1. Project D: Thermodynamics and soil-vegetation-atmosphere transfer processes Objectives and Hypotheses Biospheric Theory and Modelling, Max Planck Institute for Biogeochemistry, Jena 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. Approach and Workplan Contribution to overall WP Stan Schymanski Axel Kleidon Hypotheses Project D is based on and motivated by the following hypotheses: Thermodynamics principles will allow explain and predict a wider range of ecohydrological phenomena than energy and mass balances or empirical relations alone. The soil-vegetation system is a co-evolving self-organised system, which can be understood better if we understand the organising principles. The organising principles relate to optimality theory, in that the many degrees of freedom of the system co-evolve (at different time scales) to maximise or minimise a certain objective function, which can be formulated mathematically. The degrees of freedom include all the internal properties of the soil-vegetation- atmosphere system that are not fully constrained by physical or biological laws or by land use practices. The variability of the degrees of freedom is optimised in both time and space. Implementation of different biological and thermodynamic objective functions in the same numerical model will help identifying the most appropriate objective function to explain the observed system behaviour. Consideration of the general principles guiding the organisation of soils and vegetation will allow more robust prediction of the catchment response to environmental change. Objectives 1. Evaluate the use of thermodynamic constraints and optimality principles for predicting soil-vegetation-atmosphere transfer (SVAT) processes including their spatial and temporal variations. 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 principles (e.g. the MEP hypothesis) with biologically motivated principles (e.g. the maximum net carbon profit (NCP) hypothesis). 2. To achieve the above aims, develop a SVAT model that a. integrates with the CAOS model, b. allows the calculation of all the relevant thermodynamic properties of the system, c. permits the implementation of different organising principles, and d. simulates a range of observable and hydrologically relevant vegetation properties that can be used to falsify the proposed principles and constraints. 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 organisation of vegetation c. capturing the effects of sub-grid-scale variability Task 1. Thermodynamics-based formulation of soil- vegetation-atmosphere transfer (SVAT) processes Starting point: Vegetation Optimality Model (VOM) (Schymanski et al. 2009). To do: (a) Represent driving forces for water fluxes by generalised thermodynamic Forces, (b) Thermodynamic formulation of the carbon balance in terms of free energy transduction and dissipation, (c) Implement a detailed energy balance in the VOM, (d) Verify the internal consistency of the formulations and consistency with previous results of the VOM. 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) Free energy Entropy Radiation Rainfall 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 (TP B) Monitoring and tracers (TP G, H) Forcing data Atmospheric (TP C) Soil properties (TP B, G) Drainage strength (TP G) Land use (TP B, Lippmann) Thermodynamic constraints Conservation of Mass Conservation of Energy Production of Entropy Dynamic model output Water fluxes Vegetation dynamics 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 CAOS model (TP S) Task 2. Implementation and comparison of different optimality assumptions Biologically motivated vs. thermodynamically motivated organising principles Task 5. Evaluation with observational data (a) Temporal dynamics (TP B, G, H), (b) spatial organisation of vegetation (TP B, S), (c) spatial organisation of roots (TP G, H, S), (d) effect of the amount of input data (TP B, C, S) Task 4. Investigation of causes and effects of spatial heterogeneity and organization (a) Lateral fluxes and spatial organisation in the catchment (TP S), (b) preferential flow and organisation in the soil domain (TP I, J, S) 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). Fig. 2: Interplay of thermodynamics, organising principles, forcing data and observations for the testing of hypotheses. Fig. 3: Effect of rain (P) and patterns on simulated biomass (B v ). After Schymanski et al. (2010) Fig. 4: Free energy transfer to the soil matrix and associated entropy production. SVAT Model