Pub Date : 2024-08-21DOI: 10.1038/s44304-024-00025-8
Rebecca E. Morss, Cara L. Cuite, Julie L. Demuth
Risk theories and empirical research indicate that a variety of factors can influence people’s protective decisions for natural hazards. Using data from an online survey that presented coastal U.S. residents with a hypothetical hurricane scenario, this study investigates the relative importance of cognitive risk perceptions, negative affect, efficacy beliefs, and other factors in explaining people’s anticipated evacuation decisions. The analysis finds that multiple factors, including individual and household characteristics, previous experiences, cognitive and affective risk perceptions, and efficacy beliefs, can help predict hurricane evacuation intentions. However, the largest amount of variance in survey participants’ evacuation intentions is explained by their evacuation-related response efficacy (coping appraisals) and their perceived likelihood of getting hurt if they stay home during the storm. Additional analysis explores how risk perceptions and efficacy beliefs interact to influence people’s responses to risk information. Although further investigation in additional situations is needed, these results suggest that persuading people at high risk that evacuating is likely to reduce harm can serve as an important risk communication lever for motivating hurricane evacuation.
{"title":"What predicts hurricane evacuation decisions? The importance of efficacy beliefs, risk perceptions, and other factors","authors":"Rebecca E. Morss, Cara L. Cuite, Julie L. Demuth","doi":"10.1038/s44304-024-00025-8","DOIUrl":"10.1038/s44304-024-00025-8","url":null,"abstract":"Risk theories and empirical research indicate that a variety of factors can influence people’s protective decisions for natural hazards. Using data from an online survey that presented coastal U.S. residents with a hypothetical hurricane scenario, this study investigates the relative importance of cognitive risk perceptions, negative affect, efficacy beliefs, and other factors in explaining people’s anticipated evacuation decisions. The analysis finds that multiple factors, including individual and household characteristics, previous experiences, cognitive and affective risk perceptions, and efficacy beliefs, can help predict hurricane evacuation intentions. However, the largest amount of variance in survey participants’ evacuation intentions is explained by their evacuation-related response efficacy (coping appraisals) and their perceived likelihood of getting hurt if they stay home during the storm. Additional analysis explores how risk perceptions and efficacy beliefs interact to influence people’s responses to risk information. Although further investigation in additional situations is needed, these results suggest that persuading people at high risk that evacuating is likely to reduce harm can serve as an important risk communication lever for motivating hurricane evacuation.","PeriodicalId":501712,"journal":{"name":"npj Natural Hazards","volume":" ","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44304-024-00025-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-13DOI: 10.1038/s44304-024-00021-y
Hanbeen Kim, Gabriele Villarini
While there is growing attention toward the changes in flood magnitude and frequency, little is known about the way climate change could impact flood duration. Here we focus on 378 streamgages across the eastern United States to develop statistical models that allow the description of the year-to-year changes in flood duration above two National Weather Service (NWS) flood severity levels (i.e., minor and moderate). We use climate-related variables (i.e., basin- and season-averaged precipitation and temperature) as predictors, and show that they can be used to describe the inter-annual variability in seasonal flood durations for both NWS flood severity levels. We then use the insights from the understanding of the historical changes to provide an assessment of the projected changes in flood durations using global climate models from the Coupled Model Intercomparison Project Phase 6 and multiple shared socio-economic pathways. Our results show that the eastern United States is projected to experience longer flood durations, especially in winter (i.e., the main flood season) and under higher emission scenarios.
