  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 (CNN) 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 CNN
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 inter-annual 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
high-aridity basins with one-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.
