Alfonso Senatore

Towards fully coupled atmosphere-hydrology model systems: long-range simulation
in Southern Italy A. Senatore, University of Calabria
Towards fully coupled atmosphere-hydrology
model systems: long-range simulation in Southern
Italy with the WRF-Hydro modeling system
Alfonso Senatore
• CeSMMA - Centro Studi per il Monitoraggio e la Modellazione Ambientale
• Dept. of Environmental and Chemical Engineering, University of Calabria
Padua, September 23, 2015

Objectives / Outline
1.  Reliability
of
stand-‐alone
WRF-‐Hydro
hydrological

model
in
a
Mediterranean
catchment
(comparison
of

observed
and
simulated
streamﬂow)

2. Parameterization
and
evaluation
of
stand-‐alone
WRF

mesoscale
model,
with
particular
reference
to

precipitation

3. Comparison
of
stand-‐alone
WRF
modeling
system
with

fully
coupled
WRF/WRF-‐Hydro
modeling
system

—  Evaluating
potential
of
fully
coupled
modeling,
both
for

hydrometeorological
forecasts
(short-‐medium
range)

and
hydrological
impacts
due
to
climate
change

(long-‐range)

Scientific motivation

Atlantic
weather fronts
~
2000
mm

~
500
mm

~
35
km

Study area
Crotone

Soverato

Vibo

Sybaris

5
hours

160
mm

Study area
Crotone

Soverato

Vibo

Sybaris

Corigliano/
Rossano

12.08.2015

Platì

106
mm

Reggio
C.

province

09.09.2015

San
Luca

115
mm

Sant’Agata

113
mm

“Crati
@
S.
Soﬁa”
gauging

station

1281
km2

250
m
horizontal
resolution

Hmax

=
1856
m

Hmean
=
672
m

Hmin
=
49
m

mean
precipitation
1200
mm

mean
temperature
11.9
°C

Study area (stand-alone WRF-Hydro)
45
rain
gauges

(10)

35
thermometers

(11)

11

radiometers

(3)

12
hygrometers

(5)

8

anemometers

(2)

6

barometers

(2)

Longwave
radiation
à
GLDAS

—  Previous
studies:
one-‐way

coupling
with
RCMs

Study area
HIRHAM

res.
0.11°

A2
scenario

RegCM

res.
20
km

A2
scenario

COSMO-‐CLM

res.
0.165°

A1B
scenario

T

HIRHAM
+3.9
°C

RegCM
+3.9
°C

CLM
+3.5
°C

P
HIRHAM
-‐9%

RegCM
-‐20%

CLM
-‐12%

2070–2099
vs.
1961–1990

Groundwater

storage

-‐11.6%

-‐6.5%

-‐10.7%

Senatore
et
al.,
JoH,
2011
–
IPCC
AR5

In-‐STRHyM
hydrological
model

1
km
res.

daily
time
step

WRF-Hydro
Basic
concepts

•  Linking
the
column
structure
of
land
surface
models
with
the
‘distributed’

structure
of
hydrological
models

Credits:
David
J.
Gochis,
NCAR

Surface
routing

• Pixel-‐to-‐pixel
routing

•Steepest
descent
or
2d

•Diﬀusive
wave/backwater

permitting

•Explicit
solution

•  Flexible
coupling
architecture
designed
to
be
extensible
to
new

hydrological
parameterizations

Subsurface
routing

• Quasi
steady-‐state,

Boussinesq
flow
model

•Exfiltration
from
fully-‐
saturated
soil
columns

•Anisotropy
in
vertical
and

horizontal
Ksat

•No
‘perched’
flow

•Soil
depth
is
uniform

• Critical
initialization
value:

water
table
depth

WRF-Hydro
Credits:
David
J.
Gochis,
NCAR

Basic
concepts

•  Linking
the
column
structure
of
land
surface
models
with
the
‘distributed’

structure
of
hydrological
models

•  Flexible
coupling
architecture
designed
to
be
extensible
to
new

hydrological
parameterizations

WRF-Hydro
Credits:
David
J.
Gochis,
NCAR

Channel
routing

• Solution
Methods:

