修正Hargreaves-Samani公式估算宁夏参考作物蒸散量适用性分析

Hargreaves-Samani(HS)公式是运用气温和太阳辐射气象数据计算参考作物蒸散量(ET_0)的方法。基于贝叶斯原理(HS_(cor)公式)以及最小二乘法原理(HS_(lsq)公式),利用宁夏回族自治区(以下简称宁夏)10个代表气象站点1961—2006年逐日气象数据对HS公式中参数进行修正,并以2007—2016年Penman-Monteith(PM)公式的计算值(ET_(OPM))为标准,评价两种修正公式在宁夏北部引黄灌区(Ⅰ区)、中部干旱带(Ⅱ区)以及南部山区(Ⅲ区)的计算精度与适用性。评价结果表明,HS_(cor)公式各参数在3个分区差别不大,且均不大于FAO(Food and Agriculture Organization)提出的修正值,HS_(lsq)公式各参数在3个分区的差别较大;与逐日ET_(OPM)相比,两种修正公式计算的逐日参考作物蒸散量值ET_(Ocor)和ET_(Olsq)的归一化均方根误差值(nRMSE)在Ⅰ区从22.86%分别降至21.08%和22.16%,Ⅱ区从23.69%分别降至22.19%和22.26%,Ⅲ区从23.40%分别降至20.12%和20.22%,HS_(cor)公式的n RMSE降低幅度最大;相关性分析表明,ET_(Ocor)与ET_(OPM)相关系数最高(0.944 7),达到显著相关;在7—9月,逐月HS公式计算的参考作物蒸散量值ET_(OHS)的nRMSE最高,为7.10%,逐月ET_(Ocor)和ET_(Olsq)的n RMSE分别降低了5.11%和1.78%。因此,HS_(cor)公式在宁夏3个分区的参考作物蒸散量计算精度较高,可作为宁夏参考作物蒸散量简化计算的推荐公式。     

小时与日尺度PM公式的参照作物腾发量及其水稻单作物系数值差异

以昆山试验站2012-2013年自动气象站观测的小时气象资料为依据,分别采用ASCE PM公式和FAO56PM公式计算小时ET_0,在对比两种小时ET_0计算结果基础上,通过逐小时累积求和得到日ET_0值(分别记作ET_(0-dhA)和ET_(0-dhF)),进而与日尺度参照作物腾发量ET_0(记作ET_(0-d))进行对比,在明确ET_(0-d),ET_(0-dhA)和ET_(0-dhF)差异的基础上,分析了基于不同ET_0确定的水稻单作物系数差异(分别记为K_(c-d)、K_(c-dhA)与K_(c-dhF))。结果表明:在水稻不同生育阶段内,小时ET_0值大致呈抛物线型日变化,在中午时ET_0达到最大,午夜则最低; 2种PM公式计算得到的小时ET_0值存在微小差异,差异范围在-0. 02～0. 5 mm/h内,白天ASCE PM公式计算值偏大,夜间则无明显规律。在日尺度上,ET_(0-d),ET_(0-dhA)和ET_(0-dhF)三者之间具有良好的线性关系,大小关系表现为:ET_(0-dhA) ET_(0-d) ET_(0-dhF)。总体上,由实测日腾发量ET_a求得的节水灌溉水稻单作物系数大于按照FAO推荐方法确定的作物系数值,且采用不同日ET_0计算结果得到的水稻K_c值之间的大小关系为:K_(c-dhA)K_(c-d)K_(c-dhF),但其差异较小,差异程度在5%以内。     

气候变化条件下参考作物腾发量演变特性及影响因素分析

本文以临汾站1955—2014年共60年气象资料为基础,应用Penman-Montieth公式计算了临汾地区逐日参考作物腾发量(ET_0),采用Mann-Kendall法进行趋势检验和突变检验,统计检验方法分析影响参考作物腾发量的主要气象要素。结果表明:近60年间临汾地区ET_0总体呈现显著下降趋势,下降速度为23.24mm/10a,其中50年代为上升趋势,从20世纪60年代开始到21世纪初均呈下降趋势,1974年后下降趋势达到显著水平,且在1971年发生突变。日照时数和平均风速是影响参考作物腾发量的两个核心因素,平均温度、平均水汽压是次要因素,平均相对湿度虽有一定影响,但作用不显著,其中ET_0与风速、日照、温度呈显著正相关,与水汽压呈显著负相关,与相对湿度呈正相关,但未达到显著水平。     

