Articles | Volume 20, issue 4
https://doi.org/10.5194/nhess-20-1149-2020
© Author(s) 2020. 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-20-1149-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Invited perspectives
| 
29 Apr 2020
Invited perspectives |
| 29 Apr 2020
| 29 Apr 2020
Invited perspectives: How machine learning will change flood risk and impact assessment
Dennis Wagenaar
CORRESPONDING AUTHOR
Department of flood risk management, Deltares, Delft, the Netherlands
Institute for environmental studies, VU University, Amsterdam, the Netherlands
Alex Curran
Department of flood risk management, Deltares, Delft, the Netherlands
Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Mariano Balbi
Structural and Materials Lab, School of Engineering, Universidad de Buenos Aires, Buenos Aires, Argentina
Alok Bhardwaj
Earth Observatory of Singapore, Nanyang Technological University,
Singapore
Robert Soden
Columbia University, New York City, New York, USA
GFDRR, World Bank Group, Washington, D.C., USA
Co-Risk Labs, Oakland, California, USA
Emir Hartato
Planet, San Francisco, USA
Gizem Mestav Sarica
Institute of Catastrophe Risk Management, Nanyang Technological
University, Singapore
Laddaporn Ruangpan
Department of Water Resources and Ecosystems, IHE Delft Institute for Water Education, Delft, the Netherlands
Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Giuseppe Molinario
GFDRR, World Bank Group, Washington, D.C., USA
David Lallemant
Earth Observatory of Singapore, Nanyang Technological University,
Singapore
Co-Risk Labs, Oakland, California, USA
Related authors
Dennis Wagenaar, Jurjen de Jong, and Laurens M. Bouwer
Nat. Hazards Earth Syst. Sci., 17, 1683–1696, https://doi.org/10.5194/nhess-17-1683-2017, https://doi.org/10.5194/nhess-17-1683-2017, 2017
Short summary
Short summary
Flood damage models are an important component of cost–benefit analyses for flood protection measures. Currently flood damage models predict the flood damage often only based on water depth. Recently, some progress has been made in also including other variables for this prediction. Data-intensive approaches (machine learning) have been applied to do this. In practice the required data for this are rare. We apply these new approaches on a new type of dataset (combination of different sources).
This article is included in the Encyclopedia of Geosciences
D. J. Wagenaar, K. M. de Bruijn, L. M. Bouwer, and H. de Moel
Nat. Hazards Earth Syst. Sci., 16, 1–14, https://doi.org/10.5194/nhess-16-1-2016, https://doi.org/10.5194/nhess-16-1-2016, 2016
Short summary
Short summary
This paper discusses the differences that are found between flood damage estimation models. Based on an explanation of these differences, a method to quantify the uncertainty in flood damage models is proposed. An uncertainty estimate is made for a case study and the potential implications of uncertainty in flood damage estimation for investment decisions is shown.
This article is included in the Encyclopedia of Geosciences
Jun Yu Puah, Ivan D. Haigh, David Lallemant, Kyle Morgan, Dongju Peng, Masashi Watanabe, and Adam D. Switzer
EGUsphere, https://doi.org/10.5194/egusphere-2024-143, https://doi.org/10.5194/egusphere-2024-143, 2024
Short summary
Short summary
Coastal currents have wide implications on port activities, transport of sediments, and coral reef ecosystems, and hence a deeper understanding of their characteristics is needed. The analysis of current velocity data collected at offshore Singapore showed that currents are primarily affected by the tides. These tidal currents in turn exhibit high correlation with the land-sea breeze during the monsoon seasons, indicating that the properties of currents vary with time due to strong winds.
This article is included in the Encyclopedia of Geosciences
Mariano Balbi and David Charles Bonaventure Lallemant
Hydrol. Earth Syst. Sci., 27, 1089–1108, https://doi.org/10.5194/hess-27-1089-2023, https://doi.org/10.5194/hess-27-1089-2023, 2023
Short summary
Short summary
We proposed a methodology to obtain useful and robust probabilistic predictions from computational flood simulators using satellite-borne flood extent observations. We developed a Bayesian framework to obtain the uncertainty in roughness parameters, in observations errors, and in simulator structural deficiencies. We found that it can yield improvements in predictions relative to current methodologies and can potentially lead to consistent ways of combining data from different sources.
This article is included in the Encyclopedia of Geosciences
Anirudh Rao, Jungkyo Jung, Vitor Silva, Giuseppe Molinario, and Sang-Ho Yun
Nat. Hazards Earth Syst. Sci., 23, 789–807, https://doi.org/10.5194/nhess-23-789-2023, https://doi.org/10.5194/nhess-23-789-2023, 2023
Short summary
Short summary
This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets including high-resolution building inventories, while also leveraging recent advances in machine-learning algorithms. For three out of the four recent earthquakes studied, the machine-learning framework is able to identify over 50 % or nearly half of the damaged buildings successfully.
This article is included in the Encyclopedia of Geosciences
Constance Ting Chua, Adam D. Switzer, Anawat Suppasri, Linlin Li, Kwanchai Pakoksung, David Lallemant, Susanna F. Jenkins, Ingrid Charvet, Terence Chua, Amanda Cheong, and Nigel Winspear
Nat. Hazards Earth Syst. Sci., 21, 1887–1908, https://doi.org/10.5194/nhess-21-1887-2021, https://doi.org/10.5194/nhess-21-1887-2021, 2021
Short summary
Short summary
Port industries are extremely vulnerable to coastal hazards such as tsunamis. Despite their pivotal role in local and global economies, there has been little attention paid to tsunami impacts on port industries. For the first time, tsunami damage data are being extensively collected for port structures and catalogued into a database. The study also provides fragility curves which describe the probability of damage exceedance for different port industries given different tsunami intensities.
This article is included in the Encyclopedia of Geosciences
Laddaporn Ruangpan, Zoran Vojinovic, Silvana Di Sabatino, Laura Sandra Leo, Vittoria Capobianco, Amy M. P. Oen, Michael E. McClain, and Elena Lopez-Gunn
Nat. Hazards Earth Syst. Sci., 20, 243–270, https://doi.org/10.5194/nhess-20-243-2020, https://doi.org/10.5194/nhess-20-243-2020, 2020
Short summary
Short summary
This article aims to provide a critical review of the literature and indicate some directions for future research based on the current knowledge gaps in the area of nature-based solutions (NBSs) for hydro-meteorological risk reduction. The final full analysis was performed on 146 closely related articles. A review showed that many advancements related to NBSs have been made to date, but there are still many challenges that will play an important role in extending knowledge in the coming years.
This article is included in the Encyclopedia of Geosciences
Dennis Wagenaar, Jurjen de Jong, and Laurens M. Bouwer
Nat. Hazards Earth Syst. Sci., 17, 1683–1696, https://doi.org/10.5194/nhess-17-1683-2017, https://doi.org/10.5194/nhess-17-1683-2017, 2017
Short summary
Short summary
Flood damage models are an important component of cost–benefit analyses for flood protection measures. Currently flood damage models predict the flood damage often only based on water depth. Recently, some progress has been made in also including other variables for this prediction. Data-intensive approaches (machine learning) have been applied to do this. In practice the required data for this are rare. We apply these new approaches on a new type of dataset (combination of different sources).
This article is included in the Encyclopedia of Geosciences
D. J. Wagenaar, K. M. de Bruijn, L. M. Bouwer, and H. de Moel
Nat. Hazards Earth Syst. Sci., 16, 1–14, https://doi.org/10.5194/nhess-16-1-2016, https://doi.org/10.5194/nhess-16-1-2016, 2016
Short summary
Short summary
This paper discusses the differences that are found between flood damage estimation models. Based on an explanation of these differences, a method to quantify the uncertainty in flood damage models is proposed. An uncertainty estimate is made for a case study and the potential implications of uncertainty in flood damage estimation for investment decisions is shown.
