ISPRS WGVIII/1 Workshop on Geospatial Technology for Disaster … · 2015-12-14 · ISPRS WG VIII/1...

ISPRS WG VIII/1 Workshopon

Geospatial Technology for Disaster Risk Reduction

th17 December 2015, Jaipur, India

Abstract Volume

Organized by:ISPRS WG VIII/1 - Disaster and Risk Reduction

Jointly with:Indian Society of Geomatics (ISG)

andIndian Society of Remote Sensing (ISRS)

Hosts:J.K. Lakshmipat University, Jaipur

Indian Society of Geomatics, Jaipur Chapter

International Society for Photogrammetry and Remote Sensing

ISPRS WG VIII/1 Workshop

on

Geospatial Technology for Disaster Risk Reduction

17th December 2015, Jaipur, India

Abstract Volume

Organized by:ISPRS WG VIII/1 - Disaster and Risk Reduction

Jointly with:Indian Society of Geomatics (ISG)

and

Indian Society of Remote Sensing (ISRS)

Hosts:J.K. Lakshmipat University, Jaipur

Indian Society of Geomatics, Jaipur Chapter

International Society for Photogrammetry and Remote Sensing (ISPRS)Technical Commission VIII

Remote Sensing Applications and Policies

WG VIII/1: Disaster and Risk Reduction

Working Group Officers

Chair T. Srinivasa Kumar

ASG & In-charge, National Tsunami Warning Centre

Indian National Centre for Ocean Information Services (INCOIS)

Ministry of Earth Sciences (MoES)

"Ocean Valley", Pragathi Nagar (BO), Nizampet (SO)

Hyderabad-500090, India

[email protected] , www.incois.gov.in

Co-Chair Cees Van WestenEarth Systems Analysis DepartmentFaculty of Geo-Information Science and Earth Observation (ITC)University of TwentePO Box 2177500 AE Enschede, The [email protected] , www.itc.nl/about_itc/resumes/westen.aspx

Co-Chair Fabio Giulio TonoloITHACA - Information Technology forHumanitarian Assistance, Cooperation and ActionVia Pier Carlo Boggio, 6110138 – Torino, [email protected], www.ithacaweb.org

Secretary P.K. Champati rayGeosciences and Geo-Hazards DepartmentIndian Institute of Remote SensingIndian Space Research Organisation4, Kalidas RoadDehradun- 248001, [email protected], [email protected] , www.iirs.gov.in

Acknowledgements

ISPRS WG VIII/1 is extremely grateful to all contributors and eminent speakers for their contribution and presentations.We are also grateful to Dr. V.K. Dadhwal, President, ISPRS and all officers of Commission VIII for valuable guidanceand support. We also place on record the support received from all office bearers of WG VIII/1, Scientific committeemembers, Dr. Senthil Kumar, Director, IIRS and Dr. Satheesh C. Shenoi, Director, INCOIS for all their support andguidance. We acknowledge the support and cooperation received from the organisers of ISRS, ISG and JK LaxmipatUniversity, Jaipur to host the event.

The most important contribution in the form of editing, compilation and publicity has been made by team of scientificstaff at IIRS and INCOIS consisting of Dr. P.K. Champati ray, Dr. Pratima Pandey, Dr. S.L. Chattoraj, Mr. AshishDhiman (at IIRS), Dr. T. Srinivasa Kumar, Mr. R.S. Mahendra, Mr. N. Kiran Kumar (at INCOIS). Their immensecontribution is duly acknowledged.

International Society for Photogrammetry and Remote Sensing


Geospatial Technology for Disaster Risk Reduction17th December 2015, Jaipur, India

CONTENT

S.No. Page #Programme 1 - 4

THEME-1: SPATIAL DATABASE FOR DISASTER MANAGEMENT

1.Spatial Data Infrastructure for the Generation of Tsunami AdvisoriesN. Kiran Kumar, Ch Patanjali Kumar, R. S. Mahendra, J. Padmanabham, T.Srinivasa Kumar and B.V. Satyanarayana

5

2. Making risk visible: Spatial risk and vulnerability assessmentG. K. Bhat

THEME-2: SPATIAL MODELLING FOR RISK ASSESSMENT

3.

Multi-criteria hazard and vulnerability modeling leading to geospatialrisk zonation of riverine flood plain - An approach experimented inDhemaji district of AssamDiganta Barman, Jenita R Nongkynrih, Suranjana B Borah, Sangeeta Sarma andPLN Raju

6

4.

3-Dimensional modeling of 2014-Malin Landslide, Maharashtra usingsatellite derived data: A quantitative approach by numerical simulationtechniqueShovan Lal Chattoraj, Yateesh Ketholia, P.K. Champati ray and Sudhakar

Pardeshi

7

5.Multi-hazard Vulnerability Mapping of Kerala State: a case StudySatej Panditrao, R S Mahendra, P C Mohanty, H Shiva Kumar and T SrinivasaKumar

9

THEME-3: LANDSLIDE HAZARD AND RISK ANALYSIS

6.Sunkoshi Landslides 2014: Causes, Impacts and Feature Analysis usingRemote Sensing and GIS ApproachSmriti Ashok and Sonesh Chandra

10

7.Geomorphometric analysis of Chamoli and Karnaprayag District,Uttrakhand in respect to Hazard Zonation of the AreaA.K. Anand, P.S. Prasad and Kishor Kumar

11

8. Persistent Scatterer SAR Interferometry for Landslide MonitoringRamji Dwivedi, Ajai Kumar Singh and Onkar Dikshit 12

9.Case study on soil erosion and bank migration in and around Salmara &AphalamukhMrinal Deka,Ajit Borkotoky, Sailesh Kumar Yadav and S L Borona

13

10.Glacial lake inventory and glacial lake outburst floods (GLOF) riskassessment in of Himachal HimalayaPritam Chand and Milap Chand Sharma

14

THEME-4: EARTHQUAKE HAZARD AND RISK ANALYSIS

11.Earthquake damage scenario simulation using formulated mathematicalmodel based on multi-criteria decision making methodsShweta Sharma, B. K. Rastogi and A. K. Mathura

15

12.Site Characterization of Strong Motion Stations in Tarai Region ofUttarakhandBhavesh Pandey , Ravi S Jakka and Ashok Kumar

16

13.The role of InSAR imagery based deformation fields in mapping outpotential structural belts of the 2015 Nepal eventsRevathy M. Parameswaran, Kusala Rajendran and C. P. Rajendran

17

14.Cave deposits from the Kumaun Himalaya as potential earthquakerecordersJaishri Sanwal, C. P. Rajendran and Kusala Rajendran

18

15.

Active Fault Mapping using Geophysical Methods (GPR, IPR) in North-western part of Himalayan Foothill ZoneKannaujiya S. , Champati ray P. K. , Philip G. , Sharma G. , Mohanty S., SaikiaA. and Chattoraj S. L.

19

16. Earthquake Hazard and Risk Analysis of GujaratB.K. Rastogi 20

17.

Nepal earthquake: Earth Observations for geodynamics and hazardmitigationP. K. Champati ray, Gopal Sharma, R.S. Chatterjee, A. Senthil Kumar, SomalinNath and Anuradha Sharma

21

THEME-5: FLOOD, COASTAL AND OCEANOGENIC HAZARDANALYSIS

18.

Flood early warning with quasi-distributed hydrological model withinput from NWP rainfall forecast - An operational exercise in flood pronedistricts of Brahmaputra valleyD. Barman, S.S. Kundu, R.B. Gogoi, A.Borgohain, A. Bharali, D. Sarma,

N.Barman and P.L.N. Raju

22

19.Agriculture drought Severity during 2000-2014 from cropping seasonsJanuary-May & July-DecemberBhavani P., Roy P.S. and Charavarthi V.

23

20.Climate Extreme Analysis at West Flowing River Basin of Kutch,Saurashtra and MarwarIla Agnihotri

24

21.

Floods in Chennai and Kanchipuram Districts of Tamil Nadu, Nov 2015.Is it a Disaster? A Challenge towards Sustainability in the Indian CoastalZone? Or a Verdict?G. Arun and R. P. Hari Ram

25

22. Rainfall forecasting using artificial neural networksRohit Sambare, Vaibhav Garg and S.P. Aggarwal 26

23.Devastation in Nepal earthquake in 2015: A remote sensing basedassessmentSnehmani, Chetna Soni and Akansha Patel

27

24.Mapping and Monitoring of GLOF prone lakes in parts of Shyok Basinusing Remote Sensing and Field TechniquesRitesh Mujawadiya, H S Negi, G Arun, Anant Kumar and A Ganju

28

25. Hydrological Modeling for Flood Damage MitigationK. Sindhu and K.H.V. Durga Rao 29

26.Identification of Glacial Lakes for Potential Outbursts in Lahaul & Spiti,Himachal PradeshVaruni Pathak, Rakesh Arya and S. Sreekesh

30

27. Shoreline change analysis for east coast of Andhra Pradesh, IndiaG. Vivek 31

28.Two 'Hot Spots'' in the upper Alaknanda River valley- Chamoli District,Uttarakhand Himalayas - a cause of concernTangri A.K., Ram Chandra, Rupendra Singh and C. L. Paul

32

THEME-6: SPACE TECHNOLOGY FOR DISASTER PREPAREDNESSAND RESPONSE

29.

Space technology applications for addressing multi-hazard DRR needs inNE region of India - the concept called NER-DRRD. Barman, D.J. Chutia, R.K. Das, S.S. Kundu, K. Bhusan, K. Chakraborty, B.KHandique, J. Goswami,J.M. Nongkynrih, M.S. Singh, R.B. Gogoi and P.L.N Raju

33

30. Design of Cost Effective Disaster Monitoring GPS NetworkRamji Dwivedi and Onkar Dikshit 34

31.

Evaluation of the coral bleaching during 2015 at the coral environs of theGulf of Kutch using Remote SensingH Shiva Kumar, R S Mahendra, P C Mohanty, Satej Panditrao and T SrinivasaKumar

35

32.Validation of MODIS snow cover products using Landsat during 2002 –2012 in Menthosa Glacier, Himachal PradeshAbira Dutta Roy, Milap Chand Sharma and S. Sreekesh

36

THEME-7: EARLY WARNING SYSTEM FOR DISASTERMANAGEMENT AND WEATHER FORECASTING

33.

Flood early warning system for North West Himalaya using integration ofweather forecasting, hydrological and hydrodynamic modelsPraveen K. Thakur, S.P.Aggarwal, Vimal C. Shamra, Bhaskar R Nikam, VaibhavGarg, Arpit Chouksey, Pankaj Dhote and Charu Singh

37

34.Development of Earthquake Early Warning System for Northern IndiaBhanu Pratap Chamoli, Bhawesh Pandey, Pankaj Kumar, Govind Rathore, R.S.Jakka, Ajay Gairola and Ashok Kumar

38

35.Assessing and Improving VGI data quality as a method for generating anational disaster database for improved hazard and risk assessmentAlexander Zipf and Joao Porto de Alberquerque

39

36.Storm surge and inundation forecasting system at ESSO-INCOISPLN Murty, J Padmanabham, P S Bharadwaj, N Kiran Kumar, T SrinivasaKumar and S S C Shenoi

40

37. The status of Himalayan glaciers-A mass balance studyAshim Sattar, Ajanta Goswami, Anil V Kulkarni 41

38.Estimation of snow cover distribution using satellite data in NWHimalayasSnehmani, Sunil kumar and Akansha Patel

42

39. Estimation of mass balance of glacier using optical remote sensing dataJaydeo k.Dharpure, Snehmani and Akansha Patel 43

40.

Mapping and assessing the land cover/land use degradation in HimachalPradesh and vulnerability to degradation in Kangra districtSatya Prakash, Milap Chand Sharma, Rajesh Kumar, P.S. Dhinwa, K.L.N.Sastry and A.S. Rajawat

44

41. Early Warning System for Tsunamis - Progress & ChallengesT. Srinivasa Kumar 45

42.

Ocean State Forecasting during extreme weather conditions for betterdisaster management to save life and propertyT.M. Balakrishnan Nair, R. Harikumar, Anuradha Modi, K. Srinivas, RakhiKumari, B. Krishna Prasad, K. Kaviyazhahu and Yatin Grover

46

43.Role of Remote Sensing in Early Warning Services for Management ofCyclones over North Indian OceanM. Mohapatra

47

1


Geospatial Technology for Disaster Risk Reduction17th December 2015, Jaipur, India

Program Schedule

Plenary Session ISPRS WG VIII/1

S.No. December 17, 201509:30-11:00 Hrs

Chair: Shailesh NayakCo-Chair: T. Srinivasa Kumar Remarks

44. M. MohapatraRole of Remote Sensing in Early Warning Services forManagement of Cyclones over North IndianOcean[ISPRS15-07-0013]

Oral

45. T. Srinivasa KumarEarly Warning System for Tsunamis - Progress &Challenges[ISPRS15-07-0011]

Oral

46. G. K. Bhat Making risk visible: Spatial risk and vulnerabilityassessment Oral

47. B.K. Rastogi Earthquake Hazard and Risk Analysis of Gujarat[ISPRS15-04-0007] Oral

48.

P. K. Champati ray, Gopal Sharma,R.S. Chatterjee, A. Senthil Kumar,Somalin Nath and AnuradhaSharma

Nepal earthquake: Earth Observations forgeodynamics and hazard mitigation[ISPRS15-04-0008]

Oral

49. Tangri A.K., Ram Chandra,Rupendra Singh and C. L. Paul

Two 'Hot Spots'' in the upper Alaknanda River valley-Chamoli District, Uttarakhand Himalayas - a cause ofconcern[ISPRS15-05-0011]

Oral

50. Alexander Zipf and Joao Porto deAlberquerque

Assessing and Improving VGI data quality as amethod for generating a national disaster database forimproved hazard and risk assessment[ISPRS15-07-0003]

Oral

51.D. Barman, S.S. Kundu, R.B. Gogoi,A.Borgohain, A. Bharali, D. Sarma,N.Barman and P.L.N. Raju

Flood early warning with quasi-distributedhydrological model with input from NWP rainfallforecast - An operational exercise in flood pronedistricts of Brahmaputra valley[ISPRS15-05-0001]

Oral

2

TS – 3/4 (Technical Session – 1: Spatial database, modeling, landslide and earthquake)

S.No. December 17, 201511:30-13:00 Hrs

Chair: B.K. RastogiCo-Chair: P.K. Champati ray Remarks

1.N. Kiran Kumar, Ch Patanjali Kumar, R. S.Mahendra, J. Padmanabham, T. SrinivasaKumar and B.V. Satyanarayana

Spatial Data Infrastructure for the Generation of TsunamiAdvisories[ISPRS15-01-0002]

Oral

2.Diganta Barman, Jenita R Nongkynrih,Suranjana B Borah, Sangeeta Sarma andPLN Raju

Multi-criteria hazard and vulnerability modeling leadingto geospatial risk zonation of riverine flood plain - Anapproach experimented in Dhemaji district of Assam[ISPRS15-02-0001]

Oral

3. Shovan Lal Chattoraj, Yateesh Ketholia,P.K. Champati ray and Sudhakar Pardeshi

3-Dimensional modeling of 2014-Malin Landslide,Maharashtra using satellite derived data: A quantitativeapproach by numerical simulation technique[ISPRS15-02-0002]

Oral

4.Satej Panditrao, R S Mahendra, P CMohanty, H Shiva Kumar and T SrinivasaKumar

Multi-hazard Vulnerability Mapping of Kerala State: acase Study[ISPRS15-02-0003]

Oral

5. Smriti Ashok and Sonesh ChandraSunkoshi Landslides 2014: Causes, Impacts and FeatureAnalysis using Remote Sensing and GIS Approach[ISPRS15-03-0001]

Oral

6. A.K. Anand, P.S. Prasad and KishorKumar

Geomorphometric analysis of Chamoli and KarnaprayagDistrict, Uttrakhand in respect to Hazard Zonation of theArea[ISPRS15-03-0002]

Oral

7. Ramji Dwivedi, Ajai Kumar Singh andOnkar Dikshit

Persistent Scatterer SAR Interferometry for LandslideMonitoring[ISPRS15-03-0003]

Oral

8. Snehmani, Chetna Soni and Akansha PatelDevastation in Nepal earthquake in 2015: A remotesensing based assessment[ISPRS15-05-0006]

Oral

9. Shweta Sharma, B. K. Rastogi and A. K.Mathura

Earthquake damage scenario simulation usingformulated mathematical model based on multi-criteriadecision making methods[ISPRS15-04-0001]

Oral

10. Bhavesh Pandey , Ravi S Jakka and AshokKumar

Site Characterization of Strong Motion Stations in TaraiRegion of Uttarakhand[ISPRS15-04-0002]

Oral

11. Jaishri Sanwal, C. P. Rajendran and KusalaRajendran

Cave deposits from the Kumaun Himalaya as potentialearthquake recorders[ISPRS15-04-0004]

Oral

12.Kannaujiya S. , Champati ray P. K. , PhilipG. , Sharma G. , Mohanty S., Saikia A. andChattoraj S. L.

