California Basin Research Articles

Quantifying the Impacts of Fire‐Related Perturbations in WRF‐Hydro Terrestrial Water Budget Simulations in California's Feather River Basin

ABSTRACTWildfire activity in the western United States (WUS) is increasingly impacting water supply, and land surface models (LSMs) that do not explicitly account for fire disturbances can have critical uncertainties in burned areas. This study quantified responses from the Weather Research and Forecasting Hydrological modelling system (WRF‐Hydro) to a suite of fire‐related perturbations to hydrologic soil and runoff parameters, vegetation area, land cover classifications and associated vegetation properties, and snow albedo across the heavily burned Feather River Basin in California. These experiments were used to quantify the impacts of fire‐related perturbations in model simulations under the observed meteorological conditions during the 2000–2022 water years and determine whether applying these fire‐related perturbations enhanced post‐fire model accuracy across the 11–12 post‐fire months evaluated herein. The most comprehensive fire‐aware simulation consistently modelled enhanced annual catchment streamflow (by 8%–37%), subsurface flow (by 72%–116%), and soil moisture (by 4%–9%), relative to the baseline simulation which neglected fire impacts. Simulated fire‐enhanced streamflow was predominately attributable to fire‐induced vegetation area reductions that reduced transpiration. Simulated streamflow enhancements occurred throughout the water year, excluding early‐summer (e.g., May–June) when the baseline simulation modelled relatively more snowmelt and streamflow because fire perturbations caused earlier model snow depletion. Vegetation area reductions favoured increased model ground snow accumulation and enhanced snow ablation while imposed snow albedo darkening enhanced ablation, ultimately resulting in similar peak SWE and earlier snow disappearance (on average by 8‐days) from the most comprehensive fire‐aware simulation relative to the baseline simulation. The baseline simulation had large degradations in streamflow accuracy following major fire events that were likely partially attributable to neglecting fire disturbances. Applying fire‐related perturbations reduced post‐fire streamflow anomaly biases across the three study catchments. However, remaining large post‐fire streamflow uncertainties in the fire‐perturbed simulation underscores the importance of additional observationally constrained fire‐disturbance model developments.

Multi-site comparison and source apportionment of equivalent Black Carbon mass concentrations (eBC) in United States: Southern California Basin and Rochester, New York,

Spatial and temporal variability of equivalent black carbon (eBC) mass concentrations were studied using Aethalometers (AE33 and AE21) at 10 sites, including 5 urban and 5 near-road across the south coast of California and New York-Rochester. Statistical methods were applied to perform intra-urban and multi-site comparisons. Given that nominal eBC values provided by the Aethalometer were significantly overestimated, eBC concentrations were corrected using an appropriate multiple-scattering enhancement correction factor (C0) to accurately calculate light absorption. Annual and seasonal variations highlighted the significant contributions of traffic emissions to eBC mass concentrations at all sites. Source apportionment using the Aethalometer approach demonstrated that fuel combustion—primarily from gasoline and diesel vehicles— accounted for up to 80% of eBC emissions. Emission sources were found to be largely region specific. Our findings suggest that the implementation of restrictive regulations aimed at reducing gasoline and diesel vehicle emissions, such as California's Tier 3 (SULEV, 2015) and New York (2017), has led to a higher proportion of cleaner, emission-controlled vehicles in the South Coast Air Basin compared to Rochester. However, the effects of these measures may require more time to be fully observed in traffic-polluted areas across the US.

