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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

The Plant Phenology Monitoring Design for the National Ecological Observatory Network

Phenology is an integrative science that comprises the study of recurring biological activities or events. In an era of rapidly changing climate, the relationship between the timing of those events and environmental cues such as temperature, snowmelt, water availability, or day length are of particular interest. This article provides an overview of the observer-based plant phenology sampling conducted by the U.S. National Ecological Observatory Network (NEON), the resulting data, and the rationale behind the design. Trained technicians will conduct regular in situ observations of plant phenology at all terrestrial NEON sites for the 30-yr life of the observatory. Standardized and coordinated data across the network of sites can be used to quantify the direction and magnitude of the relationships between phenology and environmental forcings, as well as the degree to which these relationships vary among sites, among species, among phenophases, and through time. Vegetation at NEON sites will also be monitored with tower-based cameras, satellite remote sensing, and annual high-resolution airborne remote sensing. Ground-based measurements can be used to calibrate and improve satellite-derived phenometrics. NEON's phenology monitoring design is complementary to existing phenology research efforts and citizen science initiatives throughout the world and will produce interoperable data. By collocating plant phenology observations with a suite of additional meteorological, biophysical, and ecological measurements (e.g., climate, carbon flux, plant productivity, population dynamics of consumers) at 47 terrestrial sites, the NEON design will enable continental-scale inference about the status, trends, causes, and ecological consequences of phenological change.

phenology↗
NASA NTRS1973

Use of satellite imagery for wildland resource evaluation in the Great Basin

The author has identified the following significant results. Upon receipt of ERTS-1 imagery, scale and resolution determinations were made. It was found that the scale was quite close to the anticipated 1:1 million, and that each ERTS-1 frame had a coverage of approximately 12,500 square miles. A water body 275 feet across was identified on the MSS 7 band as a determination of maximum resolution. Large cities were easily distinguished on all bands. Paved highways and railroads were able to be identified when there was extreme contrast between these structures and surrounding areas. Landforms were identified on all bands, but band 5 proved superior. Vegetation of varying types was identified using band 7. Fields and agricultural areas were identified as was phreatophytic vegetation. Timber, pinyon-juniper, sagebrush, mountain brush, and annual grassland communities were identified on selected frames. Phenology comparisons of selected test sites were made using sequential U-2 imagery. It was found that gray scale and color IR changes relating to the phenology of certain annual grassland, mountain brush, and marsh communities could be identified. Water bodies were identified and delineated using band 7.

Tueller, P. T.↗
NASA NTRS2020

Connecting Users and Applications with Po.daac Hosted GHRSST Data

The 80+ GHRSST public datasets represent a rich resource for sea surface temperature research and applications given their time series length, resolution, spatial coverage, varying measurement types and processing levels, and availability in the full spectrum of PO.DAAC tools and services ecosystem. The PO.DAAC has created a publicly accessible recipe suite for the user community to perform straightforward yet powerful computations on GHRSST data using python recipes, Jupyter notebooks, R, Matlab, and the NCO programming language. These recipes include numerical computations for regional and global SST trends, anomaly derivations, EOF analysis, climate signal reproduction, and ocean phenology. For example, one recipe reproduces a famous SST based warming figure from the Fourth National Climate Assessment (USA) while another focuses on quantifying the regional changes in ocean SST phenology. Most are python-based while some contain hybrid calls and leverage the NCO programming interface too. All are available on the PO.DAAC user forum (https://podaac.jpl.nasa.gov/forum/) and/or via the open source NASA GitHub repository (https://github.com/nasa/podaac_tools_and_services). Several are available in the Jupyter notebook framework including podaacypy (https://github.com/nasa/podaacpy), a recipe for GHRSST granule metadata discovery and application, and more recently a Jupyter notebook developed to support data analysis and visualization of a cloud-based Zarr formatted Level 4 MUR dataset in the AWS Open Data Registry. Throughout the summer of 2020, the PO.DAAC intends to add and migrate more of its numerical recipes to the Jupyter notebook framework and publish them on its open source GitHub repository.

