Articles | Volume 24, issue 8
https://doi.org/10.5194/nhess-24-2647-2024
© Author(s) 2024. 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-24-2647-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Aug 2024
Research article |
| 02 Aug 2024
| 02 Aug 2024
Probabilistic flood inundation mapping through copula Bayesian multi-modeling of precipitation products
Francisco Javier Gomez
CORRESPONDING AUTHOR
Department of Civil, Construction and Environmental Engineering, Center of Complex Hydrosystems Research, The University of Alabama, 35487 Tuscaloosa, USA
Keighobad Jafarzadegan
Department of Civil, Construction and Environmental Engineering, Center of Complex Hydrosystems Research, The University of Alabama, 35487 Tuscaloosa, USA
Hamed Moftakhari
Department of Civil, Construction and Environmental Engineering, Center of Complex Hydrosystems Research, The University of Alabama, 35487 Tuscaloosa, USA
Department of Civil, Construction and Environmental Engineering, Center of Complex Hydrosystems Research, The University of Alabama, 35487 Tuscaloosa, USA
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Soheil Radfar, Georgios Boumis, Hamed R. Moftakhari, Wanyun Shao, Larisa Lee, and Alison N. Rellinger
Geosci. Commun. Discuss., https://doi.org/10.5194/gc-2024-7, https://doi.org/10.5194/gc-2024-7, 2024
Revised manuscript under review for GC
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Our study presents a method to visualize how variations in the relationship of flood drivers like discharge and surge evolve over time. This method simplifies complex relationships, making it easier to understand evolving flood risks, especially as climate change increases these threats. By surveying a diverse group, we found that this visual approach could improve communication between scientists and non-experts, helping communities better prepare for compound flooding in a changing climate.
Peyman Abbaszadeh, Keyhan Gavahi, and Hamid Moradkhani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-209, https://doi.org/10.5194/hess-2024-209, 2024
Revised manuscript accepted for HESS
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The Hybrid Ensemble and Variational Data Assimilation framework for Environmental System (HEAVEN) enhances flood predictions by refining hydrologic models through improved data integration and uncertainty management. Tested in three Southeastern U.S. watersheds during hurricanes, HEAVEN assimilates real-time USGS streamflow data, boosting forecast accuracy.
David F. Muñoz, Hamed Moftakhari, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 28, 2531–2553, https://doi.org/10.5194/hess-28-2531-2024, https://doi.org/10.5194/hess-28-2531-2024, 2024
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Linking hydrodynamics with machine learning models for compound flood modeling enables a robust characterization of nonlinear interactions among the sources of uncertainty. Such an approach enables the quantification of cascading uncertainty and relative contributions to total uncertainty while also tracking their evolution during compound flooding. The proposed approach is a feasible alternative to conventional statistical approaches designed for uncertainty analyses.
Keighobad Jafarzadegan, David F. Muñoz, Hamed Moftakhari, Joseph L. Gutenson, Gaurav Savant, and Hamid Moradkhani
Nat. Hazards Earth Syst. Sci., 22, 1419–1435, https://doi.org/10.5194/nhess-22-1419-2022, https://doi.org/10.5194/nhess-22-1419-2022, 2022
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The high population settled in coastal regions and the potential damage imposed by coastal floods highlight the need for improving coastal flood hazard assessment techniques. This study introduces a topography-based approach for rapid estimation of flood hazard areas in the Savannah River delta. Our validation results demonstrate that, besides the high efficiency of the proposed approach, the estimated areas accurately overlap with reference flood maps.
Keighobad Jafarzadegan, Peyman Abbaszadeh, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 25, 4995–5011, https://doi.org/10.5194/hess-25-4995-2021, https://doi.org/10.5194/hess-25-4995-2021, 2021
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In this study, daily observations are assimilated into a hydrodynamic model to update the performance of modeling and improve the flood inundation mapping skill. Results demonstrate that integrating data assimilation with a hydrodynamic model improves the performance of flood simulation and provides more reliable inundation maps. A flowchart provides the overall steps for applying this framework in practice and forecasting probabilistic flood maps before the onset of upcoming floods.
Sahani Pathiraja, Daniela Anghileri, Paolo Burlando, Ashish Sharma, Lucy Marshall, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 22, 2903–2919, https://doi.org/10.5194/hess-22-2903-2018, https://doi.org/10.5194/hess-22-2903-2018, 2018
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Hydrologic modeling methodologies must be developed that are capable of predicting runoff in catchments with changing land cover conditions. This article investigates the efficacy of a recently developed approach that allows for runoff prediction in catchments with unknown land cover changes, through experimentation in a deforested catchment in Vietnam. The importance of key elements of the method in ensuring its success, such as the chosen hydrologic model, is investigated.
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Sarra Kchouk, Louise Cavalcante, Lieke A. Melsen, David W. Walker, Germano Ribeiro Neto, Rubens Gondim, Wouter J. Smolenaars, and Pieter R. van Oel
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Belinda Rhein and Heidi Kreibich
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In July 2021, flooding killed 190 people in Germany, 134 of them in the Ahr valley, making it the deadliest flood in recent German history. The flash flood was extreme in terms of water levels, flow velocities and flood extent, and early warning and evacuation were inadequate. Many died on the ground floor or in the street, with older and impaired individuals especially vulnerable. Clear warnings should urge people to seek safety rather than save belongings, and timely evacuations are essential.
Paola Ceresa, Gianbattista Bussi, Simona Denaro, Gabriele Coccia, Paolo Bazzurro, Mario Martina, Ettore Fagà, Carlos Avelar, Mario Ordaz, Benjamin Huerta, Osvaldo Garay, Zhanar Raimbekova, Kanatbek Abdrakhmatov, Sitora Mirzokhonova, Vakhitkhan Ismailov, and Vladimir Belikov
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Nat. Hazards Earth Syst. Sci., 25, 335–351, https://doi.org/10.5194/nhess-25-335-2025, https://doi.org/10.5194/nhess-25-335-2025, 2025
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Deep learning methods are increasingly used as surrogates for spatio-temporal flood models but struggle with generalization and speed. Here, we propose a multi-resolution approach using graph neural networks that predicts dike breach floods across different meshes, topographies, and boundary conditions with high accuracy and up to 1000× speed-ups. The model also generalizes to larger more complex case studies with just one additional simulation for fine-tuning.