基于云模型的淮北平原参考作物蒸散量时空分布

传统的时空分布分析方法仅可以描述参考作物蒸散量(ET0)的平均时空分布情况,难以对ET0时空分布的离散程度与稳定性进行量化。根据安徽淮北平原5个站点的气象数据与地理信息资料,采用彭曼-蒙特斯公式计算ET0,基于云模型分析了其时空分布特征。结果表明:年ET0呈下降趋势,春、冬季增长,夏、秋季减小;年ET0空间分布较为均匀,季节ET0空间分布不均匀;2004年较为分散而不稳定,1956年较集中而稳定;阜阳站较为分散而不稳定,宿县站较集中而稳定;ET0时间变化的离散程度相对于空间分布较小,稳定性相近。因此,基于云模型分析ET0时空分布特性可行、有效,研究结果可为淮北平原不同作物蒸散发以及旱灾、灌溉等研究提供科学参考。     

基于信息熵原理的作物需水空间相似性分析

根据区域空间作物需水观测站点具有信息传输的特性，运用信息熵原理研究了它们之间的信息有向传输问题，并运用聚类分析法进一步研究了作物需水相似性区间，初步揭示了作物需水空间分布的结构特征。上述方法运用于湖南省83个观测站作物需水空间分布规律研究中，取得了满意的结果。     

黑龙江省参考作物蒸散量变化及气象因子分析

为研究中纬度寒区参考作物蒸散量时空变化及影响因子变化,揭示参考作物蒸散量与各气象因子间响应关系,基于黑龙江省34个标准气象站点数据资料,运用Penman-Monteith公式方法计算逐日参考作物蒸散量。利用累积距平、气候倾向率、趋势分析和突变检验、Hurst指数方法,分析了黑龙江省参考作物蒸散量时空变化特征及气象因子间响应关系,明确了产生差异性的主要原因。结果表明:整体上,黑龙江省1990—2019多年平均参考作物蒸散量呈下降趋势;春季相对湿度是影响参考作物蒸散量变化的主要气象因子,而冬季影响参考作物蒸散量较大的气象因子是平均气温;全省高蒸散区集中在以泰来为中心的西南部,低蒸散区集中在以呼中为中心的西北部;风速和气温是影响黑龙江省南部地区参考作物蒸散量变化的主要气象因素,相对湿度是影响北部地区参考作物蒸散量变化的主要气象因素;对未来变化趋势预测表明,黑龙江省Hurst指数为0.60~0.69,说明未来参考作物蒸散量变化呈与现在相同的下降趋势且具有一定持续性。     

基于LSTM深度学习模型的华北地区参考作物蒸散量预测研究

为有效提高华北地下水漏斗区参考作物蒸散量ET_0的预报精度,本文以华北地区7个气象代表站1958—2010年ET_(0-PM)(Penman-Monteith,P-M)的历史时间序列为训练集构建LSTM模型,以2011—2017年ET_(0-PM)的时间序列为验证集将LSTM模型与其他4种经验模型进行对比分析。结果表明:LSTM在华北地区预测的整体评价指标Gpi(Global performance indicator)排名第一,该模型可以作为华北地区逐月ET_0预测的推荐模型,为我国精准农业灌溉预报提供科学的依据。     

节水灌溉稻田蒸散时空尺度特征及影响因素

为研究节水灌溉稻田蒸散规律和尺度特征及其影响因素的尺度依赖性,用小型蒸渗仪和涡度相关仪分别测量了节水灌溉稻田冠层蒸散量(ETCML)和田间尺度蒸散量(ETEC),分析了ETCML和ETEC的典型日和逐日变化规律,以及在小时和日时间尺度上的影响因素。结果表明:节水灌溉稻田ETCML和ETEC变化规律基本一致,但白天ETCML均大于ETEC,且上午两尺度蒸散量大小和相位差均明显大于下午,夜晚ETCML和ETEC接近0,但ETCML呈正负交替波动。两尺度蒸散量的逐日变化总体上呈先增加后减小,高峰期出现在分蘖后期,抽穗开花期较小。ETCML日均值大于ETEC,比值平均为1.50。净辐射和饱和水汽压差是不同时空尺度蒸散量的显著影响因素,但叶面积指数、空气温度、风速和土壤含水率对不同时空尺度蒸散量的影响不同,具有明显的时空尺度效应。     