Active Fault Mapping using Geophysical Methods (GPR,IPR) in North-western part of Himalayan Foothill Zone[ISPRS15-04-0006]

Oral

3

TS – 4/4 (Technical Session – 2: Flood, coastal and ocenographic hazards)

S.No. December 17, 201514:00-15:30 Hrs

Chair: M. MohapatraCo-Chair: S. P. Aggarwal Remarks

1. Bhavani P., Roy P.S. and Charavarthi V.Agriculture drought Severity during 2000-2014 fromcropping seasons January-May & July-December[ISPRS15-05-0002]

Oral

2. Ila AgnihotriClimate Extreme Analysis at West Flowing River Basin ofKutch, Saurashtra and Marwar[ISPRS15-05-0003]

Oral

3. G. Arun and R. P. Hari Ram

Floods in Chennai and Kanchipuram Districts of TamilNadu, Nov 2015. Is it a Disaster? A Challenge towardsSustainability in the Indian Coastal Zone? Or a Verdict?[ISPRS15-05-0004]

Oral

4. Rohit Sambare, Vaibhav Garg and S.P.Aggarwal

Rainfall forecasting using artificial neural networks[ISPRS15-05-0005] Oral

5. Mrinal Deka,Ajit Borkotoky, SaileshKumar Yadav and S L Borona

Case study on soil erosion and bank migration in andaround Salmara & Aphalamukh[ISPRS15-03-0006]

Oral

6. Ritesh Mujawadiya, H S Negi, G Arun,Anant Kumar and A Ganju

Mapping and Monitoring of GLOF prone lakes in parts ofShyok Basin using Remote Sensing and Field Techniques[ISPRS15-05-0007]

Oral

7. K. Sindhu and K.H.V. Durga Rao Hydrological Modeling for Flood Damage Mitigation[ISPRS15-05-0008] Oral

8. Varuni Pathak, Rakesh Arya and S.Sreekesh

Identification of Glacial Lakes for Potential Outbursts inLahaul & Spiti, Himachal Pradesh[ISPRS15-05-0009]

Oral

9. G. VivekShoreline change analysis for east coast of Andhra Pradesh,India[ISPRS15-05-0010]

Oral

10.

D. Barman, D.J. Chutia, R.K. Das, S.S.Kundu, K. Bhusan, K. Chakraborty, B.KHandique, J. Goswami,J.M. Nongkynrih, M.S. Singh, R.B.Gogoi and P.L.N Raju

Space technology applications for addressing multi-hazardDRR needs in NE region of India - the concept called NER-DRR[ISPRS15-06-0001]

Oral

11.H Shiva Kumar, R S Mahendra, P CMohanty, Satej Panditrao and TSrinivasa Kumar

Evaluation of the coral bleaching during 2015 at the coralenvirons of the Gulf of Kutch using Remote Sensing[ISPRS15-06-0004]

Oral

12. Abira Dutta Roy, Milap Chand Sharmaand S. Sreekesh

Validation of MODIS snow cover products using Landsatduring 2002 –2012 in Menthosa Glacier, Himachal Pradesh[ISPRS15-06-0005]

Oral

4

TS – 5/4 (Technical Session – 3: Disaster early warning and monitoring)

S.No. December 17, 201516:00-17:30 Hrs

Chair: P.L.N. RajuCo-Chair: M. Punia Remarks

1.Praveen K. Thakur, S.P.Aggarwal, Vimal C.Shamra, Bhaskar R Nikam, Vaibhav Garg, ArpitChouksey, Pankaj Dhote and Charu Singh

Flood early warning system for North WestHimalaya using integration of weatherforecasting, hydrological and hydrodynamicmodels[ISPRS15-07-0001]

Oral

2.Bhanu Pratap Chamoli, Bhawesh Pandey, PankajKumar, Govind Rathore, R.S. Jakka, Ajay Gairolaand Ashok Kumar

Development of Earthquake Early WarningSystem for Northern India[ISPRS15-07-0002]

Oral

3. PLN Murty, J Padmanabham, P S Bharadwaj, NKiran Kumar, T Srinivasa Kumar and S S C Shenoi

Storm surge and inundation forecasting system atESSO-INCOIS[ISPRS15-07-0005]

Oral

4. Ashim Sattar, Ajanta Goswami, Anil V KulkarniThe status of Himalayan glaciers-A mass balancestudy[ISPRS15-07-0007]

Oral

5. Snehmani, Sunil kumar and Akansha PatelEstimation of snow cover distribution usingsatellite data in NW Himalayas[ISPRS15-07-0008]

Oral

6. Jaydeo k.Dharpure, Snehmani and Akansha PatelEstimation of mass balance of glacier usingoptical remote sensing data[ISPRS15-07-0009]

Oral

7.Satya Prakash, Milap Chand Sharma, RajeshKumar, P.S. Dhinwa, K.L.N. Sastry and A.S.Rajawat

Mapping and assessing the land cover/land usedegradation in Himachal Pradesh andvulnerability to degradation in Kangra district[ISPRS15-07-0010]

Oral

8.T.M. Balakrishnan Nair, R. Harikumar, AnuradhaModi, K. Srinivas, Rakhi Kumari, B. KrishnaPrasad, K. Kaviyazhahu and Yatin Grover

Ocean State Forecasting during extreme weatherconditions for better disaster management to savelife and property[ISPRS15-07-0012]

Oral

9. Pritam Chand and Milap Chand Sharma

Glacial lake inventory and glacial lake outburstfloods (GLOF) risk assessment in of HimachalHimalaya[ISPRS15-03-0007]

Oral

10. Revathy M. Parameswaran, Kusala Rajendran andC. P. Rajendran

The role of InSAR imagery based deformationfields in mapping out potential structural belts ofthe 2015 Nepal events[ISPRS15-04-0003]

Oral

11. Ramji Dwivedi and Onkar DikshitDesign of Cost Effective Disaster Monitoring GPSNetwork[ISPRS15-06-0003]

Oral

5

Theme-1: Spatial Database for Disaster Management

SPATIAL DATA INFRASTRUCTURE FOR THE GENERATION OF TSUNAMIADVISORIES

N. Kiran Kumara, Ch Patanjali Kumara, R. S. Mahendraa, J. Padmanabhama, T. Srinivasa Kumara and B.V. Satyanarayanaa

a Indian National Centre for Ocean Information Services (INCOIS), Hyderabad-500 090, Andhra Pradesh, India

KEY WORDS: Decision-makings, Tsunami, Disaster Management, Spatial Data Infrastructures (SDI), Advisories, etc.

ABSTRACT:

The use of spatial information and related technologies is well suited for disaster management because most of the input data used inthe generation of tsunami advisories are referenced to a geographic location and has been well-known worldwide. Effective disastermanagement system requires solutions and approaches that allow efficient and reliable access to spatial data. Timely access andsharing of accurate information for the purpose of disaster mitigation, prevention, preparedness, response, and recovery have provento be a challenge in many recent disasters. This is a very important aspect of disaster response as timely, up-to-date and accuratespatial information describing the current situation is paramount to successfully responding to an emergency. This includesinformation about available resources, access to roads and damaged areas, required resources, required responsive operations, etc.,and should be available and accessible for use in a short period of time. Sharing information between involved parties in order tofacilitate coordinated disaster response operations is another challenge in disaster management.

This paper aims to address the role of Spatial Data Infrastructures (SDI) as a framework for the generation of Tsunami Advisories. Itis argued that the design and implementation of an SDI model as a framework and consideration of SDI development factors andissues can assist the disaster management agencies in such a way that they improve the quality of their decision-makings andincrease their efficiency and effectiveness in all level of disaster management activities. This paper illustrates the development of anSDI Model for the generation of Tsunami Advisories. This includes the development of a prototype web-based system which canfacilitate sharing, access and use of data in disaster management and especially disaster response. These advisories are provided tothe public, designated ministries, disaster management offices, etc. for further necessary action as per the Standard OperatingProcedures (SOP) which helps the user to understand the information easily and take appropriate decision.

Author for correspondence – [email protected]; [email protected]

6

Theme-2: Spatial Modelling for Risk Assessment

MULTI-CRITERIA HAZARD AND VULNERABILITY MODELING LEADING TOGEOSPATIAL RISK ZONATION OF RIVERINE FLOOD PLAIN – AN APPROACH

EXPERIMENTED IN DHEMAJI DISTRICT OF ASSAM

Diganta Barman*, Jenita R Nongkynrih, Suranjana B Borah, Sangeeta Sarma, PLN Raju(North Eastern Space Applications Centre, DOS, Shillong, Meghalaya)

Commission VIII, WG VIII/1

KEY WORDS: Hazard, Risk, Embankment Breach, River Confluence, Multi-Criteria Weightage, Population Vulnerability,Building Vulnerability

ABSTRACT:

Flood risk zonation is an important pre-requisite for different non-structural flood mitigation measures such as flood plain regulation,flood damage insurance etc. In simplistic way, flood risk zonation is generally carried out by geospatial overlay of a time series ofinundation datasets totally ignoring both the physical potential hazard factors of the riverine flood plain and the socio-economicvulnerability factors of the flood plain dwellers. As risk is a multiplicative function of hazard and vulnerability, the present studyemphasizes on a multi-criteria hazard zonation followed by a multi-criteria vulnerability zonation before geo spatially combiningboth to obtain the flood risk zonation.

Localized flooding genesis such as Embankment breach, flood plain topography, confluence patterns of different river channels etc.were geo-spatially combined by assigning different weightages based on their relative effectiveness as an inundation triggeringfactor was the approach followed for hazard zonation mapping.

In the vulnerability assessment, different villages of the flood plain were considered as mapping units and field data collectedthrough survey were attributed to each village. Population Vulnerability assessment has been done using parameters, viz., age group,gender, temporal distribution during different time of the day, density and daily activities. For built-up vulnerability assessment,parameters considered were building wall material, distance from rivers/streams/drains, number of floors, return period of floods,protecting walls and water tanks etc.

Finally risk was calculated by combining results of individual hazard and vulnerability assessment. Risk indices such as Very High,High, Medium, Low and Very Low, were prepared.

7


3-DIMENSIONAL MODELING OF 2014-MALIN LANDSLIDE, MAHARASHTRA USINGSATELLITE DERIVED DATA: A QUANTITATIVE APPROACH BY NUMERICAL

SIMULATION TECHNIQUE

Shovan Lal Chattoraja, Yateesh Ketholiab, P.K. Champati raya, Sudhakar Pardeshib

a Indian Institute of Remote Sensing, 4-Kalidas Road, Dehradun-248001b University of Pune, Ganeshkhind, Pune, Maharashtra-411007


KEY WORDS: RAMMS, Landslide, 3-D Modeling

ABSTRACT:

Debris flows, a type of landslides, are not nowadays limited only to periodic devastation of the geologically fragile Himalaya butalso ubiquitous in weathered Deccan Volcanic Province of cratonic south Indian peninsula. Quite frequently, off late, rainy season inSahyadri hills of the Western Ghats witnessed many landslides causing severe loss to mankind and property. The present study aimsto address landslides/debris flow movement and simulate landslide event that occurred in Malin area in wee hours of 30th July, 2014following torrential rainfall. It engulfed 40 houses and prima facie gobbled up at least 151 people. The event was analyzed to beclassified as an unchannelized debris flow consisting mainly of semi-consolidated, basalt derived, silt to coarse sand sized, poorlysorted soil, highly saturated with water which was triggered by intense monsoonal precipitation on leeward side of a slope underlainby thick alternating basaltic layers of varied composition and physical characteristics.

Comprehensive assessment of landslide hazard, pertinently, requires process based modeling using simulation methods. Theunderlying principle of such events can be applied to a variety of processes including snow avalanche, debris flows, landslides, mudflows and even rock falls and has therefore found significant role in disaster management. Although well tested empirical methodsare available to determine dynamic characteristics of a flow, numerical simulation techniques are now applied to predict flow pathsand characterize the entrainment process. Development of precipitation triggered debris flow simulation models of real events arestill in a budding stage in India, albeit, especially in tectonically less disturbed regions. A highly objective simulation technique hastherefore been envisaged herein to model the debris flow run-out happened in Malin. In the present study RAMMS (Rapid MassMovements Software) developed by WSL institute of Snow and Avalanche, Switzerland, has been used, which is a state of the artnumerical simulation model that predicts the motion of a naturally occurring mass from head (release area) to base (deposition area)in three dimensions. This takes cues from a basic high-resolution DEM and other ancillary ground data including geotechnical andfrictional parameters. Till date, RAMMS has been used for modeling Himalayan Landslides only. However, this work is the first ofits kind to address the simulation of landslides in Deccan Traps. The algorithm is based on Voellmy frictional (dry and turbulentfrictional coefficients, μ and ξ respectively) parameters of debris flow with pre-defined release area identified on high resolutionsatellite images like LISS-IV and Cartosat-1. Once the event is simulated, model provides critical quantitative information on flow1) Velocity, 2) Height, 3) Momentum, and 4) Pressure along the entrainment path.

