HR: 0800h
AN: H51D-0746    [Abstracts]
TI: Using NASA and Earth Science Products to Improve EPA Non-point Source Water Quality Modeling for the Chesapeake Bay
AU: * Toll, D
EM: dave.toll@nasa.gov
AF: NASA/GSFC, Code 614.3, Greenbelt, MD 20771, United States
AU: Engman, T
EM: tengman@hsb.gsfc.nasa.gov
AF: SAIC, Code 614.3 NASA/GSFC, Greenbelt, MD 20771, United States
AU: Edward, P
EM: partington.ed@epamail.epa.gov
AF: EPA, 1200 Pennsylvania Ave, Washington, DC 20460, United States
AU: Magness, A
EM: angelicagmagness@comcast.net
AF: NOAA-CBPO, 410 Severn Ave, Annapolis, MD 21403, United States
AU: Townsend, P
EM: ptownsend@wisc.edu
AF: University Wisconsin, 1630 Linden Dr, Madison, WI 20460, United States
AU: N-Meister, W
EM: wenge.ni-meister@hunter.cuny.edu
AF: Hunter College, 695 Park Ave., New York, NY 10065, United States
AU: Nigro, J
EM: jnigro@hsb.gsfc.nasa.gov
AF: SSAI, Code 614.3 NASA/GSFC, Greenbelt, MD 20771, United States
AU: Lee, S
EM: angelicagmagness@comcast.net
AF: Hunter College, 695 Park Ave., New York, NY 10065, United States
AB: The Environmental Protection Agency (EPA) estimates that over 20,000 bodies of water throughout the country do not meet water quality standards. Nonpoint sources -- pollution from urban, agricultural, and forest land that is transported by runoff -- typically cause 90 percent of impairments. EPA has developed the BASINS (Better Assessment Science Integrating Point and Nonpoint Sources) modeling system for performing numerous water quality studies. The key to this suite of models is the Hydrological Simulation Program - Fortran (HSPF), which calculates daily stream flow rates and the corresponding pollutant concentrations at the watershed outlet. EPA has partnered with NASA to use high spatial and temporal hydrological variables (e.g., precipitation, evaporation, etc.) from the NASA Land Information System (LIS) and land cover/vegetative indices derived from primarily MODIS and Landsat satellite data non-point source water quality for the Chesapeake Bay Basin. For the precipitation and evaporation data, EPA-based BASINS-HSPF streamflow runs were conducted on seven study watersheds in the Chesapeake Bay Basin. Sets of runs using precipitation from default weather stations, the NASA LIS 1/8th degree precipitation, NOAA Stage IV precipitation, NASA LIS Noah land surface model evapotranspiration datasets were conducted for each watershed. The output statistics summarized reveal that for 74% of the runs, the NASA LIS 1/8th degree and Stage IV precipitation-based runs performed better than when using only the default EPA precipitation station data. In addition, an automatic calibration method (‘PEST') and Noah land surface model evapotranspiration (ET) being further incorporated. The empirical ability of generalized spectral indices and land cover derived from Landsat and MODIS was tested for predicting stream water nitrogen export from predominately forested watersheds undergoing disturbance. The disturbance index, a summary index that is easily computed from Landsat Tasseled Cap bands was found to have a good predictive power identifying spatial variability in N export caused by a severe gypsy moth defoliation event (R2 = 0.45, p = 0.002, N = 18 watersheds). A novel index that directly relates the magnitude of forest disturbance was derived to inter-annual changes in wetness. This more physically-based and generalized index also showed a good ability to predict N export (R2 = 0.38, p = 0.006, N = 18 watersheds). The MODIS sensor appears to perform better than the Landsat-based indices (R2 = 0.48, p = 0.001, N = 18 watersheds, a finding which may be attributed to the enhanced capacity of MODIS to match the date(s) of image collection to the peak of a disturbance event. In a modeling environment such as BASINS-HSPF, additional variables would be included in the modeling that may improve predictive ability. These preliminary results show that alternative remote sensing variables have the capacity to improve predictions of watershed N whereas standard LULC products by EPA and related groups may not.
DE: 0496 Water quality
DE: 1804 Catchment
DE: 1847 Modeling
DE: 1855 Remote sensing (1640)
DE: 1880 Water management (6334)
SC: Hydrology [H]
MN: 2007 Fall Meeting