• Energy Research

A TC hazard dataset for wind and surge was created for 1980-2020. The wind hazard dataset is based on the global International Best Track Archive for Climate Stewardship (IBTrACS) (Knapp et al. 2010) which includes 269-recorded events making landfall in Vietnam. Holland 2008 elaborates the internal methodologies of CLIMADA for wind field and the surge hazard dataset (flood depth) is derived from wind intensity with a linear relationship that modifies the water level according to the local elevation and distance to the coastal line (Xu 2010). The hazard dataset is created on an irregular grid with: 30 arcsecond (~1km2) resolution along the coastline and going 50km inland as well as over major population centres, which cover more than 95 percentile of the population; and a coarser resolution of 150 arcsecond for the rest of the country. To account for uncertainties and model sensitivity, each historic TC is resampled 100 times creating a large sample of stochastic events (a representative set of simulated events and process is detailed in Aznar-Siguan and Bresch 2019). For simulating surge, Climate Central's proprietary, high-accuracy Digital Elevation Model (DEM) known as CoastalDEM (Kulp and Strauss 2018) was used at a resolution of 90 arcsecond. It reduces median errors in NASA's SRTM3 DEM to near zero and thus allows a better understanding of coastal risk. High accuracy DEMs from airborne lidar are unavailable. The future hazard dataset is created for the future Representative Concentration Pathways (RCP) RCP8.5- The future climate scenarios are simulated through CLIMADA’s TC module but based on assumptions from Knutson et al. 2015, which stipulate increased intensity and reduced frequency of TCs in the Eastern Pacific basin for the RCP8.5 scenario by 2050. Further sea level rise was accommodated within the future scenarios with 30 cm rise in North-western Pacific basin according to sea level project tool published by NASA. Tropical cyclone storm surges probabilistic event dataset for 2020 and 2050 under the climate change scenario RCP8.5.

A TC hazard dataset for wind and surge was created for 1980-2020. The wind hazard dataset is based on the global International Best Track Archive for Climate Stewardship (IBTrACS) (Knapp et al. 2010) which includes 269-recorded events making landfall in Vietnam. Holland 2008 elaborates the internal methodologies of CLIMADA for wind field and the surge hazard dataset (flood depth) is derived from wind intensity with a linear relationship that modifies the water level according to the local elevation and distance to the coastal line (Xu 2010). The hazard dataset is created on an irregular grid with: 30 arcsecond (~1km2) resolution along the coastline and going 50km inland as well as over major population centres, which cover more than 95 percentile of the population; and a coarser resolution of 150 arcsecond for the rest of the country. To account for uncertainties and model sensitivity, each historic TC is resampled 100 times creating a large sample of stochastic events (a representative set of simulated events and process is detailed in Aznar-Siguan and Bresch 2019). For simulating surge, Climate Central's proprietary, high-accuracy Digital Elevation Model (DEM) known as CoastalDEM (Kulp and Strauss 2018) was used at a resolution of 90 arcsecond. It reduces median errors in NASA's SRTM3 DEM to near zero and thus allows a better understanding of coastal risk. High accuracy DEMs from airborne lidar are unavailable. The future hazard dataset is created for the future Representative Concentration Pathways (RCP) RCP8.5- The future climate scenarios are simulated through CLIMADA’s TC module but based on assumptions from Knutson et al. 2015, which stipulate increased intensity and reduced frequency of TCs in the Eastern Pacific basin for the RCP8.5 scenario by 2050. Further sea level rise was accommodated within the future scenarios with 30 cm rise in North-western Pacific basin according to sea level project tool published by NASA. Tropical cyclone storm surges probabilistic event dataset for 2020 and 2050 under the climate change scenario RCP8.5.

As climate change leads to more frequent and intense extreme weather events, industry stakeholders and policymakers must assess their business strategies, practices, and entire sector policies under these uncertain conditions. Much recent research has integrated quantitative climate risk modeling into frameworks to engage policymakers and inform adaptation decisions in a general way, but relatively little attention has been devoted to extending this to strategic business and investment decisions. This falls short of identifying economic opportunities and threats in a wider socio-economic context, such as the development of new technologies or evolving political and regulatory environments. Here, a methodology is developed to integrate quantitative climate risk modeling with SWOT analysis (strengths, weaknesses, opportunities, and threats) which is commonly used in business and investment strategic planning. This moves the focus from avoidance of negative outcomes to prospective planning in an evolving environment. This methodology is illustrated with a case study of the Japanese wind energy sector, using open-access data and the open-source climate risk-assessment platform CLIMADA. This Climate risk assessment indicates threats from increasing damages to the wind energy infrastructure, as well as the profitability of typhoon-resistant wind turbines under present and future climate. Expert interviews and extensive literature research on opportunities and threats, however, also show that the transition towards renewable energies faces restrictive market dynamics, political and social hurdles, which set external conditions surpassing physically-informed dimensions. Beyond this illustrative case study, the methodology developed here bridges established concepts in climate risk modeling and strategic management and thus can be used to identify industry-centric ways forward for climate-resilient planning across a wide range of economic sectors. Climate Risk Management, 46 ISSN:2212-0963

