Southern California Region Research Articles

‘Neoliberalism, far-right politics, and the shrinking White middle class in Southern California’s Inland Empire’

ABSTRACT This article examines far-right, authoritarian politics among middle-class White communities of the Inland Empire region of Southern California. Grounded in a materialist theoretical framework and an analysis of 30 semi-structured interviews and the recent political-economic history of the region, I interrogate the political and social attitudes of study participants in relation to their experiences and perceptions of changing political-economic and sociocultural conditions in Southern California in the twenty-first century. Drawing upon Gramsci and the critical theoretical literature on authoritarian populism and neoliberalism, I consider how study participants’ authoritarian politics and ideologies reflect a reactionary political response to processes associated with the reorganization of racialized capital accumulation under neoliberalism. I argue that the appeal of these movements is, in part, how they provide commonsense (hegemonic) explanations for economic precarity, demographic shifts, and immigration, among other issues, while at the same time normalizing this increasingly exploitative and brutal regime of capital accumulation.

Teachers’ Perspectives Toward Smartphone Usage by Students and Resulting Classroom Policies

Currently, 95% of American teenagers own a smartphone device, and smartphone users spend on average about 5.4 hours a day on them. Smartphone ownership, usage, and dependency are higher among teens and young adults than in any other age group. This study addressed a gap in research by examining high school teachers’ perspectives on two topics: the impact of smartphone usage on students and classroom smartphone policies. It contributes to a deeper understanding of the social, psychological, and academic effects of smartphone use on students and of the impact of teachers’ beliefs surrounding smartphones on the classroom policies they set regarding those devices. In addition, this study aimed to shed light on the need to create effective and equitable smartphone policies and practices to help promote a classroom environment conducive to learning. Survey data from 248 teachers from six high schools in the Southern California region of the United States of America produced results indicating that teachers who have stronger negative beliefs concerning the social, psychological and academic effects of smartphones on students typically set a stricter classroom smartphone policy. Implications include helping students regulate their smartphone usage so that these devices can be effective tools for learning, not a distraction. Based on the findings from this study, an increase in face-to-face social interactions among students is recommended to increase academic achievement and decrease problematic smartphone use.

Uncertainty-Aware Seismic Signal Discrimination using Bayesian Convolutional Neural Networks

Seismic signal classification plays a crucial role in mitigating the impact of seismic events on human lives and infrastructure. Traditional methods in seismic hazard assessment often overlook the inherent uncertainties associated with the prediction of this complex geological phenomenon. This work introduces a probabilistic framework that leverages Bayesian principles to model and quantify uncertainty in seismic signal classification by applying a Bayesian Convolutional Neural Network (BCNN). The BCNN was trained on a dataset that comprises waveforms detected in the Southern California region and achieved an accuracy of 99.1%. Monte Carlo Sampling subsequently creates a 95% prediction interval for probabilities that considers epistemic and aleatoric uncertainties. The ability to visualize both aleatoric and epistemic uncertainties provides decision-makers with information to determine the reliability of seismic signal classifications. Further, the use of Bayesian CNN for seismic signal classification provides a more robust foundation for decision-making and risk assessment in earthquake-prone regions.

The Impact of Living in a Low Food Access/Low Income Area on Obstructive Sleep Apnea.

To assess differences in obstructive sleep apnea (OSA)-related health parameters between residents of low income/low access (LILA) census tracts-food deserts-and non-LILA residents STUDY DESIGN: Retrospective review. Single institution serving a large region in Southern California from 2017 to 2023. Census tracts are defined as LILA if a significant proportion of residents live below the poverty threshold and far from healthy food vendors. Adults newly diagnosed with OSA on polysomnography were included. Food access status was determined by searching patient addresses in the US Department of Agriculture Food Access Research Atlas. Baseline and 1-year follow-up body mass index (BMI) and vitals were collected and compared based on food access and other demographic variables. A total of 379 patients in the LILA+ group and 2281 patients in the LILA- group met inclusion criteria. BMI was higher in the LILA group (36.6 ± 9.4 vs 35.2 ± 8.9; P = .006). The effect of food access was most significant in certain demographic groups: patients aged < 65, males, Asian/Pacific Islanders, Hispanics, and patients with Medicaid coverage all had a higher BMI when in the LILA+ group compared to the LILA- group. When considering insurance, LILA+ patients with Medicaid coverage had a significantly higher BMI than LILA- patients with non-Medicaid coverage (40.4 ± 10.3 vs 34.2 ± 8.4, P < .001. Blood pressure, heart rate, and apnea-hypopnea index were also significantly higher in LILA+/Medicaid group. BMI change across all demographic groups was minimal at 1-year follow-up. Living in a LILA census tract may result in worse OSA-related health parameters. When accounting for insurance status, the effects are even more profound. Intensive counseling on the importance of weight management should begin at the diagnosis of OSA.

