Vegetation Model Research Articles

Contributions of ecological restoration policies to China’s land carbon balance

Unleashing the land sector’s potential for climate mitigation requires purpose-driven changes in land management. However, contributions of past management changes to the current global and regional carbon cycles remain unclear. Here, we use vegetation modelling to reveal how a portfolio of ecological restoration policies has impacted China’s terrestrial carbon balance through developing counterfactual ‘no-policy’ scenarios. Pursuing conventional policies and assuming no changes in climate or atmospheric carbon dioxide (CO2) since 1980 would have led China’s land sector to be a carbon source of 0.11 Pg C yr−1 for 2001–2020, in stark contrast to a sink of 175.9 Tg C yr−1 in reality. About 72.7% of this difference can be attributed to land management changes, including afforestation and reforestation (49.0%), reduced wood extraction (21.8%), fire prevention and suppression (1.6%) and grassland grazing exclusion (0.3%). The remaining 27.3% come from changes in atmospheric CO2 (42.2%) and climate (−14.9%). Our results underscore the potential of active land management in achieving ‘carbon-neutrality’ in China.

Warming-induced contrasts in snow depth drive the future trajectory of soil carbon loss across the Arctic-Boreal region

The Arctic-Boreal region is projected to experience spatially divergent trends in snow depth following climate change. However, the impact of these spatial trends has remained largely unexplored, despite potentially large consequences for the carbon cycle. To address this knowledge gap, we forced a customised arctic version of the dynamic vegetation model LPJ-GUESS with daily CMIP6 outputs from a global climate model (MRI-ESM2-0) under three climate scenarios. We find that snow depths increased the most in the coldest, northernmost regions, insulating the soil, which led to increased heterotrophic respiration and reduced carbon residence times. We emphasise the need for improved projections of future snow depth - in particular diverging trends across landscapes - to more accurately simulate the strength of Arctic-Boreal carbon feedbacks and their impact on global climate.

Simulated sensitivity of the Amazon rainforest to extreme drought

Abstract The Amazon rainforest is highly biodiverse and the largest intact, tropical forest in the world. Undisturbed tropical forests act as a carbon sink by taking up about 15% of anthropogenic carbon emissions per year. In the past decades, a declining trend in the carbon sink capacity in the Amazon rainforest has been observed due to increased carbon losses and tree mortality. The causes are disputed, but increasing temperatures and more frequent severe droughts are potentially major drivers. We employ a novel modelling framework and hypothesize that previously rare, extreme droughts in the Amazon, such as the ones in 2005 and 2010, constitute the main cause behind the decline of the net carbon sink in aboveground biomass. Our dynamic vegetation model simulates process-based plant hydraulics and drought-induced mortality, and accounts for the diversity of strategies in plant responses to drought based on observed hydraulic vulnerability curves. The simulated impact of the 2005 drought event temporarily turned the annual Amazon net carbon sink to a carbon source of about 0.25 MgC ha-1. In contrast to other dynamic vegetation models our model simulated an increasing trend in carbon losses and a declining trend in the Amazon carbon sink over the past 25 years (net sink rate of -0.018 Mg C ha-1 year-1 or -0.22 Mg C ha-1 per decade) which corresponds well with long-term forest monitoring data (net sink rate of -0.016 Mg C ha-1 year-1). We show that this trend is entirely attributable to drought-induced forest mortality during extreme years. The simulations show a threshold-like behaviour between drought intensity and biomass loss, which is due to xylem vulnerability, indicating the potentially high sensitivity of Amazon forests to extreme drought. Further increases in the severity and frequency of droughts might thus lead to greater carbon release and tree mortality than previously assumed.

