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Abstract As global mean temperature rises, extreme drought events are expected to increasingly affect regions of the United States that are crucial for agriculture, forestry, and natural ecology. A pressing need is to understand and anticipate the conditions under which extreme drought causes catastrophic failure to vegetation in these areas. To better predict drought impacts on ecosystems, we first must understand how specific drivers, namely, atmospheric aridity and soil water stress, affect land surface processes during the evolution of flash drought events. In this study, we evaluated when vapor pressure deficit (VPD) and soil moisture thresholds corresponding to photosynthetic shutdown were crossed during flash drought events across different climate zones and land surface characteristics in the United States. First, the Dynamic Canopy Biophysical Properties (DCBP) model was used to estimate the thresholds that define reduced photosynthesis by assimilating vegetation phenology data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to a predictive phenology model. Next, we characterized and quantified flash drought onset, intensity, and duration using the standardized evaporative stress ratio (SESR) and NLDAS-2 reanalysis. Once periods of flash drought were identified, we investigated how VPD and soil moisture coevolved across regions and plant functional types. Results demonstrate that croplands and grasslands tend to be more sensitive to soil water limitations than trees across different regions of the United States. We found that whether VPD or soil moisture was the primary driver of plant water stress during drought was largely region specific. The results of this work will help to inform land managers of early warning signals relevant for specific ecosystems under threat of flash drought events. 

Abstract An exponential rise in the atmospheric vapour pressure deficit (VPD) is among the most consequential impacts of climate change in terrestrial ecosystems. Rising VPD has negative and cascading effects on nearly all aspects of plant function including photosynthesis, water status, growth and survival. These responses are exacerbated by land–atmosphere interactions that couple VPD to soil water and govern the evolution of drought, affecting a range of ecosystem services including carbon uptake, biodiversity, the provisioning of water resources and crop yields. However, despite the global nature of this phenomenon, research on how to incorporate these impacts into resilient management regimes is largely in its infancy, due in part to the entanglement of VPD trends with those of other co‐evolving climate drivers. Here, we review the mechanistic bases of VPD impacts at a range of spatial scales, paying particular attention to the independent and interactive influence of VPD in the context of other environmental changes. We then evaluate the consequences of these impacts within key management contexts, including water resources, croplands, wildfire risk mitigation and management of natural grasslands and forests. We conclude with recommendations describing how management regimes could be altered to mitigate the otherwise highly deleterious consequences of rising VPD. 

Abstract Understanding the severity and extent of near surface critical zone (CZ) disturbances and their ecosystem response is a pressing concern in the face of increasing human and natural disturbances. Predicting disturbance severity and recovery in a changing climate requires comprehensive understanding of ecosystem feedbacks among vegetation and the surrounding environment, including climate, hydrology, geomorphology, and biogeochemistry. Field surveys and satellite remote sensing have limited ability to effectively capture the spatial and temporal variability of disturbance and CZ properties. Technological advances in remote sensing using new sensors and new platforms have improved observations of changes in vegetation canopy structure and productivity; however, integrating measures of forest disturbance from various sensing platforms is complex. By connecting the potential for remote sensing technologies to observe different CZ disturbance vectors, we show that lower severity disturbance and slower vegetation recovery are more difficult to quantify. Case studies in montane forests from the western United States highlight new opportunities, including evaluating post‐disturbance forest recovery at multiple scales, shedding light on understory vegetation regrowth, detecting specific physiological responses, and refining ecohydrological modeling. Learning from regional CZ disturbance case studies, we propose future directions to synthesize fragmented findings with (a) new data analysis using new or existing sensors, (b) data fusion across multiple sensors and platforms, (c) increasing the value of ground‐based observations, (d) disturbance modeling, and (e) synthesis to improve understanding of disturbance. 

Abstract. In recent years, extreme droughts in the United States have increased in frequency and severity, underlining a need to improve our understanding of vegetation resilience and adaptation. Flash droughts are extreme events marked by the rapid dry down of soils due to lack of precipitation, high temperatures, and dry air. These events are also associated with reduced preparation, response, and management time windows before and during drought, exacerbating their detrimental impacts on people and food systems. Improvements in actionable information for flash drought management are informed by atmospheric and land surface processes, including responses and feedbacks from vegetation. Phenologic state, or growth stage, is an important metric for modeling how vegetation modulates land–atmosphere interactions. Reduced stomatal conductance during drought leads to cascading effects on carbon and water fluxes. We investigate how uncertainty in vegetation phenology and stomatal regulation propagates through vegetation responses during drought and non-drought periods by coupling a land surface hydrology model to a predictive phenology model. We assess the role of vegetation in the partitioning of carbon, water, and energy fluxes during flash drought and carry out a comparison against drought and non-drought periods. We selected study sites in Kansas, USA, that were impacted by the flash drought of 2012 and that have AmeriFlux eddy covariance towers which provide ground observations to compare against model estimates. Results show that the compounding effects of reduced precipitation and high vapor pressure deficit (VPD) on vegetation distinguish flash drought from other drought and non-drought periods. High VPD during flash drought shuts down modeled stomatal conductance, resulting in rates of evapotranspiration (ET), gross primary productivity (GPP), and water use efficiency (WUE) that fall below those of average drought conditions. Model estimates of GPP and ET during flash drought decrease to rates similar to what is observed during the winter, indicating that plant function during drought periods is similar to that of dormant months. These results have implications for improving predictions of drought impacts on vegetation. 

We use a land surface hydrology model with a predictive phenology model to analyze changes in how vegetation and the atmosphere interact during extreme drought events known as flash droughts. Included here are model results accompanying a manuscript to be submitted to a journal for peer review. The model outputs include soil moisture, root water uptake, evapotranspiration, gross primary productivity, stomatal conductance, infiltration, leaf area index, and the fraction of photosynthetically active radiation. We find that plants nearly halt water and carbon exchanges and limit their growth during flash drought. 

The dataset contains minimum and maximum vapor pressure deficit (VPD) and soil moisture parameters that define the range of suboptimal vegetation function (i.e., between uninhibited photosynthesis and complete shutdown). The ranges were estimated using the Dynamic Canopy Biophysical Properties model (DCBP; Lowman & Barros 2018), which consists of a phenology forecasting model based on the growing season index (GSI), which provides a unitless measure of the potential phenologic state of vegetation based on the concurrent meteorological conditions (Jolly et al. 2005).