Journal
ENVIRONMENTAL MODELLING & SOFTWARE
Volume 116, Issue -, Pages 119-130Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2019.01.007
Keywords
Python; Cloud computing; Shapefile-based data retrieval; Watershed hydrometeorology
Categories
Funding
- National Science Foundation Graduate Research Fellowship Program [DGE-1762114]
- HydroShare Cyberinfrastructure project [ACI 1148453]
- SI2: SSI Landlab project [ACI-1450412]
- Predicting Climate Change Impacts on Shallow landslide risks [CBET 1336725]
- Institute for Translational Health Sciences grant [UL1TR002319]
- Clinical and Translational Sciences Award Program National Center for Data to Health [U24TR002306]
- UW Civil & Environmental Engineering Department
- UW College of Engineering and researchers and scientists of the Sauk-Suiattle Indian Tribe
- Skagit Climate Consortium
- Bureau of Indian Affairs [A05AV00078]
- Washington Research Foundation
- Data Science Environment Project award from the Gordon and Betty Moore Foundaton [2013-10-29]
- Alfred P. Sloan Foundation [3835]
- Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI)
- NSF [EAR 1338606, OAC 1664061, OAC 1664018, OAC 1664119]
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Spatially-distributed time-series data support a range of environmental modeling and data research efforts. A critical first step to any such effort is acquiring interpolated hydrometeorological data. Standardized tools to facilitate this process into analyses have not been readily available for watershed scale research. Here, we introduce the Observatory for Gridded Hydrometeorology (OGH), an open source python library that fills this critical software gap by providing a cyberinfrastructure component to fetch and manage distributed data processed from regional and continental-scale gridded hydrometeorology products. Our approach involves annotating metadata to make gridded data products discoverable and usable within the software, enabling inter-operability and reproducibility of models that use the data. This paper presents the design, architecture, and application of OGH using four commonly practiced use-cases with gridded time-series data at watershed scales. OGH and its associated annotations are distributed via Anaconda Cloud within conda-forge package repository. The tutorial Jupyter notebooks for each example use-case are available within the Freshwater Initiative Observatory repository (https://github.com/Freshwater-Initiative/Obervation). The examples are designed to utilize the compute resources and software libraries provided by HydroShare ((https://www.hydroshare.org/resource/87dc5742cf164126a11ff45c3307fd9d)).
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