Calibration of Hargreaves–Samani equation for estimating reference evapotranspiration in semiarid and arid regions

The Penman–Monteith (FAO-56 PM) equation is suggested as the standard method for estimating evapotranspiration (ET₀) by the International Irrigation and Drainage Committee and Food and Agriculture Organization (FAO). On the other hand, the Hargreaves–Samani (HS) equation is an alternative method compared with the FAO-56 PM equation. In the present study, the original coefficient C of the HS equation is calibrated based on the FAO-56 PM equation for estimating the reference ET₀ from 15 meteorological stations in central Iran (about 170,000 km²) under semiarid and arid conditions. After calibration, the new values for C are ranged from 0.0018 to 0.0037. The mean bias error (MBE), the root mean square error (RMSE), and the ratio of average estimations of ET₀ (R) values for all stations are ranged from 0.12 to 5.38, ?5.35 to 1.15 mm d^?1 and 0.64 to 1.28 for the HS equation and from 0.12 to 2.48, ?2.2 to 0.60 mm d^?1, and 1.00 to 1.05 for the calibrated Hargreaves–Samani equation (CHS), respectively. Results indicate that the average RMSE and MBE values are decreased by 40% and 66%, respectively. Relationships for calibrating the C coefficient on the basis of annual average of daily temperature range (ΔT) and wind speed (V) are proposed, calibrated, and validated. Hence, the CHS equation can be used for ET₀ estimates with acceptable accuracy instead of the FAO-56 PM method.     

Forecasting daily potential evapotranspiration using machine learning and limited climatic data

Anticipating, or forecasting near-term irrigation demands is a requirement for improved management of conveyance and delivery systems. The most important component of a forecasting regime for irrigation is a simple, yet reliable, approach for forecasting crop water demands, which in this paper is represented by the reference or potential evapotranspiration (ET_o). In most cases, weather data in the area is limited to a reduced number of variables measured, therefore current or future ET_o estimation is restricted. This paper summarizes the results of testing of two proposed forecasting ET_o schemes under the mentioned conditions. The first or “direct” approach involved forecasting ET_o using historically computed ET_o values. The second or “indirect” approach involved forecasting the required weather parameters for the ET_o calculation based on historical data and then computing ET_o. An statistical machine learning algorithm, the Multivariate Relevance Vector Machine (MVRVM) is applied to both of the forecastings schemes. The general ET_o model used is the 1985 Hargreaves Equation which requires only minimum and maximum daily air temperatures and is thus well suited to regions lacking more comprehensive climatic data. The utility and practicality of the forecasting methodology is demonstrated with an application to an irrigation project in Central Utah. To determine the advantage and suitability of the applied algorithm, another learning machine, the Multilayer Perceptron (MLP), is used for comparison purposes. The robustness and stability of the proposed schemes are tested by the application of the bootstrap analysis.     

Hargreaves公式的改进及其在高寒地区的应用

应用黑龙江省2个气象台的逐日气象资料,引入自由搜索(Free Search)算法求解模型参数,建立了日、旬、月3种时间步长的Hargreaves公式改进式,并对这些改进式的适用性进行了评价。结果表明,以Pehman-Monteith公式计算的ET0为评价标准,Hargreaves公式改进式与FAO推荐的Hargreav...     

内蒙古不同类型草原区Hargreaves计算参考作物蒸散量的适用性分析

参考作物蒸散量是各种气象条件对作物需水量影响的综合反映,是草地管理和水资源评价的重要依据。本文选取内蒙古典型草原、草甸草原、荒漠草原6个气象站1971-2014年逐日的气象资料,以Penman-Monteith公式计算参考作物日蒸散量为标准,比较和分析了Hargreaves公式在内蒙古不同类型草原区的适用性,并按照草地类型、季节对Hargreaves模型进行订正。结果表明,与Penman-Monteith法相比Hargreaves法计算出的参考作物日蒸散量偏低,其日绝对偏差为0.539 mm,日平均偏差为20.98%,夏季偏差较大,其他季节偏差相对较小;订正后其相关系数大大提高,由订正前的0.494~0.874提升为0.863~0.985,订正结果的绝对偏差和相对偏差均显著降低,月参考作物蒸散量的绝对偏差由订正前的38.82 mm降低到5.84 mm,相对偏差由36.79%降低为7.76%。非参数检验结果表明两种方法所模拟ET₀无显著差异,其精度可以满足科研、生产等需要,在气象站点观测项目较少的我国草原区应用前景广阔。     

岷江源区Hargreaves法适用性与未来参考作物蒸散量预测

利用岷江源区1961—2010年逐日气象数据,采用FAO 56 Penman-Monteith和Hargreaves公式计算参考作物蒸散量,并以FAO 56 Penman-Monteith为标准对Hargreaves公式适用性进行评价,通过对Hargreaves公式转换系数C0进行修正,建立基于月尺度的参考作物蒸散发公式,结合Reg CM4.0区域模型生成的温度数据,对未来(2011—2099年)研究区参考作物蒸散发量变化进行预测。研究结果表明:通过通径分析发现,在岷江源区气温是影响参考作物蒸散量最重要的气象因子,采用基于温度法的参考作物蒸散发公式具有理论依据;采用未修正的Hargreaves公式明显高估了该区域参考作物蒸散量,特别是在雨季4—10月;修正后的Hargreaves公式绝对偏差与相对偏差显著减小,与FAO 56 Penman-Monteith月值之间均方根误差RMSE为3.76 mm、效率指数EF为0.39、可决系数CD为0.84,吻合系数d为0.8,能够满足研究区参考作物蒸散发估算精度;在未来气候变化情景下岷江源区参考作物蒸散量总体呈增加趋势,气候倾向率为5.6 mm/(10 a)。     

