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
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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.
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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 modellingWP1d. Provide modelling and data analysis support for other WPs. Support
Climate change scenarios (IPCC)
Regional climate models
Climate change scenarios adapted for Latvia
River runoff scenarios
Sea state scenarios (Gulf of Riga)
Impact assessment (on Latvian water environment)
Information flows
Global circulation models
Nutrient runoff scenarios
21 model (PRUDENCE)
118 observation stations and selected RCM mesh
Daily meteorological data (temperature, precipitation, wind, humidity, cloudiness)
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
Climate scenarios 1A
Histogram equalisation for moving time window [instead of daily or monthly or seasonal equalisation]
OBS
REF
A2
MODA2
MODB2
B2
MODREF
600
650
700
750
800
850
900
4 5 6 7 8 9 10 11
Temperatūra
No
kriš
ņi
Daily data series for Latvia – contemporary climate, climate change scenarios
Okt
S ep
Aug
J ūl
MayDec
MarF eb
J an
Okt
S ep
Aug
J ūl
J ūnNovDec
J an
Apr
Mar
F eb
0.5
1
1.5
2
2.5
3
-10 -5 0 5 10 15 20 25
Temperatūra, deg C
Nokrišņi, m
m/d
Mūsdienu klimats
S cenārijs A2
Insight : T-p diagram for Dobele, contemporary climate and A2 scenario
Atziņa – hidroloģiskā režīma daudzveidība Latvijā samazināsies
NOVITĀTE PASAULĒ
“Double ensemble forecast: ensemble of RCM vs. ensemble of hydrological models”
Regional climate modelRiver run-off
Hydrological model(independent from RCM)
Meteorological forcing
Modified meteorological forcing
Bias correction
Regional climate modelRegional climate model
Hydrological model(independent from RCM)
Hydrological model(independent from RCM)
River runoff 1BDouble ensemble approach
-60%
-40%
-20%
0%
20%
40%
60%
80%
-25% 0% 25% 50% 75% 100% 125%
Incr
ease
of m
axim
um
mo
nth
ly Q
, %
Increase of mean annual Q, %
Impact assessment by RCM ensemble (Bērze)
Uncertainty prevails
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
-25% -20% -15% -10% -5% 0% 5% 10% 15%
Incr
ease
of m
axim
um
mo
nth
ly Q
, %
Increase of mean annual Q, %
Impact assessment by hydrological model ensemble
Uncertainty remains but is decreased
Decrease of both annual run-off and its maximum monthly value expected
0
2
4
6
8
10
12
1 3 5 7 9 11 13
Month
Mea
n m
on
thly
dis
char
ge,
m3/
s
BASIN-CTL
SHE-CTL
FIBASIN-CTL
BASIN-A2
SHE-A2
FIBASIN-A2
Seasonal analysis by hydrological model ensemble
Spring snow-melt flood significantly decreases
Autumn rainfall period extends into winter. Winter low flow disappears
Summer low flow period longer and better pronounced
Regional analysis: data series for RBD (MIKE Basin)
0
10
20
30
40
1 3 5 7 9 11 13
Me
an
mo
nth
ly r
un
off
, m
m
Month
Abava-CTL
Abava-A2
0
10
20
30
40
1 3 5 7 9 11 13
Me
an
mo
nth
ly r
un
off
, m
m
Month
Bērze-CTL
Bērze-A2
0
10
20
30
40
1 3 5 7 9 11 13
Me
an
mo
nth
ly r
un
off
, m
m
Month
Dubna-CTL
Dubna-A2
0
10
20
30
40
1 3 5 7 9 11 13
Me
an
mo
nth
ly r
un
off
, m
m
Month
Salaca-CTL
Salaca-A2
Regional analysis (MIKE BASIN)
15
25
35
45
140 180 220 260 300
Mean annual runoff, mm
Ma
xim
um
mo
nth
ly r
un
off
, m
m
Abava-CTL
Abava-A2
Bērze-CTL
Bērze-A2
Salaca-CTL
Salaca-A2
Dubna-CTL
Dubna-A2
W and N regions become similar to each other and contemporary E region
C and E regions get closer
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.
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.
0
5000
10000
15000
20000
25000
Jan Feb Mar Apr Mai Jun Jul Aug Sep Oct Nov Dec Jan
Nit
rog
en
loa
d, t
on
ne
/mo
nth
CTL
A2
Nutrient run-off 1B*
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
Sea state modeling 1C
Model forcing
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 scenario
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 scenario
Climate data from PRUDENCE. Control: 1961-1990, Scenario A2: 2070-2100
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)
-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
0 1 2 3 4 5 6 7 8 9 10 11 12
Temperature, degC
Dep
th, m
Water 1961-1990
Water 2071-2100
Air 1961-1990
Air 2071-2100
T increase by 1,5 (bottom) to 3 (surface) degrees
Surface T increase close to air T increase
Physical model results – II(mean daily pycnocline depth and its variation)
-60
-50
-40
-30
-20
-10
0
Jan Feb Mar Apr Mai Jūn Jūl Aug Sep Okt Nov Dec
De
pth
, m
1961-1990
2071-2100
Pycnocline develops earlier...
... stratification lasts longer
Physical model results – III(mean time-depth plots of temperature)