Focus on Natural Hazards, Disasters, and Extreme Events

Considering the anticipated rise in wet extremes due to climate change, effective management of flood risks in global agriculture necessitates an initial assessment of the impact of floods on crop production. Such estimation can inform the development of strategies to enhance the resilience of the global agricultural system against floods, particularly in the face of growing demand for food. To this end, a worldwide calculation of inundation areas' return periods was conducted using a global river and inundation model output. This information was then linked to a global historical yield map, allowing for the identification of flood-induced crop yield changes. The findings revealed that for return periods over ten years, global average yield losses were estimated to be 4% for soy, 3% for rice, 2% for wheat, and 1% for maize. These losses amounted to a total production loss of 5.5 billion United States dollars during the 1982–2016 period. This first global estimation of flood impacts on crop production contributes to the advancement of flood risk management in agriculture, although the limitations identified in this study need to be addressed in future research.

In 2022, record-breaking long-lasting marine heatwaves (MHWs) occurred in the East China Sea (ECS), which persisted for 62 d during boreal summer. This exceeded the average MHWs duration of 10 d by a factor of 6. In addition, 2022 was also recorded as a year of many extreme events throughout Asia, such as summer floods in China and Pakistan, droughts and extreme heat in Europe, raising the question of whether they were caused by a 'triple-dip' La Niña, which has persisted since September 2020. Here we examine the key local and remote processes that led to the 2022 MHWs in the ECS using mixed-layer heat budget analysis. During the onset of the MHWs, a salinity-stratified shallow mixed-layer due to the large river discharge from the Yangtze–Huaihe River floods in June created favorable conditions for warm ocean temperature in the ECS. Simultaneously, an anomalous anticyclone maintained by the stationary Rossby wave, which is generated by vorticity forcings in mid-latitudes and thermal forcing in Pakistan, settled in the corresponding region and led to the long-lasting MHWs until Typhoon Hinnamnor began to dissipate the wave in early September. This study improves our understanding of the physical mechanism of flood-related MHWs that have increased with recent climate change.

Seasonal snow is an integral part of the global water supply and storage system. Snow droughts impact ecological, agricultural, and urban systems by altering the amount and timing of meltwater delivery. These droughts are characterized by a lack of on-the-ground snow (snow water equivalent, SWE) that can be caused by low total precipitation (dry drought) or low proportion of precipitation falling as snowfall (warm drought), often combined with an early melt. The standardized SWE index (SWEI) ranks the current status of SWE for a given location compared to a baseline condition and identifies the existence, but not the cause, of snow drought. In this work, we use estimates of SWE, temperature, and precipitation from nine coupled model intercomparison project phase 6 (CMIP6) models to quantify the frequency, severity, and type of snow droughts globally for historical and future scenarios. Compared to a historical baseline (1850–1900) total snow drought frequency more than doubles under socio-economic pathway (SSP)2-4.5 and SSP5-8.5; all of the increase in snow drought frequency comes from an increase in warm droughts. The probability distribution of future SWEI in major snowy basins around the world are likely to be centered on more negative values, which corresponds to more severe drought and, with only moderate changes in distribution spread, more frequent drought. CMIP6 simulations pinpoint snow drought as an emerging global threat to water resources and highlight the need to explore higher resolution future models that better capture complex mountain topography, wildland fires, and snow-forest interactions.

The government-funded retreat of homeowners from flood-prone housing is a globally ascendant policy of climate adaptation. Yet, we still know relatively little about some fairly basic questions involving its participants: e.g. How much risk do homeowners tolerate before retreating? Where do they move? Does that move reduce their future flood risk? And, to what extent do answers to these questions vary by the type of racial and ethnic communities in which they live? To answer these questions, we combine novel address-to-address residential history data with future flood risk estimates and indices of local context to better understand how retreat is unfolding across the United States. Results indicate that, when voluntarily undertaken, retreat is a highly local process that yields notable reductions in household flood risk. These movements, however, are racially segmented, with homeowners in majority-White communities being more likely to stay in the face of higher risk and less likely to relocate to nearby areas that are not also majority-White.

Drought impact prediction can improve early warning and thus preparedness for droughts. Across Europe drought has and will continue to affect environment, society and economy with increasingly costly damages. Impact models are challenged by a lack of data, wherefore reported impacts archived in established inventories may serve as proxy for missing quantitative data. This study develops drought impact models based on the Alpine Drought Impact report Inventory (EDII_ALPS) to evaluate the potential to predict impact occurrences. As predictors, the models use drought indices from the Alpine Drought Observatory and geographic variables to account for spatial variation in this mountainous study region. We implemented regression and random forest (RF) models and tested their potential (1) to predict impact occurrence in other regions, e.g. regions without data, and (2) to forecast impacts, e.g. for drought events near real-time. Both models show skill in predicting impacts for regions similar to training data and for time periods that have been extremely dry. Logistic regression outperforms RF models when predicting to very different conditions. Impacts are predicted best in summer and autumn, both also characterised by most reported impacts and therefore highlighting the relevance to accurately predict impacts during these seasons in order to improve preparedness. The model experiments presented reveal how impact-based drought prediction can be approached and complement index-based early warning of drought.

Variability in storminess, storm surge, and mean sea level (MSL) can substantially alter coastal hazards associated with extreme sea levels (ESLs). However, the detection and attribution of the past changes in tropical cyclone (TC) activity and associated storm surges are hampered by the inhomogeneous TC records. In this study, we investigate spatiotemporal changes in storm surge levels in Japan from 1980 to 2019, a period when observational platforms including tide gauges and storm records are highly consistent. We find statistical evidence supporting the increase in surge annual maxima in several places including the bay area of Tokyo since 1980. This rate of change is comparable to that observed for MSL rise over the same period. These findings cast doubt on the current hypothesis underlying the flood adaptation plan, which assumes that future surge extremes will remain the same and only considers MSL changes. We demonstrate that the changes in ESL in the last 40 years cannot be explained by the rise of MSL alone. Rather, the northeastward shifting of TC landfall location along with intensifying and widening of TCs, might have altered the likelihood of ESL, including surge extremes. The substantial influence of these TC meteorological variables on surge levels combined with the rise of MSL, suggests that current coastal planning practices including critical heights for flood defenses might be inadequate in the future.

