ling tang and caitlin moffitt cee 6900
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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 - PowerPoint PPT PresentationTRANSCRIPT
Ling Tang and Caitlin MoffittCEE 6900
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
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)
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)
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-
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
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
Study Region: TexasLatitude: 25.5N - 36.5NLongitude: 93.5W - 107.5W
Time period: one month June 09 - July 09, 2002
Three river gauge stations in southern Texas are selected for the flooding event
1. 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
Two Satellite Products: TRMM 3B42RT and Global Precipitation Climatology Project (GPCP) Ground Radar Data : Next Generation Radar (NEXRAD) Stage IV
Data Area Spatial Resolution
Temporal Resolution
TRMM 3B42RT
60˚N~60˚S 0.25˚ 3-hourly
GPCP global 1˚ daily
NEXRAD Stage IV
Mainly in US
0.04˚ hourly
-- All datasets were uniformed to 1˚ and daily resolution and cropped at the Texas region for statistical analysis.
1˚ and daily
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)
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
3. Probability of Detection (POD) RainThe 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)
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
3B42RT overestimates rainfall GPCP underestimates rainfall
3B42RT is more variable than GPCP
3B42RT NEXRAD GPCP
Mean
STD
Overall, 3B42RT hashigher bias than GPCP
• 3B42RT has higher RMSE than GPCP
POD is higher for 3B42RT
FAR is higher for 3B42RT
Flood indicated by large jump in hydrographs
Occurs immediately after rainfall begins
3B42RT shows stronger indication of high rainfall upstream from flood points
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
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)