This article is included in the Encyclopedia of Geosciences
Related subject area
Hydrological Hazards
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
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
Flash flood detection via copula-based intensity–duration–frequency curves: evidence from Jamaica
Seasonal forecasting of local-scale soil moisture droughts with Global BROOK90: a case study of the European drought of 2018
How to mitigate flood events similar to the 1979 catastrophic floods in the lower Tagus
Probabilistic Flood Inundation Mapping through Copula Bayesian Multi-Modelling of Precipitation Products
Assessing LISFLOOD-FP with the next-generation digital elevation model FABDEM using household survey and remote sensing data in the Central Highlands of Vietnam
CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment): a new model for geo-hydrological hazard assessment at the basin scale
Water depth estimate and flood extent enhancement for satellite-based inundation maps
Current and future rainfall-driven flood risk from hurricanes in Puerto Rico under 1.5 and 2 °C climate change
Using integrated hydrological–hydraulic modelling and global data sources to analyse the February 2023 floods in the Umbeluzi Catchment (Mozambique)
Model based assessment of climate change impact on inland flood risk in coastal areas caused by compounding storm tide and precipitation events
Impact-based flood forecasting in the Greater Horn of Africa
Flood Occurrence and Impact Models for Socioeconomic Applications over Canada and the United States
A downward counterfactual analysis of flash floods in Germany
This article is included in the Encyclopedia of Geosciences
Brief communication: A first hydrological investigation of extreme August 2023 floods in Slovenia, Europe
Multivariate regression trees as an “explainable machine learning” approach to explore relationships between hydroclimatic characteristics and agricultural and hydrological drought severity: case of study Cesar River basin
Review article: Towards improved drought prediction in the Mediterranean region – modeling approaches and future directions
Assessing typhoon-induced compound flood drivers: a case study in Ho Chi Minh City, Vietnam
Assessing the ability of a new seamless short-range ensemble rainfall product to anticipate flash floods in the French Mediterranean area
Sentinel-1-based analysis of the severe flood over Pakistan 2022
Sensitivity analysis of erosion on the landward slope of an earthen flood defense located in southern France submitted to wave overtopping
Better prepared but less resilient: the paradoxical impact of frequent flood experience on adaptive behavior and resilience
Assessing the spatial spread–skill of ensemble flood maps with remote-sensing observations
Hydrometeorological controls and social response for the 22 October 2019 catastrophic flash flood in Catalonia, north-eastern Spain
An integrated modeling approach to evaluate the impacts of nature-based solutions of flood mitigation across a small watershed in the southeast United States
Quantifying hazards resilience by modeling infrastructure recovery as a resource constrained project scheduling problem
Indicator-to-impact links to help improve agricultural drought preparedness in Thailand
An improved dynamic bidirectional coupled hydrologic-hydrodynamic model for efficient flood inundation prediction
The potential of open-access data for flood estimations: uncovering inundation hotspots in Ho Chi Minh City, Vietnam, through a normalized flood severity index
Analyzing the informative value of alternative hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale
A methodological framework for the evaluation of short-range flash-flood hydrometeorological forecasts at the event scale
Hydrological drought forecasting under a changing environment in the Luanhe River basin
A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe – Part 2: Historical context and relation to climate change
Brief communication: The potential use of low-cost acoustic sensors to detect rainfall for short-term urban flood warnings
Brief communication: On the extremeness of the July 2021 precipitation event in western Germany
A climate-conditioned catastrophe risk model for UK flooding
A globally applicable framework for compound flood hazard modeling
Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany
Brief communication: Inclusiveness in designing an early warning system for flood resilience
Evolution of multivariate drought hazard, vulnerability and risk in India under climate change
A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe – Part 1: Event description and analysis
Bare-earth DEM generation from ArcticDEM and its use in flood simulation
Comparison of estimated flood exposure and consequences generated by different event-based inland flood inundation maps
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
Dino Collalti, Nekeisha Spencer, and Eric Strobl
Nat. Hazards Earth Syst. Sci., 24, 873–890, https://doi.org/10.5194/nhess-24-873-2024, https://doi.org/10.5194/nhess-24-873-2024, 2024
Short summary
Short summary
The risk of extreme rainfall events causing floods is likely increasing with climate change. Flash floods, which follow immediately after extreme rainfall, are particularly difficult to forecast and assess. We develop a decision rule for flash flood classification with data on all incidents between 2001 and 2018 in Jamaica with the statistical copula method. This decision rule tells us for any rainfall event of a certain duration how intense it has to be to likely trigger a flash flood.
This article is included in the Encyclopedia of Geosciences
Ivan Vorobevskii, Thi Thanh Luong, and Rico Kronenberg
Nat. Hazards Earth Syst. Sci., 24, 681–697, https://doi.org/10.5194/nhess-24-681-2024, https://doi.org/10.5194/nhess-24-681-2024, 2024
Short summary
Short summary
This study presents a new version of a framework which allows us to model water balance components at any site on a local scale. Compared with the first version, the second incorporates new datasets used to set up and force the model. In particular, we highlight the ability of the framework to provide seasonal forecasts. This gives potential stakeholders (farmers, foresters, policymakers, etc.) the possibility to forecast, for example, soil moisture drought and thus apply the necessary measures.
This article is included in the Encyclopedia of Geosciences
Diego Fernández-Nóvoa, Alexandre M. Ramos, José González-Cao, Orlando García-Feal, Cristina Catita, Moncho Gómez-Gesteira, and Ricardo M. Trigo
Nat. Hazards Earth Syst. Sci., 24, 609–630, https://doi.org/10.5194/nhess-24-609-2024, https://doi.org/10.5194/nhess-24-609-2024, 2024
Short summary
Short summary
The present study focuses on an in-depth analysis of floods in the lower section of the Tagus River from a hydrodynamic perspective by means of the Iber+ numerical model and on the development of dam operating strategies to mitigate flood episodes using the exceptional floods of February 1979 as a benchmark. The results corroborate the model's capability to evaluate floods in the study area and confirm the effectiveness of the proposed strategies to reduce flood impact in the lower Tagus valley.
This article is included in the Encyclopedia of Geosciences
Francisco Javier Gomez, Keighobad Jafarzadegan, Hamed Moftakhari, and Hamid Moradkhani
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-26, https://doi.org/10.5194/nhess-2024-26, 2024
Revised manuscript accepted for NHESS
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.
This article is included in the Encyclopedia of Geosciences
Laurence Hawker, Jeffrey Neal, James Savage, Thomas Kirkpatrick, Rachel Lord, Yanos Zylberberg, Andre Groeger, Truong Dang Thuy, Sean Fox, Felix Agyemang, and Pham Khanh Nam
Nat. Hazards Earth Syst. Sci., 24, 539–566, https://doi.org/10.5194/nhess-24-539-2024, https://doi.org/10.5194/nhess-24-539-2024, 2024
Short summary
Short summary
We present a global flood model built using a new terrain data set and evaluated in the Central Highlands of Vietnam.
This article is included in the Encyclopedia of Geosciences
Andrea Abbate, Leonardo Mancusi, Francesco Apadula, Antonella Frigerio, Monica Papini, and Laura Longoni
Nat. Hazards Earth Syst. Sci., 24, 501–537, https://doi.org/10.5194/nhess-24-501-2024, https://doi.org/10.5194/nhess-24-501-2024, 2024
Short summary
Short summary
CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment) is a new physically based and spatially distributed rainfall-runoff model. The main novelties consist of reproducing rainfall-induced geo-hydrological hazards such as shallow landslide, debris flow and watershed erosion through a multi-hazard approach. CRHyME was written in Python, works at a high spatial and temporal resolution, and is a tool suitable for quantifying extreme rainfall consequences at the basin scale.
This article is included in the Encyclopedia of Geosciences
Andrea Betterle and Peter Salamon
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-22, https://doi.org/10.5194/nhess-2024-22, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
The study proposes a new framework, named FLEXTH, to estimate flood water depths and improve satellite-based flood monitoring using topographical data. FLEXTH aims to reduce the impact of floods and is readily available as a computer code, offering a practical and scalable solution for estimating flood depths quickly and systematically over large areas. The methodology can reduce the impacts of floods and enhance emergency response efforts, particularly where resources are limited.
This article is included in the Encyclopedia of Geosciences
Leanne Archer, Jeffrey Neal, Paul Bates, Emily Vosper, Dereka Carroll, Jeison Sosa, and Daniel Mitchell
Nat. Hazards Earth Syst. Sci., 24, 375–396, https://doi.org/10.5194/nhess-24-375-2024, https://doi.org/10.5194/nhess-24-375-2024, 2024
Short summary
Short summary
We model hurricane-rainfall-driven flooding to assess how the number of people exposed to flooding changes in Puerto Rico under the 1.5 and 2 °C Paris Agreement goals. Our analysis suggests 8 %–10 % of the population is currently exposed to flooding on average every 5 years, increasing by 2 %–15 % and 1 %–20 % at 1.5 and 2 °C. This has implications for adaptation to more extreme flooding in Puerto Rico and demonstrates that 1.5 °C climate change carries a significant increase in risk.
This article is included in the Encyclopedia of Geosciences
Luis Cea, Manuel Álvarez, and Jerónimo Puertas
Nat. Hazards Earth Syst. Sci., 24, 225–243, https://doi.org/10.5194/nhess-24-225-2024, https://doi.org/10.5194/nhess-24-225-2024, 2024
Short summary
Short summary
Mozambique is highly exposed to the impact of floods. To reduce flood damage, it is necessary to develop mitigation measures. Hydrological software is a very useful tool for that purpose, since it allows for a precise quantification of flood hazard in different scenarios. We present a methodology to quantify flood hazard in data-scarce regions, using freely available data and software, and we show its potential by analysing the flood event that took place in the Umbeluzi Basin in February 2023.
