Articles | Volume 21, issue 8
https://doi.org/10.5194/hess-21-4131-2017
© 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-21-4131-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
18 Aug 2017
Research article |
| 18 Aug 2017
Comparison of the impacts of urban development and climate change on exposing European cities to pluvial flooding
Department of Management Engineering, Technical University of Denmark,
Kgs. Lyngby, 2800, Denmark
Nanna Høegh Ravn
LNH Water, Tikoeb, 3080, Denmark
Karsten Arnbjerg-Nielsen
Department of Environmental Engineering, Technical University of
Denmark, Kgs. Lyngby, 2800, Denmark
Henrik Madsen
DHI, Hoersholm, 2970, Denmark
Martin Drews
Department of Management Engineering, Technical University of Denmark,
Kgs. Lyngby, 2800, Denmark
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Scientists and engineers need better indices to frame the hydrologic effects of land development. Existing approaches are not able to reflect the interactions due to the spatial arrangement of distinct land patches, which affect how much runoff is generated and how fast it can travel downstream, impacting flood response. Our novel, GIS-based modeling framework explicitly considers these aspects and is applicable to a wide range of problems, including peak-flow predictions in ungauged basins.
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We present a new approach to modeling extreme regional rainfall by considering the spatial structure of extreme events. The developed models allow a probabilistic exploration of how the regional drainage network may respond to extreme rainfall events and provide a foundation for how future risks may be better estimated.
Matthew Preisser, Paola Passalacqua, R. Patrick Bixler, and Julian Hofmann
Hydrol. Earth Syst. Sci., 26, 3941–3964, https://doi.org/10.5194/hess-26-3941-2022, https://doi.org/10.5194/hess-26-3941-2022, 2022
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There is rising concern in numerous fields regarding the inequitable distribution of human risk to floods. The co-occurrence of river and surface flooding is largely excluded from leading flood hazard mapping services, therefore underestimating hazards. Using high-resolution elevation data and a region-specific social vulnerability index, we developed a method to estimate flood impacts at the household level in near-real time.
Vincent Pons, Rasmus Benestad, Edvard Sivertsen, Tone Merete Muthanna, and Jean-Luc Bertrand-Krajewski
Hydrol. Earth Syst. Sci., 26, 2855–2874, https://doi.org/10.5194/hess-26-2855-2022, https://doi.org/10.5194/hess-26-2855-2022, 2022
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Different models were developed to increase the temporal resolution of precipitation time series to minutes. Their applicability under climate change and their suitability for producing input time series for green infrastructure (e.g. green roofs) modelling were evaluated. The robustness of the model was validated against a range of European climates in eight locations in France and Norway. The future hydrological performances of green roofs were evaluated in order to improve design practice.
Elhadi Mohsen Hassan Abdalla, Vincent Pons, Virginia Stovin, Simon De-Ville, Elizabeth Fassman-Beck, Knut Alfredsen, and Tone Merete Muthanna
Hydrol. Earth Syst. Sci., 25, 5917–5935, https://doi.org/10.5194/hess-25-5917-2021, https://doi.org/10.5194/hess-25-5917-2021, 2021
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This study investigated the potential of using machine learning algorithms as hydrological models of green roofs across different climatic condition. The study provides comparison between conceptual and machine learning algorithms. Machine learning models were found to be accurate in simulating runoff from extensive green roofs.
Yang Yang and Ting Fong May Chui
Hydrol. Earth Syst. Sci., 25, 5839–5858, https://doi.org/10.5194/hess-25-5839-2021, https://doi.org/10.5194/hess-25-5839-2021, 2021
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This study uses explainable machine learning methods to model and interpret the statistical correlations between rainfall and the discharge of urban catchments with sustainable urban drainage systems. The resulting models have good prediction accuracies. However, the right predictions may be made for the wrong reasons as the model cannot provide physically plausible explanations as to why a prediction is made.
Zhengzheng Zhou, James A. Smith, Mary Lynn Baeck, Daniel B. Wright, Brianne K. Smith, and Shuguang Liu
Hydrol. Earth Syst. Sci., 25, 4701–4717, https://doi.org/10.5194/hess-25-4701-2021, https://doi.org/10.5194/hess-25-4701-2021, 2021
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The role of rainfall space–time structure in flood response is an important research issue in urban hydrology. This study contributes to this understanding in small urban watersheds. Combining stochastically based rainfall scenarios with a hydrological model, the results show the complexities of flood response for various return periods, implying the common assumptions of spatially uniform rainfall in urban flood frequency are problematic, even for relatively small basin scales.