Maria Staudinger, Martina Kauzlaric, Alexandre Mas, Guillaume Evin, Benoit Hingray, and Daniel Viviroli
Nat. Hazards Earth Syst. Sci., 25, 247–265, https://doi.org/10.5194/nhess-25-247-2025, https://doi.org/10.5194/nhess-25-247-2025, 2025
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Various combinations of antecedent conditions and precipitation result in floods of varying degrees. Antecedent conditions played a crucial role in generating even large ones. The key predictors and spatial patterns of antecedent conditions leading to flooding at the basin's outlet were distinct. Precipitation and soil moisture from almost all sub-catchments were important for more frequent floods. For rarer events, only the predictors of specific sub-catchments were important.
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 24, 4609–4615, https://doi.org/10.5194/nhess-24-4609-2024, https://doi.org/10.5194/nhess-24-4609-2024, 2024
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Floods have caused significant damage in the past. To prepare for such events, we rely on historical data but face issues due to rare rainfall events, lack of data and climate change. Counterfactuals, or
what ifscenarios, simulate historical rainfall in different locations to estimate flood levels. Our new study refines this by deriving more-plausible local scenarios, using the June 2024 Bavaria flood as a case study. This method could improve preparedness for future floods.
Chinh Luu, Giuseppe Forino, Lynda Yorke, Hang Ha, Quynh Duy Bui, Hanh Hong Tran, Dinh Quoc Nguyen, Hieu Cong Duong, and Matthieu Kervyn
Nat. Hazards Earth Syst. Sci., 24, 4385–4408, https://doi.org/10.5194/nhess-24-4385-2024, https://doi.org/10.5194/nhess-24-4385-2024, 2024
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This study presents a novel and integrated approach to assessing the climate hazards of floods and wildfires. We explore multi-hazard assessment and risk through a machine learning modeling approach. The process includes collecting a database of topography, climate, geology, environment, and building data; developing models for multi-hazard assessment and coding in the Google Earth Engine; and producing credible multi-hazard susceptibility and building exposure maps.
Claudia De Lucia, Michele Amaddii, and Chiara Arrighi
Nat. Hazards Earth Syst. Sci., 24, 4317–4339, https://doi.org/10.5194/nhess-24-4317-2024, https://doi.org/10.5194/nhess-24-4317-2024, 2024
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This work describes the flood damage to cultural heritage (CH) that occurred in September 2022 in central Italy. Datasets related to flood impacts on cultural heritage are rare, and this work aims at highlighting both tangible and intangible aspects and their correlation with physical characteristics of flood (i.e. water depth and flow velocity). The results show that current knowledge and datasets are inadequate for risk assessment of CH.
Pravin Maduwantha, Thomas Wahl, Sara Santamaria-Aguilar, Robert Jane, James F. Booth, Hanbeen Kim, and Gabriele Villarini
Nat. Hazards Earth Syst. Sci., 24, 4091–4107, https://doi.org/10.5194/nhess-24-4091-2024, https://doi.org/10.5194/nhess-24-4091-2024, 2024
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When assessing the likelihood of compound flooding, most studies ignore that it can arise from different storm types with distinct statistical characteristics. Here, we present a new statistical framework that accounts for these differences and shows how neglecting these can impact the likelihood of compound flood potential.
Bruno Merz, Günter Blöschl, Robert Jüpner, Heidi Kreibich, Kai Schröter, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci., 24, 4015–4030, https://doi.org/10.5194/nhess-24-4015-2024, https://doi.org/10.5194/nhess-24-4015-2024, 2024
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Flood risk assessments help us decide how to reduce the risk of flooding. Since these assessments are based on probabilities, it is hard to check their accuracy by comparing them to past data. We suggest a new way to validate these assessments, making sure they are practical for real-life decisions. This approach looks at both the technical details and the real-world situations where decisions are made. We demonstrate its practicality by applying it to flood emergency planning.
Zhi Li, Hanqi Li, Zhibo Zhang, Chaomeng Dai, and Simin Jiang
Nat. Hazards Earth Syst. Sci., 24, 3977–3990, https://doi.org/10.5194/nhess-24-3977-2024, https://doi.org/10.5194/nhess-24-3977-2024, 2024
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This study used advanced computer simulations to investigate how earthquake-induced building collapse affects flooding of the metro stations in Shanghai. Results show that the influences of building collapse on rainfall-driven and river-driven floods are different because these two types of floods have different origination and propagation mechanisms.
Yue Zhu, Paolo Burlando, Puay Yok Tan, Christian Geiß, and Simone Fatichi
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-207, https://doi.org/10.5194/nhess-2024-207, 2024
Revised manuscript accepted for NHESS
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This study addresses the challenge of accurately predicting floods in regions with limited terrain data. By utilizing a deep learning model, we developed a method that improves the resolution of digital elevation data by fusing low-resolution elevation data with high-resolution satellite imagery. This approach not only substantially enhances flood prediction accuracy, but also holds potential for broader applications in simulating natural hazards that require terrain information.
Maria Magdalena Warter, Dörthe Tetzlaff, Christian Marx, and Chris Soulsby
Nat. Hazards Earth Syst. Sci., 24, 3907–3924, https://doi.org/10.5194/nhess-24-3907-2024, https://doi.org/10.5194/nhess-24-3907-2024, 2024
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Streams are increasingly impacted by droughts and floods. Still, the amount of water needed for sustainable flows remains unclear and contested. A comparison of two streams in the Berlin–Brandenburg region of northeast Germany, using stable water isotopes, shows strong groundwater dependence with seasonal rainfall contributing to high/low flows. Understanding streamflow variability can help us assess the impacts of climate change on future water resource management.
Samuel Jonson Sutanto, Matthijs Janssen, Mariana Madruga de Brito, and Maria del Pozo Garcia
Nat. Hazards Earth Syst. Sci., 24, 3703–3721, https://doi.org/10.5194/nhess-24-3703-2024, https://doi.org/10.5194/nhess-24-3703-2024, 2024
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A conventional flood risk assessment only evaluates flood hazard in isolation without considering wildfires. This study, therefore, evaluates the effect of wildfires on flood risk, considering both current and future conditions for the Ebro River basin in Spain. Results show that extreme climate change increases the risk of flooding, especially when considering the effect of wildfires, highlighting the importance of adopting a multi-hazard risk management approach.
Miroslav Spano and Jaromir Riha
Nat. Hazards Earth Syst. Sci., 24, 3683–3701, https://doi.org/10.5194/nhess-24-3683-2024, https://doi.org/10.5194/nhess-24-3683-2024, 2024
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The study examines the effects of hydrogeological hazard due to construction of the Skalička Dam near the Hranice Karst on groundwater discharges and water levels in the local karst formations downstream. A simplified pipe model was used to analyze the impact of two dam layouts: lateral and through-flow reservoirs. Results show that the through-flow scheme more significantly influences water levels and the discharge of mineral water, while the lateral layout has only negligible impact.
