基于灰色关联度与BP神经网络模型的日参考作物腾发量预测

参考作物腾发量是制定灌溉用水计划、水量分配计划最基本、最重要的内容之一,其精确预测可以提高灌溉预报的精度。采用灰色系统理论中的关联分析方法,对影响作物腾发量的各个气象因素进行关联度分析,挑选出影响作物腾发量的主要气象因子,并以这些主要气象因子为输入向量,以参考作物腾发量为输出向量,建立作物腾发量与主要气象因子之间的BP神经网络预测模型。通过实例证明,该方法简单可行,预测精度比较高,能够满足实际生产需要。     

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。     

基于GIS的山西省参考作物腾发量研究

该文运用FAO 1998年出版的灌溉与排水分册第56册中对参考作物腾发量的新定义和计算公式(彭曼-蒙蒂斯公式)，应用能代表山西省全部气候带的11个气象站9年(1991年至1999年)的逐日气象观测资料，通过Visual Basic 6.0编制程序实现计算功能，利用SQL Server 2000数据库服务器存储数据，同时利用GIS软件Arcmap9.0对山西省参考作物腾发量进行时间和空间上的分析。提出了用平均法(即用逐日参考作物腾发量值经过平均计算得到当月参考作物腾发量)计算逐月参考作物腾发量来代替用彭曼-蒙蒂斯公式计算的逐月参考作物腾发量，并可绘制出相应的日或月的参考作物腾发量等值线图。     

灰色GM(1，1)-小波变换-GARCH组合模型预测松花江流域水质

为了研究松花江流域水质变化情况,预测未来水质变化趋势以及对松花江流域水质的保护提供理论依据和决策方案,通过对松花江抚远段2012年前15周的实测溶解氧(dissolved oxygen,DO)、高锰酸盐指数CODMn、氨氮NH3-N数据分析,以灰色GM(1,1)模型、小波分解与重构和广义自回归条件异方差(generalized auto-regressive conditional heteroskedasticity,GARCH)模型为基础,建立了灰色GM(1,1)和灰色GM(1,1)-小波变换-GARCH组合的混合预测模型,并以抚远段实测DO、CODMn、NH3-N数据为实例进行验证,预测结果极显著(P0.01),预测误差分别为3.39%、8.56%、7.83%,表明该预测模型精度较高,适用于对水质变化的预测研究。最后,利用该模型对松花江抚远、黑河、嘉荫和同江段2013年前8周的4个污染指标进行预测分析,预测结果与实测数据误差较小,基本符合水质未来变化趋势,为相关部门对松花江流域水质预测和保护提供参考。     

基于SPAC系统的作物腾发量模型的试验研究

通过田间试验对河北省2004年和2005年棉花、冬小麦和夏玉米不同生育期的冠层温度、地表温度和叶面积指数进行测定,根据能量平衡方程和空气动力学方程,结合当地气象资料推导出作物腾发量模型,并与棉花、冬小麦和夏玉米不同生育期实际腾发量比较发现:作物腾发量模型计算值不仅反映了这3种作物不同生育期腾发量的变化规律,而且与实际腾发量平均值的相对误差2004年分别为8.46%、4.76%和12.85%,2005年分别为3.42%、1.65%和0.84%,因此可以利用作物腾发量模型来计算作物腾发量,该研究为监测土壤墒情和确定作物缺水指标提供了理论依据.     

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。     

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。     

新疆地区参考作物腾发量的灰色模型预测

该文依据新疆地区6个站的长序列逐日气象观测资料,基于Penman-Monteith公式计算了逐日参考作物腾发量（ET0）,并应用重标极差法对ET0未来变化趋势进行了分析。运用灰色关联理论计算了各站各气象因子与年ET0间的灰色关联度和关联序。在此基础上,运用灰色系统理论建立灰色不等维递补GM（1,h）模型对6个站的年ET0进行了模拟预测,并与灰色GM（1,1）模型进行了比较。结果表明：各站ET0年内变化均呈抛物线型,4－9月ET0依各站顺序为：若羌＞吐鲁番＞哈密＞喀什＞和田＞伊宁;6站年ET0赫斯特指数均大于0.5,各站未来的趋势与历史呈正相关,依然是波动递减;总体上,平均温度、日照时数、饱和气压差对各站年ET0的影响比较大;灰色不等维递补GM（1,h）模型预测相对误差限为0～7.31%,预测精度明显高于GM（1,1）模型。该研究表明采用灰色模型预测新疆地区参考作物腾发量精度较好。     

