几种蒸散计算方法在怀来地区的适用性研究

作物蒸散量即为植株蒸腾与棵间蒸发之和,准确地测定作物实际蒸散量,对研究农作物节水种植技术、加强水资源管理和制定合理的灌溉计划具有十分重要的意义。文章应用2013年生长季(5-10月)自动称重式蒸渗仪实测河北怀来地区夏玉米的蒸散量,从月、日两个时间尺度评价Penman-Monteith(FAO P-M)、Hargreaves、Priestley-Taylor三种基于参考作物蒸散量计算实际蒸散的方法在怀来地区的适用性,结果表明:(1)三种方法计算的夏玉米月、日实际蒸散相对于蒸渗仪实测结果均存在一定偏差,FAO P-M方法与Hargreaves方法表现较好,准确度相差不大,Priestley-Taylor方法准确性最差;(2)影响实际蒸散计算结果的主要气象因子为太阳净辐射和温度,Hargreaves方法计算结果在怀来地区可信度较高;(3)Hargreaves方法所需参数较少,在计算量方面占有很大的优势,在怀来地区适用性最好。     

1960-2013年中国地表潜在蒸散发时空变化及其对气象因子的敏感性

基于中国107个气象站点的常规观测资料,采用Penman-Monteith公式计算了1960-2013年的逐日潜在蒸散发（ET0）,分析了中国5大区域的ET0对最高温度、最低温度、2m风速、日照时长、平均气压、相对湿度和地表温度的敏感性及其分区特征。结果表明：（1）模拟的从1960-2013年平均ET0和与蒸发皿蒸散发量之间的比值为0.55,各逐站点的ET0与蒸发皿蒸散发的相关系数为0.84-0.98（剔除观测值为0的情况的样本）之间和0.42-0.81（未剔除观测值为0的情况的样本）。（2）本研究中模拟的ET0以6.75mm/10a的速度呈现出下降的趋势。敏感性分析表明,在1960-2013年间的全国范围内,最高气温和最低气温分别增加0.68℃和1.54℃,相应地导致ET0增加12.81mm和14.13mm;风速减少0.51m/s,日照时长减少0.61h,相对湿度减少2.84%,将分别导致蒸散量减少48.08mm,21.5mm,204.49mm,这能很好的解释“蒸发悖论”问题。（3）对中国不同地理分区的ET0,在东北区域、华北区域、和西北区域,蒸散量最敏感的气象因子是相对湿度,其次是风速;在西南区域和华中和华东区域,蒸散量最敏感的气象因子是相对湿度,其次是日照时长。     

产量对水的反应(节译)

Ⅱ、最大蒸散气候是决定作物最佳生长和产量的需水量的最重要因素之一。作物需水量通常用蒸散率(ET)mm/日或 mm/期间表示。ET 的水平与由大气决定的蒸发有关。当计算或预测气候对作物蒸散水平的影响时,用可能蒸散(ETo)表示由大气决定的蒸发。ETo 表示由株高8—15Cm绿草复盖的地表,在绿草旺盛生长、完全遮     

我国参考作物蒸散的空间分布和时间趋势

根据我国616个地面气象台站1975-2004年的观测资料,利用联合国粮农组织推荐的Penman-Monteith公式计算各年逐日、逐月参考作物蒸散值(ET0)和年总量.结果表明,我国参考作物蒸散多年平均值大多界于800～1 100 mm之间,西北地区高,东北地区低.1978年出现最大值,1993年出现最低值,青藏高原以东地区波动小,西北地区波动大.参考作物蒸散变化率在-30～30 mm·(10 a)-1之间,西部和长江流域地区显著下降,东部沿海、黄河中上游和东北显著上升.造成我国参考作物蒸散出现先降后增趋势的主要因素是日照时数(净辐射)和饱和差.     

