Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...

MC-LSTM papers, model runs


Authors:
Owners: This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource.
Type: Resource
Storage: The size of this resource is 5.9 GB
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
Views: 851
Downloads: 64
+1 Votes: Be the first one to 
 this.
Comments: No comments (yet)

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/

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
CONtiguous United States
North Latitude
49.9131°
East Longitude
-64.7106°
South Latitude
23.6708°
West Longitude
-125.7067°

Temporal

Start Date:
End Date:

Content

How to Cite

Frame, J. M. (2022). MC-LSTM papers, model runs, HydroShare, https://doi.org/10.4211/hs.d750278db868447dbd252a8c5431affd

This resource is shared under the Creative Commons Attribution-NoCommercial CC BY-NC.

http://creativecommons.org/licenses/by-nc/4.0/
CC-BY-NC

Comments

There are currently no comments

New Comment

required