Young-Don Choi

K-water & University of Virginia | Manager & Phd Student

Subject Areas: Hydrology

 Recent Activity

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys modeling workflow using the HydroShare model instance 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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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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ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys End-to-End modeling workflow using the GeoServer approach at Scotts Level Branch, Maryland

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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 Contact

Mobile 4344660926
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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 Program Resource Model Program Resource
MODFLOW_USG Model
Created: Aug. 28, 2017, 8:08 p.m.
Authors: Sorab Panday · Motomu Ibaraki · Christian D. Langevin · Richard G. Niswonger · Joseph D. Hughes

ABSTRACT:

A version of MODFLOW, called MODFLOW-USG (for UnStructured Grid), was developed to support a wide variety of structured and unstructured grid types, including nested grids and grids based on prismatic triangles, rectangles, hexagons, and other cell shapes. Flexibility in grid design can be used to focus resolution along rivers and around wells, for example, or to subdiscretize individual layers to better represent hydrostratigraphic units.

MODFLOW-USG is based on an underlying control volume finite difference (CVFD) formulation in which a cell can be connected to an arbitrary number of adjacent cells. To improve accuracy of the CVFD formulation for irregular grid-cell geometries or nested grids, a generalized Ghost Node Correction (GNC) Package was developed, which uses interpolated heads in the flow calculation between adjacent connected cells.

MODFLOW-USG includes a Groundwater Flow (GWF) Process, based on the GWF Process in MODFLOW-2005, as well as a new Connected Linear Network (CLN) Process to simulate the effects of multi-node wells, karst conduits, and tile drains, for example. The CLN Process is tightly coupled with the GWF Process in that the equations from both processes are formulated into one matrix equation and solved simultaneously. This robustness results from using an unstructured grid with unstructured matrix storage and solution schemes.

MODFLOW-USG also contains an optional Newton-Raphson formulation, based on the formulation in MODFLOW-NWT, for improving solution convergence and avoiding problems with the drying and rewetting of cells. Because the existing MODFLOW solvers were developed for structured and symmetric matrices, they were replaced with a new Sparse Matrix Solver (SMS) Package developed specifically for MODFLOW-USG. The SMS Package provides several methods for resolving nonlinearities and multiple symmetric and asymmetric linear solution schemes to solve the matrix arising from the flow equations and the Newton-Raphson formulation, respectively.

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MODFLOW Model Instance Resource MODFLOW Model Instance Resource
MODFLOW-USG model of groundwater flow in the Wood River Valley aquifer system in Blaine County, Idaho
Created: Aug. 28, 2017, 9:19 p.m.
Authors: Jason C. Fisher · James R. Bartolino · Allan H. Wylie · Jennifer Sukow · Michael McVay

ABSTRACT:

A three-dimensional numerical groundwater flow model (MODFLOW-USG) was developed for the Wood River Valley (WRV) aquifer system, south-central Idaho, to evaluate groundwater and surface-water availability at the regional scale. The U.S. Geological Survey (USGS), in cooperation Idaho Department of Water Resources, used the transient groundwater flow model to simulate historical hydraulic head conditions from 1995 to 2010. This USGS data release contains all of the input and output files for the simulation described in the associated model documentation report (http://dx.doi.org/10.3133/sir20165080).

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Composite Resource Composite Resource
A Series of Test Cases for SUMMA
Created: April 3, 2018, 12:57 a.m.
Authors: Martyn Clark · Bart Nijssen · Jessica Lundquist · Dmitri Kavetski

ABSTRACT:

There are two classes of test cases :

1. TEST CASES BASED ON SYNTHETIC OR LAB DATA

- Synthetic Test Case 1: Simulations from Celia (Water Resources Research 1990)
- Synthetic Test Case 2: Simulations from Miller (Water Resources Research 1998)
- Synthetic Test Case 3: Simulations of the lab experiment of Mizoguchi (1990) as described by Hansson et al. (Vadose Zone Journal 2005)
- Synthetic Test Case 4: Simulations of rain on a sloping hillslope from Wigmosta (Water Resources Research 1999)

2. TEST CASES BASED ON FIELD DATA, AS DESCRIBED IN THE SUMMA PAPERS (CLARK ET AL., WATER RESOURCES RESEARCH 2015)

- Field Data Test Case 1: Radiation transmission through an Aspen stand, Reynolds Mountain East
- Field Data Test Case 2: Wind attenuation through an Aspen stand, Reynolds Mountain East
- Field Data Test Case 3: Impacts of canopy wind profile on surface fluxes, surface temperature, and snowmelt (Aspen stand, Reynolds Mountain East)
- Field Data Test Case 4: Form of different interception capacity parameterizations (no model simulations conducted/needed)
- Field Data Test Case 5: Snow interception at Umpqua
- Field Data Test Case 6: Sensitivity to snow albedo representations at Reynolds MountainEast and Senator Beck
- Field Data Test Case 7: Sensitivity of ET to the stomatal resistance parameterization (Aspen stand at Reynolds Mountain East)
- Field Data Test Case 8: Sensitivity of ET to the root distribution and the baseflow parameterization (Aspen stand at Reynolds Mountain East)
- Field Data Test Case 9: Simulations of runoff using different baseflow parameterizations (Reynolds Mountain East)

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Model Program Resource Model Program Resource
SUMMA 2.0.0 Sopron version (lubuntu-16.10)
Created: April 19, 2018, 11:25 p.m.
Authors: Martyn Clark · Bart Nijssen

ABSTRACT:

SUMMA (Clark et al., 2015a;b;c) is a hydrologic modeling framework that can be used for the systematic analysis of alternative model conceptualizations with respect to flux parameterizations, spatial configurations, and numerical solution techniques. It can be used to configure a wide range of hydrological model alternatives and we anticipate that systematic model analysis will help researchers and practitioners understand reasons for inter-model differences in model behavior. When applied across a large sample of catchments, SUMMA may provide insights in the dominance of different physical processes and regional variability in the suitability of different modeling approaches. An important application of SUMMA is selecting specific physics options to reproduce the behavior of existing models – these applications of "model mimicry" can be used to define reference (benchmark) cases in structured model comparison experiments, and can help diagnose weaknesses of individual models in different hydroclimatic regimes.

SUMMA is built on a common set of conservation equations and a common numerical solver, which together constitute the “structural core” of the model. Different modeling approaches can then be implemented within the structural core, enabling a controlled and systematic analysis of alternative modeling options, and providing insight for future model development.

The important modeling features are:

The formulation of the conservation model equations is cleanly separated from their numerical solution;

Different model representations of physical processes (in particular, different flux parameterizations) can be used within a common set of conservation equations; and

The physical processes can be organized in different spatial configurations, including model elements of different shape and connectivity (e.g., nested multi-scale grids and HRUs).

This version updated for the sopron workshop in Hungary(15~18 April, 2018)

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Model Program Resource Model Program Resource
SUMMA 2.0.0 Sopron version (lubuntu-16.04.4)
Created: May 11, 2018, 12:17 a.m.
Authors: Martyn Clark · Bart Nijssen

ABSTRACT:

SUMMA (Clark et al., 2015a;b;c) is a hydrologic modeling framework that can be used for the systematic analysis of alternative model conceptualizations with respect to flux parameterizations, spatial configurations, and numerical solution techniques. It can be used to configure a wide range of hydrological model alternatives and we anticipate that systematic model analysis will help researchers and practitioners understand reasons for inter-model differences in model behavior. When applied across a large sample of catchments, SUMMA may provide insights in the dominance of different physical processes and regional variability in the suitability of different modeling approaches. An important application of SUMMA is selecting specific physics options to reproduce the behavior of existing models – these applications of "model mimicry" can be used to define reference (benchmark) cases in structured model comparison experiments, and can help diagnose weaknesses of individual models in different hydroclimatic regimes.

SUMMA is built on a common set of conservation equations and a common numerical solver, which together constitute the “structural core” of the model. Different modeling approaches can then be implemented within the structural core, enabling a controlled and systematic analysis of alternative modeling options, and providing insight for future model development.

The important modeling features are:

The formulation of the conservation model equations is cleanly separated from their numerical solution;

Different model representations of physical processes (in particular, different flux parameterizations) can be used within a common set of conservation equations; and

The physical processes can be organized in different spatial configurations, including model elements of different shape and connectivity (e.g., nested multi-scale grids and HRUs).