This article is included in the Encyclopedia of Geosciences
Helge Bormann, Jenny Kebschull, Lidia Gaslikova, and Ralf Weisse
EGUsphere, https://doi.org/10.5194/egusphere-2024-29, https://doi.org/10.5194/egusphere-2024-29, 2024
Short summary
Short summary
Inland flooding is threatening coastal lowlands. If rainfall and storm surges are coinciding, the risk of inland flooding increases. We examine how such compound events are influenced by climate change. Our model based scenario analysis shows 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.
This article is included in the Encyclopedia of Geosciences
Lorenzo Alfieri, Andrea Libertino, Lorenzo Campo, Francesco Dottori, Simone Gabellani, Tatiana Ghizzoni, Alessandro Masoero, Lauro Rossi, Roberto Rudari, Nicola Testa, Eva Trasforini, Ahmed Amdihun, Jully Ouma, Luca Rossi, Yves Tramblay, Huan Wu, and Marco Massabò
Nat. Hazards Earth Syst. Sci., 24, 199–224, https://doi.org/10.5194/nhess-24-199-2024, https://doi.org/10.5194/nhess-24-199-2024, 2024
Short summary
Short summary
This work describes Flood-PROOFS East Africa, an impact-based flood forecasting system for the Greater Horn of Africa. It is based on hydrological simulations, inundation mapping, and estimation of population and assets exposed to upcoming river floods. The system supports duty officers in African institutions in the daily monitoring of hydro-meteorological disasters. A first evaluation shows the system performance for the catastrophic floods in the Nile River basin in summer 2020.
This article is included in the Encyclopedia of Geosciences
Manuel Grenier, Mathieu Boudreault, David A. Carozza, Jérémie Boudreault, and Sébastien Raymond
EGUsphere, https://doi.org/10.22541/essoar.167751627.70583046/v2, https://doi.org/10.22541/essoar.167751627.70583046/v2, 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 details. 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 of population displaced of 18 % in Canada and 14 % in the U.S.
This article is included in the Encyclopedia of Geosciences
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-224, https://doi.org/10.5194/nhess-2023-224, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
To identify the flash flood potential in Germany, we shifted the most extreme rainfall events from the last 22 years systematically across Germany and simulated the consequent run off 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 by the factor 2 or more would be achieved for most areas. The results can aid disaster risk management.
Nejc Bezak, Panos Panagos, Leonidas Liakos, and Matjaž Mikoš
Nat. Hazards Earth Syst. Sci., 23, 3885–3893, https://doi.org/10.5194/nhess-23-3885-2023, https://doi.org/10.5194/nhess-23-3885-2023, 2023
Short summary
Short summary
Extreme flooding occurred in Slovenia in August 2023. This brief communication examines the main causes, mechanisms and effects of this event. The flood disaster of August 2023 can be described as relatively extreme and was probably the most extreme flood event in Slovenia in recent decades. The economic damage was large and could amount to well over 5 % of Slovenia's annual gross domestic product; the event also claimed three lives.
This article is included in the Encyclopedia of Geosciences
Ana Paez-Trujilo, Jeffer Cañon, Beatriz Hernandez, Gerald Corzo, and Dimitri Solomatine
Nat. Hazards Earth Syst. Sci., 23, 3863–3883, https://doi.org/10.5194/nhess-23-3863-2023, https://doi.org/10.5194/nhess-23-3863-2023, 2023
Short summary
Short summary
This study uses a machine learning technique, the multivariate regression tree approach, to assess the hydroclimatic characteristics that govern agricultural and hydrological drought severity. The results show that the employed technique successfully identified the primary drivers of droughts and their critical thresholds. In addition, it provides relevant information to identify the areas most vulnerable to droughts and design strategies and interventions for drought management.
This article is included in the Encyclopedia of Geosciences
Bouchra Zellou, Nabil El Moçayd, and El Houcine Bergou
Nat. Hazards Earth Syst. Sci., 23, 3543–3583, https://doi.org/10.5194/nhess-23-3543-2023, https://doi.org/10.5194/nhess-23-3543-2023, 2023
Short summary
Short summary
In this study, we underscore the critical importance of strengthening drought prediction capabilities in the Mediterranean region. We present an in-depth evaluation of current drought forecasting approaches, encompassing statistical, dynamical, and hybrid statistical–dynamical models, and highlight unexplored research opportunities. Additionally, we suggest viable directions to enhance drought prediction and early warning systems within the area.
This article is included in the Encyclopedia of Geosciences
Francisco Rodrigues do Amaral, Nicolas Gratiot, Thierry Pellarin, and Tran Anh Tu
Nat. Hazards Earth Syst. Sci., 23, 3379–3405, https://doi.org/10.5194/nhess-23-3379-2023, https://doi.org/10.5194/nhess-23-3379-2023, 2023
Short summary
Short summary
We propose an in-depth analysis of typhoon-induced compound flood drivers in the megacity of Ho Chi Minh, Vietnam. We use in situ and satellite measurements throughout the event to form a holistic overview of its impact. No evidence of storm surge was found, and peak precipitation presents a 16 h time lag to peak river discharge, which evacuates only 1.5 % of available water. The astronomical tide controls the river level even during the extreme event, and it is the main urban flood driver.
This article is included in the Encyclopedia of Geosciences
Juliette Godet, Olivier Payrastre, Pierre Javelle, and François Bouttier
Nat. Hazards Earth Syst. Sci., 23, 3355–3377, https://doi.org/10.5194/nhess-23-3355-2023, https://doi.org/10.5194/nhess-23-3355-2023, 2023
Short summary
Short summary
This article results from a master's research project which was part of a natural hazards programme developed by the French Ministry of Ecological Transition. The objective of this work was to investigate a possible way to improve the operational flash flood warning service by adding rainfall forecasts upstream of the forecasting chain. The results showed that the tested forecast product, which is new and experimental, has a real added value compared to other classical forecast products.
This article is included in the Encyclopedia of Geosciences
Florian Roth, Bernhard Bauer-Marschallinger, Mark Edwin Tupas, Christoph Reimer, Peter Salamon, and Wolfgang Wagner
Nat. Hazards Earth Syst. Sci., 23, 3305–3317, https://doi.org/10.5194/nhess-23-3305-2023, https://doi.org/10.5194/nhess-23-3305-2023, 2023
Short summary
Short summary
In August and September 2022, millions of people were impacted by a severe flood event in Pakistan. Since many roads and other infrastructure were destroyed, satellite data were the only way of providing large-scale information on the flood's impact. Based on the flood mapping algorithm developed at Technische Universität Wien (TU Wien), we mapped an area of 30 492 km2 that was flooded at least once during the study's time period. This affected area matches about the total area of Belgium.
This article is included in the Encyclopedia of Geosciences
Clément Houdard, Adrien Poupardin, Philippe Sergent, Abdelkrim Bennabi, and Jena Jeong
Nat. Hazards Earth Syst. Sci., 23, 3111–3124, https://doi.org/10.5194/nhess-23-3111-2023, https://doi.org/10.5194/nhess-23-3111-2023, 2023
Short summary
Short summary
We developed a system able to to predict, knowing the appropriate characteristics of the flood defense structure and sea state, the return periods of potentially dangerous events as well as a ranking of parameters by order of uncertainty.
The model is a combination of statistical and empirical methods that have been applied to a Mediterranean earthen dike. This shows that the most important characteristics of the dyke are its geometrical features, such as its height and slope angles.
This article is included in the Encyclopedia of Geosciences
Lisa Köhler, Torsten Masson, Sabrina Köhler, and Christian Kuhlicke
Nat. Hazards Earth Syst. Sci., 23, 2787–2806, https://doi.org/10.5194/nhess-23-2787-2023, https://doi.org/10.5194/nhess-23-2787-2023, 2023
Short summary
Short summary
We analyzed the impact of flood experience on adaptive behavior and self-reported resilience. The outcomes draw a paradoxical picture: the most experienced people are the most adapted but the least resilient. We find evidence for non-linear relationships between the number of floods experienced and resilience. We contribute to existing knowledge by focusing specifically on the number of floods experienced and extending the rare scientific literature on the influence of experience on resilience.
This article is included in the Encyclopedia of Geosciences
Helen Hooker, Sarah L. Dance, David C. Mason, John Bevington, and Kay Shelton
Nat. Hazards Earth Syst. Sci., 23, 2769–2785, https://doi.org/10.5194/nhess-23-2769-2023, https://doi.org/10.5194/nhess-23-2769-2023, 2023
Short summary
Short summary
Ensemble forecasts of flood inundation produce maps indicating the probability of flooding. A new approach is presented to evaluate the spatial performance of an ensemble flood map forecast by comparison against remotely observed flooding extents. This is important for understanding forecast uncertainties and improving flood forecasting systems.
