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Southeast Asia faces growing risks from extreme heat under climate change, yet existing assessments do not fully account for physiological impacts and population-level exposure, particularly at resolutions relevant for planning. Here we present a comprehensive evaluation of future heat stress in the region by integrating three methodological innovations: physiologically relevant bio-climatic indices, spatially explicit population exposure, and sub-daily temporal resolution. Using the Universal Thermal Climate Index (UTCI) and Wet-Bulb Globe Temperature (WBGT), we assess heat stress across Southeast Asia at km spatial and 3-hourly temporal resolution under low- and high-emissions scenarios. We combine these projections with population data to identify where and when risks are most acute. Our results show that even by the near-future (2030–2059), exposure to life-threatening extremes (UTCI > C, WBGT > C) increases sharply, by factors of 2.8–4.6 (UTCI) and 4.6–7.9 (WBGT) relative to historical levels. The number of people exposed to at least one consecutive week of extreme UTCI grows from 9 million historically to 23–28 million under RCP2.6 and RCP8.5, while extreme WBGT exposure increases from 0.1 million to 7–17 million. Continental Southeast Asia, including Myanmar, Thailand, and Cambodia, faces the most acute risks, with 6–9 hours of severe heat stress per day during peak months. By the far-future (2070–2099) under RCP8.5, exposure escalates to catastrophic levels, with up to 200 days per year of unsafe conditions and exposed populations increasing more than tenfold. Our findings show that dangerous levels of heat stress will emerge within decades, underscoring the urgency of adaptation and the benefits of strong mitigation.

Societal efforts to understand and mitigate threats posed by hazards are often informed by complex disaster risk models. Despite research demonstrating the disproportionate effects of disasters on vulnerable groups, current risk modeling approaches lack robust methods to account for such equity concerns. Consequently, efforts to develop evidence-based disaster risk management interventions may lack awareness of differential risks in the settings where they are applied. Here, we draw on the relevant literature to develop a typology for characterizing current approaches to incorporating equity into risk modeling. Using this typology, we then evaluated 69 risk assessments conducted by major international development organizations. We found that only ~ 28% of risk models attempt a quantitative evaluation of the differential impacts of disasters and climate change. We then used an equity-sensitive approach to reconstruct a recent risk assessment and show that important elements are missed when equity is excluded in disaster risk modeling.

Computational simulators of complex physical processes, such as inundations, require a robust characterization of the uncertainties involved to be useful for flood hazard and risk analysis. While flood extent data, as obtained from synthetic aperture radar (SAR) imagery, have become widely available, no methodologies have been implemented that can consistently assimilate this information source into fully probabilistic estimations of the model parameters, model structural deficiencies, and model predictions. This paper proposes a fully Bayesian framework to calibrate a 2D physics-based inundation model using a single observation of flood extent, explicitly including uncertainty in the floodplain and channel roughness parameters, simulator structural deficiencies, and observation errors. The proposed approach is compared to the current state-of-practice generalized likelihood uncertainty estimation (GLUE) framework for calibration and with a simpler Bayesian model. We found that discrepancies between the computational simulator output and the flood extent observation are spatially correlated, and calibration models that do not account for this, such as GLUE, may consistently mispredict flooding over large regions. The added structural deficiency term succeeds in capturing and correcting for this spatial behavior, improving the rate of correctly predicted pixels. We also found that binary data do not have information on the magnitude of the observed process (e.g., flood depths), raising issues in the identifiability of the roughness parameters, and the additive terms of structural deficiency and observation errors. The proposed methodology, while computationally challenging, is proven to perform better than existing techniques. It also has the potential to consistently combine observed flood extent data with other data such as sensor information and crowdsourced data, something which is not currently possible using GLUE calibration framework.

