Lima Water Cycle and Capacity Building Workshop

1. Lima Water Cycle and Capacity Building Workshop Angélica Gutiérrez-Magness University of Maryland – Civil & Environmental Engineering Dept. Latin America and Caribbean Capacity Building Workshop. Lima, Peru. November 30 – December 4, 2009

2. Geo Water Quality Workshop, 2007 “Monitoring of WQ using remote sensing (Earth Observation (EO)), in conjunction with strategic in situ sampling can play a crucial role in determining the current status of WQ conditions… However, the field is relatively new, especially in its application to WQ in inland and coastal regions”

3. OUTLINE • Pilot Research: Integrating Remotely Sensed Indices of Forest Disturbances into BASINS” (2008) • Improving BASINS/HSPF Prediction Nitrogen Export Using NASA Imagery (2009)

4. Pilot Research: Integrating Remotely Sensed (RS) Indices of Forest Disturbances into BASINS” (2008) • Questions • 1. How does forest disturbance affect stream water N? • 2. How can remote sensing best be used to detect this effect? • Objective • We used off-the-shelf MODIS data and derivations that are easily implemented. • Identify the biophysical relations behind the RS-WQ (i.e., explain why RS measures improve prediction). PI: Phil Townsend – University of Wisconsin

5. Shenandoah National Park (Virginia) Eshleman et al. (1998, 2001); Eshleman (2000) Defoliation by gypsy moth (Lymantria dispar) larvae Oak mortality in Shenandoah National Park

6. 2001 1999 2000 Townsend et al. (2003) showed that we could accurately use Landsat derived change detection methods to predict streamwater total N and NO3-N. For N concentrations ranging from 0.05-1.68 mg/L in Appalachian streams, we were able to use change vector analysis to predict N with R2 = 0.79 and CV-RMSE = 0.08 mg/L.

7. Results: 15 Mile Creek, MD • Landsat and MODIS metrics describing the intensity of a 2000 gypsy moth defoliation event are predictive of stream water N loading

8. Results: 15 Mile Creek, MD • Disturbed watersheds had lower foliar N and less vigorous vegetation growth in the year following defoliation • Disturbance-induced stress to vegetation may reduce an ecosystem’s ability to retain N. Figure adapted from McNeil et al. 2007 Geophysical Research Letters 34:L19406

9. Summary • Remote sensing indices predict forest disturbance-induced spatial variability in stream water N export. • Our predictions of NO3-N in Wisconsin had R2 > 0.80 with errors that were probably within the sampling error of our water quality data. • The loss of the forest canopy appears to be strongest driver of increased N export.

10. Outcome BASINS could better model N export from forested watersheds by incorporating remote sensing indices that direct account for ephemeral losses of the forest canopy.

11. Improving BASINS/HSPF Prediction Nitrogen Export Using NASA Imagery (2010) Phil Townsend – University of Wisconsin Angélica Gutiérrez-Magness – University of Maryland Keith Eshleman – University of Maryland Brenden McNeil – West Virginia University

12. Objectives Derive remotely sensed measures to improve estimates of annual non-point source loads of nitrogen (N) from forested lands to surface waters Improve model predictions of water quality for gauged and ungauged forested watersheds within the HSPF modeling framework Provide more accurate load estimates to better manage forested gauged and ungauged watersheds.

13. Hydrological Simulation Program-FORTRAN (HSPF) Basics • Lumped, empirical, deterministic mass-balance time-series model • Land-use specific calculations • Describes and quantifies watershed processes: • ET, infiltration, runoff (including ground water) • Biogeochemical processing • Riverine processes (transport)

14. Link to HSPF Model We will use the remote sensing results within the calibration framework of HSPF to provide more accurate estimation of variations in forest contributions to N loads. We will simulate N loads for the test watersheds in the Chesapeake Bay Watershed, Adirondacks, and Wisconsin. Investigate for the most efficient way to incorporate inputs derived from remote sensing effort into the HSPF simulations.

15. NASA Products to be used • TRMM : precipitation • Landsat: land use • MODIS: • MODIS phenology derivations (vegetation seasonality) • MODIS change indices – between years and between seasons • MODIS measures of net primary productivity • MODIS derivations of fractional cover (forest, soil, impervious surfaces, non-photosynthetic vegetation) Blue = data online; Black = derivations we calculate

16. Some HSPF Model Applications • EPA/Chesapeake Bay Watershed model with 697 watersheds in the Basin and only 238 Calibration Stations. The Chesapeake Bay watershed is 60% forested. • EPA/TMDL Program, with more than 500 HSPF models addressing nutrient and sediment impairments throughout the United States.

17. LAND COVER FERTILIZER USE SEPTIC SYSTEMS SOIL PERMEABILITY Chesapeake Bay Watershed Source: USGS

18. Pilot Test Results: Chesapeake Bay Watershed • Dynamic parameterization added to the HSPF for simulation of dissolved inorganic nitrogen (DIN).

19. Questions ? Questions?