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Water availability controls vegetation activity and the carbon balance of terrestrial ecosystems across a large portion of the global land surface. Although the influence of terrestrial water storage (TWS) on the land carbon balance is evident in globally aggregated measures, it remains unknown whether the large annual amplitudes in TWS are causally linked to water availability in the rooting zone of vegetation, or whether they reflect a correlation of plant water stress with water stored in other landscape elements that may not directly be connected to vegetation functioning (lakes, rivers, groundwater). Global models of the land surface typically ignore hillslope-scale variations in plant water availability, and water stores that are located beyond the soil, and beyond prescribed plant rooting depths. This simplification is partly owed to a lack of empirical information.Here, we approach this gap from two angles: from the site scale using eddy covariance observations, and from the global scale using earth observations. Water mass balance constraints derived from thermal infrared-based evapotranspiration (ET) estimates and precipitation reanalysis data indicate plant-available water stores that exceed the storage capacity of 2 m deep soils across 37% of the Earth’s vegetated surface. Large spatial variations of the rooting zone water storage capacity across topographic and hydro-climatic gradients are tightly linked to the sensitivity of vegetation activity (measured by sun-induced fluorescence and by the evaporative fraction) to water deficits. Similar patterns between ET and cumulative water deficits emerge from site-level flux measurements. We found large variations of the vegetation sensitivity to dry conditions across sites and at several sites a muted response of ET to dry conditions in spite of large (>300 mm) seasonal water deficits at some sites.Taken together, results we show here hint at a critical role of plant access to deep water stores and the need to extend the focus beyond moisture in the top 1-2 m of soil for understanding and simulating land-atmosphere exchange. Our results add to the emerging evidence that water stored in the weathered bedrock and plant access to groundwater may have a more important role in regulating land-atmosphere exchange and the carbon cycle than previously appreciated.

Terrestrial vegetation is a key component of the Earth system as it mediates the exchange of carbon, water and energy between the land and the atmosphere. Thereby, the vegetation affects the climate through changes in its structure (such as leaf area index, LAI) and its physiology (such as stomatal conductance); However, their relative contributions and respective processes on the land-atmosphere coupling are not yet understood. For instance, increased LAI, referred to as structural changes, promotes transpiration and vegetation productivity, and increases the surface albedo in most cases. In contrast, decreased surface conductance, referred to as physiological changes, could reduce transpiration and productivity. Therefore, the overall feedback of vegetation to climate change via water, carbon and energy exchange will depend on the relative importance of structural and physiological responses. Here we study to what extent dynamic changes in global vegetation structure and physiology modulate land-atmosphere coupling using satellite remote-sensing, data-driven, and earth system modelled vegetation data, as well ashydro-meteorological reanalysis. The land-atmosphere coupling is quantified through the correlation between soil moisture and lagged vapor pressure deficit determined with a moving time window. We employ random forests to quantify vegetation physiology by accounting for functional variability (e.g. GPP and ET) explained by hydro-meteorological data but not by the vegetation structure. Then using an explainable machine learning approach (SHAP), we determine the contributions of vegetation structure and physiology where we find overall larger contributions of structure on regulating land-atmosphere coupling during the growing season. The relative importance of vegetation structure differs across ecosystems, with stronger contributions in dry ecosystems. Furthermore, we analyze the variations of the relevance of vegetation structure over time and in particular during warm and dry periods. The results are partially backed up by using in-situ measurements of physiological traits to interpret the large-scale observed physiological patterns.

The rooting-zone water-storage capacity—the amount of water accessible to plants—controls the sensitivity of land–atmosphere exchange of water and carbon during dry periods. How the rooting-zone water-storage capacity varies spatially is largely unknown and not directly observable. Here we estimate rooting-zone water-storage capacity globally from the relationship between remotely sensed vegetation activity, measured by combining evapotranspiration, sun-induced fuorescence and radiation estimates, and the cumulative water defcit calculated from daily time series of precipitation and evapotranspiration. Our fndings indicate plant-available water stores that exceed the storage capacity of 2-m-deep soils across 37% of Earth’s vegetated surface. We fnd that biome-level variations of rooting-zone water-storage capacities correlate with observed rooting-zone depth distributions and refect the infuence of hydroclimate, as measured by the magnitude of annual cumulative water-defcit extremes. Smaller-scale variations are linked to topography and land use. Our fndings document large spatial variations in the efective root-zone water-storage capacity and illustrate a tight link among the climatology of water defcits, rooting depth of vegetation and its sensitivity to water stress., Nature Geoscience, 16 (3), ISSN:1752-0908, ISSN:1752-0894

The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly-determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at 6 globally-distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust, observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.