Rudolf Brázdil, Dominika Faturová, Monika Šulc Michalková, Jan Řehoř, Martin Caletka, and Pavel Zahradníček
Nat. Hazards Earth Syst. Sci., 24, 3663–3682, https://doi.org/10.5194/nhess-24-3663-2024, https://doi.org/10.5194/nhess-24-3663-2024, 2024
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Flash floods belong to natural hazards that can be enhanced in frequency, intensity, and impact during recent climate change. This paper presents a complex analysis of spatiotemporal variability and human impacts (including material damage and fatalities) of flash floods in the Czech Republic for the 2001–2023 period. The analysis generally shows no statistically significant trends in the characteristics analyzed.
Melissa Wood, Ivan D. Haigh, Quan Quan Le, Hung Nghia Nguyen, Hoang Ba Tran, Stephen E. Darby, Robert Marsh, Nikolaos Skliris, and Joël J.-M. Hirschi
Nat. Hazards Earth Syst. Sci., 24, 3627–3649, https://doi.org/10.5194/nhess-24-3627-2024, https://doi.org/10.5194/nhess-24-3627-2024, 2024
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We look at how compound flooding from the combination of river flooding and storm tides (storm surge and astronomical tide) may be changing over time due to climate change, with a case study of the Mekong River delta. We found that future compound flooding has the potential to flood the region more extensively and be longer lasting than compound floods today. This is useful to know because it means managers of deltas such as the Mekong can assess options for improving existing flood defences.
Maryam Pakdehi, Ebrahim Ahmadisharaf, Behzad Nazari, and Eunsaem Cho
Nat. Hazards Earth Syst. Sci., 24, 3537–3559, https://doi.org/10.5194/nhess-24-3537-2024, https://doi.org/10.5194/nhess-24-3537-2024, 2024
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Machine learning (ML) algorithms have increasingly received attention for modeling flood events. However, there are concerns about the transferability of these models (their capability in predicting out-of-sample and unseen events). Here, we show that ML models can be transferable for hindcasting maximum river flood depths across extreme events (four hurricanes) in a large coastal watershed (HUC6) when informed by the spatial distribution of pertinent features and underlying physical processes.
Claudia Teutschbein, Thomas Grabs, Markus Giese, Andrijana Todorović, and Roland Barthel
EGUsphere, https://doi.org/10.5194/egusphere-2024-2742, https://doi.org/10.5194/egusphere-2024-2742, 2024
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This study explores how droughts develop and spread in high-latitude regions, focusing on the unique conditions found in areas like Scandinavia. It reveals that droughts affect soil, rivers, and groundwater differently, depending on factors like land cover, water availability, and soil properties. The findings highlight the importance of tailored water management strategies to protect resources and ecosystems in these regions, especially as climate change continues to impact weather patterns.
María Carmen Llasat, Montserrat Llasat-Botija, Erika Pardo, Raül Marcos-Matamoros, and Marc Lemus-Canovas
Nat. Hazards Earth Syst. Sci., 24, 3423–3443, https://doi.org/10.5194/nhess-24-3423-2024, https://doi.org/10.5194/nhess-24-3423-2024, 2024
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This paper shows the first public and systematic dataset of flood episodes referring to the entire Pyrenees massif, at municipal scale, named PIRAGUA_flood. Of the 181 flood events (1981–2015) that produced 154 fatalities, 36 were transnational, with the eastern part of the massif most affected. Dominant weather types show a southern component flow, with a talweg on the Iberian Peninsula and a depression in the vicinity. A positive and significant trend was found in Nouvelle-Aquitaine.
Colin M. Zarzycki, Benjamin D. Ascher, Alan M. Rhoades, and Rachel R. McCrary
Nat. Hazards Earth Syst. Sci., 24, 3315–3335, https://doi.org/10.5194/nhess-24-3315-2024, https://doi.org/10.5194/nhess-24-3315-2024, 2024
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We developed an automated workflow to detect rain-on-snow events, which cause flooding in the northeastern United States, in climate data. Analyzing the Susquehanna River basin, this technique identified known events affecting river flow. Comparing four gridded datasets revealed variations in event frequency and severity, driven by different snowmelt and runoff estimates. This highlights the need for accurate climate data in flood management and risk prediction for these compound extremes.
Dongyu Feng, Zeli Tan, Darren Engwirda, Jonathan D. Wolfe, Donghui Xu, Chang Liao, Gautam Bisht, James J. Benedict, Tian Zhou, Mithun Deb, Hong-Yi Li, and L. Ruby Leung
EGUsphere, https://doi.org/10.5194/egusphere-2024-2785, https://doi.org/10.5194/egusphere-2024-2785, 2024
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Our study explores how riverine and coastal flooding during hurricanes is influenced by the interaction of atmosphere, land, river and ocean conditions. Using an advanced Earth system model, we simulate Hurricane Irene to evaluate how meteorological and hydrological uncertainties affect flood modeling. Our findings reveal the importance of a multi-component modeling system, how hydrological conditions play critical roles in flood modeling, and greater flood risks if multiple factors are present.
Anne F. Van Loon, Sarra Kchouk, Alessia Matanó, Faranak Tootoonchi, Camila Alvarez-Garreton, Khalid E. A. Hassaballah, Minchao Wu, Marthe L. K. Wens, Anastasiya Shyrokaya, Elena Ridolfi, Riccardo Biella, Viorica Nagavciuc, Marlies H. Barendrecht, Ana Bastos, Louise Cavalcante, Franciska T. de Vries, Margaret Garcia, Johanna Mård, Ileen N. Streefkerk, Claudia Teutschbein, Roshanak Tootoonchi, Ruben Weesie, Valentin Aich, Juan P. Boisier, Giuliano Di Baldassarre, Yiheng Du, Mauricio Galleguillos, René Garreaud, Monica Ionita, Sina Khatami, Johanna K. L. Koehler, Charles H. Luce, Shreedhar Maskey, Heidi D. Mendoza, Moses N. Mwangi, Ilias G. Pechlivanidis, Germano G. Ribeiro Neto, Tirthankar Roy, Robert Stefanski, Patricia Trambauer, Elizabeth A. Koebele, Giulia Vico, and Micha Werner
Nat. Hazards Earth Syst. Sci., 24, 3173–3205, https://doi.org/10.5194/nhess-24-3173-2024, https://doi.org/10.5194/nhess-24-3173-2024, 2024
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Drought is a creeping phenomenon but is often still analysed and managed like an isolated event, without taking into account what happened before and after. Here, we review the literature and analyse five cases to discuss how droughts and their impacts develop over time. We find that the responses of hydrological, ecological, and social systems can be classified into four types and that the systems interact. We provide suggestions for further research and monitoring, modelling, and management.
