Models and Predictions for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network" GitHub: https://github.com/gauchm/mts-lstm Results The file results.tar.gz contains: ensembled predictions for all models (generated from the models in models/ using the nh-results-ensemble command). These predictions were used in the results-analysis.ipynb and odelstm-analysis.ipynb notebooks on the GitHub repository for the paper. the NWM predictions nwm_chrt_v2_1h.p contains hourly NWM predictions for the CAMELS basins between 1993 and 2007. The file is derived from the reanalysis on aws. nwm_results.p is derived from nwm_chrt_v2_1h.p and contains hourly and day-aggregated results and performance metrics for the test period of our paper. a file signatures.p with hydrologic signatures that were calculated from the models' predictions. These signatures were used in the results-analysis.ipynb notebook on the GitHub repository for the paper. Models The tar.gz files prefixed with models- contain the trained MTS-LSTM, sMTS-LSTM, and ODE-LSTM models from our experiments. For each experiment, there exist 10 model setups (one for each random seed).
Besides the trained models, each model's tar.gz also contains the predictions on the test or validation perod and the configuration file used to train the model. MTS-LSTM mtslstm_seed* -- the MTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data) mtslstm_multiforcing_seed* -- the MTS-LSTM from the section on per-timescale input data, experiment "multi-forcing B" (using just NLDAS as hourly inputs) mtslstm_multiforcing_dailyhourly_seed* -- the MTS-LTSM from the section on per-timescale input data, experiment "multi-forcing A" (ingesting daily forcings into the hourly model) mtsltsm_136H1D_seed* -- the MTS-LTSM from the section on prediction at other timescales (1-, 3-, 6-hourly and daily predictions) sMTS-LSTM smtslstm_seed* -- the sMTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data) smtslstm_noregularization_seed* -- the sMTS-LSTM from the section on cross-timescale consistency (trained without regularization) Time-Continuous Experiments The file models-timecontinuous.tar.gz contains one sub-folder per basin on which we conducted our initial experiments.
Each basin directory contains: Experiment A (trained on daily and 12-hourly, evaluated on hourly): odelstm_a_seed* -- the ODE-LSTM from experiment A mtslstm_a_seed* -- the MTS-LSTM from experiment A Experiment B (trained on hourly and 3-hourly, evaluated on daily) odelstm_b_seed* -- the ODE-LSTM from experiment B mtslstm_b_seed* -- the MTS-LSTM from experiment B Related Datasets: https://doi.org/10.5281/zenodo.4072701 contains the hourly NLDAS forcings and USGS streamflow required to use the models from this dataset.

Models and Predictions for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network" GitHub: https://github.com/gauchm/mts-lstm Results The file results.tar.gz contains: ensembled predictions for all models (generated from the models in models/ using the nh-results-ensemble command). These predictions were used in the results-analysis.ipynb and odelstm-analysis.ipynb notebooks on the GitHub repository for the paper. the NWM predictions nwm_chrt_v2_1h.p contains hourly NWM predictions for the CAMELS basins between 1993 and 2007. The file is derived from the reanalysis on aws. nwm_results.p is derived from nwm_chrt_v2_1h.p and contains hourly and day-aggregated results and performance metrics for the test period of our paper. a file signatures.p with hydrologic signatures that were calculated from the models' predictions. These signatures were used in the results-analysis.ipynb notebook on the GitHub repository for the paper. Models The tar.gz files prefixed with models- contain the trained MTS-LSTM, sMTS-LSTM, and ODE-LSTM models from our experiments. For each experiment, there exist 10 model setups (one for each random seed).
Besides the trained models, each model's tar.gz also contains the predictions on the test or validation perod and the configuration file used to train the model. MTS-LSTM mtslstm_seed* -- the MTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data) mtslstm_multiforcing_seed* -- the MTS-LSTM from the section on per-timescale input data, experiment "multi-forcing B" (using just NLDAS as hourly inputs) mtslstm_multiforcing_dailyhourly_seed* -- the MTS-LTSM from the section on per-timescale input data, experiment "multi-forcing A" (ingesting daily forcings into the hourly model) mtsltsm_136H1D_seed* -- the MTS-LTSM from the section on prediction at other timescales (1-, 3-, 6-hourly and daily predictions) sMTS-LSTM smtslstm_seed* -- the sMTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data) smtslstm_noregularization_seed* -- the sMTS-LSTM from the section on cross-timescale consistency (trained without regularization) Time-Continuous Experiments The file models-timecontinuous.tar.gz contains one sub-folder per basin on which we conducted our initial experiments.
Each basin directory contains: Experiment A (trained on daily and 12-hourly, evaluated on hourly): odelstm_a_seed* -- the ODE-LSTM from experiment A mtslstm_a_seed* -- the MTS-LSTM from experiment A Experiment B (trained on hourly and 3-hourly, evaluated on daily) odelstm_b_seed* -- the ODE-LSTM from experiment B mtslstm_b_seed* -- the MTS-LSTM from experiment B Related Datasets: https://doi.org/10.5281/zenodo.4072700 contains the hourly NLDAS forcings and USGS streamflow required to use the models from this dataset.

