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.

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).

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)

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)

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)

ABSTRACT:

SUMMA & pySUMMA singularity

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

ABSTRACT:

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

ABSTRACT:

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

ABSTRACT:

This 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.

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.

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

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

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)

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)

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

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

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.

ABSTRACT:

Examples of RHESSys and SUMMA Model Simulation on Coweeta subwatershed 18

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).

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.

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.

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.

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

ABSTRACT:

RHESsys v3 singularity

ABSTRACT:

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

ABSTRACT:

RHESsys_grass_setup

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.

ABSTRACT:

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

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.

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.

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.

ABSTRACT:

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

ABSTRACT:

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

ABSTRACT:

aa

ABSTRACT:

ss

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.

ABSTRACT:

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

ABSTRACT:

aa

ABSTRACT:

RHESsys ensemble simulation in Coweeta subwatershed18

ABSTRACT:

aa

ABSTRACT:

test

ABSTRACT:

Examples of RHESSys and SUMMA Model Simulation on Coweeta subwatershed 18

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

ABSTRACT:

1

ABSTRACT:

In this

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

ABSTRACT:

aa

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).

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).

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

ABSTRACT:

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

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).

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

ABSTRACT:

Dakota_pySUMMA_HydroShare

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

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

ABSTRACT:

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

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.

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

ABSTRACT:

SUMMA simulation on HydroShare binder

ABSTRACT:

RHESSys input data at Coweeta subbasin18

ABSTRACT:

RHESSys input data of Coweeta subbasin18

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

ABSTRACT:

RHESSys input data at Coweeta subbasin 18 (in progress)

ABSTRACT:

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

ABSTRACT:

Map Visualization example of RHESSys output at Coweeta subbasin18

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)

ABSTRACT:

Example to use the RHESSys model

ABSTRACT:

Lecture_RHESSys input Coweeta sub18

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

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:

ABSTRACT:

Notebook Example to create SUMMA Modeling Sciunit Container in CUAHSI JupyterHub

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

ABSTRACT:

Coweeta sub18

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/)

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).

ABSTRACT:

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

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).

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).

ABSTRACT:

pyRHESSys Example of Coweeta sub18 in CyberGIS-Jupyter for water

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).

ABSTRACT:

SUMMA MODEL INSTANCE

ABSTRACT:

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

ABSTRACT:

RHESSys

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).

ABSTRACT:

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

ABSTRACT:

Meadow Creek RHESSys

ABSTRACT:

RHESSys notebook for Meadow Creek simulation

ABSTRACT:

SUMMA Binder

ABSTRACT:

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

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

ABSTRACT:

This 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)

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.

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.

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)

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.

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

ABSTRACT:

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

ABSTRACT:

This 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

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.

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

ABSTRACT:

difficult_run_MI

ABSTRACT:

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

ABSTRACT:

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

ABSTRACT:

This 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

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.

ABSTRACT:

HAND notebook

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.

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

ABSTRACT:

Bolin_Creek_RHESSys_Raw_Model_input

ABSTRACT:

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

ABSTRACT:

summa test case using camels and nldas forcing

ABSTRACT:

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

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

ABSTRACT:

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

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

ABSTRACT:

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

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

ABSTRACT:

This HydroShare resource provides 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.

ABSTRACT:

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

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

This Virtual Box image five local approaches:
- Approach-1 Compiling the core model software
- Approach-2 Containerizing the core model software only with Docker
- Approach-3 Containerizing all software with Docker
- Approach-4 Containerizing all software with Singularity
- Approach-5 Containerizing all software and modeling workflows with Sciunit

ABSTRACT:

This is a collection resource for "Comparing containerization-based approaches for reproducible computational modeling of environmental systems" manuscript in Environmental Modeling and Software Journal.
HS-1: Collection Resource for Comparing Approaches to Achieve Reproducible Computational Modeling for Hydrological and Environmental Systems

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

Then there is a HS resource for reproducible approaches in local computational environments.
HS-4. A Virtual Box image that includes five local approaches:
- Approach-1 Compiling the core model software
- Approach-2 Containerizing the core model software only with Docker
- Approach-3 Containerizing all software with Docker
- Approach-4 Containerizing all software with Singularity
- Approach-5 Containerizing all software and modeling workflows with Sciunit

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

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

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

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

ABSTRACT:

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

ABSTRACT:

Notebook Tutorials for RHESSys Modeling using pyRHESSys: Watts Branch example

ABSTRACT:

RHESSys East Coast version v7.2

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 to encapsulate the HAND TauDEM workflow and create a Sciunit Container, and evaluate differences in HAND due to changing the contributing area threshold used to map the drainage network. 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.

