Accurate baseflow estimation is essential for ecological protection and water resources management. Past studies have used environmental predictors to extend baseflow from gauged basins to ungauged basins, publishing several regional or global datasets on mean annual baseflow. However, time series datasets of baseflow are still lacking due to the complexity of baseflow generation processes. Here, we developed a monthly baseflow data set using a Deep learning model called the long short-term memory (LSTM) networks. To better train the networks across basins, we compared the standard LSTM architecture using 8 time series as inputs with four variant architectures using 16 additional static properties as inputs. Dividing the contiguous United States into nine ecoregions, we used baseflow calculated from 1,604 gauged basins as training targets to calibrate the five LSTM architectures for each ecoregion separately. Results show that three variant architectures (Joint, Front, and Entity-Aware-LSTM) perform better than the standard LSTM, with median Kling-Gupta Efficiencies across basins greater than 0.85. Based on Front LSTM, the monthly baseflow data set with 0.25° spatial resolution across the contiguous United States from 1981 to 2020 was obtained. Potential applications of the data set include analyzing baseflow trends under global change and estimating large-scale groundwater recharge.

中文翻译：

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使用长短期记忆 (LSTM) 网络估计美国 1981 年至 2020 年的网格化每月基流

准确的基流估算对于生态保护和水资源管理至关重要。过去的研究使用环境预测器将基流从计量盆地扩展到未计量盆地，发布了几个关于年平均基流的区域或全球数据集。然而，由于基流生成过程的复杂性，仍然缺乏基流的时间序列数据集。在这里，我们使用称为长短期记忆 (LSTM) 网络的深度学习模型开发了每月基流数据集。为了更好地训练跨盆地的网络，我们将使用 8 个时间序列作为输入的标准 LSTM 架构与使用 16 个额外静态属性作为输入的四种变体架构进行了比较。将连续的美国划分为九个生态区，我们使用从 1 计算的基流，604 个测量盆地作为训练目标，分别为每个生态区校准五个 LSTM 架构。结果表明，三种变体架构（Joint、Front 和 Entity-Aware-LSTM）的性能优于标准 LSTM，跨盆地的 Kling-Gupta 效率中值大于 0.85。基于Front LSTM，获得了1981-2020年美国本土空间分辨率为0.25°的月基流数据集。该数据集的潜在应用包括分析全球变化下的基流趋势和估计大规模地下水补给。基于Front LSTM，获得了1981-2020年美国本土空间分辨率为0.25°的月基流数据集。该数据集的潜在应用包括分析全球变化下的基流趋势和估计大规模地下水补给。基于Front LSTM，获得了1981-2020年美国本土空间分辨率为0.25°的月基流数据集。该数据集的潜在应用包括分析全球变化下的基流趋势和估计大规模地下水补给。