Using an optimality model to understand medium and long-term responses of vegetation water use to elevated atmospheric CO2 concentrations

Vegetation has different adjustable properties for adaptation to its environment. Examples include stomatal conductance at short time scale (minutes), leaf area index and fine root distributions at longer time scales (days-months) and species compositio

Adding our leaves: A communityâ wide perspective on research directions in ecohydrology

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154539/1/hyp13693.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154539/2/hyp13693_am.pd

Challenges and opportunities in land surface modelling of savanna ecosystems

The savanna complex is a highly diverse global biome that occurs within the seasonally dry tropical to sub-tropical equatorial latitudes and are structurally and functionally distinct from grasslands and forests. Savannas are open-canopy environments that encompass a broad demographic continuum, often characterised by a changing dominance between C3-tree and C4-grass vegetation, where frequent environmental disturbances such as fire modulates the balance between ephemeral and perennial life forms. Climate change is projected to result in significant changes to the savanna floristic structure, with increases to woody biomass expected through CO2 fertilisation in mesic savannas and increased tree mortality expected through increased rainfall interannual variability in xeric savannas. The complex interaction between vegetation and climate that occurs in savannas has traditionally challenged terrestrial biosphere models (TBMs), which aim to simulate the interaction between the atmosphere and the land surface to predict responses of vegetation to changing in environmental forcing. In this review, we examine whether TBMs are able to adequately represent savanna fluxes and what implications potential deficiencies may have for climate change projection scenarios that rely on these models. We start by highlighting the defining characteristic traits and behaviours of savannas, how these differ across continents and how this information is (or is not) represented in the structural framework of many TBMs. We highlight three dynamic processes that we believe directly affect the water use and productivity of the savanna system: phenology, root-water access and fire dynamics. Following this, we discuss how these processes are represented in many current-generation TBMs and whether they are suitable for simulating savanna fluxes.Finally, we give an overview of how eddy-covariance observations in combination with other data sources can be used in model benchmarking and intercomparison frameworks to diagnose the performance of TBMs in this environment and formulate road maps for future development. Our investigation reveals that many TBMs systematically misrepresent phenology, the effects of fire and root-water access (if they are considered at all) and that these should be critical areas for future development. Furthermore, such processes must not be static (i.e. prescribed behaviour) but be capable of responding to the changing environmental conditions in order to emulate the dynamic behaviour of savannas. Without such developments, however, TBMs will have limited predictive capability in making the critical projections needed to understand how savannas will respond to future global change

Leaf-scale experiments reveal an important omission in the Penman-Monteith equation

The Penman–Monteith (PM) equation is commonly considered the most advanced physically based approach to computing transpiration rates from plants considering stomatal conductance and atmospheric drivers. It has been widely evaluated at the canopy scale, where aerodynamic and canopy resistance to water vapour are difficult to estimate directly, leading to various empirical corrections when scaling from leaf to canopy. Here, we evaluated the PM equation directly at the leaf scale, using a detailed leaf energy balance model and direct measurements in a controlled, insulated wind tunnel using artificial leaves with fixed and predefined stomatal conductance. Experimental results were consistent with a detailed leaf energy balance model; however, the results revealed systematic deviations from PM-predicted fluxes, which pointed to fundamental problems with the PM equation. Detailed analysis of the derivation by Monteith(1965) and subsequent amendments revealed two errors: one in neglecting two-sided exchange of sensible heat by a planar leaf, and the other related to the representation of hypostomatous leaves, which are very common in temperate climates. The omission of two-sided sensible heat flux led to bias in simulated latent heat flux by the PM equation, which was as high as 50% of the observed flux in some experiments. Furthermore, we found that the neglect of feedbacks between leaf temperature and radiative energy exchange can lead to additional bias in both latent and sensible heat fluxes. A corrected set of analytical solutions for leaf temperature as well as latent and sensible heat flux is presented, and comparison with the original PM equation indicates a major improvement in reproducing experimental results at the leaf scale. The errors in the original PM equation and its failure to reproduce experimental results at the leaf scale (for which it was originally derived) propagate into inaccurate sensitivities of transpiration and sensible heat fluxes to changes in atmospheric conditions, such as those associated with climate change (even with reasonable present-day performance after calibration). The new formulation presented here rectifies some of the shortcomings of the PM equation and could provide a more robust starting point for canopy representation and climate change studies.ISSN:1027-5606ISSN:1607-793

