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(HS 2) Automate Workflows using Jupyter notebook to create Large Extent Spatial Datasets


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Created: May 13, 2021 at 10:38 p.m.
Last updated: Apr 14, 2024 at 11:21 a.m.
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Abstract

We implemented automated workflows using Jupyter notebooks for each state. The GIS processing, crucial for merging, extracting, and projecting GeoTIFF data, was performed using ArcPy—a Python package for geographic data analysis, conversion, and management within ArcGIS (Toms, 2015). After generating state-scale LES(Large Extent Spatial) datasets in GeoTIFF format, we utilized the xarray and rioxarray Python packages to convert GeoTIFF to NetCDF. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. Xarray facilitated data manipulation and metadata addition in the NetCDF file, while rioxarray was used to save GeoTIFF as NetCDF. These procedures resulted in the creation of three composite HydroShare resources (HS 2, HS 3 and HS 4) for sharing state-scale LES datasets. Notably, due to licensing constraints with ArcGIS Pro, a commercial GIS software, the Jupyter notebook development was undertaken on a Windows OS.

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COPY FOR ARCHIVING OLD RESOURCES: (HS 1) Toward Seamless Environmental Modeling: Integration of HydroShare with Server-side Methods for Exposing Large Datasets to Models Iman Maghami  Private &  Shareable None

How to Cite

Choi, Y. (2024). (HS 2) Automate Workflows using Jupyter notebook to create Large Extent Spatial Datasets, HydroShare, http://www.hydroshare.org/resource/a52df87347ef47c388d9633925cde9ad

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/
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