COVID-19’s impact on society and our daily habits has been unprecedented. With a decrease in vehicular traffic and industrial production, a decrease in local emissions was expected to occur. In order to capture any trends in ambient trace gas concentrations, approximately one thousand whole air samples were collected in intervals across the United States from April to July 2020 as part of the NASA Student Airborne Research Program (SARP). These samples were then analyzed by the UCI Rowland-Blake Lab using multi-column gas chromatography for over one hundred unique trace gases, including methane, non-methane hydrocarbons, and halocarbons, as described in Colman et al. (2001) and Barletta et al. (2002). Initial samples collected in April coincided with the peak of stay-at-home/social distancing orders in most states while samples collected later in the spring and early summer reflect the easing of these measures and initial state reopenings. Overall trends in emissions over time in select metropolitan areas will be discussed and compared to trends observed across the entire United States.

Regrowth after fire is critical to long-term persistence of chaparral shrub communities in southern California. This region is subject to frequent fire, habitat fragmentation, and protracted droughts linked to climatic change. Short-interval fire (SIF) is considered an inhibitor of recovery and cause of “type conversion” in chaparral, based on field studies of small extents and limited time periods. Sub-regional scale investigations based on remotely sensed data, however, suggest that SIF may explain little variance in postfire chaparral recovery. Drought may contribute to poor recovery or worsen the impact of repeated, short-interval fires. Previous studies have not shown whether drought reduces chaparral recovery significantly across the region, while variations in response among community types and climate zones are not well resolved. This research evaluates a regional pattern of chaparral recovery, based on series of Normalized Difference Vegetation Index (NDVI) from annual, June-solstice Landsat images (1984–2018). High resolution aerial images were used in validation and calibration. The main objectives were (1) to assess effects of fire-return interval and number of burns on chaparral recovery using 0.25 km2 sample plots (n = 528) which were paired and stratified for experimental control, and (2) to explain recovery variations across the region based on geospatial climate, vegetation, soil, terrain, and temporal drought metric data (seasonal precipitation, climatic water deficit (CWD), and Palmer Drought Severity Index (PDSI)) from 982 locations. Results suggest that SIF is most impactful in sites that burned three times within 25 years. More substantial effects were observed due to drought. In particular, ecotonal chaparral bounding the Colorado Desert is most subject to drought impact. We also highlight utility in landscape-scale predictors of drought impact on recovering chaparral, including Very Atmospherically Resistant Index (VARI).

The 2020 COVID-19 pandemic provided a unique opportunity to sample atmospheric gases during a period of very low industrial/human activity. Over 1000 Whole Air Samples were collected in over 30 cities and towns across the United States from April through July 2020 as part of the NASA Student Airborne Research Program (SARP). Sample locations leveraged the geographic distribution across the United States of the undergraduate and graduate students, faculty, and NASA personnel associated with the internship program (44 people total). Each person collected approximately 24 air samples in their city/town with the goal of characterizing local emissions with time during the pandemic. Samples were collected in 2-Liter stainless steel evacuated canisters at approximately 2 meters above ground level. The canisters were shipped to the Rowland/Blake Laboratory at the University of California Irvine and analyzed for methane, carbon dioxide, carbon monoxide, non-methane hydrocarbons, and halocarbons using the gas chromatographic system described in Colman et al. (2001) and Barletta et al. (2002). Initial samples collected in April coincided with the peak of stay-at-home/social distancing orders across most of the United States while samples collected later in the spring and early summer reflect the easing of these measures in most locations. Overall trends in emissions with time across the United States during the pandemic (in several large metro areas as well as rural locations) will be discussed.

Understanding the linkages between between climatic and surface properties that influence water uptake and loss by vegetation is essential for understanding the impact of drought on dryland regions. The Normalized Difference Vegetation Index (NDVI) is a common metric used to identify vegetation condition across LULC types. Here we employ empirical dynamic modeling (EDM) to forecast NDVI changes for savannas, grasslands, and croplands across East Africa at a dekadal (10-day) time scale using satellite-derived environmental forcing variables. The model relies on state space reconstruction with lagged coordinate embedding of multiple time series observations to recover the dynamic environmental system that links vegetation dynamics to environmental forcing. We apply convergent cross mapping based on Takens’ Theorem to detect the impact of landcover on directional causal interactions and time delays between driving (e.g. LST, rainfall) and response variables (NDVI). The model brings to light that certain regions are highly consistent in their trajectories and therefore easier to project while other regions are more dispersive and thus more difficult to determine anomalies. In terms of land cover, we are able to make projections with high accuracy for grasslands out to 6 months ahead while croplands and savannas show reduced forecast skill overall and prove less useful after 3 months. The use of historical seasonal NDVI patterns to diagnose the manner by which landcover and land use determine climate-land surface couplings provides a means for defining critical areas of inquiry related to the impacts of future change, particularly the expansion of agricultural areas. In addition, the EDM approach provides a robust means for creating short term vegetation forecasts across LULC types in East Africa. These predictions can assist relief organizations in advising drought management, declaring food security classifications and providing early response to famine.

Access to groundwater leaves riparian plants in drylands resistant to atmospheric drought but vulnerable to changes in climate or water use that reduce streamflow and groundwater tables. Despite the vulnerability of riparian vegetation to water balance changes few extensible methods have been developed to automatically map riparian plants at the scale of individual stands or stream reaches, to assess their response to changes in moisture due to drought and climate change, and to contrast those responses across plant functional types. We used LiDAR and a sub-annual timeseries of NDVI to map vegetation and then assessed drought response by comparing a drought index to variation in a remotely sensed metric of plant health. First, a random forest model was built to classify vegetation communities based on phenological changes in Sentinel-2 NDVI. This model produced community classes with an overall accuracy of 97.9%; accuracy for the riparian vegetation class was 98.9%. Following this initial classification, LiDAR measurements of vegetation height were used to split the riparian class into structural subclasses. Multiple Endmember Spectral Mixture Analysis was applied to a timeseries of Landsat imagery from 1984 to 2018, producing annual sub-pixel fractions of green vegetation, non-photosynthetic vegetation, and soil. Relationships were assessed within structural subclasses between mid-summer green vegetation fraction (GV) and the Standardized Precipitation-Evapotranspiration Index (SPEI), a measure of soil moisture drought. Among riparian vegetation subclasses, all groups showed significant positive correlations between SPEI and GV, indicating an increase in healthy plant material during wetter years. However, the relationship was strongest for herbaceous plants (R^2=0.509, m=0.0278), intermediate for shrubs (R^2=0.339, m=0.0262), and weakest for the largest trees (R^2=0.1373, m=0.0145). This implies decoupling of larger riparian plants from the impacts of atmospheric drought due to subsidies provided by groundwater resources. Our method was extended successfully to multiple climatically-dissimilar dryland systems in the American Southwest, and the results provide a basis for ongoing studies on the fine-scale drought response and climatic vulnerability of riparian woodlands.