Articles | Volume 18, issue 1
https://doi.org/10.5194/hess-18-155-2014
© Author(s) 2014. 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-18-155-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
14 Jan 2014
Research article |
| 14 Jan 2014
Towards modelling flood protection investment as a coupled human and natural system
P. E. O'Connell
Water Resource Systems Research Laboratory, School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, UK
G. O'Donnell
Water Resource Systems Research Laboratory, School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, UK
Related authors
No articles found.
John Ewen and Greg Martin O'Donnell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-170, https://doi.org/10.5194/hess-2021-170, 2021
Revised manuscript not accepted
Short summary
Short summary
Models for the water flow from river catchments are used when making decisions about flooding, climate change, etc. The models should depend directly and transparently on things known about how catchments generate flow, but that is not always the case. In an example, all the knowledge in a model is made visible as a set of statements in everyday English. The aim is to help improve the quality of future decisions by exploring the problem and creating a concrete example for use as a benchmark.
David M. W. Pritchard, Nathan Forsythe, Greg O'Donnell, Hayley J. Fowler, and Nick Rutter
The Cryosphere, 14, 1225–1244, https://doi.org/10.5194/tc-14-1225-2020, https://doi.org/10.5194/tc-14-1225-2020, 2020
Short summary
Short summary
This study compares different snowpack model configurations applied in the western Himalaya. The results show how even sparse local observations can help to delineate climate input errors from model structure errors, which provides insights into model performance variation. The results also show how interactions between processes affect sensitivities to climate variability in different model configurations, with implications for model selection in climate change projections.
Related subject area
Subject: Engineering Hydrology | Techniques and Approaches: Stochastic approaches
FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms
Uncertainty estimation of regionalised depth–duration–frequency curves in Germany
Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system
Characteristics and process controls of statistical flood moments in Europe – a data-based analysis
Objective functions for information-theoretical monitoring network design: what is “optimal”?
Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach
Numerical investigation on the power of parametric and nonparametric tests for trend detection in annual maximum series
Spatially dependent flood probabilities to support the design of civil infrastructure systems
Technical note: Stochastic simulation of streamflow time series using phase randomization
Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice
Ensemble modeling of stochastic unsteady open-channel flow in terms of its time–space evolutionary probability distribution – Part 1: theoretical development
Ensemble modeling of stochastic unsteady open-channel flow in terms of its time–space evolutionary probability distribution – Part 2: numerical application
Characterizing the spatial variations and correlations of large rainstorms for landslide study
Assessment of extreme flood events in a changing climate for a long-term planning of socio-economic infrastructure in the Russian Arctic
Dealing with uncertainty in the probability of overtopping of a flood mitigation dam
Flood frequency analysis of historical flood data under stationary and non-stationary modelling
Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland
A bivariate return period based on copulas for hydrologic dam design: accounting for reservoir routing in risk estimation
Examination of homogeneity of selected Irish pooling groups
Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models
Design flood hydrographs from the relationship between flood peak and volume
Introducing empirical and probabilistic regional envelope curves into a mixed bounded distribution function
HESS Opinions "A random walk on water"
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022, https://doi.org/10.5194/hess-26-5373-2022, 2022
Short summary
Short summary
A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.
Bora Shehu and Uwe Haberlandt
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-254, https://doi.org/10.5194/hess-2022-254, 2022
Revised manuscript accepted for HESS
Short summary
Short summary
Design rainfall volumes at different duration and frequencies are necessary for the planning of water-related systems and facilities. As the procedure for deriving these values is subjected to different sources of uncertainty, here we explore different methods to estimate how precise these values are for different duration, locations and frequencies in Germany. Combining local and spatial simulations, we estimate tolerance ranges from ±10–60 % for design rainfall volumes in Germany.
Andrew J. Newman, Amanda G. Stone, Manabendra Saharia, Kathleen D. Holman, Nans Addor, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 5603–5621, https://doi.org/10.5194/hess-25-5603-2021, https://doi.org/10.5194/hess-25-5603-2021, 2021
Short summary
Short summary
This study assesses methods that estimate flood return periods to identify when we would obtain a large flood return estimate change if the method or input data were changed (sensitivities). We include an examination of multiple flood-generating models, which is a novel addition to the flood estimation literature. We highlight the need to select appropriate flood models for the study watershed. These results will help operational water agencies develop more robust risk assessments.
