Fixed versus variable bulk canopy resistance for reference evapotranspiration estimation using the Penman–Monteith equation under semiarid conditions

In this paper, daily ET₀ estimates at two semiarid locations, Zaragoza and Córdoba, were obtained from the Penman–Monteith equation using either fixed (70 s m⁻¹) or variable r_c values. Variable r_c values were computed with two models, Katerji and Perrier, and Todorovic. Daily ET₀ estimates were computed from 24-h meteorological averages or from the sum of hourly estimates. Daily ET₀ measured values were obtained from a weighing lysimeter (Zaragoza) and an eddy covariance system (Córdoba). There was a good agreement at both locations between estimated and measured ET₀ values using a fixed r_c value and 24-h meteorological averages. Estimates obtained from the sum of hourly estimates were somewhat worse. When 24-h meteorological averages were used, the Katerji and Perrier model for variable r_c slightly improved ET₀ estimates at both locations. But that improvement does not support the effort to locally calibrate that model. When daily ET₀ estimates were obtained from the sum of hourly estimates, the Todorovic model improved the estimation at Zaragoza and, at a lesser degree, at Córdoba. Under the semiarid conditions of the two studied locations, the use of the Todorovic model is recommended to get hourly ET₀ estimates from which daily estimates can be obtained. If 24-h meteorological averages are used, a fixed r_c value as proposed by Allen et al. [Crop evapotranspiration: guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper No. 56, FAO, Rome, 1998] should be enough for accurate ET₀ estimates.     

江苏地区不同参考作物蒸发蒸腾量估算模型

为了研究不同参考作物蒸发蒸腾量ET₀估算方法在江苏地区的适用性,收集了江苏省徐州市、高邮市和昆山市1957年1月至2019年12月的气象数据,采用12种不同模型估算了各站点的ET₀,其中模型Priestly-Taylor,Hansen,Jensen-Haise,Makkink是基于辐射数据的模型;MC-Cloud,1985 Hargreaves,Thornthwaite是基于温度数据的;Copais,Valiantzas 1和Valiantzas 2是综合法模型;XGBoost和SVM是机器学习模型.12种ET₀的估算模型计算值分别与Penman-Monteith模型(PM)计算值进行比较,结果表明:各站点的综合评价指数GPI最高的为机器学习模型中的SVM模型;在输入参数相同的情况下,机器学习模型模拟精度优于综合法和温度法以及辐射法中的Pristley-Taylor和Makkink模型;机器学习模型随着输入参数减少,模拟精度依次降低.研究结果可以为江苏地区气象数据不完善时估算ET₀提供科学依据.     

4种人工智能模型在江西省参考作物蒸散量计算中的适用性

为了实现气象资料缺失下参考作物蒸散量ET₀的高精度预测,以江西南昌、吉安及龙南站1966-2015年每日最高气温T_max、最低气温T_min、日照时数n、相对湿度RH和2 m高风速u₂作为输入参数,以FAO-56 Penman-Monteith(P-M)公式的计算结果作为对照,建立了6种不同气象要素组合条件下的4种ET₀计算模型,并分别与输入相同数据的经验法计算结果进行了比较.结果表明,在3个站点中,多元自适应回归样条法MARS模型的精度最高,且计算简便,可作为江西省蒸散量模拟的推荐方法.当4种模型的输入数据完整时,模拟精度均达到最高,表明4种模型均可适用于对参考作物蒸散量的模拟;输入数据缺失条件下,各气象要素对智能模型模拟ET₀的影响由大到小按参数排序依次为T_max,T_min,n,RH,u₂.与传统经验公式相比,4种智能模型的ET₀计算结果精度均优于输入相同数据的经验法.     