This article is included in the Encyclopedia of Geosciences
Arnau Amengual, Romu Romero, María Carmen Llasat, Alejandro Hermoso, and Montserrat Llasat-Botija
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-130, https://doi.org/10.5194/nhess-2023-130, 2023
Revised manuscript accepted for NHESS
Short summary
Short summary
On 22 October 2019, the Francolí river basin (Catalonia, Spain) experienced a heavy precipitation event, resulting in a catastrophic flash flood. The main hydrometeorological factors are investigated. The social response times are also collected and compared with catchment dynamics in order to examine the adequacy of monitoring and warning issuance. Finally, the study provides recommendations aimed at minimizing losses and improving preparedness for similar natural hazards in the future.
This article is included in the Encyclopedia of Geosciences
Betina I. Guido, Ioana Popescu, Vidya Samadi, and Biswa Bhattacharya
Nat. Hazards Earth Syst. Sci., 23, 2663–2681, https://doi.org/10.5194/nhess-23-2663-2023, https://doi.org/10.5194/nhess-23-2663-2023, 2023
Short summary
Short summary
We used an integrated model to evaluate the impacts of nature-based solutions (NBSs) on flood mitigation across the Little Pee Dee and Lumber River watershed, the Carolinas, US. This area is strongly affected by climatic disasters, which are expected to increase due to climate change and urbanization, so exploring an NBS approach is crucial for adapting to future alterations. Our research found that NBSs can have visible effects on the reduction in hurricane-driven flooding.
This article is included in the Encyclopedia of Geosciences
Taylor Glen Johnson, Jorge Leandro, and Divine Kwaku Ahadzie
EGUsphere, https://doi.org/10.5194/egusphere-2023-1511, https://doi.org/10.5194/egusphere-2023-1511, 2023
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 buildings infrastructure to flooding hazards in Accra, Ghana.
This article is included in the Encyclopedia of Geosciences
Maliko Tanguy, Michael Eastman, Eugene Magee, Lucy J. Barker, Thomas Chitson, Chaiwat Ekkawatpanit, Daniel Goodwin, Jamie Hannaford, Ian Holman, Liwa Pardthaisong, Simon Parry, Dolores Rey Vicario, and Supattra Visessri
Nat. Hazards Earth Syst. Sci., 23, 2419–2441, https://doi.org/10.5194/nhess-23-2419-2023, https://doi.org/10.5194/nhess-23-2419-2023, 2023
Short summary
Short summary
Droughts in Thailand are becoming more severe due to climate change. Understanding the link between drought impacts on the ground and drought indicators used in drought monitoring systems can help increase a country's preparedness and resilience to drought. With a focus on agricultural droughts, we derive crop- and region-specific indicator-to-impact links that can form the basis of targeted mitigation actions and an improved drought monitoring and early warning system in Thailand.
This article is included in the Encyclopedia of Geosciences
Yanxia Shen, Zhenduo Zhu, Qi Zhou, and Chunbo Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2023-1106, https://doi.org/10.5194/egusphere-2023-1106, 2023
Short summary
Short summary
We present an improved Multigrid Dynamical Bidirectional Coupled hydrologic-hydrodynamic Model (M-DBCM) with two major improvements: 1) automated non-uniform mesh generation based on the D∞ algorithm was implemented to identify the flood-prone areas where high-resolution inundation conditions are needed; 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.
This article is included in the Encyclopedia of Geosciences
Leon Scheiber, Mazen Hoballah Jalloul, Christian Jordan, Jan Visscher, Hong Quan Nguyen, and Torsten Schlurmann
Nat. Hazards Earth Syst. Sci., 23, 2313–2332, https://doi.org/10.5194/nhess-23-2313-2023, https://doi.org/10.5194/nhess-23-2313-2023, 2023
Short summary
Short summary
Numerical models are increasingly important for assessing urban flooding, yet reliable input data are oftentimes hard to obtain. Taking Ho Chi Minh City as an example, this paper explores the usability and reliability of open-access data to produce preliminary risk maps that provide first insights into potential flooding hotspots. As a key novelty, a normalized flood severity index is presented which combines flood depth and duration to enhance the interpretation of hydro-numerical results.
This article is included in the Encyclopedia of Geosciences
Claudia Herbert and Petra Döll
Nat. Hazards Earth Syst. Sci., 23, 2111–2131, https://doi.org/10.5194/nhess-23-2111-2023, https://doi.org/10.5194/nhess-23-2111-2023, 2023
Short summary
Short summary
This paper presents a new method for selecting streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems in large-scale drought early warning systems. Indicators are classified by their inherent assumptions about the habituation of people and ecosystems to the streamflow regime and their level of drought characterization, namely drought magnitude (water deficit at a certain point in time) and severity (cumulated magnitude since drought onset).
This article is included in the Encyclopedia of Geosciences
Maryse Charpentier-Noyer, Daniela Peredo, Axelle Fleury, Hugo Marchal, François Bouttier, Eric Gaume, Pierre Nicolle, Olivier Payrastre, and Maria-Helena Ramos
Nat. Hazards Earth Syst. Sci., 23, 2001–2029, https://doi.org/10.5194/nhess-23-2001-2023, https://doi.org/10.5194/nhess-23-2001-2023, 2023
Short summary
Short summary
This paper proposes a methodological framework designed for event-based evaluation in the context of an intense flash-flood event. The evaluation adopts the point of view of end users, with a focus on the anticipation of exceedances of discharge thresholds. With a study of rainfall forecasts, a discharge evaluation and a detailed look at the forecast hydrographs, the evaluation framework should help in drawing robust conclusions about the usefulness of new rainfall ensemble forecasts.
This article is included in the Encyclopedia of Geosciences
Min Li, Mingfeng Zhang, Runxiang Cao, Yidi Sun, and Xiyuan Deng
Nat. Hazards Earth Syst. Sci., 23, 1453–1464, https://doi.org/10.5194/nhess-23-1453-2023, https://doi.org/10.5194/nhess-23-1453-2023, 2023
Short summary
Short summary
It is an important disaster reduction strategy to forecast hydrological drought. In order to analyse the impact of human activities on hydrological drought, we constructed the human activity factor based on the method of restoration. With the increase of human index (HI) value, hydrological droughts tend to transition to more severe droughts. The conditional distribution model involving of human activity factor can further improve the forecasting accuracy of drought in the Luanhe River basin.
This article is included in the Encyclopedia of Geosciences
Patrick Ludwig, Florian Ehmele, Mário J. Franca, Susanna Mohr, Alberto Caldas-Alvarez, James E. Daniell, Uwe Ehret, Hendrik Feldmann, Marie Hundhausen, Peter Knippertz, Katharina Küpfer, Michael Kunz, Bernhard Mühr, Joaquim G. Pinto, Julian Quinting, Andreas M. Schäfer, Frank Seidel, and Christina Wisotzky
Nat. Hazards Earth Syst. Sci., 23, 1287–1311, https://doi.org/10.5194/nhess-23-1287-2023, https://doi.org/10.5194/nhess-23-1287-2023, 2023
Short summary
Short summary
Heavy precipitation in July 2021 led to widespread floods in western Germany and neighboring countries. The event was among the five heaviest precipitation events of the past 70 years in Germany, and the river discharges exceeded by far the statistical 100-year return values. Simulations of the event under future climate conditions revealed a strong and non-linear effect on flood peaks: for +2 K global warming, an 18 % increase in rainfall led to a 39 % increase of the flood peak in the Ahr river.
This article is included in the Encyclopedia of Geosciences
Nadav Peleg, Herminia Torelló-Sentelles, Grégoire Mariéthoz, Lionel Benoit, João P. Leitão, and Francesco Marra
Nat. Hazards Earth Syst. Sci., 23, 1233–1240, https://doi.org/10.5194/nhess-23-1233-2023, https://doi.org/10.5194/nhess-23-1233-2023, 2023
Short summary
Short summary
Floods in urban areas are one of the most common natural hazards. Due to climate change enhancing extreme rainfall and cities becoming larger and denser, the impacts of these events are expected to increase. A fast and reliable flood warning system should thus be implemented in flood-prone cities to warn the public of upcoming floods. The purpose of this brief communication is to discuss the potential implementation of low-cost acoustic rainfall sensors in short-term flood warning systems.
