Stations In California Research Articles

Adaptive Coda‐Wave Imaging With Voronoi Tessellation

AbstractReconstructions of Green's functions by ambient noise interferometry enable the imaging of the Earth's subsurface. Coda waves from reconstructed Green's functions can be utilized to perform time‐dependent imaging of processes that vary substantially at daily to monthly time scales in the crust. Time‐lapse Coda‐Wave Imaging (CWI) can detect tiny changes in seismic velocity with high temporal resolution. While previous studies on CWI have mainly focused on the descriptions of coda waves' propagation, little attention has been paid to choosing effective inversion algorithms that are suitable for CWI. Here we address this issue by developing a pragmatic inversion approach based on Voronoi tessellation with mesh cells adapted to coda‐wave sensitivity kernels. Using seismic stations in Central California, we present both synthetic and real data imaging to demonstrate that this approach stabilizes the inversion, is computationally efficient, and provides spatially adaptive resolution. We further propose a heuristic approach for a quantitative assessment of spatial resolution based on multi‐scale checkerboard tests.

Estimates of biomass reductions of ozone sensitive herbaceous plants in California

Exposure to tropospheric ozone pollution impairs photosynthesis and growth in plants and this can have consequences for ecosystems. However, exposure-response research in the United States (U.S.) has historically focused on trees and crops, and less attention has been given to non-crop herbaceous species. We combined U.S. Environmental Protection Agency ozone monitoring data from the entirety of 2016 with published exposure-response relationships from controlled exposure experiments for twenty herbaceous plant species occurring in California. The U.S. Department of Agriculture PLANTS database was used to identify county-level occurrence data of these plant species. A kriged ozone exposure surface for 2016 was generated using data from monitoring stations in California and surrounding states, using Accumulated Ozone exposure over a Threshold of 40 ppb (AOT40) as an exposure metric. County-wide ozone exposure estimations were then combined with published exposure response functions for focal plants, and maps were created to estimate ozone-induced growth losses in the counties where the plants occur. Plant species had estimated annual growth losses from <1 % to >20 % based on exposure levels and sensitivity. Of the 20 species, 17 had predicted biomass loss >5 % in at least one county, emphasizing the vulnerability of herbaceous species at recent ozone concentrations. Butte, Nevada, Plumas, San Luis Obispo, and Shasta Counties, an area of about 31,652 km2, had the highest number of species (6) with >10 % estimated biomass loss, the loss threshold for European critical levels. White clover (Trifolium repens L.) was one of the most affected species with more than an estimated 10 % annual estimated growth loss over 59 % of the state. Overall, these estimated growth losses demonstrate potential for shifts in plant communities and negative effects on ecosystems. This study addresses critical policy needs for risk assessments on herbaceous species in a single year of ozone exposure.

Regional/Single Station Zenith Tropospheric Delay Combination Prediction Model Based on Radial Basis Function Neural Network and Improved Long Short-Term Memory

