Piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events on the Loess Plateau,China

Establishing a universal watershed‐scale erosion and sediment yield prediction model represents a frontier field in erosion and soil/water conservation. The research presented here was conducted on the Chabagou watershed, which is located in the first sub‐region of the hill‐gully area of the Loess Plateau, China. A back‐propagation artificial neural model for watershed‐scale erosion and sediment yield was established, with the accuracy of the model, then compared with that of multiple linear regression. The sensitivity degree of various factors to erosion and sediment yield was quantitatively analysed using the default factor test. On the basis of the sensitive factors and the fractal information dimension, the piecewise prediction model for erosion and sediment yield of individual rainfall events was established and further verified. The results revealed the back‐propagation artificial neural network model to perform better than the multiple linear regression model in terms of predicting the erosion modulus, with the former able to effectively characterize dynamic changes in sediment yield under comprehensive factor conditions. The sensitivity of runoff erosion power and runoff depth to the erosion and sediment yield associated with individual rainfall events was found to be related to the complexity of surface topography. The characteristics of such a hydrological response are thus closely related to topography. When the fractal information dimension is greater than the topographic threshold, the accuracy of prediction using runoff erosion power is higher than that of using runoff depth. In contrast, when the fractal information dimension is smaller than the topographic threshold, the accuracy of prediction using runoff depth is higher than that of using runoff erosion power. The developed piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events, which introduces runoff erosion power and runoff depth using the fractal information dimension as a boundary, can be considered feasible and reliable and has a high prediction accuracy. Copyright © 2013 John Wiley & Sons, Ltd.     

基于最大发展潜力值的元胞自动机城市扩张模型

在城镇化发展水平及土地开发评价的基础上,评价待扩张区域土地转变潜力分值,基于最大转变潜力分值,提出一种有别于传统元胞自动机模拟城市扩张的新方法。该方法与传统元胞自动机模拟城市扩张相比在迭代方式上存在不同,它克服了传统元胞自动机因阈值设置不同而导致结果不确定的难题。分别将该方法与传统元胞自动机模型应用于武汉主城区,模拟了其2003年到2013年的城市扩张情况,最后,将模拟结果与实际土地利用现状图进行对比发现改进后的新方法在模拟精度上大大提高。     

基于地性线分级的DEM信息量计算方法研究

主要对基于DEM提取的地性线进行信息量的定量分析,探讨地性线的信息量与分辨率、地形单元的关系。这个过程中首先利用河网的自相似理论研究与当前DEM所代表的地貌详细程度相适宜的地性线提取的阈值区间,并讨论此阈值下地性线的分级,以此为基础通过以该分级为权重进行DEM地形信息量的计算。实践证明,这种顾及地性线等级的DEM信息量计算方法能较准确地反映地性线的分布规律和不同等级的要素造成的信息量度量差异。     

小波分析去噪在腰沙围垦变形监测中的应用

由于受到许多复杂因素影响,沉降监测过程中所得到的信号数据可能会含有噪声。Matlab中小波分析功能能够对信号数据中的噪声信号进行有效的分解,最后重构处理后的信号。通过实例对比,选用合适的去噪方法和去噪函数,对信号数据进行处理,最后能够得到更适合的最优估计。     

A timescale decomposed threshold regression downscaling approach to forecasting South China early summer rainfall

A timescale decomposed threshold regression(TSDTR) downscaling approach to forecasting South China early summer rainfall(SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR.The two models are developed based on the partial least squares(PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915–84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation(PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985–2006, compared to other simpler approaches. This study suggests that the TSDTR approach,considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.     

基于阈值可调切换系统的多翅膀混沌吸引子设计及电路仿真

本文提出了一种通过调节一类状态依赖切换系统的阈值来生成多翅膀混沌吸引子的设计方案.首先,设计了两个类Lorenz子系统;然后,基于这两个子系统构造了一个状态依赖于阈值的切换策略,从而构造了一类状态依赖于阈值的切换系统;最后,通过调整切换系统的阈值来改变鞍焦平衡点的个数与分布,使得该切换系统能够产生4-6-8翅膀的混沌吸引子.此外,根据切换系统的状态方程设计了该系统的模拟电路,电路仿真结果与数值仿真结果一致.实验结果证实：该设计方案可以在不增加系统维数或无需设计复杂非线性函数的情况下生成多翅膀混沌吸引子.     

