Prediction of monthly flows for Três Marias reservoir (São Francisco river basin) using the CFS climate forecast model

De Paiva, Luana Ferreira Gomes; Montenegro, Suzana Maria; Cataldi, Marcio

doi:10.1590/2318-0331.252020190067

Luana Ferreira Gomes De Paiva

Universidade Federal de Pernambuco, Recife, PE, Brasil

http://orcid.org/0000-0002-6154-2706

Suzana Maria Montenegro

Universidade Federal de Pernambuco, Recife, PE, Brasil

http://orcid.org/0000-0002-2520-5761

Marcio Cataldi

Universidade Federal Fluminense, Niterói, RJ, Brasil

http://orcid.org/0000-0001-9769-0105

E-mails: luanag@ons.org.br(LFGP), suzanam@ufpe.br (SMM), mcataldi@id.uff.br (MC)

Authors contributions Luana Ferreira Gomes De Paiva: Data collection, analysis of information, simulations and general writing of the paper.

Suzana Maria Montenegro: General revision of the text in Portuguese and English, general orientation of the paper structure, analysis and simulations, final revision.

Marcio Cataldi: General revision of the text in Portuguese and English, general orientation of the article structure, analysis and simulations, final revision.

Figures (23)
Tables (10)

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Figure 1
Flowchart diagram of the study steps.

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Figure 2
Methodology for precipitation correction.

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Figure 3
Drainage area of the Três Marias reservoirs. Adapted from Tucci et al. (2015)Tucci, C. E. M., Collischonn, W., & Fan, F. M. (2015). Hydropower forecasting in Brazil. In Q. Duan, F. Pappenberger, A. Wood, H. L. Cloke, J. C. Schaake. Handbook of hydrometeorological ensemble forecasting (pp. 1-22). Berlin, Heidelberg: Springer. http://dx.doi.org/10.1007/978-3-642-40457-3_55-1.
http://dx.doi.org/10.1007/978-3-642-4045... .

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Figure 4
Process for stopping the training of a Neural Network of the study (output example from Matlab).

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Figure 5
Comparison of predicted rainfall by the CFS model with 1, 2, and 3 months in advance, compared to the average observed rainfall in the drainage area of the Três Marias reservoir.

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Figure 6
CFS climate x average rainfall at the Três Marias drainage area, from 1982 to 2010.

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Figure 7
PDF analysis, comparison of CFS climate x average rainfall at the Três Marias drainage area, from 1982 to 2010.

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Figure 8
Comparison of predicted, corrected predicted and observed precipitation.

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Figure 9
Pearson correlation of the Três Marias average rainfall with the climate variables for t, t-1, t-2 and t-3 lags.

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Figure 10
Pearson correlation of the natural flow of Três Marias with the climate variables for t, t-1, t-2 and t-3 lags.

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Figure 11
Principal component analysis factors by variables.

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Figure 12
SMAP model calibration for the Três Marias area.

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Figure 13
Observed flows x predicted flows by simulation 5 using NN (Neural Networks).

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Figure 14
Observed flows x predicted flows by simulation 6 that incorporate the observed rainfall.

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Figure 15
Observed flows x predicted flows by simulation 7 that incorporate observed rainfall.

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Figure 16
Results of simulation 7 incorporating rain forecasts of the CFS model (ensemble average).

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Figure 17
Outflow prediction with NN (Neural Networks) using the rain forecasts of the CFS model, PDF/PDF correction.

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Figure 18
Application of predicted rain by the CFS for the evaluation of the SMAP model for the Três Marias area. Where: Qverif represents the observed flow; Q c Pverif represents the predicted simulated flow with the observed rainfall (only for the comparison with the previous simulations in the same data period); pdfpdf_md Q represents the predicted flow with the average rainfall of the ensemble and with both climate correction and monthly correction by the PDF method; pdf_md Q represents the predicted flow with the average rain of the ensemble and with climate correction by the PDF method; linearpdf_md Q represents the predicted flow with the average rain of the ensemble and with climate correction by the Linear method and monthly correction by the PDF method; linear_md Q represents the predicted flow with the average rain of the ensemble and with climate correction by the Linear method.

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Figure 19
Evaluation of the flow range of the flow predictions for the Três Marias reservoir.

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Figure 20
Comparison of the average ranges predicted by SMAP and Neural Networks models.

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Figure 21
Scenarios predicted by GEVAZP, along with the range of the CFS model and observed flow, PMO September 2012 (predicted flows for October 2012) with SMAP range.

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Figure 22
Scenarios predicted by GEVAZP, along with the CFS model range and observed flow, PMO January 2013 (predicted flows for February 2013) with SMAP range.

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Figure 23
Scenarios predicted by GEVAZP, with the Neural Networks model range, SMAP and the observed flow, for the period from Jan./2015 to Dec./2016.

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Table 1
Relationship between the climate and the average rainfall at the Três Marias area, Linear Coefficient and PDF.

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Table 2
Results obtained with the correction of precipitation, Três Marias area.

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Table 3
General information of the variables under analysis.

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Table 4
Main parameters obtained in SMAP calibration.

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Table 5
Performance index found in the calibration.

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Table 6
Input variables considered in the MLP Neural Network for the wet period.