Impact of pharmacist managed oral epidermal growth factor receptor inhibitors.

Pharmacists are an integral part of medication management, with the positive impact of their clinical services in patient outcomes previously studied and reported in literature. The roles and responsibilities of pharmacists continue to expand, including optimizing patient medication and health outcomes related to complex oral anticancer drugs. To evaluate the impact of a pharmacist-managed oral chemotherapy clinic in patients with non-small cell lung cancer (NSCLC) taking oral epidermal growth factor receptor inhibitor (EGFRi) regimens within an integrated healthcare delivery system. This was an observational cohort study using data from Kaiser Permanente Northern and Southern California regions on adult patients who received oncology pharmacist-managed care compared to patients with usual care. Patients were newly initiated with EGFRi therapy to treat NSCLC between 2017 and 2019. The follow-up period was defined as the time from index date (first sold date of EGFRi) to December 2020 or end of membership or death, whichever occurred first. Primary outcome measures included adherence to EGFRi, frequency of imaging during drug exposure, and presence of imaging prior to treatment change. Outcomes were analyzed with Chi-square test for categorical variables, and Student's t-test or Wilcoxon rank-sum test for continuous variables. There were 613 patients in the pharmacist-managed group and 714 patients in the usual care group. Overall, the mean age was 68.2 ± 11.3 years, and 65.1% were female. In the pharmacist-managed group, there was a significantly higher mean proportion of days covered (PDC) during the first three months of therapy (0.86 ± 0.24 vs 0.82 ± 0.36, p = 0.01) and a higher percentage of patients who were adherent to EGFRi therapy (with PDC ≥0.80) during the drug exposure period (95.8% vs 92.4%, p = 0.01). The rate of computed tomography (CT) and magnetic resonance imaging (MRI) during drug exposure was higher in the pharmacist-managed group compared to the usual care group (31.8% vs 20.7%, p < 0.01) with a higher number of mean scans completed per patient (1.15 ± 2.42 vs 0.62 ± 1.63, p < 0.01) and per patient-year (2.60 ± 7.27 vs 1.58 ± 5.95, p < 0.01). Overall, 66.2% of patients had a treatment change, with a higher percentage of patients in the pharmacist-managed group who completed a CT or MRI scan prior to treatment change (36.3% vs 26.3%, p < 0.01). Additionally, the median time between the scan and treatment change was shorter for patients with pharmacist-managed care (1.8 vs 4 months, p = 0.04). Clinical pharmacy services contributed to improved adherence, higher rates of imaging, and shorter time between imaging and treatment changes in NSCLC patients who were taking EGFRi regimens.

Regional Comparisons of Sensitivities of Phytophthora citrophthora and P. syringae Causing Citrus Brown Rot in California to Four New and Two Older Fungicides.

