Articles | Volume 21, issue 2
https://doi.org/10.5194/nhess-21-807-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-807-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Mar 2021
Research article |
| 01 Mar 2021
| 01 Mar 2021
Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas
Department of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USA
Antonia Sebastian
Department of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USA
Department of Geological Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
Russell Blessing
Department of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USA
Wesley E. Highfield
Department of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USA
Laura Stearns
Department of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USA
Samuel D. Brody
Department of Marine Sciences, Texas A&M University at Galveston, Galveston, Texas, USA
Related authors
No articles found.
Julius Schlumberger, Tristian Stolte, Helena Margaret Garcia, Antonia Sebastian, Wiebke Jäger, Philip Ward, Marleen de Ruiter, Robert Šakić Trogrlić, Annegien Tijssen, and Mariana Madruga de Brito
EGUsphere, https://doi.org/10.5194/egusphere-2025-850, https://doi.org/10.5194/egusphere-2025-850, 2025
Short summary
Short summary
The risk flood of flood impacts is dynamic as society continuously responds to specific events or ongoing developments. We analyzed 28 studies that assess such dynamics of vulnerability. Most research uses surveys and basic statistics data, while integrated, flexible models are seldom used. The studies struggle to link specific events or developments to the observed changes. Our findings highlight needs and possible directions towards a better assessment of vulnerability dynamics.
Related subject area
Hydrological Hazards
The 2018–2023 drought in Berlin: impacts and analysis of the perspective of water resources management
Recent large-inland-lake outbursts on the Tibetan Plateau: processes, causes, and mechanisms
Modelling urban stormwater drainage overflows for assessing flood hazards: application to the urban area of Dakar (Senegal)
Dynamics and impacts of monsoon-induced geological hazards: a 2022 flood study along the Swat River in Pakistan
Monte Carlo-based sensitivity analysis of the RIM2D hydrodynamic model for the 2021 flood event in western Germany
Mind the gap: misalignment between drought monitoring and community realities
Post-wildfire sediment source and transport modeling, empirical observations, and applied mitigation: an Arizona, USA, case study
Causes of the exceptionally high number of fatalities in the Ahr valley, Germany, during the 2021 flood
Large-scale flood risk assessment in data-scarce areas: an application to Central Asia
Multi-scale hydraulic graph neural networks for flood modelling
The role of antecedent conditions in translating precipitation events into extreme floods at the catchment scale and in a large-basin context
Brief communication: Stay local or go global? On the construction of plausible counterfactual scenarios to assess flash flood hazards
Integrating susceptibility maps of multiple hazards and building exposure distribution: a case study of wildfires and floods for the province of Quang Nam, Vietnam
Tangible and intangible ex post assessment of flood-induced damage to cultural heritage
A multivariate statistical framework for mixed storm types in compound flood analysis
Invited perspectives: safeguarding the usability and credibility of flood hazard and risk assessments
Influence of building collapse on pluvial and fluvial flood inundation of metro stations in central Shanghai
Impact of drought hazards on flow regimes in anthropogenically impacted streams: an isotopic perspective on climate stress
The effect of wildfires on flood risk: a multi-hazard flood risk approach for the Ebro River basin, Spain
Modelling hazards impacting the flow regime in the Hranice Karst due to the proposed Skalička Dam
Spatiotemporal variability of flash floods and their human impacts in the Czech Republic during the 2001–2023 period
Risk of compound flooding substantially increases in the future Mekong River delta
Transferability of machine-learning-based modeling frameworks across flood events for hindcasting maximum river water depths in coastal watersheds
Floods in the Pyrenees: a global view through a regional database
Algorithmically detected rain-on-snow flood events in different climate datasets: a case study of the Susquehanna River basin
Disentangling Atmospheric, Hydrological, and Coupling Uncertainties in Compound Flood Modeling within a Coupled Earth System Model
Review article: Drought as a continuum – memory effects in interlinked hydrological, ecological, and social systems
