A new standardized Palmer drought index for hydro‐meteorological use          下载免费PDF全文

Mingwei Ma  Liliang Ren  Fei Yuan  Shanhu Jiang  Yi Liu  Hao Kong  Luyan Gong 《水文研究》2014,28(23):5645-5661

Accepting the concept of standardization introduced by the standardized precipitation index, similar methodologies have been developed to construct some other standardized drought indices such as the standardized precipitation evapotranspiration index (SPEI). In this study, the authors provided deep insight into the SPEI and recognized potential deficiencies/limitations in relating to the climatic water balance it used. By coupling another well‐known Palmer drought severity index (PDSI), we proposed a new standardized Palmer drought index (SPDI) through a moisture departure probabilistic approach, which allows multi‐scalar calculation for accurate temporal and spatial comparison of the hydro‐meteorological conditions of different locations. Using datasets of monthly precipitation, temperature and soil available water capacity, the moisture deficit/surplus was calculated at multiple temporal scales, and a couple of techniques were adopted to adjust corresponding time series to a generalized extreme value distribution out of several candidates. Results of the historical records (1900–2012) for diverse climates by multiple indices showed that the SPDI was highly consistent and correlated with the SPEI and self‐calibrated PDSI at most analysed time scales. Furthermore, a simple experiment of hypothetical temperature and/or precipitation change scenarios also verified the effectiveness of this newly derived SPDI in response to climate change impacts. Being more robust and preferable in spatial consistency and comparability as well as combining the simplicity of calculation with sufficient accounting of the physical nature of water supply and demand relating to droughts, the SPDI is promising to serve as a competent reference and an alternative for drought assessment and monitoring. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

Postglacial variability in near‐bottom current speed on the continental shelf off south‐west Spitsbergen          下载免费PDF全文

BEATA STERNAL  WITOLD SZCZUCISKI  MATTHIAS FORWICK  MAREK ZAJĄCZKOWSKI  STANISŁAW LORENC  JOANNA PRZYTARSKA 《第四纪科学杂志》2014,29(8):767-777

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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          下载免费PDF全文

J. L. McCreight  A. G. Slater  H. P. Marshall  B. Rajagopalan 《水文研究》2014,28(3):933-957

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.  相似文献   

日照海岸带沙滩侵蚀现状及沉积物粒度特征分析     

种衍飞  郝义 《海洋地质前沿》2020,36(1):19-29

通过对日照市海岸带2个重点沙滩的现场调查和沉积物粒度计算,综合分析了沙滩的侵蚀现状和粒度参数特征。海滨国家森林公园沙滩北部和南部处于侵蚀状态,中部为缓慢淤积状态;万平口海水浴场沙滩北部、中部总体呈侵蚀趋势,南部为淤积状态。海滨国家森林公园沙滩的平均粒径总体要小于万平口海水浴场沙滩,这与两沙滩的坡度不同有关,平均粒径从滩肩到低潮线逐渐变小。海滨国家森林公园沙滩的分选性要好于万平口海水浴场沙滩,分选系数具有由陆向海、由北向南逐渐变小的趋势。沙滩的滩肩和滩面处频率曲线主要为双峰,峰态平坦,物质来源复杂,低潮线处频率曲线主要为单峰,峰态尖锐,物质来源单一。  相似文献   

岩体结构面各向异性变异系数的尺寸效应研究     

洪陈杰  黄曼  夏才初  罗战友  杜时贵 《岩土力学》2020,41(6):2098-2109

岩体结构面的各向异性与尺寸效应特征对其力学性质影响较大，综合考虑两者之间的相互关系对工程岩体稳定性评价具有重要意义。全面分析不同尺度结构面的各向异性分布，提出考虑正交方向三维形貌参数的各向异性变异系数AVC3D。通过渐进覆盖法统计4组天然岩体结构面10种不同采样尺寸的各向异性变异系数及其尺寸效应规律。结果表明：各向异性变异系数均随结构面尺寸增大而减小至定值，其与结构面尺寸具有较好的负对数函数关系；通过归一化处理，各向异性变异系数与结构面尺寸呈现较好的线性函数关系，表明各向异性变异系数具有分形结构，分维数D可以实现其尺寸效应的规律统计。该方法揭示了影响结构面形貌特征方向性变化的最大有效倾角 和粗糙系数C的增长幅度会随采样尺寸增大趋于稳定的变化机制，体现了当结构面达到各向异性尺寸效应的阈值后，会呈现出稳定的各向异性规律。  相似文献   

