漳卫河平原农业水资源高效利用的模拟研究Ⅰ.参数率定和模拟验证

为了优化漳卫河平原地区的农田灌溉制度,本研究以SWAT（Soiland Water Assessment Tool）为工具,首先运用洗牌复形演化（SCEUA）算法在位于该平原及其比邻地区的2个典型试验站率定冬小麦和夏玉米的作物参数,再以遥感监测的蒸散数据为目标,应用拉丁超立方一单次单因素（LHOAT）方法对SWAT中的相关参数进行敏感性分析,选出了对实际蒸散最敏感的参数,并用序贯不确定性（SUFI-2）算法优化参数进行不确定性分析。在此基础上,对冬小麦一夏玉米轮作体系产量的长期模拟结果进行验证。这些参数率定和模拟验证结果为进一步深入研究该地区水分生产函数和优化灌溉制度奠定了基础。     

直接搜索-模拟退火算法在水质模型参数识别中的应用

在研究新近发展起来的模拟退火算法及其各种改进算法的基础上,提出并构造了一种能识别水质模型参数的、以记忆为基础的直接搜索—模拟退火算法,并在O connor水质模型参数识别的实例应用中,得到了更优的全局最优解,为水质模型参数识别提供了一条新途径。     

随机理论方法在水文模拟中的应用

水文模型中的参数是对实测资料进行分析研究的基础上通过优选得到的,因此存在一定的随机性。针对模型参数的随机性问题,文章以Nash模型为基础,应用随机理论对具有随机系数的汇流计算方法进行研究。将参数k作为随机变量引入到了Nash模型中,建立了具有随机系数的汇流模型,并对模型参数分别服从正态分布和Gamma分布进的情况推导出了随机S曲线的均值和方差的一般表达式,将其应用于襄阳-皇庄的汇流计算中,得到了较好的结果。该方法能为实际应用中估计各种防洪决策提供依据。     

分布式水文模型在徒骇马颊河流域灌溉管理中的应用Ⅰ.参数率定和模拟验证

【目的】建立徒骇马颊河流域的分布式水文模型,为进行该地区灌溉制度优化提供支持。【方法】首先运用洗牌复形演化（SCE-UA）算法在禹城试验站和洪门试验站对分布式水文模型（SWAT）中的作物参数进行率定,接着以遥感监测的蒸散数据为目标,应用拉丁超立方-单次单因素（LH-OAT）方法对蒸散发（ET）相关的参数进行了敏感性分析,用序贯不确定性（SUFI-2）算法优化出一套参数范围并进行不确定性分析。在此基础上,在考虑模型不确定性的情况下对冬小麦－夏玉米轮作体系产量的长期模拟结果进行验证。【结果】作物参数率定结果表明,禹城试验站上冬小麦和夏玉米产量的相对误差分别为7.02%和16.60%,洪门试验站上冬小麦和夏玉米产量的相对误差分别为0.09%和0.10%。ET参数率定和统计年鉴产量验证结果：除了NS系数,P_factor、R_factor以及R2的模拟精度较高。【结论】依据徒骇马颊河流域的特点进行SWAT模型相关参数的率定,得到较好的模拟结果,为进一步深入研究该地区水分生产函数和优化灌溉制度打下了基础。     

含蜡原油触变性测试方法

现有触变性测试方法(加载方式)包括恒剪切率、剪切率阶跃、控制剪切率滞回环、剪应力阶跃以及控制剪应力滞回环等。利用这些方法对同一条件下含蜡原油的触变性进行测试,并利用Houska触变模型和双结构参数触变模型进行拟合。利用一种方法测试数据拟合出的触变参数对其他测试方法加载条件下的应力进行预测,依据预测结果与实测值的吻合程度,对测试方法进行评价。结果表明:无论采用何种测试方法,利用剪切率高的加载条件得到的实验数据拟合的触变参数,均可成功地对较低剪切率加载条件下的应力进行预测,反之则会出现结构参数为负值进而导致预测失败的情况;利用同一测试方法的多组实验数据共同拟合触变参数,可以提高对其他加载方式下应力的预测值与实测值的吻合程度。     

