Articles | Volume 27, issue 21
https://doi.org/10.5194/hess-27-3911-2023
© 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-27-3911-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Nov 2023
Research article |
| 06 Nov 2023
| 06 Nov 2023
Understanding the compound flood risk along the coast of the contiguous United States
Dongyu Feng
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Donghui Xu
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
L. Ruby Leung
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
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The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
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Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Sol Kim, L. Ruby Leung, Bin Guan, and John C. H. Chiang
Geosci. Model Dev., 15, 5461–5480, https://doi.org/10.5194/gmd-15-5461-2022, https://doi.org/10.5194/gmd-15-5461-2022, 2022
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The Energy Exascale Earth System Model (E3SM) project is a state-of-the-science Earth system model developed by the US Department of Energy (DOE). Understanding how the water cycle behaves in this model is of particular importance to the DOE’s mission. Atmospheric rivers (ARs) – which are crucial to the global water cycle – move vast amounts of water vapor through the sky and produce rain and snow. We find that this model reliably represents atmospheric rivers around the world.
Lingcheng Li, Gautam Bisht, and L. Ruby Leung
Geosci. Model Dev., 15, 5489–5510, https://doi.org/10.5194/gmd-15-5489-2022, https://doi.org/10.5194/gmd-15-5489-2022, 2022
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Land surface heterogeneity plays a critical role in the terrestrial water, energy, and biogeochemical cycles. Our study systematically quantified the effects of four dominant heterogeneity sources on water and energy partitioning via Sobol' indices. We found that atmospheric forcing and land use land cover are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning. Our findings can help prioritize the future development of land surface models.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
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Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Donghui Xu, Gautam Bisht, Khachik Sargsyan, Chang Liao, and L. Ruby Leung
Geosci. Model Dev., 15, 5021–5043, https://doi.org/10.5194/gmd-15-5021-2022, https://doi.org/10.5194/gmd-15-5021-2022, 2022
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The runoff outputs in Earth system model simulations involve high uncertainty, which needs to be constrained by parameter calibration. In this work, we used a surrogate-assisted Bayesian framework to efficiently calibrate the runoff-generation processes in the Energy Exascale Earth System Model v1 at a global scale. The model performance was improved compared to the default parameter after calibration, and the associated parametric uncertainty was significantly constrained.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
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Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Yun Lin, Jiwen Fan, Pengfei Li, Lai-yung Ruby Leung, Paul J. DeMott, Lexie Goldberger, Jennifer Comstock, Ying Liu, Jong-Hoon Jeong, and Jason Tomlinson
Atmos. Chem. Phys., 22, 6749–6771, https://doi.org/10.5194/acp-22-6749-2022, https://doi.org/10.5194/acp-22-6749-2022, 2022
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How sea spray aerosols may affect cloud and precipitation over the region by acting as ice-nucleating particles (INPs) is unknown. We explored the effects of INPs from marine aerosols on orographic cloud and precipitation for an atmospheric river event observed during the 2015 ACAPEX field campaign. The marine INPs enhance the formation of ice and snow, leading to less shallow warm clouds but more mixed-phase and deep clouds. This work suggests models need to consider the impacts of marine INPs.
Pinya Wang, Yang Yang, Huimin Li, Lei Chen, Ruijun Dang, Daokai Xue, Baojie Li, Jianping Tang, L. Ruby Leung, and Hong Liao
Atmos. Chem. Phys., 22, 4705–4719, https://doi.org/10.5194/acp-22-4705-2022, https://doi.org/10.5194/acp-22-4705-2022, 2022
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China is now suffering from both severe ozone (O3) pollution and heat events. We highlight that North China Plain is the hot spot of the co-occurrences of extremes in O3 and high temperatures in China. Such coupled extremes exhibit an increasing trend during 2014–2019 and will continue to increase until the middle of this century. And the coupled extremes impose more severe health impacts to human than O3 pollution occurring alone because of elevated O3 levels and temperatures.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Sally S.-C. Wang, Yun Qian, L. Ruby Leung, and Yang Zhang
Atmos. Chem. Phys., 22, 3445–3468, https://doi.org/10.5194/acp-22-3445-2022, https://doi.org/10.5194/acp-22-3445-2022, 2022
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This study develops an interpretable machine learning (ML) model predicting monthly PM2.5 fire emission over the contiguous US at 0.25° resolution and compares the prediction skills of the ML and process-based models. The comparison facilitates attributions of model biases and better understanding of the strengths and uncertainties in the two types of models at regional scales, for informing future model development and their applications in fire emission projection.
Jinshi Jian, Xuan Du, Juying Jiao, Xiaohua Ren, Karl Auerswald, Ryan Stewart, Zeli Tan, Jianlin Zhao, Daniel L. Evans, Guangju Zhao, Nufang Fang, Wenyi Sun, Chao Yue, and Ben Bond-Lamberty
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-87, https://doi.org/10.5194/essd-2022-87, 2022
Manuscript not accepted for further review
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Field soil loss and sediment yield due to surface runoff observations were compiled into a database named AWESOME: Archive for Water Erosion and Sediment Outflow MEasurements. Annual soil erosion data from 1985 geographic sites and 75 countries have been compiled into AWESOME. This database aims to be an open framework for the scientific community to share field-based annual soil erosion measurements, enabling better understanding of the spatial and temporal variability of annual soil erosion.
