Data used to evaluate land surface heterogeneity captured by topography-based subgrid structures in grid-based and watershed-based computational units

Motivated by the need to improve the representation of small-scale surface heterogeneity in Earth System Models (ESMs), new algorithms have been introduced to discretize ESM computational units into a variable number of subgrid topographic units for improving model simulations with minimal increase in computational demand. The algorithms can be applied to both structured (regular grid) and unstructured (e.g., watersheds) computational units (CUs) to derive topography-based subgrid units (TGUs). This study evaluates the capability of the TGUs to capture surface heterogeneity within grid- versus watershed-based CUs. For this purpose, TGUs are derived for the grid- and watershed-based CUs at four equivalent spatial scales (1^o, 0.5^o, 0.25^o, and 0.125^o for grid-based and HUC07, HUC08, HUC09, and HUC10 for watershed-based CUs) over the CONUS domain. Statistical metrics are computed at CU and TGU levels at each spatial scale for comparison. Results show that compared to the grid-based TGUs, the watershed-based TGUs are superior in capturing spatial heterogeneity associated with topographic slope, land cover, and surface climate (precipitation, temperature, and snow water equivalent (SWE)), despite their similar capability in capturing topographic elevation. This improved capability of the watershed-based TGUs is consistently found across all spatial scales examined. At the finest spatial scales (0.125 and HUC10), the watershed-based TGUs better capture the observed precipitation, temperature, and SWE than the grid-based TGUs at 94%, 84%, and 72% of the SNOTEL sites, respectively, highlighting the potential advantage of the watershed-based TGUs for improving accuracy and realism in ESM simulations.

The data used to evaluate the capability to capture land surface heterogeneity by the topography-based subgrid structures of grid-based and watershed-based computational units include (1) input datasets that represent variability of land surface heterogeneity caused by topography [Lehner et al., 2008], land cover [Didan, K., 2021], and climate [Thornton et al., 2022]; (2) outputs of statistical metrics calculated to evaluate the capabilities of the topography-based subgrid structures of grid-based and watershed-based representations; and, (3) point climate data obtained from the SNOTEL sites utilized to evaluate capability to capture observed variability of climate fields.    

The data files are organized as follows:

Input data: 

• Topography:
  • DEM (dem.zip)
  • Slope (slope.zip)
  • Raster watershed IDs (watershed_raster_IDs.zip)
  • Watershed boundaries (watershed_boundaries.zip)
• Climate:
  • Gridded data:
    • Precipitation data: Gridded monthly precipitation data for the year 2015 (prcp.zip).
    • Temperature data: Gridded monthly maximum temperature data for the year 2015 (tmax.zip).
    • Snow water equivalent data: Gridded monthly snow water equivalent data for the year 2015 (swe.zip).
  • Point data
    • Point observation data: precipitation, temperature and snow water equivalent measured at the SNOTEL sites (SNOTEL_data.zip).
    • Site information data: Information about the SNOTEL sites (SNOTEL_siteInfo.zip).
• NDVI: High resolution (250 meter) NDVI data used as a proxy for the land cover spatial patterns (NDVI.zip). 

Output data:

• Statistical metrics (e.g., standard deviation and range) for the grid-based representation (grid_based_statistical_metrics.zip) including:
  • Surface elevation
  • Topographic slope
  • NDVI
  • Climate variables
• Statistical metrics (e.g., standard deviation and range) for the watershed-based representation (watershed_based_statistical_metrics.zip) including:
  • Surface elevation
  • Topographic slope
  • NDVI
  • Climate variables

Tools:

• Code (scripts.zip): Python scripts used to calculate the statistical metrics to evalute the capability of the grid-based and watershed-based TGUs to capture land surface heterogeneity.