Generating Regional Emissions Estimates with a Novel Hierarchy of Observations and Upscaled Simulation Experiments

通过新颖的观测层次结构和大规模模拟实验生成区域排放估算

• 批准号: NE/K002503/1
• 负责人: Bob Rees
• 金额: $ 22.4万
• 依托单位: Scotland's Rural College
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FK002503%2F1
• 关键词: Generating; Regional; Emissions; Estimates; Novel

The UK is committed to quantifying and managing its emissions of greenhouse gases (GHG, i.e. CO2, CH4, N2O) to reduce the threat of dangerous climate change. Sinks and sources of GHGs vary in space and time across the UK because of the landscape's mosaic of managed and semi-natural ecosystems, and the varying temporal sensitivities of each GHG's emissions to meteorology and management. Understanding spatio-temporal patterns of biogenic GHG emissions will lead to improvements in flux estimates, allow creation of inventories with greater sensitivity to management and climate, and advance the modelling of feedbacks between climate, land use and GHG emissions. Addressing Deliverable C of the NERC Greenhouse Gas (GHG) Emissions and Feedbacks Research Programme, we will use extensive existing UK field data on GHG emissions, supplemented with targeted new measurements at a range of scales, to build accurate GHG inventories and improve the capabilities of two land surface models (LSMs) to estimate GHG emissions.Our measurements will underpin state-of-the-art temporal and spatial upscaling frameworks. The temporal framework will evaluate diurnal, seasonal and inter-annual variation in emissions of CO2, CH4 and N2O over dominant UK land-covers, resolving management interventions such as ploughing, fertilizing and harvesting, and the effects of weather and climate variability. The spatial framework will evaluate landscape heterogeneity at patch (m), field (ha) and landscape (km2) scales, in two campaigns combining chambers, tower and airborne flux measurements in arable croplands of eastern England, and grazing and forest landscapes of northern Britain.For modelling, we will update two LSMs - JULES and CTESSEL- so that each generates estimates of CO2, CH4 and N2O fluxes from managed landscapes. The models will be updated to include the capabilities to represent changes in land use over time, to represent changes in land management over time (crop sowing, fertilizing, harvesting, ploughing etc), and the capacity to simulate forest rotations. With these changes in place, we will determine parameterisations for dominant UK land-covers and management interventions, using our spatio-temporal data. The work is organized in five science work-packages (WP). WP1: Data assembly and preliminary analysis. We will create a database of GHG flux data and ancillary data for major UK landcovers/landuses in order to calibrate and evaluate the LSMs' capabilities, and generate spatial databases of environmental and management drivers for the models.WP2. GHG measurement at multiple scales. We will deploy advanced technology to generate new information on spatial GHG processes from simultaneous measurement from chamber (<1 m) to landscape (40 km) length scales, and on temporal flux variation from minutes to years.WP3. Earth observation (EO) to support upscaling. EO data will provide: i) driving data for LSM upscaling, from flux tower to aircraft campaign scales; and ii) spatial data for testing LSM outputs at these larger scales.WP4 Upscaling GHG processes. Firstly, the two LSMs will be updated to allow the impacts of management activities on GHG emissions to be simulated, with calibration against an array of temporal flux data. Then, we will use the LSMs to model the fluxes of GHGs at larger spatial scales, based on a rigorous understanding of how the nonlinearity of responses and the non-Gaussian distribution of environmental input variables interact, for each GHG, using all available field data at finer scales. WP5 Application at the regional scale. The LSMs will upscale GHG emissions for both campaign regions (E. England, N. Britain) using 1-km2 resolution simulations with a focus on the airborne campaign periods of 4 weeks. We will determine how regional upscaling error can be reduced with intensive spatial soil and land management data.

