Ashley Van Beusekom

USGS;US Forest Service

 Recent Activity

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) resource (XSEDE Comet) through CyberGIS-Compute Service, helps the modelers to reproduce and build on the results from the paper (Van Beusekom et al., 2021).

For this purpose, three different Jupyter notebooks are developed and included in this resource which explore the paper goal for one example CAMELS site and a pre-selected period of 18-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 (XSEDE Comet) 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. Finally, the third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). 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 three 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.

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ABSTRACT:

This resource, configured for execution in connected JupyterHub compute platforms, helps the modelers to reproduce and build on the results from the paper (Van Beusekom et al., 2021). For this purpose, three different Jupyter notebooks are developed and included in this resource which explore the paper goal for one example CAMELS site and a pre-selected period of 18-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 executes SUMMA model using the input data from first notebook using original and altered forcing, as per further described in the notebook. Finally, the third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). 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 three notebooks, modelers can apply the methodology mentioned above to any (one to all) of the 671 CAMELS basins and simulation periods of their choice.

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ABSTRACT:

The overall goal of this collection is to use the basic strategy and architecture presented by Choi et al. (2021) to make components of a modern and complex hydrologic study (VBstudy; Van Beusekom et al., 2021) easier to reproduce.

In VBstudy, hydrological outputs from the SUMMA model for the 671 CAMELS catchments across the contiguous United States (CONUS) are investigated to understand their dependence on input forcing behavior across CONUS. VBstudy layes out a simple methodology that can be applied to understand the relative importance of seven model forcings (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure).

Choi et al. (2021) integrated three components through seamless data transfers for a reproducible research: (1) online data and model repositories; (2) computational environments leveraging containerization and self-documented computational notebooks; and (3) Application Programming Interfaces (APIs) that provide programmatic control of complex computational models.

Therefore, in the current research, we integrated the following three components through seamless data transfers to make components of a modern and complex hydrologic study (VBstudy) easier to reproduce:
(1) HydroShare as online data and model repository;
(2) CyberGIS-Jupyter for Water for self-documented computational notebooks as computational environment (with and without HPC notebooks);
(3) pySUMMA as Application Programming Interfaces (APIs) that provide programmatic control of complex computational models.

This collection includes three resources:

1- First resource, provides the entire NLDAS forcing datasets used in the paper.

2- Second resource provides an end-to-end workflow of CAMELS basin modeling with SUMMA for the paper simulations configured for execution in connected JupyterHub compute platforms. This resource is well-suited for a smaller scale exploration: it explores one example CAMELS site and a period of 18-month simulation to only demonstrate the capabilities of the notebooks.

3- Third resource, however, uses HPC (High-Performance Computing) through CyberGIS Computing Service. The HPC enables a high-speed running of simulations which makes it suitable for running larger simulations (even as large as the entire 671 CAMELS sites and the whole 60-month simulation period used in the VBstudy) practical and much faster than the second resource.

Greater details can be found in each resource.

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ABSTRACT:

This resource, configured for execution in connected JupyterHub compute platforms, helps the modelers to reproduce and build on the results from the paper (Van Beusekom et al., 2021). For this purpose, three different Jupyter notebooks are developed and included in this resource which explore the paper goal for one example CAMELS site and a pre-selected period of 18-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 executes SUMMA model using the input data from first notebook using original and altered forcing, as per further described in the notebook. Finally, the third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). 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 three notebooks, modelers can apply the methodology mentioned above to any (one to all) of the 671 CAMELS basins and simulation periods of their choice.

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ABSTRACT:

This notebook is created to support SUMMA general application workflows using CAMELS forcing, watershed attributes, and streamflow observation.
CAMELS datasets cover 671 basins across the USA, so users can apply SUMMA models in 671 basins.

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Resources
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MODFLOW Model Instance Resource 0
Multidimensional (NetCDF) 0
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Composite Resource Composite Resource
NLDAS Forcing NetCDF using CAMELS datasets from 1980 to 2018
Created: Nov. 4, 2020, 8:57 p.m.
Authors: Naoki Mizukami · Wood, Andrew

ABSTRACT:

This resource was created using CAMELS (https://ral.ucar.edu/solutions/products/camels) `TIME SERIES NLDAS forced model output` from 1980 to 2018.
The original NLDAS (North American Land Data Assimilation System) hourly forcing data was created by NOAA by 0.125 x 0.125 degree grid.
Through creating CAMELS datasets, hourly forcing data were reaggregated to 671 basins in the USA.
In this study, we merged all CAMELS forcing data into one NetCDF file to take advantage of OPeNDAP (http://hyrax.hydroshare.org/opendap/hyrax/) in HydroShare.
Currently, using SUMMA CAMELS notebooks (https://www.hydroshare.org/resource/ac54c804641b40e2b33c746336a7517e/), we can extract forcing data to simulate SUMMA in the particular basins in 671 basins of CAMELS datasets.

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Composite Resource Composite Resource

ABSTRACT:

These are example application notebooks to simulate SUMMA using CAMELS datasets.
There are three steps: (STEP-1) Create SUMMA input, (STEP-2) Execute SUMMA, (STEP-3) Visualize SUMMA output
Based on this example, users can change the HRU ID and simulation periods to analyze 671 basins in CAMELS datasets.