This version updated for the sopron workshop in Hungary(15~18 April, 2018)

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Composite Resource Composite Resource
SUMMA&pySUMMA singularity
Created: July 5, 2018, 7:45 p.m.
Authors: YOUNGDON CHOI

ABSTRACT:

SUMMA & pySUMMA singularity

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Composite Resource Composite Resource

ABSTRACT:

This Composite Resource is the collection of Jupyter notebooks to demonstrate SUMMA TestCases that was tested at the Clark et al., (2015b) study in the Reynolds Mountain East catchment in southwestern Idaho.
JN-1: pySUMMA_ReynoldsAspennStand_StomatalResistance_with_Plotting_module.ipynb
- The notebook demonstrates plotting library of pySUMMA
JN-2: pySUMMA_ReynoldsAspennStand_StomatalResistance.ipynb (Fig7)
- The notebook demonstrates the impact of the simple soil resistance method on total evapotranspiration (ET)
JN-3: SummaModel_ReynoldsAspenStand_RootDistribution.ipynb (Fig8, left)
- The notebook demonstrates the impact of Root Distributions Parameters on total evapotranspiration (ET)
JN-4: SummaModel_Reynolds_Evapotranspiration.ipynb (Fig8, right)
- The notebook demonstrates the impact of Lateral Flow Parameterizations on total evapotranspiration (ET)
JN-5: SummaModel_Reynolds_runoff.ipynb (Fig9)
- The notebook demonstrates the impact of Lateral Flow Parameterizations on Basin-wide Runoff
JN-6: SummaModel_ReynoldsAspenStand_ShortwaveRadiation.ipynb (Fig1-above)
-The notebook demonstrates the impact of shorwave radiation Parameterizations of below canopy shorwave radiation
JN-7: SummaModel_ReynoldsAspenStand_ShortwaveRadiation_LAI.ipynb (Fig1-below)
- The notebook demonstrates the impact of LAI parameter values of below canopy shorwave radiation
JN-8-SummaModel_ReynoldsAspenStand_WindSpeed.ipynb (Fig2)
- The notebook demonstrates the impact of the canopy wind parameter for the exponential wind profile

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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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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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Model Instance Resource Model Instance Resource

ABSTRACT:

This SUMMA Model instance is a part of the Clark et al., (2015b) study, and explored the sensitivity of different root distribution exponents (0.25, 0.5, 1.0). The sensitivity of evapotranspiration to the distribution of roots, which dictates the capability of plants to access water.

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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 the lateral flux of liquid water on total evapotranspiration (ET) using a SUMMA model for the Reynolds Mountain East catchment. This study looked at the sensitivity of the different model representation of the lateral flux of liquid water, which determines the availability of soil water.

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Collection Resource Collection Resource

ABSTRACT:

This resource is created for the dataset of the paper "Toward Open and Reproducible Environmental Modeling by Integrating Online Data Repositories, Computational Environments, and Model Application Programming Interfaces"

This resource includes;
- 4 Model Program Resources
- 7 Model Instance Resources
- 3 Composite Resources

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Composite Resource Composite Resource

ABSTRACT:

pySUMMA Simulation Procedure Diagram depicting how to use pySUMMA on HydroShare with Jupyter notebooks and Model Instances.
This is the collection resources for Jupyter notebooks and Model Instances. (https://www.hydroshare.org/resource/1b7a9af74daa4a449190f922b5db366e/)
This is a YouTube site for a video file. https://www.youtube.com/watch?v=pL-LNd474Tw

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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 the canopy shortwave radiation parameterizations on below canopy shortwave radiation using a SUMMA model for the Reynolds Mountain East catchment. This study looked at four different canopy shortwave radiation parameterizations: BeersLaw method(as implemented in VIC), NL_scatter method(Nijssen and Lettenmaier, JGR 1999:NL 1999), UEB_2stream method(Mahat and Tarboton, WRR 2011:MT 2012), CLM_2stream method(Dick 1983)

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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 the canopy shortwave radiation parameterizations on below canopy shortwave radiation using a SUMMA model for the Reynolds Mountain East catchment. This study looked at four different canopy shortwave radiation parameterizations: BeersLaw method(as implemented in VIC), NL_scatter method(Nijssen and Lettenmaier, JGR 1999:NL 1999), UEB_2stream method(Mahat and Tarboton, WRR 2011:MT 2012), CLM_2stream method(Dick 1983)

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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 the canopy wind parameter for the exponential wind profile on simulations of below canopy wind speed at the aspen site in the Reynolds Mountain East catchment. This study looked at the impact of the Canopy wind parameter[0.10, 0.28, 0.50, 0.750] as used in the parameterization described by the exponential wind profile

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Model Instance Resource Model Instance Resource

ABSTRACT:

Following the procedure of Jupyter notebook, users can create SUMMA input using *.csv files. If users want to create new SUMMA input, they can prepare input by csv format. After that, users are able to simulate SUMMA with PySUMMA and Plotting with SUMMA output by the various way.

Following the step of this notebooks
1. Creating SUMMA input from *.csv files
2. Run SUMMA Model using PySUMMA
3. Plotting with SUMMA output
- Time series Plotting
- 2D Plotting (heatmap, hovmoller)
- Calculating water balance variables and Plotting
- Spatial Plotting with shapefile

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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 the lateral flux of liquid water on Runoff using a SUMMA model for the Reynolds Mountain East catchment. This study looked at the sensitivity of the different model representation of the lateral flux of liquid water, which determines the availability of soil water.

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Composite Resource Composite Resource

ABSTRACT:

Examples of RHESSys and SUMMA Model Simulation on Coweeta subwatershed 18

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Model Program Resource Model Program Resource
SUMMA 2.0.0 master version
Created: Dec. 7, 2018, 10:35 p.m.
Authors: Martyn Clark · Bart Nijssen

ABSTRACT:

SUMMA (Clark et al., 2015a;b;c) is a hydrologic modeling framework that can be used for the systematic analysis of alternative model conceptualizations with respect to flux parameterizations, spatial configurations, and numerical solution techniques. It can be used to configure a wide range of hydrological model alternatives and we anticipate that systematic model analysis will help researchers and practitioners understand reasons for inter-model differences in model behavior. When applied across a large sample of catchments, SUMMA may provide insights in the dominance of different physical processes and regional variability in the suitability of different modeling approaches. An important application of SUMMA is selecting specific physics options to reproduce the behavior of existing models – these applications of "model mimicry" can be used to define reference (benchmark) cases in structured model comparison experiments, and can help diagnose weaknesses of individual models in different hydroclimatic regimes.

SUMMA is built on a common set of conservation equations and a common numerical solver, which together constitute the “structural core” of the model. Different modeling approaches can then be implemented within the structural core, enabling a controlled and systematic analysis of alternative modeling options, and providing insight for future model development.

The important modeling features are:

The formulation of the conservation model equations is cleanly separated from their numerical solution;

Different model representations of physical processes (in particular, different flux parameterizations) can be used within a common set of conservation equations; and

The physical processes can be organized in different spatial configurations, including model elements of different shape and connectivity (e.g., nested multi-scale grids and HRUs).

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Composite Resource Composite Resource

ABSTRACT:

This is an example of RHESSys Model on Coweeta subwatershed 18 on Rivanna which is University of Virginia HPC.
There are a model instance and source code in "rhessys_ws18_local.tar.gz".
Also, there is a jupyer notebook to explain how to simulate RHESSys model.

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Model Instance Resource Model Instance Resource
WRF-Hydro Test Case at Croton New York
Created: March 7, 2019, 9:58 p.m.
Authors: david gochis

ABSTRACT:

This example test case includes a small region (15km by 16km) encompassing the West Branch of the Croton River, NY, USA (USGS stream gage 0137462010) during hurricane Irene, 2011-08-26 to 2011-09-02. The simulation begins with a restart from a spinup period from 2010-10-01 to 2011-08-26. There are 3 basic routing configurations included in the test case, National Water Model (NWM), Gridded, and NCAR Reach.

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Model Program Resource Model Program Resource
WRF-Hydro v5.0.3 Singularity
Created: March 7, 2019, 10:12 p.m.
Authors: YOUNG-DON CHOI

ABSTRACT:

WRF-Hydro, an open-source community model, is used for a range of projects, including flash flood prediction, regional hydroclimate impacts assessment, seasonal forecasting of water resources, and land-atmosphere coupling studies. In this version, the Community WRF-Hydro code base has been merged with the NOAA National Water Model (NWM) code base to create a single, unified code base. On the ‘back end’ this means that there is now one unified code base supported by both the NCAR WRF-Hydro Team and the NOAA Office of Water Prediction. On the ‘front end’, the Community now has access to many of the features developed for the NWM, the first operational, high-resolution, hydrologic prediction model to be implemented across the continental United States. Some of these features include: additional methods for spatial transformation, enhancing the Noah-MP land surface model physics, and improving usability of model output files.

This is a singularity image file of WRF-Hydro that created from WRF-Hydro source code.

