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137 changes: 52 additions & 85 deletions README.md
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<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
**Table of Contents**

- [Purpose](#purpose)
- [About](#about)
- [Installation](#installation)
- [Contributing](#contributing)
- [Example Notebooks](#example-notebooks)
- [Overview of Repository Contents](#overview-of-repository-contents)
- [Getting started / Example notebooks](#getting-started--example-notebooks)
- [Community engagement](#community-engagement)
- [Disclaimer](#disclaimer)

<!-- END doctoc generated TOC please keep comment here to allow auto update -->

## Purpose
## About

The purpose of this repository is to refactor and redesign the [PRMS modeling
system](https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms)
while maintaining its functionality. Code modernization is a step towards
unification with [MODFLOW 6 (MF6)](https://github.com/MODFLOW-USGS/modflow6).
Welcome to the pywatershed repository!

The following motivations are taken from our [AGU poster from December
2022](https://agu2022fallmeeting-agu.ipostersessions.com/default.aspx?s=05-E1-C6-40-DF-0D-4D-C7-4E-DE-D2-61-02-05-8F-0A)
which provides additional details on motivations, project status, and current
directions of this project as of approximately January 2023.
Pywatershed is Python package for simulating hydrologic processes motivated by
the need to modernize important, legacy hydrologic models at the USGS,
particularly the
[Precipitation-Runoff Modeling System](https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms)
(PRMS, Markstrom et al., 2015) and its role in
[GSFLOW](https://www.usgs.gov/software/gsflow-coupled-groundwater-and-surface-water-flow-model>)
(Markstrom et al., 2008).
The goal of modernization is to make these legacy models more flexible as process
representations, to support testing of alternative hydrologic process
conceptualizations, and to facilitate the incorporation of cutting edge
modeling techniques and data sources. Pywatershed is a place for experimentation
with software design, process representation, and data fusion in the context
of well-established hydrologic process modeling.

Goals of the USGS Enterprise Capacity (EC) project include:

* A sustainable integrated, hydrologic modeling framework for the U.S.
Geological Survey (USGS)
* Interoperable modeling across the USGS, partner agencies, and academia

Goals for EC Watershed Modeling:

* Couple the Precipitation-Runoff Modeling System (PRMS, e.g. Regan et al,
2018)  with MODFLOW 6 (MF6, e.g. Langevin et al, 2017) in a sustainable
way
* Redesign PRMS to be more modern and flexible
* Prioritize process representations in the current National Hydrological
Model (NHM) based on PRMS 5.2.1

Prototype an EC watershed model: "pywatershed"

* Redesign PRMS quickly in python
* Couple to MF6 via BMI/XMI interface (Hughes et al, 2021; Hutton et al, 2020)
* Establish a prototyping ground for EC codes that couples to the compiled
framework: low cost proof of concepts (at the price of potentially less
computational performance) * Enable process representation hypothesis testing
* Use cutting-edge techniques and technologies to improve models
* Machine learning, automatic differentiation
* Address challenges of modeling across space and time scales
* Transition prototype watershed model to compiled EC code
For more information on the goals and status of pywatershed, please see the [pywatershed docs](https://pywatershed.readthedocs.io/).


## Installation
Expand All @@ -81,7 +61,7 @@ all platforms.
The `pywatershed` package is [available on
conda-forge](https://anaconda.org/conda-forge/pywatershed). The installation
is the quickest way to get up and running by provides only the minimal set of
dependencies (not including jupyter nor all packages needed for running the
dependencies (not including Jupyter nor all packages needed for running the
example notebooks, also not suitable for development purposes).

We recommend the following installation procedures to get fully-functional
Expand All @@ -92,7 +72,7 @@ repository before installing `pywatershed` itself. Mamba will be much faster
than Ananconda (but the conda command could also be used).

If you wish to use the stable release, you will use `main` in place of
`<branch>` in the following commands. If you want to follow developemnt, you'll
`<branch>` in the following commands. If you want to follow development, you'll
use `develop` instead.

Without using `git` (directly), you may:
Expand Down Expand Up @@ -121,67 +101,54 @@ you will also need to activate this environment by name.)

