Hydrological Predictions for the Arctic Environment

Hydrological Predictions for the Arctic Environment

Assoc. Prof., Dr. Berit ArheimerHead of Hydrological Research

Swedish Meteorological and Hydrological Institute(SMHI)

Outline:HYdrological Predictions for the Environment (HYPE) model

Model support systemsGlobal databases and needs of satellite data

Model output for sustainable exploitation of the Arctic?

HYdrological Predictionsfor the Environment

(HYPE) model

Berit Arheimer

Provide a modelling tool that, on a daily timescale, and at a high spatial resolution:

Calculates many hydrological variables, incl. water discharge and/or nutrient concentrations at any site in the basin and to the Seas.

Can be used operationally to give past and currentconditions and forecast all variables.

Can be used as a tool for examining the effects of climate change, landuse change and/or nutrient reduction scenarios.

Uses quality–assured data and is calibrated and validated according to sound scientific principles.

Objectives:

Berit Arheimer

Modelling tool: HYPE = HydrologicalPredictions for the Environment

Access to data required by model

System for streamlining input data handling and model set-up: HYSS + WHIST

Hydrological modelling competence

SMHI’s operational systems: e.g. technical forecast infrastructure, qualityassurance

Web services for data deliverable to stake-holders

SMHIOperationalProduction

HYPE

ww

w.s

mhi

.se

Continental & river specific Maps, Time series and Statistics • Present conditions• Forecasts• Climate change impact• Measure effects / scenarios

Source apportionment

Model evaluation

User interface

GMES satellite products

Global & free databases

Meteorological data

Climate projections

Policy scenarios

Local data

WH

IST

SMHIOperationalProduction

HYPE

ww

w.s

mhi

.se

Continental & river specific Maps, Time series and Statistics • Present conditions• Forecasts• Climate change impact• Measure effects / scenarios

Source apportionment

Model evaluation

User interface

GMES satellite products

Global & free databases

Meteorological data

Climate projections

Policy scenarios

Local data

GMES satellite products

Global & free databases

Meteorological data

Climate projections

Policy scenarios

Local data

WH

IST

What is required for hydrological data production?

Berit Arheimer

New, daily time-stepping, hydrological model based on widely accepted hydrological concepts (SMHI/HBV)

Integrated modules for hydrological compartments and flowpaths, nutrient and conservative tracers

Wide range of parameters modelled (runoff, turn-over, soilmoisture, snowdepth, groundwaterlevel, N, P, O18 )

Model already used at local, regional and pan-European scale for research purposes, and LaPlata

A Pan-Sweden model (> 17 000 basins) has already been set-up, calibrated and placed into production at SMHI

Introduces the ability to model very large regions at high resolution simultaneously

Introducing the HYPE model:

S1

S3

S2

N&P pools

N&P pools

Groundwater outflow, conc. ofIN, ON, SP & PP

Atmosphericdeposition

Fertilizers,Manure, Plant residues

Plantuptake

Denitrification

Evapo-transpiration

Rainfall,Snowmelt

Macro-poreflow

Regional groundwater flow

Surfacerunoff

N&P poolsGroundwater

Tile drain

Stream depth

N&P pools

N&P pools

Groundwater outflow, conc. ofIN, ON, SP & PP

Atmosphericdeposition

Fertilizers,Manure, Plant residues

Plantuptake

Denitrification

Evapo-transpiration

Rainfall,Snowmelt

Macro-poreflow

Regional groundwater flow

Surfacerunoff

N&P poolsGroundwater

Tile drain

Stream depth

Berit Arheimer

Models for predictions in ungauged basins

20 000 fresh-water bodies and 600 coastal zones in Sweden300 Water discharge 900 Nutrient conc.Forcing data:

300 Temperature, 800 Precipitation

Sweden = 450 000 km2

All models are wrong – but some may be useful!

