modeling river discharges of the heavily managed limpopo ... · wasserkreislauf flaches grundwasser...

Modeling river discharges of the heavily

managed Limpopo river using SWIM and the

open-source datasets

Shaochun Huang, Hagen Koch, Valentina Krysanova, Fred F. Hattermann

Potsdam Institute for Climate Impact Research

Research Domain II - Climate Impacts & Vulnerabilities

Contact: [email protected]



7/25/2013

Content

• Introduction

• Data

• Method

• Results – Calibration and validation results without management

information

– Calibration and validation results with management information

– Climate change impacts on river discharge

• Conclusion



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Introduction

Source: Global Trends 2025: A Transformed World (page 75), National Intelligence Council, 2008

• High water

demand in

sourthern

Africa



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Introduction

Change in annual runoff by 2041-60 relative to 1900-70, in percent, under the

SRES A1B emissions scenario and based on an ensemble of 12 climate models

(Milly et al., 2005)

Significant impact on water resources in southern Africa



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Introduction

Source: DWA 2010

Distribution of dams in the Limpopo River basin • Catchment area:

ca. 413,000 km²

• Rainfall: 530 mm/year

• Runoff: 13 mm/year

• Population: 14 million

• Human influence:

• over 100 dams

and reservoirs

• Irrigation

• 1900 mines

Any minor human

influences can lead to

significant changes in

river discharge



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Introduction

• Objective

– To simulate the Limpopo river discharge using the

eco-hydrological model SWIM (Soil and Water

Integrated Model)

– To test the usage of the global datasets and the

rough information on managements available in

internet

– To project the future climate change impacts on river

discharges of the Limpopo river



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Data

• Digital elevation model: Shuttle Radar Topography Missions (90 m)

• Soil parameters: Digital Soil Map of the World (FAO et al. 2012)

• Land use: Global Land Cover (GLC2000 2003)

• Climate data (1961-2001): WATCH (0.5°) (Weedon et al. 2011)

• Observed discharge: Global Runoff Data Centre

• Climate scenarios: five GCM outputs (HadGEM2-ES, IPSL-5 CM5A-LR, MIROC-ESM-CHEM, GFDL-ESM2M, NorESM1-M), rcp 8.5, downscaled and bias corrected, 0.5° (Hempel et al. 2013) .



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Data

Human Influence data from internet

• 8 reservoirs

(capacity,

withdrawal from

the reservoirs)

• 31 additional

irrigation

withdrawal

(annual

abstraction rate)



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Method

The Model SWIM (Soil and Water Integrated Model)

OF-Abfluss

GW-Neubildung

Perkolation

Interflow

Basisabfluss

Pflanzenaufnahme

OF-Abfluss

GW-Neubildung

Perkolation

Interflow

Basisabfluss

Pflanzenaufnahme

Landbedeckung Landnutzung

Klima: Strahlung, Temperatur & Niederschlag

Biomasse

Wurzeln

LAI

Pflanzen-

wachsum

Stickstoffkreilauf

Phosphorkreislauf

N-NO3No-ac

No-st Nres

Plab Pm-ac Pm-st

Porg Pres

Wasserkreislauf

Flaches

Grundwasser

Tiefes

Grundwasser

B

CBo

de

np

rofil

A

Landbedeckung Landnutzung

Klima: Strahlung, Temperatur & Niederschlag

Biomasse

Wurzeln

LAI

Pflanzen-

wachsum

Stickstoffkreilauf

Phosphorkreislauf

N-NO3No-ac

No-st Nres

Plab Pm-ac Pm-st

Porg Pres

Wasserkreislauf

Flaches

Grundwasser

Tiefes

Grundwasser

B

CBo

de

np

rofil

A

Wasserkreislauf

Flaches

Grundwasser

Tiefes

Grundwasser

B

CBo

de

np

rofil

A

Percolation

GW recharge Interflow

Surface

Wetlands

GW flow



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Method

SWIM structure



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• Transmision loss module in WASA (Model for Water Availability in

Semi-Arid Environments) (Hacker, 2005)

tloss(x,w) = Qin – (a(x,w) + b(x,w)*Qin)

a(x,w) = (a / (1 - b))*(1 – b(x,w))

b(x,w) = exp (-k*x*w)

• Qin is the upstream inflow volume

• tloss(x,w) is the volume of transmission losses, x is channel length

and w is channel mean width.

• a, k, and b have been related to the effective, steady-state hydraulic

conductivity K (in/h), the mean duration of inflow to the reach D (h),

and the mean volume of inflow to the reach Q

Method

Channel transmission loss module



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Result

Calibration and validation results without

management information

Calibration Validation

ecal: 4.5 ecal: general potential ET calibration factor (the

TURC-IVANOV method was used in this study),

default value = 1.



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Result

Calibration and validation results with

management information

Calibration Validation

ecal: 1.45



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Result

Climate change impacts on river discharge (Sicacate)

0 100 200 300

Julian day

0

200

400

600

800

Q (

m3

/s)

GFDL-ESM2M

HadGEM2-ES

IPSL-CM5A-LR

MIROC-ESM-CHEM

NorESM1-M

WATCH

1971-1999

0 100 200 300

Julian day

-200

0

200

400

600

800

1000

Cha

nge

in

%

Q(2071-2099) - Q(1971-1999)



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Result

Climate change impacts on low and high flows (Sicacate)

GF

DL

-ES

M2

M

Ha

dG

EM

2-E

S

IPS

L-C

M5

A-L

R

MIR

OC

-ES

M-C

HE

M

No

rES

M1

-M

-50

0

50

100

Cha

nge in %

Q90(2071-2099) - Q90(1971-1999)

GF

DL

-ES

M2

M

Ha

dG

EM

2-E

S

IPS

L-C

M5

A-L

R

MIR

OC

-ES

M-C

HE

M

No

rES

M1

-M

-50

0

50

100

150

200

Cha

nge in %

Q10(2071-2099) - Q10(1971-1999)



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Conclusion

• SWIM can reproduce the discharge well using the global dataset with and without human influence data for the Limpopo river

• The rough estimation of human interference indeed helps to simulate comparable results using more reasonable parameters settings.

• The global dataset and the sparse internet information are useful in hydrological modellings for the large-scale but data-scarce regions.

• There is a large uncertainty of climate change impacts on river discharges for the Limpopo river. Additional RCM scenarios are required.



7/25/2013

Source: americanelephant.wordpress.com

modeling river discharges of the heavily managed limpopo ... · wasserkreislauf flaches grundwasser...

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