Estimativa Da Evapotranspiração De Referência Para Botucatu-Sp Por Meio De Modelos De Regressão

Sarnighausen, Valéria Cristina Rodrigues; Gomes, Francielly Guieiro; Pai, Alexandre Dal; Rodriguês, Sérgio Augusto

doi:10.5380/rbclima.v28i0.71569

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Meteorological time series are essential to designing agrometeorological and environmental projects (Xiang et al, 2020;Sarnighausen et al, 2021). However, in Brazil, acquisition and storage of automated weather station (AWS) data are susceptible to instrumental and operational failures (Bier & Ferraz, 2017;Brubacher et al, 2020), affecting their use to assess climatic trends, stationarity, or seasonality (Box & Cox, 1964).…”

Section: Introductionmentioning

confidence: 99%

Gap-filling meteorological data series using the GapMET software in the state of Mato Grosso, Brazil

Sabino

Souza

2023

Rev. bras. eng. agríc. ambient.

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This paper aimed to introduce the GapMET software, developed by the authors, and evaluate the accuracy of its six methods for gap-filling the main meteorological variables monitored by weather station in the state of Mato Grosso, Brazil, using reference time series from neighbour weather station and/or remote sensing products. The methods were tested on seven different databases, with 25 to 80% artificial gaps, and their accuracy was given by the number of gaps left unfilled, the bias, the RMSE, and Pearson’s correlation. The GapMET software showed good results in filling meteorological gaps regardless of the method applied. Methods that use only one neighbour weather station as a reference series showed better results because, in the state, the minimum distance for a weather station to have at least three neighbours as reference was 350 km, reducing the climatic similarity between them and consequently the accuracy when more than one reference series were needed. The use of satellite reference series reduced the probability of unfilled gaps; however, it showed higher bias and RMSE and lower correlations.

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Section: Introductionmentioning

confidence: 99%

Gap-filling meteorological data series using the GapMET software in the state of Mato Grosso, Brazil

Sabino

Souza

2023

Rev. bras. eng. agríc. ambient.

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“…Para realizar tais analises, muitos estudos foram feitos com base em dados de precipitação e vazão (Betancur et al, 2020;Cirilo et al, 2020;Tozzi;Fill, 2020), mostrando a importância de tais medições. Deste modo, vários trabalhos foram desenvolvidos com diversas finalidades como modelos que representem a relação das chuvas diárias no fluxo dos rios (Teng et al, 2017), impactos das mudanças climáticas na vazão dos rios (Chakilu et al, 2020), analisar o risco de desastres naturais (Brunner et al, 2021), avaliar o grau de qualidade e precisão de modelos (Golmohammadi et al, 2014;Sarnighausen et al, 2021), entre outros.…”

Section: Introductionunclassified

Análise geoestatística das variáveis hidrometeorológicas na bacia hidrográfica do rio Xingu

Ribeiro

Gomes

Queiroz

et al. 2022

Rev. Bras. Geog. Fis.