Using environmental DNA to assess the response of steelhead/Rainbow Trout and Coastrange Sculpin populations to postfire debris flows in coastal streams of Big Sur, California

AbstractObjectiveDebris flows are among the most extreme disturbances to streams and are predicted to become more frequent under climate change. We assessed the response of steelhead Oncorhynchus mykiss (anadromous Rainbow Trout)/Rainbow Trout (hereafter, collectively referred to as O. mykiss) and Coastrange Sculpin Cottus aleuticus populations to major postfire debris flows in two small coastal basins of California using noninvasive environmental DNA (eDNA) sampling.MethodsWe analyzed water samples from disturbed reaches for eDNA in the first two summers after debris flows. In Big Creek, all Coastrange Sculpin habitat was disturbed by debris flows, but a major tributary occupied by O. mykiss was not affected. In Mill Creek, all available habitat for both species was disturbed. We also sampled two unburned basins as undisturbed reference streams.ResultIn Big Creek, O. mykiss eDNA was detected in all water samples during both years, and concentrations in some samples approached the lowest concentrations in the reference streams. Coastrange Sculpin eDNA was detected only in a single water sample in the first year and was not detected in the second year. In Mill Creek, neither species was detected in the first year, but during the second year, O. mykiss eDNA was detected at very low concentrations in 40% of samples and Coastrange Sculpin eDNA was detected in a single sample.ConclusionOur results indicate that O. mykiss and Coastrange Sculpins either survived in or quickly reoccupied the disturbed reaches from within the basins. However, the detection rates for cases in which all habitat for a species in a basin was affected by debris flows indicate that abundances were very low, suggesting that persistence may be uncertain unless there is successful reproduction or immigration. Finally, eDNA appears to be effective for monitoring sensitive fish populations after a major disturbance; however, detection rates in individual samples may be low and require appropriate sampling designs to achieve the desired detection probability.

Strategies for hydrogen storage in a depleted sandstone reservoir from the San Joaquin Basin, California (USA) based on high-fidelity numerical simulations

Depleted hydrocarbon reservoirs are promising candidates for underground hydrogen (H2) storage (UHS) due to their known (and often high) storage capacity, well-identified geological conditions, reduced cushion gas requirements, and adaptable existing infrastructure. While these reservoirs have shown success in natural gas storage, their performance for pure H2 storage remains untested, necessitating further research. To address this knowledge gap, we conduct high-fidelity multiphase compositional reservoir simulations to evaluate the technical feasibility of UHS in a depleted hydrocarbon reservoir within the Temblor Formation at the North Belridge Field from the San Joaquin Basin, California, USA. This reservoir zone comprises five distinct sand layers separated by four shale intervals and is bounded above and below by thick shales (overburden and underburden, respectively). This research has two primary objectives: (a) to assess the feasibility of two distinct UHS strategies—one that involves storing H2 across all sand layers and another that stores an equal amount of H2 in the single thickest sand layer, and (b) to evaluate the effect of interbedded shale permeability on UHS performance. Our findings indicate that UHS in the Temblor Formation is technically viable, showing enhanced H2 withdrawal efficiency and produced H2 purity over successive cycles with minimal leakage risk. The two storage strategies significantly impact UHS performance in the interbedded sandstone sequence. Storing H2 across all sand layers results in higher H2 withdrawal efficiency, but lower produced H2 purity. In contrast, storing H2 in a single sand layer leads to lower withdrawal efficiency but consistently high H2 purity. In addition, H2 storage in a single sand layer results in higher reservoir pressure buildup, implying greater geomechanical risks. The impact of interbedded shale permeability on UHS performance is negligible across the range of uncertainty for shale permeability. This research not only lays the groundwork for potential UHS implementation in the abundant sandstones of the San Joaquin Basin of California, but also offers valuable insights for other depleted hydrocarbon reservoirs under consideration for UHS that share similar properties.

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.

