Modeling greenhouse gas emissions from riverine systems: a review
Description
This database provides information about research focused on modeling greenhouse gas emissions from river systems over the 2010-2021 period. This includes characteristics of models, such as model purposes, model scales, data attributes, modeled factors and uncertainty analysis. This information results from a bibliometric analysis applied to investigate systematically related publications . Bibliometrics was first presented by Pritchard (1969), in which quantitative analyses and statistical measurements were applied on publications in order to gain a systematic, transparent, and reproducible review on the existing knowledge base, from that, allowed advancing research lines
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Bibliographic data was collected on March, 2022, on the Scopus website ( www.scopus.com ). Scopus database contains the largest international abstract and citation collection of peer-reviewed scientific literature. Scopus currently indexes 24,600 titles (journals, magazines, reports) from more than 5,000 international publishers. To investigate recent studies on riverine GHG emission modeling, we launched a detailed review of the studies published in peer-reviewed journals on the Scopus platform for the timeframe 2010-2021 by applying the query TITLE ( river* AND ( nitrous* OR "carbon dioxide*" OR "greenhouse gas*" OR CO2 OR methan* OR CH4 OR N2O ) ) AND PUBYEAR > 2009 AND PUBYEAR < 2022 AND TITLE-ABS-KEY ( model* OR method* OR framework* OR estim* OR predict* ). In total, 707 publications were collected. By reviewing the content of these studies, we eliminated irrelevant publications, such as publications on other topics and review papers, and ultimately obtained 118 relevant publications. The studies collected were used to investigate factors of interest, including model types, model purposes, model scales, riverine GHG data attributes, modeled factors, and uncertainty analysis. Based on modeling paradigm, we identified three model types: data-driven, mechanistic, and hybrid models, which, according to their purpose, were subcategorized into explicative and predictive models. Given their scale of application, these models were subclassified into site-, basin- or global-scale models. Similarly, we investigated data attributes that are used for model development by identifying data collection methods, sampling frequency, and sampling duration. Additionally, we determined driving factors that are commonly used as inputs in riverine GHG models, namely biochemical, hydrological, geomorphic factors and land use and cover (LULC) types. Finally, we evaluated the use of uncertainty analysis and model validation techniques in riverine GHG models. Note that these characteristics were selected to represent main steps in model selection and development, which can enable us to define the state-of-the-art of riverine GHG models. The classification of factors is described in Panique-Casso et al. Modeling greenhouse gas emissions from riverine systems: a review (submitted)