Jupyter Supported Interactive Hydrometeorological Data Preprocessing

Hydrological Data Quality Control and Filling Missing Values

Raw hydrometeorological datasets contain errors, gaps and unrealistic values that needs preprocessing.  The objective of this work is developing an interactive data preprocessing platform that enables acquiring and transforming publicly available raw hydrometeorological data to a ready to use  dataset. This interactive platform is at the core of the Comprehensive Hydrologic Observatory SEnsor Network CHOSEN dataset (Zhang et al. 2021 submitted to HP). CHOSEN provides a multitude of intensively measured hydrometeorological datasets (e.g., snow melt and soil moisture data besides the common precipitation, air temperature and streamflow measurements) across 30 watersheds in the conterminous US.


Please click the above binder link to launch the notebook on cloud.


The interactive data preprocessing platform starts with acquiring a raw hydrometeorological data table and proceeds with three interactive computations to fill missing values, 1. Interpolation, 2. Regression and 3. Climate Caltalog


Edom Moges, Liang Zhang, Laurel Larsen, Lindsey Heagy and Fernando Perez, 2021. EM_v01_Jupyter Supported Interactive Data Processing Workflow. Accessed 06/11/2021 at https://github.com/EMscience/CHOSENDryCreek


This work is supported by the NSF Earth Cube Program under awards 1928406 and 1928374.


Edom Moges
Environmental Systems Dynamics Laboratory (ESDL)
University of California, Berkeley