This article is included in the Encyclopedia of Geosciences
Katharina Lengfeld, Paul Voit, Frank Kaspar, and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 1227–1232, https://doi.org/10.5194/nhess-23-1227-2023, https://doi.org/10.5194/nhess-23-1227-2023, 2023
Short summary
Short summary
Estimating the severity of a rainfall event based on the damage caused is easy but highly depends on the affected region. A less biased measure for the extremeness of an event is its rarity combined with its spatial extent. In this brief communication, we investigate the sensitivity of such measures to the underlying dataset and highlight the importance of considering multiple spatial and temporal scales using the devastating rainfall event in July 2021 in central Europe as an example.
This article is included in the Encyclopedia of Geosciences
Paul D. Bates, James Savage, Oliver Wing, Niall Quinn, Christopher Sampson, Jeffrey Neal, and Andrew Smith
Nat. Hazards Earth Syst. Sci., 23, 891–908, https://doi.org/10.5194/nhess-23-891-2023, https://doi.org/10.5194/nhess-23-891-2023, 2023
Short summary
Short summary
We present and validate a model that simulates current and future flood risk for the UK at high resolution (~ 20–25 m). We show that UK flood losses were ~ 6 % greater in the climate of 2020 compared to recent historical values. The UK can keep any future increase to ~ 8 % if all countries implement their COP26 pledges and net-zero ambitions in full. However, if only the COP26 pledges are fulfilled, then UK flood losses increase by ~ 23 %; and potentially by ~ 37 % in a worst-case scenario.
This article is included in the Encyclopedia of Geosciences
Dirk Eilander, Anaïs Couasnon, Tim Leijnse, Hiroaki Ikeuchi, Dai Yamazaki, Sanne Muis, Job Dullaart, Arjen Haag, Hessel C. Winsemius, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 23, 823–846, https://doi.org/10.5194/nhess-23-823-2023, https://doi.org/10.5194/nhess-23-823-2023, 2023
Short summary
Short summary
In coastal deltas, flooding can occur from interactions between coastal, riverine, and pluvial drivers, so-called compound flooding. Global models however ignore these interactions. We present a framework for automated and reproducible compound flood modeling anywhere globally and validate it for two historical events in Mozambique with good results. The analysis reveals differences in compound flood dynamics between both events related to the magnitude of and time lag between drivers.
This article is included in the Encyclopedia of Geosciences
Omar Seleem, Georgy Ayzel, Axel Bronstert, and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 809–822, https://doi.org/10.5194/nhess-23-809-2023, https://doi.org/10.5194/nhess-23-809-2023, 2023
Short summary
Short summary
Data-driven models are becoming more of a surrogate that overcomes the limitations of the computationally expensive 2D hydrodynamic models to map urban flood hazards. However, the model's ability to generalize outside the training domain is still a major challenge. We evaluate the performance of random forest and convolutional neural networks to predict urban floodwater depth and investigate their transferability outside the training domain.
This article is included in the Encyclopedia of Geosciences
Tahmina Yasmin, Kieran Khamis, Anthony Ross, Subir Sen, Anita Sharma, Debashish Sen, Sumit Sen, Wouter Buytaert, and David M. Hannah
Nat. Hazards Earth Syst. Sci., 23, 667–674, https://doi.org/10.5194/nhess-23-667-2023, https://doi.org/10.5194/nhess-23-667-2023, 2023
Short summary
Short summary
Floods continue to be a wicked problem that require developing early warning systems with plausible assumptions of risk behaviour, with more targeted conversations with the community at risk. Through this paper we advocate the use of a SMART approach to encourage bottom-up initiatives to develop inclusive and purposeful early warning systems that benefit the community at risk by engaging them at every step of the way along with including other stakeholders at multiple scales of operations.
This article is included in the Encyclopedia of Geosciences
Venkataswamy Sahana and Arpita Mondal
Nat. Hazards Earth Syst. Sci., 23, 623–641, https://doi.org/10.5194/nhess-23-623-2023, https://doi.org/10.5194/nhess-23-623-2023, 2023
Short summary
Short summary
In an agriculture-dependent, densely populated country such as India, drought risk projection is important to assess future water security. This study presents the first comprehensive drought risk assessment over India, integrating hazard and vulnerability information. Future drought risk is found to be more significantly driven by increased vulnerability resulting from societal developments rather than climate-induced changes in hazard. These findings can inform planning for drought resilience.
This article is included in the Encyclopedia of Geosciences
Susanna Mohr, Uwe Ehret, Michael Kunz, Patrick Ludwig, Alberto Caldas-Alvarez, James E. Daniell, Florian Ehmele, Hendrik Feldmann, Mário J. Franca, Christian Gattke, Marie Hundhausen, Peter Knippertz, Katharina Küpfer, Bernhard Mühr, Joaquim G. Pinto, Julian Quinting, Andreas M. Schäfer, Marc Scheibel, Frank Seidel, and Christina Wisotzky
Nat. Hazards Earth Syst. Sci., 23, 525–551, https://doi.org/10.5194/nhess-23-525-2023, https://doi.org/10.5194/nhess-23-525-2023, 2023
Short summary
Short summary
The flood event in July 2021 was one of the most severe disasters in Europe in the last half century. The objective of this two-part study is a multi-disciplinary assessment that examines the complex process interactions in different compartments, from meteorology to hydrological conditions to hydro-morphological processes to impacts on assets and environment. In addition, we address the question of what measures are possible to generate added value to early response management.
This article is included in the Encyclopedia of Geosciences
Yinxue Liu, Paul D. Bates, and Jeffery C. Neal
Nat. Hazards Earth Syst. Sci., 23, 375–391, https://doi.org/10.5194/nhess-23-375-2023, https://doi.org/10.5194/nhess-23-375-2023, 2023
Short summary
Short summary
In this paper, we test two approaches for removing buildings and other above-ground objects from a state-of-the-art satellite photogrammetry topography product, ArcticDEM. Our best technique gives a 70 % reduction in vertical error, with an average difference of 1.02 m from a benchmark lidar for the city of Helsinki, Finland. When used in a simulation of rainfall-driven flooding, the bare-earth version of ArcticDEM yields a significant improvement in predicted inundation extent and water depth.
This article is included in the Encyclopedia of Geosciences
Joseph L. Gutenson, Ahmad A. Tavakoly, Mohammad S. Islam, Oliver E. J. Wing, William P. Lehman, Chase O. Hamilton, Mark D. Wahl, and T. Christopher Massey
Nat. Hazards Earth Syst. Sci., 23, 261–277, https://doi.org/10.5194/nhess-23-261-2023, https://doi.org/10.5194/nhess-23-261-2023, 2023
Short summary
Short summary
Emergency managers use event-based flood inundation maps (FIMs) to plan and coordinate flood emergency response. We perform a case study test of three different FIM frameworks to see if FIM differences lead to substantial differences in the location and magnitude of flood exposure and consequences. We find that the FIMs are very different spatially and that the spatial differences do produce differences in the location and magnitude of exposure and consequences.
This article is included in the Encyclopedia of Geosciences
Cited articles
Aarthi, A. D. and Gnanappazham, L.: Comparison of Urban Growth Modeling
Using Deep Belief and Neural Network Based Cellular Automata Model – A Case
Study of Chennai Metropolitan Area, Tamil Nadu, India, Journal of
Geographic Information System, 11, 1–16, 2019.
Abrahart, R. J. and See, L. M.: Neural network modelling of non-linear hydrological relationships, Hydrol. Earth Syst. Sci., 11, 1563–1579, https://doi.org/10.5194/hess-11-1563-2007, 2007.
Alshehhi, R., Marpu, P. R., Woon, W., and Dalla Maru, M.: Simultaneous
extraction of roads and buildings in remote sensing imagery with
convolutional neural networks, ISPRS J. Photogramm., 130, 139–149, 2017.
Amadio, M., Scorzini, A. R., Carisi, F., Essenfelder, A. H., Domeneghetti, A., Mysiak, J., and Castellarin, A.: Testing empirical and synthetic flood damage models: the case of Italy, Nat. Hazards Earth Syst. Sci., 19, 661–678, https://doi.org/10.5194/nhess-19-661-2019, 2019.
Ames, M. G.: Deconstructing the algorithmic sublime, Big Data &
Society, 5, 1–4, https://doi.org/10.1177/2053951718779194, 2018.
Annis, A. and Nardi, F.: Integrating VGI and 2D hydraulic models into a
data assimilation framework for real time flood forecasting and mapping,
Geo-spatial Information Science, 22, 223–236, https://doi.org/10.1080/10095020.2019.1626135, 2019.
Assumpção, T. H., Popescu, I., Jonoski, A., and Solomatine, D. P.: Citizen observations contributing to flood modelling: opportunities and challenges, Hydrol. Earth Syst. Sci., 22, 1473–1489, https://doi.org/10.5194/hess-22-1473-2018, 2018.
Bischke, B., Helber, P., Folz, J., Borth, D., and Dengel, A.: Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks, available at: https://arxiv.org/abs/1709.05932 (last access: 28 April 2020), 2017.