Guta Wakbulcho Abeshu, Hong-Yi Li, Zhenduo Zhu, Zeli Tan, and L. Ruby Leung
Earth Syst. Sci. Data, 14, 929–942, https://doi.org/10.5194/essd-14-929-2022, https://doi.org/10.5194/essd-14-929-2022, 2022
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Existing riverbed sediment particle size data are sparsely available at individual sites. We develop a continuous map of median riverbed sediment particle size over the contiguous US corresponding to millions of river segments based on the existing observations and machine learning methods. This map is useful for research in large-scale river sediment using model- and data-driven approaches, teaching environmental and earth system sciences, planning and managing floodplain zones, etc.
Hong-Yi Li, Zeli Tan, Hongbo Ma, Zhenduo Zhu, Guta Wakbulcho Abeshu, Senlin Zhu, Sagy Cohen, Tian Zhou, Donghui Xu, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 665–688, https://doi.org/10.5194/hess-26-665-2022, https://doi.org/10.5194/hess-26-665-2022, 2022
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We introduce a new multi-process river sediment module for Earth system models. Application and validation over the contiguous US indicate a satisfactory model performance over large river systems, including those heavily regulated by reservoirs. This new sediment module enables future modeling of the transportation and transformation of carbon and nutrients carried by the fine sediment along the river–ocean continuum to close the global carbon and nutrient cycles.
Claudia Tebaldi, Kalyn Dorheim, Michael Wehner, and Ruby Leung
Earth Syst. Dynam., 12, 1427–1501, https://doi.org/10.5194/esd-12-1427-2021, https://doi.org/10.5194/esd-12-1427-2021, 2021
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We address the question of how large an initial condition ensemble of climate model simulations should be if we are concerned with accurately projecting future changes in temperature and precipitation extremes. We find that for most cases (and both models considered), an ensemble of 20–25 members is sufficient for many extreme metrics, spatial scales and time horizons. This may leave computational resources to tackle other uncertainties in climate model simulations with our ensembles.
Dalei Hao, Gautam Bisht, Yu Gu, Wei-Liang Lee, Kuo-Nan Liou, and L. Ruby Leung
Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, https://doi.org/10.5194/gmd-14-6273-2021, 2021
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Topography exerts significant influence on the incoming solar radiation at the land surface. This study incorporated a well-validated sub-grid topographic parameterization in E3SM land model (ELM) version 1.0. The results demonstrate that sub-grid topography has non-negligible effects on surface energy budget, snow cover, and surface temperature over the Tibetan Plateau and that the ELM simulations are sensitive to season, elevation, and spatial scale.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Jianfeng Li, Zhe Feng, Yun Qian, and L. Ruby Leung
Earth Syst. Sci. Data, 13, 827–856, https://doi.org/10.5194/essd-13-827-2021, https://doi.org/10.5194/essd-13-827-2021, 2021
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Deep convection has different properties at different scales. We develop a 4 km h−1 observational data product of mesoscale convective systems and isolated deep convection in the United States from 2004–2017. We find that both types of convective systems contribute significantly to precipitation east of the Rocky Mountains but with distinct spatiotemporal characteristics. The data product will be useful for observational analyses and model evaluations of convection events at different scales.
Jinshi Jian, Xuan Du, Ryan D. Stewart, Zeli Tan, and Ben Bond-Lamberty
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-283, https://doi.org/10.5194/essd-2020-283, 2020
Preprint withdrawn
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Field soil loss due to surface runoff observations were compiled into a global database (SoilErosionDB). The database focuses on three erosion-related metrics – surface runoff, soil erosion, and nutrient leaching – and also records background information. Data from 99 geographic sites and 22 countries around the world have been compiled into SoilErosionDB. SoilErosionDB aims to be a data framework for the scientific community to share field-based soil erosion measurements.
Cited articles
AghaKouchak, A., Huning, L. S., Chiang, F., Sadegh, M., Vahedifard, F., Mazdiyasni, O., Moftakhari, H., and Mallakpour, I.: How do natural hazards cascade to cause disasters?, Nature, 561, 458–460, https://doi.org/10.1038/d41586-018-06783-6, 2018.
Alfieri, L., Feyen, L., and Di Baldassarre, G.: Increasing flood risk under climate change: a pan-European assessment of the benefits of four adaptation strategies, Climatic Change, 136, 507–521, https://doi.org/10.1007/s10584-016-1641-1, 2016.
Apel, H., Thieken, A. H., Merz, B., and Blöschl, G.: Flood risk assessment and associated uncertainty, Nat. Hazards Earth Syst. Sci., 4, 295–308, https://doi.org/10.5194/nhess-4-295-2004, 2004.
Asher, T. G., Luettich Jr., R. A., Fleming, J. G., and Blanton, B. O.: Low frequency water level correction in storm surge models using data assimilation, Ocean Model., 144, 101483, https://doi.org/10.1016/j.ocemod.2019.101483, 2019.