How flooding affects home values can determine the path of economic recovery for communities and have lasting impacts on national and global financial systems. Yet, our understanding of how flood insurance, community risk perception, and past flooding events shape future housing prices (HPs) remains limited. To explore this, we used a socio-environmental (SE) model and studied the temporal impacts of flooding on mean housing values across 496 coastal census tracts of New York, Connecticut, and New Jersey, US, from 1970 to 2021. The modeling exercise demonstrated that the initial economic impact of Hurricane Sandy was largely absorbed by the National Flood Insurance Program (NFIP); however, the region then exhibited a long-term decline in home values, which was well described by an interrupted time series model. We found significant correlations between SE model parameters describing HP change and those describing tract-scale behaviors and perceptions, suggesting that the salience of past flooding events and NFIP participation may be important regional drivers of HPs. Tracts with greater post-flood change in active insurance policies exhibited larger decreases in mean home values than those with more stable NFIP participation. An improved understanding of relationships between HPs, flood insurance, and community perceptions could support more equitable distributions of resources and improved policy interventions to reduce flooding risk.

Heatwaves are weather hazards that can influence societal and natural systems. Recently, heatwaves have increased in frequency, duration, and intensity, and this trend is projected to continue as a consequence of climate change. The study of heatwaves is hampered by the lack of a common definition, which limits comparability between studies. This applies in particular to the considered time scale for utilised metrics. Here, we study which durations of heatwaves are most impact-relevant for various types of impacts. For this purpose, we analyse societal metrics related to health (heat-related hospitalisations, mortality) and public attention (Google trends, news articles) in Germany. Country-averaged temperatures are calculated for the period of 2010–2019 and the warmest periods of all time scales between 1 and 90 days are selected. Then, we assess and compare the societal response during those periods to identify the heatwave durations with the most pronounced impacts. Note that these durations are based on average temperatures across the given time frame while individual days may be less warm. The results differ slightly between the considered societal metrics but indicate overall that heatwaves induce the strongest societal response at durations between 2 weeks and 2 months for Germany. Finally, we show that heatwave duration affects the societal response independent of, and additionally to, heatwave temperatures. This finding highlights the relevance of making informed choices on the considered time scale in heatwave analyses. The approach we introduce here can be extended to other societal indices, countries, and hazard types to reveal more meaningful definitions of climate extremes to guide future research on these events.

Advances in hydrological modeling and numerical weather forecasting have allowed hydro-climate services to provide accurate impact simulations and skillful forecasts that can drive decisions at the local scale. To enhance early warnings and long-term risk reduction actions, it is imperative to better understand the hydrological extremes and explore the drivers for their predictability. Here, we investigate the seasonal forecast skill of streamflow extremes over the pan-European domain, and further attribute the discrepancy in their predictability to the local river system memory as described by the hydrological regimes. Streamflow forecasts at about 35 400 basins, generated from the E-HYPE hydrological model driven with bias-adjusted ECMWF SEAS5 meteorological forcing input, are explored. Overall the results show adequate predictability for both hydrological extremes over Europe, despite the spatial variability in skill. The skill of high streamflow extreme deteriorates faster as a function of lead time than that of low extreme, with a positive skill persisting up to 12 and 20 weeks ahead for high and low extremes, respectively. A strong link between the predictability of extremes and the underlying local hydrological regime is identified through comparative analysis, indicating that systems of analogous river memory, e.g. fast or slow response to rainfall, can similarly predict the high and low streamflow extremes. The results improve our understanding of the geographical areas and periods, where the seasonal forecasts can timely provide information on very high and low streamflow conditions, including the drivers controlling their predictability. This consequently benefits regional and national organizations to embrace seasonal prediction systems and improve the capacity to act in order to reduce disaster risk and support climate adaptation.

Human bodies, ecosystems and infrastructures display a non-linear sensibility to extreme temperatures occurring during heatwave events. Preparing for such events entails to know how high surface air temperatures can go. Here we examine the maximal reachable temperatures in Western Europe. Taking the July 2019 record-breaking heatwave as a case study and employing a flow analogues methodology, we find that temperatures exceeding 50 ^∘C cannot be ruled out in most urban areas, even under current climate conditions. We analyze changes in the upper bound of surface air temperatures between the past (1940–1980) and present (1981–2021) periods. Our results show that the significant increase in daily maximum temperatures in the present period is only partially explained by the increase of the upper bound. Our results suggest that most of the warming of daily maximum surface temperatures result from strengthened diabatic surface fluxes rather than free troposphere warming.

Global warming accelerates the rate of inter-regional hydrological cycles, leading to a significant increase in the frequency and intensity of hydrological wet extremes. The Tibetan Plateau (TP) has been experiencing a rapid warming and wetting trend for decades. This trend is especially strong for the upper Brahmaputra basin (UBB) in the southern TP. The UBB is the largest river on the TP, and these changes are likely to impact the water security of local and downstream inhabitants. This study explores the spatial-temporal variability of wet extremes in the UBB from 1981–2019 using a water- and energy-budget distributed hydrological model (WEB-DHM) to simulate river discharge. The simulated results were validated against observed discharge from the Ministry of Water Resources at a mid-stream location and our observations downstream. The major findings are as follows: (1) the WEB-DHM model adequately describes land-atmosphere interactions (slight underestimation of −0.26 K in simulated annual mean land surface temperature) and can accurately reproduce daily and monthly discharge (Nash-Sutcliffe efficiency is 0.662 and 0.796 respectively for Nuxia station); (2) although extreme discharge generally occurs in July and is concentrated in the southeastern TP, extreme wet events in the UBB are becoming increasingly frequent (after 1998, the number of extreme days per year increased by 13% compared to before) and intense (maximum daily discharge increased with a significant trend of 444 (m³s⁻¹) yr⁻¹), and are occurring across a wider region; (3) Precipitation is more likely to affect the intensity and spatial distribution of wet extremes, while the air temperature is more correlated with the frequency. Our wet extreme analysis in the UBB provides valuable insight into strategies to manage regional water resources and prevent hydrological disasters.

