Power law scaling model predicts N2O emissions along the Upper Mississippi River basin

Role of biogeochemical and hydrodynamic characteristics in simulating nitrogen dynamics in river confluence

The confluence area is the link of different river systems, whose specific hydrodynamic characteristics can significantly influence mass transport and distribution, which can further make a difference to microorganism growth and biogeochemical processes. However, the specific influences of hydrodynamic characteristics in confluence on formation processes of microbial communities and the biogeochemical processes remain unclear. To this end, the present study established an indoor self-circulation confluence flume and conducted 28-day culture experiment to thoroughly investigate the characteristics of microbial communities and nitrogen dynamics in sediment of confluence area. Results illustrated that the initial homogenous microbial communities gradually emerged differences among varied hydrodynamic zones with experiment going on. Concentrations of nitrogenous materials also changed at different experiment period, NO3- concentrations peaked at day 14, and then exhibited significant downtrend. The mean NO3- concentrations decreased the most in flow separation zone, with a 62 % decrease from day 14 to day 28. A numerical model was further established following the thermodynamics of enzyme catalysis reactions to simulate nitrogen transformation rates based on abundances of associated functional genes (gene-centric model). The average relative deviation between simulated and measured N2 production rates was 32 %. To further investigate the influence of hydrodynamic characteristics on nitrogen dynamics, DamKöhler numbers were calculated as the ratio of characteristic residence time to reaction time. DamKöhler numbers were better fitted with measured N2 production rates than simulated results of gene-centric model, signifying the importance of hydrodynamic characteristics in simulating nitrogen dynamics in confluence area.

Nitrous oxide (N2O) emissions at the air-water-sediment interfaces of cascade reservoirs in the Yunnan-Guizhou Plateau: Spatial patterns and environmental controls

The construction of cascade reservoirs can interfere with the natural hydrologic cycles of basins, causing negative environmental effects such as altering the emission patterns of the Nitrous oxide (N2O), a potent greenhouse gas. To elucidate the impact of cascade reservoirs construction on river N2O emissions, we utilized the thin boundary model and the incubation experiments to estimate the N2O fluxes at the air-water interface and at the water-sediment interface of cascade reservoirs on the Yunnan-Guizhou Plateau, respectively. Additionally, we explored the influence of various factors, with particular emphasis on damming, on N2O emissions and production. Moreover, we identified the main pathways of N2O production and proposed management strategies to mitigate N2O emissions from cascade reservoirs. The findings revealed that N2O fluxes at the air-water interface and the water-sediment interface were 4.73 ± 1.32 μmol m-2 · d-1 and 15.56 ± 1.98 μmol m-2 · d-1, respectively. Influenced by temperature, dissolved oxygen (DO), resource substances (active nitrogen substrates and dissolved organic carbon (DOC)) and reservoir properties (scale, hydraulic retention time (HRT), reservoir age, etc.), the N2O concentration and flux exhibited notable spatial heterogeneity, gradually increasing downstream. Temperature has a significant direct impact on N2O flux, as well as indirect effects through DO and resource chemicals. Furthermore, the correlation between dissolved oxygen utilization rate (AOU) and net N2O flux (ΔN2O) indicated that N2O emissions at the water-air interface were primarily attributable to nitrification, whereas those at the water-sediment interface were predominantly driven by denitrification. These findings not only enhance our comprehension of N2O emissions at various interfaces of cascade reservoirs but also offer theoretical backing for the formulation of management strategies aimed at efficiently mitigating N2O emissions from continuously dammed rivers.

Modeling greenhouse gas emissions from riverine systems: A review

Despite the recognized importance of flowing waters in global greenhouse gas (GHG) budgets, riverine GHG models remain oversimplified, consequently restraining the development of effective prediction for riverine GHG emissions feedbacks. Here we elucidate the state of the art of riverine GHG models by investigating 148 models from 122 papers published from 2010 to 2021. Our findings indicate that riverine GHG models have been mostly data-driven models (83%), while mechanistic and hybrid models were uncommonly applied (12% and 5%, respectively). Overall, riverine GHG models were mainly used to explain relationships between GHG emissions and biochemical factors, while the role of hydrological, geomorphic, land use and cover factors remains missing. The development of complex and advanced models has been limited by data scarcity issues; hence, efforts should focus on developing affordable automatic monitoring methods to improve data quality and quantity. For future research, we request for basin-scale studies explaining river and land-surface interactions for which hybrid models are recommended given their flexibility. Such a holistic understanding of GHG dynamics would facilitate scaling-up efforts, thereby reducing uncertainties in global GHG estimates. Lastly, we propose an application framework for model selection based on three main criteria, including model purpose, model scale and the spatiotemporal characteristics of GHG data, by which optimal models can be applied in various study conditions.