Basin effects from 3D simulated ground motions in the Greater Los Angeles region for use in seismic hazard analyses

We develop basin-depth-scaling models (i.e. “basin terms”) from the long-period ([Formula: see text]) simulated ground motions of the Southern California Earthquake Center (SCEC) CyberShake project for use in seismic hazard analyses at sites within the sedimentary basins of southern California. Basin terms use the Next Generation Attenuation (NGA)-West-2 ground-motion models (GMMs) as reference models and use their functional forms with slight modifications. We investigate the use of two approaches to incorporate the time-averaged shear-wave velocity in the upper 30 m ([Formula: see text]) in these calculations and find that the use of site-specific and uniform [Formula: see text] has minor effects on the resulting basin terms for this data set. By centering the simulated ground motions on the basin terms, we separate the information from the simulations about absolute ground-motion level from information relating to the relative amplifications, such as the differences between shallow- and deep-basin sites. Recent observations from sedimentary basins of southern California indicate that additional amplification effect may persist at relatively shallow basin depths (i.e. the GMM basin terms should have positive values when differential depths, [Formula: see text], are near zero), and we present models for “centered” and “adjusted” basin-depth scaling models that reflect this potential. The simulation-modified GMMs are appropriate for crustal sources and for deep-basin sites ([Formula: see text]) within basins of the Greater Los Angeles region, for the magnitudes and distances defined by each of the reference NGA-West-2 GMMs.

On-road NOx and NH3 emissions measurements from in-use heavy-duty diesel and natural gas trucks in the South Coast air Basin of California

This study characterized nitrogen oxides (NOx) and ammonia (NH3) emissions from five in-use goods movement vehicles, including one diesel vehicle without selective catalytic reduction (SCR), two diesel vehicles with SCR, and two ultra-low NOx natural gas vehicles equipped with three-way catalysts (TWCs). Emissions testing was performed under real-world driving conditions in typical goods movement routes in the South Coast Air Basin (SCAB) of California. NOx emissions varied depending on the vehicle and the route. The no-SCR diesel vehicle showed the highest NOx emissions over all the routes. One of the SCR-equipped vehicles showed NOx emission rates that were two times higher than the other SCR-equipped vehicle with similar engine model year and mileage, which may be attributed to catalyst deterioration. The SCR-equipped diesel vehicles were within two to three times 0.2 g/bhp-hr certification standard over the different routes. The natural gas vehicles showed average NOx emissions around or below the optional Low NOx emissions standard of 0.02 g/bhp-hr. The three-bin moving average window (MAW) method was utilized to show the NOx emissions across various modes of operation, including idle, low load, and medium/high load. The highest fraction of vehicle operation was found in either the low load or medium/high load bins, depending on the route. NOx emissions for each of the bins varied between different vehicles, and are further analyzed in this paper. NH3 emissions formation was favored for the TWC-equipped natural gas vehicles, which produced about 20 times more NH3 emissions than the SCR-equipped diesel vehicles.

Trace elements and organic geochemical fingerprinting of natural crude oils from the Monterey Formation, offshore Santa Maria Basin, California

Based on our recent analytical method development using microwave digestion and triple-quad ICP-MS, we have analyzed up to 57 elements for 20 natural crude oil samples from the offshore Santa Maria Basin of California, a prolific producing basin thought to be sourced dominantly from the Monterey Formation. Our goal is mainly to see if there were oil-oil correlations between wells (fractured reservoir mixing) or no correlation (locally sourced from an unconventional restricted depth reservoir or compartment), and which part (upper and/or lower) of the Monterey Formation acted as the primary source of oil. The results show distinct fingerprints based on V/Mo and Ni/Mo ratios derived from each of the wells where the oils were sampled during stem tests, indicating that they might be partially correlated with restricted intervals of the stratigraphy (or lithofacies). The V/Mo and Ni/Mo ratios of the oils also show that the lower Phosphatic-carbonate lithofacies of Monterey Formation may be a primary source of the oils. Vanadium and Ni contents, V/(Ni + V) ratios, and sulfur content of the oils indicate that the Monterey Formation was deposited under reducing marine conditions. The linear correlation between Mg and Ca contents and non-linear correlation between P and Ca contents in the oil samples may indicate that dolomite-rich rather than apatite-rich portions of Monterey Formation may be a critical source rock component. The similarity between REY distribution patterns of the oils and authigenic carbonate implies that carbonates might have been correlated with formation of the oils. Positive Eu anomalies in most oils reveal possible hydrothermal influences associated with magmatic and slab window effects as a consequence of the Farallon-Pacific Ridge subduction, higher heat flow, and later transtensional and transpressional deformation during deposition of Monterey Formation. The comparable REY distribution patterns for the oils and outcrop shales of Lions Head reveal potential oil-source correlations.

