Embed Size (px)
Citation preview
Joel Dahné
Swedish meteorological and
hydrological institute
In this presentaion
Goals (deliverables)
Indata
HYPE model
Calibration/Evaluation
Simulation results for Future
climate and reduction scenario
Goals (deliverables)
-Input to oceanographic
models
Predicted discharge at catchment
outlets 1960-2100
Net load of N and P to the sea,
1960-2100
Indata for a pan-Baltic model
• Topography: HYDRO1k• Landuse and Soils:
ECOCLIMAPEuropean Soils Database
• Forcing Data (meteorology): ERAMESAN (Patched)ECHAM5(KNMI) + RCA3(SMHI)
•Agricultural Practices Data: CAPRIS-data(agg-eco-model)
•Atmospheric Deposition Data: MATCH model (SMHI)
•Point sources and wwtEEA, WHO, EUROSTAT
•Observed water quality (N & P):EEA
•Observed RunoffGRDC + BALTEX
Runoff
Topography Meteorology Landuse
Quality
EEA
Program for raster indata preparation WHIST
5128 sub catchments
Median size
= 325km2
HYdrological Predictions for the Environment (HYPE) model
S 1
S 3
S 2
A ltitude.
Soil classes (7)
+
Landuse classes (13)
=
SLC classes (55)
Total: 78 000 Hydrological
response units in 5128 sub
catchments
Sources for N&P:
• Fertilization
• Atm. Dep.
• Residues
• Mineralization
Sinks for N&P:
• Denitrification
• Crop up-take
• Adsorption
Fertilizer,residuals
--
Regional groundwater flow
Macropore flow
Discharge
Evapotranspiration
Surface flow
PrecipitationSnow melt
Crop uptake
N&P storage
N&P storage
Atmosphericdeposition
Denitrification
N&P storage
Processes in hydrological response units
Calibration/Evaluation
Calibration of Water model
against observed daily
streamflow.
Calibration of Water quality model
against observed seasonal and
annual concentration in rivers
Most parameters dependent on
soil or landuse. => not calibrated
to regions!
Proper evaluation yet to be done
RIVER AREA km2
1 VISTULA 1939352 ODER 1112423 NEMANUS 979464 DAUGUVA 900015 NARVA 582166 KEMIJOKI 556477 GÖTAÄLV 512748 GLAMA 414329 MUONIO 3920610 DALÄLVEN 2910911 KOKEMAENKOJI 2730312 UMEÄLV 2639413 INDALSÄLVEN 2581014 LJUSNAN 2037515 NORRSTRÖM 1925716 MOTALA STRÖM 1502617 LIELUPE 1409018 BÖLEBYN 1319119 LIVAJOKI 1258820 SKELLEFTEÄLV 1229421 VENTA 847722 PÄRNU 596723 KYRONJOKI 482924 SIIKAJOKI 466925 LAPUANJOKI 455526 LJUNGBYÅN 429927 EMÅN 417828 WARNOW 397129 PEENE 380930 GIDEÄLVEN 373631 HELGE Å 364232 INA 363933 ÄTRAN 303334 KASARI 263935 LESTIJOKI 263836 AURAJOKI 199537 RICKLEÅN 185138 SLUPIA 167339 UECKER 157440 EURAJOKI 146041 TUDE A 57942 PARSETA 443
Some prelimary results
River:
Oder
River:
Kokemäenjoki
Relative error [%]
Compared to 41 rivers
Total Phosphorus
Yearly load to sea
Relative error [%]
Compared to 41 rivers
Total Nitrogen
Yearly load to sea
1971-2000 2071-2100
Load as today
Reduced load
Clim. scen
•Climate scenario: GCM: ECHAM5(KNMI), RCM: RCA3(SMHI)
•Reduction scenario: Loads from Point sources and waste water reduced by 20%
Referens
Load
Going from observation based forcing data to Regional Climate Models
1971-2000 2071-2100
Load as today
Reduced load
•Climate scenario: ECHAM5(KNMI), RCA3(SMHI)
•Reduction scenario: Point sources and WWT reduced by 20%
RCM
Referens
Load
1971-2000 2071-2100
Load as today
Reduced load
RCM
•Climate scenario: ECHAM5(KNMI), RCA3(SMHI)
•Reduction scenario: Point sources and WWT reduced by 20%
Referens
Load
1971-2000 2071-2100
Load as today
Reduced load
•Climate scenario: ECHAM5(KNMI), RCA3(SMHI)
•Reduction scenario: Point sources and WWT reduced by 20%
RCM
Referens
Load
Evaluating the combined effects of nutrient load reduction and climate scenarios for the Baltic Sea catchment
Preliminary conclusions
• Climate run:-Increase in phosphorus load due to more intense rainfall-Decrease in nitrogen load due to deacreased flow => increase in retention time.
Combined effects: Nutrient loads will decrease
Future work
• Validation of results => Further calibration and model improvement.
• Validate that the model can reproduce changes in external loads of nutrients
• Reduction scenarios from HELCOM-BSAP
• GCM/RCM runs
• …
Thank you for your attention
SMHI - Hydrological research department