山西潇河灌区参考作物腾发量和降水的随机特性

气象要素的随机变化对于农田水分的动态变化与优化调控具有重要影响。根据山西潇河灌区1978～2003年共26年的气象资料，利用FAO推荐的Penman-Monteith公式计算了逐旬的参考作物腾发量(ET₀)。采用时间序列分析方法对ET₀序列和降水(P)序列的随机特性进行了分析，并将以上序列分解为趋势项、周期项(包括均值和标准差)和平稳随机项。结果表明：近20多年来潇河灌区ET₀序列具有递增趋势，而降水具有递减趋势，同时二序列存在负相关关系；去除趋势项的ET₀和P序列的旬均值和标准差具有周期性变化的特征，可以用Fourier级数的二阶分量来描述；二序列的平稳随机成分可以用自回归模型来描述。以上结果可以进一步用于农田墒情的随机预报和作物灌溉制度的随机优化。     

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.     

Spatial stochastic volatility for lattice data

Spatial heteroscedasticity may arise jointly with spatial autocorrelation in lattice data collected from agricultural trials and environmental studies. This leads to spatial clustering not only in the level but also in the variation of the data, the latter of which may be very important, for example, in constructing prediction intervals. This article introduces a spatial stochastic volatility (SSV) component into the widely used conditional autoregressive (CAR) model to capture the spatial clustering in heteroscedasticity. The SSV component is a mean zero, conditionally independent Gaussian process given a latent spatial process of the variances. The logarithm of the latent variance process is specified by an intrinsic Gaussian Markov random field. The SSV model relaxes the traditional homoscedasticity assumption for spatial heterogeneity and brings greater flexibility to the popular spatial statistical models. The Bayesian method is used for inference. The full conditional distribution of the heteroscedasticity components can be shown to be log-concave, which facilitates an adaptive rejection sampling algorithm. Application to the well-known wheat yield data illustrates that incorporating spatial stochastic volatility may reveal the spatial heteroscedasticity hidden from existing analyses.     

似然函数形式对水稻物候期模型品种参数校正的影响

针对传统高斯正态似然函数(Gaussian likelihood function,GLF)在观测数据存在测量误差和模型算法结构复杂时无法描述模型残差异方差特点,造成马尔科夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)算法进行模型参数校正时结果存在偏差的问题,通过引入变异系数(coefficient of variation,CV)变换的高斯似然函数(GLF with CV transformation,GLF-CV)和BC(Box-Cox)变换的高斯似然函数(GLF with BC transformation,GLF-BC)对观测数据和模型结构造成的异方差进行特征描述,并比较了参数校正效果及模型不确定度(uncertainty ratio,UR)。以2004—2009年高要雪花粘(早熟)、2001—2004年兴化武育粳3号(中熟)、1991—2004年六安汕优63号(晚熟)3个生态点的田间栽培试验数据为基础,RiceGrow和Oryza2000物候期模型为对象,利用仿射不变马尔科夫链蒙特卡洛集成采样(ensemble sampling for affin...     

基于HP滤波与ARIMA-GARCH模型的柱塞泵泄漏量预测

柱塞泵关键摩擦副磨损造成的泄漏增大是其性能退化的主要原因,预测泄漏量的变化趋势有助于定量分析柱塞泵性能退化过程。该研究使用HP（Hodrick-Proscott）滤波对柱塞泵泄漏量进行分解,结合滤波后得到的趋势数据具有非线性及方差异性的特征,基于时间序列方法建立HP-ARIMA-GARCH（HP-Auto Regressive Integrated Moving Average- Generalized Autoregressive Conditionally Heteroscedastic）模型预测柱塞泵泄漏量变化。通过不同时段泄漏量预测结果比较可知,根据HP滤波分解后得到的趋势数据序列建立的HP-ARIMA-GARCH模型较传统时间序列模型预测结果的平均相对误差最高可减小5.42个百分点,能够实现对泄漏量的有效预测。研究结论可为柱塞泵性能退化的定量预测提供理论参考。     

Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests

Geospatial measurements of ancillary sensor data, such as bulk soil electrical conductivity or remotely sensed imagery data, are commonly used to characterize spatial variation in soil or crop properties. Geostatistical techniques like kriging with external drift or regression kriging are often used to calibrate geospatial sensor data to specific soil or crop properties. More traditional statistical methods such as ordinary linear regression models are also commonly used. Unfortunately, some soil scientists see these as competing and unrelated modeling approaches and are unaware of their relationship. In this article we review the connection between the ordinary linear regression model and the more comprehensive geostatistical mixed linear model and describe when and under what conditions ordinary linear regression models represent valid spatial prediction models. The formulas for the ordinary linear regression model parameter estimates and best linear unbiased predictions are derived from the geostatistical mixed linear model under two different residual error assumptions; i.e., strictly uncorrelated (SU) residuals and effectively uncorrelated (EU) residuals. The theoretically optimal (best linear unbiased) and computable (linear unbiased) predictions and variance estimates derived under the EU error assumption are examined in detail. Statistical tests for detecting spatial correlation in LR model residuals are also reviewed, in addition to three LR model validation tests derived from classical linear modeling theory. Two case studies are presented that highlight and demonstrate the various parameter estimation, response variable prediction and model validation techniques discussed in this article.     

The use of surface resistance—soil moisture relationships in soil water budget models

An important feature of a soil water budget is the reduction of transpiration from a canopy below the rate of atmospheric demand with increasing soil dryness. Commonly, an empirical relationship between the ratio of actual evaporation (AE) to potential evaporation (PE) and soil water storage is adopted. Alternatively the Penman—Monteith equation can be used with a specified relationship between surface resistance and soil water storage.Using actual evaporation rates determined from instrumented soil water profiles, a relationship between surface resistance and soil water storage can be inferred, and results are presented for different crops and soil-types in the United Kingdom. These results are compared with the surface resistance values implicit in the performance of two layer soil moisture models adopting an empirical AE/PE relationship with soil moisture deficit. The performance of the two approaches with respect to soil moisture estimation is compared.     

以水面蒸发量为参考推求土壤实际蒸发量的数学模型

为了准确估算土壤在实际条件下的蒸发量,该文以水面蒸发量为参考,结合能量平衡方程及微气象学方法,推导计算土壤实际蒸发量的数学模型.结果表明所建模型所需参数为水面及蒸发土壤表面的日最高温度和日平均温度、水面日蒸发量、风速等.模型的验证结果表明计算的土壤蒸发量与实测蒸发量比较吻合(R2=0.90).模型所引入的参考蒸发面使其避开了土壤蒸发复杂的物理本质,从而使得对土壤蒸发的计算变得简单易行.     

How much influence does landscape-scale physiography have on air temperature in a mountain environment?

Spatio-temporal patterns of temperature in mountain environments are complex due to both regional synoptic-scale and landscape-scale physiographic controls in these systems. Understanding the nature and magnitude of these physiographic effects has practical and theoretical implications for the development of temperature datasets used in ecosystem assessment and climate change impact studies in regions of complex terrain. This study attempts to quantify the absolute and relative influence of landscape-scale physiographic factors in mediating regional temperatures and assess how these influences vary in time. Our approach was to decompose the variance in in situ temperature measurements into components associated with regional free-air temperature estimates and local physiographic effects. Near-surface air temperature data, collected between 1995 and 2006 from 16 meteorological stations in the Lake Tahoe region of California, USA were regressed against free-air temperature (North American Regional Reanalysis dataset) for the same period. Residuals from this fit represent spatial deviations from the regional mean and were modeled as a function of physiographic position on the landscape using variables derived from terrain analysis techniques. Linear models relating temperature residuals to physiographic variables explained roughly 10–90% of the variance in temperature residuals and had root mean squared error of 1.2–2.0 °C, depending upon the type of measurement and time of year. Results demonstrate that: (1) regional temperature patterns were the principle driver of surface temperatures explaining roughly 70–80% of the variance in in situ measurements; (2) the remaining variance was largely explained by spatial variability in landscape-scale physiographic variables; (3) the influence of physiographic drivers varied seasonally and was influenced by regional conditions. Periods of well-mixed atmospheric conditions lend themselves to the use of simple elevation-based lapse rate models for temperature estimation whereas other physiographic effects become more prominent during periods of enhanced atmospheric stability; and lastly (4) small differences in temperature due to landscape position, when integrated over time, can have a prominent effect on water balance and thus hydrologic and ecologic processes.     