Gentemann, Chelle↗
NASA NTRS2024

Deciphering the Spectra of Flowers to Map Landscape-scale Blooming Dynamics

Like leaves, floral coloration is driven by inherent optical properties, which are determined by pigments, scattering structure, and thickness. However, establishing the relative contribution of these factors to canopy spectral signals is usually limited to in-situ observations. Modeling flowering dynamics (e.g., blooming duration, spatial distribution) at the landscape scale may reveal insights into ecological processes and phenological adaptations to environmental changes. Multitemporal visible to shortwave infrared (VSWIR) imaging spectroscopy observations are especially suited for such efforts. Reflectance in this spectral range is sensitive to major flower pigments, flowering phenology traces, and biophysical differences between flowers and other plant parts. We explored how flowers contribute to spectral signals using a time series of imagery from the Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) collected as part of the SBG High-Frequency Time Series (SHIFT) campaign as a case study. Airborne data were collected weekly during the spring of 2022 across two natural reserves in California. Field spectra were gathered from blooming plots at leaf, flower, and canopy levels at two time points during the campaign. The processed data was used to investigate flowering species' spectro-temporal variation and spatial distribution using Spectral Mixture Residual, Gaussian clustering techniques, and a proposed narrow-band flowering index. Linear spectral unmixing allowed the computation of the weighted contribution of four major high-variance endmembers (leaves, flowers, soil, dark) and low-variance residual signal that comprises subtle spectral features used to track biophysical processes. The reflectance residual was projected on a low principal component basis to characterize flowering clusters' variation and spatial distribution based on the Gaussian mixture model, providing an uncertainty metric to assess the results. Mapping flowering events from modeling spectro-temporal dynamics throughout the season, from pre-blooming to post-flowering stages, allowed us to identify gradient variations in spectral features within the VSWIR spectral range linked to flowering pigments. Time series of the Mixture Residual Blooming Index and the Red-Edge Normalized Difference Vegetation Index revealed specific flowering and greenness phenophases across the two main species (Coreopsis gigantea, Artemisia californica) in the flowering areas. Overall, our approach opens opportunities for future satellite monitoring of floral cycles at broader scales.

Yoseline Angel↗
DOE OSTI2021

Evaluation and modification of ELM seasonal deciduous phenology against observations in a southern boreal peatland forest

Phenological transitions determine the timing of changes in land surface properties and the seasonality of exchanges of biosphere-atmosphere energy, water, and carbon. Accurate mechanistic modeling of phenological processes is therefore critical to understand and correctly predict terrestrial ecosystem feedbacks with changing atmospheric and climate conditions. However, the phenological components in the land model of the US Department of Energy's (DOE) Energy Exascale Earth System Model (ELM of E3SM) were previously unable to accurately capture the observed phenological responses to environmental conditions in a well-studied boreal peatland forest. In this research, we introduced new seasonal-deciduous phenology schemes into version 1.0 of ELM and evaluated their performance against the PhenoCam observations at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment in northern Minnesota from 2015 to 2018. We found that phenology simulated by the revised ELM (i.e., earlier spring onsets and stronger warming responses of spring onsets and autumn senescence) was closer to observations than simulations from the original algorithms for both the deciduous conifer (Larix laricina) and mixed shrub layers. Moreover, the revised ELM generally produced higher carbon and water fluxes (e.g., photosynthesis and evapotranspiration) during the growing season and stronger flux responses to warming than the default ELM. A parameter sensitivity analysis further indicated the significant contribution of phenology parameters to uncertainty in key carbon and water cycle variables, underscoring the importance of precise phenology parameterization. Furthermore, this phenological modeling effort demonstrates the potential to enhance the E3SM representation of land-climate interactions at broader spatiotemporal scales, especially under anticipated elevated CO 2 and warming conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Progress Towards an Interdisciplinary Science of Plant Phenology: Building Predictions Across Space, Time and Species Diversity

Climate change has brought renewed interest in the study of plant phenology - the timing of life history events. Data on shifting phenologies with warming have accumulated rapidly, yet research has been comparatively slow to explain the diversity of phenological responses observed across latitudes, growing seasons and species. Here, we outline recent efforts to synthesize perspectives on plant phenology across the fields of ecology, climate science and evolution. We highlight three major axes that vary among these disciplines: relative focus on abiotic versus biotic drivers of phenology, on plastic versus genetic drivers of intraspecific variation, and on cross-species versus autecological approaches. Recent interdisciplinary efforts, building on data covering diverse species and climate space, have found a greater role of temperature in controlling phenology at higher latitudes and for early-flowering species in temperate systems. These efforts have also made progress in understanding the tremendous diversity of responses across species by incorporating evolutionary relatedness, and linking phenological flexibility to invasions and plant performance. Future research with a focus on data collection in areas outside the temperate mid-latitudes and across species' ranges, alongside better integration of how risk and investment shape plant phenology, offers promise for further progress.