We install the `environment_w_jupyter.yml` to provide all known dependencies
including those for running the example notebooks. (The `environment.yml`
does not contain jupyter or jupyterlab because this interferes with installation
on WholeTale, see Example Notebooks seection below.)
does not contain Jupyter or JupyterLab because this interferes with installation
on WholeTale, see Getting Started section below.)

## Contributing

See the [developer documentation](./DEVELOPER.md) for instructions on setting up
a development environment. See the [contribution guide](./CONTRIBUTING.md) to
contribute to this project.
## Getting started / Example notebooks

## Example Notebooks
Please note that you can browse the API reference, developer info, and index
in the [pywatershed docs]((https://pywatershed.readthedocs.io/)). But
*the best way to get started with pywatershed is to dive into the example
notebooks*.

For introductory example notebooks, look in the
[`examples/`](https://github.com/EC-USGS/pywatershed/tree/main/examples>)
directory in the repository. Numbered starting at 00, these are meant to be
completed in order. Non-numbered notebooks coveradditional topics. These
notebooks are note yet covered by testing and so may be expected to have some
issues until they are added to testing. In `examples/developer/` there are
notebooks of interest to developers who may want to learn about running the
software tests.

Though no notebook outputs are saved in Github, these notebooks can easily
navigated to and run in WholeTale containers (free but sign-up or log-in
required). This is a very easy and quick way to get started without needing to
install pywatershed requirements yourself. WholeTale is an NSF funded project
and supports logins from many institutions, e.g. the USGS, and you may not need
to register.

There are containers for both the `main` and `develop` branches.
completed in order. Numbered starting at 00, these are meant to be completed
in order. Notebook outputs are not saved in Github. But you can run these
notebooks locally or using WholeTale (an NSF funded project supporting logins
from many institutions, free but sign-up or log-in required)
where the pywatershed environment is all ready to go:

[![WholeTale](https://raw.githubusercontent.com/whole-tale/wt-design-docs/master/badges/wholetale-explore.svg)](https://dashboard.wholetale.org)

* [WholeTale container for latest release (main
branch)](https://dashboard.wholetale.org/run/64ae29e8a887f48b9f173678?tab=metadata)
* [WholeTale container for develop
branch](https://dashboard.wholetale.org/run/64ae25c3a887f48b9f1735c8?tab=metadata)
* [Run latest release in WholeTale](https://dashboard.wholetale.org/run/64ae29e8a887f48b9f173678?tab=metadata)
* [Run the develop branch in WholeTale](https://dashboard.wholetale.org/run/64ae25c3a887f48b9f1735c8?tab=metadata)

WholeTale will give you a jupyter-lab running in the root of this
WholeTale will give you a JupyterLab running in the root of this
repository. You can navigate to `examples/` and then open and run the notebooks
of your choice. The develop container may require the user to update the
repository (`git pull origin`) to stay current with development.

## Overview of Repository Contents
Non-numbered notebooks in `examples/` cover additional topics. These
notebooks are not yet covered by testing and you may encounter some
issues. In `examples/developer/` there are notebooks of interest to
developers who may want to learn about running the software tests.

The contents of directories at this level is described. Therein you may discover
another README.md for more information.

```
.github/: Github actions, scripts and Python environments for continuous integration (CI) and releasing,
asv_benchmarks/: preformance benchmarking by ASV
autotest/: pywatershed package testing using pytest
autotest_exs/: pywatershed example notebook testing using pytest
bin/:PRMS executables distributed
doc/:Package/code documentation source code
evaluation/: tools for evaluation of pywatershed
examples/:How to use the package, mostly jupyter notebooks
prms_src/:PRMS source used for generating executables in bin/
pywatershed/:Package source
reference/:Ancillary materials for development
resources/:Static stuff like images
test_data/:Data used for automated testing
```
## Community engagement

We value your feedback! Please use [discussions](https://github.com/EC-USGS/pywatershed/discussions)
or [issues](https://github.com/EC-USGS/pywatershed/issues) on Github.
For more in-depth contributions, please start by reading over
the pywatershed
[DEVELOPER.md](https://github.com/EC-USGS/pywatershed/blob/develop/DEVELOPER.md) and
[CONTRIBUTING.md](https://github.com/EC-USGS/pywatershed/blob/develop/CONTRIBUTING.md)
guidelines.