{"title":"Floods across the eastern United States are projected to last longer","authors":"Hanbeen Kim, Gabriele Villarini","doi":"10.1038/s44304-024-00021-y","DOIUrl":"10.1038/s44304-024-00021-y","url":null,"abstract":"While there is growing attention toward the changes in flood magnitude and frequency, little is known about the way climate change could impact flood duration. Here we focus on 378 streamgages across the eastern United States to develop statistical models that allow the description of the year-to-year changes in flood duration above two National Weather Service (NWS) flood severity levels (i.e., minor and moderate). We use climate-related variables (i.e., basin- and season-averaged precipitation and temperature) as predictors, and show that they can be used to describe the inter-annual variability in seasonal flood durations for both NWS flood severity levels. We then use the insights from the understanding of the historical changes to provide an assessment of the projected changes in flood durations using global climate models from the Coupled Model Intercomparison Project Phase 6 and multiple shared socio-economic pathways. Our results show that the eastern United States is projected to experience longer flood durations, especially in winter (i.e., the main flood season) and under higher emission scenarios.","PeriodicalId":501712,"journal":{"name":"npj Natural Hazards","volume":" ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44304-024-00021-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141973751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Flash floods are a major threat for Mediterranean countries and their frequency is expected to increase in the next years due to the climatic change. Civil protection agencies are called to deal with increasing hydrological risk, but existing hydro-meteorological monitoring networks might not be enough for detecting, tracking, and characterizing rapidly evolving floods produced by severe convective storms. Nowadays, hydro-meteorological information in several watersheds particularly in small and mid-size in orographically complex regions or in third-world countries, is still not available or insufficient. To improve our observational capability of these events, we propose to exploit the seismic recordings, which act as opportunistic signals and can complement well-established procedures to early detect the occurrence of flash floods at regional scale. Here, we investigate the hydro-meteorological event that hit central Italy in September 2022 and resulted in a devastating flash flood. We compare seismic data from a national monitoring network with raingauges and hydrometer data. Our evidence suggests that the main stages of the hydro-meteorological events can be tracked by the spatio-temporal evolution of the seismic noise confirming the capability of this multi-sensor approach in detecting and characterizing such kind of events.
{"title":"Seismic signature of an extreme hydro-meteorological event in Italy","authors":"Velio Coviello, Mauro Palo, Elisa Adirosi, Matteo Picozzi","doi":"10.1038/s44304-024-00018-7","DOIUrl":"10.1038/s44304-024-00018-7","url":null,"abstract":"Flash floods are a major threat for Mediterranean countries and their frequency is expected to increase in the next years due to the climatic change. Civil protection agencies are called to deal with increasing hydrological risk, but existing hydro-meteorological monitoring networks might not be enough for detecting, tracking, and characterizing rapidly evolving floods produced by severe convective storms. Nowadays, hydro-meteorological information in several watersheds particularly in small and mid-size in orographically complex regions or in third-world countries, is still not available or insufficient. To improve our observational capability of these events, we propose to exploit the seismic recordings, which act as opportunistic signals and can complement well-established procedures to early detect the occurrence of flash floods at regional scale. Here, we investigate the hydro-meteorological event that hit central Italy in September 2022 and resulted in a devastating flash flood. We compare seismic data from a national monitoring network with raingauges and hydrometer data. Our evidence suggests that the main stages of the hydro-meteorological events can be tracked by the spatio-temporal evolution of the seismic noise confirming the capability of this multi-sensor approach in detecting and characterizing such kind of events.","PeriodicalId":501712,"journal":{"name":"npj Natural Hazards","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44304-024-00018-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1038/s44304-024-00012-z
Stefania Ondei, Owen F. Price, David M.J.S. Bowman
Destructive wildfire disasters are escalating globally, challenging existing fire management paradigms. The establishment of defensible space around homes in wildland and rural urban interfaces can help to reduce the risk of house loss and provide a safe area for residents and firefighters to defend the property from wildfire. Although defensible space is a well-established concept in fire management, it has received surprisingly limited scientific discussion. Here we reviewed guidelines on the creation of defensible space from Africa, Europe, North America, South America, and Oceania. We developed a conceptual model of defensible space framed around the key recommended approaches to mitigate fire attack mechanisms, which address fuel types, amount, and spatial distribution. We found that zonation within the defensible space is commonly recommended; reduction (or removal) of all fuels, and particularly dead plant material, is usually suggested in close ( < 1.5 m; Fuel-free zone) proximity to a house. Conversely, in an intermediate space (1.5–10 m; Open zone), guidelines focus predominantly on minimizing fuel horizontal and vertical connectivity. Finally, in the outer part of the garden (10–30 m; Tree zone) trees can provide canopy shielding from ember attack and radiant energy, but management of on-ground fuel is still recommended. Evidence from the scientific literature broadly supported these defensible space design elements, although many studies were highly localised. Further empirical and modelling research is required to identify optimal zonation surrounding houses, and to better understand how garden structure, species composition and moisture status affects risk of ignition from embers, radiant heat, and flames.