The southwestern Korean Peninsula had experienced cumulative precipitation deficits from the early spring of 2022, causing a severe meteorological drought in March 2023. As a growing season was forthcoming, the sub-seasonal to seasonal outlook of this ongoing drought came into question. This study aims to investigate a key driver of the ongoing drought and the required precipitation for its termination, and examine the sub-seasonal and seasonal outlooks of the ongoing drought via probabilistic and climate model-based forecasts. Results show a comparable contribution of springtime and summertime precipitation deficits in 2022, indicating that six-month accumulated precipitation deficit of 2022 was a key driver of the ongoing drought. We find that at least 80, 150, and 210 mm (170, 310, and 440 mm) of accumulated precipitation are required for the recovery (full recovery) in March, April, and May 2023, respectively. These required cumulative precipitation are found from 25% and 20% of empirical and dynamic precipitation forecasts, respectively. This study highlights the importance of the collaborative effort of national and local governments and stakeholders on mitigating negative impacts of the ongoing drought.

As the risk of forest fires increases around the globe, the issues of how to control, suppress, and prevent them are the subjects of growing public and political attention. This study focuses on the political debate in Germany regarding forest fires and provides insights into the conceptualization of forest fires and forest fire management at the federal and state policymaking levels. By examining forest fire narratives through the policy lens, this case study takes an exemplary extreme weather event exacerbated by climate change as an opportunity to examine the policy response to this problem. In this way, we examine the role of policy narratives in civil and environmental protection and disaster management. The findings reveal that all politicians examined in this study agree that forest fire management is an urgent matter that needs to be supported. In the prevailing human-centered narrative, policymakers see active forest management and use as tools to improve forest resilience to fire and other calamities. Those who advocate a nature-based narrative assert that it is natural processes in protected forest areas that most effectively enhance resilience. The policy solutions derived from these views include financial support, recognition of the work of foresters, forest fire managers, and civil protection agencies as well as, depending on the type of narrative argument favored, either increased forest management or improved protection of forest ecologies. This suggests that narrative analysis may illuminate the rationales underlying previous policy decisions and the framework for future ones. This contribution throws light on how narratives shape policymaking and, by extension, disaster management. Future studies should therefore take into account the influence of prevailing narratives when it comes to evaluating the potential that policymaking can offer for disaster management in the future.

A severe compound cold-wet event occurred in southern China (hereafter referred to as CWESC) in February 2022, leading to enormous socioeconomic losses. In this study, we proposed a new index to denote the severity of the compound cold-wet event. Based on the multivariate survival method, the CWESC in February 2022 is identified as the severest event during the past six decades. Our results indicate that the CWESC in 2022 is jointly regulated by the La Niña-like SST condition in the tropical Pacific and the warm SST anomalies in the North Atlantic, and a teleconnection in the Northern Hemisphere during winter (hereafter referred to as TNHW) plays the key role. The TNHW pattern originates from the tropical Pacific, and it splits into two routes over the North Atlantic. The northern branch of TNHW propagates via the Arctic and Siberia, causing intensified near-surface northerly wind and partially inducing an anomalous anticyclone over the western North Pacific (WNP). The southern branch of TNHW propagates via the Mediterranean and western Asia, inducing a deepened India–Burma trough and partially inducing the anomalous anticyclone over WNP. The intensified near-surface northerly wind causes enhanced cold advection over southern China, while the deepened India–Burma trough and the anomalous anticyclone over WNP cause increased southerly warm and moist air flow towards southern China, resulting in the CWESC in 2022. Moreover, four groups of numerical experiments forced by tropical Pacific, North Pacific, and North Atlantic SST anomalies are conducted based on the Community Atmosphere Model version 5. The results confirm the important roles of the La Niña-like condition and the warm SST anomalies in the North Atlantic in causing the CWESC in 2022.

Harmful algal blooms (HABs) and hypoxia, as common ecological disasters, are typically site-specific and recurrent, causing enduring environmental problems for coastal communities. Although these issues are often attributed to coastal eutrophication, in certain low-nutrient areas, such ecological disasters still frequently occur while the underlying cause is poorly understood. A prime example is the Qinhuangdao coastal waters in North China. This study intends to investigate the controlling factors of such incongruous ecological issues recurring in those low-nutrient areas with a case study of Qinhuangdao, utilizing numerical models and satellite observations. The result indicates that the weak tide-induced upwelling during summer creates favorable conditions (warm water with high transparency) for the occurrence of HABs and hypoxia in this region. It is due to that Qinhuangdao is precisely situated at the current amphidromic point of semi-diurnal tides, characterized by weak tide mixing. Likewise, the same story happens on the northern shelf of the Shandong Peninsula, where ecological problems are also prominent in China. The present study implies that shallow waters with weak local upwelling are susceptible to ecological issues during the warm season. This finding challenges the traditional view that strong-upwelling zones are more vulnerable to ecological disasters due to ample nutrient supply within the euphotic layer. It implies that tidal dynamics can greatly affect the vulnerability of coastal waters to ecological issues, which can be of significance to coastal management. Furthermore, the finding may have broader global applicability, given the ubiquity of tide-induced upwelling in various other coastal regions.

Heavy rainfall events in western Japan during early July have become more frequent, yet the underlying mechanism behind this trend during the late stage of the Meiyu-Baiu rainy season remains unclear. Our long-term analysis of short-duration events revealed that a quasi-stationary Rossby wave train enhances the poleward transport of moisture from the western Pacific, contributing to the frequent occurrence of heavy rainfall events over western Japan. The local-scale circulation over the East China Sea plays a substantial role in producing this quasi-stationary Rossby wave train, which is closely linked to enhanced deep convection over the Kuroshio warm current, characterized by a distinct sea surface temperature (SST) front. The coarse resolution of both the model and SST data may hinder the ability of climate simulations to capture the local-scale circulation, underscoring the importance of quasi-stationary atmospheric circulation for a better understanding of heavy rainfall events through poleward moisture transport.

The 2022 Indus floods in Pakistan underscore the urgency of adapting to more frequent and severe natural disasters in a warming world. Post-disaster reconstruction offers a chance to built-in adaptation measures, but identifying feasible and cost-effective adaptation options is challenging, especially in data-scarce regions. Here we employ a high-resolution rapid assessment of flood stages combined with demographic data to identify adaptation opportunities and costs for the Indus floodplains. Under a plausible set of assumptions, we find that rebuilding houses in a flood-proof, elevated manner ('moving up') or (temporary) relocation ('moving over') could have protected 13%, respectively 16% of people affected during the 2022 floods, while the remaining 70% of people were exposed to shallow water levels that could have been addressed with low-cost adaptation. Implementing these measures during ongoing reconstruction could be an effective adaptation to future floods but will come with substantial costs. Rebuilding in a flood-proofed manner ('moving up') alone would already increase costs by 26%–63% (\$1.5bn–\$3.6bn) compared to estimated reconstruction costs without adaptation (\$5.8bn). Additional costs would be incurred by relocation and adaptation of other infrastructure. The absence of local flood stage and socio-economic data creates uncertainty and points to future research avenues. Yet, our prototype approach demonstrates the value of rapid assessments for guiding post-disaster adaptation of livelihoods to future floods.