Nature-based solutions can strengthen climate resilience, but they remain underfinanced. Innovative financial instruments that transfer or reduce climate risk while enhancing ecosystem services help close the adaptation and nature finance gaps. Here we synthesize evidence on nature-positive climate risk transfer and financing instruments, with attention to how the effectiveness and economic viability of supported nature-based interventions are reported. We screened ~3,200 academic publications and 78 institutional databases, identifying 33 distinct nature-positive financial instruments. We introduced a typology that organizes them by financial instrument category. This typology, alongside a global database of 313 projects found in the literature and an inventory of 76 implemented projects, supports replication, adaptation, and scaling of nature-based solutions in diverse contexts. The systematic review highlights evidence gaps, including limited equity considerations and uncertainties in risk modeling. Together, the typology, inventory, and synthesis provide a foundation for advancing nature-positive finance through future research, investment, and policy design. Nature-positive financial instruments help scale ecosystem-based interventions for climate resilience, finds a systematic review that identifies 33 instruments and compiles a global database of 313 projects as well as an inventory of 76 implemented projects.

The recent destruction of thousands of homes by lava flows from La Palma volcano, Canary Islands, and Nyiragongo volcano, Democratic Republic of Congo, serves as a reminder of the devastating impact that lava flows can have on communities living in volcanically active regions. Damage to buildings and infrastructure can have widespread and long-lasting effects on rehabilitation and livelihoods. Our understanding of how lava flows interact with buildings is limited and based upon sparse empirical data. Often a binary impact is assumed (destroyed when in contact with the flow and intact when not in contact with the flow), although previous events have shown this to be an oversimplification. Empirical damage data collected after past events provide an evidence base from which to better understand lava flow impacts across a range of building types, environments, and eruption styles, as well as to explore the temporal and spatial trends in these impacts. However, information on lava flow impacts is scattered across literature, reports, and maps; no comprehensive dataset of lava flow impacts exists. In this study, we compile and standardise lava flow impact information from previously compiled data, eruption records, and published literature to create the first comprehensive global dataset of impacts on the built environment from lava flows. We found that since the first recorded event between 5494 yr B.P. and 5387 yr B.P., lava flows from at least 155 events have impacted buildings or infrastructure (e.g., roads, electricity pylons, ski-lifts), with most (47%, n  = 73) recorded as located in Europe. Over the last century, there have been approximately seven lava flow impact events per decade ( n  = 71 total). This greatly expands on the past compilations of lava flow impact events. Since ca. 1800 CE, impacts have been consistently documented for less than 14% of recorded eruptions with lava flows globally; prior to 1800 CE, impacts were recorded much more variably (between 0 and 70% of lava flows in any 10-year time bin). The most destructive recorded events were the 1669 CE lava flows at Etna volcano, Italy, which destroyed up to 12 villages and part of the city of Catania, and the 2002 CE lava flows at Nyiragongo volcano, Democratic Republic of Congo, which destroyed up to 14,000 buildings. We found that few studies in the dataset report building typology, damage severity, or hazard intensity at the building-level scale, limiting our ability to assess past building-lava interactions. Future collection of building-level hazard and impact data, supplemented with non-English language records, can be used to inform models that forecast future impacts, support lava flow risk assessments, and develop potential mitigation measures.

Following a disaster, crucial decisions about recovery resources often prioritize immediate damage, partly due to a lack of detailed information on who will struggle to recover in the long term. Here, we develop a data-driven approach to provide rapid estimates of non-recovery, or areas with the potential to fall behind during recovery, by relating surveyed data on recovery progress with data that would be readily available in most countries. We demonstrate this approach for one dimension of recovery—housing reconstruction—analyzing data collected five years after the 2015 Nepal earthquake to identify a range of ongoing social and environmental vulnerabilities related to non-recovery in Nepal. If such information were available in 2015, it would have exposed regional differences in recovery potential due to these vulnerabilities. More generally, moving beyond damage data by estimating non-recovery focuses attention on those most vulnerable sooner after a disaster to better support holistic and nuanced decisions.

