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。     

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。     

参考作物腾发量计算方法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。该文选用6种计算公式利用新疆4个典型气候区的气象资料计算了ET₀。并以Penman-Monteith方法作为标准，对其它方法进行评价。结果表明在新疆各气候区1948Penman法估算的ET₀值较FAO-24 Penman与FAO-24 Radiation方法更接近于P-M法的计算结果；在缺少资料的地区，Hargreaves方法或湿润区用Priestley-Taylor方法均可以得到与P-M法估值相当的结果；同时分析了P-M法计算的ET₀值和水面蒸发量之间的关系，为利用水面蒸发资料估算新疆地区ET₀值提供参考。     

温室内香蕉树蒸腾量预报的试验研究

在温室内研究了香蕉树蒸腾量和小气候的关系，用5种方法计算了温室内的参考作物腾发量，用20 cm蒸发皿测定温室内的水面蒸发力，并和测定的香蕉树蒸腾量进行对比。试验结果显示香蕉树蒸腾量和蒸发皿水面蒸发量的回归系数(R²)最高，为0.94，而和5种公式计算的参考作物腾发量的回归系数为0.47～0.60，以蒸发皿水面蒸发量计算温室内的作物蒸腾量要优于以参考作物腾发量计算作物蒸腾量的方法。温室内香蕉树的蒸腾量和20 cm蒸发皿蒸发量线性相关，可以此计算温室内作物的蒸腾量。     

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。     

西北旱区石羊河流域作物耗水点面尺度转化方法的研究

基于DEM与GIS空间分析功能研究了石羊河流域主要农作物春小麦需水量ET_c的时空分异规律。根据8个气象站近50年气象资料，应用1998年FAO推荐的Penman-Monteith公式计算参考作物蒸发蒸腾量ET₀，由收集到的春小麦需水量试验资料获得多年平均作物系数K_c。近50年来流域上游的古浪、天祝春小麦全生育期ET_c呈微弱的增加趋势，中游的凉州区表现出极显著的减少趋势，其他站减少趋势不显著。确立了ET_c与海拔高度、纬度、坡向的多元回归关系，借助Arcview3.3、ArcGIS9.0与Visual Basic6.0软件实现了春小麦ET_c的空间尺度转换，并分析了石羊河流域25%、50%、75%三个不同水文年春小麦ET_c的空间变异情况。石羊河流域春小麦ET_c由山区向绿洲平原递增，多年平均值为270～591 mm。估计值与计算值相差在11.1%以内。     

Prediction of daily reference evapotranspiration by a multiple regression method based on weather forecast data

Prediction of daily reference evapotranspiration (ET ₀) is the basis of real-time irrigation scheduling. A multiple regression method for ET ₀ prediction based on its seasonal variation pattern and public weather forecast data was presented for application in East China. The forecasted maximum temperature (T _max), minimum temperature (T _min) and weather condition index (WCI) were adopted to calculate the correction coefficient by multilinear regression under five time-division regimes (10 days, monthly, seasonal, semi-annual and annual). The multiple regression method was tested for its feasibility for ET ₀ prediction using forecasted weather data as the input, and the monthly regime was selected as the most suitable. Average absolute error (AAE) and root mean square error (RMSE) were 0.395 and 0.522 mm d^?1, respectively. ET ₀ prediction errors increased linearly with the increase in temperature prediction error. A temperature error within 3 K is likely to result in acceptable ET ₀ predictions, with AAE and average absolute relative error (AARE) <0.142 mm d^?1 and 5.8%, respectively. However, one rank error in WCI results in a much larger error in ET ₀ prediction due to the high sensitivity of the correction coefficient to WCI and the large relative error in WCI caused by one rank deviation. Improving the accuracy of weather forecasts, especially for WCI prediction, is helpful in obtaining better estimations of ET ₀ based on public weather data.     

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.     

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。     

Ermittlung des Spannungszustandes von Böden aus Werten des Eindringwiderstandes von Sonden

Evaluation of the soil consolidation state by using data from penetration resistance probes Penetration resistance data (EW) from handdriven equipment are easily obtainable because the equipment is simple, cheap, and easily carriable. Measurements are performed quickly without extensive destruction of the site. It is the only method to measure soil strength directly and in situ. Therefore, it is worthwhile to propose an interpretation of the results in order to obtain more conclusive statements on the structural soil state. The procedure applied in our study consists in assigning EW values to the principal stress σ_x and in using an auxiliary construction for the vertical component (σ_z ) as a function of penetration depth. The EW value obtained at the final soil depth is assumed to represent stresses at rest, i.e., K₀ = σ_x/σ_z = 1. Drawing a straight line from this point towards the origin of the coordinates EW and soil depth supplies values of the hydrostatic condition for each depth; e.g., values for σ_z are available for each depth. The coefficient for the equivalent stress at rest (K_0E) per depth can now be calculated simply by comparing the measured EW values with the assumed (hydrostatic) vertical depth function of EW. From a total volume of 29 sets of EW versus depth relations, means and standard deviation of K_0E are presented for arable and forest soils from central Europe. K_0E of forest soils tends to be close to 1 showing approximately normal compaction. In arable sites, K_0E > 2 prevail, indicating precompation. These results confirm the general feasibility of the approach to evaluate the compaction state of soil from EW data. Examples are given to show the K_0E characteristics for special cases of mechanical stress situations.     

