Pywr-DRB Training#
Overview#
This training is designed to get you familiar with the core functionality of the Pywr-DRB water resource model.
The training consists of several different jupyter notebook files which introduce key concepts and workflows associated with the model.
After stepping through the notebooks, you can replicate the study presented in Hamilton, Andrew L., Amestoy, Trevor J. and Reed, Patrick M., Pywr-Drb: An Open-Source Python Model for Water Availability and Drought Risk Assessment in the Delaware River Basin (In Review). A pre-print is available at SSRN: https://ssrn.com/abstract=4765247 or http://dx.doi.org/10.2139/ssrn.4765247
More trainings will be added in the future. If you have any questions or requests please contact Trevor at tja73@cornell.edu.
Learning objectives#
Become familiar with core Pywr-DRB model functionality
Learn how to run simulations using NHMv1.0 and NWMv2.1 streamflow inputs
Learn how to access and plot key results
Prerequisites#
Before starting this training it is recommended to read the preprint for Hamilton, Amestoy and Reed (In Review) available here.
Pywr-DRB is a python based model; you will need to have Python as well as the GLPK or similar linear programming solver installed.
You can access the Pywr-DRB github repository here: Pywr-DRB/Pywr-DRB
Activities#
Activity |
Est. Time |
Topic |
Resources |
Readings |
Tasks |
---|---|---|---|---|---|
1 |
2 days |
Background context on Pywr-DRB |
- |
[1] |
- |
2 |
2 days |
Tutorial 01 Introduction to PywrDRB |
[1] |
- Complete first simulation |
|
3 |
1 day |
Tutorial 02 Prepare Input Data |
- |
- |
|
4 |
2 days |
Model Parameters |
- |
- |
|
5 |
5 days |
Replication of [1] |
Instructions in Pywr-DRB README |
- |
- |
Reading list#
[1] Hamilton, Andrew L., Amestoy, Trevor J. and Reed, Patrick M., Pywr-Drb: An Open-Source Python Model for Water Availability and Drought Risk Assessment in the Delaware River Basin (In Review). A pre-print is available at SSRN: https://ssrn.com/abstract=4765247 or http://dx.doi.org/10.2139/ssrn.4765247