Airborne Measurements Reveal High Spatiotemporal Variation and the Heavy-Tail Characteristic of Nitrous Oxide Emissions in Iowa

IF 3.8 2区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
Natasha Dacic, Genevieve Plant, Eric A. Kort
{"title":"Airborne Measurements Reveal High Spatiotemporal Variation and the Heavy-Tail Characteristic of Nitrous Oxide Emissions in Iowa","authors":"Natasha Dacic,&nbsp;Genevieve Plant,&nbsp;Eric A. Kort","doi":"10.1029/2024JD041403","DOIUrl":null,"url":null,"abstract":"<p>Nitrous oxide (N<sub>2</sub>O) emissions from croplands contribute substantially to the climate impact of agriculture. Emissions of N<sub>2</sub>O are controlled by both anthropogenic and environmental factors and can vary by orders of magnitude over short times and distances. This nature of emissions is difficult to capture with models and presents an observational challenge. In the 2021 and 2022 growing season, we collected airborne measurements of N<sub>2</sub>O over Iowa to characterize N<sub>2</sub>O emissions at the farm to multi-county spatial scales across days. We link our airborne observations to surface emissions using a Lagrangian particle dispersion model and quantify emissions using a Bayesian inversion framework. We find emissions magnitudes across Iowa, showing greater skew than modeled predictions, with a small fraction of fields contributing disproportionately to the total [25% of the domain contributing to 52%–77% of total emissions]. In addition to the high spatial variation, we find high temporal variability between flight days by a factor of 2 at the 100 km scale, and an order of magnitude at the 2 km scale], with peak emissions occurring at median soil moisture. This work illustrates the importance of transient, intense emissions from concentrated areas in explaining total cropland emissions, and demonstrates how airborne measurements can provide insights into variation that may be missed by other observational systems. Investigation into environmental factors highlights the need for observations, models and their input data to be spatiotemporally resolved to enable direct comparison, facilitating evaluation of predicted and reported emissions and providing guidance and feedback on mitigation strategies.</p>","PeriodicalId":15986,"journal":{"name":"Journal of Geophysical Research: Atmospheres","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024JD041403","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geophysical Research: Atmospheres","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024JD041403","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Nitrous oxide (N2O) emissions from croplands contribute substantially to the climate impact of agriculture. Emissions of N2O are controlled by both anthropogenic and environmental factors and can vary by orders of magnitude over short times and distances. This nature of emissions is difficult to capture with models and presents an observational challenge. In the 2021 and 2022 growing season, we collected airborne measurements of N2O over Iowa to characterize N2O emissions at the farm to multi-county spatial scales across days. We link our airborne observations to surface emissions using a Lagrangian particle dispersion model and quantify emissions using a Bayesian inversion framework. We find emissions magnitudes across Iowa, showing greater skew than modeled predictions, with a small fraction of fields contributing disproportionately to the total [25% of the domain contributing to 52%–77% of total emissions]. In addition to the high spatial variation, we find high temporal variability between flight days by a factor of 2 at the 100 km scale, and an order of magnitude at the 2 km scale], with peak emissions occurring at median soil moisture. This work illustrates the importance of transient, intense emissions from concentrated areas in explaining total cropland emissions, and demonstrates how airborne measurements can provide insights into variation that may be missed by other observational systems. Investigation into environmental factors highlights the need for observations, models and their input data to be spatiotemporally resolved to enable direct comparison, facilitating evaluation of predicted and reported emissions and providing guidance and feedback on mitigation strategies.

Abstract Image

机载测量揭示了爱荷华州氧化亚氮排放的高时空变化和重尾特征
耕地的一氧化二氮(N2O)排放对农业的气候影响很大。一氧化二氮的排放受人为因素和环境因素的控制,在短时间和短距离内可以有数量级的变化。这种排放性质很难通过模型来捕捉,也给观测带来了挑战。在 2021 年和 2022 年的生长季节,我们收集了爱荷华州上空的一氧化二氮机载测量数据,以描述农场到多县空间尺度的一氧化二氮跨日排放特征。我们使用拉格朗日粒子扩散模型将机载观测结果与地面排放联系起来,并使用贝叶斯反演框架对排放进行量化。我们发现爱荷华州各地的排放量比模型预测的偏差更大,一小部分田地的排放量与总排放量不成比例(25% 的区域占总排放量的 52%-77% )。除了空间变化大之外,我们还发现飞行日之间的时间变化也很大,在 100 千米尺度上是 2 倍,在 2 千米尺度上是一个数量级],排放峰值出现在土壤湿度中位数时。这项工作说明了集中区域的瞬时高强度排放对解释耕地总排放量的重要性,并展示了机载测量如何能够深入了解其他观测系统可能忽略的变化。对环境因素的调查突出表明,需要对观测、模型及其输入数据进行时空分辨,以便进行直接比较,促进对预测和报告排放量的评估,并为减排战略提供指导和反馈。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Geophysical Research: Atmospheres
Journal of Geophysical Research: Atmospheres Earth and Planetary Sciences-Geophysics
CiteScore
7.30
自引率
11.40%
发文量
684
期刊介绍: JGR: Atmospheres publishes articles that advance and improve understanding of atmospheric properties and processes, including the interaction of the atmosphere with other components of the Earth system.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信