Yangzi Qiu, Igor da Silva Rocha Paz, Feihu Chen, Pierre-Antoine Versini, Daniel Schertzer, and Ioulia Tchiguirinskaia
Hydrol. Earth Syst. Sci., 25, 3137–3162, https://doi.org/10.5194/hess-25-3137-2021, https://doi.org/10.5194/hess-25-3137-2021, 2021
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Our original research objective is to investigate the uncertainties of the hydrological responses of nature-based solutions (NBSs) that result from the multiscale space variability in both the rainfall and the NBS distribution. Results show that the intersection effects of spatial variability in rainfall and the spatial arrangement of NBS can generate uncertainties of peak flow and total runoff volume estimations in NBS scenarios.
Kaihua Guo, Mingfu Guan, and Dapeng Yu
Hydrol. Earth Syst. Sci., 25, 2843–2860, https://doi.org/10.5194/hess-25-2843-2021, https://doi.org/10.5194/hess-25-2843-2021, 2021
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This study presents a comprehensive review of models and emerging approaches for predicting urban surface water flooding driven by intense rainfall. It explores the advantages and limitations of existing models and identifies major challenges. Issues of model complexities, scale effects, and computational efficiency are also analysed. The results will inform scientists, engineers, and decision-makers of the latest developments and guide the model selection based on desired objectives.
Everett Snieder, Karen Abogadil, and Usman T. Khan
Hydrol. Earth Syst. Sci., 25, 2543–2566, https://doi.org/10.5194/hess-25-2543-2021, https://doi.org/10.5194/hess-25-2543-2021, 2021
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Flow distributions are highly skewed, resulting in low prediction accuracy of high flows when using artificial neural networks for flood forecasting. We investigate the use of resampling and ensemble techniques to address the problem of skewed datasets to improve high flow prediction. The methods are implemented both independently and in combined, hybrid techniques. This research presents the first analysis of the effects of combining these methods on high flow prediction accuracy.
Ico Broekhuizen, Günther Leonhardt, Jiri Marsalek, and Maria Viklander
Hydrol. Earth Syst. Sci., 24, 869–885, https://doi.org/10.5194/hess-24-869-2020, https://doi.org/10.5194/hess-24-869-2020, 2020
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Urban drainage models are usually calibrated using a few events so that they accurately represent a real-world site. This paper compares 14 single- and two-stage strategies for selecting these events and found significant variation between them in terms of model performance and the obtained values of model parameters. Calibrating parameters for green and impermeable areas in two separate stages improved model performance in the validation period while making calibration easier and faster.
Xuehong Zhu, Qiang Dai, Dawei Han, Lu Zhuo, Shaonan Zhu, and Shuliang Zhang
Hydrol. Earth Syst. Sci., 23, 3353–3372, https://doi.org/10.5194/hess-23-3353-2019, https://doi.org/10.5194/hess-23-3353-2019, 2019
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Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering human mobility. Several scenarios, including diverse flooding types and various responses of residents to flooding, were considered.
Joong Gwang Lee, Christopher T. Nietch, and Srinivas Panguluri
Hydrol. Earth Syst. Sci., 22, 2615–2635, https://doi.org/10.5194/hess-22-2615-2018, https://doi.org/10.5194/hess-22-2615-2018, 2018
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This paper demonstrates an approach to spatial discretization for analyzing green infrastructure (GI) using SWMM. Besides DCIA, pervious buffers should be identified for GI modeling. Runoff contributions from different spatial components and flow pathways would impact GI performance. The presented approach can reduce the number of calibration parameters and apply scale–independently to a watershed scale. Hydrograph separation can add insights for developing GI scenarios.
Elena Cristiano, Marie-Claire ten Veldhuis, Santiago Gaitan, Susana Ochoa Rodriguez, and Nick van de Giesen
Hydrol. Earth Syst. Sci., 22, 2425–2447, https://doi.org/10.5194/hess-22-2425-2018, https://doi.org/10.5194/hess-22-2425-2018, 2018
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In this work we investigate the influence rainfall and catchment scales have on hydrological response. This problem is quite relevant in urban areas, where the response is fast due to the high degree of imperviousness. We presented a new approach to classify rainfall variability in space and time and use this classification to investigate rainfall aggregation effects on urban hydrological response. This classification allows the spatial extension of the main core of the storm to be identified.