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Composite Resource Composite Resource

ABSTRACT:

These are Jupyter Notebooks for the WRF-Hydro training.
You can follow this procedure
1. Download "Download_WRF_Hydro_data_from_HydroShare_Resources.ipynb" on your local computer.
- Move into “Notebook_for_CyberGIS” folder and download Jupyter Notebooks
- Notebook name: Download_WRF_Hydro_data_from_HydroShare_Resources.ipynb
2. Start CyberGIS WebApp(Discover tab - search "CyberGIS HPC") and upload previous Jupyter Notebook
- Create “wrfhydro” directory in you personal directory in CyberGIS and upload previous Jupyer Notebook into “wrfhydro” directory
>> mkdir wrfhydro
3. Open and run Jupyter Notebook
- Download WRF-Hydro Jupyter Notebooks from HydroShare (https://www.hydroshare.org/resource/0dd2b44ad47e428c83187ad0cef8cc08/)
- Download WRF-Hydro Test Case at Cronton New York (https://www.hydroshare.org/resource/0ef1e94ac2794ea587c1cb9006399626/)
- Download WRF-Hydro v5.0.3 Singularity from HydroShare (https://www.hydroshare.org/resource/81bffca13aa34594aa49e6b79d1026b7/)
- Create Kernel for WRF-Hydro to use WRF-Hydro v5.0.3 Singularity container
>> mkdir /data/hsjupyter/a/davidchoi76/.local/share/jupyter/kernels/wrfhydro/
>> cp ~/wrfhydro/kernel.json /data/hsjupyter/a/davidchoi76/.local/share/jupyter/kernels/wrfhydro/
4. Open and run each Jupyter Notebooks
- Lesson 1- Getting started, Lesson 2- Running WRF-Hydro, Lesson 3- Working with WRF-Hydro inputs and outputs
- Lesson 4- Run-time options for Gridded configuration, Lesson 5- Exploring other configurations, Lesson 6- Bringing it All Together

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Composite Resource Composite Resource
RHESsys v3 singularity
Created: April 19, 2019, 5:34 a.m.
Authors: YOUNG-DON CHOI

ABSTRACT:

RHESsys v3 singularity

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Composite Resource Composite Resource
pySUMMA ensemble Singularity Container
Created: April 20, 2019, 7:41 p.m.
Authors: YOUNGDON CHOI

ABSTRACT:

pySUMMA ensemble Singularity Container has an ensemble method however, we need more review for this method.

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Composite Resource Composite Resource
RHESsys_grass_setup
Created: April 21, 2019, 5:22 a.m.
Authors: YOUNG-DON CHOI

ABSTRACT:

RHESsys_grass_setup

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Composite Resource Composite Resource

ABSTRACT:

This Jupyter Notebook created by Laurence lin and Young-Don Choi to simulate the Paine Run subwatershed (12.7 km2) of Shenandoah National Park.
This notebook shows how to create RHESssys input using grass GIS from GIS data, simulate RHESsys Model and visualize the output of RHESsys model.

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Model Program Resource Model Program Resource
RHESsys Model 5.20
Created: May 8, 2019, 12:34 a.m.
Authors: Miles, Brian

ABSTRACT:

This is the RHESsys 5.20 version from RHESsys github (https://github.com/RHESSys/RHESSys/releases/tag/RHESSys-5.20.0)

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Composite Resource Composite Resource

ABSTRACT:

This Jupyter Notebook created by Laurence lin and Young-Don Choi to simulate the Paine Run subwatershed (12.7 km2) of Shenandoah National Park.
This notebook shows how to create RHESssys input using grass GIS from GIS data, simulate RHESsys Model and visualize the output of RHESsys model.

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Composite Resource Composite Resource

ABSTRACT:

This Jupyter Notebook created by Laurence lin and Young-Don Choi to simulate the Paine Run subwatershed (12.7 km2) of Shenandoah National Park.
This notebook shows how to create RHESssys input using grass GIS from GIS data, simulate RHESsys Model and visualize the output of RHESsys model.

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Composite Resource Composite Resource

ABSTRACT:

This is an example of how to create sciunit package from Jupyter Notebooks. For this example, I divided a well organized Jupyer Notebook to general three steps such as 1) get data, 2) analysis, 3) output visualization. So I recreated (1) getdata.ipynb, (2) analysis.ipynb, (3) output_viz.ipynb from a well organized Jupyter Notebook.
Then, we can create a sciunit package using "1_creating_sciunit_package_using_three_step_getdata_analysis_outputviz_jupyter_notebook.ipynb".
From the previous step, we can create a sciunit package and upload it on HydroShare as a new resource. In addition, we will add "2_testing_sciunit_repeat_and_visualization.ipynb" into new HydroShare resource. After that, we open new HydroShare resource, and use "2_testing_sciunit_repeat_and_visualization.ipynb" to repeat sciunit package to test reproducibility.

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Collection Resource Collection Resource

ABSTRACT:

This is a collection resource to collect 2019 EarthCube Conference Demo presentation material for advanced collaborative modeling framework.

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Composite Resource Composite Resource
Coweeta subwatershed 18 input for RHESsys
Created: July 3, 2019, 3:49 a.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

aa

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Composite Resource Composite Resource

ABSTRACT:

ss

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Composite Resource Composite Resource

ABSTRACT:

Hydrologic research is tackling more and more complex questions, requiring researchers to collaborate in teams to build complex, integrated model simulations. Accordingly, the use of cyberinfrastructure is increasing due to the need for collaborative modeling, high throughput computing, and reproducibility and usability. However, the design and implementation in cyberinfrastructure to support community hydrologic modeling are still challenging because much functionality, such as the user interface for modeling, online data sharing, and different model execution environments are necessary to support modeling cyberinfrastructure. In this research, we present a collaborative, cloud-based modeling system built on the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic model as an example paradigm for the design and implementation of cyberinfrastructure. The general paradigm consists of three main components: (i) a Python-based model Application Programming Interface (API) for interacting with hydrologic models, (ii) an online repository for storing model input and output files for different simulation runs, and (iii) a public JupyterHub environment for creating and running model simulations that leverages both the Python API and the online data repository. In this instance, we first created pySUMMA as an example API for interacting with the SUMMA modeling framework. Second, we used HydroShare as an online repository for sharing data and models. Finally, we used a JupyterHub instance tailored for running SUMMA model simulations and hosted by the Consortium of Universities for the Advancement of Hydrologic Science, Inc (CUAHSI). Together, these three components serve as a general example of a cloud-based modeling environment that can be used along with other models and modeling frameworks, in addition to SUMMA, to foster a community supported cyberinfrastructure for collaborative hydrologic modeling.

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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
pySUMMA_ensemble_example
Created: Aug. 8, 2019, 6:45 a.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

aa

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Composite Resource Composite Resource
RHESsys ensemble simulation in Coweeta subwatershed18
Created: Aug. 16, 2019, 4:04 a.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

RHESsys ensemble simulation in Coweeta subwatershed18

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Composite Resource Composite Resource
grass_sample_data
Created: Aug. 19, 2019, 6:43 p.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

aa

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Composite Resource Composite Resource
WRF-Hydro - NWM - Clear Creek IA
Created: Aug. 21, 2019, 5:52 p.m.
Authors: Anthony Michael Castronova

ABSTRACT:

test

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Composite Resource Composite Resource

ABSTRACT:

Examples of RHESSys and SUMMA Model Simulation on Coweeta subwatershed 18

[Modified in JupyterHub on 2019-08-21 20:22:46.378088]

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rhessys model
Created: Sept. 11, 2019, 8:15 p.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

1

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RHESsys input of subwatershed 18 in Coweeta
Created: Sept. 12, 2019, 2:43 a.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

In this

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Composite Resource Composite Resource

ABSTRACT:

Hydrologic models are growing in complexity: spatial representations, model coupling, process representations, software structure, etc. New and emerging datasets are growing, supporting even more detailed modeling use cases. This complexity is leading to the reproducibility crisis in hydrologic modeling and analysis. We argue that moving hydrologic modeling to the cloud can help to address this reproducibility crisis.
- We create two notebooks:
1. The first notebook demonstrates the process of collecting and manipulating GIS and Time-series data using GRASS GIS, Python and R to create RHESsys Model input.
2. The second notebook demonstrates the process of model compilation, parallel simulation, and visualization.

- The first notebook includes:
1. Create Project Directory and Download Raw GIS Data from HydroShare
2. Set GRASS Database and GISBASE Environment
3. Preprocessing GIS Data for RHESsys Model using GRASS GIS and R script
4. Preprocess Time series data for RHESsys Model
5. Construct worldfile and flowtable to RHESSys

- The second notebook includes:
1. Download and compile RHESsys Execution file
2. Simulate RHESsys model
3. Plotting RHESsys output

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ABSTRACT:

aa

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RHESsys input of subwatershed 18 in Coweeta
Created: Oct. 15, 2019, 5:53 p.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

In this model instance, there are spatial data and observed time-series data.
For spatial data, there is gis_data folder, and in the gis_data folder, there are DEM.tif, NLCD.tif, MapunitPolyExtended.shp(soil), and gage.shp etc.
For observed time-series data, there are climate (9/1/1983~12/31/2014) and streamflow data (1/1/2000~12/31/2006).

