Author Correction: Confined and mined: anthropogenic river modification as a driver of flood risk change
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Simulating flood risk in Tampa Bay using a machine learning driven approach
Machine learning (ML) models can simulate flood risk by identifying critical non-linear relationships between flood damage locations and flood risk factors (FRFs). To explore it, Tampa Bay, Florida, is selected as a test site. The study’s goal is to simulate flood risk and identify dominant FRFs using historical flood damage data as target variable, with 16 FRFs as predictor variables. Five different ML models such as decision tree (DT), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and random forest (RF) were adopted. RF classifies 2.42% of Tampa Bay as very high risk and 2.54% as high risk, while XGBoost classifies 3.85% as very high risk and 1.11% as high risk. Moreover, the communities reside at low altitudes and near the waterbodies, with dense man-made infrastructure, are at high flood risk. This study introduces a comprehensive framework for flood risk assessment and helps policymakers mitigate flood risk.
Pluvial flood impacts and policyholder responses throughout the United States
Pluvial floods pose a significant threat to properties, yet comprehensive impact analysis is hindered by data limitations on pluvial inundation. To assess pluvial flood impacts, we leveraged U.S. flood insurance claims and policy records for a subset of properties outside 100-year floodplains, streamflow records, and nationwide precipitation data, enabling us to distinguish damage claims caused by pluvial floods over 1978–2021. Strikingly, 87.1% of the claims analyzed from this subset were due to pluvial floods. Utilizing these pluvial flood claims unveiled distinct regional patterns of pluvial impacts across the contiguous U.S. These patterns are informed by the relationship between claim frequency and precipitation within each region. Remarkably, despite the pervasiveness of impacts, many states are seeing declining uptake in pluvial flood insurance coverage. Our study highlights regions facing heightened pluvial flood risks and underscores the critical need for enhanced consideration of pluvial inundation within risk management frameworks.
Adaptation portfolio – a multi-measure framework for future floods and droughts
Adaptation is critically important for coping with climate change. However, quantitative studies on which adaptation measures should be taken to maintain the present water risk level in the context of climate change have been explored little, particularly at large basin scales. Here, we devised three adaptation portfolios composed of combinations of measures to alleviate floods and drought with explicit basin-wide modelling in the Chao Phraya River basin, Thailand. Two portfolios mitigated future water scarcity to the present level but failed to eliminate extreme floods. The remaining portfolio with basin-wide reforestation substantially reduced the number of future flooding days but enhanced the number of drought months to 3–6 months a year, resulting from increased evapotranspiration by 7–11%. Overall, future flood adaptation remains challenging even in highly regulated rivers. We also observed that adaptation effects differ substantially by sub-basins. It highlights the necessity of spatio-temporal detailed impact modelling, including multiple adaptation measures.
Confined and mined: anthropogenic river modification as a driver of flood risk change
Rivers are often confined by structures and subjected to aggregate mining. In dynamic rivers, these interventions cause changes to riverbed and bank topography that potentially cause changes in hydraulics and flood risk. Repeat, system-scale, high-resolution topographic surveys of the gravel-bed Bislak River, the Philippines, are used to quantify annual morphological change and, using two-dimensional hydraulic modelling, to simulate changes to flood risk. Aggregate mining exports sediment and creates pitted topography, and embankments cause both deeper channels and disconnect the river from its floodplain. The consequently increased channel capacity reduces flood risk, with up to a 5% decrease in inundated areas for 10- to 100-year return periods. Sediment deprivation also increases bed shear stress that can induce scour, infrastructure damage and increased flood impacts. Rising global floodplain populations and increasing demand for aggregate ensure that sustainably managing geomorphologically dynamic rivers to support floodplain development and mitigate flood impacts remains a pertinent challenge.
Multivariate compound events drive historical floods and associated losses along the U.S. East and Gulf coasts
Compound flooding events are a threat to many coastal regions and can have widespread socio-economic implications. However, their frequency of occurrence, underlying flood drivers, and direct link to past socio-economic losses are largely unknown despite being key to supporting risk and adaptation assessments. Here, we present an impact-based analysis of compound flooding for 203 coastal counties along the U.S. Gulf and East coasts by combining data from multiple flood drivers and socio-economic loss information from 1980 to 2018. We find that ~80% of all flood events recorded in our study area were compound rather than univariate. In addition, we show that historical compound flooding events in most counties were driven by more than two flood drivers (hydrological, meteorological, and/or oceanographic) and distinct spatial clusters exist that exhibit variability in the underlying driver of compound flood events. Furthermore, we find that in more than 80% of the counties, over 80% of recorded property and crop losses were linked to compound flooding. Nearly 80% of counties have a higher median loss from compound than univariate events. For these counties, the median property loss is over 26 times greater, and the median crop loss is over 76 times greater for compound events on average. Our analysis overcomes some of the limitations of previous compound-event studies based on pre-defined flood drivers and offers new insights into the complex relationship between hazards and associated socio-economic impacts.
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