Articles | Volume 22, issue 4
https://doi.org/10.5194/hess-22-2511-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-2511-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Apr 2018
Research article |
| 24 Apr 2018
| 24 Apr 2018
Managing uncertainty in flood protection planning with climate projections
Engineering Risk Analysis Group, Technische Universität München, Arcisstr. 21, 80333 Munich, Germany
Olga Špačková
Engineering Risk Analysis Group, Technische Universität München, Arcisstr. 21, 80333 Munich, Germany
Lukas Schoppa
Engineering Risk Analysis Group, Technische Universität München, Arcisstr. 21, 80333 Munich, Germany
Daniel Straub
Engineering Risk Analysis Group, Technische Universität München, Arcisstr. 21, 80333 Munich, Germany
Related authors
Beatrice Dittes, Maria Kaiser, Olga Špačková, Wolfgang Rieger, Markus Disse, and Daniel Straub
Nat. Hazards Earth Syst. Sci., 18, 1327–1347, https://doi.org/10.5194/nhess-18-1327-2018, https://doi.org/10.5194/nhess-18-1327-2018, 2018
Short summary
Short summary
We study flood protection options in a pre-alpine catchment in southern Germany. Protection systems are evaluated probabilistically, taking into account climatic and other uncertainties as well as the possibility of future adjustments. Despite large uncertainty in damage, cost, and climate, we arrive at a rough recommendation. Hence, one can make good decisions under large uncertainty. The results also show it is preferable to plan risk-based rather than protecting from a specific design flood.
Hugo Rosero-Velásquez, Mauricio Monsalve, Juan Camilo Gómez Zapata, Elisa Ferrario, Alan Poulos, Juan Carlos de la Llera, and Daniel Straub
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-186, https://doi.org/10.5194/nhess-2023-186, 2023
Revised manuscript under review for NHESS
Short summary
Short summary
Seismic risk management uses scenarios of earthquake events, but the selection criteria do not always consider consequences to exposed assets. For this reason, we adopt a definition of representative scenario, associated to a return period and loss level, for selecting such scenarios among a big set of possible earthquakes. We identify the scenarios for the residential buildings and power supply in Valparaíso and Viña del Mar, Chile. The selected scenarios vary in function of the exposed assets.
Beatrice Dittes, Maria Kaiser, Olga Špačková, Wolfgang Rieger, Markus Disse, and Daniel Straub
Nat. Hazards Earth Syst. Sci., 18, 1327–1347, https://doi.org/10.5194/nhess-18-1327-2018, https://doi.org/10.5194/nhess-18-1327-2018, 2018
Short summary
Short summary
We study flood protection options in a pre-alpine catchment in southern Germany. Protection systems are evaluated probabilistically, taking into account climatic and other uncertainties as well as the possibility of future adjustments. Despite large uncertainty in damage, cost, and climate, we arrive at a rough recommendation. Hence, one can make good decisions under large uncertainty. The results also show it is preferable to plan risk-based rather than protecting from a specific design flood.
M. Sättele, M. Bründl, and D. Straub
Nat. Hazards Earth Syst. Sci., 16, 149–166, https://doi.org/10.5194/nhess-16-149-2016, https://doi.org/10.5194/nhess-16-149-2016, 2016
Short summary
Short summary
We suggest a generic classification of early warning systems for natural hazards, which distinguishes alarm, warning, and forecasting systems. On the basis of this classification, we developed a three-step framework for evaluating the effectiveness of such systems and illustrate its applicability using case studies. Our results will support practitioners in comparing the effectiveness of early warning systems with those of structural mitigation measures.
Related subject area
Subject: Engineering Hydrology | Techniques and Approaches: Theory development
A pulse-decay method for low (matrix) permeability analyses of granular rock media
A signal-processing-based interpretation of the Nash–Sutcliffe efficiency
Impact of cry wolf effects on social preparedness and the efficiency of flood early warning systems
Impact of detention dams on the probability distribution of floods
Hess Opinions: An interdisciplinary research agenda to explore the unintended consequences of structural flood protection
A physical approach on flood risk vulnerability of buildings
Development of streamflow drought severity–duration–frequency curves using the threshold level method
Understanding flood regime changes in Europe: a state-of-the-art assessment
Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter
On teaching styles of water educators and the impact of didactic training
T-shaped competency profile for water professionals of the future
Ideal point error for model assessment in data-driven river flow forecasting
On the return period and design in a multivariate framework
Estimating strategies for multiparameter Multivariate Extreme Value copulas
Tao Zhang, Qinhong Hu, Behzad Ghanbarian, Derek Elsworth, and Zhiming Lu
Hydrol. Earth Syst. Sci., 27, 4453–4465, https://doi.org/10.5194/hess-27-4453-2023, https://doi.org/10.5194/hess-27-4453-2023, 2023
Short summary
Short summary
Tight rock is essential to various emerging fields of energy geosciences such as EGS and CCUS, but its ultra-low permeability is not easily measurable as a rigorous and rapid theory-based measurement technique for sub-nanodarcy levels is lacking. For the first time, we resolve this by providing an integrated technique (termed gas permeability technique) with coupled theoretical development, experimental procedures, and a data interpretation workflow.
