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This paper examines the relationship between daily carbon emissions for California's savanna and forest wildfires and regional meteorology over the past 18 years. For each fuel type, the associated weather [daily maximum wind, daily vapor pressure deficit (VPD), and 30-day-prior VPD] is determined for all fire days, the first day of each fire, and the day of maximum emissions of each fire at each fire location. Carbon emissions, used as a marker of wildfire existence and growth, for both savanna and forest wildfires are found to vary greatly with regional meteorology, with the relationship between emissions and meteorology varying with the amount of emissions, fire location, and fuel type. Weak emissions are associated with climatologically typical dryness and wind. For moderate emissions, increasing emissions are associated with higher VPD from increased warming and only display a weak relationship with wind speed. High emissions, which encompass ∼85% of the total emissions but only ∼4% of the fire days, are associated with strong winds and large VPDs. Using spatial meteorological composites for California subregions, we find that weak-to-moderate emissions are associated with modestly warmer-than-normal temperatures and light winds across the domain. In contrast, high emissions are associated with strong winds and substantial temperature anomalies, with colder-than-normal temperatures east of the Sierra Nevada and warmer-than-normal conditions over the coastal zone and the interior of California. Significance Statement: The purpose of this work is to better understand the influence of spatially and temporally variable meteorology and spatially variable surface fuels on California's fires. This is important because much research has focused on large climatic scales that may dilute the true influence of weather (here, high winds and dryness) on fire growth. We use a satellite-recorded fire emissions dataset to quantify daily wildfire existence and growth and to determine the relationship between regional meteorology and wildfires across varying emissions in varying fuels. The result is a novel view of the relationship between California wildfires and rapidly variable, regional meteorology. [ABSTRACT FROM AUTHOR]

In recent works by Yang et al. (2017a, 2017b), and Yang et al. (2019), geographical, temporal, and sequential deterministic reconciliation of hierarchical photovoltaic (PV) power generation have been considered for a simulated PV dataset in California. In the first two cases, the reconciliations were carried out in spatial and temporal domains separately. To further improve forecasting accuracy, in the third case these two reconciliation approaches were applied sequentially. During the replication of the forecasting experiment, some issues emerged about non-negativity and coherency (in space and/or in time) of the sequentially reconciled forecasts. Furthermore, while the accuracy improvement of the considered approaches over the benchmark persistence forecasts is clearly visible at any data granularity, we argue that an even better performance may be obtained by a thorough exploitation of spatio-temporal hierarchies. To this end, in this paper the spatio-temporal point forecast reconciliation approach is applied to generate non-negative, fully coherent (both in space and time) forecasts. New spatio-temporal reconciliation approaches are adopted, exploiting for the first time some relationships between two-step, iterative and simultaneous spatio-temporal reconciliation procedures. Non-negativity issues of the final reconciled forecasts are discussed and correctly dealt with in a simple and effective way. The spatio-temporal reconciliation procedures are applied to the base forecasts with forecast horizon of 1 day, of PV generated power at different time granularities (1 h to 1 day), of a geographical hierarchy consisting of 324 series along 3 levels. The normalized Root Mean Square Error (nRMSE) and the normalized Mean Bias Error are used to measure forecasting accuracy, and a statistical multiple comparison procedure is performed to rank the approaches. In addition to assuring full coherence and non-negativity of the reconciled forecasts, the results show that for the considered dataset, spatio-temporal forecast reconciliation significantly improves on the sequential procedures proposed by Yang et al. (2019), at any level of the spatial hierarchy and for any temporal granularity. For example, the forecasted hourly PV generated power by the new spatio-temporal forecast reconciliation approaches improve on the NWP 3TIER forecasts in a range from 4.7% to 18.4% in terms of nRMSE. • New approach to produce fully coherent solar forecasts both in space and time. • Complete previous discussions on deterministic solar forecast reconciliation. • Avoid negative forecasts and preserve full spatio-temporal coherence. • Significant improvement of the forecast accuracy through hierarchical forecasting. • Forecast skills over NWP hourly forecasts between 4.7% and 18.4%. [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]

We propose a new machine learning‐based method for nowcasting earthquakes to image the time‐dependent earthquake cycle. The result is a timeseries that may correspond to the process of stress accumulation and release. The timeseries are constructed by using principal component analysis of regional seismicity. The patterns are found as eigenvectors of the cross‐correlation matrix of a collection of seismicity timeseries in a coarse grained regional spatial grid (pattern recognition via unsupervised machine learning). The eigenvalues of this matrix represent the relative importance of the various eigenpatterns. Using the eigenvectors and eigenvalues, we compute the weighted correlation timeseries of the regional seismicity. This timeseries has the property that the weighted correlation generally decreases prior to major earthquakes in the region, and increases suddenly just after a major earthquake occurs. As in a previous paper (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020ea001097), we find that this method produces a nowcasting timeseries that resembles the hypothesized regional stress accumulation and release process characterizing the earthquake cycle. We then address the problem of whether the timeseries contain information regarding future large earthquakes. For this, we compute a receiver operating characteristic and determine the decision thresholds for several future time periods of interest (optimization via supervised machine learning). We find that signals can be detected that can be used to characterize the information content of the timeseries. These signals may be useful in assessing present and near‐future seismic hazards. Plain Language Summary: Major earthquakes on fault systems in a tectonically active region are thought to occur in approximately repetitive cycles as a result of the buildup and release of tectonic forces (stress). Nowcasting is a technique adopted from weather, finance, and other fields that use readily observable proxy data to represent the unobservable stress accumulation process of interest. This paper presents a method that computes a timeseries representing the weighted correlation of small earthquake activity in the California region from 1950 to 2020. Prior to major magnitude M > 7 earthquakes, the timeseries trends toward lower values. Just after the earthquake occurs, the timeseries increases suddenly in association with the earthquake, before resuming its gradual trend toward lower values. Plotting the timeseries on an inverted scale, one sees a cyclic behavior that strongly resembles the hypothesized earthquake cycle. In principle, we can therefore use this timeseries for nowcasting, as a proxy for stress accumulation and release. Using methods of signal detection first developed for radar by the British in the 1940's, we find that the timeseries contain information about future large earthquakes that can be used for hazard assessment. Key Points: The current state of the earthquake cycle of tectonic stress accumulation and release is unobservable with existing methodsWe show that readily observable small earthquake correlations can be used to nowcast the current state of the earthquake cycleMachine learning techniques indicate that signals corresponding to future large earthquakes can be detected in a correlation time series [ABSTRACT FROM AUTHOR]

