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Compound coastal flooding in San Francisco Bay under climate change

The risk of compound coastal flooding in the San Francisco Bay Area is increasing due to climate change yet remains relatively underexplored. Using a novel hybrid statistical-dynamical downscaling approach, this study investigates the impacts of climate change induced sea-level rise and higher river discharge on the magnitude and frequency of flooding events as well as the relative importance of various forcing drivers to compound flooding within the Bay. Results reveal that rare occurrences of flooding under the present-day climate are projected to occur once every few hundred years under climate change with relatively low sea-level rise (0.5 m) but would become annual events under climate change with high sea-level rise (1.0 to 1.5 m). Results also show that extreme water levels that are presently dominated by tides will be dominated by sea-level rise in most locations of the Bay in the future. The dominance of river discharge to the non-tidal and non-sea-level rise driven water level signal in the North Bay is expected to extend ~15 km further seaward under extreme climate change. These findings are critical for informing climate adaptation and coastal resilience planning in San Francisco Bay.

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.

The role of rivers in the origin and future of Amazonian biodiversity

The rich biodiversity of Amazonia is shaped geographically and ecologically by its rivers and their cycles of seasonal flooding. Anthropogenic effects, such as deforestation, infrastructure development and extreme climatic events, threaten the ecological processes sustaining Amazonian ecosystems. In this Review, we explore the coupled evolution of Amazonian rivers and biodiversity associated with terrestrial and seasonally flooded environments, integrating geological, climatic, ecological and genetic evidence. Amazonia and its fluvial environments are highly heterogeneous, and the drainage system is historically dynamic and continually evolving; as a result, the discharge, sediment load and strength of rivers as barriers to biotic dispersal has changed through time. Ecological affinities of taxa, drainage rearrangements and variations in riverine landscape caused by past climate changes have mediated the evolution of the high diversity found in modern-day Amazonia. The connected history of the region’s biodiversity and landscape provides fundamental information for mitigating current and future impacts. However, incomplete knowledge about species taxonomy, distributions, habitat use, ecological interactions and occurrence patterns limits our understanding. Partnerships with Indigenous peoples and local communities, who have close ties to land and natural resources, are key to improving knowledge generation and dissemination, enabling better impact assessments, monitoring and management of the riverine systems at risk from evolving pressures.

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.

Perspectives on transport pathways of microplastics across the Middle East and North Africa (MENA) region

This perspective will focus for the first time on the occurrence and potential transport pathways of MPs within the MENA region. The delivery mechanism of MPs and characteristics of ocean currents and air patterns are discussed in detail within the Arabian Gulf -Gulf of Oman complex, the Red Sea-Gulf of Aden complex, the southern Arabian margin, and non-MENA region to the south, as well as the Mediterranean Sea respectively. Significant variable dissemination and seasonal delivery across different locations in the MENA regions are revealed from this analysis. The review provides guidance for researchers and government authorities in conducting MPs research and proposing actionable measures to mitigate risks associated with chemical and biological contamination.

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