A spectral–structural characterization of European temperate, hemiboreal, and boreal forests

Abstract. Radiative transfer models of vegetation play a crucial role in the development of remote sensing methods by providing a theoretical framework to explain how electromagnetic radiation interacts with vegetation in different spectral regions. A limiting factor in model development has been the lack of sufficiently detailed ground reference data on both the structural and spectral characteristics of forests needed for testing and validating the models. In this data description paper, we present a dataset on the structural and spectral properties of 58 stands in temperate, hemiboreal, and boreal European forests. It is specifically designed for the development and validation of radiative transfer models for forests but can also be utilized in other remote sensing studies. It comprises detailed data on forest structure based on forest inventory measurements, terrestrial and airborne laser scanning, and digital hemispherical photography. Furthermore, the data include spectral properties of the same forests at multiple scales: reflectance spectra of tree leaves and needles (based on laboratory measurements), the forest floor (based on in situ measurements), and entire stands (based on airborne measurements), as well as transmittance spectra of tree leaves and needles and entire tree canopies (based on laboratory and in situ measurements, respectively). We anticipate that these data will have wide use in testing and validating radiative transfer models for forests and in the development of remote sensing methods for vegetation. The data can be accessed at Hovi et al. (2024a, https://doi.org/10.23729/9a8d90cd-73e2-438d-9230-94e10e61adc9) (for laboratory and field data) and Hovi et al. (2024b, https://doi.org/10.23729/c6da63dd-f527-4ec9-8401-57c14f77d19f) (for airborne data).

Analysis of Quantitative Estimates of the Greenhouse Gases Net Flow in the Russian Land Use Sector

A comparative analysis of the actual greenhouse gas net runoff by the Russian land use sector has been carried out, which showed that depending on the methodologies and approaches used, as well as the type of initial data, information on the absorptive capacity can be aggregated into three groups: 1) studies based on process (inverse) modeling and VNIILM methodology; 2) studies with statistical net runoff modeling and approaches regulated by the IPCC on the basis of the state forest registry; 3) studies based on dynamic global vegetation models (DGVM) and ROBUL methodology, verified by the IPCC, but using as input data information from the state forest registry. An assumption is made that the existing official Russian ROBUL methodology, which coincides with the IPCC methodology, currently reflects a conservative estimate of the absorption capacity of the Russian land use sector and can be adjusted with a potential increase to 35-45% of the current values due to additional accounting of reserve forests in the calculations, as well as the results of the state forest inventory and a new state forest registry with a high degree of spatial resolution.

Antimicrobial Potential of Cedrus deodara Essential Oil to Preservative Effect for the Vegetables and Fruits

Abstract A unique species of pine, Cedrus deodara is known for its wood oil. Its traditional therapeutic use is mainly antibacterial and anti-inflammatory. The aim of this study was to investigate the antibacterial properties of Cedrus deodara essential oil (CDEO) obtained from the crushed wood. The antimicrobial activity of CDEO was evaluated against Gram-negative (G-) bacteria which included Pseudomonas aeruginosa CCM 1595, Salmonella enterica subs. enterica CCM 3807 and Gram-positive (G+) bacteria Yersinia enterocolitica CCM 5671. Listeria monocytogenes CCM 4699, Staphylococcus aureus subs. aureus CCM 2461 and Streptococcus consellatus CCM 4043 in vitro and in situ. The best antimicrobial activity for the disc diffusion method ranged from 4.67 to 9.67 mm and the minimum inhibitory concentration ranged from 1.48 to 5.44 mg.mL-1. The most effective antimicrobial effect was found against S. aureus and L. monocytogenes. The vapour phase used showed the best antimicrobial effect against P. aeruginosa in the kiwifruit model and L. monocytogenes in the banana model at a lower CDEO concentration of 62.5 µg.L-1 and against P. aeruginosa in the potato model and Y. enterocolitica in the cucumber model at a higher CDEO concentration of 500 µg.L-1. CDEO showed good antimicrobial activity against bacteria on vegetable and fruit model and may be a new preservative for storage of vegetables and fruits.