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。     

ECOWAT—A model for ecosystem evapotranspiration estimation

In recent years, the availability of near real-time and forecast standardized reference evapotranspiration (E₀) has increased dramatically. Use of the E₀ information in conjunction with calibration coefficients that adjust for differences between the vegetation and the reference surface provides a method to greatly improve the estimates of actual evapotranspiration (E_a) from landscapes (or ecosystems). Difficulties in estimating evapotranspiration (ET) of well-watered vegetation in an ecosystem depend on local advection and edge effects, wide variations in radiation resulting from undulating terrain, wind blockage or funnelling, and differences in temperature due to spatial variation in radiation, wind, etc. Estimating the ET of an ecosystem that is water stressed is even further complicated because of stomatal closure and reduced transpiration. The Ecosystem Water Program (ECOWAT) was developed to help improve estimates of E_a of ecosystems by accounting for microclimate, vegetation type, plant density, and water stress. The first step in estimating E_a is to calculate E₀ using monthly climate data from one representative weather station in the study area. Then, local microclimate data are used to determine a standardized reference evapotranspiration for the local microclimate (E_m). The ratio K_m = E_m/E₀ is calculated and applied as a microclimate correction factor to estimate E_m. The product of E_m and a vegetation coefficient (K_v = E_v/E_m) is used to estimate the evapotranspiration of the ecosystem vegetation (E_v) under well-watered conditions with a full-canopy cover within the same microclimate. Next, a coefficient for plant density (K_d), which is based on the percentage ground cover, is used to adjust the full-canopy E_v to the evapotranspiration of a sparse canopy from a well-watered ecosystem (E_w). A stress (K_s) coefficient, which varies between 1.0 with no stress to 0.0 with full stress, is determined as a function of available water in the root zone. The predicted actual ecosystem evapotranspiration (E_p) is estimated as E_p = E_w × K_s. In this paper, we present how the ECOWAT model works and how it performs when the predicted actual evapotranspiration (E_p) is compared with measured actual evapotranspiration (E_a) collected in several Mediterranean ecosystems (three in Italy and two in California) over a number of years. The potential use of ECOWAT in integrated fire danger systems is discussed.     

单作与间作的棵间蒸发量差异及其主要影响因子

在甘肃河西走廊区, 通过大田试验, 研究了不同供水水平下小麦间作玉米与单作小麦、单作玉米的耗水量和棵间蒸发量差异, 探讨了影响作物棵间蒸发量的关键因子。结果表明, 小麦间作玉米的耗水量较单作小麦、单作玉米耗水量的平均值增加了41.44%~47.15%; 间作全生育期的总棵间蒸发量显著大于单作, 但间作的日均棵间蒸发量显著低于单作玉米、高于单作小麦; 间作的棵间蒸发量占总耗水量的比重显著低于单作玉米, 说明间作可提高农田水分利用的有效性。随灌水水平的提高,间作总耗水量显著增加,单作相邻灌水处理间的差异不显著;灌水水平对单作玉米、间作棵间蒸发量的影响不显著,说明间作耗水量增加主要是由蒸腾作用造成的。作物的日均棵间蒸发量与0~30 cm的土壤含水量、0~25 cm的土壤温度、全生育期的平均叶面积指数均呈显著正相关关系。单作玉米日均棵间蒸发量较大的主要原因是0~30 cm的土壤含水量、0~25 cm的土壤温度均相对较高。小麦间作玉米可提高作物的土地利用率, 其水分利用效率较单作平均提高25%以上。     

