Atmospheric processes leading to extreme flood events

Transcription

1 Atmospheric processes leading to extreme flood events A. P. Dimri School of Environmental Sciences Jawaharlal Nehru University New Delhi, India

2 Observational and modelling limitations in understanding of extreme events

3 Observations details IMD vs ERA-Interim

4 Yatagai et al (2012)

5 Daily maximum precipitation for the NW subset of India (see inset) from 1998 to The time-series for each year are overlaid. The inset map also shows the average annual accumulated precipitation for this region (1998 to 2013). Also, shown is the CDF of the daily maximum rainfall for JJAS (1998 to 2013).The exceedance probability of 200 mm/day was estimated as 1.6%.

6 Regional scale

7 Model and Observation at station point

8 Sctotenv (2012)

9 Temp Model and Observation at station point

10 Western Disturbance at event scale evolution and decay is presented precipitation scale -?

11 Siachen Glacier, Karakoram Himalayas wet bias over glaciated region with higher topography meeting, 10 Nov 2011)

12 m DJF mean temperature ( ) m 5995 m DJF mean temperature ( ) Obs. HadRM3 ERA Interim Obs. HadRM3 ERA Interim DJF mean temperature ( ) Obs. HadRM3 ERA Interim Lapse rate: 6 /km

13 280 HadRM3 (Dec Nov. 2008), Siachen Glacier Mean Air Temperature (K) [117 grids] y = x R 2 = Altitude (m)

14 Standard deviation of precipitation (mm/day) for over the study area for 11 CORDEX-South Asia ensemble Ensemble spread among the 11 CORDEX experiments during JJAS precipitation (mm/day) averaged over the study area. The ensemble is shown by the red line, the +/- one standard deviation of the ensemble is denoted with the purple bars and the minimum and the maximum values among all the 11 CORDEX experiments

15 (a) Scatter spread of the precipitation (mm/day) of the ICHEC and ensemble with reference to the observation and (b) probability distribution function showing percentage of precipitation data falling within a particular range (in bar) and normal distribution (in line).

16 Storm diagnostics: Cloudburst in the Central Himalayas during September 2012

17 Cloudbursts rare phenomena - high amounts of energy required for the generation of such high intensity storms (Wooley, 1946) Localized severe storm events may not be captured at observation stations. Do not get attention if these occur over regions with no discernable impact in terms of life or property (Das et al., 2006) Not all disasters are cloudbursts, as in the case of Kedarnath disaster (Mishra and Srinivasan, 2013) Thus, a case study of cloudburst is included to understand cause and impact of such storm. Fig. Daily rainfall (mm) for 1-20 September 2012 at Ukhimath (location reported for cloudburst event) (Source: DMMC, 2012)

18 a) Daily accumulated precipitation on 13 Sept from TRMM 3B42 data b) Daily accumulated precipitation on 13 September 2012 simulated using COSMO (D2 domain). The two bands of precipitation are outlined, along with the borders of Uttarkhand state. c) Zoomed modeled accumulated precipitation with the local topography at the resolution of D2 domain (2.8km) The topography contour interval is 500m from 2000 to 6000 m elevation. d) Time-series of accumulated precipitation at location marked o and the surrounding grid cells, starting from 12 Sep The precipitation accumulation for spatial plot is from 0000 UTC to 2330 UTC.

19 Time Lapse Video of 600 hpa wind vectors and sum of vertically integrated hydrometeors (qc,qi,qs,qg,qr)

20 Time Lapse Video vertical cross-section of wind vectors and hydrometeors(qx=qc+qr+qs+qg+qi). UX is plotted for regions with qx<0.5 and UZ is plotted for regions with qx>0.5

21 a) Simulated variables for D2 domain on 13 Sep 1845 UTC: Vertically integrated rainwater mixing ratio (g/kg, color shading) and wind vectors (m/s) at 500 hpa. The x and o mark represents the Ukhimath town and the location of maximum precipitation respectively. b) Averaged vertical profile of hydrometeors at precipitation maximum, along the c-s AA. c) Cross-section AA of hydrometeors (qr in color shading, qc, qi, qg, qs in solid line from 0.2 to 2.4 g/kg at interval of 0.4 g/kg), wind vectors and temperature (black contour, showing melting level), along the c-s AA.

