Articles | Volume 21, issue 6
https://doi.org/10.5194/hess-21-2615-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-2615-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Estimating extreme river discharges in Europe through a Bayesian network
Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 11, 2628 CN Delft, the Netherlands
Oswaldo Morales-Nápoles
Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 11, 2628 CN Delft, the Netherlands
Related authors
Dominik Paprotny, Belinda Rhein, Michalis I. Vousdoukas, Paweł Terefenko, Francesco Dottori, Simon Treu, Jakub Śledziowski, Luc Feyen, and Heidi Kreibich
Hydrol. Earth Syst. Sci., 28, 3983–4010, https://doi.org/10.5194/hess-28-3983-2024, https://doi.org/10.5194/hess-28-3983-2024, 2024
Short summary
Short summary
Long-term trends in flood losses are regulated by multiple factors, including climate variation, population and economic growth, land-use transitions, reservoir construction, and flood risk reduction measures. Here, we reconstruct the factual circumstances in which almost 15 000 potential riverine, coastal and compound floods in Europe occurred between 1950 and 2020. About 10 % of those events are reported to have caused significant socioeconomic impacts.
Anna Buch, Dominik Paprotny, Kasra Rafiezadeh Shahi, Heidi Kreibich, and Nivedita Sairam
EGUsphere, https://doi.org/10.5194/egusphere-2024-2340, https://doi.org/10.5194/egusphere-2024-2340, 2024
Short summary
Short summary
Many households in Vietnam depend on revenues from microbusinesses (shop-houses). However, losses caused by regular flooding to the microbusinesses are not modelled. Business turnover, building age and water depth are found to be the main drivers of flood losses to microbusinesses. We built and validated probabilistic models (Non-parametric Bayesian Networks) that estimate flood losses to microbusinesses. The results help in flood risk management and adaption decision making for microbusinesses.
Aloïs Tilloy, Dominik Paprotny, Stefania Grimaldi, Goncalo Gomes, Alessandra Bianchi, Stefan Lange, Hylke Beck, and Luc Feyen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-41, https://doi.org/10.5194/essd-2024-41, 2024
Revised manuscript under review for ESSD
Short summary
Short summary
This article presents a reanalysis of Europe's rivers streamflow for the period 1950–2020, using a state-of-the-art hydrological simulation framework. The dataset, called HERA (Hydrological European ReAnalysis), uses detailed information about the landscape, climate, and human activities to estimate river flow. HERA can be a valuable tool for studying hydrological dynamics, including the impacts of climate change and human activities on European water resources, flood and drought risks.
Dominik Paprotny, Paweł Terefenko, and Jakub Śledziowski
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-321, https://doi.org/10.5194/essd-2023-321, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Knowledge about past natural disasters can help adapting to their future occurrences. Here, we present a dataset of 2521 riverine, pluvial, coastal and compound floods that have occurred in 42 European countries between 1870 and 2020. The dataset contains available information on the area inundated, fatalities, persons affected or economic loss, and was obtained by extensive data-collection from more than 800 sources ranging from news reports through government databases to scientific papers.
Dominik Paprotny and Matthias Mengel
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-194, https://doi.org/10.5194/gmd-2022-194, 2022
Preprint withdrawn
Short summary
Short summary
Population and economic growth over past decades have increased risk posed by natural hazards. The model presented here generates high-resolution maps of land use, population and assets (exposure) from 1870 to 2020 for 42 countries. It combines multiple methods with a large database of historical statistical data to approximate past anthropogenic environment of Europe. It enables attributing losses from past disasters to climate change by removing the influence of changes in exposure.
Dominik Paprotny, Heidi Kreibich, Oswaldo Morales-Nápoles, Paweł Terefenko, and Kai Schröter
Nat. Hazards Earth Syst. Sci., 20, 323–343, https://doi.org/10.5194/nhess-20-323-2020, https://doi.org/10.5194/nhess-20-323-2020, 2020
Short summary
Short summary
Houses and their contents in Europe are worth trillions of euros, resulting in high losses from natural hazards. Hence, risk assessments need to reliably estimate the size and value of houses, including the value of durable goods kept inside. In this work we show how openly available or open datasets can be used to predict the size of individual residential buildings. Further, we provide standardized monetary values of houses and contents per square metre of floor space for 30 countries.
