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Curve Number Development using Normalized Difference Vegetation Index for the Contiguous United States in Hydrologic Micro Services
Citation:
Muche, M., R. Parmar, D. Smith, J. Johnston, AND K. Wolfe. Curve Number Development using Normalized Difference Vegetation Index for the Contiguous United States in Hydrologic Micro Services. 2019 AGU Fall Meeting, San Francisco, CA, December 09 - 13, 2019.
Impact/Purpose:
Poster presented at the 2019 AGU Fall Meeting
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.