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Large-scale estimation of evapotranspiration (ET) remains challenging because no direct remote sensing estimates of ET exist and because most data-driven estimation approaches require assumptions about the impact of moisture conditions and biogeography on ET. The surface flux equilibrium (SFE) approach offers an alternative, deriving ET directly from atmospheric temperature and humidity under the assumption that conditions in the atmospheric boundary layer reflect ET's land boundary condition. We present a 4 km resolution, continental United States-wide, daily ET dataset spanning from 1979 to 2025 using the SFE method. The Bowen ratio is first calculated using the SFE method solely based on temperature and specific humidity estimates from gridMET and then converted to ET using net radiation and ground heat fluxes from ERA5-Land. We evaluate its performance using extended triple collocation to estimate the standard deviation of the random error and the correlation coefficient of SFE ET compared to true ET, as well as those of three widely used alternative ET datasets: GLEAM, FluxCom, and ERA5-Land. Despite its extreme simplicity, SFE ET achieves performance comparable to or exceeding the other datasets across large portions of CONUS, particularly in the Western U.S., while requiring no information about land surface, vegetation, or soil properties and no assumptions about ET's response to environmental and climate drivers. Our results support the use of SFE as a scalable, observation-driven method for estimating ET. 

Abstract. Root-zone water storage capacity (Sr) – the maximum water volume available for vegetation uptake – bolsters ecosystem resilience to droughts and heatwaves, influences land–atmosphere exchange, and controls runoff and groundwater recharge. In land models, Sr serves as a critical parameter to simulate water availability for vegetation and its impact on processes like transpiration and soil moisture dynamics. However, Sr is difficult to measure, especially at large spatial scales, hindering an accurate understanding of many biophysical processes, such as photosynthesis, evapotranspiration, tree mortality, and wildfire risk. Here, we present a global estimate of Sr using measurements of total water storage (TWS) anomalies from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On satellite missions. We find that the median Sr value for global vegetated regions is at least 220±40 mm, which is over 50 % larger than the latest estimate derived from tracking storage change via water fluxes and 380 % larger than that calculated using a typical soil and rooting-depth parameterization. These findings reveal that plant-available water stores exceed the storage capacity of 2 m deep soil in nearly half of Earth's vegetated surface, representing a notably larger extent than previous estimates. Applying our Sr estimates in a global hydrological model improves evapotranspiration simulations compared to other Sr estimates across much of the globe, particularly during droughts, highlighting the robustness of our approach. Our study highlights the importance of continued refinement and validation of Sr estimates and provides a new observational approach for further exploring the impacts of Sr on water resource management and ecosystem sustainability. 

These images depict drainage canals and roads in peatlands in Borneo, Sumatra, and Peninsular Malaysia at 5 meter resolution. These canals were detected from July-September 2017 Planet Basemaps satellite imagery using a convolutional neural network. Please contact Nathan Dadap (ndadap@stanford.edu) with any questions. 

ABSTRACT ‘Water potential’ is the biophysically relevant measure of water status in vegetation relating to stomatal, canopy and hydraulic conductance, as well as mortality thresholds; yet, this cannot be directly related to measured and modelled fluxes of water at plot‐ to landscape‐scale without understanding its relationship with ‘water content’. The capacity for detecting vegetation water content via microwave remote sensing further increases the need to understand the link between water content and ecosystem function. In this review, we explore how the fundamental measures of water status, water potential and water content are linked at ecosystem‐scale drawing on the existing theory of pressure‐volume (PV) relationships. We define and evaluate the concept and limitations of applying PV relationships to ecosystems where the quantity of water can vary on short timescales with respect to plant water status, and over longer timescales and over larger areas due to structural changes in vegetation. As a proof of concept, plot‐scale aboveground vegetation PV curves were generated from equilibrium (e.g., predawn) water potentials and water content of the above ground biomass of nine plots, including tropical rainforest, savanna, temperate forest, and a long‐term Amazonian rainforest drought experiment. Initial findings suggest that the stored water and ecosystem capacitance scale linearly with biomass across diverse systems, while the relative values of ecosystem hydraulic capacitance and physiologically accessible water storage do not vary systematically with biomass. The bottom‐up scaling approach to ecosystem water relations identified the need to characterise the distribution of water potentials within a community and also revealed the relevance of community‐level plant tissue fractions to ecosystem water relations. We believe that this theory will be instrumental in linking our detailed understanding of biophysical processes at tissue‐scale to the scale at which land surface models operate and at which tower‐based, airborne and satellite remote sensing can provide information. 

