Work

Unveiling the Patterns and Drivers of Global Riverine Dissolved Oxygen via a Transformer-Based Deep Learning

Year: 2026

Type: article

Abstract: Riverine dissolved oxygen (DO) dynamics are critical to aquatic ecosystem health, yet global understanding has long been constrained by sparse, temporally discontinuous, and heterogeneous monitoring data. Conventional statistical and process-based approaches also face difficulties integrating multi-source predictors across chemically ungauged basins. We developed a Transformer-based framework integrating hydroclimatic forcing, basin attributes, land cover, and demographic context to reconstruct daily DO dynamics across 403 river stations from 2001 to 2022. The model achieved a median Nash–Sutcliffe efficiency (NSE) of 0.61 in testing and 0.58 in hold-out validation at unseen stations, but performance varied substantially by region and was lower for some extreme DO values. Integrated Gradients identified 30-day cumulative air temperature as the most influential individual input predictor in the trained Transformer model. The reconstructed records indicate deoxygenation at 95% of the 222 stations selected for trend analysis, with stronger declines in agricultural basins. This work provides a data-driven framework for reconstructing continuous daily DO records from fragmented monitoring observations, with potential to support candidate hotspot identification and inform climate-adaptive freshwater management, subject to regional validation.

Source: Water

DOI: 10.3390/w18212757

Authors: Kai Tan, Wang Yi, Jianrong Ma, Kun Shan

Institutions: Chinese Academy of Sciences, Chongqing Institute of Green and Intelligent Technology, University of Chinese Academy of Sciences

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Climate action

Open Access status: gold

Estimated APC paid: $3,137