中国西北地区东部沙尘暴区划研究

利用西北地区东部98个气象站的沙尘暴日数、持续时间资料,采用经验正交分解(EOF、REOF)、滑动t检验、Mann-Kendall突变检验及小波变换,对其时空分布规律进行了诊断分析。结果表明:整个区域沙尘暴日数大致呈北多南少型分布;受大尺度气候异常的影响,全区一致的沙尘暴异常是该区域最主要的表现形式。西北地区东部沙尘暴日数空间异常可分为4个沙尘暴气候区:河西走廊—阿拉善区、鄂尔多斯—乌兰布和区、柴达木—茶卡寒旱区、陕甘宁黄土高原区。河西走廊—阿拉善区沙尘暴频次最高,陕甘宁黄土高原区频次最少;近40多年来西北地区东部沙尘暴日数呈减少的态势;沙尘暴日数在20世纪80年代中期到末期发生气候突变,突变起源于沙漠及相邻地区,并由外围沙漠区向中间地带传播。沙尘暴持续时间最长的不是高频区河西走廊—阿拉善区,而是鄂尔多斯—乌兰布和区,最短的也不在频次最低的陕甘宁黄土高原区,而在柴达木—茶卡寒旱区;沙尘暴高发的河西走廊—阿拉善区、鄂尔多斯—乌兰布和区,持续时间没有发生明显的突变,这两个区域20世纪80年代中期以后沙尘暴持续时间变率增大;90年代以后沙尘暴持续时间有增长的趋势。     

西北地区沙尘暴引发的荒漠化问题

从西北地区继1993～1995年连续3a出现沙尘暴之后，1996年甘肃和内蒙古西部的阿盟等地再次遭到沙尘暴的袭击入手，分析了沙尘暴发生的原因，指出它归根结底是荒漠化的直接后果。概述了荒漠化的状况及危害，并分析了其成因。     

近14年西北地区甲醛柱浓度的时空变化研究

基于臭氧监测仪（OMI）卫星反演数据,对2005~2018年西北4省区域大气甲醛柱浓度数据进行提取及分析,探讨其时空变化特征及影响因素.结果表明：在时间变化上,14a甲醛柱浓度整体呈先上升后下降的波动变化趋势,夏秋季显著高于冬春季,且冬季均值略高于春季.在空间分布上,甲醛柱浓度自西向东、自北向南逐渐升高,高值区集中于陕西和甘肃东南部及青海西南部;低值区集中于宁夏、青海和甘肃的西北部;稳定性呈现出东部分散、西部集聚、差异显著的分布格局.影响甲醛柱浓度变化的因素包括自然和人为因素,自然因素中,甲醛柱浓度受地形影响显著,与风向、气温均呈现显著正相关;人为因素中,甲醛柱浓度与人口密度、地区生产总值、工业废气排放量及建筑房屋竣工面积均表现出正相关关系,与工业废气排放量的相关度最高.大气中甲醛分子与气溶胶粒子二者间呈显著正相关关系,这进一步说明甲醛浓度受到了诸多因素的综合影响,但气溶胶粒子、气温及工业废气的排放是主导因素.     

草地生态农业与产品革新是西北地区农业的出路

针对西北地区农业越来越依赖化肥、农药来提高产量以及污染问题日趋严重的现状 ,提出将发展草地生态农业与革新产品作为该地区农业可持续发展的出路。建议先主抓多途径开辟有机肥源 ,大田作物资源化增值 ,快速解决奶牛牛源与增加系统生物多样性 4项 ,可改善生态环境 ,使农民增收 ,将农业生态系统导入良性循环     

西北地区昼夜增温的不对称性对植被动态的影响

全球变暖有很大的差异性,表现在昼夜增温速度和各个季节的温度增加的速率具有不一致性,夜间增温的速度要快于白天增温的速度,夜间气温的增温对植被变化的影响更为明显。文章利用34年逐月最高温、最低温以及降水量数据,用一元线性回归分析法、反距离权重插值以及二阶偏相关分析法,分析了西北地区白天和夜间气温的非对称时空变化和非对称增温趋势对植被动态的影响,结果表明:西北地区昼夜温度都呈现出上升的趋势,且上升趋势比较明显,白天增温的速度和夜间增温的速度呈现出不对称性,夜间温度升高的速率是白天温度升高速率的1.2倍,昼夜的温差呈现出减小的趋势;西北地区昼夜增温趋势存在不一致性,白天气温的增温速率在-0.02-0.11℃?a-1,夜间气温的增温速率在-0.06-0.2℃?a-1。tmax和tmin在绝大部分地区都呈现上升趋势,但在空间变化的区域上具有不对称性;西北地区植被生长对昼夜增温的响应具有明显的空间差异性,大部分地区的植被对昼夜增温表现为积极的响应,夜间增温对植被的影响要比白天增温对植被的影响是比较明显的;白天和夜间增温的不对称性影响着不同类型的植被,白天气温的升高有利于针叶林植被、草地和阔叶林植被的生长,而夜间气温的升高对灌丛和荒漠植被的生长有积极的影响。     