Calibration of Hargreaves–Samani and Priestley–Taylor equations in estimating reference evapotranspiration in the Northwest of Iran

Accurate estimation of reference evapotranspiration (ET_o) is essential for water resources management and irrigation systems scheduling, especially in arid and semiarid regions such as Iran. In the present research, constant coefficients of Hargreaves–Samani (C_H–S) and Priestley–Taylor (C_P–T) equations were locally calibrated to estimate the ET_o based on the FAO–Penmen–Monteith (PM) method as standard method. For this purpose, meteorological data of eight synoptic stations located in the northwest of Iran were used during the period of 1997–2008. The outcomes showed that the values of C_H–S and C_P–T were 0.0026 (instead of 0.0023) and 1.68 (instead of 1.26), respectively. Also, at stations with high wind speed, the values of calibrated coefficients of C_H–S and C_P–T were maximum. Then, the estimated ET_o values using adjusted C_H–S and C_P–T coefficients were compared to the obtained actual ET_o values by PM method using root mean square error and mean bias error indices. The results indicated that the new calibrated H–S and P–T equations have good agreement with the PM method for estimation of the ET_o. Moreover, the equation of Ravazzani et al. was calibrated in the studied region. It was concluded that in general, the mentioned equation was shown better performance than original H–S equation.     

Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone

The major problem when dealing with modeling evapotranspiration process is its nonlinear dynamic high complexity. Researchers developed reference evapotranspiration (ET-ref) estimation models in rich and poor data situations. Thus, the well-known Penman-Monteith (PM) model always performs the highest accuracy results of ET-ref from a rich data situation. Its application in many areas particularly in developing countries such as Burkina Faso has been limited by the unavailability of the enormous climatic data required. In such circumstances, simple empirical Hargreaves (HARG) equation is often used despite of its non-universal suitability. The present study assesses the artificial neural network (ANN) performance in ET-ref modeling based on temperature data in Bobo-Dioulasso region, located in the Sudano-Sahelian zone of Burkina Faso. The models of feed forward backpropagation neural network (BPNN) algorithm type ANN and Hargreaves (HARG) were employed to study their performance by comparing with the true PM. From the statistical results, BPNN temperature-based models perform better than HARG. Beside, when wind speed is introduced into the neural network models, the coefficient of determination (r²) increases significantly up to 9.52%. While, sunshine duration and relative humidity might cause only 3.51 and 6.69% of difference, respectively. Wind is found to be the most effective variable extremely required for modeling with high accuracy the nonlinear complex process of ET-ref in the Sudano-Sahelian zone of Burkina Faso.     

Hargreaves模型在黄土高原地区的应用与改进

利用黄土高原地区55个气象站30年的气象资料,以Penman-Monteith(PM)公式作为计算ET0的标准,探讨了Hargreaves公式在黄土高原地区的适用性,并对Hargreaves公式进行了修正。结果表明,Hargreaves公式不宜直接应用于黄土高原地区,但修正后的Hargreaves公式计算的3d或更长时间尺度的ET0与PM公式计算的结果吻合度较高。可见,修正后的Hargreaves公式在黄土高原地区具有可行性。     

Hargreaves does not evaluate nociception following a surgical laparotomy in Xenopus leavis frogs

The present study was performed to determine the effectiveness of the Hargreaves test for the evaluation of nociception in frogs, more precisely to determine if cutaneous thresholds to a radiant heat stimulus would increase with analgesics following an abdominal laparotomy performed under general anaesthesia. Non breeding female Xenopus leavis frogs (3 groups (non-anaesthetized, anaesthetized with tricaine methanesulfonate (MS222), with or without an abdominal laparotomy) were used to evaluate the effectiveness of the Hargreaves test. Cutaneous thresholds were evaluated at baseline and following anaesthetic recovery (over 8 h) at six different body locations. Increased reaction times were observed in the gular area only at 1 h post-recovery following a MS222 bath immersion in frogs with (p < 0.02) and without the abdominal laparotomy (p < 0.002). In conclusion, the Hargreaves test does not provide an adequate test to evaluate nociception induced by an abdominal laparotomy and consequently cannot be used to evaluate analgesics in X. leavis frogs.     

Artificial neural network for estimating monthly reference evapotransiration under arid and semi arid environments

The objective of this study is to investigate the potential of artificial neural networks (ANNs) for estimating reference monthly evapotranspiration under arid and semi-arid environments. A simple leave one out data analysis was carried out; one neural network solution on six inputs and another six network solutions on five inputs for each monitoring station were done. Comparison of the results showed that the accuracy of ANNs is decreased when relative humidity, wind speed and solar or extraterrestrial radiation are excluded as input variables. The results also showed that monthly evapotranspiration could be computed with relatively good accuracy compared with local calibrated Hargreaves equation based on air temperature using trained ANNs at another location. We conclude, based on our overall results, that temperature-based method ANNs can be used with relatively good accuracy for water resource management, irrigation scheduling and management, and environmental assessment when data are not enough using trained ANNs from another location.