The First Environmental Digital Twin Dedicated to Understanding Tropical Wetland Methane Emissions for Improved Predictions of Climate Change

第一个致力于了解热带湿地甲烷排放以改进气候变化预测的环境数字孪生

• 批准号: MR/X033139/1
• 负责人: Robert Parker
• 金额: $ 161.82万
• 依托单位: University of Leicester
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX033139%2F1
• 关键词: First; Environmental; Digital; Twin; Dedicated

Methane (CH4) is a major greenhouse gas. Its short atmospheric lifetime (~9 years) means we can mitigate its emissions and warming effects. At COP26, countries signed up to the Methane Pledge, strengthened at COP27, committing to reduce emissions in 2030 by 30%, eliminating over 0.2C of global warming by 2050.The challenge is that methane has many sources, man-made and natural. Man-made emissions include significant contributions from fossil fuels (111 Tg CH4 yr-1) and agriculture/waste (217 Tg CH4 yr-1), with natural signals dominated by wetland emissions (181 Tg CH4 yr-1, >30% of total emissions). Estimates suggest tropical wetlands contribute >65% of all wetland emissions, over 20% of the total global methane budget. However, these estimates are hugely uncertain. To fully understand the methane budget, we must monitor these natural emissions and understand how, when and where they are produced and how they might change under future climate scenarios. Failure to do so would restrict capability to inform policy and take mitigation action.The problem is becoming more urgent. Recent years have seen a rapid and surprising increase in atmospheric methane. Global values increased by 15 ppb in 2020 and 18 ppb in 2021, compared to 5-12 ppb in recent years. This acceleration is alarming and points to significant climate-feedbacks that are not fully understood nor expected. Studies using satellite data generated by my work (e.g. Qu et al., 2022, Feng et al., 2022) have reached the conclusion that tropical wetlands are the likely source of this new, and as of yet unexplained, increase in the methane growth rate. We know methane is produced in wetlands by microbes but questions remain on the effect of factors such as temperature, water level and soil type. State-of-the-art process-based land surface models can produce wetland methane emissions but huge discrepancies between model estimates limit their utility and assessing these models against observations is key. Importantly, we also do not know how large these methane-producing wetland areas are, as they continually change in size in response to rainfall and riverflow. Therefore, even if models capture the correct wetland methane climate-response, the wetland extent itself will limit ability to accurately estimate emissions. The problem therefore is two-fold: 1) Can we reconcile large discrepancies in our ability to model the wetland methane emission response to climate feedbacks? 2) Can we dramatically improve our estimates of wetland extent to constrain the spatial/temporal changes in methane emissions?This fellowship will use satellite observations and land surface models to build an innovative and dedicated Wetland Digital Twin; a machine-learning system capable of estimating methane produced by wetlands, transforming our understanding of the causes of methane emissions and responses to the changing climate.In parallel, we need much better knowledge of wetland locations and how they change over time. By applying new machine-learning methods to very-high-resolution satellite imagery and combining with advanced hydrological modelling, I will better map these wetland areas and understand their dynamics. To achieve this, I will work closely with Project Partners, specialising in land surface modelling (GCP, UKCEH, UK Met Office), machine learning and artificial intelligence (ESA Phi-Lab, NEODAAS), IT infrastructure (NEODAAS, JASMIN, CGI), high-resolution remote sensing (Planet) and climate modelling (UK Met Office) while also engaging with a range of Stakeholders from wetland ecosystem specialists to policymakers (e.g. COP/IPCC, UNEP, RAMSAR, CIFOR, CEOS/GCOS).This new Wetland Digital Twin capability, driven by Earth Observation data and powered by machine learning, will allow us to develop climate services that are capable of providing decision-support for policymakers and enable better understanding of the climate response of these critical ecosystems.

甲烷（CH4）是主要的温室气体。它的大气寿命短（〜9年）意味着我们可以减轻其排放和变暖的影响。在COP26，国家签署了甲烷承诺，在COP27时加强了，承诺在2030年将排放量减少30％，消除了2050年全球变暖的0.2摄氏度。挑战是甲烷具有许多来源，人造和自然。人造的排放包括化石燃料（111 TG CH4 YR-1）和农业/废物（217 TG CH4 YR-1）的重大贡献，其自然信号由湿地排放（181 TG CH4 YR-1，> 30％的排放量中的30％）主导。估计表明，热带湿地占所有湿地排放量的65％，超过全球甲烷预算的20％。但是，这些估计值非常不确定。为了充分了解甲烷预算，我们必须监视这些自然排放，并了解如何，何时何地生产它们以及它们如何在未来的气候情况下改变。否则，将限制能够为政策提供信息并采取缓解行动的能力。问题变得越来越紧迫。近年来，大气中的甲烷迅速增加。 2020年，全球价值在2021年增加了15 ppb，近年来为5-12 ppb。这种加速度令人震惊，并指出了尚未完全理解或期望的重要气候反馈。使用我的工作产生的卫星数据（例如，Qu等，2022，Feng等，2022）的研究得出的结论是，热带湿地可能是这种新的来源，但尚未解释，甲烷生长速率的提高。我们知道甲烷是由微生物在湿地中产生的，但问题仍然取决于温度，水位和土壤类型等因素的影响。基于过程的最先进的陆地表面模型可以产生湿地甲烷排放，但是模型估算之间的巨大差异限制了它们的效用，并根据观察评估这些模型是关键。重要的是，我们也不知道这些产生甲烷的湿地区域有多大，因为它们会因降雨和河流而不断变化。因此，即使模型捕获正确的湿地甲烷气候响应，湿地范围本身也将限制准确估计排放的能力。因此，问题是两个方面：1）我们可以在对湿地甲烷排放对气候反馈的响应建模能力方面是否可以解决巨大的差异？ 2）我们能否显着改善湿地范围的估计，以限制甲烷排放的空间/时间变化？这项奖学金将使用卫星观测和陆地表面模型来建立创新和专用的湿地数字双胞胎；一个能够估算湿地产生的甲烷的机器学习系统，改变了我们对甲烷排放的原因和对气候变化的反应的理解。在平行的情况下，我们需要更好地了解湿地位置及其随着时间的变化。通过将新的机器学习方法应用于非常高分辨率的卫星图像并与先进的水文建模相结合，我将更好地绘制这些湿地区域并了解它们的动态。为此，我将与项目合作伙伴紧密合作，专门研究陆地表面建模（GCP，UKCEH，英国大都会办公室），机器学习和人工智能（ESA PHI-LAB，NEODAAS），IT基础架构（NE​​ODAAS，JAMSIN，JAMSIN，CGI），高分辨率的远程感应（Planet）和ECOSS的ECORISTIST（UK METSISTIST），以及ECORSISTION（UK METSISTING），范围为范围，范围为范围，在范围内启动了一项范围，以范围内的范围来启动一项范围。决策者（例如COP/IPCC，UNEP，RAMSAR，CIFOR，CEOS/GCOS）。这是由地球观察数据驱动并由机器学习提供支持的新湿地数字双胞胎能力，将使我们能够开发能够为政策制定者提供决策的气候服务，并能够更好地了解这些关键的Ecososystems的气候响应。