参考作物蒸散模型对比分析及评价

利用山东省6个气象台站45 a(1960-2004年)的逐日气象资料并选用7种参考作物蒸散模型,分别计算了上述各地的参考作物蒸散,对模型结果进行时空分布对比分析;进而以FAO推荐的Penman-Monteith模型为对照,利用最小一乘法对其余6种模型进行优化并对优化前后的模型进行时空比较.结果表明:Makkink模型在6个台站的时空分布模拟效果均最好,Mass-transfer模型在7-8月明显偏低,Net Radiation模型各站全年基本都偏高;根据不同月份的相对偏差情况,采用最小一乘法进行分月优化,优化后的模型预测月参考作物蒸散标准误差小于5 mm,平均相对误差小于8.5%,台站的年参考作物蒸散相对误差也基本小于10%,说明这些含参数较少的模型经优化后基本上可用,当资料缺损时不失为Penman-Monteith模型的替代模型.     

吉林省参考作物蒸散量的时空变化特征及影响因素

基于吉林省50个气象站1960—2014年逐日最高气温、最低气温、日照时数、风速数据,采用Penman-Monteith算法,计算各站逐日参考作物蒸散量,进而计算各站及全省四季和年平均参考作物蒸散量,利用数理统计方法,结合地理信息系统软件,分析参考作物蒸散量的时空变化特征及主要气候影响因子。结果表明:近55 a,吉林省年平均参考作物蒸散量为876 mm,年参考作物蒸散量呈显著下降趋势(p <0. 01);空间分布差异显著,由东南向西北逐级递增,56%的站点呈显著下降趋势(p <0. 05)。参考作物蒸散量夏季最大、春季次之、冬季最小,且均呈下降趋势,但只有春季的下降趋势显著(p <0. 01);春、夏、秋、冬季与年平均参考作物蒸散量在空间分布上基本一致,但气候倾向率为负值以及通过显著性检验的站点数依次减少。全省四季和年参考作物蒸散量均与降水呈显著负相关,与日照时数、风速、最高气温呈显著正相关;其中年、春、夏、秋季与气温日较差以及春、夏、秋季与平均气温也呈显著正相关;冬季与最低气温、平均气温呈显著正相关;而典型站点参考作物蒸散量各季节影响因素及影响大小略有差异,各气象因子的共同作用导致了参考作物蒸散量的变化。     

北疆地区参考作物蒸散量时空变化特征

为明确北疆地区在全球气候变暖背景下合理的灌溉制度,利用北疆地区22个气象站49 a(1962~2010年)的逐日气象资料,运用Penman-Monteith公式计算北疆地区1962~2010年的参考作物蒸散量ET0(reference crop evapotranspiration),并用Mann-Kendall方法对其进行突变检验,基于Arc GIS9.3空间分析功能模块对北疆参考作物蒸散量进行了空间变化分析。结果表明:研究区域的ET0在1983年发生向下突变,ET0在时间分布上整体呈下降趋势,主要受该地区相对湿度和风速的影响;ET0从北疆的东北部和西南部向中间逐渐升高,东南部和西部表现略高,具有明显的区域差异;4~10月ET0对全年ET0的分布具有显著影响。     

基于碳水通量耦合原理改进Penman-Monteith蒸散发模型

陆地蒸散(ET)涵括地表和潮湿叶片的蒸发和植物的蒸散发,是陆地水循环的重要组成部分。Penman-Monteith方程是估算陆地蒸散的重要方法,方程中的叶片或冠层气孔导度是提高估算精度的关键因子。根据碳水循环的耦合原理,植物光合作用模型可用于估算叶片或冠层气孔导度。植物光合作用模型可分为三类:1)使用总冠层导度的大叶模型(BL),2)区别阴、阳叶冠层导度的双大叶模型(TBL),3)区别阴、阳叶叶片导度的双叶模型(TL)。与这三类光合作用模型相对应,衍生出基于不同导度计算方法的三种蒸散估算模型。三种蒸散模型之间的主要区别在于是否进行从叶片尺度到冠层尺度的气孔导度集成。这三种模型中,双叶模型使用叶片尺度的气孔导度,集成度最低。反之,大叶模型使用冠层尺度的气孔导度,集成度最高。由于在Penman-Monteith中,蒸腾和气孔导度之间的关系是非线性的,气孔导度的集合会导致负偏差。因此,与通量测量相比,大叶蒸散模型的估算偏差最大,而双叶蒸散模型的估算偏差最小。     

