基于随机样本的神经网络模型估算参考作物腾发量

参考作物腾发量(ET₀)是计算作物需水量、制定灌溉制度和进行水资源管理的主要参数之一。计算参考作物腾发量(ET₀)的方法众多，为规范ET₀的求法，联合国粮农组织(FAO)推荐采用修改的Penman-Monteith方法。该文指出不需要收集长序列气象资料，而以随机样本建立学习速率和动量因子自适应的BP神经网络模型估算参考作物腾发量(ET₀)的方法，并且与FAO推荐的Penman-Monteith法计算值对比分析，结果表明：利用随机样本建立的的BP神经网络模型可以很好的反映气象因子(最高温度、最低温度、最大湿度、最小湿度、净辐射和风速)与参考作物腾发量(ET₀)的非线性函数映射关系，并且取得了良好的估算效果，给出了国家自然科学基金重点项目研究区内蓝旗试验站2004年的时间尺度为日、十日参考作物腾发量(ET₀)的计算及对比分析过程。     

内蒙古地区ET0时空变化与相关分析

该文根据内蒙古135个站点，30年气象资料，利用Penman—Monteith公式计算得参考作物腾发量(ET₀)。在此基础上，对ET₀在我国北方干旱寒冷区时空上变化进行了分析，同时就ET₀与4项主要气象因子(温度T、湿度RH、风速U、日照时数N)的关系进行了分区分月的分析，提出了适合我国北方干旱寒冷地区不同条件下的ET₀计算模型。     

利用小蒸发皿观测资料确定参考作物蒸散量方法研究

参考作物蒸散量是土壤－植被－大气系统水分能量平衡模型的重要参数，如何准确获得将直接影响模型应用和最终模拟预测精度。该文利用分布于黄土高原地区65个气象站1971～2000年的气象资料，以FAO推荐的Penman-Monteith方法确定的参考作物蒸散量为标准，提出了根据平均相对湿度与风速为变量确定由20 cm小蒸发皿观测的水面蒸发量计算参考作物蒸散量的系数Kp。结果表明：由蒸发皿观测值计算的3 d或更长尺度的ET₀与Penman-Monteith方法计算的ET₀结果一致性很高，在对Kp方程系数进行适当的地域性调整后，由蒸发皿观测值和Kp确定的ET₀与Penman-Monteith方法确定的ET₀结果一致，从而认为在黄土高原地区参考作物蒸散量计算可以应用20 cm蒸发皿系数法。     

石羊河流域气候变化对参考作物蒸发蒸腾量的影响

根据甘肃省气象局石羊河流域的6个气象站近50年的观测资料，应用1998年FAO最新推荐的Penman-Monteith公式计算了50年各月参考作物蒸发蒸腾量ET₀，分析了ET₀的月际变化和年际变化特征，除武威与肃南站ET₀呈逐年显著减少趋势外，其他各站的ET₀值均表现为逐年增加趋势，各个站ET₀ 20世纪90年代较80年代均有明显增加，说明气候变化对ET₀的影响较大；并分析了平均气温、平均最高气温、年日照时数、平均风速、平均相对湿度、年降水量、年蒸发量、海拔高度与ET₀的相关性，各站ET₀与平均相对湿度相关性最好；石羊河流域ET₀空间变化也较大，从山区到绿洲平原ET₀多年平均值呈递增趋势。     

分别利用Hargreaves和PM公式计算西北干旱区ET0的比较

该文根据甘肃张掖气象站1991～2000年的气象资料，利用Hargreaves公式和Penman-Monteith公式计算了参照作物需水量(ET₀)。对比分析结果表明：Hargreaves公式计算的ET_0H年值比Penman-Monteith公式的计算ET_0PM偏低，而在年内6、7、8月份，ET_0H>ET_0PM，9月份两种方法计算结果几乎相等，其他月份ET_0H<ET_0PM。造成这种结果的原因是风速和降雨的影响。根据两种方法的计算结果，提出了适合西北干旱区ET₀的计算公式。     

太子河流域参考作物腾发量演变特征及气候影响因素分析

采用太子河流域内8个气象站1960～2005年间气象资料，应用Penman-Montieth公式计算了46年间逐月参考作物腾发量(ET₀)，对参考作物腾发量及气象要素的年际变化特征、月际变化特征及趋势进行了分析，应用统计检验方法分析了影响流域参考作物腾发量变化的主要气象因素。结果表明：近46年间太子河流域ET₀值呈现缓慢下降趋势，年内ET₀值分布以5、6月份最高，1月份最低。影响ET₀的主要气候要素按影响程度强弱依次为日照、风速、温度、相对湿度。     

