Detecting forest response to droughts with global observations of vegetation water content

Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil-plant-atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demanding in situ measurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem-scale analog of the pressure–volume curve, the non-linear relationship between average leaf or branch water potential and water content commonly used in plant hydraulics. The sources of variability in these ecosystem-scale pressure-volume curves and their relationship to forest response to water stress are discussed. We further show to what extent diel, seasonal, and decadal dynamics of VWC reflect variations in different processes relating the tree response to water stress. VWC can also be used for inferring belowground conditions—which are difficult to impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne observational system for VWC, when combined with existing datasets, can capture diel and seasonal water dynamics to advance the science and applications of global forest vulnerability to future droughts

A river runs through it: Robust automated mapping of riparian woodlands and land surface phenology across dryland regions

Riparian woodlands in drylands are critically important to human society, global biodiversity, and regional water and energy budgets. These sensitive ecosystems have experienced substantial degradation over the last several decades from climatic change and direct human activity. Nevertheless, quantifying long-term change in dryland riparian woodlands remains a major challenge, and much uncertainty exists in their remaining extent, historical breadth, and likely future trajectories. Dryland landscapes show large, fine-scale spatial heterogeneity in seasonal greenness patterns, driven in part by spatial variation in water availability. Riparian woodlands occur where water is concentrated in the landscape, either as aboveground streamflow or subsurface groundwater. In arid and semi-arid climates, this renders them phenologically distinctive from upland ecosystems. However, despite their importance and distinctiveness, there are currently no automated methods for delineating dryland riparian woodlands across regional extents in the cloud. Here we designed and implemented a cloud-based algorithm to retrieve dryland land surface phenology patterns from multispectral satellite imagery and conducted sensitivity analyses using real and simulated data to demonstrate that the approach is robust for MODIS, Sentinel-2, and Landsat over realistic ranges of noise and cloud cover. We then designed a series of random forest vegetation classifiers that integrate phenological and spectral information, vegetative structure from LiDAR, and topography from LiDAR or the Shuttle Radar Topography Mission. We implemented classifiers for three local study sites and then generalized our model to run regionally across the southwestern United States, with balanced accuracy for the riparian woodland class ranging from 94.5% to 97.5% when validated with local to regional datasets. Generally, phenological information proved more important than any other data source for mapping riparian woodlands, which showed more stability in interannual phenology than did upland vegetation types. To our knowledge, ours is the first regional, annual, automatically-generated and updated approach for mapping dryland riparian woodlands in the southwestern United States, paving the way for improved modeling and management efforts on watershed to regional scales. We also provide one of the first operational, exclusively cloud-based methods to extract dryland land surface phenology patterns using Landsat, Sentinel-2, MODIS, or other sensors, providing a framework for future studies investigating other aspects of long-term or spatial variation in dryland vegetative seasonality across the globe

Evapotranspiration regulates leaf temperature and respiration in dryland vegetation

Evapotranspiration regulates energy flux partitioning at the leaf surface, which in turn regulates leaf temperature. However, the mechanistic relationship between evapotranspiration and leaf temperature remains poorly constrained. In this study, we present a novel mechanistic model to predict leaf temperature as a linearized function of the evaporative fraction. The model is validated using measurements from infrared radiometers mounted on two flux towers in Arizona, USA, which measure canopies of Prosopis velutina with contrasting water availability. Both the observations and model predictions reveal that leaf temperature equilibrates with air temperature when latent heat flux consumes all of the energy incident on the leaf surface. Leaf temperature exceeds air temperature when there is a net input of energy into the leaf tissue. The flux tower observations revealed that evaporative cooling reduced canopy leaf temperature by ca. 1–5 °C, depending on water availability. Evaporative cooling also enhanced net carbon uptake by reducing leaf respiration by ca. 15% in the middle of the growing season. The regulation of leaf temperature by evapotranspiration and the resulting impact on net carbon uptake represents an important link between plant water and carbon cycles that has received little attention in literature. The model presented here provides a mechanistic framework to quantify leaf evaporative cooling and examine its impacts on plant physiological function

Causes and Implications of Extreme Atmospheric Moisture Demand during the Record-Breaking 2011 Wildfire Season in the Southwestern United States