Dominik Paprotny, Michalis I. Vousdoukas, Oswaldo Morales-Nápoles, Sebastiaan N. Jonkman, and Luc Feyen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-132, https://doi.org/10.5194/hess-2018-132, 2018
Preprint withdrawn
Dominik Paprotny, Oswaldo Morales-Nápoles, and Sebastiaan N. Jonkman
Earth Syst. Sci. Data, 10, 565–581, https://doi.org/10.5194/essd-10-565-2018, https://doi.org/10.5194/essd-10-565-2018, 2018
Short summary
Short summary
Natural hazards affect areas with various population density, economic production value and preparedness. This database will help to assess the impact of hazards in Europe in a long-term perspective. It contains data on losses, dates and location of 1564 floods from 1870–2016 in 37 countries. For the same area and timeframe, land use, population and asset value were reconstructed. Combining both data sets, one can correct the amount of losses from past events for demographic and economic growth.
Dominik Paprotny, Oswaldo Morales-Nápoles, and Sebastiaan N. Jonkman
Nat. Hazards Earth Syst. Sci., 17, 1267–1283, https://doi.org/10.5194/nhess-17-1267-2017, https://doi.org/10.5194/nhess-17-1267-2017, 2017
D. Paprotny and P. Terefenko
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-3-2493-2015, https://doi.org/10.5194/nhessd-3-2493-2015, 2015
Revised manuscript not accepted
Dominik Paprotny, Belinda Rhein, Michalis I. Vousdoukas, Paweł Terefenko, Francesco Dottori, Simon Treu, Jakub Śledziowski, Luc Feyen, and Heidi Kreibich
Hydrol. Earth Syst. Sci., 28, 3983–4010, https://doi.org/10.5194/hess-28-3983-2024, https://doi.org/10.5194/hess-28-3983-2024, 2024
Short summary
Short summary
Long-term trends in flood losses are regulated by multiple factors, including climate variation, population and economic growth, land-use transitions, reservoir construction, and flood risk reduction measures. Here, we reconstruct the factual circumstances in which almost 15 000 potential riverine, coastal and compound floods in Europe occurred between 1950 and 2020. About 10 % of those events are reported to have caused significant socioeconomic impacts.
Anna Buch, Dominik Paprotny, Kasra Rafiezadeh Shahi, Heidi Kreibich, and Nivedita Sairam
EGUsphere, https://doi.org/10.5194/egusphere-2024-2340, https://doi.org/10.5194/egusphere-2024-2340, 2024
Short summary
Short summary
Many households in Vietnam depend on revenues from microbusinesses (shop-houses). However, losses caused by regular flooding to the microbusinesses are not modelled. Business turnover, building age and water depth are found to be the main drivers of flood losses to microbusinesses. We built and validated probabilistic models (Non-parametric Bayesian Networks) that estimate flood losses to microbusinesses. The results help in flood risk management and adaption decision making for microbusinesses.
Guus Rongen, Oswaldo Morales-Nápoles, and Matthijs Kok
Hydrol. Earth Syst. Sci., 28, 2831–2848, https://doi.org/10.5194/hess-28-2831-2024, https://doi.org/10.5194/hess-28-2831-2024, 2024
Short summary
Short summary
This study proposes a new method for predicting extreme events such as floods on the river Meuse. The current method was shown to be unreliable as it did not predict a recent flood. We developed a model that includes information from experts and combines this with measurements. We found that this approach gives more accurate predictions, particularly for extreme events. The research is important for predictions of extreme flood levels that are necessary for protecting communities against floods.
Aloïs Tilloy, Dominik Paprotny, Stefania Grimaldi, Goncalo Gomes, Alessandra Bianchi, Stefan Lange, Hylke Beck, and Luc Feyen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-41, https://doi.org/10.5194/essd-2024-41, 2024
Revised manuscript under review for ESSD
Short summary
Short summary
This article presents a reanalysis of Europe's rivers streamflow for the period 1950–2020, using a state-of-the-art hydrological simulation framework. The dataset, called HERA (Hydrological European ReAnalysis), uses detailed information about the landscape, climate, and human activities to estimate river flow. HERA can be a valuable tool for studying hydrological dynamics, including the impacts of climate change and human activities on European water resources, flood and drought risks.
