1st progress meeting - milano june 29, july 2 2009 1 ceop-aegis coordinated asia-european long-term...
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1st Progress Meeting - Milano June 29, July 2 20091st Progress Meeting - Milano June 29, July 2 2009 11
CEOP-AEGISCEOP-AEGISCoordinated Asia-European long-term Observing system of Qinghai–Tibet Plateau hydro-meteorological processes and
the Asian-monsoon systEm with Ground satellite Image data and numerical Simulations
www.ceop-aegis.orgMassimo MenentiDepartment of Earth Observation Systems, Delft University of Technology, The Netherlands
Laboratoire des Sciences de l’Image, de l’Informatique et de la Télédétection - LSIIT, Universitè Louis PasteurIllkirch, France
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CEOP-AEGIS ObjectivesCEOP-AEGIS Objectives Construct an observing system to determine and
monitor the water yield of the Plateau Incorporate existing ground measurements and
current / future satellites Requires estimating snowfall, rainfall,
evapotranspiration and changes in soil moisture Monitor the evolution of surface conditions and
analyze the linkage with convective activity, precipitation events and the Asian Monsoon
Monitor snow, vegetation and surface fluxes as precursors of intense precipitation towards improving forecasts of (extreme) precipitations in SE Asia.
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CEOP – AEGIS ParticipantsCEOP – AEGIS ParticipantsParticipant organization name Local contact Country
Université Louis Pasteur LSIIT ULP Massimo Menenti France
International Institute for Geo-information science and Earth Observation ITC
Bob Su The Netherlands
ARIES Space Guido D’Urso Italy
University of Bayreuth UBT Thomas Foken Germany
Alterra - Wageningen University and Research Centre Li Jia The Netherlands
University of Valencia UVEG José Sobrino Spain
Institute for Tibetan Plateau Research ITP – Lhasa, Tibet Yaoming Ma China
China Meteorological Administration CMA – Beijing Liping Liu China
Beijing Normal University BNU – Beijing Li Xiaowen China
National Institute of Technology NIT - Rourkela R. Jha India
University of Tsukuba – UNITSUK Kenichi Ueno Japan
WaterWatch Wim Bastiaanssen The Netherlands
Cold and Arid Regions Environmental and Engineering Research Institute CAREERI–Lanzhou, Gansu
Wang Jian China
University of Ferrara Federico Porcù Italy
Institute of Geographical Sciences and Natural Resources Research IGSNRR CAS – Beijing
Liu Changming China
Institute for Remote Sensing Applications IRSA CAS – Beijing
Future Water
Delft University of Technology
Liu Qinhuo
Walter van Immerzeel
Massimo Menenti
China
The Netherlands
The Netherlands
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Interrelation of project technical elements
Drought Ea-War Flood Ea-War
Monsoon + rainfall forecasts Plateau Water Balance
Ground
energ
y-water m
eas.
Sa
tellite ve
galbedo
temp
er
Sa
tellite e
nerg
y-water flu
xes
Sa
tellite top so
il moisture
Ground
+ satellite pre
cip
Glaciers +
snow m
eltw
ater
Drought Ea-War Flood Ea-War
Monsoon + rainfall forecasts Plateau Water Balance
Ground
energ
y-water m
eas.
Sa
tellite ve
galbedo
temp
er
Sa
tellite e
nerg
y-water flu
xes
Sa
tellite top so
il moisture
Ground
+ satellite pre
cip
Glaciers +
snow m
eltw
ater
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CA1: Ground based observations of fluxes
Left: Nam Co site Right: Everest site
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Monthly variation of LAS flux contribution area(8:00am -17:00pm Jan – Dec, 2007)
Transmitter
Receiver
Flux tower
Land use map
Footprint
Eddy covariance, scintillometers and footprints
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Analysis of data quality of Tibetan energy balance
measurements
Data qualityand its dependence on land use and footprint
Metzger et al., Adv. Earth Sci. 21 (2006) 1260-1267New Everest site
Namco site
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CA2: Optical observation of the land surface
Variable Sensor Properties:• # Bands, spectral resolution• Position of bands• Spatial resolution • Acquisition geometry• Time and place of data take• Noise and history• ….