Fluid Force Reduction and Flow Structure at a Coastal Building with Different Outer Frame Openings Following Primary Defensive Alternatives: An Experiment-Based Review

A well-constructed tsunami evacuation facility can be crucial in a disaster. Understanding a tsunami’s force and the flow structure variation across various building configurations are essential to engineering designs. Hence, this study assessed the steady-state flow structure at building models (BM) incorporating outer frame openings, including piloti-type designs with a different width-to-spacing ratio of piloti-type columns following an embankment model (EM) with a vegetation model (VM). The experiments also demonstrated the outer frame opening percentage’s impact and orientation toward the overtopping tsunami flow at the BM. The results show that the arrangement of an opening on the outer frame and the piloti-type columns are critical in reducing the tsunami force concerning the experimental setup. Moreover, allowing a free surface flow beneath the BM implies that the correct piloti-pillar arrangement is crucial for resilient structure design. In addition, the three-dimensional numerical simulation was utilized to explain the turbulence intensity of the overtopping flow around the critical BM type. The derived resistance coefficient (CR) defined the drag and the hydrostatic characteristics at the BM due to the overtopping tsunami flow. Furthermore, for the impervious BM, the value CR was consistent with the previous studies, while the CR value for the BMs with an outer frame opening was directly coincident with the percentage of porosity.

Empirical evidence and theoretical understanding of ecosystem carbon and nitrogen cycle interactions.

Interactions between carbon (C) and nitrogen (N) cycles in terrestrial ecosystems are simulated in advanced vegetation models, yet methodologies vary widely, leading to divergent simulations of past land C balance trends. This underscores the need to reassess our understanding of ecosystem processes, given recent theoretical advancements and empirical data. We review current knowledge, emphasising evidence from experiments and trait data compilations for vegetation responses to CO2 and N input, alongside theoretical and ecological principles for modelling. N fertilisation increases leaf N content but inconsistently enhances leaf-level photosynthetic capacity. Whole-plant responses include increased leaf area and biomass, with reduced root allocation and increased aboveground biomass. Elevated atmospheric CO2 also boosts leaf area and biomass but intensifies belowground allocation, depleting soil N and likely reducing N losses. Global leaf traits data confirm these findings, indicating that soil N availability influences leaf N content more than photosynthetic capacity. A demonstration model based on the functional balance hypothesis accurately predicts responses to N and CO2 fertilisation on tissue allocation, growth and biomass, offering a path to reduce uncertainty in global C cycle projections.

The decline in tropical land carbon sink drives high atmospheric CO2 growth rate in 2023

Abstract Atmospheric CO2 growth rate (CGR), reflecting the carbon balance between anthropogenic emissions and net uptake from land and ocean, largely determines the magnitude and speed of global warming. CGR at Mauna Loa Baseline Observatory reached record high in 2023. Here we quantified major components of global carbon balance for 2023, by developing a framework which integrated fossil fuel CO2 emissions data, an atmospheric inversion from Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) with two artificial intelligence (AI) models derived from dynamic global vegetation models. We attributed the record high CGR increase in 2023 compared to 2022 primarily to the large decline in land carbon sink (1803 ± 197 TgC year−1), with minor contributions from a small reduction in ocean carbon sink (184 TgC year−1) and a slight increase in fossil fuel emissions (24 TgC year−1). At least 78% of the global decline in land carbon sink was contributed by the decline in tropical sink, with GONGGA inversion (1354 TgC year−1) and AI simulations (1578 ± 666 TgC year−1) showing similar declines in the tropics. We further linked this tropical decline to the detrimental impact of El Niño-induced anomalous warming and drying on vegetation productivity in water-limited Sahel and southern Africa. Our successful attribution of CGR increase within the framework combining atmospheric inversion with AI simulations enabled near-real-time tracking of the global carbon budget which had a one-year reporting lag.

Low latency carbon budget analysis reveals a large decline of the land carbon sink in 2023

