1. EU
Project
CEOP-­‐AEGIS
Collabora(ve
Project
/
Small
or
medium-­‐scale
focused
research
project
–
Specific
Interna(onal
Co-­‐opera(on
Ac(on
Coordinated
Asia-­‐European
long-­‐term
Observing
system
of
Qinghai– ENV.2007.4.1.4.2
Improving
observing
systems
for
Tibet
Plateau
hydro-­‐metOeorological
processes
and
the
Asian-­‐ water
resource
management
Period:
May
2008
–
May
2012
monsoon
systEm
with
Ground
satellite
Image
data
and
numerical
Simula(ons
An
interna(onal
coopera(on
project
between
Europe
and
Asia
to
improve
knowledge
on
hydrology
and
meteorology
of
the
Tibetan
Plateau
and
its
role
in
climate,
monsoon
and
extreme
meteorological
events
 To
construct
out
of
exis(ng
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
 To
monitor
the
evolu(on
of
snow,
vegeta(on
cover,
surface
wetness
and
surface
fluxes
and
analyze
the
linkage
with
convec(ve
ac(vity,
(extreme)
precipita(on
events
and
the
Asian
Monsoon.
19
beneficiaries
from
8
european
and
asian
countries
www.ceop-­‐aegis.org

2. Contributions to GEO WP 2012 - 2015
• GEO
Water
Tasks:
 Data
integra(on
using
hydrological
and
atmospheric
models
 Use
of
data
and
informa(on
systems
on
water
resources
to
assess
the
resilience
and
vulnerability
of
large
watersheds
towards
adapta(on
to
climate
variability
and
change
 Virtual
constella(on
terrestrial
water
cycle

3. Climate Change Adaptation
• hydrological
observaIons,
analyses
and
predicIons
• miIgate
the
impacts
of
climate
change
• remove
the
underlying
causes
of
vulnerability
to
climate
change
• Themes:
– Short-­‐
and
long-­‐term
hydrological
variability
and
climate
change
– Resilience
versus
vulnerability
of
hydrologic
systems
– Resilience
of
hydrological
systems
to
climate
variability
– IdenIfying
and
removing
the
causes
of
the
vulnerability
of
hydrological
systems
to
climate
change
– ReconstrucIon
of
adaptaIon
mechanisms
to
hydrological
variability
in
the
recent
past
• Need
to
integrate
very
diverse
data
and
models
river-­‐wise