Tasks: convert real sensor measure-ments into measurement of a Virtual Land Surface Imager
• Calibrate input sensor• Atmospheric and illumination correction• Invert surface variables using generic RT• Convert to common spectral sampling• Convert to standard viewing geometry• Convert to common geometry• Derive measurement uncertainties • Analyse error propagation• ….
Sensor 1
Sensor 2
Sensor n
Land Data Assimilation SchemeReal Sensors Virtual Land Surface Imager
V-LSI
Sensor 3
…
…
…
…
Fixed Virtual Sensor Properties:• # Bands, spectr. res.•Position Bands•Spatial Resolution •Acquisition geometry•Geolocation of data take
•Uncertainty of radiance fields
•….
BOA Radiances to Land Surface Process Models
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GroundProcessing
GISinfo
Radiativetransfer models
TOA radianceimages
(measured)
TOA radianceimages (simulated)
Surface variablesMission data
Approach
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CA3: Satellite based estimates of energy and water fluxes
LE scales with Tsurf IF radiative and convective forcing is normalized first
Additional constraints needed to solve SEB + parameterizations
Iterative procedures lead to multiple solutions
Inversion of detailed models abandoned many years ago new algorithms + easier access to computing power LUT-s may be worth a second life
Additional equations by segmenting images and assuming some parameters (e.g. ra) constant within the segment
Add experimental constraints by using limiting cases (reference system states)
Dry and wet reference states assumed to exist within image (SEBAL, S-SEBI)
Dry and wet reference states evaluated from theory (SEBI SEBS MSSEBS)
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Limiting cases and additional constraints on SEB
Evaporation controlled vs. radiation controlled T0=T0(r0)
const
r
T
0
0
00
0
r
T
Empirical dry – wet references
SEBAL, S-SEBI
Tmax and Tmin from full combination equation
SEBI, SEBS
Reference Ta cannot be local: applies to an area much larger than the length-scale of land heterogeneity
MS-SEBS
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MSSEBS 2 Framework
Multiple SEB algorithms
Core
n-D NWPM fields
Image files
DEM
GDAL
Project file
XML
parser
Log file
Node
H, LE, ET, ef...
Meta
Web form
Jobspooler
GEOSS provider
Data repository
MSSEBS 2
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CA4: Satellite based estimates of top soil moisture
Different retrieval approaches
Empirical approaches
Semi-empirical approaches
Change detection methods
Theoretical scattering and emission models
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sigma zero triplet mid values
sigma zero triplet for values
sigma zero triplet after values
METOP-A
ASCAT
Soil moisture (m3/m3 )
Soil moisture results ASCAT data
14
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CA5: Integrated ground and satellite observations of precipitations
Refining data processing of ground based rain radars
Improvement of interpolation method and 3D gridding
Analysis of precipitation regimes over Tibetan Plateau and surrounding areas
Evaluation of disdrometric measurements and algorithms to retrieve precipitation with VIS-IR data
Development of algorithms for TIR data
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4 radar data mosaic
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CA6: Estimation of glaciers and snow meltwater
Prototyping new algorithm for snow cover mapping
Simulation of snow water equivalent based on SEB
Collection and analysis of observations of reference glaciers
Evaluation of SWE algorithms and data products
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A snow cover extent prototype algorithm
Calibrated radiance
NDSI>=ThresholdSnow, cloud, water Cloud, water, snow in forest
Water mask
Cloud products
Water mask
Cloud products
Water
Snow
Cloud
Snow
Water
Cloud
Snow in forest
Forest maps & NDVI Threshold
DEM
Snow map
YES
YES
YES
YES
YES
NO
NO
NONO
YES
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CA7: Numerical Weather and Climate Modeling System
Improving parameterization of subgrid processes
Higher spatial resolution of GRAPES: 15 km
