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 subject area
Hydrological Hazards
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
How uncertain are precipitation and peak flow estimates for the July 2021 flooding event?
Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis
A multi-strategy-mode waterlogging-prediction framework for urban flood depth
Multiscale flood risk assessment under climate change: the case of the Miño River in the city of Ourense, Spain
Interactions between precipitation, evapotranspiration and soil-moisture-based indices to characterize drought with high-resolution remote sensing and land-surface model data
Rare flood scenarios for a rapidly growing high-mountain city: Pokhara, Nepal
A climate-conditioned catastrophe risk model for UK flooding
Brief communication: Impact forecasting could substantially improve the emergency management of deadly floods: case study July 2021 floods in Germany
Brief communication: Western Europe flood in 2021 – mapping agriculture flood exposure from synthetic aperture radar (SAR)
Comprehensive space–time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin
A new index to quantify the extremeness of precipitation across scales
Effectiveness of Sentinel-1 and Sentinel-2 for flood detection assessment in Europe
Assessing flood hazard changes using climate model forcing
Characterizing multivariate coastal flooding events in a semi-arid region: the implications of copula choice, sampling, and infrastructure
Different drought types and the spatial variability in their hazard, impact, and propagation characteristics
More than heavy rain turning into fast-flowing water – a landscape perspective on the 2021 Eifel floods
Integrated drought risk assessment to support adaptive policymaking in the Netherlands
INSYDE-BE: adaptation of the INSYDE model to the Walloon region (Belgium)
Assessing flooding impact to riverine bridges: an integrated analysis
Warming of 0.5 °C may cause double the economic loss and increase the population affected by floods in China
First application of the Integrated Karst Aquifer Vulnerability (IKAV) method – potential and actual vulnerability in Yucatán, Mexico
Brief communication: Seismological analysis of flood dynamics and hydrologically triggered earthquake swarms associated with Storm Alex
System vulnerability to flood events and risk assessment of railway systems based on national and river basin scales in China
Machine-learning blends of geomorphic descriptors: value and limitations for flood hazard assessment across large floodplains
A performance-based approach to quantify atmospheric river flood risk
Estimating soil moisture conditions for drought monitoring with random forests and a simple soil moisture accounting scheme
A globally-applicable framework for compound flood hazard modeling
Extreme-coastal-water-level estimation and projection: a comparison of statistical methods
Spatiotemporal evolution and meteorological triggering conditions of hydrological drought in the Hun River basin, NE China
The Cambodian Mekong floodplain under future development plans and climate change
Geo-historical database of flood impacts in Alpine catchments (HIFAVa database, Arve River, France, 1850–2015)
Compound flood modeling framework for surface–subsurface water interactions
Assessing tropical cyclone compound flood risk using hydrodynamic modelling: a case study in Haikou City, China
An approach to identify the best climate models for the assessment of climate change impacts on meteorological and hydrological droughts
Flash flood warnings in context: combining local knowledge and large-scale hydro-meteorological patterns
A comparative flood damage and risk impact assessment of land use changes
Temporal changes in rainfall intensity–duration thresholds for post-wildfire flash floods in southern California
Compound inland flood events: different pathways, different impacts and different coping options
Review article: Factors leading to the occurrence of flood fatalities: a systematic review of research papers published between 2010 and 2020
Modeling of a compound flood induced by the levee breach at Qianbujing Creek, Shanghai, during Typhoon Fitow
Improving flood damage assessments in data-scarce areas by retrieval of building characteristics through UAV image segmentation and machine learning – a case study of the 2019 floods in southern Malawi
Assessment of direct economic losses of flood disasters based on spatial valuation of land use and quantification of vulnerabilities: a case study on the 2014 flood in Lishui city of China
Evaluating integrated water management strategies to inform hydrological drought mitigation
Global riverine flood risk – how do hydrogeomorphic floodplain maps compare to flood hazard maps?
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.
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.
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.
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.
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.
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.
Mohamed Saadi, Carina Furusho-Percot, Alexandre Belleflamme, Ju-Yu Chen, Silke Trömel, and Stefan Kollet
Nat. Hazards Earth Syst. Sci., 23, 159–177, https://doi.org/10.5194/nhess-23-159-2023, https://doi.org/10.5194/nhess-23-159-2023, 2023
Short summary
Short summary
On 14 July 2021, heavy rainfall fell over central Europe, causing considerable damage and human fatalities. We analyzed how accurate our estimates of rainfall and peak flow were for these flooding events in western Germany. We found that the rainfall estimates from radar measurements were improved by including polarimetric variables and their vertical gradients. Peak flow estimates were highly uncertain due to uncertainties in hydrological model parameters and rainfall measurements.