Intensifying climate extremes and the ageing of built infrastructure have prompted the idea of replacing the ageing built infrastructure with natural infrastructure. In this paper, we discuss how a distributed portfolio of smaller wetlands performs compared to a flood control reservoir in terms of flood mitigation. Using a framework of a loosely coupled land surface model with a hydrodynamic model, in the Brazos basin (Texas), we find that (i) two smaller wetlands have more impact on flood mitigation than one double sized wetland, and (ii) creating multiple wetlands (prioritized based on storage) increases flood mitigation. Further, we find that a portfolio of wetlands equivalent to the size of the submergence area of the biggest flood control reservoir (Whitney) in the basin, can create additional flood control storage, equivalent to ∼10% of the total storage of Lake Whitney. Creating a portfolio of wetlands can increase the overall resiliency of the basin.

Coastal ecosystems have the potential to contribute to disaster risk reduction and adaptation to climate change. While previous studies have estimated the value of current coastal ecosystems for reducing coastal risk, there have been relatively few studies that look at changes in ecosystem service provision, in the past and under climate change. We employ the probabilistic, event-based CLImate ADAptation platform (CLIMADA) to quantify the protection from tropical cyclones (TCs) provided by coastal ecosystems, modeling the number of beneficiaries in the past and under future climate change. We also investigate the potential of nature-based solutions (NbS), such as mangrove restoration. We find that currently, one in five (21%) of all people impacted annually by TCs in the global low-elevation coastal zone is within the protection distance of coastal ecosystems. Over the last 30 years, the share of protected people has decreased by approximately 2%, due to ecosystem loss. With climate change, the average annual number of people impacted will increase by 40%. Simultaneously, the proportion of people protected by coastal ecosystems with climate change decreases due to changes in TC distribution (−1%). The importance of current coastal protection, and the potential for increasing protection by NbS, varies widely between countries. While the number of people protected globally only increases slightly with mangrove restoration, the share of people protected in individual countries can increase by up to 39%. Our findings provide a basis for NbS planning and adaptation policy, by highlighting areas which will be crucial for coastal protection services in a world altered by climate change.

Coastal cities like Shenzhen are confronting escalating flood risks under the combined impact of climate change and rapid urbanization, especially the tropical cyclones (TC)-induced flood. Incorporating the impact of climate change and urbanization on the flood, this study constructed a new TC-induced flood model on western Shenzhen embedded with a unique statistical approach. Based on extensive historical data and machine learning techniques, the temporal characteristics and changes of flooding were revealed. The results reveal an increase in the frequency of TC-induced floods between 1964–2022, especially after the 1990s, which is attributed to a decrease in the distance of the location of the maximum intensity of TCs (observed within an 800 km range of the study area) relative to the land, averaging a reduction of 11.4 km per decade. This shift towards land is likely due to changes in the locations of TC genesis. Furthermore, the 'rainfall sea level' threshold for western Shenzhen was accordingly derived from the results of modelling, which would enable decision-makers to quickly assess TC-induced flood risks. The study's proposed methods offer alternative approaches for predicting TC-induced floods in regions where the gathering of hydro-meteorological data is challenging or where economic and technological resources are limited.

Compound dry and hot extremes (CDHE) will have an adverse impact on socioeconomic factors during the Indian summer monsoon, and a future exacerbation is anticipated. The occurrence of CDHE is influenced by teleconnections, which play a crucial role in determining its likelihood on a seasonal scale. Despite the importance, there is a lack of studies unraveling the teleconnections of CDHE in India. Previous investigations specifically focused on the teleconnections between precipitation or temperature and climate indices. Hence, there is a need to unravel the teleconnections of CDHE. In this study, we present a framework that combines event coincidence analysis (ECA) with complexity science. ECA evaluates the synchronization between CDHE and climate indices. Subsequently, complexity science is utilized to construct a driver-CDHE network to identify the key drivers of CDHE. To evaluate the effectiveness of the proposed drivers, a logistic regression model is employed. The occurrence of CDHE exhibits distinct patterns from July to September when considering intra-seasonal variability. Our findings contribute to the identification of drivers associated with CDHE. The primary driver for Eastern, Western India and Central India is the indices in the Pacific Ocean and Atlantic Ocean, respectively, followed by the indices in the Indian Ocean. These identified drivers outperform the traditional Niño 3.4-based predictions. Overall, our results demonstrate the effectiveness of integrating ECA and complexity science to enhance the prediction of CDHE occurrences.

Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the community atmosphere model, we explore how Irma's precipitation would change under various levels of climate warming. Analysis is focused on a 48 h period where the simulated hurricane tracks reasonably represent Irma's observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3–7% K⁻¹ compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10–13% K⁻¹ and 17–21% K⁻¹, respectively. Over Florida, the simulated mean rainfall accumulations increase by 16–26% K⁻¹, with local maxima increasing by 18–43% K⁻¹. All percent changes increase monotonically with warming level.

Hydroclimatic stresses can negatively impact crop production via water deficits (low soil water supply and high atmospheric demand) or surpluses (high soil water supply and low atmospheric demand). However, the impact of both stresses on crop yields at regional scales is not well understood. Here we quantified yield sensitivities and corresponding spatio-temporal yield losses of US rainfed maize, soybeans, sorghum, and spring wheat to hydroclimatic stresses by considering the joint impacts of root-zone soil moisture and atmospheric evaporative demand from 1981 to 2020. We show that crop yields can be reduced similarly by two major hydroclimatic hazards, which are defined as the most yield damaging conditions over time: 'Low Supply + High Demand' and 'High Supply + Low Demand'. However, more exposure to 'Low Supply + High Demand' hazard led to the largest annual yield losses (7%–17%) across all four crops over time. Modeled yield losses due to these hazards were significantly associated with crop insurance lost costs. The extent of yield losses varies considerably by crop and location, highlighting the need for crop-specific and regionally tailored adaptation strategies.