Bakhtyar, R., Maitaria, K., Velissariou, P., Trimble, B., Mashriqui, H., Moghimi, S., Abdolali, A., Van der Westhuysen, A., Ma, Z., and Clark, E.: A new 1D/2D coupled modeling approach for a riverine-estuarine system under storm events: Application to Delaware River Basin, J. Geophys. Res.-Oceans, 125, e2019JC015822, https://doi.org/10.1029/2019JC015822, 2020.
Bates, P. D., Quinn, N., Sampson, C., Smith, A., Wing, O., Sosa, J., Savage, J., Olcese, G., Neal, J., Schumann, G., Giustarini, L., Coxon, G., Porter, J. R., Amodeo, M. F., Chu, Z., Lewis-Gruss, S., Freeman, N. B., Houser, T., Delgado, M., Hamidi, A., Bolliger, I., McCusker, K., Emanuel, K., Ferreira, C. M., Khalid, A., Haigh, I. D., Couasnon, A., Kopp, R., Hsiang, S., and Krajewski, W. F.: Combined modeling of US fluvial, pluvial, and coastal flood hazard under current and future climates, Water Resour. Res., 57, e2020WR028673, https://doi.org/10.1029/2020wr028673, 2021.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., Van Dijk, A. I., McVicar, T. R., and Adler, R. F.: MSWEP V2 global 3-hourly 0.1 precipitation: methodology and quantitative assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019.
Bermúdez, M., Farfán, J., Willems, P., and Cea, L.: Assessing the effects of climate change on compound flooding in coastal river areas, Water Resour. Res., 57, e2020WR029321, https://doi.org/10.1029/2020WR029321, 2021.
Bevacqua, E., Maraun, D., Vousdoukas, M., Voukouvalas, E., Vrac, M., Mentaschi, L., and Widmann, M.: Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change, Sci. Adv., 5, eaaw5531, https://doi.org/10.1126/sciadv.aaw5531, 2019.
Bilskie, M. and Hagen, S.: Defining flood zone transitions in low-gradient coastal regions, Geophys. Res. Lett., 45, 2761–2770, https://doi.org/10.1002/2018GL077524, 2018.
Bouwer, L. M.: Projections of future extreme weather losses under changes in climate and exposure, Risk Anal., 33, 915–930, https://doi.org/10.1111/j.1539-6924.2012.01880.x, 2013.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001.
Caldwell, P. M., Terai, C. R., Hillman, B., Keen, N. D., Bogenschutz, P., Lin, W., Beydoun, H., Taylor, M., Bertagna, L., and Bradley, A.: Convection-permitting simulations with the E3SM global atmosphere model, J. Adv. Model. Earth Syst., 13, e2021MS002544, https://doi.org/10.1029/2021MS002544, 2021.
Camelo, J., Mayo, T. L., and Gutmann, E. D.: Projected Climate Change Impacts on Hurricane Storm Surge Inundation in the Coastal United States, Front. Built Environ., 207, 588049, https://doi.org/10.3389/fbuil.2020.588049, 2020.
Camus, P., Haigh, I. D., Nasr, A. A., Wahl, T., Darby, S. E., and Nicholls, R. J.: Regional analysis of multivariate compound coastal flooding potential around Europe and environs: sensitivity analysis and spatial patterns, Nat. Hazards Earth Syst. Sci., 21, 2021–2040, https://doi.org/10.5194/nhess-21-2021-2021, 2021.
Cook, A. and Merwade, V.: Effect of topographic data, geometric configuration and modeling approach on flood inundation mapping, J. Hydrol., 377, 131–142, https://doi.org/10.1016/j.jhydrol.2009.08.015, 2009.
Couasnon, A., Eilander, D., Muis, S., Veldkamp, T. I., Haigh, I. D., Wahl, T., Winsemius, H. C., and Ward, P. J.: Measuring compound flood potential from river discharge and storm surge extremes at the global scale, Nat. Hazards Earth Syst. Sci., 20, 489–504, https://doi.org/10.5194/nhess-20-489-2020, 2020.
Dullaart, J., Muis, S., Bloemendaal, N., and Aerts, J. C.: Advancing global storm surge modelling using the new ERA5 climate reanalysis, Clim. Dynam., 54, 1007–1021, https://doi.org/10.1007/s00382-019-05044-0, 2020.
Dung, N. V., Merz, B., Bárdossy, A., and Apel, H.: Handling uncertainty in bivariate quantile estimation – An application to flood hazard analysis in the Mekong Delta, J. Hydrol., 527, 704–717, https://doi.org/10.1016/j.jhydrol.2015.05.033, 2015.
Dykstra, S. and Dzwonkowski, B.: The propagation of fluvial flood waves through a backwater-estuarine environment, Water Resour. Res., 56, e2019WR025743, https://doi.org/10.1029/2019WR025743, 2020.
Dykstra, S. and Dzwonkowski, B.: The Role of Intensifying Precipitation on Coastal River Flooding and Compound River-Storm Surge Events, Northeast Gulf of Mexico, Water Resour. Res., 57, e2020WR029363, https://doi.org/10.1029/2020WR029363, 2021.
Egorova, R., van Noortwijk, J. M., and Holterman, S. R.: Uncertainty in flood damage estimation, Int. J. River Basin Manage., 6, 139–148, https://doi.org/10.1080/15715124.2008.9635343, 2008.
