Water Research Horizon Conference Water quality in a ...

Climate change impact assessmenton river water quality – methods

and results for the Elbe river basin

28 June 2016Umweltbundesamt Dessau

7 Water Research Horizon Conference Water quality in a changing world

Cornelia Hesse, Valentina KrysanovaPotsdam‐Institute for Climate Impact Research (PIK)

Outline

2

1. Background 2. SWIM

3. Application for the Elbe river 4. Uncertainty and conclusions

WRHC Dessau 28 June 2016

1. Background• General method and modelling sequence• Water quality in eco‐hydrological modelling• ENSEMBLES climate change scenarios

2. Soil and Water Integrated Model (SWIM)• Model description• Relevant processes for water quality modelling• Model setup

3. Application for the Elbe river basin• The Elbe river catchment• SWIM model calibration and validation• ENSEMBLES climate change signals• Climate change impacts

4. Uncertainty and conclusions

Modelling sequence

3



Downscaling

Emission scenario(e.g. A2, B1, A1B)

Model calibration/validation

a set of scenarios and/or models allows to get a range of uncertainty

Global climate projection (GCM)

Regional climate projection (RCM)

Climate change impact assessment(running the eco‐hydrological model driven by regional climate scenario)

Eco‐hydrological model

Model setup (spatial and temporal input data)


4



Water quality in eco‐hydrological modelling (I)

• Development of watershed or river models including nutrient/pollutant transport and transformation processes since the 70th

• Initially conservative substances (e.g. chloride)

• Since the 90th implementation of reactive substances in different levels of complexity

• Watershed models: substances are transported and transformed in the catchment, reach the river and are routed through the river network

• River models: represent the transport and transformation processes in the river channel, but neglect processes in the landscape


5



Water quality in eco‐hydrological modelling (II)• Routing of nutrient/pollutants represents not

only natural conditions it is necessary to connect watershed and river processes

• Integrated watershed modelling in catchments is more and more important, especially for political and water protection requirements („good ecological status“ of the WFD)

• Models with climate and land use as boundary conditions can help to assess possible impacts of climate and/or socio‐economic changes on river ecosystems and to find suitable adaptation measures


http://prairierivers.org/ wp‐content/uploads/2008/ 08/watershed_diagram1.jpg

6



ENSEMBLES project (van der Linden and Mitchell, 2009)o intermediate emission scenario (A1B)o several projections of future European climate produced by a set of

different Regional Climate Models (RCMs) using the boundary conditions of six Global Circulation Models (GCMs)

o multi‐model approach improves the quality of projections and allows assessing the uncertainties in simulations of future climate

Selection of scenarios:o simulation period: 1951 – 2050 / 2100o resolution: 25 or 50 km

Climate scenarios from ENSEMBLES

Teutschbein and Seibert, 2010


F. Giorgi, 2008

7



ENSEMBLES climate scenarios applicationID Institute GCM RCM Reso‐

lution

S1 SMHI HadCM3Q3 RCA 25S2 HC HadCM3Q0 HadRM3Q0 25S3 HC HadCM3Q3 HadRM3Q3 25S4 HC HadCM3Q16 HadRM3Q16 25S5 C4I HadCM3Q16 RCA3 25S6 ETHZ HadCM3Q0 CLM 25S7 KNMI ECHAM5‐r3 RACMO 25S8 SMHI BCM RCA 25S9 SMHI ECHAM5‐r3 RCA 25S10 MPI ECHAM5‐r3 REMO 25S11 CNRM ARPEGE_RM5.1 Aladin 25S12 DMI ARPEGE HIRHAM 25S13 DMI ECHAM5‐r3 DMI‐HIRHAM5 25S14 DMI BCM DMI‐HIRHAM5 25S15 ICTP ECHAM5‐r3 RegCM 25S16 KNMI ECHAM5‐r1 RACMO 50S17 KNMI ECHAM5‐r2 RACMO 50S18 KNMI ECHAM5‐r3 RACMO 50S19 KNMI MIROC RACMO 50

19 scenarios chosen (S1‐S19)

3 time periods• 1971 – 2000 (p0 – reference)• 2021 – 2050 (p1)• 2071 – 2098 (p2)

6 climate parameters• minimum temperature• maximum temperature• average temperature• precipitation• air humidity• solar radiation

downloading and interpolation to the subbasin centroids by an inverse distance method


8



SWIM model overview


9



Nitrogen cycle in SWIM

fresh organic nitrogenfrom plant residues

Decom‐positionrate

Soil waterfactor of

denitrification

Comb. temp.‐carbon ‐factor of

denitrification

Fieldcapacity

Soil watercontent

Formcoefficient

Temp. factor for mineral.

