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An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncertainty were defined. Hydrologic model calibration and validation were performed using a Dynamically Dimensioned Search algorithm. A generalized likelihood uncertainty estimation method was used for quantifying uncertainty. To draw on the potential benefits of the proposed methodology, a flash-flood-prone urban watershed in the Greater Toronto Area, Canada, was selected. The developed floodplain maps were updated considering climate change impacts on the input uncertainty with rainfall Intensity–Duration–Frequency (IDF) projections of RCP8.5. The results indicated that the hydrologic model input poses the most uncertainty to floodplain delineation. Incorporating climate change impacts resulted in the expansion of the potential flood area and an increase in water depth. Comparison between stationary and non-stationary IDFs showed that the flood probability is higher when a non-stationary approach is used. The large inevitable uncertainty associated with floodplain mapping and increased future flood risk under climate change imply a great need for enhanced flood modeling techniques and tools. The probabilistic floodplain maps are beneficial for implementing risk management strategies and land-use planning.

Wetlands and reservoirs are important water flow and storage regulators in a river basin; therefore, they can play a crucial role in mitigating flood and hydrological drought risks. Despite the advancement of river basin theory and modeling, our knowledge is still limited about the extent to which these two regulators could perform such a role, especially under future climate extremes. To improve our understanding, we first coupled wetlands and reservoir operations into a semi-spatially explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. The projection of future floods and hydrological droughts was performed using the hydrological model during different periods (near future: 2026–2050, middle century: 2051–2075, and end century: 2076–2100) under five future climate change scenarios. We found that the risk of future floods and hydrological droughts can vary across different periods – in particular, it will experience relatively large increases and slight decreases. This large river basin will experience flood events of longer duration, with larger peak flows and volume, and of enhanced flashiness compared to the historical period. Simultaneously, the hydrological droughts will be much more frequent, with longer durations and more serious deficits. Therefore, the risk of floods and droughts will, overall, increase further under future climate change even under the combined influence of reservoirs and wetlands. These findings highlight the hydrological regulation function of wetlands and reservoirs and attest that the combining of wetlands with reservoir operation cannot fully eliminate the increasing future flood and drought risks. To improve a river basin's resilience to the risks of future climate change, we argue that the implementation of wetland restoration and the development of accurate forecasting systems for effective reservoir operation are of great importance. Furthermore, this study demonstrated a wetland–reservoir integrated modeling and assessment framework that is conducive to risk assessment of floods and hydrological droughts and that can be used for other river basins in the world.

Abstract Current flood risk mapping, relying on historical observations, fails to account for increasing threat under climate change. Incorporating recent developments in inundation modelling, here we show a 26.4% (24.1–29.1%) increase in US flood risk by 2050 due to climate change alone under RCP4.5. Our national depiction of comprehensive and high-resolution flood risk estimates in the United States indicates current average annual losses of US$32.1 billion (US$30.5–33.8 billion) in 2020’s climate, which are borne disproportionately by poorer communities with a proportionally larger White population. The future increase in risk will disproportionately impact Black communities, while remaining concentrated on the Atlantic and Gulf coasts. Furthermore, projected population change (SSP2) could cause flood risk increases that outweigh the impact of climate change fourfold. These results make clear the need for adaptation to flood and emergent climate risks in the United States, with mitigation required to prevent the acceleration of these risks.

Floods can cause extensive damage proportional to their magnitude, depending on the watershed hydrology and terrain characteristics. Flood studies generally assume bathymetry as steady, while in reality it is constantly changing due to sediment transport. This study seeks to quantify the impact of different lake bathymetry conditions on flood dynamics. The Hydrotel and Telemac2D models are used to simulate floods for a lake with bathymetries from multiple year surveys. The bathymetries differ in bed elevation due to sediment accumulation and/or remobilisation. Results show that bathymetric differences produce a more noticeable effect for moderate flows than for maximum flows. During moderate flows, shallower bathymetries induce higher water levels and larger water extents. For peak flows, differences in water levels and extent are practically negligible for the different bathymetries tested. Higher water levels during moderate flows could produce longer flooding times and affect the community’s perception of flood impacts.

Habitat loss and degradation is a leading cause of the current biodiversity crisis. In the lake Saint-Pierre floodplain, agricultural intensification has led to the loss of substantial spawning and rearing areas for the yellow perch ( Perca flavescens Mitchill). Restoring perennial vegetation cover is key to ensure the persistence of the population, but the floodplain conditions limit our ability to do so. In this study, we tested the impact of companion plants ( Avena sativa L., Lolium multiflorum L.) and sowing rate on the establishment success of reed canarygrass ( Phalaris arundinacea L.; RCG) in year 2. RCG tolerates a wide range of environmental conditions and can provide the plant cover essential to the reproduction of yellow perch. We hypothesized that companion plants would reduce weed pressure and in turn improve RCG establishment, and that increasing the sowing rate would improve the establishment success. Contrary to our expectations, using companion plants generally reduced the cover and biomass of RCG. It also led to increased weed prevalence in most treatments. In addition, sowing at high rates did not impact RCG cover and biomass. Sowing RCG alone appears to be the most effective option to establish perennial vegetation supporting the recovery of the yellow perch population.