Suresh Hettiarachchi, Conrad Wasko, and Ashish Sharma
Hydrol. Earth Syst. Sci., 22, 2041–2056, https://doi.org/10.5194/hess-22-2041-2018, https://doi.org/10.5194/hess-22-2041-2018, 2018
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The study examines the impact of higher temperatures expected in a future climate on how rainfall varies with time during severe storm events. The results show that these impacts increase future flood risk in urban environments and that current design guidelines need to be adjusted so that effective adaptation measures can be implemented.
Stephanie Clark, Ashish Sharma, and Scott A. Sisson
Hydrol. Earth Syst. Sci., 22, 1793–1810, https://doi.org/10.5194/hess-22-1793-2018, https://doi.org/10.5194/hess-22-1793-2018, 2018
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This study investigates global patterns relating urban river flood impacts to socioeconomic development and changing hydrologic conditions, and comparisons are provided between 98 individual cities. This paper condenses and communicates large amounts of information to accelerate the understanding of relationships between local urban conditions and global processes, and to potentially motivate knowledge transfer between decision-makers facing similar circumstances.
Abdellah Ichiba, Auguste Gires, Ioulia Tchiguirinskaia, Daniel Schertzer, Philippe Bompard, and Marie-Claire Ten Veldhuis
Hydrol. Earth Syst. Sci., 22, 331–350, https://doi.org/10.5194/hess-22-331-2018, https://doi.org/10.5194/hess-22-331-2018, 2018
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This paper proposes a two-step investigation to illustrate the extent of scale effects in urban hydrology. First, fractal tools are used to highlight the scale dependency observed within GIS data inputted in urban hydrological models. Then an intensive multi-scale modelling work was carried out to confirm effects on model performances. The model was implemented at 17 spatial resolutions ranging from 100 to 5 m. Results allow the understanding of scale challenges in hydrology modelling.
Elena Cristiano, Marie-Claire ten Veldhuis, and Nick van de Giesen
Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, https://doi.org/10.5194/hess-21-3859-2017, 2017
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In the last decades, new instruments were developed to measure rainfall and hydrological processes at high resolution. Weather radars are used, for example, to measure how rainfall varies in space and time. At the same time, new models were proposed to reproduce and predict hydrological response, in order to prevent flooding in urban areas. This paper presents a review of our current knowledge of rainfall and hydrological processes in urban areas, focusing on their variability in time and space.
Chiara Arrighi, Hocine Oumeraci, and Fabio Castelli
Hydrol. Earth Syst. Sci., 21, 515–531, https://doi.org/10.5194/hess-21-515-2017, https://doi.org/10.5194/hess-21-515-2017, 2017
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In developed countries, the majority of fatalities during floods occurs as a consequence of inappropriate high-risk behaviour such as walking or driving in floodwaters. This work addresses pedestrians' instability in floodwaters. It analyses both the contribution of flood and human physical characteristics in the loss of stability highlighting the key role of subject height (submergence) and flow regime. The method consists of a re-analysis of experiments and numerical modelling.
Hjalte Jomo Danielsen Sørup, Stylianos Georgiadis, Ida Bülow Gregersen, and Karsten Arnbjerg-Nielsen
Hydrol. Earth Syst. Sci., 21, 345–355, https://doi.org/10.5194/hess-21-345-2017, https://doi.org/10.5194/hess-21-345-2017, 2017
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In this study we propose a methodology changing present-day precipitation time series to reflect future changed climate. Present-day time series have a much finer resolution than what is provided by climate models and thus have a much broader application range. The proposed methodology is able to replicate most expectations of climate change precipitation. These time series can be used to run fine-scale hydrological and hydraulic models and thereby assess the influence of climate change on them.
Tsun-Hua Yang, Gong-Do Hwang, Chin-Cheng Tsai, and Jui-Yi Ho
Hydrol. Earth Syst. Sci., 20, 4731–4745, https://doi.org/10.5194/hess-20-4731-2016, https://doi.org/10.5194/hess-20-4731-2016, 2016
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Taiwan continues to suffer from floods. This study proposes the integration of rainfall thresholds and ensemble precipitation forecasts to provide probabilistic urban inundation forecasts. Utilization of ensemble precipitation forecasts can extend forecast lead times to 72 h, preceding peak flows and allowing response agencies to take necessary preparatory measures. This study also develops a hybrid of real-time observation and rainfall forecasts to improve the first 24 h inundation forecasts.