Regional analysis of irrigation water requirements using kriging: Application to potato crop (Solanum tuberosum L.) at Trás-os-Montes

Aiming at the analysis of the regional variation of potato crop irrigation water requirements over the Trás-os-Montes region, data from 106 rainfall stations and eight weather stations were utilized in an irrigation scheduling simulation model to estimate net irrigation water requirements of the potato crop. The simulation model was first validated using a field experiment which allows to derive the required crop data to be used in the simulations. The reference evapotranspiration (ET₀) was estimated using the FAO Penman–Monteith method. The model was applied to all 106 locations, each with a data set spanning a 19-year period. As a result of this application, series of the net irrigation water requirements for a 19-year period were obtained for each location. The resulting 106 point values of the net water requirements of the potato crop have been treated as a regionalized variable. The respective semivariograms have been computed and the kriging method then applied to estimate the spatial distribution of the water requirements in the region. Contour lines of this regionalized variable have been drawn using a GIS system. Results show an estimation error averaging 5% for the entire region.     

改进Hargreaves-Samani模型预测广西盆地参考作物蒸散量

准确估算参考作物蒸散量(ET₀)对于区域水资源管理和灌溉决策有着重要意义.Hargreaves-Samani模型(HS)是目前公认结构最简单且精度较高的ET₀估算模型.为了进一步提高HS模型预测精度,采用蜂群理论和广西盆地20个气象站(1961—2019年)数据对HS模型全局校准,使用1961—2000年数据对HS模型进行校准,2001—2019年数据在日、月、年尺度上验证.结果表明：全局优化后的经验参数C,m和a中,参数a随地形起伏差异较大,而参数C和m差异较小;校准后HS模型(平均MAE和R²分别为1.06 mm/d和0.86)优于原始HS模型(MAE和R²分别为2.20 mm/d和0.68);在日、月和年时间尺度上,校准HS模型和原始HS模型都高估了ET₀,但校准的HS模型与P-M模型计算的ET₀更为接近.因此,对仅有温度数据的地区,推荐采用校准的HS模型估算ET₀.     

云贵高原参考作物蒸散量时空特征及成因分析

为了探究云贵高原地区参考作物蒸散量(ET₀)的时空分布特征,基于云贵高原42个代表性气象站点近56 a(1961—2016年)逐日气象数据,利用Mann-Kendall检验探究ET₀主要的气象驱动因子,并运用多元回归分析量化了各气象因子对ET₀的贡献率.结果表明：云贵高原近56 a风速和气温呈逐年上升趋势,增幅分别为0.001 2 (m·s^-1)/a和0.018℃/a,太阳辐射和相对湿度呈下降趋势,降幅分别为0.007 3 (MJ·m^-2·d^-1)/a和0.074 6%/a;近56 a来ET₀整体呈波动式上升趋势,增幅为0.287 1 mm/a,空间分布西高东低,差异特征显著,西部地区24个站点年均ET₀为1 100～1 200 mm,东部18个站点年均ET₀为843～950 mm;在春、秋、冬季太阳辐射是云贵高原ET₀的主要驱动因子,夏季气温是ET₀     

Evapotranspiration modeling using a wavelet regression model

The present study proposes a simple wavelet regression (WR) approach for modeling reference evapotranspiration (ET₀). The WR model was improved combining two methods: discrete wavelet transform (DWT) and a linear regression model. The accuracy of the WR models was compared with that of the single linear regression (LR) models. The daily climatic data from three stations in central California are used as inputs to the WR models to estimate ET₀ obtained using the FAO-56 Penman–Monteith equation. The comparison of these results revealed that the WR models could increase the forecast accuracy of the LR models. A comparison is also made between the estimates provided by the WR models and those of the following empirical models: CIMIS Penman, Hargreaves, Ritchie and Turc. Based on a comparison of these results, the WR models were found to perform better than the empirical models in daily ET₀ modeling.     

