Articles | Volume 27, issue 8
https://doi.org/10.5194/hess-27-1627-2023
© Author(s) 2023. 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-27-1627-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methodology for constructing a flood-hazard map for a future climate
Yuki Kimura
CORRESPONDING AUTHOR
Risk Assessment Department, MS&AD InterRisk Research &
Consulting, Inc., 2-105 Kanda Awajicho, Chiyoda-ku, Tokyo 101-0063, Japan
Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
Yukiko Hirabayashi
Department of Civil Engineering, Shibaura Institute of Technology,
3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan
Yuki Kita
Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
Xudong Zhou
Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
Dai Yamazaki
Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
Related authors
Yuki Kimura, Yukiko Hirabayashi, and Dai Yamazaki
EGUsphere, https://doi.org/10.22541/essoar.170365204.46854879/v1, https://doi.org/10.22541/essoar.170365204.46854879/v1, 2024
Preprint archived
Short summary
Short summary
The limited number of ensemble members causes uncertainty in future climate predictions. To address this, using multiple simulations under a single future climate scenario can increase the sample size, but data availability is limited in the scenario-based future projection experiment of climate model intercomparison projects. Our proposed method integrates multiple climate scenarios at specific temperature increases, effectively reducing uncertainty in future flood hazard assessments globally.
Bernhard Lehner, Mira Anand, Etienne Fluet-Chouinard, Florence Tan, Filipe Aires, George H. Allen, Philippe Bousquet, Josep G. Canadell, Nick Davidson, Meng Ding, C. Max Finlayson, Thomas Gumbricht, Lammert Hilarides, Gustaf Hugelius, Robert B. Jackson, Maartje C. Korver, Liangyun Liu, Peter B. McIntyre, Szabolcs Nagy, David Olefeldt, Tamlin M. Pavelsky, Jean-Francois Pekel, Benjamin Poulter, Catherine Prigent, Jida Wang, Thomas A. Worthington, Dai Yamazaki, Xiao Zhang, and Michele Thieme
Earth Syst. Sci. Data, 17, 2277–2329, https://doi.org/10.5194/essd-17-2277-2025, https://doi.org/10.5194/essd-17-2277-2025, 2025
Short summary
Short summary
The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33 non-overlapping wetland classes, providing a static map of the world’s inland surface waters. It contains cell fractions of wetland extents per class at a grid cell resolution of ~500 m. The total combined extent of all classes including all inland and coastal waterbodies and wetlands of all inundation frequencies – that is, the maximum extent – covers 18.2 × 106 km2, equivalent to 13.4 % of total global land area.
Yanbin Qiu, Xudong Zhou, Jiaquan Wan, Tao Yang, Lvfei Zhang, Yuanzhuo Zhong, Leqi Shen, and Xinwu Ji
EGUsphere, https://doi.org/10.5194/egusphere-2024-4053, https://doi.org/10.5194/egusphere-2024-4053, 2025
Short summary
Short summary
Floods pose a significant risk to cities, so fast and accurate information is essential for disaster management. This study used social media images to assess flood levels by analyzing submerged buses, a reliable reference object. An advanced AI model (YOLOv8) trained on different datasets achieved high flood detection accuracy. The results provide a scalable solution for real-time flood monitoring, enhancing urban transportation safety, and supporting emergency planning.
Yuki Kimura, Yukiko Hirabayashi, and Dai Yamazaki
EGUsphere, https://doi.org/10.22541/essoar.170365204.46854879/v1, https://doi.org/10.22541/essoar.170365204.46854879/v1, 2024
Preprint archived
Short summary
Short summary
The limited number of ensemble members causes uncertainty in future climate predictions. To address this, using multiple simulations under a single future climate scenario can increase the sample size, but data availability is limited in the scenario-based future projection experiment of climate model intercomparison projects. Our proposed method integrates multiple climate scenarios at specific temperature increases, effectively reducing uncertainty in future flood hazard assessments globally.