Arefeh Safaei-Moghadam, David Tarboton, and Barbara Minsker
Nat. Hazards Earth Syst. Sci., 23, 1–19, https://doi.org/10.5194/nhess-23-1-2023, https://doi.org/10.5194/nhess-23-1-2023, 2023
Short summary
Short summary
Climate change, urbanization, and aging infrastructure contribute to flooding on roadways. This study evaluates the potential for flood reports collected from Waze – a community-based navigation app – to predict these events. Waze reports correlate primarily with low-lying depressions on roads. Therefore, we developed two data-driven models to determine whether roadways will flood. Analysis showed that in the city of Dallas, drainage area and imperviousness are the most significant contributors.
Zongjia Zhang, Jun Liang, Yujue Zhou, Zhejun Huang, Jie Jiang, Junguo Liu, and Lili Yang
Nat. Hazards Earth Syst. Sci., 22, 4139–4165, https://doi.org/10.5194/nhess-22-4139-2022, https://doi.org/10.5194/nhess-22-4139-2022, 2022
Short summary
Short summary
An innovative multi-strategy-mode waterlogging-prediction framework for predicting waterlogging depth is proposed in the paper. The framework selects eight regression algorithms for comparison and tests the prediction accuracy and robustness of the model under different prediction strategies. Ultimately, the accuracy of predicting water depth after 30 min can exceed 86.1 %. This can aid decision-making in terms of issuing early warning information and determining emergency responses in advance.
Diego Fernández-Nóvoa, Orlando García-Feal, José González-Cao, Maite deCastro, and Moncho Gómez-Gesteira
Nat. Hazards Earth Syst. Sci., 22, 3957–3972, https://doi.org/10.5194/nhess-22-3957-2022, https://doi.org/10.5194/nhess-22-3957-2022, 2022
Short summary
Short summary
A multiscale analysis, where the historical and future precipitation data from the CORDEX project were used as input in a hydrological model (HEC-HMS) that, in turn, feeds a 2D hydraulic model (Iber+), was applied to the case of the Miño-Sil basin (NW Spain), specifically to Ourense city, in order to analyze future changes in flood hazard. Detailed flood maps indicate an increase in the frequency and intensity of future floods, implying an increase in flood hazard in important areas of the city.
Jaime Gaona, Pere Quintana-Seguí, María José Escorihuela, Aaron Boone, and María Carmen Llasat
Nat. Hazards Earth Syst. Sci., 22, 3461–3485, https://doi.org/10.5194/nhess-22-3461-2022, https://doi.org/10.5194/nhess-22-3461-2022, 2022
Short summary
Short summary
Droughts represent a particularly complex natural hazard and require explorations of their multiple causes. Part of the complexity has roots in the interaction between the continuous changes in and deviation from normal conditions of the atmosphere and the land surface. The exchange between the atmospheric and surface conditions defines feedback towards dry or wet conditions. In semi-arid environments, energy seems to exceed water in its impact over the evolution of conditions, favoring drought.
Melanie Fischer, Jana Brettin, Sigrid Roessner, Ariane Walz, Monique Fort, and Oliver Korup
Nat. Hazards Earth Syst. Sci., 22, 3105–3123, https://doi.org/10.5194/nhess-22-3105-2022, https://doi.org/10.5194/nhess-22-3105-2022, 2022
Short summary
Short summary
Nepal’s second-largest city has been rapidly growing since the 1970s, although its valley has been affected by rare, catastrophic floods in recent and historic times. We analyse potential impacts of such floods on urban areas and infrastructure by modelling 10 physically plausible flood scenarios along Pokhara’s main river. We find that hydraulic effects would largely affect a number of squatter settlements, which have expanded rapidly towards the river by a factor of up to 20 since 2008.
Paul D. Bates, James Savage, Oliver Wing, Niall Quinn, Christopher Sampson, Jeffrey Neal, and Andrew Smith
EGUsphere, https://doi.org/10.5194/egusphere-2022-829, https://doi.org/10.5194/egusphere-2022-829, 2022
Short summary
Short summary
In this work we present and validate a new flood model for the UK that simulates pluvial, fluvial and coastal flooding. We show that previous UK flood losses based on government data and used in national climate change risk assessments are overestimated by a factor of ~3. These official estimates lie well outside our modelled loss distribution, which is plausibly centred on the observations.
