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

Sabino, Marlus; de Souza, Adilson Pacheco

doi:10.3390/earth4030038

Cited by 6 publications

(6 citation statements)

References 59 publications

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“…In Table 4, the parameters required for ETo estimates are denoted by " ▀▀ ", while "X" denotes that they were not employed in the corresponding method. Sabino and Souza [78] indicated that changes in solar radiation, relative humidity, and wind speed are the main driving forces that impact the ETo. In addition, RHmean and Ws have higher sensitivity indices during the dry season, which is also affirmed in our study area as it is arid in nature.…”

Section: Study Discussionmentioning

confidence: 99%

Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate

Raza,

Fahmeed,

Syed

et al. 2023

Water

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The Food and Agriculture Organization recommends that the Penman–Monteith Method contains Equation 56 (PMF) as a widely accepted standard for reference evapotranspiration (ETo) calculation. Despite this, the PMF cannot be employed when meteorological variables are constrained; therefore, alternative models for ETo estimation requiring fewer variables must be chosen, which means that they perform at least as well as, if not better than, the PMF in terms of accuracy and efficiency. This study evaluated five machine learning (ML) algorithms to estimate ETo and compared their results with the standardized PMF. For this purpose, ML models were trained using monthly time series climatic data. The created ML models underwent testing to determine ETo under varying meteorological input combinations. The results of ML models were compared to assess their accuracy and validate their performance using several statistical indicators, errors (root-mean-square (RMSE), mean absolute error (MAE)), model efficiency (NSE), and determination coefficient (R2). The process of evaluating ML models involved the utilization of radar charts, Smith graphs, heatmaps, and bullet charts. Based on our findings, satisfactory results have been obtained using RBFFNN based on M12 input combinations (mean temperature (Tmean), mean relative humidity (RHmean), sunshine hours (Sh)) for ETo estimation. The RBFFNN model exhibited the most precise estimation as RMSE obtained values of 0.30 and 0.22 during the training and testing phases, respectively. In addition, during training and testing, the MAE values for this model were recorded as 0.15 and 0.17, respectively. The highest R2 and NSE values were noted as 0.98 and 0.99 for the RBFNN during performance analysis, respectively. The scatter plots and spatial variations of the RBFNN and PMF in the studied region indicated that the RBFNN had the highest efficacy (R2, NSE) and lowest errors (RMSE, MAE) as compared with the other four ML models. Overall, our study highlights the potential of ML models for ETo estimation in the arid region (Jacobabad), providing vital insights for improving water resource management, helping climate change research, and optimizing irrigation scheduling for optimal agricultural water usage in the region.

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Section: Study Discussionmentioning

confidence: 99%

Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate

Raza,

Fahmeed,

Syed

et al. 2023

Water

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“…The increase in ETo from 2050 onwards in RCP 4.5 and 8.5 projections is, in general, justified due to the gradual and continuous increase in air temperature values since the RCP 2.6 scenario presents temperature stabilization and consequent stagnation of ETo averages in the mid-21st century (Figure 4) [36,39]. However, changes in other meteorological variables have important contributions to changes in evapotranspiration since ETo estimates using the PM method in the state are more sensitive to changes in RH and WS than to temperature [40]. Thus, the distinction between the ETo averages estimated in RCPs 4.5 and 8.5 is mainly due to the reduction in relative humidity and increase in wind speed (Figure 4), which occur more significantly in RCPs 8.5, enabling the scenario pessimism to become statically different from the current conditions observed already between the years 2050-2075 (Table 2).…”

Section: Eto (Mm Daymentioning

confidence: 98%

“…In general, the two models with the best results for estimating ETo in Mato Grosso in the current period (2007-2020) were CESM1-CAM5 and HadGEM2-ES. However, since the HadGEM2-ES model presented less dispersion of ETo estimation errors, as well as better performance in estimating the variables SRD, RH and SW, these variables have a greater impact on ETo estimates using the method of Penman-Monteith for the region [40]; in the following stages of this research, the MCG HadGEM2-ES will be the model used in the evaluations of ETo projections.…”