Isolates of the citrus brown rot pathogens Phytophthora citrophthora and P. syringae from the Inland Empire (IE) and Ventura Co. (VE) regions of southern California were evaluated for their sensitivity to ethaboxam, fluopicolide, mandipropamid, and oxathiapiprolin, and the previously published baselines that were generated for Central Valley (CV) isolates of California were expanded. Fungicides were generally more toxic to CV isolates of both species for all four fungicides. Specific differences were found in the toxicity of ethaboxam to P. syringae where CV isolates on average were 6.8 or 8.2 times more sensitive than those from the VE or IE regions, respectively. Based on the grouping of isolates in an unweighted pair-group method with arithmetic mean (UPGMA) dendrogram, as well as fastStructure analyses and plotting of principal component analyses (PCAs), differences in ethaboxam sensitivity could be related to differences in genetic background of the isolates. Isolates of P. citrophthora from the IE and VE had slightly reduced (i.e., 1.5×) sensitivity to mandipropamid as compared with isolates from the CV and were found on distinct branches in the UPGMA dendrogram. Differences in genetic background of less sensitive isolates within each species indicate that these two phenotypes emerged multiple times independently. IE and VE isolates of both species were sensitive to mefenoxam. Moderate resistance to potassium phosphite (EC50 values of 25 to 75 μg/ml) was present in IE and VE isolates of P. syringae, whereas some IE isolates of P. citrophthora were considered resistant with EC50 values of up to 113.69 μg/ml. Resistance to potassium phosphite did not relate to distinct genotypes.

Empirical ground-motion basin response in the California Great Valley, Reno, Nevada, and Portland, Oregon

We assess how well the Next-Generation Attenuation-West 2 (NGA-West2) ground-motion models (GMMs), which are used in the US Geological Survey’s (USGS) National Seismic Hazard Model (NSHM) for crustal faults in the western United States, predict the observed basin response in the Great Valley of California, the Reno basin in Nevada, and Portland and Tualatin basins in Oregon. These GMMs rely on site parameters such as the time-averaged shear-wave velocity ( VS) in the upper 30 m of Earth’s crust ( VS30) and depths to 1.0 and 2.5 km/s shear-wave isosurfaces ( Z1.0 and Z2.5) to capture basin effects and were developed using observations and simulations primarily from the Los Angeles region in southern California. Using ground-motion records from mostly small-to-moderate earthquakes and mixed-effects regression analysis, we find that the GMMs perform well with our local basin-depth models for the California Great Valley. With our local basin-depth models for Reno, the GMMs do not perform as well for this relatively shallow basin and exhibit little sensitivity to the basin parameters used in the NGA-West2 GMMs. We also find good performance for the local Z1.0 model across the Portland region, whereas the local Z2.5 model provides little predictive power except at sites in the deepest part of the Tualatin basin. Additional work could improve the performance of the site and basin terms in the NGA-West2 GMMs for regions with geologic structure different than the deep basins in southern California and the Great Valley. In addition, we find significant discrepancies among the GMMs in how the uncertainty in the ground motion varies with basin depth and pseudospectral period. Our results can help guide seismic hazard analyses on whether to include these local basin-depth models.

Data-driven optimal placement of minichannel-based solar water heater using satellite-derived and ground-telemetry weather information

The aluminum minichannel solar collector is a novel technology for solar water heating. Minichannel-based solar collectors have higher thermal efficiency than conventional flat plate collectors and do not suffer from potential loss of vacuum as evacuated-tube collectors. This technology can play a significant role in reducing natural gas consumption that translates into lower greenhouse gas emissions to the atmosphere. However, the performance of solar collectors depends on the geographical location of the installation due to solar resource availability and weather pattern. The potential reduction in natural gas consumption using aluminum minichannel solar collectors is assessed using solar irradiance, ambient temperature, and wind data obtained from ground weather station and satellite-derived data. A data-driven numerical analysis is performed using a validated solar water heater (SWH) model, population, and natural gas consumption data for the entire state of California to assess the best locations to install these systems. The SWH model is validated based on data collected from an actual SWH system installed at a single-family house in Northridge, California. A K-means clustering method is then applied to select the best regions for installation of this technology. Based on performance, population density, and natural gas consumption, the regions of Southern California and the Central Valley are chosen as having the highest potential for reduction of natural gas consumption. The analysis was performed from weather data obtained based on two full years (2020 and 2022), where the effect of COVID-19 (year 2020) is observed as having higher water tank temperatures and higher solar fractions, which could be associated with lower hot water consumption.