Berit Arheimer

Water discharge (mm)Correlation: 0.96

NSE R2: 0.92

Total NitrogenCorrelation: 0.94

NSE R2: 0.88

Total PhosphorusCorrelation: 0.79

NSE R2: 0.59

Tot-N [ug/L]

0

2000

4000

6000

8000

10000

12000

0 2000 4000 6000 8000 10000 12000

obs

mod

Q [mm/år]

0

200

400

600

800

1000

1200

1400

0 500 1000 1500 2000

Obs

Mod

Tot-P [ug/L]

0

20

40

60

80

100

120

140

160

180

200

0 50 100 150 200

obs

mod

mod

el

observation

Spatial fit: Long-term average (10 yrs)

mod

el

mod

el

observationobservation1996 1997 1998 1999 2000 2001 2002 2003 2004 20050

200

400

600

800

Vat

tenf

örin

g (m

3 /s)

1996 1997 1998 1999 2000 2001 2002 2003 2004 20058.4

8.8

9.2

9.6

10

Sjöv

atte

nstå

nd (m

)

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005400

800

1200

1600

2000

Tot-N

1996 1997 1998 1999 2000 2001 2002 2003 2004 20050

200

400

600

Tot-P

Temporal fit: Median model performance (S-HYPE)

Water discharge (m3/s)

Total Nitrogen (μg/L) Total Phosphorus (μg/L)

Lake water level (m)

1996 1997 1998 1999 2000 2001 2002 2003 2004 20050

200

400

600

800

Vat

tenf

örin

g (m

3 /s)

1996 1997 1998 1999 2000 2001 2002 2003 2004 20058.4

8.8

9.2

9.6

10

Sjöv

atte

nstå

nd (m

)

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005400

800

1200

1600

2000

Tot-N

1996 1997 1998 1999 2000 2001 2002 2003 2004 20050

200

400

600

Tot-P

Temporal fit: Median model performance (S-HYPE)

Water discharge (m3/s)

Total Nitrogen (μg/L) Total Phosphorus (μg/L)

Lake water level (m)

1996 1997 1998 1999 2000 2001 2002 2003 2004 20050

20

40

60

80

Vat

tenf

örin

g (m

3 /s)

1996 1997 1998 1999 2000 2001 2002 2003 2004 20058.4

8.8

9.2

9.6

10

10.4

Sjöv

atte

nstå

nd (m

)

1996 1997 1998 1999 2000 2001 2002 2003 2004 20050

4000

8000

12000

16000

Tot-N

1996 1997 1998 1999 2000 2001 2002 2003 2004 20050

200

400

600

Tot-P

Temporal fit: Best model performance (S-HYPE)

Water discharge (m3/s)

Total Nitrogen (μg/L) Total Phosphorus (μg/L)

Lake water level (m)

1996 1997 1998 1999 2000 2001 2002 2003 2004 20050

20

40

60

80

Vat

tenf

örin

g (m

3 /s)

1996 1997 1998 1999 2000 2001 2002 2003 2004 20058.4

8.8

9.2

9.6

10

10.4

Sjöv

atte

nstå

nd (m

)

1996 1997 1998 1999 2000 2001 2002 2003 2004 20050

4000

8000

12000

16000

Tot-N

1996 1997 1998 1999 2000 2001 2002 2003 2004 20050

200

400

600

Tot-P

Temporal fit: Best model performance (S-HYPE)

Water discharge (m3/s)

Total Nitrogen (μg/L) Total Phosphorus (μg/L)

Lake water level (m)

Models for predictions in ungauged basins

Berit Arheimer

Model applications so farSweden (S-HYPE): 450 000 km2, 17 000 subbasins, 15 yrs

Baltic Sea basin (Balt-HYPE): 1.7 milj. km2,5000 subbasins, 140 yrs

Europe (E-HYPE): 7 milj. km2, 8500 subbasins, 20 yrs

La Plata basin (LPB-HYPE): 3.6 milj. km2, 4000 subbasins, 30 yrs

Possible:

Arctic (Arc-HYPE?): 2.9 miljoner km2, 290 subbasins, 140 yrs?

Snow depths

Model support systems

Berit Arheimer

Input data on relevant scale

RCM

Vunduzi

Tacuraminga

Bue Maria

Nhazonia

Pungue SulHonde Mavonde

KatiyoPungwe Falls

Frontiera

Pungwe

Hydrological modelling

GCM

Future runoff, water resources

and status

Regional application

Vunduzi

Tacuraminga

Bue Maria

Nhazonia

Pungue SulHonde Mavonde

KatiyoPungwe Falls

Frontiera

Pungwe

Hydrological modelling

Global data

Hydrological variables, water resources

and status

MatchingInformationLevels(Streamlining)

Berit Arheimer

Not just a model: Model support systems

HYdrological Simulation System (HYSS):

New environment for hydrologicalmodelling

Allows for different hydrologicalmodels to be used in the same modelling environment

High portability, easy to extractsubmodels for stand-aloneapplications.

