Helmut Fischer, Volker Kirchesch, Katrin Quiel, Andreas Schöl Bundesanstalt für Gewässerkunde

GLOWA-Elbe II Statuskonferenz 14. Dez. 2006 Potsdam H. Fischer, BfG Koblenz

Helmut Fischer, Volker Kirchesch, Katrin Quiel, Andreas Schöl

Bundesanstalt für Gewässerkunde

Influence of global change on the water quality of the Elbe/Labe

Einfluss des globalen Wandels auf die Gewässergüte der Elbe


Contents

1. Introduction

Position in the model network

Factors influencing water quality

The water quality model QSim

2. Results

Longitudinal development of water quality

Model results (validation)

Temporal development of water quality

3. Conclusions and outlook


Einleitung:

Gliederung

Introduction - position of the project in the model network

Water management

(WBALMO)

Water use

Energy / Mining

(KASIM)

Households /business

(HAUSHALT WASSER)

Industry

(INDUSTRIE WASSER)

Agriculture / Irrigation

Wetlands

(MODAM)

Development of water technologies

Inflow

Evaporation

Irrigation

Wetlands

Water suppliers

Industry

Mining

Power plants

Nutrient load

(MONERIS)

Point source: Industry

Regionalization of global change

Future climate

(STAR)

Development of agricultural sector

(RAUMIS)

Economics and demography

(REGE)

Minimal flow for conservation

Transport on inland waterways

Point source: Sewage plant

Diffuse source: Sealed surfaces

Diffuse source: Erosion

Diffuse source: Atmospheric Deposition

Diffuse source: Drainage

Diffuse source: Surface denudation

Diffuse source: Groundwater

Diffuse source / Sink: Wetlands

Land use and regional water balance

Hydrological cycle and crop yields

(SWIM)

Land use

(LAND USE SCANNER)

Development of energy sector

(KASIM)

Transport on inland waterways

X1

X13

X15

X4

X3

X2

Y2

Y1

Y5

Y3

Y4

Y7

Z8

Y8

Z1

Y6

Z6

Z7

Z9

Z5

Z1

Z2

Z3

Z4

Water quality

(QSim)

Nutrient concentr.Phytoplankton

Oxygen


Water use

Agriculture / Irrigation

Development of water technologies

Nutrient load

(MONERIS)

Point source: dir. ind. discharges

Regionalization of global change

Future climate

(STAR)

Development of agricultural sector

(WATSIM-RAUMIS)

Economics and demography

(REGE)

Point discharges: WWTP

Diffuse Source: imp. surfaces

Diffuse Source: Erosion

Diffuse Source: Atm. Deposition

Diffuse Source: Drainage

Diffuse Source: Surface runoff

Diffuse Source: Groundwater

Sinks: Surface waters/Wetlands

Land use and regional water balance

Land use

(LAND USE SCANNER)

Y3

Y8

X1

X15

X2

Y2

Y1

Y5

Y7

Y6

Introduction - position of the project in the model network

Households / business

(HAUSHALT WASSER)

Industry

(INDUSTRIE WASSER)

Wetlands

(MODAM)

Hydrological cycle and crop yields

(SWIM)

Water quality

(QSim)

Nutrient concentr.Phytoplankton

Oxygen


Introduction

Climate (temperature, light regime, precipitation) model “STAR“, PIK

Discharge (water depth, retention times) model “SWIM“, PIK

Nutrients (inputs, concentrations) model “MONERIS“, IGB

nutrient budget Algal growth and biomass

Factors influencing algal biomass and nutrient budget and

Models providing input data for QSim


Introduction – the model QSim

O2

heterotrophic bacteria, nitrifying bacteria

phytoplankton diatoms, green and bluegreen algae

POC/DOC zooplanktonrotifers

discharge

mussels

NH4, NO2, NO3, P, Si

dissolved nutrients

organic carbon

hydraulics generating segments

heat- balance

planktonic modules

benthicmodules

Schöl et al. 1999, 2002


Longitudinal („Lagrangian“) sampling campaigns: chlorophyll a

Results

0

50

100

150

200

250

0 100 200 300 400 500 6000

50

100

150

200

0 100 200 300 400 500 600

Elbe-km

Chl

orop

hyll

a [µ

g/l]

