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Simulation of the biogeochemical cycle of phosphorus in the ORCHIDEE land surface model : evaluation against local and global observational data

Abstract : Phosphorus (P) plays a critical role in controlling metabolic processes, soil organic matter dynamics, plant growth and ecosystem productivity, thereby affecting greenhouse gas balance (GHG) of land ecosystems. A small number of land surface models have incorporated P cycles but their predictions of GHG balances remain highly uncertain. The reasons are: (1) scarce benchmarking data for key P-related processes (e.g. continental to global scale gridded datasets), (2) lack of comprehensive global evaluation strategy tailored for d P processes and interlinkages with carbon and nitrogen (N) cycles, and (3) insufficient model calibration limited by the high computation cost to simulate coupled CNP cycles which operate on timescales of minutes to millenia. Addressing those research gaps, I apply a combination of statistical methods (machine learning), LSMs and observational data among various scales.Firstly (Chapter 2), to address the lack of benchmarking data, I applied two machine-learning methods with the aim to produce spatial gridded maps of acid phosphatase (AP) activity on continental scale by scaling up scattered site observations of potential AP activity. AP secreted by fungi, bacteria and plant roots play an important role in recycling of soil P via transforming unavailable organic P into assimilable phosphate. The back-propagation artificial network (BPN) method that was chosen explained 58% of AP variability and was able to identify the gradients in AP along three transects in Europe. Soil nutrients (total nitrogen, total P and labile organic P) and climatic controls (annual precipitation, mean annual temperature and temperature amplitude) were detected to be the dominant factors influencing AP variations in space.Secondly (Chapter 3), I evaluated the performance of the global version of the land surface model ORCHIDEE-CNP (v1.2) using the data from chapter 2 as well as additional data from remote-sensing, ground-based measurement networks and ecological databases. Simulated components of the N and P cycle at different levels of aggregation (from local to global) are in good agreement with data-driven estimates. We identified model biases, in the simulated large-scale patterns of leaf and soil stoichiometry and plant P use efficiency, which point towards an underestimation of P availability towards the poles. Based on our analysis, we propose ways to address the model biases by giving priority to better representing processes of soil organic P mineralization and soil inorganic P transformation.Lastly (Chapter 4), I designed and tested a Machine Learning (ML)-based procedure for acceleration of the equilibration of biogeochemical cycles to boundary conditions (spinup) which is causing the low computational efficiency of current P-enabled LSMs. This ML-based acceleration approach (MLA) requires to spin-up only a small subset of model pixels (14.1%) from which the equilibrium state of the remaining pixels is estimated by ML. MLA predicts the equilibrium state of soil, biomass and litter C, N and P on both PFT and global scale sufficiently well as indicated by the minor error introduced in simulating current land carbon balance. The computational consumption of MLA is about one order of magnitude less than the currently used approach, which opens the opportunity of data assimilation using the ever-growing observation datasets.In the outlook, specific applications of the MLA approach and future research priorities are discussed to further improve the reliability and robustness of phosphorus-enabled land surface models.
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Submitted on : Monday, May 3, 2021 - 6:31:20 PM
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Yan Sun. Simulation of the biogeochemical cycle of phosphorus in the ORCHIDEE land surface model : evaluation against local and global observational data. Earth Sciences. Université Paris-Saclay, 2021. English. ⟨NNT : 2021UPASJ001⟩. ⟨tel-03216186⟩

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