Coupling WRF with HEC-HMS and WRF-Hydro for flood forecasting in typical mountainous catchments of northern China
Precursors and pathways: dynamically informed extreme event forecasting demonstrated on the historic Emilia-Romagna 2023 flood
Demonstrating the use of UNSEEN climate data for hydrological applications: case studies for extreme floods and droughts in England
Exploring the use of seasonal forecasts to adapt flood insurance premiums
Are 2D shallow-water solvers fast enough for early flood warning? A comparative assessment on the 2021 Ahr valley flood event
Water depth estimate and flood extent enhancement for satellite-based inundation maps
Brief Communication: Rapid high-resolution flood impact-based early warning is possible with RIM2D: a showcase for the 2023 pluvial flood in Braunschweig
Probabilistic flood inundation mapping through copula Bayesian multi-modeling of precipitation products
Flood occurrence and impact models for socioeconomic applications over Canada and the United States
Model-based assessment of climate change impact on inland flood risk at the German North Sea coast caused by compounding storm tide and precipitation events
An improved dynamic bidirectional coupled hydrologic–hydrodynamic model for efficient flood inundation prediction
Quantifying hazard resilience by modeling infrastructure recovery as a resource-constrained project scheduling problem
Hydrometeorological controls of and social response to the 22 October 2019 catastrophic flash flood in Catalonia, north-eastern Spain
A downward-counterfactual analysis of flash floods in Germany
Hyper-resolution flood hazard mapping at the national scale
Compound droughts under climate change in Switzerland
Brief communication: SWM – stochastic weather model for precipitation-related hazard assessments using ERA5-Land data
It could have been much worse: spatial counterfactuals of the July 2021 flood in the Ahr valley, Germany
Text mining uncovers the unique dynamics of socio-economic impacts of the 2018–2022 multi-year drought in Germany
The value of multi-source data for improved flood damage modelling with explicit input data uncertainty treatment: INSYDE 2.0
Limited effect of the confluence angle and tributary gradient on Alpine confluence morphodynamics under intense sediment loads
Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods?
Added value of seasonal hindcasts to create UK hydrological drought storylines
Ina Pohle, Sarah Zeilfelder, Johannes Birner, and Benjamin Creutzfeldt
Nat. Hazards Earth Syst. Sci., 25, 1293–1313, https://doi.org/10.5194/nhess-25-1293-2025, https://doi.org/10.5194/nhess-25-1293-2025, 2025
Short summary
Short summary
Climate change, the lignite mining phase-out and structural changes challenge water resources management of the German capital Berlin. Reduced water availability and rising demand are creating latent water quality problems. The 2018–2023 drought uniquely impacted temperature, precipitation, groundwater and surface water. Analysing the impacts of the 2018–2023 drought helps to address water-related challenges and implement effective measures in Berlin and its surrounding areas.
Fenglin Xu, Yong Liu, Guoqing Zhang, Ping Zhao, R. Iestyn Woolway, Yani Zhu, Jianting Ju, Tao Zhou, Xue Wang, and Wenfeng Chen
Nat. Hazards Earth Syst. Sci., 25, 1187–1206, https://doi.org/10.5194/nhess-25-1187-2025, https://doi.org/10.5194/nhess-25-1187-2025, 2025
Short summary
Short summary
Glacial lake outbursts have been widely studied, but large-inland-lake outbursts have received less attention. Recently, with the rapid expansion of inland lakes, signs of potential outbursts have increased. However, their processes, causes, and mechanisms are still not well understood. Here, the outburst processes of two inland lakes were investigated using a combination of field surveys, remote sensing mapping, and hydrodynamic modeling. Their causes and mechanisms were also investigated.
Laurent Pascal Malang Diémé, Christophe Bouvier, Ansoumana Bodian, and Alpha Sidibé
Nat. Hazards Earth Syst. Sci., 25, 1095–1112, https://doi.org/10.5194/nhess-25-1095-2025, https://doi.org/10.5194/nhess-25-1095-2025, 2025
Short summary
Short summary
We propose a decision support tool that detect the occurrence of flooding by drainage overflow, with sufficiently short calculation times. The simulations are based on a drainage topology on 5 m grids, incorporating changes to surface flows induced by urbanization. The method can be used for flood mapping in project mode and in real time. It applies to the present situation as well as to any scenario involving climate change or urban growth.