Heiko Apel, Sergiy Vorogushyn, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 22, 3005–3014, https://doi.org/10.5194/nhess-22-3005-2022, https://doi.org/10.5194/nhess-22-3005-2022, 2022
Short summary
Short summary
The paper presents a fast 2D hydraulic simulation model for flood propagation that enables operational forecasts of spatially distributed inundation depths, flood extent, flow velocities, and other flood impacts. The detailed spatial forecast of floods and flood impacts is a large step forward from the currently operational forecasts of discharges at selected gauges, thus enabling a more targeted flood management and early warning.
Kang He, Qing Yang, Xinyi Shen, and Emmanouil N. Anagnostou
Nat. Hazards Earth Syst. Sci., 22, 2921–2927, https://doi.org/10.5194/nhess-22-2921-2022, https://doi.org/10.5194/nhess-22-2921-2022, 2022
Short summary
Short summary
This study depicts the flood-affected areas in western Europe in July 2021 and particularly the agriculture land that was under flood inundation. The results indicate that the total inundated area over western Europe is about 1920 km2, of which 1320 km2 is in France. Around 64 % of the inundated area is agricultural land. We expect that the agricultural productivity in western Europe will have been severely impacted.
Daniel Viviroli, Anna E. Sikorska-Senoner, Guillaume Evin, Maria Staudinger, Martina Kauzlaric, Jérémy Chardon, Anne-Catherine Favre, Benoit Hingray, Gilles Nicolet, Damien Raynaud, Jan Seibert, Rolf Weingartner, and Calvin Whealton
Nat. Hazards Earth Syst. Sci., 22, 2891–2920, https://doi.org/10.5194/nhess-22-2891-2022, https://doi.org/10.5194/nhess-22-2891-2022, 2022
Short summary
Short summary
Estimating the magnitude of rare to very rare floods is a challenging task due to a lack of sufficiently long observations. The challenge is even greater in large river basins, where precipitation patterns and amounts differ considerably between individual events and floods from different parts of the basin coincide. We show that a hydrometeorological model chain can provide plausible estimates in this setting and can thus inform flood risk and safety assessments for critical infrastructure.
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 22, 2791–2805, https://doi.org/10.5194/nhess-22-2791-2022, https://doi.org/10.5194/nhess-22-2791-2022, 2022
Short summary
Short summary
To better understand how the frequency and intensity of heavy precipitation events (HPEs) will change with changing climate and to adapt disaster risk management accordingly, we have to quantify the extremeness of HPEs in a reliable way. We introduce the xWEI (cross-scale WEI) and show that this index can reveal important characteristics of HPEs that would otherwise remain hidden. We conclude that the xWEI could be a valuable instrument in both disaster risk management and research.
Angelica Tarpanelli, Alessandro C. Mondini, and Stefania Camici
Nat. Hazards Earth Syst. Sci., 22, 2473–2489, https://doi.org/10.5194/nhess-22-2473-2022, https://doi.org/10.5194/nhess-22-2473-2022, 2022
Short summary
Short summary
We analysed 10 years of river discharge data from almost 2000 sites in Europe, and we extracted flood events, as proxies of flood inundations, based on the overpasses of Sentinel-1 and Sentinel-2 satellites to derive the percentage of potential inundation events that they were able to observe. Results show that on average 58 % of flood events are potentially observable by Sentinel-1 and only 28 % by Sentinel-2 due to the obstacle of cloud coverage.
David P. Callaghan and Michael G. Hughes
Nat. Hazards Earth Syst. Sci., 22, 2459–2472, https://doi.org/10.5194/nhess-22-2459-2022, https://doi.org/10.5194/nhess-22-2459-2022, 2022
Short summary
Short summary
A new method was developed to estimate changes in flood hazard under climate change. We use climate projections covering New South Wales, Australia, with two emission paths of business as usual and one with reduced emissions. We apply our method to the lower floodplain of the Gwydir Valley with changes in flood hazard provided over the next 90 years compared with the previous 50 years. We find that changes in flood hazard decrease over time within the Gwydir Valley floodplain.