Data for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network" GitHub: https://github.com/gauchm/mts-lstm This dataset contains the hourly NLDAS forcings and USGS streamflow data. For training with our codebase, we recommend using the combined NetCDF file, but you can also use the csv files (but it will take much longer to load the data). Related Datasets: https://doi.org/10.5281/zenodo.4071885 contains the models trained with the forcings and streamflow from this dataset.

Data for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network" GitHub: https://github.com/gauchm/mts-lstm This dataset contains the hourly NLDAS forcings and USGS streamflow data. For training with our codebase, we recommend using the combined NetCDF file, but you can also use the csv files (but it will take much longer to load the data). Related Datasets: https://doi.org/10.5281/zenodo.4071885 contains the models trained with the forcings and streamflow from this dataset.

Models and Predictions for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network" GitHub: https://github.com/gauchm/mts-lstm Results The file results.tar.gz contains: ensembled predictions for all models (generated from the models in models/ using the nh-results-ensemble command). These predictions were used in the results-analysis.ipynb and odelstm-analysis.ipynb notebooks on the GitHub repository for the paper. the NWM predictions nwm_chrt_v2_1h.p contains hourly NWM predictions for the CAMELS basins between 1993 and 2007. The file is derived from the reanalysis on aws. nwm_results.p is derived from nwm_chrt_v2_1h.p and contains hourly and day-aggregated results and performance metrics for the test period of our paper. a file signatures.p with hydrologic signatures that were calculated from the models' predictions. These signatures were used in the results-analysis.ipynb notebook on the GitHub repository for the paper. Models The tar.gz files prefixed with models- contain the trained MTS-LSTM, sMTS-LSTM, and ODE-LSTM models from our experiments. For each experiment, there exist 10 model setups (one for each random seed).
Besides the trained models, each model's tar.gz also contains the predictions on the test or validation perod and the configuration file used to train the model. MTS-LSTM mtslstm_seed* -- the MTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data) mtslstm_multiforcing_seed* -- the MTS-LSTM from the section on per-timescale input data, experiment "multi-forcing B" (using just NLDAS as hourly inputs) mtslstm_multiforcing_dailyhourly_seed* -- the MTS-LTSM from the section on per-timescale input data, experiment "multi-forcing A" (ingesting daily forcings into the hourly model) mtsltsm_136H1D_seed* -- the MTS-LTSM from the section on prediction at other timescales (1-, 3-, 6-hourly and daily predictions) sMTS-LSTM smtslstm_seed* -- the sMTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data) smtslstm_noregularization_seed* -- the sMTS-LSTM from the section on cross-timescale consistency (trained without regularization) Time-Continuous Experiments The file models-timecontinuous.tar.gz contains one sub-folder per basin on which we conducted our initial experiments.
Each basin directory contains: Experiment A (trained on daily and 12-hourly, evaluated on hourly): odelstm_a_seed* -- the ODE-LSTM from experiment A mtslstm_a_seed* -- the MTS-LSTM from experiment A Experiment B (trained on hourly and 3-hourly, evaluated on daily) odelstm_b_seed* -- the ODE-LSTM from experiment B mtslstm_b_seed* -- the MTS-LSTM from experiment B Related Datasets: https://doi.org/10.5281/zenodo.4072700 contains the hourly NLDAS forcings and USGS streamflow required to use the models from this dataset.