The simulated flow revealed approximately 97% pixel level matching with post event satellite images of the event for µ (Mu) = 0.49,ζ (Xi) = 460 m/s2 and cohesion (c) value of 100kpa. It was observed that an increase in the dry friction coefficient (µ) caused adecrease in the run-out distance due to increase in the basal resistance opposing the flow. On the other hand, variation in the value ofviscous turbulent friction (ζ) did not influence the run-out distance significantly. These frictional inputs were compared withinstrumentally derived shear outputs of samples collected from field at same saturation state which enabled to check the validity ofthe model. The Mohr-Coulomb criteria was employed to determine the cohesion (c) and angle of internal shear resistance (φ) of semiconsolidated debris which are 98-116 Kpa and 25-32° (i.e. µ= 0.4 to 0.6) respectively which are at par with modeled inputs.As for the results, the simulated velocity of about 16m/s at mid-way the slide plummeted to 6.2 m/s at the base with intermittentlyincreased and decreased values. The simulated maximum height was 3.9m which gradually declined to 1.5m near base. Momentumalso decelerated similarly from its peak mid-way value till it reached base of the flow. The simulated pressure varied in tandem withvelocity reaching the maximum value of 440 KPa.

This work enhanced the understanding of numerical models by studying their resemblance with real landslide/debris flow thatcontributed to the unprecedented disaster in Malin. The vital output parameters viz. velocity, height, momentum and pressure can beused to provide insight of the event and extent of run out zone of future potential flows. Thus, this work bespeaks that numericalsimulation modeling is capable of emulating natural events and outputs can be used for mitigation measures. The results can be veryuseful in engineering intervention like construction of check dams to digest the initial thrust of the flow and other remedial measuresdesigned for vulnerable slope protection. Integrated with extensive landslide mapping, 3-dimensional modeling of landslides willcomplimentarily provide the stake holders actual insight of the cause of this type of event vis-à-vis its effective corrective measure.

8

The model has not only produced reliable simulation results but also established the efficacy and versatility in application of modelsin wide range of mass wasting events pertaining to different causative factors.

Corresponding Author:Shovan Lal ChattorajEmail: [email protected]: 0135-2524157

9


MULTI-HAZARD VULNERABILITY MAPPING OF KERALA STATE: A CASE STUDY

Satej Panditrao, R S Mahendra, P C Mohanty, H Shiva Kumar and T Srinivasa Kumar

Indian National Centre for Ocean Information Services, (ESSO- INCOIS), Hyderabad, India


KEY WORDS: ALTM, DEM, Extreme Water Level, GIS

ABSTRACT:

Kerala is the South-Eastern state of India, which has very large coastal area having significant economical and social importance.This study accentuates the Multi-hazard Vulnerability Mapping (MVHM) of Kerala’s coast with the help of remote sensing and GIS.The parameters used were, Historical Extreme Water Levels (from tide gauge records and historical events from the publishedliterature); Historical Shoreline Change; Sea Level Change and Return period (Projected to 100 years). High resolution topographicdata from ALTM and IRS Cartosat-1 DEM were used as the main Remote Sensing datasets and all these datasets were processed andsynthesized in GIS environment to calculate the composite hazard line. Quantification of area at the taluka level was done whichshowed that 1477.3 km2 area of Kerala state comes under vulnerability zone. Cannanore, Hosdurg, Kanayannur and Kuttanad etc.talukas constitute most of the vulnerable area. This mapping depicted that; Southern part of Kerala was more vulnerable than theNorthern part which was corroborated by Extreme Water Level and Return period values.

10

Theme-3: Landslide Hazard and Risk Analysis

SUNKOSHI LANDSLIDES 2014: CAUSES, IMPACTS AND FEATURE ANALYSIS USINGREMOTE SENSING AND GIS APPROACH

*Dr. Smriti Ashok and Sonesh Chandra


KEY WORDS: Landslide, LULC, Landsat 8 OLI/TIRS

ABSTRACT:

The aim of the present study is to identify the cause and impact of the landslide in valley of Sunkoshi mountain river and resultantflood in the Koshi river. The massive landslide happened in Jure village on 02 August 2014, which is in the catchment area ofSunkoshi river. This landslide and flood has affected approximately 2.7 million people (Wikipedia, 2014) and damaged propertiesworth Rs. 1.46 billion (NASA, 2014).

To fulfill the above objective remote sensing and GIS approach were used. First of all the Mapping of the affected area was doneusing GIS approach. Secondly, analysis of the affected area was done based on remote sensor satellite imagery (two Landsat 8satellite imagery data – OLI (September 15, 2013) & Landsat 8 – OLI (September 18, 2014 were used). The Landsat data wascollected from the U.S. Geological Survey Website. Landsat 8 – OLI data proved very useful for the present analysis as thisimagery is capable of yielding useful information with respect to Natural Hazard and disaster, agriculture, science and governmentetc.The above analysis was carried out through open source software - Quantum GIS 1.8.0 (Lisboa) used for GIS Mapping of theaffected area and GRASS GIS 6.4.3RC2 used for Land use and land cover change detection. Unsupervised image classificationtechnique was used for change detection. This is a two-step process (i) First, a clustering algorithm groups, pixel values with similarstatistical properties according to - user definitions of minimum cluster size, separability, number of clusters, etc. This processrecognizes the pixels with similar reflectance values in the various channels. The resulting signature file is used as input to generatean unsupervised image classification. (ii) The second step is to perform unsupervised classification using i.maxlik command, whichhelps in change detection analysis. Six LULC classifications were done viz. dense vegetation, moderately dense vegetation, lightvegetation, no vegetation, clouds and water. The Landslide area maps of two different periods i.e. 2013 and 2014 were generated on1:50,000 scale. The change analysis was carried out to derive land use/land cover changes.

The result shows progressive encroachments done by the villagers in the affected area and a high natural dam being created in Jurevillage area due to heavy rains causing massive landslides between the two periods undertaken for assessment. The landslideoccurred from the height of 1.4 km, created over 100 meter high natural dam across Sunkoshi river which blocked the Sunkosi Riverand buried the Araniko Highway. As the flow of water was stopped for about 12 hours, an artificial lake got created with millions ofcusecs of water that submerged dozens of houses and a hydropower substation. It also resulted into the change of the river’s courseupto 95%. Due to the active support of Nepalese Army and widespread evacuations of downstream villages during the impactedhours, the loss was minimized otherwise the landslide would have resulted into much more severe damage.

Studies say that this is not the first time the Sunkoshi valley has experienced a lethal flood, and this is certainly not the last time. Sothere is need of in-depth analysis of the causes and impact of Landslide occurrence in this area, so that the information gatheredthrough this can be used for zoning river corridors and preparing land use plans for scientific support for disaster risk management infuture.

*Corresponding Author

11


GEOMORPHOMETRIC ANALYSIS OF CHAMOLI AND KARNAPRAYAG DISTRICT,UTTRAKHAND IN RESPECT TO HAZARD ZONATION OF THE AREA

A.K. Ananda, P.S. Prasada and Kishor Kumara

a Geotechnical Engineering Area, CSIR-Central Road Research Institute, New Delhi 110025, India.E-mail: [email protected], [email protected], [email protected]


KEY WORDS: Morphometric Analysis, ASTER, DEM, GIS

ABSTRACT:

Uttrakhand lies in tectonically active zone of the country. Remote sensing techniques along with GIS have proved to be anindispensable tool in Hazard Zonation. Chamoli and Karnaprayag district of Uttrakhand comprises of fragile lithological formationsand lies in highly vulnerable zone in terms of natural disasters. It's latitude and Longitude ranges between 29 ⁰ 50 ′ N to 30 ⁰ 40 ′ Nand 78⁰ 40 ' E to 79⁰ 50 ' E. It’s prone towards frequent occurrences of various tectonic activities like landslide, earthquake, cloudburst and flash floods. Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) has been used for preparingDigital Elevation Model (DEM),Slope, Aspect and various other maps which was used in evaluation of linear and Areal parameters.Morphometric analysis refers to quantitative assessment of earth surface form and process. Linear and areal Morphometricparameters studies had been carried out on 64 selected 4th order river basins in Chamoli and Karnaprayag districts using ARC GIS.There are various parameters like stream order, stream number, Bifurcation ratio, Drainage density, Form Factor, Elongation ratioetc. had been analysed on the river basins. The lower values of Bifurcation ratio in basins represent geological heterogeneity, highpermeability and less structural control. In study area value of Drainage Density varies between 1.3 – 2.2 Km-1 which impliesdevelopment of coarse grained texture in the area. The complex relationship between the values of Elongation ratio, Circularity ratioand Form factor in some basins represents that it’s passing through structural thrust. The anomalous values of linear and arealparameters suggest that basins of the study area are geologically, structurally and lithologically controlled. The occurrences oflandslide, earthquake has dependable relation with computed parameters. Since the outcome of the study will act as precursors fornatural disasters.

12


PERSISTENT SCATTERER SAR INTERFEROMETRY FOR LANDSLIDEMONITORING

Ramji Dwivedia*

, Ajai Kumar Singha, Onkar Dikshit

b

a Geographic Information System (GIS) Cell, MNNIT Allahabad, Allahabad-211004, UP - ([email protected];[email protected])

b Department of Civil Engineering, IIT Kanpur, Kanpur-208016, UP ([email protected])


KEY WORDS: InSAR, PS-InSAR, SBAS, Landslide

ABSTRACT:

In the past few years, SAR Interferometry specially InSAR and D-InSAR were extensively used for deformation monitoringrelated applications. Due to temporal and spatial decorrelation in dense vegetated areas, effectiveness of InSAR and D-InSAR observations were always under scrutiny. Therefore, in recent years, an advanced InSAR algorithm i.e. PersistentScatterers SAR Interferometry (PS-InSAR) is developed which overcomes the decorrelation problem. PS-InSAR identifiesmeasurement pixels which have a stable phase history over a time period. In this research work, PS-InSAR is used to monitorslope stability of landslides prone areas in Nainital, Uttarakhand, India. Although, in past few years, no major landslipevent has been reported in the city but previous research work have identified several active slide zones. For the proposed work,Stanford Method for Persistent Scatterers (StaMPS) based PS-InSAR is adopted for processing 13 ENVISAT ASAR C-Bandimages covering 2008-2010, which resulted in time series 1D-Line of Sight (LOS) map of surface displacement. PS-InSARprocessing resulted in 1D-LOS velocity map which shows that a few zones in the Nainital town have shown annualdisplacement of the order of 18 mm. These zones are susceptible to movements and termed as active. It is also observed thatthere are some signs of subsidence near Nainital lake. Hence, we conclude that the generated maps are indicative of slopestability of the study areas and commensurate to the hazard areas observed through landslide hazard zonation mapsprepared by government authorities and researchers. It is also proposed to take up a few major landslides area inUttarakhand for slope stability assessment.

*Corresponding Author ([email protected])

13


CASE STUDY ON SOIL EROSION AND BANK MIGRATION IN AND AROUNDSALMARA & APHALAMUKH OF MAJULI ISLAND, JORHAT DISTRICT, ASSAM,

INDIA

Mrinal Deka a, Ajit Borkotokyb, Sailesh Kumar Yadavc, S L Boronad

a Ph.D Research Scholar, Gauhati University - ([email protected])b Ajit Borkotoky, Head of the Deptt. of Geology ([email protected])

c Sailesh Kumar Yadav, DRDO, Jodhpur – ([email protected])d S L Borona, DRDO, Jodhpur – ([email protected])


KEY WORDS: Bank Migration, Erosion, Majuli Island, Brahmaputra River, GIS & Remote Sensing

ABSTRACT:

The largest fresh Water mid-river deltaic island in the world is named as the “Majuli” which situated in the upper reaches of theBrahmaputra in the district Jorhat, Assam. The geographical extent of the study area is 26°57′0″N 94°10′0″E / 26.95000°N94.16667° with mean height of 84.5 m above MSL. The north of this river island Majuli is bounded by the river Subansiri andmighty Brahmaputra River to the south. The population according to census 2011 in Majuli Island is about 1.68. People living inthis world’s largest island is endangered because of the erratic behavior of the river. Erosion in Majuli island is a continuousprocess since historical times and possess a significant concern. The land area as evidenced till 1966-1975, 1998 and 2008 were706.14, 578.38 and 484.34km2 respectively (Earth Science India, Vol. 3 (IV), October, 2010, pp. 206-216). Having established theimportance of the problem for research and analysis, the present study is an attempt to investigate the bank line migration in andround the area of Salmara to Aphalamukh, given that Remote Sensing data and analysis using Geographical Information Systems(GIS) is widely and increasingly used for such studies.

14


GLACIAL LAKE INVENTORY AND GLACIAL LAKE OUTBURST FLOODS (GLOF)RISK ASSESSMENT IN OF HIMACHAL HIMALAYA1

Pritam Chand and Milap Chand SharmaCentre for the Study of Regional Development, Jawaharlal Nehru University (J.N.U), New Delhi, India

(Contact at: - [email protected] & [email protected] and +91-9650966260)


KEYWORDS: Glacial Lake, Glacial Lake Outburst Floods (GLOFs), Inventory of Glacial Lakes, GIS Modelling, Potential HazardMaps and Risk Assessment

ABSTRACT:

It is estimated that there are over 8,000 glacial lakes in the Hindu Kush-Himalayan region with more than 200 of them identified aspotentially dangerous (ICIMOD). A common view is that global warming gives rise to glacier retreat, resulting in expanding glaciallakes behind the newly exposed moraines, which are susceptible to sudden breach and consequent flood. Glacial lakes pose a threatto their downstream communities, but they are also a potential source of water storage for sustaining agriculture, forest-basedlivelihoods and for the hydro projects.

In the Himachal Himalaya, 156 glacial lakes and ponds have been identified and amongst which 16 glacial lakes are characterized aspotentially dangerous glacial lakes. The objectives of present study are to assess the GLOF parameters by creating an inventory ofexisting glacial lakes and monitoring the GLOF events on a regular basis and secondly, develop effective early warning system tomonitor GLOF hazards using RS and GIS for Ravi basin as a case study and further apply for the entire Himachal Himalaya.

The present assessment based on the remote sensing and GIS techniques which are the primary tool to achieve this in view of theserious challenges, in terms of accessibility, posed by high altitude, vast areal extent, and unpredictable weather. The Landsatsatellite images of 1989, 2002 and 2010 years are used for the temporal inventory of glacial lakes for whole basin which is the firststage in the risk assessment process and the baseline information for the further analysis. In next stage, identification of those lakesthat may pose a potential danger, for this, image analysis and GIS modelling based on multi-source data from Landsat satelliteimagery and ASTER digital elevation model (DEM) are applied. It helps to derived the important parameters for hazard assessmentsuch as boundary conditions of the identified glaciers (frontal retreat and thinning) and lakes (enlargement) over time, distancebetween the lake and the glacier, rating of moraines includes height, width and steepness and surroundings of the lake area includefactors such as rock or debris slides and hanging glacier avalanche paths. Next, the risk assessment entails rating or ranking of thepotentially dangerous lakes based on factors related to the physical stability of lake surroundings and the moraine dams (i.e.,likelihood of failure) , and socioeconomic parameters (i.e., potential impact). Final results, including the detailed update inventory ofglacial lakes of the upper Ravi basin and the potential hazard map of the GLOF in the basin are prepared for future planning of waterstorage and mitigation of GLOF hazard in the basin.

