ABSTRACT:

HydroMET-EO (Hydrologic Model Evaluation Tool - Earth Observation) is the first-of-its-type desktop-based semi-automatic software designed for Soil and Water Assessment Tool (SWAT) hydrologic model. In its current version, HydroMET-EO harmonizes EO datasets of evapotranspiration, Leaf Area Index, and Soil Moisture from varying sources and resolutions to SWAT’s structure and time-step, enabling subbasin-level evaluation of model outputs. Guided by a user-friendly graphical interface with reproducible workflows, statistical metrics, and visual aids, HydroMET-EO streamlines EO integration and thus advances how we typically evaluate the accuracy and physical consistency of hydrologic models and watershed management decisions. Our case studies highlight the tool’s effectiveness, demonstrating that models calibrated solely on streamflow data, despite excellent performance, may fail to capture other critical hydrologic processes.

Here we provide HydroMET-EO software executable, a user guide, and an example SWAT project with pre-processed EO data.

Developed by Linji Wang, Purdue University.

ABSTRACT:

This data collection consists of landuse and soil rasters andcn grind for Cyber Training Class week 12 works. Initial codes were provided by the instructor Dr. Venkatesh Merwade.

The landuse raster file is a reclassified National Land Cover Dataset (NLCD) raster. Original landuse raster file contains integer values 11-95 to represent different types of land cover (e.g. 11 represents open water). It was reclassified based on a .csv file that contains original value ranges and new values.

The soil raster was created from Gridded Soil Survey Geographic (gSSURGO) database file. It contains multiple features that represent different soil properties. One field named "HSG_Index" gives information about hydrologic soil group (1-HSG A, 2-HSG B, 3-HSG C, 4-HSG D).

The CN grind is generated based on the landuse raster file, the soil raster file, and a curve number lookup table. The curve number lookup table contains combinations of landuse value and soil HSG_Index value combinations and respective CN value.

All calculations were completed though python codes using Jupyter notebook with Anaconda 5.1 on MyGeoHub.