ceop-aegis aegis final report (fp7 n°212921) ... the project started with a kick-off meeting held...

CEOP AEGIS Final Report (FP7 n°212921)

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Coordinated 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

CEOP-AEGIS

Final Report

Website: http://www.ceop-aegis.org/

Contact:

Scientific Coordinator:

Prof. Dr. Massimo Menenti Faculty of Civil Engineering and Geosciences, TU Delft, Delft, The Netherlands Tel: +31 15 2784244 Fax: +31 15 278348 E-mail: [email protected] Deputy coordinator:

Dr. Li Jia Tel: +31 317 481610 Fax: +31 317 419000 E-mail: [email protected] Web site: http://www.alterra.wur.nl/UK/

Project Director:

Dr.J.Colin Tel: +33 3 68 854528 E-mail: [email protected] Web site: http://icube-trio.unistra.fr/fr/index.php/Accueil

Massimo Menenti1, Li Jia2 and Jerome Colin3

1 Faculty of Civil Engineering and Geosciences, TU Delft, Delft, The Netherlands,

2 Alterra, Wageningen University and Research Centre, Wageningen, The Netherlands

3 ICube Laboratory, University of Strasbourg, Illkirch, France

http://www.ceop-aegis.org/

mailto:[email protected]


http://www.alterra.wur.nl/UK/


http://icube-trio.unistra.fr/fr/index.php/Accueil


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Contents

Executive Summary ................................................................................................................................... 5

1. Project Context and Main Objectives ............................................................................................... 6

1.1 Context ............................................................................................................................................ 6

1.2 Objectives ........................................................................................................................................ 6

1.3 Work Performed ............................................................................................................................. 7

1.4 Main Results .................................................................................................................................... 8

2. Science and Technology Results ....................................................................................................... 9

2.1 Approach ................................................................................................................................... 9

2.2 Ground observations ................................................................................................................ 9

2.3 Retrieval of surface bio-geophysical variables from EO ......................................................... 11

2.4 Radiative and Energy Balance from EO ................................................................................... 15

2.5 Soil Moisture from EO ............................................................................................................. 18

2.6 Precipitation from EO.............................................................................................................. 21

2.7 Snow and Glaciers ................................................................................................................... 25

2.8 Monsoon and Rainfall Forecasts ............................................................................................. 28

2.9 Plateau Water Balance ............................................................................................................ 32

2.10 Drought Early Warning ............................................................................................................ 35

2.11 Flood Early Warning ................................................................................................................ 38

3. Impact, Dissemination and Exploitation of Results ........................................................................ 41

3.1 Dissemination activities ................................................................................................................ 41

3.1.1 Training sessions .................................................................................................................... 41

3.1.2 Meetings ................................................................................................................................ 45

3.1.3 Website .................................................................................................................................. 46

3.2 Exploitation of Results .................................................................................................................. 47

3.2.1 Data portal ............................................................................................................................. 47

3.2.2 Transnational water resources .............................................................................................. 47

3.3 Impact ........................................................................................................................................... 48

3.3.1 MSc and PhD students ........................................................................................................... 48

3.3.2 Contribution to GEO Activities ............................................................................................... 48

3.3.3 Publications ............................................................................................................................ 51

References .......................................................................................................................................... 51


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Annex 1 List of Publications .................................................................................................................... 53

Project reports ................................................................................................................................ 53

Other reports .................................................................................................................................. 55

International conferences ............................................................................................................... 56

Journal articles and book chapters ................................................................................................. 57


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Executive Summary

The headwater areas of seven major rivers in SE Asia, i.e. Yellow River, Yangtze, Mekong, Salween,

Irrawaddy, Brahmaputra and Ganges, are located in the Tibetan Plateau. Estimates of the Plateau water

balance rely on sparse and scarce observations that cannot provide the required accuracy, spatial density

and temporal frequency. Fully integrated use of satellite and ground observations is necessary to support

water resources management in SE Asia and to clarify the roles of the interactions between the land

surface and the atmosphere over the Tibetan Plateau in the Asian monsoon system.

The goal of this project was to:

Construct out of existing ground measurements and current / future satellites an observing system to

determine and monitor the water yield of the Plateau, i.e. how much water is finally going into the

seven major rivers of SE Asia;

Monitor the evolution of snow, vegetation cover, surface wetness and surface fluxes and analyze the

linkage with convective activity, (extreme) precipitation events and the Asian Monsoon;

New or significantly improved algorithms have been developed and evaluated against ground

measurements. Variables retrieved include land surface properties, rain rate, aerosol optical depth, water

vapour, snow cover and water equivalent, soil moisture and lake level. The three years time series of gap-

free daily and hourly evaporation derived from geostationary data collected by the FY-2D satellite is a

major achievement of the project. All expected data products has been generated and made available on

the project data portal.

The hydrologic modeling system has been implemented and applied to the Qinghai Tibet Plateau and the

headwaters of the major rivers in South and East Asia. Case studies on response of atmospheric

circulation and specifically of convective activity to land surface conditions have been completed and the

controlling land surface conditions and processes have been documented.

Two new drought indicators have been developed: Normalized Temperature Anomaly Index (NTAI) and

Normalized Vegetation Anomaly Index (NVAI). Case study in China and India showed that these

indicators capture effectively drought severity and evolution. A new method has been developed for

monitoring and early warning of flooded areas at the regional scale.

The 126 Team Members produced 200 Journal Publications and Technical Reports. Three Stakeholders

Meetings and Advanced Trainings were held and attended by 150 participants from both partner and

external organizations. There are 122 PhD and 110 MSc students completing their education in the

framework of the project. The project was concluded with an International Workshop attended by 150

Participants from 13 Countries.

In 2013 the French Ministère de l'Enseignement Supérieur et de la Recherche awarded to the project the

prize Etoiles de l‘ Europe for the theme Environment: http://www.enseignementsup-

recherche.gouv.fr/cid75867/horizon-2020-lancement-du-programme-europeen-pour-la-recherche-et-l-

innovation.html ; http://multimedia.enseignementsup-recherche.gouv.fr/H2020/index.html#/4/

http://www.enseignementsup-recherche.gouv.fr/cid75867/horizon-2020-lancement-du-programme-europeen-pour-la-recherche-et-l-innovation.html



http://multimedia.enseignementsup-recherche.gouv.fr/H2020/index.html#/4/


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1. Project Context and Main Objectives

1.1 Context

Human life and the entire ecosystem of South East Asia depend upon the monsoon climate and its

predictability. More than 40% of the earth's population lives in this region. Droughts and floods

associated with the variability of rainfall frequently cause serious damage to ecosystems in these

regions and, more importantly, injury and loss of human life.

The headwater areas of seven major rivers in SE Asia, i.e. Yellow River, Yangtze, Mekong, Salween,

Irrawaddy, Brahmaputra and Ganges, are located in the Tibetan Plateau. Estimates of the Plateau water

balance rely on sparse and scarce observations that cannot provide the required accuracy, spatial

density and temporal frequency. Fully integrated use of satellite and ground observations is necessary

to support water resources management in SE Asia and to clarify the roles of the interactions between

the land surface and the atmosphere over the Tibetan Plateau in the Asian monsoon system.

A series of international efforts initiated in 1996 with the GAME-Tibet project. The CEOP – AEGIS

was built upon 10 years of experimental and modeling research and the consortium includes many key-

players and pioneers of this long term research initiative. The identified the elements of a

comprehensive system to observe timely the state of land surface over the Plateau, extract precursor

information to improve forecast precipitation over the headwater areas of the Yellow River, Yangtze,

Mekong, Salween, Irrawaddy, Brahmaputra and Ganga, monitor the water balance of the Plateau and

its water yield, and improve timely detection of floods and droughts, towards an infrastructure for the

Group on Earth Observations (GEO) water theme and capacity building in S and E Asia.

1.2 Objectives

The goal of this project was to:

1. Construct out of existing ground measurements and current / future satellites an observing

system to determine and monitor the water yield of the Plateau, i.e. how much water is finally

going into the seven major rivers of SE Asia; this requires estimating snowfall, rainfall,

evapotranspiration and changes in soil moisture;

2. Monitor the evolution of snow, vegetation cover, surface wetness and surface fluxes and

analyze the linkage with convective activity, (extreme) precipitation events and the Asian

Monsoon; this aims at using monitoring of snow, vegetation and surface fluxes as a precursor

of intense precipitation towards improving forecasts of (extreme) precipitations in SE Asia.

The specific objectives were:

Improve spatial density and temporal frequency of observations with:

1. Ground based observations of radiative and turbulent fluxes and soil moisture over the Plateau at a

limited but representative set of permanent sites; data quality and footprint analysis for upscaling

on satellite grid elements;

2. Satellite observations of snow and vegetation cover, of surface albedo and temperature over the

Plateau;

3. Satellite based estimates of energy and water fluxes over the Plateau;

4. Satellite based estimates of top soil moisture over the Plateau;

5. Integrated ground and satellite observations of precipitation over the Plateau;

6. Estimation of glaciers and snow meltwater using ground and satellite observations;

Contribute to advance understanding of land-atmosphere interactions, monsoon system and

precipitation:

7. Numerical Weather and Climate Prediction modelling system to link these observations with

precipitation forecasts over the Plateau and surrounding areas;


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Establish a prototype observing system for large area water management by:

8. Monitoring the water balance and water yield of the Plateau

Demonstrate the benefits of the observing system with pilot projects on:

9. Satellite based drought monitoring system of pilot areas of China and India;

10. Satellite based flood monitoring system of pilot areas of China and India;

Contribute to a GEO water theme and capacity building infrastructure for SE Asia

11. Dissemination and Stakeholders Panel

1.3 Work Performed

The project started with a Kick-Off Meeting held in Beijing on May 1st – 5

th 2008 attended by 65

participants. In preparation of the meeting all partners were requested to define more precisely their

contribution and roles. A project mailing list system was established to handle internal communication,

given the complexity of the consortium. There are registered 126 Team Members to date.

The 1st Annual Progress Meeting was held in Milano, Italy on June 29

th through July 3

rd 2009,

including a joint workshop with the CEOP High Elevation Initiative (HE). The meeting was attended

by 30 participants.

The 2nd

Annual Progress and Mid Term Review Meeting was held in Beijing at the Institute for

Remote Sensing Application of the Chinese Academy of Sciences on July 15th and 16

th 2010 The first

half-day session was dedicated to a review of related programs in the region and included presentations

on the CAS Third Pole Environment Program (TPE), the SHARE - PAPRIKA project of Italy and

France, the Water and Development Information for Arid Lands-A Global Network (G-WADI) of

UNESCO and the new WCRP/GEWEX program. The meeting was attended by 56 participants. The

Annual Meeting was shorter than usual because it was followed by two International Workshops held

in Lhasa, Tibet and co-organized by CEOP-AEGIS: a) The 4th International Workshop on Catchment-

scale Hydrological Modeling and Data Assimilation (CAHMDA-IV) and b) The 2nd CAS-CEOP

International Workshop on Energy and Water Cycle over the Tibetan Plateau and High Elevations. 150

participants from 11 Countries attended the two workshops.

The 3rd

Annual Progress Meeting was held in Strasbourg was held at the Image Sciences, Computing

Sciences and Remote Sensing Laboratory (UMR 7005 CNRS/UDS) from June 14th to 17

th 2011. After

an open session dedicated to invited speakers reporting on other European projects on Tibet and

Himalayas and on current research activities in remote sensing for water management in Asia.

The 4th Annual Progress Meeting was held in Delft at the Faculty of Civil Engineering and

Geosciences of the Delft University of Technology on June 13th and 14

th 2012. The 5

th and Final

Progress meeting was held on April 25th 2013 at the Remote Sensing and Digital Earth Institute

(RADI) – Chinese Academy of Sciences in Beijing. The meeting was attended by 30 participants. The

project was concluded with an International Workshop (WATGLOBS

http://watglobs.csp.escience.cn/dct/page/1) attended by 150 Participants from 13 Countries.

The bulk of the research work was focused on improving and completing the ground and satellite data

sets on the different terms of the terrestrial water cycle. The first experiments with the Plateau water

balance model identified some inconsistencies in the snow water equivalent and precipitation data sets,

which were re-processed. Work addressed methods to detect and monitor convective events using a

combination of satellite observations and numerical experiments. The impact of land surface

conditions on meso-scale atmospheric circulation was analysed by numerical experiments based on

hypotheses on surface fluxes and surface wetness. All data sets were used to model the water balance

of the Plateau was completed. Time series analysis of satellite data for drought and flood early warning

was focused on the evaluation of new indicators to improve the detection of anomalies. The 1st

stakeholders and training event was organized at the Headquarters of the China Meteorological

http://watglobs.csp.escience.cn/dct/page/1


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Administration in Beijing and saw 39 participants from both partner and external organizations. The

2nd

Training Session and Stakeholders Panel Meeting was held in Rourkela, India, 16-20 April 2012.

More than 90 participants followed the lectures and took part in the interactive sessions of the

Stakeholders Panel meeting. The 3rd

Stakeholders and Training event saw 20 participants from both

partner and external organizations A full list of publications is available on the project web-site.

1.4 Main Results

Improvement of retrieval algorithms, process models and land-atmospheric models. New or

significantly improved algorithms have been developed and evaluated against ground measurements.

Variables retrieved include land surface properties, rain rate, aerosol optical depth, water vapour, snow

cover and water equivalent, soil moisture and lake level. Common to all algorithms developed is the

capability to make use of heterogeneous raw data. A new system has been implemented to monitor

actual evaporation and evaluated against new heat flux data from eddy-covariance and scintillometer

raw data. The three years time series of gap-free daily and hourly evaporation derived from

geostationary data collected by the FY-2D satellite is a major achievement of the project.

Production of data sets. An improved and final version of all expected data products has been

generated and made available on the project data portal.

Case-studies with hydrologic and atmospheric models. The hydrologic modeling system has been

implemented and applied to the Qinghai Tibet Plateau and the headwaters of the major rivers in South

and East Asia. Evaluation against river flow data in China and India has been done. Case studies on

response of atmospheric circulation and specifically of convective activity to land surface conditions

have been completed and the controlling land surface conditions and processes have been documented.

Case-studies on drought monitoring and early warning. A tool-kit has been developed and applied to

monitor recent and severe drought events for the evaluation of the system. Two new drought indicators

were proposed which are Normalized Temperature Anomaly Index (NTAI) and Normalized

Vegetation Anomaly Index (NVAI). Case study in China and India showed that these two drought

indicators were able to capture the drought evolution at different stage of drought occurrence. i.e. the

NTAI responds earlier than the NVAI.

Modeling and visualization of flood events and inundation. A new method has been developed for

monitoring and early warning of flooded areas at the regional scale. Several case studies have been

completed in China and India making use of very diverse satellite data.

Stakeholders and capacity building. Three Stakeholders Meetings and Advanced Trainings were held

and attended by 150 participants from both partner and external organizations. There are 122 PhD and

110 MSc students completing their education in the framework of the project. The project was

concluded with an International Workshop attended by 150 Participants from 13 Countries.

Final Results

- Data base containing ground observations, satellite data and higher level products, hydrologic and

atmospheric model fields for the period 2008 – 2010 over the Qinghai – Tibet Plateau.

- System to generate daily streamflow in the upper catchment of all major river in SE Asia gridded

to 5 km x 5 km.

Potential Impact and Use of Results

Implementation and demonstration of an observing system of water balance and water flow on and

around the Qinghai – Tibet Plateau will provide to all countries information on water resources and the

role of the Plateau in determining weather and climate in the region. The data portal developed and

populated by the project is resident at the Institute of Tibetan Plateau Research of the Chinese

Academy of Sciences, the leading Chinese organization of the Third Pole Environment Program. The

project has contributed to GEO in several ways which are detailed below (see 3.3.2). This included

presenting the project at GEO Plenaries and the GEO Workshops on European Projects and led to the

inclusion of the project in the GEO ST Portfolio and to significant contributions to the GEO water

Strategy and the new Initiative on Cold Regions.


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2. Science and Technology Results

2.1 Approach

The project is constructed around 10 technical elements towards achieving the specific objectives detailed

above. The interrelation of these elements is summarized in Fig. 1. Six of these elements have the

function of providing the data-streams required to determine the water balance of the Qinghai – Tibet

Plateau and form the components of the prototype water resources observation system. Two elements

have the function of generating information from the six data streams: on the one hand a time series of the

overall water balance of the Qinghai – Tibet Plateau, covered by the combined satellite and ground

observations, on the other hand improved forecasts of the onset of the monsoon and of precipitation

events. Finally data and information are used to feed satellite data, time-series based early warning

systems for drought and floods.

Figure 1 Simplified work flow of the prototype water resources observation system developed by CEOP - AEGIS

2.2 Ground observations

Time series of flux measurements. A 3-year time-series of ground-based fluxes and evapotranspiration

data has been prepared from 2008 to 2010 at four stations (Table 1) on the Tibetan Plateau, measured

with the eddy-covariance method. Beside state-of-the-art quality assurance indispensable to this

sophisticated method (see deliverable reports De 1.2 and 1.3), the data has been corrected according to the

observed energy balance closure gap for the first time with a new method (Babel 2013). This improves

the compatibility with remote sensing products of fluxes and evapotranspiration, which is necessary for

validation of such products with the ground-based data. Furthermore, a land surface model has been

adapted to the specific measurements sites for filling gaps in the observations (Biermann et al., 2013),

increasing the data coverage by 35% on average. In comparison to other internationally available eddy-

covariance data sets like FLUXNET, the new data set, including energy balance closure correction and

highly sophisticated gap-filling, attains a higher level which is particularly suitable for the validation of

meso-scale models and remote sensing products.