Exploitation of spatial information in high resolution digital imagery to map leaf area index

Crop responses to management practices and the environment, as quantified by leaf area index (LAI), provide decision-making criteria for the delineation of crop management zones. The objective of this work was to investigate whether spatial correlations inferred from remotely sensed imagery can be used to interpolate and map LAI using a relatively small number of ground-based LAI measurements. Airborne imagery was recorded with the Airborne Imaging Spectrometer for Applications (AISA) radiometer over a 3.2 ha corn field. Spectral vegetation indexes (SVI) were derived from the image and aggregated to cells of 2 × 2 m², 4 × 4 m², and 8 × 8 m² resolution. The residual maximum likelihood method was used to estimate the LAI variogram parameters. A generalized least squares regression was used to relate ground truth LAI data and collocated image pixels. This regression result was then used to convert variograms from the imagery to LAI units as well as to interpolate and map LAI. The decrease in resolution by merging pixels led to an increase in the value of the r ² and to a decrease in root mean-squared error (RMSE) values. The accuracy of kriged estimates from the variogram of the measured LAI and that from the image derived variograms was estimated by cross-validation. There was no difference in the accuracy of the estimates using either variograms from measured LAI values or from those of converted SVIs. Maps of LAI from ground-based measurements made by kriging the data with image-derived variogram parameters were similar to those obtained by with kriging with the variogram of measured LAI. Similar coarse spatial trends of high, medium and low LAI were evident for both maps. Variogram parameters from ground-based measurements of LAI compared favorably with those derived from remotely sensed imagery and could be used to provide reasonable results for the interpolation of LAI measurements.     

品种间双列杂交的Gardner-Eberhart模式的参数估计量方差与预测变量方差

本文给出了品种间双列杂交的 Gardner-Eberhart 模式的四个模型分别被接受时参数估计量、参数估计量之差、预测变量、预测变量之差的方差估计公式,并用一个例子作了说明。     

差分生长模型的应用分析与研究进展

差分生长模型是一种特殊的混合参数模型,通过指定一个随林分而变化的参数来解释不同林分生长曲线间的差异。由于这一特征,差分生长模型在生长收获预估中得到了广泛应用。本文一般性地介绍了差分生长模型在林分和单木生长过程中的应用、差分方程的数学性质及数学性质间的相互关系;从统计角度详细论述了变量初值y1的统计意义和与y1相关联的随机误差e1对预测的影响作用;由于差分生长模型最新研究成果主要在于方程推导、参数估计和预测误差分析等方面,因此详细讨论了模型推导方法中的广义代数差分法(GADA)、主要的参数估计方法和预测误差分析方法。     

基于分层贝叶斯方法的高山松单木生物量模型

以香格里拉市典型森林生态系统高山松林为对象,在前期进行Ⅰ区和Ⅱ区共115株高山松单木地上生物量实测基础上,以异速生长方程为单木生物量基础模型,并采用分层贝叶斯方法、非线性混合模型法、贝叶斯方法和非线性最小二乘法进行异速生长参数拟合,运用决定系数(R²)、估测精度(E)、均方根误差(RMSE)等指标对模型参数拟合效果进行评价。结果表明:1)从拟合精度看,4种方法的模型拟合效果均较好,R²均达到了0.98以上。但分层贝叶斯方法估计结果更优,其R²=0.985 6,E=84.76%和R_MSE=39.75 kg;2)通过对比不同方法的差异发现,加入了区域随机效应的分层贝叶斯方法和非线性混合模型法的拟合效果均优于未加入区域随机效应的贝叶斯方法和非线性最小二乘法。分层贝叶斯方法在拟合高山松单木生物量模型中具有更大优势,模型拟合效果最好。加入了随机效应的分层贝叶斯方法和非线性混合模型法可以提高单木生物量模型的估计精度,采用分层贝叶斯方法进行高山松单木生物量模型参数估测,为大尺度样本数据模型参数估测方法提供新思路。     

Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas

The transport of sediment and nutrients from land application areas is an environmental concern. New methods are needed for estimating soil and nutrient concentrations of runoff from cropland areas on which manure is applied. Artificial Neural Networks (ANNs) trained with a backpropagation (BP) algorithm were used to estimate soil erosion, dissolved P (DP) and NH₄–N concentrations of runoff from a land application site near Lincoln, Nebraska, USA. Simulation results from ANN-derived models showed that the amount of soil eroded is positively correlated with rainfall and runoff. In addition, concentrations of DP and NH₄–N in overland flow were related to measurements of runoff, EC and pH. Coefficient of determination values (R²) relating predicted versus measured estimates of soil erosion, DP, and NH₄–N were 0.62, 0.72 and 0.92, respectively. The ANN models derived from measurements of runoff, electrical conductivity (EC) and pH provided reliable estimates of DP and NH₄–N concentrations in runoff.     

巴氏消毒及冷藏温度作用下副溶血弧菌菌株失活异质性的比较

温度是影响细菌生长与失活的关键因素,在食品生产中常用来控制致病微生物潜在的风险。但由于不同菌株间个体的差异,细菌在相同温度作用下呈现出不同的失活趋势,这种行为方式称为菌株的失活异质性,容易导致微生物风险控制的不确定性和变异性。比较了19株副溶血性弧菌(16株临床菌株和3株环境菌株)在巴氏消毒温度(65℃)及冷链温度(10℃)作用下的失活情况,并结合Weibull模型,拟合相应的失活参数(t_R值),探究了不同菌株间的失活异质性。在65℃处理条件下,19株副溶血性弧菌的t_R值介于22.62～67.23 s,VPC-1为耐热性最强菌株,而VPC-10为耐热性最弱菌株,热失活参数t_R值最适的概率分布为Normal (44.82, 12.27)。在10℃条件下,t_R值介于113.96～371.38 h,VPC-3为耐冷性最强菌株,VPC-2为耐冷性最弱菌株,冷失活参数t_R值最适的概率分布为Loglogistic (51.45,148.88,4.67)。结果表明,副溶血性弧菌的热失活和冷失活间没有显著的相关性,菌株的失活异质性广泛存在于副溶血性弧菌之中,仅基于单一菌株进行失活模型的拟合,很难描述其整体的失活趋势。同时,初步构建了菌株失活异质性的随机模型,并使用概率分布代替了传统的失活参数。     

AMMI模型应用于桤木种源区域试验的分析研究

对桤木 1 3个种源在 5个测试点进行了生长性状的遗传测定。AMMI(加性主效应和复合交互效应 )模型和传统稳定性分析模型相比较 ,AMMI分析和生态价法的结果很吻合 ,而与回归分析法的结果有所差异 ,但排序结果基本是一致的。AMMI的Di 值法相对比较精确可靠。桤木树高在种源间差异极显著。树高相对稳定的种源是四川泸定沙湾、四川雅安和四川金堂淮口 ,最不稳定的是种源四川金堂长乐和四川沐川     

Soil moisture estimation from inverse modeling using multiple criteria functions

Soil hydraulic parameters are essential inputs to agricultural and hydrologic models for simulating soil moisture. These parameters however are difficult to obtain especially when the application is aimed at the regional scale. Laboratory and field methods have been used for quantifying soil hydraulic parameters but they are proved to be laborious and expensive. An emerging alternative of estimating soil hydraulic parameters is soil moisture model inversion using remote sensing (RS) data. Although soil hydraulic parameters could not be derived directly from remote sensing, they could be quantified by the inverse modeling of RS data. In this study, we conducted a multi-criteria inverse modeling approach to estimate the rootzone soil hydraulic parameters in a rainfed rice field at depths 3, 12, 28 and 60 cm, respectively. The conditioning data used in the inverse modeling are leaf area index (LAI) and actual evapotranspiration (ET_a) from satellite imageries, and soil moisture (SM) data from in situ measurements. The performances of all the model inversion experiments were evaluated against observed soil moisture in the field, and measured LAI during the growing season. The results showed that using remotely sensed LAI and ET_a in the inverse modeling provided a good matching between observed and simulated soil moisture down to 28 cm depth from the soil surface. With the addition of soil moisture information from the site, the model inversion significantly improved the soil moisture simulation up to a depth of 60 cm.     