This paper proposes a modeling approach for characterizing the probability of wildfire ignition caused by faults on power distribution systems. The proposed model serves as a starting point in research literature to illustrate, from an analytical perspective, the many factors that influence wildfire ignitions in power distribution systems. This paper presents the series of events that leads to power-system-related wildfire ignitions, and characterizes the wildfire ignition probability as a combination of the probability that a fault occurs along a power distribution line segment, and the probability that the fault results in the sustained ignition of a vegetation fuel bed surrounding the line. The proposed model integrates a variety of data including environmental conditions, power system protection settings, and power system line flows. A case study is performed on a test 33-bus distribution system using observed historical weather data from a high-threat fire district in California. The California case study is utilized to investigate the effects of three primary factors (wind speed, line congestion, and protection settings) on wildfire ignition probability. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to show how Californian libraries deliver value, by taking a preliminary value framework previously developed from a study of academic literature, and applying it to the public library landscape in California. A "bricolage" approach was used to develop an evidence-based framework of value and subsequently to gather data about Californians' use of and attitudes about public libraries. The framework was developed through an extended literature review and applied across data gathered through a survey of residents, interviews with library stakeholders, a review of statistics on library usage, and a review of the outcomes of grant-funded programs in California public libraries. The most significant outcome of our project is a unique value proposition for California's public libraries. These libraries deliver value for individuals, families, groups, communities, and society as a whole. They provide opportunities and support for learning and knowledge development, health and wellbeing, community development, and economic development. Value is delivered through a unique combination of resources, people, and space. California's public libraries are largely offering services that are valued by the public; however, public awareness of the range and nature of services offered remains lower than desired. Practitioners can use the evidence-based framework and the unique value proposition to better understand the aspects of their library services that offer most value to their communities, and to make more persuasive cases for library support. This paper is the first to use an evidence-based framework of value in assessing the impact of California's public libraries. [ABSTRACT FROM AUTHOR]

Simple Summary: Excessive use of groundwater resources in the Central Valley in California has led to major land sinking over the years. In this study, we rely on satellite imagery to monitor and assess the extent of this sinking. Specifically, we use images from a satellite that emits microwaves which allows us to directly obtain the deformation at a specific time. Then, we apply a machine learning algorithm to the resulting data in an attempt to effectively predict short-term future deformation. We find that the algorithm applied has a low error when compared to the actual data. This shows that machine learning algorithms could be incorporated into models that assess potential hazards associated with land sinking. Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers' integrity. [ABSTRACT FROM AUTHOR]

Conceptualizing and measuring trends in segregation in higher education is difficult as both vertical and horizontal sorting is prevalent and patterns vary across racial groups. In this paper, we measure various trends in racial segregation in California for 20 years. We find significant sorting by race both between and within sectors of higher education, though the overall levels of segregation are lower in California's colleges than they are in California's high schools. These trends have and remained relatively stable over time. We also find important differences between groups. We see that most Latinx-White and Black-White segregation is due to students attending different schools within the same sector, while Asian-White segregation is increasingly due to students attending schools in different sectors. We also find evidence that policy and structural changes, such as opening a new campus, can affect patterns of segregation across and within sectors. [ABSTRACT FROM AUTHOR]

California has experienced one of the most intense and prolonged droughts in its history in the last decade, prompting the state government to undertake a series of policy responses. This paper compares the impact of the 2015 urban water-use reduction mandate with respect to pre-policy voluntary conservation efforts and post-policy water consumption trends by examining utility-level water demand dynamics of the Metropolitan Water District in Southern California. The findings overall indicate that despite voluntary measures resulting in a gradual decline in urban water-use, the introduction of the regulatory stick achieved swift reduction targets that remained even after removal of the mandatory policy. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to describe the COVID-19 risk mitigation strategies implemented in California prisons and the impact of these policies on the mental health of incarcerated women. Design/methodology/approach: The authors conducted semi-structured qualitative interviews with ten women who were over the age of 50 and/or had a chronic illness and had been incarcerated in California prisons during the COVID-19 pandemic. The authors also interviewed ten health-care providers working in California jails or prisons during the pandemic. Interviews were analyzed using a grounded theory coding framework and triangulated with fieldnotes from ethnographic observations of medical and legal advocacy efforts during the pandemic. Findings: Participants described being locked in their cells for 23 hours per day or more, often for days, weeks or even months at a time in an effort to reduce the spread of COVID-19. For many participants, these lockdowns and the resulting isolation from loved ones both inside and outside of the prison were detrimental to both their physical and mental health. Participants reported that access to mental health care for those in the general population was limited prior to the pandemic, and that COVID-19 risk mitigation strategies, including the cessation of group programs and shift to cell-front mental health services, created further barriers. Originality/value: There has been little qualitative research on the mental health effects of the COVID-19 pandemic on incarcerated populations. This paper provides insight into the mental health effects of both the COVID-19 pandemic and COVID-19 risk mitigation strategies for the structurally vulnerable older women incarcerated in California prisons. [ABSTRACT FROM AUTHOR]

The article presents the discussion on major tectonic event occurred since the late Cretaceous. Topics include assumptions determining the timing from the river profile forms being summarized in the original paper and again in Gabet's comment; and rock uplifted during the pulse being rapidly incised for bringing the river elevation, steepness, and erosion ratign back to the pre-tilt equilibrium condition.

Unnatural disasters, such as floods and wildfires, are making many areas difficult to inhabit. For relocation to unfold in a safer and more equitable way, it must be done in a manner that (1) aligns with community values in each locality, (2) navigates legal barriers to managed retreat, and (3) creates blue-sky funding for adaptation, including managed retreat planning and implementation. This paper argues that developers continuing to build in climate vulnerable areas could and should help cover the risk of their actions. Part I lays out the legal importance of planning for retreat, as well as the need for initial funding for community-level planning and experienced personnel. Few scholars have explored options for municipalities to fund difficult conversations about and initial steps towards managing retreat. Thus, Part II explores how community benefit agreements between communities and developers in climate-vulnerable areas could play a role in bridging the gap between research and implementation. Part II also introduces the idea of a climate resilience development fee, which could provide the needed blue-sky funding to implement managed retreat. Part III analyzes the validity of a climate resilience impact fee in California and Florida, two states in which the consequences of climate change are severe enough for communities to begin to consider managed retreat. [ABSTRACT FROM AUTHOR]

Latinx families who become involved with the child welfare system are expected to engage with an unfamiliar government entity to avoid separation of their families. Unfortunately, many of these families struggle with receiving appropriate services due to their inability to fully understand English. This paper explores the experiences of bilingual child welfare social workers who serve Latinx families throughout California. The results of the study highlight the multiple areas that child welfare agencies need to address and develop to improve their existing strategies--and develop new ones--to mitigate language barriers with Latinx families who have limited English proficiency. [ABSTRACT FROM AUTHOR]

The existing machine learning (ML) models for vibration-based damage assessments often use highly compressed features or are restricted to preprocessed signals with fixed durations and sampling rates. Additionally, the learning capacities of ML models and computational resources are limited, which restricts using raw signals as direct input. This paper studied Mel filter banks (MFBs) for seismic signal processing, inspired by speech recognition technology. It is argued that the same filter designs in audio engineering may not be appropriate for seismic records, and therefore, a customized filter bank formulation was developed. Hybrid deep learning models for rapid assessments (HyDRA) were introduced as multibranch neural network architectures that enable end-to-end training for different types of processed vibration data structures. Moreover, the performance-based earthquake engineering (PBEE) equation was adjusted to integrate ML model uncertainties for probabilistic assessments. The proposed concepts were validated in a case study based on a data set of 32,400 nonlinear time-history analyses of a highway bridge in California. Insights and guidelines are provided for optimum filter design based on 5,184 experiments. Several HyDRA architectures were compared with benchmark models. The optimized MFB feature type outperformed features obtained from continuous wavelet transform and a stacked vector of conventional earthquake engineering indexes. A Bayesian variant of HyDRA was investigated to showcase its integration in the modified PBEE equation. Adopting custom filter banks with the HyDRA architecture enables effective feature extraction from raw vibration records by diversifying feature space and preserving information in the time and frequency domains. [ABSTRACT FROM AUTHOR]