ABSTRACT:

These are example application notebooks to simulate SUMMA using CAMELS datasets.
There are three steps: (STEP-1) Create SUMMA input, (STEP-2) Execute SUMMA, (STEP-3) Visualize SUMMA output
Based on this example, users can change the HRU ID and simulation periods to analyze 671 basins in CAMELS datasets.

(STEP-1) A_1_camels_make_input.ipynb
- The first notebook creates SUMMA input using Camels dataset using `summa_camels_hydroshare.zip` in this resource and OpenDAP(https://www.hydroshare.org/resource/a28685d2dd584fe5885fc368cb76ff2a/).
(STEP-2) B_1_camels_pysumma_default_prob.ipynb, B_2_camels_pysumma_lhs_prob.ipynb, B_3_camels_pysumma_config_prob.ipynb, and
B_4_camels_pysumma_lhs_config_prob.ipynb
- These four notebooks execute SUMMA considering four different parameters and parameterization combinations
(STEP-3) C_1_camels_analyze_output_default_prob.ipynb, C_2_camels_analyze_output_lhs_prob.ipynb, C_3_camels_analyze_output_config_prob.ipynb,
C_4_camels_analyze_output_lhs_config_prob.ipynb
- The final four notebooks visualize SUMMA output of B-1, B-2, B-3, and B-4 notebooks.

ABSTRACT:

This resource, configured for execution in connected JupyterHub compute platforms using the CyberGIS-Jupyter for Water (CJW) environment's supported High-Performance Computing (HPC) resource (XSEDE Comet) through CyberGIS-Compute Service, helps the modelers to reproduce and build on the results from the paper (Van Beusekom et al., 2021).

For this purpose, three different Jupyter notebooks are developed and included in this resource which explore the paper goal for one example CAMELS site and a pre-selected period of 18-month simulation to demonstrate the capabilities of the notebooks. The first notebook processes the raw input data from CAMELS dataset to be used as input for SUMMA model. The second notebook utilizes the CJW environment's supported HPC resource (XSEDE Comet) through CyberGIS-Compute Service to executes SUMMA model. This notebook uses the input data from first notebook using original and altered forcing, as per further described in the notebook. Finally, the third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). More information about each Jupyter notebook and a step-by-step instructions on how to run the notebooks can be found in the Readme.md fie included in this resource. Using these three notebooks, modelers can apply the methodology mentioned above to any (one to all) of the 671 CAMELS basins and simulation periods of their choice. As this resource uses HPC, it enables a high-speed running of simulations which makes it suitable for larger simulations (even as large as the entire 671 CAMELS sites and the whole 60-month simulation period used in the paper) practical and much faster than when no HPC is used.

ABSTRACT:

This 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).

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

ABSTRACT:

Baisman Basin for RHESSys Model Input

ABSTRACT:

Baisman Basin for RHESSys Model Input_edit

ABSTRACT:

Baisman, MD, for RHESSys Model Input

ABSTRACT:

DEM and Outlet in the Little Bear River, UT

ABSTRACT:

Camels USGS Streamflow NetCDF from 1980 to 2014

ABSTRACT:

This notebook is created to support SUMMA general application workflows using CAMELS forcing, watershed attributes, and streamflow observation.
CAMELS datasets cover 671 basins across the USA, so users can apply SUMMA models in 671 basins.

ABSTRACT:

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

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

ABSTRACT:

This resource, configured for execution in connected JupyterHub compute platforms, helps the modelers to reproduce and build on the results from the paper (Van Beusekom et al., 2021). For this purpose, three different Jupyter notebooks are developed and included in this resource which explore the paper goal for one example CAMELS site and a pre-selected period of 18-month simulation to demonstrate the capabilities of the notebooks. The first notebook processes the raw input data from CAMELS dataset to be used as input for SUMMA model. The second notebook executes SUMMA model using the input data from first notebook using original and altered forcing, as per further described in the notebook. Finally, the third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). More information about each Jupyter notebook and a step-by-step instructions on how to run the notebooks can be found in the Readme.md fie included in this resource. Using these three notebooks, modelers can apply the methodology mentioned above to any (one to all) of the 671 CAMELS basins and simulation periods of their choice.

ABSTRACT:

RHESSys notebooks for Spout run simulation

ABSTRACT:

The overall goal of this collection is to use the basic strategy and architecture presented by Choi et al. (2021) to make components of a modern and complex hydrologic modeling study (VB study; Van Beusekom et al., 2022) easier to reproduce. The design and implemention of the developed cyberinfrastructure to achieve this goal are fully explained by Maghami et al. (2023).