Jingyu Lin, Peng Wang, Jinzhu Wang, Youping Zhou, Xudong Zhou, Pan Yang, Hao Zhang, Yanpeng Cai, and Zhifeng Yang
Earth Syst. Sci. Data, 16, 1137–1149, https://doi.org/10.5194/essd-16-1137-2024, https://doi.org/10.5194/essd-16-1137-2024, 2024
Short summary
Short summary
Our paper provides a repository comprising over 330 000 observations encompassing daily, weekly, and monthly records of surface water quality spanning the period 1980–2022. It included 18 distinct indicators, meticulously gathered at 2384 monitoring sites, ranging from inland locations to coastal and oceanic areas. This dataset will be very useful for researchers and decision-makers in the fields of hydrology, ecological studies, climate change, policy development, and oceanography.
Menaka Revel, Xudong Zhou, Prakat Modi, Jean-François Cretaux, Stephane Calmant, and Dai Yamazaki
Earth Syst. Sci. Data, 16, 75–88, https://doi.org/10.5194/essd-16-75-2024, https://doi.org/10.5194/essd-16-75-2024, 2024
Short summary
Short summary
As satellite technology advances, there is an incredible amount of remotely sensed data for observing terrestrial water. Satellite altimetry observations of water heights can be utilized to calibrate and validate large-scale hydrodynamic models. However, because large-scale models are discontinuous, comparing satellite altimetry to predicted water surface elevation is difficult. We developed a satellite altimetry mapping procedure for high-resolution river network data.
Md Safat Sikder, Jida Wang, George H. Allen, Yongwei Sheng, Dai Yamazaki, Chunqiao Song, Meng Ding, Jean-François Crétaux, and Tamlin M. Pavelsky
Earth Syst. Sci. Data, 15, 3483–3511, https://doi.org/10.5194/essd-15-3483-2023, https://doi.org/10.5194/essd-15-3483-2023, 2023
Short summary
Short summary
We introduce Lake-TopoCat to reveal detailed lake hydrography information. It contains the location of lake outlets, the boundary of lake catchments, and a wide suite of attributes that depict detailed lake drainage relationships. It was constructed using lake boundaries from a global lake dataset, with the help of high-resolution hydrography data. This database may facilitate a variety of applications including water quality, agriculture and fisheries, and integrated lake–river modeling.
Youjiang Shen, Karina Nielsen, Menaka Revel, Dedi Liu, and Dai Yamazaki
Earth Syst. Sci. Data, 15, 2781–2808, https://doi.org/10.5194/essd-15-2781-2023, https://doi.org/10.5194/essd-15-2781-2023, 2023
Short summary
Short summary
Res-CN fills a gap in a comprehensive and extensive dataset of reservoir-catchment characteristics for 3254 Chinese reservoirs with 512 catchment-level attributes and significantly enhanced spatial and temporal coverage (e.g., 67 % increase in water level and 225 % in storage anomaly) of time series of reservoir water level (data available for 20 % of 3254 reservoirs), water area (99 %), storage anomaly (92 %), and evaporation (98 %), supporting a wide range of applications and disciplines.
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, https://doi.org/10.5194/gmd-16-2583-2023, 2023
Short summary
Short summary
The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Dirk Eilander, Anaïs Couasnon, Tim Leijnse, Hiroaki Ikeuchi, Dai Yamazaki, Sanne Muis, Job Dullaart, Arjen Haag, Hessel C. Winsemius, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 23, 823–846, https://doi.org/10.5194/nhess-23-823-2023, https://doi.org/10.5194/nhess-23-823-2023, 2023
Short summary
Short summary
In coastal deltas, flooding can occur from interactions between coastal, riverine, and pluvial drivers, so-called compound flooding. Global models however ignore these interactions. We present a framework for automated and reproducible compound flood modeling anywhere globally and validate it for two historical events in Mozambique with good results. The analysis reveals differences in compound flood dynamics between both events related to the magnitude of and time lag between drivers.