考虑“蒸发悖论”的洱海灌区逐日参考作物蒸散发预测

【目的】为估算参考作物蒸散发(ET₀)和灌溉实时预报调度、区域农业干旱评估提供依据。【方法】以滇中高原上洱海湖滨灌区的大理气象站为例,探究“蒸发悖论”现象出现的时期,采用气象因子线性回归模型、蒸发皿折算系数K_p模型、气象因子+蒸发皿蒸发(E_pan)多元回归模型、Normal Copula模型等4种方法计算逐日ET₀进行预测对比,并与Penman-Monteith公式计算所得的ET₀进行对比。【结果】①1954—2018年大理站20 cm蒸发皿蒸发量呈下降趋势,ET₀和气温呈上升趋势,但ET₀的上升趋势更平缓;虽然在长时间序列上ET₀和蒸发皿蒸发量有相反的变化趋势,但在年代际存在显著的差异性,1960年和2000全年以及四季均出现“蒸发悖论”,1970年则是全年以及夏、秋、冬三季出现“蒸发悖论”,1990年仅夏季出现“蒸发悖论”,2010年秋季出现“蒸发悖论”。②在未出现“蒸发悖论”时期,加入E_pan后的气象因子多元回归模型法(ET_0,Epan+Metr)所得逐日ET₀预测结果与标准值的误差最小,其次为单纯的气象因子多元线性回归模型法(ET₀,Metr),最差为K_p模型法(ET₀,K_p);加入E_pan后的气象因子多元回归模型(ET₀,ET_0,Epan+Metr)逐日ET₀预测的相对误差(ERR)小于15%、20%、25%的样本数达到了79.18%~90.16%、89.32%~97.23%、94.79%~98.36%。③出现“蒸发悖论”时,蒸发皿蒸发与ET₀的变化趋势相反,只能采用Copula联合分布函数模型预测,构建T-T_max二维Normal Copula模型的精度更高,ERR小于15%、20%、25%的样本数为73.70%~86.56%,82.51%~92.95%,89.89%~98.52%。【结论】通过M-K检验判别是否处于“蒸发悖论”期,以决策选用加入E_pan后的气象因子多元回归模型,还是T-T_max二维Normal Copula模型,二者均可显著提高逐日ET₀预测模拟的精度。     

基于NARX模型的参考作物蒸散发预测

为了实现气象资料缺失情况下参考作物蒸散量(ET₀)精确计算和预测,以攀枝花站点为例,构建非线性自回归滤波器(NARX)模型预测ET₀.以Penman-Monteith模型计算的ET₀作为预测标准,将日最高温、日最低温、日照时数、风速和相对湿度作为模型的输入参数,建立11种不同气象因子组合的NARX模型,并与Hargreaves-Samani模型、Irmak-Allen模型、Makkink模型、Priestley-Taylor模型的计算结果进行比较.结果表明:不同气象因子输入下的NARX模型模拟精度表现出明显的差异.其中,基于全部气象因子的NARX-1模型的RMSE,MAE和MBE分别为0.425 mm/d,0.320 mm/d和0.069 mm/d,NSE为0.920,GPI排名第11,精度最差;而基于风速的NARX-9模型精度最高,其RMSE,MAE和MBE分别为0.285 mm/d,0.237 mm/d和0.019 mm/d,NSE为0.964,GPI排名第1.在输入相同气象参数情况下,基于温度和日照时数的NARX-4模型模拟精度优于Irmak-Allen,Makkink,Priestley-Taylor模型;基于温度的NARX-7模型模拟精度明显高于Hargreaves-Samani模型.因此,可将NARX模型作为四川西南山地缺失较多气象资料情况下计算ET₀的推荐模型,为农田精准灌溉提供科学依据.     

基于公共天气预报的广东青年运河灌区参考作物腾发量预报方法比较

为提出高精度适合广东青年运河灌区参考作物腾发量(ET₀)预报方法,制定精准的灌溉预报,降低农业用水量,本研究以灌区内的湛江站为研究对象,收集了该站点2003-01-01-2017-05-31逐日气象观测数据和2016-01-01-2017-05-31日的预见期为7 d的逐日公共天气预报数据,采用FAO-56 Penman-Monteith计算值作为基准,比较Hargreaves-Samani(HS)法、简化Penman-Monteith(PT)、逐日均值修正法的预报效果.结果表明:以上3种方法1~7 d预见期平均绝对误差平均值分别为0.908 3,0.903 1,0.947 9 mm/d,平均绝对误差分别为1.099 1,1.099 9,1.192 4 mm/d,相关系数分别为0.649 5,0.649 8,0.615 9,PT法的平均绝对误差以及相关系数均最好.就每个预见期而言,1~5 d预见期的最优预报方法均为PT法,6~7 d为HS法.因此,建议采用PT法进行青年运河灌区的ET₀预报.     

