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首页> 外文期刊>Acta Horticulturae >Use of numerical weather forecast and time series models for predicting reference evapotranspiration.
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Use of numerical weather forecast and time series models for predicting reference evapotranspiration.

机译：使用数值天气预报和时间序列模型来预测参考蒸散量。

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Providing forecast of water balance components such as precipitation, evapotranspiration, deep percolation and runoff is important for water management and irrigation scheduling. Reference evapotranspiration (ETo) prediction will greatly enhance our capability to manage high-frequency irrigation systems and shallow-rooted crops. Reference evapotranspiration can be calculated on daily or hourly basis using analytical models (Penman-Monteith, Penman, etc.) and meteorological forecasts from numerical weather prediction models. One can also use time series analysis of ETo and meteorological variables related to evapotranspiration process. For example, autoregressive integrated moving average (ARIMA) models and artificial neural networks (ANN) can be applied in time series modelling and forecasting. The main objective of this study was to analyse and compare the performance of the above-mentioned techniques in short-term prediction of hourly and daily ETo. Reference evapotranspiration rates were calculated using the hourly Penman-Monteith equation, weather data provided by the Agrometeorological Service of Sardinia, Italy (SAR), and weather forecasts from a limited area model (BOLAM2000). Both ARIMA and ANN models were developed using four years of hourly meteorological data from three meteorological stations of SAR. Models were validated using a two-year data set from the same locations. The accuracy of the models was evaluated by comparing the forecasts with ETo values calculated using observed weather data from SAR weather stations. The use of meteorological variables from numerical weather forecast gave better results than those obtained from ARIMA and ANN models. The limited area model gave root mean squared difference values of the forecasted ETo smaller than 0.15 mm on an hourly basis and near 1.0 mm on a daily basis. However, the analysis showed a large scatter of calculated versus predicted ETo values, in particular for hourly values. The evaluation of the effect of weather forecast variables on forecast ETo accuracy showed that solar irradiance is the main parameter affecting ETo forecast.
机译：提供诸如降水，蒸散，深层渗流和径流之类的水平衡要素的预测对于水管理和灌溉计划很重要。参考蒸散量（ETo）的预测将大大增强我们管理高频灌溉系统和浅根作物的能力。参考蒸发蒸腾量可以使用分析模型（Penman-Monteith，Penman等）和数值天气预报模型的气象预报每天或每小时进行计算。还可以使用ETo的时间序列分析和与蒸散过程有关的气象变量。例如，自回归综合移动平均值（ARIMA）模型和人工神经网络（ANN）可以应用于时间序列建模和预测。这项研究的主要目的是分析和比较上述技术在每小时和每天ETo的短期预测中的性能。使用每小时的Penman-Monteith方程，意大利撒丁岛农业气象服务局（SAR）提供的天气数据以及来自有限区域模型的天气预报（BOLAM2000）计算参考蒸散率。 ARIMA和ANN模型都是使用来自SAR的三个气象站的四年每小时气象数据开发的。使用来自相同位置的两年数据集对模型进行了验证。通过将预测与使用从SAR气象站观察到的气象数据计算出的ETo值进行比较，来评估模型的准确性。与从ARIMA和ANN模型获得的结果相比，使用数字天气预报得出的气象变量产生了更好的结果。有限区域模型给出了每小时ETo的预测均方根差均方根值小于0.15 mm，每天均小于1.0 mm。但是，分析显示，计算得出的ETo值与预测的ETo值之间存在较大的差异，尤其是对于小时值而言。对天气预报变量对ETo准确性的影响的评估表明，太阳辐照度是影响ETo预报的主要参数。

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