Air temperature estimation techniques in Minas Gerais state, Brazil, Cwa and Cwb climate regions according to the Köppen-Geiger climate classification system

Santos, Pietros André Balbino dos; Monti, Cássio Augusto Ussi; Carvalho, Luiz Gonsaga de; Lacerda, Wilian Soares; Schwerz, Felipe

doi:10.1590/1413-7054202145023920

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…e Caracterização da Área Experimental Apresentando um clima altamente influenciado pela sua continentalidade e características geomorfológicas, o estado de Minas Gerais manifesta duas estações do ano bem definidas, sendo uma chuvosa, entre o período de outubro e março, e outra seca, entre os meses de abril e setembro, vide sua predominância pelos climas tropical e subtropical úmido com inverno seco (Ferreira et al, 2019;Santos et al, 2021).…”

Section: Localizaçãounclassified

Comparação de modelos geoestatísticos para estimativa de precipitação em Minas Gerais, Brasil, entre os anos hidrológicos de 2000 e 2021

Guimarães

Alves²

2023

Rev. Bras. Geog. Fis.

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A gestão integrada dos recursos hídricos no Brasil e no mundo tem passado por desafios provenientes de processos naturais e antrópicos. Assim, o conhecimento preciso das condições quantitativas e qualitativas dos recursos hídricos é de fundamental importância, sendo o monitoramento pluviométrico por estações pluviométricas distribuídas no estado um componente central deste, no que diz respeito ao entendimento da dinâmica espacial e temporal da precipitação. Em Minas Gerais, tais estações estão concentradas em determinadas áreas, dificultando o processo de tomada de decisão pelos agentes gestores. As técnicas de geoestatística têm se mostrado acuradas para estimativa de precipitação em espaços sem estações pluviométricas, sendo que tal trabalho visou aplicá-las no estado, nos anos hidrológicos de outubro de 2000 a setembro de 2021. Verificou-se o potencial dos modelos Circular, Gaussiano, Esférico, Tetraesférico, Pentaesférico e Exponencial, e o Grau de Dependência Espacial entre as variáveis, utilizando-se do tratamento de dados por linguagem de programação Python e R e por técnicas de geoprocessamento. Os resultados apontaram para maior aplicabilidade do modelo Gaussiano em toda série histórica, sendo sucedido pelo modelo Exponencial em períodos chuvosos, e pelo modelo Circular na estação seca. Os modelos Pentaesférico, Esférico e Tetraesférico foram os menos aplicados. Viu-se também uma relação irregular entre o desempenho dos modelos e o Grau de Dependência Espacial das variáveis.Palavras-chave: krigagem, dependência espacial, estações pluviométricas, gestão integrada dos recursos hídricos. Comparison of geostatistical models for rainfall forecast in Minas Gerais, Brazil, between 2000 and 2021 hydrological years A B S T R A C TThe integrated water resources management in Brazil and worldwide had been through challenges from anthropic activities and natural processes. In that regard, the precise knowledge of water resources quality and quantity conditions has major importance, with the rainfall monitoring network by rainfall stations as a core component for understanding the precipitation spatial and temporal dynamics. These stations are concentrated in a few sites within Minas Gerais, which makes the decision-making process difficult for the manager agents. Geostatistical analysis has been shown as an accurate method for rainfall forecast in spaces with lack of rainfall stations, and this work aimed to apply it in Minas Gerais, between October 2000 and September 2021 hydrological years. It was checked Circular, Gaussian, Spherical, Tetraspherical, Pentaspherical, and Exponential models potential, and the degree of spatial dependence, using data treatment by Python and R programming language and geoprocessing tools. The results showed that Gaussian model has greater applicability in the time series, being succeed by Exponential model in wet season, and Circular model in dry season. Pentaspherical, Spherical, and Tetraspherical models were the least applied. It was found an irregular relationship between models’ performance and the degree of spatial dependence.Keywords: kriging, degree of spatial dependence, rainfall stations, integrated water resources management.

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Section: Localizaçãounclassified

Comparação de modelos geoestatísticos para estimativa de precipitação em Minas Gerais, Brasil, entre os anos hidrológicos de 2000 e 2021

Guimarães

Alves²

2023

Rev. Bras. Geog. Fis.

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Determination of Heavy Metals by Inductively Coupled Plasma Optical Emission Spectrometry in Water Samples from Lake Iripixi, Oriximiná, PA, Brazil

Cruz

Ribeiro²,

Moura³

et al. 2022

Water Air Soil Pollut

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Estimating Soil Organic Matter (SOM) Using Proximal Remote Sensing: Performance Evaluation of Prediction Models Adjusted at Local Scale in the Brazilian Cerrado

Cezar,

Alberton,

Lemos

et al. 2023

Remote Sensing

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The quantification of soil organic matter (SOM) has increased over the years, especially in the Brazilian Cerrado region, one of the most important areas for grain production in the country. In this area, SOM content tends to be low, which directly impacts the physical, chemical and biological quality of soils. Thus, the use of spectroradiometry has been widely evaluated to investigate whether it can be used as a faster, more reliable and cheaper solution to meet the SOM estimation. In this context, the objective of the present paper was to evaluate the performance of a local spectral model for SOM prediction generated through the spiking strategy. The research was developed in the municipality of Passos, Minas Gerais State, located in the Brazilian Cerrado. Soil samples (0–0.2 m and 0.2–0.4 m depths) were collected in a zigzag pattern and split in calibration of the local models from a test area (90 soil samples) and recalibration and validation from a target area (46 soil samples). After this stage, the SOM contents were determined in a laboratory, and the spectral responses (350–2500 nm) of each soil sample were collected. From the target area, 10, 25 and 50% of soil spectra were selected for recalibration of the local models generated for the test area. Although median results were observed in the post-recalibration, due to the type of sample selected and the relative similarity among the spectral curves of both areas, improvement was observed for all statistical indices, especially when using 50% (23) of samples for recalibration of the local models, reaching r2 = 0.43, RMSEP = 2.34 gdm−3 and RPIQ = 4.58. These results are important for the SOM estimation in the Brazilian Cerrado considering its importance to the food security and socioeconomic activities. However, considering the lack of similar research in the study area, it is necessary to further investigate the development of spectral models on a local scale and their contribution to improve the identification of SOM spatial variability.

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Air temperature estimation techniques in Minas Gerais state, Brazil, Cwa and Cwb climate regions according to the Köppen-Geiger climate classification system

Cited by 3 publications

References 48 publications

Comparação de modelos geoestatísticos para estimativa de precipitação em Minas Gerais, Brasil, entre os anos hidrológicos de 2000 e 2021

Comparação de modelos geoestatísticos para estimativa de precipitação em Minas Gerais, Brasil, entre os anos hidrológicos de 2000 e 2021

Determination of Heavy Metals by Inductively Coupled Plasma Optical Emission Spectrometry in Water Samples from Lake Iripixi, Oriximiná, PA, Brazil

Estimating Soil Organic Matter (SOM) Using Proximal Remote Sensing: Performance Evaluation of Prediction Models Adjusted at Local Scale in the Brazilian Cerrado

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