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RHESsys input of Paine Run in Shenandoah National Park
Created: Oct. 15, 2019, 6:08 p.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

In this model instance, there are spatial data and observed time-series data.
For spatial data, there is gis_data folder, and in the gis_data folder, there are dem30m.tif, NLCD30m.tif, soilmu_a_va015.shp, soilmu_a_va165.shp, and gage.shp etc.
For observed time-series data, there is climate data (9/2/1992~12/31/2017).

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Composite Resource Composite Resource

ABSTRACT:

Hydrologic models are growing in complexity: spatial representations, model coupling, process representations, software structure, etc. New and emerging datasets are growing, supporting even more detailed modeling use cases. This complexity is leading to the reproducibility crisis in hydrologic modeling and analysis. We argue that moving hydrologic modeling to the cloud can help to address this reproducibility crisis.
- We create two notebooks:
1. The first notebook demonstrates the process of collecting and manipulating GIS and Time-series data using GRASS GIS, Python and R to create RHESsys Model input.
2. The second notebook demonstrates the process of model compilation, parallel simulation, and visualization.

- The first notebook includes:
1. Create Project Directory and Download Raw GIS Data from HydroShare
2. Set GRASS Database and GISBASE Environment
3. Preprocessing GIS Data for RHESsys Model using GRASS GIS and R script
4. Preprocess Time series data for RHESsys Model
5. Construct worldfile and flowtable to RHESSys

- The second notebook includes:
1. Download and compile RHESsys Execution file
2. Simulate RHESsys model
3. Plotting RHESsys output

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Model Program Resource Model Program Resource

ABSTRACT:

SUMMA Model Singularity Image with GRASS GIS, SUMMA2.0 and pySUMMA ensemble

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SUMMA input of subwatershed 18 in Coweeta
Created: Oct. 17, 2019, 1:13 a.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

In this model instance, there are spatial data and observed time-series data.
For spatial data, there is gis_data folder, and in the gis_data folder, there are DEM.tif, NLCD.tif, MapunitPolyExtended.shp(soil), and gage.shp etc.
For observed time-series data, there are climate (9/1/1983~12/31/2014) and streamflow data (1/1/2000~12/31/2006).

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Composite Resource Composite Resource

ABSTRACT:

Hydrologic models are growing in complexity: spatial representations, model coupling, process representations, software structure, etc. New and emerging datasets are growing, supporting even more detailed modeling use cases. This complexity is leading to the reproducibility crisis in hydrologic modeling and analysis. We argue that moving hydrologic modeling to the cloud can help to address this reproducibility crisis.
- We create two notebooks:
1. The first notebook demonstrates the process of collecting and manipulating GIS and Time-series data using GRASS GIS, Python and R to create RHESsys Model input.
2. The second notebook demonstrates the process of model compilation, parallel simulation, and visualization.

- The first notebook includes:
1. Create Project Directory and Download Raw GIS Data from HydroShare
2. Set GRASS Database and GISBASE Environment
3. Preprocessing GIS Data for RHESsys Model using GRASS GIS and R script
4. Preprocess Time series data for RHESsys Model
5. Construct worldfile and flowtable to RHESSys

- The second notebook includes:
1. Download and compile RHESsys Execution file
2. Simulate RHESsys model
3. Plotting RHESsys output

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Dakota_pySUMMA_HydroShare
Created: Oct. 18, 2019, 6:41 a.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

Dakota_pySUMMA_HydroShare

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ABSTRACT:

This Composite Resource is the collection of Jupyter notebooks to demonstrate SUMMA TestCases that was tested at the Clark et al., (2015b) study in the Reynolds Mountain East catchment in southwestern Idaho.
JN-1: pySUMMA_ReynoldsAspennStand_StomatalResistance_with_Plotting_module.ipynb
- The notebook demonstrates plotting library of pySUMMA
JN-2: pySUMMA_ReynoldsAspennStand_StomatalResistance.ipynb (Fig7)
- The notebook demonstrates the impact of the simple soil resistance method on total evapotranspiration (ET)
JN-3: SummaModel_ReynoldsAspenStand_RootDistribution.ipynb (Fig8, left)
- The notebook demonstrates the impact of Root Distributions Parameters on total evapotranspiration (ET)
JN-4: SummaModel_Reynolds_Evapotranspiration.ipynb (Fig8, right)
- The notebook demonstrates the impact of Lateral Flow Parameterizations on total evapotranspiration (ET)
JN-5: SummaModel_Reynolds_runoff.ipynb (Fig9)
- The notebook demonstrates the impact of Lateral Flow Parameterizations on Basin-wide Runoff
JN-6: SummaModel_ReynoldsAspenStand_ShortwaveRadiation.ipynb (Fig1-above)
-The notebook demonstrates the impact of shorwave radiation Parameterizations of below canopy shorwave radiation
JN-7: SummaModel_ReynoldsAspenStand_ShortwaveRadiation_LAI.ipynb (Fig1-below)
- The notebook demonstrates the impact of LAI parameter values of below canopy shorwave radiation
JN-8-SummaModel_ReynoldsAspenStand_WindSpeed.ipynb (Fig2)
- The notebook demonstrates the impact of the canopy wind parameter for the exponential wind profile

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Composite Resource Composite Resource

ABSTRACT:

Hydrologic models are growing in complexity: spatial representations, model coupling, process representations, software structure, etc. New and emerging datasets are growing, supporting even more detailed modeling use cases. This complexity is leading to the reproducibility crisis in hydrologic modeling and analysis. We argue that moving hydrologic modeling to the cloud can help to address this reproducibility crisis.
- We create two notebooks:
1. The first notebook demonstrates the process of collecting and manipulating GIS and Time-series data using GRASS GIS, Python and R to create RHESsys Model input.
2. The second notebook demonstrates the process of model compilation, parallel simulation, and visualization.

- The first notebook includes:
1. Create Project Directory and Download Raw GIS Data from HydroShare
2. Set GRASS Database and GISBASE Environment
3. Preprocessing GIS Data for RHESsys Model using GRASS GIS and R script
4. Preprocess Time series data for RHESsys Model
5. Construct worldfile and flowtable to RHESSys

- The second notebook includes:
1. Download and compile RHESsys Execution file
2. Simulate RHESsys model
3. Plotting RHESsys output

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Model Program Resource Model Program Resource
SUMMA sopron and pySUMMA 1.0.0 version Singularity
Created: Nov. 4, 2019, 2:53 p.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

This resource included a singularity image for SUMMA sopron and pySUMMA 1.0.0 version. Detail description is in definition file.

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Composite Resource Composite Resource

ABSTRACT:

This Composite Resource is the collection of Jupyter notebooks to demonstrate SUMMA TestCases that was tested at the Clark et al., (2015b) study in the Reynolds Mountain East catchment in southwestern Idaho.
JN-1: pySUMMA_ReynoldsAspennStand_StomatalResistance_with_Plotting_module_CyberGIS.ipynb
- The notebook demonstrates plotting library of pySUMMA
JN-2: pySUMMA_ReynoldsAspennStand_StomatalResistance_CyberGIS.ipynb (Fig7)
- The notebook demonstrates the impact of the simple soil resistance method on total evapotranspiration (ET)
JN-3: SummaModel_ReynoldsAspenStand_RootDistribution_CyberGIS.ipynb (Fig8, left)
- The notebook demonstrates the impact of Root Distributions Parameters on total evapotranspiration (ET)
JN-4: SummaModel_Reynolds_Evapotranspiration_CyberGIS.ipynb (Fig8, right)
- The notebook demonstrates the impact of Lateral Flow Parameterizations on total evapotranspiration (ET)
JN-5: SummaModel_Reynolds_runoff_CyberGIS.ipynb (Fig9)
- The notebook demonstrates the impact of Lateral Flow Parameterizations on Basin-wide Runoff
JN-6: SummaModel_ReynoldsAspenStand_ShortwaveRadiation_CyberGIS.ipynb (Fig1-above)
-The notebook demonstrates the impact of shorwave radiation Parameterizations of below canopy shorwave radiation
JN-7: SummaModel_ReynoldsAspenStand_ShortwaveRadiation_LAI_CyberGIS.ipynb (Fig1-below)
- The notebook demonstrates the impact of LAI parameter values of below canopy shorwave radiation
JN-8-SummaModel_ReynoldsAspenStand_WindSpeed_CyberGIS.ipynb (Fig2)
- The notebook demonstrates the impact of the canopy wind parameter for the exponential wind profile

The procedure to simulate these notebooks in CyberGIS
1) Download "Download_pySUMMA_Jupyter_Notebooks_from_HydroShare.ipynb" Jupyter Notebook manually on your local computer.
2) Move to the CyberGIS-Jupyter web app (https://www.hydroshare.org/resource/c477900488744e4a8e1df21326e4789b/) and click "Open Web App" to start CyberGIS JupyterHub.
3) Upload "Download_pySUMMA_Jupyter_Notebooks_from_HydroShare.ipynb" Jupyter Notebook into CyberGIS JupyterHub and run the Jupyter Notebook.