纵向岭谷区参考作物腾发量变化的特点和趋势

以Penman Montieth方程分析了西南纵向岭谷区大理、元江、保山、昆明、景洪站46～48年的逐日ET₀及其余25个站1961～2000年逐月ET₀系列。研究结果表明：日最高温度是年内ET₀变化主导因素，年际变化主要受日照时数影响，个别站为最高气温或风速，短期ET₀变化与雾无直接关系。利用Mann-Kendall法对各站年际、年内分季节ET₀趋势检验，56.7%站点的年ET₀呈显著增加趋势，分布于澜沧江耿马-思茅-勐海一带以及横断山区维西、福贡等地。分季节逐日ET₀变化趋势为，昆明夏秋季显著下降，景洪冬春季显著增加，元江、保山、大理有增有减。降水量增加、气温升高，蒸发和日照时数减少，导致80%的站ET₀呈下降趋势，湿润指数普遍增加。     

用微型计算机计算分析黄土高原的蒸发蒸腾量(续)

在1984年《水土保持通报》第3期上,介绍了用微型计算机计算分析黄土高原地区蒸发蒸腾量（ET₀）的程序思路与主要步骤,简述了应用的方法,并以陕北杏子河流域为应用实例。本文现根据ET₀计算分析研究工作的进展情况,结合应用方面的实际需要,再介绍一下ET₀磁盘资料的设计和应用技术,以及运用微型计算机对ET₀资料进行各种表格化处理的基本方法,并列出IPT₀应用程序框图,展示新的应用成果,以供有关部门参考。     

中国参考作物腾发量时空变化特性分析

分析参考作物腾发量的时空变化特征，有助于了解中国农业及生态需水的分布与演变规律。基于全国范围200多个气象站测站逐日气象观测资料，应用FAO-Penman-Monteith公式，计算得出各站历年逐日参照作物腾发量ET₀。利用GIS的空间分析功能，采用反距离空间插值方法得到全国参考腾发量的分布图，统计分析了不同分区不同时段ET₀的变化情况。结果表明：西北河西走廊地区和南方岭南地区的参考作物腾发量较大，最大值超过1500 mm。而东北黑龙江一带和四川盆地附近，参考作物腾发量较小，在600～700 mm之间。此外，夏季ET₀的分布特征决定了全年ET₀的分布特征。选取4个代表气象站，对其ET₀的历年变化及其与气象因素的关系进行了分析。分析表明，受风速减小和气温增加的共同影响，干旱地区、半干旱地区和半湿润地区的参考作物腾发量呈现减少趋势，湿润地区则相对稳定。     

干旱区人工绿洲间作农田蒸散研究

在黑河流域中游的张掖绿洲区建立了大田环境下的春小麦和夏玉米间作农田能水平衡研究观测点，以气象观测资料为基础，采用波文比能量平衡法(BREB)和参考作物蒸散量—作物系数法(ET₀-K_c)对作物的蒸散进行了计算。结果表明：在一个完整的生长期内，利用波文比能量平衡法得到的间作作物蒸散量为688 mm，日均3.4 mm/d，用参考作物蒸散量—作物系数法得到的作物蒸散量为666 mm，日均3.3 mm/d，两种计算方法得到的蒸散量总值差别小。同期，水文平衡法计算结果为733 mm。利用波文比能量平衡法所得结果的分析表明，试验地在不同生长阶段，ET变化剧烈，生长初期、中期、末期分别为1.19、4.41和2.58 mm/d，其蒸散量分别占全年蒸散总量的7.79%、78.73%和13.48%。ET月变化显示，3月维持在一个较低水平；4月和5月剧烈增加；6月达到最大；此后的7月和8月降低，但仍维持在一个高水平；9月，随着作物进入生长末期，蒸散急剧减小。对ET日内变化分析可知，作物蒸散开始于早晨7∶00～8∶00，在14∶00左右达到最大，19∶00～20∶00趋于0 mm/d。不同生长阶段蒸散强度差异明显。     

Calibration and validation of four common ET0 estimation equations by lysimeter data in a semi-arid environment

In this study, four different methods for reference crop evapotranspiration (ET₀) were calibrated and validated for estimation of daily to mean monthly ET₀ by weighing lysimeter data during 2005–2006 and 2004–2005, respectively, in a semi-arid region. The value of the constant in the Hargreaves–Samani method changed from 0.0023 to 0.0026 for daily to mean monthly ET₀, and can be used in stations with only air temperature data. The constant of the aerodynamic resistance equation in the FAO-56 Penman–Monteith method (208.0) changed to 85.0. The value of coefficient a in the FAO-24-Radiation method was between ?0.5 and ?0.67. Further, the empirical equations were modified to estimate the value of b in the FAO-24-Radiation method and C in the FAO-24 corrected Penman method. The results showed that the modified FAO-56, corrected Penman–Monteith and FAO-24-Radiation methods are the most appropriate for estimating daily to mean monthly ET₀. Furthermore, the modified FAO-24 corrected Penman method was ranked in fourth place and its accuracy was lower than that of the other methods. However, it is appropriate for estimating mean monthly ET₀. Smoothing the daily data decreased the fluctuation in measured daily weather data and ET₀ measured by lysimeter, and consequently resulted in a higher accuracy in the estimation of daily ET₀.     