The Canada Centre for Mapping and Earth Observation (CCMEO) uses Radarsat Constellation Mission (RCM) data for near-real time flood mapping. One of the many advantages of using SAR sensors, is that they are less affected by the cloud coverage and atmospheric conditions, compared to optical sensors. RCM has been used operationally since 2020 and employs 3 satellites, enabling lower revisit times and increased imagery coverage. The team responsible for the production of flood maps in the context of emergency response are able to produce maps within four hours from the data acquisition. Although the results from their automated system are good, there are some limitations to it, requiring manual intervention to correct the data before publication. Main limitations are located in urban and vegetated areas. Work started in 2021 to make use of deep learning algorithms, namely convolutional neural networks (CNN), to improve the performances of the automated production of flood inundation maps. The training dataset make use of the former maps created by the emergency response team and is comprised of over 80 SAR images and corresponding digital elevation model (DEM) in multiple locations in Canada. The training and test images were split in smaller tiles of 256 x 256 pixels, for a total of 22,469 training tiles and 6,821 test tiles. Current implementation uses a U-Net architecture from NRCan geo-deep-learning pipeline (https://github.com/NRCan/geo-deep-learning). To measure performance of the model, intersection over union (IoU) metric is used. The model can achieve 83% IoU for extracting water and flood from background areas over the test tiles. Next steps include increasing the number of different geographical contexts in the training set, towards the integration of the model into production.

In recent years, the utility of earlywood vessels anatomical characteristics in identifying and reconstructing hydrological conditions has been fully recognized. In riparian ring-porous species, flood rings have been used to identify discrete flood events, and chronologies developed from cross-sectional lumen areas of earlywood vessels have been used to successfully reconstruct seasonal discharge. In contrast, the utility of the earlywood vessel chronologies in non-riparian habitats has been less compelling. No studies have contrasted within species their earlywood vessel anatomical characteristics, specifically from trees that are inversely exposed to flooding. In this study, earlywood vessel and ring-width chronologies were compared between flooded and non-flooded control Fraxinus nigra trees. The association between chronologies and hydroclimate variables was also assessed. Fraxinus nigra trees from both settings shared similar mean tree-ring width but floodplain trees did produce, on average, thicker earlywood. Vessel chronologies from the floodplain trees generally recorded higher mean sensitivity (standard deviation) and lower autocorrelation than corresponding control chronologies indicating higher year-to-year variations. Principal components analysis (PCA) revealed that control and floodplain chronologies shared little variance indicating habitat-specific signals. At the habitat level, the PCA indicated that vessel characteristics were strongly associated with tree-ring width descriptors in control trees whereas, in floodplain trees, they were decoupled from the width. The most striking difference found between flood exposures related to the chronologies' associations with hydroclimatic variables. Floodplain vessel chronologies were strongly associated with climate variables modulating spring-flood conditions as well as with spring discharge whereas control ones showed weaker and few consistent correlations. Our results illustrated how spring flood conditions modulate earlywood vessel plasticity. In floodplain F. nigra trees, the use of earlywood vessel characteristics could potentially be extended to assess and/or mitigate anthropogenic modifications of hydrological regimes. In absence of major recurring environmental stressors like spring flooding, our results support the idea that the production of continuous earlywood vessel chronologies may be of limited utility in dendroclimatology.

Purpose This study aims to understand the socioeconomic impact of flood events on households, especially household welfare in terms of changes in consumption and coping strategies to deal with flood risk. This study is based on Bihar, one of the most frequently flood-affected, most populous and economically backward states in India. Design/methodology/approach Primary data were collected from 700 households in the seven most frequently flood-affected districts in Bihar. A total of 100 individuals from each district were randomly selected from flood-affected villages. Based on a detailed literature review, an econometric (probit) model was developed to test the null hypothesis of the availability of consumption insurance, and the multivariate probability approach was used to analyze the various coping strategies of these households. Findings The results of this study suggest that flood-affected households maintain their consumption by overcoming various losses, including income, house damage and livestock loss. Households depend on financial transfers, borrowings and relief, and migrate to overcome losses. Borrowing could be an extra burden as the government compensates for house damage and crop loss late to the affected households. Again, there is no compensation to overcome livelihood loss and deal with occurrences of post-flood diseases, which further emphasizes the policy implications of strengthening the health infrastructure in the state and generating alternative livelihood opportunities. Originality/value This study discusses flood risk in terms of changes in household welfare, identifies the most effective risk-coping capabilities of rural communities and contributes to the shortcomings of the government insurance and relief model. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2023-0569

Abstract Flood exposure has been linked to shifts in population sizes and composition. Traditionally, these changes have been observed at a local level providing insight to local dynamics but not general trends, or at a coarse resolution that does not capture localized shifts. Using historic flood data between 2000-2023 across the Contiguous United States (CONUS), we identify the relationships between flood exposure and population change. We demonstrate that observed declines in population are statistically associated with higher levels of historic flood exposure, which may be subsequently coupled with future population projections. Several locations have already begun to see population responses to observed flood exposure and are forecasted to have decreased future growth rates as a result. Finally, we find that exposure to high frequency flooding (5 and 20-year return periods) results in 2-7% lower growth rates than baseline projections. This is exacerbated in areas with relatively high exposure to frequent flooding where growth is expected to decline over the next 30 years.