Sheik Umar Jam-Jalloh, Jia Liu, Yicheng Wang, and Yuchen Liu
Nat. Hazards Earth Syst. Sci., 24, 3155–3172, https://doi.org/10.5194/nhess-24-3155-2024, https://doi.org/10.5194/nhess-24-3155-2024, 2024
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Our paper explores improving flood forecasting using advanced weather and hydrological models. By coupling the WRF model with WRF-Hydro and HEC-HMS, we achieved more accurate forecasts. WRF–WRF-Hydro excels for short, intense storms, while WRF–HEC-HMS is better for longer, evenly distributed storms. Our research shows how these models provide insights for adaptive atmospheric–hydrologic systems and aims to boost flood preparedness and response with more reliable, timely predictions.
Joshua Dorrington, Marta Wenta, Federico Grazzini, Linus Magnusson, Frederic Vitart, and Christian M. Grams
Nat. Hazards Earth Syst. Sci., 24, 2995–3012, https://doi.org/10.5194/nhess-24-2995-2024, https://doi.org/10.5194/nhess-24-2995-2024, 2024
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Extreme rainfall is the leading weather-related source of damages in Europe, but it is still difficult to predict on long timescales. A recent example of this was the devastating floods in the Italian region of Emiglia Romagna in May 2023. We present perspectives based on large-scale dynamical information that allows us to better understand and predict such events.
Alison L. Kay, Nick Dunstone, Gillian Kay, Victoria A. Bell, and Jamie Hannaford
Nat. Hazards Earth Syst. Sci., 24, 2953–2970, https://doi.org/10.5194/nhess-24-2953-2024, https://doi.org/10.5194/nhess-24-2953-2024, 2024
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Hydrological hazards affect people and ecosystems, but extremes are not fully understood due to limited observations. A large climate ensemble and simple hydrological model are used to assess unprecedented but plausible floods and droughts. The chain gives extreme flows outside the observed range: summer 2022 ~ 28 % lower and autumn 2023 ~ 42 % higher. Spatial dependence and temporal persistence are analysed. Planning for such events could help water supply resilience and flood risk management.
Viet Dung Nguyen, Jeroen Aerts, Max Tesselaar, Wouter Botzen, Heidi Kreibich, Lorenzo Alfieri, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 24, 2923–2937, https://doi.org/10.5194/nhess-24-2923-2024, https://doi.org/10.5194/nhess-24-2923-2024, 2024
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Our study explored how seasonal flood forecasts could enhance insurance premium accuracy. Insurers traditionally rely on historical data, yet climate fluctuations influence flood risk. We employed a method that predicts seasonal floods to adjust premiums accordingly. Our findings showed significant year-to-year variations in flood risk and premiums, underscoring the importance of adaptability. Despite limitations, this research aids insurers in preparing for evolving risks.
Shahin Khosh Bin Ghomash, Heiko Apel, and Daniel Caviedes-Voullième
Nat. Hazards Earth Syst. Sci., 24, 2857–2874, https://doi.org/10.5194/nhess-24-2857-2024, https://doi.org/10.5194/nhess-24-2857-2024, 2024
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Early warning is essential to minimise the impact of flash floods. We explore the use of highly detailed flood models to simulate the 2021 flood event in the lower Ahr valley (Germany). Using very high-resolution models resolving individual streets and buildings, we produce detailed, quantitative, and actionable information for early flood warning systems. Using state-of-the-art computational technology, these models can guarantee very fast forecasts which allow for sufficient time to respond.
Andrea Betterle and Peter Salamon
Nat. Hazards Earth Syst. Sci., 24, 2817–2836, https://doi.org/10.5194/nhess-24-2817-2024, https://doi.org/10.5194/nhess-24-2817-2024, 2024
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The study proposes a new framework, named FLEXTH, to estimate flood water depth and improve satellite-based flood monitoring using topographical data. FLEXTH is readily available as a computer code, offering a practical and scalable solution for estimating flood depth quickly and systematically over large areas. The methodology can reduce the impacts of floods and enhance emergency response efforts, particularly where resources are limited.
Manuel Grenier, Mathieu Boudreault, David A. Carozza, Jérémie Boudreault, and Sébastien Raymond
Nat. Hazards Earth Syst. Sci., 24, 2577–2595, https://doi.org/10.5194/nhess-24-2577-2024, https://doi.org/10.5194/nhess-24-2577-2024, 2024
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Modelling floods at the street level for large countries like Canada and the United States is difficult and very costly. However, many applications do not necessarily require that level of detail. As a result, we present a flood modelling framework built with artificial intelligence for socioeconomic studies like trend and scenarios analyses. We find for example that an increase of 10 % in average precipitation yields an increase in displaced population of 18 % in Canada and 14 % in the US.
Helge Bormann, Jenny Kebschull, Lidia Gaslikova, and Ralf Weisse
Nat. Hazards Earth Syst. Sci., 24, 2559–2576, https://doi.org/10.5194/nhess-24-2559-2024, https://doi.org/10.5194/nhess-24-2559-2024, 2024
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Inland flooding is threatening coastal lowlands. If rainfall and storm surges coincide, the risk of inland flooding increases. We examine how such compound events are influenced by climate change. Data analysis and model-based scenario analysis show that climate change induces an increasing frequency and intensity of compounding precipitation and storm tide events along the North Sea coast. Overload of inland drainage systems will also increase if no timely adaptation measures are taken.
Yanxia Shen, Zhenduo Zhu, Qi Zhou, and Chunbo Jiang
Nat. Hazards Earth Syst. Sci., 24, 2315–2330, https://doi.org/10.5194/nhess-24-2315-2024, https://doi.org/10.5194/nhess-24-2315-2024, 2024
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We present an improved Multigrid Dynamical Bidirectional Coupled hydrologic–hydrodynamic Model (IM-DBCM) with two major improvements: (1) automated non-uniform mesh generation based on the D-infinity algorithm was implemented to identify flood-prone areas where high-resolution inundation conditions are needed, and (2) ghost cells and bilinear interpolation were implemented to improve numerical accuracy in interpolating variables between the coarse and fine grids. The improved model was reliable.
Taylor Glen Johnson, Jorge Leandro, and Divine Kwaku Ahadzie
Nat. Hazards Earth Syst. Sci., 24, 2285–2302, https://doi.org/10.5194/nhess-24-2285-2024, https://doi.org/10.5194/nhess-24-2285-2024, 2024
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Reliance on infrastructure creates vulnerabilities to disruptions caused by natural hazards. To assess the impacts of natural hazards on the performance of infrastructure, we present a framework for quantifying resilience and develop a model of recovery based upon an application of project scheduling under resource constraints. The resilience framework and recovery model were applied in a case study to assess the resilience of building infrastructure to flooding hazards in Accra, Ghana.
