东北地区参考作物蒸发蒸腾量随时间变化的研究

根据选取的东北地区9个代表站1973-2003年的气象资料,应用Penman-Monteith公式计算了31年间逐月参考作物蒸发蒸腾量(ET0),对参考作物蒸发蒸腾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东北地区参考作物蒸发蒸腾量变化的主要气象因素。结果表明:近31年间东北地区ET0值呈现缓慢下降趋势,年内ET0值分布以5-8月份最高,1月份最低。影响ET0的主要气候要素为日照、风速和温度。     

参考蒸发蒸腾量测定仪器的研究与开发

分析了现有作物需水量测定方法与计算方法在我国应用于灌溉预报时的局限性。根据土壤的蒸发机制与植物的蒸腾机理,按照参考蒸发蒸腾量(ET0)的定义,利用生物膜技术,研制了1种参考蒸发蒸腾量的测定仪器。该仪器可较好地模拟土壤蒸发与植物蒸腾的自然物理过程,仪器的试验结果显示,仪器的水位变化可与ET0有着很好的线性关系,若以此计算作物需水量,能满足灌溉预报精度要求。仪器使用方便、维护简单、价格低廉,将有良好的商品化开发及广阔的市场应用前景。     

基于CatBoost的温室日参考作物蒸发蒸腾量估算模型研究

参考作物蒸发蒸腾量(Reference Evapotranspiration,ET₀)是估算作物需水量、制定灌溉制度、提高用水效率,实现农业节水的重要参数。针对传统Penman-Monteith(P-M)公式计算作物蒸发蒸腾量需要参数多,计算复杂等问题,提出了一种基于支持分类特征的梯度提升决策树(CatBoost)算法估算温室日参考作物蒸发蒸腾量。以温室修正型Penman-Monteith公式计算的ET₀作为标准值,通过Pearson’s方法对输入参数与ET₀之间的相关性进行分析,组合不同输入特征向量。当输入参数组合为3参数,即平均室内温度、平均相对湿度、累积太阳辐射时,CatBoost性能最优,测试集估算精度MAE为0.220 mm/d,RMSE为0.310 mm/d。进一步对比了6种其他机器学习模型(XGBoost、AdaBoost、随机森林、决策树、KNN、SVM)的估算精度,结果表明CatBoost模型具有最佳的估算精度和稳定性,能够较好地模拟参考作物蒸发蒸腾量。构建的日参考作物蒸发蒸腾量估算模型为水肥精准化管理、灌溉控制系统研发提供了一种新的思路。     

基于温湿度的ET_0估算模型应用研究

针对大面积灌区作物蒸发蒸腾量(ET0)分布式监测所需参数较多的问题,开展利用易获取的少量气象参数估算ET0的研究对我国灌区的作物需水量监测和灌区水资源的管理有重要意义。利用人工神经网络技术建立基于温、湿度的ET0月份估算模型,对作物蒸发蒸腾量进行了估算;在此基础上,针对ET0的季节性特征,将估算模型由月份尺度拓展到季节尺度;最后运用陕西省6个基本站点的气象数据对该优化模型进行普适性分析。结果表明,优化后的季节估算模型,在春夏二季的平均相对误差在10%以内,在秋冬二季的平均绝对误差在0.20mm以下,且每对基准站点和邻近站点得到的估算结果具有很好的一致性和稳定性,表明该模型在作物需水估算方向上较强的实用推广价值。     

淮北平原冬小麦蒸发蒸腾量与不同土层土壤含水率关系初探

利用大型称重式蒸渗仪测定了冬小麦不同生育期的农田蒸发蒸腾量,分析了冬小麦的蒸发蒸腾变化规律,探讨了参考作物蒸发蒸腾量(ET0)、土壤含水率与作物蒸发蒸腾量(ET)之间的关系。结果表明,ET0和蒸渗仪实测的ET生育期内变化趋势基本一致;冬小麦ET受0~60cm土层土壤含水率的影响,尤其是0~40cm土层土壤含水率对作物ET影响显著,80cm以下土层土壤含水率基本对作物ET无明显影响。     

