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Streamlining land surface model Initialization: Automated data retrieval for VELMA using HMS REST API and GDAL.

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Area of Science:

  • Environmental science
  • Hydrology
  • Geospatial analysis

Background:

  • Gridded land surface models (LSMs) require extensive continuous monitoring data.
  • Data acquisition and management for LSMs are challenging and error-prone.
  • Existing workflows hinder efficient environmental model simulations.

Purpose of the Study:

  • To develop an automated workflow for retrieving and processing essential data for LSM simulations.
  • To streamline the initialization of environmental models like the Visualizing Ecosystem and Land Management Assessment (VELMA).
  • To enhance the efficiency and reduce errors in hydrological modeling processes.

Main Methods:

  • Automated retrieval of digital elevation models (DEMs) from Google Earth Engine (GEE).
  • Processing of meteorologic drivers and surface runoff time series data.
  • Utilized the USEPA's Hydrologic Micro Services (HMS) REST API and GDAL with Python.

Main Results:

  • Demonstrated a reusable methodology for automated data processing in LSMs.
  • Significantly reduced data preparation time and minimized manual processing errors.
  • Enabled more efficient initialization of VELMA and other gridded LSMs.

Conclusions:

  • The automated workflow enhances operational capabilities for users of gridded LSMs.
  • This innovation improves the efficiency and reliability of environmental model simulations.
  • The methodology offers a scalable solution for future hydrological and land surface modeling studies.