Ecosystem Demography Research Articles

Drivers of forest productivity in two regions of the United States: Relative impacts of management and environmental variables.

Climate, environmental conditions, and management strategies are key factors affecting forest net ecosystem production (NEP). However, little is known about the relationship between management approaches and regional to continental-scale forest productivity. In this study, we utilized forests of the U.S. Southeast (SEUS) and Pacific Northwest (PNW), two ecologically and socio-politically distinct regions, to answer the question: Does management exert a stronger influence on NEP than environmental factors? We estimated Geographically Weighted Regression models of NEP derived from the Ecosystem Demography Model as a function of soil, topography, climate, and forest management practices for the period 2000-2015 using 383 and 407 10×10km2 landscapes in SEUS and PNW, respectively. Results showed that forest management practices were important in predicting NEP only in mountainous northeastern areas of the SEUS; in the PNW, NEP had a more complex relationship with management and was positively related to ecological, preservation, and passive management. Management, topography, and soil were more strongly correlated with NEP in the PNW than in the SEUS, in which 81%, 83%, and 83% of PNW locations showed significant relationships with at least one management, topography, or soil variable, repectively. In contrast, seasonal precipitation and temperature were stronger predictors of NEP in the SEUS than other drivers, with 99% and 84% of the locations influenced by at least one seasonal temperature or precipitation variable, respectively. The findings of this study may provide a valuable framework for forest management - climate change strategies that could be extended across regional scales.

Calibrating Tropical Forest Coexistence in Ecosystem Demography Models Using Multi‐Objective Optimization Through Population‐Based Parallel Surrogate Search

AbstractTropical forest diversity governs forest structures, compositions, and influences the ecosystem response to environmental changes. Better representation of forest diversity in ecosystem demography (ED) models within Earth system models is thus necessary to accurately capture and predict how tropical forests affect Earth system dynamics subject to climate changes. However, achieving forest coexistence in ED models is challenging due to their computational expense and limited understanding of the mechanisms governing forest functional diversity. This study applies the advanced Multi‐Objective Population‐based Parallel Local Surrogate‐assisted search (MOPLS) optimization algorithm to simultaneously calibrate ecosystem fluxes and coexistence of two physiologically distinct tropical forest species in a size‐ and age‐structured ED model with realistic representation of wood harvest. MOPLS exhibits satisfactory model performance, capturing hydrological and biogeochemical dynamics observed in Barro Colorado Island, Panama, and robustly achieving coexistence for the two representative forest species. This demonstrates its effectiveness in calibrating tropical forest coexistence. The optimal solution is applied to investigate the recovery trajectories of forest biomass after various intensities of clear‐cut deforestation. We find that a 20% selective logging can take approximately 40 years for aboveground biomass to return to the initial level. This is due to the slow recovery rate of late successional trees, which only increases by 4% over the 40‐year period. This study lays the foundation to calibrate coexistence in ED models. MOPLS can be an effective tool to help better represent tropical forest diversity in Earth system models and inform forest management practices.

Remote Sensing‐Based Forest Modeling Reveals Positive Effects of Functional Diversity on Productivity at Local Spatial Scale

AbstractForest biodiversity is critical for many ecosystem functions and services. Yet, it remains uncertain how plant functional diversity influences ecosystem functioning across environmental gradients and contiguous larger areas. We integrated remote sensing and terrestrial biosphere modeling to explore functional diversity–productivity relationships at multiple spatial scales for a heterogeneous forest ecosystem in Switzerland. We initialized forest structure and composition in the ecosystem demography model (ED2) through a combination of ground‐based surveys, airborne laser scanning and imaging spectroscopy for forest patches at 10 × 10‐m spatial grain. We derived morphological and physiological forest traits and productivity from model simulations at patch‐level to relate morphological and physiological aspects of functional diversity to the average productivity from 2006 to 2015 at 20 × 20 to 100 × 100‐m spatial extent. We did this for model simulations under observed and experimental conditions (mono‐soils, mono‐cultures and mono‐structures). Functional diversity increased productivity significantly (p < 0.001) across all simulations at 20 × 20 to 30 × 30 m scale, but at 100 × 100‐m scale positive relationships disappeared under homogeneous soil conditions potentially due to the low beta diversity of this forest and the saturation of functional richness represented in the model. Although local functional diversity was an important driver of productivity, environmental context underpinned the variation of productivity (and functional diversity) at larger spatial scales. In this study, we could show that the integration of remotely sensed information on forest composition and structure into terrestrial biosphere models is important to fill knowledge gaps about how plant biodiversity affects carbon cycling and biosphere feedbacks onto climate over large contiguous areas.

Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling.

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.

Impacts of scenarios RCP4.5 and RCP8.5 on plant physiology in Tapajos National Forest in the Brazilian Amazon using the ED2.2 model

ABSTRACT Models that simulate the process of stomatal conductance (gs) for a given set of environmental conditions are important, as this process is the main mechanism that controls the gas exchange of terrestrial plants absorbing atmospheric CO2 in tropical forests. Simulations were performed for the Tapajós National Forest, in the western Brazilian Amazon, observing the gs process under the current climate scenario (control) and under the scenarios RCP4.5 and RCP8.5 (2071 - 2100), using the ED2.2 ecosystem demography model. The results showed that the lower availability of soil water for the plants reduced photosynthesis due to the closing of the stomata. The model results for gross primary productivity (GPP) are similar to those observed in the field, varying about ≈24 MgC ha-1 year-1 for the rainy season and ≈23 MgC ha-1 year-1 for the dry season (average 2002 to 2010) in the control scenario. In the RCP4.5 scenario, simulated GPP was 30.7 and 30 MgC ha-1year-1 for the rainy and dry season, respectively (30.5 and 25 MgC ha-1year-1, respectively, for the RCP8.5 scenario). Our results also show that there may be a limitation on the increase in biomass carbon with the concentration of CO2, as GPP was lower in RCP8.5, despite this scenario having a higher value of atmospheric CO2 relative to RCP4.5.

A Framework to Calibrate Ecosystem Demography Models Within Earth System Models Using Parallel Surrogate Global Optimization

AbstractThe climatic feedbacks from vegetation, particularly from tropical forests, can alter climate through land‐atmospheric interactions. Expected shifts in species composition can alter these interactions with profound effects on climate and terrestrial ecosystem dynamics. Ecosystem demographic (ED) models can explicitly represent vegetation dynamics and are a key component of next‐generation Earth System Models (ESMs). Although ED models exhibit greater fidelity and allow more direct comparisons with observations, their interacting parameters can be more difficult to calibrate due to the complex interactions among vegetation groups and physical processes. In addition, while representation of forest successional coexistence in ESMs is necessary to accurately capture forest‐climate interactions, few models can simulate forest coexistence and few studies have calibrated coexisted forest species. Furthermore, although both vegetation characteristics and soil properties affect vegetation dynamics, few studies have paid attention to jointly calibrating parameters related to these two processes. In this study, we develop a computationally‐efficient and physical model structure‐based framework that uses a parallel surrogate global optimization algorithm to calibrate ED models. We calibrate two typically coexisted tropical tree species, early and late successional plants, in a state‐of‐the‐art ED model that is capable of simulating successional diversity in forests. We concurrently calibrate vegetation and soil parameters and validate results against carbon, energy, and water cycle measurements collected in Barro Colorado Island, Panama. The framework can find optimal solutions within 4–12 iterations for 19‐dimensional problems. The calibration for tropical forests has important implications for predicting land‐atmospheric interactions and responses of tropical forests to environmental changes.

