Rahul patil

2. NATURAL DISASTER: EARLY
WARNING SYSTEMS
RAHUL PATIL
II PH.D
DEPT. OF SOIL AND WATER ENGINEERING
PHD16AEG6047

3. OUTLINE
History of disasters in India
Agencies for monitoring and
assessment of natural hazards
Types of natural hazards
Early warnings, Elements of EWS
and Needs
EWS for natural disasters
Case studies
References

4. HISTORY OF DISASTERS IN INDIA

5. Major disaster in known history of india
Name of event Year Fatalities
Bengal Earthquake 1737 300,000
Kangra Earthquake 1905 20,000
Bihar Earthquake 1934 6,000
Latur Eathquake 1993 7,928 death & 3,000 injured
Gujrat Earthquake 2001 20,000
Kashmir Earthquake 2005 86000 deaths
Indian occean Tsunami 2004 10,749 deaths 5,600 missing
Bengal cyclone 1864 60,000
Maharashtra cyclone 1882 1,00,000
A P cyclone 1977 10,000
Orissa super cyclone 1999 10,000
Calcutta cyclone 1737 3,20,000
Coringa cyclone 1839 20000

6. Agencies for monitoring and assessment of natural
hazards
–India Meteorological Department :
Cyclone, Drought and other
meteorological hazards
–Central Water Commission : Floods
–Geological Survey of India :
Landslides
–Indian National Centre for Ocean
Information Services (INCOIS) :
Tsunami
–Defence Research and Development
Organisation : Snow and Avalanches
MADRAS MSLP 1796-2004
995
1000
1005
1010
1015
1020
1796
1808
1820
1832
1844
1856
1868
1880
1892
1904
1916
1928
1940
1952
1964
1976
1988
2000
YEAR
PRESSURE(hPa)
January
February
March
April
May
June
July
August
September
October
November
December
Source: Rob
Allan, UKMO

7. India’s Vulnerability to
Disasters
• 57% land is vulnerable to
earthquakes. Of these, 12% is
vulnerable to severe earthquakes.
• 68% land is vulnerable to drought.
• 12% land is vulnerable to floods.
• 8% land is vulnerable to cyclones.
• Apart from natural disasters, some
cities in India are also vulnerable
to chemical and industrial disasters
and man-made disasters.
(Hatwar et al., 2000 )
http://www.ndma.gov.in/en/vulnerability-
profile.html

8. Types of Natural Hazards
• Volcanoes, floods,
earthquakes, tornadoes,
tsunamis, etc.
• –can act adversely on human
processes
• –can occur:
• without warning (e.g.
earthquakes)
• with warnings (precursors)
(e.g. satellite monitoring of
cyclone tracks, or the presence
of ground deformation at a
volcano before an eruption)

9. EARLY WARNING SYSTEM`
• An Early Warning System (EWS) can be defined as a set of capacities needed to
generate and disseminate timely and meaningful warning information of the
possible extreme events or disasters (e.g. floods, drought, fire, earthquake and
tsunamis) that threatens people‘s lives.
• The purpose of this information is to enable individuals, communities and
organizations threatened to prepare and act appropriately and in sufficient time to
reduce the possibility of harm, loss or risk.

10. Elements of Early warning
• Risk Knowledge: Risk assessment provides essential information to set
priorities for mitigation and prevention strategies and designing early
warning systems.
• Monitoring and Predicting: Systems with monitoring and predicting
capabilities provide timely estimates of the potential risk faced by
communities, economies and the environment
• Disseminating Information: Communication systems are needed for
delivering warning messages to the potentially affected locations to alert
local and regional governmental agencies. The messages need to be
reliable, synthetic and simple to be understood by authorities and public
• Response: Coordination, good governance and appropriate action plans
are a key point in effective early warning. Likewise, public awareness and
education are critical aspects of disaster mitigation

11. Need of Early Warning System
• Early Warning for disaster reduction is a legitimate matter of
public policy at the highest national levels for two main
reasons:
• The first one, clearly, is public safety, and the protection of
human lives.
• The second is the protection of the nation‘s resource base and
productive assets

12. What is an Earthquake?
• Ground movement caused by the sudden release of seismic energy
due to tectonic forces.
The focus of an earthquake is the actual
location of the energy released inside the
Earth’s crust.
The epicentre is the point on the Earth’s
surface directly above the focus.
Why do earthquakes occur?
Seismic energy is usually caused by the brittle
failure (fracturing) of rocks under stress

15. EARTHQUAKE

17. • A user receives a message like
this on the screen of his computer.
• The message alerts the user to
how many seconds before the
shaking waves arrive at their
location and the expected
intensity of shaking at that site.
• The warning message also
displays a map with the location
of the epicenter, the magnitude of
the quake, and the current
position of the P and S waves.