Determination of differences in crop injury from aerial application of glyphosate using vegetation indices

Crop injury caused by off-target drift of herbicide can seriously reduce growth and yield and is of great concern to farmers and aerial applicators. Farmers can benefit from identifying an indirect method for assessing the level of crop injury. This study evaluates the combined use of statistical methods and vegetation indices (VIs) derived from multispectral images to assess the level of crop injury. An experiment was conducted in 2009 to determine glyphosate injury differences among the cotton, corn, and soybean crops. The crops were planted in eight rows spaced 102 cm apart and 80 m long with four replications. Seven VIs were calculated from multispectral images collected at 7 and 21 days after the glyphosate application (DAA). At each image collection date, visual injury estimates were assessed and data were collected for plant height, chlorophyll content, and shoot dry weight. From the seven VIs evaluated as surrogate for glyphosate injury identification using a canonical correlation analysis (CCA), the Chlorophyll Vegetation Index (CVI) showed the highest correlation with field-measured plant injury data. CVI image values were subtracted from the CVI average values of the non-injured area to generate CVI residual images (CVI_res). Frequency distribution histograms of CVI_res image values were calculated to assess the level of injury between crops. These data suggested that injury increased from 7DAA to 21DAA with corn exhibiting higher severity of injury than cotton or soybean, while only moderate injury was observed for cotton. The techniques evaluated in this study are promising for estimating the level of glyphosate herbicide drift, which can be used to make appropriate management decisions considering crop proximity.     

地表参数对AERMOD模型预测浓度精确度的影响

AERMOD是一个新的先进的烟羽扩散模型,它主要应用到大气扩散的法规烟羽模式。在AERMOD模型中,它需要输入3个地表参数因子,分别是波文比、地表反照率和地面粗糙度。这些因子代表着不同的土地利用类型,影响着AERMOD模型的预测浓度。为了确保输入的地表参数因子值能更精确有效地模拟预测浓度,了解这些因子如何影响模拟结果是非常重要的。探讨了不同数值的地表反照率、波文比和地面粗糙度对AERMOD模式的模拟预测浓度值的影响变化情况,并通过4种土地利用类型来评价不同的地表参数因子对AERMOD的浓度影响,从而提出AERMOD模式中地表参数波文比、地表反照率和地面粗糙度的精确性的建议。     

基于WheatGrow和CERES模型的区域小麦生育期预测与评价

【目的】研究小麦生长模型在区域应用中的关键技术,并利用WheatGrow和CERES-Wheat两套模型模拟区域小麦生育期,以检验和评价模型区域应用方法的有效性。【方法】首先利用薄盘样条法（thin plate spline,TPS）对各站点逐日气象数据进行空间插值,得到研究区域气象要素表面数据;其次利用TPS方法对各站点历史多年小麦播种期进行空间插值,并将插值后的结果进行多年平均得到研究区域播种期表面数据;进一步将Markov Chain Monte Carlo（MCMC）方法与生长模型相结合,利用典型站点历史多年小麦生育期实测数据,估算出典型站点的品种参数,并将其作为各省份的代表性生态型品种参数;最后将生成的气象要素和播种期表面数据以及生态型品种参数等输入到WheatGrow和CERES-Wheat模型中,并以栅格为单元进行研究区域小麦生育期的模拟,进一步结合不同站点历史年份的生育期观测资料,检验和评价模型区域应用方法的有效性,并量化区域生育期模拟结果的不确定性。【结果】两个模型在区域尺度上的生育期预测效果均较好,区域尺度上拔节期、抽穗期、成熟期的预测值和观测值之间的R2分别为0.85、0.87和0.86（WheatGrow）,0.87、0.85和0.82（CERES）;RMSE分别为9.6、7.2和6.3 d（WheatGrow）,9.4、7.8和6.6 d（CERES）。另外,WheatGrow模型对抽穗期和成熟期区域预测的准确度略高于CERES-Wheat,但由品种参数导致的区域模拟结果的不确定性也相对较高。【结论】通过气象数据和小麦播期数据的TPS插值技术结合MCMC方法的品种参数估计技术,将基于机理的生育期模型拓展到区域尺度,较好地预测区域小麦生育期,量化了由品种参数导致模拟结果的不确定性,研究结果可为进一步量化中国小麦主产区的区域生产力提供技术支撑。     

