Assessment of the catchment-scale energy and water balance using fully coupled simulations and observations

1. A
synthesis
of
modeling
and
observa4onal
data
for
an
integrated
assessment
of
the
catchment-­‐scale
energy
and
water
cycle
Mauro
Sulis
Meteorological
Ins4tute,
University
of
Bonn
Workshop
on
Coupled
Hydrological
Modeling
Padova,
September
23-­‐24
2015

2. Collaborators
Prabhakar
Shrestha
(MIUB)
Sandra
Steinke
(Uni-­‐Köln)
Susanne
Crewell
(Uni-­‐Köln)
Clemens
Simmer
(MIUB)
Stefan
Kollet
(IBG3)

3. Introduc4on
The
hydrological
and
meteorological
community
have
recently
converged
toward
a
new
integrated
simula5on
paradigm.
Holis5c
and
physically-­‐based
view
of
the
energy,
water,
and
ma=er
cycle
across
a
range
of
spa5al
and
temporal
scales.
New
opportuni5es
and
grand
challenges:
Integrated
diagnosis
of
the
catchment-­‐scale
energy
and
water
cycle
using
fully-­‐coupled
simula5ons
and
observa5ons.
Mo#va#ons
of
the
work:
• Powerful
tools
to
test
scien5fic
hypothesis.
• Integrated
assessment
of
the
water
cycle
for
long-­‐term
climate
projec5ons
and
short-­‐
and
medium-­‐term
weather
forecasts.
• Improved
monitoring
networks
(e.g.,
mul5ple
co-­‐located
measurements)
that
cover
the
SVA
con5nuum.

4. Outline
• Study
area
• Observa4onal
dataset
• TerrSysMP
• Model
setup
• Results
• Conclusions

5. Study
area
North-­‐Rhine
Westphalia
(NRW)
domain
Land
use
classes:
Topography:
Al4tude
range:
15
–
700
m
• Cropland
(~34
%)
• Evergreen
forest
(~14
%)
• Deciduous
forest
(~17%)
• Grassland
(~25
%)

6.
• Study
area
• Observa4onal
dataset
• TerrSysMP
• Model
setup
• Results
• Conclusions

7. Observa4onal
dataset
–
descrip4on
1HD(CP)2
Observa4onal
Prototype
Experiment
(HOPE);2TERrestrial
ENvironmental
Observatories
(TERENO)
3Jülich
ObservatorY
for
Cloud
Evolu4on
(JOYCE);4Transregional
Collabora4ve
Research
Centre
–
32
(TR32)
Data
sources:
TERENO2,
JOYCE3,
Er`
Verband,
and
TR324
Time
period:
April
–
May
2013
HOPE1
campaign
Variables:
States,
fluxes,
and
diagnos5cs
across
the
subsurface,
land
surface,
and
atmosphere
compartments
of
the
terrestrial
system.
• Radia4on
balance
composites
(radiometers)
• Energy
fluxes
(eddy
covariance
measurements)
• Soil
moisture
(cosmic-­‐ray
probes)
• Precipita4on
(X-­‐band
composites)
• Boundary
layer
height
• Water
table
depth
• Humidity
and
temperature
profiles
(mul4ple
meas.)

8. Observa4onal
dataset
–
temporal
distribu4on
Average
data
coverage:
70%
56%
64%
70%
67%
67%
66%
67%
86%
76%
76%
Latent
heat
Sensible
heat
2m
humidty
Incoming
longwave
Emiged
longwave
Incoming
shortwave
Reflected
shortwave
2m
temperature
10m
u-­‐wind
10m
v-­‐wind

9. Observa4onal
dataset
–
spa4al
distribu4on

10.
• Study
area
• Observa4onal
dataset
• TerrSysMP
• Model
setup
• Results
• Conclusions

