1. Yudong Tian (UMD/NASA/GSFC), Fritz Policelli (NASA/GSFC)
Global Flood Estimation Using Satellite Precipitation Observations and Hydrological Models: Status and Future
Robert Adler
Huan Wu
(U. of Maryland)
Yang Hong (U. of OK),
Yudong Tian (UMD/NASA/GSFC),
Fritz Policelli (NASA/GSFC)
Flood Detection/Intensity (Depth above Threshold [mm])
18Z 7 October 2012

2. Tropical Rainfall Measuring Mission (TRMM)
TRMM Multi-Satellite Precipitation Analysis (TMPA/3B42)
3-hr, 0.25° lat./long/ resolution
Polar-orbit passive microwave with gaps filled by geo-IR estimates —all calibrated by TRMM
Real-time product (~6 hrs. after observations) and research product (incorporating monthly gauges over land—14+ years)
Used in over 200 journal publications; most highly downloaded TRMM product
Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, E.J. Nelkin, K.P. Bowman, Y. Hong, E.F. Stocker, D.B. Wolff, 2007: The TRMM Multi-satellite Precipitation Analysis: Quasi-Global, Multi-Year, Combined-Sensor Precipitation Estimates at Fine Scale. J. Hydrometeor., 8(1),

3. http://trmm.gsfc.nasa.gov (Floods and Landslides)
Monitoring Heavy Rainfall Events with Satellite Rainfall
Ending 12Z 8 Dec 2010
(Floods and Landslides)

4. Coupled Routing and Excess STorage (CREST) Model:
Real-Time Utilization of TRMM-based Rainfall into Global Hydrological Model for Flood Calculations
Coupled Routing and Excess STorage (CREST) Model:
U. of Oklahoma/NASA
 rainfall-runoff generation module, modified from UW-VIC model1,
 parallel multi-linear storage module, modified from Xinanjiang model2
 newly developed grid-to-grid routing scheme.
1/8th degree global
3-hr time steps
1Liang,et al. 1994
2Zhao & Liu, 1995
Wang, J., et al., The coupled routing and excess storage (CREST) distributed hydrological model. Hydrol. Sci. J. 56(1), 84–98.
No snowmelt processes
Cell-to-Cell Flow Routing

5. Flood Detection and Intensity Estimate--Method
Relative to Reference Level (mm)
Reference Level at each grid calculated from 12-year global hydrology model run using satellite rainfall data. Reference Level is 95th percentile of Routed Runoff (water depth) + factors related to basin size
FLOOD IDENTIFICATION uses the calculated water depth (mm) relative to the Reference Level at each grid (1/8th degree)

6. Example in Southeast Asia (17 November 2010)
Relative to Reference Level (mm)

7. Global to Regional Flood Detection
Flood Detection/Intensity
(Depth above Threshold [mm])
18Z 7 October 2012

8. 4 October GMT
Flood Detection/Intensity (Depth above Threshold [mm])
Stream Flow [m3/s]
Rainfall (7 day Accumulation [mm])

9. Flood Detection/Intensity (Depth above Threshold [mm])
Time History
Flood Detection/Intensity (Depth above Threshold [mm])
5 Oct.
1 Oct. 12Z
9 Oct.
2 Oct.
6 Oct.
10 Oct.
3 Oct.
7 Oct.
4 Oct.
8 Oct.
11 Oct.

10. 13 October 2012 00Z Rainfall (7 day Accumulation [mm])
Stream Flow [m3/s]
Flood Detection/Intensity (Depth above Threshold [mm])

11. 13 October 2012 00Z 4.5S, 75.5W Stream Flow [m3/s]
Flood Detection/Intensity (Depth above Threshold [mm])
4.5S, 75.5W

12. Flood Detection/Intensity (Depth above Threshold [mm])
28 August GMT)
Flood Detection/Intensity (Depth above Threshold [mm])
Senegal
4 killed
1200 displaced
Niger
44 killed
125,000 displaced
Nigeria
10 killed
20,000 displaced
South Sudan
32 killed
154,000 displaced
Disaster numbers from GDACS

13. Detection of Flooding over North Korea

14. Evaluation of Flood Model vs. Global Flood Inventory Data
Dartmouth Flood Observatory and U. of Oklahoma
Number of Flood Events as function of flood duration
Reported Flood Duration (days)
Method 3
(used in real-time)
Probability of Detection (POD) as function of flood duration
Reported Flood Duration (days)
Wu et al., (2012) J. Hydromet.

15. Evaluation of Flood Model vs. Global Flood Inventory Data
False Alarm Rate (FAR) for floods > 3 days
Method 3
(used in real-time)
“D” is for areas with dams
Effect of Large Dams Clearly Seen in FAR Statistics
Wu et al., (2012) J. Hydromet.

16. *M4 has issue with longer false alarm durations
POD and FAR analysis for global flood system. The different flood identifications tested include a traditional statistical (Pearson III) return time and three methods based on the 95th and 98th percentiles of water depth. Method 2 adds a constant depth to the 95th percentile, while Method 3 uses the variability () of the depth and a  factor which is a function of Flow ACcumulation (FAC), or upstream area. Method 4 is simply the 98th percentile of water depth.
Method 3 is judged as best at locating floods both in smaller and larger drainages and is used in the real-time system

17. Conclusions
Global flood model running in real-time with satellite precipitation estimates. Results are generally positive and useful, but definitely areas for significant improvement.
Future
 Improved precipitation information via time-space integration, geo-IR, ancillary data, model input, Global Precipitation Measurement (GPM) mission [2014]
Big issue is shallow orographic rainfall—passive techniques tend to underestimate significantly
 Improved global hydrological modeling via finer resolution, nested approach, regional and basin tuning, accounting for water management (dams), inclusion of snow melt processes
 Use of NWP precipitation information in both global and regional context— as models improve joint use of satellite and model rainfall for flood applications will improve