Christopher A. Sanchez, Benjamin L. Ruddell, Roy Schiesser, and Venkatesh Merwade
Hydrol. Earth Syst. Sci., 20, 1289–1299, https://doi.org/10.5194/hess-20-1289-2016, https://doi.org/10.5194/hess-20-1289-2016, 2016
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The use of authentic learning activities is especially important for place-based geosciences like hydrology, where professional breadth and technical depth are critical for practicing hydrologists. The current study found that integrating computerized learning content into the learning experience, using only a simple spreadsheet tool and readily available hydrological data, can effectively bring the "real world" into the classroom and provide an enriching educational experience.
G. Bruni, R. Reinoso, N. C. van de Giesen, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Hydrol. Earth Syst. Sci., 19, 691–709, https://doi.org/10.5194/hess-19-691-2015, https://doi.org/10.5194/hess-19-691-2015, 2015
P. Licznar, C. De Michele, and W. Adamowski
Hydrol. Earth Syst. Sci., 19, 485–506, https://doi.org/10.5194/hess-19-485-2015, https://doi.org/10.5194/hess-19-485-2015, 2015
J. Herget, T. Roggenkamp, and M. Krell
Hydrol. Earth Syst. Sci., 18, 4029–4037, https://doi.org/10.5194/hess-18-4029-2014, https://doi.org/10.5194/hess-18-4029-2014, 2014
F. Beck and A. Bárdossy
Hydrol. Earth Syst. Sci., 17, 4851–4863, https://doi.org/10.5194/hess-17-4851-2013, https://doi.org/10.5194/hess-17-4851-2013, 2013
D. H. Trinh and T. F. M. Chui
Hydrol. Earth Syst. Sci., 17, 4789–4801, https://doi.org/10.5194/hess-17-4789-2013, https://doi.org/10.5194/hess-17-4789-2013, 2013
J. Y. Wu, J. R. Thompson, R. K. Kolka, K. J. Franz, and T. W. Stewart
Hydrol. Earth Syst. Sci., 17, 4743–4758, https://doi.org/10.5194/hess-17-4743-2013, https://doi.org/10.5194/hess-17-4743-2013, 2013
H. Ozdemir, C. C. Sampson, G. A. M. de Almeida, and P. D. Bates
Hydrol. Earth Syst. Sci., 17, 4015–4030, https://doi.org/10.5194/hess-17-4015-2013, https://doi.org/10.5194/hess-17-4015-2013, 2013
Y. Seo, N.-J. Choi, and A. R. Schmidt
Hydrol. Earth Syst. Sci., 17, 3473–3483, https://doi.org/10.5194/hess-17-3473-2013, https://doi.org/10.5194/hess-17-3473-2013, 2013
J. Epting, F. Händel, and P. Huggenberger
Hydrol. Earth Syst. Sci., 17, 1851–1869, https://doi.org/10.5194/hess-17-1851-2013, https://doi.org/10.5194/hess-17-1851-2013, 2013
H.-Y. Shen and L.-C. Chang
Hydrol. Earth Syst. Sci., 17, 935–945, https://doi.org/10.5194/hess-17-935-2013, https://doi.org/10.5194/hess-17-935-2013, 2013
M. H. Spekkers, M. Kok, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Hydrol. Earth Syst. Sci., 17, 913–922, https://doi.org/10.5194/hess-17-913-2013, https://doi.org/10.5194/hess-17-913-2013, 2013
J. J. Lian, K. Xu, and C. Ma
Hydrol. Earth Syst. Sci., 17, 679–689, https://doi.org/10.5194/hess-17-679-2013, https://doi.org/10.5194/hess-17-679-2013, 2013
H. T. L. Huong and A. Pathirana
Hydrol. Earth Syst. Sci., 17, 379–394, https://doi.org/10.5194/hess-17-379-2013, https://doi.org/10.5194/hess-17-379-2013, 2013
S. Oraei Zare, B. Saghafian, and A. Shamsai
Hydrol. Earth Syst. Sci., 16, 4531–4542, https://doi.org/10.5194/hess-16-4531-2012, https://doi.org/10.5194/hess-16-4531-2012, 2012
R. Archetti, A. Bolognesi, A. Casadio, and M. Maglionico
Hydrol. Earth Syst. Sci., 15, 3115–3122, https://doi.org/10.5194/hess-15-3115-2011, https://doi.org/10.5194/hess-15-3115-2011, 2011