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Composite Resource Composite Resource

ABSTRACT:

Hydrologic models are growing in complexity: spatial representations, model coupling, process representations, software structure, etc. New and emerging datasets are growing, supporting even more detailed modeling use cases. This complexity is leading to the reproducibility crisis in hydrologic modeling and analysis. We argue that moving hydrologic modeling to the cloud can help to address this reproducibility crisis.
- We create two notebooks:
1. The first notebook demonstrates the process of collecting and manipulating GIS and Time-series data using GRASS GIS, Python and R to create RHESsys Model input.
2. The second notebook demonstrates the process of model compilation, simulation, and visualization.

- The first notebook includes:
1. Create Project Directory and Download Raw GIS Data from HydroShare
2. Set GRASS Database and GISBASE Environment
3. Preprocessing GIS Data for RHESsys Model using GRASS GIS and R script
4. Preprocess Time series data for RHESsys Model
5. Construct worldfile and flowtable to RHESSys

- The second notebook includes:
1. Download and compile RHESsys Execution file
2. Simulate RHESsys model
3. Plotting RHESsys output

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Composite Resource Composite Resource
SUMMA simulation on HydroShare binder
Created: Feb. 5, 2020, 7:34 p.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

SUMMA simulation on HydroShare binder

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Model Instance Resource Model Instance Resource
RHESSys input data at Coweeta subbasin18
Created: Feb. 20, 2020, 3:21 a.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

RHESSys input data at Coweeta subbasin18

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RHESSys input data of Coweeta subbasin18
Created: Feb. 20, 2020, 8:02 a.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

RHESSys input data of Coweeta subbasin18

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Composite Resource Composite Resource

ABSTRACT:

pyRHESSys (Python-the Regional Hydro-Ecologic Simulation System) is an Object-Oriented Python wrapper for model input creation and manipulation, model execution, model output visualization and model analysis. Detail information for pyRHESSys: https://github.com/DavidChoi76/pyRHESSys

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Model Instance Resource Model Instance Resource

ABSTRACT:

RHESSys input data at Coweeta subbasin 18 (in progress)

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WRF-Hydro Binder
Created: Feb. 24, 2020, 4:32 a.m.
Authors: Castronova, Anthony Michael

ABSTRACT:

A binderhub configuration for running WRF-Hydro configured as the National Water Model v1.2.2

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Composite Resource Composite Resource

ABSTRACT:

Map Visualization example of RHESSys output at Coweeta subbasin18

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SUMMA 2.0.0 Sopron version (lubuntu-16.04.4)
Created: March 27, 2020, 6:21 p.m.
Authors: Martyn Clark · Bart Nijssen

ABSTRACT:

SUMMA (Clark et al., 2015a;b;c) is a hydrologic modeling framework that can be used for the systematic analysis of alternative model conceptualizations with respect to flux parameterizations, spatial configurations, and numerical solution techniques. It can be used to configure a wide range of hydrological model alternatives and we anticipate that systematic model analysis will help researchers and practitioners understand reasons for inter-model differences in model behavior. When applied across a large sample of catchments, SUMMA may provide insights in the dominance of different physical processes and regional variability in the suitability of different modeling approaches. An important application of SUMMA is selecting specific physics options to reproduce the behavior of existing models – these applications of "model mimicry" can be used to define reference (benchmark) cases in structured model comparison experiments, and can help diagnose weaknesses of individual models in different hydroclimatic regimes.

SUMMA is built on a common set of conservation equations and a common numerical solver, which together constitute the “structural core” of the model. Different modeling approaches can then be implemented within the structural core, enabling a controlled and systematic analysis of alternative modeling options, and providing insight for future model development.

The important modeling features are:

The formulation of the conservation model equations is cleanly separated from their numerical solution;

Different model representations of physical processes (in particular, different flux parameterizations) can be used within a common set of conservation equations; and

The physical processes can be organized in different spatial configurations, including model elements of different shape and connectivity (e.g., nested multi-scale grids and HRUs).

This version updated for the sopron workshop in Hungary(15~18 April, 2018)

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Example to use the RHESSys model
Created: April 1, 2020, 3:16 p.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

Example to use the RHESSys model

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Lecture_RHESSys input Coweeta sub18
Created: April 6, 2020, 3:49 a.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

Lecture_RHESSys input Coweeta sub18

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Composite Resource Composite Resource

ABSTRACT:

These notebooks are created to evaluate the reproducibility and replicability using Sciunit in different computational environment.
You can open these notebooks using CyberGIS-Jupyter for water from `Open with button`.
Notebook1 and notebook2 were created in local computer to test computational environment and create Sciunit containers to encapsulate SUMMA simulation workflow.
- Notebook1: N_1_Reproducibility_Evaluation_of_the_SUMMA_Model_in_the_Model_Agnostic_Framework.ipynb
- Notebook2: N_2_Creating_and_Executing_the_Sciunit_Container_to_Encapsulating_and_Evaluating_the_immutable_computational_environment.ipynb

So you can start with notebook3.
- Notebook3: N_3_Reproducibility_and_Replicability_Evaluation_using_the_Sciunit_Container_in_CyberGIS_for_water.ipynb

From this process, you can evaluate the reproducibility and replicability of SUMMA simulation with repeating of Sciunit container and changing the SUMMA configuration.
- Reproduced SUMMA application: Three different stomatal Resistance Parameterizations (BallBerry, Jarvis, and Simple Stomatal Method)
- Replicated SUMMA application: Changing the function for the soil moisture control on stomatal resistance from NoahType to CLM_Type

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Composite Resource Composite Resource
MyAnalysis
Created: April 18, 2020, 12:21 a.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

This resource demonstrates the steps to package the workflow analysis using the Sciunit tool.
These steps are
1.) create a new sciunit “MyAnalysis.” This will create a virtual directory, which will include the captured execution of the computational workflow with all the dependencies and provenance metadata associated with it;
2.) open the “MyAnalysis” sciunit to begin working in the desired sciunit;
3) execute the code required to be packaged as a virtual environment in order to repeat the analysis;
4.) place the packaged sciunit on HydroShare as a digital resource, and
5.) test the runnability of the package by executing the sciunit on the CUAHSI HydroShare JupyterHub app linked to HydroShare and configured to open and execute scripts acting on content from Resources in HydroShare (Note: To run a sciunit again requires the Sciunit tool, which is installed on CUAHSI HydroShare JupyterHub).

This resource contains the sciunit package for reproducing The total ET for the Ball Berry and Jarvis stomatal resistance methods from Clark et al., 2015:

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2_SUMMA Modeling Sciunit Container
Created: April 22, 2020, 1:14 a.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

Using this notebook, you can evaluate the reproducibility and replicability of SUMMA simulation with repeating of Sciunit container and changing the SUMMA configuration.
- Reproduced SUMMA application: Three different stomatal Resistance Parameterizations (BallBerry, Jarvis, and Simple Stomatal Method)
- Replicated SUMMA application: Changing the function for the soil moisture control on stomatal resistance from NoahType to CLM_Type

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Composite Resource Composite Resource

ABSTRACT:

Notebook Example to create SUMMA Modeling Sciunit Container in CUAHSI JupyterHub

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Coweeta sub18 raw data for RHESSys input
Created: April 22, 2020, 8:12 p.m.
Authors: CHOI, YOUNG-DON

ABSTRACT:

Coweeta sub18

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Composite Resource Composite Resource

ABSTRACT:

These are examples to test Data Processing Kernel in CyberGIS-Jupyter for water.
The 1_watershed_delineation folder is an example of a watershed delineation which is the basic step to analyze an interesting watershed. We used GRASS GIS 7.8 version and shell script to apply GRASS GIS library.
The 2_map_visualization folder is an example of an interactive map visualization which is the high-level visualization using PyViz tools as post-processing of environmental modeling. For this example, we used the following PyViz tools:
- geopandas (https://geopandas.org/), cartopy (https://scitools.org.uk/cartopy/), geoviews (https://geoviews.org/), and holoviews (https://holoviews.org/)

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Composite Resource Composite Resource

ABSTRACT:

This is a HydroShare resource to demonstrate an approach for open and reproducible Environmental Environmental Modeling during EarthCube2020 meeting (https://www.earthcube.org/EC2020).