Le Duc and Yohei Sawada
Hydrol. Earth Syst. Sci., 27, 1827–1839, https://doi.org/10.5194/hess-27-1827-2023, https://doi.org/10.5194/hess-27-1827-2023, 2023
Short summary
Short summary
The Nash–Sutcliffe efficiency (NSE) is a widely used score in hydrology, but it is not common in the other environmental sciences. One of the reasons for its unpopularity is that its scientific meaning is somehow unclear in the literature. This study attempts to establish a solid foundation for NSE from the viewpoint of signal progressing. This approach is shown to yield profound explanations to many open problems related to NSE. A generalized NSE that can be used in general cases is proposed.
Yohei Sawada, Rin Kanai, and Hitomu Kotani
Hydrol. Earth Syst. Sci., 26, 4265–4278, https://doi.org/10.5194/hess-26-4265-2022, https://doi.org/10.5194/hess-26-4265-2022, 2022
Short summary
Short summary
Although flood early warning systems (FEWS) are promising, they inevitably issue false alarms. Many false alarms undermine the credibility of FEWS, which we call a cry wolf effect. Here, we present a simple model that can simulate the cry wolf effect. Our model implies that the cry wolf effect is important if a community is heavily protected by infrastructure and few floods occur. The cry wolf effects get more important as the natural scientific skill to predict flood events is improved.
Salvatore Manfreda, Domenico Miglino, and Cinzia Albertini
Hydrol. Earth Syst. Sci., 25, 4231–4242, https://doi.org/10.5194/hess-25-4231-2021, https://doi.org/10.5194/hess-25-4231-2021, 2021
Short summary
Short summary
In this work, we introduce a new theoretically derived probability distribution of the outflows of in-line detention dams. The method may be used to evaluate the impact of detention dams on flood occurrences and attenuation of floods. This may help and support risk management planning and design.
Giuliano Di Baldassarre, Heidi Kreibich, Sergiy Vorogushyn, Jeroen Aerts, Karsten Arnbjerg-Nielsen, Marlies Barendrecht, Paul Bates, Marco Borga, Wouter Botzen, Philip Bubeck, Bruna De Marchi, Carmen Llasat, Maurizio Mazzoleni, Daniela Molinari, Elena Mondino, Johanna Mård, Olga Petrucci, Anna Scolobig, Alberto Viglione, and Philip J. Ward
Hydrol. Earth Syst. Sci., 22, 5629–5637, https://doi.org/10.5194/hess-22-5629-2018, https://doi.org/10.5194/hess-22-5629-2018, 2018
Short summary
Short summary
One common approach to cope with floods is the implementation of structural flood protection measures, such as levees. Numerous scholars have problematized this approach and shown that increasing levels of flood protection can generate a false sense of security and attract more people to the risky areas. We briefly review the literature on this topic and then propose a research agenda to explore the unintended consequences of structural flood protection.
