Ecosystem metabolism estimates from the National Ecological Observatory Network (NEON) stream and river sites.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Nicholas S Marzolf, Weston M Slaughter, Michael J Vlah, Spencer A Rhea, Amanda G DelVecchia, Emily S Bernhardt
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引用次数: 0

Abstract

Expanded availability of estimates of ecosystem metabolism and gas exchange from the worlds streams and rivers is rapidly revising estimates of river contributions to global carbon budgets. Here, we present estimates of gross primary production, ecosystem respiration, and gas exchange from 27 streams and rivers across North America, including Puerto Rico, using data from the National Ecological Observatory Network (NEON). Further, we explore how aggregating and processing input data influences model outputs, expanding the methodological knowledge in approaching sensor collection and manipulation for ecosystem-scale modelling. We apply filters to input data to determine how different approaches to quality control of raw data influence the quantity and precision of estimates of ecosystem metabolism. Model estimates are high priority measures of ecosystem function that integrate additional NEON data products that will allow further understanding of stream and river biogeochemistry and ecosystem function across time and space.

从世界溪流和河流中获得的生态系统新陈代谢和气体交换估算数据的不断扩大,正在迅速修正河流对全球碳预算的贡献估算。在此,我们利用国家生态观测网络(NEON)的数据,对包括波多黎各在内的北美地区 27 条溪流和河流的初级生产总量、生态系统呼吸和气体交换进行了估算。此外,我们还探讨了输入数据的汇总和处理如何影响模型输出,从而扩展了生态系统尺度建模中传感器收集和处理的方法知识。我们对输入数据进行过滤,以确定不同的原始数据质量控制方法如何影响生态系统代谢估算的数量和精度。模型估算是生态系统功能的优先测量指标,它整合了更多的 NEON 数据产品,将有助于进一步了解溪流和河流的生物地球化学以及跨时空的生态系统功能。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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