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Greg Easson Joel Kuszmaul Elizabeth Johnson

Integration of NASA Global Precipitation Measurement Mission Data into the SERVIR Flood Decision Support System for Mesoamerica. Greg Easson Joel Kuszmaul Elizabeth Johnson. Goals. To test and validate GPM Mission data to enhance the current satellite-based inputs into SERVIR,

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Greg Easson Joel Kuszmaul Elizabeth Johnson

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  1. Integration of NASA Global Precipitation Measurement Mission Data into the SERVIR Flood Decision SupportSystem for Mesoamerica Greg Easson Joel Kuszmaul Elizabeth Johnson

  2. Goals • To test and validate GPM Mission data to • enhance the current satellite-based inputs into SERVIR, • replace AMSR-E data used for SERVIR’s flood Decision Support System (DSS) for floods and • calibrate flood extent information generated from AMSR-E data with gauging data in Central America

  3. Project Team • Faisal Hossain - Tennessee Tech University (TTU) • Robert Brakenridge - Dartmouth Flood Observatory (DFO). • Timothy Gubbels – SSAI – GSFC • Dan Irwin – NASA – MSFC • Emil Cherrington – CATHALAC

  4. Background - SERVIR DSS SERVIR is a Mesoamerican –focused decision support system that integrates satellite and geospatial data • disasters • ecosystems • biodiversity • weather • water • climate • oceans • health • agriculture • energy

  5. SERVIR - Flood DSS AMSR-E - passive microwave instrument measures surface water condition changes in watersheds in Central America

  6. Objectives • Generation of discharge estimator from AMSR-E data under the guidance of Dartmouth Flood Observatory • Compare existing AMSR-E 36 GHz Microwave with TRMM 37 GHz • Simulated GPM precipitation estimates by Tennessee Technological University • Evaluation of results of discharge estimator from GPM with gauge and ASMR-E measurements for calibration.

  7. Study Site and Timing • Hurricane Stan Over Central America • Oct 1-10, 2005 • Up to 500 mm rain (200 inches) • Loss of life between 1600 and 2000 people • Most lives lost in Guatemala • Damages greater than $1billion USD

  8. Hurricane Stan Rainfall Accumulation map Panabaj – city lost to mudslides in Guatemala

  9. Satellite Data For Project • TRMM Products • 3D42 RT • 3D42 RT V6 • TMI 37 GHz • AMSR-E • 36.5 GHz • Simulated GPM • Precipitation product AMSR-E imagery of Hurricane Stan

  10. AMSR-E on Aqua The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) is a passive-microwave radiometer system. It measures horizontally and vertically polarized brightness temperatures at 6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz. Spatial resolution of the individual measurements varies from 5.4 km at 89 GHz to 56 km at 6.9 GHz. AMSR-E was developed by the Japan Aerospace Exploration Agency (JAXA) and launched by the U.S. aboard Aqua in mid-2002.

  11. Simulated GPM Data • TRMM data to be used to simulate GPM data • rainfall for discharge estimation • ~36Ghz microwave channel that duplicates AMSR-E • ~5km spatial resolution at nadir

  12. Objective 1:Comparison of 36Ghz Response Example: Wabash River near Mount Carmel, Indiana, USA Black square shows measurement pixel White square is calibration pixel Ratio C/M M C Higher ratios = higher discharge, more land flooded

  13. Objective 1 – M/C Ratio

  14. Objective 1 – Flood Map Dartmouth Flood Observatory flood maps from MODIS and AMSR-E imagery data

  15. Objective 1 – Kappa Statistic • Dartmouth Flood Algorithm on 36 GHz response from AMSR-E versus TRMM • Comparison region by region • each zone classified as flooded or not flooded • Use Kappa Statistic • corrects for chance agreement • Kappa calculated in the general case as: k = 0 chance agreement k < 0 worse than chance k > 0 better than chance

  16. Objective 2: Comparison of Discharge Volumes • DFO will use ground data for calibration of daily AMSR-E-based discharge volumes • TT will use ground data and rating curves for calibration of Simulated GPM precipitation-based discharge volumes • Regression analysis • 9 watersheds

  17. Hurricane Stan • Comparison of discharge as estimated by DFO with discharge as estimated by simulated GPM • GPM provides Precipitation • Convert precipitation to discharge using watershed data and rating curves Usumacinta River Watershed area 59,902 km2 Estimated discharge (m3/s)

  18. Ground Data • Gage height data • Hourly rain data • Watershed morphometric data • Currently trying to acquire rating curves

  19. Rating Curve • Relates discharge to stream stage Gage height Stage stage Rating Curve depth Datum Discharge Discharge= velocity x depth x width

  20. From Gage To Discharge (via a rating curve)

  21. Kirpich equation • Tc = 0.02 L 0.77 S – 0.385 • Tc is the collection time for precipitation • L is the maximum length of flow (m); • S is the watershed gradient (m/m).

  22. Rational Formula • qp = (C*I*A)/360 • where qp is the peak flow • C is dimensionless runoff coefficient • I is the intensity of a storm duration (Tc) for a given return period (worst case of runoff) • A is the area of catchment

  23. Timeline

  24. Questions? For more information please contact: Elizabeth Johnson University of Mississippi Geoinformatics Center (662) 915-7651 egj@olemiss.edu

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