Strain localization instability in seafloor spreading centers of the Carmen Basin, southern Gulf of California

This study provides new insights into the geological evolution of the Carmen Basin (CB) in the southern Gulf of California (GC). Using high-resolution bathymetry and seismic reflection profiles, we establish a plate-kinematic framework for this oblique-divergent rift system. By analyzing the crustal composition, we investigate the growth of the bounding transform faults and their role in accommodating transtensional shearing. We propose that the mantle upwelling beneath the CB is a northward extension of the East Pacific Rise. The CB consists of three sub-basins with distinct geometries, morphologies, and evolutionary histories. While the southern and central sub-basins are mostly abandoned, the northern sub-basin is currently experiencing seafloor spreading. This is supported by the presence of younger oceanic crust, approximately less than 1.5 Ma, juxtaposed with older oceanic crust that aligns in age with the nearby Guaymas and Farallon basins to the north and south, respectively. Our findings also indicate favorable geological conditions in the CB for the development of hydrothermal systems similar to those observed in the neighboring Guaymas and Pescadero basins in the southern GC.

The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations

AbstractWe present a synthetic multi‐scale, multi‐physics dataset constructed from the Kimberlina 1.2 CO2 reservoir model based on a potential CO2 storage site in the Southern San Joaquin Basin of California. Among 300 models, one selected reservoir‐simulation scenario produces hydrologic‐state models at the onset and after 20 years of CO2 injection. Subsequently, these models were transformed into geophysical properties, including P‐ and S‐wave seismic velocities, saturated density where the saturating fluid can be a combination of brine and supercritical CO2, and electrical resistivity using established empirical petrophysical relationships. From these 3D distributions of geophysical properties, we have generated synthetic time‐lapse seismic, gravity and electromagnetic responses with acquisition geometries that mimic realistic monitoring surveys and are achievable in actual field situations. We have also created a series of synthetic well logs of CO2 saturation, acoustic velocity, density and induction resistivity in the injection well and three monitoring wells. These were constructed by combining the low‐frequency trend of the geophysical models with the high‐frequency variations of actual well logs collected at the potential storage site. In addition, to better calibrate our datasets, measurements of permeability and pore connectivity have been made on cores of Vedder Sandstone, which forms the primary reservoir unit. These measurements provide the range of scales in the otherwise synthetic dataset to be as close to a real‐world situation as possible. This dataset consisting of the reservoir models, geophysical models, simulated time‐lapse geophysical responses and well logs forms a multi‐scale, multi‐physics testbed for designing and testing geophysical CO2 monitoring systems as well as for imaging and characterization algorithms. The suite of numerical models and data have been made publicly available for downloading on the National Energy Technology Laboratory's (NETL) Energy Data Exchange (EDX) website.

Exploring Climate-Driven agricultural water shortages in a Snow-Fed basin using a water allocation model and Machine learning