Hargraeves公式计算参考作物腾发量在新疆地区的适用性研究

新疆维吾尔族自治区地域辽阔,气候特征空间差异性显著。准确估算各地区的参考作物腾发量(ET0)是新疆节水灌溉设计的基础。以阿克苏地区30年的气象资料为基础计算了ET0,并以Penman-Monteith公式和修正Penman公式为参考标准,进行对比分析评价Hargraeves公式的精度和地区适应性。结果显示Hargraeves公式计算的参考作物蒸发蒸腾量,精度较Penman公式高,较Penman-Monteith公式低,但满足实际生产精度要求,特别适用与阿克苏地区气候类似的西部地区,基础气象资料不全的地区的参考作物蒸发蒸腾量的计算。     

青海东部农业区ET_0变化特征及气候影响因子分析

利用FAO-56推荐的Penman-Monteith公式计算了青海省东部农业区12个气象站47年(1960—2006)逐月参考作物蒸发蒸腾量(ET0),对参考作物蒸发蒸腾量、气象因子随时间变化特征进行了分析。结果显示,ET0年内呈单峰型分布,月际变化较大,最大值出现在7月份,最小值出现在12月份;近47年ET0随时间呈极显著下降趋势变化;风速是影响ET0的最主要影响因子,最高温度对该地区ET0的影响也较为显著;海拔与ET0具有明显的相关性,R2为0.68,且ET0随海拔的增高具有明显下降的趋势。研究表明,在高寒高海拔区,风速是影响ET0的最主要气候影响因子,海拔与ET0呈负相关关系。     

关中地区参考作物蒸发蒸腾量时空变化特征及主要因子分析

[目的]充分认识关中地区参考作物蒸发蒸腾量(ET0)时空变化特征及其主要影响因子.[方法]使用1980—2019年关中地区43个气象观测站逐日气象资料,结合Penman-Monteith(P-M)模型、反距离权重、逐步回归分析和通径分析等方法,研究1980—2019年关中地区ET0时空变化特征及其主要影响因子.[结果]...     

基于灰色马尔科夫的参考作物腾发量预测

参考作物腾发量(ET0)是估算作物蒸发蒸腾量的关键参数,它的准确预测对提高作物需水量预报精度具有十分重要的意义。针对GM(1,1)模型在原始数据变化幅度较大且趋势不明显时预测效果差的情况,作者提出了用马尔科夫对GM(1,1)模型修正的组合模型,该模型结合了灰色模型可以揭示预测数据的发展趋势以及马尔科夫预测适合描述随机波动性较大的预测问题的优点,将其应用于沈阳地区参考作物腾发量预测中,对比分析了GM(1,1)模型与灰色马尔科夫模型的预测结果。结果表明,灰色马尔科夫模型不仅能反映系统的动态特性,还具有比GM(1,1)更高的预测精度、逼近性和稳定性,具有较好的应用价值。     

参考作物蒸发蒸腾量计算方法在海河流域的适用性

参考作物蒸发蒸腾量（ET0）的计算公式很多，各公式所需参数各异，为寻找一种所需资料少而又精度较高的替代方法，选用1998年FAO-56分册推荐的Penman－Monteith（PM）、Hargreaves、Irmark-Allen等6种方法分别计算海河流域10个典型气象站30 a的参考作物蒸发蒸腾量，并以PM公式为标准，对其他方法进行评价。结果表明，10个站点中除了五台山地区，Hargreaves与FAO-24 Radiation 这2种方法更接近于PM方法的计算结果，其误差较小，在海河流域缺少辐射和风速     

Single and dual crop coefficients and water requirements for onion (Allium cepa L.) under semiarid conditions