You can start a Jupyter notebook ("First_NB_An_Approach_for_Open_Reproducible_Environmental_Modeling.ipynb") using "CyberGIS-Jupyter for water" on "Open with ..." Button.
Through the 1st notebook demonstration, you can experience open and reproducible environmental modeling using three main components which are online repository (HydroShare), computational environment (CyberGIS-Jupyter for water), and model API(pySUMMA).
Also, you will create a Sciunit container to encapsulate every dependency for SUMMA execution in CybeGIS-Jupyter into the Sciunit container.

Then you can move to the 2nd notebook (Second_NB_An_Approach_for_Open_Reproducible_Environmental_Modeling.ipynb) to evaluate reproducibility and replicability of the SUMMA Sciunit container in different cyberinfrastructure (CUAHSI JupyterHub).

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource includes SUMMA Sciunit Container, Notebook for demonstration and

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Composite Resource Composite Resource

ABSTRACT:

This is a HydroShare resource to demonstrate an approach for open and reproducible Environmental Environmental Modeling during EarthCube2020 meeting (https://www.earthcube.org/EC2020).

You can start a Jupyter notebook ("First_NB_An_Approach_for_Open_Reproducible_Environmental_Modeling.ipynb") using "CyberGIS-Jupyter for water" on "Open with ..." Button.
Through the 1st notebook demonstration, you can experience open and reproducible environmental modeling using three main components which are online repository (HydroShare), computational environment (CyberGIS-Jupyter for water), and model API(pySUMMA).
Also, you will create a Sciunit container to encapsulate every dependency for SUMMA execution in CybeGIS-Jupyter into the Sciunit container.

Then you can move to the 2nd notebook (Second_NB_An_Approach_for_Open_Reproducible_Environmental_Modeling.ipynb) to evaluate reproducibility and replicability of the SUMMA Sciunit container in different cyberinfrastructure (CUAHSI JupyterHub).

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Composite Resource Composite Resource

ABSTRACT:

This is a HydroShare resource to demonstrate an approach for open and reproducible Environmental Environmental Modeling during EarthCube2020 meeting (https://www.earthcube.org/EC2020).

You can start a Jupyter notebook ("First_NB_An_Approach_for_Open_Reproducible_Environmental_Modeling.ipynb") using "CyberGIS-Jupyter for water" on "Open with ..." Button.
Through the 1st notebook demonstration, you can experience open and reproducible environmental modeling using three main components which are online repository (HydroShare), computational environment (CyberGIS-Jupyter for water), and model API(pySUMMA).
Also, you will create a Sciunit container to encapsulate every dependency for SUMMA execution in CybeGIS-Jupyter into the Sciunit container.

Then you can move to the 2nd notebook (Second_NB_An_Approach_for_Open_Reproducible_Environmental_Modeling.ipynb) to evaluate reproducibility and replicability of the SUMMA Sciunit container in different cyberinfrastructure (CUAHSI JupyterHub).

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Composite Resource Composite Resource

ABSTRACT:

pyRHESSys Example of Coweeta sub18 in CyberGIS-Jupyter for water

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Model Instance Resource Model Instance Resource

ABSTRACT:

This is a Model instance for SUMMA develop branch (https://github.com/DavidChoi76/summa, June/2/2020) and pySUMMA v2.0 develop branch (https://github.com/UW-Hydro/pysumma, June/2/2020).

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Composite Resource Composite Resource
SUMMA MODEL INSTANCE
Created: June 2, 2020, 5:53 a.m.
Authors: Choi, Young-Don

ABSTRACT:

SUMMA MODEL INSTANCE

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Composite Resource Composite Resource
raw_data_diff_run1
Created: June 10, 2020, 5:56 a.m.
Authors: Saby, Linnea

ABSTRACT:

Raw data for a pyRHESSys model of Difficult Run above Fox Lake.

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Composite Resource Composite Resource
RHESSys notebook for Difficult run simulation
Created: June 10, 2020, 1:07 p.m.
Authors: Choi, Young-Don · Saby, Linnea

ABSTRACT:

RHESSys

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Model Program Resource Model Program Resource
SUMMA 2.0.0 develop (2020-06-12)
Created: June 13, 2020, 2:18 a.m.
Authors: Choi, Young-Don

ABSTRACT:

SUMMA (Clark et al., 2015a;b;c) is a hydrologic modeling framework that can be used for the systematic analysis of alternative model conceptualizations with respect to flux parameterizations, spatial configurations, and numerical solution techniques. It can be used to configure a wide range of hydrological model alternatives and we anticipate that systematic model analysis will help researchers and practitioners understand reasons for inter-model differences in model behavior. When applied across a large sample of catchments, SUMMA may provide insights in the dominance of different physical processes and regional variability in the suitability of different modeling approaches. An important application of SUMMA is selecting specific physics options to reproduce the behavior of existing models – these applications of "model mimicry" can be used to define reference (benchmark) cases in structured model comparison experiments, and can help diagnose weaknesses of individual models in different hydroclimatic regimes.

SUMMA is built on a common set of conservation equations and a common numerical solver, which together constitute the “structural core” of the model. Different modeling approaches can then be implemented within the structural core, enabling a controlled and systematic analysis of alternative modeling options, and providing insight for future model development.

The important modeling features are:
The formulation of the conservation model equations is cleanly separated from their numerical solution;
Different model representations of physical processes (in particular, different flux parameterizations) can be used within a common set of conservation equations; and
The physical processes can be organized in different spatial configurations, including model elements of different shape and connectivity (e.g., nested multi-scale grids and HRUs).

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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
RHESSys Model Instatnce: Meadow Creek
Created: June 24, 2020, 5:31 p.m.
Authors: Herbst, Seth

ABSTRACT:

Meadow Creek RHESSys

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Composite Resource Composite Resource
RHESSys notebook for Meadow Creek simulation
Created: June 24, 2020, 7:10 p.m.
Authors: Choi, Young-Don · Herbst, Seth

ABSTRACT:

RHESSys notebook for Meadow Creek simulation

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Composite Resource Composite Resource
SUMMA Binder
Created: June 25, 2020, 10:14 a.m.
Authors: Choi, Young-Don

ABSTRACT:

SUMMA Binder

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Model Program Resource Model Program Resource

ABSTRACT:

This HydroShare resource provides a Singularity image for Remote Approach 11: Using an HPC Cluster in the manuscript of "Comparing Approaches to Achieve Reproducible Computational Modeling for Hydrological and Environmental Systems" in Environmental Modeling and Software.

For more detailed information, please see this GitHub
https://github.com/DavidChoi76/Using_Singularity_in_Local_Approach_4_and_HPC_Cluster_Approach_11_for_the_reproducibility_of_SUMMA

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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 the canopy shortwave radiation parameterizations on below canopy shortwave radiation using a SUMMA model for the Reynolds Mountain East catchment. This study looked at four different canopy shortwave radiation parameterizations: BeersLaw method(as implemented in VIC), NL_scatter method(Nijssen and Lettenmaier, JGR 1999:NL 1999), UEB_2stream method(Mahat and Tarboton, WRR 2011:MT 2012), CLM_2stream method(Dick 1983)

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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 the lateral flux of liquid water on Runoff using a SUMMA model for the Reynolds Mountain East catchment. This study looked at the sensitivity of the different model representation of the lateral flux of liquid water, which determines the availability of soil water.

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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 sensitivity of different root distribution exponents (0.25, 0.5, 1.0). The sensitivity of evapotranspiration to the distribution of roots, which dictates the capability of plants to access water.

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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 the canopy shortwave radiation parameterizations on below canopy shortwave radiation using a SUMMA model for the Reynolds Mountain East catchment. This study looked at four different canopy shortwave radiation parameterizations: BeersLaw method(as implemented in VIC), NL_scatter method(Nijssen and Lettenmaier, JGR 1999:NL 1999), UEB_2stream method(Mahat and Tarboton, WRR 2011:MT 2012), CLM_2stream method(Dick 1983)

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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 the lateral flux of liquid water on total evapotranspiration (ET) using a SUMMA model for the Reynolds Mountain East catchment. This study looked at the sensitivity of the different model representation of the lateral flux of liquid water, which determines the availability of soil water.

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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 the canopy wind parameter for the exponential wind profile on simulations of below canopy wind speed at the aspen site in the Reynolds Mountain East catchment. This study looked at the impact of the Canopy wind parameter[0.10, 0.28, 0.50, 0.750] as used in the parameterization described by the exponential wind profile

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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

ABSTRACT:

This Composite Resource is the collection of Jupyter notebooks to demonstrate SUMMA TestCases that was tested at the Clark et al., (2015b) study in the Reynolds Mountain East catchment in southwestern Idaho.

