Jupyer Supported Interactive Data Processing Workflow for Intensively Monitored Watersheds across the US

Abstract:

Recent releases of large sample hydrological datasets have provided unique opportunities for comparative studies, process understanding and model development. Often, these datasets are restricted to a few variables focusing on streamflow, precipitation and air temperature - lacking key water balance components. By capitalizing on publicly available but unorganized/individual intensively monitored watersheds, we synthesize, organize and disseminate a comprehensive hydrometeorological dataset to the wider hydrological community. The hydrometeorological dataset includes daily streamflow, precipitation, snowmelt, isotope and soil moisture observations from the CZO and LTER initiatives, as well as other hydrologic observatories, comprising 30 sites total. Here, we employ the Jupyter machinery (JupyterLab, Widgets, and Voila) to interactively acquire and transform the unorganized raw data to a quality controlled - gap filled ready to use hydrometeorological data. By focusing the machinery on reproducibility and accessibility to facilitate contributions from different groups, this work will form the basis for future community initiatives to benchmark data processing approaches, comparative hydrological studies and geographically comprehensive data-driven forecasting. This work is part of the Jupyter meets the Earth project, supported by the NSF EarthCube program under awards 1928406, 1928374.