黄淮海平原冬小麦最大可能蒸散的估算

作物最大可能蒸散考虑了作物及当地地表状况,为当地地表实际覆盖情况下实际蒸散的理论上限值,能客观分析作物对水分的需求程度和农业干旱状况。基于遥感（叶面积指数和地表反照率）数据和逐日气象数据,利用Penman-Monteith公式,计算黄淮海平原小麦种植区27个气象站冬小麦生育期2000-2015年逐日蒸散,提取得到冬小麦生育期逐日最大可能蒸散数据集,并分析其时空变化特征及成因。结果表明:与联合国粮农组织（FAO）单作物系数法计算的最大可能蒸散E_k对比,区域平均最大可能蒸散E_c的时间变化趋势与E_k一致,空间分布上E_c符合客观实际。黄淮海平原冬小麦全生育期、越冬期和返青-拔节期E_c均呈北低南高的分布特征,日平均值分别为1.99 mm,0.44 mm和2.75 mm;其余3个生育期（越冬前、抽穗期、乳熟-成熟期）在空间分布上差异不大,日平均值分别为1.23 mm,4.71 mm和3.74 mm。冬小麦不同生育期（含全生育期）E_c的空间分布主要受叶面积指数分布特征的影响,二者呈显著正相关关系。     

Regional estimates of evapotranspiration over Northern China using a remote-sensing-based triangle interpolation method

Regional estimates of evapotranspiration (ET) are critical for a wide range of applications. Satellite remote sensing is a promising tool for obtaining reasonable ET spatial distribution data. However, there are at least two major problems that exist in the regional estimation of ET from remote sensing data. One is the conflicting requirements of simple data over a wide region, and accuracy of those data. The second is the lack of regional ET products that cover the entire region of northern China. In this study, we first retrieved the evaporative fraction (EF) by interpolating from the difference of day/night land surface temperature ( T ) and the normalized difference vegetation index (NDVI) triangular-shaped scatter space. Then, ET was generated from EF and land surface meteorological data. The estimated eight-day EF and ET results were validated with 14 eddy covariance (EC) flux measurements in the growing season (July-September) for the year2008 over the study area. The estimated values agreed well with flux tower measurements, and this agreement was highly statistically significant for both EF and ET (p <0.01), with the correlation coefficient for EF (R2 =0.64) being relatively higher than for ET (R2 =0.57). Validation with EC-measured ET showed the mean RMSE and bias were 0.78 mm d-1 (22.03 W m-2 ) and 0.31 mm d-1 (8.86 W m-2 ), respectively. The ET over the study area increased along a clear longitudinal gradient, which was probably controlled by the gradient of precipitation, green vegetation fractions, and the intensity of human activities. The satellite-based estimates adequately captured the spatial and seasonal structure of ET. Overall, our results demonstrate the potential of this simple but practical method for monitoring ET over regions with heterogeneous surface areas.     

Error assessment of climate variables for FAO-56 reference evapotranspiration

Meteorological stations, which measure all the required meteorological parameters to estimate reference evapotranspiration (ET_o) using the Food and Agriculture Organization Penman?CMonteith (FAO56-PM) method, are limited in Korea. In this study, alternative methods were applied to estimate these parameters, and the applicability of these methods for ET_o estimation was evaluated by comparison with a complete meteorological dataset collected in 2008 in Korea. Despite differences between the estimation and observation of radiation and wind speed, the comparison of ET_o showed small differences [i.e., mean bias error (MBE) varying ?0.22 to 0.25?mm?day^?1 and root-mean-square-error (RMSE) varying 0.06?C0.50?mm?day^?1]. The estimated vapor pressure differed considerably from the observed, resulting in a larger discrepancy in ET_o (i.e., MBE of ?0.50?mm?day^?1 and RMSE of 0.60?C0.73?mm?day^?1). Estimated ET_o showed different sensitivity to variations of the meteorological parameters??in order of vapor pressure?>?wind speed?>?radiation. It is clear that the FAO56-PM method is applicable for reasonable ET_o estimation at a daily time scale especially in data-limited regions in Korea.     

