March e-Newsletter Community Guest Spotlight with Taher Chegini
Posted Mar 10, 2025
HySetter: Hydroclimate Data Subsetter
Taher Chegini, Postdoctoral Research Associate, Purdue University
Receiving the Hydroinformatics Innovation Fellowship from CUAHSI has empowered me to address critical challenges facing the hydrology research community through the development of HySetter. My experiences conducting hydrological studies over recent years highlighted a recurring issue: researchers, including myself, frequently need diverse hydroclimate datasets—such as climate forcing data, soil types, land cover information, river network details, and topographic data—to successfully perform both physical and data-driven hydrological modeling. However, accessing and preparing these datasets is often cumbersome, requiring extensive navigation through various data sources, handling multiple file formats, and writing substantial boilerplate code, consuming considerable time and resources.
With the support provided by CUAHSI's fellowship funding, I developed HySetter, an intuitive, open-source Python library specifically designed to simplify access to critical hydroclimate datasets. HySetter leverages the powerful HyRiver software stack (https://docs.hyriver.io), granting straightforward access to essential datasets including NHDPlusV2 catchments, Daymet and GridMet climate data, gNATSGO soil data, the 3D Elevation Program, and various remote raster datasets. Its user-friendly command-line interface (CLI) enables researchers, irrespective of their programming expertise, to easily specify data requirements via intuitive YAML-based configuration files, dramatically reducing barriers to accessing and utilizing complex hydrological data.
HySetter is particularly valuable in regional or large-scale modeling projects where data integration is typically challenging due to the complexity and heterogeneity of available datasets. For instance, when performing hydrological simulations or developing predictive models that span large geographic areas, consistently accessing, aligning, and preprocessing data can be a major logistical challenge. HySetter efficiently manages this complexity by automating these processes and ensuring consistency across datasets. Researchers can readily adapt and extend published studies to new areas simply by modifying the configuration file, thereby enhancing reproducibility and promoting FAIR (Findable, Accessible, Interoperable, and Reusable) data principles.
Ultimately, the Hydroinformatics Innovation Fellowship from CUAHSI has been instrumental in transforming HySetter from concept to reality, directly addressing critical community needs and substantially enhancing research efficiency, reproducibility, and accessibility. Detailed documentation and additional resources and examples are available on HySetter's website at https://hysetter.readthedocs.io.