Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

盐碱荒地开垦年限对表层土壤盐分的影响

 以典型荒漠区原生盐碱荒地及开垦农田为研究对象,通过对比研究,以阐明原生盐碱荒地开垦后不同管理措施下土壤可溶盐总量的变化规律,开垦后土壤剖面中可溶盐总量的垂直分布和季节变化规律。结果表明：开垦显著降低了0～50 cm土壤的可溶性盐含量,开垦达100 a以上老耕地的可溶性盐含量（2.31 g/kg）仅为原生盐碱荒地（26.62 g/kg）的11%;长期的耕作改变了土壤可溶性盐分在土层中的分布,原生荒漠土壤盐分具有鲜明的表聚特征,0～30 cm土层的盐分含量占整个土壤剖面的79%,开垦50 a后,土壤中的盐分分布趋于均匀,各离子浓度的垂直分布不再明显;开垦使土壤中的盐分离子组成也发生了变化,Cl^-、K⁺、Na⁺离子浓度降低,而SO₄^2-、Mg²⁺、Ca²⁺离子浓度增加,耕作层土壤由SO₄^-2-Cl^-1盐土转变为Cl^-1-SO₄^-2盐土;盐碱荒地土壤可溶盐浓度伴随着降水变化具有显著的季节变化特征,而季节对开垦耕地没有显著影响。总体而言,盐碱荒地经过多年开垦利用后,土壤可溶性盐分和pH值均显著降低。     

Spatial Distribution of Halite in Kuqa Basin from Paleogene to Neogene and Signification of Potash Survey

正Evaporites with gigantic thickness had been developed in Kuqa Basin from Paleocene to early Miocene,and the sediment thickness changed from tens to thousands of meters.By 3D mine software,spatial distribution model of     

Plant distribution at the mobile dune scale and its relevance to soil properties and topographic features

A survey was conducted in an 11-year recovery mobile dune (RMD11) and a 20-year recovery mobile dune (RMD20), in Horqin Sandy Land, Northern China, to determine plant distribution at the mobile dune scale and its relevance to soil properties and topographic features. The results showed that (1) vegetation cover and species number increased from dune top to bottom in the restoration process of mobile dune; (2) the average value of soil organic C, total N, pH, relative height of sampling site, very fine sand content and soil water contents (40−60 and 60−80 cm) of RMD11 were less than that of RMD20, respectively, and there were significant differences (P < 0.05) between the two dunes; (3) soil resources were redistributed by shrub restoration and relative height of sampling site on dune. The distribution of sand pioneer plant, Agriophyllum squarrosum, was positively related to the relative height of sampling site and soil water content, while that of other herbaceous plants was positively related to soil nutrients in the restoration process of mobile dune. These results suggest that at mobile dune scale, plant distributions are determined by a combination of soil properties and topographic feature. Much effort should be made to preserve the interdune lowland and to improve the level of soil nutrients on mobile dune.     

膨胀地球基本参数的初值及其平均变化率

本文讨论了地球膨胀过程的初始半径R0、膨胀起始时间t0以及半径R、转动惯量I、地表重力加速度g和反映地球内部物质分布状态参数y等膨胀基本参数的平均变化率。研究结果表明,在地球的地质年龄之内,地球膨胀的初始半径R0>4619km,膨胀的起始时间t0>43.8×102Ma,膨胀过程中的地球半径R、物质分布状态参数y、转动惯量I、地表重力加速度g和表面积S随时间的平均变化率分别为:dR/dt=4×10-4m/a,dy/dt=-6.7×1012/a,dI/dt=8.49×1027kg·m~2/a,dg/dt=-1.23×10-10kg·s-2/a和ds/dt=6.41×104m2/a。地球膨胀过程中的半径平均增长率,是首次用天文学方法研究得出,且该值介于其他作者用古地磁、古地理等方法所求得的地球半径增长率若干数值之间。     

