Simulating Typhoon-Induced Flooding Using Mesoscale and Gauge Data in Mountainous Taiwan

1. Simulating Typhoon Floods with Gauge Data and Mesoscale-Modeled Rainfall in aMountainous Watershed Li, Ming-Hsu, Ming-Jen Yang, Ruitang Soong, Hsiao-Ling Huang, 2005: Simulating Typhoon Floods with Gauge Data and Mesoscale-Modeled Rainfall in a Mountainous Watershed. J. Hydrometeor, 6, 306–323. 報告人:蘇奕叡 指導教授:楊明仁 教授 日期:2012/11/06

2. Outline • Keywords • Introduction • Modeldescription • FLO-2D and MM5 • Background description • Three events and their responses • Typhoon Zeb (1998), Nari (2001) and Herb (1996) • Conclusion

3. Keywords • Flood responses • the small discrepancy in rainfall peaks and phase lags could be significantly amplified in simulated flood responses of a mountainous watershed. • Runoff Ratio (RR) • the ratio of observed (predicted) flood over the corresponding rainfall forcing

4. Introduction • Improper resolutions and inadequate interpretation of the land hydrological processes may significantly affect the modeling skill (Thieken et al. 1999; Smith et al. 2002; Wigmosta et al. 1994; Dutta et al. 2000; Morrison and Smith 2001). • Accurate rainfall information which is the most important forcing for a hydrologic model during typhoon events is crucial in modeling basin flood responses(Zhang and Smith 2003). • Spatial variability of rainfall can translate into significant variations of simulated runoff peaks and phases (Faures et al. 1995; Syed et al.2003).

5. Introduction • For flood warnings, ground-based gauge stations can’t give sufficient leadtime, and Doppler surveillance radar have limited by blocking over watersheds (Giannoniet al. 2003). • Promising results of coupling of meteorological models with hydrological models for flood forecasting(Westrick and Mass 2001; Ibbitt et al. 2001; Jasper et al. 2002). • Purpose : investigate the feasibility and uncertainty of simulating typhoon floods with a distributed hydrological model using both the observed rain gauge data and the predicted rainfall over a mountainous watershed in Taiwan.

6. Keywords • Introduction • Modeldescription • Hydrological model: FLO-2D • Mesoscale meteorological model: MM5 • Assimilation of radar-derived winds • Background description • Three events and their responses • Conclusion

7. FLO-2D(O’Brien et al. 1993, 1998) • the fully dynamic wave momentum equation and a central finite-difference routing scheme. • 1-D River flow, 2-D runoff • Green–Ampt(1911) infiltration model. • Use spatially varied parameter (surface roughness) to calibration

8. MM5(Grellet al. 1994) • In 3-D limited area, primitive equation, nested-grid model with 31 level σ coordinate • Physical parameterization: • Grell(1993)subgrid-scale cumulus parameterization scheme • Blackadar(1979) planetary BL scheme • Radiation scheme with interaction between clear sky and cloudy (Dudhia 1989) • Simple ice microphysisscheme (Dudhia 1989) • Sea surface temperature is kept constant

9. MM5 (Grell et al. 1994) • I.C and B.C take from ECMWFanalyses with 1.25˚X1.25˚ • Horizontal configuration: • Zerb: 3 nest grids (60,20,6.67km) • Nari: 4 nest grids (60,20,6.67,2.22km) • Herb: grid size down to 2.22km (Wu et al.2002) and assimilated the rader-retrieved winds

10. Assimilation of radar-derived winds • WSR-88D at Wu-Fen Mountant • Ground-Based Velocity Track Display (GBVTD) • Fixed the horizontal wind, geopotential, and temperature fields into MM5 domain • Just for the case of Herb

11. Keywords • Introduction • Modeldescription • Background description • Geographical description • Data description • Three events and their responses • Conclusion

12. Geographical description • Hsia-Yun station,the upstream of the Shihmen Reservoir • Ten gauge station(R1～R2) • Surface elevationby Digital Terrian Model(DTM) • Spatial land-use for Green-Amt model

13. Data description • Spatial data: • ArcView(Environmental Systems Research Institute 1996) combine the GIS map with 200m X 200m spatial resolution • Hydrometeorological data • Thiessen (1911) polygons method