18. 08:21:00 08:30:00 0:09:00 7600 km 11 min 20 min 10 mins

19. Magnitude determination

20. INDIA LEADS WITH 26 OTHER COUNTRIES TO DEVELOP EARLY
EARTHQUAKE WARNING SYSTEM (THURSDAY, JULY 16, 2015, TOI )
26

21. Success stories
An earthquake early warning system helped Mexico City
• The seismic warning system in place during the most recent
magnitude-7.1 quake on Sept. 19, which had its epicenter about 75
miles from the country’s capital, successfully gave people crucial
seconds to flee vulnerable buildings, and prepare for the worst.
• SkyAlert: Millions of Mexicans turn to earthquake early warning
app after deadly quakes kill 460

22. Tsunami
• A sea wave of local or distant origin that results from large-scale seafloor
displacements associated with large earthquakes, major submarine slides,
or exploding volcanic islands

23. • WORKING OF TSUNAMI WARNING SYSTEM (TWS)
• Network of seismic monitoring station at sea floor detects
presence of earthquake.
• Seismic monitoring station determines location and depth of
earthquake having potential to cause tsunami.
• Any resulting tsunami are verified by sea level monitoring
station such as DART buoys, tidal gauge.
TYPES OF TSUNAMI WARNING SYSTEM (TWS)
TWS
International Warning System National Warning System

24. DETECTION METHOD
GEOLOGICAL
ACTIVITY
WAVE
ACTIVITY
COASTLINE
ACTIVITY
DETECTING TSUNAMI
• Three types of technologies are used for detecting tsunami:
1 • SEISMOMETERS
2 • COAST TIDAL GAUGES
3
• DART BUOYS

25. Current State of the Art

26. BOTTOM PRESSURE RECORDER:
 Digiquartz broadband depth sensor is the main sensing element
 This sensor continuously monitors pressure and if pressure
exceeds threshold value, it automatically report to warning
Centre
SURFACE BUOYS
 Surface buoys makes satellite communication to warning centers
that evaluate the threat and issue a tsunami warning

27. ADVANTAGES OF DART BUOYS
 Seismometer do not measure tsunami.
 Tidal gauge do not provide direct measurement of deep
ocean tsunami energy propagating.
 DART overcomes drawback of both
ADVANTAGES OF TSUNAMI WARNING SYSTEM
 Deep water pressure produce low false reading.
 Multiple sensor can detect wave propagation.
Good advance warning system.
DISADVANTAGES OF TSUNAMI WARNING SYSTEM
 Expensive equipments.
 High maintenance cost.
 Require multiple communication link
.

28. Success story
• India undertook a very ambitious project to set up an state
of art Tsunami and Storm surge warning capability in a
short time of 30 months. This was achieved by end of
August 2007, and tested by September 12, 2007
earthquake. Over the last three years the system has
performed satisfactorily.

29. TSUNAMI WARNING CENTERS

30. FLOODS
• Natural Phenomena
• Heavy Losses
• Disrupt Normal Life
• In some river valleys, floods
have been turned to economic
advantage.
• Millions of People grow their
rice, wheat, millet and corn on
flood plains in India, China &
Countries in the East where
they are subjected to inundation
and death.
• Competition between people
and flood water for same land
area

33. ACHIEVEMENTS ON FLOOD CONTROL MEASURES
(TILL MARCH 2005)
Embackements 35007 km
Drainage channels 51678 Km
Town protection works 2450 Nos
Village Raised 4721 Nos
Area Benefited 17.77 Mha
 Flood Forecasting & Warning System Plays significant role in
reducing the loss of life and movable property during floods.
 CWC has 175 Forecasting sites. About 666 Nos. of Forecasts
were issued during monsoon of 2006
 Accuracy of forecast is about 95.8%.