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Table 7
Results obtained for the one-month lag forecasts for the Três Marias reservoir.

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Table 8
Results of simulation 6 that incorporate the observed rainfall.

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Table 9
Results of simulation 7, with one network for the wet period and another for the dry period.

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Table 10
Deviations from the average flow range predicted by SMAP and Neural Networks models for the period from May 2011 to December 2016.

Três Marias	CFS Climate	Verif	Clinear	Cpdf
Jan	354.0	263.8	0.75	0.62
Feb	260.1	177.6	0.68	0.63
Mar	261.6	169.6	0.65	0.70
Apr	106.4	63.0	0.59	0.72
May	47.2	33.9	0.72	0.85
Jun	21.3	11.2	0.52	0.59
Jul	17.6	10.5	0.59	0.71
Aug	16.9	14.9	0.89	0.65
Sep	59.3	50.5	0.85	0.68
Oct	142.9	101.7	0.71	0.69
Nov	265.7	183.5	0.69	0.75
Dec	377.9	288.2	0.76	0.76
Average	160.9	114.0	0.70	0.70

Deviation (mm)	Predicted Raw	Refined Predicted Climate	Refined Predicted Period
PDF	47.2	34.5	37.5
Linear Coef.	47.2	35.3	36.8

Variable	Average	Minimum	Maximum	Standard deviation
SOI_norm	0.0	-3.6	2.9	1.0
NATL	26.9	24.8	28.9	1.0
SATL	25.0	22.3	27.9	1.5
TROP	27.8	26.8	29.3	0.5
NINO1+2	23.3	18.6	29.2	2.4
NINO3	26.0	23.0	29.1	1.4
NINO4	28.6	26.4	30.3	0.8
NO3,4	27.1	24.3	29.6	1.1
PDO	-0.2	-3.1	2.4	1.1
P_UTM	108.8	0.0	508.1	107.8
Nat_UTM	655.5	40.0	3744.0	631.7

Str	K2t	Capc	K
524	5.21	7.80	6.0

MAPE	RMSE	MAE	Nash	Nash-Log	R2
24.7%	237.5	144.5	0.84	0.89	0.85

Case	Number Variables	Variables
Simul1	8	NATL	SATL	TROP	NINO1+2	NINO3	NINO4	NO3,4	P_UTM
Simul1	8	t-1	t-1	t-1	t-1	t-1	t-1	t-1	t-1
Simul2	18	NATL	NATL	SATL	SATL	TROP	TROP	NINO1+2	NINO1+2	NINO3
		t-2	t-1	t-2	t-1	t-2	t-1	t-2	t-1	t-2
		NINO3	NINO4	NINO4	NO3,4	NO3,4	P_UTM	P_UTM	Nat_UTM	Nat_UTM
		t-1	t-2	t-1	t-2	t-1	t-2	t-1	t-2	t-1
Simul3	4	P_UTM	P_UTM	Nat_UTM	Nat_UTM
Simul3	4	t-2	t-1	t-2	t-1
Simul4	27	NATL	NATL	NATL	SATL	SATL	SATL	TROP	TROP	TROP
		t-3	t-2	t-1	t-3	t-2	t-1	t-3	t-2	t-1
		NINO1+1	NINO1+2	NINO1+2	NINO2	NINO3	NINO3	NINO3	NINO4	NINO4
		t-3	t-2	t-1	t-3	t-2	t-1	t-3	t-2	t-1
		NO3,4	NO3,4	NO3,4	P_UTM	P_UTM	P_UTM	Nat_UTM	Nat_UTM	Nat_UTM
		t-3	t-2	t-1	t-3	t-2	t-1	t-3	t-2	t-1
Simul5	12	SATL	SATL	SATL	NINO1+1	NINO1+2	NINO1+2	P_UTM	P_UTM	P_UTM
		t-3	t-2	t-1	t-3	t-2	t-1	t-3	t-2	t-1
		Nat_UTM	Nat_UTM	Nat_UTM
		t-3	t-2	t-1

Simulation	MAPE	RMSE	MAE	Nash	Nash-Log
Simul1	42.6	384	253	0.64	0.75
Simul2	34.3	360	219	0.69	0.80
Simul3	38.0	378	240	0.65	0.79
Simul4	35.7	331	211	0.73	0.80
Simul5	39.2	333	218	0.73	0.79

Simulation	MAPE	RMSE	MAE	Nash	Nash-Log
Simul6	21.8	211	133	0.89	0.92

Simulation	MAPE	RMSE	MAE	Nash	Nash-Log
Simul7	16.0	180	103.7	0.92	0.94

Simulation	MAPE	RMSE	MAE	Nash	Nash-Log
Smap	51.8	303	157	0.49	0.65
Neural network	73.5	339	198	0.36	0.52

[1] E-mails: luanag@ons.org.br(LFGP), suzanam@ufpe.br (SMM), mcataldi@id.uff.br (MC)

Brasil

Brasil

Prediction of monthly flows for Três Marias reservoir (São Francisco river basin) using the CFS climate forecast model

Previsão de vazões mensais ao reservatório de Três Marias usando a previsão climática do modelo CFS

ABSTRACT