Variation in canopy water content (CWC) that can be detected from microwave remote sensing of vegetation optical depth (VOD) has been proposed as an important measure of vegetation water stress. However, the contribution of leaf surface water (LWs ), arising from dew formation and rainfall interception, to CWC is largely unknown, particularly in tropical forests and other high-humidity ecosystems. We compared VOD data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and CWC predicted by a plant hydrodynamics model at four tropical sites in Brazil spanning a rainfall gradient. We assessed how LWs influenced the relationship between VOD and CWC. The analysis indicates that while CWC is strongly correlated with VOD (R2 = 0.62 across all sites), LWs accounts for 61-76% of the diurnal variation in CWC despite being < 10% of CWC. Ignoring LWs weakens the near-linear relationship between CWC and VOD and reduces the consistency in diurnal variation. The contribution of LWs to CWC variation, however, decreases at longer, seasonal to inter-annual, time scales. Our results demonstrate that diurnal patterns of dew formation and rainfall interception can be an important driver of diurnal variation in CWC and VOD over tropical ecosystems and therefore should be accounted for when inferring plant diurnal water stress from VOD measurements.

Plant hydraulic and photosynthetic responses to individual rain pulses are not well understood because field experiments of pulse behavior are sparse. Understanding individual pulse responses would inform how rainfall intermittency impacts terrestrial biogeochemical cycles, especially in drylands, which play a large role in interannual global atmospheric carbon uptake variability. Using satellite-based estimates of predawn plant and soil water content from the Soil Moisture Active Passive (SMAP) satellite, we quantify the timescales of plant water content increases following rainfall pulses, which we expect bear the signature of whole-plant mechanisms. In wetter regions, we find that plant water content increases rapidly and dries along with soil moisture, which we attribute to predawn soil–plant water potential equilibrium. Global drylands, by contrast, show multi-day plant water content increases after rain pulses. Shorter increases are more common following dry initial soil conditions. These are attributed to slow plant rehydration due to high plant resistances using a plant hydraulic model. Longer multi-day dryland plant water content increases are attributed to pulse-driven growth, following larger rain pulses and wetter initial soil conditions. These dryland responses reflect widespread drought recovery rehydration responses and individual pulse-driven growth responses, as supported by previous isolated field experiments. The response dependence on moisture pulse characteristics, especially in drylands, also shows ecosystem sensitivity to intra-annual rainfall intensity and frequency, which are shifting with climate change.

Accurate estimation and forecasts of net biome CO

Accurate forecasts of net biosphere exchange (NBE) are vital for understanding the role of terrestrial ecosystems in a changing climate. It is therefore problematic that NBE projections from most state-of-the-art terrestrial biosphere models (TBMs) diverge considerably from one another. Efforts to reduce this divergence have historically focused on improving models’ structural realism, but several lines of evidence have brought the role of poorly determined and/or over-generalized parameters into sharper focus. Here we investigate how different parameterization assumptions propagate into NBE prediction errors across the globe. To do so, we simulate two methods for parameter assignment within a flexible model–data fusion framework (CARDAMOM): (a) the traditional plant functional type (PFT)-based approach, whereby parameters retrieved at a small number of select locations are applied broadly within regions sharing similar land cover characteristics; and (b) a novel top-down “environmental filtering” (EF) approach, whereby a pixel’s parameters are predicted based on relationships with climate, soil, and canopy properties. In an effort to isolate the role of parametric from structural uncertainty, we benchmark the resulting PFT-based and EF-based NBE predictions with estimates from a Bayesian optimization approach (whereby “true” parameters consistent with a suite of data constraints are retrieved on a pixel-by-pixel basis). We find that the EF-based approach outperforms the PFT-based approach at twice as many pixels as the converse and across multiple performance metrics. However, NBE estimates from the EF-based approach may be more susceptible to compensation between errors in component flux predictions. This work provides insight into the relationship between TBM performance and parametric uncertainty, informing efforts to improve model parameterization via nontraditional approaches.