–Gridded:
1-‐d
diffusive

wave:
fully-‐unsteady,

explicit,
finite-‐difference

–Reach:
Muskingam,

Muskingam-‐Cunge

• A
priori
function
of

Strahler
order

• Trapezoidal
channel

(bottom
width,
side
slope)

Basic
concepts

•  Linking
the
column
structure
of
land
surface
models
with
the
‘distributed’

structure
of
hydrological
models

•  Flexible
coupling
architecture
designed
to
be
extensible
to
new

hydrological
parameterizations

Methodology
Stand-‐alone
WRF-‐HYDRO
with
observed
forcing

Oct,1st

2002

Nov,1st

Dec,1st

Feb,1st

Mar,1st

Apr,1st

May,1st

Jun,1st

Jul,1st

Aug,1st

Sep,1st

Jan,1st

2003

Oct,1st

2003

Jan,
6

WRF-‐only

Fully-‐coupled
WRF-‐HYDRO

Oct,1st
2005

Oct,1st
2005

Stand-alone WRF-Hydro
Noah
LSM

2.5
km
res

à
250
m

à
2.5
km

—  Calibration
procedure

www.pesthomepage.org

Minimization
of
the
objective
function
Φ,

given
by
the
sum
of
squared
deviations

between
model-‐generated
observations
and

experimental
observations,
by
means
of
the

Gauss-‐Marquardt-‐Levenberg
method
(non-‐
linear
estimation
technique)

Hourly
streamﬂow
—  Experimental
observations?

—  Parameters
involved
in
the
calibration
process

Several
(dozens!)
preliminary
simulations
in
order
to
understand

sensitivity
of
the
model
to
single
parameters
and
stepwise

approach
for
the
calibration
of
the
parameters
controlling
water

volume
(1st
step)
and
hydrograph
shape
(2nd
step)

• 
4
Manning
roughness
coefficients
(CHANPARM.TBL)

• 
Bucket
model
exponent
(GWBUCKPARM.TBL
)

• 
Slope
coefficient
modifying
the
drainage
out
the
bottom
of
the
last
soil
layer

(GENPARM.TBL)

• 
Noah
surface
runoff
parameter
retdt
(GENPARM.TBL)

• 
Accompanying
parameter

refdk
(corresponding
to
Ksat
for
silty
clay
loam)

(GENPARM.TBL
–
SOILPARM.TBL)

• 
Ksat
for
sandy
loam
(most
diffused
texture
in
the
basin)
(SOILPARM.TBL)

• 
depth
of
the
bottom
of
the
first
soil
layer
(namelist.hrldas,
hydro.namelist)

• 
gridded
values
of
the
overland
flow
roughness
scaling
factor
(OVROUGHRTFAC)

—  Calibration
results

Calibration

Validation

Calibration
N.S.
=
0.93
Validation
N.S.
=
0.72
Overall
N.S.
=
0.80

Issues:

1.  Recession
curves

2.  GW
bucket
model

3.  Reservoir
management
rules

Study area (WRF, WRF-Hydro)
Large
domain

12.5
km
hor.
res.

(172
x
154
grid
points)

Small
domain

2.5
km
hor.
res.

(95
x
90
grid
points)