15

Theme-4: Earthquake Hazard and Risk Analysis

EARTHQUAKE DAMAGE SCENARIO SIMULATION USING FORMULATEDMATHEMATICAL MODEL BASED ON MULTI-CRITERIA DECISION MAKING

METHODS

Shweta Sharma a,*, B. K. Rastogi b and A. K. Mathur a

a Space Applications Centre-ISRO, Ahmedabad, Gujarat, Indiab Institute of Seismological Research, Gandhinagar, Gujarat, India

* [email protected]


KEY WORDS: Earthquake, Multi-criteria Decision making Methods, Damage Scenario Simulation, Bhuj Earthquake, UrbanPlanning

ABSTRACT:

In this work, a mathematical equation has been formulated based on the multi-criteria decision making methods to estimate theconsequences of a scenario earthquake to a city or region. This study helps in identifying areas with higher and lower risk bycalculating, mapping and displaying the damage scenario due to particular earthquake parameters. By changing the earthquakeparameters different damage scenarios can be generated.

In order to calculate, map and display damage scenario a user friendly tool Earthquake Damage Scenario Simulation Tool(EqDSST) has been developed using MATLAB language and graphical user interface (GUI) has also been designed. The inputparameters required for the tool are earthquake parameters (magnitude, peak ground acceleration, fault location and depth) and site-specific parameters (bedrock depth, geology, soil type, building damage index and ground water depth). In order to validate theformulated mathematical model, 2001 Bhuj earthquake parameters have been used. After preparing the input parameters’ files forGandhidham city in Kutchch, developed EqDSST tool was used to generate the damage scenario for a part of the city with 2001Bhuj earthquake parameters. The results obtained were compared with the actual damage map prepared after Bhuj earthquake usingpre- and post-earthquake satellite data analysis. Landsat-7 data of 8th January 2001 and 9th February 2001 was used to map thedamage using visual interpretation technique. The comparison reveals that the scenario generated using the developed tool showconsiderable matching with the actual damage map.

The results showed the potential of the developed tool to generate earthquake scenarios which help to develop strategies for post-event response and recovery. The most unfavourable site-conditions and areas, as well as the most vulnerable buildings can beidentified from the outputs. This allows for actions before an earthquake occurs, for example the retrofitting of buildings and long-term urban planning.

16


SITE CHARACTERIZATION OF STRONG MOTION STATIONS IN TARAI REGIONOF UTTARAKHAND.

Bhavesh Pandeya, Ravi S Jakkab, Ashok Kumarc

a Research Scholar, Earthquake Engineering Department, IIT Roorkee - ([email protected])b Assistant Professor, Earthquake Engineering Department, IIT Roorkee - ([email protected])

c Professor, Earthquake Engineering Department, IIT Roorkee - ([email protected])


KEYWORDS: Site Characterization, Local Site Effects, Response Spectra, MASW, HVSR

ABSTRACT:

Site characterization and study of local site conditions of strong motion stations is a very important aspect. In this study sitecharacterization of strong motion stations located in Tarai region of Uttarakhand is conducted and influence of local site conditionson characteristics of strong ground motion records and their further effects on hazard studies were investigated. Site characterizationof each of the strong ground motion station is conducted using MASW tests to obtain shear wave velocity profiles of the sites.Further, site specific ground response analysis is carried out using SHAKE2000 to investigate local site effects on strong groundmotion records and HVSR of available strong motion records from these stations. Comparison of response spectrum from IS1893:2002 (Part-1) and the one obtained from ground response analyses is also conducted. For sites having Vs30 around 200m/s,constant acceleration frequency band is significantly widened in comparison to 5% damping response spectrum of IS-1893:2002(Part-1).This study further suggests the importance of thorough site characterization of Strong motion instrumentation sites.

17


THE ROLE OF INSAR IMAGERY BASED DEFORMATION FIELDS IN MAPPING OUTPOTENTIAL STRUCTURAL BELTS OF THE 2015 NEPAL EVENTS

Revathy M. Parameswaran*a, Kusala Rajendrana and C.P. Rajendranb

a Centre for Earth Sciences, Indian Institute of Science, Bangalore, Indiab Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore

*[email protected]


KEY WORDS: InSAR, Earthquake, Himalaya

ABSTRACT:

The April 25, 2015 (Mw 7.8) earthquake near Gorkha, central Nepal, and another one that followed on May 12 (Mw 7.3), located~140 km to its east, provide exceptional opportunities to understand some new facets of Himalayan earthquakes. The rupture of theMw 7.8 earthquake was east directed, with no part relayed to the MFT. Using InSAR data, we zoomed into regions where broadzones of uplift and subsidence were juxtaposed in closest proximity, and therefore would most likely express any surfacedeformation or at least secondary effects of near-surface blind thrusts. Field evidence, supported by the available InSAR imagery ofthe deformation field, suggests that a component of slip could have emerged through a previously identified out-of-sequencethrust/active thrust in the region that parallels the Main Central Thrust (MCT), known in the literature as a co-linear physiographictransitional zone called PT2. Termination of the first rupture, triggering of the second large earthquake, and distribution ofaftershocks are also spatially constrained by the eastern extremity of PT2, which lead us to explore the role of structuralheterogeneities in constraining the rupture progression. We used teleseismic moment inversion of P- and SH-waves, and Coulombstatic stress-changes to map the slip distribution, growth of aftershocks, and their relation to the thrust systems. Our results based onIRIS data from 38 stations suggest that the maximum values of the coseismic slip coinciding with the down-dip projection of thesouthern boundary of PT2. Most aftershocks were sourced outside the stress-shadows of slip >1.65m. The May 12 (Mw 7.3)earthquake on a contiguous patch coinciding with the eastern terminus of PT2, is the first known example of a late, distant, and largeaftershock associated with any large earthquake in the Himalaya. Our analyses of the pair of Nepal earthquakes support the fieldobservations made using the InSAR-based deformation field, and highlight the role of out-of-sequence thrusts and the deep crustal,channel-flow-driven extrusion mechanism in Himalayan seismicity, an aspect that has not been discussed previously.

18


CAVE DEPOSITS FROM THE KUMAUN HIMALAYA AS POTENTIAL EARTHQUAKERECORDERS

Jaishri Sanwala, C. P. Rajendran

aand Kusala Rajendran

b

a Geodynamics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064 Indiab Centre for Earth Sciences, Indian Institute of Science, Bangalore 560 012 India


KEY WORDS: Paleoseismology, Cave deposits, Kumaon Himalaya

ABSTRACT:

The cave deposits are the possible paleoseismic recorders and their absolute chronology gives a prospect to use them as a mostpotential proxy to understand the past seismic history. The prolonged seismic quiescence of central Himalaya makes it morevulnerable to the great earthquake so it is important to identify the longer-term time series of past earthquakes tounderstand their earthquake frequency in this segment. The Central Himalaya hosts number of natural caves between major activethrusts forming potential storehouses for paleoseismological records. The deformation preserved within speleothems in karstsystem can be analysed to obtain continuous record of past earthquakes. Here we present the results obtained from thelimestone caves in the Kumaun Himalaya, and discuss the implications of growth perturbations identified in the stalagmites aspossible earthquake recorders. The U-Th age data from three specimens allow us to constrain the intervals of growthanomalies, and these were dated at 4273±410 yr BP (2673-1853 BC), 2782±79 yr BP (851-693 BC), 2498±117 yr BP (605-371 BC), 1503±245 yr BP (262-752 AD), 1346±101 yr BP (563-765 AD), and 687±147 yr BP (1176-1470 AD). The datesmay correspond to the timings of major/great earthquakes in the region and the youngest event (1176-1470 AD) showschronological correspondence with either one of the great medieval earthquakes (1050-1250 and 1259–1433 AD) evident fromtrench excavations across the Himalayan Frontal Thrust.

19


ACTIVE FAULT MAPPING USING GEOPHYSICAL METHODS (GPR, IPR) IN NORTH-WESTERN PART OF HIMALAYAN FOOTHILL ZONE

Kannaujiya S.*a, Champati ray P. K.a, Philip G.b, Sharma G.a, Mohanty S.c, Saikia A.a and Chattoraj S. L.a

a Indian Institute of Remote Sensing, Dehradun, Dehradun- [email protected] Wadia Institute of Himalayan Geology, Dehradun- [email protected]

c Indian School of Mines, Dhanbad- [email protected]


KEYWORDS: Ground Penetrating Radar (GPR), Induced Polarization Resistivity (IPR), RES2DINV, Active fault, Trenching

ABSTRACT:

The geomorphology of Indian subcontinent has been modified by multiple tectonic movements along the various fault systemswhose origin and subsequent tectonic history are related to the break-up and the northward drift of the Indian plate (Maurya et al.,2005). Many of the faults are seismic in origin and have a history of tectonic reactivation. Detailed shallow subsurface geophysicalstudies were carried out across these faults at frontal Himalaya to understand their nature, potential for stress accumulation andrelease, neotectonic evolution and palaeoseismic studies. The present paper argues for extensive use of the Ground Penetrating Radar(GPR) and Induced Polarization Resistivity (IPR) primarily to delineate the subsurface geology across various faults followed bytrenching at selected location to reconstruct the neotectonic and palaeoseismic history.

The main objective of this paper is to validate GPR (Ground Penetrating Radar) based observation for active fault mapping inHimalayan Region. GPR method was applied with 100 and 200/600 MHz dual frequency antennas in and around of Solani river(foothill of Himalaya) and Bhauwala village (Doon valley). The depth of GPR profiles acquired across fault scarp vary from 3 m to10 m depending on the antenna frequency and subsurface soil condition. With 200/600 MHz dual frequency antenna, high resolutionsubsurface image with penetration depth up to 5 m were obtained. The 2D profiles interpretation reveals correlation between GPRanomaly and fault plane. Based on the study it is concluded that GPR method was useful and quick tool compared to other methodfor active fault confirmation in Himalayan foothill zone. It helped in locating and understanding the nature of the fault andquantifying the amount of fault scarp retreat. Continuous Electrical Resistivity Tomography (ERT) surveys were carried using 40electrodes system at various locations of the Himalayan region to ascertain the electrical properties of the formations to confirmactive faults. 2D multiple IPR profiles were acquired with electrode separations varying from 1 m to 5 m. The apparent resistivitydata was inverted using the least square inversion technique (Loke and Barker, 1996) into subsurface electrical structures usingRES2DINV software. The results on the profiles indicate the boundary layer in the electrical resistivity tomography suggestingpresence of the fault scarp which has developed due to tectonic activity. Results also show that ERT technique can be usedeffectively to demarcate different stratigraphic units of comparatively different lithology and decipher major structural control.Results of both the techniques (GPR and IPR) confirm the presence of faults that were initially interpreted from satellite images andfield observation. Therefore satellite image interpretation followed by field observation and geophysical surveys have emerged asbest integrated approach for geological fault mapping in recent sediments/ alluvium.

20


EARTHQUAKE HAZARD AND RISK ANALYSIS OF GUJARAT

B.K. Rastogi a

a Institute of Seismological Research, Raisan, Gandhinagar, India – 382 009 – ([email protected])


KEY WORDS: Seismic Hazard Map, SHAKE, Gujrat

ABSTRACT:

For earthquake preparedness in Gujarat ISR has prepared Probabilistic and Deterministic Seismic Hazard maps of Gujarat stateand also maps of Vs30, resonance frequencies and amplifications corresponding to them and correlated these parameters withgeology. Seismicity in Gujarat is assessed by a dense network of 60 broadband seismographs and 55 accelerographs. Faults havebeen mapped and active fault studies have been carried out. Orientation and depths of faults are estimated using seismic, gravity-magnetic and EM surveys. Vs30 is measured by shallow seismic and PS logging methods. Geotechnical studies are done throughnumerous boreholes to 50m depth. Soil properties every 1.5m depth are measured in a well-equipped geotechnical lab. Strongground motion at surface is estimated using SHAKE program. Input for this are expected strong ground motion at any strong soil /rock layer at depth and soil properties above this depth.

Specific region study includes Vulnerability Assessment of Ports. At micro level seismic microzonation has been done for severalcities and areas. These include Gandhidham-Kandla -Anjar area, Ahmedabad and Gandhinagar, Dholera Special Investment Region.In Dholera SIR a no. of cities with high-rise buildings, industrial hubs, an airport, a railway station are planned. The cities of Surat& Bharuch have been taken up in collaboration with Geological Survey of India. Suggestions have been made for incorporation ofseismic hazard in Town Planning of Dholera SIR and Guwahati City. Deliverables of seismic microzonation are Deterministic PGAMaps and Response Spectra at 500m grid.

At small areas or local scale earthquake hazard assessments are made for critical structures like Nuclear Power Plants, LNG storageTerminals and tall buildings and cluster of skyscrapers coming up in Gujarat International Finance Tec (GIFT) city. At GIFT Citynumerous buildings of 28 to 100 storeys are planned. For the two buildings of 28 storeys each already constructed the foundationdesign and seismic force to be considered for resistant designing is with our advice. ISR has also done seismic hazard study for182m tall Statue of Unity.

An important finding of the strong motion mapping is that the low-rise buildings of 3-5 stories (natural periods 0.1-0.5 sec) need tohave 50-100% higher seismic factor than that recommended in the National Code up to a distance of 20km from Narmada andCambay faults with potential of magnitude 6. For Kachchh faults with potential of magnitude 8, this distance is 40 km andvulnerability is for both low - and high - rise buildings.

21


NEPAL EARTHQUAKE: EARTH OBSERVATIONS FOR GEODYNAMICS ANDHAZARD MITIGATION

P. K. Champati ray a, G. Sharma b, R.S. Chatterjee c, A. Senthil Kumar d, Somalin Nath e and Anuradha Sharma f

a [email protected], b [email protected], c [email protected],e [email protected], f [email protected] and Disaster Management Studies Group

IIRS (ISRO), 4-Kalidas Road, Dehradund Director, IIRS (ISRO), [email protected]


KEY WORDS: Earthquake, GNSS, TEC, Crustal Deformation, Earth Observation

ABSTRACT:

The 7.8 Mw Nepal earthquake of 25th April 2015 was a major event associated with the Main Central Thrust (MCT) on thesurface and Main Himalayan Thrust (MHT) at a depth of 15 km. This event has occurred on a relatively quiet block withintectonic frame work of Nepal Himalaya. Towards north and south there lies a zone where both MCT and South TibetanDetachment System (STDS) attain lowest topographic elevation due to massive denudation. The rupture propagationaccompanied by presence of thick piles of soft sediments and poor construction style attributes to the large scale damage inKathmandu valley. Other seismic hazards such as landslides, snow avalanche and liquefaction have been reported from Nepaland India. Very interestingly, the signals of regional deformation have been captured by Continuously Operation ReferenceStation/Global Satellite Navigation Satellite System (CORS/GNSS) in India at a distance of 580-690 km from the epicentre inNepal. Attempts were made to analysis surface/crustal deformation by (Differential Interferometry Synthetic Aperture Radar)DInSAR observation using ALOSPALSAR-2, associated avalanches and TEC (Total Electron Content in the ionosphere)anomaly using GNSS observation data. Overall, the aim was to understand the geodynamic implications of this earthquakeusing EO and allied techniques.