Eilander, D., Couasnon, A., Ikeuchi, H., Muis, S., Yamazaki, D., Winsemius, H. C., and Ward, P. J.: The effect of surge on riverine flood hazard and impact in deltas globally, Environ. Res. Lett., 15, 104007, https://doi.org/10.1088/1748-9326/ab8ca6, 2020.
Eilander, D., Couasnon, A., Leijnse, T., Ikeuchi, H., Yamazaki, D., Muis, S., Dullaart, J., Haag, A., Winsemius, H. C., and Ward, P. J.: A globally applicable framework for compound flood hazard modeling, Nat. Hazards Earth Syst. Sci., 23, 823–846, https://doi.org/10.5194/nhess-23-823-2023, 2023.
Fan, Y., Yu, L., Shi, X., and Duan, Q.: Tracing Uncertainty Contributors in the Multi-Hazard Risk Analysis for Compound Extremes, Earth's Future, 9, e2021EF002280, https://doi.org/10.1029/2021EF002280, 2021.
Feng, D.: Compound flood risk analysis in CONUS, Zenodo [code], https://doi.org/10.5281/zenodo.7588256, 2023.
Feng, D., Tan, Z., Engwirda, D., Liao, C., Xu, D., Bisht, G., Zhou, T., Li, H. Y., and Leung, L. R.: Investigating coastal backwater effects and flooding in the coastal zone using a global river transport model on an unstructured mesh, Hydrol. Earth Syst. Sci., 26, 5473–5491, https://doi.org/10.5194/hess-26-5473-2022, 2022.
Ferson, S. and Ginzburg, L. R.: Different methods are needed to propagate ignorance and variability, Reliabil. Eng. Syst. Safe., 54, 133–144, https://doi.org/10.1016/S0951-8320(96)00071-3, 1996.
Gallien, T. W., Kalligeris, N., Delisle, M.-P. C., Tang, B.-X., Lucey, J. T., and Winters, M. A.: Coastal flood modeling challenges in defended urban backshores, Geosciences, 8, 450, https://doi.org/10.3390/geosciences8120450, 2018.
Ghanbari, M., Arabi, M., Kao, S. C., Obeysekera, J., and Sweet, W.: Climate Change and Changes in Compound Coastal-Riverine Flooding Hazard Along the US Coasts, Earth's Future, 9, e2021EF002055, https://doi.org/10.1029/2021EF002055, 2021.
Golaz, J. C., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., Bradley, A. M., Tang, Q., Maltrud, M. E., and Forsyth, R. M.: The DOE E3SM Model Version 2: Overview of the physical model and initial model evaluation, J. Adv. Model. Earth Syst., 14, e2022MS003156, https://doi.org/10.1029/2022ms003156, 2022.
Gori, A. and Lin, N.: Projecting compound flood hazard under climate change with physical models and joint probability methods, Earth's Future, 10, e2022EF003097, https://doi.org/10.1029/2022EF003097, 2022.
Gori, A., Lin, N., and Smith, J.: Assessing compound flooding from landfalling tropical cyclones on the North Carolina coast, Water Resour. Res., 56, e2019WR026788, https://doi.org/10.1029/2019WR026788, 2020.
Grimaldi, S. and Serinaldi, F.: Asymmetric copula in multivariate flood frequency analysis, Adv. Water Resour., 29, 1155–1167, https://doi.org/10.1016/j.advwatres.2005.09.005, 2006.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Hall, J. W.: Handling uncertainty in the hydroinformatic process, J. Hydroinform., 5, 215–232, https://doi.org/10.2166/hydro.2003.0019, 2003.
Hanson, S., Nicholls, R., Ranger, N., Hallegatte, S., Corfee-Morlot, J., Herweijer, C., and Chateau, J.: A global ranking of port cities with high exposure to climate extremes, Climatic Change, 104, 89–111, https://doi.org/10.1007/s10584-010-9977-4, 2011.
Hendry, A., Haigh, I. D., Nicholls, R. J., Winter, H., Neal, R., Wahl, T., Joly-Laugel, A., and Darby, S. E.: Assessing the characteristics and drivers of compound flooding events around the UK coast, Hydrol. Earth Syst. Sci., 23, 3117–3139, https://doi.org/10.5194/hess-23-3117-2019, 2019.
Hersbach, H., de Rosnay, P., Bell, B., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Alonso-Balmaseda, M., Balsamo, G., and Bechtold, P.: Operational global reanalysis: progress, future directions and synergies with NWP, ECMWF, https://doi.org/10.21957/tkic6g3wm, 2018.
Hinkel, J., Lincke, D., Vafeidis, A. T., Perrette, M., Nicholls, R. J., Tol, R. S., Marzeion, B., Fettweis, X., Ionescu, C., and Levermann, A.: Coastal flood damage and adaptation costs under 21st century sea-level rise, P. Natl. Acad. Sci. USA, 111, 3292–3297, https://doi.org/10.1073/pnas.1222469111, 2014.
Hodges, B. R.: Challenges in continental river dynamics, Environ. Model. Softw., 50, 16–20, https://doi.org/10.1016/j.envsoft.2013.08.010, 2013.
