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Accurate and efficient modeling of stomatal conductance (g s) has been a key challenge in vegetation models across scales. Current practice of most land surface models (LSMs) assumes steady-state g s and predicts stomatal responses to environmental cues as immediate jumps between stationary regimes. However, the response of stomata can be orders of magnitude slower than that of photosynthesis, and often cannot reach a steady state before the next model time-step, even on half-hourly time scales. Here, we implemented a simple dynamic g s model in the vegetation module of an LSM developed within the Climate Modeling Alliance, and investigated the potential biases caused by the steady-state assumption from leaf to canopy scales. In comparison with steady-state models, the dynamic model better predicted the coupled temporal response of photosynthesis and stomatal conductance to changes in light intensity using leaf measurements. In ecosystem flux simulations, while the impact of g s hysteresis response may not be substantial in terms of daily or monthly integrated canopy fluxes, our results suggested the importance of considering this effect when quantifying fluxes in the mornings and evenings, and interpreting diurnal hysteresis patterns observed in ecosystem fluxes. Furthermore, prognostic modeling can bypass the A-Ci iterations required for steady-state simulations and can be robustly run with comparable computational costs. Overall, our study highlights the implications of dynamic g s modeling in improving the accuracy and efficiency of LSMs, and for advancing our understanding of plant-environment interactions. [ABSTRACT FROM AUTHOR]

Accurate and efficient modeling of stomatal conductance (gs) has been a key challenge in vegetation models across scales. Current practice of most land surface models (LSMs) assumes steady-state gs and predicts stomatal responses to environmental cues as immediate jumps between stationary regimes. However, the response of stomata can be orders of magnitude slower than that of photosynthesis, and often cannot reach a steady state before the next model time-step, even on half-hourly time scales. Here, we implemented a simple dynamic gs model in the vegetation module of an LSM developed within the Climate Modeling Alliance, and investigated the potential biases caused by the steady state assumption from leaf to canopy scales. In comparison with steady-state models, the dynamic model better predicted the coupled temporal response of photosynthesis and stomatal conductance to changes in light intensity using leaf measurements. In ecosystem flux simulations, while the impact of gs hysteresis response may not be substantial in terms of daily or monthly integrated canopy fluxes, our results sug gested the importance of considering this effect when quantifying fluxes in the mornings and evenings, and interpreting diurnal hysteresis patterns observed in ecosystem fluxes. Furthermore, prognostic modeling can bypass the A-Ci iterations required for steady-state simulations and can be robustly run with comparable computational costs. Overall, our study highlights the implications of dynamic gs modeling in improving the accuracy and efficiency of LSMs, and for advancing our understanding of plant-environment interactions. [ABSTRACT FROM AUTHOR]

The risks harmful algal blooms (HAB) pose to aquatic ecosystems, public health, and coastal economies necessitate supplementation of current observation strategies. Herein, we explore the use of red solar induced fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI), a remote sensing measurement retrievable in variable cloud conditions, for Karenia brevis detection. Along the West Florida Shelf from 2018 to 2020, we compare red SIF with normalized fluorescence line height (nFLH) from MODIS‐Aqua, a standard remote sensing HAB indicator limited to clear sky days. A strong positive linear relationship is found between nFLH and red SIF during severe bloom conditions in 2018 (N = 33,376, r‐value = 0.79, R2 = 0.63) permitting direct comparison. Red SIF provided nearly double the amount of spatiotemporal fluorescence information than nFLH. This work presents the first application of TROPOMI's red SIF for HAB monitoring and illuminates an approach for bolstering early warning systems for HABs beyond clear sky conditions. Plain Language Summary: When microscopic algae receive their favorable amount of light and nutrients, they can rapidly multiply and some types of algae have harmful impacts on the environment and humans. As algae photosynthesize, they emit a faint glow in the red wavelengths and this glow, known as fluorescence, is detectable by satellite instruments. We compared two satellite instruments capable of measuring fluorescence, Sentinel 5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) and MODIS‐Aqua, during massive harmful algal blooms along the West Florida Shelf from 2018 to 2020. We found that the two instruments were strongly correlated during severe harmful algal blooms and found that TROPOMI was able to gather more fluorescence information in space and time than MODIS‐Aqua because it can retrieve fluorescence in optically thin cloud conditions. Key Points: First application of red solar induced fluorescence (SIF) for harmful algal bloom (HAB) monitoringDuring severe HAB conditions, red SIF was consistent with existing monitoring toolsRed SIF provides nearly double the amount of HAB information [ABSTRACT FROM AUTHOR]