The objectives of this study were to determine onion water requirements with a sprinkler irrigation system, the most usual irrigation method in Spain. A weighing lysimeter was used to measure single (Kc) and dual (Kcb + Ke) crop coefficient curves and obtain the relationship between Kc-ground cover (GC) and Kcb-GC. Experimental work was carried out in 2005 at “Las Tiesas” farm, located in Albacete (Central Spain). To determine actual onion evapotranspiration (ETc), we used a weighing lysimeter with continuous electronic data recording. Daily measured ETc values obtained by the lysimeter were compared to calculated ETc values obtained through the standard FAO methodology [Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrig. and Drain. Paper 56. Rome, Italy]. Seasonal evapotranspiration measured in the lysimeter (893.34 mm) was higher than the seasonal ETc calculated by FAO-56 method (832.90 mm). The percentage of GC was found through the supervised classification technique of digital photographic images with the maximum probability algorithm [Calera, A., Martínez, C., Melia, J., 2001. A procedure for obtaining green plant cover: relation to NDVI in a case study for barley. Int. J. Remote Sensing, 22, 3357-3362]. The values derived from lysimetric measurements are Kc ini: 0.65, Kc mid: 1.20 and Kc end: 0.75, similar to values given in FAO-56. Lysimetric measurements showed that the evaporative component was high during the growing season, due to the high frequency of irrigation and the fact that the onion crop does not completely cover the ground; maximum GC was 72%. Therefore, the dual crop coefficient was calculated, which allowed differentiation between crop transpiration (basal crop coefficient, Kcb) and evaporation from the soil (evaporation coefficient, Ke). With the aim of facilitating extrapolation of the results to other areas, Kc and Kcb were linearly correlated to fractional GC.     

基于气温预报和HS公式的参考作物腾发量预报

为探索精确预报未来短期参考作物腾发量ET0的方法,提出基于气温预报和HargreavesSamani(HS)公式进行ET0预报.收集了南京站2001—2011年逐日气象观测数据和2011年预见期为4 d的逐日天气预报数据,采用FAO-56Penman-Monteith公式计算逐日ET0,用2001—2010年计算的ET0率定HS公式参数;用率定后的公式和2011年的天气预报气温数据进行未来4 d的ET0预报;比较2011年ET0的计算值与预报值、气温观测值与预报值以评价ET0预报精度及误差原因.结果表明:最低气温预报准确率达81.9%,最高气温预报准确率为80.1%;经过参数校正后,HS公式精度较高.ET0预报准确率为85.7%,平均绝对误差为1.01 mm/d,均方根误差为1.42 mm/d,相关系数为0.74;各项预报误差随着预见期的增大而增大.产生误差的主要原因为气温预报误差和HS公式未考虑平均风速和相对湿度的影响.总体而言,基于气温预报和HS公式的ET0预报方法精度较高,可为灌溉预报及决策提供较为准确的ET0预报数据.     

Evapotranspiration components and dual crop coefficients of coffee trees during crop production

Quantifying crop water consumption is essential for many applications in agriculture, such as crop zoning, yield forecast and irrigation management. The objective of this study was to determine evaporation (E), transpiration (T) and dual crop coefficients (Ke and Kcb) of coffee trees during crop production (3rd and 4th year of cultivation), conducted under sprinkler and drip irrigation and no irrigation, in Londrina, Paraná State, Brazil. Crop evapotranspiration (ET) was measured by weighing lysimeters cultivated with plants of cultivar IAPAR 59, E was measured by microlysimeters installed on the lysimeters and T was obtained by the difference between ET and E. The crop coefficient (Kc) was determined for the irrigated treatments as the ratio between ET and the reference evapotranspiration (ETo). Similarly, evaporation coefficient (Ke) and basal crop coefficient (Kcb) were determined as the ratio of E and T, respectively, to the value of ETo, which was estimated by the ASCE Penman-Monteith method on an hourly basis. The values of E and Ke varied due to atmospheric demand and water application method. Those factors, in addition to crop phenology and leaf area evolution, also influenced T and Kcb. Regardless irrigation treatment, the measured values of E represented 35% of ET, while T was 65% of ET. The recommended values of Ke were 0.46 and 0.26 for sprinkler and drip irrigation, respectively. The recommended values of Kcb were 0.52 and 0.82 for sprinkler-irrigated, and 0.5 and 0.65 for drip-irrigated treatments, varying as a function of daily ETo (ETo ≥ or <3 mm day⁻¹, respectively).     