Simulating the Impacts of Drought and Warming in Summer and Autumn on the Productivity of Subtropical Coniferous Forests

The impacts of drought and/or warming on forests have received great attention in recent decades. Although the extreme drought and/or warming events significantly changed the forest demography and regional carbon cycle, the seasonality quantifying the impacts of these climate extremes with different severities on the productivity of subtropical coniferous forests remains poorly understood. This study evaluated the effects of seasonal drought and/or warming on the net primary productivity (NPP) of subtropical coniferous forests (i.e., Cunninghamia lanceolata and Pinus massoniana forests) from Hengyang–Shaoyang Basin in southern China using the Ecosystem Demography model, Version 2.2 (ED-2.2) and based on the datasets from forest inventory, meteorological reanalysis, and remotely sensed products. The results showed that the goodness of fit of the DBH-height allometric equations was better than that of the default in ED-2.2 after model calibration; the ED-2.2 model qualitatively captured the seasonality of NPP in the subtropical coniferous forests; and the mismatch between simulated annual NPP and MODIS-NPP (MOD17A3HGF) became smaller over time. The effect of seasonal drought on NPP was greater than that of warming; the decline rate of NPP gradually increased and decreased with time (from July to October) under the seasonal drought and warming scenarios, respectively; NPP decreased more seriously under the combined drought-warming scenario in October, with an average decrease of 31.72%, than the drought-only and warming-only scenarios; seasonal drought had an obvious legacy impact on productivity recovery of subtropical coniferous forests, but it was not the case for warming. With the increase in drought severity, the average values of soil available water and NPP together showed a downward trend. With the increase in warming severity, the average values of canopy air space temperature increased, but NPP decreased. Seasonal drought and/or warming limit forest production through decreasing soil moisture and/or increasing canopy air space temperature, which impact on plant photosynthesis and productivity, respectively. Our results highlight the significance of taking into account the impacts of seasonal warming and drought when evaluating the productivity of subtropical coniferous forests, as well as the significance of enhancing the resistance and resilience of forests to future, more severe global climate change.

Simulation results for "A framework to calibrate ecosystem demography models within Earth system models using parallel surrogate global optimization"

Simulation results for "A framework to calibrate ecosystem demography models within Earth system models using parallel surrogate global optimization"

Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics

Abstract. Drought stress is an increasing threat for vegetation in tropical regions, within the context of human-induced increase of drought frequency and severity observed over South American forests. Drought stress is induced when a plant's water demand is not met with its water supply through root water uptake. The latter depends on root and soil properties, including soil texture (i.e. the soil clay and sand fractions) that determines the soil water availability and its hydraulic properties. Hence, soil clay content is responsible for a significant fraction of the spatial variability in forest structure and productivity. Soil-textural properties largely vary at the spatial resolution used by Terrestrial Biosphere Models (TBMs) and it is currently unclear how this variability affects the outputs of these models used to predict the response of vegetation ecosystems to future climate change scenarios. In this study, we assessed the sensitivity of the carbon cycle of three state-of-the-art TBMs, i.e. ORganizing Carbon and Hydrology in Dynamic EcosystEms (ORCHIDEEv2.2), Ecosystem Demography model version 2 (ED2), and Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS) to soil-textural properties at the regional level over the South American tropics using model default pedotransfer functions. For all three TBMs, the model outputs, including gross primary productivity (GPP), aboveground biomass (AGB), soil carbon content and drought stress, were shown to be mostly insensitive to soil-texture changes representative of the spatial variability in soil properties, except for a small region characterised by very low water availability in ORCHIDEEv2.2 and ED2. We argue that generic pedotransfer and simple drought stress functions, as currently implemented in TBMs, should be reconsidered to better capture the role of soil texture and its coupling to plant processes. Similarly, we suggest that better estimates of the soil-texture uncertainty resulting from soil-texture data aggregate should be considered in the future. Those steps forward are critical to properly account for future increasing drought stress conditions in tropical regions.

Future Hurricanes Will Increase Palm Abundance and Decrease Aboveground Biomass in a Tropical Forest

AbstractHurricanes are expected to intensify throughout the 21st century, yet the impact of frequent major hurricanes on tropical ecosystems remains unknown. To investigate tropical forest damage and recovery under different hurricane regimes, we generate a suite of scenarios based on Coupled Model Intercomparison Project Phase 6 climate projections and increased hurricane recurrence and intensity for the Luquillo Experimental Forest, Puerto Rico. We then use the Ecosystem Demography model to predict changes in carbon stocks, forest structure and composition. Our results indicate that frequent hurricane disturbances in the future would decrease the overall aboveground biomass, decrease the dominance of late‐successional species, but increase the dominance of palm species. Warmer climates with increased CO2 would have little effect on the functional‐type composition but increase the aboveground biomass. However, the predicted climate and CO2 fertilization effects would not compensate for the biomass loss due to more frequent severe‐hurricane disturbances.