JN-1: SummaModel_ReynoldsAspennStand_StomatalResistance_Basic_Plot.ipynb
- The notebook demonstrates plotting library of pySUMMA

JN-2: SummaModel_ReynoldsAspennStand_StomatalResistance_Figure7.ipynb (Fig7)
- The notebook demonstrates the impact of the simple soil resistance method on total evapotranspiration (ET)

JN-3: SummaModel_ReynoldsAspenStand_RootDistribution_Figure8_Left.ipynb (Fig8, left)
- The notebook demonstrates the impact of Root Distributions Parameters on total evapotranspiration (ET)

JN-4: SummaModel_Reynolds_Evapotranspiration_Figure8_Right.ipynb (Fig8, right)
- The notebook demonstrates the impact of Lateral Flow Parameterizations on total evapotranspiration (ET)

JN-5: SummaModel_Reynolds_runoff_Figure9.ipynb (Fig9)
- The notebook demonstrates the impact of Lateral Flow Parameterizations on Basin-wide Runoff

JN-6: SummaModel_ReynoldsAspenStand_ShortwaveRadiation_Figure1_Top.ipynb (Fig1-Top)
-The notebook demonstrates the impact of shorwave radiation Parameterizations of below canopy shorwave radiation

JN-7: SummaModel_ReynoldsAspenStand_ShortwaveRadiation_LAI_Figure1_Bottom.ipynb (Fig1-Bottom)
- The notebook demonstrates the impact of LAI parameter values of below canopy shorwave radiation

JN-8-SummaModel_ReynoldsAspenStand_WindSpeed_Figure2.ipynb (Fig2)
- The notebook demonstrates the impact of the canopy wind parameter for the exponential wind profile

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Model Program Resource Model Program Resource
SUMMA 3.0.0
Created: July 25, 2020, 8:46 p.m.
Authors: Choi, Young-Don · Bennett, Andrew · Nijssen, Bart · Clark, Martyn · Goodall, Jonathan

ABSTRACT:

SUMMA (Clark et al., 2015a;b;c) is a hydrologic modeling framework that can be used for the systematic analysis of alternative model conceptualizations with respect to flux parameterizations, spatial configurations, and numerical solution techniques. It can be used to configure a wide range of hydrological model alternatives and we anticipate that systematic model analysis will help researchers and practitioners understand reasons for inter-model differences in model behavior. When applied across a large sample of catchments, SUMMA may provide insights in the dominance of different physical processes and regional variability in the suitability of different modeling approaches. An important application of SUMMA is selecting specific physics options to reproduce the behavior of existing models – these applications of "model mimicry" can be used to define reference (benchmark) cases in structured model comparison experiments, and can help diagnose weaknesses of individual models in different hydroclimatic regimes.

SUMMA is built on a common set of conservation equations and a common numerical solver, which together constitute the “structural core” of the model. Different modeling approaches can then be implemented within the structural core, enabling a controlled and systematic analysis of alternative modeling options, and providing insight for future model development.

This version was released on July 20, 2020.

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Collection Resource Collection Resource

ABSTRACT:

This resource is created for the dataset of the paper "Toward Open and Reproducible Environmental Modeling by Integrating Online Data Repositories, Computational Environments, and Model Application Programming Interfaces"

This resource includes;
- 1 Model Program Resources
- 7 Model Instance Resources
- 2 Composite Resources

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Model Instance Resource Model Instance Resource
difficult_run_MI
Created: July 28, 2020, 1:42 p.m.
Authors: Choi, Young-Don

ABSTRACT:

difficult_run_MI

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Composite Resource Composite Resource
RHESSys Model Different Run Example
Created: July 30, 2020, 4:23 p.m.
Authors: Choi, Young-Don

ABSTRACT:

This is RHESSys inut for a pyRHESSys model of Difficult Run above Fox Lake.

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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

ABSTRACT:

This Composite Resource is the collection of Jupyter notebooks to demonstrate SUMMA TestCases that was tested at the Clark et al., (2015b) study in the Reynolds Mountain East catchment in southwestern Idaho.

JN-1: SummaModel_ReynoldsAspennStand_StomatalResistance_Basic_Plot.ipynb
- The notebook demonstrates plotting library of pySUMMA

JN-2: SummaModel_ReynoldsAspennStand_StomatalResistance_Figure7.ipynb (Fig7)
- The notebook demonstrates the impact of the simple soil resistance method on total evapotranspiration (ET)

JN-3: SummaModel_ReynoldsAspenStand_RootDistribution_Figure8_Left.ipynb (Fig8, left)
- The notebook demonstrates the impact of Root Distributions Parameters on total evapotranspiration (ET)

JN-4: SummaModel_Reynolds_Evapotranspiration_Figure8_Right.ipynb (Fig8, right)
- The notebook demonstrates the impact of Lateral Flow Parameterizations on total evapotranspiration (ET)

JN-5: SummaModel_Reynolds_runoff_Figure9.ipynb (Fig9)
- The notebook demonstrates the impact of Lateral Flow Parameterizations on Basin-wide Runoff

JN-6: SummaModel_ReynoldsAspenStand_ShortwaveRadiation_Figure1_Top.ipynb (Fig1-Top)
-The notebook demonstrates the impact of shorwave radiation Parameterizations of below canopy shorwave radiation

JN-7: SummaModel_ReynoldsAspenStand_ShortwaveRadiation_LAI_Figure1_Bottom.ipynb (Fig1-Bottom)
- The notebook demonstrates the impact of LAI parameter values of below canopy shorwave radiation

JN-8-SummaModel_ReynoldsAspenStand_WindSpeed_Figure2.ipynb (Fig2)
- The notebook demonstrates the impact of the canopy wind parameter for the exponential wind profile

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Composite Resource Composite Resource
Google Earth Engine - NPP Image Extraction Example
Created: Sept. 12, 2020, 5:46 p.m.
Authors: Choi, Young-Don

ABSTRACT:

This example is about how to use Google Earth Engine API on Jupyter Notebooks.
We show the example of how to get Landsat Net Primary Production (NPP) CONUS DataSet from Google Earth Engine Data Catalog.

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Composite Resource Composite Resource

ABSTRACT:

HAND notebook

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Composite Resource Composite Resource
SUMMA and pySUMMA Training Tutorial for Beginners
Created: Oct. 21, 2020, 7:27 p.m.
Authors: Choi, Young-Don

ABSTRACT:

This resource created to understand how to use pySUMMA for SUMMA simulation step-by-step. Each notebook explains with SUMM online document (https://summa.readthedocs.io/en/latest/) to understand SUMMA at first, then how to use pySUMMA for each part of SUMMA simulation. I recommend users to use "CyberGIS-Jupyter for water" for this pySUMMA training Tutorial.

The first five notebooks are created to understand how to set and manipulate SUMMA input configuration files.
In the SUMMA model, "file manager" is a master configuration file; therefore the first notebook (A. Explore file manger of SUMMA 3.0.3.ipynb) explains how to set and manipulate the file manager file.
The second notebook (B. Explore decision file of SUMMA 3.0.3.ipynb) explains how to set and manipulate the decision file which controls different parameterization in the SUMMA model.
The third notebook (C. Explore forcing data of SUMMA 3.0.3.ipynb) explains how to set and plot the forcing data as SUMMA input.
The fourth notebook (D. Explore local attributes, local parameters (global hru) and basin parameters (global gru) of SUMMA 3.0.3.ipynb) explains how to set and manipulate local attributes, local parameters, and basin parameters.
The fifth notebook (E. Explore Trial parameters, Output control and Noah-MP tables of SUMMA 3.0.3.ipynb) explains how to set and manipulate trial parameters and output control text file.

Then, the next seven notebooks are created to understand how to execute SUMMA with different cases.
The sixth notebook (F-1. SUMMA simulation using Output Control.ipynb) demonstrate the comparison of SUMMA output with different set of output control text file (hourly vs daily simulation)
The seventh notebook (F-2. Ensemble simulation using different parameterizations (decisions).ipynb) demonstrates how to execute SUMMA using different parameterizations.
The eighth notebook (F-3. Ensemble simulation using different values of a Local Parameters (Global HRU).ipynb) demonstrates how to execute SUMMA using different local parameter settings.
The ninth notebook (F-4. Ensemble simulation using different values of a Local Attributes.ipynb) demonstrates how to execute SUMMA using different local attributes settings.
The tenth notebook (F-5. Ensemble simulation using different values of a Trial Parameters.ipynb) demonstrates how to execute SUMMA using different trial parameters settings.
The eleventh notebook (F-6. Ensemble simulation using different file manager files.ipynb) demonstrates how to execute SUMMA using different file manager file settings.
The twelfth notebook (F-7. Ensemble simulation using different decisions and parameter trials setting.ipynb) demonstrate how to execute SUMMA using the combination of different configuration settings (different parameterization using decision file and parameter trial netcdf file).

After understanding these SUMMA and pySUMMA training tutorials, users will understand the next tutorial notebook (Application notebooks) better.