Thank you for your interest.


## Disclaimer

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Expand Up @@ -7,7 +7,7 @@ pywatershed: A hydrologic model in Python
Welcome to the `pywatershed` docs!

Pywatershed is Python package for simulating hydrologic processes motivated by
the need to modernize important, legacy hydrologic models at the USGS
the need to modernize important, legacy hydrologic models at the USGS,
particularly the
`Precipitation-Runoff Modeling System <https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms>`__
(PRMS, Markstrom et al., 2015) and its role in
Expand All @@ -18,12 +18,12 @@ representations, to support testing of alternative hydrologic process
conceptualizations, and to facilitate the incorporation of cutting edge
modeling techniques and data sources. Pywatershed is a place for experimentation
with software design, process representation, and data fusion in the context
of well established hydrologic process modeling.
of well-established hydrologic process modeling.

The Python language was choosen because it is accessible to a wide audience of
potential contributors which will help foster community development and
experimentation. A large number of advanced libraries available for Python can
be applied to hdyrologic modeling, including libraries for parallelism, data
also be applied to hdyrologic modeling, including libraries for parallelism, data
access and manipulation, and machine learning.

Following the conceptual design of PRMS, pywatershed calculates explicit solutions
Expand All @@ -36,12 +36,12 @@ land use change at temporal scales ranging from days to centuries.
Pywatershed enhances PRMS with a new software design that is object-oriented and highly
flexible, allowing users to easily run "sub-models", replace process representations, and
incorporate new data. There are base classes which manage mass and energy conservation
and the implementation of concrete, process classes follows a self-describing design
which allows for Model class to properly connect hydrologic proecsses based on their
own descriptions of themselves. A variety of input data sources is managed by the
and the implementation of concrete process classes follows a self-describing design
which allows for a Model class to connect hydrologic process classes based on their
descriptions of themselves. A variety of input data sources is managed by the
Adapter class which implements subclasses for different sources. The design of
pywatershed is documented in these docs and also deomonstrated by numbered jupyter
notebooks in the `examples/` directory.
pywatershed is documented in these docs and also demonstrated by numbered Jupyter
Notebooks in the `examples/` directory.

The flexible structure of pywatershed helps it to couple with other hydrologic
models. We can easily one-way couple pywatershed to
Expand All @@ -56,7 +56,7 @@ sustainable manner that allows individual software components to evolve independ


=========================
Current Version: 1.0.0
Current version: 1.0.0
=========================
With pywatershed version 1.0.0, we have faithfully reproduced the PRMS process representations used in
the USGS `National Hydrolgical Model <https://pubs.usgs.gov/publication/tm6B9>`__ (NHM, Regan et al.,
Expand All @@ -73,11 +73,11 @@ gridded configurations and cascading flows.
We are also working on reservoir representations.

=================
Getting Started
Getting started
=================
Please note that you can browse the API reference, developer info, and index
using the table of contents on the left. But *the best way to get started
with pywatershed is to dive in to the example notebooks*.
with pywatershed is to dive into the example notebooks*.

| For introductory example notebooks, look in the `examples/ <https://github.com/EC-USGS/pywatershed/tree/main/examples>`_ directory in the repository. Numbered starting at 00, these are meant to be completed in order. Notebook outputs are not saved in Github. But you can run these notebooks locally or using WholeTale (free but sign-up or log-in required) where the pywatershed environment is all ready to go:
Expand All @@ -92,10 +92,10 @@ on both `running locally <https://github.com/EC-USGS/pywatershed#installation>`_
or `using WholeTale <https://github.com/EC-USGS/pywatershed#example-notebooks>`_.

========================
Community Engagement
Community engagement
========================
We value your feedback! Please use `discussions <https://github.com/EC-USGS/pywatershed/discussions>`_
or `issues <https://github.com/EC-USGS/pywatershed/issues>`_ on our Github page. You may also suggest
or `issues <https://github.com/EC-USGS/pywatershed/issues>`_ on Github. You may also suggest
edits to this documentation or open an issue by clicking on the Github Octocat at the top of the page.
For more in-depth contributions, please start by reading over
the `DEVELOPER.md file <https://github.com/EC-USGS/pywatershed/blob/develop/DEVELOPER.md>`_.
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