Water allocation models (WAM) can capture complex feedbacks between water infrastructure, water supply, water demand, and water rights structure. They are an important tool for water managers to reduce conflict and aid in future water resource planning. Future streamflow reductions and shifts in timing are anticipated in snow-dominated watersheds of the western United States due to projected reductions in snow water storage. These basins tend to be water-limited and rely on groundwater to support agricultural demand. Water managers in the snow-fed, agricultural Walker River Basin (WRB) in California and Nevada use a dynamically coupled WAM and physically based groundwater flow model as a decision support tool. Large computation time is required to simulate groundwater dynamics, limiting the number of model scenarios that can feasibly be run to test alternative management scenarios under climate change. Machine learning (ML) techniques have been used to capture complex nonlinear hydrologic dynamics in other contexts, but to our knowledge there have not been efforts to use ML to increase computational efficiency in modeling groundwater responses to agricultural water use. We use extreme gradient boosting to replace the groundwater sub-model for the WRB decision support tool, resulting in accurate estimates of reservoir storage (monthly error almost always < 5% of reservoir capacity), streamflow (total outflow errors from −3% to 20% depending on scenario) and agricultural water shortages (-12% to 18% error in total shortage depending on scenario). For comparison, we found that omitting groundwater modeling from the WAM results in a larger overprediction of outflows (2 to 58%) and underprediction in agricultural water shortages (0 to 41%) across a range of scenarios. We use this validated ML method to explore the sensitivity of agricultural water availability to a range of streamflow volumes and streamflow timings. Results indicate that agricultural water shortages are more sensitive to annual streamflow volume in comparison to shifts in streamflow timing. Our model shows that reservoirs can buffer the impact of altered flow timing on agricultural production, except under extreme conditions in which all precipitation falls as rain (no snow storage) and streamflow peaks in the winter. Future work will explore these thresholds in more detail. While the results here are site-specific, the methodology for coupling a WAM with a ML representation of surface–groundwater interactions can be applied to a wide variety of water resource modeling applications. These results also provide a stark example of the importance of snowpack storage and groundwater management to western US water infrastructure.

Unexpected deterioration of O3 pollution in the South Coast Air Basin of California: The role of meteorology and emissions

Tropospheric ozone (O3) pollution has long been a prominent environmental threat due to its adverse impacts on vulnerable populations and ecosystems. In recent years, an unexpected increase in O3 levels over the South Coast Air Basin (SoCAB) of California has been observed despite reduced precursor emissions and the driving factors behind this abnormal condition remain unclear. In this work, we combine ambient measurements, satellite data, and air quality modeling to investigate O3 and precursor emission trends and explore the impacts of meteorological variability and emission changes on O3 over the SoCAB from 2012 to 2020. Changes in O3 trends were characterized by declining O3 in 2012–2015, and increasing O3 afterwards with the most extreme O3 exceedances in 2020. Basin-wide increases of MDA8 O3 concentrations over warm season were depicted between 2012 and 2020, with the most significant enhancements (5–10 ppb) observed in San Bernardino County. Persistent heatwaves and weak ventilation on consecutive days were closely correlated with O3 exceedances (r2 above 0.6) over inland SoCAB. While decreasing trends in NOx (−4.1%/yr) and VOC emissions (−1.8%/yr) inferred from emission inventory and satellites during 2012–2020 resulted in a slow transition for O3 sensitivity from VOCs-limited to NOx-limited, model simulations performed with fixed meteorology indicate that unfavorable meteorological conditions could largely offset regulation benefits, with meteorology anomaly-induced monthly O3 changes reaching 20 ppb (May 2020) and the deterioration of O3 pollution in 2016, 2017, and 2020 was largely attributed to unfavorable meteorological conditions. Nevertheless, anthropogenic emission changes may act as the dominant factor in governing O3 variations across the SoCAB when net effects of meteorology are neutral (typically 2018). This work provides a comprehensive assessment of O3 pollution and contributes valuable insights into understanding the long-term changes of O3 and precursors in guiding future regulation efforts in the SoCAB.

Emissions, meteorological and climate impacts on PM2.5 levels in Southern California using a generalized additive model: Historic trends and future estimates