phenology↗
DOE OSTI2023

Remote sensing of seasonal variation of LAI and fAPAR in a deciduous broadleaf forest

Climate change is affecting the phenology of terrestrial ecosystems. In deciduous forests, phenology in leaf area index (LAI) is the primary driver of seasonal variation in the fraction of absorbed photosynthetically active radiation (fAPAR), which drives photosynthesis. Remote sensing has been widely used to estimate LAI and fAPAR. However, while many studies have examined both empirical and model-based relationships among LAI, fAPAR, and spectral vegetation indices (SVI) from remote sensing, few studies have systematically and empirically examined how relationships among these variables change over the growing season. In this study, we examine how and why seasonal-scale covariation differs among time series of remotely sensed SVIs and both LAI and fAPAR based on current understanding and theory. To do this we use newly available remote sensing data sets in combination with time series of in-situ measurements and a canopy radiative transfer model to analyze how seasonal variation in canopy and environmental conditions affect relationships among remotely sensed SVIs, LAI, and fAPAR at a temperate deciduous forest site in central Massachusetts. Our results show that accounting for seasonal variation in canopy shadowing, which is driven by variation in solar zenith angle, improved remote sensing-based estimates of LAI, fAPAR, and daily total APAR. Specifically, we show that the phenology of SVIs is strongly influenced by seasonal variation in near infrared (NIR) reflectance arising from systematic variation in the canopy shadow fraction that is independent of changes in LAI or fAPAR. Therefore, results of this work provide a refined basis for understanding how remote sensing can be used to monitor and model the phenology of LAI, fAPAR, APAR, and gross primary productivity in temperate deciduous forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Predicting spring phenology in deciduous broadleaf forests: NEON phenology forecasting community challenge

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative's National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate when, where, and for what model type phenology forecast skill is highest. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying the long-term changes of terrestrial water storage and their driving factors

Global warming is expected to cause changes in terrestrial water storage (TWS) across the land surface, with widespread impacts on ecosystems and society. Although extensive research has been performed to analyze TWS changes and possible drivers during the post-2000 period, longer-term evolution of TWS and associated environmental forcings remain relatively unexplored. In this study, we evaluated the performance of the Energy Exascale Earth System model (E3SM) land model ELM version 1 (ELM v1) in simulating global TWS, and used factorial simulations of ELMv1 to quantify global TWS changes and their drivers during 1948–2012. We found that ELM’s agreed best with existing satellites and reconstruction datasets in temperate regions unaffected by irrigation. Biome- and climate zone-averaged TWS mainly increased at rates between 0 and 10 mm/year over 1948–2012, but the second half of that period saw smaller positive trends than the first half or even negative trends. Climate change explained >80 % of the TWS trends across most biomes and climate zones, followed by land use and land cover change. The physiological and phenological effects of CO2 primarily induced noticeable TWS trends in the more humid biomes and climate zones across different latitudes. In contrast, nitrogen deposition and aerosol deposition generally had smaller and negative impacts across the biomes and climate regions. Among the meteorological drivers analyzed, the long-term average imbalance between precipitation (P), evapotranspiration (E), and runoff (Q) contributed >50 % of the TWS trends in most biomes and climate zones, with nonlinearity being induced by spatially heterogenous changes in E/P and Q/P ratios. The accumulated detrended anomalies in P, E, and Q also often contributed substantially, while the trends difference between P, E, and Q contributed little. Together, these findings unveiled an intensification of the global TWS and its diverse patterns of climate change and different non-withdrawal human-induced alterations, contributing to a more comprehensive understanding and projection of the global water cycle.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors

Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.

Jeff W. Atkins↗
NASA NTRS2020

Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data

Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R(exp 2), RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p < 0.01), and harvesting dates were delayed by 0.78 days/year (p < 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p < 0. 01) or a 0.66-day harvest delay (r = 0.10, p < 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p < 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p < 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. The derived crop phenological datasets and cropping intensity maps can be used in regional climate assessments and in developing adaptation strategies.

