Dataset Information

Selecting models for the estimation of reference evapotranspiration for irrigation scheduling purposes.

ABSTRACT: Alternative models for the estimation of reference evapotranspiration (ETo) are typically assessed using traditional error metrics, such as root mean square error (RMSE), which may not be sufficient to select the best model for irrigation scheduling purposes. Thus, this study analyzes the performance of the original and calibrated Hargreaves-Samani (HS), Romanenko (ROM) and Jensen-Haise (JH) equations, initially assessed using traditional error metrics, for use in irrigation scheduling, considering the simulation of different irrigation intervals/time scales. Irrigation scheduling was simulated using meteorological data collected in Viçosa-MG and Mocambinho-MG, Brazil. The Penman-Monteith FAO-56 equation was used as benchmark. In general, the original equations did not perform well to estimate ETo, except the ROM and HS equations used at Viçosa and Mocambinho, respectively. Calibration and the increase in the time scale provided performance gains. When applied in irrigation scheduling, the calibrated HS and JH equations showed the best performances. Even with greater errors in estimating ETo, the calibrated HS equation performed similarly or better than the calibrated JH equation, as it had errors with greater potential to be canceled during the soil water balance. Finally, in addition to using error metrics, the performance of the models throughout the year should be considered in their assessment. Furthermore, simulating the application of ETo models in irrigation scheduling can provide valuable information for choosing the most suitable model.

SUBMITTER: Ferreira LB

PROVIDER: S-EPMC7799801 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Selecting models for the estimation of reference evapotranspiration for irrigation scheduling purposes.

Ferreira Lucas Borges LB da Cunha Fernando França FF Zanetti Sidney Sara SS

PloS one 20210111 1

Alternative models for the estimation of reference evapotranspiration (ETo) are typically assessed using traditional error metrics, such as root mean square error (RMSE), which may not be sufficient to select the best model for irrigation scheduling purposes. Thus, this study analyzes the performance of the original and calibrated Hargreaves-Samani (HS), Romanenko (ROM) and Jensen-Haise (JH) equations, initially assessed using traditional error metrics, for use in irrigation scheduling, consider ...[more]

PMID: 33428674

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Project description:The electrical response of plants to environmental stimuli can be measured and quantitatively related to the intensity of several stimulating sources, like temperature, solar radiation, soil water content, evapotranspiration rates, sap flow and dendrometric cycles. These relations can be used to assess the influence of different environmental situations on soil water availability to plants, defined as a steady state condition between leaf transpirative flow and soil water flow to plant roots. A restricted soil water flow due to soil dryness can trigger water stress in plants, if the atmospheric evaporative demand is high, causing partial stomata closure as a physiological response to avoid plant dehydration; water stressed and unstressed plants manifest a differential electrical response. Real time plant electrical response measurements can anticipate actions that prevent the plant reaching actual stress conditions, optimizing stomata gas exchange and photosynthetic rates. An electrophysiological sensor developed in this work, allows remote real-time recording information on plant electrical potential (EP) in the field, which is highly related to EP measurements obtained with a laboratory Keithley voltmeter sensor used in an highly controlled experimental setup. Our electrophysiological sensor is a wireless, autonomous devise, which transmits EP information via Internet to a data server. Using both types of sensors (EP electrodes with a Keithley voltmeter and the electrophysiological sensor), we measured in real time the electrical responses of Persea americana and Prunus domestica plants, to induced water deficits. The differential response for 2 scenarios: irrigation and water restriction is identified by a progressive change in slope on the daily maximal and minimal electric signal values in stressed plants, and a zero-slope for similar signals for well-watered plants. Results show a correspondence between measured signals obtained by our electrophysiological sensor and the EP electrodes connected to the Keithley voltmeter in each irrigation stage. Also, both sensors show a daily cyclical signal (circadian cycle).

Project description:Reference evapotranspiration (ETo) is a fundamental parameter for hydrological studies and irrigation management. The Penman-Monteith method is the standard to estimate ETo and requires several meteorological elements. In developing countries, the number of weather stations is insufficient. Thus, free products of remote sensing with evapotranspiration information must be used for this purpose. In this context, the objective of this study was to estimate monthly ETo from potential evapotranspiration (PET) made available by MOD16 product. In this study, the monthly ETo estimated by Penman-Monteith method was considered as the standard. For this, data from 265 weather station of the National Institute of Meteorology (INMET), spread all over the Brazilian territory, were acquired for the period from 2000 to 2014 (15 years). For these months, monthly PET values from MOD16 product for all Brazil were also downloaded. By using machine learning algorithms and information from WorldClim as covariates, ETo was estimated through images from the MOD16 product. To perform the modeling of ETo, eight regression algorithms were tested: multiple linear regression; random forest; cubist; partial least squares; principal components regression; adaptive forward-backward greedy; generalized boosted regression and generalized linear model by likelihood-based boosting. Data from 2000 to 2012 (13 years) were used for training and data of 2013 and 2014 (2 years) were used to test the models. The PET made available by the MOD16 product showed higher values than those of ETo for different periods and climatic regions of Brazil. However, the MOD16 product showed good correlation with ETo, indicating that it can be used in ETo estimation. All models of machine learning were effective in improving the performance of the metrics evaluated. Cubist was the model that presented the best metrics for r2 (0.91), NSE (0.90) and nRMSE (8.54%) and should be preferred for ETo prediction. MOD16 product is recommended to be used to predict monthly ETo, which opens possibilities for its use in several other studies.

Dataset Information

Selecting models for the estimation of reference evapotranspiration for irrigation scheduling purposes.

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Selecting models for the estimation of reference evapotranspiration for irrigation scheduling purposes.

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