Summary Predictive relationships between plant traits and environmental factors can be derived at global and regional scales, informing efforts to reorient ecological models around functional traits. However, in a changing climate, the environmental variables used as predictors in such relationships are far from stationary. This could yield errors in trait–environment model predictions if timescale is not accounted for.Here, the timescale dependence of trait–environment relationships is investigated by regressingin situtrait measurements of specific leaf area, leaf nitrogen content, and wood density on local climate characteristics summarized across several increasingly long timescales.We identify contrasting responses of leaf and wood traits to climate timescale. Leaf traits are best predicted by recent climate timescales, while wood density is a longer term memory trait. The use of sub‐optimal climate timescales reduces the accuracy of the resulting trait–environment relationships.This study concludes that plant traits respond to climate conditions on the timescale of tissue lifespans rather than long‐term climate normals, even at large spatial scales where multiple ecological and physiological mechanisms drive trait change. Thus, determining trait–environment relationships with temporally relevant climate variables may be critical for predicting trait change in a nonstationary climate system. 

Abstract Stomatal optimization theory is a commonly used framework for modeling how plants regulate transpiration in response to the environment. Most stomatal optimization models assume that plantsinstantaneouslyoptimize a reward function such as carbon gain. However, plants are expected to optimize over longer timescales given the rapid environmental variability they encounter. There are currently no observational constraints on these timescales. Here, a new stomatal model is developed and is used to analyze the timescales over which stomatal closure is optimized. The proposed model assumes plants maximize carbon gain subject to the constraint that they cannot draw down soil moisture below a critical value. The reward is integrated over time, after being weighted by a discount factor that represents the timescale (τ) that a plant considers when optimizing stomatal conductance to save water. The model is simple enough to be analytically solvable, which allows the value ofτto be inferred from observations of stomatal behavior under known environmental conditions. The model is fitted to eddy covariance data in a range of ecosystems, finding the value ofτthat best predicts the dynamics of evapotranspiration at each site. Across 82 sites, the climate metrics with the strongest correlation toτare measures of the average number of dry days between rainfall events. Values ofτare similar in magnitude to the longest such dry period encountered in an average year. The results here shed light on which climate characteristics shape spatial variations in ecosystem‐level water use strategy. 

Abstract Spatiotemporal patterns of plant water uptake, loss, and storage exert a first‐order control on photosynthesis and evapotranspiration. Many studies of plant responses to water stress have focused on differences between species because of their different stomatal closure, xylem conductance, and root traits. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density. Here, we seek to understand the relative importance of the dominant species for regional‐scale variations in woody plant responses to water stress. We map plant water sensitivity (PWS) based on the response of remotely sensed live fuel moisture content to variations in hydrometeorology using an auto‐regressive model. Live fuel moisture content dynamics are informative of PWS because they directly reflect vegetation water content and therefore patterns of plant water uptake and evapotranspiration. The PWS is studied using 21,455 wooded locations containing U.S. Forest Service Forest Inventory and Analysis plots across the western United States, where species cover is known and where a single species is locally dominant. Using a species‐specific mean PWS value explains 23% of observed PWS variability. By contrast, a random forest driven by mean vegetation density, mean climate, soil properties, and topographic descriptors explains 43% of observed PWS variability. Thus, the dominant species explains only 53% (23% compared to 43%) of explainable variations in PWS. Mean climate and mean NDVI also exert significant influence on PWS. Our results suggest that studies of differences between species should explicitly consider the environments (climate, soil, topography) in which observations for each species are made, and whether those environments are representative of the entire species range. 

Abstract Under increasingly variable rainfall, trends toward more intense and less frequent daily‐scale precipitation have been identified using regional and global averages. However, it has not been explicitly demonstrated whether and where these trends are co‐located, which is important given their potential impacts on land surface processes. Here, using global observation and model‐based data sets, we find that trends toward fewer, larger daily precipitation events are common and relatively distributed across terrestrial ecosystems; they are approximately as common as trends toward more, larger daily precipitation events (which underpin increases in annual precipitation totals). Therefore, widespread precipitation intensification is not consistently increasing annual precipitation totals partly because precipitation events, especially of small‐to‐moderate depths (<10 mm/day), are simultaneously becoming less frequent. Independent of the consequences of changes in mean annual precipitation, these daily‐scale precipitation alterations can substantially impact water resource availability, floods, land‐atmosphere interactions, crop yields, wildfire fuel loads, and carbon sequestration.