NWM SWE Point-Scale Evaluation for the Tuolumne River Watershed

To support this evaluation, this HydroShare resource leverages evaluation code developed as part of an ongoing NSF-funded project in which the authors of this HydroShare resource are involved in. The source code included in this resource provides evaluation utilities and is installed through the provided environment file as a local Python package. This package-based structure helps organize reusable code for evaluating modeled results against observations.

The package includes shared statistical metrics such as RMSE, MSE, Pearson correlation, Spearman rank correlation, Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency, R-squared, bias, percent bias, absolute relative bias, total difference, and Condon category. It also includes plotting utilities for site-level time series and mapped summaries of evaluation metrics across sites.

Repository structure

The contents of this HydroShare resource include:

• nwm_swe_point_scale_evaluation.ipynb: Main Jupyter Notebook for retrieving NWM SWE data, accessing CCSS observations, comparing modeled and observed SWE, and visualizing model performance.
• domain_data: Domain-specific input files used by the notebook, such as watershed boundary data or other spatial files needed to define the study area.
• img: Images and figures used in the notebook or supporting documentation.
• src: Source code for the local cssi_evaluation Python package used by the notebook.
• cssi_env.yml: Conda environment file used to create the recommended computational environment for running the notebook.
• pyproject.toml: Python package metadata and dependency configuration used to install the local package.
• LICENSE: License information for the Python package.

The evaluation framework lives under src/cssi_evaluation/:

.
├── nwm_swe_point_scale_evaluation.ipynb   # Main notebook
├── domain_data/                           # Watershed/domain input files
├── img/                                   # Images used in the notebook or README
├── cssi_env.yml                           # Conda environment for running the notebook
├── pyproject.toml                         # Package metadata and installation settings
├── LICENSE                                # License information
└── src/
    └── cssi_evaluation/                   # Core evaluation framework code
        ├── external_data_access/          # Observation and reference-data access helpers
        ├── models/                        # Model-specific adapters
        ├── utils/                         # Shared evaluation utilities
        └── variables/                     # Variable-specific evaluation workflows

Getting Started

Launch the Notebook

To launch the notebook, click the Open with button in the upper-right corner of this HydroShare resource and select CIROH-2i2c JupyterHub. When prompted to choose a server option, select a medium machine using the New Pangeo image.

Set up the recommended local environment

Run these commands from the repository root. This assumes that conda and mamba are already installed (e.g., conda install mamba -n base -c conda-forge).

mamba env create -f cssi_env.yml
conda activate cssi_evaluation
python -m ipykernel install --user --name=cssi_evaluation

Once the enviroment is instlled, it may take a few mintues for it to appear. you may need to harf refersh the browser. Then click on the kernel from the toolbar and click on change kernel and choose the one that is named cssi_evaluation.

After the environment is installed, it may take a few minutes for the new kernel to appear in JupyterLab. You may need to hard refresh the browser. Then, from the notebook toolbar, click the current kernel name or go to Kernel -> Change Kernel, and select the kernel named cssi_evaluation.