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 extent spatial (LES) 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 access the uploaded LES datasets on HydroShare-GeoServer and THREDDS using this HS resource id. This resource was created using HS 2.

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

ABSTRACT:

Test MI resource for netcdf metadata recognition

ABSTRACT:

This HydroShare resource was created to share large extent spatial (LES) 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 access the uploaded LES datasets on HydroShare-GeoServer and THREDDS using this HS resource id. This resource was created using HS 2.

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

ABSTRACT:

This HydroShare resource was created to share large extent spatial (LES) 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 access the uploaded LES datasets on HydroShare-GeoServer and THREDDS using this HS resource id. This resource was created using HS 2.

Then, through the RHESSys workflows, users can subset LES 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:

ATTENTION: All 3 model instances are now presnted in one resouce as 3 model instance aggregations. This resource is kept only for archiving purpose.

This HydroShare resource provides raw spatial input data for executing RHESSys workflows at Coweeta Subbasin18, North Carolina. Assessing the conventional data distribution approach, these spatial datasets were manually collected and shared at the file level through small files.

ABSTRACT:

ATTENTION: All 3 model instances are now presnted in one resouce as 3 model instance aggregations. This resource is kept only for archiving purpose.

This HydroShare resource provides raw spatial input data for executing RHESSys workflows at Scotts Level Branch, Maryland. Assessing the conventional data distribution approach, these spatial datasets were manually collected and shared at the file level through small files.

ABSTRACT:

ATTENTION: All 3 model instances are now presnted in one resouce as 3 model instance aggregations. This resource is kept only for archiving purpose.

This HydroShare resource provides raw spatial input data for executing RHESSys workflows at Spout Run, Virginia. Assessing the conventional data distribution approach, these spatial datasets were manually collected and shared at the file level through small files.

ABSTRACT:

We implemented automated workflows using Jupyter notebooks for each state. The GIS processing, crucial for merging, extracting, and projecting GeoTIFF data, was performed using ArcPy—a Python package for geographic data analysis, conversion, and management within ArcGIS (Toms, 2015). After generating state-scale LES(Large Extent Spatial) datasets in GeoTIFF format, we utilized the xarray and rioxarray Python packages to convert GeoTIFF to NetCDF. 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. Xarray facilitated data manipulation and metadata addition in the NetCDF file, while rioxarray was used to save GeoTIFF as NetCDF. These procedures resulted in the creation of three composite HydroShare resources (HS 2, HS 3 and HS 4) for sharing state-scale LES datasets. Notably, due to licensing constraints with ArcGIS Pro, a commercial GIS software, the Jupyter notebook development was undertaken on a Windows OS.

ABSTRACT:

ATTENTION: All 9 workflows for RHESSys modeling are now condensed to one. This resource is kept only for archiving purpose.

This HydroShare resource offers Jupyter Notebooks for the RHESSys modeling workflow, employing the conventional approach at Coweeta Subbasin18, NC. For instructions on running the Jupyter Notebooks, please refer to the provided README file within this resource.

ABSTRACT:

ATTENTION: All 9 workflows for RHESSys modeling are now condensed to one. This resource is kept only for archiving purpose.

This HydroShare resource offers Jupyter Notebooks for the RHESSys modeling workflow, employing the GeoServer approach at Coweeta Subbasin18, NC. For instructions on running the Jupyter Notebooks, please refer to the provided README file within this resource.

ABSTRACT:

ATTENTION: All 9 workflows for RHESSys modeling are now condensed to one. This resource is kept only for archiving purpose.

This HydroShare resource offers Jupyter Notebooks for the RHESSys modeling workflow, employing the THREDDS approach at Coweeta Subbasin18, NC. For instructions on running the Jupyter Notebooks, please refer to the provided README file within this resource.

ABSTRACT:

ATTENTION: All 9 workflows for RHESSys modeling are now condensed to one. This resource is kept only for archiving purpose.

This HydroShare resource offers Jupyter Notebooks for the RHESSys modeling workflow, employing the conventional approach at Scotts Level Branch, MD. For instructions on running the Jupyter Notebooks, please refer to the provided README file within this resource.

ABSTRACT:

ATTENTION: All 9 workflows for RHESSys modeling are now condensed to one. This resource is kept only for archiving purpose.

This HydroShare resource offers Jupyter Notebooks for the RHESSys modeling workflow, employing the GeoServer approach at Scotts Level Branch, MD. For instructions on running the Jupyter Notebooks, please refer to the provided README file within this resource

ABSTRACT:

ATTENTION: All 9 workflows for RHESSys modeling are now condensed to one. This resource is kept only for archiving purpose.

This HydroShare resource offers Jupyter Notebooks for the RHESSys modeling workflow, employing the THREDDS approach at Scotts Level Branch, MD. For instructions on running the Jupyter Notebooks, please refer to the provided README file within this resource.

ABSTRACT:

ATTENTION: All 9 workflows for RHESSys modeling are now condensed to one. This resource is kept only for archiving purpose.

This HydroShare resource offers Jupyter Notebooks for the RHESSys modeling workflow, employing the conventional approach at Spout Run, VA. For instructions on running the Jupyter Notebooks, please refer to the provided README file within this resource.

ABSTRACT:

ATTENTION: All 9 workflows for RHESSys modeling are now condensed to one. This resource is kept only for archiving purpose.

This HydroShare resource offers Jupyter Notebooks for the RHESSys modeling workflow, employing the THREDDS approach at Spout Run, VA. For instructions on running the Jupyter Notebooks, please refer to the provided README file within this resource.