Ikeuchi, H., Hirabayashi, Y., Yamazaki, D., Muis, S., Ward, P. J., Winsemius, H. C., Verlaan, M., and Kanae, S.: Compound simulation of fluvial floods and storm surges in a global coupled river-coast flood model: Model development and its application to 2007 Cyclone Sidr in Bangladesh, J. Adv. Model. Earth Syst., 9, 1847–1862, https://doi.org/10.1002/2017MS000943, 2017.
Judi, D. R., Rakowski, C. L., Waichler, S. R., Feng, Y., and Wigmosta, M. S.: Integrated modeling approach for the development of climate-informed, actionable information, Water, 10, 775, https://doi.org/10.3390/w10060775, 2018.
Kalyanapu, A. J., Judi, D. R., McPherson, T. N., and Burian, S. J.: Annualised risk analysis approach to recommend appropriate level of flood control: application to S wannanoa river watershed, J. Flood Risk Manage., 8, 368–385, https://doi.org/10.1111/jfr3.12108, 2015.
Kendall, M. G.: A new measure of rank correlation, Biometrika, 30, 81–93, https://doi.org/10.2307/2332226, 1938.
Kew, S., Selten, F., Lenderink, G., and Hazeleger, W.: The simultaneous occurrence of surge and discharge extremes for the Rhine delta, Nat. Hazards Earth Syst. Sci., 13, 2017–2029, https://doi.org/10.5194/nhess-13-2017-2013, 2013.
Kiang, J. E., Gazoorian, C., McMillan, H., Coxon, G., Le Coz, J., Westerberg, I. K., Belleville, A., Sevrez, D., Sikorska, A. E., and Petersen-Øverleir, A.: A comparison of methods for streamflow uncertainty estimation, Water Resour. Res., 54, 7149–7176, https://doi.org/10.1029/2018WR022708, 2018.
Kojadinovic, I. and Yan, J.: Modeling multivariate distributions with continuous margins using the copula R package, J. Stat. Softw., 34, 1–20, https://doi.org/10.18637/jss.v034.i09, 2010.
Kron, W.: Flood risk = hazard ⋅ values ⋅ vulnerability, Water Int., 30, 58–68, https://doi.org/10.1080/02508060508691837, 2005.
Kulp, S. A. and Strauss, B. H.: New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding, Nat. Commun., 10, 1–12, https://doi.org/10.1038/s41467-019-12808-z, 2019.
Kumbier, K., Carvalho, R. C., Vafeidis, A. T., and Woodroffe, C. D.: Investigating compound flooding in an estuary using hydrodynamic modelling: a case study from the Shoalhaven River, Australia, Nat. Hazards Earth Syst. Sci., 18, 463–477, https://doi.org/10.5194/nhess-18-463-2018, 2018.
Lamb, M. P., Nittrouer, J. A., Mohrig, D., and Shaw, J.: Backwater and river plume controls on scour upstream of river mouths: Implications for fluvio-deltaic morphodynamics, J. Geophys. Res.-Earth, 117, F01002, https://doi.org/10.1029/2011JF002079, 2012.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., and Kennedy, D.: The Community Land Model version 5: Description of new features, benchmarking, and impact of forcing uncertainty, J. Adv. Model. Earth Syst., 11, 4245–4287, 2019.
Lehner, B. and Grill, G.: Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems, Hydrol. Process., 27, 2171–2186, https://doi.org/10.1002/hyp.9740, 2013.
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from spaceborne elevation data, Eos Trans. Am. Geophys. Union, 89, 93–94, https://doi.org/10.1029/2008EO100001, 2008.
Li, H., Wigmosta, M. S., Wu, H., Huang, M., Ke, Y., Coleman, A. M., and Leung, L. R.: A physically based runoff routing model for land surface and earth system models, J. Hydrometeorol., 14, 808–828, https://doi.org/10.1175/JHM-D-12-015.1, 2013.
Li, H. Y., Ruby Leung, L., Tesfa, T., Voisin, N., Hejazi, M., Liu, L., Liu, Y., Rice, J., Wu, H., and Yang, X.: Modeling stream temperature in the Anthropocene: An earth system modeling approach, J. Adv. Model. Earth Syst., 7, 1661–1679, 2015a.
Li, H.-Y., Leung, L. R., Getirana, A., Huang, M., Wu, H., Xu, Y., Guo, J., and Voisin, N.: Evaluating global streamflow simulations by a physically based routing model coupled with the community land model, J. Hydrometeorol., 16, 948–971, https://doi.org/10.1175/JHM-D-14-0079.1, 2015b.
Li, H.-Y., Tan, Z., Ma, H., Zhu, Z., Abeshu, G. W., Zhu, S., Cohen, S., Zhou, T., Xu, D., and Leung, L. R.: A new large-scale suspended sediment model and its application over the United States, Hydrol. Earth Syst. Sci., 26, 665–688, https://doi.org/10.5194/hess-26-665-2022, 2022.
Lian, J., Xu, K., and Ma, C.: Joint impact of rainfall and tidal level on flood risk in a coastal city with a complex river network: a case study of Fuzhou City, China, Hydrol. Earth Syst. Sci., 17, 679–689, https://doi.org/10.5194/hess-17-679-2013, 2013.