Spatiotemporal variability of reference evapotranspiration and its contributing climatic factors in Yunnan Province, SW China, 1961–2004

Evapotranspiration is an important flux term in the water cycle that integrates atmospheric demand and surface conditions. Using the FAO Penman–Monteith method, we calculated monthly reference evapotranspiration (ET0) for 119 stations during 1961–2004 over Yunnan Province (YP), southwest China. Linear trend analysis shows that area-averaged annual and seasonal ET0 rates declined, with most remarkable decreases during pre-monsoon (?1.5 mm decade^?1, Mar–May) and monsoon (?0.6 mm decade^?1, Jun–Aug) seasons. Most of the stations with negative trends were concentrated in the eastern and northern parts of YP. Over the 44–year period, wind speed (WS), relative sunshine duration (SD) and relative humidity (RH) all showed decreasing trends, whereas maximum temperature (TMX) increased slightly. Multivariate regression analysis indicated that the variability of ET0 rates is most sensitive to the variations of SD, followed by RH, TMX and WS. The temporal evolution of these contributing factors was not stable during the study period, with an increasing contribution of SD and a decreasing contribution of TMX after the 1970s. Temporally changing contributions of climatic variables to ET0 should be taken into account when evapotranspiration rates are calculated with equations that rely on parameterization of climatic variables. Linking the changing contributions of climatic variables to ET0 rates to circulation features may help to better understand how ET0 responds to regional climatic change.     

Performance evaluation of modified versions of Hargreaves equation across a wide range of Iranian climates

Reference evapotranspiration (ET_o) is significant for water resources planning and environmental studies. Many equations have been developed for ET_o estimation in various geographic and climatic conditions, of which, the Penman–Monteith FAO 56 (PMF-56) equation was accepted as reference method. A major complication in estimating ET_o by the PMF-56 model is the requirement for meteorological data that may not be readily available from typical weather stations in many areas of the globe. This restriction necessitates use of simpler models which require less input data. In this study, the original and five modified versions of the Hargreaves equation that require only temperature and rainfall were evaluated in humid, semi-humid, semi-arid and arid climates in Iran. The results showed that the original and modified versions of the Hargreaves equation had the poorest performance in semi-humid climate and the best performance in windy humid environment. Further, the ET_o estimations with the Hargreaves equations having rainfall parameter were poor as compared to those from the PMF-56 method under majority of the climatic situations studied.     

Estimating sunshine duration from other climatic data by artificial neural network for ET0 estimation in an arid environment

Sunshine duration data are desirable for calculating daily solar radiation (R _s) and subsequent reference evapotranspiration (ET₀) using the Penman–Monteith (PM) method. In the absence of measured R _s data, the Ångström equation has been recommended by the Food and Agriculture Organization (FAO) of the United Nations. This equation requires actual sunshine duration that is not commonly observed at many weather stations. This paper examines the potential for the use of artificial neural networks (ANNs) to estimate sunshine duration based on air temperature and humidity data under arid environment. This is important because these data are commonly available parameters. The impact of the estimated sunshine duration on estimation of R _s and ET₀ was also conducted. The four weather stations selected for this study are located in Sistan and Baluchestan Province (southeast of Iran). The study demonstrated that modelling of sunshine duration through the use of ANN technique made acceptable estimates. Models were compared using the determination coefficient (R ²), the root mean square error (RMSE) and the mean bias error (MBE). Average R ², RMSE and MBE for the comparison between measured and estimated sunshine duration were calculated resulting 0.81, 6.3 % and 0.1 %, respectively. Our analyses also demonstrate that the difference between the measured and estimated sunshine duration has less effect on the estimated R _s and ET₀ by using Ångström and FAO-PM equations, respectively.     