Y.-M. Chiang, L.-C. Chang, M.-J. Tsai, Y.-F. Wang, and F.-J. Chang
Hydrol. Earth Syst. Sci., 15, 185–196, https://doi.org/10.5194/hess-15-185-2011, https://doi.org/10.5194/hess-15-185-2011, 2011
I. Andrés-Doménech, J. C. Múnera, F. Francés, and J. B. Marco
Hydrol. Earth Syst. Sci., 14, 2057–2072, https://doi.org/10.5194/hess-14-2057-2010, https://doi.org/10.5194/hess-14-2057-2010, 2010
Yen-Ming Chiang, Li-Chiu Chang, Meng-Jung Tsai, Yi-Fung Wang, and Fi-John Chang
Hydrol. Earth Syst. Sci., 14, 1309–1319, https://doi.org/10.5194/hess-14-1309-2010, https://doi.org/10.5194/hess-14-1309-2010, 2010
Cited articles
Angel, S., Parent, J., Civco, D. L., Blei, A., and Potere, D.: The dimensions of global urban expansion: estimates and projections for all countries, 2000–2050, Prog. Plan., 75, 53–107, https://doi.org/10.1016/j.progress.2011.04.001, 2011.
Arnbjerg-Nielsen, K.: Past, present, and future design of urban drainage systems with focus on Danish experiences, Water Sci. Technol., 63, 527–535, https://doi.org/10.2166/wst.2011.253, 2011.
Arnbjerg-Nielsen, K.: Quantification of climate change effects on extreme precipitation used for high resolution hydrologic design, Urban Water J., 9, 57–65, https://doi.org/10.1080/1573062X.2011.630091, 2012.
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.
Bauer, M. E., Doyle, J. K., and Heinert, N. J.: Impervious surface mapping using satellite remote sensing, Int. Geosci. Remote Se., 2334–2336, https://doi.org/10.1109/IGARSS.2002.1026536, 2002.
Bauer, M. E., Loffelholz, B. C., and Wilson, B.: Estimating and mapping impervious surface area by regression analysis of Landsat imagery, in: Remote Sensing of Impervious Surfaces, CRC Press, Boca Raton, p. 454, 2008.
BMLFUW: eHYD, Bundesministerium für Land- und Forstwirtschaft, Umwelt und Wasserwithtschaft, available at: http://ehyd.gv.at/, last access: 15 January, 2016.
BMT Group Ltd.: TUFLOW, available at: http://www.tuflow.com/Default.aspx, last access: 15 January, 2016.
Butler, D. and Davies, J. W.: Urban drainage, 3rd Edn., Spon Press, London, New York, 2011.
Chocat, B., Ashley, R., Marsalek, J., Matos, M. R., Rauch, W., Schilling, W., and Urbonas, B.: Toward the Sustainable Management of Urban Storm-Water, Indoor Built Environ., 16, 273–285, https://doi.org/10.1177/1420326X07078854, 2007.
Chormanski, J., Van De Voorde, T., De Roeck, T., Batelaan, O., and Canters, F.: Improving distributed run-off prediction in urbanized catchments with remote sensing based estimates of impervious surface cover, Sensors, 8, 910–932, 2008.
Chow, V. T., Maidment, D. R., and Mays, L. W.: Applied hydrology, McGraw-Hill series in water resources and environmental engineering, McGraw-Hill, New York, 1988.
CORINE: CORINE Land Cover (CLC) Inventory, available at: http://www.eea.europa.eu/publications/COR0-landcover (last access: 15 January 2016), 2012.
Coste, C. and Loudet, M.: L'assainissement en milieu urbain ou rural – Tome 1: Les réseaux et les ouvrages de retenue, Paris, France, 1987.
Dams, J., Dujardin, J., Reggers, R., Bashir, I., Canters, F., and Batelaan, O.: Mapping impervious surface change from remote sensing for hydrological modeling, J. Hydrol., 485, 84–95, https://doi.org/10.1016/j.jhydrol.2012.09.045, 2013.
EEA: Digital Elevation Model over Europe (EU-DEM), European Energy Agency, available at: http://www.eea.europa.eu/data-and-maps/data/eu-dem (last access: 15 January 2016), 2013.