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Theme-5: Flood, Coastal and Oceanogenic Hazard Analysis

FLOOD EARLY WARNING WITH QUASI-DISTRIBUTED HYDROLOGICAL MODELWITH INPUT FROM NWP RAINFALL FORECAST – AN OPERATIONAL EXERCISE

IN FLOOD PRONE DISTRICTS OF BRAHMAPUTRA VALLEY

D. Barman**, S.S. Kundu, R.B. Gogoi, A.Borgohain, A. Bharali, D. Sarma, N.Barman, P.L.N. Raju

(North Eastern Space Applications Centre, Shillong, Meghalaya, India)** Presenting Author


KEY WORDS: Quasi-distributed Hydrological Model, Numerical Weather Prediction, Automatic Weather Station, GriddedRainfall Forecast, Flood Hydro-graph

ABSTRACT:

Flood is a chronic disaster occurring almost every year in the flood plains of Brahmaputra valley. Along with structural measureslike the construction of embankment etc. non-structural measures like flood forecasting, flood plain zonation and regulation etc. inrecent past has gained importance among researchers, technocrats and policy makers. In this context an attempt has been made tomitigate the flood damage by developing an operational flood forecasting system for some districts in the state of Assam byjudiciously using the strength of geospatial technology coupled with established relationships among important hydro-meteorological parameters. The major technical component of the present exercise comprises of two sub-components, where the firstone deals with a numerical weather prediction model called Weather Research Forecast (WRF) giving gridded rainfall forecast, thesecond component is a robust quasi distributed hydrological model that runs with the input of the distributed grid wise forecastedrainfall values to generate flood hydrographs at different reaches of a river system, which when compared with flooding thresholdsfor those reaches establishes the decision support for issue of flood alerts to the concerned authorities. Alerts have been issued indistrict and revenue circle level with an average lead time of 12 to 24 hours. Both the met model and the hydro model have beenvalidated satisfactorily with ground data from met and hydro observation stations spread across the river basins under consideration.This activity has been recognized both at regional and national level.

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AGRICULTURE DROUGHT SEVERITY DURING 2000-2014 FROM CROPPINGSEASONS JANUARY–MAY & JULY-DECEMBER

Bhavani P., Roy P.S., Charavarthi V.

Centre for Earth and Space Sciences, University of Hyderabad, Hyderabad, India - (*[email protected])


KEY WORDS: Drought, NDVI, SPI

ABSTRACT:

Assessment of spatial severity is of significant importance in drought studies. Andhra Pradesh (undivided) is one of the major statewhere agriculture production is high, at the same time the state has experienced major droughts during recent decades. During 2000-2014, the state has experienced drought in 2001, 2002, 2004, 2009 & 2011 years. These droughts are studied based on satellite andmeteorological data of Kharif crop growth period (i.e. July-December). The present study attempts to investigate the impact of poormonsoon on the crop grown during January to May. For this purpose the whole year fortnightly data has been analyzed. The paperpresents the interesting outcomes of drought condition during January-May and July-Dec. The results indicate that SPI andDevNDVI/VCI together can make assessment of agriculture drought during the total crop growth period. Based on the results, thestudy isolates drought years during the period of January-May and July-December for the assessment period. Within the droughtyears intensity has been evaluated for the drought affected years. The district wise classification of severity is based on intensity andreoccurrence of drought during assessment period. The study identifies the districts which experience higher severity duringdroughts. Such an approach could provide better predictability of drought vulnerability.

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CLIMATE EXTREME ANALYSIS AT WEST FLOWING RIVER BASIN OF KUTCH,SAURASHTRA AND MARWAR

Ila Agnihotri

RRSC-W/NRSC/ISRO,Jodhpur


KEY WORDS: Extreme Events, Climate, RClimDex

ABSTRACT:

Climate variability always exists in nature at several scales. Extremes are a natural part of even a stable climate system. ClimateExtreme or extreme weather or climate event indicate the occurrence of a value of a weather or climate variable above (orbelow) a threshold value near the upper (or lower) ends of the range of observed values of the variable. Precipitation andtemperature data act as climate indicators in the analysis of climate variability and change in an area. The goal of this researchis to analyze the temperature and precipitation extremes in this basin. The indices of temperature and precipitation extremesconsidered in the present study were selected from the list of indices for surface data recommended by the joint working groupon climate change detection of the World Meteorological Organization–Commission for Climatology (WMO–CCL) and theResearch Programme on Climate Variability and Predictability (CLIVAR; Peterson et al. 2001).

West Flowing River Basins of Kutch, Saurashtra and Marwar lies between 67°52’ to 75°19’ east longitudes and 20°53’ to 26°57’north latitudes. The basin is bounded by Aravalli range and Gujarat plains in the east, Rajasthan desert in north, Arabian Seain the south and the west. The basin has maximum length and width of about 865 km and 445 km, respectively. The basincovers large areas in Rajasthan and Gujarat and covers whole of Diu. Hydrologically the West flowing rivers of Kutch andMarwar basin is divided into six sub basins: Luni Upper (38.03%), Luni Lower (15.81%), Saraswati (14.77%), Drainage of Rann(11.50%), Bhadar and other West Flowing Rivers (10.02%) and Shetrunji and other East Flowing Rivers (9.87%). Six subbasins have been further clustered into 268 Watersheds each of which represents a different tributary system for size rangingfrom 332 Sq. Km to 1448 Sq. Km with maximum number of watersheds falling in Luni Upper Sub Basin.

For the analyses of climate extreme in the basin, RClimDex has been used on IMD daily rainfall and temperature gridded data.Daily rainfall data for IMD Precipitation grid (0.25° x 0.25°) with a temporal resolution of 1964-2013; and daily maximum &minimum temperature data for IMD Temperature grid (1°x1°) with a temporal resolution of 1969-2004 has been used for thecalculation of climate extremes. Theil Sen slope were used as trend estimators for climate data. Climate extremes were analyzedby calculating 16 Extreme Indices for Temperature with their trend and 11 Extreme Indices for Precipitation with their trend.

The specific conclusion for climate variations is derived for the WFR-KSM basin and the sub basins of WFR-KSM basin.Overall, the climate analysis indicated that the Rainfall trend is indicating wetter climate extremes. The intensificationvaries spatially. Temperature trend is indicating a shift towards hotter climate and increase in temperature extremes.

25


FLOODS IN CHENNAI AND KANCHIPURAM DISTRICTS OF TAMIL NADU, NOV2015. IS IT A DISASTER? A CHALLENGE TOWARDS SUSTAINABILITY IN THE

INDIAN COASTAL ZONE? OR A VERDICT?

G Arun, R P Hari Ram


KEY WORDS: Landsat 8 OLI/TIRS, Sentinal-1, SAR data, Media, Sustainability, Anthropogenic, Accretion, Erosion

ABSTRACT:

The current study approaches a broad view of consistent low pressure zone induced floods in parts of Chennai and KanchipuramDistricts during November 2015 monsoon season. Landsat 8 OLI/TIRS images of the pre-event has been analysed for extraction ofwetlands, existing lakes and waterbodies prior to the event. Sentinal-1 interferometric wide swath data (C-band SAR data) has beenused for extraction of post-disaster waterbodies. The area affected in this region has been quantified. Apart from that communicateddata from the social and mass media has been collected which indirectly shows various responses and overall governance after thedisaster event. A study has been made over organizations, Institutes and Universities’ efforts over the current disaster event. Basedon the cumulative study evidences are brought out showing the challenges that are being faced towards the sustainability.Anthropogenic activities carried out in the 21st Century underestimating the coastal processes such as accretion and erosion and theirrisk has been analyzed from the literatures.

Corresponding Author: [email protected]

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RAINFALL FORECASTING USING ARTIFICIAL NEURAL NETWORKS

Rohit Sambarea, Dr. Vaibhav Gargb, S.P. Aggarwalc

a,b,c Indian Institute of Remote Sensing Dehradun, - ([email protected]), ([email protected]), ([email protected])


KEY WORDS: Digital Elevation Model, Artificial Neural Network, Hydro Processing, Rainfall Forecasting

ABSTRACT:

Forecasting rainfall temporally is one of the major scientific topic discussed today because rainfall is a very key factor in predictingnatural disasters like floods, landslides etc. Accurate, timely forecasts of rainfall are important for accurately predictingstreamflow and flash floods in river basins. The ensemble approach of mathematics, statistics & other computational techniquesdeveloped in recent past has enhanced the accuracy of climate model for predicting rainfall. But still as rainfall is a verynonlinear natural event especially in India the accuracy of these models are not good. Besides these statistical and mathematicalmodels requires high computational powers. Therefore many researchers have considered the use of Artificial Neural Network (ANN)for forecasting of rainfall events which is less complex than other statistical models. ANN is machine learning technique withflexible mathematical structure which is capable of identifying complex non-linear relationships between input and output datawithout attempting to reach understandings as to a nature of phenomenon. In this study we have proposed we have used severalnetwork type, learning function and transfer function of ANN to analyse and to decide better type of network, learningfunction and transfer function to get best results. Based on algorithm we are using three network layer (one input, one hidden& one output layer). The Basin of Tel River which is one of the largest tributary of Mahanadi in state of Odisha is considered for thisstudy. Digital Elevation Model (DEM) of SRTM 30 meter is used for delineating watershed of river using Hydro processing toolArc-Hydro tool of Arc-MAP. Various rainfall causative factors (Air Temperature, Wind Speed, Atmospheric Pressure andHumidity) are considered for predicting rainfall. Air Temperature, Wind Speed, Atmospheric Pressure and Humidity data areacquired from Automatic Weather Stations (AWS’s) of Indian Meteorological Department (IMD) which are located in river basinand these data were used for training dataset for ANN. Rainfall events of past events are also used for training data which is acquiredfrom AWS’s. Training, Testing and validation data were divided and implemented in network and with correlation coefficient ofMean Square Error (MSE). The dataset is trained and tested separately for each of neural network architecture. The results showsthat the best models have R values of 0.93-0.99 range. These results also show that forecast ability is enhanced. Hence ANN hasconsiderable potential to be used for rainfall forecasting.

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DEVASTATION IN NEPAL EARTHQUAKE IN 2015: A REMOTE SENSING BASEDASSESSMENT

Snehmani a, Chetna Soni a, Akansha Patel a

a Snow and Avalanche Study Estt, Him parisar, Sector 37-A, Chandigarh, 160036 – ([email protected])


KEY WORDS: Earthquake, Landsat 8, CossiCorr, Optical Remote Sensing, Kathmandu

ABSTRACT:

Nepal earthquake which is also known as Gorkha earthquake was the worst natural disaster. Nepal earthquake was more damagingthan earthquakes that originate deeper in ground. The earthquake occurred on 25th April 2015 at 11:56am with its epicenter34 kilometers east-southeast of Lamjung, Nepal. The intensity was measured 7.9 on Richter scale. Total 39 shocks along with themajor aftershock occurred on 12th may 2015 with the 7.3 intensity on Richter scale. The epicenter of aftershock earthquake was nearthe Chinese border between the capital of Kathmandu and Mount Everest.

Satellite remote sensing is ideal tool for disaster management as it covers large area with temporal data of the same area in repeatpass. The availability of optical imagery allows monitoring of earth surface and changes occurred in land surfaces due to climatechange and geologic process. Optical image correlation is efficient in measuring the fault-parallel and fault-perpendicularcomponents of co-seismic displacements.

Present study shows the displacement and magnitude occurred due to Nepal earthquake. Study area covers Kathmandu city, capitalof Nepal. Ground deformation Information has been retrieved from sub-pixel correlation of pre- and post-earthquake remotelysensed optical images. Co-Registration of Optically Sensed Images and Correlation (CossiCorr) has been used in software packageto process the co-seismic deformation.

Landsat 8 imagery has been used to process pre and post-earthquake events. 9th February 2015 Landsat 8 cloud free image has beentaken as a pre event image and 1st June 2015 Landsat 8 has been taken as post event image. Pre and post event images have been co-registered first at 1/10th sub-pixel level using Fourier based frequency correlator. Displacement has been estimated using frequencycorrelation in two patches. First, roughly pixel wise displacement has been measured than correlation has been operated to retrievethe sub-pixel displacement. North/South and East/West displacement along with signal to noise ratio image has been generated.Vector field has been created using North/South and East/West displacement image. Magnitude has been measured using vectorfield to find displacement. Magnitude has been found to be similar to intensity of Richter scale.

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MAPPING AND MONITORING OF GLOF PRONE LAKES IN PARTS OF SHYOKBASIN USING REMOTE SENSING AND FIELD TECHNIQUES

Ritesh Mujawadiya, H S Negi, G Arun, Anant Kumar, A Ganju

Snow & Avalanche Studies Establishment, Him-parisar, Sector 37-A, Chandigarh-160036.


KEY WORDS: GLOFS, ASTER DEM, Shyok Basin

ABSTRACT:

Himalayan terrain is associated with a number of hazards and disasters. Glacial lake outburst flood (GLOF) is one of the majorthreats formed due to glacier receding and moraine damming. These are vulnerable for disaster in the valley region due to the floodsassociated with them. Catchment area of the Shyok River, i.e. 27,360 Sq.km is taken for study where 4091 Sq.km is glaciated. Multi-temporal satellite imageries of 2000-2002 and 2014-2015 along with ASTER DEM (30m resolution) were used for mapping andmonitoring of Glacier lakes. Total 57 lakes were identified in the study area and categorized as natural lakes/water bodies,moraine/Ice dammed lakes and supra-glacier lakes. Area-volume estimation has been carried out using empirical methods andvolume changed was calculated. The temporal changes and terrain parameters of moraine and ice dammed lakes helps invulnerability assessment. Field studies has been carried out for the assessment of moraine/ice dammed lakes in the study area.

Correspondence: [email protected]

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HYDROLOGICAL MODELING FOR FLOOD DAMAGE MITIGATION

K.Sindhua, K.H.V.Durga Raob

a National Institute of Technology, Warangal, India, [email protected] National Remote Sensing Centre, Hyderabad, India, [email protected]


KEY WORDS: Brahmani-Baitarani Basin, Digital Elevation Model, Hydrological Modeling

ABSTRACT:

The Hydrologic Modeling System is designed to simulate the precipitation-runoff processes of watershed systems. In this paper, acontinuous simulation based hydrological model is developed through a distributed hydrological modeling approach and ahydrodynamic model is developed for computing spatial inundation for the Brahmani-Baitarani river basin, India using space inputs.The basin is geographically located between 200 29’ N to 230 38’ N latitude and 830 54’E to 870 02’ E longitude. The aerial extentof the basin is approximately 50,066 sq. km.

The hydrologic modeling approach includes rainfall-runoff modeling, flow routing, calibration and validation of the model with thefield discharge data. The study basin is divided into 17 sub basins to improve the model accuracy. CARTO Digital Elevation Model(DEM) generated from Indian Remote Sensing Satellite Cartosat-1 of 30m resolution, land use/land cover derived from the IndianRemote Sensing Satellite (IRS-P6) AWiFS data, and soil textural data obtained from National Bureau of Soil Sciences and Land UsePlanning (NBSS&LUP) of the study area are used in the modeling to compute topographic and hydraulic parameters of each subbasin and channel. The model is calibrated for the years 2006 and 2009 using the observed data and validated for 2008 and 2011.From the results, it is found that computed discharges are matching well with the observed discharges. The model is useful for runoffestimation and flood damage mitigation.