David Lun, Alberto Viglione, Miriam Bertola, Jürgen Komma, Juraj Parajka, Peter Valent, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 5535–5560, https://doi.org/10.5194/hess-25-5535-2021, https://doi.org/10.5194/hess-25-5535-2021, 2021
Short summary
Short summary
We investigate statistical properties of observed flood series on a European scale. There are pronounced regional patterns, for instance: regions with strong Atlantic influence show less year-to-year variability in the magnitude of observed floods when compared with more arid regions of Europe. The hydrological controls on the patterns are quantified and discussed. On the European scale, climate seems to be the dominant driver for the observed patterns.
Hossein Foroozand and Steven V. Weijs
Hydrol. Earth Syst. Sci., 25, 831–850, https://doi.org/10.5194/hess-25-831-2021, https://doi.org/10.5194/hess-25-831-2021, 2021
Short summary
Short summary
In monitoring network design, we have to decide what to measure, where to measure, and when to measure. In this paper, we focus on the question of where to measure. Past literature has used the concept of information to choose a selection of locations that provide maximally informative data. In this paper, we look in detail at the proper mathematical formulation of the information concept as an objective. We argue that previous proposals for this formulation have been needlessly complicated.
Manuela I. Brunner and Eric Gilleland
Hydrol. Earth Syst. Sci., 24, 3967–3982, https://doi.org/10.5194/hess-24-3967-2020, https://doi.org/10.5194/hess-24-3967-2020, 2020
Short summary
Short summary
Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but yet unobserved streamflow time series with the same characteristics as the observed data. We propose a stochastic simulation approach in the frequency domain instead of the time domain. Our evaluation results suggest that the flexible, continuous simulation approach is valuable for a diverse range of water management applications.
Vincenzo Totaro, Andrea Gioia, and Vito Iacobellis
Hydrol. Earth Syst. Sci., 24, 473–488, https://doi.org/10.5194/hess-24-473-2020, https://doi.org/10.5194/hess-24-473-2020, 2020
Short summary
Short summary
We highlight the need for power evaluation in the application of null hypothesis significance tests for trend detection in extreme event analysis. In a wide range of conditions, depending on the underlying distribution of data, the test power may reach unacceptably low values. We propose the use of a parametric approach, based on model selection criteria, that allows one to choose the null hypothesis, to select the level of significance, and to check the test power using Monte Carlo experiments.
Phuong Dong Le, Michael Leonard, and Seth Westra
Hydrol. Earth Syst. Sci., 23, 4851–4867, https://doi.org/10.5194/hess-23-4851-2019, https://doi.org/10.5194/hess-23-4851-2019, 2019
Short summary
Short summary
While conventional approaches focus on flood designs at individual locations, there are many situations requiring an understanding of spatial dependence of floods at multiple locations. This research describes a new framework for analyzing flood characteristics across civil infrastructure systems, including conditional and joint probabilities of floods. This work leads to a new flood estimation paradigm, which focuses on the risk of the entire system rather than each system element in isolation.
Manuela I. Brunner, András Bárdossy, and Reinhard Furrer
Hydrol. Earth Syst. Sci., 23, 3175–3187, https://doi.org/10.5194/hess-23-3175-2019, https://doi.org/10.5194/hess-23-3175-2019, 2019
Short summary
Short summary
This study proposes a procedure for the generation of daily discharge data which considers temporal dependence both within short timescales and across different years. The simulation procedure can be applied to individual and multiple sites. It can be used for various applications such as the design of hydropower reservoirs, the assessment of flood risk or the assessment of drought persistence, and the estimation of the risk of multi-year droughts.
Cong Jiang, Lihua Xiong, Lei Yan, Jianfan Dong, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 23, 1683–1704, https://doi.org/10.5194/hess-23-1683-2019, https://doi.org/10.5194/hess-23-1683-2019, 2019
Short summary
Short summary
We present the methods addressing the multivariate hydrologic design applied to the engineering practice under nonstationary conditions. A dynamic C-vine copula allowing for both time-varying marginal distributions and a time-varying dependence structure is developed to capture the nonstationarities of multivariate flood distribution. Then, the multivariate hydrologic design under nonstationary conditions is estimated through specifying the design criterion by average annual reliability.
Alain Dib and M. Levent Kavvas
Hydrol. Earth Syst. Sci., 22, 1993–2005, https://doi.org/10.5194/hess-22-1993-2018, https://doi.org/10.5194/hess-22-1993-2018, 2018
Short summary
Short summary
A new method is proposed to solve the stochastic unsteady open-channel flow system in only one single simulation, as opposed to the many simulations usually done in the popular Monte Carlo approach. The derivation of this new method gave a deterministic and linear Fokker–Planck equation whose solution provided a powerful and effective approach for quantifying the ensemble behavior and variability of such a stochastic system, regardless of the number of parameters causing its uncertainty.