In this paper, we report on the scaffold-level assembled genome for the federally endangered, California endemic crustacean Lepidurus packardi (the Vernal Pool Tadpole Shrimp). L. packardi is a key food source for other conserved California species including the California Tiger Salamander Ambystoma californiense. It faces significant habitat loss and fragmentation as vernal pools are threatened by urbanization, agricultural conversion, and climate change. This resource represents the first scaffold-level genome of any Lepidurus species. The assembled genome spans 108.6 Mbps, with 6 chromosome-length scaffolds comprising 71% of total genomic length and 444 total contigs. The BUSCO score for this genome is 97.3%, suggesting a high level of completeness. We produced a predicted gene set for this species trained on the Daphnia magna set of genes and predicted 17,650 genes. These tools can aid researchers in understanding the evolution and adaptive potential of alternative reproductive modes within this species. [ABSTRACT FROM AUTHOR]

Traffic flow prediction has been essential for traffic management and road network planning. However, the complex urban road network and the strong spatialtemporal correlation of traffic flow data make this problem difficult. Existing prediction methods cannot fully utilize the spatial-temporal correlations in traffic flow data. Therefore, we propose a deep learning model called ResGAT-ABiGRU which combines Residual Network (ResNet), Graph Attention Network (GAT), Attention Mechanism, and the Bidirectional Gated Recurrent Unit (BiGRU). Firstly, GAT is used to capture the spatial correlations of traffic flow data, and then the time characteristics are extracted by Bidirectional GRU. Secondly, the ResNet module stacks multiple GAT layers and designs the attention mechanism to assign weights for different flow sequences to further capture spatial relations. Finally, we obtain the output through the fully connected layers. Validation traffic data from California, USA, is used for verification. The results show that the ResGAT-ABiGRU model proposed in this paper has higher prediction accuracy. Compared the model's performance with the Gated Recurrent Unit (GRU) baseline model, and the root means square error (RMSE) is reduced by 22.75%, and compared to the T-gcn model, the root mean square error is reduced by 3.29%. [ABSTRACT FROM AUTHOR]

Background: Research advancements in Alzheimer's disease (AD) raise opportunities for genetic testing to improve diagnostic and risk assessment. Despite emerging developments, it is unclear how geriatricians perceive the potential clinical and personal utility of genetic testing for their patients. Geriatricians' perspectives are essential to understanding potential ethical, policy, and clinical challenges.Objective: In this paper, we report on geriatricians' perspectives on the utility of genetic testing for AD.Methods: Semi-structured interviews with California geriatricians within different practices settings to collect and characterize their perspectives on genetic testing for AD. We used an adapted grounded theory approach to analyze recorded and transcribed interviews.Results: We identified geriatricians' (n = 10) perspectives on the clinical and personal utility of testing, alongside their views on clinical care approaches for older adults. Geriatricians perceived minimal clinical utility of genetic testing for AD, though that may change with the availability of disease-modifying therapies. Yet, they recognized the potential personal utility of testing (e.g., assisting with future financial planning). Finally, geriatricians expressed concerns regarding patients' anxiety from learning about genetic status, particularly through direct-to-consumer (DTC) testing.Conclusion: Our data highlight that the decision to order genetic testing requires clinical and ethical considerations, including balancing limited clinical utility with the potential personal utility. Although DTC testing is available, geriatricians perceive that they have an important role in managing the decision to test and interpreting the results. Further research is needed to inform policy and ethical guidelines to support geriatricians' critical role to counsel patients considering clinical and DTC genetic testing. [ABSTRACT FROM AUTHOR]

This paper presents a new direct maximum power point tracking (MPPT) with a reference voltage ( V r e f ) based on the metaheuristic earthquake algorithm (EA) where the optimization variable is the V r e f for hard-switching converters. The efficiency and performance of EA-MPPT- V r e f is compared with the perturb-and-observe (P&O) counterpart technique due to the fact that it is widely used for commercial products. Static and dynamic responses for both MPPT strategies are evaluated, which correspond to steady-state oscillations when they are near the maximum power point (MPP), and the tracking-speed, respectively. The efficiency was evaluated with the EN 50530 standard. The results show that the new MPPT proposed is a competitive method using the EA to obtain the optimal voltage reference. From static results, EA-MPPT VP presented a better efficiency of 5.13% and 3.23% for European and California energy commission (CEC) efficiency, respectively. Whereas, from dynamic results, MPPT- V r e f techniques presented an efficiency from 95.13% to 99.91%, and 99.01% to 99.91% of the total power of the PV system for P&O and EA strategies, respectively. [ABSTRACT FROM AUTHOR]

Despite lower‐than‐average total precipitation in the western states of the U.S., the 2021 "precipitation roller coaster" defined as large precipitation swings has pointed to a strong hydroclimatic intensity (HYINT). Here we examine the 2021 HYINT using an index—a product of the average precipitation intensity (INT) and dry spell length (DSL). HYINT exhibited an extremely high value in the western U.S. in 2021. INT and DSL contribute differently to the 2021 HYINT, with large spatial variability. Overall, the 2021 extreme HYINT in central California and Utah is tied more to large INT, than to DSL. Meanwhile, the historical trends in INT and DSL may have contributed to the extreme 2021 HYINT event. The fraction of attributable risk framework reveals that the 2021 extreme HYINT is more likely to occur with anthropogenic forcing (e.g., 7.3 times more likely for HYINT exceeding 1.3) than natural forcing alone. Plain Language Summary: The western U.S. is a hotspot for studying climate change impacts on the hydrological cycle. Despite lower‐than‐average total precipitation in 2021, the contrasting dryness and wetness in the western U.S. has been widely reported as a "precipitation roller coaster." In this paper we quantified the "precipitation roller coaster" using an index (hydroclimatic intensity [HYINT])—a product of average precipitation intensity during wet days and dry spell length (DSL). The study found that the 2021 extreme HYINT event was largely attributable to the combined impacts of precipitation intensity and DSL in California and Utah, with precipitation intensity playing a more important role. In contrast, the 2021 precipitation event in other western states exhibited divergent contributions from precipitation intensity and DSL. The southwestern U.S. has been identified as a hotspot for increasing HYINT, which is tied more to the increasing DSL than the precipitation intensity. The trends in DSL and precipitation intensity may have played a key role in driving the 2021 extreme HYINT event. Using climate model experiments with and without anthropogenic forcing, an extreme HYINT event in the western U.S. is more likely to occur with anthropogenic forcing. Key Points: Hydroclimatic intensity (HYINT) exhibited extremely high values in parts of the western U.S. in 2021, mainly caused by average precipitation intensityHYINT shows a significant rising trend in most of the southwestern U.S. mainly tied to a rising dry spell length trendThe extreme HYINT event is more likely to occur under anthropogenic forcing than natural forcing alone [ABSTRACT FROM AUTHOR]

