Science Inventory

NHDPlusV2 Catchment Scale Curve Number and NDVI Dataset

Citation:

SMITH, D., M. MUCHE, K. L. WOLFE, R. S. PARMAR, AND J. M. JOHNSTON. NHDPlusV2 Catchment Scale Curve Number and NDVI Dataset. U.S. Environmental Protection Agency, Washington, DC, EPA/600/C-21/001, 2021.

Impact/Purpose:

This dataset is a useful resource for watershed modelers for event rainfall-runoff calculation. The Curve Number (CN) is a simple yet widely used method of calculating runoff from rainfall. The method needs hydrologic soil group, land use, and hydrologic condition. The primary challenge in automating the generation of runoff time series using the Curve Number method is the selection of the hydrologic condition. The customary approach to specifying hydrologic condition requires site specific expert analysis that hinders scaling the approach to larger areas. We used MODIS NDVI satellite vegetation change data as an indicator of hydrologic condition. In our calculation of CN, 250-meter resolution MODIS NDVI satellite raster data was aggregated by NHDPlusV2 catchment by using spatially weighted approach in order to calculate the mean NDVI value for each 16-day timestep of the data, from 2001 through 2017. The mean NDVI values were then used to determine the hydrologic condition being Poor, Normal or Good for their corresponding timespans, for the NLCD land-cover types and NDVI ranges specified. The spatially weighted aggregations of the NDVI raster data for approximately 2.65 million CONUS catchments were performed using Google Earth Engine. We used the EPA StreamCat dataset to obtain catchment level NLCD 2011 landcover data. We also used StreamCat dataset to obtain catchment level STATSGO derived sand and clay soil composition percentages; these were used to determine the hydrologic soil group (HSG) of each catchment. Using the land cover, hydrologic soil group, and 16-day timestep hydrologic condition values we determined 16-day timestep CN for each CONUS catchment for 2001 through 2017 from USDA’s Soil Conservation Service curve number tables. In order to use these CN values for time spans outside the NDVI data range, we averaged each 16-day period over the 17 years to have CN values resulting in 23 values for each catchment.

Description:

Hydrologic Micro Services (HMS) is a computational platform developed by the U.S. Environmental Protection Agency (USEPA) with a collection of data and software components for building hydrologic and water quality workflows. The platform is devised for providing national capability to address emerging challenges related to hydrology and water quality with advances in computational technologies and data availability. HMS components include data provisioning such as precipitation and runoff; and simulation algorithms for water quantity and quality modeling. The platform uses NHDPlus catchments to standardize the overall system for data interoperability in hydrologic sciences. In HMS, Curve Number (CN), an empirical parameter to estimate runoff from rainfall events, has been developed at the NHDPlus catchments level using 16-day and 250 m resolution Moderate Resolution Imaging Spectro Radiometer (MODIS) - Normalized Difference Vegetation Index (NDVI). Traditionally, CN integrates the combined hydrologic influences of soil type, land use, land management, and hydrologic condition to estimate runoff from single-event rainfall which was developed by the United States Department of Agriculture - Soil Conservation Service (USDA-SCS), now the Natural Resources Conservation Service (NRCS). In HMS, NDVI is used to develop a time-series of CN to account for seasonal land cover changes and capture the spatial and temporal variability of hydrologic conditions which influence the rainfall-runoff relationship. The Google Earth Engine was used for download and processing of MODIS-NDVI from 2001 to 2017 with 23 composite images per year to estimate time-series CN values for each 16-day period. Developing nationally capable catchment-based CN could help modelers and managers to conduct more timely water and land management decisions using available resources.

Record Details:

Record Type: DOCUMENT ( DATA/SOFTWARE/ SCIENTIFIC DATA)
Product Published Date: 04/08/2021
Record Last Revised: 06/30/2022
OMB Category: Other
Record ID: 351307