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Table 1 Characteristics and data coverage at the flux measurements sites on the Tibetan Plateau; data coverage for

evapotranspiration is expressed as percentage of the maximum possible number of observations in 2008 - 2010.

Naqu (BJ) Nam Co Linzhi Qomolangma

Latitude 31°22‘7‘‘N 30°46‘22‘‘N 29°45‘56‘‘N 28°21‘29‘‘N

Longitude 91°53‘55‘‘E 90°57‘47‘‘E 94°44‘18‘‘E 86°56‘47‘‘E

Altitude 4502m 4745m 3327m 4293m

Land Cover (target land use) Alpine steppe Alpine steppe Alpine grassland Gravel

Coverage of quality checked

measurements

18.5% 53.4% 34.2% 70.6%

Coverage of gap-filled data 82.2% 83.6% 55.5% 95.0%

Lake evaporation. Evaporation above a lake surface in the monsoon season has been measured with the

eddy-covariance method for the first time on the Tibetan Plateau (Babel, 2013; Biermann et al., 2013).

For the technical realization of such measurements a footprint analysis method has been adapted to

distinguish evaporation flux contributions of the lake surface from the land surface for observations

conducted at the shoreline. The data has been successfully reproduced by a model specially tailored for

lake surfaces which has not been done on the Tibetan Plateau before with this degree of accuracy and

temporal resolution. The measurements and simulations pose a valuable basis for further quantification of

lake surface evaporation on the Plateau scale.

Figure 2 The distribution (left) and frequency statistics (right) of free convection events (FCEs) times at Nam Co

site

Momentum and heat transfer coefficients. Eddy covariance flux data collected from three stations

(Qomolangma station, Namco station and Southeast Tibet station-Linzhi station) on the Tibetan Plateau

were used (Wang and Ma, 2011) to analyze the variation of momentum transfer coefficient (CD), heat

transfer coefficient (CH), aerodynamic roughness length (z0m), thermal roughness length (z0h) and

excess resistance to heat transfer (kB-1). All the data was checked under the data quality control firstly.

The monthly average surface roughness, bulk transfer coefficient and excess resistance to heat transfer at

all three sites are obtained. Momentum transfer coefficient (CD) is quite variable during the day but

relatively stable and lower in the night. The parameter kB-1 exhibits clear diurnal variations with lower

values in the night and higher values in the daytime, especially in the afternoon. Negative values of kB-

1are often observed in the night for relatively smooth surfaces on the Tibetan Plateau.

Free Convection Events. The spatial and temporal structure in the quality of eddy covariance (EC)

measurements at Nam Co site was analyzed (Zhou et al., 2010), by using the comprehensive software

package TK2 together with a footprint model, and the high quality turbulent flux data have been obtained

0 6 12 18 0

Apr

Jun

Aug

Oct

Local time

(a)

Monsoon

Sunrise

Sunset

FCEs

n.V.

6 8 10 12 14 16 180

20

40

60

80

100

120

Local time

FC

Es

freq

uen

cy (

-)

(b) Before monsoon

During monsoon


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for the investigation of free convection events (FCEs). The research of FCEs at Nam Co site indicates that

the generation of FCEs not only can be detected (Fig.2) in the morning hours, when the diurnal

circulation system changes its previously prevailed wind direction, but also can be triggered by the quick

variation of heating difference between different types of land use during the daytime when clouds cover

the underlying surface or move away. FCEs at Nam Co site are found to occur frequently, which can lead

to the effective convective release of near ground air masses into the atmosphere boundary layer (ABL)

and may strongly influence its local moisture and temperature profiles and its structure.

Figure 3 Location of precipitation sampling sites and ice core drilling sites. The shadowed line in the middle of the

map shows the north boundary of summer Indian monsoon.

Stable Isotopes. Tibetan Network on Stable Isotopes in Precipitation has been set up in the Tibetan

Plateau to study the climate controlling on stable isotopes (Fig.3). These data are also used to study the

atmospheric circulation and moisture transport to the Tibetan Plateau. The seasonality and spatial

variations of precipitation isotope reveal different moisture supplies. The seasonal pattern of precipitation

reveal two zones: (1) The southern Tibetan Plateau, (2) Northern Tibetan Plateau, and also (3) the

boundary zone.

2.3 Retrieval of surface bio-geophysical variables from EO

Instrument development. A multispectral canopy imager (MCI) was developed for the field measurements

of forestry canopy LAI (Fig.4). It can capture image pairs in three different wavelength bands at arbitrary


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zenithal and horizontal directions. The MCI image pairs can be used to discriminate the sky, leaves, cloud

and woody components. As a result, this instrument is capable of measuring the woody area index .

Finally, the LAI values were obtained in several locations after consider the correcting of the clumping

effects and woody components.

Canon 40D

IS-1

LinkPan tilt

Tripod

Metallic arm

Figure 4 Multispectral canopy imager: MCI structure (top left); the clumping indices (bottom left)and woody-to-

total area ratio(bottom right) of one plot at Heihe River in June 2008

Generic inversion algorithm. In order to enable the application of the method to several satellite sensors,

the observation model SLC (soil-leaf-canopy) was extended for applications in the thermal domain. In

addition, look-up table (LUT) techniques were optimized in order to allow for efficient image simulations

under various conditions. A unified equation was derived to describe the TOA radiance as a function of

surface and atmospheric parameters in the optical and thermal domains with the incorporation of

topographic effects. The MODTRAN interrogation technique was also extended into the thermal domain,

and several MODTRAN outputs were identified with physical quantities of four-stream radiative transfer

theory.

Retrieval models to estimate land surface radiative flux under all sky conditions. New methods have been

proposed to simultaneously derive surface shortwave (or longwave) radiative flux components based on

MODIS products using an artificial neural network (ANN), and were validated using in situ measurement

and at Tibetan Plateau region. Based on the ANN, a MODTRAN-CF model driven from the MODTRAN


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model by incorporating two cloud fraction related factors (hemispherical effective cloud fraction and

regional cloud fraction), was developed to estimate the shortwave flux components under cloudy sky

from MODIS 1-7 channels‘ TOA radiances, and topographic correction was also perform on the

estimated flux.

Albedo retrieval model for topography and scale effect. A scale effect correction factor was defined to

correct the topography and scale effects on the albedo product. This factor is only dependent on DEM and

the geometry of sun and sensor. The correction algorithm includes three steps: (1) a database for slope

and aspect angle, and scale effect correction factor; (2) topography effect correction for finer directional

reflectance; (3) topography and scale effect correction for coarser directional reflectance.

Fractional Vegetation Cover (FVC) retrieval model. The fractional vegetation cover was retrieved by

using a gap fraction model and the 2nd order polynomial form of NDVI and FVC. The 7 days composed

FVC maps over Tibet Plateau were produced in about 3 years, from May 2008 to December 2010. The

daily FVC maps were interpolated (Fig. 5). The FVC was validated with field measurement, and the field

FVC was extracted from RGB photos by using the CIE L*a*b* color space technique.

Figure 5 Retrieval of Fractional Vegetation Cover: Coverage of Tibetan Plateau and time series at two sites (left);

Validation against ground measurements in 2010 at two sites in the HeiHe Basin (right).

Leaf Area Index (LAI) retrieval model. The general regression neural network (GRNN) was developed to

retrieve LAI products (see Fig.6) from the reprocessed time series MODIS reflectance, where the neural

networks are trained over time series composited LAI products from MODIS and CYCLOPES and

reprocessed MODIS reflectance for each of the MODIS biome classes.

Figure 6 Retrieved LAI maps over the Tibet Plateau

Land Surface Temperature (LST) retrieval model. A global algorithm was developed to get LST from two

thermal bands from 10 to 12 µm for polar-orbiting satellite; a single-channel parametric model algorithm


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and a generalized split-window algorithm were proposed to retrieve LST from HJ-1B/IRS and FY-2C

data, respectively; a diurnal temperature cycle (DTC) model was developed to make temporal

normalization to LST from geostationary satellite data (see Fig.7); LST was also derived from microwave

data based on the relationship between vertical emissivity and the ratio of vertical against horizontal

emissivity.

Figure 7 Map of the LST estimated from FY-2C satellite data at 11:00 local time on May 15, 2006

Cross-calibration of MIR and TIR channels of FY-2C data with well calibrated MODIS/Terra and

AIRS/Aqua channels. Inter-calibration of SVISSR/FY-2C infrared channels 1, 2 and 4 against

MODIS/Terra and AIRS/Aqua channels was done using the Ray-Matching (RM) method, the Radiative

Transfer Modeling (RTM) method and the High Spectral Convolution (HSC) method. For the RM

method, the qualified measurement in December of 2006 and 2007 in SVISSR and MODIS channels with

|∆VZA|<2.0º, |∆SZA|<2.0º and |∆RAA|<5.0º were directly graphed in Figure 8. More than 1000 pixels are

qualified for each month and the VZAs are distributed between 7° to 52°. The MODIS measurements are

linearly related to the SVISSR measurements with correlation coefficients greater than 0.96. The results

(Fig.8) reveal that the calibrations are basically consistent between the two months for SVISSR channel 1,

but they are greatly different for SVISSR channels 2 and 4.


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180 200 220 240 260 280 300 320

180

200

220

240

260

280

300

320

Value Error

Intercept 21.673 1.548

Slope 0.9218 0.0056

R 0.980

Stdev 3.857

N 1134

Value Error


Slope 0.9345 0.0049

R 0.986

Stdev 3.300

N 1032

M

ea

su

rem

en

ts in

MO

DIS

ch

an

ne

l 3

1 (

K)

Measurements in SVISSR channel 1 (K)

Dec., 2006

Linear Fit

(a) Dec., 2007

Linear Fit

180 200 220 240 260 280 300 320

180

200

220

240

260

280

300

320

Measure

ments

in M

OD

IS c

hannel 32 (

K)


Dec., 2006

Linear Fit

(b)

Value Error


Slope 0.9404 0.0061

R 0.977

Stdev 4.098

N 1134

Value Error


Slope 0.9851 0.0058

R 0.983

Stdev 3.735

N 1032

Dec., 2007

Linear Fit

180 200 220 240 260 280 300 320

180

200

220

240

260

280

300

320

Value Error

Intercept -6.921 2.683

Slope 1.0212 0.0092

R 0.961

Stdev 3.153

N 1032

Me

asu

rem

en

ts in

MO

DIS

ch

an

ne

l 2

0 (

K)


Dec., 2006

Linear fit

(c)

Value Error

Intercept -16.005 2.671

Slope 1.0510 0.0092

R 0.960

Stdev 3.718

N 1134 Dec., 2007

Linear fit

180 200 220 240 260 280 300 320

-9

-6

-3

0

3

6

9

MO

DIS

-SV

ISS

R t

em

pe

ratu

re a

dju

stm

en

t (K

)

BT in SVISSR channels (K)

IR1 (2006) IR1 (2007)

IR2 (2006) IR2 (2007)

IR4 (2006) IR4 (2007)

(d)

Figure 8 Relationships between the MODIS/Terra measurements and the SVISSR/FY-2C measurements (a, b, and c)

and the MODIS-SVISSR temperature adjustments for the BT in SVISSR channels changing from 190 K to 310 K (d)

2.4 Radiative and Energy Balance from EO

A new, dedicated application code name SEBI-Common Framework (SEBI-CF) was developed to

perform the Surface Energy Balance (SEB) calculations over the Tibetan Plateau at high temporal and

spatial resolution. Following a review of existing algorithms, the SEBI-CF has been implemented to

include multiple Surface Energy Balance algorithms. Details about the implementation of the SEBI-

Common Framework were detailed by Colin et al. (2011). The SEBI-CF system provided a gap—free

time series of daily soil heat flux for the period 1/1/2008 – 31/12/2010. This time series was combined

with the corresponding time series of Land Surface Temperature to estimate the thermal admittance of the

land surface for each one of the dominant periodic components identified in LST time series by Fourier

analysis. This spectral thermal admittance was then used to infer the soil thermal properties at different

depths.

Land Surface Temperature (LST): gap-filled hourly time series. At any given time a number of image

pixel capture emittance by clouds, since such observations do not provide valid retrievals of Land Surface

Temperature. Cloud-screening, such as the one included in the retrieval procedure described above, does


16

not completely remove cloud-affected observations. We have developed a new procedure which refines

detection and removal of invalid observations due to erroneous retrieval and clouds and fills the gap thus

created in the time series. Using several surface observations per day (albedo, LST, etc.), which is

feasible when using radiometric data acquired by geo-stationary platforms, makes estimating and

monitoring surface heat fluxes accurate and reliable. The HANTS algorithm is based on discrete Fourier

transform (Menenti et al., 1993; Verhoef et al., 1996) and it was developed to deal with time series of

irregularly spaced observations and to identify and remove cloud-contaminated observations. Under

CEOP – AEGIS it was adapted to deal with the high temporal resolution of observations by geo-

stationary platforms. The results (Fig. 9) of a test on 1 month of hourly data over the entire Qinghai –

Tibet Plateau show that such complex signals are reconstructed accurately, notwithstanding frequent and

long gaps due to clouds. To evaluate the accuracy of signal modeling and reconstruction the gaps have

been created by removing segments of observations and the r.m.s.e. of the reconstructed signal has been

evaluated against the removed observations.

Figure 9 Modeling and gap-filling of LST time series retrieved from radiometric data collected by the Feng-Yun 2C

satellite

Monitoring of turbulent fluxes with geostationary satellite data. The hourly LST observations made it

feasible to calculate actual Evapo-Transpiration hourly for the entire period 2008 – 2010 and the entire

study area (2.4 106 km2), although the computational load of hourly estimates remains very considerable.

The gain obtained during the month of June 2008 by integrating the estimations of hourly evaporation at

the Qomolangma station, in comparison with the two methods to estimate the total daily ETd described

below is clearly documented by the scatter plots in Fig. 10. The data products on radiative and turbulent

fluxes for the period 2008 – 2010 and the entire study area were improved on the basis of the evaluations

described above. Although the intended temporal frequency was initially one week, we succeeded in the

production of a continuous, gap-free daily ET data product for the reference period and the entire study

area (Fig.11).


17

Figure 10 Time-series of measured and estimated hourly and daily actual evaporation: Comparison between the

results with the sine function (left) and hourly integration (right) for all stations during the month of June 2008

Figure 11 Daily Evapo-Transpiration ETd over the Tibetan Plateau on October 14

th 2008

Validation of surface heat fluxes. Two approaches were applied to estimate daily ET with a single set of

satellite observations at one time of the day and evaluated against the daily integral of hourly satellite data

collected by the geostationary satellite Feng-Yun 2D. The first method assumes that the EF values at the

time of observation can be applied to the daily value of net radiation, while the second method assumes

that the ratio of instantaneous to daily ET is a sine function of the time of observation (Fig.12). The


18

method based on the sinusoidal dependence on the time of observation gave a better agreement with the

ground based measurements of latent heat flux (evaporation). The error analysis demonstrates clearly that

for any sky conditions, the sine method for daily scaling of ET always provide better results, even at

Qomolangma station. The improvement in ET-daily estimates is illustrated in Figure 13 and by RMSE

being always lower than 1 mm d-1

.

Figure 12 Time-series of measured vs estimated daily actual evaporation for the Linzhi station (2008-2010):

comparison between constant EF (left) and sine function method (right )

Figure 13 Time-series of measured and estimated daily actual evaporation with two methods to estimate the daily

total ET using a single instantaneous satellite observation; Linzhi station (2008-2010)

2.5 Soil Moisture from EO

The long-term global soil moisture products are being used to identify the links between monsoon onset,

precipitation intensity and variability and soil moisture. Work focused on developing remote sensing and

modeling methodologies for better estimation of soil moisture at plateau and sub-continental scales by


19

using available and future space observations, and validations of the developed methodologies using

observation data from both routine observations from the monitoring sites and dedicated field

experiments.

We aimed at building up continuous in-situ soil moisture measurements at regional scale of selected sites

on the Tibetan Plateau after understanding the state of the art algorithms and available soil moisture

products. This task leads to the establishment of the Tibetan-Plateau Soil Moisture (SM) and Soil

Temperature (ST) Observatory (Figure 14). This observatory includes three observation networks

distributed in three different climate conditions (e.g. cold-humid, cold semi-arid, and cold arid) [Su et al.

2011].

Figure 14 Location of the three regional networks of the Tibetan Plateau soil moisture and soil temperature

Observatory (Tibet-Obs).

The continuous 3+ year in-situ soil moisture and soil temperature data have been collected from this

observatory [van de Velde et al. 2008, Su et al. 2011, Dente et al. 2012, Bhatti et al. 2013]. The data

collection is still on-going and data analysis is maintained. The in-situ data has been used to examine the

ECMWF‘s soil moisture analysis [Su et al. 2013], to calibrate and validate soil moisture retrieval from

ASAR data [van der Velde et al. 2012], to calibrate and validate the blended soil moisture from both

AMSR-E and ASCAT-L2 data [Zeng et al. 2013], and to be used as one of core validation sites for SMOS

and SMAP satellite missions. The preliminary results and documentation of uncertainties have been

reported. It is suggested that the in-situ soil moisture and soil temperature measurements are valuable

references in this particular environment and shall be maintained and updated whenever it is possible [Su

et al. 2011; Zeng et al. 2013].


20

Figure 15 Simulated ASCAT & AMSR-E signals by the optimized TVG model compared with the satellite data.

