Ocean Acidification

Abstract

The chemical nature of river water significantly influences the coastal carbonate system, contributing to coastal acidification and creating suboptimal conditions for marine calcifiers. While several regional efforts have assessed observationally based riverine concentrations and fluxes of total alkalinity (TA) and dissolved inorganic carbon (DIC), these values in global ocean biogeochemical models have generally been simplified, often set to zero or balanced against global sediment calcium carbonate burial. To enhance our understanding of rivers’ role in the coastal carbonate system, we applied multiple linear regression (MLR) to develop global empirical relationships for estimating river TA and DIC from watershed properties. We find that river TA values are primarily controlled by forest, carbonate rock coverage, and annual mean precipitation, explaining 74% of the spatial variability in TA. The variability explained improves to 77% with the inclusion of permafrost and glacial coverage, especially in high latitude and altitude regions. Additionally, nearly 30% of the spatial variability in the river DIC-to-TA ratio can be explained by terrestrial gross primary production and carbonate rock coverage. Applying these MLR-derived TA and DIC concentrations to a 1/4° resolution global ocean model reduces the high bias in model estimates of global coastal CO₂ uptake by 69% (equivalent to 0.11 Pg C yr⁻¹ less CO₂ uptake) compared to the case with zero river TA and DIC. This study elucidates key drivers of the river carbonate system and underscores the importance of accurately representing riverine inputs to improve predictions of global coastal carbon dynamics and ecosystem responses to environmental changes.

Plain Language Summary

Rivers play a critical role in shaping the chemistry of coastal waters, influencing how much carbon dioxide (CO₂) the ocean absorbs and creating conditions that affect marine life, such as shellfish and corals. Global models are essential for predicting carbon dynamics at large scales, offering insights into the interactions between rivers, coastal systems, and the global ocean. However, global models often simplify or partially overlook key chemical contributions from rivers, leading to biases in predictions. In this study, we analyzed how river chemistry, particularly river carbon inputs, is influenced by factors such as forest cover, carbonate rocks, rainfall, permafrost, and glaciers on land. We developed statistical models to estimate two key properties: total alkalinity and dissolved inorganic carbon. Incorporating these improved river chemistry estimates into a global ocean model markedly reduced the overestimation of coastal CO₂ absorption. This research underscores the importance of accurately including riverine inputs in global models to enhance predictions of coastal carbon dynamics and ecosystem responses to climate change.

Key Points

• Global empirical relationships are developed using multiple linear regression (MLR) to estimate river TA and DIC concentrations from watershed properties
• Forest and carbonate rock coverage, and annual mean precipitation explain 74% of the spatial variability in global river TA values
• Applying MLR-derived river TA and DIC concentrations to a global ocean model substantially reduces biases in coastal CO₂ uptake estimates

Da F., Stock C. A., Dunne J. P., Liu X., Luo J. Y., Lee M. & Shevliakova E., 2025. A global perspective on river alkalinity: drivers and implications for coastal ocean carbonate chemistry. Global Biogeochemical Cycles 39: e2025GB008528. doi: 10.1029/2025GB008528. Article.