muraro, D., Kalas, M., and Baugh, C.: Chapter 11 – On the Operational Implementation of the European Flood Awareness System (EFAS), in: Flood Forecasting, edited by: Adams, T. E. and Pagano, T. C., Academic Press, Boston, 313–348,
https://doi.org/10.1016/B978-0-12-801884-2.00011-6, 2016. a, b, c, d
Tabeart, J. M., Dance, S. L., Lawless, A. S., Nichols, N. K., and Waller,
J. A.: Improving the condition number of estimated covariance matrices,
Tellus A, 72, 1–19, 2020. a
Thiboult, A., Anctil, F., and Ramos, M.: How does the quantification of
uncertainties affect the quality and value of flood early warning systems?,
J. Hydrol., 551, 365–373,
https://doi.org/10.1016/j.jhydrol.2017.05.014, 2017. a
Thielen, J., Bartholmes, J., Ramos, M.-H., and de Roo, A.: The European Flood Alert System – Part 1: Concept and development, Hydrol. Earth Syst. Sci., 13, 125–140, https://doi.org/10.5194/hess-13-125-2009, 2009. a
Todini, E.: From HUP to MCP: Analogies and extended performances, J. Hydrol., 477, 33–42, 2013. a
Todini, E., Coccia, G., and Ortiz, E.: On the proper use of ensembles for
predictive uncertainty assessment, in: EGU General Assembly Conference
Abstracts, 10365 pp., https://meetingorganizer.copernicus.org/EGU2015/EGU2015-10365.pdf (last access: 13 September 2021) 2015. a
van Andel, S. J., Weerts, A., Schaake, J., and Bogner, K.: Post-processing
hydrological ensemble predictions intercomparison experiment, Hydrol. Process., 27, 158–161, 2013. a
Van Der Knijff, J., Younis, J., and De Roo, A.: LISFLOOD: a GIS-based
distributed model for river basin scale water balance and flood simulation,
Int. J. Geogr. Inf. Sci., 24, 189–212,
2010. a
Venables, W. N. and Ripley, B. D.: Modern Applied Statistics with S, Springer, New York, fourth edn., http://www.stats.ox.ac.uk/pub/MASS4 (last access: 20 September 2021), 2002.
a
Verkade, J., Brown, J., Reggiani, P., and Weerts, A.: Post-processing ECMWF
precipitation and temperature ensemble reforecasts for operational hydrologic
forecasting at various spatial scales, J. Hydrol., 501, 73–91,
2013. a
Verkade, J., Brown, J., Davids, F., Reggiani, P., and Weerts, A.: Estimating
predictive hydrological uncertainty by dressing deterministic and ensemble
forecasts; a comparison, with application to Meuse and Rhine, J. Hydrol., 555, 257–277, 2017. a
Weerts, A. H., Winsemius, H. C., and Verkade, J. S.: Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales), Hydrol. Earth Syst. Sci., 15, 255–265, https://doi.org/10.5194/hess-15-255-2011, 2011. a
Węglarczyk, S.: Kernel density estimation and its application, in: ITM
Web of Conferences, vol. 23, EDP Sciences, https://doi.org/10.1051/itmconf/20182300037, 2018. a
Wu, W., Emerton, R., Duan, Q., Wood, A. W., Wetterhall, F., and Robertson,
D. E.: Ensemble flood forecasting: Current status and future opportunities,
Wiley Interdisciplinary Reviews: Water, 7, e1432, https://doi.org/10.1002/wat2.1432, 2020. a
Ye, A., Duan, Q., Yuan, X., Wood, E. F., and Schaake, J.: Hydrologic
post-processing of MOPEX streamflow simulations, J. Hydrol., 508,
147–156, 2014. a
Zamo, M. and Naveau, P.: Estimation of the continuous ranked probability score
with limited information and applications to ensemble weather forecasts,
Math. Geosci., 50, 209–234, 2018. a
Zhao, L., Duan, Q., Schaake, J., Ye, A., and Xia, J.: A hydrologic
post-processor for ensemble streamflow predictions, Advances in Geosciences, 29, 51–59, 2011. a
Zhong, Y., Guo, S., Xiong, F., Liu, D., Ba, H., and Wu, X.: Probabilistic
forecasting based on ensemble forecasts and EMOS method for TGR inflow,
Front. Earth Sci.-PRC., 14, 188–200, 2020. a
Short summary
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.
The European Flood Awareness System creates flood forecasts for up to 15 d in the future for the...