基于模糊熵的GLLE熵阈值分割方法

图像分割是计算机视觉中基础且重要的一个问题.熵阈值图像分割作为一种有效的分割方法,被广泛应用于模式识别和图像处理中.传统的图像分割方法并不能获得足够有效的图像特征.为解决这个问题且进一步探究熵阈值在图像分割中的应用,引入一种GLLE（Gray Level and Local Entropy）二维直方图改进熵阈值图像分割模型,并提出了基于模糊熵的方法计算所建立的二维直方图模型.通过标准实验数据集上的对比实验表明,基于模糊熵的GLLE熵阈值分割方法可以得到更加准确的阈值,提高了分割精度.同时在处理不同类型图像的表现上优于往常的算法,具有更强的鲁棒性.     

基于WRF模式的云南短时强降水物理量特征

为了解云南短时强降水发生前本地化中尺度WRF(Weather Research Forecast)模式输出结果的物理量特征及其对短时强降水预报的作用,使用WRF模式对2016年云南主汛期(6—8月)5次短时强降水过程进行模拟,利用模式输出的高时空分辨率资料计算5次过程中85个样本在短时强降水发生前6 h水汽类、动力类及不稳定条件类的部分物理量值,使用箱线图分析各物理量的分布特征及其与短时强降水的关系,应用经验累积分布函数图确定各物理量的阈值。研究表明,水汽类物理量样本数据值分布较为集中,随着短时强降水的临近数值逐渐增大;动力类的6 km垂直风切变中位数值及平均值随时间变化很小,所有时次的6 km垂直风切变阈值均低于12 m/s,表明短时强降水发生前有弱垂直风切变;不稳定条件类中对流有效位能样本数据的离散程度较大,对短时强降水无指示意义;LI指数、K指数和700 hPa假相当位温样本数据离散度较小,其中K指数中位数值、平均值及阈值的上下限在短时强降水发生前1 h有显著增大的特征,且数据集中度达到最高,大的K指数值与短时强降水有较好的对应关系。使用物理量阈值推算短时强降水落点的方法对云南本地化WRF模式短时强降水的预报性能有改进作用。     

基于富营养化阈值的松花湖水环境容量分析

通过对松花湖水体中浮游植物的绝对优势种——铜绿微囊藻(Microcystis aeruginosa)单藻株生长与磷、氮定量关系的室内模拟研究,得出松花湖富营养化发生的阈值为：总磷浓度0．065 mg/L,总氮浓度0．843 mg/L．利用风险分析理论和蒙特卡罗随机模拟方法,在分析2002-2004年松花湖水体中总磷和总氮浓度实际分布规律的基础上,提出了计算湖、库总磷和总氮水环境容量的不确定性方法．通过对松花湖磷、氮水环境容量的计算,得出松花湖流域总磷和总氮的最大允许排放量为2123．78 t/a和7018．82 t/a,为了使松花湖富营养化发生的概率在0．001以下,总磷和总氮需要分别削减3208．34 t/a和18648．91 t/a．     

农业集聚对农业面源污染的影响——基于中国地级市面板数据门槛效应分析

农业集聚的环境效应相比其他产业更为复杂,然而学术界对此探讨较少。在理论分析的基础上,利用2007—2015年中国337个地级市面板数据,核算农业集聚水平与农业面源污染排放量,并采用空间分析技术、计量模型,实证探究农业集聚与农业面源污染的时空特征及农业集聚对农业面源污染的门槛效应。结果显示：① 农业集聚与COD、TN、TP三种农业面源污染在时序变化、空间布局、空间关联以及空间趋势变化上特征相异。② 农业集聚对COD、TN、TP农业面源污染皆呈现正相关的单门槛效应。低于门槛值阶段,农业集聚水平的提高显著加剧了农业污染,集聚水平跨过门槛值后,污染加剧形势有所缓和。③ 农业集聚与农业面源污染之间经历的两个阶段,取决于规模效应、结构效应、技术效应、社会效应、累积效应分别对农业面源污染排放的加剧与制约作用。随着农业集聚水平提高,各效应增污作用明显,导致污染水平显著提高,进入高于门槛值的阶段,各效应对污染排放的抑制能力提升,削弱了部分负环境外部效应。④ 农业经济水平、农业生产条件在一定程度上加剧了COD、TN、TP污染;畜牧业结构、环境规制对COD、TP污染存在显著的正相关关系;生产者素质的提高可以影响农户生产行为并改善农业环境。⑤ 农业集聚对COD污染的降污效应较弱,农业COD污染较为严重且主要来源于畜牧业养殖,畜牧业污染防治亟待加强。