Atmospheric water vapor is an essential source of information that predicts global climate change, rainfall, and disaster-natured weather. It is also a vital source of error for Earth observation systems, such as the global navigation satellite system (GNSS). The Zenith Tropospheric Delay (ZTD) plays a crucial role in applications, such as atmospheric water vapor inversion and GNSS precision positioning. ZTD has specific temporal and spatial variation characteristics. Real-time ZTD modeling is widely used in modern society. The conventional back propagation (BP) neural network model has issues, such as local, optimal, and long short-term memory (LSTM) model needs, which help by relying on long historical data. A regional/single station ZTD combination prediction model with high precision, efficiency, and suitability for online modeling was proposed. The model, called K-RBF, is based on the machine learning algorithms of radial basis function (RBF) neural network, assisted by the K-means cluster algorithm (K-RBF) and LSTM of real-time parameter updating (R-LSTM). An online updating mechanism is adopted to improve the modeling efficiency of the traditional LSTM. Taking the ZTD data (5 min sampling interval) of 13 international GNSS service stations in southern California in the United States for 90 consecutive days, K-RBF, R-LSTM, and K-RBF were used for regions, single stations, and a combination of ZTD prediction models regarding research, respectively. Real-time/near real-time prediction results show that the root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and training time consumption (TTC) of the K-RBF model with 13 station data are 8.35 mm, 6.89 mm, 0.61, and 4.78 s, respectively. The accuracy and efficiency of the K-RBF model are improved compared with those of the conventional BP model. The RMSE, MAE, R2, and TTC of the R-LSTM model with WHC1 station data are 6.74 mm, 5.92 mm, 0.98, and 0.18 s, which improved by 67.43%, 66.42%, 63.33%, and 97.70% compared with those of the LSTM model. The comparison experiments of different historical observation data in 24 groups show that the real-time update model has strong applicability and accuracy for the time prediction of small sample data. The RMSE and MAE of K-RBF with 13 station data are 4.37 mm and 3.64 mm, which improved by 47.70% and 47.20% compared to K-RBF and by 28.48% and 31.29% compared to R-LSTM, respectively. The changes in the temporospatial features of ZTD are considered, as well, in the combination model.

Effect of Large Height Difference on Global Positioning System Solutions from a Commercially Available Software Package

Commercially available global navigation satellite system (GNSS) processing software is user-friendly and can be used in a wide variety of positioning applications from establishing geodetic control over large regions to deformation monitoring on a local scale. It is important to study the accuracy of positioning from commercial software for presurvey planning. Previously, this was done in several studies; however, none of them focused on the effect of large height differences in positioning solutions. It is known in the literature that large height differences on a baseline degrade GNSS solutions. In this study, we investigate the effect using TOPCON MAGNET v 4.0.1 through analysis of continuously operating reference stations (CORS) in California, US. Because the effect of height differences on global positioning system (GPS) baseline components has not yet been properly quantified, the positioning accuracy standards given in the surveying guidelines may not be appropriate and are often overoptimistic. When the height difference was less than about 300 m, the root mean square error was about 2–3 mm for the horizontal components and about 20 mm for the vertical component for baseline lengths of 10–20 km. However, for a height difference of 1,500 m, the error became 5–8 mm for the horizontal and 70 mm for the vertical components. In all cases, the vertical error was greater than the horizontal by a factor of 5–9, which is much greater than the expected 2–4 for GNSS. These results are likely only applicable to the version of the MAGNET software used in this study.

Effects of expanding electric vehicle charging stations in California on the housing market

Effects of expanding electric vehicle charging stations in California on the housing market

Estimation of Site Terms in Ground-Motion Models for California Using Horizontal-to-Vertical Spectral Ratios from Microtremor

ABSTRACT The horizontal-to-vertical spectral ratios from microtremor (mHVSR) data obtained at 196 seismic stations in California are used to evaluate three alternative microtremor-based proxies for site amplification for use in ground-motion models (GMMs): the site fundamental period (f0), the period-dependent amplitude of the mHVSR(T), and the normalized amplitude of the mHVSR(T). The alternative parameters are evaluated for the sites with and without measurements of VS30. If a VS30 measurement is not available for a site, then f0 has the highest correlation with the site amplification for short periods (T &amp;lt;1 s) and the normalized amplitude of the mHVSR(T) has the highest correlation for long periods (T ≥1 s). If a measurement of the VS30 is available for a site, then the normalized amplitude of the mHVSR(T) has the highest correlation for the site amplification not explained by VS30 for all periods. For both cases, the correlations are strongest at the longer periods as mHVSR(T) measurements excel at providing valuable information for sites with long-period amplification due to the deeper velocity structure. In particular, for sites with a VS30 measurement, the normalized mHVSR(T) amplitude provides more information about the long-period site terms than the basin depth currently used in GMMs. Empirical models of the median and standard deviation of the site terms based on the normalized mHVSR(T) curves are developed for the two cases. These models can be used directly in the ASK14 GMM to modify the median and aleatory standard deviation or they can be used to estimate the site-specific site term in the context of a partially nonergodic GMM. Including the mHVSR(T) measurement can have a significant effect on estimates of the ground motion at a site: the range 5%–95% on the observed HVSR(T) values corresponds to factors of 0.6–1.6 for the median spectral acceleration for periods between 0.5 and 4 s.