Arnau Amengual, Romu Romero, María Carmen Llasat, Alejandro Hermoso, and Montserrat Llasat-Botija
Nat. Hazards Earth Syst. Sci., 24, 2215–2242, https://doi.org/10.5194/nhess-24-2215-2024, https://doi.org/10.5194/nhess-24-2215-2024, 2024
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On 22 October 2019, the Francolí River basin experienced a heavy precipitation event, resulting in a catastrophic flash flood. Few studies comprehensively address both the physical and human dimensions and their interrelations during extreme flash flooding. This research takes a step forward towards filling this gap in knowledge by examining the alignment among all these factors.
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 24, 2147–2164, https://doi.org/10.5194/nhess-24-2147-2024, https://doi.org/10.5194/nhess-24-2147-2024, 2024
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To identify flash flood potential in Germany, we shifted the most extreme rainfall events from the last 22 years systematically across Germany and simulated the consequent runoff reaction. Our results show that almost all areas in Germany have not seen the worst-case scenario of flood peaks within the last 22 years. With a slight spatial change of historical rainfall events, flood peaks of a factor of 2 or more would be achieved for most areas. The results can aid disaster risk management.
Günter Blöschl, Andreas Buttinger-Kreuzhuber, Daniel Cornel, Julia Eisl, Michael Hofer, Markus Hollaus, Zsolt Horváth, Jürgen Komma, Artem Konev, Juraj Parajka, Norbert Pfeifer, Andreas Reithofer, José Salinas, Peter Valent, Roman Výleta, Jürgen Waser, Michael H. Wimmer, and Heinz Stiefelmeyer
Nat. Hazards Earth Syst. Sci., 24, 2071–2091, https://doi.org/10.5194/nhess-24-2071-2024, https://doi.org/10.5194/nhess-24-2071-2024, 2024
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A methodology of regional flood hazard mapping is proposed, based on data in Austria, which combines automatic methods with manual interventions to maximise efficiency and to obtain estimation accuracy similar to that of local studies. Flood discharge records from 781 stations are used to estimate flood hazard patterns of a given return period at a resolution of 2 m over a total stream length of 38 000 km. The hazard maps are used for civil protection, risk awareness and insurance purposes.
Christoph Nathanael von Matt, Regula Muelchi, Lukas Gudmundsson, and Olivia Martius
Nat. Hazards Earth Syst. Sci., 24, 1975–2001, https://doi.org/10.5194/nhess-24-1975-2024, https://doi.org/10.5194/nhess-24-1975-2024, 2024
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The simultaneous occurrence of meteorological (precipitation), agricultural (soil moisture), and hydrological (streamflow) drought can lead to augmented impacts. By analysing drought indices derived from the newest climate scenarios for Switzerland (CH2018, Hydro-CH2018), we show that with climate change the concurrence of all drought types will increase in all studied regions of Switzerland. Our results stress the benefits of and need for both mitigation and adaptation measures at early stages.
Melody Gwyneth Whitehead and Mark Stephen Bebbington
Nat. Hazards Earth Syst. Sci., 24, 1929–1935, https://doi.org/10.5194/nhess-24-1929-2024, https://doi.org/10.5194/nhess-24-1929-2024, 2024
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Precipitation-driven hazards including floods, landslides, and lahars can be catastrophic and difficult to forecast due to high uncertainty around future weather patterns. This work presents a stochastic weather model that produces statistically similar (realistic) rainfall over long time periods at minimal computational cost. These data provide much-needed inputs for hazard simulations to support long-term, time and spatially varying risk assessments.
Sergiy Vorogushyn, Li Han, Heiko Apel, Viet Dung Nguyen, Björn Guse, Xiaoxiang Guan, Oldrich Rakovec, Husain Najafi, Luis Samaniego, and Bruno Merz
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-97, https://doi.org/10.5194/nhess-2024-97, 2024
Revised manuscript accepted for NHESS
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The July 2021 flood in Central Europe was one of the deadliest floods in Europe in the past decades and the most expensive flood in Germany. In this paper we show that the hydrological impact of this event in the Ahr valley could have been even worse if the rainfall footprint trajectory was only slightly different. The presented methodology of spatial counterfactuals generates plausible unprecedented events and helps better prepare for future extreme floods.
Jan Sodoge, Christian Kuhlicke, Miguel D. Mahecha, and Mariana Madruga de Brito
Nat. Hazards Earth Syst. Sci., 24, 1757–1777, https://doi.org/10.5194/nhess-24-1757-2024, https://doi.org/10.5194/nhess-24-1757-2024, 2024
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We delved into the socio-economic impacts of the 2018–2022 drought in Germany. We derived a dataset covering the impacts of droughts in Germany between 2000 and 2022 on sectors such as agriculture and forestry based on newspaper articles. Notably, our study illustrated that the longer drought had a wider reach and more varied effects. We show that dealing with longer droughts requires different plans compared to shorter ones, and it is crucial to be ready for the challenges they bring.
Mario Di Bacco, Daniela Molinari, and Anna Rita Scorzini
Nat. Hazards Earth Syst. Sci., 24, 1681–1696, https://doi.org/10.5194/nhess-24-1681-2024, https://doi.org/10.5194/nhess-24-1681-2024, 2024
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INSYDE 2.0 is a tool for modelling flood damage to residential buildings. By incorporating ultra-detailed survey and desk-based data, it improves the reliability and informativeness of damage assessments while addressing input data uncertainties.
Théo St. Pierre Ostrander, Thomé Kraus, Bruno Mazzorana, Johannes Holzner, Andrea Andreoli, Francesco Comiti, and Bernhard Gems
Nat. Hazards Earth Syst. Sci., 24, 1607–1634, https://doi.org/10.5194/nhess-24-1607-2024, https://doi.org/10.5194/nhess-24-1607-2024, 2024
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Mountain river confluences are hazardous during localized flooding events. A physical model was used to determine the dominant controls over mountain confluences. Contrary to lowland confluences, in mountain regions, the channel discharges and (to a lesser degree) the tributary sediment concentration control morphological patterns. Applying conclusions drawn from lowland confluences could misrepresent depositional and erosional patterns and the related flood hazard at mountain river confluences.
Cited articles
Abbaszadeh, P., Moradkhani, H., and Daescu, D. N.: The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework, Water Resour. Res., 55, 2407–2431, https://doi.org/10.1029/2018WR023629, 2019.