Annual fine particulate matter (PM2.5) mass concentrations in the South Coast Air Basin (SoCAB) of California decreased from around 30 μg/m3 to 11 μg/m3 between 2000 and 2013 but rose from 11 μg/m3 to 13 μg/m3 between 2014 and 2018, raising important questions about the effectiveness of ongoing emission control policies. A two-step generalized additive model (GAM)-least squares approach was developed to explore the effects of emissions, large-scale climate events and meteorological factors on daily PM2.5 mass concentrations from 2000 to 2019 to quantitatively link impacts of emissions and meteorological on PM2.5 and to assess factors leading to the increase. The GAM had an R2 = 0.99 and root mean square error (RMSE) = 0.7 μg/m3 for the annual average PM2.5 concentrations. The two-step method had an R2 = 0.93 and RMSE = 4.07 μg/m3 for the 98th percentile 24-hr average PM2.5 concentrations. Variations in both emissions and relative humidity were of high importance compared with other included factors. Interactions of NH3 emissions with NOx and SO2 emissions, which lead to ammonium nitrate and sulfate aerosol formation, were the most important factors. Meteorological effects on PM2.5 explained the majority of the daily PM2.5 fluctuations. Emission changes (increases in SO2 and PM2.5) led to increases in predicted PM2.5 between 2014 and 2018. Predicted future PM2.5, using projected emissions and meteorological data from model simulations of representative concentration pathway (RCP) scenarios, are around 12 μg/m3 (annual) and 30 μg/m3 (98th percentile daily), which are both close to the current National Ambient Air Quality Standards (NAAQS) for PM2.5. Meteorological impacts on the predicted PM2.5 in future years lead to variations of ±2 μg/m3 for the annual average and ±5 μg/m3 for the 98th percentile daily level. Future climate changes lead to a probable year-to-year variation that will let PM2.5 levels in some years exceed the standard.

Escaping the heat: Climate change and visitation to the Lake Tahoe basin

The Lake Tahoe Basin of California and Nevada is a popular destination for visitors. As temperatures rise globally, visitation to this climatic refuge is likely to grow. Increased visitation contributes to increases in vehicle miles of travel (VMT), congestion, crashes, emissions, and deteriorating travel experiences at Lake Tahoe, while also contributing to friction between visitors and residents. Policymakers are exploring ways to mitigate these problems, but one key uncertainty to first address is exactly how many visitors are in the Tahoe Basin at a given time and how and where they travel while there. It is also unclear how visitation is influenced by heat in the nearby lower-elevation population centers during the summertime, which is when visitation is the highest. This study thus seeks to 1) quantify day and overnight visitors in the Tahoe Basin, 2) estimate VMT attributable to each group, and 3) model the influence of hot temperatures nearby on visitation and VMT within the Tahoe Basin. We use existing survey data, network GIS analysis, and publicly available weather data to model the relationship between particularly hot conditions and vehicle flow through two of the region’s primary entry points and estimate in-basin VMT attributable to travel through them. We evaluate several ways of defining “hot conditions” nearby and find that between 500 and 1,200 additional vehicles enter the basin at these two points alone, generating an additional in-basin daily VMT of about 12,000 to 33,000 when hot conditions exist. Quantifying the magnitude of the challenge enables managers to design and scale interventions to mitigate impacts.

An improved rescaling algorithm for estimating groundwater depletion rates using the GRACE satellite

ABSTRACT The Gravity Recovery And Climate Experiments (GRACE) satellite mission has been instrumental in estimating large-scale groundwater storage changes across the globe. GRACE observations include significant errors, so pre-processing is normally required before the data are used. In particular, the terrestrial water storage anomalies (TWSA) are usually filtered to reduce the effects of measurement errors and then rescaled to reduce the unintended impacts of the filtering. The scaling is typically selected to maximize the Nash-Sutcliffe Efficiency (NSE) between the rescaled filtered TWSA and the original TWSA from large-scale hydrologic models that represent an incomplete water budget. The objectives of this study are as follows (1) to evaluate the use of NSE in the current GRACE rescaling methodology, (2) develop an improved methodology that incorporates a complete regional water budget, and (3) examine the impacts of the rescaling methodology on regional assessments of groundwater depletion. To evaluate the use of NSE as a performance metric, we compare it to an analytical solution that restores the relative variability between the rescaled filtered and original GRACE TWSA series. The relative variability approach produces more reliable estimates when comparing to TWSA estimates from global positioning systems (GPS) for the Sacramento and San Joaquin River basins in California. Rescaling the complete regional water budget results in a larger scale factor than the scale factor from the large-scale hydrologic model outputs, and the new TWSA results are more consistent with those from GPS. The large scale factor also suggests that regional groundwater depletion is more severe than previously estimated.