The intensities and occurrences of heat extremes are projected to increase in a warmer climate, and relevant policies have been established to address different warming levels. However, how climate extremes change at regional warming levels is not well-known because changes in temperature vary over different regions. This study investigated climate extremes and population exposure to these extremes at regional and global 1.5 °C or 2.0 °C warming over 58 reference regions with 16 Coupled Model Intercomparison Project, 6th phase models. The years of reaching local 1.5 °C or 2.0 °C warming occurred earlier than the timing of global warming over certain land areas, with more than 30 years advance in northern high latitude land areas. Heat extremes are projected to increase in all reference regions under regional and global 1.5 °C or 2.0 °C warming. Moving from regional to global 1.5 °C or 2.0 °C warming, heat extremes were found to increase over most land areas, especially over mid- and high-latitude areas. Population exposure to climate extremes increased over more than half the land regions under regional to global 1.5 °C or 2.0 °C warming. Changes in population exposure to absolute heat extremes were mainly generated by changes in population over about 34 land regions, whereas changes in population exposure to percentile-based heat extremes over more than 40 land regions were mostly due to changes in climate extremes. These results provided references to establish relevant strategies at regional scale to address possible risks related to climate extremes.

Weather extremes become more frequent and intense with climate change, but how weather extremes impact household wealth in the Global South remains elusive in many regions. We combined nationally representative quarterly household panel data with climate data to evaluate the impact of weather extremes on household poverty in Kyrgyzstan between 2013 and 2020. We evaluated multiple dimensions of poverty by quantifying changes in nutrition, education, health, and living standards. We used a linear quantile mixed model to relate the poverty dimensions with four salient weather extremes: cold winters, hot summers, excessive rains, and dry spells. Our findings show that all weather extremes harmed household wealth but with substantial spatial variation. Cold winters were the most detrimental, with negative consequences that continued into the subsequent year. Poor households suffered disproportionally more from extremes than rich ones. Our results underscore the need to initiate place-based adaptation options to cushion the adverse effects of extreme weather events on household wealth.

As wildfires continue to worsen across western United States, forest managers are increasingly employing prescribed burns as a way to reduce excess fuels and future wildfire risk. While the ecological benefits of these fuel treatments are clear, little is known about the smoke exposure tradeoffs of using prescribed burns to mitigate wildfires, particularly among at-risk populations. Outdoor agricultural workers are a population at increased risk of smoke exposure because of their time spent outside and the physical demands of their work. Here, we assess the smoke exposure impacts among outdoor agricultural workers resulting from the implementation of six forest management scenarios proposed for a landscape in the Central Sierra, California. We leverage emissions estimates from LANDIS-II to model daily PM_2.5 concentrations with the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) and link those to agricultural employment data from the Bureau of Labor Statistics. We find a u-shaped result, in that moderate amounts of prescribed burning result in the greatest reduction in total smoke exposure among outdoor agricultural workers, particularly during months of peak agricultural activity due to wildfire-specific smoke reductions. The reduction in total smoke exposure, relative to scenarios with the least amount of management, decreases as more prescribed burning is applied to the landscape due to the contributions of the fuel treatments themselves to overall smoke burden. The results of this analysis may contribute to preparedness efforts aimed at reducing smoke exposures among outdoor agricultural workers, while also informing forest management planning for this specific landscape.

Hydrological extremes can affect nutrient export from catchments to streams, posing a threat to aquatic ecosystems. In this study, we investigated the effects of hydrological drought on nitrate concentrations in the streamflow of 182 German catchments from 1980 to 2020. We found that across all seasons, 40% and 25% of the catchments showed significantly lower nitrate concentrations during drought and post-droughts, respectively, when compared to non-drought conditions. However, we observed pronounced spatial variability in the responses, particularly during winter droughts and post-droughts, with more catchments exhibiting higher nitrate concentrations. Specifically, nitrate concentrations were significantly higher in 25% of the study catchments during winter droughts, particularly in wetter catchments with low nitrogen retention. During winter post-droughts, nitrate concentrations are significantly higher in 19% of the catchments, especially in wetter catchments with more nitrogen surplus. Moreover, the likelihood of nitrate seasonal extremes increased by 6% during winter post-drought in our study catchments. Considering the projected increase in the frequency of droughts in Germany, the increase in nitrate concentrations during the corresponding post-drought periods poses a potential threat to aquatic ecosystem health.

Given its high population density and degree of urbanization, the eastern United States (US) is a region vulnerable to the impacts from hailstorms. Small changes in hail activity may indicate large impacts on the potential hail risks faced by the region. While contrasting hailstorm-favorable environmental changes between the northeastern and southeastern US have been documented, the meridional shift of hail activity in the eastern US has not been directly revealed based on observed hailstorm records. In this letter, using the official hailstorm database, we find a significant northward migration of hail activity (+0.33° N decade⁻¹) in the eastern US since 2000, which is mainly contributed by the increasing proportion of large hailstorm events (hail size 0.75–2.0 inch) hitting the northeast in July and August (+0.93° N decade⁻¹). The spatially inhomogeneous climatic mean state changes over the past two decades contribute a leading role: the intensified Bermuda High and the eastward shift of upper-level jet stream over the central US tended to moisten (dry) the atmosphere over the northeastern (southeastern) US by enhancing the low-level poleward moisture transport. This not only provides more moisture for hailstorm formation in the northeast but also destabilizes (stabilizes) the atmosphere in the northeast (southeast) under an overall increase in dry instability over the eastern US. These factors together lead to a northward shift of large hailstorms toward the northeastern US, where hailstorms were relatively seldom reported. Incorporating this shift in knowledge may improve contingency and risk management strategies of both the public and private sectors in the future.

Flash drought events (FDEs) are projected to increase frequently in a warming world, significantly impacting ecosystem productivity and the global carbon cycle. The development of FDEs, induced by anomalies in different environmental variables, may cause different responses to the ecosystem's gross primary productivity (GPP). However, the GPP variations and underlying mechanisms during the FDEs have rarely been quantified. This study collected long-term (>10 years) high-quality flux observations from the FLUXNET 2015 dataset to investigate GPP variations and their driving mechanisms during FDEs. Results showed that all vegetation types have two contrasting GPP variations during FDEs. One variation is a decreasing then increasing standardized GPP anomaly (V-shape response). The other shows an increase followed by decreasing standardized GPP anomaly (inverted V-shape response). The V-shape GPP response to FDEs was induced by increased soil water content deficit at the onset stage of FDEs. In contrast, the inverted V-shape GPP response to FDEs was induced by increased net radiation at the onset of FDEs. Such results indicated competing moisture supply and atmospheric moisture demand at the onset of FDEs, controlling the two contrasting ecosystem's carbon responses with its development. Moreover, the contribution of water use efficiency to the magnitude of the V-shape GPP response (64.5 ± 22.4%) is greater than that to the inverted V-shape GPP response (47.6 ± 18.7%). This study identified the two contrasting types of GPP variations during FDEs and their driving mechanisms across multiple ecosystem types which can improve our ability to predict the future effects of more frequent FDEs on ecosystem productivity.

