Model output and code for a long short-term memory model forecasting streamflow drought across the Conterminous United States by focusing on percentiles below 50 percent

Machine learning (ML) models were used to forecast streamflow drought from 1 to 13 weeks into the future at more than 3,000 streamgage locations across the conterminous United States. Long short-term memory [LSTM] neural network models were used to predict weekly streamflow percentiles with independent models for each forecast horizon. In this data release we specifically include an LSTM model where only percentiles below 50% (LSTM<50) are prioritized. Model performance was evaluated regionally and nationally for drought occurrence (the classification performance for a future date) and for drought onset and termination (performance identifying drought starts and ends). This data release contains model outputs (model_outputs.zip) and model code (model_code.zip). The model_outputs.zip folder contains files with model performance data (performance_data.feather) and observed and predicted streamflow percentile time series data (timeseries_data.feather). The model_code.zip folder contains Jupyter notebooks with Python scripts for running the LSTM models.