Evaluation of sensitivity to soil initial conditions
Case – studies on different land surface parameterizations
Document relevant convective events over the Tibetan Plateau using TIR geostationary obs
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70E 75E 80E 85E 90E 95E 100E 105E 110E 115E 120E
Ic (<250) a ll day
20N
25N
30N
35N
40N
Na Qu
LhasaDing Ri
Lin Zhi
Gai Ze
Li JiangTeng ChongKun Ming
Meng Zi
Da Li
Gan ZiW en Jiang
Yi BinXi Chang
Da Zhou (Da Xian)Li Tang
Blue: Averaged convective index, black circle: occurrence of MCS
Observation of convective events
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CA8: Water Balance and Water Yield of the Tibetan Plateau
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Indicator SWAT HBV GRAPES LARSIM PCR-WB
Spatial discretization 5 5 15 15 15 Soil water balance 12 8 12 8 8 Date requirements 4 8 8 12 12 Permafrost 3 2 3 2 3 Forcing P by EO 3 3 9 9 9 Forcing ET by EO 4 4 4 4 12 Forcing snow by EO 1 1 1 1 1 Routing 15 10 5 15 15 Lumped glaciers 2 2 2 2 2 Lakes and reservoirs 9 3 3 6 9 Detailed outputs 9 6 6 9 9 Model efficiency 8 12 8 8 12 Code availability 15 5 5 15 15 Support from developers 10 5 5 15 15
Total score 100 74 86 121 137
Selection of Water Balance Model
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Long-term changes of meteorological factors (1966-2003)
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CA9: Satellite Drought Monitoring System
Algorithms developed to improve screening of MODIS data
Algorithms to monitor drought using combined LST, VI, fAPAR
Review of drought monitoring systems in China and India
Document major drought events in China and India
Analysis of AVHRR time series of LST and NDVI
Ground and high resolution data for validation of low resolution drought indicators
Improved Land Cover Land Use maps of several areas
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LST vs NDVI correlation
-90 -75 -60 -45 -30 -15 0 15 30 45 60 75 90º
IHS of LST vs. NDVI correlation
Colors indicate θ values;
Intensity codes d values, characterizing NDVI-LST annual cycle;
Saturation codes R2 values;
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Drought monitoring and Early Warning by detecting anomalies
40
50
60
70
80
90
100ja
n 1
jan
17
feb
2
feb
18
ma
r 0
6
ma
r 2
2
ap
r 0
7
ap
r 2
3
ma
y 0
9
ma
y 2
5
jun
10
jun
26
jul 1
2
jul 2
8
au
g 1
3
au
g 2
9
sep
14
sep
30
oct
16
no
v 0
1
no
v 1
7
de
c 0
3
de
c 1
9
DOY
fAP
AR
5-year mean
2006
Sichuan-Chongqing drought, 2006
Anomaly: Deviation of current state from historical average
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Drought monitoring and Early Warning by detecting anomalies
9 May
10 June
12 July
13 Aug
LST anomaly EVI anomaly
Tibet Plateau and Sichuan-Chongqing,
2006
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CA10: Satellite Flood Monitoring System of Pilot Areas
Document flood drought prone areas in China and India
Review flood forecast in India
Development of flood hazard evaluation system
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•Flow Data is being collected from Government of Bihar for Bgmti river basin.•Flow Data of key stations of Main Ganga river and Brahmputra river would be collected from shortly.•Precipitation data is being procured from Meteorological Department on payment basis for flood and drought studies (both).• All the map are being digitized in ArcGIS, which would be input to the model.•IRS-LISS II and PAN data is being procured and landuse map is being prepared using ERDAS 9.3 (latest version). • ANN model is being tested for some of the flood events.
India
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China
Vulnerability factors
data
Flood risk and impact maps
Flood risk analysis
Hydro-dynamic model (HEC-RAS)
Remote Sensing image
Socio-Economic data
Population
Hazard factors data
Population density map
Economic Value map
Flood impact analysis
Hydrologic data
DEM
Land use
Flood area Flood depth
Flood frequency
Flood risk assessment and impact evaluation
model
Assessment method
Remote Sensing image
Partly-Finished
Unfinished
Finished
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Thank you!