{"title":"Garden design can reduce wildfire risk and drive more sustainable co-existence with wildfire","authors":"Stefania Ondei, Owen F. Price, David M.J.S. Bowman","doi":"10.1038/s44304-024-00012-z","DOIUrl":"10.1038/s44304-024-00012-z","url":null,"abstract":"Destructive wildfire disasters are escalating globally, challenging existing fire management paradigms. The establishment of defensible space around homes in wildland and rural urban interfaces can help to reduce the risk of house loss and provide a safe area for residents and firefighters to defend the property from wildfire. Although defensible space is a well-established concept in fire management, it has received surprisingly limited scientific discussion. Here we reviewed guidelines on the creation of defensible space from Africa, Europe, North America, South America, and Oceania. We developed a conceptual model of defensible space framed around the key recommended approaches to mitigate fire attack mechanisms, which address fuel types, amount, and spatial distribution. We found that zonation within the defensible space is commonly recommended; reduction (or removal) of all fuels, and particularly dead plant material, is usually suggested in close ( < 1.5 m; Fuel-free zone) proximity to a house. Conversely, in an intermediate space (1.5–10 m; Open zone), guidelines focus predominantly on minimizing fuel horizontal and vertical connectivity. Finally, in the outer part of the garden (10–30 m; Tree zone) trees can provide canopy shielding from ember attack and radiant energy, but management of on-ground fuel is still recommended. Evidence from the scientific literature broadly supported these defensible space design elements, although many studies were highly localised. Further empirical and modelling research is required to identify optimal zonation surrounding houses, and to better understand how garden structure, species composition and moisture status affects risk of ignition from embers, radiant heat, and flames.","PeriodicalId":501712,"journal":{"name":"npj Natural Hazards","volume":" ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44304-024-00012-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1038/s44304-024-00016-9
Trang Minh Duong, Remi Meynadier, Roshanka Ranasinghe, Manuel Andres Diaz Loaiza, Jeremy D. Bricker, Johan Reyns, Arjen Luijendijk, Janaka Bamunawala
Coastal flooding is projected to become more severe over the 21st century, necessitating effective adaptation, which in turn requires detailed local scale information that can only be provided by detailed numerical modelling. The current lack of information on flood protection measures and the high resource requirements of traditional hydrodynamic models presents concurrent challenges for detailed coastal flood modelling. But how comprehensive do the representation of coastal flood defences and hydrodynamic forcing need to be for adequately accurate modelling of coastal flooding? Here, we attempt to answer this question through strategic numerical simulations of the flooding that occurred at Île de Ré (France) during the Xynthia storm (2010), using the flexible mesh model Delft3D FM, with an over-land grid resolution of ~10 m. The model is validated against the flood extents observed in Île de Ré during Xynthia. We use three levels of detail in flood defence representation: a 5 m resolution DEM (i.e. base case DEM), the same 5 m DEM augmented with defences extracted from a 1 m DEM and Google Earth images (i.e. moderately augmented DEM), and the moderately augmented DEM further augmented with in-situ measurements of flood defences (i.e. highly augmented DEM). Simulations with these three DEMs are performed with and without flow-wave coupling (thus, 6 simulations in total), and results are analysed in terms of four flood indicators: maximum flood depths, flood extents, flood current velocities and flood damages. Our analysis indicates that both detailed representation of flood defences and the inclusion of waves have substantial effects on coastal flood modelling at local scale, with the former having a more pronounced effect. The return on the investment in implementing highly detailed in-situ measurements to represent flood defences appears to be low in this case, and adequately accurate results are obtained with a moderately augmented DEM. The combined effect of using the moderately augmented DEM together with waves, relative to using the base case DEM without waves, is to decrease maximum flood depths (up to 2 m), flood extent (by ~10%), maximum current velocities (in ~50% flooded area) and total flood damage (by ~27% or ~€ 188 million).