In 2011, exceptionally low atmospheric moisture content combined with moderately high temperatures to produce a record-high vapor pressure deficit (VPD) in the southwestern United States (SW). These conditions combined with record-low cold-season precipitation to cause widespread drought and extreme wildfires. Although interannual VPD variability is generally dominated by temperature, high VPD in 2011 was also driven by a lack of atmospheric moisture. The May–July 2011 dewpoint in the SW was 4.5 standard deviations below the long-term mean. Lack of atmospheric moisture was promoted by already very dry soils and amplified by a strong ocean-to-continent sea level pressure gradient and upper-level convergence that drove dry northerly winds and subsidence upwind of and over the SW. Subsidence drove divergence of rapid and dry surface winds over the SW, suppressing southerly moisture imports and removing moisture from already dry soils. Model projections developed for the fifth phase of the Coupled Model Intercomparison Project (CMIP5) suggest that by the 2050s warming trends will cause mean warm-season VPD to be comparable to the record-high VPD observed in 2011. CMIP5 projections also suggest increased interannual variability of VPD, independent of trends in background mean levels, as a result of increased variability of dewpoint, temperature, vapor pressure, and saturation vapor pressure. Increased variability in VPD translates to increased probability of 2011-type VPD anomalies, which would be superimposed on ever-greater background VPD levels. Although temperature will continue to be the primary driver of interannual VPD variability, 2011 served as an important reminder that atmospheric moisture content can also drive impactful VPD anomalies

Correlations between components of the water balance and burned area reveal new insights for predicting forest fire area in the southwest United States

We related measurements of annual burned area in the southwest United States during 1984–2013 to records of climate variability. Within forests, annual burned area correlated at least as strongly with spring–summer vapour pressure deficit (VPD) as with 14 other drought-related metrics, including more complex metrics that explicitly represent fuel moisture. Particularly strong correlations with VPD arise partly because this term dictates the atmospheric moisture demand. Additionally, VPD responds to moisture supply, which is difficult to measure and model regionally due to complex micrometeorology, land cover and terrain. Thus, VPD appears to be a simple and holistic indicator of regional water balance. Coupled with the well-known positive influence of prior-year cold season precipitation on fuel availability and connectivity, VPD may be utilised for burned area forecasts and also to infer future trends, though these are subject to other complicating factors such as land cover change and management. Assuming an aggressive greenhouse gas emissions scenario, climate models predict mean spring–summer VPD will exceed the highest recorded values in the southwest in nearly 40% of years by the middle of this century. These results forewarn of continued increases in burned forest area in the southwest United States, and likely elsewhere, when fuels are not limiting

Detecting forest response to droughts with global observations of vegetation water content

Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil-plant-atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demanding in situ measurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem-scale analog of the pressure-volume curve, the non-linear relationship between average leaf or branch water potential and water content commonly used in plant hydraulics. The sources of variability in these ecosystem-scale pressure-volume curves and their relationship to forest response to water stress are discussed. We further show to what extent diel, seasonal, and decadal dynamics of VWC reflect variations in different processes relating the tree response to water stress. VWC can also be used for inferring belowground conditions-which are difficult to impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne observational system for VWC, when combined with existing datasets, can capture diel and seasonal water dynamics to advance the science and applications of global forest vulnerability to future droughts

Supplementary Model Output to "Climate, soil organic layer, and nitrogen jointly drive forest development after fire in the North American boreal zone"

This dataset contains all the model output used to generate the figures and data reported in the article "Climate, soil organic layer, and nitrogen jointly drive forest development after fire in the North American boreal zone". The data was generated during spring 2015 using the a modified version of the Ecosystem Demography model version 2, provided as a supplement accompanying the article. The data was generated using the computational resources supported by the PICSciE OIT High Performance Computing Center and Visualization Laboratory at Princeton University. The dataset contains a pdf Readme file which explains in detail how the data can be used. Users are recommended to go through this file before using the data.This research was supported by a National Science Foundation Graduate Research Fellowship to Anna Trugman (DGE 1148900) and from the NASA Carbon Cycle Science Program Award