Improving USGS National Hydrologic Model Parameterization with Satellite-Based Phenology Products

Thursday, 18 December 2014
Paul D Micheletty1,2, Terri S Hogue1, Lauren Hay3, Steven L Markstrom4 and Robert S. Regan2,4, (1)Colorado School of Mines, Golden, CO, United States, (2)USGS Colorado Water Science Center Lakewood, Lakewood, CO, United States, (3)USGS, Baltimore, MD, United States, (4)USGS, National Research Program, Baltimore, MD, United States
Hydrologists and water resource engineers are simulating hydrologic processes at the continental scale assisted by the advancement of high-performance computing and the accessibility of large-scale climate and hydrologic datasets. The United States Geological Survey (USGS) is developing a National Hydrologic Model (NHM) that supports coordinated, comprehensive, and consistent hydrologic model development and simulations of the conterminous United States (CONUS). The goal of this project is to improve model parameterization and ultimately streamflow predictions across the CONUS using remotely sensed data products. The current work will specifically improve estimates of the growing season in the NHM through the integration of satellite-based phenology products developed at the USGS Earth Resources Observation and Science (EROS) Center. Currently, the NHM defines the growing season using one of three temperature-index methods: 1) first and last freezing air temperatures; 2) temperature threshold for a specified begin and end month; and 3) dynamic specification. The USGS/EROS RSP products are based on a timeseries analysis of the normalized difference vegetation index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors. Using the phenological metrics derived from AVHRR, we define a new growing season parameter set for the CONUS from 1989 to 2013, which ultimately will enhance estimations of daily transpiration rates throughout the model domain. Using default temperature-index based estimates of growing season and RSP derived estimates, we provide statistical evaluation and comparison of the NHM simulations related to growing season. The RSP growing season dates may improve model hydrologic simulations especially in drought periods when water availability, demand, and usage are critical, or in areas where the temperature-index based growing season estimates lack skill, such as some California grasslands which have winter growing seasons.