Summer 200026.6. – 5.7. (9 days)

low discharge

Summer 200524.7. – 1.8. (7 days)medium discharge

Spring 20068.5. – 15.5. (6 days)

high discharge

18:00Elbe

6:00

18:00tributaries

6:00

High phytoplankton biomass leads to oxygen supersaturation in the Middle Elbe (up to 200% in summer) and and heavy loads of organic matter in the tidal area.

n = 3

0

50

100

150

200

0 100 200 300 400 500 600


Chlorophyll a – measured and modelled data

Results

Sommer 200026.6. – 5.7.

low discharge

18:00Elbe

6:00

18:00tributaries

6:000

50

100

150

200

250

0 100 200 300 400 500 600

Chl

orop

hyll

a [µ

g/l]

Elbe-km


Longitudinal sampling campaigns: dissolved phosphorus (ortho-phosphate)

Resultsor

tho-

PO

4-P

[m

g/l]

Elbe Tributaries

n = 6

0.00

0.05

0.10

0.15

0.20

0.25

0 100 200 300 400 500 6000.00

0.05

0.10

0.15

0.20

0.25

0 100 200 300 400 500 6000.00

0.02

0.04

0.06

0.08

0.10

0 100 200 300 400 500 600

Summer 200026.6. – 5.7. (9 days)

low discharge


Spring 20068.5. – 15.5. (6 days)

high discharge

Elbe-km


Sommer 200026.6. – 5.7.

low discharge

Elbe

tributaries0.00

0.05

0.10

0.15

0.20

0.25

0 100 200 300 400 500 600

orth

o-P

O4-

P [m

g/l]

Elbe-km

Results

Phosphate – measured and modelled data


Results

Elbe Tributaries

n = 6

0

1

2

3

4

5

0 100 200 300 400 500 6000

1

2

3

4

5

0 100 200 300 400 500 6000

1

2

3

4

5

0 100 200 300 400 500 600

SiO

2-S

i [m

g/l]

Summer 200026.6. – 5.7. (9 days)

low discharge


Spring 20068.5. – 15.5. (6 days)

high discharge

Elbe-km

Longitudinal sampling campaigns: silica


Sommer 200026.6. – 5.7.

low discharge

Elbe

tributaries0

1

2

3

4

5

0 100 200 300 400 500 600

SiO

2-S

i [m

g/l]

Elbe-km

Results

Silica – measured and modelled data


Longitudinal sampling campaigns: nitrogen (nitrate and total nitrogen)

Results

n = 6

NO

3-N

, tot

al N

[m

g/l]

Summer 200026.6. – 5.7. (9 days)

low discharge


Elbe-km

0

1

2

3

4

5

6

7

0 100 200 300 400 500 6000

1

2

3

4

0 100 200 300 400 500 6000

1

2

3

4

0 100 200 300 400 500 600

Total-NNO3-N

Total-NNO3-N

Elbe

Tributaries

0

1

2

3

4

5

6

7

0 100 200 300 400 500 600

Spring 20068.5. – 15.5. (6 days)

high discharge


Sommer 200026.6. – 5.7.

low discharge

Elbe

tributaries0

1

2

3

4

5

0 100 200 300 400 500 600

NO

3-N

[mg/

l]

Elbe-km

Results

Nitrate – measured and modelled data


Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

50

100

150

200

250

300Jan Feb Mar Apr May Jun Jul Aug Sep Okt Nov Dec

0

50

100

150

200

250

300Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

50

100

150

200

250

300Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

50

100

150

200

250

300

km 474.5

1998

left mean (left, right)

Ch

loro

ph

yll

a [µ

g/l

]

km 318.1

km 172.6

modelled measured

km 3.9Chlorophyll a

Jun

Dec

Chlorophyll a [µg/l]

Sep

Nov

Jul

Aug

Feb

Oct

Apr

May

Jan

Elbe-km

Mar

0102030405060708090100110120130140150160170180190200210220230240

100 200 300 400 5000

Results

Chlorophyll a (1998)

measured and modelled(Schöl et al. 2006; measured data by ARGE Elbe)

modelled


Results

Comparison of

phytoplankton

composition

in the years

1998 and 2003

2003 a warm and dry summer

development of cyanobacteria (data by ARGE Elbe)

1998, Elbe-km 318

0

50000

100000

150000

200000

7.1

4.2

4.3

18.3 1.