ESGF: CORDEX @ IPSL, Earth System Grid Federation, available at: https://esgf-node.ipsl.upmc.fr/search/cordex-ipsl/, last access: 15 January, 2016.
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., Midgley, P. M.: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaption, A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, Cambridge University Press, New York, USA, 2012.
Flood, N.: Continuity of Reflectance Data between Landsat-7 ETM+ and Landsat-8 OLI, for Both Top-of-Atmosphere and Surface Reflectance: A Study in the Australian Landscape, Remote Sens., 6, 7952–7970, https://doi.org/10.3390/rs6097952, 2014.
Fowler, A. M. and Hennessy, K. J.: Potential impacts of global warming on the frequency and magnitude of heavy precipitation, Nat. Hazards, 11, 283–303, https://doi.org/10.1007/BF00613411, 1995.
Giorgi, F., Jones, C., and Asrar, G.: Addressing climate information needs at the regional level: The CORDEX framework, WMO Bull., 58, 175–183, 2009.
Green, W. and Ampt, G.: Studies on soil physics Part I – The flow of air and water through soils, J. Agric. Sci., 4, 1–24, 1911.
Gregersen, I. B., Madsen, H., Linde, J. J., and Arnbjerg-Nielsen, K.: Updated climate factors and rain intensities for dimensioning (Skrift nr. 30) (Opdaterede klimafaktorer og dimensionsgivende regnintensiteter), Spildevandskommiteen, 2014.
Hall, J., Arheimer, B., Borga, M., Brázdil, R., Claps, P., Kiss, A., Kjeldsen, T. R., Kriauciuniene, 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., Plavcovà, 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.
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in projections of regional precipitation change, Clim. Dynam. 37, 407–418, https://doi.org/10.1007/s00382-010-0810-6, 2011.
Henonin, J., Russo, B., Mark, O., and Gourbesville, P.: Real-time urban flood forecasting and modelling – a state of the art, J. Hydroinformatics, 15, 717–736, https://doi.org/10.2166/hydro.2013.132, 2013.
Hollis, G. E.: The effect of urbanization on floods of different recurrence interval, Water Resour. Res., 11, 431–435, https://doi.org/10.1029/WR011i003p00431, 1975.
Horton, R. E.: The Rôle of infiltration in the hydrologic cycle, EOS T. Am. Geophys. Un., 14, 446–460, https://doi.org/10.1029/TR014i001p00446, 1933.
Intergovernmental Panel on Climate Change: Climate Change 2013 – The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 2014.
Jensen, M.: Rain-Runoff Parameters for Six Small Gauged Urban Catchments, Nordic Hydrol., 21, 165–184, 1990.
JRC: Dominant Soil Texture, Text-SRF-DOM raster data, available at: http://eusoils.jrc.ec.europa.eu/library/esdac/index.html, last access: 15 January, 2016.
Kaspersen, P., Fensholt, R., and Drews, M.: Using Landsat Vegetation Indices to Estimate Impervious Surface Fractions for European Cities, Remote Sens., 7, 8224–8249, https://doi.org/10.3390/rs70608224, 2015.
Larsen, A. N., Gregersen, I. B., Christensen, O. B., Linde, J. J., and Mikkelsen, P. S.: Potential future increase in extreme one-hour precipitation events over Europe due to climate change, Water Sci. Technol., 60, 2205–2216, https://doi.org/10.2166/wst.2009.650, 2009.
Leandro, J., Chen, A. S., Djordjević, S., and Savić, D. A.: Comparison of 1D/1D and 1D/2D Coupled (Sewer/Surface) Hydraulic Models for Urban Flood Simulation, J. Hydraul. Eng., 135, 495–504, https://doi.org/10.1061/(ASCE)HY.1943-7900.0000037, 2009.
Lu, D., Li, G., Kuang, W., and Moran, E.: Methods to extract impervious surface areas from satellite images, Int. J. Digit. Earth, 7, 93–112, https://doi.org/10.1080/17538947.2013.866173, 2014.
Maksimović, Č., Prodanović, D., Boonya-Aroonnet, S., Leitão, J. P., Djordjević, S., and Allitt, R.: Overland flow and pathway analysis for modelling of urban pluvial flooding, J. Hydraul. Res., 47, 512–523, https://doi.org/10.1080/00221686.2009.9522027, 2009.