Climate-induced hazards exert uneven impacts on communities. However, conventional risk models rarely consider these disparities, which are critical for informing risk reduction decisions. Instead, they quantify risk solely based on the value of assets at risk, without accounting for how communities are differentially exposed and vulnerable to particular hazards. This has significant consequences for low-income populations, who tend to suffer most from disasters. Our study introduces an equity-sensitive framework that considers inequities in exposure and vulnerability, demonstrating how these inequities compound into well-being risks. We apply this framework in a large-scale study of coastal flooding and sea-level rise risk in the Philippines, highlighting both quantitative and spatial variations in asset and well-being risks. Findings indicate that accounting for income-driven inequities yields a more comprehensive understanding of coastal flood risks across groups. This framework is adaptable for other hazards and contexts, and aims to promote more equitable disaster risk reduction outcomes.

Urban populations are increasingly moving into hazardous areas, which leads to widespread and frequent impacts from hazard events. Although these exposure trends have been quantified for other hazards, global exposure analyses for volcanic hazards remain limited. Here, we quantify global and regional changes in city exposure to volcanic hazards through time. With GHS-UCDB and GHS-POP datasets, we use spatio-temporal metrics to track urban expansion within 100 km of volcanoes active in the Holocene from 1975 to 2020, with projections to 2030. The number of cities within 100 km of volcanoes is projected to more than double, with populations increasing by 154%. The proportion of people within 100 km of volcanoes who live in cities increases from 44% (~186 million) in 1975 to over 50% by 2030 (~473 million). Exposed city populations concentrate within 20–30 km from volcanoes, and average urban population density generally decreases closer to volcanoes. Exposure growth is highest in Southeast Asia and East Africa. Most cities expand and densify, with many growing faster toward nearby volcanoes. Case studies show how urban expansion intersects with volcanic impact pathways. These trends indicate that urban expansion is amplifying volcanic risk and highlight the need to integrate hazard data into urban planning.

This paper presents the advantages and opportunities for rapid preliminary intervention screening to enhance inclusion of green infrastructures in regional scale stormwater management. Stormwater flooding is widely recognised as a significant and worsening natural hazard across the globe; however, current management approaches aimed at the site scale do not adequately explore opportunities for integrated management at the regional scale at which decisions are made. This research addresses this gap through supporting the development of stormwater management strategies, including green infrastructure, at a regional scale. This is achieved through upscaling a modelling approach using a spatially explicit inundation model (CADDIES) coupled with an economic model of inundation loss (OpenProFIA) to support widescale evaluation of green infrastructure during the informative early-stage development of stormwater management strategies. This novel regional scale approach is demonstrated across a case study of the San Francisco Bay Area, spanning 8300 sq km. The main opportunity from this regional approach is to identify spatial and temporal trends which are used to inform regional planning and direct future detailed modelling efforts. The study highlights several limitations of the new method, suggesting it should be applied as part of a suite of landscape management approaches; however, highlights that it has the potential to complement existing stormwater management toolkits.

In Maritime Continent, the shift of intertropical convergence zone (ITCZ) location directly regulates the distribution of black carbon and hence affects public health in the region, but the mechanism and human health impacts have not yet been comprehensively revealed. Here we used multiple reanalysis datasets to investigate the long-term shift of seasonal-mean zonal-mean ITCZ location in this region from 1980 to 2014, and to assess the influences on black carbon distribution and the resultant health impact in terms of premature mortality. Results show that recent human-related equatorial warming contributed to an equatorward shift (∼2.1°) of ITCZ location in Maritime Continent. Spatially, the equatorward shift of ITCZ reduced surface black carbon concentration over the maritime area by enhancing updrafts and wet deposition, but raised the concentration in the continental area by inhibiting updrafts. Meanwhile, anomalous low-level northeasterlies weakened summer circulation and prevented black carbon from being transported to the Philippines. Our results also suggest that the equatorward shift decreased ∼13% of black carbon-associated monthly premature mortality in maritime countries, but increased ∼6% of that in continental countries based on the population and mortality rate in 2010. We therefore recommend considering climate change impacts in the design of adaptation strategies against regional air pollution.