Evaluation of the P-wave seismogram approach to estimate VS30 in California

The P-wave seismogram method is utilized to estimate the V S30 at 194 ground motion recording stations in California. Comparison with the measured V S30 at these sites shows an average overestimation of 9%, which is similar to values reported by other studies for the P-wave seismogram method. The estimated V S30 are within ±50% of the measured V S30 for 85% of the sites. The V S30 residuals are interpreted with respect to the peak frequency obtained from horizontal-to-vertical spectral ratio (HVSR) measurements, and the results indicate that stations with estimated V S30 greater than 500 m/s may be systematically overestimated by the P-wave seismogram method when a distinct peak is identified in the HVSR. Thus, the peak frequency from HVSR can be used to identify sites with potential bias and also to correct this bias. The P-wave seismogram V S30 estimates from this study are compared to estimates from a geology/slope proxy, and the results suggest that the P-wave seismogram method achieves similar accuracy and less variability relative to the measured V S30 compared with the geology/slope proxy-based method.

A novel hybridized neuro-fuzzy model with an optimal input combination for dissolved oxygen estimation

Dissolved oxygen (DO) is one of the main prerequisites to protect amphibian biological systems and to support powerful administration choices. This research investigated the applicability of Shannon’s entropy theory and correlation in obtaining the combination of the optimum inputs, and then the abstracted input variables were used to develop three novel intelligent hybrid models, namely, NF-GWO (neuro-fuzzy with grey wolf optimizer), NF-SC (subtractive clustering), and NF-FCM (fuzzy c-mean), for estimation of DO concentration. Seven different input combinations of water quality variables, including water temperature (TE), specific conductivity (SC), turbidity (Tu), and pH, were used to develop the prediction models at two stations in California. The performance of proposed models for DO estimation was assessed using statistical metrics and visual interpretation. The results revealed the better performance of NF-GWO for all input combinations than other models where its performance was improved by 24.2–66.2% and 14.9–31.2% in terms of CC (correlation coefficient) and WI (Willmott index) compared to standalone NF for different input combinations. Additionally, the MAE (mean absolute error) and RMSE (root mean absolute error) of the NF model were reduced using the NF-GWO model by 9.9–46.0% and 8.9–47.5%, respectively. Therefore, NF-GWO with all water quality variables as input can be considered the optimal model for predicting DO concentration of the two stations. In contrast, NF-SC performed worst for most of the input combinations. The violin plot of NF-GWO-predicted DO was found most similar to the violin plot of observed data. The dissimilarity with the observed violin was found high for the NF-FCM model. Therefore, this study promotes the hybrid intelligence models to predict DO concentration accurately and resolve complex hydro-environmental problems.

P- andS-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California

SUMMARYThis 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.

Benefits of sustainable transportation infrastructure: Evidence from the housing market response to electric vehicle charging stations in California

Benefits of sustainable transportation infrastructure: Evidence from the housing market response to electric vehicle charging stations in California

Do early adopters pass on convenience? Access to and intention to use geographically convenient hydrogen stations in California

This study addresses two topics relevant to the expanding research on how early adopters of hydrogen fuel cell vehicles (FCVs) evaluate stations. First, we assess FCV adopters' access to available stations near home or on the way when they adopted their FCV. Second, we analyze characteristics of geographically convenient stations that drivers did not intend to use (“unlisted stations”) and compare to those they did (“listed stations”). Responses from a web-based survey distributed to FCV adopters in California indicate that nearly half lacked a station within 10 min’ drive of home, while nearly all had one on the way. Drivers did not intend to use nearly half of their geographically convenient stations. Compared to listed stations, unlisted stations are closer to other available ones and commonly only on the way, and several neighborhood-level differences are observed. These findings are important in the context of efforts to expand FCV uptake.

