Iman Maghami

University of Virginia

 Recent Activity

ABSTRACT:

This is a test resource for MI/MP aggregation for schema validation.

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ABSTRACT:

test

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ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys modeling workflow using the GeoServer approach at Spout Run, VA

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file which is provided in this resource.

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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) 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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 Contact

Resources
All 0
Collection 0
Composite Resource 0
Generic 0
Geographic Feature 0
Geographic Raster 0
HIS Referenced Time Series 0
Model Instance 0
Model Program 0
MODFLOW Model Instance Resource 0
Multidimensional (NetCDF) 0
Script Resource 0
SWAT Model Instance 0
Time Series 0
Web App 0
Model Instance Resource Model Instance Resource
HEC-HMS Model for Rapidan River Watershed
Created: Nov. 21, 2017, 3:51 a.m.
Authors: Iman Maghami

ABSTRACT:

This HEC-HMS model presents calibration data as well as validation data for Rapidan River near Ruckersville, Virginia. (USGS Station 01665500). The storm event used for calibrations is from 23 June 1995 to 3 July 1995 and the storm event period for validation is 27 September 1999 to 3 October 1999.

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Model Instance Resource Model Instance Resource

ABSTRACT:

This SUMMA Model instance is a part of the Clark et al., (2015b) study, and explored the impact of different stomatal resistance parameterizations on total evapotranspiration (ET) in the Reynolds Mountain East catchment in southwestern Idaho. This study applied three different stomatal resistance parameterizations: the simple soil resistance method, the Ball Berry method, and the Jarvis method.

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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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Collection Resource Collection Resource

ABSTRACT:

This is a collection resource for "Comparing Approaches to Achieve Reproducible Computational Modeling for Hydrological and Environmental Systems" manuscript in Environmental Modeling and Software.

HS-1: Collection Resource for Comparing Approaches to Achieve Reproducible Computational Modeling for Hydrological and Environmental Systems

For SUMMA simulation, we created two SUMMA model instances.
HS-2. Model Instance for the Impact of Stomatal Resistance Parameterizations on ET of SUMMA Model in Aspen stand at Reynolds Mountain East
HS-3. Model Instance for the Impact of Lateral Flow Parameterizations on Runoff of SUMMA Model at Reynolds Mountain East

For SUMMA simulation, we created a SUMMA model program
HS-4: Remote Approach-11: Using HPC Cluster (Rivanna: HPC at University of Virginia) for the reproducibility of SUMMA modeling

There are five HS resources for reproducible approaches.
HS-5. A Virtual Box image that includes five local approaches:
- Approach-1 Compiling the core model software
- Approach-2 Containerizing the core model software only with Docker
- Approach-3 Containerizing all software with Docker
- Approach-4 Containerizing all software with Singularity
- Approach-5 Containerizing all software and modeling workflows with Sciunit
HS-6. Approach-6 Using CUAHSI JupyterHub
HS-7. Approach-7 Using CyberGIS-Jupyter for water
HS-8. Approach-8 Using Sciunit in CUAHSI JupyterHub
HS-9. Approach-9 Using Sciunit in CyberGIS-Jupyter for water

Lastly, we created a notebook for performance tests using the different reproducible approaches.
HS-10. Jupyter notebook for performance test using the different reproducible approaches

In addition, there are three GitHub repositories for reproducible approaches in related resources in the reference section.
Git-1. Approach-10 Using Binder
Git-2. Description of Approach-3 to show how to create Docker environments
Git-3. Description of Approach-4 and 11 to show how to use a Singularity image in HPC

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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:

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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Composite Resource Composite Resource
Run RHESSys model with CyberGIS-Compute Service on CJW
Created: March 1, 2021, 8:54 p.m.
Authors: Choi, Young-Don

ABSTRACT:

RHESSys (Regional Hydro-Ecological Simulation System) is a GIS-based, terrestrial ecohydrological modeling framework designed to simulate carbon, water and nutrient fluxes at the watershed scale. RHESSys models the temporal and spatial variability of ecosystem processes and interactions at a daily time step over multiple years by combining a set of physically-based process models and a methodology for partitioning and parameterizing the landscape. Detailed model algorithms are available in Tague and Band (2004).

This notebook demonstrates parallel job submissions of RHESSys ensemble simulations from CyberGIS-Jupyer for water to HPC (XSEDE), visualizes RHESSys output, and evaluate RHESSys efficiency with simulation runoff and observation streamflow

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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.