基于ELM的西北旱区参考作物蒸散量预报模型

为实现气象资料缺失情况下ET₀的精确预报,选取中国西北旱区4个代表性站点的气象资料,建立15种基于极限学习机(ELM)的ET₀预报模型,并通过与其他ET₀计算模型对比和可移植性分析探究ELM在西北旱区的适用性.结果表明:基于温度和风速的ELM₇预报精度较高(整体评价指标GPI排名第4);基于温度和辐射的ELM₅预报精度(GPI排名第6)明显高于Iramk模型和Jensen-Haise模型;仅基于温度的ELM₉预报精度(GPI排名第8)高于Hargreaves-Samani模型.通过模型可移植性分析发现,ELM₇在西北旱区内各训练站点和预测站点组合下预报精度良好.因此,可将ELM₅(输入温度和辐射)、ELM₇(输入温度和风速)和ELM₉(输入温度)作为西北旱区较少气象参数输入情况下精确预报ET₀的推荐模型.     

Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain)

Reference evapotranspiration (ETo) determination is a key factor for water balance and irrigation scheduling. Evapotranspiration can be measured directly by high-cost micrometeorological techniques, or estimated by mathematical models. The combination equation of Penman–Monteith, modified by Allen et al. [Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrigation and Drainage, Paper no. 56. FAO, Rome] (PM56), is the reference equation for ETo estimation. This method is also appropriate for the calibration of other ETo estimation equations. The utilization of these calibrated ETo equations is recommended in the absence of data of any of the meteorological parameters necessary for the application of PM56. In addition to the use of classic ETo equations, the adoption of artificial neural network (ANN) models for the estimation of daily ETo has been evaluated in this study. ANNs are mathematical models, whose architecture has been inspired by biological neural networks. They are highly appropriate for the modelling of non-linear processes, which is the case of the evapotranspiration process. Seven ANNs (with different input combinations) have been implemented and compared with ten locally calibrated empirical and semi-empirical ETo equations and variants of these equations (with estimated meteorological parameters as inputs). The comparisons have been based on statistical error techniques, using PM56 daily ETo values as a reference. ANNs have obtained better results than the locally calibrated ETo equations in the three groups of evaluated methods: temperature and/or relative humidity-based methods (0.385 mm d⁻¹ of root mean square error (RMSE)), solar radiation-based methods (0.238 mm d⁻¹ of RMSE), and methods based on similar requirements to those of PM56 except for the estimation of solar radiation and/or relative humidity (0.285 mm d⁻¹ of RMSE).     

基于改进Makkink模型的四川盆地参考作物蒸散量估算

为了有效提高四川盆地参考作物蒸散量ET₀的预报精度,选取四川盆地16个代表性气象站点1961-2019年逐日气象数据,基于差分进化算法(DE)对辐射模型的经验参数校准改进Makkink模型并估算四川盆地ET₀,在日、月尺度上对改进的Makkink模型(M1-M6)和Jennsen-Haise(JH)及Irmak(IK)模型评价.结果表明:在日尺度上,改进的Makkink(M1-M6)模型(R²为0.77~0.87)模拟结果比JH和IK模型(R²为0.74~0.76)更精确,改进的Makkink模型中,M4模型估算精度最高,综合性指标GPI中位数为1.05;在月尺度上,改进的Makkink模型模拟结果(误差为3.59~15.71 mm/月)也优于JH和IK模型(误差为6.84~25.31 mm/月),其中M4模型估算精度最佳,综合性指标GPI为1.72.总体而言,推荐以温度和相对湿度作为输入数据的M4模型模拟四川盆地ET₀.     