基于云模型的云南参考作物蒸散量时空变化及影响因素分析

云模型可定量描述参考作物蒸散量(ET₀)的随机性和模糊性，基于云模型分析云南ET₀的时空变化，结果可为云南农业灌溉、水旱灾害等研究提供参考。以云南省31个气象站1958—2013年的逐日气象资料为基础计算ET₀,基于云模型并结合线性倾向、M-K趋势检验、偏相关分析等研究云南参考作物蒸散量及影响因素的变化特征。结果表明：1958—2013年，云南ET₀在时间和空间上分布不均匀，空间分布较时间变化更不均匀且不稳定。56 a间ET₀呈不显著增加趋势，2000年后ET₀显著大幅增加且分布极不均匀极不稳定；春季ET₀最大，冬季ET₀最小，冬春ET₀分散且不稳定；ET₀呈“中高东西低、南多北少”的空间分布和“西增中东减”的变化规律，滇中高值区ET₀变化不均匀且不稳定；湿度、日照时数和风速是影响ET₀的主要因素。     

基于敏感系数的贡献值法对参考作物腾发量变化的成因分析

本文采用FAO Penman-Monteith公式对大连地区1970—2006年间的参考作物腾发量进行计算,分析了该地区近37年来ET_0的变化成因及各气象要素对ET_0变化的贡献值。结果表明:37年来,大连地区生长季ET_0逐渐减少,年际ET_0呈不明显增加趋势,从生长季来看,ET_0对相对湿度的变化最敏感,太阳辐射是对ET_0变化贡献最大的因素,也是引起ET_0变化的主要因素;而从年际来看,ET_0对相对湿度的变化最敏感,尽管太阳辐射对ET_0变化的贡献值最大,但对ET_0变化成因起主导作用的却是相对湿度和平均温度。     

汉江上游流域潜在蒸散量敏感性分析

为研究全球变暖背景下汉江上游流域潜在蒸散量的变化特征,根据汉江上游流域1960—2015年汉中、石泉和安康3个气象站的逐日实测气象数据,采用彭曼-蒙蒂斯方程计算逐日潜在蒸散量ET_0。应用敏感性公式计算ET_0对5个主要气象因子的敏感系数,并结合气象因子的多年相对变化分析ET_0变化成因。结果表明:受太阳周年运动及地形等地理要素的共同影响,汉江上游不同气象因子及ET_0的年内分布不一;汉江上游ET_0对相对湿度最为敏感,各气象因子年敏感系数多呈显著下降趋势,敏感程度均达到"中"以上等级;ET_0同气象因子表现出复杂非线性关系,日照是汉中站ET_0变化的主导气象因子,石泉和安康站ET_0变化的主导气象因子是相对湿度。     

西藏高海拔地区气象数据缺失条件下的ET0计算研究

西藏高海拔地区低氧低压(平均不足海平面的2/3)、太阳辐射强(年太阳辐射6 000~8 000 MJ/m2)、近地层空气湿度变化大,加之西藏地区气象资料系列短、站点少,该地区ET_0计算具有特殊性及不便性。本研究基于西藏地区9个典型站点20年逐日气象资料,通过引入海拔因子与修正温度常数对Hargreaves(HS)模型进行改进,旨在得到一种少参、准确的高海拔地区ET_0简易计算方法。结果表明,海拔2 000 m以上地区Hargreaves-Elevation(HS-E)改进模型在不同时间尺度条件下的修正结果均明显优于HS模型且避免了原HS模型在高海拔地区ET_0计算出现负值的情况,提升了ET_0计算值的实用性与精度。以PM模型ET_0计算值为标准进行误差分析,HS-E模型逐日ET_0计算的纳什效率系数(NSE)、均方根误差(RMSE)和平均相对误差(MRE)分别为0.8、0.53mm/d和13.80%,逐月ET_0计算的NSE、RMSE和MRE分别为0.84、11.90 mm/month和12.50%;对比不同时间尺度条件下(日、月)误差分析结果可知,计算时间尺度越大HS-E模型结果越优。HS-E改进模型在高海拔地区适应性较强,具有较高的计算精度,可作为西藏海拔2000 m以上地区气象数据缺失条件下ET_0计算的推荐模型。     

考虑气象因子不确定性的参考作物蒸散量预报方法

参考作物蒸散量(ET0)的准确预测预报对于制定作物灌溉制度与实时灌溉调度具有重要意义,然而气象因子的不确定性极大的影响着ET0的预测精度.因此本研究采用马尔科夫蒙特卡罗模拟与自适应采样算法相结合的方法(AM-MCMC)对气象因子的不确定性进行修正,以气象站实测ET0作为标准值,利用径向基神经网络(RBF)模型建立气象因...     

