Transcript Document

Impact of climate change on Latvian water
environment
WP1: Impact of the climate change on runoff,
nutrient fluxes and regime of Gulf of Riga
Uldis Bethers
Laboratory for mathematical modelling of environmental and
technological processes
Faculty of Physics and mathematics,
University of Latvia
WP1. GOAL
Modelling of several scenarious of the change of water environment using the
existing climate change scenarious for Baltic Sea region
WP1. TASKS
WP1a. Evaluate and adapt the results from the regional climate models, and
design the series of data which form the state of the water objects. Scenarios
WP1b. Modeling of surface water and nutrients runoff for Latvia. Preparation of
data series of river runoff for climate change scenarios
Calculation of data series of nutrient runoff to the Gulf of Riga
WP1c. Adapt 3D sea state models to produce the data series for the forecast of
biogeochemical processes and sea ecosystem evolution.
Oceanographic modelling
WP1d. Provide modelling and data analysis support for other WPs. Support
Climate change
scenarios (IPCC)
Information flows
Global circulation
models
Regional climate
models
River runoff scenarios
Nutrient runoff scenarios
Climate change
scenarios adapted
for Latvia
Sea state scenarios
(Gulf of Riga)
Impact assessment (on Latvian water environment)
Climate scenarios 1A
Methodics for measurement and
comparison of RCM skill for
control period
Double downscaling: bias
correction (statistical downscaling
via histogram equalisation) of
dynamically (via RCM) downscaled
GCM data – T, p, r
Histogram equalisation for moving
time window [instead of daily or
monthly or seasonal equalisation]
21 model (PRUDENCE)
118 observation stations
and selected RCM mesh
Daily meteorological data
(temperature,
precipitation, wind,
humidity, cloudiness)
900
Daily data series
for Latvia –
contemporary climate, climate
850
change scenarios
A2
B2
Nokrišņi
800
REF
750
700
MODA2
MODB2
650
MODREF
OBS
600
4
5
6
7
8
Temperatūra
9
10
11
Insight : T-p diagram for Dobele, contemporary climate and A2 scenario
3
J ūl
Rainy, long
autumn
Pleasant
Sep/Oct
Aug
Dec
J ūn
Aug
2
J an
O kt
Winter
only
in Feb
O kt
Summer 2
S ep
months
longer, hot,
Mūs dienu klimats
dry
F eb
Dec
May
Apr
J an
Mar
F eb
Spring 2
weeks earlier
Mar
1.5
1
S cenārijs A2
0.5
-10
-5
0
5
10
T emperatūra, deg C
15
20
25
Nokriš ņi, mm/d
S ep
Nov
2.5
J ūl
Dobele: comparison scenarios vs. observations
1.80
1.80
1.80
A2
A2
A2
1.75
1.75
1.75
BBB222
1.70
1.70
1.70
p,
p,mm/d
mm/d
p,
mm/d
Novērotās klimata
iz maiņas
1.65
1.65
1.65
1.60
1.60
1.60
1961-1990
1961-1990RR
RKKKMM
M
1978-2007
1961-1990
1978-2007
1.55
1.55
1.55
1961-1990
novērojumi
1961-1990novērojumi
novērojumi
1961-1990
1.50
1.50
1.50
555
666
777
888
deg
degCCC
TTT, ,,deg
999
10
10
10
11
11
11
Noteces modelēšana 1B
• Hidroloģisko modeļu ansambļa pieeja 2008
• Noteces kalibrācija / verifikācija ūdensobjektiem
• Upju noteces scenāriji
Atziņa 2008 – upju notece RJL sateces baseinā samazināsies
vismaz par 5-10%
• Reģionālā upju noteces izmaiņu analīze Latvijai 2009
• UBA noteces scenāriji 2009
• Biogēnu noteces scenāriji 2009 (paldies Bārbelei )
Atziņa – hidroloģiskā režīma daudzveidība Latvijā
samazināsies
NOVITĀTE PASAULĒ
“Double ensemble forecast: ensemble of RCM vs.