C contentof layer

Soil waterfactor of

mineralisation

Temperaturefactor of

mineralisation

C:N ratiofactor of

mineralisation

C:P ratiofactor of

mineralisation

C:N ratio

C:P ratio

Soil tem‐perature

plantresidues

Humus rateconstant for N

mineralnitrogenin soil

Nitrate

Exchange constant

stable organicnitrogen

Part of activeN pools (=0.15)

active or already mineralizableorganic nitrogen

Fertilization

Washout

N fe

rtilizatio

n

Precipiatio

n

Nutrientuptakeof plants

Denitrification

Labile phosphorus

Freshorganic

phosphorus

Decompo

sitionDe

compo

sition M

ineralisa

tion


10



fertilisation

wet atm

ospheric deposition

organic N(soil humus)

organic N(plant residue)

decompo

sition

erosionammonium (NH4‐N)

nitrate nitrogen (NO3‐N)

mineralisa

tion

nitrificatio

n

plant uptake

leaching

/ wash ou

tvolatilisa

tion

decomposition

mineralisation

constraint:if H2O > 80%T < 5°C; T > 40°C

Ammonium cycle in SWIM


11



Phosphorus cycle in SWIM

Decompo‐sition rate

Plantresidues

Field‐capacity

Soilwatercontent

Soil waterfactor of

mineralisation

Temperaturefactor of

mineralisation

C:P ratiofactor of

mineralisation

C:N ratiofactor of

mineralisation

C:P ratio

C:N ratio

Soil temperature

Humus rateconstant for P

Mineralnitrogenin soil

Fresh organic phosphorusfrom plant residues

Labile phosphorusActivemineral

phosphorus

Stablemineral

phosphorus

Organicphosphorus

Loss ofsoluble P by washout

Decompo

sition

Minerali‐

satio

nDe

compo

sition

Erosion

Nutrientuptakeof plants


12



Model extension: In‐stream processes in the river

Sediment

Algae /Chlorophyll a

O2 CBOD

Norg

NH4-N

NO2-N

NO3-N

Porg

PO4-P

Diffusion

Set

tling

Set

tling

Diffusion

Mineral

isatio

n

Deco

mpo

sition

Decomposition

Mineralisation

Uptake

Uptake Uptake

Respiration

Photo-

synthesis

Nitrifikation

Nitrifik

ation

Water body

Atmosphere and Watersides

Oxidation

Oxidation

Point Sources /Diffuse Pollution

Atm

osph

eric

Rea

erat

ion


Set

tling

CarbonaceousDeoxygenation

light

temperature

Sed

imen

tatio

n

Sediment

Algae /Chlorophyll a

O2 CBOD

Norg

NH4-N

NO2-N

NO3-N

Porg

PO4-P

Diffusion

Set

tling

Set

tling

Diffusion

Mineral

isatio

n

Deco

mpo

sition

Decomposition

Mineralisation

Uptake

Uptake Uptake

Respiration

Photo-

synthesis

Nitrifikation

Nitrifik

ation

Water body

Atmosphere and Watersides

Oxidation

Oxidation


Atm

osph

eric

Rea

erat

ion


Set

tling

CarbonaceousDeoxygenation

light

temperature

Sed

imen

tatio

n

(based on SWAT (QUAL2E))


Data needs for model setup and calibration

• Spatial data (DEM, land use, soil, subbasin)

• Soil and vegetation parameters

• Climate parameters (min/max/av. temperatures, precipitation, solar radiation, air humidity)

• Input from point sources (location and amount )

• Time and amount of fertilizer inputs

• Crop types and yields on agricultural areas

• Water discharge and water quality observation data at basin outlet and selected tributaries

Adjusting several calibration parameters for water quantity and quality (also partly spatially distributed)

13




For climate change impact assessment these parameters are variable, all other parameters remain the same.

14



The Elbe river catchmentca. 150.000 km², about 700 m³/s

River

gauge (discharge

/ water qua

lity)

Elbe

Neu

Darchau

/ Schn

ackenb

urg

Vltava

Vraň

any / Zelčín

Ohře

Loun

y / Terezín

Schw

arze Elster

Löbe

n / Gorsdorf

Mulde

Bad Düb

en / Dessau

Saale

Calbe‐Grizeh

ne/ Groß Ro

senb

urg

Havel

Havelbe

rg/ To

ppel

Length [km] 907 430 305 179* 314 434 334Discharge [m³/s] 711 145 38 21 67 117 114Catchment [km²] 131 950 28 090 5 614 5 705 7 400 24 079 23 858Av. altitude [m] 281 523 507 131 394 287 74Average temp. [°C] 8.9 7.8 7.6 9.7 8.9 9.2 9.6Av. prec. [mm/y] 698 713 771 652 822 680 616Land use [%]

