IRDR DATA session Meeting  Date:  Avenue: RADI Station, Sanya, China  Time: 9:00-12:00

Correlation analysis between disaster events and different data types -flood as showcase Zhang Hongyue Sanya,China

Contents  Targets of this research  Investigation of taxonomy of natural disasters  Analysis of information requirement of disaster  Correlation analysis between disaster event and data  Knowledge base construction  Concept model building  Flood showcase  Outlook

Targets of this research  Heterogeneity Lifespan: rapid-onset or slow-onset extent of affected area : local,regional,global Incentives: weather, hydrology, geology  Uncertainty regular or irregular, Seasonal or unseasonal  Profound impact  …...  Multi-source  Unstructured  Distributed  Large amount  Multidisciplinary  …….. Natural Hazards/ Disasters Disaster Related Data How to link targets: To study the mechanism for connecting exiting data to enable simple and faster discovery and access. To research on a unified data query and retrieval method by attributes of disaster events in response to research need to past disaster events.

Investigation: Domestic  Four category  Five category  Seven category  Sect oral classification  the Eleventh Five-Year Plan  GB/T  others International  EM-DAT  NatCatSERVICE  GLIDE  GRIP  et al Comparison and analysis Taxonomy of natural disaster for data-oriented management Map and integration Taxonomy of Natural Disasters

Information requirement analysis topography, geology, geomorphology, soils, hydrology, land use, vegetation etc. infrastructure, settlements, population, socioeconomic data etc location, frequency, magnitude etc. hospitals, fire brigades, police stations, warehouses etc environment disastrous phenomena Destroyed elements Emergency relief resources In general the following types of Information are required:

Meteorological factors Hydrological factors Geological factors Exposure factors Other environment factors Induce Disaster information (time, location, strength) Response mechanism Relief organization remedy Damage assessment( population, architecture, livestock, crops, infrastructure ） Reconstruction plan Et al. Pre-disaster ： Early warning and preparedness Disaster happening ： Relief and Rescue Post-disaster ： Damage assessment and recovery Domain data Measure data Remote sensing data Topographic data Et al; Basic geographic data Topographic data Remote sensing data Population and transportation data Statistical yearbook Government report Aerial data Et al Knowledge inference of disaster event process Correlation analysis: Knowledge inference

Correlation analysis: Data requirement Remote sensing data Platform: Spatial resolution Temporal resolution Sensor: Spectral resolution Radiometric resolution Meteorological data temperature, rainfall, wind, barometric pressure, relative humidity et al. Hydrological data Water level & water depth & peak flow et al. Geophysical data Tectonic movement Surface deformation Socio-economic data Government report Statistical yearbook Et al. Disaster event - time,location,magnitude hazards- hydrological& meteorological& geophysical& topographical elements Damaged features- socio-economic elements& infrastructure& transportation et al. Recovery features- transportation & building et al. Monitor Record describe

Knowledge collection- Content of each data type Meteorological data A - aerological data B - surface meteorological data C - meteorological radiation data D - marine meteorological data E - agro-meteorological data F - cryosphere data G - atmospheric chemistry and atmospheric physics data H - hydrological and meteorological data I - terrestrial physics data J - analysis data K - meteorological disaster information L - history and alternate data M - soil and vegetation data S - radar data T - satellite data Hydrological elements  Evaporation and evaporation assisted data  Water level  Flow rate (water)  Sediment  Water temperature  Ice  Tides  station properties  Other Temporal information  Extracted value  date value  mid-value  monthly values ​​  annual value  the measured and survey values  ​​ the constant rate value  basic information  data instructions  other values Geophysical data  Gravity data,  Magnetic data,  Deep seismic reflection data,  Broadband seismic data,  Stress measurement data Hydrological data  Hydrological stations  water level stations  rainfall stations  evaporation stations  Groundwater Monitoring Station  water quality stations  moisture stations  water flow stations  water boundary data  water body data  water resources partition data  water function zoning data  flood data

Airborne remote sensing  Aviation Scanning  Aerial Photography  Microwave radar imaging Remote sensing satellite Terrestrial Observation  Landsat series  SPOT series  Canada Radarsat  CBERS  Ikonos  Quickbird  others Marine Satellite  Ocean color satellite  Ocean topography Satellite  Ocean dynamic environment satellites Meteorological satellite LEO satellites  American-TIROS  American-NIMBUS  American-ESSA  American-NOAA  China-FY1 High-orbit satellites  American—SMS/GOES  Japan-GMS  Russia-ELECTRO GOMS N1  China-FY2 Reconnaissance satellites Communications satellite  international Communications satellite  domestic Communications satellite  regional Communications satellite Navigation Satellite  GPS  GLONASS  Galileo  Beidou Ground measure data  Vegetation  soil remote sensing  water environment remote sensing  Atmospheric remote sensing Spaceborne remote sensing Earth Observation data There is a possibility of linking Earth Observation and Communication and Navigation Satellites to develop global information infrastructure which could offer viable solutions to many of the problems related to disaster management; but it can be enhanced only with international cooperation. socio-economic data  Statistical yearbook  Government reports  Disaster relief plan  Disaster report  Economic and social development report  Post-disaster reconstruction plan damaged features  Population statistics  GDP  Building  Crop  Livestock  Infrastructure  Schools  Pipeline  Road  Others emergency and relief information  Emergency agencies  Relief supplies  Relief plan  Emergency rescue routes  Hospitals  Other materials Reconstruction information  Reconstruction plan  Annual report  Etc Socio-economic information