Nazir Ahmed Bazai, Mehtab Alam, Peng Cui, Wang Hao, Adil Poshad Khan, Muhammad Waseem, Yao Shunyu, Muhammad Ramzan, Li Wanhong, and Tashfain Ahmed
Nat. Hazards Earth Syst. Sci., 25, 1071–1093, https://doi.org/10.5194/nhess-25-1071-2025, https://doi.org/10.5194/nhess-25-1071-2025, 2025
Short summary
Short summary
The 2022 monsoon in Pakistan's Swat River basin brought record rainfall, exceeding averages by 7–8%, triggering catastrophic debris flows and floods. Key factors include extreme rainfall, deforestation, and steep slopes. Fieldwork, remote sensing, and simulations highlight land degradation's role in intensifying floods. Recommendations include reforestation, early warning systems, and land use reforms to protect communities and reduce future risks
Shahin Khosh Bin Ghomash, Patricio Yeste, Heiko Apel, and Viet Dung Nguyen
Nat. Hazards Earth Syst. Sci., 25, 975–990, https://doi.org/10.5194/nhess-25-975-2025, https://doi.org/10.5194/nhess-25-975-2025, 2025
Short summary
Short summary
Hydrodynamic models are vital for predicting floods, like those in Germany's Ahr region in July 2021. We refine the RIM2D model for the Ahr region, analyzing the impact of various factors using Monte Carlo simulations. Accurate parameter assignment is crucial, with channel roughness and resolution playing key roles. Coarser resolutions are suitable for flood extent predictions, aiding early-warning systems. Our work provides guidelines for optimizing hydrodynamic models in the Ahr region.
Sarra Kchouk, Louise Cavalcante, Lieke A. Melsen, David W. Walker, Germano Ribeiro Neto, Rubens Gondim, Wouter J. Smolenaars, and Pieter R. van Oel
Nat. Hazards Earth Syst. Sci., 25, 893–912, https://doi.org/10.5194/nhess-25-893-2025, https://doi.org/10.5194/nhess-25-893-2025, 2025
Short summary
Short summary
Droughts impact water and people, yet monitoring often overlooks impacts on people. In northeastern Brazil, we compare official data to local experiences, finding data mismatches and blind spots. Mismatches occur due to the data's broad scope missing finer details. Blind spots arise from ignoring diverse community responses and vulnerabilities to droughts. We suggest enhanced monitoring by technical extension officers for both severe and mild droughts.
Edward R. Schenk, Alex Wood, Allen Haden, Gabriel Baca, Jake Fleishman, and Joe Loverich
Nat. Hazards Earth Syst. Sci., 25, 727–745, https://doi.org/10.5194/nhess-25-727-2025, https://doi.org/10.5194/nhess-25-727-2025, 2025
Short summary
Short summary
Post-wildfire flooding and debris are dangerous and damaging. This study used three different sediment models to predict post-wildfire sediment sources and transport amounts downstream of the 2019 Museum Fire in northern Arizona, USA. The predictions were compared with real-world measurements of sediment that was cleaned out of the city of Flagstaff after four large floods in 2021. Results provide avenues for continued model refinement and an example of potential mitigation strategies.
Belinda Rhein and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 25, 581–589, https://doi.org/10.5194/nhess-25-581-2025, https://doi.org/10.5194/nhess-25-581-2025, 2025
Short summary
Short summary
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
Nat. Hazards Earth Syst. Sci., 25, 403–428, https://doi.org/10.5194/nhess-25-403-2025, https://doi.org/10.5194/nhess-25-403-2025, 2025
Short summary
Short summary
A fully probabilistic flood risk assessment was carried out for five Central Asian countries to support regional and national risk financing and insurance applications. The paper presents the first high-resolution regional-scale transboundary flood risk assessment study in the area aiming to provide tools for decision-making.