Lilly, J. R., Capodaglio, G., Petersen, M. R., Brus, S. R., Engwirda, D., and Higdon, R. L.: Storm Surge Modeling as an Application of Local Time-Stepping in MPAS-Ocean, J. Adv. Model. Earth Syst., 15, e2022MS003327, https://doi.org/10.1029/2022MS003327, 2023.
Luo, X., Li, H.-Y., Leung, L. R., Tesfa, T. K., Getirana, A., Papa, F., and Hess, L. L.: Modeling surface water dynamics in the Amazon Basin using MOSART-Inundation v1.0: impacts of geomorphological parameters and river flow representation, Geosci. Model Dev., 10, 1233–1259, https://doi.org/10.5194/gmd-10-1233-2017, 2017.
Maskrey, A., Peduzzi, P., Chatenoux, B., Herold, C., Dao, Q.-H., and Giuliani, G.: Revealing Risk, Redefining Development, Global Assessment Report on Disaster Risk Reduction, United Nations Strategy for Disaster Reduction, 17–51, https://www.undrr.org/publication/global-assessment-report-disaster-risk-reduction-2011 (last access: 3 November 2023), 2011.
Mehran, A., AghaKouchak, A., Nakhjiri, N., Stewardson, M. J., Peel, M. C., Phillips, T. J., Wada, Y., and Ravalico, J. K.: Compounding impacts of human-induced water stress and climate change on water availability, Sci. Rep., 7, 1–9, https://doi.org/10.1038/s41598-017-06765-0, 2017.
Moftakhari, H., Schubert, J. E., AghaKouchak, A., Matthew, R. A., and Sanders, B. F.: Linking statistical and hydrodynamic modeling for compound flood hazard assessment in tidal channels and estuaries, Adv. Water Resour., 128, 28–38, https://doi.org/10.1016/j.advwatres.2019.04.009, 2019.
Moftakhari, H. R., Salvadori, G., AghaKouchak, A., Sanders, B. F., and Matthew, R. A.: Compounding effects of sea level rise and fluvial flooding, P. Natl. Acad. Sci. USA, 114, 9785–9790, https://doi.org/10.1073/pnas.1620325114, 2017.
Muis, S., Verlaan, M., Nicholls, R. J., Brown, S., Hinkel, J., Lincke, D., Vafeidis, A. T., Scussolini, P., Winsemius, H. C., and Ward, P. J.: A comparison of two global datasets of extreme sea levels and resulting flood exposure, Earth's Future, 5, 379–392, https://doi.org/10.1002/2016EF000430, 2017.
Muis, S., Apecechea, M. I., Dullaart, J., de Lima Rego, J., Madsen, K. S., Su, J., Yan, K., and Verlaan, M.: A high-resolution global dataset of extreme sea levels, tides, and storm surges, including future projections, Front. Mar. Sci., 7, 263, https://doi.org/10.3389/fmars.2020.00263, 2020.
Muis, S., Apecechea, M. I., Álvarez, J. A., Verlaan, M., Yan, K., Dullaart, J., Aerts, J., Duong, T., Ranasinghe, R., Erikson, L., O'Neill, A., Bars, D. l., Haarsma, R., and Roberts, M.: Global sea level change time series from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections, CDS – Copernicus Climat Data Store [data set], https://doi.org/10.24381/cds.a6d42d60, 2022.
Muñoz, D., Moftakhari, H., and Moradkhani, H.: Compound effects of flood drivers and wetland elevation correction on coastal flood hazard assessment, Water Resour. Res., 56, e2020WR027544, https://doi.org/10.1029/2020WR027544, 2020.
Nasr, A. A., Wahl, T., Rashid, M. M., Camus, P., and Haigh, I. D.: Assessing the dependence structure between oceanographic, fluvial, and pluvial flooding drivers along the United States coastline, Hydrol. Earth Syst. Sci., 25, 6203–6222, https://doi.org/10.5194/hess-25-6203-2021, 2021.
NOAA: NOAA tides & currents, https://tidesandcurrents.noaa.gov/ (last access: 19 January 2023), 2022.
Olbert, A. I., Comer, J., Nash, S., and Hartnett, M.: High-resolution multi-scale modelling of coastal flooding due to tides, storm surges and rivers inflows. A Cork City example, Coast. Eng., 121, 278–296, https://doi.org/10.1016/j.coastaleng.2016.12.006, 2017.
Pal, N., Barton, K. N., Petersen, M. R., Brus, S. R., Engwirda, D., Arbic, B. K., Roberts, A. F., Westerink, J. J., and Wirasaet, D.: Barotropic tides in MPAS-Ocean (E3SM V2): impact of ice shelf cavities, Geosci. Model Dev., 16, 1297–1314, https://doi.org/10.5194/gmd-16-1297-2023, 2023.
Paprotny, D., Vousdoukas, M. I., Morales-Nápoles, O., Jonkman, S. N., and Feyen, L.: Pan-European hydrodynamic models and their ability to identify compound floods, Nat. Hazards, 101, 933–957, https://doi.org/10.1007/s11069-020-03902-3, 2020.
Parodi, M. U., Giardino, A., Van Dongeren, A., Pearson, S. G., Bricker, J. D., and Reniers, A. J.: Uncertainties in coastal flood risk assessments in small island developing states, Nat. Hazards Earth Syst. Sci., 20, 2397–2414, https://doi.org/10.5194/nhess-20-2397-2020, 2020.