Predicting nitrous oxide emissions through riverine networks

Nitrous oxide (N₂O) is currently the leading ozone-depleting gas and is also a potent greenhouse gas. Predictions of N₂O emissions from riverine systems are difficult and mostly accomplished via regression equations based on dissolved inorganic nitrogen (DIN) concentrations or fluxes, although recent studies have shown that hydromorphological characteristics can influence N₂O emissions in riverine reaches. Here, we propose a predictive model for N₂O riverine concentrations and emissions at the reach scale. The model is based on Damköhler numbers and captures the primary effects of reach-scale biogeochemical and hydromorphological characteristics in flowing waters. It explains the change in N₂O emissions from small streams to large rivers under varying conditions including biome, land use, climate, and nutrient availability. The model and observed data show that dimensionless N₂O concentrations and emission rates have higher variability and mean values for small streams (reach width <10 m) than for larger streams due to high spatial variability of stream hydraulics and morphology.

Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery

Methane (CH₄) emission estimates from top-down studies over oil and gas basins have revealed systematic underestimation of CH₄ emissions in current national inventories. Sparse but extremely large amounts of CH₄ from oil and gas production activities have been detected across the globe, resulting in a significant increase of the overall oil and gas contribution. However, attribution to specific facilities remains a major challenge unless high-spatial-resolution images provide sufficient granularity within the oil and gas basin. In this paper, we monitor known oil and gas infrastructures across the globe using recurrent Sentinel-2 imagery to detect and quantify more than 1200 CH₄ emissions. In combination with emission estimates from airborne and Sentinel-5P measurements, we demonstrate the robustness of the fit to a power law from 0.1 tCH4/h to 600 tCH4/h. We conclude here that the prevalence of ultraemitters (>25tCH4/h) detected globally by Sentinel-5P directly relates to emission occurrences below its detection threshold in the range >2tCH4/h, which correspond to large emitters covered by Sentinel-2. We also verified that this relation is also valid at a more local scale for two specific countries, namely, Algeria and Turkmenistan, and the Permian basin in the United States.

Modeling Riverine N2O Sources, Fates, and Emission Factors in a Typical River Network of Eastern China

Estimates of riverine N₂O emission contain great uncertainty because of the lack of quantitative knowledge concerning riverine N₂O sources and fates. Using a 3.5-year record of monthly N₂O measurements from the Yongan River network of eastern China, we developed a mass-balance model to address the riverine N₂O source and sink processes. We achieved reasonable model efficacies (R² = 0.44-0.84, Nash-Sutcliffe coefficients = 0.40-0.80) across three tributaries and the entire river system. Estimated riverine N₂O loads originated from groundwater (38-88%), surface runoff (3-26%), and in-stream production (4-48%). Estimated in-stream losses via atmospheric release + complete denitrification accounted for 76, 95, 25, and 89% of riverine N₂O fate for the agricultural, residential, forest, and entire river system, respectively. Considering limited complete denitrification, the model estimated an upper-bound riverine N₂O emission rate of 2.65 ton N₂O-N km^-2 year^-1 for the entire river system. Riverine N₂O emission estimates were of comparable magnitude to those estimated with a power-law scaling model. Riverine N₂O emissions using the IPCC default emission factor (0.26%) overestimated emissions by 3-15 times, whereas the dissolved N₂O concentration-based emission factor overestimated or underestimated emissions. This study highlights the importance of combining comprehensive information on N₂O sources and fates to achieve accurate riverine N₂O emission estimates.

Global riverine nitrous oxide emissions: The role of small streams and large rivers

Nitrous oxide, N₂O, is the leading cause of stratospheric ozone depletion and one of the most potent greenhouse gases (GHG). Its concentration in the atmosphere has been rapidly increasing since the green revolution in the 1950s and 1960s. Riverine systems have been suggested to be an important source of N₂O, although their quantitative contribution has been estimated with poor precision, ranging between 32.2 and 2100 GgN₂O - N/yr. Here, we quantify reach scale N₂O emissions by integrating a data-driven machine learning model with a physically-based upscaling model. The application of this hybrid modeling approach reveals that small streams (those with widths less than 10 m) are the primary sources of riverine N₂O emissions to the atmosphere. They contribute nearly 36 GgN₂O - N/yr; almost 50% of the entire N₂O emissions from riverine systems (72.8 Gg₂O - N/yr), although they account for only 13% of the total riverine surface area worldwide. Large rivers (widths wider than 175 m), such as the main stems of the Amazon River (~ 6 GgN₂O - N/yr), the Mississippi River (~ 2 GgN₂O - N/yr), the Congo River (~ 1 GgN₂O - N/yr) and the Yang Tze River (~ 0.7 GgN₂O - N/yr), only contribute 26% of global N₂O emissions, which primarily originate from their water column. This study identifies, for the first time, near-global N₂O emission and NO₃ removal hot spots within watersheds and thus can aid the development of local- to global-scale management and mitigation strategies for riverine systems with respect to N₂O emissions. The presented framework can be extended to quantified biogeochemical, besides N₂O emissions, processes at the global scale.