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Ling Tang and Caitlin Moffitt CEE 6900

Determining River Flooding Using Satellite-based Rainfall Products: Satellite Rainfall Flood Project. Ling Tang and Caitlin Moffitt CEE 6900. Presentation Outline. Introduction Flooding in Southern Texas Satellite Rainfall Data GPCP and TRMM Dartmouth Flood Observatory Objectives

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Ling Tang and Caitlin Moffitt CEE 6900

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  1. Determining River Flooding Using Satellite-based Rainfall Products:Satellite Rainfall Flood Project Ling Tang and Caitlin Moffitt CEE 6900

  2. Presentation Outline • Introduction • Flooding in Southern Texas • Satellite Rainfall Data • GPCP and TRMM • Dartmouth Flood Observatory • Objectives • Methodology • Study Region and Time Period • Datasets • Statistical Analysis • Qualitative Analysis • Results • Conclusions

  3. Flooding in Southern Texas- June 30-July 9, 2002 • Torrential rains totaled as much as 2-3 feet • River levels reached record heights with crests as high as 30-40 feet above flood stage • 9 fatalities • 48,000 homes damaged- 5,000 people evacuated • $1 billion in damage • Extensive impact to livestock and agriculture in the region Flooding not just a localized issue- since1970 more than 7,000 major flooding and drought events have caused $2 trillion in damage and 2.5 million casualties world-wide. (World Water Assessment Programme, 2009)

  4. Satellite Rainfall Data • Satellite-based flood data could be solution to early flood warning and disaster management • Important component for flood analysis is rainfall • Two satellite rainfall products considered in this study- • TRMM • Sensor Packages: • TRMM Microwave Imager (TMI) • Precipitation Radar • Visible Infrared Scanner (VIRS) • GPCP • Sensor Packages: • Special Sensor Microwave/Imager (SSMI) • GPCP Version 2.1 Satellite-Gauge (SG) combination • Atmospheric Infrared Sounder (AIRS) • low-orbit IR (leo-IR) GOES Precipitation Index (GPI) data from NOAA • Television Infrared Observation Satellite Program (TIROS) Operational Vertical Sounder (TOVS)

  5. Dartmouth Flood Observatory • Uses satellite observations from MODIS to monitor flooding as it occurs • MODIS • Visible and infrared bands • Used to determine properties of Earth’s surface and atmosphere • MODIS observations confirmed by flooding reports

  6. Objectives • To understand the level of agreement of two satellite-based rainfall products • To understand which satellite-based rainfall and flood product would be more appropriate for early flood warning and disaster management

  7. Study Region and Time Period Study Region: Texas Latitude: 25.5N - 36.5N Longitude: 93.5W - 107.5W Time period: one month June 09 - July 09, 2002

  8. Study Region and Time Period Three river gauge stations in southern Texas are selected for the flooding event • Frio River • 28˚28'02“ N 98˚32'50"W • 2. Nueces River • 28˚18'31“N • 98˚33'25“W • 3. San Antonio River • 28˚57'05“N • 98˚03'50“W

  9. Datasets • Two Satellite Products: TRMM 3B42RT and Global Precipitation Climatology Project (GPCP) • Ground Radar Data : Next Generation Radar (NEXRAD) Stage IV 1˚ and daily -- All datasets were uniformed to 1˚ and daily resolution and cropped at the Texas region for statistical analysis.

  10. Statistical Analysis 1. Estimate the statistical properties of the datasets - Calculate the mean and standard deviation of the datasets in each day in the study time period. 2. Compare the level of agreement between the two satellite datasets - Estimate the correlation between the satellite products, and also with the ground data. 3. Estimate the uncertainty of satellite products based on the truth data (ground radar) This includes the estimation of four error metrics: 1). Bias 2). Root Mean Square Error (RMSE) 3). Probability of Detection (POD) 4). False Alarm Ratio (FAR)

  11. Statistical Analysis Error assessment 1. Bias the average of difference between the study data and the truth of the days being estimated 2. RMSE the second moment of error, for an unbiased estimator, RMSE is the standard deviation

  12. 3. Probability of Detection (POD) Rain The fraction of observed events that were correctly forecast 4. False Alarm Ratio (FAR) The fraction of forecast events that were observed to be non-events (source from : Ebert E. et. al 2007)

  13. Qualitative Analysis • Compare hydrographs for point locations along satellite-claimed “flooded” rivers to determine if flooding occurred • Side-by-side comparison of DFO flood maps with 3B42RT and GPCP to determine which satellite product would be better for early flood detection and disaster management

  14. Statistical Results- Mean and Standard Deviation of Rainfall • 3B42RT overestimates rainfall • GPCP underestimates rainfall • 3B42RT is more variable than GPCP

  15. Statistical Results- Mean and Standard Deviation of Rainfall 3B42RT NEXRAD GPCP Mean STD

  16. Statistical Results-Correlation

  17. Statistical Results-Bias • Overall, 3B42RT has higher bias than GPCP

  18. Statistical Results-Root Mean Square Error • 3B42RT has higher RMSE than GPCP

  19. Statistical Results-Probability of Detection • POD is higher for 3B42RT

  20. Statistical Results-False Alarm Ratio • FAR is higher for 3B42RT

  21. Qualitative Results- Hydrographs • Flood indicated by large jump in hydrographs • Occurs immediately after rainfall begins

  22. Qualitative Results-DFO vs. 3B42RT and GPCP • 3B42RT shows stronger indication of high rainfall upstream from flood points

  23. Conclusions • Overall, both satellite-based rainfall products indicated areas of high accumulation upstream of flooding points • 3B42RT had higher probability of detecting rainfall and flooding, and a higher correlation with ground measurement • However, 3B42RT also has higher uncertainty compared to GPCP • GPCP is more appropriate for climatologic analysis or application

  24. References • World Water Assessment Programme (2009). The United Nations World Water Development Report 3: Water in a Changing World. Paris:UNESCO, and London: Earthscan • Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin, E. Nelkin 2003: The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present). J. Hydrometeor., 4,1147-1167. • Huffman, G.J., R.F. Adler, M. Morrissey, D.T. Bolvin, S. Curtis, R. Joyce, B McGavock, J. Susskind, 2001: Global Precipitation at One-Degree Daily Resolution from Multi-Satellite Observations. J. Hydrometeor., 2, 36-50. • Kummerow, Christian et al. “The Tropical Rainfall Measurement Mission (TRMM) Sensor Package.” Journal of Atmospheric and Oceanic Technology. Volume 15 (June 1998). 809-817 • http://trmm.gsfc.nasa.gov/ (TRMM) • http://www.ncdc.noaa.gov/ (NEXRAD) • http://precip.gsfc.nasa.gov/ (GPCP) • http://www.dartmouth.edu/~floods/ (Dartmouth Flood Observatory)

  25. Thank you for your time and attention.(A special thank you to Dr. Faisal Hossainfor his guidance on this project.)

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