Menaka Revel, Xudong Zhou, Dai Yamazaki, and Shinjiro Kanae
Hydrol. Earth Syst. Sci., 27, 647–671, https://doi.org/10.5194/hess-27-647-2023, https://doi.org/10.5194/hess-27-647-2023, 2023
Short summary
Short summary
The capacity to discern surface water improved as satellites became more available. Because remote sensing data is discontinuous, integrating models with satellite observations will improve knowledge of water resources. However, given the current limitations (e.g., parameter errors) of water resource modeling, merging satellite data with simulations is problematic. Integrating observations and models with the unique approaches given here can lead to a better estimation of surface water dynamics.
Robert J. Parker, Chris Wilson, Edward Comyn-Platt, Garry Hayman, Toby R. Marthews, A. Anthony Bloom, Mark F. Lunt, Nicola Gedney, Simon J. Dadson, Joe McNorton, Neil Humpage, Hartmut Boesch, Martyn P. Chipperfield, Paul I. Palmer, and Dai Yamazaki
Biogeosciences, 19, 5779–5805, https://doi.org/10.5194/bg-19-5779-2022, https://doi.org/10.5194/bg-19-5779-2022, 2022
Short summary
Short summary
Wetlands are the largest natural source of methane, one of the most important climate gases. The JULES land surface model simulates these emissions. We use satellite data to evaluate how well JULES reproduces the methane seasonal cycle over different tropical wetlands. It performs well for most regions; however, it struggles for some African wetlands influenced heavily by river flooding. We explain the reasons for these deficiencies and highlight how future development will improve these areas.
Toby R. Marthews, Simon J. Dadson, Douglas B. Clark, Eleanor M. Blyth, Garry D. Hayman, Dai Yamazaki, Olivia R. E. Becher, Alberto Martínez-de la Torre, Catherine Prigent, and Carlos Jiménez
Hydrol. Earth Syst. Sci., 26, 3151–3175, https://doi.org/10.5194/hess-26-3151-2022, https://doi.org/10.5194/hess-26-3151-2022, 2022
Short summary
Short summary
Reliable data on global inundated areas remain uncertain. By matching a leading global data product on inundation extents (GIEMS) against predictions from a global hydrodynamic model (CaMa-Flood), we found small but consistent and non-random biases in well-known tropical wetlands (Sudd, Pantanal, Amazon and Congo). These result from known limitations in the data and the models used, which shows us how to improve our ability to make critical predictions of inundation events in the future.
Naota Hanasaki, Hikari Matsuda, Masashi Fujiwara, Yukiko Hirabayashi, Shinta Seto, Shinjiro Kanae, and Taikan Oki
Hydrol. Earth Syst. Sci., 26, 1953–1975, https://doi.org/10.5194/hess-26-1953-2022, https://doi.org/10.5194/hess-26-1953-2022, 2022
Short summary
Short summary
Global hydrological models (GHMs) are usually applied with a spatial resolution of about 50 km, but this time we applied the H08 model, one of the most advanced GHMs, with a high resolution of 2 km to Kyushu island, Japan. Since the model was not accurate as it was, we incorporated local information and improved the model, which revealed detailed water stress in subregions that were not visible with the previous resolution.
Dirk Eilander, Willem van Verseveld, Dai Yamazaki, Albrecht Weerts, Hessel C. Winsemius, and Philip J. Ward
Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, https://doi.org/10.5194/hess-25-5287-2021, 2021
Short summary
Short summary
Digital elevation models and derived flow directions are crucial to distributed hydrological modeling. As the spatial resolution of models is typically coarser than these data, we need methods to upscale flow direction data while preserving the river structure. We propose the Iterative Hydrography Upscaling (IHU) method and show it outperforms other often-applied methods. We publish the multi-resolution MERIT Hydro IHU hydrography dataset and the algorithm as part of the pyflwdir Python package.
Daisuke Tokuda, Hyungjun Kim, Dai Yamazaki, and Taikan Oki
Geosci. Model Dev., 14, 5669–5693, https://doi.org/10.5194/gmd-14-5669-2021, https://doi.org/10.5194/gmd-14-5669-2021, 2021
Short summary
Short summary
We developed TCHOIR, a hydrologic simulation framework, to solve fluvial- and thermodynamics of the river–lake continuum. This provides an algorithm for upscaling high-resolution topography as well, which enables the representation of those interactions at the global scale. Validation against in situ and satellite observations shows that the coupled mode outperforms river- or lake-only modes. TCHOIR will contribute to elucidating the role of surface hydrology in Earth’s energy and water cycle.
