ClearWater-Riverine

The ClearWater-riverine package is a two-dimensional (2D) water quality transporter model to calculate conservative advection and diffusion of constituents from an unstructured grid of flows within complex river systems and floodplains. It is developed with modern Python by the the U.S. Army Engineer Research and Development Center (ERDC), Environmental Laboratory (EL).

The goal of this model is to simulate the transport (advection and diffusion) of heat and water quality constituents in riverine systems by coupling it to ERDC's ClearWater (Corps Library for Environmental Analysis and Restoration of Watersheds) modules that simulates water quality processes and kinetics. At present, the Temperature Simulation Module (TSM) and Nutrient Simulation Module (NSM) have been successfully coupled to HEC-RAS-2D models via ClearWater-Riverine, simulating fundamental eutrophication processes such as the interactions between temperature, nutrients, algae, dissolved oxygen, and organic matter. ClearWater-Riverine assumes vertical homogeneity. Therefore, it is best suited for evaluating riverine systems during conditions where vertical stratification does not contribute significantly to the water quality dynamics, but where the longitudinal and lateral changes of water quality are important.

A secondary goal is to develop a suite of easy-to-use modern Python tools that build on community-developed scientific workflows, standards, and libraries to automate model setup, prepare input datasets, store output data, and visualize results using Python-based user interfaces such as Jupyter Notebooks.

Example applications

The following plot shows an animation of E. Coli transport in the Ohio River in June, 2010. A sudden inflow of E. Coli enters the Ohio River at Covington on the south shore of the river. The downstream flow and lateral spread of E. Coli over time is due to the transport and mixing processes (advection-diffusion) in the river.

ClearWater-Riverine performance was compared to an existing EFDC model of the Ohio River, and both models were verified with observed data. These comparisons verified that ClearWater-Riverine is accurately capaturing the transport processes in this system. A side-by-side comparison of the two models is shown below.

Repository Directories

src contains the source code to create and run the clearwater_riverine.

examples contains tutorials and useful Juptyer Notebooks.

docs contains relevant reference documentation.

tests will contain clearwater_riverine tests once they are developed.

Getting Started

Installation

Clearwater Riverine is designed to run with Python 3.10.

Follow these steps to install.

1. Install Miniconda or Anaconda Distribution

We recommend installing the light-weight Miniconda that includes Python, the conda environment and package management system, and their dependencies.

NOTE: Follow conda defaults to install in your local user director. DO NOT install for all users, to avoid substantial headaches with permissions.

If you have already installed the Anaconda Distribution, you can use it to complete the next steps, but you may need to update to the latest version.

2. Clone or Download this Clearwater-riverine repository

From this Github site, click on the green "Code" dropdown button near the upper right. Select to either Open in GitHub Desktop (i.e. git clone) or "Download ZIP". We recommend using GitHub Desktop, to most easily receive updates.

Place your copy of this repo folder in any convenient location on your computer.

3. Create a Conda Environment for Clearwater Riverine Modeling

We recommend creating a custom virtual environment with the Conda package, dependency, and environment management for any language (i.e. easily install C++ packages such as GDAL).

We provide an environment.yml file that specifies for Conda how to create a virtual environment that contains the same software dependencies that we've used in development and testing.

Create a ClearWater-modules environment using this conda command in your terminal or Anaconda Prompt console. If necessary, replace environment.yml with the full file pathway to the environment.yml file in the local cloned repository.

conda env create --file environment.yml

Alternatively, use the faster libmamba solver with:

conda env create -f environment.yml --solver=libmamba

If users are experiencing issues with plots NOT displaying in jupyter notebooks once a cell is executed, then we recommend using the environment_working.yml file. We have noticed that later versions of some libraries might be creating a conflict, but we have not been able to track down the root cause since no warnings/errors are given when the plot does NOT display:

conda env create -f environment_working.yml --solver=libmamba

Activate the environment using the instructions printed by conda after the environment is created successfully.

To update your environment to the latest versions of dependencies and/or add additional dependencies to your environment (by first editting environment.yml), run the following command:

conda env update -f environment.yml --solver=libmamba --prune

or to recreate from scratch:

conda env create -f environment.yml --solver=libmamba --force

For additional information on managing conda environments, see Conda's User Guide on Managing Environments.

4. Add your ClearWater-riverine Path to Miniconda/Anaconda sites-packages

To have access to the clearwater_riverine module in your Python environments, it is necessary to have a path to your copy of Clearwater Riverine in Anaconda's sites-packages directory (i.e. something like $HOME/path/to/anaconda/lib/pythonX.X/site-packages or $HOME/path/to/anaconda/lib/site-packages similar).

The easiest way to do this is to use the conda develop command in the console or terminal like this, replacing '/path/to/module/src' with the full file pathway to the local cloned Clearwater-riverine repository:

conda develop '/path/to/module/src'

You should now be able to run the examples and create your own Jupyter Notebooks!

Examples

We recommend viewing or interactively running our Ohio River Jupyter Notebook.

We recommend using JupyterLab to run our tutorial Juptyer Notebooks in the example folder, due to many additional built-in features and extensions. The following JupyterLab extensions are particularly useful:

• lckr-jupyterlab-variableinspector

Contributing

Acknowlgements

This library is developed by ERDC-EL through funding from the ECOMOD project. Dr. Todd E. Steissberg (ERDC-EL) developed the vision for this library as an example of how to couple at water transport model with a water quality reaction model :