The development of a high-quality wildfire occurrence model is an essential component in mapping present wildfire risk, and in projecting future wildfire dynamics with climate and land-use change. Here, we develop a new model for predicting the daily probability of wildfire occurrence at 0.1° (∼10 km) spatial resolution by adapting a generalised linear modelling (GLM) approach to include improvements to the variable selection procedure, identification of the range over which specific predictors are influential, and the minimisation of compression, applied in an ensemble of model runs. We develop and test the model using data from the contiguous United States. The ensemble performed well in predicting the mean geospatial patterns of fire occurrence, the interannual variability in the number of fires, and the regional variation in the seasonal cycle of wildfire. Model runs gave an area under the receiver operating characteristic curve (AUC) of 0.85–0.88, indicating good predictive power. The ensemble of runs provides insight into the key predictors for wildfire occurrence in the contiguous United States. The methodology, though developed for the United States, is globally implementable.

Wildfires lead to dramatic increases in fine particulate matter pollution concentrations. Based on the premise that higher-income households purchase more defensive investments to reduce the degree to which outdoor pollution infiltrates indoors, in this study, we investigate how income contributes to outdoor–indoor pollution infiltration rates during wildfire events. Using crowd-sourced data from the PurpleAir Real-Time Air Quality Monitoring Network and econometric models that explore variations in monitor readings over time, we find increases in outdoor pollution lead to significant increases in indoor pollution, but disproportionately so in lower-income areas. The results highlight a new inequality in pollution exposure: not only are outdoor pollution levels higher for lower-income individuals, but indoor pollution levels are higher even for similar outdoor pollution levels.

China is highly susceptible to landslides and debris flow disasters as it is a mountainous country with unique topography and monsoon climate. In this study, an efficient statistical model is used to predict the landslide risk in China under the Representative Concentration Pathway 8.5 by 2050, with the precipitation data from global climate models (GCMs) as the driving field. Additionally, for the first time, the impact of future changes in land use types on landslide risk is explored. By distinguishing between landslide susceptibility and landslide risk, the results indicate that the landslide susceptibility in China will change in the near future. The occurrence of high-frequency landslide risks is concentrated in southwestern and southeastern China, with an overall increase in landslide frequency. Although different GCMs differ in projecting the future spatio-temporal distribution of precipitation, there is a consensus that the increased landslide risk in China's future is largely attributed to the increase in extremely heavy precipitation. Moreover, alterations in land use have an impact on landslide risk. In the Huang-Huai-Hai Plain, Qinghai Tibet Plateau, and Loess Plateau, changes in land types can mitigate landslide risks. Conversely, in other areas, such changes may increase the risk of landslides. This study aims to facilitate informed decision-making and preparedness measures to protect lives and assets in response to the changing climate conditions.

Drought is a recurrent climatic hazard impacting natural and built environmental systems, including human lives. Although several studies have assessed streamflow droughts and their multivariate characterization, very few studies have focused on understanding spatiotemporal changes in drought attributes, such as drought seasonality, severity and duration across global tropics. Further, the nonlinear response between onset time and severity of streamflow droughts at a large scale are unknown. Leveraging ground-based streamflow observations, this study for the first time investigate changes in streamflow drought characteristics across global tropics using two 30 year climate normal periods: 1961–1990 and 1991–2020. Our analyses of changes in probability distributions of onset time and severity (deficit volume) of streamflow droughts over the two time windows show significant shifts towards higher values for Northeast and South American Monsoon region, Western Africa, eastern South Africa, north and eastern Australia. Around 55% of the sites show an increase in drought frequency in recent times. We found that in the recent times, only 27% of sites depict an increase in deficit volume accompanied by delayed onset. Further, we identify a few regional hotspots, such as Northeast and South American monsoon region, and eastern coast of Australia show an increased frequency of droughts with an upward trend in deficit volume in recent years. As expected, the individual changes in drought attributes have translated into changes in joint occurrences of their interdependent attributes, assuming the correlation between onset time and deficit volume. Our analyses show robust dependence strengths between onset time and deficit volume, which strengthen further in the recent time window over 50% of catchments. The nonstationary changes identified here in individual drought attributes and their joint dependence can alter the hazard potential of extreme droughts, which has consequences in risk management, climate adaptation and water resources planning.

The increase in heatwave intensity, causing heat stress and crop failures in many regions is a concerning impact of global climate change. In northern Europe, significant interannual variability previously prevented robust assessments of trends in heat extremes. However, with a large-ensemble seasonal hindcasts and archived forecasts dataset covering 1981–2022 multiple realisations of weather patterns can be pooled and assessed. What are recent trends of extreme temperatures? Has the risk for a 100-year heatwave event increased in Northern Europe? We apply the UNSEEN (UNprecedented Simulated Extremes using ENsembles) approach to assess the credibility of the model ensemble and use non-stationary extreme value analysis to quantify recent trends in extreme 3-day heatwaves in late spring and early summer (May to July). We find significant non-stationarity and positive trends in annual maximum heatwave intensity. We also show that heatwave volatility, i.e. the risk of clearly outstanding heatwaves, is highest in central Scandinavia.

Human activities have led to a global temperature increase, and the primary objective of the Paris Agreement is to limit this rise to 1.5 °C of warming level. Understanding the impact of global warming beyond preindustrial conditions on precipitation intensity is crucial for devising effective adaptation and mitigation strategies, particularly in densely populated global land monsoon (GLM) regions. However, the time of emergence (ToE) of extreme summer monsoon precipitation and its dependency on global warming targets has rarely been investigated. Using large ensemble simulations forced by the SSP3–7.0 scenario, we reveal that the impacts of anthropogenic forcing on extreme precipitation intensity become evident in GLM regions before 2050, accompanied by a sudden expansion in areas where the ToE of extreme precipitation occurs. Furthermore, our study demonstrates that achieving the Paris Agreement goal at 1.5 °C of global warming level can prevent the ToE of extreme precipitation in Asian and African monsoon regions. This, in turn, has the potential to halve the number (over one billion) of individuals exposed to extreme precipitation. These findings highlight the urgent need for action to mitigate the risk associated with anthropogenic warming induced climate change.