Abstract In 2023, the CO2 growth rate was 3.37 ± 0.11 ppm at Mauna Loa, 86% above the previous year, and hitting a record high since observations began in 1958, while global fossil fuel CO2 emissions only increased by 0.6 ± 0.5%. This implies an unprecedented weakening of land and ocean sinks, and raises the question of where and why this reduction happened. Here we show a global net land CO2 sink of 0.44 ± 0.21 GtC yr−1, the weakest since 2003. We used dynamic global vegetation models, satellites fire emissions, an atmospheric inversion based on OCO-2 measurements, and emulators of ocean biogeochemical and data driven models to deliver a fast-track carbon budget in 2023. Those models ensured consistency with previous carbon budgets. Regional flux anomalies from 2015–2022 are consistent between top-down and bottom-up approaches, with the largest abnormal carbon loss in the Amazon during the drought in the second half of 2023 (0.31 ± 0.19 GtC yr−1), extreme fire emissions of 0.58 ± 0.10 GtC yr−1 in Canada and a loss in South-East Asia (0.13 ± 0.12 GtC yr−1). Since 2015, land CO2 uptake north of 20°N declined by half to 1.13 ± 0.24 GtC yr−1 in 2023. Meanwhile, the tropics recovered from the 2015–16 El Niño carbon loss, gained carbon during the La Niña years (2020–2023), then switched to a carbon loss during the 2023 El Niño (0.56 ± 0.23 GtC yr−1). The ocean sink was stronger than normal in the equatorial eastern Pacific due to reduced upwelling from La Niña's retreat in early 2023 and the development of El Niño later. Land regions exposed to extreme heat in 2023 contributed a gross carbon loss of 1.73 GtC yr−1, indicating that record warming in 2023 had a strong negative impact on the capacity of terrestrial ecosystems to mitigate climate change.

Study on the Sand Reduction Effect of Slope Vegetation Combination in Loess Areas

Slope erosion in the Loess Plateau region has long been a concern, and vegetation plays an important role in slowing down erosion and controlling sedimentation. However, a single vegetation model shows some limitations when facing complex natural conditions and variable rainfall events. Therefore, this study investigated the influence mechanism of vegetation configuration on slope sand production at different slopes through theoretical analyses and indoor experiments. The results of the study showed that certain factors, such as vegetation configuration mode, flow rate, runoff power, runoff velocity, and runoff shear, are closely related to slope runoff sand production. The specific findings are as follows: (1) Under the condition of slope gradient of 2°, the sand reduction effect of the rigid–flexible single-row staggered configuration is the most significant, and the sediment production is reduced by 29.89%. (2) With the increase in the slope gradient and flow rate, the sand production on the slope surface rises significantly, and when the slope gradient is increased from 2° to 6°, the average sand production is increased from 1.43 kg to 2.51 kg.(3) The erosion reduction effects of different vegetation configurations were in the order of rigid–flexible single-row staggered combination > flexible vegetation single combination > rigid–flexible double-row staggered combination > rigid vegetation single combination > upstream rigid downstream flexible combination > bare slope. This study provides a theoretical basis for optimizing the vegetation configuration for effective sand reduction and provides an important reference for the sustainable development of the Yellow River Basin.

Carbon dioxide fertilization enhanced carbon sink offset by climate change and land use in Amazonia on a centennial scale

The Amazon, the Earth's largest tropical forest, plays a critical role in the global carbon cycle, acting as a significant carbon sink. Recent studies, however, indicate a decline in its carbon sequestration capacity due to climate variability, intensive deforestation, and fires. This study aims to examine the impacts of these factors on the carbon dynamics of the Amazon over a centennial scale based on dynamic global vegetation models (DGVMs) of Trendy-v11. It was found that the Amazon region exhibited significant spatiotemporal variations in net land carbon (C) fluxes, and was a net C sink (40.02 ± 242.64 Tg C yr−1) during 1901–2021. The Amazonian net biome productivity (NBP) showed a 6-decades-scale shift from a decreasing trend (−3.78 Tg C yr−2) during 1901–1959 to an increasing trend (2.39 Tg C yr−2) during 1960–2021. The Amazonian NBP was negatively related to air temperature while positively related to dry-season precipitation during 1901–2021. Furthermore, the increase of atmospheric CO2 concentration during 1901–2020 enhanced Amazonian NBP by 36.40 ± 8.39 Pg C, which was largely offset by land use change (−18.84 ± 12.02 Pg C) and climate change (−10.03 ± 5.00 Pg C). Our findings underscore the critical need for sustainable management practices in the Amazon to enhance its C sink and preserve its function in the global climate system.