Joseph T. D. Lucey and Timu W. Gallien
Nat. Hazards Earth Syst. Sci., 22, 2145–2167, https://doi.org/10.5194/nhess-22-2145-2022, https://doi.org/10.5194/nhess-22-2145-2022, 2022
Short summary
Short summary
Coastal flooding can result from multiple flood drivers (e.g., tides, waves, river flows, rainfall) occurring at the same time. This study characterizes flooding events caused by high marine water levels and rain. Results show that wet-season coinciding sampling may better describe extreme flooding events in a dry, tidally dominated region. A joint-probability-based function is then used to estimate sea wall impacts on urban coastal flooding.
Erik Tijdeman, Veit Blauhut, Michael Stoelzle, Lucas Menzel, and Kerstin Stahl
Nat. Hazards Earth Syst. Sci., 22, 2099–2116, https://doi.org/10.5194/nhess-22-2099-2022, https://doi.org/10.5194/nhess-22-2099-2022, 2022
Short summary
Short summary
We identified different drought types with typical hazard and impact characteristics. The summer drought type with compounding heat was most impactful. Regional drought propagation of this drought type exhibited typical characteristics that can guide drought management. However, we also found a large spatial variability that caused distinct differences among propagating drought signals. Accordingly, local multivariate drought information was needed to explain the full range of drought impacts.
Michael Dietze, Rainer Bell, Ugur Ozturk, Kristen L. Cook, Christoff Andermann, Alexander R. Beer, Bodo Damm, Ana Lucia, Felix S. Fauer, Katrin M. Nissen, Tobias Sieg, and Annegret H. Thieken
Nat. Hazards Earth Syst. Sci., 22, 1845–1856, https://doi.org/10.5194/nhess-22-1845-2022, https://doi.org/10.5194/nhess-22-1845-2022, 2022
Short summary
Short summary
The flood that hit Europe in July 2021, specifically the Eifel, Germany, was more than a lot of fast-flowing water. The heavy rain that fell during the 3 d before also caused the slope to fail, recruited tree trunks that clogged bridges, and routed debris across the landscape. Especially in the upper parts of the catchments the flood was able to gain momentum. Here, we discuss how different landscape elements interacted and highlight the challenges of holistic future flood anticipation.
Marjolein J. P. Mens, Gigi van Rhee, Femke Schasfoort, and Neeltje Kielen
Nat. Hazards Earth Syst. Sci., 22, 1763–1776, https://doi.org/10.5194/nhess-22-1763-2022, https://doi.org/10.5194/nhess-22-1763-2022, 2022
Short summary
Short summary
Many countries have to prepare for droughts by proposing policy actions to increase water supply, reduce water demand, or limit the societal impact. Societal cost–benefit analysis is required to support decision-making for a range of future scenarios, accounting for climate change and socio-economic developments. This paper presents a framework to assess drought policy actions based on quantification of drought risk and exemplifies it for the Netherlands’ drought risk management strategy.
Anna Rita Scorzini, Benjamin Dewals, Daniela Rodriguez Castro, Pierre Archambeau, and Daniela Molinari
Nat. Hazards Earth Syst. Sci., 22, 1743–1761, https://doi.org/10.5194/nhess-22-1743-2022, https://doi.org/10.5194/nhess-22-1743-2022, 2022
Short summary
Short summary
This study presents a replicable procedure for the adaptation of synthetic, multi-variable flood damage models among countries that may have different hazard and vulnerability features. The procedure is exemplified here for the case of adaptation to the Belgian context of a flood damage model, INSYDE, for the residential sector, originally developed for Italy. The study describes necessary changes in model assumptions and input parameters to properly represent the new context of implementation.
Maria Pregnolato, Andrew O. Winter, Dakota Mascarenas, Andrew D. Sen, Paul Bates, and Michael R. Motley
Nat. Hazards Earth Syst. Sci., 22, 1559–1576, https://doi.org/10.5194/nhess-22-1559-2022, https://doi.org/10.5194/nhess-22-1559-2022, 2022
Short summary
Short summary
The interaction of flow, structure and network is complex, and yet to be fully understood. This study aims to establish rigorous practices of computational fluid dynamics (CFD) for modelling hydrodynamic forces on inundated bridges, and understanding the consequences of such impacts on the surrounding network. The objectives of this study are to model hydrodynamic forces as the demand on the bridge structure, to advance a structural reliability and network-level analysis.