Towards an adaptive model for greenhouse control

Application of advanced controllers in horticultural practice requires detailed models. Even highly sophisticated models require regular attention from the user due to changing circumstances like plant growth, changing material properties and modifications in greenhouse design and layout. Moreover, their calibration is data demanding and laborious. This study explores the suitability of the extended Kalman filter (EKF) for automatic, on-line estimation and adaptation of parameters in a physics-based greenhouse model. The method was tested with measured data recorded over a period of 1 year, and with a model that describes the air temperature and moisture content in the Watergy greenhouse. In order to keep the parameters estimation problem tractable, and to improve the local accuracy of the parameters, separate EKFs are applied to sub-systems, using observation data at the sub-system boundaries. The filter adequately adjusts parameter values, thus significantly improving the model fit as compared to simulations with no-varying parameters. It appears that the filter is robust with respect to sudden changes in the system; when a disturbance occurs, such as pruning of plants or emergency opening of the windows, the EKF changes the parameter values accordingly. The result suggests that the extended Kalman filter is, indeed, a suitable method to provide the required automatic adaptation to time-varying phenomena when modeling is impractical.     

电网动态负荷有功模型参数辨识研究

针对动态负荷模型的参数辨识问题进行研究。通过对实测电网有功功率和电压信号的实验分析,借助MATLAB系统优化工具箱,给出了动态负荷模型参数辨识的具体方法和步骤。结果表明:利用参数辨识方法得到的负荷模型能够较好地体现电网实测有功功率的变化规律,对于深入研究电网动态负荷模型的特性有重要意义。     

Confidence bands for measured economically optimal nitrogen rates

While numerous researchers have computed economically optimal nitrogen rate (EONR) values from measured yield–N rate data, nearly all have neglected to compute or estimate the statistical reliability of these EONR values. In this study, a simple method for computing EONR and its confidence bands is described and demonstrated. The method is illustrated for seven yield response functions, namely, the linear plateau, quadratic, quadratic plateau, square root quadratic, spherical plateau, and exponential and exponential plateau. Only the quadratic and square root quadratic functions are linear in their parameters, with least squares regression yielding parameters that are normally distributed. The other five functions are non-linear and give parameter estimates that are non-normal and biased in their distribution when fit by least squares. Prior to computing EONR distributions, the non-linear functions were reparameterized to give fitted parameters that were nearly unbiased and normally distributed. EONR distributions were computed using a Monte Carlo method to generate 1,000 realizations of EONR based on the fitted response function parameters. From the 1,000 realizations, the expectation, confidence bands, and cumulative probability distributions for EONR were easily computed. Applying the approach to six yield data sets from the literature illustrated that the 68% confidence bands for computed EONR can span several tens of kilograms per hectare and are typically skewed about the expectation. There were considerable differences among the distributions of EONR computed from the seven functions, with the cumulative probability distributions sometimes not overlapping. Given the limited statistical reliability possible for EONR, it is essential that confidence bands always be reported when EONR values are computed from yield data.     

冬小麦群体参数优化设计方法

为探讨冬小麦生产中合理群体结构的构建方法,在2009/2011和2012/2013年冬小麦生长季,利用3个田间试验建立不同冬小麦群体,分析冬小麦群体动态变化特征对产量的影响,并提出冬小麦群体参数优化设计的方法。研究结果表明:产量的形成与干物质最大积累速率、干物质快速增长的延续点及叶面积指数(LAI)动态变化特征参数相关,所建立的关系模型达到极显著水平(P0.01),对模型进一步用独立样本进行配对t检验,模拟值与实际值差异不显著(P0.05);冬小麦参数设计中,不同时期的干物质积累、LAI变化和群体生长特征参数的范围以及产量与群体的关系构成约束条件;该方法能定量描述冬小麦群体最优的生长动态过程,且灵活方便。本方法能为作物生产管理中合理群体结构的构建提供理论分析手段。