据预测，21 世纪的沿海洪水将变得更加严重，因此必须采取有效的适应措施，而这反过来 又需要详细的地方尺度信息，只有详细的数值模拟才能提供这些信息。目前，由于缺乏防洪措施方面的信息，而传统的流体力学模型又需要大量的资源，因此，详细的沿岸洪水模拟同时也面临着挑战。但是，要充分准确地模拟沿岸洪水，需要多全面地反映沿岸防洪和水动力强迫呢？在这里，我们试图通过对 Xynthia 风暴期间（2010 年）发生在法国雷岛的洪水进行战略数值模拟来回答这个问题，模拟采用了柔性网格模型 Delft3D FM，陆上网格分辨率约为 10 米。该模型根据 Xynthia 期间在雷岛观测到的洪水范围进行了验证。我们在洪水防御表示中使用了三种详细程度：5 米分辨率的 DEM（即基本情况 DEM）、用从 1 米 DEM 和谷歌地球图像中提取的防御数据增强的相同 5 米 DEM（即适度增强 DEM），以及用现场洪水防御测量数据进一步增强的适度增强 DEM（即高度增强 DEM）。使用这三种 DEM 进行了有流波耦合和无流波耦合的模拟（因此，共进行了 6 次模拟），并根据四项洪水指标对结果进行了分析：最大洪水深度、洪水范围、洪水流速和洪水损失。我们的分析表明，对防洪设施的详细描述和波浪的加入都会对局部尺度的沿岸洪水模 拟产生重大影响，而前者的影响更为明显。在这种情况下，采用非常详细的原位测量来表示洪水防御工事的投资回报率似乎很低， 而采用适度增强的 DEM 可以得到足够精确的结果。与使用不带波浪的基础 DEM 相比，使用带波浪的适度增强 DEM 的综合效果是减少最大洪水深度（达 2 米）、洪水范围（减少约 10%）、最大流速（约 50% 的洪水淹没区）和洪水损失总量（减少约 27% 或约 1.88 亿欧元）。
{"title":"On detailed representation of flood defences and flow-wave coupling in coastal flood modelling","authors":"Trang Minh Duong, Remi Meynadier, Roshanka Ranasinghe, Manuel Andres Diaz Loaiza, Jeremy D. Bricker, Johan Reyns, Arjen Luijendijk, Janaka Bamunawala","doi":"10.1038/s44304-024-00016-9","DOIUrl":"10.1038/s44304-024-00016-9","url":null,"abstract":"Coastal flooding is projected to become more severe over the 21st century, necessitating effective adaptation, which in turn requires detailed local scale information that can only be provided by detailed numerical modelling. The current lack of information on flood protection measures and the high resource requirements of traditional hydrodynamic models presents concurrent challenges for detailed coastal flood modelling. But how comprehensive do the representation of coastal flood defences and hydrodynamic forcing need to be for adequately accurate modelling of coastal flooding? Here, we attempt to answer this question through strategic numerical simulations of the flooding that occurred at Île de Ré (France) during the Xynthia storm (2010), using the flexible mesh model Delft3D FM, with an over-land grid resolution of ~10 m. The model is validated against the flood extents observed in Île de Ré during Xynthia. We use three levels of detail in flood defence representation: a 5 m resolution DEM (i.e. base case DEM), the same 5 m DEM augmented with defences extracted from a 1 m DEM and Google Earth images (i.e. moderately augmented DEM), and the moderately augmented DEM further augmented with in-situ measurements of flood defences (i.e. highly augmented DEM). Simulations with these three DEMs are performed with and without flow-wave coupling (thus, 6 simulations in total), and results are analysed in terms of four flood indicators: maximum flood depths, flood extents, flood current velocities and flood damages. Our analysis indicates that both detailed representation of flood defences and the inclusion of waves have substantial effects on coastal flood modelling at local scale, with the former having a more pronounced effect. The return on the investment in implementing highly detailed in-situ measurements to represent flood defences appears to be low in this case, and adequately accurate results are obtained with a moderately augmented DEM. The combined effect of using the moderately augmented DEM together with waves, relative to using the base case DEM without waves, is to decrease maximum flood depths (up to 2 m), flood extent (by ~10%), maximum current velocities (in ~50% flooded area) and total flood damage (by ~27% or ~€ 188 million).","PeriodicalId":501712,"journal":{"name":"npj Natural Hazards","volume":" ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44304-024-00016-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-19DOI: 10.1038/s44304-024-00024-9
Daniel Nohrstedt, Elena Mondino, Giuliano Di Baldassarre, Charles F. Parker
{"title":"Author Correction: Assessing the myth of disaster risk reduction in the wake of catastrophic floods","authors":"Daniel Nohrstedt, Elena Mondino, Giuliano Di Baldassarre, Charles F. Parker","doi":"10.1038/s44304-024-00024-9","DOIUrl":"10.1038/s44304-024-00024-9","url":null,"abstract":"","PeriodicalId":501712,"journal":{"name":"npj Natural Hazards","volume":" ","pages":"1-1"},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44304-024-00024-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1038/s44304-024-00013-y
Ravi Ranjan, Subhankar Karmakar
