adapting swat to a lowland catchment and using model ... · 1 adapting swat to a lowland catchment...

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Adapting SWAT to a lowland catchment and using model results for ecohydrological assessments

Nicola Fohrer, Jens Kiesel, Britta Schmalz, Mike White

Department of Hydrology &Water Resources Management

Department of Applied Zoology & Hydrobiology

Daniel Hering&

2

Motivation

I N T R O D U C T I O N

Outline

INTRODUCTION

METHODOLOGY

RESULTS

DISCUSSION

OUTLOOK

Study area

Hypotheses to be tested

Initial setup and auto calibration

Drainage setup

Depression setup

Drainage and depression setup

Model results for the different setups

Assessment of model results

Are the hypotheses supported?

Input for a habitat model

3

Lowland characteristics

(JOSE 2006)

Northern German lowland

Precipitation870 mm/yr

Mean annual temperature

8.2°C

4

Motivation


Study catchment: Kielstau

Landuse

Topography

56% arable land30% grassland/fallow

9% forest3% urban areas

79-27mASL2% mean slope

> 7 depressions per 100ha(RIEDEL & UMLAND 1983)

Soils50% Stagnic Luvisols20% Haplic Luvisols

10% Stagnic Gleysoils10% Sapric Histosols

38% artificially drained(FOHRER et al. 2007)

50 km² area16 km river length

2 lakes1 gauge at the outlet

Some numbers

DEFICITS

Impact on the catchment scale scarcely researched

Lack of information about location and characteristics

Depressions are depicted in DEM but erased during delineation process

Lack of information about location and characteristics

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Motivation


Hypotheses to be tested

(1)Incorporating drainages will improve model performance and enhance process depiction

(2)Incorporating depressions will improve model performance and enhance process depiction

(3)It is a further improvement to jointly incorporate depressions and drainages

6

Methodology

M E T H O D O L O G Y

Input drainages

Setup 1initial

Drain Setup

Manual calibration

Autocalibration

Sensitivityanalysis

Setup 1auto calibrated

Setup 1manual calibrated

Input depressions

Depression Setup

Combined Setup

Input drains & depressions

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Input depressions

Drain Setup

Initial Setup


Input drainagesSetup 1initial

Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup

Combined SetupInput drains & depr.

Climate (1993-2009)

Land use (25x25m)

Soil (1:200.000)

Topography (25x25m)

(DWD 2009, IFM 2007) (BGR 1999, LANU 2006)

(DLR 1995) (LVA 2004)

Calculating ALPHA_BF

(ARNOLD et al. 1995)

Adjusting CN-Values according

to HRU slope

8

Input depressions

Drain Setup

Sensitivity analysis



Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


Global sensitivity analysis tool from VAN GRIENSVEN et al. 2006

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Input depressions

Drain Setup

Manual calibration



Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


Groundwater parameters

Soil available water capacity

Soil hydraulic conductivity


Calibration to reach at least positive NSE

10

Input depressions

Drain Setup

Auto-calibration



Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup



Auto-calibration tool from VAN GRIENSVEN et al. 2006

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Input depressions

Drain Setup

Tile drain location



Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


Estimated drainage area:

38% of the catchment

Topography-factor

Drainprobability

FOHRER et al. 2007

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Input depressions

Drain Setup

Tile drain implementation



Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


1. Overlay soil- and drainage map

Drain Setup

2. Assign unique names to (un-)drained soils

3. Calibrate parameters for drained HRUs

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Input depressions

Drain Setup

Depression properties



Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


Raw DEM

Land use

River

Depression map and

parameters

Raw sinks

Corrected sink map 1

Calculate flow accumulation, area and depth for each sinkRaw DEM

Embankment

Contributing area to sink

Input Internal calculation processes and maps

Output

Fill sinks and subtract from raw DEM


Sinks in floodplains

Remove sinks within one grid cellto the river network

Calculate values for SubbasinsSpatial info

Sinks due towater bodies &embankments

Remove sinks within one grid cell towater bodies and embankments

Database Area (ha)

Depressions on DEM (%)

Over-estimation

(%)

GPS 5.44 - -

5 m LiDAR 7.60 99.83 39.63

(SN-SH 2006)

(LVA 2007)

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Input depressions

Drain Setup

Depression location



Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


Raw DEM

Land use

River

Depression map and

parameters

Raw sinks



Embankment



Output








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Input depressions

Drain Setup

Depression implementation



Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


Raw DEM

Land use

River

Depression map and

parameters

Raw sinks



Embankment



Output








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Input depressions

Drain Setup




Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


Raw DEM

Land use

River

Depression map and

parameters

Raw sinks



Embankment



Output








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Input depressions

Drain Setup




Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


Raw DEM

Land use

River

Depression map and

parameters

Raw sinks



Embankment



Output








Surface area: 580ha; Volume: 1.7 Mill. m³

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Input depressions

Drain Setup

Drain and depression implementation



Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


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17.8

0

1

2

3

4

5

1999 2000 2001 2002 2003 2004

Observed

Setup 1 initial

R E S U L T S

Input depressions

Drain SetupInput drainagesSetup 1initial

Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


Setup 1 initial

RMSE 0.38r² 0.16NSE -0.31

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0

1

2

3

4

5

1999 2000 2001 2002 2003 2004

Observed

Setup 1 man.cal.