A comparison of different methods of estimating energy‐limited evapotranspiration in the peace river region of British Columbia

Abstract

The performances of eight methods for estimating daily energy‐limited evapotranspiration (E_e) were compared with reliable values for the Peace River region of British Columbia. They included the methods of Priestley and Taylor (PT), Jensen and Haise (JH), Hargreaves (H) and Makkink (M), the first and second equations of Baier and Robertson (BR1 and BR2), and the modified methods of Blaney and Criddle (BC) and Thornthwaite (T). The reference data were obtained from previous workers, who in 1977 and 1979 made micrometeorological measurements of E_e using the Bowen ratio method.

The relationships between estimated and measured E_e values excluding the T method in 1977 had correlation coefficients (R) ranging from 0.65 to 0.84. These were not significantly different at the 5% level. The Standard Errors of the Estimate (SEE) ranged from 0.43 to 1.69 mm d^?1. The PT, JH, BR1 and T methods had low SEE values, whereas the BR2, H, BC and M methods had high SEE values. Six of the eight methods (PT, JH, H, M, BR2 and BC) were calibrated for local conditions using 1979 data. After calibration, the methods were tested with the 1977 data. The results indicated significant improvement in the fit of four of the methods (H, M, BR2 and BC). Overall, it was concluded that after calibration, six of the eight methods had predictive power and fit that were not significantly different at the 5% level.     

Applicability and improvement of different evapotranspiration methods of reference crops in Jiangxi Province

To achieve accurate evaluation of evapotranspiration of reference crops (ET0) in Jiangxi, China, in the absence of systematic climatological data, with reference to the FAO-56 Penman–Monteith (P-M) equation, the Priestley-Taylor (P–T) method, the Makkink method, the Hargreaves-Samani (H–S) method, the Irmak-Allen (I-A) method, the Penman1948 (48PM) method, the Penman-Van Bavel (PVB) method, the Baier-Robertson (B-R) method, the improved Baier-Robertson (M-B-R) method, the Schendel (Sch) method, the Turc method, the Jensen-Haise (J-H) method, and the Brutsaert-Stricker (B-S) method were used to evaluate the daily climatological data collected by 26 weather stations in Jiangxi, China, and 17 weather stations in adjacent provinces. The results were compared with each other and parameter rate determination was conducted. The results indicated that the Turc method exhibited optimized applicability before parameter rate determination and the average root mean square error (R_MSE) and the average normalized root mean square error (N_RMSE) by this method were 0.39 mm/d and 0.157 mm, respectively. However, parameter rate determination led to negligible improvement in accuracy for this method. The Turc method could be directly applied in Jiangxi (except Nanchang). For special distribution of error after parameter rate determination, all methods exhibited significant errors in Northern Jiangxi. Herein, the 48PM method and the B-S method showed good applicability after parameter rate determination and R_MSE and N_RMSE of data by these methods ranged in 0.06 ~ 0.34 mm/d and 0.08 ~ 0.27, 8 ~ 27%, respectively, and their d-indices were close to 1. The annual over-estimations in weather stations in Jiangxi were below 30 mm. In the absence of data about relative humidity and wind speed, the P–T method was an appropriate simplified method for Jiangxi. In this case, α was slightly lower than the default value (1.05 ~ 1.18), R_MSE was within 0.21 ~ 0.66 mm/d, and N_RMSE was within 0.08 ~ 0.308 ~ 30%. Accuracy of R_MSE, d-index, and N_RMSE of data by the P–T method, the I-A method, and the PVB method was consistent with all stations, while that by the Mak method was slightly lower, which could be attributed to severe over-estimation in July and August. R_MSE of the H–S method, the B-R method, the M-B-R method, the J-H method, and the Sch method were above 0.75 mm/d and these methods were not suitable for accurate evaluation of ET0 in Jiangxi, China. The annual ET0 was calculated by various methods (except the 48PM method and the B-S method) exhibited significant variation around 2003. This may be attributed to significant changes in certain meteorological factors over recent years.