Jun'ya Takakura, Shinichiro Fujimori, Kiyoshi Takahashi, Naota Hanasaki, Tomoko Hasegawa, Yukiko Hirabayashi, Yasushi Honda, Toshichika Iizumi, Chan Park, Makoto Tamura, and Yasuaki Hijioka
Geosci. Model Dev., 14, 3121–3140, https://doi.org/10.5194/gmd-14-3121-2021, https://doi.org/10.5194/gmd-14-3121-2021, 2021
Short summary
Short summary
To simplify calculating economic impacts of climate change, statistical methods called emulators are developed and evaluated. There are trade-offs between model complexity and emulation performance. Aggregated economic impacts can be approximated by relatively simple emulators, but complex emulators are necessary to accommodate finer-scale economic impacts.
Xudong Zhou, Wenchao Ma, Wataru Echizenya, and Dai Yamazaki
Nat. Hazards Earth Syst. Sci., 21, 1071–1085, https://doi.org/10.5194/nhess-21-1071-2021, https://doi.org/10.5194/nhess-21-1071-2021, 2021
Short summary
Short summary
This article assesses different uncertainties in the analysis of flood risk and found the runoff generated before the river routing is the primary uncertainty source. This calls for attention to be focused on selecting an appropriate runoff for the flood analysis. The uncertainties are reflected in the flood water depth, inundation area and the exposure of the population and economy to the floods.
Zun Yin, Catherine Ottlé, Philippe Ciais, Feng Zhou, Xuhui Wang, Polcher Jan, Patrice Dumas, Shushi Peng, Laurent Li, Xudong Zhou, Yan Bo, Yi Xi, and Shilong Piao
Hydrol. Earth Syst. Sci., 25, 1133–1150, https://doi.org/10.5194/hess-25-1133-2021, https://doi.org/10.5194/hess-25-1133-2021, 2021
Short summary
Short summary
We improved the irrigation module in a land surface model ORCHIDEE and developed a dam operation model with the aim to investigate how irrigation and dams affect the streamflow fluctuations of the Yellow River. Results show that irrigation mainly reduces the annual river flow. The dam operation, however, mainly affects streamflow variation. By considering two generic operation rules, flood control and base flow guarantee, our dam model can sustainably improve the simulation accuracy.
Cited articles
Alfieri, L., Bisselink, B., Dottori, F., Naumann, G., de Roo, A., Salamon,
P., Wyser K, and Feyen, L.: Global projections of river flood risk in a
warmer world, Earth's Future, 5, 171–182.
https://doi.org/10.1002/2016EF000485, 2017.
Aqueduct Floods Hazard Maps: https://www.wri.org/aqueduct/data, last access:
25 October 2022.
Bates, P. D., Savage, J., Wing, O., Quinn, N., Sampson, C., Neal, J., and Smith, A.: A climate-conditioned catastrophe risk model for UK flooding, Nat. Hazards Earth Syst. Sci., 23, 891–908, https://doi.org/10.5194/nhess-23-891-2023, 2023.
Beck, H. E., De Roo, A., and van Dijk, A. I.: Global maps of streamflow
characteristics based on observations from several thousand catchments, J. Hydrometeorol., 16, 1478–1501,
https://doi.org/10.1175/JHM-D-14-0155.1, 2015.
Bernhofen, M. V., Whyman, C., Trigg, M. A., Sleigh, P. A., Smith, A. M.,
Sampson, C. C., Yamazaki, D., Ward, J. P., and Winsemius, H. C.: A first
collective validation of global fluvial flood models for major floods in
Nigeria and Mozambique, Environ. Res. Lett., 13, 104007, https://doi.org/10.1088/1748-9326/aae014, 2018.