Abbaszadeh, P., Gavahi, K., Alipour, A., Deb, P., and Moradkhani, H.: Bayesian Multi-modeling of Deep Neural Nets for Probabilistic Crop Yield Prediction, Agr. Forest Meteorol., 314, 108773, https://doi.org/10.1016/j.agrformet.2021.108773, 2022a.
Abbaszadeh, P., Muñoz, D. F., Moftakhari, H., Jafarzadegan, K., and Moradkhani, H.: Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting, iScience, 25, 105201, https://doi.org/10.1016/j.isci.2022.105201, 2022b.
Alipour, A., Jafarzadegan, K., and Moradkhani, H.: Global sensitivity analysis in hydrodynamic modeling and flood inundation mapping, Environ. Modell. Softw., 152, 105398, https://doi.org/10.1016/j.envsoft.2022.105398, 2022.
Andreas, E. L., Mahrt, L., and Vickers, D.: A New Drag Relation for Aerodynamically Rough Flow over the Ocean, J. Atmos. Sci., 69, 2520–2537, https://doi.org/10.1175/JAS-D-11-0312.1, 2012.
Bates, P. D.: Flood Inundation Prediction, Annu. Rev. Fluid Mech., 54, 287–315, https://doi.org/10.1146/annurev-fluid-030121-113138, 2022.
Bates, P. D., Horritt, M. S., Aronica, G., and Beven, K.: Bayesian updating of flood inundation likelihoods conditioned on flood extent data, Hydrol. Process., 18, 3347–3370, https://doi.org/10.1002/hyp.1499, 2004.
Blake, E. and Zelinsky, D.: National Hurricane Center Tropical Cyclone Report Hurricane Harvey, National Hurricane Center, https://www.nhc.noaa.gov/data/tcr/AL092017_Harvey.pdf (last access: 2 February 2024), 2018.
Brauer, N. S., Basara, J. B., Homeyer, C. R., McFarquhar, G. M., and Kirstetter, P. E.: Quantifying Precipitation Efficiency and Drivers of Excessive Precipitation in Post-Landfall Hurricane Harvey, J. Hydrometeorol., 21, 433–452, https://doi.org/10.1175/JHM-D-19-0192.1, 2020.
Chen, F.-W. and Liu, C.-W.: Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan, Paddy Water Environ., 10, 209–222, https://doi.org/10.1007/s10333-012-0319-1, 2012.
Chen, M., Nabih, S., Brauer, N. S., Gao, S., Gourley, J. J., Hong, Z., Kolar, R. L., and Hong, Y.: Can Remote Sensing Technologies Capture the Extreme Precipitation Event and Its Cascading Hydrological Response? A Case Study of Hurricane Harvey Using EF5 Modeling Framework, Remote Sens., 12, 445, https://doi.org/10.3390/rs12030445, 2020.
Chen, M., Li, Z., Gao, S., Luo, X., Wing, O. E. J., Shen, X., Gourley, J. J., Kolar, R. L., and Hong, Y.: A comprehensive flood inundation mapping for Hurricane Harvey using an integrated hydrological and hydraulic model, J. Hydrometeorol., 22, 1713–1726, https://doi.org/10.1175/JHM-D-20-0218.1, 2021.
Chen, M., Li, Z., Gao, S., Xue, M., Gourley, J. J., Kolar, R. L., and Hong, Y.: A flood predictability study for Hurricane Harvey with the CREST-iMAP model using high-resolution quantitative precipitation forecasts and U-Net deep learning precipitation nowcasts, J. Hydrol., 612, 128168, https://doi.org/10.1016/j.jhydrol.2022.128168, 2022.
Costabile, P., Costanzo, C., Ferraro, D., Macchione, F., and Petaccia, G.: Performances of the New HEC-RAS Version 5 for 2-D Hydrodynamic-Based Rainfall-Runoff Simulations at Basin Scale: Comparison with a State-of-the Art Model, Water, 12, 2326, https://doi.org/10.3390/w12092326, 2020.
David, A. and Schmalz, B.: A Systematic Analysis of the Interaction between Rain-on-Grid-Simulations and Spatial Resolution in 2D Hydrodynamic Modeling, Water, 13, 2346, https://doi.org/10.3390/w13172346, 2021.
Dewitz, J. and U.S. Geological Survey: National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021), U.S. Geological Survey data release, https://doi.org/10.5066/P9KZCM54, 2021.
Di Baldassarre, G., Schumann, G., Bates, P. D., Freer, J. E., and Beven, K. J.: Flood-plain mapping: a critical discussion of deterministic and probabilistic approaches, Hydrolog. Sci. J., 55, 364–376, https://doi.org/10.1080/02626661003683389, 2010.
Du, J.: NCEP/EMC 4KM Gridded Data (GRIB) Stage IV Data. Version 1.0 (Version 1.0) Hourly precipitation amount, UCAR/NCAR – Earth Observing Laboratory, https://doi.org/10.5065/D6PG1QDD, 2011.
Duan, Q., Ajami, N. K., Gao, X., and Sorooshian, S.: Multi-model ensemble hydrologic prediction using Bayesian model averaging, Adv. Water Resour., 30, 1371–1386, https://doi.org/10.1016/j.advwatres.2006.11.014, 2007.
Dullo, T. T., Gangrade, S., Morales-Hernández, M., Sharif, M. B., Kao, S., Kalyanapu, A. J., Ghafoor, S., and Evans, K. J.: Simulation of Hurricane Harvey flood event through coupled hydrologic-hydraulic models: Challenges and next steps, J. Flood Risk Manag., 14, https://doi.org/10.1111/jfr3.12716, 2021.
Fagnant, C., Gori, A., Sebastian, A., Bedient, P. B., and Ensor, K. B.: Characterizing spatiotemporal trends in extreme precipitation in Southeast Texas, Nat. Hazards, 104, 1597–1621, https://doi.org/10.1007/s11069-020-04235-x, 2020.
Gao, S., Zhang, J., Li, D., Jiang, H., and Fang, Z. N.: Evaluation of Multiradar Multisensor and Stage IV Quantitative Precipitation Estimates during Hurricane Harvey, Nat. Hazards Rev., 22, 04020057, https://doi.org/10.1061/(ASCE)NH.1527-6996.0000435, 2021.
Garcia, M., Juan, A., and Bedient, P.: Integrating Reservoir Operations and Flood Modeling with HEC-RAS 2D, Water, 12, 2259, https://doi.org/10.3390/w12082259, 2020.