In VB study, hydrological outputs from the SUMMA model for the 671 CAMELS catchments across the contiguous United States (CONUS) and a 60-month actual simulation period are investigated to understand their dependence on input forcing behavior across CONUS. VB study layes out a simple methodology that can be applied to understand the relative importance of seven model forcings (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure).

Choi et al. (2021) integrated three components through seamless data transfers for a reproducible research: (1) online data and model repositories; (2) computational environments leveraging containerization and self-documented computational notebooks; and (3) Application Programming Interfaces (APIs) that provide programmatic control of complex computational models.

Therefore, Maghami et al. (2023), integrated the following three components through seamless data transfers to make components of a modern and complex hydrologic study (VB study) easier to reproduce:
(1) HydroShare as online data and model repository;
(2) CyberGIS-Jupyter for Water for self-documented computational notebooks as computational environment (with and without HPC notebooks);
(3) pySUMMA as Application Programming Interfaces (APIs) that provide programmatic control of complex computational models.

This collection includes three resources:

1- First resource, provides the entire NLDAS forcing datasets used in the VB study.
2- Second resource provides an end-to-end workflow of CAMELS basin modeling with SUMMA for the paper simulations configured for execution in connected JupyterHub compute platforms. This resource is well-suited for a smaller scale exploration: it is preconfigured to explore one example CAMELS site and a period of 60-month actual simulation to demonstrate the capabilities of the notebooks. Users still can change the CAMELS site, the number of sites being explored or even the simulation period. To quickly assess the capabilities of the notebooks in this resource, we even recommend running an actual simulation period as short as 12 months.
3- Third resource, however, uses HPC (High-Performance Computing) through CyberGIS Computing Service. The HPC enables a high-speed running of simulations which makes it suitable for running larger simulations (even as large as the entire 671 CAMELS sites and the whole 60-month actual simulation period used in the VB study) practical and much faster than the second resource. This resource is preconfigured to explore four example CAMELS site and a period of 60-month actual simulation to only demonstrate the capabilities of the notebooks. Users still can change the CAMELS sites, the number of sites being explored or even the simulation period.

Greater details can be found in each resource.

ABSTRACT:

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

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

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

ABSTRACT:

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

Users can access the uploaded LES datasets on HydroShare-GeoServer and THREDDS using this HS resource id. This resource was created using HS 2.

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

ABSTRACT:

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

Users can access the uploaded LES datasets on HydroShare-GeoServer and THREDDS using this HS resource id. This resource was created using HS 2.

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

ABSTRACT:

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

Users can access the uploaded LES datasets on HydroShare-GeoServer and THREDDS using this HS resource id. This resource was created using HS 2.

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

ABSTRACT:

Aspen stand at Reynolds Mountain East

ABSTRACT:

SUMMA model Instance in col-de-port

ABSTRACT:

SUMMA Model Instance in YAKIMA RIVER AT MABTON, WA

ABSTRACT:

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

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

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

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

ABSTRACT:

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

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

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/

ABSTRACT:

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

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

ABSTRACT:

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

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

ABSTRACT:

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

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

ABSTRACT:

We implemented automated workflows using Jupyter notebooks for each state. The GIS processing, crucial for merging, extracting, and projecting GeoTIFF data, was performed using ArcPy—a Python package for geographic data analysis, conversion, and management within ArcGIS (Toms, 2015). After generating state-scale LES (large extent spatial) datasets in GeoTIFF format, we utilized the xarray and rioxarray Python packages to convert GeoTIFF to NetCDF. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. Xarray facilitated data manipulation and metadata addition in the NetCDF file, while rioxarray was used to save GeoTIFF as NetCDF. These procedures resulted in the creation of three HydroShare resources (HS 3, HS 4 and HS 5) for sharing state-scale LES datasets. Notably, due to licensing constraints with ArcGIS Pro, a commercial GIS software, the Jupyter notebook development was undertaken on a Windows OS.

ABSTRACT:

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

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

ABSTRACT:

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

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

ABSTRACT:

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

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

ABSTRACT:

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

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

ABSTRACT:

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

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

ABSTRACT:

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

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

ABSTRACT:

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

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

ABSTRACT:

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.