Seismotectonics of the San Andreas Fault System in the San Gorgonio Pass region: A synthesis

In the San Gorgonio Pass region of Southern California, USA, the San Andreas fault exhibits greater geometric complexity than anywhere else along its length, with multiple nonparallel fault strands that branch and join both laterally and at depth (Fig. 1). These structures are associated with regional contraction within the San Gorgonio Pass region due to the nearby Big Bend of the San Andreas fault. The complexity of past and present faulting in this region has led Jon Matti, Doug Yule, and others to call this the “San Gorgonio knot.” Due to its complexity, this part of the San Andreas fault system may serve as both a barrier and an initiation point for devastating earthquakes. Research to understand and unravel the San Gorgonio knot has important implications for seismic hazard in southern California. The first author of this manuscript, Doug Yule, investigated slip rates and earthquake records in this enigmatic region from the mid-1990s until his death in 2022 (Fig. 2). During the 2010s, many researchers, including co-authors Cooke and Oglesby, were inspired by Doug Yule’s work in this area. Together, the three authors coordinated a five-year research effort to investigate active faulting within the San Gorgonio Pass, involving many researchers in the Southern California Earthquake Center (SCEC) community with support from SCEC, their various organizations, and institutions. Many of the results of this effort are compiled in this Geosphere thematic issue, with 11 papers that span earthquake geology, geomorphology, geodesy, potential field geophysics, crustal deformation models, and dynamic rupture models. Here, we highlight findings from these studies and honor the role that Doug played in bringing together earthquake scientists from a variety of disciplines to better understand the rupture potential through the complex faults of the San Gorgonio Pass. Much of this science can be organized around a few key questions.

Do stormy seas lead to better boats? Exploring the origins of the southern Californian plank canoe through ocean voyage modeling

What constraints and conditions are conducive to the innovation of more advanced watercraft technology? This paper explores this question by modeling ancient voyages in the Channel Island region of southern California. The Chumash and Tongva cultures of this region invented an advanced form of boat, the sewn plank canoe, around 500 CE. This new technology led to a rapid increase in maritime travel and transformed the maritime political economy of the region. In this paper we use agent-based ocean voyage modeling to examine the capacities of a range of indigenous boat types to travel important routes in the Channel Region at different times of the year. Our results indicate that while several different boat types would have been conducive for voyaging from the mainland coast to adjacent islands such as Limuw (Santa Cruz) and Pimu (Catalina), voyages to outlying islands, including Tuqan (San Miguel) and especially Haraasnga (San Nicolas), would have been difficult for much of the year in dugout or reed boats. We argue that early mariners plying these routes would have been under strong pressure to innovate faster and more seaworthy craft, possibly leading to the eventual development of the sewn plank canoe.

Artificial neural network modeling of solar photovoltaic panel energy output

Solar panel energy output is an essential parameter for the design and operation of renewable energy systems. Previously, little was known about the precise relationship between the energy outputs of solar panels with various meteorological, radiometric, and weather conditions in the southern California region. Without precise modeling or prediction systems, solar energy can potentially be wasted due to grid energy fluctuation. Thus, it is intended to use an artificial neural network (ANN) to develop solar panel energy output prediction model with a high degree of accuracy. A self-developed feedforward ANN model utilizing the Rectified linear unit (ReLu) activation function was used in the present study. Meteorological, weather, and sun irradiation data collected throughout the last year from a residential location have been used to train the models. The model’s performance was identified based on the minimum mean absolute error (MAE) and root mean square error (RMSE) and maximum linear correlation coefficient (R2). Further, the present self-developed ANN model was consistent with other solar energy experimental results and theoretical analysis. The developed ANN model using the Python programming language achieved a high R2 of more than 85% which ascertains the accuracy and suitability of the model to predict the daily solar energy output in local southern California area. This ANN modeling approach can be extended to many other applications such as SCORE, commercial, and residential building design.