ensemble of hydrological models”
River runoff 1B
Double ensemble approach
Regional climate model
Regional climate model
Regional climate model
River run-off
Meteorological forcing
Hydrological model
Bias correction
(independent from RCM)
Modified
meteorological
forcing
Hydrological model
(independent from RCM)
Hydrological model
(independent from RCM)
Impact assessment by RCM ensemble (Bērze)
Increase of maximum monthly Q, %
80%
60%
40%
20%
0%
-25%
0%
25%
50%
75%
-20%
-40%
Uncertainty prevails
-60%
Increase of mean annual Q, %
100%
125%
Impact assessment by hydrological model ensemble
Increase of maximum monthly Q, %
25%
20%
Uncertainty remains but is decreased
15%
10%
5%
0%
-25%
-20%
-15%
-10%
-5% -5% 0%
-10%
Decrease
5%
10%
15%
of both annual
-15%
run-off and its maximum
monthly value expected
-20%
-25%
Increase of mean annual Q, %
Seasonal analysis by hydrological model ensemble
Mean monthly discharge, m3/s
12
Spring snow-melt flood significantly decreases
10
8
6
BASIN-CTL
SHE-CTL
FIBASIN-CTL
BASIN-A2
SHE-A2
FIBASIN-A2
Summer low flow period longer
and better pronounced
4
2
Autumn rainfall period
0extends into winter. Winter
low
1 flow disappears
3
5
7
Month
9
11
13
Regional analysis: data series for RBD (MIKE Basin)
40
40
Salaca-CTL
Mean monthly runoff, mm
Abava-A2
30
20
Salaca-A2
30
20
10
10
0
0
1
1
3
5
7
9
11
3
5
13
7
9
11
13
Month
Month
40
40
Mean monthly runoff, mm
Dubna-CTL
Bērze-CTL
Mean monthly runoff, mm
Mean monthly runoff, mm
Abava-CTL
Bērze-A2
30
20
Dubna-A2
30
20
10
10
0
1
0
1
3
5
7
Month
9
11
13
3
5
7
Month
9
11
13
Regional analysis (MIKE BASIN)
Maximum monthly runoff, mm
45
W and N regions become similar to
each other and contemporary E region
35
25
C and E regions get closer
15
140
180
220
Abava-CTL
Abava-A2
Bērze-CTL
Bērze-A2
Salaca-CTL
Salaca-A2
Dubna-CTL
Dubna-A2
260
Mean annual runoff, mm
300
Nutrient run-off 1B*
River run-off was calculated for
control period and A2 scenario
by MIKE BASIN hydrological
model with daily time-step.
Model was set-up for the
drainage basin of the Gulf of
Riga, dividing it into 42
subbasins.
Yearly average nutrient loads are
assumed to remain the same in the A2
scenario, while their seasonal
distribution have been changed.
Loads of Norg, N-NH4, N-NO3, Porg,
P-PO4 with monthly time-step are used
as the input for the nutrient model of the
Gulf of Riga.
25000
CTL
A2
Nitrogen load, tonne/month
20000
15000
10000
5000
0
Jan
Feb
Mar
Apr
Mai
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Sea state modeling 1C
ORIGINAL PLAN – 3D climatic
modeling failed
Gulf of Riga: vertical temperature
distribution
General Ocean Turbulence Model (GOTM)
Coefficients of second order model: Cheng
(2002)
Dynamic equation (k-ε style) for TKE
Dynamic dissipation rate equation
Model forcing
Climate data from PRUDENCE. Control: 1961-1990, Scenario A2: 2070-2100
Ins titute
Model
Driving data
Ac ronym
E xperiment
S MHI
R C AO high res .
HadAM3H A2
HC C T L _22
control
S MHI
R C AO high res .
HadAM3H A2
HC A2_22
s cenario
Extra downscaling of RCM data (bias correction via histogram equalisation):
relative humidity (used variable td2m)
air temperature (used variable t2m)
Original RCM data:
sea level pressure (used variable MSLP)
cloudiness (used variable clcov)
wind speed (used variable w10m)
wind direction (used variable w10dir)
Calculations made for Gulf of Riga (50 m), 30 year
period, daily output data – water temperature
Physical model results – I
(mean temperature distribution over depth)
Surface T increase close to air T increase
Depth, m
0
-5
Water 1961-1990
-10
Water 2071-2100
-15
Air 1961-1990
-20
Air 2071-2100
-25
T increase by 1,5 (bottom)
to 3 (surface) degrees
-30
-35
-40
-45
-50
0
1
2
3
4
5
6
7
Temperature, degC
8
9
10
11
12
Physical model results – II
(mean daily pycnocline depth and its variation)
0
1961-1990
2071-2100
-10
... stratification
lasts longer
Depth, m
-20
-30
-40
-50
-60
Jan
Feb
Pycnocline
Mar
Apr earlier...
Mai
develops
Jūn
Jūl
Aug
Sep
Okt
Nov
Dec
Physical model results – III
(mean time-depth plots of temperature)
Contemporary
climate
Climate change
scanario A2