bob adler robert.f.adler@nasa yang hong and george huffman
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National Aeronautic Space Administration Goddard Space Flight Center, Greenbelt, Maryland 20771. Flood and Landslide Applications of High Time Resolution Satellite Rain Products. Bob Adler [email protected] Yang Hong and George Huffman - PowerPoint PPT PresentationTRANSCRIPT
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Yang Hong and George HuffmanNASA Goddard Space Flight Center, Greenbelt, MD 20771
National Aeronautic Space AdministrationNational Aeronautic Space Administration Goddard Space Flight Center, Greenbelt, Goddard Space Flight Center, Greenbelt, Maryland 20771Maryland 20771
Flood and Landslide Applications of High Time Resolution Satellite Rain Products
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Motivation:
• Floods and landslides associated with heavy rainfall impact more people globally than any other type of natural disaster
• Detecting or forecasting such events globally is important to understand the causative processes, for mitigation, and potentially for warning
• Insufficient in situ data, long delays in data transmission and absence of data sharing in many trans-boundary river basins
• Recent satellite remote sensing data sets of precipitation and land surface characteristics (e.g., elevation, soil conditions, vegetation) are now available for research and potential applications in this area.
In this talk:
• A framework for global flood estimation
• A global landslide forecast technique--in early test phase
Global Monitoring/Warning Systems for Floods and Landslides
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A Framework For Global-scale Flood Detection System
TRMM Multi-satellite Precipitation Analysis (TMPA)TRMM 3-hour real-time
Output/Decision Support System
Flood Inundation Map-
News ReportInventory
Satellite Inundation Information
Stream Flow Model
Hydrograph
WEB: 3-h update
Cell-to-CellFlow Routing
Basin/river networkFlow Accumulation
Implementation interface
Module. 1: Input
Rain-InfiltrationPartitioning
Qi
Pi(t)
Iii(t) Oik(t)QkQi
Grid-based Water Balance Model
Basin-based WaterBalance Model
Module. 3: Model
DEM, Topography, Slope gradient, Flow Direction, Flow Accumulation, River Network, watershed, Soil Property, Soil Moisture, Hydrological Soil Groups, Land Use, Vegetation cover, Field Capacity, Profile Water Content, Saturated Hydrological Conductivity
Land Surface Characteristics
Soil Parameters Routing Parameters Basin Parameters
Module. 2: Surface
Validation
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Tropical Rainfall Measuring Mission (TRMM)Tropical Rainfall Measuring Mission (TRMM)TRMM Multi-satellite Precipitation Analysis
(TMPA) [3B42]Real-time update every 3-hour at http://trmm.gsfc.nasa.gov
7 day accumulation
TMPA uses polar-orbit microwave satellites (NOAA, DoD, NASA) and geosynchronous IR satellites, all calibrated by TRMM
Recent Typhoon-related heavy rains over Philippines
Module 1:Quasi-global rainfallModule 1:Quasi-global rainfall
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From Space-borne Rainfall to Flood Potential Mapshttp://trmm.gsfc.nasa.gov
Simple rain amount thresholds: 24 hrs
ending 28 Oct. 2005 0000GMT
Threshold: 35 mm
131 mm MADRAS 214 mm NELLORE 234 mm CUDDAPAH 336 mm (13”) TIRUPATHI
From web site text page:
24 hr rainfall
NEWS STORY:More than 100 die in India floods More than 100 people have died in five days of heavy rains in the southern Indian states of Tamil Nadu and Karnataka, officials say.
More than 50,000 people have been evacuated from their homes in affected areas of Tamil Nadu.
Thousands of people have been displaced and air, rail and road services hit.
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24 hrs ending 0300 GMT 19 Oct.
Potential Flood Areas from TRMM Web Site
http://trmm.gsfc.nasa.gov
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Module 2: Development of Hydrologic Parameters at global scale Geospatial Database
(a) USGS GTOPO30 DEM (1km) (b) NASA SRTM DEM (30- or 90-m)
Topography Parameters:
Puerto Rico SRTM DEM (30-meter) Derived Slope (degree)
SRTM--Shuttle Radar Topography Mission
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Global Slope (degree) Flow Accumulation (upper stream)
Global River NetworkFlow Length (cell to watershed outlet)
Module 2: Development of Hydrologic Parameters at global scale Level II Derived Flow Routing Parameters
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Module 2: Development of Hydrologic Parameters at global scale Geospatial Database (not from satellite information)
Soil Parameters:
(b) Clay (%)
(c) Sand (%) (d) Silt (%)
(a) Soil Texture (b) FAO Soil Type Classification
Land Use/Vegetation Information from NASA MODIS
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Spatial variation: each cell has its own CN Time-variant: Antecedent Precipitation Index
Surface Runoff Generated by uniform Rainfall (=100mm/hr) at normal moisture condition
Rainfall-infiltration Partitioning (Spatio-temporal variation)
CN=95
CN=85
CN=75
…..