Alain Dib and M. Levent Kavvas
Hydrol. Earth Syst. Sci., 22, 2007–2021, https://doi.org/10.5194/hess-22-2007-2018, https://doi.org/10.5194/hess-22-2007-2018, 2018
Short summary
Short summary
A newly proposed method is applied to solve a stochastic unsteady open-channel flow system (with an uncertain roughness coefficient) in only one simulation. After comparing its results to those of the Monte Carlo simulations, the new method was found to adequately predict the temporal and spatial evolution of the probability density of the flow variables of the system. This revealed the effectiveness, strength, and time efficiency of this new method as compared to other popular approaches.
Liang Gao, Limin Zhang, and Mengqian Lu
Hydrol. Earth Syst. Sci., 21, 4573–4589, https://doi.org/10.5194/hess-21-4573-2017, https://doi.org/10.5194/hess-21-4573-2017, 2017
Short summary
Short summary
Rainfall is the primary trigger of landslides. However, the rainfall intensity is not uniform in space, which causes more landslides in the area of intense rainfall. The primary objective of this paper is to quantify spatial correlation characteristics of three landslide-triggering large storms in Hong Kong. The spatial maximum rolling rainfall is represented by a trend surface and a random field of residuals. The scales of fluctuation of the residuals are found between 5 km and 30 km.
Elena Shevnina, Ekaterina Kourzeneva, Viktor Kovalenko, and Timo Vihma
Hydrol. Earth Syst. Sci., 21, 2559–2578, https://doi.org/10.5194/hess-21-2559-2017, https://doi.org/10.5194/hess-21-2559-2017, 2017
Short summary
Short summary
This paper presents the probabilistic approach to evaluate design floods in a changing climate, adapted in this case to the northern territories. For the Russian Arctic, the regions are delineated, where it is suggested to correct engineering hydrological calculations to account for climate change. An example of the calculation of a maximal discharge of 1 % exceedance probability for the Nadym River at Nadym is provided.
Eleni Maria Michailidi and Baldassare Bacchi
Hydrol. Earth Syst. Sci., 21, 2497–2507, https://doi.org/10.5194/hess-21-2497-2017, https://doi.org/10.5194/hess-21-2497-2017, 2017
Short summary
Short summary
In this research, we explored how the sampling uncertainty of flood variables (flood peak, volume, etc.) can reflect on a structural variable, which in our case was the maximum water level (MWL) of a reservoir controlled by a dam. Next, we incorporated additional information from different sources for a better estimation of the uncertainty in the probability of exceedance of the MWL. Results showed the importance of providing confidence intervals in the risk assessment of a structure.
M. J. Machado, B. A. Botero, J. López, F. Francés, A. Díez-Herrero, and G. Benito
Hydrol. Earth Syst. Sci., 19, 2561–2576, https://doi.org/10.5194/hess-19-2561-2015, https://doi.org/10.5194/hess-19-2561-2015, 2015
Short summary
Short summary
A flood frequency analysis using a 400-year historical flood record was carried out using a stationary model (based on maximum likelihood estimators) and a non-stationary model that incorporates external covariates (climatic and environmental). The stationary model was successful in providing an average discharge around which value flood quantiles estimated by non-stationary models fluctuate through time.
L. Gaál, P. Molnar, and J. Szolgay
Hydrol. Earth Syst. Sci., 18, 1561–1573, https://doi.org/10.5194/hess-18-1561-2014, https://doi.org/10.5194/hess-18-1561-2014, 2014
A. I. Requena, L. Mediero, and L. Garrote
Hydrol. Earth Syst. Sci., 17, 3023–3038, https://doi.org/10.5194/hess-17-3023-2013, https://doi.org/10.5194/hess-17-3023-2013, 2013
S. Das and C. Cunnane
Hydrol. Earth Syst. Sci., 15, 819–830, https://doi.org/10.5194/hess-15-819-2011, https://doi.org/10.5194/hess-15-819-2011, 2011
B. A. Botero and F. Francés
Hydrol. Earth Syst. Sci., 14, 2617–2628, https://doi.org/10.5194/hess-14-2617-2010, https://doi.org/10.5194/hess-14-2617-2010, 2010
L. Mediero, A. Jiménez-Álvarez, and L. Garrote
Hydrol. Earth Syst. Sci., 14, 2495–2505, https://doi.org/10.5194/hess-14-2495-2010, https://doi.org/10.5194/hess-14-2495-2010, 2010
B. Guse, Th. Hofherr, and B. Merz
Hydrol. Earth Syst. Sci., 14, 2465–2478, https://doi.org/10.5194/hess-14-2465-2010, https://doi.org/10.5194/hess-14-2465-2010, 2010
D. Koutsoyiannis
Hydrol. Earth Syst. Sci., 14, 585–601, https://doi.org/10.5194/hess-14-585-2010, https://doi.org/10.5194/hess-14-585-2010, 2010
Cited articles
Ackerman, F., DeCanio, S. J., Howarth, R. B., and Sheeran, K.: Limitations of integrated assessment models of climate change, Clim. Change, 95, 297–315, 2009.