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Composite Resource Composite Resource

ABSTRACT:

These original notebooks and datasets for pySUMMA (https://github.com/arbennett/pysumma-tutorial) were developed by Andrew Bennett. Young-Don Choi review and edit these notebooks to apply these notebooks to CyberGIS-Jupyter for water. You can use these notebooks on CyberGIS-Jupyer for water

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Composite Resource Composite Resource
Bolin_Creek_RHESSys_Raw_Model_input
Created: Oct. 29, 2020, 4:05 p.m.
Authors: Choi, Young-Don

ABSTRACT:

Bolin_Creek_RHESSys_Raw_Model_input

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Composite Resource Composite Resource
NLDAS Forcing NetCDF using CAMELS datasets from 1980 to 2018
Created: Nov. 4, 2020, 8:57 p.m.
Authors: Naoki Mizukami · Wood, Andrew

ABSTRACT:

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

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Composite Resource Composite Resource

ABSTRACT:

summa test case using camels and nldas forcing

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Composite Resource Composite Resource

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.

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Composite Resource Composite 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.

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Composite Resource Composite 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.

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Composite Resource Composite Resource

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for the reproducibility of SUMMA modeling using Sciunit in 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.

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Composite Resource Composite 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

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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:

This HydroShare resource has the results of performance tests that are computational_time.csv, size.csv, and complexity.csv.
Using Jupyter notebook, we created four performance plots in the manuscript of "Comparing Approaches to Achieve Reproducible Computational Modeling for Hydrological and Environmental Systems" in Environmental Modeling and Software.

Figure 9. The total scores of complexity on reproducible approaches for developer and user work
Figure 10 Comparison of the size for reproducible artifacts in five local reproducible approaches
Figure 11 Comparison of computational time in five local reproducible approaches
Figure 12 Comparison of computational time in six remote reproducible approaches

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Composite Resource Composite Resource

ABSTRACT:

SUMMA Simulation in East Branch Delaware River at Margaretville New York using Camels Datasets in on CyberGIS Jupyter for water
There are four Jupyter notebooks to demonstrate SUMMA Simulations
1. Use installation.ipynb to install required dependencies
2. Create SUMMA input using Camels dataset via this HS resource and OpenDAP(https://www.hydroshare.org/resource/a28685d2dd584fe5885fc368cb76ff2a/)
3. Execute SUMMA using pySUMMA
4. Plot SUMMA output

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Composite Resource Composite Resource

ABSTRACT:

Notebook Tutorials for RHESSys Modeling using pyRHESSys: Watts Branch example

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Model Program Resource Model Program Resource
RHESSys East Coast version v7.2
Created: Dec. 19, 2020, 7:16 a.m.
Authors: Choi, Young-Don

ABSTRACT:

RHESSys East Coast version v7.2

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Composite Resource Composite Resource

ABSTRACT:

This is an example of Geoscience Use Case 4: Height Above the Nearest Drainage (HAND) of "Improving Reproducibility of Geoscience Models with Sciunit" in the Geological Society of America publication. In this resource, there are two notebooks: 1) HANDWorkFlow.ipynb and 2) HAND_Sciunit.ipynb.

Using these two notebooks, we demonstrate the capabilities of Sciunit: 1) encapsulating HAND TauDEM workflows and creating a Sciunit Container, and 2) `diff` command to compare the results of drainage network according to the contributing area threshold. During computation of the drainage network, a minimum contributing area threshold is used to identify the channel beginning. With a lower threshold value, the density of the resulting drainage network increases. Scientists running this experiment might be interested in finding out how the threshold makes a difference in the execution and result of the HAND model.

The first notebook demonstrates the general procedure to calculate HAND (Height above the Nearest Drainage) using TauDEM (https://hydrology.usu.edu/taudem/taudem5/).
Then using the second notebook we demonstrate how to create a Sciunit container for HAND Workflow and compare two Sciunit containers (5000 vs 50000 thresholds) using `diff` command.

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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 is a HydroShare resource to demonstrate an approach for open and reproducible Environmental Environmental Modeling during EarthCube2020 meeting (https://www.earthcube.org/EC2020).

You can start a Jupyter notebook ("First_NB_An_Approach_for_Open_Reproducible_Environmental_Modeling.ipynb") using "CyberGIS-Jupyter for water" on "Open with ..." Button.
Through the 1st notebook demonstration, you can experience open and reproducible environmental modeling using three main components which are online repository (HydroShare), computational environment (CyberGIS-Jupyter for water), and model API(pySUMMA).
Also, you will create a Sciunit container to encapsulate every dependency for SUMMA execution in CybeGIS-Jupyter into the Sciunit container.

Then you can move to the 2nd notebook (Second_NB_An_Approach_for_Open_Reproducible_Environmental_Modeling.ipynb) to evaluate reproducibility and replicability of the SUMMA Sciunit container in different cyberinfrastructure (CUAHSI JupyterHub).

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Composite Resource Composite Resource

ABSTRACT:

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

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

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Model Instance Resource Model Instance Resource
Baisman Basin for RHESSys Model Input
Created: Jan. 16, 2021, 4:50 a.m.
Authors: Choi, Young-Don

ABSTRACT:

Baisman Basin for RHESSys Model Input

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Composite Resource Composite Resource
Baisman Basin for RHESSys Model Input_edit
Created: Jan. 16, 2021, 7:41 a.m.
Authors: Choi, Young-Don

ABSTRACT:

Baisman Basin for RHESSys Model Input_edit

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Model Instance Resource Model Instance Resource
Baisman, MD, for RHESSys Model Input
Created: Jan. 17, 2021, 2:25 p.m.
Authors: Choi, Young-Don

ABSTRACT:

Baisman, MD, for RHESSys Model Input

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Composite Resource Composite Resource
DEM and Outlet in the Little Bear River, UT
Created: Jan. 29, 2021, 8:37 p.m.
Authors: Choi, Young-Don

ABSTRACT:

DEM and Outlet in the Little Bear River, UT

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Composite Resource Composite Resource
Camels USGS Streamflow NetCDF from 1980 to 2014
Created: Feb. 10, 2021, 8:15 a.m.
Authors: Choi, Young-Don

ABSTRACT:

Camels USGS Streamflow NetCDF from 1980 to 2014

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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
RHESSys notebooks for Spout run simulation
Created: March 19, 2021, 8:42 p.m.
Authors: Choi, Young-Don

ABSTRACT:

RHESSys notebooks for Spout run simulation

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Collection Resource Collection Resource

ABSTRACT:

The overall goal of this collection is to provide the hydrologic modelers with the datasets and an end-to-end workflow to explore the sensitivity of hydrologic model simulations to variability in the characteristics of meteorological forcings that is further described in the research paper, Van Beusekom et al. (2021). In this paper, 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. The paper lays 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).

This collection includes three resources which help the modelers to reproduce and build on the results from the paper.

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 of the paper goal: explores the paper goal mentioned above for 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 to address the same goal as the second resource. 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 paper) 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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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:

Aspen stand at Reynolds Mountain East

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Composite Resource Composite Resource
SUMMA model Instance in col-de-port
Created: April 25, 2021, 8:01 p.m.
Authors: Choi, Young-Don

ABSTRACT:

SUMMA model Instance in col-de-port

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Composite Resource Composite Resource
SUMMA Model Instance in YAKIMA RIVER AT MABTON, WA
Created: April 25, 2021, 8:10 p.m.
Authors: Choi, Young-Don

ABSTRACT:

SUMMA Model Instance in YAKIMA RIVER AT MABTON, WA

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

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Composite Resource Composite Resource

ABSTRACT:

Sciunit (https://sciunit.run/) is a tool that encapsulates a set of executions into an isolated, independent container. It allows computational scientists to create research objects, which can be reused and transferred to other computational environments for reproducibility. Sciunit containerizes a program by capturing the trace of its execution using system utilities. It stores the sequence of instructions to run the program and the input and output data content used by that program. Programs in this self-contained sandbox are reproduced on the system or transported to another system for re-execution.

In this resource, users can show how to reproduce a Sciunit Container that encapsulates MODFLOW-NWT Use Case in the James River watershed upstream of Richmond, VA, USA

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Composite Resource Composite Resource

ABSTRACT:

Variable Infiltration Capacity (VIC) model in hydrology use case packaged using provenance to use (PTU) module used by Sciunit.
Relevant code and data are in the cde-root directory. Main script to execute is script_6.scr located at:
/cde-root/var/lib/irods/iRODS/server/bin/cmd/

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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 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 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 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
End-to-End RHESSys Notebooks at Spout Run Virginia
Created: May 13, 2021, 10:47 p.m.
Authors: Choi, Young-Don

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys End-to-End 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

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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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 HydroShare resource provides the Jupyter Notebooks for RHESSys End-to-End modeling workflow using the GeoServer approach at Scotts Level Branch, Maryland

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 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

ABSTRACT:

This HydroShare resource provides the Jupyter Notebooks for RHESSys modeling workflow using the HydroShare model instance 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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