Meteorological & hydrological & Geophysical information, etc Remote sensing data information Concept model Concept model of disaster event and data

relationship association Time & location& hydrological features Hydrological stations Time & location& meteorological features Meteorological stations Time & location& observation requirements Sensor & satellite Time & location& disaster-loss features Socio-economic data Time & location& Geological features Geophysical elements match Search for match Search for Socio-economic elements access Geophysical data access Search for Hydrological elements Meteorological elements Search for Remote sensing Application access Hydrological data meteorological data Remote sensing data Main concept and relationshipof the inference process Inference steps

Flood showcase Flood event information ExposureRainfall hazardWater hazardDamage informationRespond information Happen time Place of occurrence Population, GDP, farmland area, urban land, woodland, grass land, water land,other land Precipitation Duration, amount and coverage Hydrological station name, river basin name, river stage and discharge,historical highest level, warning level Affected area, affected population, casualties, houses destroyed, housed damaged, direct economic loss, number of industrial and mining enterprises affected, number of infrastructure affected, number of transport and communication facilities affected, National relief, input of flood resistance supplies, Number of Flood resistance people Related Knowledge Types of flood: river floods, flash floods, dam-break floods or coastal floods Different characteristics of flood: time of occurrence, the magnitude, frequency, duration, flow velocity and the areal extension Factors: the intensity and duration of rainfall, snowmelt, deforestation, land use practices, sedimentation in riverbeds, and natural or man made obstructions. Parameters: depth of water during flood, the duration of flood, the flow velocity, the rate of rise and decline, and the frequency of occurrence. Required information: Time information: time of occurrence, Rainfall duration ， flood duration, submerged duration Spatial information : place of occurrence, submerged area and scope Meteorological information : rainfall(precipitation, levels) Hydrological information: water situation, river stage, discharge, flow velocity, water temperature, peak water level, peak flow Social and economic information: injuries and deaths, Collapsed buildings; Livestock casualties; crop losses Natural and environmental effects Related information analysis of Flood event

Flood showcase: spatial and temporal resolution requirement of flood detection ApplicationPhaseThresholdOptimum Land use post-floodpre-flood30 meter (MSI)4-5 meter (MSI) Infrastructure status post-floodpre-flood5 meter (pan-vis)<= 1 meter (pan-vis) Vegetation post-floodpre-flood<= 250 meter (M/HSI)<= 30 meter (M/HSI) Soil Moisturepre-flood1 km100 meter Snow Packpre-flood1 km100 meter DEM (vertical) post-floodpre-flood1-3 meter (INSAR/pan-vis) meter Flood development and flood peakduring flood post-flood<= 30 meter (SAR/MSI/ vis-pan/IR)<= 5 meter Damage assessment (incl. feedback/lessons learned) post flood2-5 meter (MSI/pan-vis/ SAR)0.3 meter Bathymetry (near-shore)< 1 km (SAR/MSI)90 meter Spatial Resolution Requirements (by application MSI = multi-spectral imagery (2 to 50 bands) HSI = hyper-spectral imagery (> 50 bands) pan-vis = panchromatic visible imagery SAR = synthetic aperture radar INSAR = interferometric SAR ApplicationImage refresh rate (Threshold/Optimum) Image delivery time (Threshold/Optimum) Infrastructure status1-3 yrs / 6 monthsmonths Land use1-3 yrs / 6 monthsmonths Vegetation3 months / 1 monthmonths Soil Moisture1 week/daily1 day Snow Pack2 month/1 week1 day DEM pre- and post-flood1-3 yrs / monthsmonths Flood development, Flood peak, 24-hr from tasking to deliveryhours-days (function of drainage basin)hours-days (function of drainage basin) / Damage assessmentn/a2-3 days / < 1 day Bathymetry pre- and post-flood1-3 yrs / monthsmonths Temporal resolution requirements (by application)

Knowledge base: Concept and property Next step is to complete the concept and property ontology, further to build relationship between flood and data in order to realize the inference process

Yearly indices Observation period (day) NOAA 0.5 Landsat TM 16 Spot 26 ERS-1/2JERS-1 44 Radarsat 3-4 Real-time aviation Spatial resolution(meters) Imaging width80 Weather capability  Submerged area Submerged water depth  Last time  Flooded area bottom  Working condition monitoring  Disaster assessment Suitability of remote sensing data to flood monitoring WaveLengthWave bandApplicationSensor examples Visible mmVegetation mappingSPOT; Landsat TM Assessment of buildingAVHRR; MODIS; IKONOS Population densityIKONOS; MODIS Digital elevation models ASTER; PRISM NIR mmVegetation mappingSPOT; Landsat TM; AVHRR; MODIS Flood mappingMODIS SWIR mm Water vapor AIRS Thermal infrared mmActive fire detectionMODIS Fire slash mapping MODIS hotspotsMODIS; AVHRR Volcanic activity Hyperion Microwave (radar) cm Earth deformation and ground motion Radarsat SAR; PALSAR rainfallMeteosat; Microwave Imager (carried by TRMM) Streamflow AMSR-E Flood mapping and forecasting AMSR-E Surface windQuikScat Rardar Three-dimensional storm structure (carried by TRMM) Application of different wavebands on disaster management Knowledge base : remote sensing data for flood detection

Outlook Disaster phenomenon is the combined effect of the natural environment on human kind, the related elements are complex and intricate. The related information almost involves all kind of data, so it is urgent to link open data for disaster research which call for cooperation between multi-discipline agencies. I need help from field expert for the knowledge base building and inference rules of hydrological data & meteorological data as well as socio-economic data. Also welcome suggestions on the inference ontology construction.