India is the worst affected region in the world by tropical cyclones (TCs), causing an average 2% annual GDP loss. TCs instigate many other natural hazards that have a compounding effect on the adversely affected population and present significant challenges to the resilience of emergency response systems and infrastructure. Hence, any risk assessment on TC is inherently multivariate/compound in nature. This study investigates co-occurring wind and rainfall extremes during TCs across India (1979–2020) using a novel quasi-Lagrangian approach, focusing on location-specific hazards. Eastern coastal states and adjacent inland areas experience the highest frequency (≥10 cyclones in 40 years) of concurrent extremes (wind gusts ≥ 16 m/s and rainfall ≥ 18 mm/h). Whereas duration-wise, the eastern coastal states and Gujarat state experience frequent concurrent extremes lasting more than a day annually, with the Krishna–Godavari delta region particularly vulnerable to highly severe events (duration of concurrent extremes ≥ 24 h). This study provides a high-resolution cartographic product of compound hazard from TC-induced extremes for the first time over the entire India, highlighting regional heterogeneity and aiding targeted national-level risk mitigation and adaptation planning.
{"title":"Compound hazard mapping for tropical cyclone-induced concurrent wind and rainfall extremes over India","authors":"Ravi Ranjan, Subhankar Karmakar","doi":"10.1038/s44304-024-00013-y","DOIUrl":"10.1038/s44304-024-00013-y","url":null,"abstract":"India is the worst affected region in the world by tropical cyclones (TCs), causing an average 2% annual GDP loss. TCs instigate many other natural hazards that have a compounding effect on the adversely affected population and present significant challenges to the resilience of emergency response systems and infrastructure. Hence, any risk assessment on TC is inherently multivariate/compound in nature. This study investigates co-occurring wind and rainfall extremes during TCs across India (1979–2020) using a novel quasi-Lagrangian approach, focusing on location-specific hazards. Eastern coastal states and adjacent inland areas experience the highest frequency (≥10 cyclones in 40 years) of concurrent extremes (wind gusts ≥ 16 m/s and rainfall ≥ 18 mm/h). Whereas duration-wise, the eastern coastal states and Gujarat state experience frequent concurrent extremes lasting more than a day annually, with the Krishna–Godavari delta region particularly vulnerable to highly severe events (duration of concurrent extremes ≥ 24 h). This study provides a high-resolution cartographic product of compound hazard from TC-induced extremes for the first time over the entire India, highlighting regional heterogeneity and aiding targeted national-level risk mitigation and adaptation planning.","PeriodicalId":501712,"journal":{"name":"npj Natural Hazards","volume":" ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44304-024-00013-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141489091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1038/s44304-024-00014-x
Amy McGovern, Julie Demuth, Ann Bostrom, Christopher D. Wirz, Philippe E. Tissot, Mariana G. Cains, Kate D. Musgrave
Artificial Intelligence applications are rapidly expanding across weather, climate, and natural hazards. AI can be used to assist with forecasting weather and climate risks, including forecasting both the chance that a hazard will occur and the negative impacts from it, which means AI can help protect lives, property, and livelihoods on a global scale in our changing climate. To ensure that we are achieving this goal, the AI must be developed to be trustworthy, which is a complex and multifaceted undertaking. We present our work from the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES), where we are taking a convergence research approach. Our work deeply integrates across AI, environmental, and risk communication sciences. This involves collaboration with professional end-users to investigate how they assess the trustworthiness and usefulness of AI methods for forecasting natural hazards. In turn, we use this knowledge to develop AI that is more trustworthy. We discuss how and why end-users may trust or distrust AI methods for multiple natural hazards, including winter weather, tropical cyclones, severe storms, and coastal oceanography.