B. Mazzorana, S. Simoni, C. Scherer, B. Gems, S. Fuchs, and M. Keiler
Hydrol. Earth Syst. Sci., 18, 3817–3836, https://doi.org/10.5194/hess-18-3817-2014, https://doi.org/10.5194/hess-18-3817-2014, 2014
J. H. Sung and E.-S. Chung
Hydrol. Earth Syst. Sci., 18, 3341–3351, https://doi.org/10.5194/hess-18-3341-2014, https://doi.org/10.5194/hess-18-3341-2014, 2014
J. Hall, B. Arheimer, M. Borga, R. Brázdil, P. Claps, A. Kiss, T. R. Kjeldsen, J. Kriaučiūnienė, Z. W. Kundzewicz, M. Lang, M. C. Llasat, N. Macdonald, N. McIntyre, L. Mediero, B. Merz, R. Merz, P. Molnar, A. Montanari, C. Neuhold, J. Parajka, R. A. P. Perdigão, L. Plavcová, M. Rogger, J. L. Salinas, E. Sauquet, C. Schär, J. Szolgay, A. Viglione, and G. Blöschl
Hydrol. Earth Syst. Sci., 18, 2735–2772, https://doi.org/10.5194/hess-18-2735-2014, https://doi.org/10.5194/hess-18-2735-2014, 2014
V. R. N. Pauwels, G. J. M. De Lannoy, H.-J. Hendricks Franssen, and H. Vereecken
Hydrol. Earth Syst. Sci., 17, 3499–3521, https://doi.org/10.5194/hess-17-3499-2013, https://doi.org/10.5194/hess-17-3499-2013, 2013
A. Pathirana, J. H. Koster, E. de Jong, and S. Uhlenbrook
Hydrol. Earth Syst. Sci., 16, 3677–3688, https://doi.org/10.5194/hess-16-3677-2012, https://doi.org/10.5194/hess-16-3677-2012, 2012
S. Uhlenbrook and E. de Jong
Hydrol. Earth Syst. Sci., 16, 3475–3483, https://doi.org/10.5194/hess-16-3475-2012, https://doi.org/10.5194/hess-16-3475-2012, 2012
C. W. Dawson, N. J. Mount, R. J. Abrahart, and A. Y. Shamseldin
Hydrol. Earth Syst. Sci., 16, 3049–3060, https://doi.org/10.5194/hess-16-3049-2012, https://doi.org/10.5194/hess-16-3049-2012, 2012
G. Salvadori, C. De Michele, and F. Durante
Hydrol. Earth Syst. Sci., 15, 3293–3305, https://doi.org/10.5194/hess-15-3293-2011, https://doi.org/10.5194/hess-15-3293-2011, 2011
G. Salvadori and C. De Michele
Hydrol. Earth Syst. Sci., 15, 141–150, https://doi.org/10.5194/hess-15-141-2011, https://doi.org/10.5194/hess-15-141-2011, 2011
Cited articles
Aghakouchak, A., Easterling, D., Hsu, K., Schubert, S., and Sorooshian, S. (Eds.):
Extremes in a Changing Climate, Springer, Dordrecht, 2013.
Alfieri, L., Burek, P., Feyen, L., and Forzieri, G.: Global warming increases
the frequency of river floods in Europe, Hydrol. Earth Syst. Sci., 19, 2247–2260,
https://doi.org/10.5194/hess-19-2247-2015, 2015.
Arnbjerg-Nielsen, K., Willems, P., Olsson, J., Beecham, S., Pathirana, A.,
Bülow Gregersen, I., Madsen, H., and Nguyen, V. T. V: Impacts of climate
change on rainfall extremes and urban drainage systems: A review, Water Sci.
Technol., 68, 16–28, https://doi.org/10.2166/wst.2013.251, 2013.
Arnbjerg-Nielsen, K., Funder, S. G., and Madsen, H.: Identifying climate
analogues for precipitation extremes for Denmark based on RCM simulations from
the ENSEMBLES database, Water Sci. Technol., 71, 418–425, https://doi.org/10.2166/wst.2015.001, 2015.
Benjamin, J. R. and Cornell, C. A.: Probability, Statistics and Decisions for
Civil Engineers, Mc Graw – Hill Book Company, New York City, 1970.
Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., and Savenije, H.:
Runoff Prediction in Ungauged Basins: Synthesis Across Processes, in: Places and
Scales, Cambridge University Press, Cambridge, 2013a.
Blöschl, G., Nester, T., Komma, J., Parajka, J., and Perdigão, R. A. P.:
The June 2013 flood in the Upper Danube Basin, and comparisons with the 2002,
1954 and 1899 floods, Hydrol. Earth Syst. Sci., 17, 5197–5212, https://doi.org/10.5194/hess-17-5197-2013, 2013b.
Bosshard, T., Kotlarski, S., Ewen, T., and Schär, C.: Spectral representation
of the annual cycle in the climate change signal, Hydrol. Earth Syst. Sci., 15,
2777–2788, https://doi.org/10.5194/hess-15-2777-2011, 2011.
Bosshard, T., Carambia, M., Goergen, K., Kotlarski, S., Krahe, P., Zappa, M.,
and Schär, C.: Quantifying uncertainty sources in an ensemble of hydrological
climate-impact projections, Water Resour. Res., 49, 1523–1536, https://doi.org/10.1029/2011WR011533, 2013.
Brigode, P., Oudin, L., and Perrin, C.: Hydrological model parameter instability:
A source of additional uncertainty in estimating the hydrological impacts of
climate change?, J. Hydrol., 476, 410–425, https://doi.org/10.1016/j.jhydrol.2012.11.012, 2013.
Central European Flood Risk Assessment and Management in CENTROPE: Current
standards for flood protection, available at: http://www.floodcba2.eu/site/wp-content/uploads/CEframe_363_Current_standards_for_flood_protection.pdf
(last access: 22 April 2018), 2013.