Figure 16 Inter-comparison between the blended soil moisture and the original AMSR-E and ASCAT-L2 data, and

the in-situ over semi-humid (Maqu), semiarid (Naqu) and arid areas (Ali & Shiquanhe).

Apart from using either a passive microwave emission radiometry or an active microwave scatterometry

in retrieving soil moisture at top surface layer, it is investigated the advantage of combining both active

and passive microwave remote sensing data to improve the modelling of emission and scattering

mechanisms over the natural grassland in the Maqu region, located at eastern Tibetan Plateau [Dente et al.


21

2013]. It was found that the optimization of input variables, which are affecting utmost the model output,

was crucial in the synergistic use of active and passive data. The model was able to simulate with good

accuracy the backscattering coefficient measured by ASCAT and the brightness temperature measured by

AMSR-E (Figure 15).

It proves that the integration of the active and passive microwave data is a crucial point to better

understand the microwave signature interactions with the land surface and to improve their modelling. It

is expected that the soil moisture retrieval obtained by model inversion will be improved, with the

developed sensor independent system [Dente et al. 2013].

For retrieving LST, a Generalized Split-Window (GSW) algorithm for the Chinese geostationary

FengYun meteorological satellite was developed [Zhao et al. 2011a]. For generating soil moisture, an

improved two-parameter retrieval algorithm was developed to retrieve soil moisture data from AMSR-E

land surface brightness temperature observation data [Zhao et al. 2011b]. Furthermore, a blending

approach was developed to generate a consistent soil moisture product by using both AMSR-E data and

ASCAT data (e.g. level 2 data) simultaneously. The blended soil moisture data product was quality

checked and cross-compared with different soil moisture products (i.e. GLDAS, ECMWF, SMOS and In-

situ data) [Zeng et al. 2013]. The comparison indicates that the blended soil moisture product performed

better over the cold-humid region (Maqu) than the cold-semi-arid (Naqu) and cold-arid regions (Ali &

Shiquanhe) (Figure 16). The main factor behind this unsatisfied performance over cold-semi-arid and

cold-arid regions was attributed to the algorithm used for retrieving soil moisture from the level 1 satellite

data [Zeng et al. 2013].

2.6 Precipitation from EO


22

Figure 17 The hourly precipitation produced by radar and raingauge network at 1400 UTC 2 Aug 2009 (top),

relationships between precipitation observed by radar and raingauge before bright band removed (top left) and

after bright band removed (top right)

The challenge to measure precipitation is tackled along different lines, aiming to improve quality and

reliability of precipitation characteristics measurements at the ground (with radar, raingauges, and

disdrometers) and to estimate precipitation from space with different approaches and data.

Two Doppler, C-band radar networks, one on central Plateau (four radars) and one in Xining (two radars)

are operated by CAMS, and a number of algorithm are implemented and tested to improve the

Quantitative Precipitation Estimation (QPE). Radar data preprocessing is first performed to mitigate the

impact of ground clutter: an original fuzzy-logic algorithm is set up to detect clutter in the Doppler

velocity maps and to remove the signal from the reflectivity data. Radar reflectivity volumes are also

inspected to detect possible bright band signatures and the effects are corrected in accordance to the

vertical profile of reflectivity. A hybrid scan is reconstructed, by using, for each azimuth value, the lowest

elevation not affected by beam blocking, due to mountains and/or other obstacles (Zhuang and Liu, 2012).

The reflectivity volume is then converted to instantaneous rainrate by means of an appropriate

exponential relationship between reflectivity (Z) and rainrate (R). In order to select the most suitable Z-R

relationship, a preliminary classification of rain type is performed, distinguishing between convective and

stratiform areas, and the coefficients of Z-R are selected accordingly. Finally, to improve the quality of

the estimate, a runtime adjustment routine compares the radar instantaneous estimates with a number of 6-

minutes cumulated rain amounts measured by reference raingauges. A linear relationship is derived

between radar and raingauges values over the calibration sites and it is used to correct the radar estimates

all over the domain. An example of radar product and the impact of bright band correction is shown in

Fig. 17.

In this framework, a disdrometric campaign has been carried out for the first time over the Plateau, to

study the rainfall microphysical structure at the ground. The output of the campaign are used to infer the

impact of altitude on the raindrop velocity and maximum size (Porcù et al., 2013a), to characterize


23

raindrop spectra of liquid precipitation (D‘Adderio et al., 2013), and to calibrate radar estimates,

improving the relationship between actual rainrate and measured reflectivity.

Radar data and estimates contributed in different ways to the implementation of the satellite precipitation

estimates. First, the reflectivity volumes are used, for selected case studies, to assess the consistency of

the cloud resolving model outputs used for the development of the passive microwave part of the

algorithm. Secondly, the radar derived rainrate maps are assumed as ground reference to construct the

supervised dataset used to train of the artificial neural network, which is the framework for the multi-

spectral rain retrieval techniques. Finally, case studies are performed, comparing different global satellite

precipitation products, the output of the multi-spectral technique, and the ground radar estimates.

0 200 400 600 800

monthly rain amount (mm)

Figure 18 Total rain amount over the Tibetan Plateau and surrounding areas for August 2009 as computed by

TANN-R (left), TANN-S with old (centre), and new screening (right).

Satellite precipitation observation is a hard task of the Tibetan Plateau, given a number of reasons that

makes difficult to separate atmospheric and surface signals in the measured radiation and to establish a

robust relation between radiation measured at different frequencies and precipitation at the ground.

Recent studies (Porcù et al., 2013b, Gao and Liu, 2013) proved these difficulties also for widely used

global precipitation products.

A multi-spectral technique has been implemented, calibrated and tested over the Tibetan Plateau, to

provide daily precipitation maps over a spatial grid of 5x5 km, for three years (2008, 2009 and 2010), and

to study the high resolution precipitation pattern over the Plateau. The proposed technique (Tibetan-

Plateau Artificial Neural Network, TANN) rely of the frequent observations (half hourly) of geostationary

IR sensor (Capacci and Porcù, 2009). The one-hidden-layer, perceptron type, network takes as input the

radiances (and their local variability features)of the IR channels (6.7 and 11 µm) of MVIRI sensor on

board the METEOSAT-7 spacecraft, operated by EUMETSAT at 57 degrees East on a geostationary

orbit. The output is the probability of precipitation, and the training to set-up the weights that define the

network is carried on a supervised dataset, where the input are experimentally matched with real

precipitation as measured by independent instruments.

For the cold months (October to May) the reference precipitation field is provided by the snowfall

estimates computed from the Cloud Profiling Radar on board the CloudSAT satellite mission, by using a

revised version of the Kulie and Bennartz (2009) algorithm.

For the monsoon season, two different strategies are pursued. A first release of the algorithm (TANN-R)

is obtained using QPE maps obtained by radar for selected case studies as ground reference, while for the


24

second release (TANN-S) the output of a passive microwave algorithm, based on SSMIS data, used in the

training set. The passive algorithm used here is the Cloud Radiation Database (CRD, Mugnai et al., 2008)

that makes use of a Cloud Resolving Model to construct a database of cloud profiles in the region of

interest; a radiative transfer model is applied to the cloud profile to retrieve a corresponding brightness

temperature (Tb) set, simulating the sensors observation. To perform the retrieval, the Tb database is

searched for the Tb set closest to the observed one and the precipitation rate at the ground of the

corresponding cloud profile is assigned. In general, the screening of surface signal is a key issue of any

passive microwave precipitation algorithm.

mm h-1

10

5

0

Figure 19 Three hourly averaged rainrate as estimated by radar QPE (a), TMPA-3B42 (b), C-MORPH (c), CNAW

(d), TANN-R (e), TANN-S (f) and TANN-S with the new screening (g), for August 24, 2009.

For the applications on the Tibetan Plateau, two schemes have been applied. For a first release of the

microwave estimates a multispectral threshold system is applied, making also use of the water vapor and

oxygen absorption channels of SSMIS. A detailed inspection of the precipitation maps, in comparison

with radar data and global products outputs, showed, however, a marked overestimation of rain area,

especially over northwestern Plateau where glaciers are present (see Fig. 18). Moreover, the use of the

final multisensory estimates as input in the hydrological model shows an overestimation of the rain

amount over the river basins including northwestern Plateau, such as Indus. These findings lead to the

implementation of a further screening procedure that mitigated the problem and provided new

precipitation maps for the monsoon season. Examples of impact of the screening procedure (Fig.18) show

the tendency of TANN-R to overestimate precipitation over the Plateau, while relatively lower rain

amount is estimated on the southern Himalayan slopes and northern India. The impact of the new

screening procedure is mostly evident in northeastern and central Plateau.

In Fig 19 an example of a case study is shown for August 24 2009, when around 18:00 UTC widespread

precipitation are detected in the Lhasa region by the radar network (a). Two global products (C-MORPH

(b) and 3B42 (c)) underestimate rain rate and area, while all the other products (d to g) make use of

infrared data, resulting in larger wet areas. TANN-R (e), calibrated with radar data, is able to better

reproduce the rain pattern as seen by the radar network (a), while the marked overestimate of the first

TANN-S version (f), due to the misclassification of surface features, is mitigated by the latest version of

the algorithm (g).

a b


25

A B 0 500 1000 1500 2000 mm y-1

Figure 20 Daily precipitation data product Tibetan Plateau 2008 - 2010: daily averages over the headwaters of

main river basins (a) and mean annual precipitation (b)

These algorithms were applied to generate a daily time series of precipitation for the entire Tibetan

Plateau and the period 2008 – 2010 (Fig.20a). The time series of 10-days averaged daily precipitation

over four river basins (restricted to the Plateau area) show different seasonal and annual variability

moving from East (Indus) to West (Yangtze). The mean annual precipitation amount shown in Fig. 20b

has been computed for the three years of the Project using TANN-S technique

2.7 Snow and Glaciers

Meltwater from snow, glaciers and permafrost if an important term of the Plateau water balance. First, we

developed a prototype system for mapping snow cover, fractional snow cover, and snow depth. Next a

modeling and data assimilation procedure was developed to use satellite retrievals of Snow Water

Equivalent, SWE, and maps of the soil freeze/thaw status to produce data sets on these variables from

2008 to 2010 and the entire Plateau area. The data set have been issued in project ftp. We also provided a

high-resolution forcing data set and soil parameter data sets from a microwave land data assimilation

system from 2008 to 2010. As regards the glaciers meltwater, we have carried out the first glacier

investigation data set on China. We modeled the energy and mass balance of the Zhadang glacier surface

in the Nam Co basin. In addition, we monitored the lake level change from ICESat and determined

geometric dependency of 244 lakes directly fed by glacial runoff on QTP. Finally, we presented a

synthesized scheme for simulation of snow distribution and melt on the QTP.

Snow Cover Mapping and SWE Retrieval. We developed a new blending daily snow cover algorithm

through improving the NSIDC snow algorithms and combining the Terra and Aqua MODIS with AMSR-

E data on the Qinghai Tibet Plateau (QTP). There are three major steps in our methodology. At first,

AMSR-E Snow Depth (SD) data and MODIS Fractional Snow Cover (FSC) data were collected and

processed. Also, we have demonstrated a cubic spline interpolation method for improving the accuracy of

FSC data with cloudy removal in the QTP region. There we proposed a combination rule to produce a

new SD dataset (with spatial resolution 0.1◦), using the AMSR-E SD data (with spatial resolution 0.25◦)

and MODIS FSC data (with spatial resolution 500m). The new synthetic SD dataset combining AMSR-E

and MODIS was named AMSD. Secondly, meteorological fields were modeled using the WRF model.

Driven by the distributed meteorological data, parameters of snow model were calibrated against the

combined SD and station observation in-situ. Thirdly, combined SD was assimilated into snow model


26

using Ensemble Kalman Filter (ENKF) method. The results were validated by different methods. Figure

21 shows the flowchart of simulation of snowmelt using different RS data.

Figure 21 The flowchart of simulation of snowmelt using different RS data

The combination rule determines the merging of MODIS SCF data and AMSR-E SD data and is based on

two key-assumptions. First, in a AMSR-E grid with the spatial resolution 0.25°, the snowpack is

inhomogeneously distributed in the regions where MODIS SCF value is greater than zero. Second, the SD

value in one grid from AMSR-E is an average value over the entire grid. Figure 22 shows the

combination of AMSR-E SWE data and MODIS FSC data, where the blended product shos the variability

of FSC modulating the low resolution SWE.


27

Figure 22 Combination of AMSR-E SWE data and MODIS FSC data. A is the filtered FSC data with spatial

resolution of 0.1 degree, B is the AMSR-E SWE data with spatial resolution of 0.25 Degree. C is the combined SWE

data with resolution of 0.1 Degree.

The snow cover area was further validated using the in-situ snow depth data and the snow cover area data

set were produced from 2008 to 2010.

Observations of lake levels with ICESat/GLAS. Direct glacial elevation changes on the Tibetan plateau

are difficult to obtain. It is however possible to use elevation data from the GLAS laser altimetry

instrument on board of the ICESat satellite to accurately monitor lake level changes. Lake level variations

between 2003 and 2009 for 157 Tibetan lakes were obtained and trends of each lake evaluated (Fig.23).


28

Figure 23 Lake level trends on the Tibetan plateau between 2003 and 2009 determined with ICESAT / GLAS

observations (Phan Hien et al., 2011)

Next we linked glaciers and lakes in the entire QTP using the SRTM DTM and evaluated the contribution

of glacier meltwater to the water balance of each lake (Fig.24).

Figure 24 Dependency of the Tibetan lakes on direct glacial runoff determined by integrating the SRTM and

glaciers inventory (Phan Hien et al., 2013)

2.8 Monsoon and Rainfall Forecasts

The purpose of this study was to understand the land-atmosphere interaction processes in influencing the

daily precipitation events, and to help improving the heavy rainfall forecast skill of meso-scale numerical

models over the Tibetan Plateau (TP) and surrounding regions. The work had a two-fold focus: (a)

Numerical experiments on effects of underlying surface heterogeneities on initiations of meso-scale


29

convections and (b) Impact of surface heterogeneity on the forecasts of rainfall and other near surface

variables and evaluation of land surface effects on daily rainfall forecasts.

Moisture intrusion into the plateau by a synoptic sale trough and importance of east-west heterogeneity in

soil moisture over the plateau to initiate MCS. Numerical simulation with sensitivity experiments were

performed to diagnose the systematic development of MCSs over eastern TP, and revealed (1) the

formation of horizontal wind shear between southwesterlies and northeasterlies over the strong heated

land‐surface causing a thermal low in the northwest, (2) the eastward propagation of the vorticity due to

intensification of upper westerlies in the night, and (3) the MCS genesis by low‐level convergences

behind the migrated vortex with a convective instability condition over the eastern wetland surface

(Figure 25). Namely, the enhanced southeast‐northwest gradation of the plateau‐scale soil moisture

distribution could effectively form the MCSs in the eastern plateau during the monsoon season.

Figure 25 Schematic diagram showing the MCS formation in the eastern Tibetan Plateau due to a propagation of

western thermal low.

Figure 26 MCS formation on June 30 18 UTC 2008 by METEOSAT IR image (left) and the same for MRF

simulation cloud distribution with q*V vectors at 500 hPa (Ueno et al., 2011)

Function of Sichuan basin and diurnal variation of northerly winds along the eastern periphery of the

plateau to erupt a MCS with heavy rains. The generation processes of MCSs, that occurred over the

Sichuan Basin without direct migration of the vortex generated from the plateau surface, were revealed

(Figure 26). MCS occurrences detected in METEOSAT geostationary satellite images are associated with

the traveling of midlatitude troughs after the onset of the Indian monsoon. Numerical simulations showed


30

that the MCS was triggered in the evening by strengthening of the low‐level wind convergences with

horizontal shear between the southerly monsoon flow, with large convective available potential energy,

and the northerly dry intrusion. Sensitivity numerical experiments revealed that the topography of the

Sichuan Basin was able to cause the sudden onset of MCSs apart from the plateau and higher soil

moisture in the basin could enhance a heavy precipitation zone.

Study on the interaction between land surface, PBL and convective activities by using a single column

model. A Single Column Model (SCM) for GRAPES is constructed for the purpose of evaluating

parameterizations of physical processes. Two observational datasets including Wangara and the third

Global Energy and Water Cycle Experiment Atmospheric Boundary Layer Study (GABLS-3) SCM field

observations have been applied to evaluate this SCM. CASE3 (CoLM land surface scheme coupled with

ABL scheme) simulates less sensible heat fluxes and smaller surface temperature which corresponds with

its lower potential temperature at the bottom of the ABL. Moreover, CASE3 simulates turbulence that is

weaker during the daytime and stronger during nighttime which corresponds with a wind speed at 200 m

higher during daytime and lower during nighttime. CASE1 (observed surface temperature taken as lower

thermal forcing) and CASE2 have no soil moisture prediction and simulate a similar water vapor mixing

ratio, while CASE3 has a soil moisture prediction and simulates wetter conditions.

Figure 27 Seasonal mean daily precipitation (mm d

-1) of 24-hour forecast and GPCP. The left and right panels are

for winter and summer respectively. (a) DJF-mean daily precipitation of 24-hour forecast using initial soil data

from T639; (b) DJF-mean daily precipitation of 24-hour forecast using initial soil data from GLDAS; (c) DJF-mean

daily precipitation of GPCP. (d), (e) and (f) are similar to (a), (b) and (c) but for JJA-mean.