Shoreline predictions are essential for coastal management. In this era of increasing amounts of data from different sources, it is imperative to use observations to ensure the reliability of shoreline forecasts. Data assimilation has emerged as a powerful tool to bridge the gap between episodic and imprecise spatiotemporal observations and the incomplete mathematical equations describing the physics of coastal dynamics. This research seeks to maximize this potential by assessing the effectiveness of different data assimilation algorithms considering different observational data characteristics and initial system knowledge to guide shoreline models towards delivering results as close as possible to the real world. Two statistical algorithms (stochastic ensemble and extended Kalman filters) and one variational algorithm (4D-Var) are incorporated into an equilibrium cross-shore model and a one-line longshore model. A twin experimental procedure is conducted to determine the observation requirements for these assimilation algorithms in terms of accuracy, length of the data collection campaign and sampling frequency. Similarly, the initial system knowledge needed and the ability of the assimilation methods to track the system nonstationarity are evaluated under synthetic scenarios. The results indicate that with noisy observations, the Kalman filter variants outperform 4D-Var. However, 4D-Var is less restrictive in terms of initial system knowledge and tracks nonstationary parametrizations more accurately for cross-shore processes. The findings are demonstrated at two real beaches governed by different processes with different data sources used for calibration. In this contribution, the coastal processes assimilated thus far in shoreline modelling are extended, the 4D-Var algorithm is applied for the first time in the field of shoreline modelling, and guidelines on which assimilation method can be most beneficial in terms of the available observational data and system knowledge are provided.

Several recent widespread temperature extremes across the United States (U.S.) have been associated with power outages, disrupting access to electricity at times that are critical for the health and well-being of communities. Building resilience to such extremes in our energy infrastructure needs a comprehensive understanding of their spatial and temporal characteristics. In this study, we systematically quantify the frequency, extent, duration, and intensity of widespread temperature extremes and their associated energy demand in the six North American Electric Reliability Corporation regions using ERA5 reanalysis data. We show that every region has experienced hot or cold extremes that affected nearly their entire extent and such events were associated with substantially higher energy demand, resulting in simultaneous stress across the entire electric gird. The western U.S. experienced significant increases in the frequency (123%), extent (32%), duration (55%) and intensity (29%) of hot extremes and Texas experienced significant increases in the frequency (132%) of hot extremes. The frequency of cold extremes has decreased across most regions without substantial changes in other characteristics. Using power outage data, we show that recent widespread extremes in nearly every region have coincided with power outages, and such outages account for between 12%–52% of all weather-related outages in the past decade depending on the region. Importantly, we find that solar potential is significantly higher during widespread hot extremes in all six regions and during widespread cold extremes in five of the six regions. Further, wind potential is significantly higher during widespread hot or cold extremes in at least three regions. Our findings indicate that increased solar and wind capacity could be leveraged to meet the higher demand for energy during such widespread extremes, improving the resilience and reliability of our energy systems in addition to limiting carbon emissions.

Flood-prone people and decision-makers are often unwilling to discuss and prepare for exceptional events, as such events are hard to perceive and out of experience for most people. Once an exceptional flood occurs, affected people and decision-makers are able to learn from this event. However, this learning is often focussed narrowly on the specific disaster experienced, thus missing an opportunity to explore and prepare for even more severe, or different, events. We propose spatial counterfactual floods as a means to motivate society to discuss exceptional events and suitable risk management strategies. We generate a set of extreme floods across Germany by shifting observed rainfall events in space and then propagating these shifted fields through a flood model. We argue that the storm tracks that caused past floods could have developed several tens of km away from the actual tracks. The set of spatial counterfactual floods generated contains events which are more than twice as severe as the most disastrous flood since 1950 in Germany. Moreover, regions that have been spared from havoc in the past should not feel safe, as they could have been badly hit as well. We propose spatial counterfactuals as a suitable approach to overcome society's unwillingness to think about and prepare for exceptional floods expected to occur more frequently in a warmer world.

Flash drought affects agricultural activities and water availability. However, the rate of flash drought development and termination and their controlling mechanisms remain mostly unexplored. Using climate reanalysis (ERA5) datasets, we examine the flash drought development and recovery rates in seventeen climate regions across the globe during the 1981–2020 period. In most global climate regions, flash drought recovery (25.2 percentile/pentad) is faster than its development rate (17.2 percentile/pentad). The tropical and sub-tropical humid areas, particularly eastern North America, northern South America, South Asia, Southeast Asia, and the Islands groups, are the hotspots of rapid flash drought development and faster recovery rates. In most climate regions, flash drought development and recovery rates have considerably increased during the recent two decades. Pluvial events (heavy-to-extreme precipitation) associated with increased soil moisture and decreased atmospheric aridity vapor pressure deficit are the primary driver of the rapid flash drought recovery. Globally, 10 of 17 regions showed the dominance of extreme precipitation in flash drought recovery, primarily due to an increase in the frequency of extreme precipitation. A fraction of flash droughts terminated by extreme precipitation has increased significantly across the most regions during 1981–2020. Considering the increase in flash drought frequency, development rate, and rapid termination, the compound risk of flash droughts followed by extreme precipitation and flooding has enhanced. The abrupt transition from flash drought to wet conditions makes drought and flood management more challenging, with consequences for agriculture and water resources.

Siberia experienced a prolonged heatwave in the spring of 2020, resulting in extreme summer drought and major wildfires in the North-Eastern Siberian lowland tundra. In the Arctic tundra, plants play a key role in regulating the summer land surface energy budget by contributing to land surface cooling through evapotranspiration. Yet we know little about how drought conditions impact land surface cooling by tundra plant communities, potentially contributing to high air temperatures through a positive plant-mediated feedback. Here we used high-resolution land surface temperature and vegetation maps based on drone imagery to determine the impact of an extreme summer drought on land surface cooling in the lowland tundra of North-Eastern Siberia. We found that land surface cooling differed strongly among plant communities between the drought year 2020 and the reference year 2021. Further, we observed a decrease in the normalized land surface cooling (measured as water deficit index) in the drought year 2020 across all plant communities. This indicates a shift towards an energy budget dominated by sensible heat fluxes, contributing to land surface warming. Overall, our findings suggest significant variation in land surface cooling among common Arctic plant communities in the North-Eastern Siberian lowland tundra and a pronounced effect of drought on all community types. Based on our results, we suggest discriminating between functional tundra plant communities when predicting the drought impacts on energy flux related processes such as land surface cooling, permafrost thaw and wildfires.