Roberto Bentivoglio, Elvin Isufi, Sebastiaan Nicolas Jonkman, and Riccardo Taormina
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Shahin Khosh Bin Ghomash, Heiko Apel, Kai Schröter, and Max Steinhausen
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-139, https://doi.org/10.5194/nhess-2024-139, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
This work introduces RIM2D, a hydrodynamic model for precise and rapid flood predictions, ideal for early warning systems. We demonstrate RIM2D's ability to deliver detailed and localized flood forecasts using the June 2023 flood in Braunschweig, Germany, as a case study. This research highlights the readiness of RIM2D and the required hardware for integration into operational flood warning and impact-based forecasting systems.
Francisco Javier Gomez, Keighobad Jafarzadegan, Hamed Moftakhari, and Hamid Moradkhani
Nat. Hazards Earth Syst. Sci., 24, 2647–2665, https://doi.org/10.5194/nhess-24-2647-2024, https://doi.org/10.5194/nhess-24-2647-2024, 2024
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Nils Poncet, Philippe Lucas-Picher, Yves Tramblay, Guillaume Thirel, Humberto Vergara, Jonathan Gourley, and Antoinette Alias
Nat. Hazards Earth Syst. Sci., 24, 1163–1183, https://doi.org/10.5194/nhess-24-1163-2024, https://doi.org/10.5194/nhess-24-1163-2024, 2024
Short summary
Short summary
High-resolution convection-permitting climate models (CPMs) are now available to better simulate rainstorm events leading to flash floods. In this study, two hydrological models are compared to simulate floods in a Mediterranean basin, showing a better ability of the CPM to reproduce flood peaks compared to coarser-resolution climate models. Future projections are also different, with a projected increase for the most severe floods and a potential decrease for the most frequent events.
Wilson C. H. Chan, Nigel W. Arnell, Geoff Darch, Katie Facer-Childs, Theodore G. Shepherd, and Maliko Tanguy
Nat. Hazards Earth Syst. Sci., 24, 1065–1078, https://doi.org/10.5194/nhess-24-1065-2024, https://doi.org/10.5194/nhess-24-1065-2024, 2024
Short summary
Short summary
The most recent drought in the UK was declared in summer 2022. We pooled a large sample of plausible winters from seasonal hindcasts and grouped them into four clusters based on their atmospheric circulation configurations. Drought storylines representative of what the drought could have looked like if winter 2022/23 resembled each winter circulation storyline were created to explore counterfactuals of how bad the 2022 drought could have been over winter 2022/23 and beyond.
Cited articles
Albers, S. J. and Déry, S. J.: Flooding in the Nechako River Basin of Canada: A random forest modeling approach to flood analysis in a regulated reservoir system, Can. Water Resour. J., 41, 250–260, https://doi.org/10.1080/07011784.2015.1109480, 2015. a
Anderson, B., Rutherfurd, I., and Western, A.: An analysis of the influence of riparian vegetation on the propagation of flood
waves, Environ. Modell. Softw., 21, 1290–1296, 2006. a
Anderson, D. G.: Effects of urban development on floods in northern Virginia,
US Government Printing Office, Washington DC, 27 pp., 1970. a
Apel, H., Aronica, G., Kreibich, H., and Thieken, A.: Flood risk analyses – how detailed do we need to be?, Nat. Hazards, 49, 79–98, 2009. a
Arnold Jr., C. L. and Gibbons, C. J.: Impervious surface coverage: the emergence of a key environmental
indicator, J. Am. Plann. Assoc., 62, 243–258, 1996. a
Bates, P. D., Quinn, N., Sampson, C., et al.: Combined modelling of US
fluvial, pluvial and coastal flood hazard under current and future
