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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. [ABSTRACT FROM AUTHOR]

In recent years, extreme drought events in the United States have seen increases in frequency and severity underlining a need to improve our understanding of vegetation resilience and adaptation. Flash droughts are extreme events marked by 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 which exacerbate 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 interacts with the atmosphere. We investigate how uncertainty in vegetation phenology propagates through vegetation responses during drought and non-drought periods by coupling a land-surface hydrology model to a predictive phenology model. We identify plant processes that influence vegetation responses to drought and assess the role of vegetation in the partitioning of carbon, water, and energy fluxes. We selected study sites in Kansas, USA where extreme drought events have been observed, in particular the flash drought of 2012, and where AmeriFlux eddy covariance towers provide data which can be used to evaluate water movement between the land (surface and subsurface) and the atmosphere. We evaluate the evolution of plant phenology, water use, and productivity using different water stress events. Results show that phenological responses using model parameters generated from periods of average precipitation show slower responses to drought as compared to parameters generated to reflect isohydric or anisohydric tendencies. Evapotranspiration (ET) and gross primary productivity (GPP) show similarly timed responses to water stress. We find plants alter water use strategies under extreme drought, with plants nearly halting atmospheric water and carbon exchanges when under stress. Decreases in uncertainty from ensemble estimates of GPP and ET during the flash drought period reduce to winter levels implying variability in plant life stage and functionality during drought periods are similar to those of dormant months. These results have implications for improving predictions of drought impacts on vegetation. [ABSTRACT FROM AUTHOR]

Soil moisture can be obtained from in-situ measurements, satellite observations, and model simulations. This study evaluates different methods of combining model, satellite, and in-situ soil moisture data to provide an accurate and spatially-continuous soil moisture product. Three independent soil moisture datasets are used, including an in situ-based product that uses regression kriging (RK) with precipitation, SMAP L4 soil moisture, and model-simulated soil moisture from the Noah model as part of the North American Land Data Assimilation System. Triple collocation (TC), relative error variance (REV), and RK were used to estimate the error variance of each parent dataset, based on which the least squares weighting (LSW) was applied to blend the parent datasets. These results were also compared with that using simple average (AVE). The results indicated no significant differences between blended soil moisture datasets using errors estimated from TC, REV or RK. Moreover, the LSW did not outperform AVE. The SMAP L4 data have a significant negative bias (-18 %) comparing with in-situ measurements, and in-situ measurements are valuable for improving the accuracy of hybrid results. In addition, datasets using anomalies and percentiles have smaller errors than using volumetric water content, mainly due to the reduced bias. Finally, the in situ-based soil moisture and the simple-averaged product from in situ-based and Noah soil moisture are the two optimal datasets for soil moisture mapping. The in situ-based product performs better when the sample density is high, while the simple-averaged product performs better when the station density is low, or measurement sites are less representative. [ABSTRACT FROM AUTHOR]