Center for International Earth Science Information Network (CIESIN),
Columbia University: Documentation for the Gridded Population of the World,
Version 4 (GPWv4), Revision 11 Data Sets, NASA Socioeconomic Data and
Applications Center (SEDAC) [data set], Palisades, NY,
https://doi.org/10.7927/H45Q4T5F, 2018.
Dankers, R. and Feyen, L.: Climate change impact on flood hazard in Europe:
An assessment based on high-resolution climate simulations, J. Geophys. Res.-Atmos., 113, D19105, https://doi.org/10.1029/2007JD009719, 2008.
de Moel, H., van Alphen, J., and Aerts, J. C. J. H.: Flood maps in Europe – methods, availability and use, Nat. Hazards Earth Syst. Sci., 9, 289–301, https://doi.org/10.5194/nhess-9-289-2009, 2009.
Dottori, F., Szewczyk, W., Ciscar, J. C., Zhao, F., Alfieri, L.,
Hirabayashi, Y., Bianchi, A., Mongelli, I., Frieler, K., Betts, R. A., and
Feyen, L.: Increased human and economic losses from river flooding with
anthropogenic warming, Nat. Clim. Change, 8, 781–786,
https://doi.org/10.1038/s41558-018-0257-z, 2018.
Dottori, F., Alfieri, L., Bianchi, A., Skoien, J., and Salamon, P.: A new dataset of river flood hazard maps for Europe and the Mediterranean Basin, Earth Syst. Sci. Data, 14, 1549–1569, https://doi.org/10.5194/essd-14-1549-2022, 2022.
Earth System Grid Federation: CMIP6 Data Search, https://esgf-node.llnl.gov/search/cmip6/, last access: 14 April 2023.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Federal Emergency Management Agency (FEMA): Flood Maps and Zones Explained,
https://www.fema.gov/blog/fema-flood-maps-and-zones-explained (last access: 16 October 2022), 2018 (last
updated 17 March 2021).
Global Assessment Report 2015 (GAR 2015): Global Risk Data Platform,
https://preview.grid.unep.ch/ (last access 16 October 2022), 2015.
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam., 4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013.
Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D.,
Watanabe, S., Kim, H., and Kanae, S.: Global flood risk under climate change,
Nat. Clim. Change, 3, 816–821, https://doi.org/10.1038/NCLIMATE19,
2013.
Hirabayashi, Y., Tanoue, M., Sasaki, O., Zhou, X., and Yamazaki, D.: Global
exposure to flooding from the new CMIP6 climate model projections,
Sci. Rep., 11, 1–7, https://doi.org/10.1038/s41598-021-83279-w,
2021.
Hirabayashi, Y., Yamada, K., Yamazaki, D., Ishikawa, Y., Arai, M., Inuzuka,
T., Hisamatsu, R., and Ogawada, D.: Comparative Evaluation of Global Flood
Hazard Maps and Recommendations for Corporate Practice,
Journal of Japan Society of Hydrology and Water Resources, 35, 175–191,
https://doi.org/10.3178/jjshwr.35.175, 2022.
Hosking, J. R. M.: L-Moments, in: Wiley StatsRef: Statistics Reference
Online, John Wiley and Sons, Ltd., Hoboken, USA, 1–8,
https://doi.org/10.1002/9781118445112.stat00570.pub2, 2015.
Huang, S., Krysanova, V., and Hattermann, F. F.: Does bias correction
increase reliability of flood projections under climate change? A case study
of large rivers in Germany, Int. J. Climatol., 34,
3780–3800, https://doi.org/10.1002/joc.3945, 2014.
Japan Institute of Country-ology and Engineering 39th report,
https://www.jice.or.jp/tech/reports/detail/16/43 (last access: 16 October 2022), 2021.
Joint Research Centre (JRC): River Flood Hazard Maps at European and
Global Scale, https://data.jrc.ec.europa.eu/collection/id-0054, last access:
16 October 2022.
Kita, Y. and Yamazaki, D.: Verification of the Usability of Global River
Inundation Model Output for Hazard Maps in Japan, Journal of Japan Society of Hydrology and Water Resources, 35, 267–278, https://doi.org/10.3178/jjshwr.35.1743, 2022.