Model Error Propagation and Data Collection Design. An Application in Water Quality Modelling

The scope of this paper is to analyse the error propagation structure of a simple two compartment water quality model, and to describe the effect on model predictions from uncertainties in initial conditions and forcing inflow. Following a brief review of linear dynamic systems, a variance estimation model for the water quality model is described. The methodology employed in relating model prediction uncertainty and data collection design, is based on systems or ordinary differential equations combined with first-order second-moment analysis (FOSMA). In the paper some important characteristics of uncertain water quality systems are discussed, namely the steady-state concentration and system time scale. Applying FOSMA, the variance expressions of the system characteristics lead to some specific suggestions in the practical design of water quality models and the relation to data collection accuracy. Further it was found that the variance estimator for the steady-state concentration, provided that the system matrix is deterministic, is expressed as a weighted sum of the variances of the initial concentrations and the inflow transport. The weights are functions of the system time scales and thus related to the model parameters.     

Development and evaluation of a soil water evaporation model to assess the effects of soil texture, tillage and crop residue management under field conditions

Abstract. A number of mathematical models to predict soil water evaporation are available in the literature which generally require complex input data. In the present study, a simple parametric model has been developed by coupling existing and newly developed equations to assess soil water evaporation and drainage under field conditions in relation to potential evaporation rate, soil texture, time and depth of tillage and crop residue management. The model has moderate input data requirements and predicts well the effects of tillage and crop residue management practices on soil water loss (evaporation+drainage) with multi-drying and -wetting cycles prevailing under natural conditions. The root mean squares of deviations between observed and predicted cumulative water loss at different periods of study were 0.82, 2.04, 2.31 and 1.74  cm for untreated, residue-mulch, tillage and residue-incorporated treatments, respectively. Simulation analysis on cumulative evaporation and evaporation rate has shown that the evaporation reduction with different combinations of tillage and crop residue followed the order of residue-undercut>residue-mulch>residue-incorporated>tillage. Thus, the magnitude of beneficial effects of crop residues and tillage on soil water evaporation reduction are associated with amount of residues, mode of residue management (mulched or incorporated in the soil) and time and depth of tillage.     

喀斯特森林近地表层覆盖对碳酸盐岩红土蒸发过程的影响

蒸发是西南喀斯特地区薄层土壤水分损失的主要途径,浅层土壤水分的存蓄对喀斯特地区农业生产和生态恢复至关重要。以西南喀斯特森林碳酸盐岩红土为研究对象,基于室内蒸渗试验设置4个苔藓生物量(0,0.32,0.64,0.95 kg/m~2)和3个松针生物量(0,0.32,0.64 kg/m~2)共12种处理,分析森林近地表层覆盖对碳酸盐岩红土蒸发过程及表层温度时空分布的影响规律,并对比3种蒸发模型(Black、Rose、空气动力学蒸发模型)在喀斯特森林碳酸盐岩红土的适用性。结果表明:苔藓和松针覆盖显著降低累积蒸发量和蒸发速率(P0.05),接种苔藓0.95 kg/m~2和覆盖松针0.64 kg/m~2处理比裸土累积蒸发量小36.9%;苔藓和松针导致土壤含水量显著增加(P0.05);苔藓和松针增加了表层土壤的平均温度,松针对土壤温度的提升作用强于苔藓;Black、Rose和空气动力学模型均能较好地模拟碳酸盐岩红土蒸发过程,Black蒸发模型的拟合精度高于Rose和空气动力学蒸发模型。研究结果能为西南喀斯特地区的水量平衡分析提供理论支撑并加强对喀斯特森林地表水文过程的认知。