Multi-variable SWAT model calibration with remotely sensed evapotranspiration and observed flow

Franco, Ana Clara Lazzari; Bonumá, Nadia Bernardi

doi:10.1590/2318-0331.011716090

Ana Clara Lazzari Franco

Universidade Federal de Santa Catarina, Florianópolis, SC, Brasil

Nadia Bernardi Bonumá

Universidade Federal de Santa Catarina, Florianópolis, SC, Brasil

E-mails: anaclaralfranco@gmail.com (ACLF), nadia.bonuma@ufsc.br (NBB)

Ana Clara Lazzari Franco: Paper conception, data analysis, simulations, literature review, discussion, paper writing.

Nadia Bernardi Bonumá: Paper writing and discussion.

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Figure 1
Location of the upper Negro river basin, rainfall and streamflow gauges and meteorologic stations.

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Figure 2
Calibration strategies, according the variable considered for the objective function of each iteration.

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Figure 3
Monthly streamflow calibration (2006-2008) and validation (2009) for strategies S3’ and S3.

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Figure 4
Monthly streamflow calibration (2007-2009) and validation (2006) for strategies S3’ and S3.

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Figure 5
Daily streamflow from 01 July to 31 December of 2009, calibrated with monthly data from 2006 to 2008.

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Figure 6
Daily streamflow from 01 July to 31 December of 2006, calibrated with monthly data from 2007 to 2009.

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Figure 7
Average monthly rainfall and streamflow.

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Figure 8
Daily streamflow performance (2006-2009) using parameter values interval (Range_Par) and best parameter set (Best_Par).

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Figure 9
Monthly streamflow performance (2006-2009) using parameter values interval (Range_Par) and best parameter set (Best_Par).

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Table 1
Rainfall (P), streamflow (F) and meteorological (M) stations.

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Table 2
Land use and land cover for the upper Negro river basin.

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Table 3
Calibrated parameters and initial range.

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Table 4
Performance indicators for the uncalibrated model (S0).

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Table 5
Daily streamflow validation for the time interval of 2006 to 2009.

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Table 6
Uncertainty and performance indicators for all strategies. Monthly simulation.

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Code	Name	Available data from	Latitude	Longitude	Altitude (m)	Responsable	Type
2549003	Rio da Várzea dos Lima	1/9/1940	-26.0	-49.4	810	ANA	P
2649006	Rio Negro	1/12/1922	-26.1	-49.8	770	ANA	P
2649017	Doutor Pedrinho	1/12/1953	-26.7	-49.5	250	ANA	P
2649018	Fragosos	1/05/1967	-26.2	-49.4	790	ANA	P
2649054	Moema	1/07/1976	-26.5	-49.8	950	ANA	P
2649055	Corredeira	1/07/1976	-26.4	-49.6	750	ANA	P
2649056	Itaiópolis	1/07/1976	-26.3	-49.9	990	ANA	P
2649057	Campo Alegre	1/07/1976	-26.2	-49.3	870	ANA	P
2649058	Barra do Prata	1/11/1977	-26.7	-49.8	450	ANA	P
65100000	Rio Negro	1/05/1930	-26.1	-49.8	766	COPEL	F
86945	Rio Negrinho	1/06/1990	-26.2	-49.6	857	EPAGRI/INMET	M

Class	Area		SWAT
Class	Km²	%	code
Water	14.2	0.4	WATR
Urban	96.8	2.8	URHD
Reforestation	610.0	17.7	PINE
Native Forest	1,713.8	49.7	FRST
Agriculture	926.5	26.9	AGRL
Pasture	87.4	2.5	PAST

Parameter	Minimum	Maximum
CN2 (relative)^* * The existing parameter value is multiplied by (1+ a given value). Parameter definitions and further details can be found in Neitsch et al. (2011).	-0.25	0.25
SOL_AWC (relative)^* * The existing parameter value is multiplied by (1+ a given value). Parameter definitions and further details can be found in Neitsch et al. (2011).	-0.25	0.25
SOL_K (relative)^* * The existing parameter value is multiplied by (1+ a given value). Parameter definitions and further details can be found in Neitsch et al. (2011).	-0.25	0.25
ALPHA_BF	0	1
GW_DELAY	0	500
GWQMN	0	5000
LAT_TTIME	1	180
CH_K1	1	300
CH_N1	0.01	0.5
RCHRG_DP	0	1
SURLAG	1	24

		Monthly time step				Daily time step
Time interval	Variable	R²	NS	PBIAS	KGE	R²	NS	PBIAS	KGE
2006-2008	Q	0.68	0.32	-40.0	0.55	0.14	-4.52	-58.2	-0.66
2006-2008	ET	0.52		40.9	0.43
2007-2009	Q	0.67	0.04	-43.9	0.47	0.01	-3.03	-18.8	-0.24
2007-2009	ET	0.61		43.5	0.44
2006	Q	0.76	-8.40	-67.1	-1.29	0.17	-24.31	-66.1	-3.25
2006	ET	0.37		44.7	0.29
2009	Q	0.78	0.74	-3.7	0.67	0.01	-0.76	43.4	0
2009	ET	0.81		33.6	0.60

