calibration and sensitivity analysis of swat for a small forested … · 2011. 6. 16. ·...

UNIÃO EUROPEIA Fundo social Europeu

Rial-Rivas, M.E.1; Santos, J. 1;Bernard-Jannin L. 1 ; Boulet, A.K. 1 ; Coelho, C.O.A. 1; Ferreira, A.J.D.4; Nunes, J.P. 1 ; Rodríguez-Suárez J.A. 1, 2; Rodríguez-Blanco M.L. 1,3;

Keizer, J.J. 1

Calibration and sensitivity analysis of SWAT for a small forested catchment, north-

central Portugal

2011 International SWAT Conference- June 15-17 Toledo, Spain

1CESAM and Dept. Environment & Planning. University of Aveiro. PORTUGAL E-mail: [email protected]

2Vegetal Biology and Soil Science Department. University of Vigo. SPAIN. 3 Faculty of Sciences. University of A Coruña. SPAIN.

4CERNAS, Coimbra Agrarian Technical School. PORTUGAL

HIDRIA project


HIDRIA project

“A multi-stage approach for addressing input data uncertainties in

process-based rainfall-runoff modeling for small forested

catchments upstream of the Ria de Aveiro”

The project foresees the development of:

A stepwise approach to rainfall-runoff modelling

To assess the implications of

existing data constraints

To establish priorities for additional field

and laboratory data gathering


SPECIFIC OBJECTIVE WITHIN THIS WORK

Assess the influence of:

Nash-Sutcliffe efficiency (NSE)

Percent bias (PBIAS)

Root Mean Square Error (RMSE)

METHODS

MODEL EVALUATION

SENSITIVITY ANALYSIS

(ArcSWAT 2009 interface)

AUTO-CALIBRATION

(ArcSWAT 2009 interface)

Latin Hypercube (LH) and One-factor-At-a-Time (OAT)

sampling.

Parameter Solution (ParaSol)

with uncertainty analysis

Ranges of variation of these

parameters

in the SWAT model auto-calibration results for the study catchment.

Number of parameters included in

the auto-calibration.

HIDRIA project

STUDY AREA

HIDRIA project

Serra de Cima

Rainfall:

1000-2500 mm/yr

4 micro-catchments

Area <1 km2

Caramulo mountain

range

STUDY AREA

HIDRIA project

HYDROLOGICAL MODELLING: Input Data

LAND-USE SOIL-TYPES

Data widely available in Portugal (e.g. to

hydrologists from a consultancy company developing a

Watershed Management Plan)

