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1. Groundwater dependence of riparian woodlands and the disrupting effect of anthropogenically altered streamflow

   https://doi.org/10.1073/pnas.2026453118  

   Rohde, Melissa M.; Stella, John C.; Roberts, Dar A.; Singer, Michael Bliss (June 2021, Proceedings of the National Academy of Sciences)

   null (Ed.)

   Riparian ecosystems fundamentally depend on groundwater, especially in dryland regions, yet their water requirements and sources are rarely considered in water resource management decisions. Until recently, technological limitations and data gaps have hindered assessment of groundwater influences on riparian ecosystem health at the spatial and temporal scales relevant to policy and management. Here, we analyze Sentinel-2–derived normalized difference vegetation index (NDVI; n = 5,335,472 observations), field-based groundwater elevation ( n = 32,051 observations), and streamflow alteration data for riparian woodland communities ( n = 22,153 polygons) over a 5-y period (2015 to 2020) across California. We find that riparian woodlands exhibit a stress response to deeper groundwater, as evidenced by concurrent declines in greenness represented by NDVI. Furthermore, we find greater seasonal coupling of canopy greenness to groundwater for vegetation along streams with natural flow regimes in comparison with anthropogenically altered streams, particularly in the most water-limited regions. These patterns suggest that many riparian woodlands in California are subsidized by water management practices. Riparian woodland communities rely on naturally variable groundwater and streamflow components to sustain key ecological processes, such as recruitment and succession. Altered flow regimes, which stabilize streamflow throughout the year and artificially enhance water supplies to riparian vegetation in the dry season, disrupt the seasonal cycles of abiotic drivers to which these Mediterranean forests are adapted. Consequently, our analysis suggests that many riparian ecosystems have become reliant on anthropogenically altered flow regimes, making them more vulnerable and less resilient to rapid hydrologic change, potentially leading to future riparian forest loss across increasingly stressed dryland regions. 

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2. Flowering Phenology in a Dryland Ecosystem as Affected by Climate

   Foss, Anessa (June 2025, keep.lib.asu.edu)

   Phenology is the study of the timing of annually recurring biological events. Plant species and communities around the world are experiencing shifts in phenological stages. However, the mechanisms that drive these shifts in dryland ecosystems remain uncertain. Here, I investigate the effects of precipitation and temperature on the flowering phenology of a variety of grasses and shrubs in the Northern Chihuahuan Desert by asking two questions: (1) Do different plant species flower at the same time and form temporal clusters? (2) What is the effect of precipitation and temperature on the timing of flowering for two dominant species? To answer these questions, I used three complementary approaches: (i) Field experiments where precipitation has been reduced or enhanced; (ii) long-term monthly observations and (iii) supplemental snapshot observations. Monthly phenological observations revealed distinct temporal clusters of flowering, identifying early bloom and late bloom species. For dominant grass Bouteloua eriopoda and dominant shrub Prosopis glandulosa, precipitation accumulated over the previous 90 days showed a strong influence on flowering day of year. For example, an additional 100 mm of rainfall to the average rainfall amount could advance flowering of P. glandulosa by up to 5 days, suggesting that consistent moisture availability may advance flowering timing even in more drought-adapted species. These findings support the conclusion that precipitation, not temperature, is the primary climatic driver of flowering phenology in drylands. 

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

   https://doi.org/10.3390/drones4030056  

   Atkins, Jeff W.; Stovall, Atticus E.; Yang, Xi (September 2020, Drones)

   null (Ed.)

   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. 

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4. Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

   https://doi.org/10.34133/2021/9859103  

   Taylor, Shawn D.; Browning, Dawn M.; Baca, Ruben A.; Gao, Feng (October 2021, Journal of Remote Sensing)

   Land surface phenology (LSP) enables global-scale tracking of ecosystem processes, but its utility is limited in drylands due to low vegetation cover and resulting low annual amplitudes of vegetation indices (VIs). Due to the importance of drylands for biodiversity, food security, and the carbon cycle, it is necessary to understand the limitations in measuring dryland dynamics. Here, using simulated data and multitemporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, VI uncertainty, and two different detection algorithms. Using simulated data, we found that plants with distinct VI signals, such as deciduous shrubs, can require up to 60% fractional cover to consistently detect LSP. Evergreen plants, with lower seasonal VI amplitude, require considerably higher cover and can have undetectable phenology even with 100% vegetation cover. Our evaluation of two algorithms showed that neither performed the best in all cases. Even with adequate cover, biases in phenological metrics can still exceed 20 days and can never be 100% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. We showed how high-resolution UAV imagery enables LSP studies in drylands and highlighted important scale effects driven by within-canopy VI variation. With high-resolution imagery, the open canopies of drylands are beneficial as they allow for straightforward identification of individual plants, enabling the tracking of phenology at the individual level. Drylands thus have the potential to become an exemplary environment for future LSP research. 

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5. A Comparison of Land Surface Phenology in the Northern Hemisphere Derived from Satellite Remote Sensing and the Community Land Model

   https://doi.org/10.1175/JHM-D-21-0169.1  

   Li, Xiaolu; Melaas, Eli; Carrillo, Carlos M.; Ault, Toby; Richardson, Andrew D.; Lawrence, Peter; Friedl, Mark A.; Seyednasrollah, Bijan; Lawrence, David M.; Young, Adam M. (June 2022, Journal of Hydrometeorology)

   Abstract Large-scale changes in the state of the land surface affect the circulation of the atmosphere and the structure and function of ecosystems alike. As global temperatures increase and regional climates change, the timing of key plant phenophase changes are likely to shift as well. Here we evaluate a suite of phenometrics designed to facilitate an “apples to apples” comparison between remote sensing products and climate model output. Specifically, we derive day-of-year (DOY) thresholds of leaf area index (LAI) from both remote sensing and the Community Land Model (CLM) over the Northern Hemisphere. This systematic approach to comparing phenologically relevant variables reveals appreciable differences in both LAI seasonal cycle and spring onset timing between model simulated phenology and satellite records. For example, phenological spring onset in the model occurs on average 30 days later than observed, especially for evergreen plant functional types. The disagreement in phenology can result in a mean bias of approximately 5% of the total estimated Northern Hemisphere NPP. Further, while the more recent version of CLM (v5.0) exhibits seasonal mean LAI values that are in closer agreement with satellite data than its predecessor (CLM4.5), LAI seasonal cycles in CLM5.0 exhibit poorer agreement. Therefore, despite broad improvements for a range of states and fluxes from CLM4.5 to CLM5.0, degradation of plant phenology occurs in CLM5.0. Therefore, any coupling between the land surface and the atmosphere that depends on vegetation state might not be fully captured by the existing generation of the model. We also discuss several avenues for improving the fidelity between observations and model simulations. 

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