During droughts, low water availabilities limit soil evaporation and induce stomatal closure to prevent transpiration, leading to reductions in evapotranspiration (ET). At the same time, drought-associated meteorological conditions such as high temperature elevate atmospheric evaporative demand, acting to increase ET. However, the overall effect of drought on the sign of ET anomalies remains unknown, as are the determinants of this response. Positive anomalies during drought (ET+), in particular, are of concern because they quickly deplete water resources, may cause flash droughts, and exacerbate ecosystem stress. Because remotely sensed ET datasets implicitly assume a stomatal response to drought, they cannot provide direct observational constraints of the prevalence of ET+. Eddy covariance tower records are often too short and sparse to adequately sample drought conditions. To avoid these shortcomings, we used a water balance approach to derive a new estimate of ET+ occurrence during droughts by combining total terrestrial water storage (TWS) observations from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) with Global Precipitation Climatology Project precipitation data. The robustness of this approach is demonstrated across 104 hydrological basins. With this new water balance-based estimate, we showed that ET+ during droughts are globally widespread. On average, ET+ occurs in ~45% of drought periods, and it is more likely to occur during milder droughts (with relatively lower P reductions and ample available TWS). CMIP6 Earth system models (ESMs) underestimate the observed ET+ probability by nearly half. This underestimation is particularly large in relatively dry locations with an aridity index (P/PET) below ~1.5 and can be attributed in part to an overly strong ET response to decreases in soil moisture in these regions. Furthermore, ESM’s lack of accounting for variability in plant water stress response traits within plant functional types exacerbates their underestimation of ET+. This demonstrates for the first time that local adaptation of plant water stress response traits reduces the impact of droughts on ET. These process representations should be improved to reduce model uncertainties in predicting drought impacts on the energy-water-carbon nexus.

Evapotranspiration (ET) from tropical forests serves as a critical moisture source for regional and global climate cycles. However, the magnitude, seasonality, and interannual variability of ET in the Congo Basin remain poorly constrained due to a scarcity of direct observations, despite the Congo being the second-largest river basin in the world and containing a vast region of tropical forest. In this study, we applied a water balance model to an array of remotely sensed and in situ datasets to produce monthly, basin-wide ET estimates spanning April 2002 to November 2016. Data sources include water storage changes estimated from the Gravity Recovery and Climate Experiment (GRACE) satellites, in situ measurements of river discharge, and precipitation from several remotely sensed and gauge-based sources. An optimal precipitation dataset was determined as a weighted average of interpolated data by Nicholson et al. (2018), Climate Hazards InfraRed Precipitation with Station data version 2 (CHIRPS2) , and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record product (PERSIANN-CDR), with the relative weights based on the error magnitudes of each dataset as determined by triple collocation. The resulting water-balance-derived ET (ETwb) features a long-term average that is consistent with previous studies (117.2±3.5 cm yr−1) but displays greater seasonal and interannual variability than seven global ET products. The seasonal cycle of ETwb generally tracks that of precipitation over the basin, with the exception that ETwb is greater in March–April–May (MAM) than in the relatively wetter September–October–November (SON) periods. This pattern appears to be driven by seasonal variations in the diffuse photosynthetically active radiation (PAR) fraction, net radiation (Rn), and soil water availability. From 2002 to 2016, Rn, PAR, and vapor-pressure deficit (VPD) all increased significantly within the Congo Basin; however, no corresponding trend occurred in ETwb. We hypothesize that the stability of ETwb over the study period despite sunnier and less humid conditions may be due to increasing atmospheric CO2 concentrations that offset the impacts of rising VPD and irradiance on stomatal water use efficiency (WUE).

Transpiration, the dominant component of terrestrial evapotranspiration (ET), directly connects the water, energy and carbon cycles and is typically restricted by soil and atmospheric (for example, the vapour pressure deficit (VPD)) moisture stresses through plant hydraulic processes. These sources of stress are likely to diverge under climate change, with a globally enhanced VPD but more variable and uncertain changes in soil moisture. Here, using a model–data fusion approach, we demonstrate that the common empirical approach used in most Earth system models to evaluate the ET response to soil moisture and VPD, which neglects plant hydraulics, underestimates ET sensitivity to VPD and compensates by overestimating the sensitivity to soil moisture stress. A hydraulic model that describes water transport through the plant better captures ET under high VPD conditions for wide-ranging soil moisture states. These findings highlight the central role of plant hydraulics in regulating the increasing importance of atmospheric moisture stress on biosphere–atmosphere interactions under elevated temperatures. Evapotranspiration links productivity with water cycling between land and atmosphere. A model including plant hydraulics better describes the response of evapotranspiration to stress from vapour pressure deficit and soil moisture under rising temperatures than approaches common in Earth system models.

Soil hydrological processes (SHP) support ecosystems, modulate the impact of climate change on terrestrial systems and control feedback mechanisms between water, energy and biogeochemical cycles. However, land-use changes and extreme events are increasingly impacting these processes. In this Review, we describe SHP across scales and examine their links with soil properties, ecosystem processes and climate. Soil structure influences SHP such as infiltration, soil water redistribution and root water uptake on small scales. On local scales, SHP are driven by root water uptake, vegetation and groundwater dynamics. Regionally, SHP are impacted by extreme events such as droughts, floods, heatwaves and land-use change; however, antecedent and current SHP partially determine the broader effects of extreme events. Emerging technologies such as wireless and automated sensing, soil moisture observation through novel synthetic aperture radars satellites, big data analysis and machine learning approaches offer unique opportunities to advance soil hydrology. These advances, in tandem with the inclusion of more key soil types and properties in models, will be pivotal in predicting the role of SHP during global change.