Dominik Paprotny, Paweł Terefenko, and Jakub Śledziowski
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-321, https://doi.org/10.5194/essd-2023-321, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Knowledge about past natural disasters can help adapting to their future occurrences. Here, we present a dataset of 2521 riverine, pluvial, coastal and compound floods that have occurred in 42 European countries between 1870 and 2020. The dataset contains available information on the area inundated, fatalities, persons affected or economic loss, and was obtained by extensive data-collection from more than 800 sources ranging from news reports through government databases to scientific papers.
Dominik Paprotny and Matthias Mengel
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-194, https://doi.org/10.5194/gmd-2022-194, 2022
Preprint withdrawn
Short summary
Short summary
Population and economic growth over past decades have increased risk posed by natural hazards. The model presented here generates high-resolution maps of land use, population and assets (exposure) from 1870 to 2020 for 42 countries. It combines multiple methods with a large database of historical statistical data to approximate past anthropogenic environment of Europe. It enables attributing losses from past disasters to climate change by removing the influence of changes in exposure.
Elisa Ragno, Markus Hrachowitz, and Oswaldo Morales-Nápoles
Hydrol. Earth Syst. Sci., 26, 1695–1711, https://doi.org/10.5194/hess-26-1695-2022, https://doi.org/10.5194/hess-26-1695-2022, 2022
Short summary
Short summary
We explore the ability of non-parametric Bayesian networks to reproduce maximum daily discharge in a given month in a catchment when the remaining hydro-meteorological and catchment attributes are known. We show that a saturated network evaluated in an individual catchment can reproduce statistical characteristics of discharge in about ~ 40 % of the cases, while challenges remain when a saturated network considering all the catchments together is evaluated.
Dominik Paprotny, Heidi Kreibich, Oswaldo Morales-Nápoles, Paweł Terefenko, and Kai Schröter
Nat. Hazards Earth Syst. Sci., 20, 323–343, https://doi.org/10.5194/nhess-20-323-2020, https://doi.org/10.5194/nhess-20-323-2020, 2020
Short summary
Short summary
Houses and their contents in Europe are worth trillions of euros, resulting in high losses from natural hazards. Hence, risk assessments need to reliably estimate the size and value of houses, including the value of durable goods kept inside. In this work we show how openly available or open datasets can be used to predict the size of individual residential buildings. Further, we provide standardized monetary values of houses and contents per square metre of floor space for 30 countries.
Dominik Paprotny, Michalis I. Vousdoukas, Oswaldo Morales-Nápoles, Sebastiaan N. Jonkman, and Luc Feyen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-132, https://doi.org/10.5194/hess-2018-132, 2018
Preprint withdrawn
Dominik Paprotny, Oswaldo Morales-Nápoles, and Sebastiaan N. Jonkman
Earth Syst. Sci. Data, 10, 565–581, https://doi.org/10.5194/essd-10-565-2018, https://doi.org/10.5194/essd-10-565-2018, 2018
Short summary
Short summary
Natural hazards affect areas with various population density, economic production value and preparedness. This database will help to assess the impact of hazards in Europe in a long-term perspective. It contains data on losses, dates and location of 1564 floods from 1870–2016 in 37 countries. For the same area and timeframe, land use, population and asset value were reconstructed. Combining both data sets, one can correct the amount of losses from past events for demographic and economic growth.
