Windows下二维气象绘图软件——客观分析诊断图形系统

客观分析诊断图形系统是一款操作界面友好的二维气象绘图软件，它运行于微软Windows操作系统下，具有多种数据接口，能够绘制等值线、流线、矢量、多种投影地图及不规则数据分布图等多种图形。客观分析诊断图形系统还能够对气象地面、探空数据进行客观分析诊断，并能根据用户所选择气压层、变量由客观分析诊断的结果绘制相应图形。     

贺兰山以北乌兰布和沙漠地下水水化学特征演化规律研究

乌兰布和沙漠对中国西北部干旱地区气候变化和水文循环过程研究具有指示意义, 以水文地球化学运移为导向, 通过分析33个地下水样, 探讨了该区地下水的补给与演化过程. 结果表明: 巴彦乌拉山浅层地下水水化学类型为Cl^--HCO₃^--SO₄^2--Na⁺; 从吉兰泰盐湖至乌兰布和沙漠, 主要水化学类型变化浅层地下水为Cl^--HCO₃^--Na⁺到Cl^--HCO₃^--SO₄^2--Na⁺, 深层地下水为HCO₃^--Cl^--Na⁺再到HCO₃^--Cl^--Na⁺或HCO₃^--Cl^--Ca²⁺. Gibbs图表明, 研究区地下水的水化学特征主要受蒸发浓缩作用影响和岩石风化作用控制, 大气降雨机制影响甚微. 主要离子相关关系图及饱和指数表明, 岩盐、芒硝、钠长石、方解石、白云石、石膏等矿物的风化溶解是该区地下水主要离子来源. 此外, 氯碱指数和Ca/Na的指示, 说明阳离子交换作用也是形成该地区地下水水化学组分的重要机制.     

Supporting USLE‐MM reliability by analyzing soil loss measurement errors

Sampling the collected suspension in a storage tank is a common procedure to obtain soil loss data. A calibration curve of the tank has to be used to obtain actual concentration values from those measured by sampling. However, literature suggests that using a tank calibration curve was not a common procedure in the past. For the clay soil of the Sparacia (Italy) experimental station, this investigation aimed to establish a link between the relative performances of the USLE‐M and USLE‐MM models, usable to predict plot soil loss at the event temporal scale, and soil loss measurement errors. Using all available soil loss data, lower soil loss prediction errors were obtained with the USLE‐MM (exponent of the erosivity term, b₁ > 1) than the USLE‐M (b₁ = 1). A systematic error of the soil loss data is unexpected for the Sparacia soil because the calibration curve does not depend on the water level in the tank. In any case, this type of error does not have any effect on the b₁ exponent. Instead, this exponent decreases as the level of underestimation increases for increasing soil loss values. This type of error can occur at Sparacia if it is assumed that a soil loss measurement can be obtained by a bottle sampler dipped close to the bottom of the tank after mixing the suspension and assuming that the measured concentration coincides with the actual one. In this case, the risk is to obtain a lower b₁ value than the actual one. In conclusion, additional investigations on the factors determining errors in soil loss data collected by a sampling procedure are advisable because these errors can have a noticeable effect on the calibrated empirical models for soil loss prediction.     

Are spatial patterns of soil moisture at plot scales generalisable across catchments,climates, and other characteristics? A synthesis of synoptic soil moisture across the Mid-Atlantic

The spatial variation of soil moisture over very small areas (<100 m²) can have nonlinear impacts on cycling and flux rates resulting in bias if it is not considered, but measuring this variation is difficult over extensive temporal and spatial scales. Most studies examining spatial variation of soil moisture were conducted at hillslope (0.01 km²) to multi-catchment spatial scales (1000 km²). They found the greatest variation at mid wetness levels and the smallest variation at wet and dry wetness levels forming a concave down relationship. There is growing evidence that concave down relationships formed between spatial variation of soil moisture and average soil moisture are consistent across spatial scales spanning several orders of magnitude, but more research is needed at very small, plot scales (<100 m²). The goal of this study was to characterise spatial variation in shallow soil moisture at the plot scale by relating the mean of measurements collected in a plot to the standard deviation (SD). We combined data from a previous study with thousands of new soil moisture measurements from 212 plots in eight catchments distributed across the US Mid-Atlantic Region to (1) test for a generalisable mean–SD relationship at plot scales, (2) characterise how landcover, land use, season, and hillslope position contribute to differences in mean–SD relationships, and (3) use these generalised mean–SD relationships to quantify their impacts on catchment scale nitrification and denitrification potential. Our study found that 98% of all measurements formed a generalised mean–SD relationship like those observed at hillslope and catchment spatial scales. The remaining 2% of data comprised a mean–SD relationship with greater spatial variation that originated from two riparian plots reported in a previous study. Incorporating the generalised mean–SD relationship into estimates of nitrification and denitrification potential revealed strong bias that was even greater when incorporating mean–SD observations from the two riparian plots with significantly greater spatial variation.     