climates, Water Resour. Res., e2020WR028673, 2020. a
Belgiu, M. and Drăgut, L.: Random forest in remote sensing: A review of
applications and future directions, 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011,
2016. a
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin
versant, Hydrolog. Sci. J., 24, 43–69, 1979. a
Blessing, R., Sebastian, A., and Brody, S. D.: Flood risk delineation in the
United States: How much loss are we capturing?, Nat. Hazards Rev., 18,
04017002, https://doi.org/10.1061/(ASCE)NH.1527-6996.0000242, 2017. a, b, c
Breiman, L.: Out-of-bag estimation, Techinical report, Department of Statistics, University of California, 1996. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Brody, S., Blessing, R., Sebastian, A., and Bedient, P.: Examining the impact
of land use/land cover characteristics on flood
losses, J. Environ. Plann. Man., 57, 1252–1265, https://doi.org/10.1080/09640568.2013.802228, 2013. a
Brody, S., Sebastian, A., Blessing, R., and Bedient, P.: Case study results
from southeast Houston, Texas: identifying the impacts of residential
location on flood risk and loss, J. Flood Risk Manag., 11, 110–120, 2018. a
Brody, S. D., Zahran, S., Highfield, W. E., Bernhardt, S. P., and Vedlitz, A.: Policy Learning for Flood Mitigation: A Longitudinal Assessment of the
Community Rating System in Florida, Risk Anal., 29, 912–929,
https://doi.org/10.1111/j.1539-6924.2009.01210.x, 2009. a
Bui, D. T., Ngo, P. T. T., Pham, T. D., Jaafari, A., Minh, N. Q., Hoa, P. V.,
and Samui, P.: A novel hybrid approach based on a swarm intelligence
optimized extreme learning machine for flash flood susceptibility mapping,
Catena, 179, 184–196, https://doi.org/10.1016/j.catena.2019.04.009, 2019. a, b, c, d, e, f
Chen, C., Liaw, A., and Breiman, L.: Using random forest to learn imbalanced
data, University of California, Berkeley, USA, 2004. a
Center for Texas Beaches and Shores (CTBS): https://dataverse.tdl.org/dataverse/M3FR, last access: 31 March 2020. a
Dodangeh, E., Choubin, B., Eigdir, A. N., Nabipour, N., Panahi, M.,
Shamshirband, S., and Mosavi, A.: Integrated machine learning methods with
resampling algorithms for flood susceptibility
prediction, Sci. Total Environ., 705, 135983, https://doi.org/10.1016/j.scitotenv.2019.135983, 2020. a, b, c
Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., and Yates,
C. J.: A statistical explanation of MaxEnt for
ecologists, Divers. Distrib., 17, 43–57, 2011. a
Galloway, G. E., Reilly, A., Ryoo, S., Riley, A., Haslam, M., Brody, S.,
Highfield, W., Gunn, J., Rainey, J., and Parker, S.: The Growing Threat of
Urban Flooding: A National Challenge, College Park and Galveston: University
of Maryland and Texas A&M University, 44 pp., 2018. a
Garousi-Nejad, I., Tarboton, D. G., Aboutalebi, M., and Torres-Rua, A. F.:
Terrain analysis enhancements to the height above nearest drainage flood
inundation mapping method, Water Resour. Res., 55, 7983–8009, 2019. a
Gillies, S., Stewart, A. J., Snow, A. D., et al.: Rasterio: geospatial raster I/O for Python programmers, available at:
https://github.com/mapbox/rasterio (4 March 2020), 2013. a
Gori, A., Blessing, R., Juan, A., Brody, S., and Bedient, P.: Characterizing
urbanization impacts on floodplain through integrated land use, hydrologic,
and hydraulic modeling, J. Hydrol., 568, 82–95, 2019. a
Hall, M. J.: Urban hydrology, Elsevier Applied Science Publishing, London, UK, 299, 1984. a
Harris, C. R., Millman, K. J., van der Walt, S. J., et al: Array programming with NumPy, Nature, 585, 357–362, https://doi.org/10.1038/s41586-020-2649-2, 2020. a