英国致力于量化和管理其温室气体（GHG，即CO2，CH 4，N2 O）的排放，以减少危险的气候变化的威胁。温室气体的汇和源在整个英国的空间和时间上各不相同，因为景观的马赛克管理和半自然的生态系统，以及每个温室气体的排放气象和管理的不同时间敏感性。了解生物源温室气体排放的时空模式将导致通量估计的改进，允许创建对管理和气候更敏感的清单，并推进气候，土地使用和温室气体排放之间的反馈模型。针对NERC温室气体（GHG）排放和反馈研究计划的可验证C，我们将使用大量现有的英国温室气体排放现场数据，辅以一系列尺度的有针对性的新测量，以建立准确的温室气体清单，并提高两个陆面模型（LSM）估计温室气体排放的能力。我们的测量将支持最先进的时间和空间升级框架。该时间框架将评估联合王国主要土地覆盖物上CO2、CH 4和N2 O排放量的日变化、季节变化和年际变化，解决耕作、耕作和收获等管理干预措施，以及天气和气候变化的影响。该空间框架将在斑块（m）、田地（ha）和景观（km 2）尺度上评估景观异质性，在两个活动中结合了英格兰东部可耕地和北方英国放牧和森林景观的室内、塔和空中通量测量。来自管理景观的CH 4和N2 O通量。将对这些模型进行更新，使其具有反映土地使用随时间变化的能力，反映土地管理随时间变化的能力（作物播种、播种、收获、耕作等），并具有模拟森林轮作的能力。随着这些变化的地方，我们将确定参数化占主导地位的英国土地覆盖和管理干预措施，使用我们的时空数据。这项工作分为五个科学工作包（WP）。WP 1：数据汇编和初步分析。我们将为英国主要的土地覆盖/土地利用创建一个温室气体通量数据和辅助数据的数据库，以校准和评估LSM的能力，并为模型生成环境和管理驱动因素的空间数据库。多尺度温室气体计量。我们将部署先进的技术，以产生空间温室气体过程的新信息，从同时测量室（<1米）到景观（40公里）的长度尺度，以及从分钟到年的时间通量变化。地球观测，以支持升级。EO数据将提供：i）用于LSM升级的驱动数据，从通量塔到飞机活动规模;以及ii）用于在这些更大规模上测试LSM输出的空间数据。首先，将更新两个LSM，以便模拟管理活动对温室气体排放的影响，并根据一系列时间通量数据进行校准。然后，我们将使用最小二乘模型在更大的空间尺度上模拟温室气体的通量，基于对响应的非线性和环境输入变量的非高斯分布如何相互作用的严格理解，对于每种温室气体，使用所有可用的现场数据在更精细的尺度。WP 5在区域范围内的应用。LSM将提高两个活动区域的GHG排放量（E。英格兰，N。英国）使用1平方公里的分辨率模拟，重点是空降战役为期4周。我们将确定如何利用密集的空间土壤和土地管理数据来减少区域放大误差。

项目成果

Information Properties of Boundary Line Models for N2O Emissions from Agricultural Soils

• DOI: 10.3390/e15030972
• 发表时间: 2013-03
• 期刊: Entropy
• 影响因子: 2.7
• 作者: C. Topp;Weijin Wang;J. Cloy;R. Rees;Gareth Hughes

The use of biogeochemical models to evaluate mitigation of greenhouse gas emissions from managed grasslands.

• DOI: 10.1016/j.scitotenv.2018.06.020
• 发表时间: 2018-11
• 期刊: The Science of the total environment
• 作者: R. Sándor;F. Ehrhardt;L. Brilli;M. Carozzi;S. Recous;Pete Smith;V. Snow;J. Soussana;C. Dorich;K. Fuchs;N. Fitton;Kate Gongadze;K. Klumpp;M. Liebig;Raphaël Martin;L. Merbold;P. Newton;R. Rees;S. Rolinski;G. Bellocchi

A systematic approach to identifying key parameters and processes in agroecosystem models

• DOI: 10.1016/j.ecolmodel.2017.12.009
• 发表时间: 2018-01
• 期刊: Ecological Modelling
• 影响因子: 3.1
• 作者: V. Myrgiotis;R. Rees;C. Topp;M. Williams

Model evaluation in relation to soil N2O emissions: An algorithmic method which accounts for variability in measurements and possible time lags

与土壤 N2O 排放相关的模型评估：一种考虑测量变化和可能时滞的算法方法

• DOI: 10.1016/j.envsoft.2016.07.002
• 发表时间: 2016
• 期刊: Environmental Modelling & Software
• 影响因子: 4.9
• 作者: Myrgiotis V

Improving model prediction of soil N2O emissions through Bayesian calibration.

通过贝叶斯校准改进土壤 N2O 排放的模型预测。

• DOI: 10.1016/j.scitotenv.2017.12.202
• 发表时间: 2018
• 期刊: The Science of the total environment
• 作者: Myrgiotis V