Models and Predictions for "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"

<strong>Models and Predictions for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"</strong> GitHub: https://github.com/gauchm/mts-lstm <strong>Results</strong> 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. <strong>Models</strong> 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).<br> 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. <em>MTS-LSTM</em> `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) <em>sMTS-LSTM</em> `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) <em>Time-Continuous Experiments</em> The file `models-timecontinuous.tar.gz` contains one sub-folder per basin on which we conducted our initial experiments.<br> 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 <em>Related Datasets: </em>https://doi.org/10.5281/zenodo.4072701 contains the hourly NLDAS forcings and USGS streamflow required to use the models from this dataset.