One-‐way
nesting

Era-‐Interim
reanalysis

—  WRF
conﬁgurations

WRF parameterization
Acronyms
M2P1
M2P2
M6P1
M6P2
M8P1
M8P2
M2P2C3
M6P2C3

Microphysics
2
2
6
6
8
8
2
6

PBL
1
2
1
2
1
2
2
2

Cum.
param.
1
1
1
1
1
1
3
3

• 
Microphysics:
2
-‐
Purdue
Lin;
6
-‐
WSM6;
8
-‐
Thompson
graupel

• 
PBL:
1
-‐
YSU
scheme;
2
–
MYJ

• 
Cumulus
parameterization:
1
-‐
Kain-‐Fritsch;
3
-‐
Grell-‐Devenyi
ensemble

• 
Rapid
Radiative
Transfer
Model
(RRTM)
for
longwave
radiation

• 
Dudhia
scheme
for
shortwave
radiation

• 
Uniﬁed
Noah
Land-‐Surface
Model

Senatore
et
al.,
JoHM,
Dec
2014

“Wet
period”
Nov
2008
–
Jan
2009

Obs
M2P1
M2P2
M6P1
M6P2
M8P1
M8P2
M2P2C3
M6P2C3

Mean (mm)
743.5
791.6
738.1
745.9
710.4
718.7
585.5
699.6
660.8

St.Dev. (mm)
257.8
256.1
243.9
239.7
239.6
246.6
211.2
257.0
258.3

precipitation

“Dry
period”
Oct
2001
–
Sep
2002

Obs
M2P1
M2P2
M6P1
M6P2
M8P1
M8P2

Mean (mm)
832.7
972.8
859.0
975.2
855.2
799.8
687.1

St.Dev. (mm)
296.6
309.9
269.8
370.5
312.5
295.8
222.7

precipitation

—  Maps
of
simulated
precipitation
ﬁelds

Dry
period
Wet
period

—  Sensitivity
to
SST
and
coastal
SST

Tracks
of
pressure
minima

from
8
Jan

2009,
12:00
to
9
Jan
2009,
21:00,
with
SST,

SST-‐0.5
°C
and
SST+0.5
°C

Daily
precipitation
patterns
on
9
Jan

2009

7
Nov
2014

3.

Senatore
et
al.,
Journal
of
Advances
in
Modeling
Earth
Systems
-‐
AGU,
2015

Forcing
data

from
WRF

(P,
T...)

WRF-Hydro (with NOAH LSM)
SH,
LH
from

Hydro+LSM

SH,
LH

from

WRF

Surface
+
Hydrological
model
SH

LH

P

Results: preliminary considerations
One-‐way
coupling

vs.

Fully-‐coupling

Soil
moisture

evolution

SM

SM

—  What
should
we
expect?

Results: preliminary considerations
WRF

vs.

WRF-‐Hydro

Soil
layers

Re-‐inﬁltration

more
soil

moisture

more

runoﬀ

generation

—  Averaged
precipitation
maps
(Nov
2002
–
Sep
2005)

Results - precipitation
WRF-‐only
WRF-‐Hydro

2118
mm
2110
mm
3502
mm
3422
mm

WRF-‐only
-‐
WRF-‐Hydro

—  Performance
indices
of
WRF-‐only
and
fully-‐coupled

WRF/WRF-‐Hydro
modeled
precipitation
ﬁelds

Bias
lower
with

WRF-‐Hydro
in

25

stations,
with

WRF
in
13
stations

RMSE
lower
with

WRF-‐Hydro
in

22

stations,
with

WRF
in
16
stations

—  Averaged
precipitation
evolution
in
the
Crati
catchment

3319

3207

3434

Absolute
diﬀerences
with
obs
daily
precipitation
>
10
mm

in:

• 
7.3%
of
cases
with
WRF

• 
6.9%
of
cases
with
WRF-‐Hydro

—  Days
with
absolute
precipitation
diﬀerences
between
WRF-‐only

and
fully-‐coupled
WRF/WRF-‐Hydro
higher
than
10
mm

Day

Observed

(mm)

WRF

(mm)

WRF-‐Hydro

(mm)

Diﬀerences

WRF
-‐
WRF-‐Hydro

15/11/2004
24.6
29.1
45.5
-‐16.4

05/06/2004
5.9
14.0
27.4
-‐13.4

24/12/2003
4.8
38.5
23.9
14.6

26/01/2003
6.5
40.4
24.9
15.5

10/12/2002
5.5
17.4
0.5
16.9

14/11/2003
0.0
74.5
45.8
28.7

26/07/2004
4.8
100.2
27.9
72.3

—  Jul
26,
2004

WRF
WRF-‐Hydro

Western

station

Eastern

station

—  Jul
26,
2004

IWV
(kgm-‐2),
sea
level
pressure
(contours;
hPa),
and
10-‐m
winds
(barbs;
ms-‐1)
on
26
Jul
2004,
0900