22 Cloudburst mechanism is led by convective triggering + orographic forced locking At times simulation failed to capture this triggering and hence no event

23 Kedarnath (Uttrakhand, India) flood disaster June 2013

24 Objectives To understand and describe a heavy precipitation event over Uttarakhand (Kedarnath) June 2013 using numerical simulation technique. To study in detail the various physical, dynamical and thermodynamical processes associated with the storm formation.

25 What is the study about? Torrential rains from beginning of 16Jun :15IST continued to the second event which was the floods at Kedarnath temple on 17Jun :45IST (Dhobal et al., 2013). Due to this devastation aggravated as drainage system of the whole watershed catchment area got saturated (USAC Report, 2013) small landslides throughout the catchment area led to changes in drainage paths (Rao et al. 2014) above were the reasons for the overflow (due to blocked natural drains or speculative glacial lake outburst flow) forming flashfloods towards the Kedarnath region (Srinivasan, 2013).

26 Further, in the present context, prolonged and continuous heavy precipitation over a large region possibly cannot be considered as cloudburst (Mishra and Srinivasan, 2013) Uttarakhand received rainfall 375% > monsoon daily normal. Fast advance of monsoon (IMD, 2013). Interaction of Westerlies and Monsoon (Rao et al. 2014).

27 Table 1: Daily rainfall (mm) over different Uttrakhand cities from June 2013 (IMD, 2013b) 14-Jun Jun Jun Jun Jun-2013 Chamoli Dehradun Tehri Rudraprayag Uttarkashi Table 2: Impact of Uttarakhand disaster (Rautela 2013; Sphere India, 2013a; Sphere India, 2013b) IMACT OF THE DISASTER No of districts (villages) affected 13 (4,200) No of human lives lost (injured) 580 (4,463) No of people missing (rescued) 3,078 (1,08,653) No of roads/bridges/houses damaged 1,642/147/2,232 Electrical (water) supply systems damaged 382(968) Communication line cut off -> Difficulty in rescue operation ->Rescue operations also suffered (two rescue helicopters crashed)

28 What was unusual about the storm? Fastest advance of monsoon ( ) and reached over Himalayan region by 15Jun2013 (End of the season report 2013 IMD). Westerlies and monsoon system interacted exactly over the Uttarkhand region (IMD, 2013; Rao et al. 2014). This interaction influenced the moisture from SE flow with the WD over the terrain caused severe rainfall over the region (IMD, 2013). Fig: The locations of low pressure system at 0000 UTC of (a) 15 June 2013, (b) 16 June 2013, (c) 17 June 2013, (d) 18 June 2013 (Source: IMD Report on Kedarnath Disaster)

29 (a) 15Jun2013 (0600UTC) (b) 16Jun2013 (0600UTC) (c) 17Jun2013 (0600UTC) Kalpana satellite images (Source: IMD, 2013) for (a) 15Jun2013 (0600UTC), (b) 16Jun2013 (0600UTC) and (c) 17Jun2013 (0600UTC).

30 Experimental Design Table 3: Model description Model WRF Version Map Projection Mercator Horizontal Resolution Triple Nest: 27km, 9km & 3km Simulation temporal 00UTC 15Jun2013 to 00UTC extent 19Jun2013 Central Point of 20.0 N 70.0 E Domain Horizontal Grid Arakawa C-grid Scheme Time Step 108 s Microphysics WSM 6 Scheme Land surface model Noah Land Surface Model Surface layer model MM5 Similarity Model Radiation Scheme Shortwave Dudhia Scheme Longwave - RRTM Planetary boundary Yonsei University Scheme layer Cumulus Kain-Fritsch Scheme Parametrization (Explicit calulation for 3km nest) Fig. 2: Model domain and topography (*10 3 m; shaded). Shaded region corresponds to model domain 1 (27 km horizontal model resolution), and boxes with solid black lines indicate model domain 2 (9 km horizontal model resolution) and domain 3 (3 km horizontal model resolution). Plus sign indicates location of Kedarnath.