Allenby, B.: Earth systems engineering and management, Technology and Society Magazine, IEEE, 19, 10–24, https://doi.org/10.1109/44.890078, 2000.
An, L.: Modeling human decisions in coupled human and natural systems: Review of agent-based models, Ecol. Modell., 229, 25–36, 2012.
Bankes, S. C.: Tools and techniques for developing policies for complex and uncertain systems, Proc. Natl. Acad. Sci. USA, 99, 7263–7266, 2002.
Barredo, J. I.: Normalised flood losses in Europe: 1970–2006, Nat. Hazards Earth Syst. Sci., 9, 97–104, https://doi.org/10.5194/nhess-9-97-2009, 2009.
Batten, D. F.: Simulating human behaviour: the invisible choreography of self-referential systems, The International Environmental Modelling & Software Society (IEMSS) Conference, Osnabrück, Germany, 14–17 June, 203–208, 2004.
Becu, N., Perez, P., Walker, A., Barreteau, O., and Page, C. L.: Agent based simulation of a small catchment water management in northern Thailand: Description of the CATCHSCAPE model, Ecol. Modell., 170, 319–331, 2003.
Blöschl, G., Viglione, A., and Montanari, A.: Emerging Approaches to Hydrological Risk Management in a Changing World, in: Climate Vulnerability: Understanding and Addressing Threats to Essential Resources, edited by: Pielke, R., Elsevier, Vol 5, 3–10, 2013.
Bras, R. L. and Rodríguez-Iturbe, I.: Random Functions and Hydrology, Dover Publications, New York, 559 pp., 1985.
Brath, A., Montanari, A., and Moretti, G.: Assessing the effect on flood frequency of land use change via hydrological simulation (with uncertainty), J. Hydrol., 324, 141–153, https://doi.org/10.1016/j.jhydrol.2005.10.001, 2006.
Brouwers, L. and Boman, M.: A Computational Agent Model of Flood Management Strategies, in: Computational Methods for Agricultural Research: Advances and Applications, IGI Global, Hershey, PA, USA, 296–307, 2011.
Brown, C. and Wilby, R. L.: An alternate approach to assessing climate risks, Eos, Transactions American Geophysical Union, 93, 401–402, https://doi.org/10.1029/2012EO410001, 2012.
Burningham, K., Fielding, J., and Thrush, D.: "It'll never happen to me": understanding public awareness of local flood risk, Disasters, 32, 216–238, 2008.
Cashman, A. C.: Alternative manifestations of actor responses to urban flooding: case studies from Bradford and Glasgow, Water Sci. Technol., 60, 77–85, 2009.
Crooks, A., Castle, C., and Batty, M.: Key challenges in agent-based modelling for geo-spatial simulation, Computers, Environ. Urban Syst., 32, 417–430, 2008.
Dawson, R., Hall, J., Sayers, P., Bates, P., and Rosu, C.: Sampling-based flood risk analysis for fluvial dike systems, Stoch. Env. Res. Risk A., 19, 388–402, 2005.
Deffuant, G., Amblard, F., Weisbuch, G., and Faure, T.: How can extremism prevail? A study based on the relative agreement interaction model in: Journal of Artificial Societies and Social Simulation, Vol. 5, available at: http://jasss.soc.surrey.ac.uk/5/4/1.html (last access: 20 June 2013), 2002.
Defra: Taking forward a new Government strategy for flood and coastal erosion risk management in England First Government response to the autumn 2004: Making space for water consultation exercise, Defra, London, 45, 2004.
Defra: The UK Government Sustainable Development Strategy, London, 188, 2005.
Defra: Appraisal of flood and coastal erosion risk management: A Defra policy statement June 2009, Defra, London, 52, 2009.
Defra: Flood and Coastal Resilience Partnership Funding: Defra policy statement on an outcome-focused, partnership approach to funding flood and coastal erosion risk management Defra, London, 11, 2011.
Defra/EA: Understanding the risks, empowering communities, building resilience: the national flood and coastal erosion risk management strategy for England, The Stationary Office, London, 63, 2011.
DeMarzo, P. M., Vayanos, D., and Zwiebel, J.: Persuasion Bias, Social Influence, and Unidimensional Opinions, The Quarterly Journal of Economics, 118, 909–968, 2003.