Calibration Strategy	Calibration period	p-factor	r-factor	R²	NS	PBIAS	KGE	$\bar{S}$	$σ_{s}$	( $\bar{O})$	( $σ_{o}$ )
S1	2006-2008	0.67	0.93	0.21	-0.34	2.7	0.42	59	69	(60)	(57)
S1	2007-2009	0.86	1.11	0.57	0.49	-4.1	0.75	63	58
S2	2006-2008	0.96	1.27	0.67	0.65	1.9	0.80	59	53
S2	2007-2009	0.96	1.24	0.66	0.65	1.9	0.80	59	53
S3	2006-2008	0.32	0.70	0.59	0.53	-19.3	0.52	72	35
S3	2007-2009	0.36	0.74	0.59	0.54	-14.7	0.52	69	35
S3'	2006-2008	0.69	1.08	0.69	0.67	-12.7	0.77	68	52
S3'	2007-2009	0.71	1.00	0.70	0.67	-13.0	0.78	68	53

Calibration Strategy	Calibration Period	(Validation Period)	Variable	p-factor	r-factor	R²	NS	PBIAS	KGE
S1	2007-2009	(2006)	Q	0.94(0.75)	1.27(3.42)	0.78(0.75)	0.78(0.66)	-2.9(-8.9)	0.84(0.84)
			ET	0.01(0.01)	0.24(0.29)	0.58(0.36)		46.4(47.1)	0.40(0.26)
			Average						0.62(0.55)
	2006-2008	(2009)	Q	0.89(0.92)	1.08(0.75)	0.76(0.58)	0.73(0.58)	0.1(0)	0.87(0.63)
			ET	0.01(0.02)	0.24(0.23)	0.48(0.77)		46.5(39.4)	0.35(0.52)
			Average						0.61(0.57)
S2	2007-2009	(2006)	Q	1(1)	1.60(4.83)	0.79(0.88)	0.78(0.80)	-2.3(-9.1)	0.89(0.87)
			ET	0.02(0.04)	0.11(0.12)	0.63(0.39)		41.3(40.6)	0.48(0.34)
			Average						0.68(0.61)
	2006-2008	(2009)	Q	1(1)	1.77(1.15)	0.76(0.89)	0.76(0.82)	5.6(-14.0)	0.83(0.80)
			ET	0.02(0.03)	0.11(0.11)	0.51(0.81)		41.9(34.5)	0.41(0.59)
			Average						0.62(0.69)
S3'	2007-2009	(2006)	Q	0.81(0.50)	1.15(4.63)	0.85(0.84)	0.84(0.64)	1.5(-7.7)	0.83(0.67)
			ET	0.02(0.04)	0.17(0.19)	0.63(0.39)		40.3(41.4)	0.49(0.33)
			Average						0.66(0.50)
	2006-2008	(2009)	Q	0.69(0.75)	1.36(0.95)	0.80(0.93)	0.73(0.90)	-13.9(-11.9)	0.82(0.87)
			ET	0.02(0.03)	0.18(0.17)	0.50(0.80)		43.1(35.9)	0.40(0.57)
			Average						0.61(0.72)
S3	2007-2009	(2006)	Q	0.42(0.33)	0.86(3.68)	0.65(0.52)	0.45(-4.10)	-27.8(-73.3)	0.53(0.21)
			ET	0.02(0.04)	0.13(0.15)	0.65(0.40)		37.6(38.6)	0.53(0.37)
			Average						0.53(0.29)
	2006-2008	(2009)	Q	0.31(0.25)	0.96(0.68)	0.63(0.80)	0.56(0.74)	-17.3(-8.9)	0.60(0.65)
			ET	0.02(0.03)	0.14(0.13)	0.51(0.82)		41.3(33.0)	0.42(0.61)
			Average						0.51(0.63)

R^{2} = \frac{{(\sum_{i = 1}^{n} (O_{i} - \bar{S}) (S_{i} - \bar{S}))}^{2}}{\sum_{i = 1}^{n} {(O_{i} - \bar{O})}^{2} \sum_{i = 1}^{n} {(S_{i} - \bar{S})}^{2}}

[1] E-mails: anaclaralfranco@gmail.com (ACLF), nadia.bonuma@ufsc.br (NBB)

Brasil

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Multi-variable SWAT model calibration with remotely sensed evapotranspiration and observed flow

Calibração multi-variável do modelo SWAT com evapotranspiração proveniente de sensoriamento remoto e vazão observada.

ABSTRACT