World Hydrological Input Set-upTool (WHIST):

• Handles input data

• Can increase the model area and resolution without increasing modelcomplexity

• Easy to compile input data to model new areas from existing (and free) databases

Berit Arheimer

Not just a model: A production system

SMHIOperationalProduction

HYPE

ww

w.s

mhi

.se

Continental & river specific Maps, Time series and Statistics • Present conditions• Forecasts• Climate change impact• Measure effects / scenarios

Source apportionment

Model evaluation

User interface

GMES satellite products

Global & free databases

Meteorological data

Climate projections

Policy scenarios

Local data

WH

IST

SMHIOperationalProduction

HYPE

ww

w.s

mhi

.se

Continental & river specific Maps, Time series and Statistics • Present conditions• Forecasts• Climate change impact• Measure effects / scenarios

Source apportionment

Model evaluation

User interface

GMES satellite products

Global & free databases

Meteorological data

Climate projections

Policy scenarios

Local data

GMES satellite products

Global & free databases

Meteorological data

Climate projections

Policy scenarios

Local data

WH

IST

Global databases and future needs

Berit Arheimer

Readily Available Global Databases

Topography: Hydro1k, Hydrosheds (USGS)

Land use + soil: ECOCLIMAP (1 km), (MeteoFrance)

Forcing data (P & T): A combination of ERA-40/Interim, and forecasts (ECMWF)

Major Dams: ICOLD

Agricultural Data: FAO

Point Sources: Population data from HYDE database, treatment level and standard values for emissions (van Drecht et al. 2009)

Atmospheric Deposition: Long term averages from national monitoring

Berit Arheimer

Readily Available DatabasesObserved river discharge: GRDC and BALTEX (daily and monthly)

Possibility for additional data throughcollaboration and partnership?

Data for model calibrationand evaluation

Russia

Sweden

Finland

Norway

Ukraine

PolandGermany

Belarus

Latvia

Lithuania

Estonia

Czech RepublicSlovakia

DenmarkDenmark

Estonia

France

Denmark

-

Catchment area (km2)no info

0 - 10000

10001 - 50000

50001 - 100000

100001 - 281000

Baltex stations for measuring daily Q

Berit Arheimer

Needs of satellite data for the Arctic

Model input:

Land cover

Water surface

Leaf area index

Phenology

Glaciar, ice-sheets

Comparision and Validation:

Soil moisture

Water surface

Snow cover

Snow depths

Ice caps

In the future (?):

Discharge

Lake level fluctuations

Groundwater level fluctuations

Model output for sustainable exploitation

of the Arctic?

Berit Arheimer

Median resolution (10 000 km2), daily model of water variables (e.g. flow rates, soil moisture)possibility of adding water qualityover the entire region

WHAT?

WHY?• Homogenous model (impartial platform),

• Systematically-Implemented (easily run for

new scenarios),

• Ensemble member and reference model

(compared with local and basin scale models)

To sum up: High resolution hydrological modelof the Arctic region?

Berit Arheimer

% change of P concentrationin surface water

μg P L-1

National HYPE modelling of P concentrations 1961-2100

Berit Arheimer

So far:Results of E-HYPE discharge modelling(test run)

What sort of resultscan be expected?

Local correctionsare possible whereobserved datais available!

Berit Arheimer

Example of HYPE model output for sustainable exploitation of the Arctic

HYPE can assist in:

Determining WMO:s Efficient Climate Variables (discharge and water use) of past conditions.

Assessment of present ecological status.

Climate change impact studies of future conditions.

e.g.:

Oceanographic circulation patterns demand fresh-water inflow.

Design of hydropower demands a hydrological tool.

Prediction of biological status is related to hydrological variables (on land, in freshwater and eustaries).

The model may calculate transport of substances and pollutants in the region, and to the sea.

Changes in frozen soil is crucial for infrastructure and ecology.

Conclusions

Berit Arheimer

Conclusions

The HYPE model introduces the ability to model very large regions at high resolution simultaneously.

The model is supported by tools for handling global databases and an operational production system at SMHI.

The present need of satellite data include mainly: Land cover, Water surface, Leaf area index, Phenology, Glaciar and ice-sheets fluctuations, and Snow.

In the future Discharge, Lake level and Groundwater fluctuationswould be appreciated.

This hydrological model can assist in many aspects of sustainable exploitation of the Arctic

Thank you!