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IDENTIFICATION OF GLACIAL LAKES FOR POTENTIAL OUTBURSTS IN LAHAUL& SPITI, HIMACHAL PRADESH

Varuni Pathak, Rakesh Arya, S.Sreekesh

CSRD/SSS, JNU, New Delhi-110067 – ([email protected])


KEYWORD: GLOFs, Climate Change, Remote Sensing

ABSTRACT:

The glaciers are nature’s most valuable resource, supplying fresh water continually to mankind for drinking, agricultural, industrialand hydroelectric power generations for present and future needs for the people in mountainous and foreland areas, besidesmoderating climate of the region. Climate changes in recent years have caused recession in these glaciers, leading to formation oflarge number of glacial lakes in the Himalaya, at different. These glacial lakes store large amount of water which may burst out oncestoring capacity of the lake is achieved due to increased inflow, causing abrupt breaching of glacial, leading to multitude of damagesin downstream areas, having cross boundary impacts. Such records are available for a breach of glacial lake, however infrequent inthe past, in the Upper Chandrabhaga 1860s.

Technically speaking, a GLOF is a catastrophic discharge of water from a glacial lake, be it pro, supra or moraine dammed thatmobilizes large boulder and ice downstream as a gush where huge amount of devastation and destruction is caused because ofenormous amount of debris and washed out material such as trees and huge boulders. Therefore, these GLOFs events are severegeomorphologic hazards. Over the period of time, GLOFs once infrequent are increasing globally and more specifically in the entireHimalayan region. Regular monitoring of glaciers and glacial lakes is therefore mandatory in order to propagate adaptationmeasures, early warning systems and mitigation measure in areas vulnerable to GLOFs knowing that the frequency of such event areincreasing.

In India number of hydroelectric power (HE) projects are being installed in the mountainous state of Himachal Pradesh, coupledwith several other infrastructural activities worth billions of rupees. These are being developed amidst growing danger of GLOFhazard due to rising number of lakes and areal extent of existing ones, dammed by highly porous soil material. These lakes maybeach anytime either due to piping or due to overtopping can uproot the entire megaproject besides taking a toll of millions ofpopulation. Hence it is necessary to map the newly forming lakes and changes in the pre-existing lakes in order to assess and predictthe vulnerability of event, probability of its occurrence and damage potential in the impact areas to minimize the loss and damage oflife and property. Since earlier one of such floods in Chandrabhaga River in Lahaul valley is recorded to have damaged all bridgeseven beyond the present national boundaries in 1860s, emanating from Bara Shigri glacier on breaching. Therefore, GLOFs in thepresent context needs to be taken up seriously for monitoring, hazard assessment and mitigation in such areas. Geospatial tools andtechniques used here can play a decisive role in assessing the GLOF hazard prepare strategy measures and preparedness mechanismfor mitigation.

This paper focuses on the Lahaul valley in Himachal Pradesh which needs a more comprehensive, precise and detailed analysis ofglacial lakes to identity the potentially dangerous glacial lakes that may lead to floods in near future, damaging newly lined upinfrastructural property extending across different reaches. There are 212 glacial lakes in the state of Himachal Pradesh, out of which11 glacial lakes are identified as potentially dangerous lakes. Four major glacial lakes have been identified within the administrativeboundary of the present study area which has potential of breaching in the near future.

The boundary of the study area was derived from the Shuttle Radar Topography Mission Digital Elevation Models (SRTM DEMs)using an altitude of ≥ 2,500 m. All of these lakes have been mapped using Landsat TM and ETM images with 30 Meters resolution,superimposed with Google earth images wherever possible, for a period from 1990 to 20013, using NDWI in ArcGIS to extractglacial lake boundaries and types (i.e., glacier-fed or non-glacier-fed). Synchronization in such exercise is critical as the higherspatial-resolution images used in Google Earth, including SPOT 5 (spatial resolution of 2.5m) can improve the process ofidentification of lakes and boundaries. For prioritising and classifying glacial lakes with potential threat of breaching, spatio-temporal changes in areal extent and volume of existing glacial lakes is mapped along with formation of new small or large glaciallakes in the vicinity. Glacial lake inventory includes supra glacial, pro-glacial or moraine dammed glacial lakes, with volumetricassessment using Huggel and Evans’ empirical estimation method. From change analysis of lake area and volume thus measured, abuffer zone being created around the potentially identified glacial lakes that may lead to GLOFs in the future, having a reach/path tonearby settlements or infrastructures for a probable impact area.

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SHORELINE CHANGE ANALYSIS FOR EAST COAST OF ANDHRA PRADESH, INDIA

Vivek Ga

a Dept. of Civil Engineering, SRM University, Kattankulathur, Kancheepuram Dt., Chennai-603203, India,([email protected])


KEY WORDS: DSAS, Shoreline Change Map, Risk Ratings

ABSTRACT:

Coastal shorelines are always subjected to changes due to coastal processes, which are controlled by wave characteristics and theresultant near-shore circulation, sediment characteristics, beach form, etc. From the coastal vulnerability point of view, coastssubjected to accretion will be considered as less vulnerable areas as they move toward the ocean and result in the addition ofland areas, whereas areas of coastal erosion will be considered as more vulnerable because of the resultant loss of private andpublic property and important natural habitats such as beaches, dunes, and marshes. It also reduces the distance betweencoastal population and ocean, thereby increasing the risk of exposure of population to coastal hazards. Landsat MSS, ETM andTM images covering the Visakhapatnam coastline for the years (1973, 1977, 1988, 1994, 2001, 2002, 2003, 2011, 2014 and 2015)were downloaded from USGS. The data have been projected to the Universal Transverse Mercator (UTM) projection system withWGS-84 datum. The shoreline along the Vishakhapatnam coastline was digitized using ArcMap 10.2 and ERDAS Imaginesoftware using the on-screen point mode digitization technique. The near infrared band that is most suitable for the demarcationof the land–water boundary has been used to extract the shoreline. The digitized shoreline in the vector format were used asthe input to the Digital Shoreline Analysis System (DSAS) to calculate the rate of shoreline change. The inputs required forthis tool are shoreline in the vector format, date of each vector layer, and transect distance. The rate of shoreline change iscalculated for the entire study area, and risk ratings are assigned.

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TWO ‘HOT SPOTS’ IN THE UPPER ALAKNANDA RIVER VALLEY- CHAMOLIDISTRICT, UTTARAKHAND HIMALAYAS – A CAUSE OF CONCERN

Tangri A.K.*, Ram Chandra, Rupendra Singh & C.L. Paul - ([email protected])


KEY WORDS: Cloud Burst, Landslides, Flash Floods, HOT SPOTS, Kharak Galciers

ABSTRACT:

Cloud burst, landslides and the resultant flash floods in Uttarakhand Himalayas are quite conspicuous by their recurrence in recentyears. Although the 10 to 20 year cycle of floods and landslides in Alaknanda river and its tributaries has long been reported (Bhatt1994), it is the recent amalgamation of conventional and space borne technologies that has made their impact assessment moreaccurate and has also led to predictions and mitigation of their consequential impacts.

Over the course of recent glaciological & snow cover studies in Satopanth & Bhagirath Kharak glaciers, north of Bhagirath inChamoli district of Uttarakhand, two important ‘HOT SPOTS’ have been identified which are quite prone to cause large scalenatural disasters, in form of lake bursts and flash floods.

These two HOT SPOT have been temporal monitored using multi-date satellite data in conjunction with field technologies.

Alaknanda river is the combined melt water of Satopanth & Bhagirath Kharak glaciers. Bhagnue nala descending down the BhagnueBank glacier and meeting Alaknanda river (30*47’94”N, 79*26’96”E), almost two kilomaters downstream of the snout ofBhagirath Kharak glacier, has a high discharge and carries down enormous quantum of bed load in form of boulders, pebbles andcobbles of all sizes, which are dumped to form a huge fan deposit at the confluence with Alaknanda river. These deposits at timesblock the main course of Alaknanda river, as was experienced during the ablation period of 2013, immediately following the heavyrainfall, which also caused the in-famous Kedarnath tragedy.

During the field travers of May 2013, the confluence of Bhagnue nala with Alaknanda river seemed normal and no blockade of theletter was visible. However, the heavy rainfall of mid- June, resulted in heavy discharge in Bhagnue nala. This carried down a lot ofbed- load and dumped it into the Alaknanda river. This blocked the course of the river and a lake started forming. This lake startedswelling and gradually increased in volume. The satellite data of June 22nd, 2013, showed that the lake had swelled to 25,701.04(±1800 M2) Sq.mts. and subsequently the satellite data of July 9th, 2013 showed that the lake had further increased to an alarmingsize of 35,751.43 (±1800 M2) sq.mts. Any further blockade of Alaknanda river could have caused a giant lake which in case ofbursting could have resulted in large scale devastations in the downstream. Fortunately, this giant lake breached on the side andentire accumulated water rolled downstream. Thus a major tragedy was averted.

Likewise, a Hot-Spot no. 2 has also been temporally monitored in the study area. A pro- glacial lake on Satopanth glacier has alsoswelled enormously in spatial extent and volume with time. This moraine dammed lake also needs to be regularly monitored usingmulti-date satellite data to avoid any disastrous impact in the long run.

These “Hot-Spots” in the Himalayas are a cause of concern and need to be regularly monitored to avoid and mitigate anyconsequential disaster.

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Theme-6: Space Technology for Disaster Preparedness and Response

SPACE TECHNOLOGY APPLICATIONS FOR ADDRESSING MULTI-HAZARD DRRNEEDS IN NE REGION OF INDIA – THE CONCEPT CALLED NER-DRR

D. Barman, D.J. Chutia, R.K. Das, S.S. Kundu, K. Bhusan, K. Chakraborty, B.K Handique, J. Goswami, J.M. Nongkynrih, M.S.Singh, R.B. Gogoi, P.L.N Raju**

(North Eastern Space Applications Centre, Shillong, Meghalaya, India)** Presenting Author


KEY WORDS: Disaster Risk Reduction, Actionable products, Early Warnings, Risk Zoning, Standard Operating Procedures

ABSTRACT:

The North Eastern Region (NER) of our country, consisting of eight States, covering about 8% of India’s geographical area andabout 4% of India’s population can be broadly divided into three geographical regions: the Surma valley, North Eastern hillbasin and the Brahmaputra valley. The hills and plains differ significantly in terms of availability of natural resources, populationdensity, habitation pattern, climate conditions, and differ in their proneness to natural disasters, besides their unique culturaldiversity. NER experiences a number of natural hazards almost every year with severity varying across spatial and temporalscales in different States. Natural disasters such as Earthquake, floods, river bank erosion, landslides, severe cyclones,thunderstorms, forest fires, agricultural drought and vector borne diseases are annual events in different parts of the region. Due torapid industrialization in some parts of the region, industrial hazards such as explosion, fatal water and air pollution etc. are alsopresent. Both Natural and anthropogenic factors are responsible for the occurrence of these calamities. With the advent of opening ofnew frontiers in space based technologies in earth observations, communications and information systems, geo-portals etc. differentvalue added products have become possible for providing meaningful disaster risk reduction services. Keeping this in view, theNorth Eastern Regional node for Disaster Risk Reduction (NER-DRR) has been conceptualized. The node is presently gettingdeveloped with few major objectives such as development of disaster-wise interactive database based on state govt. user needassessment, development of geo-spatial support tools for decision making for management of different disasters, development ofactionable products and services in tune with the requirements of the region etc. Few successes have already been achieved in areaslike Flood early warning, multi-hazard risk and vulnerability assessment, development of disaster relevant information kiosk,development of mobile app for field data transmission etc. Once NER-DRR becomes fully functional, it is expected to play animportant mitigating role for management of various disasters in NE region of India.

34


DESIGN OF COST EFFECTIVE DISASTER MONITORING GPS NETWORK DESIGN

Ramji Dwivedia, Onkar Dikshit

b

aGeographic Information System (GIS) Cell, MNNIT Allahabad, Allahabad-211004, UP

bDepartment of Civil Engineering, IIT Kanpur, Kanpur-208016, UP


KEY WORDS: Optimization and Design of GPS networks; D-optimality; N-optimality; Global optimization techniques; PSO

ABSTRACT:

GPS networks are established for disaster monitoring related activities around the globe. A disaster monitoring network isestablished in the field with desired quality criteria such as high precision in final estimates with good reliability at minimalcost. Cost serves an important factor while dealing with these networks. It is, therefore, essentially required to achieve thedesired quality standards in field at minimal cost which indirectly points to optimal design of monitoring network. Optimaldesign of GPS networks i.e. optimal planning of GPS survey is essential in order to achieve high precision at minimalcost. This paper aims at discussing optimization problem of first order design (FOD) and second order design (SOD) of GPSnetwork at minimal cost. Particle Swarm Optimization (PSO), a search heuristic, is explored for providing optimal solution totwo aforesaid design stages of GPS network. PSO efficiently provides near optimal solutions for FOD- finding an optimalnetwork geometry and SOD- finding optimal baselines to be observed. The developed PSO algorithm is demonstrated on solvingFOD and SOD problem and achieving optimal solution of a complex GPS network. The algorithm successfully demonstratedthe application of global optimization technique in solving complex design problem of GPS network. Finally, an optimalsession of baselines to be measured is presented based on the availability of GPS receivers at minimal cost.

aCorresponding Author, Email: [email protected]

35


EVALUATION OF THE CORAL BLEACHING DURING 2015 AT THE CORALENVIRONS OF THE GULF OF KUTCH USING REMOTE SENSING

H Shiva Kumar, R S Mahendra, P C Mohanty, Satej Panditrao, T Srinivasa Kumar

Indian National Centre for Ocean Information Services, (ESSO- INCOIS), Hyderabad, India


KEYWORDS: Gulf of Kutch, Hot Spot, Degree of heating weeks, Thermal stress, Sea surface temperature

ABSTRACT:

Satellite remote sensing is widely used as a tool in many parts of the world for the management of the resources and activities withinthe continental shelf containing reefs, Islands, mangroves, shoals and nutrient rich waters associated with major estuaries. Coral reefbleaching is caused by various anthropogenic and natural variations in the reef environment including sea temperature, solarirradiance, sedimentation, xenobiotics, subaerial exposure, inorganic nutrients, freshwater dilution, and epizootics. Increasedseawater temperatures have been proposed as the most likely cause of coral reef bleaching. In this study Sea Surface Temperature(SST) derived using the satellite Remote Sensing data of NOAA AVHRR were used to generate the Degree of Heating Weeks(DHW) and Hot Spot (HS) products. Combination of the cumulative temperature anomalies and the thermal stress studies wereyielded to synoptically identify the probable areas of bleaching. The bleaching status of the Gulf of Kutch region was assessed basedon the DHW and HS for the bleaching event occurred in the Gulf of Kutch region in 24th April, 2015 to 5th July, 2015. The bleachingstatus up to Alert Level-1 was recorded with the maximum HS of 3o conducted in the Gulf of Kutch Sea confirmed the coralbleaching event. This study focused on detection of coral bleaching warning based on the SST in compliment with the in-situobservations.

