Dataset for Global Daily Discharge Estimation Based on Grid-Scale Long Short-Term Memory (LSTM) Model and River Routing

Corresponding peer-reviewed publication Yang, Y., Feng, D., Beck, H.E., Hu, W., Sengupta, A., Delle Monache, L., Hartman, R., Lin, P., Shen, C. and Pan, M. Global Daily Discharge Estimation Based on Grid-Scale Long Short-Term Memory (LSTM) Model and River Routing. Water Resources Research (Under Review). DOI: 10.22541/essoar.169724927.73813721/v1. For updates and revisions to the original manuscript, please refer to the AGU 2024 presentation available on the GRADES-hydroDL website: https://www.reachhydro.org/home/records/grades-hydrodl. When using any of the files in this dataset, please cite both the article as mentioned above and the dataset herein. Summary This dataset contains input files for developing GRADES-hydroDL (global reach level daily discharge based on machine learning and river routing model) dataset and final evaluation metrics. GRADES-hydroDL.pdf: The details of GRADES-hydroDL dataset, including overview, download links, instructions, etc. training_gauge_information.csv: The basic information of the 4215 basins selected for LSTM training. training_gauge_attr.csv: The static attributes of the 4215 training basins used in the LSTM training process. 10fold_training_basin.zip: The training basins and corresponding test basins for the 10-fold cross-validation experiments. global_025d_gridinfo.csv: The basic information of the global 0.25-degree grids used for LSTM application. metrics.zip: All evaluation results of all experiments used in the article.