Gavahi, K., Foroumandi, E., and Moradkhani, H.: A deep learning-based framework for multi-source precipitation fusion, Remote Sens. Environ., 295, 113723, https://doi.org/10.1016/j.rse.2023.113723, 2023.
Grønneberg, S. and Hjort, N. L.: The Copula Information Criteria, Scandinavian J. Statistics, 41, 436–459, https://doi.org/10.1111/sjos.12042, 2014.
Habibi, H., Awal, R., Fares, A., and Temimi, M.: Performance of Multi-Radar Multi-Sensor (MRMS) product in monitoring precipitation under extreme events in Harris County, Texas, J. Hydrol., 598, 126385, https://doi.org/10.1016/j.jhydrol.2021.126385, 2021.
Han, S. and Coulibaly, P.: Bayesian flood forecasting methods: A review, J. Hydrol., 551, 340–351, https://doi.org/10.1016/j.jhydrol.2017.06.004, 2017.
He, S., Guo, S., Liu, Z., Yin, J., Chen, K., and Wu, X.: Uncertainty analysis of hydrological multi-model ensembles based on CBP-BMA method, Hydrol. Res., 49, 1636–1651, https://doi.org/10.2166/nh.2018.160, 2018.
Hofert, M., Kojadinovic, I., Maechler, M., and Yan, J.: copula: Multivariate Dependence with Copulas, https://doi.org/10.32614/CRAN.package.copula, 2023.
Huang, T. and Merwade, V.: Uncertainty Analysis and Quantification in Flood Insurance Rate Maps Using Bayesian Model Averaging and Hierarchical BMA, J. Hydrol. Eng., 28, 04022038, https://doi.org/10.1061/JHYEFF.HEENG-5851, 2023.
Huang, W., Ye, F., Zhang, Y. J., Park, K., Du, J., Moghimi, S., Myers, E., Pe'eri, S., Calzada, J. R., Yu, H. C., Nunez, K., and Liu, Z.: Compounding factors for extreme flooding around Galveston Bay during Hurricane Harvey, Ocean Modell., 158, 101735, https://doi.org/10.1016/j.ocemod.2020.101735, 2021.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/gpm/imerg/3b-hh/07, 2019.
Jafarzadegan, K., Merwade, V., and Moradkhani, H.: Combining clustering and classification for the regionalization of environmental model parameters: Application to floodplain mapping in data-scarce regions, Environ. Modell. Softw., 125, 104613, https://doi.org/10.1016/j.envsoft.2019.104613, 2020.
Jafarzadegan, K., Abbaszadeh, P., and Moradkhani, H.: Sequential data assimilation for real-time probabilistic flood inundation mapping, Hydrol. Earth Syst. Sci., 25, 4995–5011, https://doi.org/10.5194/hess-25-4995-2021, 2021a.
Jafarzadegan, K., Alipour, A., Gavahi, K., Moftakhari, H., and Moradkhani, H.: Toward improved river boundary conditioning for simulation of extreme floods, Adv. Water Resour., 158, 104059, https://doi.org/10.1016/j.advwatres.2021.104059, 2021b.
Jafarzadegan, K., Moradkhani, H., Pappenberger, F., Moftakhari, H., Bates, P., Abbaszadeh, P., Marsooli, R., Ferreira, C., Cloke, H. L., Ogden, F., and Duan, Q.: Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling, Rev. Geophys., 61, e2022RG000788, https://doi.org/10.1029/2022RG000788, 2023.
Jiang, H., Zhang, J., Liu, Y., Li, J., and Fang, Z. N.: Does flooding get worse with subsiding land? Investigating the impacts of land subsidence on flood inundation from Hurricane Harvey, Sci. Total Environ., 865, 161072, https://doi.org/10.1016/j.scitotenv.2022.161072, 2023.
Juan, A., Gori, A., and Sebastian, A.: Comparing floodplain evolution in channelized and unchannelized urban watersheds in Houston, Texas, J. Flood Risk Manage., 13, e12604, https://doi.org/10.1111/jfr3.12604, 2020.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
Liu, Z. and Merwade, V.: Accounting for model structure, parameter and input forcing uncertainty in flood inundation modeling using Bayesian model averaging, J. Hydrol., 565, 138–149, https://doi.org/10.1016/j.jhydrol.2018.08.009, 2018.
Liu, Z. and Merwade, V.: Separation and prioritization of uncertainty sources in a raster based flood inundation model using hierarchical Bayesian model averaging, J. Hydrol., 578, 124100, https://doi.org/10.1016/j.jhydrol.2019.124100, 2019.
Liu, Z., Merwade, V., and Jafarzadegan, K.: Investigating the role of model structure and surface roughness in generating flood inundation extents using one- and two-dimensional hydraulic models, J. Flood Risk Manage., 12, e12347, https://doi.org/10.1111/jfr3.12347, 2019.
Madadgar, S. and Moradkhani, H.: Improved Bayesian multimodeling: Integration of copulas and Bayesian model averaging, Water Resour. Res., 50, 9586–9603, https://doi.org/10.1002/2014WR015965, 2014.
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.
Moftakhari, H., AghaKouchak, A., Sanders, B. F., Matthew, R. A., and Mazdiyasni, O.: Translating Uncertain Sea Level Projections Into Infrastructure Impacts Using a Bayesian Framework, Geophys. Res. Lett., 44, 914–921, https://doi.org/10.1002/2017GL076116, 2017.
Muñoz, D. F., Abbaszadeh, P., Moftakhari, H., and Moradkhani, H.: Accounting for uncertainties in compound flood hazard assessment: The value of data assimilation, Coast. Eng., 171, 104057, https://doi.org/10.1016/j.coastaleng.2021.104057, 2022.
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.e2161bac, 2019.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
National Centers for Environmental Information: Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado, Boulder, 2014: Continuously Updated Digital Elevation Model (CUDEM) –
Nelson, B. R., Prat, O. P., Seo, D.-J., and Habib, E.: Assessment and Implications of NCEP Stage IV Quantitative Precipitation Estimates for Product Intercomparisons, Weather Forecast., 31, 371–394, https://doi.org/10.1175/WAF-D-14-00112.1, 2016.
Noh, S., Lee, J.-H., Lee, S., and Seo, D.-J.: Retrospective Dynamic Inundation Mapping of Hurricane Harvey Flooding in the Houston Metropolitan Area Using High-Resolution Modeling and High-Performance Computing, Water, 11, 597, https://doi.org/10.3390/w11030597, 2019.
Omranian, E., Sharif, H., and Tavakoly, A.: How Well Can Global Precipitation Measurement (GPM) Capture Hurricanes? Case Study: Hurricane Harvey, Remote Sens., 10, 1150, https://doi.org/10.3390/rs10071150, 2018.
