Enhanced Rock Weathering (ERW) is gaining prominence as a viable option among Carbon Dioxide Removal strategies, offering a sustainable way to reduce atmospheric CO₂ levels, along with additional benefits such as improved soil pH and nutrient release. However, a detailed understanding of how ERW affects river systems—a critical factor in assessing its net efficiency in consuming CO₂—is still lacking, impeding its broader acceptance as a consistent carbon management method. This study aims to bridge this gap using a comprehensive integrated approach that combines machine learning and numerical models, specifically targeting river systems in North America. A key element of our methodology is the implementation of an innovative dynamic river network model, designed to provide a thorough analysis of river responses to ERW application. Our research indicates relatively low carbon leakage in most river segments over a two-year period. Nevertheless, we also highlight significant spatial and seasonal variations in these responses, paving the way for a strategic plan to optimize ERW deployment by selecting the most suitable watersheds and optimal times for application.
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Zhang et al. (2024) studied this question.
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