Bishop, C. M.: Pattern Recognition and Machine Learning,
Springer, Cambridge, UK, ISBN 978-0-387-31073-2, 2006.
Bouwer, L. M., Haasnoot, M., Wagenaar, D., and Roscoe, K.: Assessment
of alternative flood mitigation strategies for the C-7 Basin in Miami,
Florida, Deltares, Delft, the Netherlands, 1230718, 2017.
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J.:
Classification and regression trees, Wadsworth &
Brooks/Cole Advanced Books & Software, Monterey, CA, USA, ISBN 978-0-412-04841-8, 1984.
Burton, H. V., Miles, S. B., and Kang, H.: Integrating Performance-Based Engineering and Urban Simulation to Model Post-Earthquake Housing Recovery, Earthq. Spectra, 34, 1763–1785, https://doi.org/10.1193/041017EQS067M, 2018.
Cammerer, H., Thieken, A. H., and Lammel, J.: Adaptability and transferability of flood loss functions in residential areas, Nat. Hazards Earth Syst. Sci., 13, 3063–3081, https://doi.org/10.5194/nhess-13-3063-2013, 2013.
Campolo, M., Soldati, A., and Andreussi, P.: Artificial neural network
approach to flood forecasting in the River Arno, Hydrolog. Sci. J., 48,
381–398, https://doi.org/10.1623/hysj.48.3.381.45286, 2003.
Carisi, F., Schröter, K., Domeneghetti, A., Kreibich, H., and Castellarin, A.: Development and assessment of uni- and multivariable flood loss models for Emilia-Romagna (Italy), Nat. Hazards Earth Syst. Sci., 18, 2057–2079, https://doi.org/10.5194/nhess-18-2057-2018, 2018.
Carvajal, T. M., Viacrusis, K. M., Hernandez, L. F. T., Ho, H. T., Amalin, D. M.,
and Watanabe, K.: Machine learning methods reveal the temporal pattern
of dengue incidence using meteorological factors in metropolitan Manila,
Philippines, BMC Infect. Dis., 18, p. 183, 2018.
Castelletti, A., Galelli, S., Restelli, M., and Soncini-Sessa, R.:
Tree-based reinforcement learning for optimal water reservoir operation,
Water Resour. Res., 46, W09507, https://doi.org/10.1029/2009WR008898, 2010.
Chang, L., Amin, M. Z., Yang, S. N., and Chang, F.: Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models, Water, 10, 1283, https://doi.org/10.3390/w10091283, 2018.
Chinh, D., Gain, A., Dung, N., Haase, D., and Kreibich, H.: Multi-Variate
Analyses of Flood Loss in Can Tho City, Mekong Delta, Water, 8, 6,
https://doi.org/10.3390/w8010006, 2015.
Chojaczyk, A., Teixeira, A. P., Neves, L. C., Cardoso, J. B., and Guedes
Soares C.: Review and application of Artificial Neural Networks
models in reliability analysis of steel structures, Struct. Saf., 52,
78–89, 2015.
Corzo, P. G. A. and Solomatine, D.: Comparative analysis of conceptual models with error correction, artificial neural networks and committee models, EGU General Assembly 2014, 27 April–2 May 2014, Vienna, Austria, 2014.
Coughlan de Perez, E., van den Hurk, B., van Aalst, M. K., Amuron, I., Bamanya, D., Hauser, T., Jongma, B., Lopez, A., Mason, S., Mendler de Suarez, J., Pappenberger, F., Rueth, A., Stephens, E., Suarez, P., Wagemaker, J., and Zsoter, E.: Action-based flood forecasting for triggering humanitarian action, Hydrol. Earth Syst. Sci., 20, 3549–3560, https://doi.org/10.5194/hess-20-3549-2016, 2016.
Curran, A., de Bruijn, K. M., Klerk, W. J., and Kok, M.: Large Scale Flood Hazard
Analysis by Including Defence Failures on the Dutch River System, Water, 11, 1732, https://doi.org/10.3390/w11081732, 2019.
De Waal, J. P.: Basisrapport WBI 2017, Deltares 1230086-002, Delft, the Netherlands, 2016.
Dibike, Y. B. and Solomatine, D. P.: River flow forecasting using
artificial neural networks, Phys. Chem. Earth Pt. B, 26, 1–7, 2001.
Eilander, D., Trambauer, P., Wagemaker, J., and Van Loenen, A.: Harvesting
social media for generation of near real-time flood maps, 12th International
Conference on Hydroinformatics, HIC, 21 August 2016, Incheon, South Korea, 2016.
Eubanks, V.: Automating inequality: How high-tech tools profile,
police, and punish the poor, St. Martin's Press, New York, USA, 2018.
Fohringer, J., Dransch, D., Kreibich, H., and Schröter, K.: Social media as an information source for rapid flood inundation mapping, Nat. Hazards Earth Syst. Sci., 15, 2725–2738, https://doi.org/10.5194/nhess-15-2725-2015, 2015.
Gao, X., Klaiber, C., Patel, D., and Underwood, J.: AI is supercharging
the creation of maps around the world, Tech@Facebook, available at:
https://tech.fb.com/ai-is-supercharging-the-creation-of-maps-around-the-world/,
last access: 21 August 2019.
Gauss, C. F.: Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium, sumtibus Perthes, F. and Besser, I. H., Hamburg, Germany, https://doi.org/10.3931/e-rara-522, 1809.
GFDRR: Machine Learning for Disaster Risk Management,
GFDRR, Washington, D.C., USA, 2018.
Ghalkhani, H., Golian, S., Saghafian, B., Farokhnia, A., and Shamseldin, A.: Application of surrogate artificial intelligent models for real-time flood routing, Water Environ. J., 27, https://doi.org/10.1111/j.1747-6593.2012.00344.x, 2013.
Giacinto, G. and Roli, F.: Design of effective neural network ensembles for
image classification purposes, Image Vis. Comput., 19, 699–707,
https://doi.org/10.1016/S0262-8856(01)00045-2, 2001.
Goldblatt, R., You, W., Hanson, G., and Khandelwal, A.: Detecting the
boundaries of urban areas in india: A dataset for pixel-based image
classification in google earth engine, Remote Sens., 8, 634, https://doi.org/10.3390/rs8080634, 2016.
Goldblatt, R., Stuhlmacher, M. F., Tellman, B., Clinton, N., Hanson, G.,
Georgescu, M., Wang, C., Serrano-Candela, F., Khandelwal, A. K., Cheng, W., and
Balling, R.: Using Landsat and nighttime lights for supervised
pixel-based image classification of urban land cover, Remote Sens.
Environ., 205, 253–275, 2018.
Heermann, P. D. and Khazenie, N.: Classification of multispectral remote
sensing data using a back-propagation neural network, IEEE T. Geosci.
Remote, 30, 81–88, 1992.
Ivers, L. C. and Ryan, E. T.: Infectious diseases of severe
weather-related and flood-related natural disasters, Curr. Opin.
Infect. Dis., 19, 408–414, 2006.
Jonkman, S. N.: Global Perspectives on Loss of Human Life Caused by Floods,
Nat. Hazards, 34, 151–175, 2005.
Jonkman, S. N., Voortman, H. G., Klerk, W. J., and van Vuren, S.:
Developments in the management of flood defences and hydraulic
infrastructure in the Netherlands, Struct. Infrastruct. Eng., 14, 895–910, 2018.
Kang, H., Burton, H., and Miao, H.: Replicating the Recovery following the 2014 South Napa Earthquake using Stochastic Process Models, Earthq. Spectra, 34, 1247–1266, https://doi.org/10.1193/012917EQS020M, 2018.
Keyes, O.: The misgendering machines: Trans/HCI implications of
automatic gender recognition, Proceedings of the ACM on Human-Computer
Interaction, 2, 88, https://doi.org/10.1145/3274357, 2018.
Khan, A., Khan, H., and Vasilescu, L.: Disaster Management CYCLE –
a theoretical approach, Management and Marketing Journal, 6,
43–50, 2008.
Kind, J., Botzen, W. J., and Aerts, C. J. H.: Accounting for risk aversion,
income distribution and social welfare in cost-benefit analysis for flood
risk management, WIREs Clim. Change2016, 8, e446, https://doi.org/10.1002/wcc.446, 2016.
Kingston, G. B., Rajabalinejad, M. Gouldby, B. P., and Van Gelder,
P. H. A. J. M: Computational intelligence methods for the efficient
reliability analysis of complex flood defence structures, Struct. Saf.,
33, 64–73, 2011.
Klemas, V.: Remote Sensing of Floods and Flood-Prone Areas: An
Overview, J. Coastal Res., 31, 1005–1013, 2015.