LaFond, K. M., Griffis, V. W., and Spellman, P.: Forcing hydrologic models with GCM output: Bias correction vs. the “delta change” method, American Society of Civil Engineers, https://doi.org/10.1061/9780784413548.214, 2014
Li, C., Zwiers, F., Zhang, X., Li, G., Sun, Y., and Wehner, M.: Changes in
annual extremes of daily temperature and precipitation in CMIP6 models,
J. Climate, 34, 3441–3460,
https://doi.org/10.1175/JCLI-D-19-1013.1, 2021.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res.-Atmos.,
99, 14415–14428, https://doi.org/10.1029/94JD00483, 1994.
Liang, X., Wood, E. F., and Lettenmaier, D. P.: Surface soil moisture
parameterization of the VIC-2L model: Evaluation and modification, Global Planet. Change, 13, 195–206,
https://doi.org/10.1016/0921-8181(95)00046-1, 1996.
Lu, J., Carbone, G. J., and Crego, J. M.: Uncertainty and hotspots in 21st
century projections of agricultural drought from CMIP5 models, Sci. Rep., 9, 4922, https://doi.org/10.1038/s41598-019-41196-z, 2019.
Panofsky, H. A. and Brier, G. W.: Some applications of statistics to meteorology,
The Pennsylvania State University Press, 224 pp., 1968.
Reachhydro.org: Global Reach-level Flood Reanalysis, https://www.reachhydro.org/home/records/grfr, last access: 14 April 2023.
Sampson, C. C., Smith, A. M., Bates, P. D., Neal, J. C., Alfieri, L., and
Freer, J. E.: A high-resolution global flood hazard model, Water Resour. Res., 51, 7358–7381, https://doi.org/10.1002/2015WR016954, 2015.
Seneviratne, S. I., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca,
A., Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I.,
Satoh, M., Vicente-Serrano, S. M., Wehner, M., and Zhou, B.: Weather and
Climate Extreme Events in a Changing Climate, in: Climate Change 2021: The
Physical Science Basis. Contribution of Working Group I to the Sixth
Assessment Report of the Intergovernmental Panel on Climate Change, edited by: MassonDelmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom
and New York, NY, USA, 1513–1766, https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/ (last access: 14 April 2023), 2021.
Smith, A., Bates, P. D., Wing, O., Sampson, C., Quinn, N., and Neal, J.: New
estimates of flood exposure in developing countries using high-resolution
population data, Nat. Commun., 10, 1–7,
https://doi.org/10.1038/s41467-019-09282-y, 2019.
Taguchi, R., Tanoue, M., Yamazaki, D., and Hirabayashi, Y.: Global-Scale
Assessment of Economic Losses Caused by Flood-Related Business Interruption,
Water, 14, 967, https://doi.org/10.3390/w14060967, 2022.
Trigg, M. A., Birch, C. E., Neal, J. C., Bates, P. D., Smith, A., Sampson,
C. C., Yamazaki, D., Hirabayashi, Y., Pappenberger, F., Dutra, E., Ward, P.
J., Winsemius, H. C., Salamon, P., Dottori, F., Rudari, R., Kappes, M. S.,
Simpson, A. L., Hadzilacos, G., and Fewtrell, T. J.: The credibility challenge
for global fluvial flood risk analysis, Environ. Res. Lett.,
11, 094014, https://doi.org/10.1088/1748-9326/11/9/094014, 2016.
United Nations Office for Disaster Risk Reduction (UNISDR): Sendai Framework
for Disaster Risk Reduction 2015–2030, United Nations,
http://www.unisdr.org/files/43291_sendaiframeworkfordrren.pdf (last access: 17 October 2022), 2015.
Watanabe, S., Kanae, S., Seto, S., Yeh, P. J. F., Hirabayashi, Y., and Oki,
T.: Intercomparison of bias-correction methods for monthly temperature and
precipitation simulated by multiple climate models, J. Geophys. Res.-Atmos., 117, D23114, https://doi.org/10.1029/2012JD018192, 2012.
