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

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.