Di Baldassarre, G., Castellarin, A., and Brath, A.: Analysis of the effects of levee heightening on flood propagation: example of the River Po, Italy, Hydrol. Sci. J., 54, 1007–1017, https://doi.org/10.1623/hysj.54.6.1007, 2009.
Di Baldassarre, G., Kooy, M., Kemerink, J. S., and Brandimarte, L.: Towards understanding the dynamic behaviour of floodplains as human-water systems, Hydrol. Earth Syst. Sci., 17, 3235–3244, https://doi.org/10.5194/hess-17-3235-2013, 2013a.
Di Baldassarre, G., Viglione, A., Carr, G., Kuil, L., Salinas, J. L., and Blöschl, G.: Socio-hydrology: conceptualising human-flood interactions, Hydrol. Earth Syst. Sci., 17, 3295–3303, https://doi.org/10.5194/hess-17-3295-2013, 2013b.
Downing, T., Moss, S., and Pahl-Wostl, C.: Understanding Climate Policy Using Participatory Agent-Based Social Simulation, in: Multi-Agent-Based Simulation, edited by: Moss, S. and Davidsson, P., Lecture Notes in Computer Science, Springer Berlin Heidelberg, 198–213, 2001.
Dyer, M.: Performance of flood embankments in England and Wales, Proc. ICE – Water Manage., 157, 177–186, 2004.
Eiser, R. J., Bostrom, A., Burton, I., Johnston, D. M., McClure, J., Paton, D., van der Pligt, J., and White, M. P.: Risk interpretation and action: A conceptual framework for responses to natural hazards, Int. J. Disaster Risk Red., 1, 5–16, https://doi.org/10.1016/j.ijdrr.2012.05.002, 2012.
Evans, E. P., Ashley, R., Hall, J. W., Penning-Rowsell, E. C., Saul, A., Sayers, P. B., Thorne, C. R., and Watkinson, A. R.: Foresight Future Flooding, Scientific Summary: Vol. 1: Future Risks and their Drivers, Office of Science and Technology, London, 2004a.
Evans, E. P., Ashley, R., Hall, J. W., Penning-Rowsell, E. C., Sayers, P. B., Thorne, C. R., and Watkinson, A. R.: Foresight Future Flooding. Scientific Summary: Vol. 2: Managing Future Risks, Office of Science and Technology, London 2004b.
Filatova, T., Parker, D. C., and Veen van der, A.: The Implications of Skewed Risk Perception for a Dutch Coastal Land Market: Insights from an Agent-Based Computational Economics Model, Agr. Resour. Econom. Rev., 40, 405–423, 2011.
Fraedrich, K., Blender, R., and Zhu, X.: Continuum climate variability: Long-term memory scaling, and 1/f-noise, Int. J. Modern Phys. B, 23, 5403–5416, 2009.
Geris, J., Ewen, J., and O'Connell, E.: Natural hydrological variability and its effect on flood impact of land use and management change: Insights from a multiscale monitoring experiment, J. Hydrol., submitted, 2013.
Grelot, F. and Barreteau, O.: Simulation of Resilience of an Insurance System to Flood Risk, Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany, 1–5 July, 8 pp., 2012.
Grothmann, T. and Reusswig, F.: People at Risk of Flooding: Why Some Residents Take Precautionary Action While Others Do Not, Nat. Hazards, 38, 101–120, 2006.
Gulati, G. J., Hadlock, C. R., and Gainsborough, J. F.: VODYS: An Agent-Based Model for Exploring Campaign Dynamics, Soc. Sci. Comput. Rev., 29, 250–272, 2011.
Hall, J. W., Evans, E. P., Penning-Rowsell, E. C., Sayers, P. B., Thorne, C. R., and Saul, A. J.: Quantified scenarios analysis of drivers and impacts of changing flood risk in England and Wales: 2030–2100, Global Environm. Change Part B, 5, 51–65, https://doi.org/10.1016/j.hazards.2004.04.002, 2003.
Hamill, L. and Gilbert, N.: Social Circles: A Simple Structure for Agent-Based Social Network Models, in: Journal of Artificial Societies and Social Simulation, Vol. 12, available at: http://jasss.soc.surrey.ac.uk/12/2/3.html (last access: 20 June 2013), 2009.
Harries, T. and Penning-Rowsell, E.: Victim pressure, institutional inertia and climate change adaptation: The case of flood risk, Global Environ. Change, 21, 188–197, 2011.
HM Treasury: The Green Book: Appraisal and Evaluation in Central Government, The Stationery Office, London, 118 pp., 2011.
