Mapping Rainfall Variability in the São Francisco River Basin: Insights for Water Resource Management

Medeiros, Elias Silva de; Oliveira, Willian dos Santos; Silva, Paulo Vinicius da; Souza, Amaury de; Bicalho, Carolina Cristina; Mauad, Munir; Souza, Sidnei Azevedo de

doi:10.1590/0102-77863910050

Acessibilidade / Reportar erro

Brasil

Revista Brasileira de Meteorologia

Español English

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Article • Rev. bras. meteorol. 39 • 2024 • https://doi.org/10.1590/0102-77863910050 copy

Mapping Rainfall Variability in the São Francisco River Basin: Insights for Water Resource Management

Mapeando a Variabilidade das Chuvas na Bacia do Rio São Francisco: Insights para a Gestão de Recursos Hídricos

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

The São Francisco River Basin (SFRB) is extremely important for both the local rural and urban population, running through several states and municipalities in Brazil. Thus, the study of the spatial distribution of rainfall in the SFRB, supported by the application of geostatistics, offers valuable insights for water management and planning. In this study, data from 341 rainfall stations were used, corresponding to the period between 1989 and 2018, and monthly accumulated rainfall analyzed. About the trends, positive estimates for latitude, except for April, highlight that the accumulated precipitation decreases from south to north each month. In December, 85.10% of the spatial variability of rainfall was explained by the trend of rainfall concerning geographical coordinates. Concerning spatial dependence, the analyzed data predominantly exhibited the exponential variogram model. The conducted analyses offered a holistic view of rainfall in the region, facilitating the understanding of spatial patterns and shedding light on challenges in the face of climate change. Our research limited on the spatial modeling of rainfall in the SFRB, and future work should include modeling socio-economic and environmental impacts in the region concerning the spatial irregularity of rainfall.

Keywords
climate change; geostatistics; rainfall; universal kriging

Model	Equation	SDI (%)
Spherical	$γ (h) = C_{0} + C_{1} [\frac{3 h}{2 r} - {\frac{1}{2} (\frac{h}{r})}^{3}], 0 < h \leq r$ $γ (h) = C_{0} + C_{1}, h > r$	$\frac{C_{0}}{C_{0} + C_{1}} \times 100 %$
Exponential	$γ (h) = C_{0} + C_{1} [1 - e x p (- \frac{h}{r})], h > 0$
Gaussian	$γ (h) = C_{0} + C_{1} [1 - e x p (- \frac{h^{2}}{r^{2}})], 0 < h \leq r$ $γ (h) = C_{0} + C, h > r$

Month	Minimum	Mean	Median	Maximum	Standard deviation	CV (%)
January	19.49	174.46	169.16	451.72	81.89	46.94%
February	16.3	125.04	125.18	308.17	42.79	34.27%
March	25.91	139.7	142.23	383.26	46.69	33.42%
April	26.29	59.8	55.71	147.58	20.46	34.22%
May	1.44	23.79	18.82	206.58	23.49	9871,0%
June	0.00	11.05	5.06	199.63	21.67	195.90%
July	0.00	8.14	3.4	193.2	19.83	243.62%
August	0.00	7.83	4.59	112.97	12.08	154.25%
September	0.04	22.66	15.63	67.65	18.45	81.44%
October	3.82	65.08	69.11	157.7	31.69	48.69%
November	5.007	163.67	188.81	591.39	74.32	45.41%
December	13.27	201.47	216.88	610.09	93.48	46.40%

Month	Variable				R ²
Month	Intercept	Latitude	Longitude	Longitude²	R ²
January	1651.06**	-15.39**	88.73**	1.13**	75.34%
February	1381.15**	-3.35**	72.91**	0.98**	65.02%
March	1512.26**	-3.98**	79.46**	1.06**	65.42%
April	2465.96**	1.50**	116.11**	1.40**	34.94%
May	4053.11**	-3.054**	185.60**	2.10**	69.71%
June	3495.67**	-2.44**	159.40**	1.80**	70.72%
July	3124.92**	-1.98**	142.05**	1.60**	73.41%
August	2001.21**	-1.96**	91.89**	1.04**	67.99%
September	1498.28**	-5.10**	70.50**	0.79**	77.57%
October	1363.19**	-4.91**	70.52**	0.89**	80.25%
November	-654.00**	-7.41**	-13.55**	0.057**	81.27%
December	-579.70**	-16.33**	-11.55**	0.015**	85.1%

Month	Spherical			Exponential			Gaussian
Month	MAE	RMSE	R ²	MAE	RMSE	R ²	MAE	RMSE	R ²
January	18.66	28.98	87.58%	17.74	27.69	88.61%	194.50	683.70	4.07%
February	13.43	20.86	76.68%	13.38	20.74	76.89%	9480	93600	~ 0.00
March	15.83	23.48	75.01%	15.06	22.27	77.31%	1941.56	8452.37	0.09%
April	8.73	12.24	64.52%	8.43	11.89	66.35%	84.99	282.08	0.02%
May	4.22	6.49	92.34%	4.23	6.49	92.34%	1103.39	5293.21	0.05%
June	2.61	5.30	94.07%	2.59	5.23	94.20%	1147.89	5538.84	0.06%
July	2.26	5.85	91.38%	2.22	5.77	91.57%	184.87	1002.87	0.02%
August	2.27	4.60	85.55%	1.89	3.80	90.08%	10.05	33.37	26.65%
September	3.62	5.29	91.80%	3.45	5.07	92.47%	212.06	1148.69	0.87%
October	7.57	1.36	87.20%	7.46	11.47	86.95%	73.55	344.49	1.39%
November	15.72	29.70	84.22%	15.22	28.87	85.01%	264.00	12200.00	0.08%
December	16.67	23.65	93.23%	16.24	23.05	93.56%	16.05	22.87	93.66%

Month	Variogram	Sill	Range (km)
January	Exponential	1.417.081	73.086
February	Exponential	835.396	176.430
March	Exponential	822.054	74.586
April	Exponential	249.213	65.629
May	Spherical	107.524	274.445
June	Exponential	84.73	194.16
July	Exponential	81.428	267.30
August	Exponential	18.36	42.57
September	Exponential	59.07	74.89
October	Spherical	153.93	63.83
November	Exponential	1260.58	88.92
December	Gaussian	225.63	83.52

Sociedade Brasileira de Meteorologia Rua. Do México - Centro - Rio de Janeiro - RJ - Brasil, +55(83)981340757 - São Paulo - SP - Brazil
E-mail: sbmet@sbmet.org.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] Autor de correspondência: Elias Silva de Medeiros, eliasmedeiros@ufgd.edu.br.