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YOUNG-DON CHOI

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

Subject Areas: Hydrology

 Recent Activity

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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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

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ABSTRACT:

This resource included a singularity image for SUMMA sopron and pySUMMA 0.0.1 version. Detail description is in definition file.

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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

Show More

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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 Contact

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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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Generic Generic
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
Test Cases of SUMMA modeling that include model instances and Jupyter notebooks for SUMMA 2nd Paper(2015)
Created: Sept. 13, 2018, 6:51 p.m.
Authors: Martyn Clark · Bart Nijssen · Jessica Lundquist · Dmitri Kavetski · David E. Rupp · Ross Woods · Jim E. Freer · Ethan D. Gutmann · Andrew Wood · david gochis · Roy M. Rasmussen · David Tarboton · Vinod Mahat · Gerald N. Flerchinger · Danny G. Marks

ABSTRACT:

This resource aggregates several resources related to Test Cases of SUMMA Simulation on the Clark et al., (2015b)(https://doi.org/10.1002/2015WR017200) research. These Test Cases simulate SUMMA with pySUMMA which is a Python library for wrapping the SUMMA modeling framework. The resources include SUMMA Model Instances and notebooks to use pySUMMA. pySUMMA provides an object-oriented approach for manipulating Model Instance(configuration files), executing a SUMMA simulation on HydroShare and visualizing SUMMA model outputs.

A pySUMMA Simulation Procedure on HydroShare showing how these resources simulate is shown in the "pySUMMA Simulation Procedure on HydroShare" resource.

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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 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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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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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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Composite Resource Composite Resource

ABSTRACT:

This resource holds the notebooks and scripts to demonstrate Reproducible Science Use Cases With HydroShare and Sciunit for EarthCube 2019 annual meeting session. This resource is designed to be opened with the CUAHSI JupyterHub HydroShare app. Then open the notebook file “my_analysis.ipynb” and step through it. It is designed to be somewhat self explanatory and call upon scripts that are part of this resource when needed.

The work illustrated follows the following steps
1. Set up the problem and visualize the study area
2. Run a script to repeat the analysis needed to produce Fig 7 in Clark et al. (2015). This establishes repeatability.
3. Encapsulate the script in a sciunit container. This is a further component of repeatability.
4. Push the sciunit to HydroShare to that it can be accessed by others. This demonstrates runnability on a different computer
5. Access the HydroShare resource containing the sciunit from a different user and repeat the sciunit. This demonstrates reproducibility.
6. Use sciunit to run a slightly changed script that adds another evapotranspiration option to the results. This is an illustration of replicability, a separate user adding new data or analysis to the work.

Reference
Clark, M. P., B. Nijssen, J. D. Lundquist, D. Kavetski, D. E. Rupp, R. A. Woods, J. E. Freer, E. D. Gutmann, A. W. Wood, D. J. Gochis, R. M. Rasmussen, D. G. Tarboton, V. Mahat, G. N. Flerchinger and D. G. Marks, (2015), "A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies," Water Resources Research, 51(4): 2515-2542, http://doi.org/10.1002/2015wr017200.

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Composite Resource Composite Resource

ABSTRACT:

This is a poster for 2019 EarthCube Conference in Denver June 12~14.

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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:

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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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Composite Resource Composite Resource
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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Composite Resource Composite Resource

ABSTRACT:

aa

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Model Instance Resource Model Instance Resource
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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Model Instance Resource Model Instance Resource
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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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 Instance Resource Model Instance Resource
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:

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 0.0.1 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 0.0.1 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

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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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