34. Flood warning system
Use of Sonar IoT sensors that are mounted to
bridges or at the bank of river in order to
measure the water level below.
Sonar IoT sensors make use of the same
principal to detect water level that the bats use
to see
The same way the sensors mounted on the
bridge send high-frequency sounds onto the
water and measure the time that it takes for the
echo to reflect back. As and when the water
level rises, the rivers flood, making the return
time for the echo to shrink as there’s a shorter
distance to cover.
Using this process, makes the solution
financially viable, as the components cost
around a decent $300.
(https://www.indianweb2.com/2016/07/07/intro
ducing-sonar-iot-system-can-provide-early-
warning-floods/

35. State-wise distribution of flood forecasting stations
State
No’s of
sites
State
No’s of
sites
Andhra
Pradesh
16
Madhya
Pradesh
3
Assam 24 Orissa 12
Bihar 33 Tripura 2
Chattisgar
h
1 Uttar Pradesh 35
Gujarat 10 Uttaranchal 3
Haryana 1 West Bengal 14
Jharkhand 4
NCT Delhi
Haveli 2
Karnataka 4
Dadra &
Nagar
2
Marashtra 9 Total 175
Year No of Stations
1958 1
1965 2
1970 43
1975 79
1980 84
1985 145
1990 157
2001 159
2004 172
2005 173
2006 175

36.  Under the bilateral Flood Forecasting and
warning system on rivers common to
India & Nepal, Hydro-meteorological
stations have been set up in Nepal and
India for exchange of data on real time
basis
 42 hydro-meteorological stations in
Nepal territory. Data received by CWC
are shared with Bihar & U.P.
Co-operation with Nepal Co-operation with Bangladesh
 Transmission of water level,
discharge of rivers and rainfall data
from India to Bangladesh during
monsoon season since 1972
 Water level, weather report and
discharge data of Farakka on Ganga
and Pandu on Brahmaputra twice
daily (0800 hrs. and 2000 hrs)
Co-operation with China
 Data of 3 stations namely Yanghen, Nugesha and Nuxia on Siang river located
in China are being received twice a day at 05:30 hrs. and 17:30 hrs. since 2002.
 Historical data of last ten years received from China are being used for
development of flood forecasting model
 Data are used for formulation of flood forecast and shared with the Govt. of
Arunachal Pradesh & Assam.

37. Ongoing scheme for modernisation
• TELEMETRY SYSTEM UNDER INSTALLATION
Godavari basins - 63 no
Krishna basin - 41 no.
Brahmaputra basin - 21 no.
Damodar basin - 20 no.
Yamuna basin - 15 no.
Mahanadi basin - 8 no

38. • Assam flood alarm system goes places(2014)
Altogether 20,000 people living in the catchment areas of two rivers, Jiadhal in
Dhemaji and Singora in Lakhimpur districts in Assam, have been benefiting
from this initiative directly and indirectly since 2010-11.
• Flood warning sounded in Bhima, Kabani River basins
Authorities today sounded flood warning at two places in Karnataka and
asked people living on the banks of Kabini reservoir, built across river
Cauvery and Bhima river in Kalaburagi District, to move to safer places in
view of receiving heavy inflows due to heavy rain in the catchment areas.

39. Tropical Cyclone Hazards in India
• One of the most devastating natural hazards
• The whole of east coast and the Gujarat coast in the west
are highly vulnerable.
• The recent experience
 The Orissa Super Cyclone of October 1999 affected
around 13 million people.
 10,000 people lost their lives and about one-third of the total
population of the state was affected

40. Cyclones

41. Three Types of Forecasting
• PERSISTENCE
• SYNOPTIC
• STATISTICAL

42. PERSISTENCE METHOD
• The first of these methods is the Persistence Method; the
simplest way of producing a forecast. The persistence method
assumes that the conditions at the time of the forecast will not
change.
• Persistence forecasting is based on the concept that current
weather conditions can reveal clues to tomorrow's forecast.
• The persistence method works well when weather patterns
change very little and features on the weather maps move very
slowly.
http://ww2010.atmos.uiuc.edu/(Gl)/guides/mt
r/fcst/mth/prst.rxml

43. SYNOPTIC
• Synoptic, or analogue forecasting is a method of
predicting the weather based on accepted theories and
principles of meteorology.
• This technique requires some skill and training, and
incorporates weather maps, radar and satellite images.
• Forecasters combine these tools with information
about atmospheric pressure, air flow and
temperatures to come up with a forecast.
• Synoptic forecasting served as the primary method of
predicting the weather through the 1950s and '60s. It's
still used today for short term predictions.