基于公共天气预报的参考作物腾发量预报

针对Penman Monteith公式的应用局限性,以公共天气预报可测因子及历史气象数据计算ET₀为基准,对广州站2017-01-01-2019-03-31预报气象信息风力状况进行量化后,以2017,2018年气象预报信息为输入因子、ET₀为输出因子,分别建立基于回归型支持向量机(SVR)预报模型与BP神经网络预报模型,选择性能较优预报模型对2019年ET₀进行预报,并与计算值进行对比分析.结果表明:回归型支持向量机参考作物腾发量预报模型测试集确定性系数为0.896、均方误差为0.206,BP神经网络参考作物腾发量预报模型测试集确定性系数为0.851、均方误差为0.305,SVR参考作物腾发量预报模型均方误差及决定系数要明显优于BP神经网络;基于SVR模型的预报值与PM公式计算值相关系数为0.761,没有明显差异,表现出显著的相关性以及整体吻合度,可为灌溉预报及决策提供较为准确的ET₀预报数据.     

Evapotranspiration and irrigation effeciency of mature orange orchards in Valencia (Spain)

Actual evapotranspiration (ETc) of three mature sweet orange orchards (cv. Salustiana and Washington Navel on sour orange), under border irrigation and typical cultural practices was measured by the water balance method during 1981 to 1984. Soil water content was measured at 7 to 10 day intervals using a neutron meter and soil sampling of the 0–10 cm surface layer. Zero flux plane was calculated by measurements with mercury tensiometers. Irrigation water in these and other 5 similar orchards was measured by broad crested weirs. Rainfall and other climatic data for calculation of reference evapotranspiration by FAO's methods (ETo) were collected in a nearby meteorological station. Average yearly ETc ranged from 750 to 660 mm and mean monthly maximum was 3.7 and 3.2 mm/day in July for Salustiana and W. Navel orchards, respectively.ETo estimates for the different methods used were highly correlated (r ²0.94). Monthly crop coefficients (Kc) based on pan evaporation ranged from 0.5–0.6 in spring and summer to 0.8 in autumn and were about 10% higher than those for Penman or radiation methods. Average annual Kc for the three plots studied was 0.64, 0.61 and 0.51, respectively, and correlated well (r ²=0.99) with tree ground cover. Irrigation efficiency was about 50% for orchards with soils with less water holding capacity and more applied water per irrigation and 70–80% in orchards with deeper soils or with a higher water holding capacity. Increasing irrigation frequency and applying smaller amounts of water per irrigation with good uniformity can improve on-farm irrigation efficiency.     

基于气温预报的参考作物腾发量预报方法比较

为探索精确预报未来短期ET0的方法,比较了4种基于气温预报ET0预报模型,即Hargreaves-Samani(HS)、Thornthwaite(TH)、简化的Penman-Monteith(PT)及McCloud(MC)模型。收集了西藏林芝站2001年1月1日至2013年12月31日的实测逐日气象数据和2012年6月6日至2013年12月31日逐日对未来7d的气象预报数据,在气温预报精度评价的基础上,采用4种基于温度的参考腾发量计算模型直接进行ET0预报,然后采用率定后的模型进行ET0预报,最后与实测气象数据和FAO-56PM公式计算的ET0值进行比较。结果表明,未率定的4种模型预报误差均较大,其中PT公式精度稍高。经率定后,4种公式的预报精度都有所提高,平均准确率为70%,MAE值HS模型最小,平均为0.57mm/d,其他3个模型为1.27~1.50mm/d;RMSE都在2.0mm/d左右;r值总体仍不高,TH模型平均仅有0.19,其他3种模型在0.6左右。综合来看,PT模型的预报效果稳定性优于其他3个模型。对于林芝地区附近的灌区,无论有无气象观测数据供模型率定,建议采用PT模型进行ET0预报。     