This paper presents a methodological framework to evaluate the resilience, with the primary focus on performance degradation, of levee-protected electric power networks to flooding in a changing climate. To this end, a multi-disciplinary framework is established by integrating climate science, hydrology, and electric power network analysis. The framework quantifies the effect of climate change on flood hazard levels in a levee-protected area and the subsequent changes in the resilience of the electric power network. In the first step, the changes in the exposure of levee-protected regions to flooding hazard in a warming climate are calculated. Then, the probabilities of failure of power network components due to flooding under current and projected future climate are determined. Finally, the power system resilience index is used to assess the system resiliency for pre-flooding (baseline), historic flooding, and two projected future flooding scenarios. For demonstration, the proposed framework is applied to a levee-protected area in Northern California. The IEEE 118-bus standard test system is employed to represent the power network in the study area. Results reveal that climate change can considerably decrease the system resilience of the levee-protected electric power network. The findings of this study can contribute towards more resilient power network systems under a changing climate. [ABSTRACT FROM AUTHOR]

The Hearing Voices (HV) Movement promotes diverse understandings of voice-hearing and seeing visions, which mental health professionals commonly refer to as 'auditory hallucinations,' 'schizophrenia,' or 'psychosis.' Central to this movement are peer support groups through which attendees connect with others who have similar experiences. This paper describes an adaptation of a Hearing Voices group facilitation training at VA Greater Los Angeles (VAGLA) and discusses training modifications, along with trainee perceptions and implementation and intervention outcomes. This is a first step towards adapting HV-inspired groups to VA systems of care. Data collection involved surveys of trainees (n = 18) and field notes throughout the 24 h online training. Findings indicate high acceptability and appropriateness of the training and high feasibility in implementation, suggesting the training was well-adapted to VAGLA. This research contributes to global efforts to integrate the Hearing Voices approach in diverse settings and increase awareness about its benefits among providers. [ABSTRACT FROM AUTHOR]

This paper presents heave and pullout studies of plain granular pile-anchors (GPAs) and geogrid-encased GPAs. Experiments were conducted in California bearing ratio (CBR) moulds and fabricated moulds of size 381 × 381 × 457 mm in expansive clays. The GPA method is an innovative technique developed for reducing heave and improving bearing capacity and pullout capacity. In this study, GPAs were encased in geogrids to augment the engineering performance of the system. Tests were conducted in CBR moulds and fabricated larger moulds to study and compare the heave and pullout behaviour of plain GPAs and geogrid-encased GPAs. The results indicated that the clay beds reinforced with geogrid-encased GPAs showed better heave and pullout behaviour. Heave decreased by 90.8% and 92.5%, respectively, in the case of plain and geogrid-encased GPAs installed in the CBR moulds and by 95.12% and 98.3% in the case of plain and geogrid-encased GPAs installed in fabricated large moulds. Pullout load on geogrid-encased GPAs in CBR moulds improved by 42.17% over that on plain GPAs and pullout load on geogrid-encased GPAs in the fabricated large moulds improved by 52.25% over that on plain GPAs. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic required significant public health interventions from local governments. Although nonpharmaceutical interventions often were implemented as decision rules, few studies evaluated the robustness of those reopening plans under a wide range of uncertainties. This paper uses the Robust Decision Making approach to stress-test 78 alternative reopening strategies, using California as an example. This study uniquely considers a wide range of uncertainties and demonstrates that seemingly sensible reopening plans can lead to both unnecessary COVID-19 deaths and days of interventions. We find that plans using fixed COVID-19 case thresholds might be less effective than strategies with time-varying reopening thresholds. While we use California as an example, our results are particularly relevant for jurisdictions where vaccination roll-out has been slower. The approach used in this paper could also prove useful for other public health policy problems in which policymakers need to make robust decisions in the face of deep uncertainty. [ABSTRACT FROM AUTHOR]

Scholars from different political approaches argue that governance may harmonize antagonistic interests. However, governance's concept is still imprecise, and it has been used in a contradictory way. This paper's aim is to discuss governance limitations to overcome obstacles regarding land monopolization and water 'commoditization'. Through California experience, this paper shows how historic legacies have reinforced changes in commodity market, narrowing the margin of maneuver of vulnerable households. Inclusive governance may be the best shot to reduce inequalities regarding water access, but it is not a magical solution. There is a long way to follow and political fights still must happen. [ABSTRACT FROM AUTHOR]

Accessory dwelling units (ADUs) have been championed as a low-cost solution—both monetarily and politically—for America's housing affordability crisis. However, do these micro-units provide a city's lower-income community members with much needed affordable housing? And which income groups benefit from the theoretical supplemental income created by ADUs? Using socioeconomic data, building permit information, and geospatial analysis, this paper explores these questions by conducting a case study of the archetype of America's housing affordability crisis: San Francisco, California. Finding that, within the city and sample frame, few ADU permits have been filed in the most price-vulnerable communities, this paper challenges the theoretical benefits of ADUs espoused by politicians and academics. If the city continues to lean on ADU legislation, local legislators must strengthen current enforcement mechanisms and more narrowly tailor ADU-related rental restrictions. By offering model legislation and code language, this paper illustrates how San Francisco and other communities across the country can better ensure ADUs live up to their promise of affordability and access. [ABSTRACT FROM AUTHOR]

This paper proposes the concept of zombie infrastructure to understand the entangled histories of railroad colonialism, Indigenous dispossession, and corporate power in the California desert. I examine debates over the Cadiz project, a contemporary water project that proposes to take water from a California desert aquifer and transport it to the California coast. I argue that the life of the Cadiz project depends on Cadiz Inc.'s ability to revive the legal rights and body of a little-used railroad shortline, thus bringing back a legal infrastructure and corporate power from the late nineteenth century in the service of a new corporation. In so doing, the Cadiz project enlivens the racialized dispossession of land and labor that the railroad initially required. Routing the politics of a contemporary infrastructure project through the railroad and its octopus past, I argue, places the politics of infrastructure at the intersection of laws, monstrosity, and dispossession. Drawing on economic and legal geography, this paper proposes the concept of zombie infrastructure, a concept that builds on what activists call zombie projects in order to show the life and death of infrastructure, and reveals how contemporary capitalists enliven old infrastructures for new purposes. [ABSTRACT FROM AUTHOR]