(STEP-1) A_1_camels_make_input.ipynb
- The first notebook creates SUMMA input using Camels dataset using `summa_camels_hydroshare.zip` in this resource and OpenDAP(https://www.hydroshare.org/resource/a28685d2dd584fe5885fc368cb76ff2a/).
(STEP-2) B_1_camels_pysumma_default_prob.ipynb, B_2_camels_pysumma_lhs_prob.ipynb, B_3_camels_pysumma_config_prob.ipynb, and
B_4_camels_pysumma_lhs_config_prob.ipynb
- These four notebooks execute SUMMA considering four different parameters and parameterization combinations
(STEP-3) C_1_camels_analyze_output_default_prob.ipynb, C_2_camels_analyze_output_lhs_prob.ipynb, C_3_camels_analyze_output_config_prob.ipynb,
C_4_camels_analyze_output_lhs_config_prob.ipynb
- The final four notebooks visualize SUMMA output of B-1, B-2, B-3, and B-4 notebooks.

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Composite Resource Composite Resource

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) resource (XSEDE Comet) through CyberGIS-Compute Service, helps the modelers to reproduce and build on the results from the paper (Van Beusekom et al., 2021).

For this purpose, three different Jupyter notebooks are developed and included in this resource which explore the paper goal for one example CAMELS site and a pre-selected period of 18-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 (XSEDE Comet) 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. Finally, the third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). 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 three 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.

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Composite Resource Composite Resource

ABSTRACT:

SUMMA Simulation in MERCED R A HAPPY ISLES BRIDGE NR YOSEMITE CA using Camels Datasets in on CyberGIS Jupyter for water

There are three Jupyter notebooks to demonstrate SUMMA Simulations
1. Create SUMMA input using Camels dataset via this HS resource and OpenDAP(https://www.hydroshare.org/resource/a28685d2dd584fe5885fc368cb76ff2a/)
2. Execute SUMMA using pySUMMA
3. Plot SUMMA output

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Composite Resource Composite Resource

ABSTRACT:

This notebook is created to support SUMMA general application workflows using CAMELS forcing, watershed attributes, and streamflow observation.
CAMELS datasets cover 671 basins across the USA, so users can apply SUMMA models in 671 basins.

Show More
Composite Resource Composite Resource

ABSTRACT:

This resource, configured for execution in connected JupyterHub compute platforms, helps the modelers to reproduce and build on the results from the paper (Van Beusekom et al., 2021). For this purpose, three different Jupyter notebooks are developed and included in this resource which explore the paper goal for one example CAMELS site and a pre-selected period of 18-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 executes SUMMA model using the input data from first notebook using original and altered forcing, as per further described in the notebook. Finally, the third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). 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 three notebooks, modelers can apply the methodology mentioned above to any (one to all) of the 671 CAMELS basins and simulation periods of their choice.

Show More
Collection Resource Collection Resource

ABSTRACT:

The overall goal of this collection is to use the basic strategy and architecture presented by Choi et al. (2021) to make components of a modern and complex hydrologic study (VBstudy; Van Beusekom et al., 2021) easier to reproduce.

In VBstudy, hydrological outputs from the SUMMA model for the 671 CAMELS catchments across the contiguous United States (CONUS) are investigated to understand their dependence on input forcing behavior across CONUS. VBstudy layes out a simple methodology that can be applied to understand the relative importance of seven model forcings (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure).

Choi et al. (2021) integrated three components through seamless data transfers for a reproducible research: (1) online data and model repositories; (2) computational environments leveraging containerization and self-documented computational notebooks; and (3) Application Programming Interfaces (APIs) that provide programmatic control of complex computational models.

Therefore, in the current research, we integrated the following three components through seamless data transfers to make components of a modern and complex hydrologic study (VBstudy) easier to reproduce:
(1) HydroShare as online data and model repository;
(2) CyberGIS-Jupyter for Water for self-documented computational notebooks as computational environment (with and without HPC notebooks);
(3) pySUMMA as Application Programming Interfaces (APIs) that provide programmatic control of complex computational models.

This collection includes three resources:

1- First resource, provides the entire NLDAS forcing datasets used in the paper.

2- Second resource provides an end-to-end workflow of CAMELS basin modeling with SUMMA for the paper simulations configured for execution in connected JupyterHub compute platforms. This resource is well-suited for a smaller scale exploration: it explores one example CAMELS site and a period of 18-month simulation to only demonstrate the capabilities of the notebooks.

3- Third resource, however, uses HPC (High-Performance Computing) through CyberGIS Computing Service. The HPC enables a high-speed running of simulations which makes it suitable for running larger simulations (even as large as the entire 671 CAMELS sites and the whole 60-month simulation period used in the VBstudy) practical and much faster than the second resource.

Greater details can be found in each resource.

Show More
Composite Resource Composite Resource

ABSTRACT:

This resource, configured for execution in connected JupyterHub compute platforms, helps the modelers to reproduce and build on the results from the paper (Van Beusekom et al., 2021). For this purpose, three different Jupyter notebooks are developed and included in this resource which explore the paper goal for one example CAMELS site and a pre-selected period of 18-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 executes SUMMA model using the input data from first notebook using original and altered forcing, as per further described in the notebook. Finally, the third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). 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 three notebooks, modelers can apply the methodology mentioned above to any (one to all) of the 671 CAMELS basins and simulation periods of their choice.

Show More
Composite Resource Composite Resource

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) resource (XSEDE Comet) through CyberGIS-Compute Service, helps the modelers to reproduce and build on the results from the paper (Van Beusekom et al., 2021).

For this purpose, three different Jupyter notebooks are developed and included in this resource which explore the paper goal for one example CAMELS site and a pre-selected period of 18-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 (XSEDE Comet) 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. Finally, the third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). 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 three 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.

Show More