Chen, J., Brissette, F. P., and Lucas-picher, P.: Assessing the limits of
bias-correcting climatemodel outputs for climate change impact studies, J.
Geophys. Res.-Atmos., 120, 1123–1136, https://doi.org/10.1002/2014JD022635, 2015.
Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer, London, 2004.
Coles, S., Pericchi, L. R., and Sisson, S.: A fully probabilistic approach to
extreme rainfall modelling, J. Hydrol., 273, 35–50, https://doi.org/10.1016/S0022-1694(02)00353-0, 2003.
Curry, J. A. and Webster, P. J.: Climate science and the uncertainty monster,
B. Am. Meteorol. Soc., 92, 1667–1682, https://doi.org/10.1175/2011BAMS3139.1, 2011.
Custer, R. and Nishijima, K.: Hierarchical decision making for flood risk
reduction, in: 11th International Conference on Structural Safety & Reliability,
ICOSSAR, New York, 4865–4872, 2013.
Davis, D. R., Kisiel, C. C., and Duckstein, L.: Bayesian decision theory applied
to design in hydrology, Water Resour. Res., 8, 33–41, 1972.
De Kok, J. L., Hoekstra, A. Y., Defence, F., and Change, C.: Living with peak
discharge uncertainty: The self-learning dike, in 4th Biennial Meeting of
the International Congress on Environmental Modelling and Software, iEMSs,
Barcelona, 1542–1549, 2008.
Delgado, J. M., Apel, H., and Merz, B.: Flood trends and variability in the
Mekong river, Hydrol. Earth Syst. Sci., 14, 407–418, https://doi.org/10.5194/hess-14-407-2010, 2010.
Deutscher Wetterdienst: Deutscher Klimaatlas, available at: https://www.dwd.de/DE/klimaumwelt/klimaatlas/klimaatlas_node.html,
last access: 22 April 2018.
Dittes, B., Špačková, O., and Straub, D.: Managing uncertainty in
design flood magnitude: Flexible protection strategies vs. safety factors,
J. Flood Risk Manage., accepted, 2018.
Dobler, C., Hagemann, S., Wilby, R. L., and Stätter, J.: Quantifying
different sources of uncertainty in hydrological projections in an Alpine
watershed, Hydrol. Earth Syst. Sci., 16, 4343–4360, https://doi.org/10.5194/hess-16-4343-2012, 2012.
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., and Liebert, J.: Should
we apply bias correction to global and regional climate model data?, Hydrol.
Earth Syst. Sci., 16, 3391–3404, https://doi.org/10.5194/hess-16-3391-2012, 2012.
Fatichi, S., Rimkus, S., Burlando, P., Bordoy, R., and Molnar, P.: Elevational
dependence of climate change impacts on water resources in an Alpine catchment,
Hydrol. Earth Syst. Sci. Discuss., 10, 3743–3794, https://doi.org/10.5194/hessd-10-3743-2013, 2013.
Foley, A. M.: Uncertainty in regional climate modelling: A review, Progress
in Physical Geography, 34(5), 647–670, doi:10.1177/0309133310375654, 2010.
Götzinger, J. and Bárdossy, A.: Generic error model for calibration
and uncertainty estimation of hydrological models, Water Resour. Res., 44,
W00B07, https://doi.org/10.1029/2007WR006691, 2008.
Graf, M., Nishijima, K., and Faber, M.: Bayesian updating in natural hazard
risk assessment, Aust. J. Struct. Eng., 9, 35–44, https://doi.org/10.1080/13287982.2009.11465008, 2007.
Gregersen, I. B., Madsen, H., Rosbjerg, D., and Arnbjerg-Nielsen, K.: Long term
variations of extreme rainfall in Denmark and southern Sweden, Clim. Dynam.,
44, 3155–3169, https://doi.org/10.1007/s00382-014-2276-4, 2014.
Grundmann, J.: Analyse und Simulation von Unsicherheiten in der Flächendifferenzierten
Niederschlags-Abfluss-Modellierung, in: Dresdner Schriften zur Hydrologie,
PhD Thesis, 165 pp., https://d-nb.info/1008804363/34 (last access: 22 April 2018), 2010.
Hall, J. and Solomatine, D.: A framework for uncertainty analysis in flood risk
management decisions, Int. J. River Basin Manage., 6, 85–98, https://doi.org/10.1080/15715124.2008.9635339, 2008.