A dataset of 0.05-degree leaf area index in China during 1983–2100 based on deep learning network

Leaf Area Index (LAI) is a critical parameter in terrestrial ecosystems, with high spatial resolution data being extensively utilized in various research studies. However, LAI data under future scenarios are typically only available at 1° or coarser spatial resolutions. In this study, we generated a dataset of 0.05° LAI (F0.05D-LAI) from 1983–2100 in a high spatial resolution using the LAI Downscaling Network (LAIDN) model driven by inputs including air temperature, relative humidity, precipitation, and topography data. The dataset spans the historical period (1983–2014) and future scenarios (2015–2100, including SSP-126, SSP-245, SSP-370, and SSP-585) with a monthly interval. It achieves high accuracy (R² = 0.887, RMSE = 0.340) and captures fine spatial details across various climate zones and terrain types, indicating a slightly greening trend under future scenarios. F0.05D-LAI is the first high-resolution LAI dataset and reveals the potential vegetation variation under future scenarios in China, which benefits vegetation studies and model development in earth and environmental sciences across present and future periods.

Estimation of surface soil moisture from Sentinel-1 synthetic aperture radar imagery using machine learning method

Surface soil moisture (SM) is a crucial variable representing the water content in soil at the topmost soil layer. Accurate data of SM are essential for various purposes, including the monitoring of drought, vegetation modeling, weather forecasting, and agriculture management. Over the past decades, microwave remote sensing has been employed to estimate SM at large scales, with the Sentinel-1 satellite mission recently offering Synthetic Aperture Radar (SAR) data and enabling to estimate SM at high spatial resolution. Machine learning (ML) techniques have further enhanced these estimations, with random forest (RF) emerging as a promising method due to its strong capabilities. However, the performance of RF in estimating SM with satellite data has not yet been thoroughly evaluated across large areas. This study presents a new RF-based model for estimating SM over Europe with SAR data from Sentinel-1, along with the climate and terrain data. The predictions from the RF model were compared against the ground measurements from the International Soil Moisture Network (ISMN), Integrated Carbon Observation System (ICOS), and official SMAP/Sentinel-1 SM products. The results demonstrated that the RF model yielded accurate predictions with a Pearson's correlation coefficient (R) of 0.847 outperforming the official SMAP/Sentinel-1 product at spatial resolutions of 1 km and 3 km, which achieved R values of 0.599 and 0.616, respectively. Additionally, the impact of vegetation cover that is represented by using multiple satellite vegetation products on the RF model was investigated. Despite a moderate correlation between backscattering coefficients and vegetation cover, no correlation was found between the RF model's errors and vegetation cover. The results suggest that the RF model and Sentinel-1 SAR data can be used to provide SM estimations at high spatial resolution, characterizing the fine-scale variations in SM, to support various applications such as precision agriculture at the small, localized scales.

Fire weakens land carbon sinks before 1.5 °C

To avoid the worst impacts of climate change, the Paris Agreement committed countries to pursue efforts to limit global warming to 1.5 °C by urgently reducing greenhouse gas emissions. However, the Paris temperature ambitions and remaining carbon budgets mostly use models that lack feedback among fire, vegetation and carbon, which are essential for understanding the future resilience of ecosystems. Here we use a coupled fire–vegetation model to explore regional impacts and feedbacks across global warming levels. We address whether the 1.5 °C goal is consistent with avoiding significant ecosystem changes when considering shifts in fire regimes. We find that the global warming level at which fire began to impact global carbon storage significantly was 1.07 °C (0.8–1.34 °C) above pre-industrial levels and conclude that fire is already playing a major role in decreasing the effectiveness of land carbon sinks. We estimate that considering fire reduces the remaining carbon budget by 25 Gt CO2 (~5%) for limiting temperature rise to 1.5 °C and 64 GtCO2 (~5%) for 2.0 °C compared to previous estimates. Whereas limiting warming to 1.5 °C is still essential for avoiding the worst impacts of climate change, in many cases, we are already reaching the point of significant change in ecosystems rich in carbon and biodiversity.

