Displays

Water limiting conditions for the growth and physiology of trees as well as episodes of tree mortality triggered by drought have recently been documented in several bioregions across the world. In parallel to these major vegetation alterations, the impact of water scarcity also has prominent effects on soil processes mediated by the microbiome such as the transformation of organic matter, heterotrophic respiration, microbial uptake as well as nutrient mineralization. Although currently little explored, shifts in the interplay occurring between tree functioning and soil microbial processes may be crucial during tree mortality events and may feed back on ecosystem carbon and nitrogen cycling. We will present a multidisciplinary setup to mechanistically explore how water limitation acts synergistically on the interplay between trees and soil microorganisms, with potential consequences for ecosystem biogeochemical fluxes.

The experimental setup focusses on a key temperate forest species, Scots pine (Pinus sylvestris L.), which is currently facing high mortality rates in several inner-Alpine valleys of Europe due to drier climatic conditions during parts of the year. We make use of small scale mesocosms featuring young trees and soil collected from a drought-affected natural forest. The mesocosms are treated with different levels of water availability under controlled conditions. Plant growth and physiological changes related to water limitation are investigated in parallel to various soil properties. State-of-the-art isotopic labelling techniques are used to trace alterations in carbon and nitrogen transfers within the plant-soil-microbe continuum. We will specifically test whether extended periods of drought suppress the flux of carbon from plants to soil and lead plants to invest more in the maintenance of fine root systems. Moreover, we will follow the potential changes in the rates of decomposition, mineralization and incorporation of plant debris into soil organic matter over time and link them to potential alterations of the soil microbiota. These experimental observations will be validated by measurements in drought-affected Scots pine forests in inner-Alpine valleys. We expect the outcomes of this work to advance the fundamental understanding of the alterations occurring in the plant-soil-microbe system related to drought as well as to improve the detection of mechanisms leading to Scots pine mortality.

How to cite: Solly, E. F., Jaeger, A. C. H., Six, J., and Hartmann, M.: Potential consequences of water limitation and drought-induced tree mortality on carbon and nitrogen cycling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3963, https://doi.org/10.5194/egusphere-egu2020-3963, 2020.

Managed grasslands are extensive terrestrial ecosystems that provide a range of services. In addition to supporting the world’s various livestock production systems they contain climatically significant amounts of carbon (C). Understanding and quantifying the C dynamics of managed grasslands is complicated yet crucial.This presentation describes a process-model of C dynamics in managed grasslands (DALEC-Grass). DALEC-Grass is a model of intermediate complexity, which calculates primary productivity, dynamicallyallocates C to biomass tissues and describes the impacts of grazing/harvesting activities. The model is integrated into a Bayesian model-data fusion framework (CARDAMOM). CARDAMOM uses observations of ecosystem functioning (e.g. leaf area, biomass, C fluxes) to optimise the model’s parameters while respecting a set of biogeochemical and physiological rules. The model evaluation results presented demonstrate the model’s skill in predicting primary productivity and C allocation patterns in UK grasslands using both ground and satellite based leaf area index (LAI) time series as observational constraints.

How to cite: Myrgiotis, V., Clement, R., Jones, S. K., Keane, B., Lee, M., Levy, P. E., Rees, R. M., Skiba, U. M., Smallman, L. T., Toet, S., Williams, M., and Blei, E.: Understanding and quantifying carbon cycling in managed grasslands through model-data fusion, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4984, https://doi.org/10.5194/egusphere-egu2020-4984, 2020.

