Abstract

Evapotranspiration (ET) is the main component of water balance in agricultural systems and the most active variable of the hydrological cycle. In the literature, few studies have used the forecast the day before via Artificial Neural Networks (ANNs) for the northern region of São Paulo state, Brazil. Therefore, this aimed to predict the reference evapotranspiration for Jaboticabal, the major sugarcane-producing region of São Paulo state. We used a historical series of data on average air temperature, wind speed, net radiation, soil heat flux, and daily relative humidity from 2002 to 2012, for Jaboticabal, SP (Brazil). ET was estimated by Penman-Monteith method. To forecast reference evapotranspiration, we used a feed-forward Multi-Layer Perceptron (MLP), which is a traditional Artificial Neural Network. Numerous topologies and variations were tested between neurons in intermediate and outer layers until the most accurate were obtained. We separated 75% from data for network training (2002 to 2010) and 25% for testing (2011 to 2013). The criteria for assessing the ANN performance were accuracy, precision, and trend. ET could be accurately estimated with a day to spare at any time of the year, by means of artificial neural networks, and using only air temperature data as an input variable.

Highlights

  • Brazil climate patterns are widely diverse (Alvares et al, 2014)

  • The southern region of Brazil is characterized by medium predictability, and due to its latitudinal location, suffers more influence of mid-latitude systems, where frontal systems are the main causes of rainfall during the year (Sampaio & Silva Dias, 2014)

  • ET is a key parameter for watershed management studies (Raziei & Pereira, 2013), for crop water requirement estimates and for irrigation project and management (Kumar et al, 2008)

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Summary

Introduction

In the North region, there is a rainy equatorial climate without any dry season, while in the Northeast, the rainy season has low rainfall and is restricted to a few months, featuring a semi-arid climate and presenting high climate predictability. Tropical systems and mid-latitudes influence Southeast and Midwest, which present a well-defined dry season (winter), and a rainy season (summer) with convective rainfall. The southern region of Brazil is characterized by medium predictability, and due to its latitudinal location, suffers more influence of mid-latitude systems, where frontal systems are the main causes of rainfall during the year (Sampaio & Silva Dias, 2014). Weather conditions have marked influence on ET; subsequently, small mistakes in its estimate have a high impact on the water balance calculation for a region (Carvalho et al, 2015)

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