内蒙古东部牧区缺测数据条件下适宜ET_0计算方法优选

内蒙古东部牧区多地处偏远边疆,气象站点有限,应用FA056 Penman-Monteith计算ET0相对困难。为了在缺测气象数据条件下准确计算ET0,本文依据该区内典型气象站点资料,以FAO56 Penman-Monteith为标准方法,以FAO17 Penman、Priestley-Taylor、Irmark-Allen拟合法、Hargreaves-Samani法为对照方法分别对ET0进行计算,并对4种方法适用性进行评价。结果表明:Priestley-Taylor与Hargreaves-Samani法计算值较FAO56 PM法计算结果偏大,不适于该地区ET0计算。FAO17 Penman法和Irmark-Allen拟合法与FAO56 PM法计算结果平均相对误差小于15%,计算精度较高,但Irmark-Allen拟合法仅需气温和日照时间气象资料,因此,Irmark-Allen拟合法适宜缺测气象条件下内蒙古东部牧区ET0计算。     

The Effect of Temperature Adjustment on Reference Evapotranspiration and Reconnaissance Drought Index (RDI) in Iran

The reference evapotranspiration (ET₀) is necessary to calculate Reconnaissance Drought Index (RDI). To estimate ET₀, FAO56 Penman-Monteith method which needs reference stations data is commonly used. Most of the meteorological stations in Iran are classified as non-reference satations and The use of their data in ET₀ calculation can affect the RDI. The objective of the present study is to evaluate the effect of temperature adjustment based on the reference condition on ET₀ and RDI values in non-reference stations of Iran. For this purpose, the meteorological data, recorded during 1960–2014 in 27 non-reference stations located in arid and semi-arid regions, were used. First, the values of ET₀ were determined using observed values of temperature. Using these values, RDI were computed by Log-Normal and Gamma distributions at annual and 6-month scales. Then the values of minimum, maximum and dew point temperatures were adjusted on the basis of the reference condition. The values of ET₀ and consequently RDI were calculated using adjusted data. On the basis of obtained results, at annual and 6-month scales, using observed values of temperature instead of adjusted values in non-reference stations cause to overestimate the value of ET₀. Also, using observed data with no adjustment can change the drought class which was determined on the basis of RDI. According to these results, temperature adjustment based on reference condition can change the values of ET₀ and RDI which was calculated by using Log-Normal or Gamma distributions at annual and 6-month scales.     

Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags

The reference evapotranspiration (ET₀) plays a significant role especially in agricultural water management and water resources planning for irrigation. It can be calculated using different empirical equations and forecasted by applying various artificial intelligence techniques. The simulation result of a machine learning technique is a function of its structure and model inputs. The purpose of this study is to investigate the effect of using the optimum set of time lags for model inputs on the prediction accuracy of monthly ET₀ using an artificial neural network (ANN). For this, the weather data time-series i.e. minimum and maximum air temperatures, vapour pressure, sunshine hours, and wind speed were collected from six meteorological stations in Serbia for the period 1980–2010. Three ANN models were applied to monthly ET₀ time-series to study the impacts of using the optimum time lags for input time-series on the performance of ANN model. Achieved results of goodness–of–fit statistics approved the results obtained by scatterplots of testing sets - using more time lags that are selected based on their correlation to the dataset is more efficient for monthly ET₀ prediction. It was realized that all the developed models showed the best performances at Loznica and Vranje stations and the worst performances at Nis station. Simultaneous assessment of the impact of using a different number of time lags and the set of time lags that show a stronger correlation to the dataset for input time-series, on the performance of ANN model in monthly ET₀ prediction in Serbia is the novelty of this study.