Setup 1 initial

R E S U L T S

Input depressions


Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


11.4

Setup 1 manual calibrated

RMSE 0.17r² 0.54NSE 0.42

21R E S U L T S

Input depressions


Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


Setup 1 auto calibrated

0

1

2

3

4

5

1999 2000 2001 2002 2003 2004

Observed

Setup 1 auto-cal.(calibration)

0

1

2

3

4

5

2004 2005 2006 2007 2008 2009

Observed

Setup 1 auto-cal.(verification)

Problem Peak flows: Underestimation in winter; Overestimation in summer

RMSE 0.07r² 0.71NSE 0.65

RMSE 0.08r² 0.72NSE 0.72

22R E S U L T S

Input depressions


Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


Drain Setup

Increase of peak flows in winter; Summer peaks not changed

0

1

2

3

4

5

1999 2000 2001 2002 2003 2004

Observed

Setup 1 auto cal.

Drain Setup(calibration)

0

1

2

3

4

5

2004 2005 2006 2007 2008 2009

Observed

Setup 1 auto cal.

Drain Setup(verification)

RMSE 0.07r² 0.78NSE 0.66

RMSE 0.07r² 0.78NSE 0.76

23R E S U L T S

Input depressions


Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


Depression Setup

0

1

2

3

4

5

1999 2000 2001 2002 2003 2004

Observed

Setup 1 auto cal.

Depr. setup(calibration)

0

1

2

3

4

5

2004 2005 2006 2007 2008 2009

Observed

Setup 1 auto cal.

Depr. Setup(verification)

Reduction of all peak flows, higher impact on summer peaks

RMSE 0.06r² 0.73NSE 0.70

RMSE 0.09r² 0.74NSE 0.69

24R E S U L T S

Input depressions


Manual calibration

Autocalibration

Sensitivityanalysis



Depression Setup


0

1

2

3

4

5

1999 2000 2001 2002 2003 2004

04080120160200240280

PCP

Observed

Combined Setup(calibration)

0

1

2

3

4

5

2004 2005 2006 2007 2008 2009

04080120160200240280

PCP

Observed

Combined Setup(verification)

Combined Setup

RMSE 0.04r² 0.82NSE 0.78

RMSE 0.06r² 0.82NSE 0.78

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Motivation

D I S C U S S I O N

Hypothesis 1: Drain improvement

…increase of winter peak flows while summer peaks are not affected

…higher increase of peak flows at the beginning of winterperiod compared to end of winter period

…better depiction of hydrograph recession limb

The model response supports hypothesis 1 due to…

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Motivation

D I S C U S S I O N

Hypothesis 2: Depression improvement

…the introduction of the depressions improves summer peak flow depiction

but can induce a too slow catchment response in winter periods

For hypothesis 2 no conclusion can be drawn because…

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Motivation

D I S C U S S I O N

Hypothesis 3: Combined improvement

…the plausible counteraction of reducing retention through drainages and increasing retention through depressions

…the best model performance, because formerly under-and overestimated peak flows are suitably increased and reduced

The model response supports hypothesis 3 due to…

It is recommended to jointly incorporate the two landscape characteristics

28O U T L O O K

One stepping stone for a habitat model

0

1

2

3

4

5

1999 2000 2001 2002 2003 2004

04080120160200240280

PCP

Observed

Modelled (calibration)

High & lowflow duration

Hydraulic stress

Profile alteration

Substratestability

Straightening

Substratedegradation

Rivercleaning

Bank and bed fixation

Hydraulic model

GIS mapping

Data Model Output Assessment

Hydrologic stress

Fine sediment intake

Substrate preference

Flow preference

ResilienceΦ1max

t

vΦ2

Φ4

dΦ3

Φ5

Φ6

Φ7

Φ8

Φ9

Φ10

Φ = f (Φ1 Φ2…Φ10)

Φ1min

t

Eco-hydrologic model

Agriculture

Urbanization

Flood control

Gravel

Bed & bankmaterial

Wood debris

Velocity

Silt & clay

Sand

Stones

In-streamvegetation

High & low flow duration

Φ4

Φ5

Φ6

Φ7

Φ8

Φ9

Φ10

Water depth

Φ2

Φ3

Φ1 hiΦ1 lo

(KIESEL et al. 2009)

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Thank you for your attention and interest!

Contact:

[email protected]@hydrology.uni-kiel.de

Depression-Script Download (for ArcGIS 9.2):http://www.hydrology.uni-kiel.de/~jkiesel/ERPL.zip

Further Information:

KIESEL et al. (2009) in review in Hydrological Processes

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−×

+

−×−×

−=

−

−

−l

id t

tilestoret

w

dwtile eqeFCSW

hhh

q2424

11)( 1

Model algorithms: Drainages


td hw

hd

SW = Soil moisture

FC = Field capacityqtile = Tile drain discharge

hw = Height of water table above imp. zone

hd = Height of tile drain pipe above imp. zone

td = Time to drain soil to field capacity

qtilestore = Tile flow stored previous day

t1 = Transfer time from pipe to channel

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seepevapflowoutflowinpcpstored VVVVVVV −−−++=

Model algorithms: Depressions (potholes)


adapting swat to a lowland catchment and using model ... · 1 adapting swat to a lowland catchment...

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