     

Evaluation of solar radiation estimation methods for reference evapotranspiration estimation in Canada

The accuracy of nine solar radiation (R _s ) estimation models and their effects on reference evapotranspiration (ET _o ) were evaluated using data from eight meteorological stations in Canada. The R _s estimation models were FAO recommended Angstrom-Prescott (A-P) coefficients, locally calibrated A-P coefficients, Hargreaves and Samani (H-S) (1982), Annandale et al., (2002), Allen (1995), Self-Calibrating (S-C, Allen, 1997), Samani (2000), Mahmood and Hubbard (M-H) (2002), and Bristow and Campbell (B-C) (1984). The estimated R _s values were then compared to measured R _s to check the appropriateness of these models at the study locations. Based on root mean square error (RMSE), mean bias error (MBE) and modelling efficiency (ME) ranking, calibrated A-P coefficients performed better than all other methods. The calibrated H-S method (using new K _RS 0.15) estimated R _s more accurately than FAO-56 recommended A-P in Elora, and Winnipeg. The RMSE of the calibrated H-S method ranged between 1-6% and the RMSE of the calibrated and FAO recommended Angstrom-Prescott (A-P) methods ranged between 1-9%. The models with the least accuracy at the eight locations are the Mahmood & Hubbard (2002) and Self-Calibrating models. The percent deviation in ET _o calculated with estimated R _s was reduced by about 50% as compared to deviation in measured versus estimated R _s .     

地面有效辐射气候学模型评估和参数优化

基于中国19个辐射站1993-2012年的地面辐射平衡资料和气象资料,分析评估了布朗特法、彭曼法、别尔良德法、FAO24法、FAO56-PM法、邓根云法和童宏良法7种参数化方案计算中国地面有效辐射的适用性;并以均方根误差最小为目标函数,利用步长加速法和多元回归法迭代求解最优参数,建立适合于中国的最优参数化逐日有效辐射估算方法。结果表明:参与评估的7种方案都不同程度低估了中国的有效辐射;从全中国总体误差水平看,童宏良法的平均绝对百分比误差和均方根误差小于其他6种方案,分别为27.0%和24.5 W/m2,估算效果较好;其次是彭曼法和邓根云法;FAO56-PM法精度较低,不适用于中国的有效辐射估算。针对单站来说,邓根云法在东部平原地区的精度最高,童宏良法由于考虑了海拔高度的订正,适用于西部高原地区。相关分析表明水汽压是影响有效辐射估算误差的最关键因素,因此根据水汽压的地理分布规律,分东部区和西部区建立分区方案。基于观测资料建立的全中国方案和分区方案的均方根误差分别为20.8和21.4 W/m2,精度均高于已有参与评估的7种方案;而且在绝大多数站点,分区方案的误差小于全中国方案,所以划分东部区和西部区进行有效辐射模型参数化很有必要。同时发现,分区方案在西部区明显优于邓根云法,在东部区明显优于童宏良法,因此推荐其作为中国有效辐射的计算方法。      

Estimating daily pan evaporation using artificial neural network in a semi-arid environment

The objective of this study was to test an artificial neural network (ANN) for estimating the evaporation from pan (E _Pan) as a function of air temperature data in the Safiabad Agricultural Research Center (SARC) located in Khuzestan plain in the southwest of Iran. The ANNs (multilayer perceptron type) were trained to estimate E _Pan as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1996–2001) of daily climatic data collected in weather station of SARC. The empirical Hargreaves equation (HG) is also considered for the comparison. The HG equation calibrated for converting grass evapotranspiration to open water evaporation by applying the same data used for neural network training. Two historical series (2002–2003) were utilized to test the network and for comparison between the ANN and calibrated Hargreaves method. The results show that both empirical and neural network methods provided closer agreement with the measured values (R ²?>?0.88 and RMSE?<?1.2 mm day^?1), but the ANN method gave better estimates than the calibrated Hargreaves method.