Last update: 2020-11-23

Jupyter Notebooks for Use with the TopoFlow Model #

Note: Please do not view these notebooks in GitHub, which is slow and does not work well. Instead, copy their URL from the top of your browser and paste them into the box at: https://nbviewer.jupyter.org. There, notebooks render correctly and very quickly. Both internal and external links will also work!

• An overview and introduction for new users. TopoFlow Getting Started

• How to prepare almost all of the input files that are needed to apply the TopoFlow hydrologic model to a new geographic region. TopoFlow_Prepare_Input_Data

• How to create visualizations (optimized color plots and animations) from TopoFlow output files (netCDF). TopoFlow_Visualization

• How to do basic model calibration with gauging station data. TopoFlow_Calibration_Gauge_Data

• How to do basic model calibration with remote sensing data. TopoFlow_Calibration_Remote_Sensing

• How to prepare rainfall data for a river basin, using the Baro River as an example. TopoFlow_Baro_Rainfall_Prep

• A comparison of multiple, global, remotely-sensed rainfall products (GPM, GLDAS, CHIRPS). TopoFlow_Rainfall_Inputs

• Several other notebooks can be found in the TopoFlow Notebooks Folder. These notebooks show how to use numerous utilities in the TopoFlow 3.6 Python package.

Data Services #

The MINT Data Catalog (see overview) provides access to a curated collection of a datasets in the MINT Data Catalog.

The MINT Data Catalog API provides a programmatic way of interacting with the datasets. Some of this functionality is demonstrated in the following:

• A Python notebook for registering a dataset using the MINT Data Catalog API

• A Python notebook for searching for a dataset using the MINT Data Catalog API

Scientific Variables #

• A Python notebook for performing scientific variable searches/exploration and generating SVO-compatible knowledge graphs grounded to SVO variables and WM indicators.