Impact of dyke and vegetation on fluid force and moment reduction under sub and supercritical flow conditions

Flooding is the most common natural disaster throughout the world and requires efficient management. Therefore, the current investigation aimed to explore the impact of a composite defense system comprising dyke and vegetation on flow dynamics and velocity reduction. Experiments were conducted in an open channel setup with an adjustable bed slope and transparent sidewalls, and the vegetation model was replicated as real trees such as Eucalyptus trees. The study involved calculating several parameters, including flow velocity, reduction of fluid force index (RFI%), reduction in moment index (RMI%), and hydrostatic and hydrodynamic forces. These calculations were done by changing the channel bed slope and keeping the flow rate (discharge) constant while considering both subcritical and supercritical flow conditions. Moreover, regression analysis was performed for the prediction of RFI% and RMI% under various flow conditions. Also, statistical analyses were performed to assess the effectiveness of the defense system in reducing fluid force and moment indices. The result of the current investigation indicates that the highest values of RFI% and RMI% under subcritical flow conditions were 79% and 88%, while under supercritical flow conditions they were 94% and 78%, respectively. Moreover, a velocity reduction of 69% was observed under subcritical flow, while 84% was observed under supercritical flow conditions. Under subcritical flow conditions, RFI% and RMI% enhanced by enhancing Froude number (Fr) because of an increase in velocity reduction and hydraulic jump formation. Similar trends were observed under supercritical flow conditions, with effective mitigation of high-velocity flows by the composite system. The finding of current research helps in providing effective techniques for flood management.

Atmospheric CO2 fertilization effect on cereal yields in Morocco using the CARAIB dynamic vegetation model

Climate change and rising atmospheric CO2 levels are critical factors influencing agricultural productivity, particularly in Morocco, where cereal crops are essential for food security. The primary objective of this study is to evaluate the combined effects of atmospheric CO2 variations and climatic changes on cereal yields up to 2099 using the CARAIB dynamic vegetation model. This evaluation is driven by four future scenarios based on the Euro-CORDEX initiative's regional climate models under the Representative Concentration Pathway 8.5. As part of this evaluation, the study also validates the CARAIB model for Morocco’s major cereal crops: soft wheat, durum wheat, and barley, in order to ensure the model's accuracy in simulating crop responses under projected environmental conditions. The CARAIB model effectively simulated historical cereal yield trajectories across major farming regions in Morocco from 2000 to 2016, demonstrating its robust predictive capability. Our future projections suggest that elevated CO2 levels might initially sustain cereal yields at approximately 90 % of current levels until 2050. This trend indicates that the increase in atmospheric CO2 may exert a moderating influence on the negative impacts of other environmental stressors on crop yields. However, despite this initial buffering effect, the overall yield trend from the present until 2099 indicates a decrease for most combinations of crop, zone, and climate model, even with the CO2 fertilization effect, except in some cases, the model exhibits slight increases or stabilization in yields. Additionally, the CARAIB model predicts potential yield shocks of 10–35 % below current levels from the 2080 s onwards, primarily due to periodic droughts. This variation underscores the complexity of the interplay between CO2 fertilization and climatic changes, emphasizing the urgent need for Morocco to develop adaptive agricultural strategies for long-term sustainability in the face of climatic challenges.

Leaf habit drives leaf nutrient resorption globally alongside nutrient availability and climate

Abstract. Nutrient resorption from senescing leaves can significantly affect ecosystem nutrient cycling, making it an essential process to better understand long-term plant productivity under environmental change that affects the balance between nutrient availability and demand. Although it is known that nutrient resorption rates vary strongly between different species and across environmental gradients, the underlying driving factors are insufficiently quantified. Here, we present an analysis of globally distributed observations of leaf nutrient resorption to investigate the factors driving resorption efficiencies for nitrogen (NRE) and phosphorus (PRE). Our results show that leaf structure and habit, together with indicators of nutrient availability, are the two most important factors driving spatial variation in NRE. Overall, we find higher NRE in deciduous plants (65.2 % ± 12.4 %, n=400) than in evergreen plants (57.9 % ± 11.4 %, n=551), likely associated with a higher share of metabolic N in leaves of deciduous plants. Tropical regions show the lowest resorption for N (NRE: 52.4 % ± 12.1 %), and tundra ecosystems in polar regions show the highest (NRE: 69.6 % ± 12.8 %). At the same time, the PRE is lowest in temperate regions (57.8 % ± 13.6 %) and highest in boreal regions (67.3 % ± 13.6 %). Soil clay content, N and P atmospheric deposition (globally available proxies for soil fertility), and mean annual precipitation (MAP) play an important role in this pattern. The statistical relationships developed in this analysis indicate the important role of leaf habit and type for nutrient cycling and guide improved representations of plant-internal nutrient recycling and nutrient conservation strategies in vegetation models.