14. Keywords • Introduction • Modeldescription • Background description • Three events and their responses • Typhoon Zeb (1998) • Typhoon Nari (2001) • Typhoon Herb (1996) • Runoff ratio • Conclusion

15. Typhoon Zeb (1998) • Background • coupled circulation • Without landfall, but take heavy rainfall over eastern and northern since terrain

16. Typhoon Zeb (1998) • In Hsia-Yun watershed, the basin-averaged rainfall hyetographs • Error for peaks: 23% & 17% 49.2mm/h 42.1mm/h 33.7mm/h 27.5mm/h

17. Error in gauge: overestimated of initial precipitation losses • Error in MM5: underestimate of 1st peak by timing phase; 2nd peak error so more as 34% • Flood responses are highly sensitive to extreme rainfall predictions

18. Spatial distribution for Zeb • First period(left) • MM5 underestimated • Second period(right) • MM5 overestimated • Higher rainfall intensity in the east domain • River in downstream have higher depths

19. Typhoon Nari (2001) • Grew from category 1 on 2001,sep 6 • Passed four times over an area for 8 days • 16 sep, landfall with 49 hour • Record-breaking 24-48h accumulated rainfall over northern by high ocean temperature, slow-moving and steep terrain.(Sui et al. 2002)

20. Typhoon Nari (2001) • Error peak for 1st peak with 0 timing delay and 56.5% overestimation of peak amount • 2nd peak with 4%underestimationwith 2h delay 58.8mm/h 37.6mm/h 35.4mm/h 34.0mm/h

21. Observed rainfall demonstrate successful application of physically based model with error 5% and 4% • MM5 peak error 77% & 17% • For the large error of 1st peak: The flood response is fast and the rainfall error may significantly amplified in place with persistent intensive rainfall and steep terrain slops

22. Spatial distribution for Nari • First period(left) • MM5 underestimated • Second period(right) • MM5 overestimated • Higher rainfall intensity in the north domain • River at north have higher depths

23. Typhoon Herb (1996) • Struck from 1400 UTC JUL 31 to 2000 UTC • Extensive rainfall and gusty wind induce flood in northern and mudslides in central • This event compare with the the simulated floods with and without the rader data assimilation. (Wu et al.2002;Chiang et al.2001)

24. Typhoon Herb (1996) • MM5 predicted taken in period of 6h with radar, 18h with not • Although overestimated, the MM5-predicted rainfall with assimilated radar-retrieved wind greatly reduced the peak without introducing any additional phase error (both +1h) 112.4mm/h 76.3mm/h 48.0mm/h

25. In MM5-predicte, assimilating radar-derived wind reduce error 106% to 67% • The result suggest a promising potential of integrating Dopplor radar observation

26. Spatial distribution for Herb • First period(left) • MM5 overestimated • Second period(right) • MM5 overestimated • Higher rainfall intensity in the southwest and north domain • River at north have higher depths

27. Runoff Ratio • Calculating the runoff ratein the table • RR of MM5 closer the observed than RR of gauge ∴gauge data has poorly predicted • Computed flood responses (Q), MM5 higher than gauge • But for the Rmse and GOF, MM5 is worsethan gauge

28. Zeb Nari Herb • Reason Discussion: • MM5-predicted flood are usually overestimate and lag • MM5’s first rising limb are underestimated, because the first phase and peak error can’t neglect. • Gauge have larger of the falling limb • Hydrological model neglect of the return flow

29. Conclusions • Predicted flood hydrographs with gauge data is good, MM5-predicted are overestimated with some timing error. • Through MM5 successfully simulate with horizontal resolution 2.22km. • But 2.22km grid can’t resolve the detailed rainfall distribution in small watershed with complex terrain • The different spatial resolutions may become animportant limiting factorfor long-period hydrological simulations • Hydrological model is limited in predicting the falling limb ∵neglect of the return flow from saturated ground-water, can use DHSVM(Wigmosta et al. 1994) to do it in future. • Fast response over the mountainous watershed can only be successfully forecasted when rainfall is accurately predicted

30. Thanks for Your Attention

31. References • Li, Ming-Hsu, Ming-Jen Yang, Ruitang Soong, Hsiao-Ling Huang, 2005: Simulating Typhoon Floods with Gauge Data and Mesoscale-Modeled Rainfall in a Mountainous Watershed. J. Hydrometeor, 6, 306–323. • CWB颱風資料庫http://rdc28.cwb.gov.tw/data.php