Lulu Liu, Jiangbo Gao, and Shaohong Wu
Nat. Hazards Earth Syst. Sci., 22, 1577–1590, https://doi.org/10.5194/nhess-22-1577-2022, https://doi.org/10.5194/nhess-22-1577-2022, 2022
Short summary
Short summary
The impact of extreme events is increasing with global warming. Based on future scenario data and an improved quantitative assessment model of natural-disaster risk, this study analyses the spatial and temporal patterns of floods in China at 1.5 °C and 2 °C of global warming, quantitatively assesses the socioeconomic risks posed by floods, and determines the integrated risk levels. Global warming of 1.5 °C can effectively reduce the population affected and the economic risks of floods.
Miguel Moreno-Gómez, Carolina Martínez-Salvador, Rudolf Liedl, Catalin Stefan, and Julia Pacheco
Nat. Hazards Earth Syst. Sci., 22, 1591–1608, https://doi.org/10.5194/nhess-22-1591-2022, https://doi.org/10.5194/nhess-22-1591-2022, 2022
Short summary
Short summary
Current vulnerability methods, as tools to protect groundwater resources from pollution, present some limitations and drawbacks: the roles of population and economic activities are not considered by such methods. The methodology presented in this work combines natural characteristics and human-driven conditions of a given region to improve the process of groundwater vulnerability analysis. Results indicate the reliability of this alternative method to improve groundwater protection strategies.
Małgorzata Chmiel, Maxime Godano, Marco Piantini, Pierre Brigode, Florent Gimbert, Maarten Bakker, Françoise Courboulex, Jean-Paul Ampuero, Diane Rivet, Anthony Sladen, David Ambrois, and Margot Chapuis
Nat. Hazards Earth Syst. Sci., 22, 1541–1558, https://doi.org/10.5194/nhess-22-1541-2022, https://doi.org/10.5194/nhess-22-1541-2022, 2022
Short summary
Short summary
On 2 October 2020, the French Maritime Alps were struck by an extreme rainfall event caused by Storm Alex. Here, we show that seismic data provide the timing and velocity of the propagation of flash-flood waves along the Vésubie River. We also detect 114 small local earthquakes triggered by the rainwater weight and/or its infiltration into the ground. This study paves the way for future works that can reveal further details of the impact of Storm Alex on the Earth’s surface and subsurface.
Weihua Zhu, Kai Liu, Ming Wang, Philip J. Ward, and Elco E. Koks
Nat. Hazards Earth Syst. Sci., 22, 1519–1540, https://doi.org/10.5194/nhess-22-1519-2022, https://doi.org/10.5194/nhess-22-1519-2022, 2022
Short summary
Short summary
We present a simulation framework to analyse the system vulnerability and risk of the Chinese railway system to floods. To do so, we develop a method for generating flood events at both the national and river basin scale. Results show flood system vulnerability and risk of the railway system are spatially heterogeneous. The event-based approach shows how we can identify critical hotspots, taking the first steps in developing climate-resilient infrastructure.
Andrea Magnini, Michele Lombardi, Simone Persiano, Antonio Tirri, Francesco Lo Conti, and Attilio Castellarin
Nat. Hazards Earth Syst. Sci., 22, 1469–1486, https://doi.org/10.5194/nhess-22-1469-2022, https://doi.org/10.5194/nhess-22-1469-2022, 2022
Short summary
Short summary
We retrieve descriptors of the terrain morphology from a digital elevation model of a 105 km2 study area and blend them through decision tree models to map flood susceptibility and expected water depth. We investigate this approach with particular attention to (a) the comparison with a selected single-descriptor approach, (b) the goodness of decision trees, and (c) the performance of these models when applied to data-scarce regions. We find promising pathways for future research.
Corinne Bowers, Katherine A. Serafin, and Jack Baker
Nat. Hazards Earth Syst. Sci., 22, 1371–1393, https://doi.org/10.5194/nhess-22-1371-2022, https://doi.org/10.5194/nhess-22-1371-2022, 2022
Short summary
Short summary
Atmospheric rivers (ARs) cause significant flooding on the US west coast. We present a new Performance-based Atmospheric River Risk Analysis (PARRA) framework that connects models of atmospheric forcings, hydrologic impacts, and economic consequences to better estimate losses from AR-induced river flooding. We apply the PARRA framework to a case study in Sonoma County, CA, USA, and show that the framework can quantify the potential benefit of flood mitigation actions such as home elevation.
