Weather-Related Disaster in a Diverse Cohort of Aging Adults

As climate change contributes to increasing frequency and intensity of weather-related disasters, it is critical to define characteristics that increase risk of poor health outcomes during and after events. Given the aging of the United States (US) population and over-representation of older adults in disaster-prone areas, disaster-related impacts on older adults present a growing public health challenge. We linked data from the REGARDS study, a cohort of 30,107 Black and White adults (mean age 65 years at baseline, 2003-2007), with community data from the National Establishment Time Series database and longitudinal weather-related disaster data from the Spatial Hazard Events and Losses Database for the US. We calculated disaster exposure for each year for the county in which the respondents lived from 2003 – 2015: 84% of county-years showed at least some impacts, including 16% of counties experiencing medium impacts ($10- $50 property damage per capita or 2 fatalities) and 12% severe (greater than $50 per capital or 3 fatalities); this mirrors that of the continental US (77% some impact, 15% medium, 13% severe). REGARDS participants exposed to moderate or severe disasters were more likely to be Black and low socioeconomic status compared to those who were not exposed. For community characteristics, higher disaster exposure was associated with a greater density of resources including ambulatory care, food stores, social services, and destinations for daily living. Our approach showcases how disaster preparedness systems need better data about specific individual-and community-level factors that increase risk among older adults to better serve communities.

The Racially Unequal Impacts of Disasters and Federal Recovery Assistance on Local Self-Employment Rates

This study examines racial inequalities in changing self-employment rates associated with natural hazard impacts and federal recovery assistance in ethnoracially diverse metropolitan counties between 2000 and 2010. It advances the viewpoint that such inequalities can stem from hoarded opportunities tied to white privilege in addition to commonly highlighted social vulnerabilities tied to racial inequities and exclusion. To test that proposition, we conduct change-score analyses using county-level data from the US Census Bureau, the Spatial Hazard Events and Losses Database for the United States, and the Federal Emergency Management Agency. Results indicate that (a) overall, self-employment rates increase with local property damages from natural hazards, especially among white and Latino workers; (b) those increases are largely explained by the amount of federal public assistance received for disaster recovery, not property damages themselves; and (c) white workers experience the most positive and consistent increases in self-employment from federal recovery assistance. Implications for understanding racial inequities stemming from current and future disasters and government assistance are discussed.

Impact of information presentation on interpretability of spatial hazard information: Lessons from a study in avalanche safety

Avalanche warning services publish avalanche condition reports, often called avalanche bulletins, to help backcountry recreationists make informed risk management decisions about when and where to travel in avalanche terrain. To be successful, the information presented in bulletins must be properly understood and applied prior to entering avalanche terrain. However, few avalanche bulletin elements have been empirically tested for their efficacy in communicating hazard information. The objective of this study is to explicitly test the effectiveness of three different graphics representing the aspect and elevation of avalanche problems on users’ ability to apply the information. To address this question, we conducted an online survey that presented participants with one of three graphic renderings of avalanche problem information and asked them to rank a series of route options in order of their exposure to the described hazard. Following completion of route ranking tasks, users were presented with all three graphics and asked to rate how effective they thought the graphics were. Our analysis dataset included responses from 3,056 backcountry recreationists with a variety of backgrounds and avalanche safety training levels. Using a series of generalized linear mixed effects models, our analysis shows that a graphic format that combines the aspect and elevation information for each avalanche problem is the most effective graphic for helping users understand the avalanche hazard conditions because it resulted in higher success in picking the correct exposure ranking, faster completion times, and was rated by users to be the most effective. These results are consistent with existing research on the impact of graphics on cognitive load and can be applied by avalanche warning services to improve the communication of avalanche hazard to readers of their avalanche bulletins.