人工智能的应用正在天气、气候和自然灾害领域迅速扩展。人工智能可用于协助预测天气和气候风险，包括预测灾害发生的几率及其负面影响，这意味着在不断变化的气候中，人工智能可在全球范围内帮助保护生命、财产和生计。为确保实现这一目标，我们必须开发出值得信赖的人工智能，这是一项复杂而多方面的工作。我们将介绍美国国家科学基金会人工智能研究所（NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography，AI2ES）在天气、气候和沿海海洋学领域值得信赖的人工智能（AI2ES）方面所做的工作。我们的工作深度融合了人工智能、环境和风险交流科学。这包括与专业终端用户合作，调查他们如何评估人工智能方法在预测自然灾害方面的可信度和实用性。反过来，我们利用这些知识来开发更值得信赖的人工智能。我们将讨论终端用户如何以及为什么会信任或不信任人工智能方法来预测多种自然灾害，包括冬季天气、热带气旋、强风暴和沿岸海洋学。
{"title":"The value of convergence research for developing trustworthy AI for weather, climate, and ocean hazards","authors":"Amy McGovern, Julie Demuth, Ann Bostrom, Christopher D. Wirz, Philippe E. Tissot, Mariana G. Cains, Kate D. Musgrave","doi":"10.1038/s44304-024-00014-x","DOIUrl":"10.1038/s44304-024-00014-x","url":null,"abstract":"Artificial Intelligence applications are rapidly expanding across weather, climate, and natural hazards. AI can be used to assist with forecasting weather and climate risks, including forecasting both the chance that a hazard will occur and the negative impacts from it, which means AI can help protect lives, property, and livelihoods on a global scale in our changing climate. To ensure that we are achieving this goal, the AI must be developed to be trustworthy, which is a complex and multifaceted undertaking. We present our work from the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES), where we are taking a convergence research approach. Our work deeply integrates across AI, environmental, and risk communication sciences. This involves collaboration with professional end-users to investigate how they assess the trustworthiness and usefulness of AI methods for forecasting natural hazards. In turn, we use this knowledge to develop AI that is more trustworthy. We discuss how and why end-users may trust or distrust AI methods for multiple natural hazards, including winter weather, tropical cyclones, severe storms, and coastal oceanography.","PeriodicalId":501712,"journal":{"name":"npj Natural Hazards","volume":" ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44304-024-00014-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141489063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-19DOI: 10.1038/s44304-024-00019-6
Stephen M. Strader, Victor A. Gensini, Walker S. Ashley, Amanda N. Wagner
Tornado risk, as determined by the occurrence of atmospheric conditions that support tornado incidence, has exhibited robust spatial trends in the United States Southern Plains and Mid-South during recent decades. The consequences of these risk changes have not been fully explored, especially in conjunction with growing societal vulnerability. Herein, we assess how changes in risk and vulnerability over the last 40 years have collectively and individually altered tornado-housing impact potential. Results indicate that escalating vulnerability and exposure have outweighed the effects of spatially changing risk. However, the combination of increasing risk and exposure has led to a threefold increase in Mid-South housing exposure since 1980. Though Southern Plains tornado risk has decreased since 1980, amplifying exposure has led to more than a 50% increase in mean annual tornado-housing impact potential across the region. Stakeholders should use these findings to develop more holistic mitigation and resilience-building strategies that consider a dynamically changing tornado disaster landscape.
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