Project D: Thermodynamics and soil-vegetation-atmosphere transfer processes

1. Project D: Thermodynamics and soil-vegetation-atmosphere transfer processes Free energy Radiation Rainfall Entropy Biospheric Theory and Modelling, Max Planck Institute for Biogeochemistry, Jena Stan SchymanskiAxel Kleidon Approach andWorkplan Hypotheses and Objectives Task 3. Adaptation of the VOM to the Attert catchment (a) Deciduous trees, (b) land use types, (c) rainfall interception, (d) parameterisation for each elementary functional unit (EFU), (e) integration of VOM in the CAOS model (Proj. S). • Task 1. Thermodynamics-based formulation of soil-vegetation-atmosphere transfer (SVAT) processes • Starting point: Vegetation Optimality Model (VOM) (Schymanski et al. 2009). • To do: • Use generalised thermodynamic forces as driving forces for water fluxes . • Thermodynamic formulation of the carbon balance in (free energy). • Implement a detailed energy balance in the VOM. • Hypotheses • Thermodynamics principles can explain and predict a wide range of ecohydrological phenomena. • The soil-vegetation system is a co-evolving self-organisedsystem. • The many degrees of freedom of the system co-evolve following organising principles that can be formulated mathematically as an objective function. • Objectives • Evaluate the use of thermodynamic constraints and optimality principles for predicting soil-vegetation-atmosphere transfer (SVAT). In particular: • a. Formulate SVAT processes as exchange of thermodynamic quantities between open systems. • b. Analyse the relationship between free energy dissipation, work and entropy. • c. Compare thermodynamic principleswith biologically motivated principles. • 2. To achieve the above aims, develop a SVAT model that • a. allows the calculation of the system’s relevant thermodynamic properties • b. permits the implementation of different organising principles, and • c. simulates a range of observable vegetation properties that can be used to falsify the model. • 3. Test the use of the organising principles in the SVAT model for • a. predicting the dynamics of water use by different vegetation types • b. explaining the spatial organisationof vegetation • c. capturing the effects of sub-grid-scale variability Task 4. Investigation of causes and effects of spatial heterogeneity and organization (a) Lateral fluxes and spatial organisation in the catchment (Proj. S), (b) preferential flow and organisation in the soil domain (Proj. I, J, S) MA,p MA,s yr,s yr,p Fig. 2: The Vegetation Optimality Model (VOM). Perennial and seasonal plants are represented by separate “big leaves” with attached root systems. Perennials: constant cover (MA), deep roots (yr); seasonals: variable cover, shallow roots. Fig. 5: Effect of rain (P) and patterns on simu-lated biomass (Bv) (Schymanski et al. 2010). Fig. 6: Free energy transfer to the soil matrix and associated entropy production. Task 5. Evaluation with observational data (a) Temporal dynamics (Proj. B, G, H), (b) spatial organisation of vegetation (Proj. B, S), (c) spatial organisation of roots (Proj. G, H, S), (d) effect of the amount of input data (Proj. B, C, S) Fig. 3: Simulated and observed evapo-transpiration (top) and CO2 uptake (bottom) at a savanna site (Schymanski et al. 2009). Fig. 1: Soil, vegetation and atmosphere as thermodynamic systems, with boundaries shown as dotted lines. Arrows: mass fluxes across system boundaries; boxes: dissipative processes. subscripts: P = precipitation, S = soil, V = vegetation, A = atmosphere, O = ocean. From Kleidon and Schymanski (2008). Task 2. Implementation and comparison of different optimality assumptions Biologically motivated vs. thermodynamically motivated organising principles Contributiontooverall WP • WP 2.1 “Surface and vegetation domain”: • Closure relations for the SVAT pathway (Tasks 1, 3) • Dynamic adaptation of natural vegetation to its environment (Tasks 2, 5) • Differential root water uptake in the soil profile (Tasks 2, 5) • WP 2.2 “Subsurface flow domain”: • Effect of roots on preferential flow paths (Task 4) • WP 3.2 “Synthesis of organising principles and rules for forming dynamic functional units”: • Vegetation sensitivity to external boundary conditions (Task 3) • WP 3.3 “Multi-objective validation and assessment of minimum data needs”: • Effect of input data on accuracy of results (Task 5) • Forcing data • Atmospheric (Proj. C) • Soil properties (Proj. B, G) • Drainage strength (Proj. G) • Land use (Proj. B, Lippmann) • Thermodynamic constraints • Conservation of Mass • Conservation of Energy • Production of Entropy VOM • Degrees of Freedom • Vegetation properties • Macropores • Spatial organisation • Organising principles • Max. Net Carbon Profit • Max. Gross Primary Productivity • Max. Entropy Production • Min. Energy Expenditure … • Observations • Remote sensing (Proj. B) • Monitoring and tracers (Proj. G, H) • Dynamic model output • Water fluxes • Vegetation dynamics Fig. 4: Interplay of thermodynamics, organising principles, forcing data and observations for the testing of hypotheses. Literature/Refs Kleidon, A. and Schymanski, S. (2008): Thermodynamics and optimality of the water budget on land: A review. Geophysical Research Letters 35(20), p.L20404. doi: 10.1029/2008GL035393. Schymanski, S.J., Sivapalan, M., Roderick, M.L., Hutley, L.B. and Beringer, J. (2009): An Optimality-Based Model of the Dynamic Feedbacks between Natural Vegetaton and the Water Balance. Water Resources Research 45, p.W01412. doi: 10.1029/2008WR006841. Schymanski, S.J., Kleidon, A., Stieglitz, M. and Narula, J. (2010): Maximum Entropy Production allows a simple representation of heterogeneity in semiarid ecosystems. Phil. Trans. R. Soc. London, Ser. B 365, p.1449–1455.