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| Created: | Jan 16, 2022 at 5:32 p.m. | |
| Last updated: | Jan 17, 2022 at 9:13 p.m. | |
| DOI: | 10.4211/hs.d750278db868447dbd252a8c5431affd | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 690 |
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Abstract
Runs from two papers exploring the use of mass conserving LSTM. Model results used in the papers are 1) model_outputs_for_analysis_extreme_events_paper.tar.gz, and 2) model_outputs_for_analysis_mass_balance_paper.tar.gz.
The models here are trained/calibrated on three different time periods. Standard Time Split (time split 1): test period(1989-1999) is the same period used by previous studies which allows us to confirm that the deep learning models (LSTM andMC-LSTM) trained for this project perform as expected relative to prior work. NWM Time Split (time split 2): The second test period (1995-2014) allows us to benchmark against the NWM-Rv2, which does not provide data prior to 1995. Return period split: The third test period (based on return periods) allows us to benchmark only on water years that contain streamflow events that are larger (per basin) than anything seen in the training data (<= 5-year return periods in training and > 5-year return periods in testing).
Also included are an ensemble of model runs for LSTM, MC-LSTM for the "standard" training period and two forcing products. These files are provided in the format "<model_type>_<forcing_product>_standard_training.tar.gz". Note that these individual ensemble member runs we used to produce the runs in the files "model_outputs_for_analysis_<*>_paper.tar.gz".
IMPORTANT NOTE: This python environment should be used to extract and load the data: https://github.com/jmframe/mclstm_2021_extrapolate/blob/main/python_environment.yml, as the pickle files serialized the data with specific versions of python libraries. Specifically, the pickle serialization was done with xarray=0.16.1.
Code to interpret these runs can be found here:
https://github.com/jmframe/mclstm_2021_extrapolate
https://github.com/jmframe/mclstm_2021_mass_balance
Papers are available here:
https://hess.copernicus.org/preprints/hess-2021-423/
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This resource is shared under the Creative Commons Attribution-NoCommercial CC BY-NC.
http://creativecommons.org/licenses/by-nc/4.0/
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