Highfield, W. E., Norman, S. A., and Brody, S. D.: Examining the 100-Year
Floodplain as a Metric of Risk, Loss, and Household
Adjustment, Risk Anal., 33, 186–191, https://doi.org/10.1111/j.1539-6924.2012.01840.x, 2013. a, b
Hosseini, F. S., Choubin, B., Mosavi, A., Nabipour, N., Shamshirband, S.,
Darabi, H., and Haghighi, A. T.: Flash-flood hazard assessment using
ensembles and Bayesian-based machine learning models: Application of the
simulated annealing feature selection
method, Sci. Total Environ., 711, 135161, https://doi.org/10.1016/j.scitotenv.2019.135161, 2019. a, b, c, d, e
Hutson, M.: Artificial intelligence faces reproducibility crisis, American Association for the Advancement of Science, 359, 725–726, https://doi.org/10.1126/science.359.6377.725, 2018. a
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 Sy., 9, 1847–1862, 2017. a
Janizadeh, S., Avand, M., Jaafari, A., Phong, T. V., Bayat, M., Ahmadisharaf,
E., Prakash, I., Pham, B. T., and Lee, S.: Prediction Success of Machine
Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh
Watershed, Iran, Sustainability-Basel, 11, 5426, https://doi.org/10.3390/su11195426,
2019a. a, b, c, d
Jenson, S. K. and Domingue, J. O.: Extracting topographic structure from
digital elevation data for geographic information system
analysis, Photogramm. Eng. Rem. S., 54, 1593–1600, 1988. a
Kalyanapu, A. J., Burian, S. J., and McPherson, T. N.: Effect of land use-based surface roughness on hydrologic model output, Journal of Spatial Hydrology, 9, 51–71, 2010. a
Knighton, J., Buchanan, B., Guzman, C., Elliott, R., White, E., and Rahm, B.:
Predicting flood insurance claims with hydrologic and socioeconomic
demographics via machine learning: exploring the roles of topography,
minority populations, and political
dissimilarity, J. Environ. Manage., 272, 111051, https://doi.org/10.1016/j.jenvman.2020.111051, 2020. a, b
Kousky, C. and Shabman, L.: Policy nook: “Federal funding for flood risk
reduction in the US: Pre- or post-disaster?”, Water Economics and Policy, 3,
1771001, https://doi.org/10.1142/S2382624X17710011, 2017. a
LandFire: Homepage of the LANDFIRE Program, available at: https://www.landfire.gov/ (last access: 4 March 2020), 2010. a
Lee, S., Kim, J.-C., Jung, H.-S., Lee, M. J., and Lee, S.: Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea, Geomat. Nat. Haz. Risk, 8, 1185–1203, https://doi.org/10.1080/19475705.2017.1308971, 2017. a, b, c
Li, B., Wang, Q., and Hu, J.: A fast SVM training method for very large
datasets, in: Neural Networks, 2009. IJCNN 2009. International Joint
Conference on, 1784–1789, 2009. a
Liaw, A. and Wiener, M.: Classification and regression by randomForest, R
news, 2, 18–22, 2002. a
Liu, Y. Y., Maidment, D. R., Tarboton, D. G., Zheng, X., Yildirim, A., Sazib,
N. S., and Wang, S.: A CyberGIS approach to generating high-resolution height
above nearest drainage (HAND) raster for national flood mapping, CyberGIS,
16, 24–26, 2016. a
Metz, C. E.: BASIC PRINCIPLES OF ROC ANALYSIS, Seminars in Nuclear Medicine, 8,
283–298, https://doi.org/10.1016/s0001-2998(78)80014-2, 1978. a
Mobley, W.: Flood Hazard Modeling Output, V2, Texas Data Repository, https://doi.org/10.18738/T8/FVJFSW, 2020. a
Moradkhani, H. and Sorooshian, S.: General review of rainfall-runoff modeling:
model calibration, data assimilation, and uncertainty analysis, in:
Hydrological modelling and the water cycle, edited by: Sorooshian, S., Hsu, K. L., Coppola, E., Tomassetti, B., Verdecchia, M., and Visconti, G., Springer, Berlin, Heidelberg, 1–24, https://doi.org/10.1007/978-3-540-77843-1_1, 2009. a