Monitoring Human Activity at a Very Local Scale with Ground-Motion Records: The Early Stage of COVID-19 Pandemic in California, U.S.A., New York City, U.S.A., and Mexicali, Mexico

Abstract In this article, we analyze the change in anthropogenic seismic noise level within a frequency range of 4–14 Hz, through a survey of seismic stations in California, United States, New York City, United States, and Mexicali, Baja California, Mexico from early December 2019 to late April 2020. Our analysis shows that some stations recorded a drop in anthropogenic seismic noise during the COVID-19 pandemic, and the timing of the anthropogenic noise decrease typically correlates with the timing of a strict curtailment of personal and economic activity issued by the local government. In other locations, the drop in the anthropogenic seismic noise appears not to follow the lockdown timing perfectly. During our analysis, we observed that many stations did not record a drop during the early stage of COVID-19 pandemic. Of the 19 stations of the Southern California Seismic Network that were surveyed, we found that only five show a similar extent of drop in anthropogenic seismic noise comparable to the Christmas holiday break in 2019. This suggests that the human activity that caused seismic noise did not significantly reduce during the COVID-19 pandemic near most surveyed stations in southern California. A further analysis implies that the primary seismic noise source in southern California might be traffic, and the continuation of industrial traffic, such as cargo transportation, during the COVID-19 pandemic may be the reason why many stations did not record a noise drop. Our results show that the anthropogenic seismic noise recorded by seismic stations is capable of indicating human activity, and that this metric is, particularly, powerful in measuring how localized communities initially responded to the COVID-19 pandemic.

Towards structural imaging using seismic ambient field correlation artefacts

SUMMARY Correlations of the ambient seismic field recorded by seismic stations carry information about the wave propagation between the stations. They also contain information about the ambient field—both the source of the ambient field, and sources of scattering that contribute to it. The waves that comprise the ambient field are subject to scattering due to the heterogeneous Earth, which can generate supplementary arrivals on the correlation functions. We use these effects to locate sources of signals linked to scattering. For this analysis, we use correlation functions computed from continuous signals recorded between 2013 and 2015 by a line of seismic stations in Central California. We identify spurious arrivals on the Vertical to Vertical and Transverse to Transverse correlation functions and use array analysis to map the source of scattering, which is linked to strong structural variations in the Coast ranges and at the border of the Great Valley.

An Unknown Maximum Lag‐Correlation Between Rainfall and Aerosols at 140–160 Minutes

Rainfall and aerosols play major roles in the Earth climate system and substantially influence our life. Here, the focus is on the local near-surface aerosol/rainfall correlations with time scales of minutes to days. We investigated 29 experiments including 14 specific rain events, with time resolutions of daily and 60, 30, and 10 min at 10 stations in Israel and California. The highest negative correlations were consistently at a positive lag of about 140–160 min where a positive lag means that the aerosol time series follows that of the rain. The highest negative value is suggested to be the probable outcome of immediate scavenging along with the rise in aerosol concentration after rain depending on aerosol sources, hygroscopic growth, and transport. The scavenging dominance is expressed by the mostly negative lag-correlation values in all experiments. In addition, the consistent lack of significant correlation found at negative lags suggests a weak aerosol effect on precipitation.