Mark, O., Weesakul, S., Apirumanekul, C., Aroonnet, S. B., and Djordjević, S.: Potential and limitations of 1D modelling of urban flooding, J. Hydrol., 299, 284–299, https://doi.org/10.1016/j.jhydrol.2004.08.014, 2004.
Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J.-F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M., and van Vuuren, D. P. P.: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Clim. Change, 109, 213–241, https://doi.org/10.1007/s10584-011-0156-z, 2011.
MIKE powered by DHI: MIKE Flood, available at: https://www.mikepoweredbydhi.com/products/mike-flood, last access: 15 January, 2016.
Obermayer, A., Guenthert, F. W., Angermair, G., Tandler, R., Braunschmidt, S., and Milojevic, N.: Different approaches for modelling of sewer caused urban flooding, Water Sci. Technol., 62, 2175–2182, https://doi.org/10.2166/wst.2010.439, 2010.
Paludan, B., Nielsen, N. H., Brink-Kjær, A., Jense, L. N., Linde, J. J., and Mark, O.: The Climate Cookbook, 2nd Edn., Forskningsog udredningsprojekt nr. 19 Danva, ISBN 978-87-92651-04-4, 2011.
Parkinson, J. and Mark, O.: Urban stormwater management in developing countries, IWA Pub., London, 2005.
Poelmans, L., Van Rompaey, A., and Batelaan, O.: Coupling urban expansion models and hydrological models: How important are spatial patterns?, Land Use Policy, 27, 965–975, https://doi.org/10.1016/j.landusepol.2009.12.010, 2010.
Semadeni-Davies, A., Hernebring, C., Svensson, G., and Gustafsson, L.-G.: The impacts of climate change and urbanisation on drainage in Helsingborg, Sweden: Suburban stormwater, J. Hydrol., 350, 114–125, https://doi.org/10.1016/j.jhydrol.2007.11.006, 2008.
Skougaard Kaspersen, P., Høegh Ravn, N., Arnbjerg-Nielsen, K., Madsen, H., and Drews, M.: Influence of urban land cover changes and climate change for the exposure of European cities to flooding during high-intensity precipitation, Proc. IAHS, 370, 21–27, https://doi.org/10.5194/piahs-370-21-2015, 2015.
Stone, J. J., Paige, G. B., and Hawkins, R. H.: Rainfall Intensity-Dependent Infiltration Rates on Rangeland Rainfall Simulator Plots, T. ASABE, 51, 45–53, https://doi.org/10.13031/2013.24226, 2008.
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., Kriauciuniene, 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.
Tomicic, B.: Storm Water Runoff from Green Urban Areas – Modellers' Guideline, CRC for Water Sensitive Cities, DHI, 2015.
United Nations: World urbanization prospects: the 2014 revision?: highlights, Department of Economic and Social Affairs, Population Division, available at: https://esa.un.org/unpd/wup/publications/files/wup2014-highlights.pdf, 2014.
Urich, C. and Rauch, W.: Exploring critical pathways for urban water management to identify robust strategies under deep uncertainties, Water Res., 66, 374–389, https://doi.org/10.1016/j.watres.2014.08.020, 2014.
USDA: Urban Hydrology for Small Watersheds – TR-55 (No. Technical Release 55), United States Department of Agriculture, Natural Resources Conservation Service, Conservation Engineering Division, 1986.
USDA: Soil Infiltration Rates, United States Department of Agriculture, available at: http://qcode.us/codes/sacramentocounty/view.php?topic=14-14_10-14_10_110, 2016.
Verbeiren, B., Van De Voorde, T., Canters, F., Binard, M., Cornet, Y., and Batelaan, O.: Assessing urbanisation effects on rainfall-runoff using a remote sensing supported modelling strategy, Int. J. Appl. Earth Obs., 21, 92–102, https://doi.org/10.1016/j.jag.2012.08.011, 2013.
Weng, Q.: Modeling Urban Growth Effects on Surface Runoff with the Integration of Remote Sensing and GIS, Environ. Manage., 28, 737–748, https://doi.org/10.1007/s002670010258, 2001.
Weng, Q.: Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends, Remote Sens. Environ., 117, 34–49, https://doi.org/10.1016/j.rse.2011.02.030, 2012.
Willems, P., Olsson, J., Arnbjerg-Nielsen, K., Beecham, S., Pathirana, A., Bulow Gregersen, I., Madsen, H., and Nguyen, V.: Impacts of climate change on rainfall extremes and urban drainage systems, IWA, London, 2012.
