the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impacts of land-use change on biospheric carbon: an oriented benchmark using ORCHIDEE land surface model
Abstract. Land-use change (LUC) impacts biospheric carbon, encompassing biomass carbon and soil organic carbon (SOC). Despite the use of dynamic global vegetation models (DGVMs) in estimating the anthropogenic perturbation of biospheric carbon stocks, critical evaluations of model performance concerning LUC impacts are scarce. Here, we present a systematic evaluation of the performance of the DGVM ORCHIDEE to reproduce observed LUC impacts on biospheric carbon stocks over Europe. First, we compare model predictions with observation-based gridded estimates of net and gross primary productivity (NPP and GPP), biomass growth patterns, and SOC stocks. Second, we evaluate the predicted response of carbon stocks to LUC based on data from forest inventories, paired plots, chronosequences and repeated sampling designs. Third, we use interpretable machine learning to identify factors contributing to discrepancies between simulations and observations, including drivers and processes not resolved in ORCHIDEE (e.g. erosion, soil fertility). Results indicate agreement between the model and observed spatial patterns and temporal trends, such as the increase in biomass with age, when simulating biosphere carbon stocks. The direction of the SOC responses to LUC generally aligns between simulated and observed data. However, the model underestimates carbon gains for cropland-to-grassland and carbon losses for grassland-to-cropland and forest-to-cropland conversions. These discrepancies are attributed to bias arising from soil erosion rate, which is not fully captured in ORCHIDEE. Our study provides an oriented benchmark for assessing the DGVMs against observations and explores its potential in studying the impact of LUCs on SOC stocks.
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RC1: 'Comment on gmd-2024-42', Anonymous Referee #1, 23 Apr 2024
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This study evaluates the performance of the ORCHIDEE dynamic global vegetation model (DGVM) in accurately depicting the effects of land-use change (LUC) on biospheric carbon stocks across Europe. Through a systematic evaluation, the authors compare ORCHIDEE's predictions with observation-based estimates of key variables such as net and gross primary productivity (NPP and GPP), biomass growth patterns, and soil organic carbon (SOC) stocks. Additionally, they employ interpretable machine learning techniques to uncover factors contributing to discrepancies between model simulations and observational data.
A notable aspect of this study is its comprehensive analysis of ORCHIDEE's ability to reproduce spatial patterns and temporal trends in biospheric carbon stocks. The results demonstrate encouraging agreement between model predictions and observed data, particularly in capturing biomass accumulation with age and the general trends in SOC responses to LUC. Moreover, the authors adeptly discuss both the strengths and weaknesses of ORCHIDEE, providing a balanced assessment of its performance.
Overall, this paper makes a significant contribution to the fields of biogeochemistry and dynamic vegetation model development. Its meticulous methodology, insightful findings, and implications for model refinement enhance our understanding of the complex interactions between land-use change and biospheric carbon dynamics. I commend the authors for their rigorous approach and eagerly anticipate further advancements in this important area of research.
Citation: https://doi.org/10.5194/gmd-2024-42-RC1
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