Hosking, J. R. M. and Wallis, J. R.: Regional frequency analysis: an approach based on L-moments, Cambridge University Press, Cambridge, UK, 1997.
IPCC: Summary for Policymakers., in: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, edited by: Field, C. B., Barros, V., Stocker, T. F., Qin, D., Dokken, D. J., Ebi, K. L., Mastrandrea, M. D., Mach, K. J., Plattner, G.-K., Allen, S. K., Tignor, M., and Midgley, P. M., Cambridge University Press, Cambridge, UK, and New York, NY, USA, 1–19, 2012.
Irvin, R. A. and Stansbury, J.: Citizen Participation in Decision Making: Is It Worth the Effort?, Public Administration Review, 64, 55–65, 2004.
Irwin, E. G.: New directions for urban economic models of land use change: Incorporating spatial dynamics and heterogeneity, J. Reg. Sci., 50, 65–91, https://doi.org/10.1111/j.1467-9787.2009.00655.x, 2010.
Johnson, C. L. and Priest, S. J.: Flood Risk Management in England: A Changing Landscape of Risk Responsibility?, Int. J. Water Resour. Develop., 24, 513–525, 2008.
Johnson, C., Penning-Rowsell, E., and Parker, D.: Natural and imposed injustices: the challenges in implementing "fair" flood risk management policy in England, Geogr. J., 173, 374–390, 2007.
Johnson, F., Westra, S., Sharma, A., and Pitman, A. J.: An Assessment of GCM Skill in Simulating Persistence across Multiple Time Scales, J. Clim., 24, 3609–3623, https://doi.org/10.1175/2011JCLI3732.1, 2011.
Jones, P. D., Lister, D. H., Wilby, R. L., and Kostopoulou, E.: Extended riverflow reconstructions for England and Wales, 1865–2002, Int. J. Climatol., 26, 219–231, https://doi.org/10.1002/joc.1252, 2006.
Kellens, W., Terpstra, T., and De Maeyer, P.: Perception and Communication of Flood Risks: A Systematic Review of Empirical Research, Risk Analysis, 33, 24–49, 2013.
Kim, J. E., Rheem, S. K. K., and Jeong, C. H.: Disaster Management of Local Government: Comparison between the UK and South Korea, Korea Association for Public Administration 2012 International Conference, Seoul, Korea, 27–29 June, 16 pp., 2012.
Koutsoyiannis, D.: Hurst-Kolmogorov Dynamics and Uncertainty, JAWRA J. Am. Water Resour. Assoc., 47, 481–495, 2011.
Krieger, K.: Norms, structures, procedures and variety in risk-based governance: the case of flood management in Germany and England, 4th Biennal Conference of the ECPR Standing Group on Regulation & Governance at the University of Exeter, 27–29 June, 2012.
Krieger, K.: The limits and variety of risk-based governance: The case of flood management in Germany and England, Regul. Govern., 7, 236–257, 2013.
Kurtz, C. F. and Snowden, D. J.: The new dynamics of strategy: Sense-making in a complex and complicated world, IBM Systems J., 42, 462–483, 2003.
Lane, S. N.: Climate change and the summer 2007 floods in the UK, Geography., 93, 91–97, 2008.
Lara, A., Saur\'i, D., Ribas, A., and Pavón, D.: Social perceptions of floods and flood management in a Mediterranean area (Costa Brava, Spain), Nat. Hazards Earth Syst. Sci., 10, 2081–2091, https://doi.org/10.5194/nhess-10-2081-2010, 2010.
Lavery, S. and Donovan, B.: Flood risk management in the Thames Estuary looking ahead 100 years, Philos. Trans. Royal Soc. A, 363, 1455–1474, 2005.
Lempert, R.: Agent-based modeling as organizational and public policy simulators, Proc. Natl. Acad. Sci. USA, 99, 7195–7196, 2002.
Ligtenberg, A., Wachowicz, M., Bregt, A. K., Beulens, A., and Kettenis, D. L.: A design and application of a multi-agent system for simulation of multi-actor spatial planning, J. Environ. Manage., 72, 43–55, 2004.
Liu, J., Dietz, T., Carpenter, S. R., Alberti, M., Folke, C., Moran, E., Pell, A. N., Deadman, P., Kratz, T., Lubchenco, J., Ostrom, E., Ouyang, Z., Provencher, W., Redman, C. L., Schneider, S. H., and Taylor, W. W.: Complexity of Coupled Human and Natural Systems, Science, 317, 1513–1516, 2007a.