31

Systematic evaluation of land surface effects on daily rainfall forecasts over Tibetan Plateau. Successive

24-hour forecast experiments by using GRAPES_Meso have been conducted with 15-km horizontal

resolution for winter (December to February of 2011/2012) and summer months (June to August of

2012). The experiments are designed to utilize different initial values of soil moisture and temperature,

which are from the CMA operational global forecast model - TL639 and NCEP global land data

assimilation system (GLDAS) respectively. The experiments show that GRAPES model can well capture

the rainfall distribution, but exhibits excessive rainfall amount on average. Despite of the obvious

difference of initial soil data, there exists little difference in rainfall forecast both in winter and summer.

This is understandable because the two initial soil moisture values exhibit little difference comparing with

the two initial soil temperature. And, it is believed that soil moisture should play much more important

roles. This conclusion is preliminary and in an average sense.

The seasonal mean daily precipitation of 24-hour forecast using initial soil data from TL639 and GLDAS

was compared with observations from GPCP (Fig.27). Clearly, GRAPES model can well capture the

rainfall distribution. The observational precipitation in winter as shown in Fig.27c has three rainfall areas

located at north-western part of Tibetan Plateau, south-eastern part of China and north-eastern Indian

subcontinent. Comparing with the observations, the GRAPES_Meso reasonably forecasts these three

rainfall areas except for the excessive rainfall over the Sichuan Basin of China. Also, it is noted that the

two forecasts using different initial soil conditions exhibit little difference at the 24-hour forecast range

(Figs.27a and 27b). Summer is the rainy season in south-eastern and eastern Asia, characterizing as the

Asian monsoon season. As shown in Fig.27f, observational main rain belt extends from the Indian

subcontinent to the southern and eastern China. Again, the GRAPES_Meso does good job in forecasting

the main features of monsoon rainfall (Figs.27d and 27e). Comparing the two forecasts with different

initial soil conditions as plotted in Figs.27d and 27e, again there exists little difference between the two,

indicating the minor effects of LSM as having found in the winter-time forecast. It seems that beyond the

12-hour forecast range, initial soil conditions play little roles in influencing the rainfall amount and

distribution on average. However, whether the initial soil conditions influence the precipitating systems or

not still needs to be investigated further.

Figure 28 Frequency of low-level vortex occurrence in summer months (JJA) for the period from 2005-2010


32

Statistical analysis of low-level vortex occurrence and its relationship with Plateau-scale land surface

inhomogeneity over Qinghai – Tibet Plateau. Statistical analysis of low-level vortex occurrence are

conducted by using the ECMWF reanalysis data for the boreal summer during 2005-2010. It is interesting

to note that most of the low–level vortices always occur over the almost fixed region as shown in Fig.28.

And, further analysis show that this rather fixed region corresponds to the strong east-west gradients of

soil moisture and thermal conditions of PBL. Except for 2 cases, 98% of low-level vortex occurrence

show significant correlations with soil moisture at 90% significance level, and 93% exhibit high

correlations with east-west gradient of soil moisture at 95% significance level. If the occurred low-level

vortices are classified into developing (life time more than 36 hours) and short duration ones, the

developing vortices show much higher correlations with soil moisture and its gradient. Also, the PBL

thermal conditions and their east-west gradients show the similar relationship with low-level vortex

occurrence. These indicate the strong coupling between the low-level vortex occurrence and plateau-scale

land surface inhomogeneities. However, to what extent land surface inhomogeneities influence the

occurrence of low-level vortex, or there exists only simultaneous coupling between the two, need to be

further analysed.

2.9 Plateau Water Balance

We developed a GIS-based hydrological model, named ‗Qinghai-Tibetan Plateau Spatial Processes in

Hydrology (QTP-SPHY), to monitor the water balance of the QTP for 2008-2010. In addition, long-term

changes in the water balance elements of the QTP have been analyzed for the period 1970 to 2010 based

on the data from the ground meteorological stations and E.O. database. The SPHY is based on the

PCRGLOB-WB model, but with additional improved routines for cryospheric processes. The SPHY

model is a raster based highly detailed full distributed cryospheric-hydrological model, based on

commonly accepted standards from multiple proven hydrological models. The model is setup at a high

spatial resolution (5km) and simulates the complete hydrological cycle on a daily step. The actual runoff

which is calculated for each grid cell consists of four contributing factors. These are: runoff originating

from rain, runoff originating from snow melt, runoff originating from glacial melt, and baseflow. Runoff

and groundwater base flow are transferred to the to the drainage network and routed along the digital

elevation model.

The long-term ground observed dataset has been built based on the observed climate and hydrologic data

during the period 1970 to 2010 from 89 meteorological stations of the Qinghai – Tibet Plateau. Spatial

interpolation has been processing to provide 5×5 km climate dataset for the entire plateau considering the

location and the elevation of the grids. The climate and hydrologic variables include mean, maximum and

minimum daily temperature, vapor pressure, wind speed and sunshine duration. For the water balance

elements, changes in precipitation, potential evapotranspiration, evapotranspiration and streamflow have

been evaluated using the Mann-Kendall method.


33

Figure 29 Water balance components Tibetan Plateau (area within red boundary) 2008-2010 (mm/yr).

Water balance of the QTP from the hydrologic model. The SPHY model simulates the contribution of

glacier melt, snow melt, rainfall-runoff and base flow to the total flow from the plateau. For 2008-2010

their relative contributions to total flow were 4%, 55%, 15% and 26%, respectively (Fig.29). Besides, the

SPHY model is used to assess the average annual water yield for each of the major river basins with

sources on the Tibetan Plateau (Table 2).

Table 2 Annual water yield for major river basins originating on the Tibetan Plateau. Numbers are representative

for the area of the river basins on the Plateau (area within the red boundary in Figure 25) for 2008-2010.

River basin Annual water yield (km3/yr) Annual water yield (mm/yr)

Indus 9 94

Ganges-Brahmaputra 285 747

Mekong 74 855

Salween 68 631

Yangtze 475 916

Yellow River 185 809

Time series analysis of water balance of the QTP. Fig.30a shows the variation of annual precipitation

from 1970 to 2010. Annual precipitation had increasing trend (P<0.1), and the increasing rate was about

0.11mm/10a. Fig.30b shows the spatial distribution of changes in annual precipitation from 1970 to 2010.

Annual precipitation increased at 66 stations (74.2%), and the increase trend is significant at 37 stations.


34

Figure 30 (a) Annual precipitation from 1970 to 2010 averaged over the entire Plateau and (b) spatial distribution

of changes in annual precipitation from 1970 to 2010.

Figure 31 The variation of annual potential evapotranspiration for the Plateau from 1970 to 2010

The variation of annual potential evapotranspiration (ET0) for the Plateau from 1970 to 2010 is shown in

Fig. 31. A change point for ET0 series was identified around the year 1996. Annual ET0 decreased

significantly (P<0.001) at -1.45 mm yr-2 from 1970 to 1996, while ET0 increased significantly (P<0.001)

at 3.79 mm yr-2 from 1997 to 2010. Annual ET0 (Fig. 32) in most stations decreased from 1970 to 1996

and increased from 1997 to 2010. The attribution results show that the significant decreases in wind speed

and solar radiation offset the effect of increasing air temperature and led to the decrease in ET0 by 1.45

mm yr-2 from 1970 to 1996. However, from 1997 to 2010, the rate of increase in air temperature rose

seriously, while the rate of decrease in wind speed and solar radiation declined. Increasing air temperature

dominated the change in ET0, which did offset the effect of wind speed and led to the increase in ET0.


35

Figure 32 Spatial distribution of changes in annual potential evapotranspiration: from 1970 to 1996 (left) and from

1997 to 2010 (right).

2.10 Drought Early Warning

Prototyping and demonstration of a Drought Early Warning system was pursued along two simultaneous

lines of R/D:

Develop improved methods for time series analysis of satellite indicators of land surface response to

drought;

Integrate algorithms and multiple drought indicators into a prototype Drought Early Warning and

Monitoring System;

Time series of satellite indicators of land surface response to drought. A new drought indicators was

proposed which is the Normalized FPAR Anomaly Index (NFAI). Case study in China and India showed

that major drought events occurred in this period were identified well in the anomaly maps (Fig.33). The

new drought indicator was functioning to capture the drought evolution at different stage of drought

occurrence. A complementary, new drought indicator, the Normalized Temperature Anomaly Index

(NTAI), was developed to present the vegetation response to drought evolution (Jia et al., 2011). NTAI

represents relative anomaly in land surface temperature normalized by the difference of the historical

maximum and minimum value of LST. Positive NTAI indicates warmer deviation from the historical

mean status.

Anomaly maps of LST and FAPAR over China and India were produced between 2001 and 2010 (for

FPAR is 2003 - 2010). Anomaly maps of NFAI and NTAI between 2001 – 2010 over China and India

were produced. A few case studies were carried out to evaluate the capability of NTAI in detecting

drought against drought indicator from other resources, e.g . the Comprehensive Index (CI) for drought

monitoring from The Beijing Climate Center (BCC) of the China Meteorological Administration (CMA) .

CI is a function of the last 30-day and 90-day SPI and the corresponding potential evapotranspiration.

Precipitation-based drought indicators provide an objective measure of less-than-average precipitation,

but this does not necessarily imply an impact on vegetation, particularly on agricultural crops. This is

shown clearly by the comparison of the BCC indicator with NTAI (Fig.34).


36

2009145

2009153

2009161

2009169

2009177

2009185

2009193

2009201

2009209

Figure 33 Maps of NFAI covering western China and northern India between DOY145 – DOY209 in 2009

(a) NTAI, 2006201

(b) NTAI, 2006217

(c) NTAI, 2006233

(d) BCC-CI 2006.07.21

(e) BCC-CI 2006.08.05

(f) BCC-CI 2006.08.31

Figure 34 Spatial distribution of NTAI (a, b, c) and BCC drought index (CI) (d, e, f) on the three periods

corresponding to China Sichuan-Chongqing 2006 summer drought.

The severe to extreme anomaly assessed with the BCC indicator does not appear in the NTAI maps,

which indicate a more limited and localized drought event. Moreover both NTAI indicate more extensive


37

anomalies, particularly in the North – East of India along the southern side of the Himalayas, where the

BCC does not indicate any anomaly.

The difference is not inherent to the indicators, but related to how the processes linking precipitation, land

surface energy balance and vegetation growth interact in a specific region and during each event. This is

shown clearly by the results in Fig.34. Spatial patterns of NVAI (not shown here) and NTAI in the three

periods of China Sichuan-Chongqing drought in 2006 were very consistent to the BCC drought index

(Fig. 34).

Figure 35 Workflow of the East-South Asia Drought Monitor (ESADroM)

Drought early warning and monitoring system integration. A drought early warning and monitoring

system was developed (Fig.35; Jia et al., 2013) by integrating the tools for precipitation, land surface

temperature and vegetation response to drought. What the system deals with is the agricultural drought,

say starting from the precipitation deficit to the impact on the agricultural and nature ecosystem. The lake

levels and river streams, that are associated to hydrological drought, are not concerned. Since all the

droughts are resulted by lacking of precipitation for a certain time, the anomaly in precipitation is taken as

the first alerting indicator. Lacking of precipitation does not necessarily lead to agricultural drought due to

many other factor such as irrigation and plant extraction of shallow ground water. Vegetation will respond

to drought by increasing foliage temperature and thermal emission and by changing spectral properties


38

since the photosynthesis and transpiration are reduced by insufficient water supply. Land surface

temperature is an indicator with fast response to water deficit, higher canopy and soil temperature occur

during drought. Therefore LST is the second indicator to be monitored. If water deficit continues and lasts

for a longer period, vegetation may becoming wilting and decreased vegetation index than normal

conditions is observed. Indicators based on vegetation index or fPAR is therefore depicting the

consequence of meteorological drought (i.e. lack of precipitation) and can be used to assess the ultimate

impact or damage of drought on ecosystems (agriculture, prairie, and forest). According to the logic

mentioned above, a drought early warning and monitoring system for the East and South Asia region,

ESADroM - East-South Asia Drought Monitor, was developed by implementing (Fig.35) two sub-

systems for drought indicator analysis: system for rainfall deficit index (RainMonitor), system for time

series of land surface temperature anomaly analysis (i.e. the Normalized Temperature Anomaly Index -

NTAI) and for vegetation response analysis (i.e. the Normalized Vegetation Anomaly Index - NVAI) .

The two indices, NTAI and NVAI, together with the combined index Normalized Drought Anomaly

Index (NDAI) (Jia et al., 2012) are used to identify the drought evolution and impact.

2.11 Flood Early Warning

Four main objectives were pursued: (a) Early flood warning using surface wetness indicator derived from

satellite data; (b) Real time flood forecasting using data from the atmospheric-hydrologic network; (c)

Mapping and visualizing flood inundation, flood risk using combined satellite data and hydraulic models,

and (d) real time forecasting flood inundation, depth, areal extent and return period of flooding.

Figure 36 Fractional area of water saturated soil at a reference pixel and the river stage at Xiangjiang

hydrological station from 2000 to 2001.

Hydrological model and microwave measurements for flood early warning. For flood early warning, the

estimation of the soil water storage capacity and the prediction of the precipitation are the major two

problems. In a river basin with the evident rainy season, such as the Xiangjiang river basin, the soil water

30

32

34

36

38

40

42

44

46

48

50

0

100

200

300

400

500

600

Frac

tio

nal

are

a o

f w

ate

r sa

tura

ted

so

il (k

m2 )

DOY

fractional area of water saturated soil

river stage


39

storage capacity plays the most important role in the flood early warning system. The Polarization

Difference Brightness Temperature (PDBT) of 37GHz has been proved to be very sensitive to the soil

water saturation of top soil. At regional scale, we defined the fractional area of water-saturated soil as the

degree of soil water saturation at top soil. Due to the shallow detected depth, this is a good indication of

the full soil water retention, because when the top soil is saturated, the additional precipitation will

become surface runoff. Thus we used the PDBT value of 37GHz to retrieve (Shang et al., 2009) the

fractional area of water-saturated soil to indicate the residual water retention capacity of a certain area.

This work by combining the satellite observations and the hydrological model identifies critical areas,

whose soil water storage capacity has a close relationship with the river stage at XiangJiang hydrological

station (Fig.36).

Real Time Flood Forecasting Using Data from Atmospheric Hydrologic Network. Stochastic time series

model (ARIMA), SCS-Curve Number, HEC-GeoHMS model, Probability Distributed Model (PDM) and

ANN models were applied for (a) Kosi River System, which is an important sub-basin of Ganga river

system, India, and (b) Heihe upstream river basin and Xiangjiang basin, China. All the models were

applied to estimate the runoff at different locations.

Figure 37 Observed and computed runoff using different methods: Runoff from sub-basins- SCS Unit Hydrograph

(left) and Base flow separation - Constant mean monthly values (right)

A comparison (Fig.37) is carried out once the model calibration and testing is done. In the model various

important parameter were computed using the following equations methods: (a) Runoff from sub-basins-

SCS Unit Hydrograph; (b) Base flow separation - Constant mean monthly values; (c) Flood Routing -

Muskingum-Cunge method; (d) Soil Moisture Accounting- Initial Loss Method.

Mapping and Visualizing Flood Inundation, Flood Risk using combined Satellite Data and Hydraulic

Models. The Chang Sha channel segment of Xiang Jiang River, China and Kosi river, India were selected

as study areas. Water level data of each day were collected for the analysis. Socio-economic data of each

district were collected from 1998 to 2004. The digital 2.5-meter intervals contours and 0.1-meter intervals

spot heights were used to generate the DEM with 5-meter grid size. The final DEM of study area is

generated using ArcView and ArcGIS software. Flood frequency of every pixel is calculated by

overlaying flood area of eight years. A hierarchical classification system of 18 land-use classes was

applied to the Landsat ETM data. The flood risk assessment model (FRAM) was developed using Model

Builder of the ArcGIS 9.x. The model is used to produce vulnerability class map, hazard class map and

flood risk class map. After running the flood risk assessment model and inputting all need data, the flood

risk map is produced by combining all the hazard map and vulnerability map where the pixel by pixel

sum of each map.


40

Figure 38 Flood inundation using real time non-linear models

The core element of the risk assessment is the simulation (Fig.38) of the flood depth and areal extent in

combination with the return period of flooding.


41

3. Impact, Dissemination and Exploitation of Results

3.1 Dissemination activities

The interface of the CEOP-AEGIS project with its stake-holders was a combination of dissemination

activities. We aimed at disseminating the information about the project and the results generated within

each element of the project. Three main instruments were used: 1) training sessions and workshops, 2)

tailored capacity building and 3) a website. Furthermore, several other means were utilized, among others,

scientific publications, presentations, videos, posters, etc.

3.1.1 Training sessions

Three training sessions were held during the project lifetime, as it is described in Table 3. A total of 106

participants completed the courses, representing 49 institutions from China, India, Nepal and Pakistan.

The lecturers were the PI‘s and selected researchers of the project Team.

Table 3 Schedule of training sessions

Event Date Location Status

1st Training 17-19 May 2011 Beijing, China Completed

2nd

Training 16-20 April 2012 Rourkela, India Completed

3rd

Training April 26-May 3, 2013 Beijing, China Completed

Final Workshop April 26-30, 2013 Beijing, China Completed

Two partially overlapping groups of the stakeholders panel were targeted by the training sessions and the

capacity building plans: the water management related institutions (including meteorological

organizations) sent middle level practitioners for the trainings, and the young researchers of the

universities and research organizations participated in the MSc and PhD research. Some members of this

latter group joined the courses as well. Interactive stakeholders panel meetings were held during the

training courses, as forums for exchanging ideas and getting feedbacks from the participants. More than

250 representatives of stakeholders participated on these events.

