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Jonathan Goodall

University of Virginia | Associate Professor

Subject Areas: Hydrology, Hydroinformatics, Water Resources

 Recent activity

ABSTRACT:

This presentation was given at the iEMSs conference held in Fort Collins, CO in June 2018. http://iemss2018.engr.colostate.edu/

Reproducibility of computational workflows is an important challenge that calls for open and reusable code and data, well-documented workflows, and controlled environments that allow others to verify published findings. HydroShare (http://www.hydroshare.org) and GeoTrust (http://geotrusthub.org/), two new cyberinfrastructure tools under active development, can be used to improve reproducibility in computational hydrology. HydroShare is a web-based system for sharing hydrologic data and model resources. HydroShare allows hydrologists to upload model input data resources, add detailed hydrologic-specific metadata to these resources, and use the data directly within HydroShare for collaborative modeling using tools like JupyterHub. GeoTrust provides tools for scientists to efficiently reproduce, track and share geoscience applications by building ‘sciunits,’ which are efficient, lightweight, self-contained packages of computational experiments that can be guaranteed to repeat or reproduce regardless of deployment challenges. We will present a use case example focusing on a workflow that uses the MODFLOW model to demonstrate how HydroShare and GeoTrust can be integrated to easily and efficiently reproduce computational workflows. This use case example automates pre-processing of model inputs, model execution, and post-processing of model output. This work demonstrates how the integration of HydroShare and Geotrust ensures the logical and physical preservation of computation workflows and that reproducibility can be achieved by replicating the original sciunit, modifying it to produce a new sciunit and finally, preserving and sharing the newly created sciunit by using HydroShare's JupyterHub.

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ABSTRACT:

This presentation was given at the iEMSs conference in Fort Collins, CO in June 2018. http://iemss2018.engr.colostate.edu/

The Structure for Unifying Multiple Modeling Alternatives (SUMMA) is a hydrologic modeling framework that allows hydrologists to systematically test alternative model conceptualizations. The objective of this project is to create a Python library for wrapping the SUMMA modeling framework called pySUMMA. Using this library, hydrologists can create Python scripts that document the alternative model conceptualizations tested within different experiments. To this end, pySUMMA provides an object-oriented means for updating SUMMA model configurations, executing SUMMA model runs, and visualizing SUMMA model outputs. This work is part of the HydroShare web-based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) that seeks to make hydrologic data and models discoverable and shareable online. Creating pySUMMA is a first step toward the longer-term goal of creating an interactive SUMMA-based modeling system by combining HydroShare.org, JupyterHub, and High Performance Computing (HPC) resources. In the current version of HydroShare, different data and model resources can be uploaded, shared, and published. This current development will result in a tighter integration between the SUMMA modeling process and HydroShare.org with the goal of making hydrologic models more open, reusable, and reproducible. Ultimately, SUMMA serves as a use case for modeling in HydroShare that advances a general approach for leveraging JupyterHub and HPC that can be repeated for other modeling systems.

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ABSTRACT:

Diagram depicting the relationship between 10 different HydroShare resources used to produce results for data-driven street flood severity modeling done for Norfolk, VA for 2010-2016. The analysis is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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ABSTRACT:

Script and accompanying ipython notebook written in Python 2.7 for aggregating sub-daily environmental data (rainfall, tide, wind, groundwater) to a daily timescale. The input data are from Norfolk, Virginia. Several different methods of aggregation are used including averages and maximums. The processed/aggregated data are combined with street flood report data to be used in data-driven, predictive modeling. The script in this resource was used in the analysis described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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ABSTRACT:

Daily observations data for rainfall, wind, tide, and water table levels. These variables are more fully defined in the raw source data. These data are used as input for data-driven prediction of street flood severity in Norfolk, VA 2010-2016. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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 Contact

Resources
All 0
Collection 0
Composite Resource 0
Generic 0
Geographic Feature 0
Geographic Raster 0
HIS Referenced Time Series 0
Model Instance 0
Model Program 0
MODFLOW Model Instance Resource 0
Multidimensional (NetCDF) 0
Script Resource 0
SWAT Model Instance 0
Time Series 0
Web App 0
Composite Resource Composite Resource
Hampton Roads Environmental Time Series Data
Created: July 20, 2017, 7:18 p.m.
Authors: Jeff Sadler