Liu, J., Dietz, T., Carpenter, S. R., Folke, C., Alberti, M., Redman, C. L., Schneider, S. H., Ostrom, E., Pell, A. N., Lubchenco, J., Taylor, W. W., Ouyang, Z., Deadman, P., Kratz, T., and Provencher, W.: Coupled Human and Natural Systems, AMBIO: A J. Human Environ., 36, 639–649, 2007b.
Matalas, N.: Stochastic Hydrology in the Context of Climate Change, Clim. Change, 37, 89–101, 1997.
Matthews, R., Gilbert, N., Roach, A., Polhill, J. G., and Gotts, N.: Agent-based land-use models: a review of applications, Landscape Ecol., 22, 1447–1459, https://doi.org/10.1007/s10980-007-9135-1, 2007.
Mauget, S. A.: Intra- to Multidecadal Climate Variability over the Continental United States: 1932–99, J. Climate, 16, 2215–2231, https://doi.org/10.1175/2751.1, 2003a.
Mauget, S. A.: Multidecadal Regime Shifts in U.S. Streamflow, Precipitation, and Temperature at the End of the Twentieth Century, J. Climate, 16, 3905–3916, 2003b.
McEwen, L. and Jones, O.: Building local/lay flood knowledges into community flood resilience planning after the July 2007 floods, Gloucestershire, UK, Hydrol. Res., 43, 675–688, 2012.
McEwen, L., Krause, F., Hansen, J. G., and Jones, O.: Flood histories, flood memories and informal flood knowledge in the development of community resilience to future flood risk, BHS Eleventh National Symposium, Hydrology for a changing world, Dundee, 9–11 July, 2012a.
McEwen, L. J., Krause, K., Jones, O., and Garde-Hansen, J.: Sustainable flood memories, informal knowledges and the development of community resilience to future flood risk, WIT Press, Ashurst, UK, 253–264, 2012b.
Merz, B., Kreibich, H., Schwarze, R., and Thieken, A.: Review article "Assessment of economic flood damage", Nat. Hazards Earth Syst. Sci., 10, 1697–1724, https://doi.org/10.5194/nhess-10-1697-2010, 2010.
Mesa, O. J., Gupta, V. K., and O'Connell, P. E.: Dynamical System Exploration of the Hurst Phenomenon in Simple Climate Models, in: Extreme Events and Natural Hazards: The Complexity Perspective, American Geophysical Union, Washington D. C., 209–230, 2012.
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., and Stouffer, R. J.: Stationarity Is Dead: Whither Water Management?, 5863, 573–574, 2008.
Monticino, M., Acevedo, M., Callicott, B., Cogdill, T., and Lindquist, C.: Coupled human and natural systems: A multi-agent-based approach, Environ. Modell. Softw., 22, 656–663, 2007.
Moss, S., Downing, T., and Rouchier, J.: Demonstrating the Role of Stakeholder Participation: An Agent Based Social Simulation Model of Water Demand Policy and Response, Centre for Policy Modelling, Manchester Metropolitan University, Manchester, UK, 2001.
Munich, Re: Natural catastrophes 2007 Analyses, assessments, positions Munich, 54, 2008.
Naess, L. O., Bang, G., Eriksen, S., and Vevatne, J.: Institutional adaptation to climate change: Flood responses at the municipal level in Norway, Global Environ. Change, 15, 125–138, 2005.
Nancarrow, B. E. and Syme, G. J.: Fairness principles in allocating water: integrating views of different agents, International Congress "Complexity and Integrated Resources management", Univ. Osnabrück, 14–17 June, 172-176, 2004.
Ntegeka, V. and Willems, P.: Trends and multidecadal oscillations in rainfall extremes, based on a more than 100-year time series of 10 min rainfall intensities at Uccle, Belgium, Water Resour. Res., 44, W07402, https://doi.org/10.1029/2007WR006471, 2008.
O'Connell, P. E.: Catchments as coupled human and natural systems, Conference on Integrated Assessment of Water Resources and Global Change: A North-South Analysis, Bonn, Germany, 23–25 February, 2005.
O'Connell, E., Blenkinsop, S., O'Donnell, G., and Hall, J.: Evaluating strategies for adaptation investment in a highly variable climate., BHS Third International Symposium: Role of Hydrology in Managing Consequences of a Changing Global Environment, Newcastle upon Tyne, 19–23 July, 552–557, 2010a.
O'Connell, E., Ewen, J., and O'Donnell, G.: Strategic Overview of Land Use Management in the Context of Catchment Flood Risk Management Planning, in: Flood Risk Science and Management, edited by: Pender, G. and Faulkner, H., Wiley-Blackwell, 2010b.
O'Connell, P. E.: A simple stochastic modelling of Hurst's Law, IAHS Mathematical Models in Hydrology Symposium, Warsaw, 169–187, 1971.