Using local sources (roof runoff, stormwater, graywater, and onsite wastewater) to meet non-potable water demands can minimize potable water use in buildings and increase supply reliability. In 2017, an Independent Advisory Panel developed a risk-based framework to identify pathogen log reduction targets (LRTs) for onsite non-potable water systems (ONWSs). Subsequently, California's legislature mandated the development and adoption of regulations--including risk-based LRTs--for use in multifamily residential, commercial, and mixed-use buildings. A California Expert Panel was convened in 2021 to (1) update the LRT requirements using new, quantitative pathogen data and (2) propose treatment trains capable of meeting the updated LRTs. This paper presents the updated risk-based LRTs for multiple pathogens (viruses, protozoa, and bacteria) and an expanded set of end-uses including toilet flushing, clothes washing, irrigation, dust and fire suppression, car washing, and decorative fountains. The updated 95th percentile LRTs required for each source water, pathogen, and end-use were typically within 1-log10 of the 2017 LRTs regardless of the approach used to estimate pathogen concentrations. LRT requirements decreased with influent pathogen concentrations from wastewater to graywater to stormwater to roof runoff. Cost and footprint estimates provide details on the capital, operations and maintenance, and siting requirements for ONWS implementation. [ABSTRACT FROM AUTHOR]

Marginalized individuals are less likely to participate or have their interests represented in political processes than historically privileged individuals. Interest groups are considered the best means to address this gap, but there is little research on the role of interest groups in mobilizing people to participate in political processes and none on marginalized communities in particular. This paper is the first to test hypotheses about organizational strategies used to mobilize vulnerable communities for political participation around unconventional oil and gas policies in California and Colorado. Based on a survey of interest groups in both states, the results show that interest groups working in vulnerable communities do more outside advocacy (i.e., connecting residents to representatives) and use more personal communication methods (i.e., door-to-door canvassing) than interest groups working in historically privileged communities. However, organizational strategies in general are not well predicted by the target community's composition, suggesting that decisions around mobilization strategies are driven by other factors. [ABSTRACT FROM AUTHOR]

Persian gum as a newly introduced hydrocolloid with unique adhesive and gel forming properties has found wide applications in various industries such as medicine, food and textile. Recently, it has been employed in soil environment as a biocompatible stabilizing agent. However, due to its novelty, there is no study about its application in cohesionless sandy soil. This paper provides a comprehensive evaluation of Persian gum treated sand through macro- and microscale tests including compaction, unconfined compressive strength (UCS), direct shear (DS), triaxial (UU), California bearing ratio (CBR), falling head permeability, scanning electron microscopy (SEM), stereo zoom microscopy (SZM), Brunner, Emmet and Teller (BET), particle size analysis (PSA) and thermal gravimetric analysis (TGA). The tests were conducted on untreated and treated soil with different Persian gum contents and curing times. The obtained results represent the powerful performance of this novel gum in mechanical strength and bearing capacity enhancement through binding soil particles and formation of large agglomerated grains. Also, the remarkable ability of this novel gum in pore filling and compacting soil interstructure makes it a favorable additive in soil projects with permeability reduction purposes. [ABSTRACT FROM AUTHOR]

In 1997, thirty-nine members of a religious cult from California, US, committed mass suicide to gain abnormal powers exchanging their present body form. This is popularly known as Heaven's Gate. In 2021, in India, two young women were killed by their learned parents to get abnormal powers by giving-up their present body form. The paper connects such incidences and answers questions like why we feel that our present form of life is worth quitting? Ten eminent personalities from different fields were interviewed. Their responses are presented in a "belief-emotion-behavior," a novel format, analyzing the relationship between the three. [ABSTRACT FROM AUTHOR]

This study uses an ensemble Kalman method for near-surface seismic site characterization of 154 network earthquake monitoring stations in California to improve the resolution of S -wave velocity (VS) and P -wave velocity (VP) profiles—up to the resolution depth—coupled with better quantification of uncertainties compared to previous site characterization studies at this network. These stations were part of the Yong et al. site characterization project, with selected stations based on future recordings of ground motions that are expected to exceed 10 per cent peak ground acceleration in 50 yr. To estimate VS and VP from experimental dispersion data, Yong et al. investigated these stations using linearized (local search and iteration) routines, and Yong et al. later studied a subset of these stations using nonlinear (global search and optimization) routines. In both studies, the selection of model parameters—that is, discretization of the VS and VP profiles with only five fixed thickness layers—was mainly based on trial and error. In contrast, this paper uses an approximate Bayesian method to assimilate experimental dispersion data and sequentially update an ensemble of particle estimates that span the VS and VP parameter spaces. Doing so, we systematically determine the most probable profiles conditioned on the experimental dispersion data, the introduced noise levels, and a priori knowledge in the form of physical constraints. We consider two configurations to discretize the soil depth from the surface to half of the maximum discernible wavelength obtained from the experimental dispersion data, namely refined and coarse models, and two initial models for each configuration to study solution multiplicity. Our results suggest that using the refined model for the top surface layers improves the resolution of near-surface site characteristics and the model's success rate in capturing dispersion data at high frequencies. All models result in similar VS but distinct VP profiles, with increasing uncertainty at deeper layers, suggesting that the fundamental mode of Rayleigh wave dispersion data is not adequate to constrain the P -wave velocity profile and the S -wave velocity close to the resolution depth. [ABSTRACT FROM AUTHOR]

Native California bees and other wild pollinators, which are essential to many fruit and vegetable crops, are being threatened by climate change, pesticides and habitat degradation. Carbon farming, a set of practices that sequester carbon in the soil or woody biomass, can create habitat that supports these pollinators. This paper focuses on habitat management and farming practices that both increase carbon sequestration and benefit pollinator communities. By incentivizing and supporting conservation practices that incorporate carbon farming, we can protect wild pollinators and increase the resilience of California agriculture in the face of ongoing climate change. [ABSTRACT FROM AUTHOR]

The voices and experiences of formerly incarcerated college students are emerging throughout the social science literature. The importance of documenting their narratives is grounded in the reality that more than an estimated 12,000 system-impacted people are enrolled throughout the California postsecondary education system. This paper highlights the knowledge and skills formerly incarcerated students possess and deploy to navigate higher education successfully. Our study adds to the growing body of literature examining the experiences of formerly incarcerated Latinx students from an anti-deficit perspective. We use the theory of funds of knowledge and semi-structured interviews with 16 formerly incarcerated Latinx students at different points of the postsecondary education pipeline to understand their experiences. We find that formerly incarcerated Latinx students tapped into their "hustle" to move from surviving to thriving in higher education. These pre-college skills, acquired through their life experiences, allow students to seek academic and financial resources, create academic networks, and make personal connections with institutional agents to overcome various personal and institutional barriers. [ABSTRACT FROM AUTHOR]

• The California mandate for solar PV systems on residential rooftops will have a negative occupational safety impact. • Results indicate that 16.6 recordable non-fatal injuries are expected to occur between 2020 and 2029 as a result of this policy. • The impact could be reduced by 18% by eliminating work on roofs. Introduction: A 2018 change to the California building code mandates that new residential construction in the state include rooftop solar photovoltaic power systems beginning in 2020. As residential construction (especially work on rooftops) is among the more dangerous occupations in the United States, this paper seeks to quantify the increased risks to workers as a result of this mandate. Method: An analysis of the trends by occupation of nonfatal safety incident rates in the United States combined with a Monte Carlo simulation provide an estimate of the uncertain impact of this new mandate. Results: Recordable safety incidents are anticipated to increase by a total of 16.6 incidents (standard deviation = 1.0 incidents) over the 2020–2029 time period as a result of this policy change. However, lessons from Germany and other industries offer potential avenues to reduce the negative social impact of this mandate. Conclusions: While it is not possible to increase employment in any sector without increasing the expected number of occupational injuries to some degree, these results indicate that risks could be considerably reduced by making solar PV system design decisions that increase worker productivity and reduce roof exposure time. Practical Applications: Changes such as eliminating work on roofs could decrease the expected number of recordable injuries over the 10-year period by 0.30 incidents per year (a reduction of 18%). [ABSTRACT FROM AUTHOR]