Morphological and physiological adaptation of a desert shrub, Encelia farinosa, under drought stress

Water scarcity in arid and semi-arid regions affects ecosystem development. Therefore, elucidation of the mechanisms by which plant species in a given region respond to drought conditions may allow us to improve global vegetation. Encelia species are drought-tolerant and an important component of shrub communities in the arid and semi-arid regions of southern California, U.S.A.; these species are widely used for vegetation restoration in the region. This study aimed to investigate the morphological, and physiological responses in E. farinosa in relation to changes in soil properties under drought conditions. Plants were subjected to two levels of soil water regimes, drought and well-watered conditions (5% and 12% of gravimetric water content, respectively). After 3 months, above-ground biomass, basal diameter, leaf area, leaf and root relative water content, and root length were measured. The shoot:root ratio in dry mass and specific leaf area (SLA) were calculated. We also analyzed leaf and soil nutrients, including total C, N, and available P. In the drought treatment, E. farinosa showed significantly lower above-ground biomass, stem diameter, and shoot and root relative water content. Also, the drought treatment resulted in significantly lower leaf areas, while the SLA and root length were significantly higher. Leaf total C and N were higher in the drought treatment while leaf P was not affected by soil water content. The reduction in plant leaf area, root elongation and the increase in SLA under drought are known as drought avoidance strategies of plants, suggesting that E. farinosa hampers water loss by morphological regulations. Additionally, higher leaf content of C and N seems to be important for metabolic drought adaptation. Further research on osmotic adjustment and antioxidant defense systems will clarify the detailed drought tolerance mechanisms of E. farinosa in relation to C and N accumulation under drought conditions.

Numbers of wildlife fatalities at renewable energy facilities in a targeted development region

Increased interest in renewable energy has fostered development of wind and solar energy facilities globally. However, energy development sometimes has negative environmental impacts, such as wildlife fatalities. Efforts by regional land managers to balance energy potential while minimizing fatality risk currently rely on datasets that are aggregated at continental, but not regional scales, that focus on single species, or that implement meta-analyses that inappropriately use inferential statistics. We compiled and summarized fatality data from 87 reports for solar and wind facilities in the Mojave and Sonoran Deserts region of southern California within the Desert Renewable Energy Conservation Plan area. Our goal was to evaluate potential temporal and guild-specific patterns in fatalities, especially for priority species of conservation concern. We also aimed to provide a perspective on approaches interpreting these types of data, given inherent limitations in how they were collected. Mourning doves (Zenaida macroura), Chukar (Alectoris chukar) and California Quail (Callipepla californica), and passerines (Passeriformes), accounted for the most commonly reported fatalities. However, our aggregated count data were derived from raw, uncorrected totals, and thus reflect an absolute minimum number of fatalities for the monitored period. Additionally, patterns in the raw data suggested that many species commonly documented as fatalities (e.g., waterbirds and other nocturnal migrants, bats) are rarely counted during typical pre-construction use surveys. This may explain the more commonly observed mismatch between pre-construction risk assessment and actual fatalities. Our work may serve to guide design of future scientific research to address temporal and spatial patterns in fatalities and to apply rigorous guild-specific survey methodologies to estimate populations at risk from renewable energy development.

Nothing About Patients Without Patients: More Than Just a Catchphrase!

Health care is sometimes called a "team sport," yet patients were traditionally not considered to be "on the team" in medicine. In 2001, the Institute of Medicine (now National Academy of Medicine) published its seminal book Crossing the Quality Chasm, in which patient-centered care was identified as 1 of 6 quality aims. Many organizations have since included patient-centered care as an important aspect of quality, including The Joint Commission, Centers for Medicare and Medicaid, and many large employers. In the past 10 years, the focus on patient-centered care has expanded in the Kaiser Permanente, Southern California region to include innovative ways for patients to collaborate with health care teams to codesign improvement efforts that are truly patient-centered. We will describe 3 important approaches that have greatly increased the patient-centeredness of our organization: individual patient approaches; adding patients onto health care teams; and effectively utilizing patient and family advisory councils. We will provide examples of how all health care organizations can better partner with their patients to improve their ability to provide higher quality, safer, more equitable, and affordable health care. The slogan "Nothing About Patients Without Patients" was an early rallying cry of the patient engagement movement. It conveyed the idea that as with everything else in our society, patients now expect to have a say in the design and implementation of their care. We show that this is not only possible, but also highly effective and even necessary to improving care.