44. STATISTICAL
• statistical or climatological forecasting allows
meteorologists to make predictions based on historical
trends.
• They examine historical storm records and precipitation
amounts and use those as a basis for forecasting.
• For example, a statistical forecaster may state that the
next month will bring rain and cold temperatures because
that is considered the normal condition for this area at this
time of year.

45. How are TC monitored by IMD
• IMD has well established and time-tested
organization for monitoring and forecasting
TC.
• The conventional observations are
supplemented by observation data from
automatic weather stations, radar and satellite
systems.
• INSAT imagery obtained at hourly intervals
during cyclone situations has proved to be
immense useful in montoring the development
and movement of cyclones

46. How is cyclone monitored by satellite technique ?
• The satellite technique can be used to find out the centre and
intensity of the system. It can also be used to find out various
derived parameters which are useful for monitoring and
prediction of the cyclones and associated disastrous weather.
• Dvorak’s technique based on pattern recognition in the cloud
imagery based on satellite observation is used to determine the
intensity of cyclonic storm.
• For this purpose a T. No. where T stands for tropical cyclone is
assigned to the system. This scale of T Nos. varies from T 1.0
to T 8.0 at the interval of 0.5.
• The T 2.5 corresponds to the intensity of a cyclonic storm.

48. IMD-FUTURE PLANS
• Nation-wide network of
Automatic weather Stations
(AWS) with spatial resolution
of 50km
• High density raingauge stations
• Nation-wide Doppler Weather
Radar network
• Deployment of wind profilers

49. CASE STUDY I
• Title: INDIAN TSUNAMI WARNING SYSTEM
• Authors: Shailesh Nayak and T. Srinivasa Kumar
• Journal: The International Archives of the
Photogrammetry, Remote Sensing and Spatial
Information Sciences
• Year: 2008
• Location: Indian occean

50. Tsunami prone area

51. • Estimation of Earthquake Parameters
• INCOIS is receiving real-time seismic data from international
seismic networks as well as from India Meterological
Department (IMD) and has been detecting all earthquake
events occurring in the Indian Ocean in the less than 15
minutes of occurrence.
• Monitoring of Sea Level
• A network of Bottom Pressure Recorders (BPRs) has been
installed close to the tsunamigenic source regions to detect
tsunami, by the National Institute of Ocean Technology
(NIOT). These BPRs can detect changes of 1 cm at water
depths up to 6 km.
• A network of tidal gauges along the coast helps to monitor
progress of tsunami as well as validation of the model
scenarios

52. • Data Acquisition and Display
• The warning centre currently receives seismic data, tidal data
and BPR data through VSAT links, Internet, and INSAT from
multiple sources.
• Model Scenario Database
• TUNAMI N2 model has been used to estimate travel time and
run-up height for a particular earthquake. Since the model
cannot be run at the time of an event, due to large computing
time
• A database of pre-run scenarios has been created for 1000 unit
sources covering all the tsunamigenic sources in the Indian
Ocean region

53. • Model Scenario Database
• Tunami N2 model has been used to estimate travel time and run-up
height for a particular earthquake. Since the model cannot be run at
the time of an event, due to large computing time.
• The criteria for generation of different types of advisories
(warning/alert/watch) for a particular region of the coast are to be
based on the available warning time
• The warning criteria are based on the premise that coastal areas
falling within 60 minutes travel time from a tsunamigenic
earthquake source need to be warned based solely on earthquake
information, since enough time will not be available for
confirmation of water levels from BPRs and tide gauges.
• Those coastal areas falling outside the 60 minutes travel time from a
tsunamigenic earthquake source could be put under a watch status
and upgraded to a warning only upon confirmation of water-level
data.

55. The modelled and observed results
conclusion
This information was used to provide necessary advisories to
the concerned authorities, thus avoiding unnecessary public
evacuation for this event.