An Assessment of Global Positioning System Velocity Uncertainty in California

AbstractWe analyze data from 580 continuous global positioning system (GPS) stations in California to quantify differences in published velocity estimates from five analysis centers. Horizontal and vertical rates for individual stations can differ up to 5 mm/yr, with systematic differences in some areas comparable to deformation rates. Published velocity uncertainties vary between analysis centers and are systematically underreported in the horizontal relative to empirical uncertainties calculated from the scatter of analysis center velocities. In the vertical, published velocity uncertainties are both over and underreported and vary more widely between centers. An interpolated ensemble vertical velocity field shows high‐subsidence regions in the Central Valley and Salton Trough have the largest empirical uncertainties, while station density has a modest impact on uncertainties. Applications that rely on subcentimeter GPS accuracy should consider the possibility that formal errors published with velocity rate estimates understate true velocity uncertainties in both the horizontal and vertical.

Connection of Solar Activities and Forest Fires in 2018: Events in the USA (California), Portugal and Greece

The impact of solar activity on environmental processes is difficult to understand and complex for empirical modeling. This study aimed to establish forecast models of the meteorological conditions in the forest fire areas based on the solar activity parameters applying the neural networks approach. During July and August 2018, severe forest fires simultaneously occurred in the State of California (USA), Portugal, and Greece. Air temperature and humidity data together with solar parameters (integral flux of solar protons, differential electron flux and proton flux, solar wind plasma parameters, and solar radio flux at 10.7 cm data) were used in long short-term memory (LSTM) recurrent neural network ensembles. It is found that solar activity mostly affects the humidity for two stations in California and Portugal (an increase in the integral flux of solar protons of &gt; 30 MeV by 10% increases the humidity by 3.25%, 1.65%, and 1.57%, respectively). Furthermore, an increase in air temperature of 10% increases the humidity by 2.55%, 2.01%, and 0.26%, respectively. It is shown that temperature is less sensitive to changes in solar parameters but depends on previous conditions (previous increase of 10% increases the current temperature by 0.75%, 0.34%, and 0.33%, respectively). Humidity in Greece is mostly impacted by solar flux F10.7 cm and previous values of humidity. An increase in these factors by 10% will lead to a decrease in the humidity of 3.89% or an increase of 1.31%, while air temperature mostly depends on ion temperature. If this factor increases by 10%, it will lead to air temperature rising by 0.42%.

Concentrated solar driven thermochemical hydrogen production plant with thermal energy storage and geothermal systems

In this study, a concentrated solar and geothermal based integrated system is thermodynamically investigated to produce useful outputs, such as hydrogen, space heating, electricity, and fresh water. The present system consists of a thermochemical copper-chlorine (Cu–Cl) hydrogen production plant, a geothermal system, a trilateral ammonia Rankine cycle power plant, a multi-effect distillation (MED) desalination unit, a parabolic trough collector (PTC) concentrated solar power (CSP) system with thermal energy storage (TES), and a residential heat pump. As a case study, a hypothetical community is considered near Geysers in California. All produced commodities, except for hydrogen, are utilized for domestic consumption purposes in the community. Hydrogen is supplied to fueling stations in California. One of the most critical requirements of the system, high-grade heat for Cu–Cl cycle, is met by PTC CSP with molten salt TES system. Space heating, electricity, and fresh water productions are powered by geothermal energy systems. Several simulations have been conducted for detailed analyses of the proposed system. Energy and exergy analyses are conducted for the overall system and each component. Some of the subsystems are comparatively assessed and sensitivity analysis is performed to investigate the effects of different parameters on the performance of the entire system as well as various subsystem.Cost assessment studies are conducted for the overall system and its subsystems. 5.5 MWp geothermal and 12.97 MWt solar systems produced 296.9 tons of hydrogen fuel at $2.84/kg cost, 47.6 GWh electricity at $0.03/kWh cost with heat and fresh water due to the simulations. Both energy and exergy efficiencies are found to be 27.4% and 17.3% respectively for the overall proposed system at the ambient conditions.