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Composite Resource Composite Resource

ABSTRACT:

CAMELS (Catchment Attributes and Meteorology for Large-sample Studies: https://ral.ucar.edu/solutions/products/camels) is a large-sample hydrometeorological dataset that provides catchment attributes and forcings for 671 small- to medium-sized basins across the CONUS.

This resource contains basin attributes and parameters in NetCDF files.

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Composite Resource Composite Resource

ABSTRACT:

CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) is a large-sample hydrometeorological dataset that provides catchment attributes, forcings and GIS data for 671 small- to medium-sized basins across the CONUS (continental United States). HydroShare hosts a copy of CAMELS and exposes it through different public data access protocols (WMS, WFS and OPeNDAP) for easy visualization and subsetting of the dataset in community modeling research. This notebook demostrates how to set up SUMMA models with CAMELS dataset from HydroShare using various tools integrated in the CyberGIS-Jupyter for Water (CJW) environment and execution of ensemble model runs on a supported High-Performance Computing (HPC) resource (XSEDE Comet or UIUC Virtual Roger) through CyberGIS-Compute Service.

How to run the notebook:
1) Click on the OpenWith button in the upper-right corner;
2) Select "CyberGIS-Jupyter for Water";
3) Open the notebook and follow instructions;

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Composite Resource Composite Resource

ABSTRACT:

RHESSys (Regional Hydro-Ecological Simulation System) is a GIS-based, terrestrial ecohydrologic modeling framework designed to simulate carbon, water and nutrient fluxes at the watershed scale. RHESSys models the temporal and spatial variability of ecosystem processes and interactions at a daily time step over multiple years by combining a set of physically based process models and a methodology for partitioning and parameterizing the landscape. Detailed model algorithms are available in Tague and Band (2004).

This notebook demonstrates how to configure an ensemble RHESSys simulation with pyRHESSys, submit it to a supported HPC resource (XSEDE COMET or UIUC Virtual Roger) for execution through CyberGIS Computing Service, visualize model outputs with various tooks integrated in the CyberGIS-Jupyter for Water (CJW).

The model used here is based off of a pre-built RHESSys model for the Coweeta Subbasin 18 (0.124 𝑘𝑚2 ), a subbasins in Coweeta watershed (16 𝑘𝑚2 ), from the Coweeta Long Term Ecological Research (LTER) Program.

How to run the notebook:
1) Click on the OpenWith button in the upper-right corner;
2) Select "CyberGIS-Jupyter for Water";
3) Open the notebook and follow instructions;

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Composite Resource Composite Resource
Geospatial Analysis of Tree Canopy Cover for Streams in the Contiguous United States
Created: March 21, 2021, 4:11 a.m.
Authors: Maghami, Iman · Goodall, Jonathan · Victor A. L. Sobral · Morsy, Mohamed · John C. Lach

ABSTRACT:

The goal of this Resource is to estimate the fraction of stream length in the contiguous United States covered by dense tree canopy described in greater detail in the research paper Maghami et al. (2021). To find out more information about this Resource and the steps to reproduce this geospatial analysis, please refer to the readme file.

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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.

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource is an example to demonstrate the vPICO presentations in EGU General Assembly 2021 (https://meetingorganizer.copernicus.org/EGU21/session/40092#vPICO_presentations).
- Session: EOS5.3 session - The evolving open-science landscape in geosciences: open data, software, publications, and community initiatives
- Title: An Approach for Open and Reproducible Hydrological Modeling using Sciunit and HydroShare

Using this notebook, you can test how to create an immutable and interoperable Sciunit Container for open and reproducible hydrological modeling.

You can start using "NB_01_An_Approach_for_Open_and_Reproducible_Hydrological_Modeling_using_Sciunit_and_HydroShare.ipynb" notebook in "CyberGIS-Jupyter for water" after clicking "Open with...". in Right-Above.

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Composite Resource Composite Resource
(HS 3) Large Spatial Sample Datasets in Maryland
Created: April 10, 2021, 1:01 a.m.
Authors: Choi, Young-Don

ABSTRACT:

This HydroShare resource was created to share large spatial sample datasets in Maryland on GeoServer (https://geoserver.hydroshare.org/geoserver/web/wicket/bookmarkable/org.geoserver.web.demo.MapPreviewPage) and THREDDS (https://thredds.hydroshare.org/thredds/catalog/hydroshare/resources/catalog.html).