Phongsapan, K., Chishtie, F., Poortinga, A., Bhandari, B., Meechaiya, C., Kunlamai, T., Aung, K. S., Saah, D., Anderson, E., and Markert, K.: Operational flood risk index mapping for disaster risk reduction using Earth Observations and cloud computing technologies: a case study on Myanmar, Front. Environ. Sci., 7, 191, https://doi.org/10.3389/fenvs.2019.00191, 2019.
Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., and Weyer, N.: The ocean and cryosphere in a changing climate, IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, https://doi.org/10.1017/9781009157964, 2019.
Rashid, M., Wahl, T., Chambers, D. P., Calafat, F. M., and Sweet, W. V.: An extreme sea level indicator for the contiguous United States coastline, Sci. Data, 6, 1–14, https://doi.org/10.1038/s41597-019-0333-x, 2019.
Sadegh, M., Ragno, E., and AghaKouchak, A.: Multivariate Copula Analysis Toolbox (MvCAT): describing dependence and underlying uncertainty using a Bayesian framework, Water Resour. Res., 53, 5166-5183, https://doi.org/10.1002/2016WR020242, 2017.
Sadegh, M., Moftakhari, H., Gupta, H. V., Ragno, E., Mazdiyasni, O., Sanders, B., Matthew, R., and AghaKouchak, A.: Multihazard scenarios for analysis of compound extreme events, Geophys. Res. Lett., 45, 5470–5480, https://doi.org/10.1029/2018GL077317, 2018.
Salvadori, G., De Michele, C., Kottegoda, N. T., and Rosso, R.: Extremes in nature: an approach using copulas, Springer Science & Business Media, ISBN 978-1-4020-4414-4, https://doi.org/10.1007/1-4020-4415-1, 2007.
Salvadori, G., Durante, F., De Michele, C., Bernardi, M., and Petrella, L.: A multivariate copula-based framework for dealing with hazard scenarios and failure probabilities, Water Resour. Res., 52, 3701–3721, https://doi.org/10.1002/2015WR017225, 2016.
Santiago-Collazo, F. L., Bilskie, M. V., and Hagen, S. C.: A comprehensive review of compound inundation models in low-gradient coastal watersheds, Environ. Model. Softw., 119, 166–181, https://doi.org/10.1016/j.envsoft.2019.06.002, 2019.
Schiavina, M., Freire, S., and MacManus, K.: GHS population grid multitemporal (1975, 1990, 2000, 2015) R2019A, European Commission, JRC – Joint Research Centre, https://doi.org/10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218, 2019.
Seneviratne, S., Nicholls, N., Easterling, D., Goodess, C., Kanae, S., Kossin, J., Luo, Y., Marengo, J., McInnes, K., and Rahimi, M.: Changes in climate extremes and their impacts on the natural physical environment, Columbia University, https://doi.org/10.7916/d8-6nbt-s431, 2012.
Serafin, K. A., Ruggiero, P., Parker, K., and Hill, D. F.: What's streamflow got to do with it? A probabilistic simulation of the competing oceanographic and fluvial processes driving extreme along-river water levels, Nat. Hazards Earth Syst. Sci., 19, 1415–1431, https://doi.org/10.5194/nhess-19-1415-2019, 2019.
Sun, N., Wigmosta, M. S., Judi, D., Yang, Z., Xiao, Z., and Wang, T.: Climatological analysis of tropical cyclone impacts on hydrological extremes in the Mid-Atlantic region of the United States, Environ. Res. Lett., 16, 124009, https://doi.org/10.1088/1748-9326/ac2d6a, 2021.
Ting, M., Kossin, J. P., Camargo, S. J., and Li, C.: Past and future hurricane intensity change along the US East Coast, Sci. Re., 9, 7795, https://doi.org/10.1038/s41598-019-44252-w, 2019.
Towner, J., Cloke, H. L., Zsoter, E., Flamig, Z., Hoch, J. M., Bazo, J., Coughlan de Perez, E., and Stephens, E. M.: Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin, Hydrol. Earth Syst. Sci., 23, 3057–3080, https://doi.org/10.5194/hess-23-3057-2019, 2019.
Turnipseed, D. P. and Sauer, V. B.: Discharge measurements at gaging stations, US Geological Survey 2328-7055, US Geological Survey, https://doi.org/10.3133/tm3A8, 2010.
US Geological Survey: National Water Information System data available on the World Wide Web, USGS Water Data for the Nation, http://waterdata.usgs.gov/nwis/ (last access: 19 January 2023), 2016.
US Geological Survey: USGS 3D Elevation Program Digital Elevation Model, https://elevation.nationalmap.gov/arcgis/rest/services/3DEPElevation/ImageServer (last access: 19 January 2023), 2019.
Uusitalo, L., Lehikoinen, A., Helle, I., and Myrberg, K.: An overview of methods to evaluate uncertainty of deterministic models in decision support, Environ. Model. Softw., 63, 24–31, https://doi.org/10.1016/j.envsoft.2014.09.017, 2015.
Valle-Levinson, A., Olabarrieta, M., and Heilman, L.: Compound flooding in Houston-Galveston Bay during Hurricane Harvey, Sci. Total Environ., 747, 141272, https://doi.org/10.1016/j.scitotenv.2020.141272, 2020.