11. TerrSysMP
COSMO
Convec4on
permihng
configura4on
(COSMO-­‐DE)
(Baldauf
et
al.
2011).
CLM
Land
surface
scheme
(Oleson
et
al.
2008).
ParFlow
Integrated
surface-­‐subsurface
flow
model
with
terrain
following
coordinates
(Kollet
and
Maxwell,
2006;
Maxwell,
2012).
OASIS3
–
OASIS-­‐MCT
External
coupler
with
mul4ple
executable
approach
(Valcke
2013).
Model
developments,
improvements,
and
applicaLons:
Shrestha
et
al.,
2014
MWR;
Gasper
et
al.,
2014
GMD;
Sulis
et
al.,
2015
JHM;
Rahman
et
al.,
2015
AWR
Shrestha
et
al.,
2014
MWR

12.
• Study
area
• Observa4onal
dataset
• TerrSysMP
• Model
setup
• Results
• Conclusions

13. Model
setup
SpaLal
resoluLon:
• COSMO:
ΔX
=
ΔY
=
1000
m
• ParFlow-­‐CLM:
ΔX
=
ΔY
=
500m
Temporal
resoluLon:
• COSMO:
Δt
=
10
sec
• ParFlow-­‐CLM:
Δt
=
900
sec
Coupling
frequencies:
• COSMO-­‐CLM:
CPL1
=
900
sec
• CLM-­‐ParFlow:
CPL2
=
900
sec
Boundary
condiLons:
• COSMO:
Hourly
reanalysis
COSMO-­‐DE
forcing
• ParFlow:
No-­‐flux
condi4ons

14.
• Study
area
• Observa4onal
dataset
• TerrSysMP
• Model
setup
• Results
• Conclusions

15. Results
–
Radia4on
balance
*bias
=
(Xsim
—
Xobs)
/
Xobs
Systema4c
overes4ma4on
of
the
net
shortwave
radia4on
by
TerrSysMP.
Beger
match
of
the
net
longwave,with
the
excep4on
of
Wuestbach.

16. Results
–
Radia4on
balance
Analysis
of
the
shortwave
radia5on
composites:
screening
for
“clear-­‐sky”
days
Overes4ma4on
of
incoming
shortwave:
cloudiness
effect.
Underes4ma4on
of
reflected
shortwave:
albedo
parameterizaLon.

17. Results
–
Radia4on
balance
Analysis
of
the
longwave
radia5on
composites:
screening
for
“clear-­‐sky”
days
Underes4ma4on
of
incoming
longwave:
liquid
water
path.
Good
agreement
in
the
emiged
longwave:
land
surface
temperature.

18. Results
–
Atmospheric
states
Analysis
of
the
integrated
water
vapor
(IWV):
Slight
underes4ma4on
of
the
simulated
IWV,
especially
with
respect
to
MWR,
and
late
in
the
a`ernoon.
TerrSysMP
response
is
consistent
with
COSMO-­‐DE
lateral
BCs.

19. Results
–
Energy
fluxes
TerrSysMP
overesLmates
H,
larger
Bowen
ra4os
for
most
of
the
sta4ons.

20. Results
–
Land
surface
states
Soil
moisture
dynamics
:
Soil
porosity
Underes5ma5on
of
precipita5on

21. Results
–
Land
surface
states
Soil
moisture
dynamics
:

22.
• Study
area
• Observa4onal
dataset
• TerrSysMP
• Model
setup
• Results
• Conclusions

23. Conclusions
• Need
of
an
accurate
assessment
of
the
radia4on
balance.
• Dras4c
influence
of
local
features
in
the
soil
moisture
dynamics
and
par44oning
of
land
surface
energy
fluxes.
• Soil
moisture
dynamics
generally
well
reproduced.
• Es4mate
the
integrated
water
balance.
• Perform
ensemble
simula4ons
(e.g.,
COSMO-­‐DE-­‐EPS).
• Extend
the
simula4on
to
longer
4me
periods.
Preliminary
results:
Next
steps:
• Coherence
in
observa4ons
and
modeling
results.

24. Acknowledgments
Alexander
Graf
and
Marius
Schmidt
(IBG3-­‐FZJ)
Roland
Baatz
and
Heye
Bogena
(IBG3-­‐FZJ)
Malte
Diederich
(MIUB)
Stefan
Simon
(Er`
Verband)
Jan
Schween
(Uni-­‐Köln)
Sidney
Marschollek
(MIUB)