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VALIDATION OF MODIS SNOW COVER PRODUCTS USING LANDSAT DURING 2002– 2012 IN MENTHOSA GLACIER, HIMACHAL PRADESH

Abira Dutta Roy*, Milap Chand Sharma, S. Sreekesh

Centre for the Study Regional Development, School of Social Sciences-III, Jawaharlal Nehru University -(*[email protected])


KEY WORDS: Snow Cover, Remote Sensing, Himalaya

ABSTRACT:

Snow cover has a vital impact on climate processes, surface hydrological cycles and ecological and agricultural processes. Since the1980s, numerous methods for snow-cover mapping at regional and global scales have been carried out using data from the NationalOceanic and Atmospheric Administration (NOAA), Advanced Very High Resolution Radiometer (AVHRR) and Satellite Pourl’Observation de la Terre (SPOT) images. Latest technology for global-scale snow-cover mapping is the Moderate ResolutionImaging Spectro-radiometer (MODIS). But accuracy assessment of the snow-mapping has not been effectively monitored. However,the validation of MODIS snow products, with in situ observations have given erroneous results because most of the ground-basedobservation stations are situated at low elevation and in an open area, and thus present some locational bias. Moreover, snow andclouds show almost similar spectral behaviour and similar temperatures as well, which make discrimination difficult. Snow andclouds are often characterized by similar spectral reflectance and share similar temperatures, which make discrimination difficult.The MODIS snow algorithm uses a cloud-mask product to identify clouds in the 8 day composite product, the MOD10A2 productreflects the maximum snow cover extent during 8 days. However, large areas of snow cover may not be shown on the MOD10A2maps if the snow fell at the end of an 8-day period and clouds persisted for the rest of the compositing period. Therefore, where thereare limitations of in situ observations, additional satellite data with higher spatial resolution may serve to validate the accuracy of theMODIS snow products. In the current paper we used Landsat Enhanced Landsat TM images, Thematic Mapper Plus (ETM +) andOLI images on board of Landsat 5, 7 and 8 respectively from 2002 to 2012. These included more than 286 images. NDSI valueswere computed through band ratioing where threshold value was set to >0.4. The Landsat images were resampled to 500 mresolution. Corresponding to the date of pass of the Landsat images MODIS daily snow map were generated to be validated for theMenthosa Glacial valley in Himachal Pradesh. The cloud mask of MODIS daily snow product were also validated with the Landsatfrom 2000 to 2012. All the images were project at UTM WGS84 Zone 44N.

The data were then compared with their corresponding locations (pixels) in each satellite image product for each observation date.The accuracy assessment was included both cloud cover and cloud free images. The SCA (Snow Cover Area) was calculated in theLandsat images based on the number of snow pixels and its area. The value-attribute table (VAT) of each grid of the MOD10A1 orMOD10A2 snow cover products includes each coded integer of the image recorded and its corresponding number of pixels in theimage. At the end, total SCA and the cloud-covered area were calculated for the image based on pixel count and its resolution. Thestudy shows that the MODIS snow-mapping algorithm has lesser accuracy in the mountain area such as the Himalaya, andundervalue the SCA classification errors. Besides the snow depth and land cover types, the snow cover density as well as the slopegradient and sunlight exposure factors leads to accurate estimation of snow cover area. Because of the absence of the climatestations the probable errors caused by slope, aspect and snow cover density could not be estimated. In depth analysis through thecomparison of Landsat and MODIS images show that in the large patches of snow, MOD10A1 provides higher accuracy. However,it was identified that MOD10A1 product excluded snow cover area under in the shadows of the mountain and in the snow coveredge area, whereas, Landsat on the other hand could identify such regions. These errors in the mountain area by the MODIS snowproducts could be minimized through terrain correction method.

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Theme-7: Early Warning System for Disaster Management and Weather Forecasting

FLOOD EARLY WARNING SYSTEM FOR NORTH WEST HIMALAYA USINGINTEGRATION OF WEATHER FORECASTING, HYDROLOGICAL AND

HYDRODYNAMIC MODELS

Praveen K. Thakura, S.P.Aggarwalb, Vimal C. Shamrac, Bhaskar R Nikamd, Vaibhav Gargd Arpit Choukseyf, Pankaj Dhoteg andCharu Singhh

a to h Indian Institute of Remote Sensing (IIRS), ISRO, Dehradun, ([email protected]; [email protected]; [email protected])


KEYWORDS: Flood early warning, WRF, Hydrological model, Flood Hydrograph and Flood Inundation

ABSTRACT:

The flood early warning for any country is very important due to possible saving of human life, minimizing economic losses anddevising possible mitigation strategies. As part of early warning activities, the present work highlights the experimental flood earlywarning study for part of North Western Himalaya (NWH) for the entire monsoon of 2015 and some extreme events of 2013 and2014. The entire flood early warning is done in three parts. In first part, precipitation forecast for every three days in double nestedWeather Research and Forecasting (WRF) domain (9km for outer domain and 3 km for inner domain) has been done for NWH usingNational Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) 0.25 deg data as initialization state. Total30 simulations were done in 2015 monsoon. Based on historical data of Indian Meteorological Department (IMD), validation of2013 and 2014 monsoon has been done, the simulation accuracy of WRF in rainfall prediction above 100 mm is about 60%, butoverall R2 is low. In this year monsoon, the flood event of 05 to 08 August 2015 in part of Beas river basin in Mandi District of H.P.near Dharampur has caused very high damages. This event was picked three days in advance by WRF model based rainfall forecast.In addition, IMD-GFS based 5-day district wise ensemble rainfall forecast was also used. In second part, the forecasted precipitationof every three hours in netcdf format is used in python based codes to get zonal statistics of mean rainfall at sub-basin scale for HMShydrological model or at grid scale for VIC model. This data is used in these hydrological models for flood hydrograph generation atvarious outlets of study area. Currently part of Beas river basin, Upper Ganga basin upto Haridwar and Yamuna basin upto PoantaSahib are tested for near real time flood forecasting. Limited validation for Uttarkashi and Joshimath sites are done using historicaldata of 2000-2009 monsoon. Validation of simulated flood discharge for 2013-2015 monsoon is planned with observed dischargedata of Central Water Commission (CWC), Bhakra Beas management Board (BBMB) and state water agencies. In third part, floodinundation scenarios is prepared using Hydro-Dynamic (HD) modelling approach to see the probable areas which can come underflood inundation. The Glacier Lake Outburst Flood (GLOF) modelling (if some lake is vulnerable for breaching) can be done in 1-DHD model of Mike 11. The GLOF model for Kedarnath is already tested for 2013 flood event.

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DEVELOPMENT OF EARTHQUAKE EARLY WARNING SYSTEM FOR NORTHERNINDIA

Bhanu Pratap Chamolia, Bhawesh Pandeya, Pankaj Kumara, Govind Rathorea, R.S. Jakkab, Ajay Gairolac and Ashok Kumard

a Research Scholar, IIT Roorkeeb Assistant Professor, Department of Earthquake Engineering, IIT Roorkee

c Professor, Department of Civil Engineering, IIT Roorkeed Professor, Department of Earthquake Engineering, IIT Roorkee


KEY WORDS: Early Warning System, EEW, Earthquake

ABSTRACT:

As a measure of short-term disaster mitigation plan for Northern India, an earthquake early warning system has been envisagedand a prototype has been developed. Several seismologists have shown their apprehension of a major or great earthquake incentral Himalayas / Uttarakhand. Studies have also shown that an earthquake of magnitude 7 or more in Garhwal can endangerlife of millions of people in Northern India up to Delhi. Therefore, a region of 100 X 80 Km between Uttarkashi and Joshimathwas identified for setting up a network of approximately 100 accelerometers for this project. Station to station distances betweenaccelerometers were generally kept between 5Km to 10Km. As of now 84 sensors have already been installed. The data fromthese sensors is presently streaming in real time to central server located at IIT Roorkee. Various modules for Picking up P-phase, calculating various EEW parameters and estimating epicenter and magnitude of the earthquake within 3 seconds of P-onset have been developed. These modules have been tested for their performance using past recorded earthquakes. Theperformance of this EEW system was found to be satisfactory during the October 26, 2015 M7.5 Afghanistan earthquake. Thispaper presents details of the network and its connectivity, algorithms used for development of modules and results of fewsimulated testing carried out to test the software.

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ASSESSING AND IMPROVING VGI DATA QUALITY AS A METHOD FORGENERATING A NATIONAL DISASTER DATABASE FOR IMPROVED HAZARD

AND RISK ASSESSMENT

Alexander Zipf and Joao Porto de AlberquerqueGIScience Research Group Heidelberg University, Germany, [email protected]


KEY WORDS: Crowdsourcing, Disaster Mapping, Risk Mapping, VGI, Open Street Map, Social Media

ABSTRACT:

We discuss the potential of crowdsourced and volunteered geographic information for generating or enriching anational disaster database for improved hazard and risk assessment. This will be done by investigating the state of art inderiving spatial information from ambient and volunteered GI. Within the last years the role of geographic informationprovided not by professionals but by the crowd of citizens became increasingly important as an alternative data source fora range of applications. While VGI has demonstrated its general usefulness for disaster management, in many worldregions it is still important to consider quality constraints. The research on quality measures will be reviewed and firstresults of automated improvements of VGI will be discussed, as they are essential for enhancing national databases fordisaster management. The question is what can be learned from the research on VGI quality measures and enrichmentmethods that can be adapted to the special situation of national databases for disaster management?

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STORM SURGE AND INUNDATION FORECASTING SYSTEM AT ESSO-INCOIS

PLN Murty, J Padmanabham, P S Bharadwaj, N Kiran Kumar, T Srinivasa Kumar and S S C Shenoi


KEY WORDS: ESSO-INCOIS, Storm Surge, ADCIRC

ABSTRACT:

The high population density along the coastal stretch of India necessitates a real-time storm surge warning system. Keeping this inview, the Earth System Science Organization (ESSO) - Indian National Centre for Ocean Information Services (INCOIS) initiatedthe Storm Surge Early Warning System (SSEWS) for Indian coasts using the ADCIRC (ADvanced CIRCulation) model. ADCIRCis a finite element based, depth integrated shallow water model that can be used to model storm surges and for other coastalapplications. In this paper we highlight the performance of SSEWS at ESSO-INCOIS during the very severe cyclonic storms‘Phailin’ and ‘Hudhud’. This warning system utilizes the automated Decision Support System (DSS) based on GeographicInformation System (GIS) and database technology. Wind and pressure fields are generated using the Jelesnianski and Taylordynamic wind model. While DSS was initially tested for the very severe cyclonic storm ‘Phailin’ (October, 2013) in experimentalmode, it was used for the first time to provide real-time storm surge and inundation forecasts during ‘Hudhud’ (October, 2014).Model predicted inundation extents were well matched with field surveyed records. Further model did good job in predictingtemporal evolution of water levels. While comparing with observations, these forecasts were found to be quite promising and haveproved the capability of SSEWS at ESSO - INCOIS.

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THE STATUS OF HIMALAYAN GLACIERS - A MASS BALANCE STUDY

Ashim Sattara, Ajanta Goswami

a, Anil V Kulkarni

b

a Department of Earth Science, Indian Institute of Technology, Roorkeeb Divecha Centre for Climate Change, Indian Institute of Science, Bangalore


KEY WORDS: Mass Balance, ALA/AAR. Remote sensing

ABSTRACT:

Himalayan glaciers contribute about 40,800 km2

of the global glacier coverage, the largest of alpine type glacier on theglobe. The mass balance record of the Himalayan glaciers has been significantly limited in order to understand the behaviorof the glaciers to climate and seasonal changes over the entire Himalayan cryosphere. Here we review the various practical,direct and indirect methods like the glaciological method ,geodetic method and ELA/AAR methods used to estimate massbalance of the Himalayan glaciers. Based on the published results from 1979 till date, we discuss about the trends of massbalance of 128 different glaciers spread along the Northeastern, central and Northwestern Himalaya including Nepal andBhutan Himalaya. The two decades of mass balance estimates available for selected reference glaciers along with thesurrounding glaciers distributed over the major basins viz. Indus basin, Ganga basin and Brahmaputra basin are studied,and understanding the basinal behavior of mass changes is attempted. The biasness of mass balance estimates is towards thenorthwest Himalaya with 58 studied glaciers (1979-2014) and just 7 glaciers (1998-2014) in the Brahmaputra basin towardseast gives us a rather anomalous trends of mass balance. Here, the various advantage and disadvantages of the differenttechniques of glacial mass balance and the scope for further research on Himalayan glacial mass balance has been alsohighlighted.

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ESTIMATION OF SNOW COVER DISTRIBUTION USING SATELLITE DATA IN NWHIMALAYAS

Snehmania, Sunil Kumara, Akansha Patela

a Snow and Avalanche Study Estt, Him parisar, Sector 37-A, Chandigarh, 160036 - ([email protected])


KEYWORDS: Snow Cover, Hazard Mapping, AVHRR, MODIS, NDSI, Weather Forecasting

ABSTRACT:

Snow covered area and snow water equivalent are two essential measurements.Snow cover area is important parameter in the studyof Climatic and hydrological modelling. Glaciers and snowfields normally exist in remote and inaccessible areas and the datacollection on regular basis becomes quite difficult and hazardous. Due to the rugged terrain of Himalaya it is very difficult to collectthe snow cover data by Traditional field based method in winter season.

Remote sensing techniques have the potential to capture the data and used for investigating spatially distributed hydrological statesfor use in modelling. Normalized Difference Snow Index (NDSI) is used for the estimation of snow covered area in the differentbasins of the NW Himalaya. The extent of snow cover has been derived by threshold method based on reflectance from the visiblechannels. The High resolution Advanced Very High Resolution Radiometer (AVHRR/3) and MODIS are used over NW Himalaya.In this paper, total snow and non-snow cover area over NW Himalaya is estimated during 2014-2015.Snow may also cause anincrease in visible reflectance and decrease in mid - infrared reflectance. NDSI is used to capture this reflectance. The maximumsnow cover area (%) was examined in January. The area covered by snow is approximately 72730sq km.

Monitoring snow cover area help in dynamic studies and prevention of snow-caused disasters in pastoral areas.It is used foraddressing the environmental factors such as the surface emissivity, large range of atmospheric water vapour and also for the airtemperature difference for land. This study help in weather forecasting and hazard assessment .It become useful tool for avalanchewarning services in the future for data sparse areas.

.

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ESTIMATION OF MASS BALANCE OF GLACIER USING OPTICAL REMOTESENSING DATA

Jaydeo k.Dharpure a, Snehmani a, Akansha Patel a

a Snow and Avalanche Study Estt, Him parisar, Sector 37-A, Chandigarh, 160036 - ([email protected])


KEYWORDS: AAR, Geodetic Method, Optical Remote Sensing, Glacier, Mass Balance, Climatic Change, ELA

ABSTRACT:

Glacier is a land-borne ice that flows downward due to gravity. It is a complex interdisciplinary system which is spread all over theworld. Major rivers of Northern India which rise from the Himalayas are perennial. They fed by melt run off from the temperateglaciers and seasonal snow cover. This makes river navigable throughout the year and has enough water for irrigation, hydro powergeneration, domestic use etc.