CLIMATE

HYDROLOGICAL MODELLING: Input Data

ArcSWAT 2009

HUMIC CAMBISOL

ELEVATIONS

•Rainfall

•Temperature

•Relative Humidity

•Wind velocity

•Solar radiation

DAILY

•Temperature

•Rainfall

IM: Coimbra_G

WEATHER

GENERATOR

Study area

HYDROLOGICAL MODELLING: CLIMATE Input Data

CLC06: Transitional woodland-shrub 6.58% total area

Catchment area: 0.53 km2

CORINE LAND-COVER 2006

Young Eucalypt

plantations

HYDROLOGICAL MODELLING: LAND-USE Input Data

CLC06: Broad-leaved forest: 93.42% total area

Eucalypt

Forest

HYDROLOGICAL MODELLING: Simulated period

13.2

13.4

13.6

13.8

14

14.2

14.4

14.6

14.8

15

15.2

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Tem

pera

ture

, ºC

Rain

fall, m

m/y

r

Rainfall, mm/yr Mean Temp, ºC

HYDROLOGICAL MODELLING: Simulated period Warm-up period Study period

Mean Rainfall = 1245 mm/yr

Calibration period Validation period

0

30

60

90

120

1/1/09 3/2/09 5/1/09 6/30/09 8/29/09 10/28/09 12/27/09

Daily

Str

eam

flow

, m

m

0

30

60

90

120

150

180

Daily

Rain

fall,

mm

0

30

60

90

120

01/01/10 03/02/10 05/01/10 06/30/10 08/29/10 10/28/10 12/27/10

Daily

Str

eam

flow

, m

m

0

30

60

90

120

150

180

Daily

Rain

fall,

mm

Calibration period Validation period

HYDROLOGICAL MODELLING: Simulated period

PBouça = 1585 mm

Qmm = 1004 mm

Q max obs = 47.9 mm

Q min obs = 0 mm

PBouça = 1367 mm

Qmm = 671 mm

Q max obs = 19.9 mm

Q min obs = 0 mm

HYDROLOGICAL MODELLING: Sensitivity Analysis

HYDROLOGICAL MODELLING: Sensitivity Analysis

26 flow-related parameters Ranking

Mean

PARAMETER LO BOUND UP BOUND iMet

Alpha_Bf 0 1 1

Biomix 0 1 1

Blai 0 1 1

Canmx 0 10 1

Ch_K2 0 150 1

Ch_N2 0 1 1

Cn2 -25 25 3

Epco 0 1 1

Esco 0 1 1

Gw_Delay 0.001 10 2

Gw_Revap 0.001 0.036 2

Gwqmn 0.001 1000 2

Revapmn 0.001 100 2

Sftmp 0 5 1

Slope -25 25 3

Slsubbsn -25 25 3

Smfmn 0 10 1

Smfmx 0 10 1

Smtmp -25 25 3

Sol_Alb -25 25 3

Sol_Awc -25 25 3

Sol_K -25 25 3

Sol_Z -25 25 3

Surlag 0 10 1

Timp 0 1 1

Tlaps 0 50 1

INPUT OUTPUT

0

5

10

15

20

25

Alp

ha

_B

f

Bio

mix

Bla

i

Ca

nm

x

Ch

_K

2

Ch

_N

2

Cn

2

Ep

co

Esco

Gw

_D

ela

y

Gw

_R

eva

p

Gw

qm

n

Re

va

pm

n

Sft

mp

Slo

pe

Sls

ub

bsn

Sm

fmn

Sm

fmx

Sm

tmp

So

l_A

lb

So

l_A

wc

So

l_K

So

l_Z

Su

rla

g

Tim

p

Tla

ps

With observed data

Without observed data

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

Alp

ha

_B

f

Bio

mix

Bla

i

Ca

nm

x

Ch

_K

2

Ch

_N

2

Cn

2

Ep

co

Esco

Gw

_D

ela

y

Gw

_R

eva

p

Gw

qm

n

Re

va

pm

n

Sft

mp

Slo

pe

Sls

ub

bsn

Sm

fmn

Sm

fmx

Sm

tmp

So

l_A

lb

So

l_A

wc

So

l_K

So

l_Z

Su

rla

g

Tim

p

Tla

ps

With observed data


HYDROLOGICAL MODELLING: Sensitivity Analysis 26 flow-related parameters

Ranking

Mean

OUTPUT

0

5

10

15

20

25

Alp

ha

_B

f

Bio

mix

Bla

i

Ca

nm

x

Ch

_K

2

Ch

_N

2

Cn

2

Ep

co

Esco

Gw

_D

ela

y

Gw

_R

eva

p

Gw

qm

n

Re

va

pm

n

Sft

mp

Slo

pe

Sls

ub

bsn

Sm

fmn

Sm

fmx

Sm

tmp

So

l_A

lb

So

l_A

wc

So

l_K

So

l_Z

Su

rla

g

Tim

p

Tla

ps

With observed data


0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

Alp

ha

_B

f

Bio

mix

Bla

i

Ca

nm

x

Ch

_K

2

Ch

_N

2

Cn

2

Ep

co

Esco

Gw

_D

ela

y

Gw

_R

eva

p

Gw

qm

n

Re

va

pm

n

Sft

mp

Slo

pe

Sls

ub

bsn

Sm

fmn

Sm

fmx

Sm

tmp

So

l_A

lb

So

l_A

wc

So

l_K

So

l_Z

Su

rla

g

Tim

p

Tla

ps

With observed data


WITHOUT WITH

OBS DATA OBS DATA

Alpha_Bf Esco

Cn2 Gwqmn

Esco Sol_Awc

Canmx Canmx

Blai Cn2

Surlag Sol_Z

Ch_K2 Gw_Revap

Gw_Revap Blai

Gw_Delay Alpha_Bf

Sol_Z Sol_K

Ch_N2 Ch_K2

Sol_Awc Biomix

Sol_K Slope

Slope Epco

Biomix Revapmn

Slsubbsn Gw_Delay

Epco Surlag

Gwqmn Ch_N2

Sol_Alb Sol_Alb

Revapmn Slsubbsn

Sftmp Sftmp

Smfmn Smfmn

Smfmx Smfmx

Smtmp Smtmp

Timp Timp

Tlaps Tlaps

HYDROLOGICAL MODELLING: Auto-calibration

Three auto-calibrations were carried out using:

Auto-calibration A

26 flow-related

parameters and full

default ranges of

variation

HYDROLOGICAL MODELLING: Auto-calibration

Auto-calibration C Auto-calibration B

13 flow-related

parameters and full

default ranges of

variation

13 flow related

parameters and

narrow ranges of

variation

The 13 most sensitive parameters selected from

Sensitivity Analysis using observed data.

Upper and lower bounds for narrow ranges using

max and min values from the good parameter

sets from Auto-calibration A.

• ParaSol with uncertainty analysis

• Fixing the number of simulations runs in 20000 and the optimization settings as the default.

