Vegetation and land-surface models

Esha Mukherjee, Amity University Noida

Due to global environmental change, the potential of land ecosystems to deliver societal benefits – including the regulation of climate, the carbon cycle, and water and air quality, and the provisioning of goods including food and fibre is at risk. 

Plant demography, growth, and competition, as well as physical land-atmosphere interactions, must be accurately characterized at numerous geographic and temporal scales in order to assess and mitigate this risk.

Photosynthesis and stomatal regulation, carbon allocation, competition for light, water, and nutrients, community assembly, disturbance regimes, interactions of vegetation with climate and atmospheric composition, and yields of essential products such as crops are now all simulated using highly developed, process-based computational models that operate across scales.

The two main (overlapping) categories of current models are dynamic global vegetation models (DGVMs) and land surface models (LSMs).

LSMs are intended for use in climate models, and they clearly depict “fast” land-atmosphere exchanges, usually with half-hourly time increments. Some LSMs treat vegetation composition and structure as static; others, like DGVMs, also simulate vegetation dynamics.

Model development: Problems and solutions

Vegetation modelling as a fully integrated part of the climate system has significant scientific and computational hurdles. Many successful applications of vegetation models have diverted attention away from a number of well-documented systemic flaws, which have arisen particularly when models have attempted to recreate large-scale processes contained in atmospheric observations. For example, over the last half-century, both Earth System Models (ESMs) and offline DGVMs have failed to reproduce the full scale of amplification of the high-latitude seasonal cycle of atmospheric CO2.

Operational time-steps differentiate processes: LSMs typically represent canopy-atmosphere energy exchanges and photosynthesis at half-hourly time-steps; phenology, carbon allocation, and growth at days to months; and vegetation dynamics and disturbance in DGVMs at months to years. Most models define a small number of plant functional types (PFTs), each with its own set of properties, to represent plant adaptations to environmental conditions.

This is problematic because (a) for most quantitative plant traits, variation within PFTs is greater than the variation between PFTs and (b) acclimation and adaptation within species and PFTs account for a significant portion of the observed variation in community-mean trait values along environmental gradients.

Over the last decade, an alternative approach has gained traction: simulating quantitative traits that vary dynamically, mimicking acclimation and/or adaptation processes, and more realistically portraying ecosystem carbon uptake and the dynamic response of terrestrial ecosystems to climate change.

Many recent model “improvements” have been gained through increasing complexity, however, it is widely recognized that this does not equate to increased realism. Furthermore, developing models through accretion has invariably resulted in a loss of transparency. There has been a rising recognition in other areas of environmental modelling, particularly climate modelling, a re-examination of underlying processes, reduction of complexity, and enhanced transparency that are all important for advancement.

Identifying principles applicable across different and phylogenetically unique assemblages is a major difficulty for the global-scale modelling of biological systems (Franklin et al., 2020). EEO could play a major role because it can create coherent, testable ideas on plant and vegetation functions that transcend biome and flora differences.

Leaf-level and canopy-level optimality

Case study: By adjusting stomatal conductance, plants may control water and energy exchanges with the atmosphere. The quick, experimentally observed response to vapour pressure deposit (VPD) is the basis for most current models of gs. EEO hypotheses, which are based on a trade-off between maximizing carbon gain and reducing water loss, may provide cost-effective solutions. One approach is based on an approximate solution to the hypothesis of constant marginal water use efficiency, proposed by Cowan and Farquhar in 1977. This approach accurately predicts stomatal responses to increasing CO2 levels and variability across a range of environmental conditions (Medlyn et al., 2011).

The EEO hypothesis states that leaves minimize the sum of the maintenance costs (per unit assimilation) of transpiration and carboxylation capacities. This technique (Prentice et al., 2014), employed in the model of H. Wang et al. (2017). Alternative EEO techniques include hydraulic costs, which are based on the assumption that short- and long-term costs of transpiration at low soil water potential contribution to the total cost of maintaining the water transport system.

Conclusion

In a constantly changing environment, vegetation models have proven effective for estimating ecosystem productivity, vegetation patterns, terrestrial carbon uptake, and other ecosystem services. More reliable models, on the other hand, are needed to boost confidence in the plausibility of many of these projections. Models must be able to deal with dynamic processes such as plant migration, adaptation, acclimation, and land-use change as the rate of predicted global environmental change increases. 

EEO theory has the potential to solve these issues by drastically lowering the number of factors that must be stated. EEO techniques will make it possible to minimize the dimensionality of the trait space that needs to be explored when models progress away from PFTs and toward explicitly describing plant qualities. The use of EEO necessitates the unambiguous framing of various hypotheses, which places a premium on observation and experimentation to evaluate and compare them. There is currently no comprehensive definition of plant behaviour in terms of EEO — fact, as some of the examples above illustrate, the proper selection of optimality criteria is a hot issue of research in areas like stomatal behaviour.

Also read: The fate of transplanted stem cell is unpredictable

Source:

1. Harrison, S et al., (2021). Eco‐evolutionary optimality as a means to improve vegetation and land‐surface models. New Phytologist. DOI: https://doi.org/10.1111/nph.17558

2. Atkin OK, et al., (2017). Leaf respiration in terrestrial biosphere models. Plant Respiration: Metabolic Fluxes and Carbon Balance. DOI:10.1007/978-3-319-68703-2_6

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