Yves Tramblay and Pere Quintana Seguí
Nat. Hazards Earth Syst. Sci., 22, 1325–1334, https://doi.org/10.5194/nhess-22-1325-2022, https://doi.org/10.5194/nhess-22-1325-2022, 2022
Short summary
Short summary
Monitoring soil moisture is important during droughts, but very few measurements are available. Consequently, land-surface models are essential tools for reproducing soil moisture dynamics. In this study, a hybrid approach allowed for regionalizing soil water content using a machine learning method. This approach proved to be efficient, compared to the use of soil property maps, to run a simple soil moisture accounting model, and therefore it can be applied in various regions.
Dirk Eilander, Anaïs Couasnon, Tim Leijnse, Hiroaki Ikeuchi, Dai Yamazaki, Sanne Muis, Job Dullaart, Hessel C. Winsemius, and Philip J. Ward
EGUsphere, https://doi.org/10.5194/egusphere-2022-149, https://doi.org/10.5194/egusphere-2022-149, 2022
Short summary
Short summary
In coastal deltas flooding can occur from interactions between surge and waves, river discharge and precipitation, so-called compound flooding. Global flood models however ignore these interaction. We therefore present a framework to create a reproducible compound flood model anywhere at the globe and show how it can be used to better understand compound flooding. The framework is applied to two historical events tropical cyclone events in Mozambique with good results.
Maria Francesca Caruso and Marco Marani
Nat. Hazards Earth Syst. Sci., 22, 1109–1128, https://doi.org/10.5194/nhess-22-1109-2022, https://doi.org/10.5194/nhess-22-1109-2022, 2022
Short summary
Short summary
We comparatively evaluate the predictive performance of traditional and new approaches to estimate the probability distributions of extreme coastal water levels. The metastatistical approach maximizes the use of observational information and provides reliable estimates of high quantiles with respect to traditional methods. Leveraging the increased estimation accuracy afforded by this approach, we investigate future changes in the frequency of extreme total water levels.
Shupeng Yue, Xiaodan Sheng, and Fengtian Yang
Nat. Hazards Earth Syst. Sci., 22, 995–1014, https://doi.org/10.5194/nhess-22-995-2022, https://doi.org/10.5194/nhess-22-995-2022, 2022
Short summary
Short summary
To develop drought assessment and early warning systems, it is necessary to explore the characteristics of drought and its propagation process. In this article, a generalized and efficient drought research framework is studied and verified. It includes the evaluation of the spatiotemporal evolution, the construction of the return period calculation model, and the quantitative analysis of the meteorological trigger conditions of drought based on an improved Bayesian network model.
Alexander J. Horton, Nguyen V. K. Triet, Long P. Hoang, Sokchhay Heng, Panha Hok, Sarit Chung, Jorma Koponen, and Matti Kummu
Nat. Hazards Earth Syst. Sci., 22, 967–983, https://doi.org/10.5194/nhess-22-967-2022, https://doi.org/10.5194/nhess-22-967-2022, 2022
Short summary
Short summary
We studied the cumulative impact of future development and climate change scenarios on discharge and floods in the Cambodian Mekong floodplain. We found that hydropower impacts dominate, acting in opposition to climate change impacts to drastically increase dry season flows and reduce wet season flows even when considering the higher RCP8.5 level. The consequent reduction in flood extent and duration may reduce regional flood risk but may also have negative impacts on floodplain productivity.
Eva Boisson, Bruno Wilhelm, Emmanuel Garnier, Alain Mélo, Sandrine Anquetin, and Isabelle Ruin
Nat. Hazards Earth Syst. Sci., 22, 831–847, https://doi.org/10.5194/nhess-22-831-2022, https://doi.org/10.5194/nhess-22-831-2022, 2022
Short summary
Short summary
We present the database of Historical Impacts of Floods in the Arve Valley (HIFAVa). It reports flood occurrences and impacts (1850–2015) in a French Alpine catchment. Our results show an increasing occurrence of impacts from 1920 onwards, which is more likely related to indirect source effects and/or increasing exposure rather than hydrological changes. The analysis reveals that small mountain streams caused more impacts (67 %) than the main river.