延河流域潜在蒸散发的时空变化特征

[目的] 研究气候变化下潜在蒸散发（ET₀）的时空特征，为区域生态需水研究和水资源管理提供科学依据。[方法] 基于延河流域1978—2017年逐日气象资料，利用Penman-Monteith方法对ET₀进行计算，运用Mann-Kendall趋势检验法、Pettitt检验对ET₀时空变化特征进行分析，并通过Pearson相关性分析对ET₀变化的影响因子进行探讨。[结果] 延河流域年平均ET₀为923.53 mm，整体呈现上升趋势。月ET₀呈单峰分布，高值月份出现于5—7月。季节上ET₀表现为：夏季>春季>秋季>冬季，夏季、春季、冬季的ET₀呈上升趋势，秋季呈下降趋势，春季蒸散变化速率最大。空间上，ET₀呈现由西部向南部增加再向东南部减少的趋势。延安站蒸散量最大，志丹站蒸散量最小，除甘泉站外其他站点的ET₀均呈上升趋势，甘泉附近地区存在“蒸发悖论”现象，主导因子是日照时数、2 m高风速和降水量。ET₀变化率呈现东南高西北低的分布规律，延安站变化率最大，安塞站变化最小。平均温度、日照时数、相对湿度、气压、2 m高风速、降水量等气象因子的变化趋势和变化速率时空差异显著，同一气象因子对ET₀的影响程度具有时空差异，相同因子不同变化趋势的组合对蒸散发的影响具有显著差异。[结论] 延河流域ET₀变化与平均温度、日照时数、2 m高风速的变化为正相关关系，与相对湿度、气压、降雨量的变化为负相关关系，其中与日照时数相关最为密切。     

基于5变量局部薄盘光滑样条函数的蒸发空间插值

 高分辨率、栅格化的气候数据作为环境因子是地学模型和气候模型等相关研究的重要参数,国内外的研究多集中于温度、降水等气象要素,对陆面蒸发空间化研究较少。对黄土高原多沙粗沙区及周围共计53个气象站点(多沙粗沙区30个)蒸发皿测量值EE进行空间插值,以5变量局部薄盘样条函数(经纬度为自变量,净辐射、水气压差和风速为协变量),建立具有多元线性子模型的蒸发插值模型,以ANUSPLIN为实现软件,生成连续21年共252个蒸发表面。交叉验证表明:引入蒸发影响因子作为协变量线性子模型进行表面插值能显著提高插值精度,夏季提高幅度更大,拟合表面具有较高的精确度与平滑度。蒸发随协变量的变率显示,在多沙粗沙区,水气压差是夏季蒸发的主要控制因素,风速对蒸发的影响冬季稍强一些,净辐射的影响没有明显的季节性,只在春分和秋分时节有微小提高。     

遗传算法率定参照作物腾发量相关参数的研究

用气象资料计算参照作物腾发量(ET0)的方法需要各种气象(候)和物理参数,净辐射是其中的重要数据之一,而专业测量净辐射的设备在农业气象站里很少安装。为解决计算ET0时缺少太阳净辐射(Rn)测量值这一实际问题,该文采用浑善达克沙地东南缘南沙梁草甸草原区气象站观测的气象资料,用遗传算法模型对联合国粮农组织56号文本(FAO56)推荐值(as和bs)进行率定,计算了对应夏半年(4—9月)和冬半年(1—3月和10—12月)的太阳净辐射和参照作物腾发量,并将率定前后的模拟太阳辐射进行对比分析,用残差估计指数法对该方法模拟的参照作物腾发量模拟精度进行了分析。结果表明:在缺少太阳净辐射测量值的地区,采用FAO56参数(as和bs)推荐值与遗传算法模型率定参数(as和bs)相比,净辐射年内变化趋势一致,采用率定后参数计算的净辐射相对更不稳定,波动更大,但能有效提高参照作物腾发量计算精度。误差较大的模拟值均出现在降雨日前后,降雨虽然并未直接出现在Penman-Monteith公式中,但是降雨必然会对湿度和温度等气象条件造成一定影响,而as和bs是受湿度等因素影响而变化的,其深层次的原因有待进一步分析。     

宁夏中部干旱带砂土混合覆盖下土壤蒸发估算

为寻求一种能够有效估算宁夏中部干旱带压砂地土壤蒸发量的方法,通过微型蒸渗仪大田试验,研究了 0(S1),20％(S2),40％(S3),60％(S4),80％(S5),100％(S6)6种砂土混合比条件下土水蒸发比与表层土壤含水量的关系,并构建了压砂地土壤蒸发量估算模型.结果表明:土水蒸发比随表层土壤含水量呈分阶段变化...     

新疆参考作物蒸散发趋势转折与大尺度气候变率的关系

参考作物蒸散发(reference crop evapotranspiration,ET₀)能够全面反映一个地区的蒸散发能力,在农业高效节水灌溉等领域得到了广泛应用。近年来大多数研究通常将ET₀与局地气象因子的变化进行敏感性分析,忽略了大尺度气候变率对ET₀的遥相关影响。该研究基于新疆地区84个气象站点的逐日气象资料和气候变率指数,采用多元线性回归和Cramer’s突变检验等方法,探究了厄尔尼诺南方涛动(El Nino-Southern Oscillation,ENSO)、印度洋偶极子(Indian Ocean Dipole,IOD)、太平洋年代际振荡(Pacific Decadal Oscillation,PDO)和北大西洋多年代际振荡(Atlantic Multidecadal Oscillation,AMO)等大尺度气候变率与新疆地区ET₀趋势转折的关系。结果表明：1960—2020年ET₀总体呈下降趋势,平均递减率为0.75 mm/a;1998年为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.