Crop sensitivity to soil texture guides many agronomic operations, especially under water-limited conditions. Unlike annual and mono-cultured crops, natural vegetation is subject to continuous selection of species and traits for adaptation to local climatic conditions. We report here a systematic evaluation of natural and rainfed cropped vegetation sensitivity to soil texture across biomes, rainfall anomalies, and scales using field observations and remote sensing products. Across biomes and annual precipitation amounts, natural vegetation productivity (GPP) shows no variations with soil texture. In contrast, crops (yields at small scales and GPP at large scales) exhibit sensitivity to soil texture that varies with annual rainfall anomaly and scale. Local measurements at field scale unambiguously show correlation in dry years (in agreement with conventional agronomic practices), while the strong correlation with soil texture vanishes at large scales (250 x 250 km) using remote sensing products. Subsampling of crop GPP at smaller scale (25 x 25 km) reveals a sensitivity of crop GPP to soil texture that becomes prominent in dry years. We conclude that natural vegetation across biomes represents a condition of climatic equilibrium via trait adaptation to overcome soil texture limitations, whereas annual crops retain dependency on soil texture (in rainfed agriculture) manifested at small scales, but obscured at larger scales where topography, aspect and soil map uncertainty dominate. The study provides new insights into gauging vegetation climatic adaptation via sensitivity to soil texture and the roles of scale in expressing such sensitivities in Earth Surface Models.

How to cite: Gupta, S., Lehmann, P., Bonetti, S., and Or, D.: On sensitivity of natural vegetation and rainfed crops to soil texture , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5571, https://doi.org/10.5194/egusphere-egu2020-5571, 2020.

Plants open and close their stomata in response to changes in the environment, so they can absorb the CO₂ they need to grow, while also avoid drying out. Since the activities of leaf stomata determine the exchanges of carbon and water between the vegetation and the atmosphere, it is crucial to incorporate their responses to environmental pressure into the vegetation models predicting carbon and water fluxes on broad spatial and temporal scales. The least-cost optimality theory proposes a simple way to predict leaf behaviour, in particular changes in the ratio of leaf internal (c_i) to ambient (c_a) partial pressure of CO₂, from four environmental variables, i.e. c_a, growing-season temperature (T_g), atmospheric vapour pressure deficit (D_g), and atmospheric pressure (as indexed by elevation, z). However, even though the theory considers the effect of atmospheric demand for water on c_i/c_a, it does not predict how dry soils with reduced soil water availability further influence c_i/c_a. Recent research has shown that independent of the individual effects of T_g, D_g, c_a and z on c_i/c_a, the model tends to underestimate c_i/c_a values at high soil moisture and to overestimate c_i/c_a values at low soil moisture. Here, we will try to disentangle the relative contribution of D_g and soil moisture on changes in c_i/c_a and test a new implementation of soil moisture effect in the framework of the least-cost hypothesis. To achieve this goal, we will use stable carbon isotopes measurements in leaves and in tree rings at sites with different soil water availability and different evaporative demand. We will then incorporate the improved model based on the least-cost hypothesis into the UK vegetation model JULES and investigate leaf stomatal responses to recent environmental changes across regions.

How to cite: Lavergne, A., Graven, H., and Prentice, I. C.: Disentangling the relative contributions of atmospheric demand for water and soil water availability on the stomatal limitation of photosynthesis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7190, https://doi.org/10.5194/egusphere-egu2020-7190, 2020.

Lianas, or woody vines, are abundant throughout forests worldwide, but are especially common in the tropics. Their presence can strongly suppress tree wood production, and presumably also reduce the strength of the tropical forest carbon sink. In intact neotropical forests, liana presence has been increasing over the past few decades, though the mechanisms remain under debate. Vexingly, lianas are not represented at all in current-day climate models. Better knowledge of liana morphology and allocation is required to unravel the mechanisms of below- and aboveground liana-tree competition in tropical forests. Such knowledge is also an essential step toward incorporating lianas into mechanistic forest dynamics models. To address these liana knowledge gaps, we have initiated a new project that integrates empirical and modeling work. Our objectives in this presentation are to compare observed liana allocation patterns to allocation patterns predicted by theory, and then to demonstrate how these results can be integrated into a numerical model.