Dominik Paprotny, Oswaldo Morales-Nápoles, and Sebastiaan N. Jonkman
Nat. Hazards Earth Syst. Sci., 17, 1267–1283, https://doi.org/10.5194/nhess-17-1267-2017, https://doi.org/10.5194/nhess-17-1267-2017, 2017
D. Paprotny and P. Terefenko
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-3-2493-2015, https://doi.org/10.5194/nhessd-3-2493-2015, 2015
Revised manuscript not accepted
Related subject area
Subject: Rivers and Lakes | Techniques and Approaches: Mathematical applications
GRAINet: mapping grain size distributions in river beds from UAV images with convolutional neural networks
A wavelet-based approach to streamflow event identification and modeled timing error evaluation
Variability in epilimnion depth estimations in lakes
Hydrodynamic and environmental characteristics of a tributary bay influenced by backwater jacking and intrusions from a main reservoir
Automatic identification of alternating morphological units in river channels using wavelet analysis and ridge extraction
Stream temperature and discharge evolution in Switzerland over the last 50 years: annual and seasonal behaviour
KULTURisk regional risk assessment methodology for water-related natural hazards – Part 2: Application to the Zurich case study
Reply to D. L. Peters' Comment on "Streamflow input to Lake Athabasca, Canada" by Rasouli et al. (2013)
Temporal and spatial changes of water quality and management strategies of Dianchi Lake in southwest China
A model based on dimensional analysis for prediction of nitrogen and phosphorus concentrations at the river station Ižkovce, Slovakia
A hybrid model of self organizing maps and least square support vector machine for river flow forecasting
Spatial variability in floodplain sedimentation: the use of generalized linear mixed-effects models
Flood trends and variability in the Mekong river
Nico Lang, Andrea Irniger, Agnieszka Rozniak, Roni Hunziker, Jan Dirk Wegner, and Konrad Schindler
Hydrol. Earth Syst. Sci., 25, 2567–2597, https://doi.org/10.5194/hess-25-2567-2021, https://doi.org/10.5194/hess-25-2567-2021, 2021
Short summary
Short summary
Grain size analysis is the key to understanding the sediment dynamics of river systems and is an important indicator for mitigating flood risk and preserving biodiversity in aquatic habitats. We propose GRAINet, a data-driven approach based on deep learning, to regress grain size distributions from georeferenced UAV images. This allows for a holistic analysis of entire gravel bars, resulting in robust grading curves and high-resolution maps of spatial grain size distribution at large scale.
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
Short summary
Short summary
We present a wavelet-based approach to quantify streamflow timing errors for model evaluation and development. We demonstrate the method using real and simulated stream discharge data from several locations. We show how results can be used to identify potential hydrologic processes contributing to the timing errors. Furthermore, we illustrate how the method can document model performance by comparing timing errors across versions of the National Water Model.
Harriet L. Wilson, Ana I. Ayala, Ian D. Jones, Alec Rolston, Don Pierson, Elvira de Eyto, Hans-Peter Grossart, Marie-Elodie Perga, R. Iestyn Woolway, and Eleanor Jennings
Hydrol. Earth Syst. Sci., 24, 5559–5577, https://doi.org/10.5194/hess-24-5559-2020, https://doi.org/10.5194/hess-24-5559-2020, 2020
Short summary
Short summary
Lakes are often described in terms of vertical layers. The
epilimnionrefers to the warm surface layer that is homogeneous due to mixing. The depth of the epilimnion can influence air–water exchanges and the vertical distribution of biological variables. We compared various methods for defining the epilimnion layer and found large variability between methods. Certain methods may be better suited for applications such as multi-lake comparison and assessing the impact of climate change.
Xintong Li, Bing Liu, Yuanming Wang, Yongan Yang, Ruifeng Liang, Fangjun Peng, Shudan Xue, Zaixiang Zhu, and Kefeng Li
Hydrol. Earth Syst. Sci., 24, 5057–5076, https://doi.org/10.5194/hess-24-5057-2020, https://doi.org/10.5194/hess-24-5057-2020, 2020
Short summary
Short summary
We aim to understand the hydrodynamic and environmental characteristics of a tributary bay influenced by a main reservoir. The results showed that the tributary bay was mainly affected by backwater jacking of the main reservoir when the water level dropped and by intrusion of the main reservoir when the water level rose. An obvious quality concentration boundary existed in the tributary bay. The results of this study can provide guidance for water environment protection in tributary bays.
Mounir Mahdade, Nicolas Le Moine, Roger Moussa, Oldrich Navratil, and Pierre Ribstein
Hydrol. Earth Syst. Sci., 24, 3513–3537, https://doi.org/10.5194/hess-24-3513-2020, https://doi.org/10.5194/hess-24-3513-2020, 2020
Short summary
Short summary
We present an automatic procedure based on wavelet ridge extraction to identify some characteristics of alternating morphological units (e.g., pools to riffles). We used four hydro-morphological variables (velocity, hydraulic radius, bed shear stress, local channel direction angle). We find that the wavelengths are consistent with the values of the literature, and the use of a multivariate approach yields more robust results and ensures a consistent covariance of flow variables.