中国稀土元素地球化学背景与远景区优选     

王学求  周建  迟清华  王玮  张必敏  聂兰仕  刘东盛  徐善法  吴慧  高燕芳 《地球学报》2020,41(6):747-758

稀土元素是现代科技、新能源、特种制造的关键性材料, 以其不可替代性和稀缺性, 而备受关注, 很多国家将其列为关键资源或战略资源。地球化学是研究稀土分布和发现稀土矿床的有效方法。本文利用“化学地球”大科学计划获得的全国15个稀土元素地球化学基准数据以及“一带一路”的中蒙边境、中缅老越边境地区地球化学填图数据为基础, 阐述中国稀土地球化学背景和圈定远景区。获得全国岩石和汇水域沉积物15个稀土元素、轻稀土(ΣLREE)、重稀土(ΣHREE)和总稀土(ΣREE)背景值。全国岩石背景值分别为: ΣLREE 121 μg/g, ΣHREE 35.0 μg/g, ΣREE 157 μg/g, 轻重稀土比值为3.5。全国汇水域沉积物背景值分别为: ΣLREE 134.0 μg/g, ΣHREE 38.5 μg/g, 和ΣREE 173 μg/g, 轻重稀土比值为3.5。总体上汇水域沉积物与岩石一致, 但含量略高于岩石。全国共圈定稀土地球化学异常区35处, 其中有26处异常与已知稀土矿或稀土成矿带相吻合, 新发现稀土超富集中心的稀土异常9处, 分别位于内蒙白云鄂博以西的乌拉特中旗—乌拉特后旗、华南异常富集中心、松潘—甘孜—攀西地区、云南红河州—中越边境、三江南段—中缅边境、雅鲁藏布江东段、西藏札达地区、中塔边境、黔东正安—荔波地区。这些异常显示, 是具有寻找白云鄂博型、碱性岩型、离子吸附型、花岗伟晶岩型、磷块岩型和泥岩型稀土矿的有利地区。  相似文献   

中国避暑型与避寒型宜人气候的分布特征及差异     

邓粒子  保继刚 《地理研究》2020,39(1):41-52

宜人气候可以分为避暑型和避寒型两种类型,但现有研究鲜有关注国内两类气候的分布特征及差异。本文采用1981—2010年2132个国家气象观测站数据,基于温湿指数、风寒指数和着衣指数计算各个气象站点的气候综合舒适指数,结合协同克里金空间插值方法对全国避暑型和避寒型宜人气候的分布特征进行了研究,并对两类气候的地域差异进行了分析。结果表明：① 中国避暑型气候区包括40°N以北的西北边疆和东北地区、西北中部地区及西南地区三大集中分布区。中国避寒型气候区集中分布在北回归线以南的低纬地区。② 国内夏冬两季的气候不舒适地域广阔,包括环渤海、长三角等经济发达、人口稠密地区,避暑型与避寒型气候资源的开发潜力显著。③ 两类宜人气候呈现明显的地域分离特征,拥有避暑和避寒双重属性的地方极少。④ 国内避寒型气候是稀缺资源,具有垄断性特征;而避暑型气候相对分布广泛,是一种相对遍在性资源。本文不仅丰富了宜人气候分布特征研究的理论成果,而且可为地方气候资源的旅游开发提供科学依据。  相似文献   

PDR算法对地磁室内定位精度的提升研究     

黄鹤  张新宇  仇凯悦 《测绘通报》2019,(12):18-21

利用建筑物中金属结构引起的地磁场扰动可以对室内的行人目标进行定位，而且基于地磁场的定位无需布设任何额外设施，因此可以以低成本实现定位。但仅靠单一的地磁技术无法满足室内定位的精度要求。为了解决磁场数据中单点定位的模糊性问题，本文提出了一种利用粒子滤波算法将PDR与地磁相融合的室内定位方法，并开发了地磁室内导航系统，以智能手机为硬件平台构建磁力计传感器模型，建立匹配轨迹的均方误差准则并实现PDR累积误差实时校正的迭代计算。在68 m×1.8 m的试验区域内，产生的平均定位误差为1.13 m，最大定位误差为2.17 m。本文算法的定位精度比单独PDR算法提升了42%；与单一地磁指纹匹配算法相比，定位精度提高了57%。试验证明，本文提出的融合算法对提高室内定位精度具有显著的作用。  相似文献   

改进分数阶达尔文粒子群的多Renyi熵图像分割算法     

袁玉珠 《测绘通报》2019,(6):34-40

针对智能优化图像分割算法易陷入局部最优、分割精度不高等问题，本文融合改进的分数阶达尔文粒子群算法和二维Renyi熵多阈值，提出了一种新的多阈值遥感图像分割算法。算法利用粒子自身进化信息来定义进化因子，结合进化因子并利用高斯图函数调整分数阶次α系数以实现精确计算和快速收敛；根据局部最优概率因子对局部最优位置进行Levy飞行随机扰动以提高算法跳出局部最优的能力；同时将二维Renyi熵单阈值扩展到多阈值分割上，并结合改进的分数阶达尔文粒子群算法，将二维Renyi熵多阈值应用于遥感图像分割中。仿真结果表明，与其他2种智能优化分割算法相比，本文分割算法在细节处理和分割精度上均有明显优势，在PRI上至少提升7.27%、VOI至少降低6.5%、GCE至少降低10.4%。  相似文献   

顾及有色噪声的自适应粒子滤波UWB定位算法     

张园  谭兴龙  赵长胜  李晓明 《测绘通报》2019,(8):30-33

传统卡尔曼滤波算法要求噪声模型符合高斯分布，在UWB室内定位中，由于载体本身的机制等干扰，观测噪声不仅仅是白噪声，也存在有色噪声的情况，而粒子滤波可以处理有色噪声的问题。本文通过增加似然分布自适应调整来改进粒子滤波用于目标跟踪的精度，同时研究在白噪声、有色噪声下似然分布自适应调整粒子滤波和拓展卡尔曼滤波在UWB中的优势与不同。试验结果表明：观测噪声为白噪声时，拓展卡尔曼滤波和粒子滤波均可以较好地实现对行人的定位跟踪；观测噪声为有色噪声时，自适应粒子滤波定位效果优于粒子滤波、拓展卡尔曼滤波。  相似文献