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A questão das mudanças nos níveis dos rios (cheias e vazantes) e a variabilidade da precipitação na região Amazônica é um grande problema, pois atinge diretamente a população local. Deste modo, destaca-se a bacia hidrográfica do rio Xingu localizada nos estados do Mato Grosso e Pará. O objetivo deste trabalho é o uso dos modelos espaços-temporais baseado nas variáveis hidrometeorologicas na caracterização geoestatística de áreas na bacia hidrográfica do rio Xingu. Para análise geoestatística dos dados pluviométricos e fluviométricos utilizou-se: modelos de regressão (avaliar tendências das variáveis), correlação espacial (geoestatísticas) , mapeamento de variáveis e uso de modelos espaciais (econometria espacial) e o método k-means (grupos homogêneos) através dos software Microsoft Excel e Programa R- The R Project for Statistical Computing 2015. A variabilidade média mensal mostra o período menos chuvoso caracterizado pelos meses de Maio a Outubro se observa oscilações com valores mínimos de 17,4 mm a 155,6 mm ocasionando o período de estiagem na área. A bacia apresenta maiores vazões no trimestre Fevereiro, Março e Abril sendo o maior valor em Março (7937,5 m³/s). O estudo de tendência para as duas variáveis mostrou que a média se manteve constante, ou seja, não houve tendência. Os índices de Moran Globais calculados para precipitações (I=0,7521) e vazões (I=7833) indicam que existe uma correlação espacial positiva para as duas variáveis. Os resultados obtidos neste estudo podem contribuir para um maior conhecimento de possíveis relações entre variáveis hidrometeorológicas e auxiliar problemas de variabilidade hidrológicas e seus possíveis danos socioeconômicos e ambientais a comunidade local. Geostatistical analysis of hydrometeorological variables in the Xingu river watershedABSTRACT The issue of changes in river levels (floods and ebb tides) and rainfall variability in the Amazon region is major problem, as it directly affects the local population. So, the Xingu River hydrographic basin located in the state of Mato Grosso and Pará is highlighted. The objective of this work is the use of spatiotemporal models based on hydrometeorological variables in the geostatistical characterization of areas in the Xingu River hydrographic basin. For geostatistical analysis of rainfall and fluviometric data, we used: regression models (evaluating trends in variables), spacial correlation (geostatistics), variable mapping and use of spacial models (spacial econometrics) and the k-means method (homogeneous groups) through Microsoft Excel and R-The R Project for Statistical Computing 2015 software. The monthly average variability shows the least rainy period characterized by the months of May to October, with oscillations with minimum values from 17.4 mm to 155.6 mm, causing the dry period in the area. The basin presents the highest inflows in the February, March and April quarter, with the highest value in March (7937.5 m³/s). The trend study for the two variables showed that the mean remained constant, that is, there was no trend. The Global Moran indices calculated for rainfall (I=0.7521) and outflows (I=7833) indicate that there is a positive spatial correlation for the two variables. The results obtained in this study can contribute to a greater understanding of possible relationships between hydrometeorological variables and help problems of hydrological variability and their possible socioeconomic and environmental damage to the local community.

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Global Sensitivity of Penman–Monteith Reference Evapotranspiration to Climatic Variables in Mato Grosso, Brazil

Sabino,

de Souza

2023

Earth

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Understanding how climatic variables impact the reference evapotranspiration (ETo) is essential for water resource management, especially considering potential fluctuations due to climate change. Therefore, we used the Sobol’ method to analyze the spatiotemporal variations of Penman–Monteith ETo sensitivity to the climatic variables: downward solar radiation, relative humidity, maximum and minimum air temperature, and wind speed. The Sobol’ indices variances were estimated by Monte Carlo integration, with sample limits set to the 2.5th and 97.5th percentiles of the daily data of 33 automatic weather stations located in the state of Mato Grosso, Brazil. The results of the Sobol’ analysis indicate considerable spatiotemporal variations in the sensitivity of ETo to climatic variables and their interactions. The dominant climatic variable responsible for ETo fluctuations in Mato Grosso is incident solar radiation (53% to 93% of annual total sensitivity—Stot), which has a more significant impact in humid environments (70% to 90% of Stot), as observed in the areas of the Amazon biome in the state. Air relative humidity and wind speed have higher sensitivity indices during the dry season in the Cerrado biome (savanna) areas in Mato Grosso (20% and 30% of the Stot, respectively). Our findings show that changes in solar radiation, relative humidity, and wind speed are the main driving forces that impact the reference evapotranspiration.

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Estimativa Da Evapotranspiração De Referência Para Botucatu-Sp Por Meio De Modelos De Regressão

Cited by 4 publications

References 18 publications

Gap-filling meteorological data series using the GapMET software in the state of Mato Grosso, Brazil

Gap-filling meteorological data series using the GapMET software in the state of Mato Grosso, Brazil

Análise geoestatística das variáveis hidrometeorológicas na bacia hidrográfica do rio Xingu

Global Sensitivity of Penman–Monteith Reference Evapotranspiration to Climatic Variables in Mato Grosso, Brazil

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