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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 1.5 MB | |
| Created: | May 20, 2021 at 12:35 a.m. | |
| Last updated: | Apr 12, 2023 at 11:33 p.m. (Metadata update) | |
| Published date: | Apr 11, 2023 at 7:34 p.m. | |
| DOI: | 10.4211/hs.9d73d61696ee4f6b9c9a11e21cd44e24 | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 1629 |
| Downloads: | 77 |
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Abstract
This resource, configured for execution in connected JupyterHub compute platforms using the CyberGIS-Jupyter for Water (CJW) environment's supported High-Performance Computing (HPC) resources (Expanse or Virtual ROGER) through CyberGIS-Compute Service, helps the modelers to reproduce and build on the results from the VB study (Van Beusekom et al., 2022) as explained by Maghami et el. (2023).
For this purpose, four different Jupyter notebooks are developed and included in this resource which explore the paper goal for four example CAMELS site and a pre-selected period of 60-month simulation to demonstrate the capabilities of the notebooks. The first notebook processes the raw input data from CAMELS dataset to be used as input for SUMMA model. The second notebook utilizes the CJW environment's supported HPC resource (Expanse or Virtual ROGER) through CyberGIS-Compute Service to executes SUMMA model. This notebook uses the input data from first notebook using original and altered forcing, as per further described in the notebook. The third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). The fourth notebook, only developed for the HPC environment (and only currently working with Expanse HPC), enables transferring large data from HPC to the scientific cloud service (i.e., CJW) using Globus service integrated by CyberGIS-Compute in a reliable, high-performance and fast way. More information about each Jupyter notebook and a step-by-step instructions on how to run the notebooks can be found in the Readme.md fie included in this resource. Using these four notebooks, modelers can apply the methodology mentioned above to any (one to all) of the 671 CAMELS basins and simulation periods of their choice. As this resource uses HPC, it enables a high-speed running of simulations which makes it suitable for larger simulations (even as large as the entire 671 CAMELS sites and the whole 60-month simulation period used in the paper) practical and much faster than when no HPC is used.
Subject Keywords
Coverage
Spatial
Temporal
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Content
Readme.md
How to run the simulations
This Readme file provides the users with the step-by-step guide to successfully run the three developed notebooks.
The steps, in the order they need to be taken, are explained in what follows.
STEP_0 Preliminary step
In this step the modellers make sure that they have access to the content files of the resource and required compute platform.
- In order to be able to run the three Jupyter notebooks, modelers need to first have a HydroShare account.
- If the modeler already does not have access to CyberGIS-Jupyter for Water (CJW), they need to ask to get access to it at the CyberGIS-Jupyter for Water platform.
Important notes before running the notebooks:
- Users can change the HRU ID and simulation periods to analyze any of the 671 basins in CAMELS datasets for the simulation period of their choice.
- To run each notebook:
1. Click the OpenWith button in the upper-right corner of this HydroShare resource webpage;
2. Select "CyberGIS-Jupyter for Water";
3. Open the notebook and follow instructions;
STEP_1 Create SUMMA input using 1_camels_make_input.ipynb
The first notebook creates SUMMA input using Camels dataset using summa_camels_hydroshare.zip in this resource and OpenDAP resource.
STEP_2 Execute SUMMA using 2_camels_pysumma_hpc.ipynb
This notebook executes SUMMA using original and constant forcing, and different parameters and parameterization combinations via HPC.
STEP_3 Visualize SUMMA output using 3_camels_analyze_output.ipynb
This notebook visualizes the sensitivity of SUMMA output according to the constant forcing and output variables using KGE.
STEP_4 Transfer raw SUMMA output using 4_globus_download.ipynb
This notebook enables transferring large data from HPC to the scientific cloud service (i.e., CJW) using Globus service integrated by CyberGIS-Compute in a reliable, high-performance and fast way.
Related Resources
| The content of this resource is derived from | https://www.hydroshare.org/resource/a28685d2dd584fe5885fc368cb76ff2a/ |
| The content of this resource is derived from | http://www.hydroshare.org/resource/03dc01d36f0547f5945d93d2c47b48cc |
| Title | Owners | Sharing Status | My Permission |
|---|---|---|---|
| Facilitating Reproduction of the Components of A Complex Hydrologic Modeling Study: "Hydrologic Model Sensitivity to Temporal Aggregation of Meteorological Forcing Data: A Case Study for the Contiguous United States" | Bart Nijssen · Andrew Bennett · Ashley Van Beusekom · Young-Don Choi · Iman Maghami · Zhiyu/Drew Li · Jonathan Goodall | Published | Open Access |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Science Foundation | Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis | OAC-1664061, OAC-1664018, OAC-1664119 |
| National Science Foundation | HDR Institute: Geospatial Understanding through an Integrative Discovery Environment | OAC-2118329 |
| National Science Foundation | EarthCube Data Capabilities: Collaborative Research: Integration of Reproducibility into Community CyberInfrastructure | RISE-1928369 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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