Klerk, W., Schweckendiek, T., Den Heijer, F., and Kok, M.: Value of
information of Structural Health Monitoring in Asset Management of Flood
Defences, Infrastructures, 4,
56, https://doi.org/10.3390/infrastructures4030056, 2019.
Koks, E. E., Carrera, L., Jonkeren, O., Aerts, J. C. J. H., Husby, T. G., Thissen, M., Standardi, G., and Mysiak, J.: Regional disaster impact analysis: comparing input–output and computable general equilibrium models, Nat. Hazards Earth Syst. Sci., 16, 1911–1924, https://doi.org/10.5194/nhess-16-1911-2016, 2016.
Kreibich, H., Seifert, I., Merz, B., and Thieken, A.: Development of FLEMOcs
– a new model for the estimation of flood losses in the commercial sector,
Hydrolog. Sci. J., 55, 1302–1313, 2010.
Kreibich, H., Botto, A., Merz, B., and Schröter, K.: Probabilistic,
Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO, Risk
Anal., 37, 774–787, https://doi.org/10.1111/risa.12650, 2017.
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: ImageNet Classification
with Deep Convolutional Neural Networks, Proceedings of the 25th International Conference on Neural Information Processing Systems, 3–6 December 2012, Lake Tahoe, Nevada, USA, 2012.
Kron, W.: Flood Risk = Hazard × Exposure ×
Vulnerability, in: Flood Defence, edited by: Wu, B. S., Wang, Z. Y., Wang, G. Q., Huang, G. H., Fang, H. W., and Huang, J. C., Science Press, New
York, USA, 82–97, 2002.
Kundzewicz, Z. W., Kanae, S., Seneviratne, S. L., Handmer, J., Nicholls, N.,
Peduzzi, P., Mechler, R., Bouwer, L. M., Arnell, N., Mach, K., Muir-Wood, R.,
Brakenridge, R., Kron, W., Benito, G., Honda, Y., Takahashi, K., and
Sherstyukov, B.: Flood risk and climate change: global and regional
perspectives, Hydrolog. Sci.
J., 59, 1–28, https://doi.org/10.1080/02626667.2013.857411, 2014.
Legendre, A. M.: Nouvelles méthodes pour la détermination des
orbites des comètes, Sur la Méthode des
moindres quarrés, Firmin Didot, Paris, France, 1805.
Lignon, B. L.: Infectious Diseases that Pose Specific Challenges After Natural Disasters: A Review, Seminars in Pediatric Infectious Diseases, 17, 36–45, https://doi.org/10.1053/j.spid.2006.01.002, 2006.
Lin, Y. N., Yun, S., Bhardwaj, A., and Hill, E. M.: Urban Flood Detection
with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations
in a Bayesian Framework: A Case Study for Hurricane Matthew, Remote Sens., 11, 1778, https://doi.org/10.3390/rs11151778, 2019.
Lobbrecht A. and Solomatine, D.: Machine Learning in Real-Time Control of
Water Systems, Urban Water 4, 283–289, 2002.
Lopez-Fuentes, L., Van de Weijer, J., Bolaños, M., and Skinnemoen, H.: Multi-modal Deep Learning Approach for Flood Detection, MediaEval'17, 13–15 September 2017, Dublin, Ireland, 2017.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Hung Byers, A.: Big data: The next frontier for innovation, competition,
and productivity, McKinsey Global Institute, New York City, New York, USA, 2011.
Mayfield, H. J., Smith, C. S., Lowry, J. H., Watson, C. H., Baker, M. G., Kama,
M., Nilles, E. J., and Lau, C. L.: Predictive risk mapping of an
environmentally-driven infectious disease using spatial Bayesian networks: A
case study of leptospirosis in Fiji, PLoS Neglect. Trop. D.,
12, e0006857, https://doi.org/10.1371/journal.pntd.0006857, 2018.
Mazzoleni, M., Verlaan, M., Alfonso, L., Monego, M., Norbiato, D., Ferri, M., and Solomatine, D. P.: Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?, Hydrol. Earth Syst. Sci., 21, 839–861, https://doi.org/10.5194/hess-21-839-2017, 2017.
Menderes, A., Erener, A., and Sarp, G.: Automatic Detection of Damaged
Buildings after Earthquake Hazard by Using Remote Sensing and Information
Technologies, Proced. Earth Plan. Sc., 15,
257–262, https://doi.org/10.1016/j.proeps.2015.08.063, 2015.
Merz, B., Kreibich, H., Schwarze, R., and Thieken, A.: Review article “Assessment of economic flood damage”, Nat. Hazards Earth Syst. Sci., 10, 1697–1724, https://doi.org/10.5194/nhess-10-1697-2010, 2010.
Merz, B., Kreibich, H., and Lall, U.: Multi-variate flood damage assessment: a tree-based data-mining approach, Nat. Hazards Earth Syst. Sci., 13, 53–64, https://doi.org/10.5194/nhess-13-53-2013, 2013.
Mestav Sarica, G., Zhu, T., and Pan, T.-C.: Flood Exposure of Shenzhen
from Past to Future: A Spatio-Temporal Approach using Urban Growth
Modeling, Proceedings of the 7th Annual International Conference on
Architecture and Civil Engineering, 27–28 May 2019, Singapore, 400–405, 2019.
Modu, B., Polovina, N., Lan, Y., Konur, S., Asyhari, A., and Peng, Y.:
Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning
System, Appl. Sci., 7, 836, https://doi.org/10.3390/app7080836, 2017.
Mosavi, A., Ozturk, P., and Kwok-wing, C.: Review: Flood Prediction Using
Machine Learning Models: Literature Review, Water, 10, 1536,
https://doi.org/10.3390/w10111536, 2018.
Naghibi, F., Delavar, M. R., and Pijanowski, B.: Urban Growth Modeling
Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated
by the Artificial Bee Colony Optimization Algorithm, Sensors, 16, 2122, https://doi.org/10.3390/s16122122,
2016.
Narayanan, A.: How to recognize AI snake oil, available at: https://www.cs.princeton.edu/~arvindn/talks/MIT-STS-AI-snakeoil.pdf (last access: 27 January 2020), 2019.
National Research Council: Introduction Facing hazards and
disasters: Understanding human dimensions, The National
Academies Press, Washington, D.C., USA, https://doi.org/10.17226/11671, 2006.
Neuhold, G., Ollmann, T., Rota Bulo, S., and Kontschieder, P.: The
Mapillary Vistas for Semantic Understanding of Street Scenes, International
Conf. on Computer Vision (ICCV), 22–29 October 2017, Venice, Italy, 2017.
Neves, C., González, I., Leander, J., and Karoumi, R.: Structural
health monitoring of bridges: a model-free ANN-based approach to damage
detection, J. Civ. Struct. Heal. Monit., 7, 689–702, 2017.
Nevo, S., Anisimov, V., El-Yaniv, R., Giencke, P., Gigi, Y., Hassidim, A.,
Mushe, Z., Schlesinger, M., Shalev, G., Tirumali, A., Wiesel, A., Zlydenko,
O., and Matias, Y.: Machine Learning for Flood Forecasting at Scale, 32nd Conference on Neural Information Processing Systems (NIPS 2018), 3–8 December 2019, Montréal, Canada, 2019.
Noble, S. U.: Algorithms of oppression: How search engines reinforce
racism, nyu Press, New York City, New York, USA, ISBN 9781479837243, 2018.
Olivas, E. S., Guerrero, J. D., Martinez-Sober, M., Magdalena-Benedito, J.
R., and Serrano López, A. J.: Handbook of Research on Machine
Learning Applications and Trends: Algorithms, Methods, and Techniques, IGI Global, Hershey, PA, USA, 2, 1–852, https://doi.org/10.4018/978-1-60566-766-9, 2010.
Ong, Y. S., Nair, P., ans Keane, A. J.: Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling, AIAA Journal, 41, 4, https://doi.org/10.2514/2.1999, 2003.
Pal, S. and Ghosh, S. K.: Rule based End-to-End Learning Framework for
Urban Growth Prediction, ArXiv, abs/1711.10801, available at: https://arxiv.org/abs/1711.10801 (last access: 28 April 2020), 2017.
Penning-Rowsell, E. C., Johnson, C., and Tunstall, S.: The benefits of Flood
and Coastal Risk Management: A Manual of Assessment Techniques, Middlesex
University Press, London, UK, 2005.
Prendergast, L. J., Limongelli, M. P., Ademovic, N., Anžlin, A., Gavin,
K., Zanini, M.: Structural Health Monitoring for Performance
Assessment of Bridges under Flooding and Seismic Actions, Struct. Eng.
Int., 28, 296–307, 2018.