Adrien Michel, Tristan Brauchli, Michael Lehning, Bettina Schaefli, and Hendrik Huwald
Hydrol. Earth Syst. Sci., 24, 115–142, https://doi.org/10.5194/hess-24-115-2020, https://doi.org/10.5194/hess-24-115-2020, 2020
Short summary
Short summary
This study constitutes the first comprehensive analysis of river
temperature in Switzerland combined with discharge and key meteorological variables, such as air temperature and precipitation. It is also the first study to discuss the large-scale seasonal behaviour of stream temperature in Switzerland. This research shows the clear increase of river temperature in Switzerland over the last few decades and may serve as a solid reference for future climate change scenario simulations.
P. Ronco, M. Bullo, S. Torresan, A. Critto, R. Olschewski, M. Zappa, and A. Marcomini
Hydrol. Earth Syst. Sci., 19, 1561–1576, https://doi.org/10.5194/hess-19-1561-2015, https://doi.org/10.5194/hess-19-1561-2015, 2015
Short summary
Short summary
The aim of the paper is the application of the KULTURisk regional risk assessment (KR-RRA) methodology, presented in the companion paper (Part 1), to the Sihl River basin, in northern Switzerland. Flood-related risks have been assessed for different receptors lying in the Sihl river valley including the city of Zurich, which represents a typical case of river flooding in an urban area, by means of a calibration process of the methodology to the site-specific context and features.
K. Rasouli, M. A. Hernández-Henríquez, and S. J. Déry
Hydrol. Earth Syst. Sci., 19, 1287–1292, https://doi.org/10.5194/hess-19-1287-2015, https://doi.org/10.5194/hess-19-1287-2015, 2015
T. Zhang, W. H. Zeng, S. R. Wang, and Z. K. Ni
Hydrol. Earth Syst. Sci., 18, 1493–1502, https://doi.org/10.5194/hess-18-1493-2014, https://doi.org/10.5194/hess-18-1493-2014, 2014
M. Zeleňáková, M. Čarnogurská, M. Šlezingr, D. Słyś, and P. Purcz
Hydrol. Earth Syst. Sci., 17, 201–209, https://doi.org/10.5194/hess-17-201-2013, https://doi.org/10.5194/hess-17-201-2013, 2013
S. Ismail, A. Shabri, and R. Samsudin
Hydrol. Earth Syst. Sci., 16, 4417–4433, https://doi.org/10.5194/hess-16-4417-2012, https://doi.org/10.5194/hess-16-4417-2012, 2012
A. Cabezas, M. Angulo-Martínez, M. Gonzalez-Sanchís, J. J. Jimenez, and F. A. Comín
Hydrol. Earth Syst. Sci., 14, 1655–1668, https://doi.org/10.5194/hess-14-1655-2010, https://doi.org/10.5194/hess-14-1655-2010, 2010
J. M. Delgado, H. Apel, and B. Merz
Hydrol. Earth Syst. Sci., 14, 407–418, https://doi.org/10.5194/hess-14-407-2010, https://doi.org/10.5194/hess-14-407-2010, 2010
Cited articles
Alfieri, L., Salamon, P., Bianchi, A., Neal, J., Bates, P., and Feyen, L.: Advances in pan-European flood hazard mapping, Hydrol. Process., 28, 4067–4077, https://doi.org/10.1002/hyp.9947, 2014.
Alfieri, L., Feyen, L., Dottori, F., and Bianchi, A.: Ensemble flood risk assessment in Europe under high end climate scenarios. Global Environ. Chang., 35, 199–210, https://doi.org/10.1016/j.gloenvcha.2015.09.004, 2015.
Barredo, J. I.: Major flood disasters in Europe: 1950–2005, Nat. Hazards, 42, 125–148, https://doi.org/10.1007/s11069-006-9065-2, 2007.
Bartalev, S. A., Belward, A. S., Erchov, D. V., and Isaev, A. S.: A new SPOT4-Vegetation derived land cover map of Northern Eurasia, Int. J. Remote Sens., 24, 1977–1982, https://doi.org/10.1080/0143116031000066297, 2003.
Centro de Estudios Hidrográficos: Anuario de aforos 2011–2012, available at: http://ceh-flumen64.cedex.es/anuarioaforos/default.asp (last access: 27 January 2016), 2012.
Chow, V. T.: Applied hydrology, McGraw-Hill, New York, USA, 1988.
Couasnon, A. A. O.: Characterizing flood hazard at two spatial scales with the use of stochastic models: an application to the contiguous United States of America and the Houston Ship Channel, MSc thesis, TU Delft, Delft, the Netherlands, 2017.