ABSTRACT:

This HydroShare resource aims to assess data consistency among two server-side methods (GeoServer and THREDDS Data Server) and the conventional data distribution approach (manually collecting and sharing at file-level). The evaluation spans three different-sized watersheds: Coweeta subbasin18, Scotts Level Branch, and Spout Run with 10, 30, and 60 m DEM resolutions, respectively. The workflow for resulting nine case studies, derived from the combination of three methods and three watersheds, are presented in one HydroShare resource (HS 7), yielding a total of nine RHESSys daily streamflow output files.

Within this resource, we include these nine output files and provide three Jupyter notebooks for conducting evaluations. Each notebook is dedicated to a specific watershed and focuses on the three methods, facilitating a comprehensive analysis of data consistency.

ABSTRACT:

Ensuring the reproducibility of scientific studies is crucial for advancing research, with effective data management serving as a cornerstone for achieving this goal. Ensuring the reproducibility of scientific studies is crucial for advancing research, with effective data management serving as a cornerstone for achieving this goal. In hydrologic and environmental modeling, spatial data is used as model input and sharing of this spatial data is a main step in the data management process. However, by focusing only on sharing data at the file level through small files rather than providing the ability to Find, Access, Interoperate with, and directly Reuse subsets of larger datasets, online data repositories are missing an opportunity to foster more reproducible science. This leads to challenges when accommodating large files which benefit from consistent data quality and seamless geographic extent. To utilize the benefits of large datasets, the objective of this study is therefore to create and test an approach for exposing large extent spatial (LES) datasets to support catchment-scale hydrologic modeling needs. GeoServer and THREDDS Data Server connected to HydroShare were used to provide seamless access to LES datasets. The approach is demonstrated using the Regional Hydro-Ecologic Simulation System (RHESSys) for three different sized watersheds in the US. We assessed data consistency across three different data acquisition approaches: the ‘conventional’ approach, which involves sharing data at the file level through small files, as well as GeoServer, and THREDDS Data Server. This assessment is conducted using RHESSys to evaluate differences in model streamflow output. This approach provides an opportunity to serve datasets needed to create catchment models in a consistent way that can be accessed and processed to serve individual modeling needs.

This collection resource (HS 1) comprises 7 individual HydroShare resources (HS 2-8), each containing different datasets or workflows. These 7 HydroShare resources consist of the following: three resources for three state-scale LES datasets (HS 2-4), one resource with Jupyter notebooks for three different approaches and three different watersheds (HS 5), one resource for RHESSys model instances (i.e., input) of the conventional approach and observation data for all data access approaches in three different watersheds (HS 6), one resource with Jupyter notebooks for automated workflows to create LES datasets (HS 7), and finally one resource with Jupyter notebooks for the evaluation of data consistency (HS 8). More information on each resource is provided within it.

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:

ATTENTION: All 9 workflows for RHESSys modeling are now condensed to one. This resource is kept only for archiving purpose.

This HydroShare resource offers Jupyter Notebooks for the RHESSys modeling workflow, employing the GeoServer approach at Spout Run, VA. For instructions on running the Jupyter Notebooks, please refer to the provided README file within 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 water balance of a 1373 km2 watershed in North Carolina, the Upper Neuse watershed, due to climate change. To accomplish this, they used a SWAT model to quantify possible changes in the water balance. They first analyzed sensitivity to determine their study area's most sensitive model parameters. Next, they calibrated and validated the SWAT model using daily streamflow records within the watershed. Finally, they used the SWAT model forced with different climate scenarios for baseline, mid-century, and end-century periods using five different downscaled General Circulation Models.

​Ercan et al. (2020)​ did not formally publish the data or Model Instances (MI) used in their study, which is not uncommon. In this resource, we published their data and MIs as an example to demonstrate the design capabilities of Maghami et al. (2023)'s extensible schema for capturing environmental model metadata and show its implementation in HydroShare.

This resource includes the raw input data and preprocessing codes to prepare them as MIs for the SWAT model, four MIs, one Model Program (MP), and postprocessing codes Ercan et al. (2020) used summarize the model results as figures and tables. The contents are organized into the following seven folders:

1- InputDataAndPreprocessing
2- MI_1_SensitivityAnalysis
3- MI_2_CalibrationAndValidation
4- MI_4_ClimateModels_Historical_AfterCalibration
5- MI_5_ClimateModels_Future_AfterCalibration
6- MP
7- Postprocessing

A detailed explanation of the MIs and the MP is available in Maghami et al. (2023). It is important to note that our model metadata design treats the entire raw input data, custom preprocessing, and postprocessing tools (e.g., codes to process raw input data), along with the processed input data, as a single MI. However, since most of the raw input data, preprocessing, and postprocessing tools are common among the four MIs, to avoid repetition, we have organized them into dedicated folders. Each MI now specifically includes only the processed input data for the SWAT model.

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).

ABSTRACT:

This is a calibrated SWAT model for Cedar Creek in Indiana. It was used by Rajib and Merwade (2016) to investigate the role of SCS CN method in soil moisture accounting algorithm in SWAT.

ABSTRACT:

This HydroShare resource offers Jupyter Notebooks for the RHESSys modeling workflow, employing the conventional, GeoServer and THREDDS approaches across Coweeta Subbasin 18, NC; Spout Run, VA; and Scotts Level Branch, MD. For instructions on running the Jupyter Notebooks, please refer to the provided README file within this resource.

ABSTRACT:

This HydroShare resource provides raw spatial input data for executing RHESSys workflows at 1- Coweeta Subbasin 18, North Carolina, 2- Scotts Level Branch, Maryland, and 3- Spout Run, Virginia. Assessing the conventional data distribution approach, these spatial datasets were manually collected and shared at the file level through small files.

Additoinally, the GeoServer and TDS approach will only use the observation data from this resource.