O'Connell, P. E.: Stochastic modeling of long-term persistence in stream flow sequences, Imperial College of Science and Technology, London, 1974.
O'Connell, P. E. and O'Donnell, G. M.: Modelling Water Resources and Flood Risk Management Adaptation in a Highly Variable Climate: Is Stationarity Really Dead?, Hydrol. Sci. J., submitted, 2013.
O'Connell, P. E., Ewen, J., O'Donnell, G., and Quinn, P.: Is there a link between agricultural land-use management and flooding?, Hydrol. Earth Syst. Sci., 11, 96–107, https://doi.org/10.5194/hess-11-96-2007, 2007.
O'Neill, J. and O'Neill, M.: Social justice and the future of flood insurance, Joseph Rowntree Foundation, York, 2012.
Parker, D. J.: Floodplain development policy in England and Wales, Appl. Geogr., 15, 341–363, 1995.
Parliamentary Select Committee: Evidence from the Local Government Association (LGA) on Flooding, House of Commons, London, 2013.
Pattison, I. and Lane, S. N.: The relationship between Lamb weather types and long-term changes in flood frequency, River Eden, UK, Int. J. Climatol., 32, 1971–1989, 2012.
Pearce, D.: Cost benefit analysis and environmental policy, Oxf. Rev. Econ. Policy, 14, 84–100, 1998.
Pellizzari, P.: Complexities and simplicity: a review of agent-based artificial markets, Giornata di studi in onore di Giovanni Castellani, Venice, 26 September, 211–221, 2005.
Penning-Rowsell, E., Johnson, C., and Tunstall, S.: "Signals" from pre-crisis discourse: Lessons from UK flooding for global environmental policy change?, Global Environ. Change, 16, 323–339, 2006.
Porter, J. and Demeritt, D.: Flood-risk management, mapping, and planning: the institutional politics of decision support in England, Environ. Plann.-Part A, 44, 2359–2378, 2012.
Raubal, M.: Ontology and epistemology for agent-based wayfinding simulation, Int. J. Geogr. Inform. Sci., 15, 653–665, 2001.
Robson, A. J., Jones, T. K., Reed, D. W., and Bayliss, A. C.: A study of national trend and variation in UK floods, Int. J. Climatol., 18, 165–182, 1998.
Scolobig, A., Marchi, B., and Borga, M.: The missing link between flood risk awareness and preparedness: findings from case studies in an Alpine Region, Nat. Hazards, 63, 499–520, https://doi.org/10.1007/s11069-012-0161-1, 2012.
Sivapalan, M., Savenije, H. H. G., and Blöschl, G.: Socio-hydrology: A new science of people and water, Hydrol. Process., 26, 1270–1276, 2012.
Svensson, C. and Jones, D. A.: Dependence between extreme sea surge, river flow and precipitation in eastern Britain, Int. J. Climatol., 22, 1149–1168, 2002.
ten Brinke, W. B. M., Saeijs, G. E. M., Helsloot, I., and van Alphen, J.: Safety chain approach in flood risk management, Proc. ICE-Municipal Engineer, 161, 93–102, 2008.
Verdon, D. C. and Franks, S. W.: Long-term behaviour of ENSO: Interactions with the PDO over the past 400 years inferred from paleoclimate records, Geophys. Res. Lett., 33, L06712, https://doi.org/10.1029/2005GL025052, 2006.
Villarini, G., Smith, J. A., Vitolo, R., and Stephenson, D. B.: On the temporal clustering of US floods and its relationship to climate teleconnection patterns, Int. J. Climatol., 33, 629–640, https://doi.org/10.1002/joc.3458, 2013.
Vojinović, Z. and Abbott, M. B.: Flood risk and social justice: From quantitative to qualitative flood risk assessment and mitigation, IWA Publishing (International Water Assoc), London, 2012.
Wachinger, G., Renn, O., Begg, C., and Kuhlicke, C.: The Risk Perception Paradox–-Implications for Governance and Communication of Natural Hazards, Risk Anal., 33, 1049–1065, https://doi.org/10.1111/j.1539-6924.2012.01942.x, 2013.
Whatmore, S. J. and Landström, C.: Flood apprentices: an exercise in making things public, Econ. Soc., 40, 582–610, 2011.
Wiering, M. A. and Driessen, P. P. J.: Beyond the art of diking: interactive policy on river management in The Netherlands, Water Pol., 3, 283–296, 2001.
Wilkinson, M. E., Quinn, P. F., and Welton, P.: Runoff management during the September 2008 floods in the Belford catchment, Northumberland, J. Flood Risk Manage. 3, 285–295, 2010.