First training session of the Stakeholders Panel. The first training session took place at the HeadQuarters

of the China Meteorological Authority in Beijing, on 17-19 May, 2011 and was attended by 39

participants from the stakeholder institutions (Fig.39). The objective of the advanced training course was

to introduce the stakeholders/participants from China and Asian countries to the CEOP-AEGIS project

results related to in-situ and earth observation, retrievals and modeling of land surface processes and land-

atmosphere interactions with emphasis on the Tibetan Plateau. The course included explanations about

theory, instruments, validation, retrievals and modeling as well as applications. Practical sessions were

organized with hands-on exercises with data collected in different work packages. The lecturers were

experts responsible for the relevant project tasks. Furthermore, this occasion provided a feedback to the

CEOP-AEGIS consortium from the stakeholders of the project.


42

Figure 39 CEOP – AEGIS Advanced Training Sessions: Beijing (China) May 17 – 19 2011

The program was designed to cover the available products of the CEOP-AEGIS project according to their

relevance for the stakeholders. The course consisted of lectures, demonstrations and exercises in 3 days.

The target group is the senior technical staff of the stakeholder institutions, like meteorological and water

agencies of China and other Asia countries. Furthermore, PhD students, MSc students, and post-docs

were admitted to the available places.

The three-day program was as follows:

Day 1 –measurement and observation techniques, and the analysis of the measured data, as well as

dissemination of results via the Data Portal.

Day 2 –evapotranspiration (based on surface energy balance) and soil moisture.

Day 3 –In the morning: snow observations and drought monitoring; in the afternoon: interactive

session of the Stakeholders Panel for the evaluation of the project data and products, analysis of gaps

and suggestions how to close the gaps.

The interactive session showed that the stakeholders are very much interested in getting the results, and

they see still gaps between their daily work and the applicability of the project outputs. They had clear

suggestions about how these gaps can be closed, e.g., with validation of the results. They showed that the

easy access to the data and results is essential for operationalization.

Second training session of the Stakeholders Panel. The 2nd

Training Session and Stakeholders Panel

meeting in Rourkela, India, 16-20 April 2012. More than 90 participants followed the lectures and took

part in the interactive sessions of the Stakeholders Panel meeting. The one-day program focused on

informing the partners about the results of CEOP-AEGIS and discussing how the results can be used in


43

water management in South-East Asia. The Stakeholders phrased their expectations and comments about

the EO-based water management data, tools and products of the project.

A five-day training session followed the Stakeholders Panel meeting, where 53 participants from India

and the surrounding countries (Fig.40) learnt about the details of the project results. Besides the lectures,

most of the time was spent on hands-on training with exercises. The feedback session consisted of two

parts: i.) an evaluation part, where the course participants shared their opinion about the course and the

learnt topics with us, and ii.) an interactive feedback part, where in a forum-like setup, the course

participants shared their ideas about how EO and the project results could be the best integrated in to their

daily work.

Figure 40 CEOP – AEGIS Advanced Training Sessions: Rourkela (India) April 16 – 20 2012

The program started with and attractive opening ceremony, with the participation of state governmental

representatives and the dignitaries of NIT. A keynote lecture was delivered by Professor Dr. Massimo

Menenti and a demonstration of the CEOP-AEGIS data portal. Active involvement of the stakeholders in

the project is a basic principle of CEOP-AEGIS, thus in the afternoon, the participants introduced their

activity and their institutions in an interactive session, which focused on the recent water management

practices in the region and the expectations of the stakeholders from EO technology. Besides the scientific

insight into the role of the Tibetan Plateau related to the hydrological systems of the region, data

availability and access to analysis results were mentioned as the key expectations.

Lectures explained the theoretical background and practical exercises made the participants familiar with

the data and the processing tools. The topics covered were:

Ground-based observations: measured parameters, sites setup and data base

Satellite observations: land surface temperature and vegetation properties


44

Monitoring of surface fluxes with satellite observations

Satellite observations: rainfall

Snow cover and water equivalent

Soil moisture retrieval and time series

Water balance and water yield of the Tibetan Plateau

Drought monitoring and early warning system

Satellite based flood monitoring of pilot areas

The training part was closed by an interactive session to learn about and discuss the suggestions of the

participants in relation to the project activities and results. First, they filled up a questionnaire then Prof.

Menenti convened the discussion about the topics of the training course with the following concluding

remarks:

The course was an eye-opener in understanding how EO can contribute to water management.

Processes on the Tibetan Plateau are of extreme importance for the countries downstream of the big

rivers.

Data for flow estimates (floods) are expected from CEOP-AEGIS.

Precise snow information is needed for water resources estimates.

An evaluation form was filled up after the course by the participants anonymously, using the Internet-

based service "Survey Monkey". The results show a general satisfaction of the course participants. The

2nd

Stakeholders Meeting and Training Course in Rourkela reached out to more stakeholders than the first

one, there were more than 90 participants on the first day and 53 experts completed the course in India.

Since one busy working year has been elapsed from the first event, more research findings

(results/products) have been shown and distributed to the trainees, and the dialogue with the stakeholders

and the decision makers provided more focused suggestions and ideas.

Third training session of the Stakeholders Panel. The course was originally designed to be hosted by

International Centre for Integrated Mountain Development (ICIMOD) in Kathmandu, Nepal.

Unfortunately, the overloaded schedule of the institute finally did not allow fulfilling this plan, and the

course was organized as a joint event of the Final Workshop (WATGLOBS), hosted by the Institute for

Remote Sensing and Digital Earth (RADI) in Beijing, China. ITC was responsible for the content and

CMA was organizing the logistics, whilst selecting and inviting the participants was a joint effort of these

institutions.

Six international participants from South and East Asian countries and eight Chinese participants attended

the practical exercise part of the training session. They were water management practitioners representing

different institutions as well as some MSc and PhD students.

The course consisted of two parts: 1.) the WATGLOBS workshop (see 3.1.2), where a part of the

presentations covered the available products of the CEOP-AEGIS project and the current satellite data

products on the terrestrial water cycle. 2.) Demonstrations and exercises, the hands-on part of the course,

with examples developed by the work packages of CEOP-AEGIS.

An evaluation form was filled up after the course by the participants anonymously, using the Internet-

based service "Survey Monkey". The answers showed an overall satisfaction. Maybe the most important


45

comment about the organization and logistics was related to the short time that was available between the

announcement and the delivery of the course. The participants evaluated the content appropriate for their

work. As a generic wish, they asked for more practical exercises, which shows that they are really

interested in the implementation of the discussed techniques. The suggestions related to the possible

follow-up actions ask for cooperation between the project (represented by the lecturers) and the

stakeholders. Based on the assessment by the participants, we can conclude that the training session was

successful and the project results are relevant for the stakeholders.

3.1.2 Meetings

Each annual meeting, except the final one in 2013 was opened by a public session dedicated to

international initiatives related to the core objectives of CEOP – AEGIS. This included programs focusing

on the Qinghai – Tibet Plateau and surrounding regions, e.g. the Third Pole Environment program and the

PAPRIKA project, to Cold and High Elevation Regions, e.g. the CEOP High Elevation and GEO Cold

Regions Initiatives and Arid Regions, e.g. the UNESCO G-WADI program. Presentations on the

GEWEX program were given at the 2nd

Annual Meeting in 2010 and at the final WATGLOBS workshop

in 2013.

Two International Workshops were co-organized by CEOP – AEGIS with Chinese Partner Organizations

in 2010:

The 2nd CAS-CEOP International Workshop on Energy and Water Cycle over the Tibetan Plateau

and High Elevations, held on July 19–21, 2010;

The 4th International Workshop on Catchment-scale Hydrological Modeling and Data Assimilation,

held on July 21–23, 2010;

Both workshops were held in Lhasa and attended by 160 scientists.

Figure 41 Participants at the Terrestrial Water Cycle Observation and Modeling from Space: Innovation and

Reliability of Data Products (WATGLOBS)” international workshop

With the cooperation of State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing

and Digital Earth (RADI) of the Chinese Academy of Science, the Final Workshop took place as a part of

the ―Terrestrial Water Cycle Observation and Modeling from Space: Innovation and Reliability of Data

Products (WATGLOBS)‖ international workshop (http://watglobs.csp.escience.cn/dct/page/1). The

http://watglobs.csp.escience.cn/dct/page/1


46

workshop, among others, addressed the CEOP-AEGIS objectives and topics in a much broader context,

involving outstanding scientists worldwide. Stakeholder organizations formed the audience.

The last ten years of Earth Observation have witnessed two simultaneous major developments: an

increasing diversity of primary measurements and a concerted effort of the Earth Observation community

to develop and use higher level data products. This has been particularly true for observations to capture

the complexity of the terrestrial water cycle. Two fundamentally different but complementary groups of

state variables need to be captured: the amount of water in the Earth System and the response of the land

surface to water availability.

The workshop kept 140 young and not so young scientists (Fig.41) glued on their chairs over four and

half days through a double week-end: after a western Saturday – Sunday full work days followed a

Chinese – style three days holidays on the occasion of Labour Day 2013. The 48 posters, 40 contributed

and 21 invited presentations provided a state-of-the-art yet realistic overview of achievements and

challenges along the trail from observations to a better grip on the global terrestrial water cycle and water

resources. The Workshop covered the following topics: 1) Terrestrial water cycle – programs ; 2)

Observing the terrestrial water cycle from space: Soil moisture, Precipitation, Evapotranspiration, Snow,

Glaciers, Lakes, Groundwater; 3) Cryosphere processes and land surface hydrology; 4) Land surface state

and atmospheric convection; 5) Assimilation of terrestrial water cycle observations; 6) Global and

regional hydrological modeling; 7) Terrestrial Water Cycle Integration.

The Workshop was opened by Academician Guanhua Xu former Minister of MOST; Dr. Xiaohan Liao,

the China GEO Principal and the director of NRSCC-MOST; Prof. Huadong Guo, the director of the host

institute RADI; Mr. Guocheng Zhang, former director of NRSCC-MOST; Prof. Xiaowen Li,

Academician in Remote Sensing and Jessica Mitchell from European Commission, China Delegation;

Douglas Cripes and Richard Lawford from GEO; and Peter van Oevelen, the director of the International

GEWEX office.

The science priorities emerging from the Workshop were summarized with five questions:

Multiple data products for each variable: does it help?

Way forward: single universal algorithms or local adaptation of multiple algorithms?

Evaluation of data products: does a reference exist or should we stay with deviations from averages?

Would better modeling of measurements improve consistency with model variables?

Use of current data products for model forcing and evaluation: should we wait for better data?

3.1.3 Website

The website of CEOP-AEGIS (www.ceop-aegis.org ) provides the broadest access to the project results

and all information related to the activities. The data and tools generated by the project were first

available only for the project personnel, but by the end of the project, according to the original contract, it

was released to the public domain. The first version of the website was developed in the first phase of the

project with two access areas: 1.) the front page provided generic information for the public with several

links to relevant material; 2.) a password-protected area, accessible to the project personnel contained the

complete project documentation as well as the prototype data engine. During the last year of the project, a

lot of work was carried out to ease access to project foreground and increase the visibility of the data and

the publications of the project. Following recommendations from the stakeholders community, the home

page was redesigned to include a new ―data‖ button in the menu bar, and an ―access data portal‖ button in

http://www.ceop-aegis.org/


47

the side panel. This latter leads to three publicly available functions: 1.) The CEOP-AEGIS Open Library,

containing the links to project documents and their proper citations; 2) The CEOP-AEGIS Data Portal,

presents the key products in a user-friendly environment; and 3) The CEOP-AEGIS OpenDAP Server,

that is dedicated to advanced users familiar with the open data transfer protocol ―OpenDAP‖. This way

CEOP-AEGIS complies with the GEOSS requirements.

The visits to the website increased in the last year considerably (Table 4). The University of Strasbourg

owns the ceop-aegis.org domain name for the coming years to ensure a continuity of service beyond the

lifetime of the project. The IT service of the ICube Lab of the University of Strasbourg is hosting the

services and maintains the portal and servers free of charge.

Table 4 CEOP-AEGIS website visit statistics

Year Single visitors Visits Pages viewed Hits Bandwidth

2011 <=3205 5147 19981 46962 33.70 Go

2012 3622 5681 16300 33786 5.35 Go

2013 5719 12606 99148 120826 6.57 Go

3.2 Exploitation of Results

We have pursued the exploitation of project results in three different ways:

Making available all the ground and satellite data generated by the project through the CEOP-AEGIS

Data Portal;

Promoting the research and outcome of CEOP – AEGIS at GEO events

Publications on diverse online and hard-copy media

3.2.1 Data portal

It is crucial for the scientific community of the CEOP-AEGIS consortium that all the final products and

documents are properly cited in future scientific work. Therefore, it was agreed in the closing project

plenary session that the CEOP-AEGIS Data Section would not require a complex registration process, nor

special permission or password, but users accessing this section should first agree with a license for use of

the products. This license was developed and attached to the website.

The list of available data sets is given in Table 5. The data portal is visible and accessible through the

project web-site and it is resident at the Institute for Tibetan Plateau (ITP) research, Chinese Academy of

Sciences. ITP is the leading organizations of the Third Pole Environment program.

3.2.2 Transnational water resources

The project has generated two data sets of direct relevance to improve freely available information on

shared trans-national water resources:

Daily glacier melt, snow melt, rainfall-runoff, base flow and stream-flow for all channel sections

within each 5 km x 5 km grid over the Qinghai – Tibet Plateau and headwaters of the rivers fed by

Plateau water (see Fig. 29) for the period 2008 – 2010;

Gridded climate and hydrologic variables over the same 5 km x 5 km grid but derived from

observations at 89 stations over the Qinghai – Tibet Plateau and the period 1970 – 2010: mean daily


48

temperature, maximum daily temperature, minimum daily temperature, vapor pressure, wind speed

and sunshine duration.

These data sets are unique and have already provided a first assessment of on the dependence of water

resources in the Countries of South and East Asia on Plateau waters.

Table 5 Overview of main data sets available through the project Data Portal

Variable Spatial Coverage Temporal coverage Spatial and temporal resolution

Surface albedo 29 N – 41 N

70 E – 110 E

2008 - 2010 1 km x 1 km; weekly

Surface temperature 29 N – 41 N

70 E – 110 E

2008 - 2010 5 km x 5 km; hourly

Vegetation fractional cover 29 N – 41 N

70 E – 110 E

2008 - 2010 1 km x 1 km; weekly

Leaf Area Index 29 N – 41 N

70 E – 110 E

2008 - 2010 1 km x 1 km; weekly

Evaporation 29 N – 41 N

70 E – 110 E

2008 - 2010 5 km x 5 km; daily

Soil water content 29 N – 41 N

70 E – 110 E

2008 - 2010 25 km x 25 km; daily

Precipitation 29 N – 41 N

70 E – 110 E

2008 - 2010 5 km x 5 km; daily

Snow cover 29 N – 41 N

70 E – 110 E

2008 - 2010 500 m x 500 m; daily

Snow water equivalent 29 N – 41 N

70 E – 110 E

2008 - 2010 10 km x 10 km; daily

Stream flow 29 N – 41 N

70 E – 110 E

2008 - 2010 5 km x 5 km; daily

Turbulent fluxes 4 stations 2008 - 2010 Stations; 30 min

3.3 Impact

The impact of the project is documented by the large number of completed and on-going MSc and PhD

projects at Partner Organizations, where CEOP AEGIS provided a common framework, ideas for research

and data. A second indicator of impact is the visibility at GEO events since 2009, the specific contribution

of the project to new GEO Water Strategy and the new GEO initiative on Cold Regions. A third indicator

is the large number of publications during the period 2008 – 2013.

3.3.1 MSc and PhD students

Besides the training courses, MSc and PhD research projects were included in the tailored capacity

building plan. At the end of the project, 110 MSc and 122 PhD researches were completed or in progress

(Table 6). These projects were only partly funded by CEOP AEGIS, but do show that the project provided

a natural focus for the research of partner organizations and will lead to a long-lasting impact of CEOP

AEGIS.

3.3.2 Contribution to GEO Activities

The contribution of CEOP AEGIS to the GEO Tasks was defined in some detail in the initial phase of the

project with reference to the GEO Work Plan 2009 – 2011 (Fig. 35).

CEOP – AEGIS Delegates have been attending the following GEO meetings in the reporting period:

6th Meeting of the GEO Science and Technology Committee. 11 – 12 February 2008 Hannover,

Germany.


49

6th Meeting of the GEO Capacity Building Committee. 13 – 14 February 2008, Hannover, Germany.

GEO European Projects 2nd

Workshop. 3 – 4 September 2008. Brussels, Belgium.

GEO European Projects 3rd

Workshop. 8 – 9 October 2009. Istanbul, Turkey.

GEO European Projects 4th Workshop. 29 – 30 April 2010. Athens, Greece

7th Plenary Session and Ministerial Summit of GEO on 2 – 5 November, 2010, in Beijing, China.