Users can check the uploaded LSS datasets on HydroShare-GeoServer and THREDDS using this HS resource id.

Then, through the RHESSys workflows, users can subset LSS datasets using OWSLib and xarray.

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Model Instance Resource Model Instance Resource
Test MI resource for netcdf metadata recognition
Created: April 23, 2021, 7:23 a.m.
Authors: Maghami, Iman

ABSTRACT:

Test MI resource for netcdf metadata recognition

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Composite Resource Composite Resource
(HS 4) Large Spatial Sample Datasets in Virginia
Created: April 25, 2021, 12:26 a.m.
Authors: Choi, Young-Don

ABSTRACT:

This HydroShare resource was created to share large spatial sample datasets in Virginia on GeoServer (https://geoserver.hydroshare.org/geoserver/web/wicket/bookmarkable/org.geoserver.web.demo.MapPreviewPage) and THREDDS (https://thredds.hydroshare.org/thredds/catalog/hydroshare/resources/catalog.html).

Users can check the uploaded LSS datasets on HydroShare-GeoServer and THREDDS using this HS resource id.

Then, through the RHESSys workflows, users can subset LSS datasets using OWSLib and xarray.

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Composite Resource Composite Resource
(HS 2) Large Spatial Sample Datasets in North Carolina
Created: April 25, 2021, 12:27 a.m.
Authors: Choi, Young-Don

ABSTRACT:

This HydroShare resource was created to share large spatial sample datasets in North Carolina on GeoServer (https://geoserver.hydroshare.org/geoserver/web/wicket/bookmarkable/org.geoserver.web.demo.MapPreviewPage) and THREDDS (https://thredds.hydroshare.org/thredds/catalog/hydroshare/resources/catalog.html).

Users can check the uploaded LSS datasets on HydroShare-GeoServer and THREDDS using this HS resource id.

Then, through the RHESSys workflows, users can subset LSS datasets using OWSLib and xarray.

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks created for the study "An Approach for Creating Immutable and Interoperable End-to-End Hydrological Modeling Computational Workflows" led by researcher Young-Don Choi submitted to the 2021 EarthCube Annual meeting, Notebook Sessions.

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file provided in this resource.

For the sake of completeness, the abstract for the study submitted to the EarthCube session is mentioned below:

"Reproducibility is a fundamental requirement to advance science. Creating reproducible hydrological models that include all required data, software, and workflows, however, is often burdensome and requires significant work. Computational hydrology is a rapidly advancing field with fast-evolving technologies to support increasingly complex computational hydrologic modeling. The growing model complexity in terms of variety of software and cyberinfrastructure capabilities makes achieving computational reproducibility extremely challenging. Through recent reproducibility research, there have been efforts to integrate three components: 1) (meta)data, 2) computational environments, and 3) workflows. However, each component is still separate, and researchers must interoperate between these three components. These separations make verifying end-to-end reproducibility challenging. Sciunit was developed to assist scientists, who are not programming experts, with encapsulating these three components into a container to enable reproducibility in an immutable form. However, there were still limitations to support interoperable computational environments and apply end-to-end solutions, which are an ultimate goal of reproducible hydrological modeling. Therefore, the objective of this research is to advance the existing Sciunit capabilities to not only support immutable, but also interoperable computational environments and apply an end-to-end modeling workflow using the Regional Hydro-Ecologic Simulation System (RHESSys) hydrologic model as an example. First, we create an end-to-end workflow for RHESSys using pyRHESSys on the CyberGIS-Jupyter for Water platform. Second, we encapsulate the aforementioned three components and create configurations that include lists of encapsulated dependencies using Sciunit. Third, we create two HydroShare resources, one for immutable reproducibility evaluation using Sciunit and the other for interoperable reproducibility evaluation using library configurations created by Sciunit. Finally, we evaluate the reproducibility of Sciunit in MyBinder, which is a different computational environment, using these two resources. This research presents a detailed example of a user-centric case study demonstrating the application of an open and interoperable containerization approach from a hydrologic modeler’s perspective."

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource was created to execute RHESSys workflows at Coweeta Subbasin18, North Carolina for the evaluation of the original datasets approach which manually collected spatial datasets to represent the traditional approach.

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource was created to execute RHESSys workflows at Scotts Level Branch, Maryland for the evaluation of the original datasets approach which manually collected spatial datasets to represent the traditional approach.

