Articles | Volume 21, issue 3
https://doi.org/10.5194/nhess-21-1071-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-1071-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Mar 2021
Research article |
| 23 Mar 2021
| 23 Mar 2021
The uncertainty of flood frequency analyses in hydrodynamic model simulations
Xudong Zhou
CORRESPONDING AUTHOR
Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8505, Japan
Wenchao Ma
Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8505, Japan
Wataru Echizenya
Corporate Planning Department, MS&AD InterRisk Research & Consulting, Inc., 2-105, Kanda Awajicho, Chiyoda-ku, Tokyo 101-0063, Japan
Dai Yamazaki
Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8505, Japan
Related authors
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.
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.
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.
Yuki Kimura, Yukiko Hirabayashi, Yuki Kita, Xudong Zhou, and Dai Yamazaki
Hydrol. Earth Syst. Sci., 27, 1627–1644, https://doi.org/10.5194/hess-27-1627-2023, https://doi.org/10.5194/hess-27-1627-2023, 2023
Short summary
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.
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.
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.
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.
Yuki Kimura, Yukiko Hirabayashi, Yuki Kita, Xudong Zhou, and Dai Yamazaki
Hydrol. Earth Syst. Sci., 27, 1627–1644, https://doi.org/10.5194/hess-27-1627-2023, https://doi.org/10.5194/hess-27-1627-2023, 2023
Short summary
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.
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.
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.
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
Aerts, J. P. M., Uhlemann-Elmer, S., Eilander, D., and Ward, P. J.: Comparison of estimates of global flood models for flood hazard and exposed gross domestic product: a China case study, Nat. Hazards Earth Syst. Sci., 20, 3245–3260, https://doi.org/10.5194/nhess-20-3245-2020, 2020. a
Akaike, H.: A new look at the statistical model identification, IEEE
T Automat. Contr., 19, 716–723, 1974. a
Alvisi, S. and Franchini, M.: A grey-based method for evaluating the effects
of rating curve uncertainty on frequency analysis of annual maxima, J. Hydroinform., 15, 194–210, https://doi.org/10.2166/hydro.2012.127, 2013. a
Bales, J. D. and Wagner, C. R.: Sources of uncertainty in flood inundation
maps, J. Flood Risk Manag., 2, 139–147,
https://doi.org/10.1111/j.1753-318X.2009.01029.x, 2009. a
Bernhofen, M. V., Whyman, C., Trigg, M. A., Sleigh, P. A., Smith, A. M.,
Sampson, C. C., Yamazaki, D., Ward, P. J., Rudari, R., Pappenberger, F.,
Dottori, F., Salamon, 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. a
Beven, K., Lamb, R., Leedal, D., and Hunter, N.: Communicating uncertainty in flood inundation mapping: A case study, Int. J. River Basin Manag., 13, 285–295, https://doi.org/10.1080/15715124.2014.917318, 2015. a
CIESIN – Center for International Earth Science Information Network, Columbia University: Documentation for the Gridded Population of the World, Version 4 (GPWv4), Revision 11 Data Sets, NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY, https://doi.org/10.7927/H45Q4T5F, 2018. a
Domeneghetti, A., Castellarin, A., and Brath, A.: Assessing rating-curve uncertainty and its effects on hydraulic model calibration, Hydrol. Earth Syst. Sci., 16, 1191–1202, https://doi.org/10.5194/hess-16-1191-2012, 2012. a
Drissia, T. K., Jothiprakash, V., and Anitha, A. B.: Flood Frequency Analysis Using L Moments: a Comparison between At-Site and Regional Approach, Water Resour. Manag., 33, 1013–1037, https://doi.org/10.1007/s11269-018-2162-7, 2019. a, b, c
Hamed, K. and Rao, A. R.: Flood frequency analysis, CRC Press, Boca Raton, Florida, 2019. a
Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D.,
Watanabe, S., Kim, H., and Kanae, S.: Global flood risk under climate
change, Nat. Clim. Change, 3, 816–821, https://doi.org/10.1038/nclimate1911,
2013. a
