MESAS

Multiresolution Estimation of StorAge Selection functions

What are StorAge Selection functions?

StorAge Selection is a theoretical framework for modeling transport through control volumes. It is appropriate if you are interested a system that can be treated as a single control volume (or a collection of such), and wish to make minimal assumptions about the internal organization of the transport. SAS assumes that the material leaving a system is some combination of the material that entered at earlier times. This can be useful for constructing simple models of very complicated flow systems, and for inferring the emergent transport properties of a system from tracer data.

SAS is a very general framework – at heart, it is composed of two parts: a way of keeping track of changes in the age structure of the ‘population’ of fluid parcels in a system, and a rule (or rules) that determines how that age structure changes. By using tracers to investigate what rule best mimics the behaviour of a given system, we can learn important information about that system – most directly, something about the volume of tracer-transporting fluid that is actively ‘turning over’. SAS is a generalization of commonly-used transit time theory to the case where fluxes vary in time, and there may be more than one outflow (e.g. discharge and ET).

For more information see the free HydroLearn course: Tracers and transit times in time-variable hydrologic systems: A gentle introduction to the StorAge Selection (SAS) approach

What is mesas.py?

The package will eventually include a number of component tools for building SAS models and using them to analyze tracer data.

Currently the main repository codebase includes three submodules:

mesas.sas

The main submodule for building and running SAS models

mesas.utils

Tools for manipulating SAS model input data and results

mesas.utils.vis

Visualization of SAS model results

I hope to expand these in the future, and welcome contributors through the GitHub repository

Support, bugs, suggestions, etc.

If you find a bug please open an issue on GitHub here: https://github.com/charman2/mesas/issues

You can also use the issue tracer to make suggestions to improve the code.

If you need further help using the code or interpreting the results, please get in touch: charman1 at jhu dot edu

Table of contents

Getting started

• Installation
  • Build from source
• Quickstart
  • Input timeseries
  • Configuration information
  • Running mesas.py
  • Plot the results

User guide

• Timeseries Inputs and Outputs
  • Input and output fluxes
  • Other input timeseries
  • Output timeseries
• Configuring the model
• Specifying SAS functions
  • Using a gamma or beta distribution
  • Using parameterized distributions from scipy.stats
  • As a piecewise linear CDF
• Specifying solute parameters
  • Modifying parameters
• Optional parameters
• Extracting results
• Visualizing outputs

Reference

• SAS Functions
  • mesas.sas.functions module
• SAS Specification Class
  • mesas.sas.specs module
• SAS Model class
  • mesas.sas.model module

Indices and tables

• Index

• Module Index

• Search Page