HYDROLOGICAL MODELLING: Auto-calibration RESULTS

Calibration period

0

30

60

90

1200

30

60

90

120

1/1/2009 2/1/2009 3/1/2009 4/1/2009 5/1/2009 6/1/2009 7/1/2009 8/1/2009 9/1/2009 10/1/2009 11/1/2009 12/1/2009

Daily

Rain

fall,

mm

Daily

Str

eam

flo

w, m

m

Daily Rainfall, mm

Daily Observed Flows, mm (Calibration period)

Auto-calibration A: Daily Simulated Flows, mm (Calibration period)

Auto-calibration B: Daily Simulated Flows, mm (Calibration period)

Auto-calibration C: Daily Simulated Flows, mm (Calibration period)

Auto-calibration A Auto-calibration B Auto-calibration C

PBIAS 9.84 16.05 9.75

RMSE 4.14 4.11 4.37

NSE 0.62 0.63 0.58


Calibration period

SD=6.72

Satisfactory

Very good

0

30

60

90

1200

30

60

90

120

1/1/2009 2/1/2009 3/1/2009 4/1/2009 5/1/2009 6/1/2009 7/1/2009 8/1/2009 9/1/2009 10/1/2009 11/1/2009 12/1/2009

Daily

Rain

fall,

mm

Daily

Str

eam

flo

w, m

m

Daily Rainfall, mm

Daily Observed Flows, mm (Calibration period)

Auto-calibration A: Daily Simulated Flows, mm (Calibration period)

Auto-calibration B: Daily Simulated Flows, mm (Calibration period)

Auto-calibration C: Daily Simulated Flows, mm (Calibration period)


Validation period


Validation period

0

20

40

60

80

100

120

140

160

180

2000

20

40

60

01/01/10 01/31/10 03/02/10 04/01/10 05/01/10 05/31/10 06/30/10 07/30/10 08/29/10 09/28/10 10/28/10 11/27/10 12/27/10

Daily

Rain

fall,

mm

Daily

Str

eam

flo

w, m

m

Daily Rainfall, mm

Daily Observed Flows, mm (Validation period)

Auto-calibration A: Daily Simulated Flows, mm (Validation period)

Auto-calibration B: Daily Simulated Flows, mm (Validation period)

Auto-calibration C: Daily Simulated Flows, mm (Validation period)


PBIAS -23.6 -19.81 -21.64

RMSE 1.5 1.92 1.69

NSE 0.74 0.55 0.66

0

20

40

60

80

100

120

140

160

180

2000

20

40

60

01/01/10 01/31/10 03/02/10 04/01/10 05/01/10 05/31/10 06/30/10 07/30/10 08/29/10 09/28/10 10/28/10 11/27/10 12/27/10

Daily

Rain

fall,

mm

Daily

Str

eam

flo

w, m

m

Daily Rainfall, mm

Daily Observed Flows, mm (Validation period)

Auto-calibration A: Daily Simulated Flows, mm (Validation period)

Auto-calibration B: Daily Simulated Flows, mm (Validation period)

Auto-calibration C: Daily Simulated Flows, mm (Validation period)

SD=2.83


Comparison between best parameter sets for each auto-calibration



Alpha_Bf 0.269 0.562 0.195

Biomix 0.677 0.218 0.180

Blai 0.156 0.061 0.059

Canmx 4.412 7.327 7.421

Ch_K2 127.6 106.9 126.4

Cn2 50-95 56-100 51-97

Esco 0.131 0.198 0.139

Gw_Revap 0.050 2.009 0.060

Gwqmn 933 962 1858

Sol_Awc1 0.169 0.203 0.193

Sol_Awc2 0.130 0.157 0.149

Sol_K 3.46 4.28 4.15

Sol_K2 3.41 4.22 4.09

Sol_Z1 335 358 334

Sol_Z2 1342 1432 1337

Groundwater “revap” coefficient

Afects the amount of water that

recharges the capillary fringe after

evaporation during the dry periods.

Depth from soil surface to bottom layer

Threshold depth of water in the

shallow aquifer required for the

return flow to occur (The ground

water flow to the main channel is

allowed only when the depth of water in

the shallow aquifer is equal to or greater

than

Baseflow recesion coefficient is a direct

index of groundwater flow response to

changes in recharge

Comparison between best parameter sets for each auto-calibration

Canopy Interception and Max. Potencial

LAI

1.- Check the feasibility of the obtained parameters

2.- Check other auto-calibration methodologies and with different objective functions.

3.- Testing SWAT with data obtained from a meteorological station in the study area as well as

from fieldwork in the Serra de Cima catchment, aiming at improving model results and

decrease problems related with equifinality.

CONCLUSIONS

The best results were obtained for the set with the largest number of

parameters and the widest ranges of variation.

Sensitivity analysis was helpful in reducing the number of parameters

included in the auto-calibration and, auto-calibration time, without

seriously affecting model results.

The use of narrow ranges of variation for the parameters also reduced the

time needed for auto-calibration whilst still producing results that can be

regarded adequate, especially for general-purpose studies.

The fact that several parameter sets have given good results, indicating a

problem with equifinality of model parameterization.

ONGOING WORK


Thanks for your attention

calibration and sensitivity analysis of swat for a small forested … · 2011. 6. 16. ·...

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