Hall, J., Arheimer, B., Borga, M., Brázdil, R., Claps, P., Kiss, A., Kjeldsen,
T. R., Kriaucuniene, J., Kundzewicz, Z. W., Lang, M., Llasat, M. C., Macdonald,
N., McIntyre, N., Mediero, L., Merz, B., Merz, R., Molnar, P., Montanari, A.,
Neuhold, C., Parajka, J., Perdigão, R. A. P., Plavcová, L., Rogger, M.,
Salinas, J. L., Sauquet, E., Schär, C., Szolgay, J., Viglione, A., and
Blöschl, G.: Understanding flood regime changes in Europe: A state-of-the-art
assessment, Hydrol. Earth Syst. Sci., 18, 2735–2772, https://doi.org/10.5194/hess-18-2735-2014, 2014.
Hallegatte, S.: Strategies to adapt to an uncertain climate change, Global
Environ. Change, 19, 240–247, https://doi.org/10.1016/j.gloenvcha.2008.12.003, 2009.
Hanel, M. and Buishand, T. A.: Analysis of precipitation extremes in an ensemble
of transient regional climate model simulations for the Rhine basin, Clim.
Dynam., 36, 1135–1153, https://doi.org/10.1007/s00382-010-0822-2, 2011.
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in regional
climate predictions, B. Am. Meteorol. Soc., 90, 1095–1107, https://doi.org/10.1175/2009BAMS2607.1, 2009.
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in projections
of regional precipitation change, Clim. Dynam., 37, 407–418, 2011.
Hollweg, H.-D., Böhm, U., Fast, I., Hennemuth, B., Keuler, K., Keup-Thiel,
E., Lautenschlager, M., Legutke, S., Radtke, K., Rockel, B., Schubert, M., Will,
A., and Michael Woldt, C. W.: Ensemble Simulations over Europe with the Regional
Climate Model CLM forced with IPCC AR4 Global Scenarios, CLM Technical Report,
20, 857–861, https://doi.org/10.1089/lap.2010.0351, 2008.
Huang, S., Krysanova, V., and Hattermann, F.: Projections of climate change
impacts on floods and droughts in Germany using an ensemble of climate change
scenarios, Reg. Environ. Change, 15, 461–473, https://doi.org/10.1007/s10113-014-0606-z, 2014.
Hundecha, Y., Sunyer, M. A., Lawrence, D., Madsen, H., Willems, P., Martinkova,
M., Vormoor, K., Bürger, G., Hanel, M., Kriaučiuniene, J., Loukas, A.,
Osuch, M., and Yücel, I.: Inter-comparison of statistical downscaling methods
for projection of extreme flow indices across Europe, Hydrol. Earth Syst. Sci.,
19, 1827–1847, https://doi.org/10.5194/hess-19-1827-2015, 2016.
IPCC: IPCC Special Report – Emission Scenarios, edited by: Nakicenovic, N. and
Swart, R., Cambridge University Press, Cambridge, England, 2000.
IPCC: Climate Change 2013: The Physical Science Basis, Cambridge University Press, Cambridge, 2013.
Kennedy, M. C. and O'Hagan, A.: Bayesian calibration of computer models, J.
Roy. Stat. Soc. B, 63, 425–464, https://doi.org/10.1111/1467-9868.00294, 2001.
Kerkhoff, C., Künsch, H. R., and Schär, C.: A Bayesian hierarchical
model for heterogeneous RCM-GCM multimodel ensembles, J. Climate, 28, 6249–6266,
https://doi.org/10.1175/JCLI-D-14-00606.1, 2015.
KLIWA: Der Klimawandel in Bayern für den Zeitraum 2021–2050, Arbeitskreis
KLIWA, Mannheim, 2005.
KLIWA: Heft 9 – Regionale Klimaszenarien für Süddeutschland,
Arbeitskreis KLIWA, Mannheim, 2006.
Knutti, R., Masson, D., and Gettelman, A.: Climate model genealogy: Generation
CMIP5 and how we got there, Geophys. Res. Lett., 40, 1194–1199, https://doi.org/10.1002/grl.50256, 2013.
Kochendorfer, M. J.: Decision Making Under Uncertainty, The MIT Press,
Cambridge, Massachusetts, 2015.
Kunstmann, H. and Stadler, C.: High resolution distributed atmospheric–hydrological
modelling for Alpine catchments, J. Hydrol., 314, 105–124, https://doi.org/10.1016/j.jhydrol.2005.03.033, 2005.
Kwakkel, J., Walker, W., and Marchau, V.: Grappling with uncertainty in the
long-term development of infrastructure systems, in: 3rd International Conference
on Infrastructure Systems and Services: Next Generation Infrastructure Systems
for Eco-Cities, INFRA, Shenzhen, 2010.
Labarthe, B., Abasq, L., de Fouquet, C., and Flipo, N.: Stepwise calibration
procedure for regional coupled hydrological-hydrogeological models, in: EGU
General Assembly Conference Abstracts (Vol. 16), May 2014, Vienna, Austria, 2014.