Van de Sande, B., Lansen, J., and Hoyng, C.: Sensitivity of coastal flood risk assessments to digital elevation models, Water, 4, 568–579, https://doi.org/10.3390/w4030568, 2012.
Vousdoukas, M. I., Bouziotas, D., Giardino, A., Bouwer, L. M., Mentaschi, L., Voukouvalas, E., and Feyen, L.: Understanding epistemic uncertainty in large-scale coastal flood risk assessment for present and future climates, Nat. Hazards Earth Syst. Sci., 18, 2127–2142, https://doi.org/10.5194/nhess-18-2127-2018, 2018.
Wahl, T., Jain, S., Bender, J., Meyers, S. D., and Luther, M. E.: Increasing risk of compound flooding from storm surge and rainfall for major US cities, Nat. Clim. Change, 5, 1093–1097, https://doi.org/10.1038/NCLIMATE2736, 2015.
Wahl, T., Haigh, I. D., Nicholls, R. J., Arns, A., Dangendorf, S., Hinkel, J., and Slangen, A. B.: Understanding extreme sea levels for broad-scale coastal impact and adaptation analysis, Nature Commun., 8, 1–12, https://doi.org/10.1038/ncomms16075, 2017.
Ward, P. J., Couasnon, A., Eilander, D., Haigh, I. D., Hendry, A., Muis, S., Veldkamp, T. I., Winsemius, H. C., and Wahl, T.: Dependence between high sea-level and high river discharge increases flood hazard in global deltas and estuaries, Environ. Res. Lett., 13, 084012, https://doi.org/10.1088/1748-9326/aad400, 2018.
Woolway, R. I., Sharma, S., Weyhenmeyer, G. A., Debolskiy, A., Golub, M., Mercado-Bettín, D., Perroud, M., Stepanenko, V., Tan, Z., and Grant, L.: Phenological shifts in lake stratification under climate change, Nat. Commun., 12, 1–11, https://doi.org/10.1038/s41467-021-22657-4, 2021.
Wu, W., Westra, S., and Leonard, M.: Estimating the probability of compound floods in estuarine regions, Hydrol. Earth Syst. Sci., 25, 2821–2841, https://doi.org/10.5194/hess-25-2821-2021, 2021.
Xiao, Z., Yang, Z., Wang, T., Sun, N., Wigmosta, M., and Judi, D.: Characterizing the non-linear interactions between tide, storm surge, and river flow in the delaware bay estuary, United States, Front. Mar. Sci., 8, 715557, 10.3389/fmars.2021.715557, 2021.
Xu, D., Bisht, G., Zhou, T., Leung, L. R., and Pan, M.: Development of Land-River Two-Way Hydrologic Coupling for Floodplain Inundation in the Energy Exascale Earth System Model, J. Adv. Model. Earth Syst., 14, e2021MS002772, https://doi.org/10.1029/2021MS002772, 2022.
Xu, K., Wang, C., and Bin, L.: Compound flood models in coastal areas: a review of methods and uncertainty analysis, Nat. Hazards, 116, 469–496, https://doi.org/10.1007/s11069-022-05683-3, 2022.
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, W09508, https://doi.org/10.1029/2012WR011869, 2012.
Yan, K., Muis, S., Irazoqui Apecechea, M., and Verlaan, M.: Water level change time series for the European coast from 1977 to 2100 derived from climate projections, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.8c59054f, 2022.
Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H., Lu, H., Yang, K., and Hong, Y.: 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.
Ye, F., Zhang, Y. J., Yu, H., Sun, W., Moghimi, S., Myers, E., Nunez, K., Zhang, R., Wang, H. V., and Roland, A.: Simulating storm surge and compound flooding events with a creek-to-ocean model: Importance of baroclinic effects, Ocean Model., 145, 101526, https://doi.org/10.1016/j.ocemod.2019.101526, 2020.
Zhang, D., Shi, X., Xu, H., Jing, Q., Pan, X., Liu, T., Wang, H., and Hou, H.: A GIS-based spatial multi-index model for flood risk assessment in the Yangtze River Basin, China, Environ. Impact Assess. Rev., 83, 106397, https://doi.org/10.1016/j.eiar.2020.106397, 2020.
Zhang, Y. J., Ye, F., Yu, H., Sun, W., Moghimi, S., Myers, E., Nunez, K., Zhang, R., Wang, H., and Roland, A.: Simulating compound flooding events in a hurricane, Ocean Dynam., 70, 621–640, https://doi.org/10.1007/s10236-020-01351-x, 2020.
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., and Mahecha, M. D.: A typology of compound weather and climate events, Nature Rev. Earth Environ., 1, 333–347, https://doi.org/10.1038/s43017-020-0060-z, 2020.
Short summary
This study assesses the flood risks concurrently induced by river flooding and coastal storm surge along the coast of the contiguous United States using statistical and numerical models. We reveal a few hotspots of such risks, the critical spatial variabilities within a river basin and over the whole US coast, and the uncertainties of the risk assessment. We highlight the importance of weighing different risk measures to avoid underestimating or exaggerating the compound flood impacts.
This study assesses the flood risks concurrently induced by river flooding and coastal storm...
