1st progress meeting - milano june 29, july 2 2009 1 ceop-aegis coordinated asia-european long-term...

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


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.


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


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


CA1: Ground based observations of fluxes

Left: Nam Co site Right: Everest site


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


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


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


GroundProcessing

GISinfo

Radiativetransfer models

TOA radianceimages

(measured)

TOA radianceimages (simulated)

Surface variablesMission data

Approach


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)


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


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


CA4: Satellite based estimates of top soil moisture

Different retrieval approaches

Empirical approaches

Semi-empirical approaches

Change detection methods

Theoretical scattering and emission models


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


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


4 radar data mosaic


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


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


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


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


CA8: Water Balance and Water Yield of the Tibetan Plateau


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


Long-term changes of meteorological factors (1966-2003)


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


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;


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


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


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


•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


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


Thank you!

1st progress meeting - milano june 29, july 2 2009 1 ceop-aegis coordinated asia-european long-term...

Documents

wet reference states

ground satellite image

ceopaegis objectivesconstruct

asianmonsoon system

image sebal

land use

new everest sitenamco

precipitation events