Department of Civil and Environmental Engineering, The Pennsylvania State University, State College, Pennsylvania 16802, United States.
Department of Natural Resources & Environmental Science, The University of Nevada, Reno, Nevada 89557, United States.
Environ Sci Technol. 2021 Feb 16;55(4):2357-2368. doi: 10.1021/acs.est.0c06783. Epub 2021 Feb 3.
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.
溶解氧（DO）反映了河流的代谢脉冲，是一项重要的水质衡量标准。然而，我们对 DO 的预测能力仍然难以捉摸。水质数据，特别是这里的 DO 数据，通常存在较大的空白和稀疏的区域和时间覆盖范围。另一方面，地球表面和水文气象数据已经变得广泛可用。在这里，我们想知道：LSTM 模型是否可以从稀疏的 DO 和密集的（每日）水文气象数据中学习河流 DO 动态？我们使用了 CAMELS-chem，这是一个新的数据集合，其中包含来自美国 236 个最小干扰流域的 DO 浓度。该模型通常可以学习 DO 溶解度的理论，并捕捉到随着水温升高 DO 浓度降低的趋势。它具有预测“化学未测量流域”中 DO 的潜力，这些流域没有任何 DO 和一般水质的测量值。然而，当河流内生源地化学过程变得重要时，该模型会错过一些 DO 的峰值和低谷。令人惊讶的是，当数据更多时，模型的表现并不更好。相反，它在流量和 DO 变化较小、径流量比（>0.45）较高且冬季降水峰值较高的流域表现更好。结果表明，在 DO 的峰值和低谷以及监测稀疏地区进行更多的数据收集对于克服数据稀缺这一水质领域的突出挑战至关重要。