UTC
with
a)
WRF-‐only
and
b)
fully-‐coupled
WRF/WRF-‐Hydro
simulations.
c)
Ts
diﬀerences
in
the

skin
temperature
for
the
two
models
(WRF-‐only
minus
fully-‐coupled
WRF/WRF-‐Hydro)
at
the
same

date
and
time

Results —  Surface
runoﬀ
(a)
and
deep
drainage
(b)

1068
mm

641
mm

893
mm

723
mm

—  Soil
moisture
Results
ZSOIL(1)

0-‐0.05
m

ZSOIL(4)

0.7-‐1.5
m

Results —  Soil
moisture

1st
layer
-‐
Aug
31,
2003,
13:00

WRF-‐only
WRF-‐Hydro

Shannon
enthropy
(as
a
measure
of
spatial
variability):

• 
WRF
=
0.70

• 
WRF-‐Hydro
=
0.77

Mean
SMC
=
0.126
Mean
SMC
=
0.143

moisture

1st
layer
-‐
Feb
20,
2004,
13:00

WRF-‐only
WRF-‐Hydro

Mean
SMC
=
0.265
Mean
SMC
=
0.265

Shannon
enthropy
(as
a
measure
of
spatial
variability):

• 
WRF
=
0.65

• 
WRF-‐Hydro
=
0.67

Results —  Heat
ﬂuxes

H

LE

Results —  Heat
ﬂuxes

Dec
2002

Aug
2005

DJF
MAM
JJA
SON

Ts

WRF
(K)
286.5
277.1
284.5
296.6
288.3

WRF-‐Hydro
(K)
286.4
277.1
284.4
296.4
288.2

H

WRF
(W
m-‐2)
49.6
-‐1.0
50.6
102.0
43.6

WRF-‐Hydro
(W
m-‐2)
46.7
-‐1.2
49.2
95.5
40.0

λE

WRF
(W
m-‐2)
49.8
22.0
72.9
70.9
24.0

WRF-‐Hydro
(W
m-‐2)
53.6
23.3
74.8
78.6
28.4

Results —  LE
-‐
Aug
31,
2003,
13:00

WRF-‐only
WRF-‐Hydro

Shannon
enthropy
(as
a
measure
of
spatial
variability):

• 
WRF
=
0.83

• 
WRF-‐Hydro
=
0.91

Results
— Soil
moisture
and
ﬂuxes
validation?

Bonis

FLUXNET

station
Soil
moisture
Sensible
heat
ﬂux

moisture
feedback
on
precipitation

Results —  Models
vs.
MODIS
LST
–
Aug
31,
2003

WRF
WRF-‐H

MODIS

Inland
cells

WRF
WRF-‐H
MODIS

µ
307.3
306.4
306.1

c.v.
0.0120
0.0124
0.0144

E
0.69
0.72
0.77

Results
—  Streamﬂow

Total
volumes
at
the
outlet:

1426×106m3

1334×106m3

Observed

WRF-‐Hydro

Ratio
to
precipitation:

0.36

0.34

Nash-‐Sutcliﬀe

0.27

Conclusions (1/2)
— Reliability
of
stand-‐alone
WRF-‐Hydro
hydrological

model
in
a
Mediterranean
catchment

— WRF
vs.
fully-‐coupled
WRF-‐Hydro
à
differences
due
to

soil
water
lateral
redistribution
and
re-‐infiltration:

—  Largest
differences
for
surface
runoff
and
deep
drainage

—  Soil
moisture
and
heat
fluxes
differences
(summertime
λE
8

Wm-‐2)

—  Slightly
better
performance
for
precipitation

Conclusions (2/2)
— An
answer
not
only
to
the
question
“if”,
but
also
“to

what
extent”
does
the
fully
coupled
modeling
alter

mesoscale
model
performance
in
terms
of
land
surface

variables
and
precipitation,
at
least
in
the
analyzed

region

— More
evident
improvements
for
continental
interior

regions
(no
ﬂat
areas
/
not
too
humid
climate)?

— Results
achieved
mainly
relevant
to
long-‐range

simulations
(short-‐term
under
investigation…)

Acknowledgements: G. Mendicino (Unical),
D.J. Gochis, Wei Yu, D.N. Yates (NCAR), H.
Kunstmann, B. Fersch, T. Rummler (IMK-
IFU)

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