Empirical measurements are being carried out in tropical dry forests in Guanacaste, Costa Rica. These measurements will eventually include excavations of ~80 entire trees and lianas, which will enable measurements of belowground and aboveground biomass of co-occurring trees and lianas, coarse and fine root vertical distribution, and lateral root spread. Also being measured are liana traits (including several critical hydraulic traits), above- and belowground productivity, and species-level fine root productivity. The modeling work includes the incorporation of lianas into the TROLL model, which is a mechanistic, individual-based forest dynamics model. The model will simulate the unique features of lianas, accounting for their structural parasitism and their different allocation strategies and morphology compared to trees. The simulated trees and lianas will compete aboveground for light and belowground for water. Thus, the model will integrate above- and belowground processes and couple the carbon and water cycles. Traits measured as part of this project are being used to parameterize the model.

Thus far, 33 mature, canopy-exposed individuals (18 trees and 15 lianas) have been harvested and analyzed. For both trees and lianas, biomass partitioning to roots, stems, and leaves were consistent with the predictions of allometric biomass partitioning theory. This result thwarted our initial expectation that lianas, with their narrow-diameter stems, would allocate proportionally less to stems than trees. We also found that vertical root profiles varied across life forms: lianas had the shallowest roots, evergreen trees had the deepest roots, and deciduous trees had intermediate rooting depths. The liana root systems also had notably broader lateral extents than the tree root systems. These results run contrary to previous work that reported that lianas were relatively deeply-rooted.

Our empirical results have helped to motivate model development. Each of our modeled liana individuals is assigned a laterally-widespread root system that can potentially extend beneath many trees. The liana root system is then permitted to put up aboveground shoots that associate with trees within the footprint of the root system. Comparisons of simulated and observed above- and belowground productivity are currently being conducted to help evaluate model assumptions.

How to cite: Medvigy, D., Smith-Martin, C., Parker, S., Willson, A., Marechaux, I., Tiffin, P., Chave, J., and Powers, J.: Unraveling the mechanisms of below- and aboveground liana-tree competition in tropical forests, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7746, https://doi.org/10.5194/egusphere-egu2020-7746, 2020.

Accurate simulations of gross primary production (GPP) are vital for Earth System Models that must inform public policy decisions.  The instantaneous controls of leaf-level photosynthesis, which can be measured in manipulative experiments, are well established.  At the canopy scale, however, there is no consensus on how GPP depends on (a) light or (b) other aspects of the physical environment such as temperature and CO₂.  Models of GPP make a variety of different assumptions when ‘scaling-up’ the standard model of photosynthesis.  As a troublesome consequence, they make a variety of different predictions about how GPP responds to contemporary environmental change.

This problem can be tackled by theoretically based modelling, or by empirical analysis of GPP as reconstructed from eddy-covariance flux measurements.  Theoretical modelling has provided an explanation for why ‘light-use efficiency’ (LUE) models work well at time scales of a week or longer.  The same logic provides a justification for the use of LUE as a key metric in an empirical analysis.  By focusing on LUE, we can isolate the controls of GPP that are distinct from its over-riding control by absorbed light.  We have used open-access eddy covariance data from over 100 sites, collated over 20 years (the number of sites has grown with time).  These sites, located in a wide range of biomes and climate zones, form part of the FLUXNET network.  We have combined the flux data with a satellite product (FPAR from MODIS) that provides spatial estimates of the fraction of incident light absorbed by green vegetation.  Soil moisture at flux sites was estimated using the SPLASH model, with appropriate meteorological inputs, and soil water-holding capacity derived using SoilGrids.  LUE was then calculated as the amount of carbon fixed per unit of absorbed light.  We then considered additive models (incorporating multiple explanatory factors) that support non-linear responses, including a peaked response to temperature.  Recognising that our longitudinal data are not fully independent, we controlled for the hierarchical nature of the dataset through a variance structure that nests measurement year within site location.

