From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?

Abstract

Dissolved oxygen (DO) reflects river metabolic pulses and is an essential water quality measure. Our capabilities of forecasting DO however remain elusive. Water quality data, specifically DO data here, often have large gaps and sparse areal and temporal coverage. Earth surface and hydrometeorology data, on the other hand, have become largely available. Here we ask: can a Long Short-Term Memory (LSTM) model learn about river DO dynamics from sparse DO and intensive (daily) hydrometeorology data? We used CAMELS-chem, a new data set with DO concentrations from 236 minimally disturbed watersheds across the U.S. The model generally learns the theory of DO solubility and captures its decreasing trend with increasing water temperature. It exhibits the potential of predicting DO in “chemically ungauged basins”, defined as basins without any measurements of DO and broadly water quality in general. The model however misses some DO peaks and troughs when in-stream biogeochemical processes become important. Surprisingly, the model does not perform better where more data are available. Instead, it performs better in basins with low variations of streamflow and DO, high runoff-ratio (>0.45), and winter precipitation peaks. Results here suggest that more data collections at DO peaks and troughs and in sparsely monitored areas are essential to overcome the issue of data scarcity, an outstanding challenge in the water quality community.

Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions

“…For this work, the TL models were based on an established LSTM architecture which was already successfully tested for predictions of streamflow (Feng et al., 2020), soil moisture (Fang, Pan, & Shen, 2019; Fang & Shen, 2020; Fang, Shen, et al., 2017) with uncertainty estimates (Fang, Kifer, et al., 2020), water temperature (Rahmani et al., 2020) and other water quality variables like dissolved oxygen (Zhi et al., 2021). Long short‐term memory (LSTM) is a deep learning algorithm, a type of recurrent neural network that learns from sequential data.…”

Editorial: Broadening the Use of Machine Learning in Hydrology

“…In agreement with the general trend in the field of hydrology, the abovementioned papers have covered most components of the hydrologic cycle. Outside of this Research Topic, machine learning has been applied to soil moisture (Fang et al, 2019), soil data extraction (Chaney et al, 2019), hydrology-influenced water quality variables including in-stream water temperature (Rahmani et al, 2020) and dissolved oxygen (Zhi et al, 2021), human water management through reservoirs (Yang et al, 2019;Ouyang et al, 2021), subsurface reactive transport (Laloy and Jacques, 2019;He et al, 2020), and vadose zone hydrology (Bandai and Ghezzehei, 2021), among others. ML is not only applicable in data-rich regions but can also be leveraged by data-scarce regions (Feng et al, 2021;Ma et al, 2021).…”

Drivers of Dissolved Organic Carbon Mobilization From Forested Headwater Catchments: A Multi Scaled Approach

“…These spatial and temporal gaps currently limit statistical power and the development of a larger consensus on processes, suggesting a need for more long-term monitoring and data fusion/integration efforts as illustrated here. Overall, consistent, extensive, and multiple-perspective monitoring systems are urgently needed to record long-term alteration of water quantity and quality response to climate change and human perturbation efforts (Lovett et al, 2007;Magner and Brooks, 2008;Li et al, 2021;Zhi et al, 2021). In the meantime, we believe that such integrated datasets, novel data science tools, and process investigations will allow the catchment science community to make progress addressing the problem of scale.…”