Laprise, R.: Comment on “The added value to global model projections of climate
change by dynamical downscaling: A case study over the continental U.S. using
the GISS-ModelE2 nad WRF models” by Racherla et al., J. Geophys. Res.-Atmos.,
119, 3877–3881, https://doi.org/10.1002/2013JD019945, 2014.
Li, M., Yang, D., Chen, J., and Hubbard, S. S.: Calibration of a distributed
flood forecasting model with input uncertainty using a Bayesian framework, Water
Resour. Res., 48, W08510, https://doi.org/10.1029/2010WR010062, 2012.
MacKay, D. J. C.: Bayesian interpolation, Neural Computation, 4, 415–447,
https://doi.org/10.1162/neco.1992.4.3.415, 1992.
Madsen, H., Lawrence, D., Lang, M., Martinkova, M., and Kjeldsen, T. R.: Review
of trend analysis and climate change projections of extreme precipitation and
floods in Europe, J. Hydrol., 519, 3634–3650, https://doi.org/10.1016/j.jhydrol.2014.11.003, 2014.
Magdali, M.: Calibration of the hydrological model WaSiM for the Mangfall,
MS Thesis, Technical University of Munich, Munich, 2015.
Maraun, D.: When will trends in European mean and heavy daily precipitation
emerge?, Environ. Res. Lett., 8, 14004, https://doi.org/10.1088/1748-9326/8/1/014004, 2013.
Masson, D. and Knutti, R.: Climate model genealogy, Geophys. Res. Lett., 38,
L08703, https://doi.org/10.1029/2011GL046864, 2011.
Maurer, E. P. and Pierce, D. W.: Bias correction can modify climate model
simulated precipitation changes without adverse effect on the ensemble mean,
Hydrol. Earth Syst. Sci., 18, 915–925, https://doi.org/10.5194/hess-18-915-2014, 2014.
Merz, B., Aerts, J., Arnbjerg-Nielsen, K., Baldi, M., Becker, A., Bichet, A.,
Blöschl, G., Bouwer, L. M., Brauer, A., Cioffi, F., Delgado, J. M., Gocht,
M., Guzzetti, F., Harrigan, S., Hirschboeck, K., Kilsby, C., Kron, W., Kwon,
H. H., Lall, U., Merz, R., Nissen, K., Salvatti, P., Swierczynski, T., Ulbrich,
U., Viglione, A., Ward, P. J., Weiler, M., Wilhelm, B., and Nied, M.: Floods
and climate: Emerging perspectives for flood risk assessment and management,
Nat. Hazards Earth Syst. Sci., 14, 1921–1942, https://doi.org/10.5194/nhess-14-1921-2014, 2014.
Merz, B., Vorogushyn, S., Lall, U., Viglione, A, and Blöschl, G.: Charting
unknown water – on the role of surprise in flood risk assessment and management,
Water Resour. Res., 51, 6399–6416, https://doi.org/10.1002/2014WR016259, 2015.
Merz, B., Dung, N. V., and Vorogushyn, S.: Temporal clustering of floods in
Germany: Do flood-rich and flood-poor periods exist?, J. Hydrol., 541, 824–838,
https://doi.org/10.1016/j.jhydrol.2016.07.041, 2016.
Merz, R., Parajka, J., and Blöschl, G.: Time stability of catchment model
parameters: Implications for climate impact analyses, Water Resour. Res., 47,
6119, https://doi.org/10.1029/2010WR009505, 2011.
Mitchell, T. D. and Hulme, M.: Predicting regional climate change: living with
uncertainty, Prog. Phys. Geogr., 23, 57–78, https://doi.org/10.1191/030913399672023346, 1999.
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.
Muerth, M., St.-Denis, B. G., Ludwig, R., and Caya, D.: Evaluation of different
sources of uncertainty in climate change impact research using a hydro-climatic
model ensemble, in: 6th Biennial Meeting of the International Congress on
Environmental Modelling and Software, iEMSs, Leipzig, 2012.
Nishijima, K.: Concept of decision graphical framework for optimising adaptation
of civil infrastructure to a changing climate, Struct. Infrastruct. Eng., 12,
477–483, https://doi.org/10.1080/15732479.2015.1020496, 2015.
Paté-Cornell, E.: On “Black swans” and “Perfect storms”: Risk analysis
and management when statistics are not enough, Risk Analysis, 32, 1823–1833, 2011.
Pennell, C. and Reichler, T.: On the effective number of climate models, J.
Climate, 24, 2358–2367, https://doi.org/10.1175/2010JCLI3814.1, 2011.