How early hydrogen fuel cell vehicle adopters geographically evaluate a network of refueling stations in California

After a few years of initial sales, there is an opportunity to analyze how early hydrogen fuel cell vehicle (FCV) adopters evaluated the spatial arrangement of a network of stations prior to adoption. Since strategies differ on how best to arrange initial stations in a region to facilitate adoption, understanding how they did so informs future station planning methods. We distributed a web-based survey to 129 FCV adopters throughout California in 2019, asking them where they lived and traveled at the time of adoption, up to five stations they planned to use, and subjective reasons for listing those stations. We estimated shortest travel times to respondents' homes and other frequent locations, and deviations from frequently traveled routes. We compared differences in subjective and objective convenience for primary, secondary, and lower-ranked stations, and tabulated the different combinations of stations that satisfied adopters' various geographic criteria. Over 80% planned to rely on a portfolio of multiple stations subjectively convenient to key activity locations, and nearly 25% who provided subjective geographic criteria for listing stations did not include “near home” as their top reason for their primary or secondary station. Estimated travel times to stations subjectively considered “near” home, work, and other location types exhibit variability, but consistently decay beyond 90 min. Primary stations are subjectively and objectively more convenient to home and work than lower-ranked stations, and more associated with subjective convenience to home and objective convenience to work than secondary stations. Other destination types align with lower-ranked stations.

Multifractal Cross Correlation Analysis of Agro-Meteorological Datasets (Including Reference Evapotranspiration) of California, United States

The multifractal properties of six acknowledged agro-meteorological parameters, such as reference evapotranspiration (ET0), wind speed (U), incoming solar radiation (SR), air temperature (T), air pressure (P), and relative air humidity (RH) of five stations in California, USA were examined. The investigation of multifractality of datasets from stations with differing terrain conditions using the Multifractal Detrended Fluctuation Analysis (MFDFA) showed the existence of a long-term persistence and multifractality irrespective of the location. The scaling exponents of SR and T time series are found to be higher for stations with higher altitudes. Subsequently, this study proposed using the novel multifractal cross correlation (MFCCA) method to examine the multiscale-multifractal correlations properties between ET0 and other investigated variables. The MFCCA could successfully capture the scale dependent association of different variables and the dynamics in the nature of their associations from weekly to inter-annual time scales. The multifractal exponents of P and U are consistently lower than the exponents of ET0, irrespective of station location. This study found that joint scaling exponent was nearly the average of scaling exponents of individual series in different pairs of variables. Additionally, the α-values of joint multifractal spectrum were lower than the α values of both of the individual spectra, validating two universal properties in the MFCCA studies for agro-meteorological time series. The temporal evolution of cross-correlation determined by the MFCCA successfully captured the dynamics in the nature of associations in the P-ET0 link.

Assessing the performance of GPS tomography at retrieving water vapour fields during landfalling atmospheric rivers over southern California

AbstractAccurate monitoring of water vapour evolution is essential to gain a better understanding of the atmospheric river movement, as well as predicting the severity and life cycle of heavy precipitation. The use of hundreds of ground Global Positioning System (GPS) stations in southern California provided a good opportunity to monitor the water vapour changes associated with landfalling atmospheric rivers. Based on a tomographic technique, the 15 min‐resolution water vapour density fields of southern California were reconstructed for February 2019. The assessments by radiosonde and the European Centre for Medium‐Range Weather Forecasts' (ECMWF) Re‐Analysis Interim (ERA‐Interim) database reanalysis show that tomography can retrieve the density profiles with an overall accuracy of about 1.1 g·m–3. In addition, both assessments indicate that the tomographic solutions of altitudes &gt; 6 km are not reliable as relative root mean square error can reach up to 500% in the uppermost layer. Nevertheless, tomographic integrated water vapour is consistent with those derived from radiosonde and ERA‐Interim because their discrepancies are basically &lt; 3 mm. The tomographic density fields were then applied to monitor water vapour evolution associated with two atmospheric river landfalls that occurred on February 2 and February 13, 2019. Results show that both atmospheric rivers made landfall from the northwest and passed over southern California in a southeasterly direction. The series of density profiles reveals that the high‐elevation mountains can block the atmospheric river movement, resulting in the concentration of water vapour at the foot of the mountains.