Uncertainty Decomposition to Understand the Influence of Water Systems Model Error in Climate Vulnerability Assessments

AbstractClimate vulnerability assessments rely on water infrastructure system models that imperfectly predict performance metrics under ensembles of future scenarios. There is a benefit to reduced complexity system representations to support these assessments, especially when large ensembles are used to better characterize future uncertainties. An important question is whether the total uncertainty in the output metrics is primarily attributable to the climate ensemble or to the systems model itself. Here we develop a method to address this question by combining time series error models of performance metrics with time‐varying Sobol sensitivity analysis. The method is applied to a reduced complexity multi‐reservoir systems model of the Sacramento‐San Joaquin River Basin in California to demonstrate the decomposition of flood risk and water supply uncertainties under an ensemble of climate change scenarios. The results show that the contribution of systems model error to total uncertainty is small (∼5%–15%) relative to climate based uncertainties. This indicates that the reduced complexity systems model is sufficiently accurate for use in the context of the vulnerability assessment. We also observe that climate uncertainty is dominated by the choice of general circulation model and its interactive effects with the representative concentration pathway (RCP), rather than the RCP alone. This observation has implications for how climate vulnerabilities should be interpreted.

Mapping of snow water equivalent by a deep-learning model assimilating snow observations

To estimate snowpack water storage in mountain basins, this study presents a framework that couples a deep-learning long short-term memory (LSTM) model and a zonal bias-correction approach for assimilating ground snow observations. We explored the framework’s ability for snow water equivalent (SWE) mapping and forecasting in a domain, including the Feather, Yuba-Bear, and American River basins in California’s Sierra Nevada. Through a series of different modeling experiments, performances of the LSTM model and bias-correction approach were independently investigated. The LSTM model trained with meteorological forcing (precipitation and temperature), landscape attributes, and antecedent lagged snow conditions, showed good agreement with SWE reanalysis data (Nash-Sutcliffe efficiency, NSE = 0.99). However, for continuous SWE projection without using SWE reanalysis as the antecedent snow condition, the LSTM model underestimated basin-scale and site-scale SWE (NSE = 0.19), reflecting large discrepancies since model bias accumulates over time. Thus, a zonal inverse-distance-weighting (IDW) bias-correction approach was proposed to assimilate ground observations and correct model trajectory. With the bias correction, SWE estimates were significantly improved (NSE = 0.79). The zonal IDW approach accounts for snow differences across different areas by zonal distance, based on historical snow patterns, showing better predictions than the original IDW, which showed larger biases in complex terrain with a rain shadow. We observed that SWE estimates from the framework were not sensitive to the difference in precipitation and temperature forcings between observation-based data and one-day-ahead weather forecasts, suggesting that the framework can use weather-forecast forcing for snowpack forecasting. Comparison between results from monthly and daily bias corrections showed that increasing bias-correction frequency and integrating more snow observations can further improve SWE estimates. Using the continuous SWE products, analyses of 170 precipitation events showed that snowmelt often occurred when daily temperature was above 3.7 °C, augmenting runoff. The proposed framework leverages ground observations and a deep-learning model to provide daily gridded snowpack estimates, which are important for runoff forecasting and water supply.