风沙区参考作物需水量的计算

根据国内外相关的研究成果 ,分析选择并确定了适宜于风沙区参考作物需水量 (ET0 )的计算模式。利用典型风沙区的气象资料 ,对多年逐旬参考作物需水量及 2 0 0 1年春小麦与春玉米生育时段内逐日参考作物需水量进行了分析计算。结果表明 ,FAO最新修正的 Penman-Moteith公式可较好地用于风沙区参考作物需水量的估算 ,一般 ET0 值在年内与年际间变化较大 ,最高值发生在 6月上旬左右 ,多年平均为 5 .82 mm/ d,最低值发生在 1月上旬 ,多年平均 0 .43 mm/ d左右 ,年内各日 ET0 值受气象因素的影响变幅很大 ,因此 ,精确灌溉应设法提高短期天气预报和灌溉预报的精度     

灌区自然供水条件下的水资源短缺风险模型及应用

以河南省陆浑灌区1970—2013年的年降水量和年参考作物腾发量为基础,运用Frank Copula函数构建了灌区自然供水条件下的水资源短缺风险模型,并对灌区自然供水条件下的水资源供需联合分布和组合遭遇概率进行了分析。结果表明,Frank Copula函数能较好地模拟降水量与ET_0之间的相依关系,所构建的联合分布模型能很好地描述降水量与ET_0的联合概率分布特性;不同量级年降水量和年ET_0的2类联合分布概率可通过该模型进行耦合量化。     

作物系数影响因素分析

作物系数Kc的合理取值对作物需水量分析计算精度十分关键。以水稻为例,基于江西省灌溉试验中心站及浙江永康灌溉试验站的实测数据,分析了Kc与降雨量及ET0的关系,以及灌溉模式对Kc的影响,以江西及云南省为例,探讨了Kc在省域尺度上的空间变化规律。结果表明,Kc年内变化呈现先增大后减小的趋势,在抽穗开花期达到最大;Kc年际变化存在差异,早晚稻Kc与ET0呈负相关,晚稻Kc与降雨呈弱正相关,但关系均不明显,采用连续3年Kc实测平均值代替各年Kc满足精度要求;节水型灌溉模式下作物系数明显小于淹灌模式;采用FAO修订办法计算了江西省19个站点的Kc并进行了比较,Kcini均为1.05,Kcmid和Kcend的标准差与变异系数均在0.01左右,Kcmid、Kcend最大值与最小值相差3%左右,而云南36个站点的比较表明,Kcini标准差与变异系数在0.03~0.04,Kcmid,Kcend标准差和变异系数在0.02左右,Kcini最大值与最小值相差10%,Kcmid、Kcend最大值与最小值相差在5%左右。因此,对于省域尺度,如果省内气象要素变异不大,某个站点的作物系数可以扩展到整个省内使用,否则需进行修订。     

Determination of growth-stage-specific crop coefficients (Kc) of cotton and wheat

Development of crop coefficient (Kc), the ratio of crop evapotranspiration (ETc) to reference evapotranspiration (ETo), can enhance ETc estimates in relation to specific crop phenological development. This research was conducted to determine growth-stage-specific Kc and crop water use for cotton (Gossypium hirsutum) and wheat (Triticum aestivum) at the Texas AgriLife Research field at Uvalde, TX, USA from 2005 to 2008. Weighing lysimeters were used to measure crop water use and local weather data were used to determine the reference evapotranspiration (ETo). Seven lysimeters, weighing about 14 Mg, consisted of undisturbed 1.5 m × 2.0 m × 2.2 m deep soil monoliths. Six lysimeters were located in the center of a 1-ha field beneath a linear-move sprinkler system equipped with low energy precision application (LEPA) and a seventh lysimeter was established to measure reference grass ETo. Crop water requirements, Kc determination, and comparison to existing FAO Kc values were determined over a 2-year period on cotton and a 3-year period on wheat. Seasonal total amounts of crop water use ranged from 689 to 830 mm for cotton and from 483 to 505 mm for wheat. The Kc values determined over the growing seasons varied from 0.2 to 1.5 for cotton and 0.1 to 1.7 for wheat. Some of the values corresponded and some did not correspond to those from FAO-56 and from the Texas High Plains and elsewhere in other states. We assume that the development of regionally based and growth-stage-specific Kc helps in irrigation management and provides precise water applications for this region.