CN=40
Fixed CN=75Various NAPI
Curve Number Approach
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Module 3: Distributed Macro-scale Hydrological Model
Step I: Rainfall-infiltration Partitioning (Distributed and Time-variant)
Step 2: Flow Routing using Macro-scale Cell-to-Cell Algorithm
Step 3: Grid Point Hydrographs--Flood Inundation Mapping
Rain-InfiltrationPartitioning
Qi
Pi(t)
Iii(t) Oik(t)QkQi
Grid-based Water Balance Model
Basin-based WaterBalance Model
Constraints: computational efficiency, simplification of channel and hydraulic parameterization
Qi
Pi(t)
Iii(t) Oik(t)QkQi
Cell-to-Cell Flow Routing )()()()()( tEtOtQtIdt
tdSiiki
jji
i −−+=∑Cell-based Water Balance Model
Routing ParametersCell-based: Slope, soil type, Hydrologic conductivity, Porosity, Field Capacity, effective depth of soil column, flow direction, velocity, hydraulic radius, roughness coefficient, antecedent precipitation index;Watershed-based: flow length, area, flow accumulation, concentration time, flow time
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Basin Rainfall (mm)
Water Depth [lower basin] (mm)
Case Study: Yangtze River flooding in Sept. 2005
Yangtze Basin-averaged Hydrograph for 2005
Water depth exceeding threshold= flood
Sept 01----- 05 (day)
Verified by Dartmouth Flood Archive and News: Sept 01-05, 2005 China – Typhoon Talim caused flooding and landslides. 129 dead, 30 missing. 1.84 million people evacuated. 62,000 houses collapsed, US$960 million damages.
Basin Area: 1,722,155 km2
Population: 386 million
Lower Basin
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Preliminary Case StudiesYangtse River Basin Cell-based Flow Routing
2005 Day of Year
Cell Rainfall
Cell Water Storage/Depth (mm) after Flow Routing
(118.875oE, 31.125oN)
Grid (118.875oE, 31.125oN)
A grid-based hydrograph
Cell-based Hydrograph
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19621970
Peru (Ancash)Peru (Ancash)
5,000 death18,000 death
10/30/1998 Nicaragua >2000 death
10/08/2005 Solola, Guatemala >1800 death
2/17/2006 Layte, Philippines buried entire village( 1500)
1/10/2005 La Conchita, CA 12 death
01/04/2006 Jakarta, Indonesia entire village
Landslides Death Toll
Landslides/Mudslides/Debris Flow• Landslides are one of the most widespread natural
hazards on Earth, responsible for thousands of deaths and billions of dollars in property damage every year.
• Rainfall is the primary causative factor.
• Currently, no system exists at regional or global scale to detect heavy rainfall that may trigger landslides.
Entire village buried.
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GGlobal lobal RRainfall-induced ainfall-induced LLandslide andslide FForecast orecast SSystemystem
DEM, Slope, AspectTopography
Curvature, Concavity
Morphology
Lithological makeup Geology
Sand, Foam, Silt, ClaySoil Property
Shrub, barren, builtup Land Cover
Soil Moisture, FD, FA Hydrology
Landslide Susceptibility
Real-time Rainfall Estimation
NASA TRMM-based
Surface controlling factors
When
Where
How big
Risk
Damage
Detection/Warning
Slope-Stability Hierarchical Decision Tree
Decision MakingInventory Data
Rainfall Trigger Intensity-Duration
ClassificationSoil Moisture
SlidingProbability
Hong et al., IEEE TGRS (accepted)
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Landslide Susceptibility Category-1: Water Bodies0: Permanent Snow/Ice1: Very Low Susceptibility2: Low Susceptibility 3: Moderate Susceptibility4: High Susceptibility (yellow)5: Very High Susceptibility (orange)
Landslide Susceptability Map
Hong et al., Submitted to J. of Natural Hazards
DEM, Slope, AspectTopography
Curvature, Concavity
Morphology
Lithological makeup Geology
Sand, Foam, Silt, ClaySoil Property
Shrub, barren, urbanLand Cover
e.g., Soil MoistureHydrology
Surface controlling factors
Percentage of Grid Boxes in Each Category
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(a) (b)
Hour Day
Influence of Rainfall Characteristics on the Timing and Occurrence of Landslides
Philippines Landslide and TRMM Rainfall Accumulation
Philippines
Feb 8-17, 2006
1500 Deaths
Note that bars are the rainfall intensity and star denotes the timing of landslide occurrence.
Hong et al., Submitted to GRL
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GGlobal lobal RRainfall-induced ainfall-induced LLandslide andslide FForecast orecast SSystemystem
Decision MakingInventory Data
Rainfall Trigger Intensity-Duration
SlidingProbability
Rainfall Map Intensity-Duration Susceptibility/ Landslide Warning
TRMM Near Real-Time Rainfall at location 76.875 W, 4.125 N, April 13, Columbia 1) the last 24 hour rainfall accumulation > 103mm 2) The Susceptibility Map shows high or very high susceptibilityNews Report: 13 Apr 2006, At least 34 people missing in Colombian mudslide
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Summary and Future Work
NASA-based data sets of near real-time precipitation observations and land surface characteristics are being combined to develop a Flood and Landslide Monitoring Systems.
Next: 1) Evaluation/implementation of the first-cut, experimental systems for user
feedback (January 2007 is goal to have real-time experimental systems running in real-time);
2) Flexible Module Structure: open for new component plug-ins for testing;
3) Use of multiple precipitation estimates for ensemble forecasts;
4) Use of NWP model(s) precipitation forecasts to lengthen forecast applicability
NASA TRMM-based Global-scale Flood/landslide System
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Watershed-based VIC Simulation (1998-1999): La Plata Basin5000+ Water basins derived from DEM
The La Plata Basin
Credit: U. of Washington Lettenmeier Group