Creating Immersive Virtual Environments Based on Open Geospatial Data and Game Engines

Modern game engines like Unity allow users to create realistic 3D environments containing terrains as well as natural and artificial objects easily and swiftly. In addition, recent advances of game engine capabilities enable effortless implementation of virtual reality (VR) compatibility. 3D environments created with VR compatibility can be experienced from an egocentric and stereoscopic perspective that surpasses the immersion of the ‘classical’ screen-based perception of 3D environments. Not only game developers benefit from the possibilities provided by game engines. The ability to use geospatial data to shape virtual 3D environments opens a multitude of possibilities for geographic applications, such as construction planning, spatial hazard simulations or representation of historical places. The multi-perspective, multimodal reconstruction of three-dimensional space based on game engine technology today supports the possibility of linking different approaches of geographic work more closely. Free geospatial data that can be used for spatial reconstructions is provided by numerous national and regional official institutions. However, the file format of these data sources is not standardized and game engines only support a limited number of file formats. Therefore, format transformation is usually required to apply geospatial data to virtual 3D environments. This paper presents several workflows to apply digital elevation data and 3D city model data from OpenStreetMap and the Open.NRW initiative to Unity-based 3D environments. Advantages and disadvantages of different sources of geospatial data are discussed. In addition, implementation of VR compatibility is described. Finally, benefits of immersive VR implementation and characteristics of current VR hardware are discussed in the context of specific geographic application scenarios.

Rapid Flood Progress Monitoring in Cropland with NASA SMAP

Research in different agricultural sectors, including in crop loss estimation during flood and yield estimation, substantially rely on inundation information. Spaceborne remote sensing has widely been used in the mapping and monitoring of floods. However, the inability of optical remote sensing to cloud penetration and the scarcity of fine temporal resolution SAR data hinder the application of flood mapping in many cases. Soil Moisture Active Passive (SMAP) level 4 products, which are model-driven soil moisture data derived from SMAP observations and are available at 3-h intervals, can offer an intermediate but effective solution. This study maps flood progress in croplands by incorporating SMAP surface soil moisture, soil physical properties, and national floodplain information. Soil moisture above the effective soil porosity is a direct indication of soil saturation. Soil moisture also increases considerably during a flood event. Therefore, this approach took into account three conditions to map the flooded pixels: a minimum of 0.05 m3m−3 increment in soil moisture from pre-flood to post-flood condition, soil moisture above the effective soil porosity, and the holding of saturation condition for the 72 consecutive hours. Results indicated that the SMAP-derived maps were able to successfully map most of the flooded areas in the reference maps in the majority of the cases, though with some degree of overestimation (due to the coarse spatial resolution of SMAP). Finally, the inundated croplands are extracted from saturated areas by Spatial Hazard Zone areas (SHFA) of Federal Emergency Management Agency (FEMA) and cropland data layer (CDL). The flood maps extracted from SMAP data are validated with FEMA-declared affected counties as well as with flood maps from other sources.

On the relevance of extremal dependence for spatial statistical modelling of natural hazards

Natural hazard loss portfolios with exposure over a region are sensitive to the dependency between extreme values of the key hazard variable at different spatial locations. It is therefore important to correctly identify and quantify dependency to avoid poor quantification of risk. This study demonstrates how bivariate extreme value tail dependency methods can be used together in a novel way to explore and quantify extremal dependency in spatial hazard fields. A relationship between dependency and loss is obtained by deriving how the probability distribution of conceptual loss depends on the tail dependency coefficient. The approaches are illustrated by applying them to 6103 historical European windstorm footprints (spatial maps of 3-day maximum gust speeds). We find there is little evidence of asymptotic extremal dependency in windstorm footprints. Furthermore, empirical extremal properties and conceptual loss distributions between pairs of locations are shown to be well reproduced using Gaussian copulas but not by extremally-dependent models such as Gumbel copulas. It is conjectured that the lack of asymptotic dependence is a generic property of turbulent flows, which may extend to other spatially continuous hazards such as heat waves and air pollution. These results motivate the potential of using Gaussian process (geostatistical) models for efficient simulation of hazard fields.