Influence of daily temperature maximums on the development and short-distance movement of the Asian citrus psyllid

Temperature is a key factor in insect biology and ecology. Climate change is driving insect exposure to temperature extremes and understanding the effect of extreme temperatures on the biology of invasive agricultural pests will be key to predicting the effect of temperature increases. Here, we simulated diurnal cycles with different lengths of exposure times to maximum temperatures experienced in summer in different locations of California on the survivorship and development of the Asian citrus psyllid (ACP) (Diaphorina citri Kuwayama). ACP is the invasive vector of Huanglongbing disease (HLB), a lethal bacterial pathogen of citrus which is currently spreading in the Los Angeles, California basin. We also tested the effect of high or low humidity at high temperatures on ACP survival and development and the effect of high temperatures on short-distance dispersal. ACP were able to complete their life cycle in all temperature treatments (28–43 °C) except in daily cycles when 43 °C was maintained for 6 h. Temperature and exposure time significantly decreased adult emergence above 40 °C. High temperatures significantly increased development time with longer development as exposure times to high temperatures increased. The interaction between low humidity and high temperature increased the number of emerging adults and decreased developmental times. ACP short-distance dispersal increased over time but was not affected by temperature. These results indicate that ACP are capable to develop in temperatures higher than previously reported, suggesting that increasing temperatures may reduce the invasive capacity of ACP in regions where maximum daily temperatures are increasing along with the duration of such temperatures throughout the day.

Use of fecal DNA to estimate black bear density in an urban‐wildland interface

AbstractAmerican black bears (Ursus americanus) commonly habituate to human resources in regions of urban‐wildland interface, which frequently leads to human‐wildlife conflict. Monitoring bear densities is important to inform management, yet traditional density estimation methods may not be practical in such areas. Minimally or noninvasive genetic tools offer a potential solution, but the most common sampling method employs barbed‐wire hair snares and scented lure, which can pose dangers to people and pets. Fecal DNA, which is less invasive, has been used successfully for many species, but has not been commonly used for ursids in the United States. We conducted a pilot study from July–September 2018 to assess the feasibility of using fecal DNA in a spatial capture‐recapture analysis to estimate black bear density in the Lake Tahoe Basin in California, a region of high‐human density compared to other urban‐wildland interface localities in the United States, where bears pose significant nuisance problems. The use of fecal DNA allowed us to sample in both urban and wildland areas with minimal disturbance to the surrounding community. Based on 142 genotyped samples from 101 distinct individuals, we estimated an abundance of 0.84 bears/km2(95% CI = 0.58–1.21 bears/km2), which was similar to a previous capture‐recapture estimate in the study area and represents one of the highest densities reported for black bears. More generally, our findings indicated that fecal DNA capture‐recapture approaches provide a feasible means of estimating black bear densities in areas where hair sampling may be impractical.

Composition and reactivity of volatile organic compounds in the South Coast Air Basin and San Joaquin Valley of California

Abstract. Comprehensive aircraft measurements of volatile organic compounds (VOCs) covering the South Coast Air Basin (SoCAB) and San Joaquin Valley (SJV) of California were obtained in the summer of 2019. Combined with the CO, CH4, and NOx data, the total calculated gas-phase hydroxyl radical reactivity (cOHRTOTAL) was quantified to be 6.1 and 4.6 s−1 for the SoCAB and SJV, respectively. VOCs accounted for ∼ 60 %–70 % of the cOHRTOTAL in both basins. In particular, oxygenated VOCs (OVOCs) contributed &gt;60 % of the cOHR of total VOCs (cOHRVOC) and the total observed VOC mixing ratio. Primary biogenic VOCs (BVOCs) represented a minor fraction (&lt;2 %) of the total VOC mixing ratio but accounted for 21 % and 6 % of the cOHRVOC in the SoCAB and SJV, respectively. Furthermore, the contribution of BVOCs to the cOHRVOC increased with increasing cOHRVOC in the SoCAB, suggesting that BVOCs were important ozone precursors during high ozone episodes. Spatially, the trace gases were heterogeneously distributed in the SoCAB, with their mixing ratios and cOHR being significantly greater over the inland regions than the coast, while their levels were more evenly distributed in SJV. The results highlight that a better grasp of the emission rates and sources of OVOCs and BVOCs is essential for a predictive understanding of the ozone abundance and distribution in California.