西北地区水资源数量、质量及其分布规律

运用40年降雨、蒸发、径流的同步资料,计算了西北地区的降水总量为7981.9×10⁸m³,地表水资源总量为2164.8×10⁸m³;地下水资源量为1034.6×10⁸m³,扣除重复量后的水资源总量为2304.4×10⁸m³。分析了水资源的地区分布及年内、年际变化规律,评价了西北地区水资源质量,并总结了西北内陆河区径流主要形成于山区,散失于山前平原盆地等水资源六大特点。     

The Importance of Being Spatial (and Reserved): Assessing Northern Spotted Owl Habitat Relationships with Hierarchical Bayesian Models

Abstract:  Regional conservation planning increasingly draws on habitat suitability models to support decisions regarding land allocation and management. Nevertheless, statistical techniques commonly used for developing such models may give misleading results because they fail to account for 3 factors common in data sets of species distribution: spatial autocorrelation, the large number of sites where the species is absent (zero inflation), and uneven survey effort. We used spatial autoregressive models fit with Bayesian Markov Chain Monte Carlo techniques to assess the relationship between older coniferous forest and the abundance of Northern Spotted Owl nest and activity sites throughout the species' range. The spatial random-effect term incorporated in the autoregressive models successfully accounted for zero inflation and reduced the effect of survey bias on estimates of species–habitat associations. Our results support the hypothesis that the relationship between owl distribution and older forest varies with latitude. A quadratic relationship between owl abundance and older forest was evident in the southern portion of the range, and a pseudothreshold relationship was evident in the northern portion of the range. Our results suggest that proposed changes to the network of owl habitat reserves would reduce the proportion of the population protected by up to one-third, and that proposed guidelines for forest management within reserves underestimate the proportion of older forest associated with maximum owl abundance and inappropriately generalize threshold relationships among subregions. Bayesian spatial models can greatly enhance the utility of habitat analysis for conservation planning because they add the statistical flexibility necessary for analyzing regional survey data while retaining the interpretability of simpler models.     

Tectonic evolution of the Aravalli orogen (NW India): an inverted Proterozoic rift basin?

The Aravalli mountain range (AMR) in the northwestern part of the Indian Peninsula consists of two main Proterozoic metasedimentary and metaigneous sequences, the Aravalli and Delhi Supergroups, respectively, which rest over the Archaean gneissic basement. A synthesis and reinterpretation of the available geological, geochronological and geophysical data, including results of own field work and geophysical interpretations pertaining to the AMR, indicate its origin as an inverted basin: rifting into granitoid basement began ca. 2.5; Ga ago with Aravalli passive rifting (ca. 2.5–2.0 Ga) and Delhi active rifting (ca. 1.9–1.6 Ga). Associated mafic igneous rocks show both continental and oceanic tholeiitic geochemistry and are comparable with Phanerozoic, rift-related magmatic products. Available data showed no conclusive evidence for oceanic lithoshere and island-arc/active margin magmatic activity in the AMR. Subsequent inversion and orogeny (Delhi orogeny, ca. 1.5-1.4 Ga) lead to complex deformation and metamorphism. Only in the western and central zones has the basement been involved in this mid-Proterozoic (Delhi) deformation, whereas it is unaffected in the eastern part, except for local shear zones mainly along the basement/cover interface. The grade of metamorphism increases from the greenschist facies in the east to the amphibolite facies in the west with local HP assemblages. These latter are explained by rapid burial and exhumation of thin and cool continental lithosphere. Subsequently, during a final, mild phase of inversion, the Vindhyan basins consisting mainly of sandstones, limestones and shales, flanking the AMR formed which are comparable to foreland basins. The tectonic evolution of the AMR is therefore interpreted as an example of a major inverted continental rift and of a Proterozoic intra-continental orogen.     

西北地区可持续发展应注意的主要问题与对策

本文在分析西北地区社会、经济及自然状况的基础上 ,着重从资源与环境方面指出该地区可持续发展应注意的主要问题 ,并针对这些问题 ,提出以可持续发展为目标的资源、环境策略。     

基于LM-BP神经网络的Argo数据西北太平洋海水温度模型

以2007年西北太平洋海域Argo海表面温度、经纬度、深度为输入参数,利用LM-BP神经网络,构建了西北太平洋海水温度模型。将均方根差以及Pearson相关性系数作为检验指标,利用2008年和2009年的Argo数据对模型进行了检验。检验结果为:2008年均方根误差为0.714 0℃,Pearson相关性系数为0.996 8;2009年均方根误差为0.761 5℃,Pearson相关性系数为0.9965。表明所建立的基于LM-BP神经网络的Argo数据西北太平洋海水温度模型是可行的。