Droughts impede water balance recovery from fires in the Western United States.

A steady rise in fires in the Western United States, coincident with intensifying droughts, imparts substantial modifications to the underlying vegetation, hydrology and overall ecosystem. Drought can compound the ecosystem disturbance caused by fire, although how these compound effects on hydrologic and ecosystem recovery vary among ecosystems is poorly understood. Here we use remote sensing-derived high-resolution evapotranspiration (ET) estimates from before and after 1,514 fires to show that ecoregions dominated by grasslands and shrublands are more susceptible to drought, which amplifies fire-induced ET decline and, subsequently, shifts water flux partitioning. In contrast, severely burned forests recover from fire slowly or incompletely, but are less sensitive to dry extremes. We conclude that moisture limitation caused by droughts influences the dynamics of water balance recovery in post-fire years. This finding explains why moderate to extreme droughts aggravate impacts on the water balance in non-forested vegetation, while moisture accessed by deeper roots in forests helps meet evaporative demands unless severe burns disrupt internal tree structure and deplete fuel load availability. Our results highlight the dominant control of drought on altering the resilience of vegetation to fires, with critical implications for terrestrial ecosystem stability in the face of anthropogenic climate change in the West.

Effects of meteorological and land surface modeling uncertainty on errors in winegrape ET calculated with SIMS.

Characterization of model errors is important when applying satellite-driven evapotranspiration (ET) models to water resource management problems. This study examines how uncertainty in meteorological forcing data and land surface modeling propagate through to errors in final ET data calculated using the Satellite Irrigation Management Support (SIMS) model, a computationally efficient ET model driven with satellite surface reflectance values. The model is applied to three instrumented winegrape vineyards over the 2017-2020 time period and the spatial and temporal variation in errors are analyzed. We illustrate how meteorological data inputs can introduce biases that vary in space and at seasonal timescales, but that can persist from year to year. We also observe that errors in SIMS estimates of land surface conductance can have a particularly strong dependence on time of year. Overall, meteorological inputs introduced RMSE of 0.33-0.65 mm/day (7-27%) across sites, while SIMS introduced RMSE of 0.55-0.83 mm/day (19-24%). The relative error contribution from meteorological inputs versus SIMS varied across sites; errors from SIMS were larger at one site, errors from meteorological inputs were larger at a second site, and the error contributions were of equal magnitude at the third site. The similar magnitude of error contributions is significant given that many satellite-driven ET models differ in their approaches to estimating land surface conductance, but often rely on similar or identical meteorological forcing data. The finding is particularly notable given that SIMS makes assumptions about the land surface (no soil evaporation or plant water stress) that do not always hold in practice. The results of this study show that improving SIMS by eliminating these assumptions would result in meteorological inputs dominating the error budget of the model on the whole. This finding underscores the need for further work on characterizing spatial uncertainty in the meteorological forcing of ET. Supplementary Information: The online version contains supplementary material available at 10.1007/s00271-022-00808-9.

NASA Global Daily Downscaled Projections, CMIP6.

We describe the latest version of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). The archive contains downscaled historical and future projections for 1950-2100 based on output from Phase 6 of the Climate Model Intercomparison Project (CMIP6). The downscaled products were produced using a daily variant of the monthly bias correction/spatial disaggregation (BCSD) method and are at 1/4-degree horizontal resolution. Currently, eight variables from five CMIP6 experiments (historical, SSP126, SSP245, SSP370, and SSP585) are provided as procurable from thirty-five global climate models.

Assessing the utility of remote sensing data to accurately estimate changes in groundwater storage.

Accurate and timely estimates of groundwater storage changes are critical to the sustainable management of aquifers worldwide, but are hindered by the lack of in-situ groundwater measurements in most regions. Hydrologic remote sensing measurements provide a potential pathway to quantify groundwater storage changes by closing the water balance, but the degree to which remote sensing data can accurately estimate groundwater storage changes is unclear. In this study, we quantified groundwater storage changes in California's Central Valley at two spatial scales for the period 2002 through 2020 using remote sensing data and an ensemble water balance method. To evaluate performance, we compared estimates of groundwater storage changes to three independent estimates: GRACE satellite data, groundwater wells and a groundwater flow model. Results suggest evapotranspiration has the highest uncertainty among water balance components, while precipitation has the lowest. We found that remote sensing-based groundwater storage estimates correlated well with independent estimates; annual trends during droughts fall within 15% of trends calculated using wells and groundwater models within the Central Valley. Remote sensing-based estimates also reliably estimated the long-term trend, seasonality, and rate of groundwater depletion during major drought events. Additionally, our study suggests that the proposed method estimate changes in groundwater at sub-annual latencies, which is not currently possible using other methods. The findings have implications for improving the understanding of aquifer dynamics and can inform regional water managers about the status of groundwater systems during droughts.

Forecasting distributional responses of limber pine to climate change at management-relevant scales in Rocky Mountain National Park.

Resource managers at parks and other protected areas are increasingly expected to factor climate change explicitly into their decision making frameworks. However, most protected areas are small relative to the geographic ranges of species being managed, so forecasts need to consider local adaptation and community dynamics that are correlated with climate and affect distributions inside protected area boundaries. Additionally, niche theory suggests that species' physiological capacities to respond to climate change may be underestimated when forecasts fail to consider the full breadth of climates occupied by the species rangewide. Here, using correlative species distribution models that contrast estimates of climatic sensitivity inferred from the two spatial extents, we quantify the response of limber pine (Pinus flexilis) to climate change in Rocky Mountain National Park (Colorado, USA). Models are trained locally within the park where limber pine is the community dominant tree species, a distinct structural-compositional vegetation class of interest to managers, and also rangewide, as suggested by niche theory. Model forecasts through 2100 under two representative concentration pathways (RCP 4.5 and 8.5 W/m(2)) show that the distribution of limber pine in the park is expected to move upslope in elevation, but changes in total and core patch area remain highly uncertain. Most of this uncertainty is biological, as magnitudes of projected change are considerably more variable between the two spatial extents used in model training than they are between RCPs, and novel future climates only affect local model predictions associated with RCP 8.5 after 2091. Combined, these results illustrate the importance of accounting for unknowns in species' climatic sensitivities when forecasting distributional scenarios that are used to inform management decisions. We discuss how our results for limber pine may be interpreted in the context of climate change vulnerability and used to help guide adaptive management.