Experimental monitoring and numerical study of pesticide (carbofuran) transfer in an agricultural soil at a field site

We studied the transport of a pesticide at field scale, namely carbofuran molecule, which is known for its high mobility, especially in sandy soils with high hydraulic conductivity and low organic matter. To add to our knowledge of the future of this high-mobility molecule in this type of soils, we developed a mechanistic numerical model allowing the simulation of hydric and solute transfers (bromide and carbofuran) in the soil. We carried out this study in an agricultural plot in the region of Mnasra in Morocco. Confrontation of the measured and simulated values allowed the calibration of the parameters of hydric transfer and carbofuran. The developed model accurately reproduces the measured values. Despite a weak irrigation and precipitation regime, carbofuran was practically leached beyond the root zone. Prospective simulations show that under a more important irrigation regime, carbofuran reaches a 100-cm depth, whereas it does not exceed 60 cm under a deficit regime.     

气象观测数据等值线自动绘制系统

基于气象观测数据的自动等值线系统采用IDL作为开发工具,通过对自动气象站数据的处理和分析,自动生成等值线图(包括温度,降水等值线图,风矢量图等)并发布到WEB页面,提供动画播放功能和图像查询功能.该系统不仅为天气预报提供必要的决策支持,也是监视天气变化的有效工具.系统在北京第29届奥运会气象服务中得到了广泛使用,是气象预报人员进行个例分析和预报服务的重要支撑平台.     

构建地块二维表征及CNN模型的作物遥感分类

本文旨在研究基于地块数据约束的深度学习模型的分类特征表示方法,以识别不同作物在不同时相上光谱差异从而对作物类型进行分类。通过Google Earth Engine平台获取作物生育期内全部Landsat 8影像,利用其质量评定波段完成研究区无云时相及区域上的地块统计,提取地块级别的各波段反射率均值按照时相顺序及波长进行排列,构建波谱、时相二维特征图作为该地块的抽象表示。通过构建相对最优的卷积神经网络CNN(Convolutional Neural Network)结构完成对特征图的分类,从而完成对地块的分类。构建CNN模型并不需要手工特征和预定义功能的需求,可完成提取特征并遵循端到端原则进行分类。将该模型的分类结果与其他最为常用机器学习分类器进行了比较,获得了优于常用遥感分类算法的分类精度。结果表明地块数据的加入可以有效的缩减计算规模并提供了准确的分类边界。所提出得方法在地块特征表示及作物分类中具有突出的应用潜力,应视为基于地块的多时相影像分类任务的优选方法。     

模糊直方图及其在测量数据处理中的应用

从分析绘制异常型直方图的观测误差出发,讨论了直方图区间的模糊化问题,给出了计算模糊频数的公式和绘制模糊直方图的方法,并结合实例,说明了模糊直方图的优越性。     

普陀山植被对旅游干扰的响应研究

应用有序样方聚类法,把普陀山旅游区32个样方分为5个群落类型,分别是马尾松-青冈+檵木群丛、马尾松-青冈+檵木+红楠+窄基红褐柃群丛、马尾松+枫香+檵木-青冈+檵木群丛、马尾松+青冈-青冈+檵木群丛、马尾松+青冈-青冈+檵木+石栎群丛,并运用TW INSPAN对上述样方进行聚类分析。两种分类方法的结果具有极显著的关联,说明有序样方聚类法很好地揭示了旅游干扰和群落类型之间的关系,它更适合于旅游干扰分析中的聚类研究,在旅游干扰的研究中是一个有益且有效的尝试。     

机载LiDAR数据估算样地和单木尺度森林地上生物量

利用机载激光雷达点云数据,结合大量实测单木结构信息,分别从样地和单木尺度估算了森林地上生物量AGB。首先,利用局部最大值单木提取算法提取了每个样地内的单木结构参数,并针对样地和单木尺度分别计算了一组激光雷达变量。然后,利用激光雷达变量和地上生物量及其两者的对数形式,从样地和单木尺度分别构建了估算模型。最后,针对两种尺度估算过程中存在的不确定性进行了详细讨论。结果表明:(1)样地和单木尺度模型估算的森林地上生物量与地面实测值都具有明显的相关性,且对数模型估算效果要优于非对数模型;(2)样地尺度模型估算效果(R2=0.84,rRMSE=0.23)明显优于单木尺度模型(R2=0.61,rRMSE=0.46);(3)按树木类型分别进行估算可以提高单木地上生物量的估算精度;(4)不论是样地还是单木尺度地上生物量估算都存在一定的不确定性,与样地尺度相比,单木尺度估算过程的不确定性更大,这种不确定性主要来自单木识别过程。