Morss, R. E., Wilhelmi, O. V., Downton, M. W., and Gruntfest, E.: Flood risk,
uncertainty, and scientific information for decision making: lessons from an
interdisciplinary project, B. Am. Meteorol. Soc.,
86, 1593, https://doi.org/10.1175/BAMS-86-11-1593, 2005. a
Mosavi, A., Ozturk, P., and Chau, K.-w.: Flood prediction using machine
learning models: Literature review, Water, 10, 1536, https://doi.org/10.3390/w10111536, 2018. a, b
Moy de Vitry, M., Kramer, S., Wegner, J. D., and Leitão, J. P.: Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network, Hydrol. Earth Syst. Sci., 23, 4621–4634, https://doi.org/10.5194/hess-23-4621-2019, 2019. a
Muñoz, P., Orellana-Alvear, J., Willems, P., and Célleri, R.: Flash-Flood
Forecasting in an Andean Mountain Catchment – Development of a Step-Wise
Methodology Based on the Random Forest Algorithm, Water, 10, 1519,
https://doi.org/10.3390/w10111519, 2018. a
NHC: https://www.nhc.noaa.gov/climo/ (last access: 4 March 2020), Tropical Cyclone Climatology, 2015. a
NOAA National Centers for Environmental Information (NCEI): US Billion-Dollar Weather and Climate Disasters, available at: https://www.ncdc.noaa.gov/billions/ (last access: 4 March 2020), 2021. a
Office, C. B.: The National Flood Insurance Program: Financial Soundness and
Affordability, Report, Congress of the United States congressional budget office, Washington D.C., 44 pp., 2017. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn.
Res., 12, 2825–2830, available at: https://dl.acm.org/doi/10.5555/1953048.2078195 (last access: 4 March 2020), 2011. a, b
Phillips, S. J., Dudík, M., Elith, J., Graham, C. H., Lehmann, A., Leathwick,
J., and Ferrier, S.: Sample selection bias and presence-only distribution
models: implications for background and pseudo-absence data, Ecol.
Appl., 19, 181–197, https://doi.org/10.1890/07-2153.1, 2009. a
Quinlan, J. R.: Induction of decision trees, Mach. Learn., 1, 81–106, 1986.
Rahmati, O. and Pourghasemi, H. R.: Identification of Critical Flood Prone
Areas in Data-Scarce and Ungauged Regions: A Comparison of Three Data Mining
Models, Water Res. Manag., 31, 1473–1487,
https://doi.org/10.1007/s11269-017-1589-6, 2017. a, b, c, d
Rajib, A., Liu, Z., Merwade, V., Tavakoly, A. A., and Follum, M. L.: Towards a
large-scale locally relevant flood inundation modeling framework using SWAT
and LISFLOOD-FP, J. Hydrol., 581, 124406, https://doi.org/10.1016/j.jhydrol.2019.124406, 2020. a
Rawls, W. J., Brakensiek, D. L., and Miller, N.: Green-Ampt Infiltration
Parameters from Soils Data, J. Hydraul. Eng., 109, 62–70,
https://doi.org/10.1061/(ASCE)0733-9429(1983)109:1(62), 1983. a
Rodda, H. J.: The development and application of a flood risk model for the
Czech Republic, Nat. Hazards, 36, 207–220, 2005. a
Sebastian, A., Gori, A., Blessing, R. B., van der Wiel, K., and Bass, B.:
Disentangling the impacts of human and environmental change on catchment
response during Hurricane Harvey, Environ. Res. Lett., 14,
124023, https://doi.org/10.1088/1748-9326/ab5234, 2019. a
Singh, V.: Effect of spatial and temporal variability in rainfall and watershed
characteristics on stream flow hydrograph, Hydrol. Process., 11,
1649–1669, 1997. a
Solomatine, D. P. and Ostfeld, A.: Data-driven modelling: Some past experiences
and new approaches, J. Hydroinform., 10, 3–22, https://doi.org/10.2166/hydro.2008.015, 2008. a, b
Sørensen, R., Zinko, U., and Seibert, J.: On the calculation of the topographic wetness index: evaluation of different methods based on field observations, Hydrol. Earth Syst. Sci., 10, 101–112, https://doi.org/10.5194/hess-10-101-2006, 2006.
