  Significant strides have been made in advancing streamflow predictions,
notably with the introduction of cutting-edge machine-learning models.
Predominantly, Long Short-Term Memories (LSTMs) and Convolution Neural Networks
(CNNs) have been widely employed in this domain. While LSTMs are applicable in
both rainfall-runoff and time series settings, CNN-LSTMs have primarily been
utilized in rainfall-runoff scenarios. In this study, we extend the application
of CNN-LSTMs to time series settings, leveraging lagged streamflow data in
conjunction with precipitation and temperature data to predict streamflow. Our
results show a substantial improvement in predictive performance in 21 out of
32 HUC8 basins in Nebraska, showcasing noteworthy increases in the Kling-Gupta
Efficiency (KGE) values. These results highlight the effectiveness of CNN-LSTMs
in time series settings, particularly for spatiotemporal hydrological modeling,
for more accurate and robust streamflow predictions.