Marine heatwaves (MHWs) and marine cold-spells (MCSs) are prolonged oceanic extreme temperature events that can severely impact large-scale ecosystems, fisheries, and human activities with consequent socioeconomic impacts. Although some studies have contributed valuable insights into the vertical structure and related mechanisms of MHWs, equivalent research on MCSs remains unclear. Thus, comprehensive and systematic analysis of the vertical structures and related mechanisms of MHWs and MCSs remains area of an active research. In this study, we classified MHWs/MCSs into two types in the Kuroshio Extension region: extended MHWs/MCSs that can extend through more than 70% of the water column and shallow MHWs/MCSs that are restricted from the surface layer to less than 70% of the water column. Analysis revealed that shallow events are characterized by stronger intensity and shorter duration compared with extended events. All shallow events are driven by surface heat flux anomalies, with shortwave radiation (latent heat flux) mostly inducing those in MHWs (MCSs). However, extended MHWs/MCSs are primarily driven by ocean anticyclonic/cyclonic eddies. These findings provide deeper understanding of the statistical characteristics, vertical structures, and physical drivers of MHWs and MCSs.

California must adapt to increasing wildfire activity concurrent with climate change and expanding housing development in fire-prone areas. Recent decades have seen record-breaking fire activity, economic costs, and human health impacts. Residents more frequently face home evacuations, prolonged periods of unhealthy air quality, and power shut-offs. Understanding how these experiences influence support for risk mitigation policies is essential to inform action on climate and fire adaptation. To better understand linkages between experience and policy support, we surveyed California residents (n = 645) about their wildfire-related experiences, risk perceptions, and support for 18 wildfire risk mitigation policies. To assess how the relationship between policy support and wildfire experience is modulated by preexisting worldviews, we measured the extent to which respondents are motivated by individualistic or communitarian values as proposed in the cultural theory of risk. We surveyed residents across a gradient of wildfire impacts, spatially stratifying residences based on wildland-urban-interface type and proximity to large 2020 wildfires. Support was generally high for most policies, though most respondents opposed incorporating future risk into insurance rates and coverage. Policy support models showed that communitarian worldviews were more consistently associated with greater support for diverse wildfire mitigation policies than were measures of recent experience with wildfire. These results suggest that California residents within our sample regions already support many wildfire risk mitigation strategies, and preexisting societal beliefs are a stronger predictor of these views than personal experiences with wildfire. Policy-makers can utilize this understanding to focus on crafting policies and messaging that resonates with individualistic values.

Earth observations (EOs) have successfully been used to train artificial intelligence (AI)-based models in the field of disaster risk reduction (DRR) contributing to tools such as disaster early warning systems. Given the number of in situ and remote (e.g. radiosonde/satellite) monitoring devices, there is a common perception that there are no limits to the availability of EO for immediate use in such AI-based models. However, a mere fraction of EO is actually being used in this way. This topical review draws on use cases, workshop presentations, literature, and consultation with experts from key institutes to explore reasons for this discrepancy. Specifically, it evaluates the types of EO needed to train AI-based models for DRR applications and identifies the main characteristics, possible challenges, and innovative solutions for EO. Finally, it suggests ways to make EO more user ready and to facilitate its uptake in AI for DRR and beyond.

Global disaster databases are prone to missing data. Neglect or inappropriate handling of missing data can bias statistical analyses. Consequently, this risks the reliability of study results and the wider evidence base underlying climate and disaster policies. In this paper, a comprehensive systematic literature review was conducted to determine how missing data have been acknowledged and handled in disaster research. We sought empirical, quantitative studies that utilised the Emergency Events Database (EM-DAT) as a primary or secondary data source to capture an extensive sample of the disaster literature. Data on the acknowledgement and handling of missing data were extracted from all eligible studies. Descriptive statistics and univariate correlation analysis were used to identify trends in the consideration of missing data given specific study characteristics. Of the 433 eligible studies, 44.6% acknowledged missing data, albeit briefly, and 33.5% attempted to handle missing data. Studies having a higher page count were significantly (p < 0.01) less prone to acknowledge or handle missing data, whereas the research field of the publication journal distinguished between papers that simply acknowledged missing data, with those that both acknowledged and handled missing data (p < 0.100). A variety of methods to handle missing data (n = 24) were identified. However, these were commonly ad-hoc with little statistical basis. The broad method used to handle missing data: imputation, augmentation or deletion was significantly (p < 0.001) correlated with the geographical scope of the study. This systematic review reveals large failings of the disaster literature to adequately acknowledge and handle missing data. Given these findings, more insight is required to guide a standard practice of handling missing data in disaster research.

Urban climate-related disaster risks are set to rise, driven by the interaction of two global megatrends: urbanization and climate change. A detailed understanding of whether, where and how cities are growing within or into hazard-prone areas is an urgent prerequisite for assessing future risk trajectories, risk-informed planning, and adaptation decisions. However, this analysis has been mostly neglected to date, as most climate change and disaster risk research has focused on the assessment of future hazard trends but less on the assessment of how socio-economic changes affect future hazard exposure. Urban growth and expansion modeling provide a powerful tool, given that urban growth is a major driver of future disaster risk in cities. The paper reviews the achievements lately made in urban growth and exposure modeling and assesses how they can be applied in the context of future-oriented urban risk assessment and the planning of adaptation measures. It also analyses which methodological challenges persist in urban growth and exposure modeling and how they might be overcome. These points pertain particularly to the need to consider and integrate (1) urban morphology patterns and potential linkages to exposure as well as vulnerability, (2) long-term time horizons to consider long-term developments, (3) feedbacks between urbanization trajectories and hazard trends, (4) the integration of future urban growth drivers and adaptation responses, (5) feedbacks between adaptation and urbanization, and (6) scenarios, which are developed within a commonly defined scenario framework.