Abstract
Continuous monitoring data required for performing environmental model simulations using gridded land surface models (LSMs) are often difficult to obtain and manage, making the modeling process challenging and prone to error. In response, this study focuses on automated retrieval and processing of digital elevation models (DEMs from Google Earth Engine (GEE)), meteorologic drivers of hydrology, and surface runoff time series data, using the Visualizing Ecosystem and Land Management Assessment (VELMA) model as a case study. Our automation methodology is accomplished using the USEPA's Hydrologic Micro Services (HMS) Representation State Transfer (REST) application programming interface (API) and Geospatial Data Abstraction Library (GDAL) with Python. This workflow provides greater efficiency, minimizes data preparation time, reduces manual processing errors, and provides a reusable methodology for use in other modeling studies. With this innovation, users of VELMA and other gridded LSMs will be able to initialize simulations more efficiently, improving their operational capabilities.