Effects of water limitation and competition on tree carbon allocation in an Earth system modelling framework

Abstract Earth system models (ESMs) have a limited capacity to represent plant functional diversity and shifts in trait distributions. Approaches to improving the representation of this complexity in ESMs include (i) optimality‐based approaches that predict trait–environment responses and (ii) explicitly modelling coexistence and community assembly. These approaches are expected to converge only when optimality‐based approaches identify competitively dominant strategies, which often differ from strategies that maximize ecosystem functioning or fitness components in monoculture. We used two models, LM3‐PPA (a vegetation demographic model designed as an ESM component) and BiomeE (a computationally efficient analog for LM3‐PPA), to explore how water limitation affects carbon allocation strategies of canopy trees. We compared competitive allocation strategies and those that maximize biomass or productivity in monoculture. We did not explicitly model coexistence or community assembly. Rather, we used model experiments to identify competitive and maximizing strategies in a two‐dimensional trait space under different precipitation and mortality scenarios. At 10 eastern US locations, we simulated historical, wet and dry climate scenarios, novel drought and three different mortality scenarios (low, medium or high sensitivity to water deficit). For each site and scenario, we identified the competitive strategy and three maximizing strategies (maximum biomass, productivity or drought‐tolerance). Root: leaf ratios tended to increase and leaf area tended to decrease with increasing water stress (increasing water limitation and its effects on mortality). However, relative to maximizing strategies, competitive strategies shifted towards greater allocation to roots and leaves with increasing water stress. Competitive overinvestments (greater allocation to roots and leaves by competitive strategies compared with maximizing strategies) were robust across different modelling contexts, including vegetation parameter sets (Acer vs. Populus), models (LM3‐PPA vs. BiomeE) and uncalibrated vsersus calibrated BiomeE versions. Synthesis: The theoretical prediction that competitive and maximizing allocation strategies differ under water limitation is confirmed for a demographic model designed as an ESM component. Optimality‐based trait predictions can simplify representing trait diversity in ESMs but do not always correspond to competitive outcomes. Explicitly modelling coexistence and community assembly in ESMs is challenging but is likely the most general approach to representing trait diversity.

Enhancing ecohydrological simulation with improved dynamic vegetation growth module in SWAT

The Soil and Water Assessment Tool (SWAT) model has been widely used for simulating ecohydrological processes such as streamflow and evapotranspiration (ET) under different land use and climate conditions. The growth of vegetation is a crucial link in the ecohydrological process. However, the model uses a simplified Environmental Policy Impact Climate (EPIC) model, which has limitations in simulation of dynamic vegetation growth. Therefore, this study aims to enhance SWAT by incorporating the forest aging data and optimizing the dormancy and forest vegetation growth modules. The application of this modified model with a case study shows that the accuracy of leaf area index (LAI) simulation has significantly improved (coefficient of determination (R2) increased by 0.16, the Nash-Sutcliffe efficiency (NSE) increased from −0.18 to 0.87, and the absolute value of percent bias (PBIAS) decreased by 50.99%), and with moderate improvement in streamflow simulation (NSE increased by 0.03, PBIAS decreased by 4.60%). Furthermore, optimizing the dynamic growth model of vegetation led to significant increases in LAI (151.58%) and ET (5.25%) values in the forest area, while streamflow and water yield decreased by 4.28% and 4.16%, respectively, across the watershed. The results indicate that dynamic vegetation growth led to increased ET, resulting in a decrease in streamflow and water yield. Overall, our results suggest that the modified SWAT model effectively simulated the process of forest growth from young forest to mature stages, significantly improving the accuracy of simulated hydrological processes. This methodology can be applied to other watersheds to simulate ecohydrological processes and can be valuable in management of ecosystems and water resources at watershed scale.