The last decade has marked a period of rapid innovation in high-throughput phenotyping (HTP) of plants. This includes the establishment of robotic phenotyping infrastructure, development of new sensors, and improvements in computation for downstream analysis. While HTP approaches have revolutionized data collection, meaningful insights into plant function require a yet deeper connection between resultant HTP-based information and biological responses. We suggest that dynamic process-based plant models, which simulate growth and physiology in a time-explicit manner, can serve as a functional link between high-throughput methods and whole-plant mechanisms of growth. Using this framework, we review recent research that has leveraged HTP approaches for estimation of plant traits that are commonly used as process-based model (PBM) variables. Through this analysis, we review successes and identify emerging directions for future research. Finally, we highlight the varied ways that HTP can be used in conjunction with PBMs as a tool to advance discovery and improve prediction of plant growth.

Rising temperatures and increases in drought negatively impact the efficiency and sustainability of both agricultural and forest ecosystems. Although hydraulic limitations on photosynthesis have been extensively studied, a solid understanding of the links between whole plant hydraulics and photosynthetic processes at the cellular level under changing environmental conditions is still missing, hampering our predictive power for plant mortality. Here, we examined plant hydraulic traits and CO2 assimilation rate under progressive water limitation by implementing Photosystem II (PSII) dynamics with a whole plant process model (TREES). The photosynthetic responses to plant water status were parameterized based on measurements of chlorophyll a fluorescence, gas exchange and water potential for Brassica rapa (R500) grown in a greenhouse under fully watered to lethal drought conditions. The updated model significantly improved predictions of photosynthesis, stomatal conductance and leaf water potential. TREES with PSII knowledge predicted a larger hydraulic safety margin and a decrease in percent loss of conductivity. TREES predicted a slower decrease in leaf water potential, which agreed with measurements. Our results highlight the pressing need for incorporating PSII drought photochemistry into current process models to capture cross-scale plant water dynamics from cell to whole plant level.

SummaryPlant water uptake from the soil is a crucial element of the global hydrological cycle and essential for vegetation drought resilience. Yet, knowledge of how the distribution of water uptake depth (WUD) varies across species, climates, and seasons is scarce relative to our knowledge of aboveground plant functions. With a global literature review, we found that average WUD varied more among biomes than plant functional types (i.e. deciduous/evergreen broadleaves and conifers), illustrating the importance of the hydroclimate, especially precipitation seasonality, on WUD. By combining records of rooting depth with WUD, we observed a consistently deeper maximum rooting depth than WUD with the largest differences in arid regions – indicating that deep taproots act as lifelines while not contributing to the majority of water uptake. The most ubiquitous observation across the literature was that woody plants switch water sources to soil layers with the highest water availability within short timescales. Hence, seasonal shifts to deep soil layers occur across the globe when shallow soils are drying out, allowing continued transpiration and hydraulic safety. While there are still significant gaps in our understanding of WUD, the consistency across global ecosystems allows integration of existing knowledge into the next generation of vegetation process models.

This article is a Commentary on Robbins et al. (2024), 244: 2239–2250.

This article is a Commentary on Zhang et al. (2023), 237: 780–792.

Terrestrial disturbances are increasing in frequency and severity, perturbing the hydrologic cycle by altering vegetation-mediated water use and microclimate. Here, we synthesize the literature on post-disturbance ecohydrological coupling, including the mechanistic relationship between vegetation and streamflow, under changing disturbance regimes, atmospheric CO2, and climate. Disturbance can cause decoupling between transpiration and streamflow by altering the connectivity, size, availability, and spatial distribution of their source pools. Successional trajectories influence the dynamics of source water partitioning. Changing climate and disturbance regimes can alter succession and prolong decoupling. Increasing rates, severity, and spread of disturbances along with warming could promote greater decoupling globally. From this review emerges a framework of testable hypotheses that identify the critical processes regulating ecohydrological coupling and provide a roadmap for future research. Accurate prediction of post-disturbance coupling requires understanding the degree of hydraulic connectivity between source water pools and their response to succession under changing disturbance and climate regimes.

Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential ("risky" stomatal regulation), high xylem hydraulic conductivity and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance and low stomatal conductance at low leaf water potential ("conservative" stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits, soil characteristics and their interactions (i.e., networks), has potential to improve our understanding of crop performance in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance.

On behalf of the editorial board of Water Resources Research (WRR) and the entire water science community, we want to express our most heartfelt gratitude to all who reviewed manuscripts for the journal in 2021. Your great efforts have ensured and improved the high quality and impact of the WRR papers and generally of research in our field. In 2021, the 2,960 individuals listed below have contributed 4,788 peer reviews with the 432 people indicated in italics, reviewing three or more papers for the journal. The editors, associate editors, and authors of WRR sincerely thank all our reviewers and look forward to the exciting advances that will be published in WRR in 2022.

Drought-associated woody-plant mortality has been increasing in most regions with multi-decadal records and is projected to increase in the future, impacting terrestrial climate forcing, biodiversity and resource availability. The mechanisms underlying such mortality, however, are debated, owing to complex interactions between the drivers and the processes. In this Review, we synthesize knowledge of drought-related tree mortality under a warming and drying atmosphere with rising atmospheric CO2. Drought-associated mortality results from water and carbon depletion and declines in their fluxes relative to demand by living tissues. These pools and fluxes are interdependent and underlay plant defences against biotic agents. Death via failure to maintain a positive water balance is particularly dependent on soil-to-root conductance, capacitance, vulnerability to hydraulic failure, cuticular water losses and dehydration tolerance, all of which could be exacerbated by reduced carbon supply rates to support cellular survival or the carbon starvation process. The depletion of plant water and carbon pools is accelerated under rising vapour pressure deficit, but increasing CO2 can mitigate these impacts. Advancing knowledge and reducing predictive uncertainties requires the integration of carbon, water and defensive processes, and the use of a range of experimental and modelling approaches. Enhanced drought frequency and magnitude have impacted tree mortality, leading to multiple examples of regional-scale dieback. This Review outlines the mechanisms leading to mortality, including carbon starvation and hydraulic failure.

Summary Increasing seawater exposure is killing coastal trees globally, with expectations of accelerating mortality with rising sea levels. However, the impact of concomitant changes in atmospheric CO2 concentration, temperature, and vapor pressure deficit (VPD) on seawater‐induced tree mortality is uncertain. We examined the mechanisms of seawater‐induced mortality under varying climate scenarios using a photosynthetic gain and hydraulic cost optimization model validated against observations in a mature stand of Sitka spruce (Picea sitchensis) trees in the Pacific Northwest, USA, that were dying from recent seawater exposure. The simulations matched well with observations of photosynthesis, transpiration, nonstructural carbohydrates concentrations, leaf water potential, the percentage loss of xylem conductivity, and stand‐level mortality rates. The simulations suggest that seawater‐induced mortality could decrease by c. 16.7% with increasing atmospheric CO2 levels due to reduced risk of carbon starvation. Conversely, rising VPD could increase mortality by c. 5.6% because of increasing risk of hydraulic failure. Across all scenarios, seawater‐induced mortality was driven by hydraulic failure in the first 2 yr after seawater exposure began, with carbon starvation becoming more important in subsequent years. Changing CO2 and climate appear unlikely to have a significant impact on coastal tree mortality under rising sea levels.

Increasing severity and frequency of drought is predicted for large portions of the terrestrial biosphere, with major impacts already documented in wet tropical forests. Using a 4‐year rainfall exclusion experiment in the Daintree Rainforest in northeast Australia, we examined canopy tree responses to reduced precipitation and soil water availability by quantifying seasonal changes in plant hydraulic and carbon traits for 11 tree species between control and drought treatments. Even with reduced soil volumetric water content in the upper 1 m of soil in the drought treatment, we found no significant difference between treatments for predawn and midday leaf water potential, photosynthesis, stomatal conductance, foliar stable carbon isotope composition, leaf mass per area, turgor loss point, xylem vessel anatomy, or leaf and stem nonstructural carbohydrates. While empirical measurements of aboveground traits revealed homeostatic maintenance of plant water status and traits in response to reduced soil moisture, modeled belowground dynamics revealed that trees in the drought treatment shifted the depth from which water was acquired to deeper soil layers. These findings reveal that belowground acclimation of tree water uptake depth may buffer tropical rainforests from more severe droughts that may arise in future with climate change.