As climate change leads to more frequent and intense extreme weather events, industry stakeholders and policymakers must assess their business strategies, practices, and entire sector policies under these uncertain conditions. Much recent research has integrated quantitative climate risk modeling into frameworks to engage policymakers and inform adaptation decisions in a general way, but relatively little attention has been devoted to extending this to strategic business and investment decisions. This falls short of identifying economic opportunities and threats in a wider socio-economic context, such as the development of new technologies or evolving political and regulatory environments. Here, a methodology is developed to integrate quantitative climate risk modeling with SWOT analysis (strengths, weaknesses, opportunities, and threats) which is commonly used in business and investment strategic planning. This moves the focus from avoidance of negative outcomes to prospective planning in an evolving environment. This methodology is illustrated with a case study of the Japanese wind energy sector, using open-access data and the open-source climate risk-assessment platform CLIMADA. This Climate risk assessment indicates threats from increasing damages to the wind energy infrastructure, as well as the profitability of typhoon-resistant wind turbines under present and future climate. Expert interviews and extensive literature research on opportunities and threats, however, also show that the transition towards renewable energies faces restrictive market dynamics, political and social hurdles, which set external conditions surpassing physically-informed dimensions. Beyond this illustrative case study, the methodology developed here bridges established concepts in climate risk modeling and strategic management and thus can be used to identify industry-centric ways forward for climate-resilient planning across a wide range of economic sectors. Climate Risk Management, 46 ISSN:2212-0963

Abstract Coral reefs ecosystems, often compared to rain forests for their high biodiversity, are threatened by ocean warming causing coral bleaching when the symbiotic relationship between dinoflagellates and corals breaks under high ocean temperatures. Thermal stress from marine heatwaves (MHWs) occur both at the surface and subsurface with subsurface MHWs lasting longer with potentially higher cumulative intensities. However, global coral bleaching models generally ignore the differences in thermal stress between surface and sea-bed levels. Here, we define MHWs at sea-bed level to model coral bleaching with daily resolution from 6 May 1993 to 31 October 2023, for 9944 tropical coral reefs between 0 and 60 m depths. We show that deeper reefs experience on average higher thermal stress and bleaching compared to surface reefs. Using surface temperature data to model bleaching for deeper corals underestimates bleaching intensities by an average of 6% ± 9% compared to the subsurface calibrated model. Our study is a starting point for more accurate coral bleaching modelling, providing additional evidence to reshape our perception of deeper coral reefs as potential refugees from climate change.

Abstract Coral reefs ecosystems, often compared to rain forests for their high biodiversity, are threatened by ocean warming causing coral bleaching when the symbiotic relationship between dinoflagellates and corals breaks under high ocean temperatures. Thermal stress from marine heatwaves (MHWs) occur both at the surface and subsurface with subsurface MHWs lasting longer with potentially higher cumulative intensities. However, global coral bleaching models generally ignore the differences in thermal stress between surface and sea-bed levels. Here, we define MHWs at sea-bed level to model coral bleaching with daily resolution from 6 May 1993 to 31 October 2023, for 9944 tropical coral reefs between 0 and 60 m depths. We show that deeper reefs experience on average higher thermal stress and bleaching compared to surface reefs. Using surface temperature data to model bleaching for deeper corals underestimates bleaching intensities by an average of 6% ± 9% compared to the subsurface calibrated model. Our study is a starting point for more accurate coral bleaching modelling, providing additional evidence to reshape our perception of deeper coral reefs as potential refugees from climate change.

Abstract Climate change is a global challenge with serious impacts on human populations. Numerous studies have highlighted the impacts of climate change on natural hazard processes and associated risks for communities. Understanding future hazard and risks is crucial for effective risk management. This study focuses on assessing snow avalanche risks for inhabited areas in the context of climate-induced changes.Using hazard scenarios based on CH2018 climate projections and the RAMMS avalanche model, we generated large-scale hazard indication maps for future avalanche hazards. By employing the open-source probabilistic risk assessment platform CLIMADA, along with building data and vulnerability functions, we estimated risks for the present time and two future time frames: mid-century (2060) and the end of the century (2085). An uncertainty and sensitivity analysis complemented the study to account for potential fluctuations in model assumptions.Our mean-based approach, considering different CH2018 model chains, indicates an overall decline in avalanche risks for the future. This reduction is driven by assumed decreases in snow accumulation, rising snowpack temperatures, and a rising snowline. To cover more extreme developments, we also examined boundary model chains, which suggest that future risks can both increase and decrease, with a general trend of decreasing affected objects towards the end of the century. It is worth noting that some individual objects depending on their location may remain at consistently high avalanche risk despite climate change.This study provides a valuable tool for decision-makers to compare future risk scenarios with the present situation, supporting effective mitigation and adaptation strategies to address the challenges of climate change. By providing risk maps and identifying potential future risk hot spots, our approach contributes to enhancing community resilience and protecting their assets in a changing climate.