Wind effects on leaf transpiration challenge the concept of "potential evaporation"

Transpiration is commonly conceptualised as a fraction of some potential rate, driven by so-called "atmospheric evaporative demand". Therefore, atmospheric evaporative demand or "potential evaporation" is generally used alongside with precipitation and soil moisture to characterise the environmental conditions that affect plant water use. Consequently, an increase in potential evaporation (e.g. due to climate change) is believed to cause increased transpiration and/or vegetation water stress. In the present study, we investigated the question whether potential evaporation constitutes a meaningful reference for transpiration and compared sensitivity of potential evaporation and leaf transpiration to atmospheric forcing. A physically-based leaf energy balance model was used, considering the dependence of feedbacks between leaf temperature and exchange rates of radiative, sensible and latent heat on stomatal resistance. Based on modelling results and supporting experimental evidence, we conclude that stomatal resistance cannot be parameterised as a factor relating transpiration to potential evaporation, as the ratio between transpiration and potential evaporation not only varies with stomatal resistance, but also with wind speed, air temperature, irradiance and relative humidity. Furthermore, the effect of wind speed in particular implies increase in potential evaporation, which is commonly interpreted as increased "water stress", but at the same time can reduce leaf transpiration, implying a decrease in water demand at leaf scale.ISSN:2199-899XISSN:2199-898

LI-6800

This repository contains python scripts to import data files from a LI-COR LI-6800, re-calculate fluxes and plot results. Associated Jupyter notebooks illustrate their use. This is a renku project available at https://renkulab.io/projects/wave/li-6800, which can be run on renkulab.io or imported from its corresponding gitlab address https://renkulab.io/gitlab/wave/li-6800

Stomatal Control and Leaf Thermal and Hydraulic Capacitances under Rapid Environmental Fluctuations

Leaves within a canopy may experience rapid and extreme fluctuations in ambient conditions. A shaded leaf, for example, may become exposed to an order of magnitude increase in solar radiation within a few seconds, due to sunflecks or canopy motions. Considering typical time scales for stomatal adjustments, (2 to 60 minutes), the gap between these two time scales raised the question whether leaves rely on their hydraulic and thermal capacitances for passive protection from hydraulic failure or over-heating until stomata have adjusted. We employed a physically based model to systematically study effects of short-term fluctuations in irradiance on leaf temperatures and transpiration rates. Considering typical amplitudes and time scales of such fluctuations, the importance of leaf heat and water capacities for avoiding damaging leaf temperatures and hydraulic failure were investigated. The results suggest that common leaf heat capacities are not sufficient to protect a non-transpiring leaf from over-heating during sunflecks of several minutes duration whereas transpirative cooling provides effective protection. A comparison of the simulated time scales for heat damage in the absence of evaporative cooling with observed stomatal response times suggested that stomata must be already open before arrival of a sunfleck to avoid over-heating to critical leaf temperatures. This is consistent with measured stomatal conductances in shaded leaves and has implications for water use efficiency of deep canopy leaves and vulnerability to heat damage during drought. Our results also suggest that typical leaf water contents could sustain several minutes of evaporative cooling during a sunfleck without increasing the xylem water supply and thus risking embolism. We thus submit that shaded leaves rely on hydraulic capacitance and evaporative cooling to avoid over-heating and hydraulic failure during exposure to typical sunflecks, whereas thermal capacitance provides limited protection for very short sunflecks (tens of seconds).ISSN:1932-620

