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Reducing the global human footprint on lake water quality near river inlets
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Abstract
Human activities have degraded lake water quality globally, leading to toxic algae proliferation and anoxia. The spatial variability of these impacts within lakes and the potential for targeted nutrient pollution reduction to improve water quality remain however underexplored at the global scale. Using 742 million chlorophyll-a (chl-a) estimates from six satellite sensors (daily, 1–4 km resolution), we mapped surface chl-a across 321 large lakes, averaged over the time interval from 1997 to 2020. We bias-corrected these data through lake-specific algorithm tuning based on morphometric characteristics and in-situ water samples. Our analysis revealed distinct spatial patterns in chl-a concentrations within lakes, with the highest concentrations often occurring near river inflows, driven primarily by nutrient pollution from croplands. Importantly, we found that reductions in chl-a concentrations can be achieved across latitudes by minimizing agricultural nutrient inputs, with lower-latitude lakes across Africa, South America, and Asia, exhibiting the largest potential for reductions in chl-a concentrations and improved water quality. However, reductions in nutrient pollution may have limited influence on chl-a concentrations in open waters, suggesting that nearshore areas, which often support higher biodiversity, may benefit most from targeted interventions. These findings underscore the importance of spatially explicit management strategies to address the varying impacts of nutrient pollution across and within lakes.
DOI
https://doi.org/10.31223/X5Q17X
Subjects
Fresh Water Studies, Hydrology, Other Environmental Sciences, Remote Sensing, Water Resource Management
Keywords
lake, water quality, nutrients, croplands, Chlorophyll-a
Dates
Published: 2025-11-12 17:08
Last Updated: 2025-11-12 17:08
License
CC BY Attribution 4.0 International
Additional Metadata
Conflict of interest statement:
The co-authors report no conflict of interest
Data Availability (Reason not available):
All code and data used in this manuscript are available for download at the following DOI: https://doi.org/10.5281/zenodo.17582203
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