Statistical analysis of the effects on overland flow of spatial variability in soil hydraulic conductivity / Analyse statistique des effets de la variabilité spatiale de la conductivité hydraulique du sol sur l'écoulement de surface

Abstract

The effects of spatial variation of the saturated hydraulic conductivity (K _s ) of the soil on the variation of overland flow were tested by analysing 2000 synthetic rainfall—runoff events, all generated from real, observed rainfall events but with runoff modelled by a two-dimensional distributed model using different spatially variable K _s fields in a small (12 ha) agricultural catchment. The purpose is to determine the influence of spatial variation in K _s on runoff generation. The statistical measures used to describe the variation in the generated K _s were its coefficient of variation and correlation length. Both of these had two levels of typical values obtained from field measurements in other studies. The storms were analysed at a general event level, first using simple graphical and statistical methods and then using analysis of variance (ANOVA). The observed scale of the spatial variation of K _s does cause statistically significant variation in overland flow. The graphical analysis showed that the first flow peak in a multi-event storm had the largest variation and that differences were greater in the rising part of the hydrograph than in its recession. The greatest variation in overland flow was produced by the combination of the greater coefficient of variation and the longer correlation lengths. The smallest variation in overland flow was produced by the combination of the smaller coefficient of variation and the shorter correlation lengths. ANOVA showed that the coefficient of variation and correlation length alone did not explain all the variation of the total flow. ANOVA was not very useful due to the many restrictive assumptions that were not satisfied by the nature of the data and therefore analysis methods with less restrictive assumptions need to be tested.     

Evaluation of Composts from Agricultural Wastes with Fish Pond Sediment as Bulking Agent to Improve Compost Quality

Composts with five different ratios of agricultural wastes, viz. rice straw (RS), wheat straw (WS), potato plant (PP), and mustard stover (MS) were prepared with or without fish pond bottom sediment to investigate the compost maturity and their suitability for field application. The composting process was monitored through the changes in physico‐chemical parameters and germination index (GI) at every 7 days interval of the composting process. All the composts were dark brown and smelled like forest soil within 56 days of composting, which reflected its matured status. On the basis of the physico‐chemical parameters (bulk density: 0.84 g/cm³; pH 7.05; electrical conductivity: 3.52 mS/cm; cation exchange capacity:82.4 cmol/kg; total carbon:321.4 g/kg; total nitrogen: 16.9 g/kg; As: 6.8 mg/kg; Cd: 2.96 mg/kg; Cr: 29.6 mg/kg, Cu: 243.6 mg/kg; Hg: 0.019 mg/kg; Ni: 24.3 mg/kg; Pb: 62.1 mg/kg and Zn: 812 mg/kg) and GI (89–96%), it could be concluded that RS/WS/PP/MS, 1:1:2:1 v/v/v/v with fish pond sediment produced better compost in accordance with the Indian compost standard. Application of a combined randomized block design analysis revealed that there is a significant difference in the responses of the five composts, in relation to the time of composting. Hierarchical clustering algorithm was applied with a view to form homogeneous groups of five different composts on the basis of different physico‐chemical parameters. Therefore, the ratio of waste incorporation is an important decision for composting and addition of pond sediment can improve the quality of compost.     

基于GIS的中原经济区发展水平差异分析

通过对中原经济区内29个省辖市经济水平的数据进行比较,运用模型计算出各城市的经济强度。根据经济发展水平差异把各城市划分成4个等级,应用GIS手段将中原经济区的发展水平差异可视化,并分析了产生差异的原因,对中原经济区的建设提出了建议。     

连分式拟合模型在沉降变形监测中的应用研究

介绍了连分式拟合模型的建模思路,提出了后验差法检验其模型精度,通过对某建筑物沉降数据的建模分析,验证了连分式拟合模型应用于沉降监测数据处理的可行性。     

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引进时间遗忘因子和观测冗余度因子,有效地平衡移动窗口内不同时刻的观测数据及其冗余情况对单位权方差估值的贡献,改进单位权方差的移动开窗实时估计算法.采用载噪比模型确定观测权阵,等价权抗差估计方法处理粗差.实测车载GPS/Doppler数据的处理结果表明:采用本文算法显著提高GPS/Doppler的导航精度与可靠性.     

改进的修正预测法预报电离层

利用自回归移动平均ARMA模型的线性最小方差预测法预报电离层存在的主要问题是极值点处预测误差较大。通过对模型阶数上限及定阶准则的选取进行实验分析,确定了合适的模型阶数并建立了相应模型。修正预测法可利用新信息对线性最小方差预测法的预测结果进行修正。这是一种短期预报方法,每次向前预测一步即2 h,但结果并不理想。考虑到电离层变化的周期性,为了进一步减小极值点处预测误差,提出了改进的修正预测法。实验表明,改进的修正预测法预测结果与原始值符合较好。     

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将抗差估计的思想融入到粗差探测的算法中,设计出对模型误差,特别是粗差具有抵抗能力的粗差探测算法.基于验后方差估计原理导出的选权迭代法即为抗差佑计的一种,首先是应用最小二乘法来计算观测值的参数x、残差V、协因数阵Qvv及单位权σ0 的初值,然后再根据残差和有关的参数,按所选择的权函数,计算每个观测值的权,经过迭代计算求得...     

浅谈三因素方差分析在评价化探数据质量中的应用——以盐源县马思罗金矿土壤化探分析数据为例

三因素方差分析是对分析数据质量合格与否的一种有效检验方法.它可以对元素的自然变化,采样、重复分析的系统误差,偶然误差等进行定量的评价.应用以三因素方差分析基本原理为基础,运用盐源县马思罗金矿土壤化探分析数据为实例,查证了分析数据的可靠性,为正确的指导化探找矿、加速矿权评价奠定了基础.     

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利用多年月平均格点数据分析了北欧海主要锋面的分布特征和季节变化规律,并讨论了月平均数据分析锋面适合使用的方法。月平均数据显示的锋面出现间断或多重的现象是锋面侧向摆动造成的,这是月平均数据的一大特点。北欧海各锋面主要水文和季节变化特征差异很大。东格陵兰极地锋在夏季锋面强度大,锋面较连续完整,而冬季强度小,锋面结构零散。9月由于东格陵兰寒流势力最强,可观察到温度梯度较大且连续的东格陵兰锋。北极锋的季节变化在水平方向呈＂哑铃型＂分布,中段摆动较南北两端小。由于挪威海流在冬季出现的最大流量引起挪威海流的流幅在该处加宽,莫恩海脊锋冬季向西北移动,对前人文章中基本上没有季节性移动的说法进行了修正和补充。冰岛—法罗群岛锋随深度增加向南移动,锋面强度增强,这是溢流造成的。     

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