Forest mortality has been widely observed across the globe during recent episodes of drought and extreme heat events. But the future of forest mortality remains poorly understood. While the direct effects of future climate and elevated CO2 on forest mortality risk have been studied, the role of lateral subsurface water flow has rarely been considered. Here we demonstrated the fingerprint of lateral flow on the forest mortality risk of a riparian ecosystem using a coupled plant hydraulics-hydrology model prescribed with multiple Earth System Model projections of future hydroclimate. We showed that the anticipated water-saving and drought ameliorating effects of elevated CO2 on mortality risk were largely compromised when lateral hydrological processes were considered. Further, we found lateral flow reduce ecosystem sensitivity to climate variations, by removing soil water excess during wet periods and providing additional water from groundwater storage during dry periods. These findings challenge the prevailing expectation of elevated CO2 to reduce mortality risk and highlight the need to assess the effects of lateral flow exchange more explicitly moving forward with forest mortality projections.

Key Points The editors thank the 2020 peer reviewers

By utilizing functional relationships based on observations at plot or field scales, water quality models first compute surface runoff and then use it as the primary governing variable to estimate sediment and nutrient transport. When these models are applied at watershed scales, this serial model structure, coupling a surface runoff sub-model with a water quality sub-model, may be inappropriate because dominant hydrological processes differ among scales. A parallel modeling approach is proposed to evaluate how best to combine dominant hydrological processes for predicting water quality at watershed scales. In the parallel scheme, dominant variables of water quality models are identified based entirely on their statistical significance using time series analysis. Four surface runoff models of different model complexity were assessed using both the serial and parallel approaches to quantify the uncertainty on forcing variables used to predict water quality. The eight alternative model structures were tested against a 25-year high-resolution data set of streamflow, suspended sediment discharge, and phosphorous discharge at weekly time steps. Models using the parallel approach consistently performed better than serial-based models, by having less error in predictions of watershed scale streamflow, sediment and phosphorus, which suggests model structures of water quantity and quality models at watershed scales should be reformulated by incorporating the dominant variables. The implication is that hydrological models should be constructed in a way that avoids stacking one sub-model with one set of scale assumptions onto the front end of another sub-model with a different set of scale assumptions.

SummaryShrub encroachment, forest decline and wildfires have caused large‐scale changes in semi‐arid vegetation over the past 50 years. Climate is a primary determinant of plant growth in semi‐arid ecosystems, yet it remains difficult to forecast large‐scale vegetation shifts (i.e. biome shifts) in response to climate change. We highlight recent advances from four conceptual perspectives that are improving forecasts of semi‐arid biome shifts. Moving from small to large scales, first, tree‐level models that simulate the carbon costs of drought‐induced plant hydraulic failure are improving predictions of delayed‐mortality responses to drought. Second, tracer‐informed water flow models are improving predictions of species coexistence as a function of climate. Third, new applications of ecohydrological models are beginning to simulate small‐scale water movement processes at large scales. Fourth, remotely‐sensed measurements of plant traits such as relative canopy moisture are providing early‐warning signals that predict forest mortality more than a year in advance. We suggest that a community of researchers using modeling approaches (e.g. machine learning) that can integrate these perspectives will rapidly improve forecasts of semi‐arid biome shifts. Better forecasts can be expected to help prevent catastrophic changes in vegetation states by identifying improved monitoring approaches and by prioritizing high‐risk areas for management.