The evolution of “Hot” droughts in Southern California, USA from the 20th to the 21st century

Drought intensity and duration in southern California, USA during the 21st century has been exceptional, and the changing climate dynamics of drought in this arid and semiarid region have been linked to anthropogenic warming. We examine the frequency, intensity, and persistence of drought in southern California's two climatic divisions during 1900–2022 and use the monthly instrumental record of the Palmer Drought Severity Index (PDSI) to track how drought events have changed. We introduce an empirical definition of “hot drought” by combining mean summer PDSI values with standardized scores of mean summer minimum temperature. Droughts in southern California, USA have become increasingly more severe, frequent, and long-lived in the 21st century. Moderate drought conditions (PDSI < −2) were recorded during more than half of the months in the decade ending in 2020. Similarly, hot droughts, droughts associated with both water deficits and significantly above-normal temperatures, have become increasingly frequent in recent decades. By the 21st century, hot droughts were occurring in approximately half of the summers. Our findings support the idea that anthropogenic warming results in a changing drought climatology for arid and semiarid regions of southern California and that hot droughts will likely become the dominant drought type.

Anisotropy and Deformation Processes in Southern California From Rotational Observations

AbstractSeismic anisotropy in the upper mantle reveals geodynamic processes and the tectonic evolution of the Earth. The two most powerful methods, surface wave tomography, and shear‐wave splitting observations, cannot investigate the deep local anisotropy with good vertical and lateral resolution, resulting in poor constraints on plate deformation processes of the complex plate boundary beneath the Southern California region. Here, we show that the amplitude ratio of translational displacement and rotation makes it possible to retrieve the local anisotropy in the upper mantle. Azimuthal anisotropy in the asthenosphere is well determined and resolved in lateral and vertical directions. The fast axis retrieved from amplitude observations indicates the local rapid changes in plate deformation and complex pattern of mantle flow, which is compatible with the distributions of horizontal mantle flow illuminated by geodetic measurements, providing new insights on geodynamic processes of the Southern California region.

Potential hybridization among two species of California Salvia

Hybridization among plants is fairly common, particularly in instances where closely related species are sympatric. One example of a group in which hybridization has been detected is the genus Salvia (Lamiaceae; mint family). Salvia is a diverse genus consisting of about 1,000 species and is defined primarily by having only two stamens, each with their anther sacs separated by elongated connective tissue. The genus has diversity centers around the world, including Southwest Asia and the Mediterranean region, Mexico/ Central America, northern and central South America, and temperate East Asia. The genus also has a smaller species radiation of 19 species in western North America, subgenus Audibertia centered in California, and there has been documented evidence of hybridization within the subgenus. For this study we investigated potential hybridization among Salvia columbariae and Salvia greatae, two species with a sympatric distribution in the Orocopia Mountains region of Southern California. These species are placed in different sections of subgenus Audibertia, and hybridization has not yet been documented between sections of this subgenus. To examine relationships between the species, we compared molecular phylogenies from nuclear ribosomal DNA and chloroplast DNA. The resulting phylogenies did not show evidence of hybridization between these species. Although no evidence of hybridization was found, it is possible that additional sampling could yield different results.

QLTI-09. INTEGRATION OF NEURO-ONCOLOGY CLINICAL PATHWAYS IN PROVIDENCE SOUTHERN CALIFORNIA CLINICAL RESEARCH NETWORK: UPDATED INFO AFTER 17 MONTHS OF UTILIZATION

Abstract INTRODUCTION ClinicalPath is an evidence-based oncology decision support and analytics tool for cancer care. ClinicalPath’s treatment recommendations are prioritized based on efficacy, toxicity, and cost, by a nationwide committee of oncologists. The pathways are updated quarterly with the most recent evidenced-based treatment options that healthcare providers choose for adoption to help standardize therapy, improve quality, and decrease cost. The pathway system also allows for local clinical trial mapping to promote clinical trial awareness and help increase enrollment. ClinicalPath provides clinical pathways delivering personalized, evidence-based oncology guidance at the point of care. ClinicalPath’s network in North America serves more than 2,000 cancer physicians, within 54 practices in 31 states (15 academic medical centers, 29 hospital systems, and 9 community practices). Population: Our population is derived from multiple hospitals in Southern California within the Providence Health System. Methodology: Medical oncologists and Advanced Practice Providers received training on ClinicalPath before go-live. ClinicalPath was integrated into the Epic EHR in multiple Southern CA hospitals in a single month in Jan 2022. RESULTS On reviewing the utilization data within our Southern California region from Jan 2022-May 2023, 2681 treatment decisions were made across all cancers, with 86.7% of cancer patients treated on pathway. Of these decisions, 114 were made within the neuro-oncology specialty, and 70.2% of those cancer patients were treated on pathway. CONCLUSION We successfully integrated and initiated ClinicalPath in a multiple hospital-affiliated community oncology clinical trial network which included a strong neuro-oncology presence. We are actively working across our Southern California Region to map locally available clinical trials to promote awareness and increase enrollment. Provider utilization and patient on-pathway rates are actively monitored and will be updated.

