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An increasing amount of California's landscape has burned in wildfires in recent decades, in conjunction with increasing temperatures and vapor pressure deficit due to climate change. As the wildland-urban interface expands, more people are exposed to and harmed by these extensive wildfires, which are also eroding the resilience of terrestrial ecosystems. With future wildfire activity expected to increase, there is an urgent demand for solutions that sustain healthy ecosystems and wildfire-resilient human communities. Those who manage disaster response, landscapes, and biodiversity rely on mapped projections of how fire activity may respond to climate change and other human factors. California wildfire is complex, however, and climate-fire relationships vary across the state. Given known geographical variability in drivers of fire activity, we asked whether the geographical extent of fire models used to create these projections may alter the interpretation of predictions. We compared models of fire occurrence spanning the entire state of California to models developed for individual ecoregions and then projected end-of-century future fire patterns under climate change scenarios. We trained a Maximum Entropy model with fire records and hydroclimatological variables from recent decades (1981 to 2010) as well as topographic and human infrastructure predictors. Results showed substantial variation in predictors of fire probability and mapped future projections of fire depending upon geographical extents of model boundaries. Only the ecoregion models, accounting for the unique patterns of vegetation, climate, and human infrastructure, projected an increase in fire in most forested regions of the state, congruent with predictions from other studies. [ABSTRACT FROM AUTHOR]

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Disaster preparedness by corporate organizations is receiving international attention and is being implemented. However, the efforts taken by many local governments for facilitating corporate disaster preparedness are insufficient. Planning of corporate disaster prevention policies by local governments involves the following questions: What are the experiences of local governments in promoting corporate disaster prevention? How do businesses engage in natural disaster preparedness? This study compiled the corporate disaster prevention and relief initiatives of the California county governments' disaster prevention and relief programs and examined the corporate disaster prevention-related actions of each county government in terms of disaster prevention and relief planning. This study explored the development, nature, and operation of corporate disaster preparedness programs by the California county government and demonstrated the direction and practices of local governments and corporations for promoting disaster preparedness and the construction of response systems. [ABSTRACT FROM AUTHOR]

The evaluation of communities' vulnerability to prolonged power outages offers valuable insights for prioritizing improvements in infrastructure resilience, thereby alleviating societal consequences. This study proposes a data-driven approach aiming at developing a Social Vulnerability Index (SoVI) to prolonged power outages leveraging three county-level datasets in California including (1) demographic features, (2) power outage factors, and (3) backup power factors. Furthermore, the study conducts a sensitivity analysis on three distinct datasets under two scenarios (Scenario 1: the current SoVI in 2022, Scenario 2: the prediction of SoVI in the year 2030). The results of Scenario 1 indicate that the counties with more affected customers, the number of power outages, and less education attainment tend to be more vulnerable to power outages in 2022. Scenario 1 reveals that the number of affected customers and power outages are the primary features influencing around 29% and 18% of counties, while educational attainment, public Electric Vehicles (EVs) chargers, and homes with rooftop photovoltaic (PV) substantially impact approximately 32%, 11%, and 8% of counties, respectively. However, in Scenario 2, crucial factors affecting the anticipated SoVI in 2030 include public EV chargers, houses with rooftop PV, power outages, and adults living alone. In contrast to Scenario 1, the prevalence of adults living alone has emerged as a notable factor impacting SoVI in 2030, while both scenarios underscore the pivotal role of EV chargers in influencing SoVI concerning power outages. The proposed SoVI facilitates informed policy decisions and infrastructure improvements in energy resilience, resource allocation, and disaster preparedness, contributing valuable insights for targeted interventions in these domains. • A data-driven approach was proposed to quantify the social vulnerability index (SoVI) to power outages. • This paper applies Principal Component Analysis (PCA) to identify the most correlated features from county-level datasets. • The paper conducts a sensitivity analysis to predict SoVI in2030 and evaluate how counties' dynamics change over time. • This paper provides insights into how policymakers can reduce the social vulnerability to power outages in California. [ABSTRACT FROM AUTHOR]

Advanced Quantitative Precipitation Information (AQPI) is a synergistic project that combines observations and models to improve monitoring and forecasts of precipitation, streamflow, and coastal flooding in the San Francisco Bay Area. As an experimental system, AQPI leverages more than a decade of research, innovation, and implementation of a statewide, state-of-the-art network of observations, and development of the next generation of weather and coastal forecast models. AQPI was developed as a prototype in response to requests from the water management community for improved information on precipitation, riverine, and coastal conditions to inform their decision-making processes. Observation of precipitation in the complex Bay Area landscape of California's coastal mountain ranges is known to be a challenging problem. But, with new advanced radar network techniques, AQPI is helping fill an important observational gap for this highly populated and vulnerable metropolitan area. The prototype AQPI system consists of improved weather radar data for precipitation estimation; additional surface measurements of precipitation, streamflow, and soil moisture; and a suite of integrated forecast modeling systems to improve situational awareness about current and future water conditions from sky to sea. Together these tools will help improve emergency preparedness and public response to prevent loss of life and destruction of property during extreme storms accompanied by heavy precipitation and high coastal water levels—especially high-moisture laden atmospheric rivers. The Bay Area AQPI system could potentially be replicated in other urban regions in California, the United States, and worldwide. [ABSTRACT FROM AUTHOR]