ABSTRACT:

This is raw environmental time series data stored in a sqlite database with a data schema loosely based off of ODM1.1. This scheme is shown in the data model figure included in the resource. The geographical location of these data is in the Hampton Roads region in South East Virginia. The variables of the time series are rainfall, tide, wind, and water table elevations. These data were processed and used as input for data-driven modeling for street flood severity prediction. The processing and modeling are described in this Journal of Hydrology Paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Collection Resource Collection Resource

ABSTRACT:

This resource aggregates several resources related to street flood severity modeling in Norfolk, Virginia USA. The resources include raw and pre-processed data, scripts used to perform the pre-processing, scripts used to train data-driven algorithms, and results from the models. The models used crowd-sourced street flood reports as target values and environmental data as input values. The resources in this aggregate resource are used to generate the results for this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

A diagram showing how these resources relate is shown in the "Resource workflow diagram for street flood severity modeling in Norfolk, VA 2010-2016" resource.

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Model Program Resource Model Program Resource
SUMMA 2.0.0
Created: Dec. 11, 2017, 2:56 a.m.
Authors: Martyn Clark · Bart Nijssen

ABSTRACT:

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

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

The important modeling features are:

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

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

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

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Model Instance Resource Model Instance Resource
celia1990 SUMMA model
Created: Dec. 11, 2017, 3:06 a.m.
Authors: Martyn Clark · Bart Nijssen

ABSTRACT:

Simulations from Celia, 1990 (Water Resources Research)

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Composite Resource Composite Resource

ABSTRACT:

This is tabular output data from two data-driven models used to predict flood severity, Poisson regression and Random Forest regression. Both outputs from the training and testing phases of the modeling are included in the resource. Additionally, results indicating the relative importance of each predictor variable in the Random Forest model are provided in the "rf_impo_out.csv" file. This work is described in the following paper published in the Journal of Hydrology: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Composite Resource Composite Resource
Input data for flood severity modeling in Norfolk, VA
Created: Dec. 21, 2017, 5:14 p.m.
Authors: Jeff Sadler

ABSTRACT:

This is tabular input data originally used in two data-driven models (Poisson regression and Random Forest) for predicting flood severity. The inputs to the model (or predictor variables) are environmental conditions such as cumulative rainfall, high and low tides, etc. The outputs (or target variable) of the model is the number of flood reports per storm event. This data was used in work that is described in the following paper published in the Journal of Hydrology: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Composite Resource Composite Resource

ABSTRACT:

This is a script written in the R programming language. The script is used to train and apply two data-driven models, Random Forest and Poisson regression. The target variable is the number of flood reports per storm event in Norfolk, VA USA. The input variables for the models are environmental conditions on an event time scale (or daily if no flood reports were made for an event). This script was used to produce results published in a paper in the Journal of Hydrology: https://doi.org/10.1016/j.jhydrol.2018.01.044.
---
Original run configurations:
R version = 3.3.3
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
Packages used:
'randomForest' (version 4.6-12)
'caret' (version 6.0-73)

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Composite Resource Composite Resource
Raw street flood report data from Norfolk, VA 2010-2016
Created: Jan. 2, 2018, 9:20 p.m.
Authors: Jeff Sadler

ABSTRACT:

Street flooding reports made by mostly City of Norfolk staff from 2010-2016. The coordinate system used for the X and Y coordinates is "Virginia state plane, south zone, feet (NAD83)." These data were processed and used as target values for street data-driven flood prediction severity modeling. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Composite Resource Composite Resource
Script for processing street flood reports
Created: Jan. 2, 2018, 9:22 p.m.
Authors: Jeff Sadler

ABSTRACT:

Script and accompanying notebook written in Python 2.7 for processing street flood reports made by City of Norfolk staff. The output data from this script were used as target values for street data-driven flood prediction severity modeling. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Composite Resource Composite Resource
Processed street flood data from Norfolk, VA 2010-2016
Created: Jan. 2, 2018, 9:24 p.m.
Authors: Jeff Sadler

ABSTRACT:

Processed street flooding data from street flood reports made by City of Norfolk, VA staff 2010-2016. These data were used as target values for street data-driven flood prediction severity modeling. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Composite Resource Composite Resource