ABSTRACT:

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

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

ABSTRACT:

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

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

ABSTRACT:

This HydroShare resource was created to support the study presented in Choi et al. (2024), titled "Toward Reproducible and Interoperable Environmental Modeling: Integration of HydroShare with Server-side Methods for Exposing Large-Extent Spatial Datasets to Models." Ensuring the reproducibility of scientific studies is crucial for advancing research, with effective data management serving as a cornerstone for achieving this goal. In hydrologic and environmental modeling, spatial data is used as model input, and sharing this spatial data is a main step in the data management process. However, by focusing only on sharing data at the file level through small files rather than providing the ability to Find, Access, Interoperate with, and directly Reuse subsets of larger datasets, online data repositories have missed an opportunity to foster more reproducible science. This has led to challenges when accommodating large files that benefit from consistent data quality and seamless geographic extent.

To utilize the benefits of large datasets, the objective of the Choi et al. (2024) study was to create and test an approach for exposing large extent spatial (LES) datasets to support catchment-scale hydrologic modeling needs. GeoServer and THREDDS Data Server connected to HydroShare were used to provide seamless access to LES datasets. The approach was demonstrated using the Regional Hydro-Ecologic Simulation System (RHESSys) for three different-sized watersheds in the US. Data consistency was assessed across three different data acquisition approaches: the 'conventional' approach, which involved sharing data at the file level through small files, as well as GeoServer and THREDDS Data Server. This assessment was conducted using RHESSys to evaluate differences in model streamflow output. This approach provided an opportunity to serve datasets needed to create catchment models in a consistent way that could be accessed and processed to serve individual modeling needs. For full details on the methods and approach, please refer to Choi et al. (2024). This HydroShare resource is essential for accessing the data and workflows that were integral to the study.

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

ABSTRACT:

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

ABSTRACT:

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

ABSTRACT:

This 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.

ABSTRACT:

This resource, configured for execution in connected JupyterHub compute platforms, helps the modelers to reproduce and build on the results from the VB study (Van Beusekom et al., 2022) as explained by Maghami et el. (2023). For this purpose, three different Jupyter notebooks are developed and included in this resource which explore the paper goal for one example CAMELS site and a pre-selected period of 60-month actual simulation to demonstrate the capabilities of the notebooks. For even a faster assesment of the capabilities of the notebooks, users are recommended to opt for a shorter simulation period (e.g., 12 months of actual simulation and six months of initialization) and one example CAMELS site. The first notebook processes the raw input data from CAMELS dataset to be used as input for SUMMA model. The second notebook executes SUMMA model using the input data from first notebook using original and altered forcing, as per further described in the notebook. Finally, the third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). More information about each Jupyter notebook and a step-by-step instructions on how to run the notebooks can be found in the Readme.md fie included in this resource. Using these three notebooks, modelers can apply the methodology mentioned above to any (one to all) of the 671 CAMELS basins and simulation periods of their choice.

ABSTRACT:

This resource, configured for execution in connected JupyterHub compute platforms using the CyberGIS-Jupyter for Water (CJW) environment's supported High-Performance Computing (HPC) resources (Expanse or Virtual ROGER) through CyberGIS-Compute Service, helps the modelers to reproduce and build on the results from the VB study (Van Beusekom et al., 2022) as explained by Maghami et el. (2023).

For this purpose, four different Jupyter notebooks are developed and included in this resource which explore the paper goal for four example CAMELS site and a pre-selected period of 60-month simulation to demonstrate the capabilities of the notebooks. The first notebook processes the raw input data from CAMELS dataset to be used as input for SUMMA model. The second notebook utilizes the CJW environment's supported HPC resource (Expanse or Virtual ROGER) through CyberGIS-Compute Service to executes SUMMA model. This notebook uses the input data from first notebook using original and altered forcing, as per further described in the notebook. The third notebook utilizes the outputs from notebook 2 and visualizes the sensitivity of SUMMA model outputs using Kling-Gupta Efficiency (KGE). The fourth notebook, only developed for the HPC environment (and only currently working with Expanse HPC), enables transferring large data from HPC to the scientific cloud service (i.e., CJW) using Globus service integrated by CyberGIS-Compute in a reliable, high-performance and fast way. More information about each Jupyter notebook and a step-by-step instructions on how to run the notebooks can be found in the Readme.md fie included in this resource. Using these four notebooks, modelers can apply the methodology mentioned above to any (one to all) of the 671 CAMELS basins and simulation periods of their choice. As this resource uses HPC, it enables a high-speed running of simulations which makes it suitable for larger simulations (even as large as the entire 671 CAMELS sites and the whole 60-month simulation period used in the paper) practical and much faster than when no HPC is used.

ABSTRACT:

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

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

ABSTRACT:

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.

ABSTRACT:

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

ABSTRACT:

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

ABSTRACT:

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

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