内蒙古半干旱草原下垫面地表辐射特征

中纬度半干旱草原是全球陆地生态系统的重要类型之一,也是我国北方主要的地表类型,为深入了解我国半干旱草原区陆面对大气的辐射强迫,选取锡林浩特国家气候观象台2010年的辐射观测资料,分析了内蒙古半干旱草原下垫面地表辐射平衡各分量、地表吸收辐射和有效辐射以及地表反照率的日变化和季节变化规律,并与西北典型干旱区作了对比分析。结果表明：半干旱草原区太阳总辐射季节变化特征非常明显,季节平均日积分值由大到小依次为夏季、春季、秋季、冬季,年总量为6 036.64 MJ/m²,与黑河地区绿洲接近,明显小于戈壁和沙漠下垫面的值;半干旱草原地表反射辐射年变化趋势较为复杂,在4～10月由于下垫面植被和土壤类型、湿度等的差异,半干旱草原地表反射辐射月平均值低于黑河地区草地和荒漠的值,但高于农田的值;地表吸收辐射季节变化规律与总辐射保持一致;大气长波辐射年总量为8 155.23 MJ/m²,低于黑河地区草地、荒漠和农田下垫面的值,主要是由于半干旱草原区地理纬度较高,全年平均气温较低造成的;在植被覆盖较好的6～9月,半干旱草原区地表长波辐射月总量为4 532.14 MJ/m²,低于黑河流域的荒漠区,而高于草地和农田的值;半干旱草原区地表有效辐射年总量为2 398.75 MJ/m²,小于黑河地区沙漠和戈壁下垫面而大于绿洲;半干旱草原区地表反照率年均值为0.35,均大于西北干旱区不同下垫面,主要由于半干旱草原区冬春季地表积雪覆盖时间明显长于干旱区,可见,地表积雪对反照率的影响非常大。     

某城地下水地表水联合优化调度管理模型实例

本文阐述了利用响应矩阵法建立地下水、地表水优化调度管理模型的约束条件、并用目标规划法进行管理模型计算的一个实例。这是一个位于黄河之滨的城市,市内有许多名泉。近年来大量开采地下水已使泉水干涸,而黄河水在某些月份含沙量很大,不宜全年开采。本计算求得了这个城市几个水源地与黄河水在一个枯水年(或丰水年)内各月份的优化开采量分配方案,使之既可保证泉水全年喷涌;叉可提供所需水量。     

茂名地区海雾含水量的演变特征及其与大气水平能见度的关系

2008年3月16—19日,在广东茂名博贺海洋气象科学试验基地,利用美国DMT公司生产的FM-100型雾滴谱仪,连续观测了海雾的雾滴谱、数浓度等微物理量。在此基础上分析了海雾液态含水量的演变特征,并结合同期的气象观测与分析数据,进行了海雾中低能见度成因的天气学分析。研究表明,海雾发展过程中含水量有着上下起伏变化的特点;直径10μm以上雾滴的增多是含水量增大的主要原因;随着含水量的增大,雾滴谱分布有向大雾滴方向增宽的趋势,其峰值高度也不断增大。在相同的含水量区间,不同的大气能见度样本表现出明显不同的谱分布特征;在相同高数浓度区间的情况下,导致大气能见度降低的主要原因是含水量的增大。而海雾过程中低能见度的出现,是众多天气因素共同影响的结果:地面天气图上的均压场范围更大,大气低空及地面风速小,大气低层处于弱不稳定状态,以及高湿度层主要在近地层等等。     

东海陆架北部表层细粒级沉积物的级配及意义

本文依据东海陆架北部 2 3个表层沉积物样品的粒度分析资料 ,就东海陆架北部表层细粒级沉积物的级配及意义进行了研究。结果表明 ,研究区表层沉积物中细粒级部分以 <0 .0 16 mm为优势粒级 ,可占到总细粒级沉积物的 75 %以上。济州岛西南泥质区以细于 0 .0 0 8mm粒级的沉积物占据优势 ,长江口泥质区则以 <0 .0 16 mm粒级的沉积物为主 ,两泥质区细粒级沉积物的级配很不相同     

山西地区一次层状云降水过程的微观特征观测分析

针对山西省2010年5月27日一次层状云降水过程,利用机载DMT探头和Parsivel激光降水粒子谱仪进行探测试验,分析了云微物理特征,并对空中和地面雨滴谱进行比较。结果显示：空中云垂直和水平结构分布不均匀,CDP、CIP探测最大粒子浓度分别为165．20、1．08cm^（-3）。地面雨滴微物理量的平均值说明本次降水是典型的层状云降水,雨强主要由雨滴数密度决定,雨滴微物理参量随时间分布不均匀。建立地面雨强,与雷达反射率因子Z、雨水含量W、雨滴数浓度N、Gamma分布的谱参数眠、A的相关关系,Z-I、W-I,相关性很好,N-I、N_0-I、λ-I,相关性较差。地面平均雨滴谱较空中平均雨滴谱窄、谱型陡。结合粒子图像和雨滴特征量分析空中雨滴谱随高度的分布发现,本次降水是冷云和暖云降水共存。