a
Tehrany, M. S., Jones, S., and Shabani, F.: Identifying the essential flood
conditioning factors for flood prone area mapping using machine learning
techniques, Catena, 175, 174–192, https://doi.org/10.1016/j.catena.2018.12.011,
2019a. a, b, c, d
Tehrany, M. S., Kumar, L., and Shabani, F.: A novel GIS-based ensemble
technique for flood susceptibility mapping using evidential belief function
and support vector machine: Brisbane, Australia, PeerJ, 7, e7653, https://doi.org/10.7717/peerj.7653, 2019b. a, b, c
Thomas, H. and Nisbet, T.: An assessment of the impact of floodplain woodland
on flood flows, Water Environ. J., 21, 114–126, 2007. a
Upstill-Goddard, R., Eccles, D., Fliege, J., and Collins, A.: Machine learning
approaches for the discovery of gene-gene interactions in disease data,
Brief. Bioinform., 14, 251–60, https://doi.org/10.1093/bib/bbs024, 2013. a
van der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy Array: A
Structure for Efficient Numerical Computation, Comput. Sci.
Eng., 13, 22–30, 2011. a
Van Oldenborgh, G. J., Van Der Wiel, K., Sebastian, A., Singh, R., Arrighi, J.,
Otto, F., Haustein, K., Li, S., Vecchi, G., and Cullen, H.: Attribution of
extreme rainfall from Hurricane Harvey, August 2017, Environ. Res.
Lett., 12, 124009, https://doi.org/10.1088/1748-9326/aaa343, 2017. a
Wagenaar, D., de Jong, J., and Bouwer, L. M.: Multi-variable flood damage modelling with limited data using supervised learning approaches, Nat. Hazards Earth Syst. Sci., 17, 1683–1696, https://doi.org/10.5194/nhess-17-1683-2017, 2017. a
Ward, P. J., Jongman, B., Salamon, P., Simpson, A., Bates, P., De Groeve, T.,
Muis, S., De Perez, E. C., Rudari, R., Trigg, M. A., and Winsemius, H. C.: Usefulness and
limitations of global flood risk models, Nat. Clim. Change, 5, 712–715,
2015. a
McKinney, W.: Data Structures for Statistical Computing in
Python, in: Proceedings of the 9th Python in Science Conference, 28 June–3 July, Austin Texas, USA, 56–61,
https://doi.org/10.25080/Majora-92bf1922-00a, 2010. a
White, M. D. and Greer, K. A.: The effects of watershed urbanization on the
stream hydrology and riparian vegetation of Los Penasquitos Creek,
California, Landscape Urban Plan., 74, 125–138, 2006. a
Wing, O. E., Bates, P. D., Smith, A. M., Sampson, C. C., Johnson, K. A.,
Fargione, J., and Morefield, P.: Estimates of present and future flood risk
in the conterminous United States, Environ. Res. Lett., 13,
034023, https://doi.org/10.1088/1748-9326/aaac65, 2018. a
Wisz, M. S. and Guisan, A.: Do pseudo-absence selection strategies influence
species distribution models and their predictions? An information-theoretic
approach based on simulated data, BMC Ecology, 9, 8,
https://doi.org/10.1186/1472-6785-9-8, 2009. a
Short summary
In southeast Texas, flood impacts are exacerbated by increases in impervious surfaces, human inaction, outdated FEMA-defined floodplains and modeling assumptions, and changing environmental conditions. The current flood maps are inadequate indicators of flood risk, especially in urban areas. This study proposes a novel method to model flood hazard and impact in urban areas. Specifically, we used novel flood risk modeling techniques to produce annualized flood hazard maps.
In southeast Texas, flood impacts are exacerbated by increases in impervious surfaces, human...
Altmetrics
Final-revised paper
Preprint