415

.429

.413

.527

.510

.624

.6 8.7

22.7 5.

819

.8 2.9

16.9

30.9

14.1

028

.10

11.1

19.

12

Ab

un

dan

ce [

Ind

/ml]

0

50

100

150

200

250

Ch

loro

ph

yll

a [µ

g/l

]

Cyanophyceae

Diatomeae

Chlorophyceae

Chlorophyll-a

2003, Elbe-km 318

0

50000

100000

150000

200000

8.1

5.2

19.2 5.

319

.3 2.4

16.4

28.4

14.5

26.5

11.6

25.6 9.

723

.7 6.8

20.8 3.

917

.91.

10

15.1

029

.1012

.1110

.12

Ab

un

dan

ce [

Ind

/ml]

0

50

100

150

200

250

Ch

loro

ph

yll

a [µ

g/l

]


Results

2003

Measured

phytoplankton

composition (data ARGE Elbe)

2003, Elbe-km 318

0

50000

100000

150000

200000

Ab

un

dan

ce [

Ind

/ml]

0

50

100

150

200

250

Ch

loro

ph

yll

a [µ

g/l

]

Simulation 2003, Elbe-km 318

0

20

40

60

80

100

120

01.01.2003 01.03.2003 01.05.2003 01.07.2003 01.09.2003 01.11.2003

Ch

loro

ph

yll

a [µ

g/L

]

Cyanophyceae

Diatomeae

Chlorophyceae

2003

Modelled

phytoplankton

composition


Results

In which direction will the system change if

- temperature increases - summer discharges decrease

Model runs with database from years 1998 and 2003

Model results

slightly more zooplankton

only minor change in algal biomass

change in phytoplankton composition

(increase of cyanobacteria)


Conclusions and Outlook

Current state- strong phytoplankton growth (factor ~4)- intense turnover of nutrients (biologically controlled)- heavy organic load („secondary pollution“) impairs the oxygen budget of the Elbe estuary- high nutrient loads are problematic for coastal areas

Future state- intensification of biological processes- better growth conditions (light, temperature)- stronger nutrient limitation is possible- better conditions for cyanobacteria- open system for invading species (e.g. mussels)

Phytoplankton development

+

+ -

-


Conclusions and Outlook

Further work within GLOWA-Elbe II

- Modelling nutrient dynamics and phytoplankton growth in the Czech Elbe section (below Vltava/Moldau).

- Calculation of 5 climate scenarios x 4 socio-economic scenarios

Future state- intensification of biological processes- better growth conditions (light, temperature)- stronger nutrient limitation is possible- better conditions for cyanobacteria- open system for invading species (e.g. mussels)

Phytoplankton development

+

+ -

-


Thank You

for your attention!


Longitudinal sampling campaigns: (ortho-phosphate) and total phosphorus

Results

n = 6

0,0

0,1

0,2

0,3

0,4

0,5

0 100 200 300 400 500 600

0,0

0,1

0,2

0,3

0,4

0,5

0 100 200 300 400 500 600

0,0

0,1

0,2

0,3

0,4

0,5

0 100 200 300 400 500 600

Elbe-km

orth

o-P

O4-

P, t

otal

P [

mg/

l]

Summer 200026.6. – 5.7. (9 days)

low discharge


Spring 20068.5. – 15.5. (6 days)

high discharge

Total-Portho-PO4-P

Elbe

TributariesTotal-Portho-PO4-P

Documents

Helmut Fischer, Volker Kirchesch, Katrin Quiel, Andreas Schöl Bundesanstalt für Gewässerkunde