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.

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.

ABSTRACT:

This HydroShare resource provides a Singularity image for Local Approach 4: Containerizing all software with Singularity for "Comparing containerization-based approaches for reproducible computational modeling of environmental systems" manuscript in Environmental Modeling and Software Journal.

For more detailed information, please see this GitHub
Git-3. Description of Approach-4 to show how to use a Singularity image (https://github.com/uva-hydroinformatics/SUMMA_Singularity_In_Rivanna.git)

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.

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for the reproducibility of SUMMA modeling using CyberGIS-Jupyter for water in the manuscript of "Comparing Approaches to Achieve Reproducible Computational Modeling for Hydrological and Environmental Systems" in Environmental Modeling and Software.

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

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for the reproducibility of SUMMA modeling using CUAHSI JupyterHub in the manuscript of "Comparing Approaches to Achieve Reproducible Computational Modeling for Hydrological and Environmental Systems" in Environmental Modeling and Software.

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

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for the reproducibility of SUMMA modeling using Sciunit in CyberGIS-Jupyter for water in the manuscript of "Comparing Approaches to Achieve Reproducible Computational Modeling for Hydrological and Environmental Systems" in Environmental Modeling and Software.

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

ABSTRACT:

This HydroShare resource provides five local approaches using Virtual Box image.
First, users need to install Virtual Box (https://www.virtualbox.org/wiki/Downloads) at first.
Then import this "research.ova" to create a SUMMA and pySUMMA computational environment in Virtual Box.

After creating "research.ova" image on Virtual Box, users need to move to the "/home/hydro/project/Performance_Test" folder to start SUMMA run.
Then, users can follow the "instruction.txt" in each approach foler.
The password of this "research.ova" image is "hydro."

This Virtual Box image 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

ABSTRACT:

This is a collection resource for "Comparing containerization-based approaches for reproducible computational modeling of environmental systems" manuscript in Environmental Modeling and Software Journal.
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

Then there is a HS resource for reproducible approaches in local computational environments.
HS-4. 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

Next, there are four HS resources and a GitHub repository for reproducible approaches in remote computational environments.
HS-5. Approach-6 Using CUAHSI JupyterHub
HS-6. Approach-7 Using CyberGIS-Jupyter for water
HS-7. Approach-8 Using Sciunit in CUAHSI JupyterHub
HS-8. Approach-9 Using Sciunit in CyberGIS-Jupyter for water
Git-1. Approach-10 Using Binder (https://github.com/uva-hydroinformatics/SUMMA_Binder.git)

Lastly, we created a notebook for performance tests using the different reproducible approaches.
HS-9. Jupyter notebook for performance test using the different reproducible approaches

For additional description, we created two GitHub repositories to show how to create Docker and Singularity image for Approach-2,3, and 4.
Git-2. Description of Approach-3 to show how to create Docker environments (https://github.com/uva-hydroinformatics/SUMMA_Docker_Training.git)
Git-3. Description of Approach-4 to show how to use a Singularity image (https://github.com/uva-hydroinformatics/SUMMA_Singularity_In_Rivanna.git)

As a result, we shared a created Singularity image for a model program resource.
HS-10: A singularity image for the reproducibility of SUMMA modeling

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.

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.

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.

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

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.

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.

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;

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;

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.

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 modeling study (VB study; Van Beusekom et al., 2022) easier to reproduce. The design and implemention of the developed cyberinfrastructure to achieve this goal are fully explained by Maghami et al. (2023).

In VB study, hydrological outputs from the SUMMA model for the 671 CAMELS catchments across the contiguous United States (CONUS) and a 60-month actual simulation period are investigated to understand their dependence on input forcing behavior across CONUS. VB study 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, Maghami et al. (2023), integrated the following three components through seamless data transfers to make components of a modern and complex hydrologic study (VB study) 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 VB study.
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 is preconfigured to explore one example CAMELS site and a period of 60-month actual simulation to demonstrate the capabilities of the notebooks. Users still can change the CAMELS site, the number of sites being explored or even the simulation period. To quickly assess the capabilities of the notebooks in this resource, we even recommend running an actual simulation period as short as 12 months.
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 actual simulation period used in the VB study) practical and much faster than the second resource. This resource is preconfigured to explore four example CAMELS site and a period of 60-month actual simulation to only demonstrate the capabilities of the notebooks. Users still can change the CAMELS sites, the number of sites being explored or even the simulation period.

Greater details can be found in each 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.

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.

ABSTRACT:

Test MI resource for netcdf metadata recognition

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.

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.

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."

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

ABSTRACT:

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

ABSTRACT:

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

ABSTRACT:

This resource, configured for execution in connected JupyterHub compute platforms, 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, 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 60-month actual simulation to demonstrate the capabilities of the notebooks. For even a faster assesment of the capabilities of the notebooks, users are recommended to opt for a shorter simulation period (e.g., 12 months of actual simulation and six months of initialization) and one example CAMELS site. 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.

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.

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.

ABSTRACT:

test

ABSTRACT:

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

ABSTRACT:

This resource holds the data and models used by Ercan et al. (2020). The goal of their study was to quantify possible changes in the Upper Neuse watershed's water balance due to climate change.

This resource includes 5 model instances and 1 model program:

1- MI_1_SensitivityAnalysis
2- MI_2_Calibration
3- MI_3_Validation
4- MI_4_ClimateModels_Historical_AfterCalibration
5- MI_5_ClimateModels_Future_AfterCalibration
6- MP

ABSTRACT:

This resource is created to transfer the MI, MP and modeling workflows created for the paper by Choi et al. (2021), "Toward Open and Reproducible Environmental Modeling by Integrating Online Data Repositories, Computational Environments, and Model Application Programming Interfaces" to a composite resource containing them. The original MI, MP and modeling workflows were shared as composite resources in a collection resource by Choi (2020)

This resource includes:

- 1 Model Program content aggregation
- 7 Model Instance content aggregation
- 2 uncategorized folders for modeling workflows

ABSTRACT:

This HydroShare resource was created to show performance test using the different reproducible approaches in this paper.

ABSTRACT:

The Office of Water Prediction (OWP) collaboratively researches, develops and delivers state-of-the-science national hydrologic analyses, forecast information, data, decision-support services and guidance to support and inform essential emergency services and water management decisions.

ABSTRACT:

The Office of Water Prediction (OWP) collaboratively researches, develops and delivers state-of-the-science national hydrologic analyses, forecast information, data, decision-support services and guidance to support and inform essential emergency services and water management decisions.

ABSTRACT:

This resource contains the apps logos used in the CIROH Tethys Portal (https://tinyurl.com/cirohportal).