A transiting temperate-subtropical mixed forest: carbon cycle projection and uncertainty

Terrestrial ecosystems respond to climate change in various ways, making it crucial to improve our understanding of these dynamics and uncertainty in projections. Here, we investigate how the species composition in a temperate-subtropical mixed forest on Jeju Island, South Korea, would change by 2099 and analysed the resultant effects on phenological timings and carbon flux using an individual cohort-based model—the ecosystem demography biosphere model version 2. We use the analyses of variance to decompose the contribution of model parameters (four sets) and climate inputs (four global climate models under four representative concentration pathway (RCP) scenarios) to the total uncertainty in the leaf area index (LAI) and net ecosystem productivity (NEP) projections. We find that with increases in temperature, photosynthetically active radiation, and vapour pressure deficit, the dominance of subtropical species will gradually increase by approximately 11%, from 30.2% in 2013 to 41.1% by the end of this century, yet there was a large variation in the projections depending on the model parameter and climate inputs. We also show the increases in the LAI and length of growing season by the end of this century, resulting in an increased NEP at the rate of up to 62.7 gC m−2 yr−1 per decade under the RCP8.5. The uncertainty in the LAI projection was largely due to the model parameter (and its interaction with climate inputs); however, the uncertainty contribution of climate models is as large as the emission scenario in the NEP projection. This study highlights the importance of identifying uncertainty sources for a robust projection of terrestrial ecosystem and carbon cycle.

The impact of hurricane disturbances on a tropical forest: implementing a palm plant functional type and hurricane disturbance module in ED2-HuDi V1.0

Abstract. Hurricanes commonly disturb and damage tropical forests. Hurricane frequency and intensity are predicted to change under the changing climate. The short-term impacts of hurricane disturbances to tropical forests have been widely studied, but the long-term impacts are rarely investigated. Modeling is critical to investigate the potential response of forests to future disturbances, particularly if the nature of the disturbances is changing with climate. Unfortunately, existing models of forest dynamics are not presently able to account for hurricane disturbances. Therefore, we implement the Hurricane Disturbance in the Ecosystem Demography model (ED2) (ED2-HuDi). The hurricane disturbance includes hurricane-induced immediate mortality and subsequent recovery modules. The parameterizations are based on observations at the Bisley Experimental Watersheds (BEW) in the Luquillo Experimental Forest in Puerto Rico. We add one new plant functional type (PFT) to the model – Palm, as palms cannot be categorized into one of the current existing PFTs and are known to be an abundant component of tropical forests worldwide. The model is calibrated with observations at BEW using the generalized likelihood uncertainty estimation (GLUE) approach. The optimal simulation obtained from GLUE has a mean relative error of −21 %, −12 %, and −15 % for stem density, basal area, and aboveground biomass, respectively. The optimal simulation also agrees well with the observation in terms of PFT composition (+1 %, −8 %, −2 %, and +9 % differences in the percentages of “Early”, “Mid”, “Late”, and “Palm” PFTs, respectively) and size structure of the forest (+0.8 % differences in the percentage of large stems). Lastly, using the optimal parameter set, we study the impact of forest initial condition on the recovery of the forest from a single hurricane disturbance. The results indicate that, compared to a no-hurricane scenario, a single hurricane disturbance has little impact on forest structure (+1 % change in the percentage of large stems) and composition (<1 % change in the percentage of each of the four PFTs) but leads to 5 % higher aboveground biomass after 80 years of succession. The assumption of a less severe hurricane disturbance leads to a 4 % increase in aboveground biomass.

Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)

Abstract. Terrestrial biosphere models (TBMs) are invaluable tools for studying plant–atmosphere interactions at multiple spatial and temporal scales, as well as how global change impacts ecosystems. Yet, TBM projections suffer from large uncertainties that limit their usefulness. Forest structure drives a significant part of TBM uncertainty as it regulates key processes such as the transfer of carbon, energy, and water between the land and the atmosphere, but it remains challenging to observe and reliably represent. The poor representation of forest structure in TBMs might actually result in simulations that reproduce observed land fluxes but fail to capture carbon pools, forest composition, and demography. Recent advances in terrestrial laser scanning (TLS) offer new opportunities to capture the three-dimensional structure of the ecosystem and to transfer this information to TBMs in order to increase their accuracy. In this study, we quantified the impacts of prescribing initial conditions (tree size distribution), constraining key model parameters with observations, as well as imposing structural observations of individual trees (namely tree height, leaf area, woody biomass, and crown area) derived from TLS on the state-of-the-art Ecosystem Demography model (ED2.2) of a temperate forest site (Wytham Woods, UK). We assessed the relative contributions of initial conditions, model structure, and parameters to the overall output uncertainty by running ensemble simulations with multiple model configurations. We show that forest demography and ecosystem functions as modelled by ED2.2 are sensitive to the imposed initial state, the model parameters, and the choice of key model processes. In particular, we show that: Parameter uncertainty drove the overall model uncertainty, with a mean contribution of 63 % to the overall variance of simulated gross primary production. Model uncertainty in the gross primary production was reduced fourfold when both TLS and trait data were integrated into the model configuration. Land fluxes and ecosystem composition could be simultaneously and accurately simulated with physically realistic parameters when appropriate constraints were applied to critical parameters and processes. We conclude that integrating TLS data can inform TBMs of the most adequate model structure, constrain critical parameters, and prescribe representative initial conditions. Our study also confirms the need for simultaneous observations of plant traits, structure, and state variables if we seek to improve the robustness of TBMs and reduce their overall uncertainties.

Data associated to the paper "Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)"

Data associated to the paper "Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)"

Global evaluation of the Ecosystem Demography model (ED v3.0)

Abstract. Terrestrial ecosystems play a critical role in the global carbon cycle but have highly uncertain future dynamics. Ecosystem modeling that includes the scaling up of underlying mechanistic ecological processes has the potential to improve the accuracy of future projections while retaining key process-level detail. Over the past two decades, multiple modeling advances have been made to meet this challenge, such as the Ecosystem Demography (ED) model and its derivatives, including ED2 and FATES. Here, we present the global evaluation of the Ecosystem Demography model (ED v3.0), which, like its predecessors, features the formal scaling of physiological processes for individual-based vegetation dynamics to ecosystem scales, together with integrated submodules of soil biogeochemistry and soil hydrology, while retaining explicit tracking of vegetation 3-D structure. This new model version builds on previous versions and provides the first global calibration and evaluation, global tracking of the effects of climate and land-use change on vegetation 3-D structure, spin-up process and input datasets, as well as numerous other advances. Model evaluation was performed with respect to a set of important benchmarking datasets, and model estimates were within observational constraints for multiple key variables, including (i) global patterns of dominant plant functional types (broadleaf vs. evergreen), (ii) the spatial distribution, seasonal cycle, and interannual trends for global gross primary production (GPP), (iii) the global interannual variability of net biome production (NBP) and (iv) global patterns of vertical structure, including leaf area and canopy height. With this global model version, it is now possible to simulate vegetation dynamics from local to global scales and from seconds to centuries with a consistent mechanistic modeling framework amendable to data from multiple traditional and new remote sensing sources, including lidar.

Intra-annual variation in microclimatic conditions in relation to vegetation type and structure in two tropical dry forests undergoing secondary succession

Microclimate acts as a strong filter on species performance in restored and regenerating forests, particularly in seasonally dry tropical forests (SDTF). Yet few studies have measured microclimate patterns across succession in SDTF. Furthermore, although dynamic vegetation models simulate microclimate, evaluation of these simulated variables with field observations has been relatively uncommon. Here, we investigated the seasonal patterns of soil temperature and soil water in naturally regenerated and planted successional vegetation in SDTF in Costa Rica and Puerto Rico, using complementary approaches of intensive field observations and simulation modeling with the Ecosystem Demography model. We found that plots representing later successional stages were wetter on average, but only during the dry season. During the wet season, mean soil water did not differ across vegetation types, but open, early successional vegetation experienced more frequent extreme wet and dry conditions than older forest and plantations. Soil temperature tended to decline with forest structure, and later successional vegetation also experienced less extreme daily temperature fluctuations. Basal area and leaf area index were the best predictors of differences in soil water and temperature across plots. Model simulations were consistent with observations of wet season soil temperature and soil water, but the model failed to reproduce dry season soil moisture dynamics, suggesting that further work is needed to reduce model biases in microclimate variables. Collectively, our results imply that common assumptions about how microclimates influence successional processes in SDTF should be revisited.