Hollebrandse, F., Adams, J., and Nutt, N.: Python 3.x library to estimate linear moments for statistical distribution functions, GitHub, available at: https://github.com/OpenHydrology/lmoments3 (last access: 17 March 2021), 2015. a
Horritt, M. S. and Bates, P. D.: Effects of spatial resolution on a raster
based model of flood flow, J. Hydrol., 253, 239–249,
https://doi.org/10.1016/S0022-1694(01)00490-5, 2001. a
Hosking, J. R. M.: L-Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics, J. Roy. Stat. Soc. Ser. B, 52, 105–124, 1990. a
Hosking, J. R. M.: L-Moments, in: Wiley StatsRef: Statistics Reference Online, John Wiley & Sons, Ltd., Hoboken, USA, 1–8, https://doi.org/10.1002/9781118445112.stat00570.pub2, 2015. a
Jung, Y. and Merwade, V.: Uncertainty Quantification in Flood Inundation
Mapping Using Generalized Likelihood Uncertainty Estimate and Sensitivity
Analysis, J. Hydrol. Eng., 17, 507–520,
https://doi.org/10.1061/(asce)he.1943-5584.0000476, 2012. a
Kidson, R. and Richards, K. S.: Flood frequency analysis: Assumptions and
alternatives, Prog. Phys. Geog., 29, 392–410,
https://doi.org/10.1191/0309133305pp454ra, 2005. a, b
Kummu, M., Taka, M., and Guillaume, J. H.: Gridded global datasets for Gross
Domestic Product and Human Development Index over 1990-2015, Sci.
Data, 5, 1–15, https://doi.org/10.1038/sdata.2018.4, 2018. a
Leyk, S., Gaughan, A. E., Adamo, S. B., de Sherbinin, A., Balk, D., Freire, S., Rose, A., Stevens, F. R., Blankespoor, B., Frye, C., Comenetz, J., Sorichetta, A., MacManus, K., Pistolesi, L., Levy, M., Tatem, A. J., and Pesaresi, M.: The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use, Earth Syst. Sci. Data, 11, 1385–1409, https://doi.org/10.5194/essd-11-1385-2019, 2019. a
Liscum, F. and Massey, B. C.: Technique for estimating the magnitude and
frequency of floods in the Houston, Texas, metropolitan area, Final report., Tech. rep., U.S. Geological Survey, Water Resources Division, 1980. a
Luke, A., Sanders, B. F., Goodrich, K. A., Feldman, D. L., Boudreau, D., Eguiarte, A., Serrano, K., Reyes, A., Schubert, J. E., AghaKouchak, A., Basolo, V., and Matthew, R. A.: Going beyond the flood insurance rate map: insights from flood hazard map co-production, Nat. Hazards Earth Syst. Sci., 18, 1097–1120, https://doi.org/10.5194/nhess-18-1097-2018, 2018. a
Merwade, V., Olivera, F., Arabi, M., and Edleman, S.: Uncertainty in flood
inundation mapping: Current issues and future directions, J.
Hydrol. Eng., 13, 608–620, https://doi.org/10.1061/(ASCE)1084-0699(2008)13:7(608), 2008. a, b
Osti, R., Tanaka, S., and Tokioka, T.: Flood hazard mapping in developing
countries: Problems and prospects, Disaster Prev. Manag., 17, 104–113, https://doi.org/10.1108/09653560810855919, 2008. a
Pappenberger, F., Beven, K. J., Ratto, M., and Matgen, P.: Multi-method global
sensitivity analysis of flood inundation models, Adv. Water
Resour., 31, 1–14, https://doi.org/10.1016/j.advwatres.2007.04.009, 2008. a, b
Pappenberger, F., Dutra, E., Wetterhall, F., and Cloke, H. L.: Deriving global flood hazard maps of fluvial floods through a physical model cascade, Hydrol. Earth Syst. Sci., 16, 4143–4156, https://doi.org/10.5194/hess-16-4143-2012, 2012. a
Sampson, C. C., Smith, A. M., Bates, P. B., 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. a
Schellekens, J., Dutra, E., Martínez-de la Torre, A., Balsamo, G., van Dijk, A., Sperna Weiland, F., Minvielle, M., Calvet, J.-C., Decharme, B., Eisner, S., Fink, G., Flörke, M., Peßenteiner, S., van Beek, R., Polcher, J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo, W., and Weedon, G. P.: A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data, 9, 389–413, https://doi.org/10.5194/essd-9-389-2017, 2017. a, b, c, d
Shin, S., Pokhrel, Y., Yamazaki, D., Huang, X., Torbick, N., Qi, J.,
Pattanakiat, S., Ngo-Duc, T., and Duc Tuan, N.: High Resolution Modeling
of River-floodplain-reservoir Inundation Dynamics in the Mekong River Basin, Water Resour. Res., 56, e2019WR026449, https://doi.org/10.1029/2019wr026449, 2020. a, b
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. a
Tanaka, Y. and Yamazaki, D.: The automatic extraction of physical flood
protection parameters for global river models, J.