Table 6 Summary of MSc and PhD students active in the framework of CEOP – AEGIS

MSc completion PhD completion

Host institution 2009-

2011

2012-

2014

2009-

2011

2012-

2015

Major topics

1 BNU 13 16 6 10 ET, LST, fractional vegetation and snow,

albedo, validation of EO products, SM, radiative

fluxes

2 CAMS 6 8 1 9 Rain radar and convective activities, land

surface models

3 CAREERI 3 8 10 15 Land surface parameters, SM, LST, snow

hydrology, glaciers, ET

4 CAREERI/Duke 0 0 0 1 SAR

5 CAU 7 5 5 1 Vegetation, SM, drought, data integration

6 IGSNRR 0 2 0 6 Vegetation, climate, LST, SM

7 IRSA-CAS 0 3 1 12 Scaling effect, surface fluxes, land surface

parameters

8 ITC (UT) 3 2 2 11 Snow, SM, water level fluctuations in lakes

9 ITP 8 12 5 7 Radiation and heat flux, SM, aerosol, land

surface models, glacier mass balance

10 UDS - TRIO 0 0 0 2 Land surface emissivity, surface fluxes

11 UDS – TUD –

Alterra

0 0 0 1 Land surface albedo in rugged terrain; at surface

radiation balance

11 NIT 0 2 0 1 Flood, water pollution, rainfall runoff modelling

12 TU Delft 2 3 1 3 Glaciers, laser altimetry, river levels, energy

balance

13 TU Delft/Alterra 0 0 0 2 Time series of ET

14 TU Delft/IRSA 0 0 0 1 SAR for flood

15 TU Delft/UBT 0 1 0 0 River level

16 UBT 4 4 0 2 Carbon dioxite and energy fluxes

17 UBT/Cambridge 0 0 0 1 Convective processes

18 UCG/UVEG 0 0 1 3 ET

19 U Tsukuba 0 0 0 1 Convective clouds

20 UNIFE 0 0 0 2 Precipitation

Total 46 64 32 90


50

CEOP-AEGIS @ the GEO-VII & Beijing Ministerial Summit. CEOP-AEGIS was active and visible at

the Seventh Plenary Session of the Group on Earth Observations (GEO-VII) & Beijing Ministerial

Summit on November 2-5th 2010. The summit was hosted by China, through the Ministry of Science and

Technology of the People‘s Republic of China (MOST) and the China Meteorological Administration

(CMA), supported by the China GEO committee.

Figure 42 Schematic overview of CEOP – AEGIS contributions to the GEO Tasks identified in the Work Plan 2009

- 2011

CEOP-AEGIS was promoted at the stand of the European Commission, the stands of China and of The

Netherlands, as well as through a dedicated stand in the exhibition hall. Project goals and achievements

were presented with posters, videos and brochures during the GEO exhibition. One video summarizing

the project was running on the giant screen in the centre court of the Exhibition Hall.

The CEOP-AEGIS China Office located in Beijing supervised the logistics of our 9 m2 booth during all

the plenary session and ministerial summit.

Furthermore, our project was presented at and contributed to the Workshop GEOSS support for IPCC

assessments on the data needs of the climate impacts, adaptation and vulnerability research

community 1 – 4 February 2011, Geneva Switzerland.

GEO European Projects 5th Workshop. 8 – 9 February 2011. London, UK. PR materials (brochure,

posters and a video) were prepared to support the dissemination activities.

8th Plenary Session of GEO on 16 – 17 November 2011, in İstanbul, Turkey. CEOP – AEGIS was

present with a booth within the EU exhibit.

GEO EU Projects 6th Workshop. 7 – 8 May 2012, Rome, Italy.

GEO STC Portfolio of Compelling Examples 2012 (http://www.geo-tasks.org/geoss_portfolio)

GEO Water Strategy 2014

GEO Initiative on Cold Regions 2014

http://www.geo-tasks.org/geoss_portfolio


51

3.3.3 Publications

Here only the number of Publications by category is provided (Table 7), while full list is attached (Annex

1). The Project Reports include all Periodic Reports and Deliverables and are published as a series by the

Université de Strasbourg (ISSN 2118-7843).

Table 7 Overview of publications by CEOP AEGIS Team Members by category

Category Number Period

Project reports 58 2009 – 2013

Other reports 5 2009 – 2011

International conferences 26 2009 - 2011

Journal articles and book chapters 72 2009 – 2012

Total 161

References Babel, W.: Site-specific modelling of turbulent fluxes on the Tibetan Plateau, University of Bayreuth,

url:http://opus.ub.uni-bayreuth.de/opus4-ubbayreuth/frontdoor/index/index/docId/1254, 168p., 2013

Bhatti, Haris A.; Rientjes, Tom; Verhoef, Wouter; Yaseen, Muhammad. 2013. "Assessing Temporal Stability for

Coarse Scale Satellite Moisture Validation in the Maqu Area, Tibet." Sensors 13, no. 8: 10725-10748.

Biermann, T., Babel, W., Ma, W., Chen, X., Thiem, E., Ma, Y., and Foken, T. 2013. Turbulent flux observations

and modelling over a shallow lake and a wet grassland inthe Nam Co basin, Tibetan Plateau, Theor. Appl.

Climatol., online first, doi:10.1007/s00704-013-0953-6

Chen, X. L., X. S. Shen and H. B. Chen, 2010: Analysis of the impact of land surface process on numerical weather

prediction of intensive summer rainfall over Huai-River in 2007, J. Tropical Meteorol. (in Chinese), Vol.26, 667-

679.

D‘Adderio, L. P.,F.Porcù, F. Prodi and C. Caracciolo, 2013: Rain microphysical structure over the Tibetan Plateau.

International Workshop on Terrestrial Water Cycle Observation and Modeling from Space: Innovation and

Reliability of Data Products (WATGLOBS), Beijing, 26-30 April.

Dente L., P. Ferrazzoli, Z. Su, R. van de Velde, L. Guerriero, (2013) Combined use of active and passive microwave

satellite data for an improved modelling. Submitted to RSE

Dente, L., Vekerdy, Z., Wen, J. and Su, Z. (2012) Maqu network for validation of satellite - derived soil moisture

products. In: International Journal of Applied Earth Observation and Geoinformation : JAG, 17 (2012) pp. 55-65

Gao，Y. C.and M. F. Liu. 2013, Evaluation of high-resolution satellite precipitation products using rain gauge

observations over the Tibetan Plateau, Hydrol. Earth Syst. Sci., 17, 837-849.

Kulie, M. S., and R. Bennartz, 2009, Utilizing Spaceborne Radars to Retrieve Dry Snowfall. J. Appl. Meteor.

Climatol., 48, 2564–2580

Jia, L., J. Zhou, G. Hu, W. Verhoef, 2011. Tools for time series analysis of vegetation response to drought & Tools

for drought prediction by analyzing vegetation response. CEOP-AEGIS Deliverable Report De9.3-9.4, Ed.

University of Strasbourg, France, ISSN 2118-7843: 27 p.

Jia, L., G. Hu, J. Zhou, 2013. System applicable for drought monitoring and early warning in the pilot areas in China

and India - East-South Asia Drought Monitor ESADroM. CEOP-AEGIS Deliverable Report De9.8, Ed.


Mugnai, A., E.A. Smith, G.J. Tripoli, S. Dietrich, V. Kotroni, K. Lagouvardos and C.M. Medaglia, 2008: Explaining

discrepancies in passive microwave cloud-radiation databases in microphysical context from two different cloud-

resolving models. Meteor. Atmos. Phys.,

Porcù F., U. Gjoka, S. Dietrich, P. Sanò, D. Casella, A. Mugnai 2013b: Satellite precipitation estimation over the

Tibetan Plateau and perspectives for new satellite missions. International Workshop on Terrestrial Water Cycle

Observation and Modeling from Space: Innovation and Reliability of Data Products (WATGLOBS), Beijing, 26-

30 April.

Porcù, F., L. P. D‘Adderio, F. Prodi and C. Caracciolo, 2013a, Effects of altitude on maximum raindrop size and fall

velocity as limited by collisional breakup, J. Atmos. Sci., 70, 1129-1134.


52

Shang, H., H. Pelgrum, A. Klaasse, M.Menenti. (2009). Algorithm theoretical basis document for surface wetness

indicators retrieved from passive and active microwave satellite data. CEOP-AEGIS Deliverable Report De10.1,

Ed. University of Strasbourg, France, ISSN 2118-7843: 23 p

Su, Z., P. de Rosnay, J. Wen, L. Wang, and Y. Zeng, 2013, Evaluation of ECMWF‘s Soil Moisture Analyses using

Observations on the Tibetan Plateau, Journal of Geophysical Research, Vol. 118, doi:10.1002/jgrd.50468.

Su, Z., Wen, J., Dente, L., van der Velde, R. and ... [et al.] (2011) The Tibetan plateau observatory of plateau scale

soil moisture and soil temperature, Tibet - Obs, for quantifying uncertainties in coarse resolution satellite and

model products. In: Hydrology and earth system sciences (HESS) : open access, 15 (2011)7 pp. 2303-2016.

Sugimoto S., K. Ueno and W. Sha (2008), Transportation of water vapor into the Tibetan Plateau in the case of a

passing synoptic-scale trough. J. Meteor. Soc. Japan, 86, 935-949.

Sugimoto, S., and K. Ueno (2010), Formation of mesoscale convective systems over the eastern Tibetan Plateau

affected by plateau‐scale heating contrasts, J. Geophys. Res., 115, D16105, doi:10.1029/2009JD013609.

Ueno, K., S. Sugimoto, T. Koike, H. Tsutsui, and X. Xu (2011), Generation processes of mesoscale convective

systems following midlatitude troughs around the Sichuan Basin, J. Geophys. Res., 116, D02104,

doi:10.1029/2009JD013780.

van der Velde, R., Su, Z. and Ma, Y. (2008) Impact of soil moisture dynamics on ASAR signatures and its spatial

variability observed over the Tibetan plateau. In: Sensors : journal on the science and technology of sensors and

biosensors : open access, 8 (2008)9 pp. 5479-5491.

van der Velde, R., Su, Z., van Oevelen, P., Wen, J., Ma, Y. and Salama, M.S. (2012) Soil moisture mapping over the

central part of the Tibetan Plateau using a series of ASAR WS images. In: Remote sensing of environment, 120

(2012) pp. 175-187.

Wang, S., Y. Ma, 2011, Characteristics of land–Atmosphere interaction parameters over the Tibetan Plateau, Journal

of Hydrometeorology, 12（4）: 702-708. Capacci, D. and F. Porcù, 2009: Evaluation of a satellite multispectral

VIS/IR daytime statistical rain-rate classifier and comparison with passive microwave rainfall estimates, J. Appl.

Meteor. Clim., 48, 284-300.

Xue H., X. Shen and Y. Su, (2011), Parameterization of Turbulent Orographic Form Drag and Implementation in

GRAPES (in Chinese). Journal of Applied Meteorological Sciences, Vol.22, No.2, 169-181.

Yang J. and X. Shen, (2011), The Construction of SCM in GRAPES and Its Applications in Two Field Experiment

Simulations. Advances in Atmospheric Sciences, VOL. 28, NO. 3, 2011, 534-550.

Yang J. and X. Shen, (2012)： A case study of the GRAPES single column model. Acta Meteorologica Sinica (in

Chinese). Vol. 70, No.2,275-290

Zeng Y., Z. Su, L. Wang, K. Xu, (2013), A Blended Soil Moisture Product over Tibetan Plateau using Satellite,

Reanalysis and in-situ Data, under submission

Zhao T.J., L. X. Zhang, J. C. Shi, L. M. Jiang, (2011b), A physically based statistical methodology for surface soil

moisture retrieval in the Tibet Plateau using microwave vegetation indices, Journal of Geophysical Research,

116, D08116, doi:10.1029/2010JD015229

Zhao, W., B.-H. Tang and Z.-L. Li. (2011a). Estimation of soil moisture from Geostationary Satellite (GS) data 4

(optical remotely sensed data). CEOP-AEGIS Deliverable Report De4.3, Ed. University of Strasbourg, France,

ISSN 2118-7843: 21 p.

Zhou,D., R.Eigenmann, W.Babel,T.Foken,Y. Ma, 2010, The study of near-ground free convection conditions at

Nam Co station on the Tibetan Plateau, TheoreticalApplied Climatology,DOI 10.1007/s00704-010-0393-5.

Zhuang W. and Liu L., 2012. A reflectivity climatology algorithm for hybrid scans and its application to radar

coverage over the Tibetan Plateau. Acta Meteor. Sinica, 26(6),746-757.


53

Annex 1 List of Publications

This Annex reports all the scientific publications that fully or partly benefit from the CEOP-AEGIS project.

Project reports Note: In the following list, project reports are sorted by numbers. The numbering of the project reports is of the form

X.Y, where X is the work-package number and Y the deliverable number within a given work-package.

(1.1) Eigenmann, R., Babel, W., Foken T., Zhou D. and Ma, Y. (2009). Detailed approach and preliminary data

analysis protocol for the characterization of seasonal and inter-annual time scales fluxes. CEOP-AEGIS

Deliverable Report De1.1, Ed. University of Strasbourg, France, ISSN 2118-7843: 32 p.

(1.2) Babel. W, R.Eigenmann, Y.Ma and T.Foken. (2011). Analysis of turbulent fluxes and their representativeness

for the interaction between the atmospheric boundary layer and the underlying surface on Tibetan Plateau.

CEOP-AEGIS Deliverable Report De1.2, Ed. University of Strasbourg, France, ISSN 2118-7843: 35 p.

(1.3) Babel, W., M.Li, F. Sun, W. Ma, X.Chen, J.Colin, Y.Ma and T.Foken. (2011). Postprocessing of ground based

turbulent measurements and preparation for the CEOP-AEGIS database in NetCDF. CEOP-AEGIS Deliverable

Report De1.3, Ed. University of Strasbourg, France, ISSN 2118-7843: 90 p

(1.4) Ma, Y. (2011). Atmospheric water vapor transport. CEOP-AEGIS Deliverable Report De1.4, Ed. University of

Strasbourg, France, ISSN 2118-7843: 10 p.

(2.1) Yan, G., W. Verhoef, Q. Liu, Z. Xiao, M. Menenti, L. Jia, Q. Liu, X. Mu, J. Li, B. Zhong, H. Ren. (2009).

Generic algorithms to retrieve land surface variables with a multitude of satellite sensors. CEOP-AEGIS


(2.2) Liu, Z. and Z.-L. Li. (2009). Algorithms to produce a consistent land surface temperature from polar satellites.


(2.3) Tang, B.-H. and Z.-L. Li. (2011). Algorithms for surface albedo, emissivity and temperature from

geostationary satellite. CEOP-AEGIS Deliverable Report De2.3, Ed. University of Strasbourg, France, ISSN

2118-7843: 25 p.

(2.4) Yan, G., W. Verhoef, Q. Liu, Z. Xiao, M. Menenti, L. Jia, Q. Liu, X. Mu, J. Li, B. Zhong, H. Ren. (2011).

Multi-source remote sensing - ATBD. CEOP-AEGIS Deliverable Report De2.4, Ed. University of Strasbourg,

France, ISSN 2118-7843: 136 p.

(2.5) Yan, G., Z.-L. Li, B.-H. Tang, Z. Xiao, X. Mu, L. Jia, J. Yang, Q. Liu, J. Li, Q. Liu. (2011). Description of

land surface properties retrieved from spectro-radiometric data in the optical spectral region. CEOP-AEGIS


(2.6) Yan, G., Z.-L. Li, B.-H. Tang, Z. Xiao, X. Mu, Q. Liu, J. Li, L. Jia, Q. Liu (2013). Validated dataset of land

surface properties over a 3 years period. CEOP-AEGIS Deliverable Report De2.6, Ed. University of Strasbourg,

France, ISSN 2118-7843: 17 p.

(3.1) Jia, L., M. Menenti. (2009). Review of selected existing algorithms and models on local, regional and Plateau

scales data sets. CEOP-AEGIS Deliverable Report De3.1, Ed. University of Strasbourg, France, ISSN 2118-

7843: 27 p.

(3.2) MENENTI, M., J.COLIN, R.FAIVRE, L.ROUPIOZ, L.JIA. (2009). Generalization of the surface energy

balance calculation over the Tibetan Plateau. CEOP-AEGIS Deliverable Report De3.2, Ed. University of


(3.3) COLIN, J., L.ROUPIOZ, H.GHAFARIAN, J.BAI, L.JIA, S.M.LIU, R.FAIVRE, F.NERRY, M.MENENTI.

(2011). Surface Radiative and Energy Balance time-series processing and validation procedure document.


(3.4) FAIVRE, R., J.COLIN, L.ROUPIOZ, H.GHAFARIAN, Z.LI, L.JIA, F.NERRY, M.MENENTI (2012).

Preliminary time-series maps of fluxes over 3 years, with a target frequency of one week. CEOP-AEGIS


(3.5) FAIVRE, R., J.COLIN, L.ROUPIOZ, H.GHAFARIAN, Z. LI, L.JIA, F.NERRY, M.MENENTI (2013).

Validated time-series maps of fluxes over 3 years, with a frequency of one day. CEOP-AEGIS Deliverable

Report De3.5, Ed. University of Strasbourg, France, ISSN 2118-7843: 22 p.

(4.1) Dente, L., Z. Vekerdy, Z. Su, J.Wen. (2009). Collection of consistent continuous in-situ soil moisture

measurements at regional scale. CEOP-AEGIS Deliverable Report De4.1, Ed. University of Strasbourg, France,

ISSN 2118-7843: 17 p.


54

(4.2) Su, Z., L.Dente, R. van der Velde, J. Wen, L. Zhang, T. Zhao, L. Jiang, T. Zhang, Y. Li, X. Li, L. Xiao. (2011).

Development of a satellite sensor independent system for the soil moisture combined retrieval algorithms.


(4.3) Zhao, W., B.-H. Tang and Z.-L. Li. (2011). Estimation of soil moisture from Geostationary Satellite (GS) data

4 (optical remotely sensed data). CEOP-AEGIS Deliverable Report De4.3, Ed. University of Strasbourg, France,

ISSN 2118-7843: 21 p.