Impact of Ice-Storms and Subsequent Salvage Logging on the Productivity of Cunninghamia lanceolata (Chinese Fir) Forests

The impacts of ice-storms on forests have received growing attention in recent years. Although there is a wide agreement that ice-storms significantly affect forest structure and functions, how frequent ice-storms and subsequent salvage logging impact productivity of subtropical coniferous forests in the future still remains poorly understood. In this study, we used the Ecosystem Demography model, Version 2.2 (ED-2.2), to project the impact of salvage logging of ice-storm-damaged trees on the productivity of Cunninghamia lanceolata-dominated coniferous forest and C. lanceolata-dominated mixed coniferous and broadleaved forests. The results show that forest productivity recovery is delayed in coniferous forests when there is no shade-tolerant broadleaved species invasion after ice-storms, and C. lanceolata could continue to dominate the canopy in the mixed coniferous and broadleaved forests under high-frequency ice-storms and subsequent salvage logging. The resistance and resilience of the mixed coniferous and broadleaved forests to high-frequency ice-storms and subsequent salvage logging were stronger compared to coniferous forests. Although conifers could continue to dominate the canopy under shade-tolerant broadleaved species invasion, we could not rule out the possibility of a future forest community dominated by shade-tolerant broadleaf trees because there were few coniferous saplings and shade-tolerant broadleaf species dominated the understory. Our results highlight that post-disaster forest management should be continued after high-frequency ice-storms and subsequent salvage logging in C. lanceolata forests to prevent possible shade-tolerant, late successional broadleaf trees from dominating the canopy in the future.

Impacts of the 2012-2015 Californian drought on carbon, water and energy fluxes in the Californian Sierras: Results from an imaging spectrometry-constrained terrestrial biosphere model.

Accurate descriptions of current ecosystem composition are essential for improving terrestrial biosphere model predictions of how ecosystems are responding to climate variability and change. This study investigates how imaging spectrometry-derived ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional-scale carbon, water and energy fluxes. Incorporating imaging spectrometry-derived composition of five plant functional types (Grasses/Shrubs, Oaks/Western Hardwoods, Western Pines, Fir/Cedar and High-elevation Pines) into the Ecosystem Demography (ED2) terrestrial biosphere model improves predictions of net ecosystem productivity (NEP) and gross primary productivity (GPP) across four flux towers of the Southern Sierra Critical Zone Observatory (SSCZO) spanning a 2250m elevational gradient in the western Sierra Nevada. NEP and GPP root-mean-square-errors were reduced by 23%-82% and 19%-89%, respectively, and water flux predictions improved at the mid-elevation pine (Soaproot), fir/cedar (P301) and high-elevation pine (Shorthair) flux tower sites, but not at the oak savanna (San Joaquin Experimental Range [SJER]) site. These improvements in carbon and water predictions are similar to those achieved with model initializations using ground-based inventory composition. The imaging spectrometry-constrained ED2model was then used to predict carbon, water and energy fluxes and above-ground biomass (AGB) dynamics over a 737km2 region to gain insight into the regional ecosystem impacts of the 2012-2015 Californian drought. The analysis indicates that the drought reduced regional NEP, GPP and transpiration by 83%, 40% and 33%, respectively, with the largest reductions occurring in the functionally diverse, high basal area mid-elevation forests. This was accompanied by a 54% decline in AGB growth in 2012, followed by a marked increase (823%) in AGB mortality in 2014, reflecting an approximately 10-fold increase in per capita tree mortality from ~55 treeskm-2 year-1 in 2010-2011, to ~535 treeskm-2 year-1 in 2014. These findings illustrate how imaging spectrometry estimates of ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional carbon, water, and energy fluxes, and biomass dynamics.