Dankers, R. and Feyen, L.: Climate change impact on flood hazard in Europe: An assessment based on high resolution climate simulations, J. Geophys. Res., 113, D19105, https://doi.org/10.1029/2007JD009719, 2008.
Dankers, R. and Feyen, L.: Flood hazard in Europe in an ensemble of regional climate scenarios, J. Geophys. Res., 114, D16108, https://doi.org/10.1029/2008JD011523, 2009.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
De Jager, A. L. and Vogt, J. V.: Development and demonstration of a structured hydrological feature coding system for Europe, Hydrolog. Sci. J., 55, 661–675, https://doi.org/10.1080/02626667.2010.490786, 2010.
DHI GRAS: EU-DEM Statistical Validation Report, European Environment Agency, Copenhagen, Denmark, 2014.
European Environment Agency: CLC2006 technical guidelines, EEA Technical report No. 17/2007, European Environment Agency, Copenhagen, Denmark, 2007.
European Environment Agency: Corine Land Cover 2000 raster data, available at: http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000-raster-3 (last access: 29 January 2016), 2014a.
European Environment Agency: EEA Fast Track Service Precursor on Land Monitoring – Degree of soil sealing, available at: http://www.eea.europa.eu/data-and-maps/data/eea-fast-track-service-precursor-on-land-monitoring-degree-of-soil-sealing (last access: 29 January 2016), 2014b.
Fal, B.: Przepływy charakterystyczne głównych rzek polskich w latach 1951-1995, Materiały Badawcze – Instytut Meteorologii i Gospodarki Wodnej. Hydrologia i Oceanologia 26, IMGW, Warsaw, Poland, 137 pp., 2000.
FAO/IIASA/ISRIC/ISS-CAS/JRC: Harmonized World Soil Database (version 1.2), FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Feyen, L., Dankers, R., Bódis, K., Salamon, P., and Barredo, J. I.: Fluvial flood risk in Europe in present and future climates, Climatic Change, 112, 47–62, https://doi.org/10.1007/s10584-011-0339-7, 2012.
Gao, H., Hrachowitz, M., Fenicia, F., Gharari, S., and Savenije, H. H. G.: Testing the realism of a topography-driven model (FLEX-Topo) in the nested catchments of the Upper Heihe, China, Hydrol. Earth Syst. Sci., 18, 1895–1915, https://doi.org/10.5194/hess-18-1895-2014, 2014.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B.: Bayesian data analysis, 3rd ed., Chapman & Hall/CRC, London, UK, 2013.
Gericke, O. J. and Smithers, J. C.: Review of methods used to estimate catchment response time for the purpose of peak discharge estimation, Hydrol. Sci. J., 59, 1935–1971, https://doi.org/10.1080/02626667.2013.866712, 2014.
Gharari, S., Hrachowitz, M., Fenicia, F., and Savenije, H. H. G.: Hydrological landscape classification: investigating the performance of HAND based landscape classifications in a central European meso-scale catchment, Hydrol. Earth Syst. Sci., 15, 3275–3291, https://doi.org/10.5194/hess-15-3275-2011, 2011.
Global Runoff Data Centre: BfG – The GRDC, available at: http://www.bafg.de/GRDC/EN/Home/homepage_node.html, last access: 27 January 2016.
Hanea, A. M., Kurowicka, D., and Cooke, R. M.: Hybrid Method for Quantifying and Analyzing Bayesian Belief Nets, Qual. Reliab. Eng. Int., 22, 709–729, https://doi.org/10.1002/qre.808, 2006.
Hanea, A. M., Morales Nápoles, O., and Ababei, D.: Non-parametric Bayesian networks: Improving theory and reviewing applications, Reliab. Eng. Syst. Safe., 144, 265–284, https://doi.org/10.1016/j.ress.2015.07.027, 2015.
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., and New, M.: A European daily high-resolution gridded dataset of surface temperature and precipitation, J. Geophys. Res., 113, D20119, https://doi.org/10.1029/2008JD010201, 2008.
Hengl, T., de Jesus, J. M., MacMillan R. A., Batjes, N. H., Heuvelink, G. B. M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J. G. B., Walsh, M. G., and Gonzalez, M. R.: SoilGrids1km – Global Soil Information Based on Automated Mapping, PLoS ONE, 9, e105992, https://doi.org/10.1371/journal.pone.0105992, 2014.