Francisco Peña, Fernando Nardi, Assefa Melesse, Jayantha Obeysekera, Fabio Castelli, René M. Price, Todd Crowl, and Noemi Gonzalez-Ramirez
Nat. Hazards Earth Syst. Sci., 22, 775–793, https://doi.org/10.5194/nhess-22-775-2022, https://doi.org/10.5194/nhess-22-775-2022, 2022
Short summary
Short summary
Groundwater-induced flooding, a rare phenomenon that is increasing in low-elevation coastal cities due to higher water tables, is often neglected in flood risk mapping due to its sporadic frequency and considerably lower severity with respect to other flood hazards. A loosely coupled flood model is used to simulate the interplay between surface and subsurface flooding mechanisms simultaneously. This work opens new horizons on the development of compound flood models from a holistic perspective.
Qing Liu, Hanqing Xu, and Jun Wang
Nat. Hazards Earth Syst. Sci., 22, 665–675, https://doi.org/10.5194/nhess-22-665-2022, https://doi.org/10.5194/nhess-22-665-2022, 2022
Short summary
Short summary
The coastal area is a major floodplain in compound flood events in coastal cities, primarily due to storm tide, with the inundation severity positively correlated with the height of the storm tide. Simply accumulating every single-driven flood hazard (rainstorm inundation and storm tide flooding) to define the compound flood hazard may cause underestimation. The assessment of tropical cyclone compound flood risk can provide vital insight for research on coastal flooding prevention.
Antonio-Juan Collados-Lara, Juan-de-Dios Gómez-Gómez, David Pulido-Velazquez, and Eulogio Pardo-Igúzquiza
Nat. Hazards Earth Syst. Sci., 22, 599–616, https://doi.org/10.5194/nhess-22-599-2022, https://doi.org/10.5194/nhess-22-599-2022, 2022
Short summary
Short summary
This work studies the benefit of using more reliable local climate scenarios to analyse hydrological impacts. It has been applied in the Cenajo basin (south-eastern Spain), where we showed that the best approximations of the historical meteorology also provide the best approximations of the hydrology. The two selected climate models predict worrying changes in precipitation, temperature, streamflows and meteorological and hydrological droughts for the period 2071–2100 under the RCP8.5.
Agathe Bucherie, Micha Werner, Marc van den Homberg, and Simon Tembo
Nat. Hazards Earth Syst. Sci., 22, 461–480, https://doi.org/10.5194/nhess-22-461-2022, https://doi.org/10.5194/nhess-22-461-2022, 2022
Short summary
Short summary
Local communities in northern Malawi have well-developed knowledge of the conditions leading to flash floods, spatially and temporally. Scientific analysis of catchment geomorphology and global reanalysis datasets corroborates this local knowledge, underlining the potential of these large-scale scientific datasets. Combining local knowledge with contemporary scientific datasets provides a common understanding of flash flood events, contributing to a more people-centred warning to flash floods.
Karen Gabriels, Patrick Willems, and Jos Van Orshoven
Nat. Hazards Earth Syst. Sci., 22, 395–410, https://doi.org/10.5194/nhess-22-395-2022, https://doi.org/10.5194/nhess-22-395-2022, 2022
Short summary
Short summary
As land use influences hydrological processes (e.g., forests have a high water retention and infiltration capacity), it also impacts floods downstream in the river system. This paper demonstrates an approach quantifying the impact of land use changes on economic flood damages: damages in an initial situation are quantified and compared to damages of simulated floods associated with a land use change scenario. This approach can be used as an explorative tool in sustainable flood risk management.
Tao Liu, Luke A. McGuire, Nina Oakley, and Forest Cannon
Nat. Hazards Earth Syst. Sci., 22, 361–376, https://doi.org/10.5194/nhess-22-361-2022, https://doi.org/10.5194/nhess-22-361-2022, 2022
Short summary
Short summary
A well-constrained rainfall-runoff model forced by radar-derived precipitation is used to define rainfall intensity-duration (ID) thresholds for flash floods. The rainfall ID doubles in 5 years after a severe wildfire in a watershed in southern California, USA. Rainfall ID performs stably well for intense pulses of rainfall over durations of 30-60 minutes that cover at least 15%-25% of the watershed. This finding could help issuing flash flood warnings based on radar-derived precipitation.
Annegret H. Thieken, Guilherme Samprogna Mohor, Heidi Kreibich, and Meike Müller
Nat. Hazards Earth Syst. Sci., 22, 165–185, https://doi.org/10.5194/nhess-22-165-2022, https://doi.org/10.5194/nhess-22-165-2022, 2022
Short summary
Short summary
Various floods hit Germany recently. While there was a river flood with some dike breaches in 2013, flooding in 2016 resulted directly from heavy rainfall, causing overflowing drainage systems in urban areas and destructive flash floods in steep catchments. Based on survey data, we analysed how residents coped with these different floods. We observed significantly different flood impacts, warnings, behaviour and recovery, offering entry points for tailored risk communication and support.