56. CASE STUDY- II
• Title: SMS BASED FLOOD MONITORING AND EARLY
WARNING SYSTEM
• Authors: Sheikh Azid, Bibhya Sharma, Krishna Raghuwaiya,
Abinendra Chand, Sumeet Prasad, A Jacquier
• Journal: ARPN Journal of Engineering and Applied Sciences
Year: 2015
• Location: Fiji

57. Design Overview
Air would be trapped inside the
pipe when the setup is lowered in
the water because of one closed
end. Rise in water level would
cause the air trapped inside the
pipe to compress which would
causes an increase in pressure in
the air trapped inside the pipe.
Hence the pressure measured by
the sensor is then converted to
height by Arduino.

58. • The threshold height value set would be chosen by the user
with prior knowledge of the flood height in the particular
area. This threshold value would vary with different
locations where the system would be placed in.
• When the water level exceeds the threshold value set,
emergency alert message would be sent to numbers stored
in the phonebook of the sim which would contain numbers
of people and contact of relevant authorities disaster
management committee.

59. The barometric pressure sensor (SM5100B) is
fixed at one end of the pipe facing downwards
so that air pressure in the pipe could be
measured
An aluminium box is used to house the circuit
components requires an external support such
as a column of a bridge or a dedicated concrete
support

60. RESULTS
• The pipe was marked at 0.1m height intervals and was
lowered in water. The pressure value after each 0.1m
interval was noted till 0.9m of the pipe was in the water.
Four samples at each height level was taken and averaged

61. Level Height = Pressure – 100717Pa /8084

62. CONCLUSIONS
• It successfully verifies the use of pressure sensors in a water
level monitoring system as the relationship between the
pressure and water level height is a perfect linear
• Finally, this monitoring system is fast, cheaper and reliable
hence it helps prevent the loss of lives damage to properties.

63. CASE STUDY- III
• Title: Developing a Prototype Earthquake Early Warning
System in the Beijing Capital Region
• Authors: Hanshu Peng, Zhongliang Wu, Yih-Min Wu,
Shuming Yu, Dongning Zhang, and Wenhui Huang
• Journal: Seismological Research Letters
• Year: 2011
• Location: Beijing, China

64. Distribution of the epicenters of historical earthquakes with
magnitudes greater than M 5.0 BCR.

65. Triggered waveforms of the Fengnan MS 4.2 earthquake

66. Early warning for 6 march 2010 event
• In this case, the EEWS provided approximately 24 seconds of
warning time for the center of Beijing, which is approximately
150 km away from the epicenter.
• An automatic report of the earthquake information includes the
origin time, the location and magnitude of the earthquake, the
epicentral distance of the target region, and the estimated time
of the peak S-wave arrival.
• The report is sent automatically through mobile phone
messages and/or e-mail. Currently, as a test mode, only
members of the research group and prescribed users can
receive the warning information

67. Configuration of the EEW system flowchart

70. CONCLUSIONS AND DISCUSSION
DEALING WITH FALSE ALARMS
• For events greater than M 2.0, there were 59 triggered
reports during 1 February 2010 to 31 August 2010, among
which 16 events were false alarms, yielding a rate of
27%.
• For events greater than M 3.0, the false alarm rate
reduced to 14%.
• Among them, an “M 6.8 earthquake” reported to occur in
Changli, Hebei Province, at 04:34:16 a.m. on 27 February
2010, is the biggest false alarm. These false alarms were
caused primarily by abnormal signals recorded at
approximately the same time by several neighboring
seismic stations

71. References
• Balaka, D. and Singh, R. B., 2006, Natural hazards and disaster
management, The Secretary, Central Board of Secondary
Education, Delhi.pp.10-34.
•
• Shailesh, N. and Srinivasa Kumar, T., 2008, Indian tsunami warning
system. The International Archives of the Photogrammetry,
Remote Sensing and Spatial Information Sciences,
37(B4):1501-1506.
• Yong. Wei., Eddie, N. B., Liujuan, Tang., Robert, Weiss., Vasily, V.,
Titov., Christopher, Moore., Michael, Spillane., Mike,
Hopkins., and Utku, K., 2008, Real-time experimental forecast of
the Peruvian tsunami of August 2007 for U.S. coastlines. Geophy.
Res.Letters., 35(1):1-7.