Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales
Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales
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DOI：
10.1029/2019wr026793
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发表时间：
2020-09-01
影响因子：
5.4
通讯作者：
Shen, Chaopeng
中科院分区：
文献类型：
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作者：
Feng, Dapeng;Fang, Kuai;Shen, Chaopeng
最近的观测与不同的时间表和类型（移动平均，快照，或定期间隔）可以帮助改善径流预测，但它是具有挑战性的有效整合。基于长短期记忆（LSTM）流量模型，我们测试了多个版本的灵活程序，我们称之为数据集成（DI），以利用最近的流量测量来改善预测。DI直接或通过卷积神经网络单元接受滞后输入。DI普遍将径流预报性能提升到前所未有的水平，达到创纪录的大陆尺度中值Nash-Sutcliffe效率系数值0.86。整合移动平均流量，最近几天的流量，甚至是前一个日历月的平均流量都可以改善每日预报。直接使用滞后观测值作为输入在性能上与使用卷积神经网络单元相当。重要的是，我们获得了有关水文过程影响LSTM和DI性能的宝贵见解。在应用DI之前，基础LSTM模型在山区或积雪为主的地区工作得很好，但在排放量低的地区（由于降水量低或降水能量同步性高）和年际储存变化大的地区工作得不太好。DI是最有益的高流量自相关的地区：它大大减少了基流偏差地下水为主的西部盆地，也提高了动态地表水存储，如草原坑洼或五大湖地区的盆地峰值预测。然而，即使是DI不能提高性能在高干旱盆地1天的闪光峰值。尽管存在这种限制，但由于其性能、自动化、效率和灵活性，基于深度学习的预测范式有很大的希望。
Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-scale median Nash-Sutcliffe Efficiency coefficient value of 0.86. Integrating moving-average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow-dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation-energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high-aridity basins with 1-day flash peaks. Despite this limitation, there is much promise for a deep-learning-based forecast paradigm due to its performance, automation, efficiency, and flexibility.