Proper methods and its calibration for estimating reference evapotranspiration using limited climatic data in Southwestern China

Proper methods for estimating reference evapotranspiration (ET₀) using limited climatic data are critical, if complete weather data are unavailable. Based on the weather data of 19 stations in Guizhou Province, China, several simple methods for ET₀ estimation, including the Hargreaves, Priestley–Taylor, Irmak–Allen, McCloud, Turk, and Valiantzas methods, were involved in comparison with the standard FAO-56 Penman–Monteith (PM) method. The Turk equation performs well for estimating ET₀ in humid locations. Both the Turk method and the Valiantzas method initially performed acceptably with mean root-mean-square difference (RMSD) of 0.1472 and 0.1282 mm d^?1, respectively, with only requiring parameters of temperature (T), relative humidity (RH), and sunshine duration (n). The corresponding calibration formulas to Turk and Valiantzas method were suggested as the most appropriate method for ET₀ estimation with the RMSD of 0.0098 and 0.0250 mm d^?1, respectively. The local calibrated Hargreaves–Samani method performed well and can be applied as the substitute of FAO-56 PM method under the condition that only the daily mean, maximum, and minimum temperatures were available, and local calibrated McCloud method was acceptable if only the mean temperature was available.     

Evaluation of pan coefficient equations for estimating reference crop evapotranspiration in the arid region

Reference evapotranspiration (ET₀) can be estimated on basis of pan evaporation data (E_pan), whose measurements have the advantage of low cost, simplicity of the measuring equipment, simple data interpretation and application as well as suitability for locations with limited availability of meteorological data. E_pan values were converted to ET₀ using the pan evaporation coefficient (K_pan). In this study, seven common K_pan equations were evaluated for prediction of ET₀ in the growing season (April to October) in arid region of Iran. The Cuenca approach was best suited compared to the standard FAO Penman–Monteith method (FAO-56 PM).     

Evaluation and calibration of simple methods for daily reference evapotranspiration estimation in humid East China

The current study aims to improve the performance of simple methods for the estimation of daily reference evapotranspiration (ET₀) in humid East China, namely Priestley–Taylor 1972 (P-T 1972), Hargreaves–Samani 1985 (H-S 1985) and Turc 1961 (TU 1961). These methods were evaluated and calibrated based on well-watered grass lysimeter experiments. The FAO-56 Penman–Monteith equation (FAO-56 PM) is the best method, and the radiation-based methods (TU 1961 and P-T 1972) perform much better than the temperature-based method (H-S 1985). In the simple methods, the coefficients are calibrated to: 1.34 for P-T 1972; 0.0186, 23.47 and 17.06 for TU 1961; and 0.0027 and 0.449 for H-S 1985. The locally calibrated TU 1961 and P-T 1972 perform much better than the original, with either the observed ET_0r or the ET_0c obtained by FAO-56 PM as standard. However, local calibration does not significantly improve the performance of the H-S 1985 method. In humid East China, FAO-56 PM is the best method for daily ET₀ calculation. TU 1961, especially if locally calibrated, is the optimal choice as a simple substitute for FAO-56 PM when solar radiation is available. Otherwise, serious local calibration is strongly recommended before applying H-S 1985 for daily ET₀ estimation.     

甘肃地区参考作物蒸散量时空变化研究

区域水土平衡模型的建立通常需要确定计算参考作物蒸散量的模型,这一模型的精确与否,直接影响整体预测模型的最终预报精度.运用FAO-24 Blaney-Criddle法、FAO-24 Radiation法、FAO PPP-17 Penman法及FAO Penman-Monteith(98) 4种方法,对甘肃省1981～2000年33个站点的月参考作物蒸散量进行了计算.对比分析结果表明,AO Penman-Monteith(98)模型的精度与灵敏度均显示了较强的优越性.运用该模型对甘肃省参考作物蒸散量的时空分布特征进行研究表明:甘肃省参考作物蒸散量年内逐月演变曲线呈单峰状;年际蒸散量变化与夏季年际波动变化存在较高一致性;全年参考作物蒸散量分布具有从东南向西北递增的趋势.     