Relationship between community prevalence of obesity and associated behavioral factors and community rates of influenza-related hospitalizations in the United States.

BACKGROUND: Findings from studies examining the association between obesity and acute respiratory infection are inconsistent. Few studies have assessed the relationship between obesity-related behavioral factors, such as diet and exercise, and risk of acute respiratory infection. OBJECTIVE: To determine whether community prevalence of obesity, low fruit/vegetable consumption, and physical inactivity are associated with influenza-related hospitalization rates. METHODS: Using data from 274 US counties, from 2002 to 2008, we regressed county influenza-related hospitalization rates on county prevalence of obesity (BMI ≥ 30), low fruit/vegetable consumption (<5 servings/day), and physical inactivity (<30 minutes/month recreational exercise), while adjusting for community-level confounders such as insurance coverage and the number of primary care physicians per 100,000 population. RESULTS: A 5% increase in obesity prevalence was associated with a 12% increase in influenza-related hospitalization rates [adjusted rate ratio (ARR) 1.12, 95% confidence interval (CI) 1.07, 1.17]. Similarly, a 5% increase in the prevalence of low fruit/vegetable consumption and physical inactivity was associated with an increase of 12% (ARR 1.12, 95% CI 1.08, 1.17) and 11% (ARR 1.11, 95% CI 1.07, 1.16), respectively. When all three variables were included in the same model, a 5% increase in prevalence of obesity, low fruit/vegetable consumption, and physical inactivity was associated with 6%, 8%, and 7% increases in influenza-related hospitalization rates, respectively. CONCLUSIONS: Communities with a greater prevalence of obesity were more likely to have high influenza-related hospitalization rates. Similarly, less physically active populations, with lower fruit/vegetable consumption, tended to have higher influenza-related hospitalization rates, even after accounting for obesity.

Human risk of infection with Borrelia burgdorferi, the Lyme disease agent, in eastern United States.

The geographic pattern of human risk for infection with Borrelia burgdorferi sensu stricto, the tick-borne pathogen that causes Lyme disease, was mapped for the eastern United States. The map is based on standardized field sampling in 304 sites of the density of Ixodes scapularis host-seeking nymphs infected with B. burgdorferi, which is closely associated with human infection risk. Risk factors for the presence and density of infected nymphs were used to model a continuous 8 km×8 km resolution predictive surface of human risk, including confidence intervals for each pixel. Discontinuous Lyme disease risk foci were identified in the Northeast and upper Midwest, with a transitional zone including sites with uninfected I. scapularis populations. Given frequent under- and over-diagnoses of Lyme disease, this map could act as a tool to guide surveillance, control, and prevention efforts and act as a baseline for studies tracking the spread of infection.

Temporal connections between Culex tarsalis abundance and transmission of western equine encephalomyelitis virus in California.

Definition of targets for vector control requires an understanding of the relationship between vector abundance and the intensity of arbovirus transmission. Using an extensive surveillance dataset with observations from sentinel chicken flocks and mosquito traps paired in time and space, hierarchical autoregressive logistic regression models were developed to predict the probability of seroconversion in chickens for western equine encephalomyelitis virus (WEEV) based on the relative abundance of the principal vector, Culex tarsalis. After adjustments for confounders, the abundance of Cx. tarsalis 29-42 d before the date of chicken sampling was credibly associated with the risk of WEEV transmission in both the Central and Coachella Valleys, and a doubling of relative Cx. tarsalis abundance was associated with a 58% increase in the odds of seroconversion. The critical time windows identified in our study highlight the need for surveillance of vector populations and forecasting models to guide proactive vector control measures before the detection of transmission to sentinel chickens.

Climate and tick seasonality are predictors of Borrelia burgdorferi genotype distribution.

The blacklegged tick, Ixodes scapularis, is of significant public health importance as a vector of Borrelia burgdorferi, the agent of Lyme borreliosis. The timing of seasonal activity of each immature I. scapularis life stage relative to the next is critical for the maintenance of B. burgdorferi because larvae must feed after an infected nymph to efficiently acquire the infection from reservoir hosts. Recent studies have shown that some strains of B. burgdorferi do not persist in the primary reservoir host for more than a few weeks, thereby shortening the window of opportunity between nymphal and larval feeding that sustains their enzootic maintenance. We tested the hypothesis that climate is predictive of geographic variation in the seasonal activity of I. scapularis, which in turn differentially influences the distribution of B. burgdorferi genotypes within the geographic range of I. scapularis. We analyzed the relationships between climate, seasonal activity of I. scapularis, and B. burgdorferi genotype frequency in 30 geographically diverse sites in the northeastern and midwestern United States. We found that the magnitude of the difference between summer and winter daily temperature maximums was positively correlated with the degree of seasonal synchrony of the two immature stages of I. scapularis. Genotyping revealed an enrichment of 16S-23S rRNA intergenic spacer restriction fragment length polymorphism sequence type 1 strains relative to others at sites with lower seasonal synchrony. We conclude that climate-associated variability in the timing of I. scapularis host seeking contributes to geographic heterogeneities in the frequencies of B. burgdorferi genotypes, with potential consequences for Lyme borreliosis morbidity.