Melting of glacier alters the ocean and also increases the global sea level which affects the habitats in low lying areas and polluteground water while making it unfit for human use. Glacier is sensitive indicators of climatic variations. Fresh snow has very highalbedo and therefore presence of snow in accumulation zone of glacier also has bearing on the atmospheric temperature. Suddenchange in albedo during winter can cause formation of ice layer. Fresh snowfall will cause avalanche. Retreat and advance ofglaciers in the geological history had also synchronization with climate change of the earth.

Mass balance of a glacier is one of the key parameters to understand the influence of climate change. Measurements of mass balanceusing field parameters are a difficult and major task. The ice flow depends on the location of the glacier and their local climaticcondition. For this stake method in field was also initiated by installing stakes on glacier bed. But due to the hazard event on glacierduring winter season stakes were collapsed to limit our study to remote sensing techniques.

In this paper, mass balance study was carried out using satellite imagery. Two different methods Area Accumulation Ratio (AAR)and geodetic method were used to estimate the mass balance. Based on multi-temporal satellite records from all the three glaciersbetween 1989 and 2014, significant variation was observed in annual snow line/ELA though glaciers. The annual ELA above orbelow the average ELA indicates a negative or positive mass balance respectively, for that particular year. The AAR for Patsioglacier was found to have decreasing trend in years. The specific mass balance of Patsio glacier from AAR method shows a negativetrend in last one decade. Geodetic method deal with vertical depth difference in elevation between two date imagery. The heightdifference and mass balance (in meter water equivalent) has been evaluated and displayed. Results of this paper suggest a retreatingtrend in Patsio glacier in area, length and mass.

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MAPPING AND ASSESSING THE LAND COVER/LAND USE DEGRADATION INHIMACHAL PRADESH AND VULNERABILITY TO DEGRADATION IN KANGRA

DISTRICT

Satya Prakasha, Milap Chand Sharmaa, Rajesh Kumara, P.S. Dhinwab, K.L.N. Sastryb, A.S. Rajawatb

a Centre for the Study of Regional Development, School of Sciences, JNU, New Delhi-110067, Indiab Geo-Sciences Division, Space Applications Centre (SAC), Indian Space Research Organisation (ISRO), Ahmedabad-300015, India

- ([email protected])


KEY WORDS: Mapping Desertification Status, Processes, Land Cover/Land Use Change, Environmental Vulnerability Index,Socio-economic Vulnerability Index, Degradation, Himachal Pradesh, Kangra

ABSTRACT:

The physical and man-made processes and their interrelations generate different types of land use and land degradation. Landdegradation processes involve two interlocking, complex systems i.e. the natural ecosystem and the social system. The causes ofland degradation are not only biophysical, but also socioeconomic (e.g. land tenure, marketing, institutional support, income andhuman health) and political (e.g. incentives and political stability) (World Meteorological Organization 2005). The seasonal rainfallregimes with pronounced dry season, excess evapotranspiration and fragile ecosystem create an environment which is furtherdegraded by population pressure, inappropriate technology and poor farming practices. To analyse the processes of desertificationand subsequent change in the land cover, multi-date satellite images of Landsat TM (1990) and AWiFS (2011) satellite image havebeen used. The Landsat TM image was resampled using ERDAS Imagine software to compare with AWiFS at 56 meters. Theseimages were visually interpreted through on screen digitization method using ArcGIS software on 1:500000 scales; and landdegradation was analysed, considering earth surface process in a specific area. Field verification has been conducted in the entirestate for detailed extraction of information at micro level, accuracy assessment, validation of processes and level of degradation. Foranalysing the dominance of landscape sculpting processes, Himachal Pradesh has been divided into Upper and Lower Himachal onthe basis of altitude of 4000 m (amsl). The upper Himachal is dominated by glacial and periglacial processes whereas vegetal andfluvial processes dominate the lower Himachal. The land cover/land use classes are defined on the basis of the ISRO/SACclassification system for desertification. Total thirteen land cover/land use (LC/LU) classes have been interpreted with the earthsurface processes for the entire study area.

Land cover/land use degradation vulnerability index (DVI) has been calculated for Kangra district following the SAC/ISROguidelines. Indicators used in calculating degradation vulnerability index are; rainfall, temperature, aspect, land cover/land use,geology, soil type, slope, population density, non-worker population and illiteracy. On the basis of these indicators, socio-economicvulnerability index and environmental vulnerability index has been calculated. Multiplication of these indexes shows degradationvulnerability index. The Kangra valley between Shahpur to Baijnath comes under very high land degradation risk zone because ofhigh rainfall, poor soil condition and fragile geological base, moderate slope, agricultural land use and very high population density.The area under high risk zone is cover 17.37 % area of the district. The very high risk areas are highly sensitive to degradation underany change in climate, land use and population pressure. Land degradation map can be used for sustainable land resourcesmanagement in the district.

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EARLY WARNING SYSTEM FOR TSUNAMIS – PROGRESS & CHALLENGES

T. Srinivasa Kumar,

Scientist & In-charge, Indian National Tsunami Early Warning Centre, Indian National Centre for Ocean Information Services,Earth System Sciences Organisation, Ministry of Earth Sciences, Government of India – ([email protected])


KEY WORDS: ITEWS, Tsunamis, Policy Making

ABSTRACT:

The great Sumatra earthquake of December 26, 2004 generated a devastating tsunami that affected most of the Indian Ocean rimcountries. The tsunami resulted in catastrophic losses of life and property in near field as well as extensive far field regions such asSomalia. The recent tsunamis in Samoa (2009), Chile (2010) and Japan (2011) reminded the world, though how rare the event is, itcan be more disastrous in comparison to extreme weather conditions such as floods, cyclones etc. However, it is evident withexperience that the policy makers can take actions that save lives, reduce losses and speed up response procedures, such as, in thewake of 2004 tsunami, Government of India has established the Indian Tsunami Early Warning System (ITEWS) at Indian NationalCentre for Ocean Information Services (INCOIS), Hyderabad.

The ITEWS is a unified system comprising of hazard identification & risk assessment, detection & dissemination, capacity buildingthrough community awareness & preparedness. As part of detection & dissemination, the Indian Tsunami Early Warning Centre(ITEWC) acts as operations centre that operates on a 24 × 7 basis. The ITEWC monitors seismic stations for detection of tsunamigenic earthquakes and issues advisories based on pre-computed simulations. Subsequently, the bottom pressure recorders and tidegauge stations are monitored for the confirmation of tsunami generation. Based on severity of tsunami evaluations, following theunique standard operating procedure designed for handling near-source and far-source regions, as well as geospatial tools, advisoriesare disseminated to national emergency points of contact for necessary action. Advisories are updated at least hourly or as conditionswarrant to continue till all clear is issued. The ITEWC also acts as regional tsunami service provider for the Indian Ocean rimcountries. Also, through advanced communications the seismic and sea level data is shared worldwide in near real-time.

The nation’s tsunami mitigation efforts have improved several folds since 2004 and the ITEWC has been performing well as perinternational standards. However, current capabilities need further improvement to meet the challenges posed by tsunami, especiallyin case of near source regions. The recent Japan tsunami of March 2011 has brought to the fore several important issues that have tobe addressed for improving the accuracies of tsunami warning systems. Water level inversion, real-time inundation modeling, real-time estimation of focal mechanism of earthquake to show the style of faulting and incorporation of GPS data into the warning chainare a few key issues that ITEWC has taken up on priority. The recent communication tests and Mock Tsunami Drill have alsobrought to the fore several issues with the last mile communication of warnings, as well as the awareness and response mechanisms.The ITEWC is working with all stakeholders involved to improve upon these aspects. While we cannot prevent the occurrence of atsunami, we can surely mitigate the possible impacts by providing timely early warnings as well as by enhancing the capability ofcommunities to respond appropriately.

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OCEAN STATE FORECASTING DURING EXTREME WEATHER CONDITIONS FORBETTER DISASTER MANAGEMENT TO SAVE LIFE AND PROPERTY

T.M. Balakrishnan Nair*, R. Harikumar, Anuradha Modi, K. Srinivas, Rakhi Kumari, B. Krishna Prasad, K. Kaviyazhahu, YatinGrover

ESSO-Indian National Centre for Ocean Information Services (INCOIS), MoES, Govt. of India, Hyderabad-90,India*Correspondingauthor:[email protected]


KEY WORDS: Extreme Weather, Ocean State Forecasting, Disaster Management

ABSTRACT:

ESSO-Indian National Centre for Ocean Information Services (INCOIS) is the Indian nodal agency to provide operationalocean information, forecast and advisory services. The users include offshore industries, coastal population, fisher folks,disaster management authorities, navy, coastguard, port and harbors, maritime boards etc. At present, we provide dailyupdated user- customized forecasts of Wave height, direction and period (of wind waves and swells), Sea surface currents,SST, MLD, D20, Astronomical tides, Wind speed and direction and Oil-spill trajectory. Modern trends of Informationand Communication Technology are used right from the forecast generation, evaluation & fine-tuning until the forecastdissemination to the end users. The dissemination modes include Public Addressing Systems, Fax, Telephone, Radio, TV,E-mail, Web site and Mobile phones (both SMSs and audio messages) individually or in combination. Joint INCOIS-IMD bulletins consisting the meteorological and oceanic information and forecasts, along with separate high sea statewarnings, are issued during extreme weather conditions. Statistical bias correction to the forecasts are applied to the directocean model forecasts using real-time observations from different parts of the Indian Ocean. The Ocean State Forecastingoperations and service’s Quality Management System is conferred with ISO 9001:2008 certification in 2014. User feedbacksand delayed mode evaluation/auditing suggest not only that the forecasts are > 80% accurate, but also that theforecasts/information reach the maximum end users and disaster management authorities on time, which is also equally crucialfor saving life and property.

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ROLE OF REMOTE SENSING IN EARLY WARNING SERVICES FORMANAGEMENT OF CYCLONES OVER NORTH INDIAN OCEAN

M. Mohapatra

India Meteorological Department, Mausam Bhavan, Lodi Road, New delhi-110003 - ([email protected])


KEY WORDS: INSAT-3D, Kalpana, INSAT-3A, Phailin, Hudhud, Doppler weather radars

ABSTRACT:

The tropical warm Indian Ocean, like North Atlantic, South Pacific and the northwest Pacific, is a breeding ground for the disastroustropical cyclones (TCs). The TC (maximum sustained surface wind speed of 34 knots or more) is a multi-hazard due to associatedgale winds, torrential rains and storm surges. Normally five TCs develop over the north Indian Ocean (NIO) including four over theBay of Bengal and one over the Arabian Sea. Though the frequency of TCs over the NIO accounts for 07% of the global frequency,death due to TCs over this region has been maximum during past years. The reduction of TC disasters depends on several factorsincluding hazard & vulnerability analysis, preparedness & planning, early warning and mitigation. The early warning is a majorcomponent and it includes skill in monitoring and prediction of TC, timely, precise and effective warning products generation anddissemination, coordination with emergency response units and improvement in the public perception about the credibility of theofficial predictions and warnings. India Meteorological department (IMD) is the nodal agency in the country to monitor and predictthe TCs and issue the warning and advisory bulletins. IMD, New Delhi also acts as one of the six Regional SpecialisedMeteorological Centre (RSMC) in the world and provides TC advisories to the World Meteorological Organisation (WMO)/Economic and social cooperation for Asia and the Pacific (ESCAP) Panel member countries, viz., Bangladesh, Myanmar, Thailand,Sri Lanka, Maldives, Pakistan and Oman.

The initiatives taken by IMD and Ministry of Earth Sciences, Government of India in recent years have resulted in improved andeffective TC warning service including forecast accuracy and longer lead period of warning (up to five days) and hence reduction inloss of lives. The average track, landfall point and intensity forecast errors of IMD have improved to 97 km, 56 km and 11 knotsduring 2011-15 against 141 km, 99 km and 12 knots during 2005-09 respectively for 24 hours lead period. The success in earlywarning of extremely severe TCs, Phailin and Hudhud in 2013 and 2014 leading to 22 and 46 deaths only respectively are notisolated successes. To mention a few are (i) severe cyclonic storm, Laila in 2010, which caused only six deaths in Andhra Pradesh,(ii) very severe cyclonic storm, Thane which caused only 46 deaths in Tamil Nadu and Puducherry in 2011, (iii) cyclonic storm,Nilam, which caused only six deaths in Tamil Nadu in 2012.

Currently, the TC analysis, prediction and decision-making process in IMD is made by blending scientifically based conceptualmodels, dynamical & statistical models, meteorological datasets (including observations from conventional observational network,automatic weather stations (AWS), buoy & ship observations, Doppler weather radars and satellites), technology and humanexpertise. A number of national & international global and regional models including IMD’s global forecast system (GFS), weatherresearch and forecast (WRF), Hurricane WRF (HWRF), Japan Meteorological Agency (JMA), Meteo-France, GFS of NationalCentre for Environmental Prediction (NCEP), USA, UK Met unified model, IMD’s multi-model ensemble (MME) technique, globalensemble forecasting systems (GEFS) of National Centre for Medium Range Weather Forecasting (NCMRWF) and ensembleprediction systems of other leading numerical weather prediction (NWP) centres are utilized for cyclogenesis, track, intensity andassociated wind and rainfall prediction. IMD’s Dynamical statistical models are also utilized for cyclogenesis and intensityprediction. The TC module installed in digitized forecasting platform of IMD is utilized as a decision support system (DSS) for TCmonitoring, analysis, prediction and warning products generation. For prediction of storm surge, the dynamical model of IIT Delhi &Indian National Centre for Ocean Information Services (INCOIS), Hyderabad is utilised. Consensus TC forecasts that gather all orpart of the numerical forecast and uses synoptic and statistical guidance are utilised to issue official forecast.

Accurate and timely monitoring is possible only with satellite in the absence of dense observational network and DWR in thecountry for mainly synoptic and meso-scale weather hazards including TC. IMD using space technology since 1960s for synopticscale systems like TC monitoring with US collaboration. It got a boost with INSAT series in 1982 and current products are availablefor every half an hour from INSAT-3D, Kalpana & INSAT-3A. In addition to its utility in synoptic analysis, the satellite data areingested in numerical models for defining the initial condition in the model. It helps in physical Understanding on genesis, structureand intensity, cloud scale, storm scale and environmental scale interaction, landfall processes, especially heavy rainfall etc.

The rate of improvement in intensity forecast is less compared to that of track forecast and hence intensity forecasting is stillchallenging for forecasters. There is still scope for further improvement in TC forecast over the north Indian Ocean based on thelatest technology including further improvement in initial condition of the atmosphere and Ocean through aircraft reconnaissance

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and deployment of more buoys, assimilation of more observational data from satellite and Doppler weather radars, etc., in the NWPmodels, implementation of high resolution meso-scale ocean–atmosphere coupled model like hurricane weather research andforecast (HWRF), implementation of ensemble prediction system (EPS) for global and mesoscale models etc. IMD is already takingup all these aspects through its continuous upgradation programme.

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