Olga Petrucci
Nat. Hazards Earth Syst. Sci., 22, 71–83, https://doi.org/10.5194/nhess-22-71-2022, https://doi.org/10.5194/nhess-22-71-2022, 2022
Short summary
Short summary
This systematic review highlights flood mortality factors and the strategies to mitigate them, as obtained from 44 scientific articles published between 2010 and 2020. The findings are the classification of flood mortality drivers in two groups and the identification of strategies to cope with them. Future studies should fill the data gaps regarding flood fatalities in developing countries and information on people who have survived floods, which can be useful in educational campaigns.
Yuhan Yang, Jie Yin, Weiguo Zhang, Yan Zhang, Yi Lu, Yufan Liu, Aoyue Xiao, Yunxiao Wang, and Wenming Song
Nat. Hazards Earth Syst. Sci., 21, 3563–3572, https://doi.org/10.5194/nhess-21-3563-2021, https://doi.org/10.5194/nhess-21-3563-2021, 2021
Short summary
Short summary
This is the first time the compound flooding process of heavy rain and levee-breach-induced flooding has been modeled. Real-life cases of historical flooding events have been adequately investigated. Our results provide a comprehensive view of the spatial patterns of the flood evolution, the dynamic process, and mechanism of these cases, which can help decision makers to develop effective emergency response plans and flood adaptation strategies.
Lucas Wouters, Anaïs Couasnon, Marleen C. de Ruiter, Marc J. C. van den Homberg, Aklilu Teklesadik, and Hans de Moel
Nat. Hazards Earth Syst. Sci., 21, 3199–3218, https://doi.org/10.5194/nhess-21-3199-2021, https://doi.org/10.5194/nhess-21-3199-2021, 2021
Short summary
Short summary
This research introduces a novel approach to estimate flood damage in Malawi by applying a machine learning model to UAV imagery. We think that the development of such a model is an essential step to enable the swift allocation of resources for recovery by humanitarian decision-makers. By comparing this method (EUR 10 140) to a conventional land-use-based approach (EUR 15 782) for a specific flood event, recommendations are made for future assessments.
Haixia Zhang, Weihua Fang, Hua Zhang, and Lu Yu
Nat. Hazards Earth Syst. Sci., 21, 3161–3174, https://doi.org/10.5194/nhess-21-3161-2021, https://doi.org/10.5194/nhess-21-3161-2021, 2021
Short summary
Short summary
Taking a single flood disaster in Lishui city as an example, a rapid and refined assessment of economic loss is studied and verified, which can effectively simulate the distribution of loss ratio and loss value. It includes the construction of land use type and value based on data fusion and an expert questionnaire survey, the fitting and calibration of vulnerability curves based on an existing database and disaster loss reporting, and estimation of loss ratio and loss value by spatial analysis.
Doris E. Wendt, John P. Bloomfield, Anne F. Van Loon, Margaret Garcia, Benedikt Heudorfer, Joshua Larsen, and David M. Hannah
Nat. Hazards Earth Syst. Sci., 21, 3113–3139, https://doi.org/10.5194/nhess-21-3113-2021, https://doi.org/10.5194/nhess-21-3113-2021, 2021
Short summary
Short summary
Managing water demand and supply during droughts is complex, as highly pressured human–water systems can overuse water sources to maintain water supply. We evaluated the impact of drought policies on water resources using a socio-hydrological model. For a range of hydrogeological conditions, we found that integrated drought policies reduce baseflow and groundwater droughts most if extra surface water is imported, reducing the pressure on water resources during droughts.
Sara Lindersson, Luigia Brandimarte, Johanna Mård, and Giuliano Di Baldassarre
Nat. Hazards Earth Syst. Sci., 21, 2921–2948, https://doi.org/10.5194/nhess-21-2921-2021, https://doi.org/10.5194/nhess-21-2921-2021, 2021
Short summary
Short summary
Riverine flood risk assessments require the identification of areas prone to potential flooding. We find that (topography-based) hydrogeomorphic floodplain maps can in many cases be useful for riverine flood risk assessments, particularly where hydrologic data are scarce. For 26 countries across the global south, we also demonstrate how dataset choice influences the estimated number of people living within flood-prone zones.
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