(4.4) Zeng, Y., L.Dente, J.Wen and Z. Su (2012). A data product of the plateau using different sensors

simultaneously. CEOP-AEGIS Deliverable Report De4.4, Ed. University of Strasbourg, France, ISSN 2118-

7843: 16 p.

(4.5) Su, Z., J.Wen, L.Dente, R.van der Velde, L.Wang, Y.Ma, L.Jiang, K.Yang, Z.Hu, X.Li, L.Xiao (2012).

Preliminary results and documentation of Uncertainties. CEOP-AEGIS Deliverable Report De4.5, Ed. University

of Strasbourg, France, ISSN 2118-7843: 21 p.

(4.6) Su, Z., Y.Zeng, L.Dente, L.Wang, J.Wen, R.van der Velde (2012). Soil moisture products on the Qinghai-Tibet

Plateau. CEOP-AEGIS Deliverable Report De4.6, Ed. University of Strasbourg, France, ISSN 2118-7843: 18 p.

(5.1) Liu, L., H. Wang, Y. Xiao, Z. Hu. (2009). Algorithms for QPE, 3D mosaic and hydrometeor classification.


(5.2) Porcu, F., U. Gjoka and Y.-C. Gao. (2011). Provide rainfall estimation algorithms from satellites (MW, VIS-

IR) 5 data. CEOP-AEGIS Deliverable Report De5.2, Ed. University of Strasbourg, France, ISSN 2118-7843: 27

p.

(5.3) Liu, L., W. Zhuang, F.Porcu and U. Gjoka. (2011). Provide 3-years 3-D gridded radar data for case studies.


(5.4) Porcu, F., U.Gjoka and W.Zhuang (2012). Provide high space and temporal resolution precipitation data

retrieved from radar, satellite (both VIS-IR and MW) and ground rain gauge data for case studies. CEOP-AEGIS


(5.5) Porcu, F., L.Liu and W.Zhuang (2012). High space and temporal resolution precipitation data retrieved from

radar, satellite (both VIS-IR and MW) and ground rain gauge data for entire QT Plateau and for the years 2008,

2009 and 2010. CEOP-AEGIS Deliverable Report De5.5, Ed. University of Strasbourg, France, ISSN 2118-

7843: 10 p.

(6.1) Hao, X., J. Wang, H. Li, Z. Li, R. Jin, T. Che, K. Yang. (2009). Sample data set and Report on retrieval

performance based on MODIS and AMSR-E data. CEOP-AEGIS Deliverable Report De6.1, Ed. University of


(6.2) Hao, X., J. Wang, H. Li, Z. Li, R. Jin, T. Che, K. Yang. (2009). Preliminary Algorithm Theoretical Basis

Documents for Snow/Ice/Frozen soil Properties. CEOP-AEGIS Deliverable Report De6.2, Ed. University of


(6.3) Wang, J., X.H. Hao, T. Che, Y.C. Bo, J. Bi, L.Y. dai, R. Jin and C.L. Huang. (2011). Snow and Ice data

products - ATBD. CEOP-AEGIS Deliverable Report De6.3, Ed. University of Strasbourg, France, ISSN 2118-

7843: 90 p.

(6.4) Wang, J., X.H. Hao, T. Che, Y.C. Bo, J. Bi, L.Y. dai, R. Jin and C.L. Huang. (2011). Description of the data

sets: snow cover area, SWE, glacier area and storage from satellite data, and assimilation data sets. CEOP-

AEGIS Deliverable Report De6.4, Ed. University of Strasbourg, France, ISSN 2118-7843: 12 p.

(6.5) Wang, J., X.H.Hao, T.Che, Y.Bo, C.Huang, R.Jin, J.Bi, L. Dai, L. Yan (2012). Validated data set of snow

cover, fractional snow cover, snow depth, glacier area and assimilation data sets. CEOP-AEGIS Deliverable

Report De6.5, Ed. University of Strasbourg, France, ISSN 2118-7843: 49 p.

(6.6) Li, H., J.Wang, S. Kang, Z. Tang, T. Che, X. Pan, C. Huang, X.Wang, X. Hao and S. Sun (2012). Evaluation

of water resources contributed by the snowmelt and glacier melt on Qinghai-Tibet Plateau. CEOP-AEGIS


(7.1) Ueno, K., S. Sugimoto and X. Shen. (2011). Report and data set on results of numerical experiments to

document the response to observed evolution of snow and vegetation cover and surface heat fluxes over the

Plateau. CEOP-AEGIS Deliverable Report De7.1, Ed. University of Strasbourg, France, ISSN 2118-7843: 23 p.

(7.2) Shen, X., J. Yang and Y. Su (2013). Successive 24-hour Forecast Experiments by Using GRAPES Meso-scale

Model. CEOP-AEGIS Deliverable Report De7.2, Ed. University of Strasbourg, France, ISSN 2118-7843: 8 p.

(8.1) Zheng, H., W.Immerzeel, G.D'Urso and J.Colin. (2011). Design of a data processing protocol for E.O. based

water balance products. CEOP-AEGIS Deliverable Report De8.1, Ed. University of Strasbourg, France, ISSN

2118-7843: 34 p.


55

(8.2) Immerzeel, W., C.De Michele, N.Holzer and J.Colin (2012). Prototype of data collection, integration and

processing system to monitor the Plateau water balance. CEOP-AEGIS Deliverable Report De8.2, Ed. University

of Strasbourg, France, ISSN 2118-7843: 51 p.

(8.3) Liu, X., H. Zheng and W. Immerzeel (2013). Time series analysis of water balance data. CEOP-AEGIS


(8.4) COLIN, J., N.HOLZER, X.GUO. (2011). The CEOP-AEGIS Data Portal Software Requirement and

Specifications Document. CEOP-AEGIS Deliverable Report De8.4, Ed. University of Strasbourg, France, ISSN

2118-7843: 38 p.

(9.1) Sobrino, J.A., Y. Julien, G. Soria, J. C. Jimnez-Munoz, V. Hidalgo, B. Franch, C. Mattar, R. Oltra and J.

Cuenca. (2009). Vegetation dynamic maps over a long-term period.CEOP-AEGIS Deliverable Report De9.1, Ed.


(9.2) Jia, L., R. Sun, R. Jha, G. Hu, J.A. Sobrino, Y. Julien, N. Huang, Z. Niu. (2011). Maps of drought vulnerability

zones in the study area. CEOP-AEGIS Deliverable Report De9.2, Ed. University of Strasbourg, France, ISSN

2118-7843: 43 p.

(9.3-9.4) Jia, L., J. Zhou, G. Hu, W. Verhoef. (2011). Tools for time series analysis of vegetation response to

drought & Tools for drought prediction by analyzing vegetation response. CEOP-AEGIS Deliverable Report

De9.3-9.4, Ed. University of Strasbourg, France, ISSN 2118-7843: xx p.

(9.5) Pelgrum, H., A. Klaasse, S. Zwart, L. Jia, G. Hu and J. Zhou. (2011). Anomalies maps of rainfall, vegetation

response. CEOP-AEGIS Deliverable Report De9.5, Ed. University of Strasbourg, France, ISSN 2118-7843: 48 p.

(9.6) Pelgrum, H., A. Klaasse. (2011). Tools for evaluating drought impact on agriculture and forestry. CEOP-

AEGIS Deliverable Report De9.6, Ed. University of Strasbourg, France, ISSN 2118-7843: 27 p.

(9.7) Jia, L., L. Roupioz, G. Hu, J. Zhou (2012). Anomalies maps of net radiation, LST and FPAR. CEOP-AEGIS


(9.8) Jia, L., G. Hu, J. Zhou (2013). System applicable for drought monitoring and early warning in the pilot areas in

China and India - East-South Asia Drought Monitor ESADroM. CEOP-AEGIS Deliverable Report De9.8, Ed.


(10.1) Shang, H., H. Pelgrum, A. Klaasse, M.Menenti. (2009). Algorithm theoretical basis document for surface

wetness indicators retrieved from passive and active microwave satellite data. CEOP-AEGIS Deliverable Report

De10.1, Ed. University of Strasbourg, France, ISSN 2118-7843: 23 p.

(10.2) Jha, R., Z. Liu. (2011). Real time flood forecast models based on stochastic time series, ANN and Fuzzy logic

for the study area. CEOP-AEGIS Deliverable Report De10.2, Ed. University of Strasbourg, France, ISSN 2118-

7843: 24 p.

(10.3) Jha, R., Z. Liu (2012). Flood inundation, flood hazard and flood risk zone maps based on the hydraulic

modelling and analysis of satellite data. CEOP-AEGIS Deliverable Report De10.3, Ed. University of Strasbourg,

France, ISSN 2118-7843: 28 p.

(10.4) Jha, R., Z. Liu. (2011). Real time forecasting of flood inundation, depth, areal extent and return period of

flooding. CEOP-AEGIS Deliverable Report De10.4, Ed. University of Strasbourg, France, ISSN 2118-7843: 29

p.

(11.1.1) Menenti, M., J.Colin, Z.Vekerdy. (2009). Capacity building. CEOP-AEGIS Deliverable Report De11.1.1,

Ed. University of Strasbourg, France, ISSN 2118-7843: 16 p.

(11.1.2) VEKERDY, Z., J. COLIN, Y. YAN, B. SU, L. WANG. (2011). Dissemination and Stakeholders Panel

second reporting period. CEOP-AEGIS Deliverable Report De11.1.2, Ed. University of Strasbourg, France,

ISSN 2118-7843: 52 p.

(11.2.2) VEKERDY, Z., R. JHA, J. COLIN, Y. YAN, B. SU (2012). Training Session Nr.2 Involving Major SE

Asia Hydrometeorological Organizations, NIT Rourkela, 16-20th April 2012. CEOP-AEGIS Deliverable Report

De11.2.2, Ed. University of Strasbourg, France, ISSN 2118-7843: 44 p.

(11.2.3) Yan, Y., Z. Vekerdy, J. Colin and B. Su (2013). CEOP-AEGIS 2013 Advanced 3rd Training and closing

workshop (WATGLOBS). CEOP-AEGIS Deliverable Report De11.2.3, Ed. University of Strasbourg, France,

ISSN 2118-7843: 28 p.

Other reports Foken, T. and H.Falke. (2010). Documentation and Instruction Manual for the Krypton Hygrometer Calibration

Instrument. Work Report of the University of Bayreuth, Dept. of Micrometeorology, ISSN 1614-8916, No. 42

Liang, J., Wang, J., Feng, K., Liu, C. (2010). Scattering simulation of snow grains. Dragon 2 programme Mid-Term

results 2008-2010. 17-21 May 2010, Guillin, China.


56

Liang, J., Liu, C., Wang, J., Li, H. (2010). Snow reflectance retrieval from Hyperion imagery. Dragon 2 programme

Mid-Term results 2008-2010. 17-21 May 2010, Guillin, China.

Mauder, M. and Foken, T. (2011). Documentation and Instruction Manual of the Eddy-Covariance Software

Package TK3. Work Report of the University of Bayreuth, Dept. of Micrometeorology, ISSN 1614-8916, No.

46.

MENENTI, M., L. JIA AND J. COLIN. (2009). A Prototype Observation System for the Qinghai-Tibet Plateau in

Southeast Asia, Global Energy and Water cycle EXperiment letter, Vol. 19, no. 3.

International conferences Colin, J., M. Menenti, R. Faivre, Q. Liu, X. Shen, X. Li. (2010). Turbulent flux partitioning from local to meso scale

over high elevation arid regions of China, ESA Living Planet Symposium, Bergen, Norway, pp. 1--5, European

Space Agency (Eds.), June 2010.

COLIN, J., M.MENENTI, L.JIA, Y.MA. (2010). TOWARDS AN INTEGRATED USER-FRIENDLY PORTAL

FOR SHARING CEOP-AEGIS DATASETS, 2nd International Workshop on Energy and Water Cycle over the

Tibetan Plateau and High-elevations, Lhasa, China, 19-21 July 2010.

D'Urso, G., S. Falanga Bolognesi, C. de Michele, W. W. Immerzeel, P. Droogers, L. Changming, and Z. Hongxing.

(2011). Preliminary validation of the Tibetan plateau water balance monitoring system: the Yellow River case-

study, 3rd CEOP-AEGIS Plenary Meeting, Strasbourg, France, June 2011.

Du J. P., Sun R. (2011) Estimation of evapotranspiration for ungauged areas using MODIS measurements and

GLDAS data. The 18th Biennial Conference of International Society for Ecological Modelling, Beijing,

September 20-23, 2011

Hua Li, Qinhuo Liu, Jinxiong Jiang, Heshun Wang and Lin Sun (2011).VALIDATION OF THE LAND SURFACE

TEMPERATURE DERIVED FROM HJ-1B/IRS DATA WITH GROUND MEASUREMENTS. IGARSS

2011:293-296.

Hua Li, Qinhuo Liu, Jinxiong Jiang, Heshun Wang, Qing Li, Lin Sun. (2011). LAND SURFACE EMISSIVITY

RETRIEVAL FROM HJ-1B SATELLITE DATA USING A COMBINED METHOD. IGARSS2011:301-304.

Jinxiong Jiang, Qinhuo Liu, Hua Li and Huaguo Huang. (2011). SPLIT-WINDOW METHOD FOR LAND

SURFACE TEMPERATURE ESTIMATION FROM FY-3A/VIRR DATA. IGARSS 2011:305-308.ma

Li, X., L. Zhang, L. Jiang, S. Zhao and T. Zhao. (2010). Simulation and measurement of relief effects on passive

microwave radiation. IGARSS, 3015-3018.

Malik, M.J., Van der Velde, R., Vekerdy, Z., Su, Z., and Salman, M.F. (2011) Semi-empirical approach for

estimating broadband albedo of snow, Remote Sensing of Environment, 115, pp. 2086-2095. (IF: 4.607)

MENENTI, M., J. COLIN AND L. JIA. (2009). A prototype observation system for water resources in the Qinghai -

Tibet Plateau. Paper EGU2009-10434 EGU General Assembly, Vienna April 19th - 24th 2009.

MENENTI, M., L. JIA AND J. COLIN. (2011). Assessing ET and water use: an overview, The Water Information

Research and Development Alliance (WIRADA) Science, 1 to 5 August 2011 Melbourne, Australia (accepted).

MENENTI, M., J. COLIN AND L. JIA. (2010). Observation and modeling of land surface state and convective

activity over the Qinghai - Tibet Plateau, The Forth International Workshop on Catchment-scale Hydrological

Modeling and Data Assimilation (CAHMDA-IV), Lhasa, China, 21-23 July 2010.

MENENTI, M., J. COLIN AND L. JIA. (2010). A protoype observation system for water resources in South - East

Asia: ground and space observations, 2nd International Workshop on Energy and Water Cycle over the Tibetan

Plateau and High-elevations, Lhasa, China, 19-21 July 2010.

MENENTI, M., J. COLIN AND L. JIA. (2011). CEOP-AEGIS: Towards an integrated water cycle monitoring

system for Asia, 5th GEO European Project Workshop (GEPW5), London, 8 & 9 February 2011.

MENENTI, M., J. COLIN AND L. JIA. (2010). Assessing vulnerability and adaptation‖, GEOSS support for the

IPCC assessments: a workshop on the data needs of the climate impacts, adaptation and vulnerability research

community, Geneva, Switzerland, 1-4 February 2011.

MENENTI, M., J. COLIN AND L. JIA. (2010). The Asian Regional Showcase, 4th EU GEO Project Workshop,

Athens, Greece, 29-30 April 2010.

MENENTI, M., J. COLIN AND L. JIA. (2009). A protoype observation system for water resources in South - East

Asia: ground and space observations. Earth Observation and Water Cycle Science workshop, ESA-ESRIN,

Frascati, Italie, 18-20 novembre 2009.

MENENTI, M., J. COLIN AND L. JIA. (2009). A prototype observation system of water resources in the Qinghai -

Tibet Plateau. Indo-UK Workshop Water Resources Management under Climate and Environment Change. IIT,

Roorkee, September 11th - 13th 2009.


57

MENENTI, M., J. COLIN AND L. JIA. (2009). Coordinated 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. 5th International Symposium on Tibetan Plateau and 24th Himalaya -

Karakorum Workshop, Beijing, August 11th - 14th.

MENENTI, M., J. COLIN AND L. JIA. (2009). A prototype observation system of water resources in the Qinghai -

Tibet Plateau. 33rd International Symposium on Remote Sensing of Environment, Stresa, May 4-8th 2009.

SU Tao, WANG Peng-xin. (2009). Retrieval of crop water stress factor using remotely sensed biomass products.

ISRSE 33rd International Symposium on Remote Sensing of Environment, Stresa, Italy, May 4-8, 2009,

electronic version.

WANG Peng-xin, SUN Wei. (2009). Quantification of the vegetation temperature condition index for operational

drought monitoring. ISRSE 33rd International Symposium on Remote Sensing of Environment, Stresa, Italy,

May 4-8, 2009, electronic version.

Xu Hongwei, Sun Rui, Du Junping. (2011). Estimation of evapotranspiration in Heihe River Basin with HJ-1A/B

data. The 5th Cross Strait Conference on Remote Sensing, 2011, Harbin, China.

Zhang, Z, G. Sun, L. Zhang, Z. Guo and W. Huang. (2010). Biomass retrieval based on uavsar polarimetric data.

IGARSS, 604-607.

Zhang, T, L. Zhang, L. Jiang and T. Zhao. (2010). Effects of spatial heterogeneity of bare soil on soil moisture

retrieval from passive microwave remote sensing. IGARSS, 3886-3889.

