Regional rainfall-runoff modeling is an old but still mostly out-standing
problem in Hydrological Sciences. The problem currently is that traditional
hydrological models degrade significantly in performance when calibrated for
multiple basins together instead of for a single basin alone. In this paper, we
propose a novel, data-driven approach using Long Short-Term Memory networks
(LSTMs), and demonstrate that under a 'big data' paradigm, this is not
necessarily the case. By training a single LSTM model on 531 basins from the
CAMELS data set using meteorological time series data and static catchment
attributes, we were able to significantly improve performance compared to a set
of several different hydrological benchmark models. Our proposed approach not
only significantly outperforms hydrological models that were calibrated
regionally but also achieves better performance than hydrological models that
were calibrated for each basin individually. Furthermore, we propose an
adaption to the standard LSTM architecture, which we call an Entity-Aware-LSTM
(EA-LSTM), that allows for learning, and embedding as a feature layer in a deep
learning model, catchment similarities. We show that this learned catchment
similarity corresponds well with what we would expect from prior hydrological
understanding.

使用气象时间序列数据和静态流域属性，我们提出了一种基于数据驱动的方法，在使用 LSTMs 单个模型对 531 个流域进行训练的情况下，显著提高了性能，比符合地区性条件的水文模型和针对单个流域进行校准的水文模型的表现更好，同时还提出了 Entity-Aware-LSTM 的改进网络架构使学习、嵌入和特征层深度学习可以在其上实现，同时提出的这个背景与之前的水文理解相符。