International audience; Water potential directly controls the function of leaves, roots and microbes, and gradients in water potential drive water flows throughout the soil-plant-atmosphere continuum. Notwithstanding its clear relevance for many ecosystem processes, soil water potential is rarely measured in situ, and plant water potential observations are generally discrete, sparse, and not yet aggregated into accessible databases. These gaps limit our conceptual understanding of biophysical responses to moisture stress and inject large uncertainty into hydrologic and land-surface models. Here, we outline the conceptual and predictive gains that could be made with more continuous and discoverable observations of water potential in soils and plants. We discuss improvements to sensor technologies that facilitate in situ characterization of water potential, as well as strategies for building new networks that aggregate water potential data across sites. We end by highlighting novel opportunities for linking more representative site-level observations of water potential to remotely sensed proxies. Together, these considerations offer a road map for clearer links between ecohydrological processes and the water potential gradients that have the `potential' to substantially reduce conceptual and modelling uncertainties.

Author(s): Durand, Michael; Barros, Ana; Dozier, Jeff; Adler, Robert; Cooley, Sarah; Entekhabi, Dara; Forman, Barton A; Konings, Alexandra G; Kustas, William P; Lundquist, Jessica D; Pavelsky, Tamlin M; Rodell, Matthew; Steele‐Dunne, Susan

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 below-ground 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.

Devastating wildfires have occurred around the world in recent years. While fire has always been part of the landscape, the trend toward increasingly widespread and severe wildfires is a key sentinel of the rapidly intensifying risks caused by global warming and climate change. Using atmospheric observations and climate model simulations, Zhuang et al. (1) quantify the relative contributions of natural climate variability and anthropogenic climate forcing to the increasing area burned in the western United States. They find that atmospheric variability can explain at most approximately one-third of the historical trend in atmospheric aridity that is conducive to wildfire. By contrast, global warming has contributed at least two-thirds of the rising trend in atmospheric aridity. Understanding the causes of increasing wildfire risk is a critical scientific and societal challenge. First and foremost, the rapidly increasing size and intensity of wildfires is having severe impacts. In California, the eight largest wildfires in recorded history have occurred in the past 5 y, and 9 of the 17 largest have occurred in the past 2 y (including the two largest and six of the top seven). Recent fires have had widespread impacts on people and ecosystems. Just in California alone these impacts include dozens of lives lost and tens of thousands of homes destroyed, leading to extended displacement of individuals, families, and communities. In addition, the massive plumes of smoke that have blanketed much of the western United States have impacted millions of people and have accounted for up to half of fine particulate matter pollution in some areas of the region (2). Recent fires have also exceeded the intensity to which fire-adapted vegetation is accustomed, posing risks to ecosystems and individual species such as California’s giant sequoias (e.g., ref. 3). While California’s fires have received much public attention, similar challenges are playing … [↵][1]1To whom correspondence may be addressed. Email: diffenbaugh{at}stanford.edu. [1]: #xref-corresp-1-1

The rooting zone water storage capacity (S0) extends from the soil surface to the weathered bedrock (the Critical Zone) and determines land-atmosphere exchange during dry periods. Despite its importance to land-surface modeling, variations of S0 across space are largely unknown as they cannot be observed directly. We developed a method to diagnose global variations of S0 from the relationship between vegetation activity (measured by sun-induced fluorescence and by the evaporative fraction) and the cumulative water deficit (CWD). We then show that spatial variations in S0 can be predicted from the assumption that plants are adapted to sustain CWD extremes occurring with a return period that is related to the life form of dominant plants and the large-scale topographical setting. Predicted biome-level S0 distributions, translated to an apparent rooting depth (zr) by accounting for soil texture, are consistent with observations from a comprehensive zr dataset. Large spatial variations in S0 across the globe reflect adaptation of zr to the hydroclimate and topography and implies large heterogeneity in the sensitivity of vegetation activity to drought. The magnitude of S0 inferred for most of the Earth’s vegetated regions and particularly for those with a large seasonality in their hydroclimate indicates an important role for plant access to water stored at depth - beyond the soil layers commonly considered in land-surface models.

Extreme wildfires extensively impact human health and the environment. Increasing vapour pressure deficit (VPD) has led to a chronic increase in wildfire area in the western United States, yet some regions have been more affected than others. Here we show that for the same increase in VPD, burned area increases more in regions where vegetation moisture shows greater sensitivity to water limitation (plant-water sensitivity; R