In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become popular for constructing safe and sustainable pavement structures. The strength of the subgrade soil is routinely assessed in terms of its California bearing ratio (CBR). However, in the past, no effort was made to develop a method for evaluating the CBR of the reinforced subgrade soil. The main aim of this paper is to explore and appraise the competency of the several intelligent models such as artificial neural network (ANN), least median of squares regression, Gaussian processes regression, elastic net regularisation regression, lazy K-star, M-5 model trees, alternating model trees and random forest in estimating the CBR of reinforced soil. For this, all the models were calibrated and validated using the reliable pertinent historical data. The prognostic veracity of all the tools mentioned supra were assessed using the well-established traditional statistical indices, external model evaluation technique, multi-criteria assessment approach and independent experimental dataset. Due to the overall excellent performance of ANN, the model was converted into a trackable functional relationship to estimate the CBR of reinforced soil. Finally, the sensitivity analysis was performed to find the strength and relationship of the used parameters on the CBR value. [ABSTRACT FROM AUTHOR]

Problem: Children with chronic conditions often have difficulties with emotions, concentration, and behaviors (ECB) and are not recognized and treated adequately. In this paper, long‐term medication use (LTM) was adopted as a proxy for chronic illness due to the lack of consistent and standardized diagnostic criteria for chronic illnesses in children. Methods: Children (8–12 years) were selected from the California Health Interview Survey (2017) based on: (1) households with children (<12 years), (2) parent/adult caregivers report about child's health indicating "yes" to, (3) "does your child require prescription medicine for a health condition that has lasted or is expected to last at least 12 months or more," and (4) "difficulties with ECB in past 6 months." Findings: A total of 1600 children were included by the CHIS data set, and children whose parental report had met the selection criteria were children with LTM (n = 144; 7.4 ± 2.9 years), ECB (n = 233; 8.16 ± 2.14), and both LTM + ECB (n = 62; 8.61 ± 1.81). Children with LTM+ ECB were Caucasian (56.4%), Hispanic (19.3%), and males (64.5%). Children with both LTM + ECB had two to three (33.87%) or at least four (53.2%) physician visits, and/or receiving special therapy (45.1%). Children with LTM had prescription delays (n = 144; 5.6%) and were not able to get medical care due to lack of insurance (n = 144; 6.9%). The majority of the children with LTM (54.2%) and LTM + ECB (43.5%) had parental employment‐based insurance. More children that have both LTM and ECB (48.4%) than children with LTM, No ECB (32.9%) were on Medi‐Cal/Medicaid. Conclusion: Children with LTM need further evaluation for difficulties with ECB. Future studies are required to examine health status, healthcare use, and access for children with LTM and ECB. [ABSTRACT FROM AUTHOR]

For renewable hydrogen to be a significant part of the future decarbonized energy and transportation sectors, a rapid and massive build-out of hydrogen production facilities will be needed. This paper describes a geospatial modeling approach to identifying the optimal locations for renewable hydrogen fuel production throughout the state of California, based on least-cost generation and transport. This is accomplished by (1) estimating and projecting California renewable hydrogen demand scenarios through the year 2050, (2) identifying feedstock locations, (3) excluding areas not suitable for development, and (4) selecting optimal site locations using commercial geospatial modeling software. The findings indicate that there is a need for hundreds of new renewable hydrogen production facilities in the decades preceding the year 2050. In selecting sites for development, feedstock availability by technology type is the driving factor. • Extensive build-out of new renewable hydrogen facilities is needed for hydrogen economy. • Zoning and terrain limit the potential renewable hydrogen production facility sites. • Proximity to feedstock dominates cost-optimal site selection. [ABSTRACT FROM AUTHOR]

Many nations and jurisdictions have legalized non-medical adult use of cannabis, or are considering doing so. This paper contributes to knowledge of adult use legalization's associations with cannabis use disorder (CUD) treatment utilization. This study collected data from a dataset of all publicly funded substance use disorder treatment delivered in California from 2010 to 2021 (1,460,066 episodes). A logistic regression model estimates adult use legalization's impacts on CUD treatment utilization using an individual-level pre-post time series model, including individual and county-level characteristics and county and year-fixed effects. Adult use legalization was associated with a significant decrease in the probability of admission to CUD treatment (average marginal effect (AME): −0.005, 95 % CI: −0.009, 0.000). Adult use legalization was also associated with a decrease in the probability of admission to CUD treatment for males (AME: −0.025, 95 % CI: −0.027, −0.023) Medi-Cal beneficiaries (AME: −0.025, 95 % CI: −0.027, −0.023) adults ages 21+ (AME: −0.011, 95 % CI: −0.014, −0.009) and Whites (AME: −0.012, 95 % CI: −0.015, −0.010), and an increase in the probability of admission to CUD treatment for patients referred from the criminal justice system (AME: 0.017, 95 % CI: 0.015, 0.020) and Blacks (AME: 0.004, 95 % CI: 0.000, 0.007) and Hispanics (AME: 0.009, 95 % CI: 0.006, 0.011). Adult use legalization is associated with declining CUD treatment admissions, even though cannabis-related problems are becoming more prevalent. Policies and practices that protect public health, and engage people with CUD in treatment are needed. • Pre-legalization, 19 % of SUD admissions were CUD, compared to 9 % post-legalization. • Adult use legalization was associated with a decrease in CUD treatment admissions. • Differences existed in legalization's associations on different demographic groups. [ABSTRACT FROM AUTHOR]

Liquefaction hazard analyses are needed for regional-scale applications, such as risk analysis and upgrades of electrical transmission, water conveyance, and highway systems. In this paper, we expand well-established site-specific liquefaction triggering and manifestation analysis methods for regional scale application by explicitly addressing the uncertainty in groundwater conditions, soil behavior classification, and cyclic resistance. The uncertainty inherent to regional-scale geotechnical properties is quantified by considering the inputs into conventional liquefaction triggering and manifestation analyses as random variables. With this flexible and extensional formulation, the geostatistical properties needed for regional scale analyses may be estimated by either averaging borehole information or spatially interpolating geotechnical properties across broad geologic deposits. We demonstrate the utility of the method by analyzing the liquefaction hazard throughout the south San Francisco Bay area, California, U.S.A. The resulting estimates of the probability of liquefaction manifestation tend to be lower than previously published estimates, largely owing to the geologic structure of the area and the uncertainty in liquefaction surface manifestation once liquefaction is triggered at depth. Uncertainties in groundwater conditions, soil behavior classification, penetration resistance, and liquefaction manifestation are quantified by the regional scale analyses, which provide a complete and systematic evaluation of the liquefaction hazards, localize the uncertainties in subsurface conditions, and facilitate supplemental investigations at areas of high uncertainty to fill data gaps. • Existing liquefaction models are extended for regional-scale application. • Flexible model allows deposit-scale averages or spatially interpolated properties. • Geologic structure and uncertain groundwater depth are applied in the model. • Example application in southern San Francisco Bay area, California, U.S.A. [ABSTRACT FROM AUTHOR]

