Global methane and nitrous oxide emissions from inland waters and estuaries

Abstract

Inland waters (rivers, reservoirs, lakes, ponds, streams) and estuaries are significant emitters of methane (CH 4 ) and nitrous oxide (N 2 O) to the atmosphere, while global estimates of these emissions have been hampered due to the lack of a worldwide comprehensive data set of CH 4 and N 2 O flux components. Here, we synthesize 2997 in-situ flux or concentration measurements of CH 4 and N 2 O from 277 peer-reviewed publications to estimate global CH 4 and N 2 O emissions from inland waters and estuaries. Inland waters including rivers, reservoirs, lakes, and streams together release 95.18 Tg CH 4 year −1 (ebullition plus diffusion) and 1.48 Tg N 2 O year −1 (diffusion) to the atmosphere, yielding an overall CO 2 -equivalent emission total of 3.06 Pg CO 2 year −1 . The estimate of CH 4 and N 2 O emissions represents roughly 60% of CO 2 emissions (5.13 Pg CO 2 year −1 ) from these four inland aquatic systems, among which lakes act as the largest emitter for both CH 4 and N 2 O. Ebullition showed as a dominant flux component of CH 4 , contributing up to 62%-84% of total CH 4 fluxes across all inland waters. Chamber-derived CH 4 emission rates are significantly greater than those determined by diffusion model-based methods for commonly capturing of both diffusive and ebullitive fluxes. Water dissolved oxygen (DO) showed as a dominant factor among all variables to influence both CH 4 (diffusive and ebullitive) and N 2 O fluxes from inland waters. Our study reveals a major oversight in regional and global CH 4 budgets from inland waters, caused by neglecting the dominant role of ebullition pathways in those emissions. The estimated indirect N 2 O EF 5 values suggest that a downward refinement is required in current IPCC default EF 5 values for inland waters and estuaries. Our findings further indicate that a comprehensive understanding of the magnitude and patterns of CH 4 and N 2 O emissions from inland waters and estuaries is essential in defining the way of how these aquatic systems will shape our climate.

Tidal restoration to reduce greenhouse gas emissions from freshwater impounded coastal wetlands

“…Saltmarsh had higher N 2 O emissions (1.32 AE 0.52 μg N 2 O m À2 hour À1 ) than mangroves, similar to other results reported in the literature (Iram et al 2021;Rosentreter et al 2021). Overall, our results support the hypothesis that tidal coastal wetlands are a low source of GHG (Zheng et al 2022).…”

Inland Waters Increasingly Produce and Emit Nitrous Oxide

“…The hydrological characteristics of low-order (Strahler orders < 6) streams for each 0.5-by-0.5-degree grid are estimated based on the hydrology of high-order rivers using the parameterization method proposed by Wollheim et al, with detailed descriptions of equations and parameters used in IMAGE-DGNM in refs , . Extensive validation in open-access publications has shown that the runoff, water discharge, total water storage, evapotranspiration, precipitation, and other hydrological properties simulated by PCR-GLOBWB (hydrological module in IMAGE-DGNM) agree well with observations. ,, In this study, the lake area is from Global Lakes and Wetlands Database (GLWD1), the reservoir area is from the Global Reservoir and Dam database (GRanD v1.3) and introduced dynamically based on the construction year, and IMAGE-DGNM estimates that low-order streams account for 61–66% of the total inland-water area during 1900–2010, which is very close to the 69% estimated using data from HydroSHEDS by Raymond et al Therefore, surface water area, a factor proven important for estimating inland-water gas emissions, , is properly simulated in IMAGE-DGNM. In this study, a 1 year temporal resolution is used in all modules within IMAGE-DGNM to explore the long-term change in the inland-water N 2 O budget on the global scale.…”

Spatiotemporal Patterns of Methane and Nitrous Oxide Emissions in China’s Inland Waters Identified by Machine Learning Technique

“…It is expected that the emission patterns in different regions of China and in different seasons may vary considerably due to the huge geographical diversity in aquatic water quality and climatic conditions. − Nevertheless, directly acquiring such information through field measurement at large scale is challenging, which requires intensive time, labor, and financial inputs. In this respect, model prediction based on other readily available monitoring data associated with GHG emissions is considered as a promising alternative.…”