Herold, C. and Mouton, F.: Global flood hazard mapping using statistical peak flow estimates, Hydrol. Earth Syst. Sci. Discuss., 8, 305–363, https://doi.org/10.5194/hessd-8-305-2011, 2011.
Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., Kim, H., and Kanae, S.: Global flood risk under climate change, Nat. Clim. Change, 3, 816–821, https://doi.org/10.1038/nclimate1911, 2013.
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578. https://doi.org/10.1007/s10113-013-0499-2, 2014.
Joe, H.: Dependence Modeling with Copulas, Chapman & Hall/CRC, London, UK, 2014.
Katz, R. W., Parlange, M. B., and Naveau, P.: Statistics of extremes in hydrology, Adv. Water Resour., 25, 1287–1304, https://doi.org/10.1016/S0309-1708(02)00056-8, 2002.
Klein Goldewijk, K., Beusen, A., de Vos, M., and van Drecht, G.: The HYDE 3.1 spatially explicit database of human induced land use change over the past 12 000 years, Global Ecol. Biogeogr., 20, 73–86, https://doi.org/10.1111/j.1466-8238.2010.00587.x, 2011.
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 2014.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of the Köppen-Geiger climate classification updated, Meteorol. Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006.
Kurowicka, D. and Cooke, R.: Uncertainty analysis with high dimensional dependence modelling, John Wiley & Sons Ltd, Chichester, UK, 2006.
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., and Wisser, D.: High resolution mapping of the world's reservoirs and dams for sustainable river flow management, Front. Ecol. Environ., 9, 494–502, https://doi.org/10.1890/100125, 2011.
Meigh, J. R., Farquharson, F. A. K., and Sutcliffe, J. V.: A worldwide comparison of regional flood estimation methods and climate, Hydrol. Sci. J., 42, 225–244, https://doi.org/10.1080/02626669709492022, 1997.
Morales Nápoles, O., Worm, D., van den Haak, P., Hanea, A., Courage, W., and Miraglia, S.: Reader for course: Introduction to Bayesian Networks, TNO-060-DTM-2013-01115, TNO, Delft, the Netherlands, 2013.
Moriasi, D., Arnold, J., Van Liew, M., Binger, R., Harmel, R., and Veith T.: Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, T. ASABE, 50, 885–900, 2007.
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next generation of scenarios for climate change research and assessment, Nature, 463, 747–756, https://doi.org/10.1038/nature08823, 2010.
Mutua, F. M.: The use of the Akaike Information Criterion in the identification of an optimum flood frequency model, Hydrolog. Sci. J., 39, 235–244, https://doi.org/10.1080/02626669409492740, 1994.
Norwegian Water Resources and Energy Directorate: Historiske vannføringsdata til produksjonsplanlegging, available at: https://www.nve.no/hydrologi/hydrologiske-data/historiske-data/historiske-vannfoeringsdata-til-produksjonsplanlegging/ (last access: 27 January 2016), 2015.
Padi, P. T., Baldassarre, G. D., and Castellarin, A.: Floodplain management in Africa: Large scale analysis of flood data, Phys. Chem. Earth, 36, 292–298, https://doi.org/10.1016/j.pce.2011.02.002, 2011.
Panagos, P., Van Liedekerke, M., Jones, A., and Montanarella, L.: European Soil Data Centre: Response to European policy support and public data requirements, Land Use Policy, 29, 329–338, https://doi.org/10.1016/j.landusepol.2011.07.003, 2012.
Paprotny, D. and Morales Nápoles, O.: A Bayesian Network for extreme river discharges in Europe, in: Safety and Reliability of Complex Engineered Systems, edited by: Podofillini, L., Sudret, B., Stojadinović, B., Zio, E., and Kröger, W., CRC Press/Balkema, Leiden, the Netherlands, 4303–4311, 2015.
Paprotny, D. and Morales Nápoles, O.: Pan-European data sets of river flood probability of occurrence under present and future climate, TU Delft, dataset, https://doi.org/10.4121/uuid:968098ce-afe1-4b21-a509-dedaf9bf4bd5, 2016.
Paprotny, D., Morales-Nápoles, O., and Jonkman, S. N.: Efficient pan-European river flood hazard modelling through a combination of statistical and physical models, Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-4, in review, 2017.