Transformer Graph Convolutional Network for Relative Travel-Time Shift Prediction

Abstract Predicting surface-wave travel-time shifts is valuable for analyzing potential effects caused by changes in medium properties, station clock errors, instrument response errors, and other factors. Many current neural networks used in seismology are single-station models trained using single-station (pair) data. However, most seismic methods require knowledge of the spatial positions between multiple stations. Multiple stations contain rich interrelationships and spatial information that cannot be exploited by single-station models. We proposed a multistation neural network structure Transformer Graph Convolutional Network (TGCN) that utilizes temporal attention and spatial attention to capture spatiotemporal information for predicting relative travel-time shifts. Before that, we introduced a method that treats station pairs as nodes and constructs a graph with multiple station pairs. We collected original ambient noise waveforms from 2017 to 2019 in the Alaska region and 2010 to 2014 in the southern California region to obtain relative travel-time shift sequences of station pairs for model training and testing. To showcase the improvement of spatial information to the model, we compared TGCN with two other baseline single-station models—temporal convolutional network and long short-term memory. Our proposed method predicted travel-time values more accurately than the two baseline models, and it also exhibited slower decay in performance when predicting over larger intervals. We also found that the number of station pairs has an impact on the model. When there are a sufficient number of station pairs, the model can effectively utilize the rich spatial information and achieve higher accuracy. Our approach, which incorporates spatiotemporal information, provides outputs that are more efficient and accurate compared with the traditional single-station (pair) method that only considers temporal information, suggesting that spatial information does enhance the performance of the model.

Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California

Abstract. Environmental streamflow management can improve the ecological health of streams by returning modified flows to more natural conditions. The Ecological Limits of Hydrologic Alteration (ELOHA) framework for developing regional environmental flow criteria has been implemented to reverse hydromodification across the heterogenous region of coastal southern California (So. CA) by focusing on two elements of the flow regime: streamflow permanence and flashiness. Within ELOHA, classification groups streams by hydrologic and geomorphic similarity to stratify flow–ecology relationships. Analogous grouping techniques are used by hydrologic modelers to facilitate streamflow prediction in ungaged basins (PUB) through regionalization. Most watersheds, including those needed for stream classification and environmental flow development, are ungaged. Furthermore, So. CA is a highly heterogeneous region spanning gradients of urbanization and flow permanence, which presents a challenge for regionalizing ungaged basins. In this study, we develop a novel classification technique for PUB modeling that uses an inductive approach to group perennial, intermittent, and ephemeral regional streams by modeled hydrologic similarity followed by deductively determining class membership with hydrologic model errors and watershed metrics. As a new type of classification, this hydrologic-model-based classification (HMC) prioritizes modeling accuracy, which in turn provides a means to improve model predictions in ungaged basins while complementing traditional classifications and improving environmental flow management. HMC is developed by calibrating a regional catalog of process-based rainfall–runoff models, quantifying the hydrologic reciprocity of calibrated parameters that would be unknown in ungaged basins and grouping sites according to hydrologic and physical similarity. HMC was applied to 25 USGS streamflow gages in the “South Coast” region of California and was compared to other hybrid PUB approaches combining inductive and deductive classification. Using an average cluster error metric, results show that HMC provided the most hydrologically similar groups according to calibrated parameter reciprocity. Hydrologic-model-based classification is relatively complex and time-consuming to implement, but it shows potential for simplifying ungaged basin management. This study demonstrates the benefits of thorough stream classification using multiple approaches and suggests that hydrologic-model-based classification has advantages for PUB and building the hydrologic foundation for environmental flow management.