Estimation of daily reference evapotranspiration by neuro computing techniques using limited data in a semi-arid environment

In this paper, the daily reference evapotranspiration (ET₀) for Bulawayo Goetz was estimated from climatic data using neuro computing techniques. The region lacks reliable weather data and experiences inconsistencies in the measuring process due to inadequate and obsolete measuring equipment. This paper aims to propose neuro computing techniques as an alternative methodology to estimating evapotranspiration. Firstly, ET₀ was calculated using FAO-56 Penman-Monteith (PM) equation from available climatic data. Data was divided into training, testing and validation for neuro computing purposes. The study also investigated the effect of different normalisation techniques on neuro computing ET₀ estimation accuracy. In another application, neuro-computing ET₀ estimates were compared against those obtained using empirical methods and their calibrated versions. The Z-score normalisation technique for all data sets gave best results with a Multi-layer perceptron (5–5-1) model having RMSE, MAE and R² values in the range 0.12–0.25 mm day^?1, 0.08–0.15 mm day^?1 and 0.94–0.99 respectively. There were no significant differences in ET₀ estimation accuracy by neuro computing techniques due to normalisation technique. The Neuro computing techniques were superior to empirical methods in ET₀ estimation for Bulawayo Goetz. The Neuro computing techniques are recommended for use in cases of limited climatic data at Bulawayo Goetz.     

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.     

基于GEP和地理位置信息的湘鄂地区月参考作物腾发量模拟计算

参考作物腾发量(ET0)是计算植被蒸散发的关键因子,准确估算ET0对水资源管理、灌溉制度设计等具有重要意义。本研究利用湘鄂地区46个气象站点1955—2005年的逐月气象数据,包括月最高气温、最低气温、平均风速、日照时数以及相对湿度,用FAO-56 Penman-Monteith法计算各站的逐月ET0值。然后结合基因表达式编程(GEP)算法挖掘公式的能力,以各站点的地理位置信息(纬度、经度、海拔)及月序数为输入,以多年逐月平均ET0值为输出,建立基于地理位置信息的月ET0模型,并与传统ET0模型的计算结果进行比较。结果表明,所建立的模型具有足够的精度,校正、检验阶段的决定系数(R2)和均方根误差(RMSE)分别为0.934、0.951和10.050 mm、8.628 mm;与Hargreaves和Priestley-Taylor法相比,基于地理位置信息建立的GEP模型的结果均方根误差最小,变化范围为8.628~9.967 mm。本研究所建立的月ET0模型具有明确的表达式,简单易用,在湘鄂地区仅利用地理位置信息计算逐月ET0是可行的,可以利用该模型进行月尺度的灌溉制度设计和植被蒸散发的估算。     

利用温度资料和广义回归神经网络模拟参考作物蒸散量

参考作物蒸散量(reference evapotranspiration,ET0)精确模拟对水资源高效利用和灌溉制度制定具有重要意义,该文以四川盆地19个气象站点1961-1990年逐日最高、最低温度和大气顶层辐射作为输入参数,FAO-56 Penman-Monteith(PM)模型计算的ET0为标准值,建立基于广义回归神经网络(generalized regression neural network,GRNN)的ET0模拟模型,基于1991-2014年资料进行模型验证,将GRNN模型同Hargreaves(HS1)和改进Hargreaves(HS2)等简化模型的模拟结果进行比较,分析只有温度资料情况下不同模型模拟ET0误差的时空变异性。结果表明:GRNN、HS1和HS2模型均方根误差(root mean square error,RMSE)分别为0.41、1.16和0.70 mm/d,模型效率系数(Ens)分别为0.88、0.13和0.67。3种模型RMSE在时空上均呈现HS1HS2GRNN、Ens均呈现GRNNHS2HS1趋势;与PM模型模拟结果相比,GRNN、HS1和HS2模型模拟结果分别偏大0.8%、45.1%和17.3%。在时空尺度上的误差分析均表明利用温度资料建立的GRNN模型能够较为准确地模拟四川盆地ET0,因此可以作为资料缺失情况下ET0模拟的推荐模型。该研究可为四川盆地作物需水精确预测提供科学依据。     

Penman-Monteith与Penman修正式计算塔里木盆地参考作物潜在腾发量比较

利用1989~1996年阿克苏水平衡试验站的气象资料,对Penman-Monteith公式和Penman修正式计算的参考作物潜在腾发量进行了比较。Penman修正式计算的参考作物潜在腾发量年值略大于Penman-Monteith公式计算的年值,绝对偏差为42~128 mm,相对偏差为3.3~9.8%,且年际间变化不大。各月的参考作物潜在腾发量变化较大,绝对偏差可正可负,1、2、12月小于0,3~10月大于0,相对误差1、12月较大,2、11月较小,其它月份变化不大。导致计算偏差的原因在于两种公式采用了不同的辐射项和空气动力项计算公式和参数。两种公式计算的参考作物潜在腾发量具有显著的线性相关性。