Managing water resources in complex tropical basins: tailored SWAT ecohydrological modeling to the Rio das Velhas, Brazil

Almeida, Ana Luisa Sales Pereira; Macedo, Diego Rodrigues; Santos, Hersilia de Andrade e; Ribeiro, Sónia Maria Carvalho; Hughes, Robert Mason

doi:10.1590/2318-0331.282320230034

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Scientific/Technical Article • RBRH 28 • 2023 • https://doi.org/10.1590/2318-0331.282320230034 copy

Managing water resources in complex tropical basins: tailored SWAT ecohydrological modeling to the Rio das Velhas, Brazil

Gestão dos recursos hídricos em bacias tropicais complexas: uma abordagem customizada da modelagem eco-hidrológica SWAT para o Rio das Velhas, Brasil

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Hydrological modeling in decision-making is particularly challenging in tropical countries such as Brazil. There are numerous modeling tools; however, many applications have focused on watersheds with a total area of <20,000km². Here we tailored a customized SWAT (Soil and Water Assessment Tool) ecohydrological model application using the SWAT CUP tool for calibration and validation of the Rio das Velhas, a relatively large, complex Brazilian basin (~28,000km²). The Rio das Velhas is the longest tributary of the São Francisco River and contains heterogeneous landforms, soils, vegetation, and land uses. A multisite calibration method obtained specific regionalized parameters for each sub-basin group for successfully simulating Rio das Velhas streamflows. Our results showed a suitable adjustment of the model. Nash-Sutcliff (NS) model performance values were 0.73-0.97 (calibration) and 0.51-0.98 (validation). The percent bias (PBIAS) was -11.3 to 19.4 (calibration) and -18.6 to 24.6 (validation), and the coefficient of determination values (R²) were >0.6 in all sub-basins on a monthly basis. We also explored how four contrasting land use scenarios affected four water-flow variables (surface runoff, base flow, percolation, and total streamflow). Our results show that by using multiple flow-monitoring stations and multisite calibration approaches, ecohydrological models can be useful for managing basin-extent water resources in countries of continental dimensions such as Brazil.

Keywords:
Water resources management; Water governance; GIS spatial tools; Rio das Velhas

Model performance	NS	PBIAS
Very good	0.75 < NS ≤ 1.00	PBIAS ≤ ±10%
Good	0.65 < NS ≤ 0.75	±10% < PBIAS ≤ ±15%
Satisfactory	0.50 < NS ≤ 0.65	±15% < PBIAS ≤ ±25%
Unsatisfactory	0.0 < NS ≤ 0.50	±25% < PBIAS ≤ ±30%
Unacceptable	NS ≤ 0.0	PBIAS > ±30%

	Baseline (S1)	Natural (S2)	Agricultural (S3)	Urbanization (S4)
Agriculture	14.47%	0%	51.35%	14.47%
Urban Infrastructure	2.41%	0%	2.41%	39.29%
Natural vegetation cover ¹ 1 Forest-evergreen, range-brush, and range-grasses.	36.88%	100%	0%	0%
Others	46.24%	0%	46.24%	46.24%

Three major subbasin groups	Number of HRUs	Predominant class
Three major subbasin groups	Number of HRUs	Soil type	Climate type	Land use and cover	Elevation class	Slope class
04	276	Neosoil	AW	Natural cover	600-800m	< 4%
31	144	Argisoil	CWb	Natural cover	600-800m	13-25%
43	259	Argisoil	CWa	Natural cover	600-800m	13-25%

Sub-basin	Order of calibration	Pre-calibration			Calibration			Validation
Sub-basin	Order of calibration	NS	PBIAS	R²	NS	PBIAS	R²	NS	PBIAS	R²
15	1^st	0.59^* * Satisfactory.	-14.27^ Good.	0.62	0.73^* ***** Very good.	12.2^**	0.76	0.51^*	-2.1^***	0.62
22	1^st	-2.96	-149.28	0.79	0.77^***	17.0	0.79	0.81^***	-18.6^*	0.85
34	1^st	-20.46	-394.8	0.71	0.78^***	10.7^**	0.78	0.57^*	-15.6^*	0.65
42	1^st	0.42	-51.29	0.90	0.92^***	-11.3^**	0.94	0.86^***	-14.3^**	0.87
59	1^st	-6.28	-115	0.81	0.78^***	-4.8^***	0.81	0.90^***	-7.0^***	0.90
61	1^st	-1.06	-87.37	0.88	0.85^***	13.2^**	0.86	0.90^***	8.2^***	0.91
64	1^st	-4.38	-91.25	0.76	0.76^***	19.4^*	0.85	0.77^***	24.6^*	0.85
68	1^st	-1.45	-59.31	0.78	0.84^***	1.3^***	0.88	0.73^**	-2.4^***	0.80
76	1^st	-2.34	-34.95	0.85	0.88^***	-3.1^***	0.88	0.92^***	-3.2^***	0.92
67	2^nd	-4.33	-87.49	0.87	0.94^***	-2.5^***	0.95	0.90^***	-16.2^*	0.93
56	3^rd	-3.68	-83.52	0.89	0.91^***	7.8^***	0.93	0.97^***	4.7^***	0.97
52	4^th	-3.97	-83.73	0.89	0.93^***	2.4^***	0.93	0.98^***	4.3^***	0.98
43	5^th	-3.69	-86.37	0.88	0.93^***	-0.1^***	0.93	0.85^***	0.2^***	0.95
31	6^th	-2.27	-93.33	0.90	0.96^***	-6.8^***	0.97	0.94^***	-12.0^**	0.96
4	7^th	-1.82	-99.53	0.91	0.97^***	2.3^***	0.98	0.97^***	1.8^***	0.97

	Baseline (S1)	Natural (S2)	Agricultural (S3)	Urbanization (S4)
(1) Surface runoff/Total flow	0.178	0.154	0.192	0.479
(2) Base flow/Total flow	0.379	0.415	0.363	0.210
(3) Surface runoff/Percolation	0.190	0.153	0.208	0.847

Sub-basin	Area Km²	Dry				Wet
Sub-basin	Area Km²	S1	S2	S3	S4	S1	S2	S3	S4
15	1,361	2.65	2.88	2.65	2.76	33.98	37.55	36.61	35.34
22	2,041	7.51	6.73	7.16	6.05	24.15	22.43	25.66	32.19
34	812	0.94	1.66	1.16	1.12	4.36	6.16	4.84	7.46
42	3,971	22.76	21.89	22.01	21.91	122.88	120.36	124.26	132.11
59	239	1.73	1.88	1.61	0.76	3.89	3.28	3.75	5.17
61	562	1.31	0.99	1.45	1.24	9.35	8.14	9.39	11.58
64	704	2.07	2.55	2.28	1.82	10.4	8.48	15.45	9
68	615	2.88	3.22	1.5	2.62	14.57	14.8	14.15	17.86
76	1,535	21.61	22.15	21.21	17.72	40.88	40.56	41.06	45.14
67	3,707	35.84	38.86	35.51	29.17	83.88	79.13	81.89	99.7
56	7,048	47	52.14	45.59	35.52	124.92	120.06	121.5	160.1
52	8,021	57.56	65.09	41.28	55.79	138.62	136.21	134.78	174.23
43	10,620	81.38	92.27	79.66	58.04	171.6	179.08	169.65	213.35
31	16,590	107.28	118.16	105.2	83.33	299.36	306.02	299.68	358.16
4	26,390	127.98	139.2	125.74	103.41	405.94	417.32	413.41	497.45

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