Pohl, R.: Freibordbemessung an Hochwasserschutzanlagen, 36 Dresdner
Wasserbaukolloquium “Technischer und organisatorischer Hochwasserschutz”,
Technische Universität Dresden, Institut für Wasserbau und technische
Hydromechanik, Dresden, 2013.
Pöhler, H., Schultze, B., and Scherzer, J.: KLIWA: Vergleichende Analyse
der neuen globalen Klimaprojektionen aus CMIP5 für Süddeutschland,
Abschlussbericht, Arbeitskreis KLIWA, Mannheim, 2012.
Raiffa, H. and Schlaifer, R.: Applied Statistical Decision Theory, 5th Edn.,
The Colonial Press, Boston, 1961.
Rajczak, J., Pall, P., and Schär, C.: Projections of extreme precipitation
events in regional climate simulations for Europe and the Alpine Region, J.
Geophys. Res.-Atmos., 118, 3610–3626, https://doi.org/10.1002/jgrd.50297, 2013.
Refsgaard, J. C., Arnbjerg-Nielsen, K., Drews, M., Halsnæs, K., Jeppesen,
E., Madsen, H., Markandya, A., Olesen, J. E., Porter, J. R., and Christensen,
J. H.: The role of uncertainty in climate change adaptation strategies – A
Danish water management example, Mitig. Adapt. Strat. Global Change, 18, 337–359,
https://doi.org/10.1007/s11027-012-9366-6, 2013.
Refsgaard, J. C., Madsen, H., Andréassian, V., Arnbjerg-Nielsen, K.,
Davidson, T. A., Drews, M., Hamilton, D. P., Jeppesen, E., Kjellström, E.,
Olesen, J. E., Sonnenborg, T. O., Trolle, D., Willems, P., and Christensen, J.
H.: A framework for testing the ability of models to project climate change and
its impacts, Climatic Change, 122, 271–282, https://doi.org/10.1007/s10584-013-0990-2, 2014.
RMD Consult: Erläuterungsbericht – Hochwasserrückhaltebecken Feldolling,
Munich, Germany, 2016.
Rodwell, M. J. and Palmer, T. N.: Using numerical weather prediction to assess
climate models, Q. J. Roy. Meteorol. Soc., 133, 937–948, https://doi.org/10.1002/qj.23, 2007.
Schmid, F. J., Willkofer, F., and Ludwig, R.: Endbericht Einfluss der Biaskorrektur
dynamischer regionaler Klimamodelldaten auf die Wasserhaushaltsmodellierung und
Klimafolgeabschätzung in Bayerischen Flussgebieten – Erstellung eines
Klimamodell-Audits und ergänzende Untersuchungen (BI-KLIM-2014), Ludwig
Maximilian Universität, München, 2014.
Seifert, P.: Mit Sicherheit wächst der Schaden. Überlegungen zum Umgang
mit Hochwasser in der räumlichen Planung, Geschäftsstelle des Regionalen
Planungsverbandes Oberes Elbtal/Osterzgebirge, Meissen, 2012.
Špačková, O. and Straub, D.: Long-term adaption decisions via fully
and partially observable Markov decision processes, Sustain. Resil. Infrastruct.,
2, 37–58, 2017.
Sunyer, M. A.: Uncertainties in extreme precipitation under climate change
conditions, Technical University of Denmark, available at: http://www.orbit.dtu.dk
Copenhagen, 2014.
Sunyer, M. A., Sørup, H. J. D., Christensen, O. B., Madsen, H., Rosbjerg, D.,
Mikkelsen, P. S., and Arnbjerg-Nielsen, K.: On the importance of observational
data properties when assessing regional climate model performance of extreme
precipitation, Hydrol. Earth Syst. Sci., 17, 4323–4337, https://doi.org/10.5194/hess-17-4323-2013, 2013a.
Sunyer, M. A., Madsen, H., Rosbjerg, D., and Arnbjerg-Nielsen, K.: Regional
interdependency of precipitation indices across Denmark in two ensembles of
high-resolution RCMs, J. Climate, 26, 7912–7928, https://doi.org/10.1175/JCLI-D-12-00707.1, 2013b.
Sunyer, M. A., Gregersen, I. B., Rosbjerg, D., Madsen, H., Luchner, J., and
Arnbjerg-Nielsen, K.: Comparison of different statistical downscaling methods
to estimate changes in hourly extreme precipitation using RCM projections from
ENSEMBLES, Int. J. Climatol., 35, 2528–2539, https://doi.org/10.1002/joc.4138, 2015a.