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Composite Resource Composite Resource
(HS 16) RHESSys Raw Input at Spout Run, Virginia
Created: May 7, 2021, 11:07 p.m.
Authors: Choi, Young-Don

ABSTRACT:

This HydroShare resource was created to execute RHESSys workflows at Spout Run, Virginia for the evaluation of the original datasets approach which manually collected spatial datasets to represent the traditional approach.

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Composite Resource Composite Resource

ABSTRACT:

For the automated workflows, we create Jupyter notebooks for each state. In these workflows, GIS processing to merge, extract and project GeoTIFF data was the most important process. For this process, we used ArcPy which is a python package to perform geographic data analysis, data conversion, and data management in ArcGIS (Toms, 2015). After creating state-scale LSS datasets in GeoTIFF format, we convert GeoTIFF to NetCDF using xarray and rioxarray Python packages. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. We used xarray to manipulate data type and add metadata in NetCDF file and rioxarray to save GeoTIFF to NetCDF format. Through these procedures, we created three composite HyddroShare resources to share state-scale LSS datasets.
Due to the limitation of ArcGIS Pro license which is a commercial GIS software, we developed this Jupyter notebook on Windows OS.

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys modeling workflow using the original (HydroShare) approach at Coweeta Subbasin18, NC

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file which is provided in this resource.

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys modeling workflow using the GeoServer approach at Coweeta Subbasin18, NC

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file which is provided in this resource.

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys modeling workflow using the THREDDS approach at Coweeta Subbasin18, NC

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file which is provided in this resource.

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys modeling workflow using the original (HydroShare) approach at Scotts Level Branch, MD

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file which is provided in this resource.

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys modeling workflow using the GeoServer approach at Scotts Level Branch, MD

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file which is provided in this resource.

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys modeling workflow using the THREDDS approach at Scotts Level Branch, MD

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file which is provided in this resource.

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys modeling workflow using the original (HydroShare) approach at Spout Run, VA

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file which is provided in this resource.

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys modeling workflow using the THREDDS approach at Spout Run, VA

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file which is provided in this resource.

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks to evaluate data consistency in three different watersheds (different resolutions: 10, 30, and 60 m). In these procedures, we created 9 case studies using three different datasets (Original (HydroShare), GeoServer, and OPeNDAP) and three watersheds (Coweeta subbasin18, Scotts Level Branch, and Spout Run). For the evaluation of the original datasets approach which manually collected spatial datasets to represent the traditional approach, we created three HydroShare resources for each watershed. Then, we present the evaluation results of data consistency in three different watersheds (different resolutions: 10, 30, and 60 m) using Jupyter notebooks (HS-6 resource). For evaluation, we used regression plots for model output (RHESSys streamflow output).

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Collection Resource Collection Resource

ABSTRACT:

Reproducibility is a fundamental requirement to advance science, and data management is the basic element for reproducibility. In hydrological modeling, there have been many efforts to improve the use of spatial data as model input; however, data sharing is file-level, the use of APIs are difficult, and data distribution service is fragile from fast-evolving technologies. Currently large datasets, GeoServer, and OPeNDAP are only used separately, limiting their benefits. The objective of this study is to create and share interoperable and reusable state scale large spatial datasets on GeoServer and OPeNDAP in HydroShare for open and reproducible seamless environmental modelling. We, first, present the procedures for creating and sharing large datasets. Then, we present application workflows with an example of the Regional Hydro-Ecologic Simulation System and evaluate the data consistency of large datasets. We apply three different scales of watershed in three different states to evaluate data consistency in modeling workflows.

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Composite Resource Composite Resource

ABSTRACT:

This resource was created to share the Sciunit container that encapsulated RHESSys end-to-end workflows

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Composite Resource Composite Resource

ABSTRACT:

This resource was created to share the Sciunit container that encapsulated RHESSys end-to-end workflows

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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.

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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:

This HydroShare resource provides the Jupyter Notebooks for RHESSys modeling workflow using the GeoServer approach at Spout Run, VA

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file which is provided in this resource.

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Composite Resource Composite Resource
test
Created: May 24, 2021, 5:26 p.m.
Authors: Maghami, Iman

ABSTRACT:

test

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Composite Resource Composite Resource
MI/MP aggregation for schema validation
Created: July 14, 2021, 4:50 p.m.
Authors: Maghami, Iman

ABSTRACT:

This is a test resource for MI/MP aggregation for schema validation.

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