ABSTRACT:

Script and accompanying notebook written in Python 2.7 for combining flood report data (output) and environmental data (input) into a format suitable for a data-driven model. These data used as target values for street data-driven flood prediction severity modeling for Norfolk, VA 2010-2016. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Composite Resource Composite Resource
Daily environmental observations Norfolk, VA 2010-2016
Created: Jan. 2, 2018, 9:37 p.m.
Authors: Jeff Sadler

ABSTRACT:

Daily observations data for rainfall, wind, tide, and water table levels. These variables are more fully defined in the raw source data. These data are used as input for data-driven prediction of street flood severity in Norfolk, VA 2010-2016. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

Show More
Composite Resource Composite Resource

ABSTRACT:

Script and accompanying ipython notebook written in Python 2.7 for aggregating sub-daily environmental data (rainfall, tide, wind, groundwater) to a daily timescale. The input data are from Norfolk, Virginia. Several different methods of aggregation are used including averages and maximums. The processed/aggregated data are combined with street flood report data to be used in data-driven, predictive modeling. The script in this resource was used in the analysis described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

Show More
Composite Resource Composite Resource

ABSTRACT:

Diagram depicting the relationship between 10 different HydroShare resources used to produce results for data-driven street flood severity modeling done for Norfolk, VA for 2010-2016. The analysis is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

Show More
Composite Resource Composite Resource

ABSTRACT:

This presentation was given at the iEMSs conference in Fort Collins, CO in June 2018. http://iemss2018.engr.colostate.edu/

The Structure for Unifying Multiple Modeling Alternatives (SUMMA) is a hydrologic modeling framework that allows hydrologists to systematically test alternative model conceptualizations. The objective of this project is to create a Python library for wrapping the SUMMA modeling framework called pySUMMA. Using this library, hydrologists can create Python scripts that document the alternative model conceptualizations tested within different experiments. To this end, pySUMMA provides an object-oriented means for updating SUMMA model configurations, executing SUMMA model runs, and visualizing SUMMA model outputs. This work is part of the HydroShare web-based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) that seeks to make hydrologic data and models discoverable and shareable online. Creating pySUMMA is a first step toward the longer-term goal of creating an interactive SUMMA-based modeling system by combining HydroShare.org, JupyterHub, and High Performance Computing (HPC) resources. In the current version of HydroShare, different data and model resources can be uploaded, shared, and published. This current development will result in a tighter integration between the SUMMA modeling process and HydroShare.org with the goal of making hydrologic models more open, reusable, and reproducible. Ultimately, SUMMA serves as a use case for modeling in HydroShare that advances a general approach for leveraging JupyterHub and HPC that can be repeated for other modeling systems.

Show More
Composite Resource Composite Resource

ABSTRACT:

This presentation was given at the iEMSs conference held in Fort Collins, CO in June 2018. http://iemss2018.engr.colostate.edu/

Reproducibility of computational workflows is an important challenge that calls for open and reusable code and data, well-documented workflows, and controlled environments that allow others to verify published findings. HydroShare (http://www.hydroshare.org) and GeoTrust (http://geotrusthub.org/), two new cyberinfrastructure tools under active development, can be used to improve reproducibility in computational hydrology. HydroShare is a web-based system for sharing hydrologic data and model resources. HydroShare allows hydrologists to upload model input data resources, add detailed hydrologic-specific metadata to these resources, and use the data directly within HydroShare for collaborative modeling using tools like JupyterHub. GeoTrust provides tools for scientists to efficiently reproduce, track and share geoscience applications by building ‘sciunits,’ which are efficient, lightweight, self-contained packages of computational experiments that can be guaranteed to repeat or reproduce regardless of deployment challenges. We will present a use case example focusing on a workflow that uses the MODFLOW model to demonstrate how HydroShare and GeoTrust can be integrated to easily and efficiently reproduce computational workflows. This use case example automates pre-processing of model inputs, model execution, and post-processing of model output. This work demonstrates how the integration of HydroShare and Geotrust ensures the logical and physical preservation of computation workflows and that reproducibility can be achieved by replicating the original sciunit, modifying it to produce a new sciunit and finally, preserving and sharing the newly created sciunit by using HydroShare's JupyterHub.

Show More