Cost/Benefit Analysis of Carbon (C) allocation in Maize and Barley

<p><strong>Copyright © 2019-2023 Luxembourg Institute of Science and Technology. All Rights Reserved.</strong></p> <p><strong>The code in this repository is distributed under the terms of the GNU General Public License 3.0 or any later version, unless stated otherwise. All other material is licensed under a Creative Commons Attribution 4.0 International license, unless stated otherwise.</strong></p> <p>This repository contains code related to the development and testing of a custom growth chamber that allows simultaneous monitoring of above-gound and below-ground fluxes in plants. It also contains data and codes related to analysing the cost/benefit ratios of maize plants grown under varying bulk densities. This study was carried out as the PhD project of Emmanuella Onyinyechi Osuebi-Iyke, under the supervision of Stanislaus Schymanski. The research was funded by the Luxembourg National Research fund (FNR ATTRACT programme (A16/SR/11254288))</p> <h2><a href="#repository-structure"></a>Repository structure</h2> <ul> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/tree/master/above_ground">above-ground</a>: Above-ground fluxes data for maize and barley plants as obtained from the LI-6800.</p> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/tree/master/below_ground">below-ground</a>: Above-ground fluxes data for maize and barley plants as obtained from the LI-6800.</p> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/tree/master/multiplexer">multiplexer</a>: Multiplexer switch files required to assign fluxes to the correct growth chambers.</p> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/tree/master/Jupyter">Jupyter</a>: Jupyter notebooks with code and documentation</p> <ul> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/blob/master/JJupyter/New_GC_Fluxes_Computations_Juelich_LIST_Maize-Barley_March_2023.ipynb">Jupyter/New_GC_Fluxes_Computations_Juelich_LIST_Maize-Barley_March_2023.ipynb</a>: Notebook with initial computations for the plant growth experiments with maize and barley plants under different bulk densities.</p> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/blob/master/Jupyter/Pickle_New_GC_Fluxes_Computations_Juelich_LIST_Maize-Barley_March_2023.ipynb">Jupyter/Pickle_New_GC_Fluxes_Computations_Juelich_LIST_Maize-Barley_March_2023.ipynb</a>: Notebook with more computations and plots for the plant growth experiments (cost/benefit analysis) with maize and barley plants under different bulk densities.</p> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/blob/master/Jupyter/Feb-2023_1_4-1_6_QB_Data_Analysis-Penetration_Resistance.ipynb">Jupyter/Data_Analysis-Penetration_Resistance.ipynb</a>: Notebook with more computations and plots for the "soil" penetration resistance tests.</p> </li> </ul> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/tree/master/keithley">keithley</a>: Temperature data for out-going air from the custom growth chamber.</p> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/tree/master/pen_data_1_4-1_6BD">pen_data_1_4-1_6BD</a>: Contains data from penetration tests using the mini-penetrometer.</p> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/blob/master/llibreoffice_calc">libreoffice_calc</a>: All relevant spreadsheet documents relating to the plant growth experiments with maize plants under different bulk densities. Contains every data about the experimental plants including shoot height, leaf area etc.</p> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/tree/master/modules">modules</a>: Python files (.py) for importing elsewhere.</p> <ul> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/blob/master/Jupyter/modules/computing.py">modules/computing.py</a>: Python module for computing equations.</p> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/blob/master/Jupyter/modules/plotting.py">modules/computing.py</a>: Python module with plotting functions.</p> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/blob/master/Jupyter/modules/plotting.py">modules/computing.py</a>: Python module with file reading functions.</p> </li> </ul> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/tree/master/modules_exported">modules_exported</a>: Python files (.py) for variable and equation definitions.</p> <ul> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/blob/master/modules_exported/Mathematical_model_equations_definitions.py">modules_exported/Mathematical_model_equations_definitions.py</a>: Python module with equation definitions.</p> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/blob/master/modules_exported/Mathematical_model_variable_definitions.py">modules_exported/Mathematical_model_variable_definitions.py</a>: Python module with variable definitions.</p> </li> </ul> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/tree/master/pickles">pickles</a>: Saved files (after computation) for easy importance and further analysis.</p> </li> <li> <p><a href="../wave/CB_Ratios_Maize-and-Barley/-/tree/master/winRHIZO_txtfiles">winRHIZO_txtfiles</a>: Data for destructive plant root analysis.</p> </li> </ul&gt