In arriving at a final parsimonious model, we show that daytime air temperature and vapour pressure deficit, and soil moisture content, are all salient predictors of LUE.  The same explanatory terms are retained in iterations of this analysis run at timescales from weeks to months.  Model performance was not significantly improved by inclusion of additional variables such as rainfall, site elevation or vegetation category (e.g. Plant Functional Type, PFT).  This empirical analysis supports the notion that GPP is predictable using a single model structure that is common to different PFTs.

How to cite: Bloomfield, K., Stocker, B., and Prentice, C.: Testing across vegetation types for common environmental dependencies of Gross Primary Production , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10067, https://doi.org/10.5194/egusphere-egu2020-10067, 2020.

The land surface components of climate and Earth system models tend to utilise relatively simple representations of vegetation radiative transfer processes to determine key land surface properties such as albedo, land surface temperature and the absorption of sunlight for photosynthesis. This simplicity is driven, in large part, by a need for computational efficiency. However, a growing number of studies have pointed to the need for more complex radiative transfer in these models.

An almost ubiquitous assumption in such radiative transfer schemes is that a vegetation canopy can be represented by a plane-parallel, turbid medium – a perfectly flat box in which scattering elements (i.e. leaves, branches, trunks, etc.) are randomly distributed. Real canopies typically exhibit quite complex, non-random structures often involving the clumping of leaves and branches at multiple scales. Furthermore, the optical properties of canopies are typically assumed to be vertically and horizontally homogeneous which does not allow for realistic representation of, for example, forest stands with mixed species or understory vegetation.

This presentation examines recent developments that have the potential to overcome these and other deficiencies in land surface model radiative transfer schemes, whilst maintaining sufficient computational efficiency to make them viable for inclusion in climate and Earth system models. This is achieved by using the same solutions to the transfer problem as currently employed in climate models as the building blocks to construct canopies that can vary both vertically and horizontally. 

How to cite: Quaife, T.: New Vegetation Radiative Transfer Schemes for Land Surface Models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10282, https://doi.org/10.5194/egusphere-egu2020-10282, 2020.

Accurately representing the response of ecosystems to environmental change in land surface models (LSM) is crucial to making accurate predictions of future climate. Many LSMs do not correctly capture plant respiration and growth fluxes, particularly in response to extreme climatic events. This is in part due to the unrealistic assumption that total plant carbon expenditure (PCE) is always equal to gross carbon accumulation by photosynthesis. We present and evaluate a simple model of labile carbon storage and utilisation (SUGAR), designed to be integrated into an LSM, that allows simulated plant respiration and growth to vary independently of photosynthesis. SUGAR buffers simulated PCE against seasonal variation in photosynthesis, producing more constant (less variable) predictions of plant growth and respiration relative to an LSM that does not represent labile carbon storage. This allows the model to more accurately capture observed carbon fluxes at a large-scale drought experiment in a tropical moist forest in the Amazon, relative to the Joint UK Land Environment Simulator LSM (JULES). SUGAR is designed to improve the representation of carbon storage in LSMs and provides a simple framework that allows new processes to be integrated as the empirical understanding of carbon storage in plants improves. The study highlights the need for future research into carbon storage and allocation in plants, particularly in response to extreme climate events such as drought.

How to cite: Jones, S., Rowland, L., Cox, P., Hemming, D., Wiltshire, A., Williams, K., Parazoo, N., Liu, J., da Costa, A., Meir, P., Mencuccini, M., and Harper, A.: The role of non-structural carbohydrates in simulations of ecosystem carbon fluxes., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16583, https://doi.org/10.5194/egusphere-egu2020-16583, 2020.

How to cite: Zhang, C., Atherton, J., Rajewicz, P., Riikonen, A., Kolari, P., Fernández-Marín, B., Garcia-Plazaola, J. I., and Porcar-Castell, A.: Seasonal dynamics of spectral vegetation indices at leaf, ecosystem and satellite scales for a boreal evergreen coniferous forest , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18337, https://doi.org/10.5194/egusphere-egu2020-18337, 2020.