The substantial threat of concurrent air pollutants to public health is increasingly severe under climate change. To identify the common drivers and extent of spatiotemporal similarity of PM 2.5 and ozone (O 3), this paper proposed a log Gaussian–Gumbel Bayesian hierarchical model allowing for sharing a stochastic partial differential equation and autoregressive model of order one (SPDE-AR(1)) spatiotemporal interaction structure. The proposed model, implemented by the approach of integrated nested Laplace approximation (INLA), outperforms in terms of estimation accuracy and prediction capacity for its increased parsimony and reduced uncertainty, especially for the shared O 3 sub-model. Besides the consistently significant influence of temperature (positive), extreme drought (positive), fire burnt area (positive), gross domestic product (GDP) per capita (positive), and wind speed (negative) on both PM 2.5 and O 3 , surface pressure and precipitation demonstrate positive associations with PM 2.5 and O 3 , respectively. While population density relates to neither. In addition, our results demonstrate similar spatiotemporal interactions between PM 2.5 and O 3 , indicating that the spatial and temporal variations of these pollutants show relatively considerable consistency in California. Finally, with the aid of the excursion function, we see that the areas around the intersection of San Luis Obispo and Santa Barbara counties are likely to exceed the unhealthy O 3 level for USG simultaneously with other areas throughout the year. Our findings provide new insights for regional and seasonal strategies in the co-control of PM 2.5 and O 3. Our methodology is expected to be utilized when interest lies in multiple interrelated processes in the fields of environment and epidemiology. [Display omitted] • PM 2.5 and O 3 pollutants are jointly analysed in California through a log Gaussian–Gumbel Bayesian hierarchical joint model. • Distinct spatiotemporal interactions are revealed with a shared SPDE-AR structure; common drivers of PM 2.5 and O 3 are identified. • Hotspots at risk of simultaneously exceeding unhealthy O 3 levels are identified. [ABSTRACT FROM AUTHOR]

The quality of traffic services depends on the accuracy and completeness of the collected traffic data. However,the existing traffic data imputation methods usually only rely on the predefined road network structure to capture the spatio-temporal features and only consider the imputation effect from a single perspective, which are very limited for imputation of different missing patterns of road traffic data. In this paper, we propose a novel deep learning framework called Hierarchical Spatio-temporal Graph Convolutional Neural Networks(HSTGCN) to impute traffic data,through the macro layer and the road layer. The model constructs macro graph of the road network based on the data temporal correlation clustering, which can mine the temporal dependencies of road traffic data from a hierarchical perspective. Besides, a temporal attention mechanism and adaptive adjacency matrix are introduced in the road layer to better extract the spatio-temporal information of the road traffic data. Finally, we use graph convolution neural networks to learn the spatio-temporal feature representations of the road layer and macro layer, which are then fused to achieve data imputation. To illustrate the efficient performance of the model, experiments are conducted on traffic data collected from California and Seattle. The proposed model performs better than the comparison model for traffic data imputation. • A full dynamic graph is built based on the urban road structure. • Extracting spatio-temporal features from multiple dimensions using hierarchical thinking. • Gate-GCN perform effective feature selection. [ABSTRACT FROM AUTHOR]

• Factors affecting injury severity in rollover crashes were explored. • Random parameters logit model with unobserved heterogeneity in means and variances. • Findings reveal temporal instability of model specifications across individual years. • Aggregate-to-component shift quantified using out-of-sample prediction. • Implications for mitigating the associated driver injury severity. Single-vehicle rollover crashes have been acknowledged as a predominant highway crash type resulting in serious casualties. To investigate the heterogeneous impact of factors determining different injury severity levels in single-vehicle rollover crashes, the random parameters logit model with unobserved heterogeneity in means and variances was employed in this paper. A five-year dataset on single-vehicle rollover crashes, gathered in California from January 1, 2013, to December 31, 2017, was utilized. Driver injury severities that were determined to be outcome variables include no injury, minor injury, and severe injury. Characteristics pertaining to the crash, driver, temporal, vehicle, roadway, and environment were acknowledged as potential determinants. The results showed that the gender indicator specified to minor injury was consistently identified as a significant random parameter in four years' models and the joint five-year model, excluding the 2016 crash model where the night indicator associated with no injury was observed to produce the random effect. Additionally, two series of likelihood ratio tests were conducted to assess the year-to-year and aggregate-to-component temporal stability of model estimation results. Marginal effects of explanatory variables were also calculated and compared to analyze the temporal stability and interpret the results. The findings revealed an overall temporal instability of model specifications across individual years, while there is no significant aggregate-to-component variation. Injury severities were observed to be stably affected by several variables, including improper turn indicator, under the influence of alcohol indicator, old driver indicator, seatbelt indicator, insurance indicator, and airbag indicator. Furthermore, the year-to-year and aggregate-to-component shift was quantified and characterized by calculating the differences in probabilities between within-sample observations and out-of-sample predictions. The overall results imply that continuing to expand and refine the model to incorporate more comprehensive datasets can result in more robust and stable injury severity prediction, thus benefiting in mitigating the associated driver injury severity. [ABSTRACT FROM AUTHOR]

Forest fires cause every year damages to biodiversity, atmosphere, and economy activities. Forest fire simulation have improved significantly, but input data describing fire scenarios are subject to high levels of uncertainty. In this work the two-stage prediction scheme is used to adjust unknown parameters. This scheme relies on an input data calibration phase, which is carried over following a genetic algorithm strategy. The calibrated inputs are then pipelined into the actual prediction phase. This two-stage prediction scheme is leveraged by the cloud computing paradigm, which enables high level of parallelism on demand, elasticity, scalability and low-cost. In this paper, all the models designed to properly allocate cloud resources to the two-stage scheme in a performance-efficient and cost-effective way are described. This Cloud-based Urgent Computing (CuCo) architecture has been tested using, as study case, an extreme wildland fire that took place in California in 2018 (Camp Fire). • Data-driven calibration to deal with uncertainty in forest fire spread prediction. • Cloud-based urgent computing implementation of a two-stage prediction model. • Use of utility function to deal with the cost-performance trade-off. • Validation against a deadly and destructive wildfire with promising results. [ABSTRACT FROM AUTHOR]

Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event, thus investigating the resilience of an area over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel framework utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires. Taking the Mendocino Complex and Camp wildfires - the largest and most deadly wildfires in California to date, respectively - as case studies, we capture the robustness and vulnerability of communities based on human mobility data from 2018 to 2019. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, providing a new lens to study disasters and their long-term impacts on society. • Quantifying community resilience is an open research challenge • This paper develops a novel framework to quantify resilience after a disaster using network analysis • Using human mobility data associated with two wildfires, we measure robustness and vulnerability of communities • Results show community resilience closely tied to socio-economic and built environmental traits of the affected areas • Our approach paves a way to study disasters and their long-term impacts on society [ABSTRACT FROM AUTHOR]