Lianas Significantly Reduce Aboveground and Belowground Carbon Storage: A Virtual Removal Experiment

Lianas are structural parasites of trees that cause a reduction in tree growth and an increase in tree mortality. Thereby, lianas negatively impact forest carbon storage as evidenced by liana removal experiments. In this proof-of-concept study, we calibrated the Ecosystem Demography model (ED2) using 3 years of observations of net aboveground biomass (AGB) changes in control and removal plots of a liana removal experiment on Gigante Peninsula, Panama. After calibration, the model could accurately reproduce the observations of net biomass changes, the discrepancies between treatments, as well as the observed components of those changes (mortality, productivity, and growth). Simulations revealed that the long-term total (i.e., above- and belowground) carbon storage was enhanced in liana removal plots (+1.2 kgC m–2 after 3 years, +1.8 kgC m–2 after 10 years, as compared to the control plots). This difference was driven by a sharp increase in biomass of early successional trees and the slow decomposition of liana woody tissues in the removal plots. Moreover, liana removal significantly reduced the simulated heterotrophic respiration (−24%), which resulted in an average increase in net ecosystem productivity (NEP) from 0.009 to 0.075 kgC m–2 yr–1 for 10 years after liana removal. Based on the ED2 model outputs, lianas reduced gross and net primary productivity of trees by 40% and 53%, respectively, mainly through competition for light. Finally, model simulations suggested a profound impact of the liana removal on the soil carbon dynamics: the simulated metabolic litter carbon pool was systematically larger in control plots (+51% on average) as a result of higher mortality rates and faster leaf and root turnover rates. By overcoming the challenge of including lianas and depicting their effect on forest ecosystems, the calibrated version of the liana plant functional type (PFT) as incorporated in ED2 can predict the impact of liana removal at large-scale and its potential effect on long-term ecosystem carbon storage.

Cutting out the middleman: calibrating and validating a dynamic vegetation model (ED2-PROSPECT5) using remotely sensed surface reflectance

Abstract. Canopy radiative transfer is the primary mechanism by which models relate vegetation composition and state to the surface energy balance, which is important to light- and temperature-sensitive plant processes as well as understanding land–atmosphere feedbacks. In addition, certain parameters (e.g., specific leaf area, SLA) that have an outsized influence on vegetation model behavior can be constrained by observations of shortwave reflectance, thus reducing model predictive uncertainty. Importantly, calibrating against radiative transfer outputs allows models to directly use remote sensing reflectance products without relying on highly derived products (such as MODIS leaf area index) whose assumptions may be incompatible with the target vegetation model and whose uncertainties are usually not well quantified. Here, we created the EDR model by coupling the two-stream representation of canopy radiative transfer in the Ecosystem Demography model version 2 (ED2) with a leaf radiative transfer model (PROSPECT-5) and a simple soil reflectance model to predict full-range, high-spectral-resolution surface reflectance that is dependent on the underlying ED2 model state. We then calibrated this model against estimates of hemispherical reflectance (corrected for directional effects) from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration significantly reduced uncertainty in model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types. Using a single common set of parameters across all sites, the calibrated model was able to accurately reproduce surface reflectance for sites with highly varied forest composition and structure. However, the calibrated model's predictions of leaf area index (LAI) were less robust, capturing only 46 % of the variability in the observations. Comparing the ED2 radiative transfer model with another two-stream soil–leaf–canopy radiative transfer model commonly used in remote sensing studies (PRO4SAIL) illustrated structural errors in the ED2 representation of direct radiation backscatter that resulted in systematic underestimation of reflectance. In addition, we also highlight that, to directly compare with a two-stream radiative transfer model like EDR, we had to perform an additional processing step to convert the directional reflectance estimates of AVIRIS to hemispherical reflectance (also known as “albedo”). In future work, we recommend that vegetation models add the capability to predict directional reflectance, to allow them to more directly assimilate a wide range of airborne and satellite reflectance products. We ultimately conclude that despite these challenges, using dynamic vegetation models to predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data.