Section: Choice Of Climate Projection Modelsmentioning

confidence: 99%

“…The changes in seasonality with a greater increase during the end of the dry period and the beginning of the rainy season, as previously mentioned, may be mainly linked to reductions in relative air humidity and an increase in wind speed [43,44], since, according to Greve and Seneviratne [45], tropical regions and their transition areas in South America may present trends in the future towards intensification of arid environmental conditions characterized by low relative humidity, higher wind speed and high evapotranspiration rates. Furthermore, net radiation, despite not varying as significantly as the other variables (Figure 4), must be taken into account as one of the factors since previous studies have indicated this component as one of the main variables affecting the seasonality of ETo in the Amazon region [9,40,46].…”

Section: Eto (Mm Daymentioning

confidence: 99%

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Reference Evapotranspiration in Climate Change Scenarios in Mato Grosso, Brazil

Sabino,

Silva,

Almeida

et al. 2024

Preprint

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The understanding of spatiotemporal variations in reference evapotranspiration (ETo) and its long-term trends is of paramount importance for water cycle studies, modeling, and water resource management, especially in the context of climate change. Therefore, the primary aim of this study is to critically evaluate the performance of various CMIP5 global climate models in simulating the Penman-Monteith reference evapotranspiration and its associated climate variables (maximum and minimum air temperature, incident solar radiation, relative humidity, and wind speed). This evaluation is based on data from nine climate models and 33 automatic meteorological stations (EMA) in the state of Mato Grosso, spanning the period 2007-2020. The statistical metrics used for evaluation include Bias, root mean square error, and Pearson and Spearman correlation coefficients. The projections of the most accurate model were then used to analyze the spatial and temporal changes and trends in ETo under the RCP 2.6, 4.5, and RCP 8.5 scenarios from 2007 to 2100. The HadGEM2-ES model projections indicate static averages similar to current conditions until the end of the century in the RCP 2.6 scenario. However, in the RCP 4.5 and 8.5 scenarios, there is a continuous increase in ETo, with the most significant increase occurring during the dry period (May to September). The areas of the Amazon biome in the north of Mato Grosso exhibit the largest increases in ETo when comparing the observed (2007-2020) and projected (2020-2100) averages. The trend analysis reveals significant changes in ETo and its variables across the state of Mato Grosso in the RCP 4.5 and 8.5 scenarios. In the RCP 2.6 scenario, significant trends in ETo are observed only in the northern Amazon areas. Despite not being observed in all AWSs, the trend analysis of the observed data demonstrates more intense changes in ETo and the existence of the evapotranspiration paradox with an increase in the Cerrado areas and reductions in the Pantanal and southern Amazon areas.

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“…The results obtained by Yong et al [23] indicated that the global sensitivity analysis studies are recent and restricted to a few countries (Australia, Malaysia, the United States, and China). A recent work by Sabino and de Souza [24] presented a study of global sensitivity analysis for the Penman-Monteith model to calculate the reference evapotranspiration in the state of Mato Grosso, Brazil, using the Sobol' method.…”

Section: Previous Studiesmentioning

confidence: 99%

The Modelling of the Evapotranspiration Portion of the Water Footprint: A Global Sensitivity Analysis in the Brazilian Serra Gaúcha

Platt,

Nunes,

Martins

et al. 2024

Earth

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Water footprints have been widely used to illustrate the consumption of water in many situations, for instance, in products, processes, or regions of interest. In this work, we analyzed—using a sensitivity analysis approach—the effect of some variables in the calculation of the water footprint in the viticulture in the Brazilian Serra Gaúcha (the major producing region of Brazilian wine). The classical Penman–Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering a fraction of the vineyard, maximum temperatures for some months, the altitudes and latitudes of the site). A sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to evapotranspiration is more sensitive to the values of the mulch-covered fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

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

Cited by 6 publications

References 59 publications

Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate

Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate

Reference Evapotranspiration in Climate Change Scenarios in Mato Grosso, Brazil

The Modelling of the Evapotranspiration Portion of the Water Footprint: A Global Sensitivity Analysis in the Brazilian Serra Gaúcha

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