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo, California, USA, 1988.
Peereboom, I. O., Waagø, O. S., and Myhre, M.: Preliminary Flood Risk Assessment in Norway – An example of a methodology based on a GIS-approach, Report no. 7/2011, Norwegian Water Resources and Energy Directorate, Oslo, Norway, 2011.
Rockel, B., Will, A., and Hense, A.: Special issue regional climate modelling with COSMO-CLM (CCLM), Meteorol. Z., 17, 347–348, 2008.
Rojas, R., Feyen, L., Dosio, A., and Bavera, D.: Improving pan-European hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations, Hydrol. Earth Syst. Sci., 15, 2599–2620, https://doi.org/10.5194/hess-15-2599-2011, 2011.
Rojas, R., Feyen, L., Bianchi, A., and Dosio, A.: Assessment of future flood hazard in Europe using a large ensemble of bias-corrected regional climate simulations, J. Geophys. Res., 117, D17109, https://doi.org/10.1029/2012JD017461, 2012.
Salinas, J. L., Laaha, G., Rogger, M., Parajka, J., Viglione, A., Sivapalan, M., and Blöschl, G.: Comparative assessment of predictions in ungauged basins – Part 2: Flood and low flow studies, Hydrol. Earth Syst. Sci., 17, 2637–2652, https://doi.org/10.5194/hess-17-2637-2013, 2013.
Sampson, C. C., Smith, A. M., Bates, P. D., Neal, J. C., Alfieri, L., and Freer, J. E.: A high-resolution global flood hazard model, Water Resour. Res., 51, 7358–7381, https://doi.org/10.1002/2015WR016954, 2015.
Sando, S. K.: Techniques for Estimating Peak-Flow Magnitude and Frequency Relations for South Dakota Streams, Water-Resources Investigations Report 98-4055, U.S. Geological Survey, Denver, USA, 1998.
Savenije, H. H. G.: HESS Opinions “Topography driven conceptual modelling (FLEX-Topo)”, Hydrol. Earth Syst. Sci., 14, 2681–2692, https://doi.org/10.5194/hess-14-2681-2010, 2010.
Smith, A., Sampson, C., and Bates, P.: Regional flood frequency analysis at the global scale, Water Resour. Res., 51, 539–553, https://doi.org/10.1002/2014WR015814, 2015.
Stachý, J. and Fal, B.: Zasady obliczania maksymalnych przepływów prawdopodobnych, Prace Instytutu Badawczego Dróg i Mostów, 3–4, 91–147, 1986.
Swedish Meteorological and Hydrological Institute: Vattenweb Mätningar, available at: http://vattenweb.smhi.se/station/, last access: 27 January 2016.
Viewfinder Panoramas: Digital elevation data, available at: http://viewfinderpanoramas.org/dem3.html (last access: 28 January 2016), 2014.
Vogt, J. V., Soille, P., de Jager, A., Rimaviciute, E., Mehl, W., Foisneau, S., Bodis, K., Dusart, J., Paracchini, M. L., Haastrup, P., and Bamps, C.: A pan-European River and Catchment Database, Report EUR 22920 EN, European Commission-Joint Research Centre, Luxembourg, 120 pp., 2007.
Ward, P. J., Jongman, B., Sperna Weiland, F., Bouwman, A., and van Beek, R.: Assessing flood risk at the global scale: model setup, results, and sensitivity, Environ. Res. Lett., 8, 044019, https://doi.org/10.1088/1748-9326/8/4/044019, 2013.
Whitfield, P.: Floods in future climates: a review, J. Flood Risk Manag., 5, 336–365, https://doi.org/10.1111/j.1753-318X.2012.01150.x, 2012.
Winsemius, H. C., Van Beek, L. P. H., Jongman, B., Ward, P. J., and Bouwman, A.: A framework for global river flood risk assessments, Hydrol. Earth Syst. Sci., 17, 1871–1892, https://doi.org/10.5194/hess-17-1871-2013, 2013.
Wrede, S., Seibert, J., and Uhlenbrook, S.: Distributed conceptual modelling in a Swedish lowland catchment: a multi-criteria model assessment, Hydrol. Res., 44, 318–333. https://doi.org/10.2166/Nh.2012.056, 2013.