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Fluvial floods can cause significant damages and are expected to increase in magnitude and frequency throughout the twenty-first century due to global warming. Alongside hazard characteristics, damage potentials depend on exposure and vulnerability, which are changing in the wake of socio-economic developments. In the context of continuously evolving damage-causing factors, assessments of future changes in flood damage potentials are increasingly asked for by decision-makers in flood risk management. This study addresses this need by (a) providing a systematic review of contemporary assessment approaches to quantitatively compare direct economic losses from fluvial flooding under current and future conditions and (b) combining the reviewed approaches to an applicable methodology which is used in a case study to quantify changing flood damage potentials in the Neckar River basin in southern Germany. Therefore, a scoping study of contemporary flood damage assessment approaches supported by geographic information systems (GIS) is performed. The subsequent case study of the Neckar River prognoses a significant increase in average annual flood damages in the study area throughout the twenty-first century. The case study produces valid results with regards to current precipitation data, whereas the absence of verification data makes the validation of projected scenarios more difficult. To account for uncertainties surrounding these future projections, a nascent qualitative confidence estimation is introduced to reflect on the strength of knowledge underlying the used flood damage assessment methodology.

Carbon dioxide removal is key to climate change mitigation, yet implications of its deployment remain unclear. Recent exponential growth in literature is rapidly filling this gap but makes the synthesis of the evidence on carbon dioxide removal side effects increasingly challenging. Here we address this issue by mapping this literature and proposing a taxonomy to synthesize and compare evidence on carbon dioxide removal side effects. The expansive evidence warrants the use of machine learning to systematically select relevant research and provide an inventory of nearly 400 co-benefits, challenges, and limits. We find rich evidence in Europe but little information for Africa, South America, and Oceania, where large-scale carbon dioxide removal is nevertheless projected. There is a predominance of articles discussing negative effects compared to positive ones. Starting from the limitations of our analysis and literature gaps, we provide entry points for future studies that can build on our literature-based taxonomy.

Carbon dioxide removal (CDR) features prominently in the 1.5 °C compatible and high overshoot pathways in the IPCC’s Sixth Assessment Report (AR6, WGIII). However, the amount of CDR varies considerably among scenarios. We analyze the range in CDR volumes in AR6 WGIII pathways by exploring relationships between variables as potential driving forces, focusing on CDR in 2050 and scenario properties linked to reaching net-zero CO 2 . It is also shown how the relative and absolute contribution of CDR to total mitigation up until reaching net-zero CO 2 substantially differs across scenarios. The volumes of CDR in 2050 and 2100 and the cumulative amount throughout the 21 st century were most strongly correlated to the degree to which CO 2 emissions are reduced as a means of reaching net-zero CO 2 . CDR in 2050 is also substantially correlated to the timing of net-zero CO 2 . The robustness of the analyzed relationships was evaluated by comparing different scenario filtering and data-cleaning approaches. Beyond filtering and cleaning, additional factors that influence CDR deployment in scenarios, such as discount rates, carbon price trajectories, and scenario design choices, were discussed.

Global emission reduction efforts continue to be insufficient to meet the temperature goal of the Paris Agreement 1 . This makes the systematic exploration of so-called overshoot pathways that temporarily exceed a targeted global warming limit before drawing temperatures back down to safer levels a priority for science and policy 2 – 5 . Here we show that global and regional climate change and associated risks after an overshoot are different from a world that avoids it. We find that achieving declining global temperatures can limit long-term climate risks compared with a mere stabilization of global warming, including for sea-level rise and cryosphere changes. However, the possibility that global warming could be reversed many decades into the future might be of limited relevance for adaptation planning today. Temperature reversal could be undercut by strong Earth-system feedbacks resulting in high near-term and continuous long-term warming 6 , 7 . To hedge and protect against high-risk outcomes, we identify the geophysical need for a preventive carbon dioxide removal capacity of several hundred gigatonnes. Yet, technical, economic and sustainability considerations may limit the realization of carbon dioxide removal deployment at such scales 8 , 9 . Therefore, we cannot be confident that temperature decline after overshoot is achievable within the timescales expected today. Only rapid near-term emission reductions are effective in reducing climate risks. Aiming for declining global temperatures can limit long-term climate risks compared with a mere stabilization of global warming, including sea-level rise and cryosphere changes.

The AR6 Scenarios Database is a vital repository of climate change mitigation pathways used in the latest Intergovernmental Panel on Climate Change (IPCC) assessment cycle. In its current version, many scenarios in the database lack information about the level of anthropogenic carbon dioxide (CO2) removal via land sinks, as net-negative CO2 emissions and gross removals on land are not always separated and are not consistently reported across models. This makes scenario analyses focusing on CO2 removal challenging. We test and compare the performance of different regression models to impute missing data on land carbon sequestration for the global level and for several sub-global macro-regions from available data on net CO2 emissions from agriculture, forestry, and other land uses. We find that a k-nearest neighbors regression performs best among the tested regression models and use it to impute and provide two publicly available imputation datasets (https://doi.org/10.5281/zenodo.13373539, Prütz et al., 2024) on CO2 removal via land sinks for incomplete global scenarios (n=404) and incomplete regional R10 scenario variants (n=2358) of the AR6 Scenarios Database. We discuss the limitations of our approach, the use of our datasets for secondary assessments of AR6 scenario ensembles, and how this approach compares to other recent AR6 data reanalyses.