GRiMeDB: The global river database of methane concentrations and fluxes

Abstract

Abstract. Despite their small spatial extent, fluvial ecosystems play a significant role in processing and transporting carbon in aquatic networks, which results in substantial emission of methane (CH4) to the atmosphere. For this reason, considerable effort has been put into identifying patterns and drivers of CH4 concentrations in streams and rivers and estimating fluxes to the atmosphere across broad spatial scales. Yet progress toward these ends has been slow because of pronounced spatial and temporal variability of lotic CH4 concentrations and fluxes and by limited data availability across diverse habitats and physicochemical conditions. To address these challenges, we present the first comprehensive database of CH4 concentrations and fluxes for fluvial ecosystems along with broadly relevant and concurrent physical and chemical data. The Global River Methane database (GriMeDB; https://doi.org/10.6073/pasta/b7d1fba4f9a3e365c9861ac3b58b4a90) includes 24,024 records of CH4 concentration and 8,205 flux measurements from 5,037 unique sites that were extracted from publications, reports, data repositories, and other outlets published between 1973 and 2021. GriMeDB also includes 17,655 and 8,409 concurrent measurements of concentrations and 4,444 and 1,521 of fluxes for CO2 and nitrous oxide (N2O) respectively. Most observations are date-specific (i.e., not site averages) and many are supported by data for 12 physicochemical variables and 6 site variables. Site variables include codes to characterize marginal channel types (e.g., springs, ditches) and/or presence of human disturbance (e.g., point source inputs, upstream dams). Overall, observations in GRiMeDB encompass a broad range of the climatic, biological, and physical conditions that occur among world river basins, although some geographic gaps remain (e.g., arid regions, tropical regions, high latitudes and altitude systems). The global median CH4 concentration (0.20 μmol L-1) and diffusive flux (0.44 mmol m-2 d-1) in GRiMeDB are lower than estimates from past, site-averaged compilations, although ranges and standard deviations are greater from this larger and more temporally-resolved database. Available flux data are dominated by diffusive measurements despite the recognized importance of ebullitive and plant-mediated CH4 fluxes. Despite these limitations, GriMeDB provides a comprehensive and cohesive resource for examining relationships between CH4 and environmental drivers, estimating the contribution of fluvial ecosystems to CH4 emissions, and to contextualize site-based investigations.

Carbon uptake in Eurasian boreal forests dominates the high‐latitude net ecosystem carbon budget

“…As has been reported elsewhere (Melton et al, 2013), much of the differences in bottom‐up estimates (and inverse estimates informed by priors from bottom‐up models) stem from how CH 4 emitting regions and wetland extent are identified (e.g., Melton et al, 2013; Zhang et al, 2017). As with most models, our accounting of CH 4 was terrestrially focused and did not provide estimates for rivers (see Stanley et al, 2022), open water lakes and ponds.…”

Bottom‐Up Evaluation of the Methane Budget in Asia and Its Subregions

“…For example, Holgerson and Raymond (2016) have suggested that small ponds, in addition to wetlands, can be a strong CH 4 source. Recent studies of freshwater CH 4 emissions are likely to progressively fill the gaps in the budget (e.g., Johnson et al., 2022; Rosentreter et al., 2021; Stanley et al., 2022). Many observations have also detected substantial CH 4 emissions from tree stems (e.g., Barba et al., 2019), but those emissions are not explicitly accounted for in most biogeochemical models.…”

A Time-Integrated Sampler for Radiocarbon Analysis of Aquatic Methane

“…Usually, these water samples are collected over a short period of time, often referred to as “grab” sampling (Turnbull et al 2017), which can usually only be considered to be representative for the period of sample collection. This is a concern because other studies have shown high temporal variability in aquatic CH 4 concentration (Podgrajsek et al 2014; Sieczko et al 2020; Stanley et al 2023) and because we have very little information on the temporal variability of 14 C in aquatic CH 4 . Moreover, the propensity to undertake sample collection during convenient, daylight hours, can lead to significant bias, as illustrated by Sieczko et al (2020) who found that aquatic CH 4 emissions were 2.4 times greater during the day compared to night.…”