John Michael Volk

University of Nevada Reno

Subject Areas: Hydrology, Modeling, Geology, Data Analysis

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ABSTRACT:

Global sensitivity analysis GSA is a useful tool for diagnosing and quantifying uncertainty within hydrologic models. Facilitating advanced model analyses such as GSA of parameters has the potential to help advance our fundamental understanding of hydrologic process representations. This document acts as a working template to apply a GSA method for parameters of the well-known Preceipitation-Runoff Modeling System (PRMS) hydrologic model maintained by the United States Geological Survey. Specifically, it documents a workflow for a moment-independent, GSA method based on empirical cumulative distribution functions named PAWN. The template is a Jupyter notebook that uses an open-source Python package called PRMS-Python; installation instructions for PRMS-Python and links to both PAWN and the Python software are included. PRMS-Python has a built in routine for Monte Carlo parameter resampling that this template demonstrates and uses to implement PAWN. The template is written so that it could be modified for an arbitrary set of PRMS parameters and is heavily commented for clarity. As such, this template along with the open-source Python package aim to encourage and facilitate the greater hydrologic modeling community to conduct advanced model analyses such as GSA. Similarly, the PRMS-Python framework has tools for self-generation of metadata files that track data provenance of large model ensembles- a useful tool for sharing model results on platforms such as HydroShare.

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ABSTRACT:

The netCDF (network Common Data Form) file format is increasingly used to store and manage multidimensional scientific data. Although netCDF files offer multiple advanced features and functionality in their own right, workflows that involve netCDF files can be intimidating for new users due to their binary format. There are several methods to manage netCDF file data including via libraries in programming languages such as Fortran or Python. However these methods require knowledge of the programming languages as a prerequisite. Other user-interface applications such as Panoply, NetCDF Explorer, or ArcGIS have functionality to access, view, and in some cases modify or create netCDF files. Another tool to manage netCDF files is the netCDF operators (NCO). NCO is a set of command line tools developed and maintained by the original creators of the netCDF file, the Unidata program at the University Corporation for Atmospheric Research. As such NCO tools are highly optimized and flexible, allowing a myriad of netCDF workflows. This html-based tutorial aims to demystify basic functionalities and syntax of NCO commands that are useful for analysing netCDF scientific data. The tutorial contains multiple examples that focus on scientific data (e.g. climatic measurements or model output) analysis including code snippets, explanations, and figures. Specifically, part 1 covers basic concatenation and averaging of single and ensemble record variables using the ncrcat, ncecat, ncra, and ncea commands respectively. Part 2 builds on part 1 and focuses on basic and advanced uses of the weighted-averaging command ncwa. Examples of other common NCO commands including breif desctiptions on how to download or install the package, and tools for netCDF visualization are also included in the tutorial. Although the tutorial is not in depth, as it does not explicitly cover all the NCO commands nor all of their options, it is a good starting point as many other NCO commands follow similar syntax and conventions.

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ABSTRACT:

The netCDF (network Common Data Form) file format is increasingly used to store and manage multidimensional scientific data. Although netCDF files offer multiple advanced features and functionality in their own right, workflows that involve netCDF files can be intimidating for new users due to their binary format. There are several methods to manage netCDF file data including via libraries in programming languages such as Fortran or Python. However these methods require knowledge of the programming languages as a prerequisite. Other user-interface applications such as Panoply, NetCDF Explorer, or ArcGIS have functionality to access, view, and in some cases modify or create netCDF files. Another tool to manage netCDF files is the netCDF operators (NCO). NCO is a set of command line tools developed and maintained by the original creators of the netCDF file, the Unidata program at the University Corporation for Atmospheric Research. As such NCO tools are highly optimized and flexible, allowing a myriad of netCDF workflows. This html-based tutorial aims to demystify basic functionalities and syntax of NCO commands that are useful for analysing netCDF scientific data. The tutorial contains multiple examples that focus on scientific data (e.g. climatic measurements or model output) analysis including code snippets, explanations, and figures. Specifically, part 1 covers basic concatenation and averaging of single and ensemble record variables using the ncrcat, ncecat, ncra, and ncea commands respectively. Part 2 builds on part 1 and focuses on basic and advanced uses of the weighted-averaging command ncwa. Examples of other common NCO commands including breif desctiptions on how to download or install the package, and tools for netCDF visualization are also included in the tutorial. Although the tutorial is not in depth, as it does not explicitly cover all the NCO commands nor all of their options, it is a good starting point as many other NCO commands follow similar syntax and conventions.

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ABSTRACT:

Global sensitivity analysis GSA is a useful tool for diagnosing and quantifying uncertainty within hydrologic models. Facilitating advanced model analyses such as GSA of parameters has the potential to help advance our fundamental understanding of hydrologic process representations. This document acts as a working template to apply a GSA method for parameters of the well-known Preceipitation-Runoff Modeling System (PRMS) hydrologic model maintained by the United States Geological Survey. Specifically, it documents a workflow for a moment-independent, GSA method based on empirical cumulative distribution functions named PAWN. The template is a Jupyter notebook that uses an open-source Python package called PRMS-Python; installation instructions for PRMS-Python and links to both PAWN and the Python software are included. PRMS-Python has a built in routine for Monte Carlo parameter resampling that this template demonstrates and uses to implement PAWN. The template is written so that it could be modified for an arbitrary set of PRMS parameters and is heavily commented for clarity. As such, this template along with the open-source Python package aim to encourage and facilitate the greater hydrologic modeling community to conduct advanced model analyses such as GSA. Similarly, the PRMS-Python framework has tools for self-generation of metadata files that track data provenance of large model ensembles- a useful tool for sharing model results on platforms such as HydroShare.

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