We evaluate the effect of surface wind stress on drone-based thermal surface water velocity measurements of tidal flow in an estuary relative to in-channel flow velocity measurements. Drones are a useful platform for capturing imagery of surface flows, with the ability to support many cameras and sensors. Drone-mounted thermal infrared microbolometer cameras can retrieve subtle temperature patterns that naturally occur in many surface flows. These thermal patterns are used as signals for pattern-tracking algorithms to produce distributed measurements of velocity across the observed water surface. The effect of wind on remote surface velocity measurements is relatively unstudied, and herein we present results demonstrating the impact of wind on surface velocity measurements. This study demonstrates the feasibility of drone-based thermal velocimetry in an estuarine channel, while collecting water velocities with an acoustic current profiler deployed on the channel bottom within the microbolometer’s field of view. Drone flights were conducted at Carpinteria Salt Marsh Reserve (California, USA). Wind speed and direction significantly increased the deviation of drone-based surface velocimetry measurements relative to in-channel current profiler measured velocities. Drone-based velocity measurements deviated more from in-channel near-surface measurements when the parallel wind stress direction was opposite the tidal flow, while drone-based velocities were in closer agreement with in-channel velocities when the parallel wind stress and tidal flow directions were the same. This experiment demonstrates the feasibility of drone-based thermal surface velocity measurements in an intertidal setting, while documenting the limitations of surface-based inferences of at-depth flows due to wind stress.

Root zone water storage capacity (SR) plays a fundamental role in determining the magnitude of evapotranspiration (ET) during both average and extreme climatic conditions. While methods exist to estimate SR globally at relatively fine spatial scales, the effects of uncertainty in these broad-scale estimates on evapotranspiration are largely unknown. We present a new method to efficiently describe the relationships between SR and evapotranspiration across all possible values of SR, for a given climate. This approach replaces computationally expensive model sensitivity analyses and provides a means for characterizing the importance of uncertainty and spatial variability in SR across various climates and timescales. To demonstrate the utility of our framework, we apply our approach to nine sites across the United States that vary in their seasonal climatology. In doing so, we show that evapotranspiration can be dramatically different between sites even with the same SR. For example, a very shallow SR (15 mm) would limit evapotranspiration to 27% of its maximum value (given no storage limitation) in some sites but to only 68% in others. Furthermore, if SR was estimated to be 250 mm with an uncertainty of +- 20%, the effect on estimated evapotranspiration in Eel (a site in Northern California) would be significant (+- 10%) but negligible in Boulder (a site in the Colorado Rockies). Furthermore, we find distinct site-specific SR–ET relationships that substantially impact how uncertainty and spatial variability in landscape distributions of SRaffect evapotranspiration patterns.

Soil moisture drydown patterns encode signatures of plant water use. Previous characterizations of the drydown patterns assume a static linear relationship between water-limited transpiration and available moisture. However, ecohydrological studies show that plants exhibit a spectrum of responses to water availability, suggesting that soil moisture loss functions may be nonlinear. To represent these dynamics, we introduce a nonlinearity parameter to the loss function. Our analysis shows that the nonlinear loss model improves the characterization of the satellite-observed soil moisture drydowns. Globally, functional responses of drydowns are dominated by convex nonlinearity, showing greater declines in water use in dry soils than the linear loss function predicts. We find distinct degrees of nonlinearity among different vegetation types; areas with non-woody vegetation more frequently exhibit a concave nonlinearity, the signature of aggressive water-use strategies. We propose the nonlinear loss function as continuous and dynamic representations of plant water-use strategies under changing water availability.

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.