Summary Understanding the genetic and physiological basis of abiotic stress tolerance under field conditions is key to varietal crop improvement in the face of climate variability. Here, we investigate dynamic physiological responses to water stress in silico and their relationships to genotypic variation in hydraulic traits of cotton (Gossypium hirsutum), an economically important species for renewable textile fiber production. In conjunction with an ecophysiological process‐based model, heterogeneous data (plant hydraulic traits, spatially‐distributed soil texture, soil water content and canopy temperature) were used to examine hydraulic characteristics of cotton, evaluate their consequences on whole plant performance under drought, and explore potential genotype × environment effects. Cotton was found to have R‐shaped hydraulic vulnerability curves (VCs), which were consistent under drought stress initiated at flowering. Stem VCs, expressed as percent loss of conductivity, differed across genotypes, whereas root VCs did not. Simulation results demonstrated how plant physiological stress can depend on the interaction between soil properties and irrigation management, which in turn affect genotypic rankings of transpiration in a time‐dependent manner. Our study shows how a process‐based modeling framework can be used to link genotypic variation in hydraulic traits to differential acclimating behaviors under drought.

Crop improvement is crucial to ensuring global food security under climate change, and hence there is a pressing need for phenotypic observations that are both high throughput and improve mechanistic understanding of plant responses to environmental cues and limitations. In this study, chlorophyllafluorescence light response curves and gas-exchange observations are combined to test the photosynthetic response to moderate drought in four genotypes ofBrassica rapa. The quantum yield of PSII (phi(PSII)) is here analyzed as an exponential decline under changing light intensity and soil moisture. Both the maximum phi(PSII)and the rate of phi(PSII)decline across a large range of light intensities (0-1,000 mu mol photons m(-2)s(-1);beta(PSII)) are negatively affected by drought. We introduce an alternative photosynthesis model (beta(PSII)model) incorporating parameters from rapid fluorescence response curves. Specifically, the model uses beta(PSII)as an input for estimating the photosynthetic electron transport rate, which agrees well with two existing photosynthesis models (Farquhar-von Caemmerer-Berry and Yin). The beta(PSII)model represents a major improvement in photosynthesis modeling through the integration of high-throughput fluorescence phenotyping data, resulting in gained parameters of high mechanistic value. Chlorophyll a fluorescence rapid light response curves improve mechanistic models of drought limitations to photosynthetic electron transport.

Process-based models of plant productivity provide a means of yield prediction that can inform best practices in agriculture and land management. However, current biophysical models fail in capturing both genotypic and phenotypic variation under changing environmental conditions. Physiological traits affecting final yield and ecosystem productivity are the result of gene expression, protein translation, and metabolite formation which are controlled by specific alleles acting alone and in response to time and environmental cues. Biophysical interactions take place simultaneously across several scales of time and space, giving rise to high levels of complexity and limiting the predictive capacity of existing analytical approaches. While statistical methods can partially quantify genotype-by-environment connections, biophysical process models miss exploring genotypic diversity and they cannot currently be implemented with -omics data, i.e. the entire collection of entities such as gene transcripts, metabolites, and proteins. Here we present a novel framework that utilizes Bayesian networks to provide probabilities that link gene expression levels to trait occurrence. We propose the use of these gained probabilities to inform parameters of existing biophysical models. Merging transcriptomic information into process-based models allows for greater integration of empirical data and processes across different scales in complex systems, while increasing the likelihood of growth predictions under unknown environmental conditions.

Predictions of warmer droughts causing increasing forest mortality are becoming abundant, yet few studies have investigated the mechanisms of forest persistence. To examine the resistance of forests to warmer droughts, we used a five-year precipitation reduction (similar to 45% removal), heat (+4 degrees C above ambient) and combined drought and heat experiment in an isolated stand of mature Pinus edulis-Juniperus monosperma. Despite severe experimental drought and heating, no trees died, and we observed only minor evidence of hydraulic failure or carbon starvation. Two mechanisms promoting survival were supported. First, access to bedrock water, or 'hydraulic refugia' aided trees in their resistance to the experimental conditions. Second, the isolation of this stand amongst a landscape of dead trees precluded ingress by Ips confusus, frequently the ultimate biotic mortality agent of pinon. These combined abiotic and biotic landscape-scale processes can moderate the impacts of future droughts on tree mortality by enabling tree avoidance of hydraulic failure, carbon starvation, and exposure to attacking abiotic agents.