A framework for the fusion of earth observation and soil data to constrain soil organic carbon model parameters

S. Ugbaje, D. Pagendam, S. Karunaratne, T. Bishop, U. Mishra, S. Gautam, M. Farrell
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Abstract

: It is challenging to accurately quantify changes in soil organic carbon (SOC) stocks across landscapes and over time due to the high costs associated with soil sampling to capture inherent variabilities. While process-based models can be used to quantify management-induced changes, it is increasingly important to validate and quantify uncertainties associated with model estimates. Due to monitoring, verification, and reporting requirements under carbon accounting schemes at both project and national scales, quantifying uncertainties in SOC estimates is increasingly important. Although field SOC measurements are essential for model calibration and validation, accessing large datasets with temporally repeated measurements across the landscape is limited. As a result, there is a growing interest in using earth observation (EO) datasets to integrate and constrain model inputs/outputs to reduce uncertainties. Despite the use of surrogate information, such as EO datasets, to constrain process-based models in research, there is currently no operational framework for soil carbon models. In this study, we developed an operational framework that employs EO-derived net primary productivity and leaf area index to constrain a soil carbon model. Our case study involved using the DayCent process-based model along with field measurements from 109 sites across three catchments in New South Wales, Australia. The DayCent model is equipped with C and nitrogen cycles and biogeochemistry solutions along a 20 cm soil depth. It provides more accurate system dynamics descriptions by estimating gas exchanges of CO2, N2O, and CH4 between the soil and the atmosphere compared to RothC. The framework incorporates a Bayesian hierarchical modelling approach to account for uncertainties associated with model inputs and parameter estimates during calibration. The resulting framework is scalable, making it applicable for soil carbon projects and national-scale GHG accounting. Implementation of this framework could enhance the credibility of the Australian National Greenhouse Inventory of the land sector.
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地球观测与土壤数据融合约束土壤有机碳模型参数的框架
由于采集土壤样本以获取内在变化的高成本,准确量化不同景观和时间土壤有机碳(SOC)储量的变化具有挑战性。虽然基于过程的模型可以用来量化管理引起的变化,但是验证和量化与模型估计相关的不确定性变得越来越重要。由于项目和国家尺度的碳核算方案需要监测、验证和报告,因此对碳含量估算中的不确定性进行量化变得越来越重要。尽管现场SOC测量对于模型校准和验证至关重要,但通过在整个景观中进行临时重复测量来访问大型数据集是有限的。因此,人们对利用地球观测(EO)数据集整合和约束模式输入/输出以减少不确定性越来越感兴趣。尽管在研究中使用了替代信息(如EO数据集)来约束基于过程的模型,但目前还没有土壤碳模型的操作框架。在这项研究中,我们开发了一个操作框架,利用生态系统衍生的净初级生产力和叶面积指数来约束土壤碳模型。我们的案例研究涉及使用基于DayCent过程的模型以及来自澳大利亚新南威尔士州三个集水区109个站点的现场测量。DayCent模型沿着20厘米的土壤深度配备了C和氮循环和生物地球化学解决方案。与RothC相比,它通过估算土壤与大气之间CO2、N2O和CH4的气体交换提供了更准确的系统动力学描述。该框架结合了贝叶斯分层建模方法,以考虑与模型输入和校准期间参数估计相关的不确定性。由此产生的框架是可扩展的,使其适用于土壤碳项目和全国范围的温室气体核算。这一框架的实施可以提高澳大利亚土地部门国家温室清单的可信度。
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