Zhao, T., L. Zhang, R. Bindlish, J. Shi, L. Jiang, Y. Li, S. Zhao, T. Zhang and X. Li. (2011). Estimating vegetation

water content during a growing season of cotton. IGARSS, 791-794.

Journal articles and book chapters Babel, W., Huneke, S., and Foken, T. (2011). A framework to utilize turbulent flux measurements for mesoscale

models and remote sensing applications. Hydrol. Earth Syst. Sci. Discuss., 8, 5165-5225, doi:10.5194/hessd-8-

5165-2011.

Caracciolo, C., Porcù, F. and F. Prodi. (2011). Drop Size Distribution over the Tibetan Plateau. Geophysical

Research Abstracts, Vol. 13, EGU2011-12732, 2011, EGU General Assembly 2011.

Chen, X. L., X. S. Shen and H. B. Chen. (2010). Analysis of the impact of land surface process on numerical

weather prediction of intensive summer rainfall over Huai-River in 2007, J. Tropical Meteorol. (in Chinese),

Vol.26, 667-679.

Chen, X., Y. Ma, H. Kelder, Z. Su, and K. Yang. (2011). On the behaviour of the tropopause folding events over the

Tibetan Plateau, Atmos. Chem. Phys., 11, 5113-5122, doi:10.5194/acp-11-5113-2011.

Colin, J., Faivre, R. (2010). Aerodynamic roughness length estimation from very high-resolution imaging LIDAR

observations over the Heihe basin in China. Hydrol. Earth Syst. Sci., 14, 2661-2669, doi:10.5194/hess-14-2661-

2010, 2010.

D'Urso G., Immerzeel W., De Michele C., Zheng H.X., Menenti M. (2010). Earth Observation integrated modeling

tool for the description of water balance and run-off production of Tibetan Plateau. IAHS Remote Sensing and

Hydrology 2010 Symposium, sept.2010, Jackson Hole, WY (USA), in press (Red Book Series)

Dente, L., Vekerdy, Z., Wen, J. Su,Z. (2011) Maqu network for validation of satellite-derived soil moisture

products. Int. J. Appl. Earth Observ. Geoinf., doi:10.1016/j.jag.2011.11.004.

Gain, A. K., W. W. Immerzeel, F. C. Sperna Weiland, and M. F. P. Bierkens. (2011). Impact of climate change on

the stream flow of the lower Brahmaputra: trends in high and low flows based on discharge-weighted ensemble

modelling, Hydrology and Earth System Sciences, 15(5), 1537-1545.

Gao T., S. Kang, Peter Krause, Lan Cuo, Santoch Nepal. (2012). A test of J2000 model in a glacierized catchment in

the centralTibetan Plateau. Environment Earth Sciences. Doi: 10.1007/s12665-011-1142-5.

HAN Ping, WANG Peng-xin, ZHANG Shu-yu, ZHU De-hai. (2010). Drought forecasting based on the remote

sensing data using ARIMA models. Mathematical and Computer Modelling, 2010, 51(11-12), 1398-1403.

He H. , Z. Hu, X. Xun et al. (2011). Study on potential evapotranspiration and wet-dry condition in the seasonal

frozen soil region of northern Tibetan Plateau. Sciences in Cold and Arid Regions, 3(2): 172-178.

He H., Z. Hu X. Xun et al. (2010). THE VARIATION CHARACTERISTICS OF RADIATION OF THE

WETLAND SURFACE IN THE NORTHERN TIBETAN PLATEAU. Acta Energiae Solaris Sinica (in

Chinese), 31(5):561-567

He H., Z. Hu, X. Xun et al. (2010). Analysis on Potential Evaptranspiration and Dry-wet Condition in The Seasonal

Frozen Soil Region of Northern Tibetan Plateau. Plateau Meteorology (in Chinese), ,29(1):10-16.

HOU Shan-shan, WANG Peng-xin, TIAN Miao. (2011). Application of phase space reconstruction and RBF neural

network model in drought forecasting (In Chinese). Agricultural Research in the Arid Areas, 29(1): 224-230.


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Huang, G.H., Liu, S.M., Liang, S.L. (2012). Estimation of net surface shortwave radiation from MODIS data.

International Journal of Remote Sensing. 33(3), 804-825.

Immerzeel, W. W., L. P. H. Beek, M. Konz, A.B. Shrestha, and M. F. P. Bierkens. (2011). Hydrological response to

climate change in a glacierized catchment in the Himalayas, Climatic Change, doi:10.1007/s10584-011-0143-4.

JIA, L. and M. MENENTI. (2010). Thermal infrared observations of heterogeneous soil-vegetation systems, Chapter

10 in 'Remote Sensing Optical Observations of Vegetation Properties', Editors: Maselli, F., Menenti, M., and

Brivio, A., Research Signpost, Kerala, India, pp. 227 - 274

Jia, L., G. Xi, S. Liu, C. Huang, Y. Yan, G. Liu. (2009). Regional estimation of Daily to Annual Regional

Evapotranspiration with MODIS data in the Yellow River Delta wetland,Hydrology and Earth System Sciences,

13, 1775-1787

Jia, L., H. Shang, G. Hu, and M. Menenti. (2011). Phenological response of vegetation to upstream river flow in the

Heihe River basin by time series analysis of MODIS data,Hydrology and Earth System Sciences, 15, 1047-1064,

2011

Jianguang Wen ; Qiang Liu ; Qinhuo Liu ; Qing Xiao ; Xiaowen Li. (2009). Scale effect and scale correction of

land- surface albedo in rugged terrain. International Journal of Remote Sensing Vol. 30, No. 20: 5397-5420

Julien, Y. and Sobrino, J. A. (2010). Comparison of cloud-reconstruction methods for time series of composite

NDVI data, Remote Sensing of Environment, 114 (2010) 618-625.

Kang, S.C., Yang, Y.P., Zhu, L.P., Ma, Y.M. (2011). Modern environmental processes and changes in the Nam Co

Basin, Tibetan Plateau. Beijing: China Meteorological Press, 418 p.

Li Yuan, Sun Rui, Liu Shaomin, Xu Ziwei, BAI Jie. (2010). A preliminary study of the validation of land surface

models with large aperture scintillometer data. Advances in Earth Science, 25(11): 1237-1247.

Li, H., Wang, J. (2011). Simulation of snow distribution and melt under cloudy conditions in an Alpine watershed.

Hydrol. Earth Syst. Sci., 15, 2195-2203, 2011, www.hydrol-earth-syst-sci.net/15/2195/2011/ doi:10.5194/hess-

15-2195-2011.

Li, X., L. Zhang, L. Jiang, S. Zhao and T. Zhao. (2011). Measurement and simulation of Hill-slope effects on

passive microwave remote sensing. Remote Sensing Technology and Application, 26:1, 74-81.(in Chinese)

Li, X., L. Zhang, L. Jiang, S. Zhao and T. Zhao. (2011). Simulation and measurement of relief effects on passive

microwave radiation. Journal of Remote Sensing, 15:1, 100-104.

Liu Xinsheng, Sun Rui, Wu Fang, Hu Bo, Wang Wen. (2010). Land-cover classification for Henan Province with

time-series MODIS EVI data. Transactions of the Chinese Society of Agricultural Engineering, 26(s1): 213-219.

Liu, S.M., Bai, J., Jia, Z.Z., Jia, L., Zhou, H.Z., Lu, L. (2010). Estimation of evapotranspiration in the Mu Us

Sandland of China. Hydrology and Earth System Sciences, 14, 573-584.

MA Quan, WANG Peng-xin, ZHAO Dong-ling, TIAN Miao. (2011). Analysis of scale-effect based on the drought

monitoring results of vegetation temperature condition index(In Chinese). Geography and Geo-Information

Science, 27(Suppl.): 43-46.

Ma, W., Ma, Y. and Su, Z. (2011). Feasibility of retrieving land surface heat fluxes from ASTER data using SEBS :

a case study from the Namco area of the Tibetan plateau. In:Arctic, antarctic and alpine research, 43(2), pp. 239-

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Ma, W., Ma, Y., Hu, Z., Su, Z., Wang, J., and Ishikawa, H. (2011). Estimating surface fluxes over middle and upper

streams of the Heihe River Basin with ASTER imagery,Hydrology and Earth System Sciences, 15, 1403-1413,

doi:10.5194/hess-15-1403-2011.

MA, Y. M., M. MENENTI, and R.A. FEDDES. (2010). Parameterization of Heat Fluxes at Heterogeneous Surfaces

by Integrating Satellite Measurements with Surface Layer and Atmospheric Boundary Layer Observations. Adv.

Atmos. Sci., 27(2), 328{336, doi: 10.1007/s00376-009-9024-4

Ma, Y., B.Wang, L.Zhong, W. Ma. (2011). The regional surface heating field over heterogeneous landscape of the

Tibetan Plateau by using the MODIS and in-situ data Advances in Atmospheric Sciences, 29(1):1-7.Doi:

10.1007/s00376-011-1008-5.

Ma, Y., L. Zhong, B. Wang, W. Ma, X. Chen, and M. Li. (2011). Determination of land surface heat fluxes over

heterogeneous landscape of the Tibetan Plateau by using the MODIS and in-situ data, Atmos. Chem. Phys., 11,

10461-10469, doi:10.5194/acp-11-10461-2011.

Ma, Y., M. Li, X. Chen, S. Wang, R.Wu, W. Ma, L. Zhong, B. Wang, C.Zhu, T. Yao. (2011). Third Pole

Environment (TPE) program: a new base for the study of atmosphere-land interaction over the heterogeneous

landscape of the Tibetan Plateau and surrounding areas, IAHS Publ. 343, 110-117.

Ma, Y., Y. Wang, L. Zhong, R.Wu, S.Wang, M.Li. (2011). The characteristics of atmospheric turbulence and

radiation energy transfer and the structure of atmospheric boundary layer over the northern slope area of

Himalaya, Journal of the Meteorological Society of Japan, 89A:345-353.


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Ma, Y., Y. Wang, L. Zhong, R.Wu, S.Wang, M.Li. (2011). The characteristics of atmospheric turbulence and

radiation energy transfer and the structure of atmospheric boundary layer over the northern slope area of

Himalaya, Journal of the Meteorological Society of Japan, 89A:345-353.

Mattar, C., Sobrino, J. A., Julien, Y. & Morales, L. (2010). Trends in column integrated water vapor over Europe

from 1973 to 2003, International Journal of Climatology, n/a. doi: 10.1002/joc.2186.

MENENTI, M., L. JIA, S. AZZALI, G. ROERINK, M. GONZALEZ-LOYARTE, S. LEGUIZAMON and W.

VERHOEF. (2010). Chapter 6 in 'Remote Sensing Optical Observations of Vegetation Properties', Editors:

Maselli, F., Menenti, M., and Brivio, A., Research Signpost, Kerala, India, pp. 131-163.

Pfab, D. (2011). Wasserdampfkonzentrationsmessungen in großen Höhen und bei niedrigen Temperaturen.

Mikrometeorologie, Universität Bayreuth, 2011.

Phan Hien, R.Lindenbergh, and M.Menenti. (2011). ICESat derived elevation changes of Tibetan lakes between

2003 and 2009. Int. J. Applied Earth Observ. Geoinform., Vol 1(11).

Sobrino, J. A., Jiménez-Muñoz, J. C., Zarco-Tejada, P. J., Sepulcre-Cantó, G., de Miguel, E., Sòria, G., Romaguera,

M., Julien, Y., Cuenca, J., Hidalgo, V., Franch, B., Mattar, C., Morales, L., Gillespie, A., Sabol, D., Balick, L.,

Su, Z., Jia, L., Gieske, A., Timmermans, W., Olioso, A., Nerry, F., Guanter, L., Moreno, J., & Shen, Q. (2009).

Thermal remote sensing from Airborne Hyperspectral Scanner data in the framework of the SPARC and

SEN2FLEX projects: an overview, Hydrology and Earth System Sciences, 13, 2031-2037, 2009.

Song, M., Y. Ma, Y. Zhang, M. Li, W. Ma, F. Sun. (2011). Climate change features along the Brahmaputra Valley

in the past 26 years and possible causes, Climatic Change, 106:649-660. 10.1007/s10584-010-9950-2.

Su, Z. J. Wen, L. Dente, R. van der Velde1, L.Wang, Y. Ma, K. Yang, and Z. Hu. (2011). The Tibetan Plateau

observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse

resolution satellite and model products. Hydrol. Earth Syst. Sci., 15, 2303-2316, 2011, www.hydrol-earth-syst-

sci.net/15/2303/2011/ doi:10.5194/hess-15-2303-2011

Sugimoto, S. and K. Ueno. (2010). Formation of mesoscale convective systems over the eastern Tibetan Plateau

affected by plateau scale heating contrasts, J. Geophys. Res., 115, D16105, doi:10.1029/2009JD013609.

Sun J., Z. Hu, X. Xun et al. (2011). Albedo Characteristics in Different Underlying Surface in Mid- and Upper-

reaches of HEIFE and Its Impact Factor. Plateau Meteorology (in Chinese), 30(3): 607-613.

Sun Liang, Sun Rui, Li Xiaowen, Chen Huailiang and Zhang Xuefen. (2011). Estimating evapotranspiration using

improved fractional vegetation cover and land surface temperature space. Journal of Resources and Ecology,

2011 2(3) 225-231.

TANG Yan, LIU Jun-ming, WANG Peng-xin, SU Tao. (2011). Algorithm for retrieving soil water change amount

based on remote sensing data (In Chinese). Journal of Computer Applications, 2011, 31(suppl. 1): 188-191.

Tian H., Wen, J., Jun, Shi, X. K., Wang, X., Liu, R., Zhang, J. H., Lv, S. N. (2011). Estimation of soilmoisture in

summer by activemicrowave remote sensing for the Maqu area at the upper reaches of the Yellow R

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TIAN Miao, WANG Peng-xin, SUN Wei. (2010). A review of retrieving of land surface parameters using the land

surface temperature-vegetation index feature space (In Chinese).Advance in Earth Science, 2010, 25(7): 698-705.

Ueno, K., S. Sugimoto, T. Koike, H. Tsutsui and X. Xu. (2011). Generation processes of mesoscale convective

systems following midlatitude troughs around the Sichuan Basin, J. Geophys. Res., 116, D02104,

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WANG Wei, LIU Xiang-ge, WANG Peng-xin, LIU Chun-hong. (2011). Application of 4DVAR and EnKF

approaches for assimilating vegetation temperature condition index (In Chinese).Transactions of the Chinese

Society of Agricultural Engineering, 2011, 27(12): 184-190.

WANG Yi-ting CHEN Xiu-wan BO Yan-chen. (2010). Monitoring Glacier Volume Change Based on Multi-Source

DEM and Multi-Temporal Remote Sensing Images--a Case Study in the Mount Naimona nyi Region on the

Tibetan Plateau. Journal of Glaciology and Geocryology, 32(1):126-132

Wang, J., Li, H., Hao, X. (2010). Responses of snowmelt runoff to climatic change in an inland river basin,

Northwestern China, over the past 50 years. Hydrol. Earth Syst. Sci., 14, 1979-1987, 2010, www.hydrol-earth-

syst-sci.net/14/1979/2010/ doi:10.5194/hess-14-1979-2010.

Wang, S. and Y. Ma. (2011). Characteristics of land-Atmosphere interaction parameters over the Tibetan Plateau.

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Xiao Zhiqiang, Jindi Wang, Shunlin Liang, et al., Variational retrieval of leaf area index from MODIS time series

data: Examples from the Heihe River Basin, North-west China,International Journal of Remote Sensing, 33(3):

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Xiao Zhiqiang, Liang Shunlin, Wang Jindi, and Jiang Bo. (2011). Real-time inversion of leaf area index from

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Xu, T.R., Liu, S.M., Liang, S.L., Qin, J. (2011). Improving predictions of water and heat fluxes by assimilating

MODIS land surface temperature products into Common Land Model.Journal of Hydrometeorology, 12:227-

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Xue H., X. Shen and Y. Su. (2011). Parameterization of Turbulent Orographic Form Drag and Implementation in

GRAPES (in Chinese). Journal of Applied Meteorological Sciences, Vol.22, No.2, 169-181.

Xun X., Z. Hu, J. Sun et al. (2011). A comparative Analysis of Height Field Variations over the Tibetan Plateau

Using ECMW and NCEP Reanalysis Data. Journal of Glaciology and Geocryology (in Chinese), 33(1): 80-87.

Yang J. and X. Shen. (2011). The Construction of SCM in GRAPES and Its Applications in Two Field Experiment

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Yang, K., J. Qin, X-F Guo, D-G Zhou, Y. Ma. (2009). Method development for estimating sensible heat flux over

the Tibetan Plateau from CMA data. Journal of Applied Meteorology and Climatology, 48(12), 2474-2486, DOI:

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Yang, K., T. Koike, I. Kaihotsu, J. Qin. (2009). Validation of a dual-pass microwave land data assimilation system

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You Q., S. Kang, G. Ren, K. Fraedrich, N. Pepin, Y. Yan, L. Ma. (2011). Changes of snow depth and snow day in

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Zhang, T., L. Zhang, L. Jiang, S. Zhao, T. Zhao and Y. Li. (2012). Effects of spatial distribution of soil parameters

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Zhong, L., Zhongbo Su, Y. Ma, Mhd. Suhyb Salama, and José A. Sobrino. (2011). Accelerated Changes of

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Zhou, D., R.Eigenmann, W.Babel, T.Foken, Y. Ma. (2011). The study of near-ground free convection conditions at

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