North America↗
DOE OSTI2022

Revegetation affects the response of land surface phenology to climate in Loess Plateau, China

Land surface phenology (LSP), defined as the plant's growth rhythm retrieved from satellite sensing products, is proven to shift with climate change and affect the carbon cycles of terrestrial ecosystems. Global afforested area is largely increasing and consequently affecting local and global climate. However, how and to what extent revegetation affects LSP remains relatively unexplored. Here we investigated the difference in four LSPs (i.e., greenup, maturity, senescence, and dormancy) and the response of LSP to climate between restored and native vegetation on Loess Plateau, China, where a remarkable process of vegetation restoration happened during 1982–2015. Most study regions showed a longer growing season (LOS) over time, specifically, with a slight delay in greenup but a relatively large delay in senescence. We found that air temperature was the dominant factor affecting greenup and maturity, while precipitation mostly controlled the senescence and dormancy in the study area. Under similar climate conditions, the LSP of restored vegetation (i.e., restored forest and grassland) showed a significant difference (p < 0.05) from native ones during 1999–2015. Compared to the native forest, restored forest from cropland and grassland showed a delayed greenup date by 0.3 and 3.6 days (p < 0.05) and an advanced dormancy date of 6.6 and 9.0 days (p < 0.05), respectively. Furthermore, the restored vegetation became less sensitive to air temperature than native vegetation, while the restored forest was more sensitive to precipitation, and its growth was affected by the water limitation to a larger extent in the study area. In conclusion, our study highlights the necessity of considering land use management and its effect on the LSP change to better understand the effect of afforestation on global climate and carbon cycles.

Climate change↗
DOE OSTI2026

SPRUCE Wood Anatomy of Picea mariana and Larix laricina in SPRUCE Experimental Plots, Marcell Experimental Forest, Minnesota, July 2023

Branch samples were collected in July 2023 to measure wood anatomical traits on two dominant conifer species, Picea mariana and Larix laricina, in a bog forest at the SPRUCE (Spruce and Peatland Responses Under Changing Environments) experiment in northern Minnesota. Anatomical measurements were made on the annual rings of those branches with dates ranging from 2011-2023. Wood anatomical measurements include annual tracheid diameter, tracheid density, cell wall thickness, thickness-to-span-ratio, and conduit lumen fraction in both earlywood and latewood. Wood anatomical samples were prepared using a portable sliding microtome (G.S.L.-1 lightweight microtome, WSL) and a light microscope (Leica DM2500). This dataset spans 2011–2023, with full branch-level coverage for 2020–2023, whereas some thinner branches formed only in recent years and therefore do not contain rings from earlier years. By providing annual, treatment-specific anatomical measurements, this dataset can help quantify structural acclimation to global change, clarify links among phenology, wood formation, and hydraulic traits, and improve predictions of forest growth response under future climates. This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

annual tracheid diameter↗
NASA NTRS2014

Satellite Observed Widespread Decline in Mongolian Grasslands Largely Due to Overgrazing

The Mongolian Steppe is one of the largest remaining grassland ecosystems. Recent studies have reported widespread decline of vegetation across the steppe and about 70 percent of this ecosystem is now considered degraded. Among the scientific community there has been an active debate about whether the observed degradation is related to climate, or overgrazing, or both. Here, we employ a new atmospheric correction and cloud screening algorithm (MAIAC) to investigate trends in satellite observed vegetation phenology. We relate these trends to changes in climate and domestic animal populations. A series of harmonic functions is fitted to MODIS observed phenological curves to quantify seasonal and inter-annual changes in vegetation. Our results show a widespread decline (of about 12 percent on average) in MODIS observed NDVI across the country but particularly in the transition zone between grassland and the Gobi desert, where recent decline was as much as 40 percent below the 2002 mean NDVI. While we found considerable regional differences in the causes of landscape degradation, about 80 percent of the decline in NDVI could be attributed to increase in livestock. Changes in precipitation were able to explain about 30 percent of degradation across the country as a whole but up to 50 percent in areas with denser vegetation cover (p0.05). Temperature changes, while significant, played only a minor role (r20.10, p0.05). Our results suggest that the cumulative effect of overgrazing is a primary contributor to the degradation of the Mongolian steppe and is at least partially responsible for desertification reported in previous studies.