AgricultureForest

GrasslandSettlements

51.331.78.46.3

49.736.87.84.3

42.237.713.63.9

48.135.07.25.9

53.328.86.99.4

63.023.34.67.6

38.638.211.17.9

Point sources TN[t/year] TP

223181870

4704564

57073

18329

1673155

3557357

2768167

Nutrients [mg/L] NO3‐NNH4‐NPO4‐PDOX

3.170.160.0711.7

3.730.310.1211.7

2.380.080.0310.6

2.310.200.029.7

4.350.160.0610.6

4.680.210.0910.3

0.820.100.1310.6

Chlorophyll [µg/L] CHLA 77.1 36.7 8.0 9.3 10.7 21.8 37.6


15



Calibration of water dischargeDa

ily and

long

‐term average

daily

discharges


simulated discharge

16



Nutrient loads at two selected gaugesLong

‐term average

daily

loads(2001

‐2010)


17



Monthly averages for the main tributaries


NO3‐N NH4‐N PO4‐P

DOX ChlaQ

(Time period 2001‐2010)

18WRHC Dessau 28 June 2016 Cornelia Hesse



1.3

3.0

0

2

4

6ΔT

[°C]

p1‐p0 p2‐p0

40.5 56.6

‐100‐500

50100150200

ΔP [m

m]

p1‐p0 p2‐p0

‐15.4

‐27.2

‐150

‐100

‐50

0

50

100

ΔR [J/cm²]

p1‐p0 p2‐p0

Climate change signalsTempe

rature

Precipita

tion

Radiation

19



Monthly climate change signals


‐1

1

3

5

7

Δtemp. [°C] 25/75‐percentile (p2‐p0)

25/75‐percentile (p1‐p0)

average (p1‐p0)

average (p2‐p0)

Temperature

‐200

‐100

0

100

200

Δsol. rad. [J cm

‐2]

Solar Radiation

‐30

‐20

‐10

0

10

20

30

Δprec. [mm]

Precipitation

20



Seasonal climate change impactsDischarge

Nitrate

Ammon

ium


full lines: Neu Darchau; dashed lines: Schöna

2021 ‐ 2050 2071 ‐ 2098

21



Seasonal climate change impactsPh

osph

ate

Chloroph

yll a

Diss. oxygen


full lines: Neu Darchau; dashed lines: Schöna

2021 ‐ 2050 2071 ‐ 2098

22


3. Application for the Elbe river 4. Uncertainty and conclusions 19

percentalchangeso

f30‐year‐averages

Spatial patterns of climate change impacts (p2‐p0)


∆ discharge, m³ s-1 [%]

‐40‐20020406080100

∆ nitrate nitrogen , kg d-1 [%]

‐50

‐25

0

25

50

∆ ammonium nitrogen, kg d-1 [%]

‐50

‐25

0

25

‐50

‐25

0

25

50∆ phosphate phosphorus, kg d-1 [%]

• Uncertainties regarding measurement data– partly short or missing data sets– data sets with trends– uncertain data about point sources

• Uncertainties regarding model processes– assumptions about possible influences on algal growth– leaching of phosphorus and ammonium– subcatch method is “static” does not represent natural variability

• Very high model complexity – many unknown calibration parameters– Which processes could be neglected?

• Uncertainties regarding future scenarios– uncertain climate behaviour in the future (many scenarios)

23

Uncertainties in water quality modelling1. Background 2. SW

IM 3. Application for the Elbe river 4. U

ncertainty and conclusions


High number of in‐stream calibration parameters

+ *.bsn parameters for nutrient processes in soils

+ crop and soil conditions

High uncertainty

Similar results can be achieved with different parameter combinations

Next: to study effect of parameter uncertainty (ranges) on impacts

Next: to compare climate scenario and impact model uncertainties

24

Overparameterization – superimposing parameters? 1. Background 2. SWIM



• Modelling helps to understand the river system behaviour and to identify fractions and areas of point and diffuse pollution

• SWIM is a good tool to estimate possible future developments under changing climate and/or land use

• Multi‐model and/or multi‐scenario approach should be the favourite approach for climate change impact assessments

• Scenarios can help to find useful measures for reducing nutrient loads(e.g. for implementation of the WFD)

• More complex water quality modelling approaches require high calibration efforts and come along with quite large uncertainty

• Next step could be a detailed analysis of uncertainties and a reduction of the number of (less important) processes/parameters in the impact model to allow less complex and more robust impact assessments

25

Conclusions1. Background 2. SW

IM 3. Application for the Elbe river 4. U

ncertainty and conclusions


Thank you for yourattention!

Cornelia Hessecohesse@pik‐potsdam.de

For further information:

Hesse, C.; Krysanova, V.: Modeling Climate and Management Change Impacts On Water Quality and In‐Stream Processes in the Elbe River Basin. Water 2016, 8, 40; doi:10.3390/w8020040

modified from http://md.water.usgs.gov/ publications/fs‐091‐03/html/

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