Sunyer, M. A., Hundecha, Y., Lawrence, D., Madsen, H., Willems, P., Martinkova,
M., Vormoor, K., Bürger, G., Hanel, M., Kriaučiūnienė, J.,
Loukas, A., Osuch, M., Yücel, I., Kriaučiuniene, J., Loukas, A., Osuch,
M., Yücel, I., Kriaučiūnienė, J., Loukas, A., Osuch, M., and
Yücel, I.: Inter-comparison of statistical downscaling methods for projection
of extreme precipitation in Europe, Hydrol. Earth Syst. Sci., 19, 1827–1847,
https://doi.org/10.5194/hess-19-1827-2015, 2015b.
Tebaldi, C. and Knutti, R.: The use of the multi-model ensemble in probabilistic
climate projections, Philos. T. Roy. Soc. A, 365, 2053–2075, https://doi.org/10.1098/rsta.2007.2076, 2007.
Tebaldi, C., Smith, R., Nychka, D., and Mearns, L.: Quantifying uncertainty in
projections of regional climate change: a Bayesian approach to the analysis of
multimodel ensembles, J. Climate, 18, 1524–1540, 2004.
Tebaldi, C., O'Neill, B., and Lamarque, J.-F.: Sensitivity of regional climate
to global temperature and forcing, Environ. Res. Lett., 10, 74001,
https://doi.org/10.1088/1748-9326/10/7/074001, 2015.
Themeßl, J., Gobiet, A., and Leuprecht, A.: Empirical-statistical downscaling
and error correction of daily precipitation from regional climate models, Int.
J. Climatol., 31, 1530–1544, https://doi.org/10.1002/joc.2168, 2010.
Umweltbundesamt: Einführung in Klimaprojektionen, available at:
https://www.umweltbundesamt.de/themen/klima-energie/klimafolgen-anpassung/folgen-des-klimawandels/klimamodelle-szenarien/einfuehrung-in-klimaprojektionen#textpart-1,
last access: 22 April 2018.
Van Haren, R., Van Oldenborgh, G. J., Lenderink, G., and Hazeleger, W.: Evaluation
of modeled changes in extreme precipitation in Europe and the Rhine basin,
Environ. Res. Lett., 8, 14053, https://doi.org/10.1088/1748-9326/8/1/014053, 2013.
Velázquez, J. A., Schmid, J., Ricard, S., Muerth, M. J., Gauvin St-Denis,
B., Minville, M., Chaumont, D., Caya, D., Ludwig, R., and Turcotte, R.: An
ensemble approach to assess hydrological models' contribution to uncertainties
in the analysis of climate change impact on water resources, Hydrol. Earth Syst.
Sci., 17, 565–578, https://doi.org/10.5194/hess-17-565-2013, 2013.
Viglione, A., Merz, R., Salinas, J. L., and Blöschl, G.: Flood frequency
hydrology: 3. A Bayesian analysis, Water Resour. Res., 49, 675–692,
https://doi.org/10.1029/2011WR010782, 2013.
Wasserwirtschaftsamt Rosenheim: Das Hochwasser vom Juni 2013, Wasserwirtschaftsamt
Rosenheim, Rosenheim, 2014.
Wiedemann, C. and Slowacek, W.: Hochwasserrückhaltebecken Feldolling: Zweck,
Betrieb Bemessung und Funktionsweise, Wasserwirtschaftsamt Rosenheim, Rosenheim,
available at: http://www.wwa-ro.bayern.de/hochwasser/hochwasserschutzprojekte/mangfalltal/doc/hrb_feldolling/funktion_hrb_feldolling.pdf
(last access: 22 April 2018), 2013.
Wilby, R. L.: Uncertainty in water resource model parameters used for climate
change impact assessment, Hydrol. Process., 19, 3201–3219, 2005.
Willems, W. and Stricker, K.: Klimawandel und Wasserhaushalt: AdaptAlp –
Untersuchung zum Einfluss des Klimawandels auf Wasserbilanzen und Abflüsse
für das Inneinzugsgebiet mittels verschiedener Klimaszenarien. Endbericht,
Bayerisches Landesamt für Umwelt, Hof, 2011.
Ylhäisi, J. S., Räisänen, J., Masson, D., Räty, O., and
Järvinen, H.: How does model development affect climate projections?, Atmos.
Sci. Lett., 16.3, 414–419, https://doi.org/10.1002/asl2.577, 2015.
Short summary
There is large uncertainty in the future development of flood patterns, e.g., due to climate change. We quantify relevant uncertainties and show how they can be used for flood protection planning. We find that one ought to include an estimate of uncertainty that cannot be quantified from available data (hidden uncertainty), since projections and data at hand often cover only a limited range of the uncertainty spectrum. Furthermore, dependencies between climate projections must be accounted for.
There is large uncertainty in the future development of flood patterns, e.g., due to climate...