Jpn Soc. Civ. Eng., 75, 1099–1104, 2019 (in Japanese). a
Tate, E., Muñoz, C., and Suchan, J.: Uncertainty and Sensitivity
Analysis of the HAZUS-MH Flood Model, Nat. Haz. Rev., 16,
04014030, https://doi.org/10.1061/(asce)nh.1527-6996.0000167, 2015. a, b
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. a, b
Ward, P. J., Jongman, B., Weiland, F. S., Bouwman, A., Van Beek, R.,
Bierkens, M. F., Ligtvoet, W., and Winsemius, H. C.: Assessing flood risk at the global scale: Model setup, results, and sensitivity, Environ.
Res. Lett., 8, 044019, doi10.1088/1748-9326/8/4/044019, 2013. a
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and Viterbo, P.: The WFDEI meteorological forcing data set: WATCH Forcing data
methodology applied to ERA-Interim reanalysis data, Water Resour.
Res., 50, 7505–7514, https://doi.org/10.1002/2014WR015638, 2014. a
Wiltshire, S. E.: Identification of homogeneous regions for flood frequency
analysis, J. Hydrol., 84, 287–302,
https://doi.org/10.1016/0022-1694(86)90128-9, 1986.
a
Winsemius, H. C., Van Beek, L. P. H., Jongman, B., Ward, P. J., and Bouwman, A.: A framework for global river flood risk assessments, Hydrol. Earth Syst. Sci., 17, 1871–1892, https://doi.org/10.5194/hess-17-1871-2013, 2013. a, b
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, 1–21, https://doi.org/10.1029/2010WR009726, 2011. a, b
Yamazaki, D., Lee, H., Alsdorf, D. E., Dutra, E., Kim, H., Kanae, S., and Oki, T.: Analysis of the water level dynamics simulated by a global river model: A case study in the Amazon River, Water Resour. Res., 48, 1–15,
https://doi.org/10.1029/2012WR011869, 2012. a
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. a
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853,
https://doi.org/10.1002/2017GL072874, 2017. a, b
Yamazaki, D., Revel, M., Zhou, X., and Nitta, T.:
Global-hydrodynamics/CaMa-Flood_v4: CaMa-Flood (Version v4.00), Zenodo, https://doi.org/10.5281/zenodo.4609655, 2021. a
Yu, D. and Lane, S. N.: Urban fluvial flood modelling using a two-dimensional diffusion-wave treatment, part 1: Mesh resolution effects, Hydrol. Process., 20, 1541–1565, https://doi.org/10.1002/hyp.5935, 2006. a
Zhao, F., Veldkamp, T. I., Frieler, K., Schewe, J., Ostberg, S., Willner, S.,
Schauberger, B., Gosling, S. N., Schmied, H. M., Portmann, F. T., Leng, G.,
Huang, M., Liu, X., Tang, Q., Hanasaki, N., Biemans, H., Gerten, D., Satoh,
Y., Pokhrel, Y., Stacke, T., Ciais, P., Chang, J., Ducharne, A., Guimberteau, M., Wada, Y., Kim, H., and Yamazaki, D.: The critical role of the routing scheme in simulating peak river discharge in global hydrological models, Environ. Res. Lett., 12, 075003, https://doi.org/10.1088/1748-9326/aa7250, 2017. a, b, c
Zhou, X., Prigent, C., and Yamazaki, D.: Reasonable agreements and mismatches
between land-surface-water-area estimates based on a global river model and
Landsat data, Earth Space Sci. Open Arch.,
https://doi.org/10.1002/essoar.10504917.1, in press, 2020. a
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.
This article assesses different uncertainties in the analysis of flood risk and found the runoff...
Altmetrics
Final-revised paper
Preprint