Mongolia↗
NASA NTRS2022

New Directions in Tropical Phenology

Earth’s most speciose biomes are in the tropics, yet tropical plant phenology remains poorly understood. Tropical phenological data are comparatively scarce and viewed through the lens of a ‘temperate phenological paradigm’ expecting phenological traits to respond to strong, predictably annual shifts in climate (e.g., between subfreezing and frost-free periods). Digitized herbarium data greatly expand existing phenological data for tropical plants; and circular data, statistics, and models are more appropriate for analyzing tropical (and temperate) phenological datasets. Phylogenetic information, which remains seldom applied in phenological investigations, provides new insights into phenological responses of large groups of related species to climate. Consistent combined use of herbarium data, circular statistical distributions, and robust phylogenies will rapidly advance our understanding of tropical – and temperate – phenology.

tropical phenology↗
NASA NTRS2020

Interannual Variability in the Thermal Habitat of Alexandrium catenella in the Bay of Fundy and the Implications of Climate Change

Globally, harmful algal blooms (HABs) are an increasing problem. In the Gulf of Maine and Bay of Fundy, blooms of the toxic dinoflagellate Alexandrium catenella are annually recurrent phenomena. As this region is one of the most rapidly warming areas of the global ocean, an improved understanding of the mechanisms driving the initiation of local A. catenella blooms, their interannual variability and the implications of future climate change is critical to local monitoring strategies and marine resources management. A 27-year (1988–2014) time series of weekly A. catenella cell counts from the Bay of Fundy and concurrent satellite-measured sea surface temperature, freshwater discharge from the St. John River and wind-driven turbulence are compared to assess their relationship to variability in bloom phenology metrics. The mean thermal habitat associated with early detection of A. catenella is 6.5 ± 1.6°C, whereas that of bloom initiation averages 9.2 ± 1.5°C. Both thermal habitats for A. catenella are trending earlier over the study period. Bloom initiations that precede the arrival of the thermal habitat mean (occur in colder water) are associated with higher spring freshwater discharge and are generally weaker blooms. Increased spring freshwater discharge is also associated with earlier bloom initiation and earlier maximum concentration dates. No significant relationship was observed with the strength of wind-driven mixing. Removal of the mean thermal seasonal cycle shows that surface temperature anomalies have a strong negative relationship to the bloom phenology metrics and arrival of thermal habitat: warmer years are linked to earlier arrival of thermal habitats (∼12 days °C–1) and earlier detection and bloom initiation dates (∼33 days °C–1). Using these relationships and present trends in Bay of Fundy surface temperature warming over the period 1982–2019, we project the arrival dates of bloom thermal habitat and bloom phenology metrics out to the middle of this century. Based on current rates of sea surface temperature change, bloom phenology metrics (e.g., bloom initiation, early detection), can be expected to shift 1–2 months earlier in the season by mid-century. Such changes in the phenology of A. catenella blooms will need to be incorporated into both monitoring strategies and forecasting models for the region.

Andre F. Bucci↗
DOE OSTI2022

Impact of Shifts in Vegetation Phenology on the Carbon Balance of a Semiarid Sagebrush Ecosystem

Dryland ecosystems are critical in regulating the interannual variability of the global terrestrial carbon cycle. The responses of such ecosystems to weather and environmental conditions remain important factors that limit the accurate projections of carbon balance under future climate change. Here, we investigated how shifts in vegetation phenology resulting from changes in weather and environmental conditions influenced ecosystem carbon cycling in one semiarid ecosystem in the Hanford area of central Washington, United States. We examined two years of measurements of the phenology camera, eddy covariance, and soil chamber from an upland semiarid sagebrush ecosystem. Both years had contrasting diel and seasonal patterns of CO 2 fluxes, primarily driven by differences in vegetation phenology. The net ecosystem exchange of CO 2 (NEE) and evapotranspiration (ET) in 2019 were enlarged by shifted vegetation phenology, as a cold and snow-covered winter and warm and dry winter in 2020 resulted in constrained magnitudes of NEE and ET during the summer months. The annual gross primary productivity (GPP) was much higher in 2019 than in 2020 (-211 vs. -112 gC m -2 ), whereas ecosystem respiration was comparable in these two years (164 vs. 144 gC m -2 ). Thus, the annual NEE in 2019 was negative (-47 gC m -2 ) with the sagebrush ecosystem functioning as a carbon sink, while the positive annual NEE in 2020 indicated that the sagebrush ecosystem functioned as a carbon source. Our results demonstrate that winter snowpack can be a critical driver of annual carbon uptake in semiarid sagebrush ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