Summary
This study applies a Long Short-Term Memory (LSTM) neural network to predict river dissolved oxygen dynamics across 236 minimally disturbed U.S. watersheds using readily available hydrometeorology data from the CAMELS-chem dataset. The model successfully captures theoretical DO solubility relationships and demonstrates potential for water quality forecasting in ungauged basins, though it struggles with DO extremes where biogeochemical processes dominate. The findings suggest that targeted data collection at DO extremes and in undermonitored regions, rather than simply increasing data volume, is critical for advancing water quality prediction capacity.
UK applicability
The methodology could be adapted to UK river systems where water quality monitoring is similarly sparse, though model performance may vary given different hydrometeorology patterns and streamflow regimes. UK-specific calibration would be necessary, particularly for catchments with distinct precipitation seasonality and biogeochemical characteristics.
Key measures
Dissolved oxygen (DO) concentrations; streamflow; water temperature; precipitation; runoff ratio; model prediction accuracy across 236 watersheds
Outcomes reported
The study developed and validated an LSTM neural network model to predict dissolved oxygen concentrations across 236 minimally disturbed U.S. watersheds using the CAMELS-chem dataset and hydrometeorology variables. The model demonstrated capacity to forecast water quality in chemically ungauged basins but showed limitations in capturing dissolved oxygen extremes where biogeochemical processes dominate.
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