Pyayt, A., Mokhov, I., Lang, B., Krzhizhanovskaya, V., and Meijer, R.: Machine learning methods for environmental monitoring and flood
Protection, World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering, 5, https://doi.org/10.5281/zenodo.1075060, 2011.
Pyayt, A. L., Kozionov, A. P., Mokhov, I. I., Lang, B., Meijer, R. J.,
Krzhizhanovskaya, V. V., and Sloot, P. M. A.: Time-frequency methods for
structural health monitoring, Sensors, 14, 5147–73, 2014.
Raghavan, M., Barocas, S., Kleinberg, J., and Levy, K.: Mitigating Bias in
Algorithmic Hiring: Evaluating Claims and Practices, Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 27–30 January 2020, Barcelona, Spain, 469–481, 2019.
Reuters: Bangladesh tries new way to aid flood-hit families: cash up
front, available at: https://www.preventionweb.net/news/view/66899 (last access: 24 April 2020), 2019.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: Learning
representations by back-propagating errors, Nature, 323, 533–536, 1986.
Saadi, S. and Bensaibi, M.: Detection of Buildings height using satellite
monoscopic image, 2nd European Conference On Earthquake Engineering, 24–29 August 2014, Istanbul, Turkey, https://doi.org/10.13140/2.1.4985.6005, 2014.
Samardzic-Petrovic, M., Kovacevic, M. , Bajat, B., and Dragicevic, S.: Machine Learning Techniques for Modelling Short Term Land-Use
Change, ISPRS Int. J. Geo-Inf., 6, 387, https://doi.org/10.3390/ijgi6120387, 2017.
Schröter, K., Kreibich, H., Vogel, K., Riggelsen, C., Scherbaum, F., and
Merz, B.: How useful are complex flood damage models?, Water Resour.
Res., 50, 3378–3395, https://doi.org/10.1002/2013WR014396, 2014.
Schröter, K., Lüdtke, S., Redweik, R., Meier, J., Bochow, M., Ross,
L., Nagel, C., and Kreibich, H.: Flood loss estimation using 3D city models
and remote sensing data, Environ. Modell. Softw., 105, 118–131,
https://doi.org/10.1016/j.envsoft.2018.03.032, 2018.
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y.: Overfeat: Integrated recognition, localization and detection using
convolutional networks, arXiv:1312.6229, available at: https://arxiv.org/abs/1312.6229 (last access: 28 April 2020), 2014.
Shively, G., Sununtnasuk, C., and Brown, M.: Environmental variability and
child growth in Nepal, Health Place, 35, 37–51, 2015.
Soden, R. and Kauffman, N.: Infrastructuring the Imaginary: How
Sea-level Rise Comes to Matter in The San Francisco Bay Area, in: Proceedings
of the 2019 CHI Conference on Human Factors in Computing Systems, 4–9 May 2019, Glasgow, UK, Paper No.: 286, 1–11, https://doi.org/10.1145/3290605.3300516, 2019.
Soden, R., Wagenaar, D. Luo, D., and Tijssen, A.: Taking Ethics, Fairness,
and Bias Seriously in Machine Learning for Disaster Risk Management,
Workshop Paper, NeurIPS 2019 Workshop on Machine Learning for
the Developing World, 8–14 December 2019, Vancouver, Canada, 2019.
Solomatine, D. P. and Ostfield, A.: Data-driven modelling: some past experiences
and new approaches, J. Hydroinform., 10, 3–22, 2008.
Song, X., Sexton, J. O., Huang, C., Channan, S., and Townshend, J. R.:
Characterizing the magnitude, timing and duration of urban growth from time
series of Landsat-based estimates of impervious cover, Remote Sens.
Environ., 175, 1–13, https://doi.org/10.1016/j.rse.2015.12.027, 2015.
Spekkers, M. H., Kok, M., Clemens, F. H. L. R., and ten Veldhuis, J. A. E.: Decision-tree analysis of factors influencing rainfall-related building structure and content damage, Nat. Hazards Earth Syst. Sci., 14, 2531–2547, https://doi.org/10.5194/nhess-14-2531-2014, 2014.
Steenbergen, H. M. G. M., Lassing, B. L., Vrouwenvelder, A. C. W. M., and Waarts, P. H.: Reliability analysis of flood defence systems, HERON, vol. 49, 2004.
Suchman, L. A. and Weber, J.: Human-machine autonomies. Autonomous
Weapons Systems, Cambridge University Press, Cambridge, UK, 75–102, 2016.
Thieken, A. H., Olschewski, A., Kreibich, H., Kobsch, S., and Merz, B.:
Development and evaluation of FLEMOps – A new flood loss esimation model
for the private sector, WIT Trans. Ecol. Envir., 118, 315–324, 2008.
Tiwari, S., Jacoby, H., and Skoufias, E.: Monsoon Babies: Rainfall Shocks and Child Nutrition in Nepal (March 1, 2013). World Bank Policy Research Working Paper No. 6395, available at: https://ssrn.com/abstract=2241953 (last access: 28 April 2020), 2013.
Tkachenko, N., Jarvis, S., and Procter, R.: Predicting floods with Flickr tags, PLoS ONE, 12, e0172870, https://doi.org/10.1371/journal.pone.0172870, 2017.
Triantakonstantis, D. and Mountrakis, G.: Urban Growth Prediction: A
Review of Computational Models and Human Perceptions, Journal of
Geographic Information System, 4, 555–587, 2013.
UrbanRiskLab: https://urbanrisklab.org/work#/riskmap/, last access:
12 August 2019.
Van der Most, H., Tanczos, I., De Bruijn, K. M., and Wagenaar, D. J.:
New, Risk-Based standards for flood protection in the Netherlands, 6th
International Conference on Flood Management (ICFM6), 16–18 September 2014, Sao Paulo, Brazil, 2014.
Wagenaar, D. J., de Bruijn, K. M., Bouwer, L. M., and de Moel, H.: Uncertainty in flood damage estimates and its potential effect on investment decisions, Nat. Hazards Earth Syst. Sci., 16, 1–14, https://doi.org/10.5194/nhess-16-1-2016, 2016.
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.
Wagenaar, D., Lüdtke, S., Schröter, K., Bouwer, L. M., and Kreibich, H.: Regional and Temporal Transferability of Multivariable Flood Damage
Models, Water Resour. Res., 54, 3688–3703,
https://doi.org/10.1029/2017WR022233,
2018.
Wagenaar, D. J., Dahm, R. J., Diermanse, F. L. M., Dias, W. P. S., Dissanayake,
D. M. S. S., Vajja, H. P, Gehrels, J. C., and Bouwer, L. M.: Evaluating adaptation measures for reducing flood risk: A case study in the city of Colombo, Sri Lanka, Int. J.
Disast. Risk Re., 37, 101162, https://doi.org/10.1016/j.ijdrr.2019.101162, 2019.
Wagenaar, D. J., Hermawan, T., Van den Homberg, M., Aerts, J. C. J. H.,
Kreibich, H., De Moel, H., and Bouwer, L. M.: Improved transferability of
multi-variable damage models through sample selection bias correction, submitted, 2020.
Watmough, G. R., Marcinko, C. L. J., Sullivan, C., Tschirhart, K., Mutuo,
P. K., Palm, C. A., and Svenning, J.: Socioecologically informed use of
remote sensing data to predict rural household poverty, P. Natl. Acad. Sci. USA, 116, 1213–1218, https://doi.org/10.1073/pnas.1812969116, 2019.
Xingjian, S., Chen, Z., Wang, H., Yeung, D., Wong, W., and Woo, W.: Convolutional LSTM network: A machine learning approach for precipitation nowcasting, in: Neural Information Processing Systems,
arXiv:1506.04214, available at: https://arxiv.org/abs/1506.04214 (last access: 28 April 2020), 2015.
Xu, T., Gao, J., and Coco, G.: Simulation of urban expansion via
integrating artificial neural network with Markov chain – cellular
automata, Int. J. Geogr. Inf. Sci.,
33, 1960–1983, https://doi.org/10.1080/13658816.2019.1600701, 2019.
Yomwan, P., Cao, C., Rakwatin, P., Suphamitmongkol, W., Tian, R., and
Saokarn, A.: A study of waterborne diseases during flooding using
Radarsat-2 imagery and a back propagation neural network algorithm,
Geomatics, Natural Hazards and Risk, 6, 289–307, 2015.
Short summary
This invited perspective paper addresses how machine learning may change flood risk and impact assessments. It goes through different modelling components and provides an analysis of how current assessments are done without machine learning, current applications of machine learning and potential future improvements. It is based on a 2-week-long intensive collaboration among experts from around the world during the Understanding Risk Field lab on urban flooding in June 2019.
This invited perspective paper addresses how machine learning may change flood risk and impact...
Altmetrics
Final-revised paper
Preprint