Evaporation, together with precipitation, is the most important component of the hydrological cycle, and knowledge of the local values of lake evaporation has applications in reservoir design and management. The objective of this study was to estimate lake evaporation at locations without meteorological monitoring using ERA5 reanalysis data and artificial neural networks (ANNs). Data from 32 automatic stations in the state of Mato Grosso were used to estimate evaporation using the method of Penman (1948). The evaporation values were related to ERA5 data and radiation data at the top of the atmosphere using multilayer perceptron ANN models. The Mann-Kendall test was used for trend analysis in the estimated monthly evaporation series. From the analysis of the results, it is concluded that it is possible to quantify the spatial and temporal distribution of evaporation from lakes with data from ERA5 reanalysis and the use of ANNs. The historical evaporation series for the period 1980 to 2019 showed a positive trend in certain parts of the Brazilian Savanna and Amazon biomes. Isolated areas of the Pantanal biome also showed a positive trend for monthly evaporation. The proposed methodology allows for the precise and accurate estimation of evaporation from liquid surfaces at locations without meteorological monitoring.

中文翻译：

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通过气候再分析数据和人工神经网络估计湖泊蒸发量

蒸发与降水是水文循环中最重要的组成部分，对湖泊蒸发局部值的了解可应用于水库设计和管理。本研究的目的是使用 ERA5 再分析数据和人工神经网络 (ANN) 估算没有气象监测地点的湖泊蒸发量。来自马托格罗索州 32 个自动站的数据被用来利用 Penman (1948) 的方法估算蒸发量。使用多层感知器 ANN 模型将蒸发值与大气顶部的 ERA5 数据和辐射数据相关。曼-肯德尔检验用于估计月蒸发量系列的趋势分析。从结果分析得出的结论是，利用 ERA5 再分析数据和人工神经网络可以量化湖泊蒸发的空间和时间分布。1980年至2019年期间的历史蒸发系列显示巴西稀树草原和亚马逊生物群落某些部分呈积极趋势。潘塔纳尔生物群落的孤立区域也显示出每月蒸发量的积极趋势。所提出的方法允许在没有气象监测的情况下精确估计液体表面的蒸发量。