垄作沟灌土壤水盐分布及作物产量试验研究

对垄作沟灌土壤水盐分布及作物产量进行了田间试验.结果表明,灌水定额为400 m³/hm²时,沟底土壤水分的垂直运动明显增大.当灌水定额增加100 m³/hm²时,土壤水分水平运动高于垂直运动.沟底土壤全盐量受灌水定额影响明显:在灌水阶段,沟底表层土壤发生脱盐现象.土壤蒸发阶段,土壤发生积盐现象.垄顶土壤含盐量相对稳定,基本不发生向下运移.重度水分亏缺(处理T1,灌溉定额为1 700 m³/hm²)垄、沟积盐量分别为1.49,1.35 kg/m²,中度水分亏缺(处理T2,灌溉定额为2 100 m³/hm²)垄沟积盐量分别为1.42,1.12 kg/m²,充分灌水(处理T3,灌溉定额为2 500 m³/hm²)垄、沟积盐量分别为1.32,0.83 kg/m²,畦灌措施使0~100 cm土壤发生脱盐现象,脱盐量为1.29 kg/m².垄作沟灌下,制种玉米产量为2 765.1~4 619.5 kg/hm²,WUE为0.755~0.969 kg/m³.穗长(L_s)、穗粗(d_s)、行粒数(N_R)与作物产量呈显著正相关关系.     

螺旋槽干气密封端面非平行间隙压力分布的近似解析计算

为了研究变形后螺旋槽干气密封的气膜压力分布情况,基于MUIJDERMAN的螺旋槽窄槽理论,给出了螺旋槽干气密封端面非平行间隙气膜力的1种近似解析计算方法,并结合具体算例,与平行间隙情况进行了对比.结果表明,与平行间隙情况相比,在密封面的区域内存在某一半径R处,变形前后的气膜压力保持不变.当密封端面间形成发散型(“A字形”)间隙时,在r小于R区域,气膜压力降低,而在r大于R区域,气膜压力增大.随着偏转角θ的增大,最小膜厚减小,泄漏量增大;当形成收敛型(“V字形”)间隙时,在r小于R区域,气膜压力增大,而在r大于R区域,气膜压力降低.随着偏转角θ的增大,最小膜厚和泄漏量均先减小而后增大.     

通辽玉米滴灌灌溉制度

为更加合理制定玉米滴灌灌溉制度,以我国“节水增粮行动”为背景, 于2016年在内蒙古通辽开展玉米滴灌灌溉制度试验研究.根据试验区34 a的降雨资料进行降雨频率分析,选取不同水文年型的代表年,结合玉米滴灌试验得到实际耗水规律,对比6种灌溉处理在各生育阶段的变化情况,测定了株高、叶面积指数、玉米产量等指标.结果表明:中水处理作物性状及产量较高,且水分利用效率最高,为最佳灌水处理;以中水处理作为滴灌灌溉制度的参考依据,通过气象数据计算参考蒸发蒸腾量ET₀.利用实际耗水量获取各生育阶段作物系数,结合代表年型的ET₀计算需水量.根据降雨量,得到不同水文年型滴灌灌溉制度:枯水年覆膜滴灌灌溉定额1 575 m³/hm²,无膜滴灌灌溉定额1 785 m³/hm²;平水年覆膜滴灌灌溉定额1 125 m³/hm²,无膜滴灌灌溉定额1 425 m³/hm²;丰水年覆膜滴灌灌溉定额600 m³/hm²,无膜滴灌灌溉定额900 m³/hm².     

基于校正WOFOST模型的枣树生长模拟与水分利用评价

为实现定量化分析温、光和水资源对果树生长的影响,本研究以成龄骏枣树为研究对象,提出了基于校正WOFOST模型的枣树生长和水分运移模拟方法.利用2016和2017年的田间试验观测数据,重点校正WOFOST模型的物候学发育、初始化、绿叶、CO2同化、干物质分配、呼吸作用和水分利用参数.在田间尺度,完成总地上生物量(TAGP...     

Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration

The study investigates the ability of artificial neural networks (ANN) with artificial bee colony (ABC) algorithm in daily reference evapotranspiration (ET₀) modeling. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from two stations, Pomona and Santa Monica, in Los Angeles, USA, are used as inputs to the ANN–ABC model so as to estimate ET₀ obtained using the FAO-56 Penman–Monteith (PM) equation. In the first part of the study, the accuracy of ANN–ABC models is compared with those of the ANN models trained with Levenberg–Marquardt (LM) and standard back-propagation (SBP) algorithms and those of the following empirical models: The California Irrigation Management System (CIMIS) Penman, Hargreaves, and Ritchie methods. The mean square error (MSE), mean absolute error (MAE) and determination coefficient (R²) statistics are used for evaluating the accuracy of the models. Based on the comparison results, the ANN–ABC and ANN–LM models are found to be superior alternative to the ANN–SBP models. In the second part of the study, the potential of the ANN–ABC, ANN–LM, and ANN–SBP models in estimation ET₀ using nearby station data is investigated.     

An evaluation of two hourly reference evapotranspiration equations for semiarid conditions

The methodology proposed by the Food and Agriculture Organization (FAO) (Doorenbos, J., Pruitt, W.O., 1977. Crop water requirements. FAO irrigation and drainage. Paper No. 24. FAO, Rome) and updated by Allen et al. (Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO irrigation and drainage. Paper No. 56. FAO, Rome) for calculating crop water requirements is the most extended and accepted method worldwide. This method requires the prior calculation of reference evapotranspiration (ET_o). This study evaluates the FAO-56 and American Society of Civil Engineers (ASCE) Penman–Monteith (PM) equations for estimation of hourly ET_o under the semiarid conditions of the province of Albacete (Spain). The FAO-56 and ASCE equations (hourly time step) were compared against measured lysimeter ET_o values at Albacete for 13 days during the period of April–October 2002 and 16 days during April–October 2003. The average of estimated FAO-56 Penman–Monteith ET_o values was equal to the average of measured values. However, the average of estimated ASCE Penman–Monteith values was 4% higher than the average of measured lysimeter ET_o values. This method overestimated measured lysimeter ET_o values by 0.45 mm h⁻¹.Simple linear regression and error analysis statistics suggest that agreement between both estimation methods and the lysimeter was quite good for the province of Albacete.In this paper, the FAO-56 Penman–Monteith equation for calculating hourly ET_o values was more accurate than the ASCE Penman–Monteith method under semiarid weather conditions in Albacete.     

土壤有机质含量高光谱估测模型构建及精度对比

土壤有机质含量对作物的生长发育有着显著影响。为实现对苹果果园土壤有机质含量快速、实时估测,本研究以山东省烟台市栖霞市苹果园为研究区,采集100个土壤样本,利用ASD FieldSpec3便携式地物光谱仪获取其高光谱反射率,利用定量化学方法测定土壤有机质含量。采用移动平均法对高光谱数据进行预处理,分析果园土壤的反射光谱特征,研究光谱反射率与其有机质含量的相关关系,筛选土壤有机质含量的敏感波长并构建光谱指数后,分别建立多元线性回归模型（MLR）、支持向量机（SVM）和随机森林（RF）模型,并对模型精度进行验证比较。结果表明,筛选出的土壤有机质含量的敏感波长为678、709、1931、1939、1996和2201 nm。用筛选出的波长构建光谱参数,最终构建的光谱指数分别为NDSI_{（678,709）}、NDSI_{（678,1931）}、NDSI_{（678,2201）}、NDSI_{（709,1939）}和NDSI_{（1939,2201）}。建立的MLR、SVM和RF回归模型中,以RF模型精度最优,其校正样本集R²为0.8804,RMSE为0.1423,RPD达到2.25;验证模型的R²为0.7466,RMSE为0.1266,RPD为1.79,建立的RF定量模型反演苹果果园土壤有机质含量效果较好。因此,可以利用RF方法快速预测苹果果园土壤有机质含量,了解土壤养分分布状况,指导农民合理施肥,从而提高果园生产管理效率。