Terrestrial carbon cycle model-data fusion: Progress and challenges

Li, Xin; Ma, Hui; Ran, Youhua; Wang, Xufeng; Zhu, Guijie; Liu, Feng; He, Honglin; Zhang, Zhen; Huang, Chunlin

doi:10.1007/s11430-020-9800-3

Cited by 12 publications

(3 citation statements)

References 102 publications

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“…Yet, while those models are good at capturing some aspects of the system, they rely on several structural assumptions, and the model parameters should be carefully calibrated to improve the model accuracy (Y.-P. Wang et al, 2009). For example, Li et al (2021) lists model structure and model assumptions uncertainties among the main processes contributing to the overall model uncertainties. These uncertainties are due to our incomplete understanding of some ecological mechanisms (for instance, belowground processes and microbial interactions, (e.g., Hartmann et al, 2020)), an abundance of empirical equations with parameters that are not necessarily applicable globally, and model-specific simplifications.…”

Section: Introductionmentioning

confidence: 99%

Constraining respiration flux and carbon pools in a simple ecosystem carbon model

Skulovich,

Famiglietti,

Konings

et al. 2024

Preprint

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Incorporating observational data in carbon-cycle models provides a systematic framework for understanding complex ecosystem carbon dynamics, contributing essential insights for climate change mitigation and land ability to continue acting as a carbon sink. This study addresses the challenge of accurately quantifying carbon fluxes and pools, focusing on the information content of remote sensing observations. The research explores the impact of assimilating multiple observational datasets into the CARbon DAta MOdel fraMework (CARDAMOM). Satellite observations such as solar-induced fluorescence (SIF) and vegetation optical depth (VOD) are used as proxies for photosynthesis and aboveground biomass, respectively. The study aims to answer key questions about the reliability of remote sensing data in constraining the ecosystem respiration flux and sizes and dynamics of carbon pools and the relative usefulness of SIF and VOD across five FLUXNET sites. We conclude that assimilating remote SIF and VOD instead of site-based net ecosystem exchange did not deteriorate and even improved model predictions for all metrics except for interannual variability. Notably, the improved results correspond to a consistent shift in values for crucial model parameters across all five investigated sites.

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Section: Introductionmentioning

confidence: 99%

Constraining respiration flux and carbon pools in a simple ecosystem carbon model

Skulovich,

Famiglietti,

Konings

et al. 2024

Preprint

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“…Several methods exist to quantify the spatio-temporal dynamics of the terrestrial carbon cycle. Dynamic global vegetation models (DGVMs) and land surface models (LSMs) simulate responses of vegetation to changes in climate by parameterising ecological processes but are limited by several uncertainties that relate to their parameterisations and limited inclusion of key ecological processes (Kowalczyk et al, 2006;Li et al, 2021;Quillet et al, 2010). Uncertain-C. A.…”

Section: Introductionmentioning

confidence: 99%

Empirical upscaling of OzFlux eddy covariance for high-resolution monitoring of terrestrial carbon uptake in Australia

Burton,

Renzullo,

Rifai

et al. 2023

Biogeosciences

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Abstract. We develop high-resolution (1 km) estimates of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) over the Australian continent for the period January 2003 to June 2022 by empirical upscaling of flux tower measurements. We compare our estimates with nine other products that cover the three broad categories that define current methods for estimating the terrestrial carbon cycle and assess if consiliences between datasets can point to the correct dynamics of Australia's carbon cycle. Our results indicate that regional empirical upscaling greatly improves upon the existing global empirical upscaling efforts, outperforms process-based models, and agrees much better with the dynamics of CO2 flux over Australia as estimated by two regional atmospheric inversions. Our nearly 20-year estimates of terrestrial carbon fluxes revealed that Australia is a strong net carbon sink of −0.44 PgC yr−1 (interquartile range, IQR = 0.42 PgC yr−1) on average, with an inter-annual variability of 0.18 PgC yr−1 and an average seasonal amplitude of 0.85 PgC yr−1. Annual mean carbon uptake estimated from other methods ranged considerably, while carbon flux anomalies showed much better agreement between methods. NEE anomalies were predominately driven by cumulative rainfall deficits and surpluses, resulting in larger anomalous responses from GPP than ER. In contrast, we show that the long-term average seasonal cycle is dictated more by the variability in ER than GPP, resulting in peak carbon uptake typically occurring during the cooler, drier austral autumn and winter months. This new estimate of Australia's terrestrial carbon cycle provides a benchmark for assessment against land surface model simulations and a means for monitoring of Australia's terrestrial carbon cycle at an unprecedented high resolution. We call this new estimate of Australia's terrestrial carbon cycle “AusEFlux” (Australian Empirical Fluxes).

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“…Zhu [28] and Yang [29] took the Hetao irrigation area and Weigan-Kuqa River Basin as examples to verify the effectiveness of multisource data assimilation. Compared with other methods, the model-data assimilation methods meet the requirements of both the regional scale and time continuity [30,31].…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Estimation for Winter-Wheat Waterlogging Monitoring by Assimilating Remote Sensing Inversion Data into the Distributed Hydrology Soil Vegetation Model

Zhang

Liu

et al. 2022

Remote Sensing

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Waterlogging crop disasters are caused by continuous and excessive soil water in the upper layer of soil. In order to enable waterlogging monitoring, it is important to collect continuous and accurate soil moisture data. The distributed hydrology soil vegetation model (DHSVM) is selected as the basic hydrological model for soil moisture estimation and winter-wheat waterlogging monitoring. To handle the error accumulation of the DHSVM and the poor continuity of remote sensing (RS) inversion data, an agro-hydrological model that assimilates RS inversion data into the DHSVM is used for winter-wheat waterlogging monitoring. The soil moisture content maps retrieved from satellite images are assimilated into the DHSVM by the successive correction method. Moreover, in order to reduce the modeling error accumulation, monthly and real-time RS inversion maps that truly reflect local soil moisture distributions are regularly assimilated into the agro-hydrological modeling process each month. The results show that the root mean square errors (RMSEs) of the simulated soil moisture value at two in situ experiment points were 0.02077 and 0.02383, respectively, which were 9.96% and 12.02% of the measured value. From the accurate and continuous soil moisture results based on the agro-hydrological assimilation model, the waterlogging-damaged ratio and grade distribution information for winter-wheat waterlogging were extracted. The results indicate that there were almost no high-damaged-ratio and severe waterlogging damage areas in Lixin County, which was consistent with the local field investigation.

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Terrestrial carbon cycle model-data fusion: Progress and challenges

Cited by 12 publications

References 102 publications

Constraining respiration flux and carbon pools in a simple ecosystem carbon model

Constraining respiration flux and carbon pools in a simple ecosystem carbon model

Empirical upscaling of OzFlux eddy covariance for high-resolution monitoring of terrestrial carbon uptake in Australia

Soil Moisture Estimation for Winter-Wheat Waterlogging Monitoring by Assimilating Remote Sensing Inversion Data into the Distributed Hydrology Soil Vegetation Model

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