     

基于气候分区的甘肃省参考作物蒸发蒸腾量时空分布特征

参考作物蒸发蒸腾量(ET0)是计算作物需水量的重要参数,为了减少甘肃地区因地形和气候跨度大而引起的灌溉参数计算误差,根据甘肃省的地理特征和干湿程度将甘肃地区划分为陇南-甘南湿润区、陇中南部半湿润区、陇中北部半干旱区和河西干旱区4个区域,利用26个国家气象站点1980-2015年的逐日气象资料,采用FAO-56 Penman-Monteith方程计算ET0,并通过反距离权重空间插值法和偏相关分析法研究了甘肃省整体和不同分区ET0的时空分布特征和影响因素。结果表明:甘肃省ET0年际变化趋势为1980-1991年下降,1991-2015年上升,整体呈上升趋势;甘肃省ET0的空间分布总体为自东南向西北逐渐增加;ET0与日平均风速、日照时数、日最高气温、日最低气温、日平均气温均表现为极显著正相关,与平均相对湿度表现为极显著负相关,且影响程度顺序为,甘肃省:U>N>Tmax>RH>Tmin>Tmean,陇南-甘南湿润区:N>U>Tmax>RH>Tmin>Tmean,陇中南部半湿润区:N>U>RH>Tmax>Tmin>Tmean,陇中北部半干旱区和河西干旱区:U>N>Tmax>RH>Tmin>Tmean。结论:地形和气候对ET0影响很大,由湿润区向干旱区依次增加;各分区ET0差异较大,从东南部向西北部增加;甘肃省ET0主要影响因素为平均风速和日照时数。     

Developing Equations for Estimating Reference Evapotranspiration in Australia

Quantifying reference evapotranspiration (ET₀) is essential in water resources management. Although, many methods have been developed with different level of accuracy, in this study, two new equations were developed and optimized for estimating ET₀ using Honey-Bee Mating Optimization (HBMO) algorithm. The first eq. estimates ET₀ from extraterrestrial radiation (R_a), relative humidity (RH) and mean daily temperature (T_mean), while the second uses the same parameters except that mean daily temperatures is replaced with maximum daily air temperature (T_max). Both equations were developed using climatic data from eight weather stations in Western Australia and subsequently verified using data from ten sites across Australia. The estimated ET₀ values from both equations versus the FAO56-Penman-Monteith have a coefficient of determination, R², of larger than 0.96. Moreover, the performance of six commonly used methods of estimating ET₀ including Hargreaves-Samani, Thornthwaith, Hamon, Mc Guinness-Bordne, Irmak and Jensen-Haise were assessed and the Hargreaves-Samani method performed better than others. An attempt was made to calibrate the Hargreaves-Samani equation; however, its overall performance did not improved and the two newly proposed equations are suggested to be used in Australia.     

基于偏最小二乘回归的塔里木盆地北缘区ET0预测

利用气象因子计算ET0时,各气象因子之间经常存在多重相关性现象,从而导致所建多元回归模型失真,预测精度低。本文以新疆塔里木盆地北缘区的尉犁县为例,采用偏最小二乘回归建立ET0模型,利用主成分分析与典型相关分析思想,采用成分提取的方法,有效解决了各气象因子间的多重相关性问题,所建ET0模型取得较好效果。     

Spatio-temporal Calibration of Blaney–Criddle Equation in Arid and Semiarid Environment

Estimates of reference evapotranspiration (ET_o) are widely used in irrigation engineering to define crop water requirements. A major drawback to application of the FAO Penman-Monteith is the relatively high data demand which unfortunately, for many locations; such meteorological variables are often incomplete and/or not available. Alternatively, the Blaney–Criddle (BC) equation is a simpler method for ET_o estimation. In this study, the BC equation was calibrated using three methods: spatially calibration at each station for the whole period (ET_o-BC_S); two periods calibration (ET_o-BC_S2); and spatial and temporal calibration at each station for each month (ET_o-BC_S,T) using twelve stations a cross Jordan. The calibration coefficient of BC equation (a, b) were determined at all stations. The results of the calibration methods showed that: (1) the spatial calibration of BC had the highest RMSE, and ME and Lower R² comparing to spatial and temporal calibration and two periods calibration. (2) Improvement was achieved for the BC equation when considering the spatial and temporal calibration for all months at each station. The values of a were negative for all months of any station. The higher values of a are coincided with cold or low temperature months while the high values coincided with high month temperature. The b values were positive for the whole stations and months. As the a values, it seems that b values had higher values in warm months than the cold one. A relatively good improvement could be obtained using two periods calibration instead of one period. The maps of a and b clearly show that a and b varied considerably in the study area and being aware of the spatial temporal variations of climatologically parameters is important in managing the limited water resources. Knowing the spatial temporal changes of such parameters, accurate calculations of ET_o can be achieved which will lead to precise and elevate water resources management in the arid region such as Jordan.