• We analyzed trends and breakpoints for three streamflow signatures of drying patterns in 540 non-perennial streams over CONUS • Nonlinear changes in streamflow are more common than linear changes for all three stream intermittency signatures. • The past behavior of non-perennial streams might be a poor predictor of future drying patterns Stream drying patterns – including duration, timing, and dry-down rates – affect aquatic ecosystems and nutrient exports in non-perennial streams. Because hydrologic processes are often nonlinear, changes in drying may also be nonlinear, but analyses of historical changes in stream drying to date have not characterized the frequency or functional forms of nonlinear change. Understanding the extent of nonlinear change in non-perennial streams is essential for advancing our fundamental knowledge of hydrological processes, aquatic ecosystems, and watershed functioning under a warming climate. This paper uses a polynomial-based trend detection technique (PolyTrend) to analyze the linear and nonlinear trend behaviors of three intermittency signatures (annual no-flow days specifying longer or shorter drying duration, day of first no-flow occurrence specifying timing of stream drying, and days from peak to no-flow specifying dry-down rates) at 540 non-perennial gage stations over 38 years (1980–2017) across the continental United States (CONUS). Additionally, we carried out a breakpoint analysis to characterize the discontinuities in the time series of each intermittency signature. Analysis of annual no-flow days shows that about 37 % of the total streamflow stations are drying for longer each year, whereas about 22 % are wetter for longer than in the past. The day of first no-flow occurrence analysis shows that 10 % of the streams are drying earlier, and 19 % are drying later. On the other hand, analysis of days from peak to no-flow shows that 14 % of streams are drying faster, and 17 % are drying more slowly. For all these metrics, among the significant trends, at least half of the relationships were nonlinear. For annual no-flow days , the breakpoint analysis shows more discontinuities in the second half of the analysis period (1999 to 2017) than in the first half, with more discontinuities in the Southern Great Plains than in other regions. The other two signatures demonstrate less frequent discontinuities in the second half of the analysis period, suggesting decreased nonlinear dynamics in recent years. Nonlinear no-flow duration trends are common in Mediterranean California, and the dry-down rate has increased in recent decades. Our findings indicate that nonlinear change in stream drying is widespread and must be accounted for in watershed planning and management. [ABSTRACT FROM AUTHOR]

This paper, on the one hand, proposes a statistical technique to detect potential causal relationships when the researcher has georeferenced data but not time dimension, and, on the other, applies this new methodology to the analysis of potential partial causal determinants of home prices. In particular, we find that the direction of causality for home prices in California goes from the income level to prices. [ABSTRACT FROM AUTHOR]

This paper introduces a series of high resolution (100-meter) population grids for eight different sociodemographic variables across the state of California using data from the 2020 census. These layers constitute the 'CA-POP' dataset, and were produced using dasymetric mapping methods to downscale census block populations using fine-scale residential tax parcel boundaries and Microsoft's remotely-sensed building footprint layer as ancillary datasets. In comparison to a number of existing gridded population products, CA-POP shows good concordance and offers a number of benefits, including more recent data vintage, higher resolution, more accurate building footprint data, and in some cases more sophisticated but parsimonious and transparent dasymetric mapping methodologies. A general accuracy assessment of the CA-POP dasymetric mapping methodology was conducted by producing a population grid that was constrained by population observations within block groups instead of blocks, enabling a comparison of this grid's population apportionment to block-level census values, yielding a median absolute relative error of approximately 30% for block group-to-block apportionment. However, the final CA-POP grids are constrained by higher-resolution census block-level observations, likely making them even more accurate than these block group-constrained grids over a given region, but for which error assessments of population disaggregation is not possible due to the absence of observational data at the sub-block scale. The CA-POP grids are freely available as GeoTIFF rasters online at github.com/njdepsky/CA-POP, for total population, Hispanic/Latinx population of any race, and non-Hispanic populations for the following groups: American Indian/Alaska Native, Asian, Black/African-American, Native Hawaiian and other Pacific Islander, White, other race or multiracial (two or more races) and residents under 18 years old (i.e. minors). [ABSTRACT FROM AUTHOR]

Wildfires are natural or man‐made disasters that continuously threaten portions of the transmission and distribution grid, and thus the stability of the electric grid. This paper presents a two‐stage framework for assessing power system‐wildfire risk using a data‐driven wildfire prediction model. The first stage of the framework estimates the spatio‐temporal probability of potential wildfire ignition and propagation using a deep neural network in combination with the wildfire physical spread model. Analysis reveals similar spatial and temporal patterns between the model‐predicted wildfire ignition potential and actual wildfire ignition. Motivated by these observations, the second stage assesses the wildfire risk in the power grid operation in terms of potential loss of load by de‐energisation, through combining geospatial information system data of the power grid topology and the stochastic spatio‐temporal wildfire model developed in the first stage. The electric power utility applications introduced by the proposed framework are twofold: 1) a spatio‐temporal risk model for proactive de‐energisation against potential power system failure‐induced wildfire, and 2) a spatio‐temporal spreading model for optimal grid operations against exogenous wildfire. The proposed model, based on real‐world dataset, is demonstrated on the IEEE 24‐bus test system mapped to a study area in Northern California, while the results illustrate the proposed model can achieve the best performance in potential wildfire ignition detection (AUC of 0.995) compared to other baselines, as well as demonstrates the risk‐aware operation of the power system enabled by the proposed framework. [ABSTRACT FROM AUTHOR]

Anderson, R.B.; Carter, O.T.; Pearce, K.G., and Capdevila, L.A., 2022. User perceptions of the Pleasure Point seawall in Santa Cruz county, California, U.S.A. Journal of Coastal Research, 38(4), 828–843. Coconut Creek (Florida), ISSN 0749-0208. Communities up and down the California coast face the looming threat of rising seas, coastal erosion, and shoreline retreat. In this paper, the researchers use anthropological methods and a user survey to explore the social dimensions of the Pleasure Point seawall to assess some of the longer-term implications of coastal armoring in California and the U.S. more broadly. The users surveyed had an overall positive to neutral opinion of a coastal retention structure (in this case a seawall). About 35% had positive opinions of the seawall, 30% had neutral opinions, and 18% had negative opinions. While these results trend positive to neutral, they also reveal divided community opinions about and experiences with the seawall. The open-ended portions of the survey, combined with participant observation and follow-up interviews, add another qualitative dimension to this data. The participants in the survey and research expressed concerns about engineering and ecological issues, whether questions about how long the structure will last or observations/opinions about its impacts on local surf conditions, backwash, and loss of beach sand. Participants also expressed concerns about the social impacts of the seawall, including access issues, crowds, growing risks, problems with increasing numbers of inexperienced users, tourism growth, and fears about gentrification and local displacement. Combined, these insights illustrate how seawalls and other coastal armoring structures can and should be understood as engineered structures with critical social impacts that intersect with their physical, material, and environmental impacts. [ABSTRACT FROM AUTHOR]

Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions. [ABSTRACT FROM AUTHOR]