Oruc Baci, N., Jafarzadegan, K., and Hamid, M.: Improving flood inundation modeling skill: interconnection between model parameters and boundary conditions, Modeling Earth Systems and Environment, 10, 1–15, https://doi.org/10.1007/s40808-023-01768-5, 2023.
Parrish, M. A., Moradkhani, H., and DeChant, C. M.: Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation, Water Resour. Res., 48, 1–18, https://doi.org/10.1029/2011WR011116, 2012.
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133, 1155–1174, https://doi.org/10.1175/MWR2906.1, 2005.
Saksena, S., Dey, S., Merwade, V., and Singhofen, P. J.: A Computationally Efficient and Physically Based Approach for Urban Flood Modeling Using a Flexible Spatiotemporal Structure, Water Resour. Res., 56, 1–22, https://doi.org/10.1029/2019WR025769, 2020.
Savage, J. T. S., Bates, P., Freer, J., Neal, J., and Aronica, G.: When does spatial resolution become spurious in probabilistic flood inundation predictions?: When Spatial Resolution Becomes Spurious in Probabilistic Flood Maps, Hydrol. Process., 30, 2014–2032, https://doi.org/10.1002/hyp.10749, 2016.
Schubert, J. E., Luke, A., AghaKouchak, A., and Sanders, B. F.: A Framework for Mechanistic Flood Inundation Forecasting at the Metropolitan Scale, Water Resour. Res., 58, e2021WR031279, https://doi.org/10.1029/2021WR031279, 2022.
Scotti, V., Giannini, M., and Cioffi, F.: Enhanced flood mapping using synthetic aperture radar (SAR) images, hydraulic modelling, and social media: A case study of Hurricane Harvey (Houston, TX), J. Flood Risk Manage., 13, 1–18, https://doi.org/10.1111/jfr3.12647, 2020.
Sebastian, A., Bader, D. J., Nederhoff, C. M., Leijnse, T. W. B., Bricker, J. D., and Aarninkhof, S. G. J.: Hindcast of pluvial, fluvial, and coastal flood damage in Houston, Texas during Hurricane Harvey (2017) using SFINCS, Nat. Hazards, 109, 2343–2362, https://doi.org/10.1007/s11069-021-04922-3, 2021.
Sklar, M.: Fonctions de répartition à N dimensions et leurs marges, Annales de l'ISUP, 8, 229–231, 1959.
Stephens, T. A., Savant, G., Sanborn, S. C., Wallen, C. M., and Roy, S.: Monolithic Multiphysics Simulation of Compound Flooding, J. Hydraul. Eng., 148, 05022003, https://doi.org/10.1061/(ASCE)HY.1943-7900.0002000, 2022.
Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., and Kim, S.: Flood inundation modelling: A review of methods, recent advances and uncertainty analysis, Environ. Modell. Softw., 90, 201–216, https://doi.org/10.1016/j.envsoft.2017.01.006, 2017.
Thornton, M. M., Shrestha, R., Wei, Y., Thornton, P. E., Kao, S.-C., and Wilson, B. E.: Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4 R1, https://doi.org/10.3334/ORNLDAAC/2129, 2022.
U.S. Geological Survey: National Water Information System data available on the World Wide Web (USGS Water Data for the Nation), https://doi.org/10.5066/F7P55KJN, 2016.
USACE: HEC-RAS River Analysis System, Version 6.3.1, Hydrologic Engineering Center, https://www.hec.usace.army.mil/software/hec-ras/ (last access: 15 December 2022), 2022.
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.
Wang, S.-Y. S., Zhao, L., Yoon, J.-H., Klotzbach, P., and Gillies, R. R.: Quantitative attribution of climate effects on Hurricane Harvey's extreme rainfall in Texas, Environ. Res. Lett., 13, 054014, https://doi.org/10.1088/1748-9326/aabb85, 2018.
Wing, O. E. J., Sampson, C. C., Bates, P. D., Quinn, N., Smith, A. M., and Neal, J. C.: A flood inundation forecast of Hurricane Harvey using a continental-scale 2D hydrodynamic model, J. Hydrol. X, 4, 100039, https://doi.org/10.1016/j.hydroa.2019.100039, 2019.
Wootten, A. and Boyles, R. P.: Comparison of NCEP Multisensor Precipitation Estimates with Independent Gauge Data over the Eastern United States, J. Appl. Meteorol. Clim., 53, 2848–2862, https://doi.org/10.1175/JAMC-D-14-0034.1, 2014.
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V., Duan, Q., Mo, K., Fan, Y., and Mocko, D.: NLDAS Primary Forcing Data L4 Hourly 0.125 × 0.125 degrees V002, Edited by David Mocko, NASA/GSFC/HSL, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/6J5LHHOHZHN4, 2009.
Xie, P., Joyce, R., Wu, S., Yoo, S.-H., Yarosh, Y., Sun, F., Lin, R., and NOAA CDR Program: NOAA Climate Data Record (CDR) of CPC Morphing Technique (CMORPH) High Resolution Global Precipitation Estimates, Version 1 [Precipitation], NOAA National Centers for Environmental Information, https://doi.org/10.25921/w9va-q159, 2019.
Yan, Z., Zhou, Z., Liu, J., Han, Z., Gao, G., and Jiang, X.: Ensemble Projection of Runoff in a Large-Scale Basin: Modeling With a Global BMA Approach, Water Resour. Res., 56, 1–17, https://doi.org/10.1029/2019WR026134, 2020.
Zeiger, S. J. and Hubbart, J. A.: Measuring and modeling event-based environmental flows: An assessment of HEC-RAS 2D rain-on-grid simulations, J. Environ. Manage., 285, 112125, https://doi.org/10.1016/j.jenvman.2021.112125, 2021.
Zhang, J., Howard, K., Langston, C., Kaney, B., Qi, Y., Tang, L., Grams, H., Wang, Y., Cocks, S., Martinaitis, S., Arthur, A., Cooper, K., Brogden, J., and Kitzmiller, D.: Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities, B. Am. Meteorol. Soc., 97, 621–638, https://doi.org/10.1175/BAMS-D-14-00174.1, 2016.
Short summary
This study utilizes the global copula Bayesian model averaging technique for accurate and reliable flood modeling, especially in coastal regions. By integrating multiple precipitation datasets within this framework, we can effectively address sources of error in each dataset, leading to the generation of probabilistic flood maps. The creation of these probabilistic maps is essential for disaster preparedness and mitigation in densely populated areas susceptible to extreme weather events.
This study utilizes the global copula Bayesian model averaging technique for accurate and...
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