Watanabe, S.: Bias correction of climate model output values (1) Organizing
feature-based methods, Journal of Japan Society of Hydrology and Water
Resources, 33, 243–262, https://doi.org/10.3178/jjshwr.33.243,
2020.
Ward, P. J., Winsemius, H. C., Kuzma, S., Bierkens, M. F. P., Bouwman, A.,
Moel, H. D. E., and Luo, T.: Aqueduct floods methodology, World Resources
Institute, 1–28, https://www.wri.org/research/aqueduct-floods-methodology (last access: 17 October 2022),
2020a.
Ward, P. J., Blauhut, V., Bloemendaal, N., Daniell, J. E., de Ruiter, M. C., Duncan, M. J., Emberson, R., Jenkins, S. F., Kirschbaum, D., Kunz, M., Mohr, S., Muis, S., Riddell, G. A., Schäfer, A., Stanley, T., Veldkamp, T. I. E., and Winsemius, H. C.: Review article: Natural hazard risk assessments at the global scale, Nat. Hazards Earth Syst. Sci., 20, 1069–1096, https://doi.org/10.5194/nhess-20-1069-2020, 2020b.
Wing, O. E., Lehman, W., Bates, P. D., Sampson, C. C., Quinn, N., Smith, A.
M., Neal, J. C, Porter, J. R., and Kousky, C.: Inequitable patterns of US
flood risk in the Anthropocene, Nat. Clim. Change, 12, 156–162,
https://doi.org/10.1038/s41558-021-01265-6, 2022.
Yamazaki, D.: MERIT Hydro: global hydrography datasets, http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/, last access 14 April 2023.
Yamazaki, D., Kanae, S., Kim, H., and Oki, T.: A physically based
description of floodplain inundation dynamics in a global river routing
model, Water Resour. Res., 47, W04501, https://doi.org/10.1029/2010WR009726,
2011.
Yamazaki, D., de Almeida, G. A., and Bates, P. D.: Improving computational
efficiency in global river models by implementing the local inertial flow
equation and a vector-based river network map, Water Resour. Res.,
49, 7221–7235, https://doi.org/10.1002/wrcr.20552, 2013.
Yamazaki, D., Sato, T., Kanae, S., Hirabayashi, Y., and Bates, P. D.:
Regional flood dynamics in a bifurcating mega delta simulated in a global
river model, Geophys. Res. Lett., 41, 3127–3135,
https://doi.org/10.1002/2014GL059744, 2014.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., and
Pavelsky, T. M.: MERIT Hydro: A high-resolution global hydrography map based
on latest topography dataset, Water Resour. Res., 55, 5053–5073,
https://doi.org/10.1029/2019WR024873, 2019.
Yamazaki, D., Revel, M., Hanazaki, R., Zhou, X., and Nitta, T.:
global-hydrodynamics/CaMa-Flood_v4, CaMa-Flood (v4.01),
Zenodo [code], https://doi.org/10.5281/zenodo.4659583, 2021.
Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David,
C.H., Lu, H. Yang, K., Hong, Y., and Wood, E. F.: Global Reach-Level
3-Hourly River Flood Reanalysis (1980–2019), B. Am. Meteorol. Soc., 102, E2086–E2105,
https://doi.org/10.1175/BAMS-D-20-0057.1, 2021.
Zhou, X., Ma, W., Echizenya, W., and Yamazaki, D.: The uncertainty of flood frequency analyses in hydrodynamic model simulations, Nat. Hazards Earth Syst. Sci., 21, 1071–1085, https://doi.org/10.5194/nhess-21-1071-2021, 2021.
Short summary
Since both the frequency and magnitude of flood will increase by climate change, information on spatial distributions of potential inundation depths (i.e., flood-hazard map) is required. We developed a method for constructing realistic future flood-hazard maps which addresses issues due to biases in climate models. A larger population is estimated to face risk in the future flood-hazard map, suggesting that only focusing on flood-frequency change could cause underestimation of future risk.
Since both the frequency and magnitude of flood will increase by climate change, information on...