Satellite microwave remote sensing techniques can be used to monitor vegetation optical depth (VOD), a metric which is directly linked to vegetation biomass and water content. However, these large-scale measurements are still difficult to reference against either rare or not directly comparable field observations. So far, in situ estimates of canopy biomass or water status often rely on infrequent and time-consuming destructive samples, which are not necessarily representative of the canopy scale. Here, we present a simple technique based on Global Navigation Satellite Systems (GNSS) with the potential to bridge this persisting scale gap. Because GNSS microwave signals are attenuated and scattered by vegetation and liquid water, placing a GNSS sensor under a vegetated canopy and measuring changes in signal strength over time can provide continuous information about VOD and thus on vegetation biomass and water content. We test this technique at a forested site in southern California for a period of 8 months. We show that variations in GNSS signal-to-noise ratios reflect the overall distribution of biomass density in the canopy and can be monitored continuously. For the first time, we show that this technique can resolve diurnal variations in VOD and canopy water content at hourly to sub-hourly time steps. Using a model of canopy transmissivity to assess these diurnal signals, we find that temperature effects on the vegetation dielectric constant, and thus on VOD, may be non-negligible at the diurnal scale or during extreme events like heat waves. Sensitivity to rainfall and dew deposition events also suggests that canopy water interception can be monitored with this approach. The technique presented here has the potential to resolve two important knowledge gaps, namely the lack of ground truth observations for satellite-based VOD and the need for a reliable proxy to extrapolate isolated and labor-intensive in situ measurements of biomass, canopy water content, or leaf water potential. We provide recommendations for deploying such off-the-shelf and easy-to-use systems at existing ecohydrological monitoring networks such as FluxNet or SapfluxNet. [ABSTRACT FROM AUTHOR]

Soil and atmospheric droughts increasingly threaten plant survival and productivity around the world. Yet, conceptual gaps constrain our ability to predict ecosystem‐scale drought impacts under climate change. Here, we introduce the ecosystem wilting point (ΨEWP), a property that integrates the drought response of an ecosystem's plant community across the soil–plant–atmosphere continuum. Specifically, ΨEWP defines a threshold below which the capacity of the root system to extract soil water and the ability of the leaves to maintain stomatal function are strongly diminished. We combined ecosystem flux and leaf water potential measurements to derive the ΨEWP of a Quercus‐Carya forest from an "ecosystem pressure–volume (PV) curve," which is analogous to the tissue‐level technique. When community predawn leaf water potential (Ψpd) was above ΨEWP (=−2.0 MPa), the forest was highly responsive to environmental dynamics. When Ψpd fell below ΨEWP, the forest became insensitive to environmental variation and was a net source of carbon dioxide for nearly 2 months. Thus, ΨEWP is a threshold defining marked shifts in ecosystem functional state. Though there was rainfall‐induced recovery of ecosystem gas exchange following soaking rains, a legacy of structural and physiological damage inhibited canopy photosynthetic capacity. Although over 16 growing seasons, only 10% of Ψpd observations fell below ΨEWP, the forest is commonly only 2–4 weeks of intense drought away from reaching ΨEWP, and thus highly reliant on frequent rainfall to replenish the soil water supply. We propose, based on a bottom‐up analysis of root density profiles and soil moisture characteristic curves, that soil water acquisition capacity is the major determinant of ΨEWP, and species in an ecosystem require compatible leaf‐level traits such as turgor loss point so that leaf wilting is coordinated with the inability to extract further water from the soil. [ABSTRACT FROM AUTHOR]