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   

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   

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   

文昌B凹陷差异伸展- 走滑机制及控藏作用研究     

江汝锋  周家雄  杨希冰  游君君  李珊珊  陈林  张霞 《地质学报》2020,94(8):2422-2432

为查明文昌B凹陷油气富集差异性的原因，基于断裂展布和形成期次、构造样式组合、构造演化和断层活动的差异等标志，建立了差异伸展- 走滑机制，识别出19洼为伸展- 强走滑、14洼为伸展- 中等走滑、30洼为伸展- 弱走滑。差异伸展- 走滑作用控制了优质烃源岩的展布和供烃方向，控制输导体系类型和运移动力，提供物源通道和改造储层，还控制了圈闭类型和力学性质。进一步深化了走滑增压理论，基于先存断裂形态、伸展应力场右旋演化和应变差异，建立了走滑增压圈闭识别方法，识别出S型增压、右行左阶增压、斜交型增压3种走滑增压构造。研究表明，文昌B凹陷的油气富集受伸展- 走滑构造背景、源- 运时空配置关系所控制，强—中等走滑变形、发育优质中深湖源岩的文昌19洼和14洼是油气富集区。该研究成果成功推动了文昌凹陷近期的多个勘探发现。  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   

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   

2001-2015年中国植被覆盖人为影响的时空格局   总被引：3，自引：0，他引：3  

王建邦  赵军  李传华  朱钰  康重阳  高超 《地理学报》2019,74(3):504-519

基于MODIS-NDVI和气温、降水数据,使用基于变异系数的人为影响模型定量计算了2001-2015年中国植被覆盖人为影响,辅以趋势分析、Hurst指数等方法探讨了中国植被覆盖人为影响的时空变化特征及未来演变趋势。研究发现：① 2001-2015年,中国植被覆盖人为影响由南向北空间分异愈发明显,年均值为-0.0102,植被覆盖在人类活动影响下轻微减少,负影响面积占51.59%,略大于正影响面积。② 中国植被覆盖人为影响年际变化特征明显,整体呈负影响波动减少趋势,降速为0.5%/10a,其中正影响、负影响均呈增大趋势,正影响增速（0.3%/10a）远大于负影响（0.02%/10a）。③ 2001-2015年间,中国植被覆盖人为正影响重心向东北方向移动,负影响重心向西南方向移动,东北部植被覆盖在人为影响下不断改善,西南部人类活动对植被破坏程度不断增大。④ 中国植被覆盖人为影响主要呈负影响减少和正影响增大趋势,面积占比分别为28.14%和25.21%,生态环境趋于改善。⑤ Hurst指数分析表明,中国植被覆盖人为影响未来演变趋势的反向特征强于正向特征,主要呈人为负影响先减少后增大趋势,面积占比15.59%。  相似文献   

Frontiers and route‐ways from Europe: the Early Middle Palaeolithic of Britain          下载免费PDF全文

Nick Ashton  Claire R. E. Harris  Simon G. Lewis 《第四纪科学杂志》2018,33(2):194-211

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南海南部次表层叶绿素a质量浓度最大值及其影响因子*     

廖秀丽  戴明  巩秀玉  刘华雪  黄洪辉 《热带海洋学报》2018,37(1):45-56

文章分析了2013年南海南部4个季节航次的叶绿素a (Chl a)调查数据, 结果显示: 150m以浅水柱Chl a质量浓度均值分别为早春0.14mg•m^-3、初夏0.12mg•m^-3、初秋0.18mg•m^-3、初冬0.16mg•m^-3。早春和初夏偏低的原因与早春风速小, 初夏水温高, 不利于水体的垂直混合, 限制了深层海水中丰富的营养盐向上层水体补充有关。4个季节中海水次表层Chl a质量浓度最大值层(SCML)均出现在50m和75m, 这两个水层的Chl a质量浓度差异小, 季节变化不大, 平均值变化范围分别为0.24~0.26mg•m^-3和0.22~0.26mg•m^-3。受混合层深度和温跃层上界深度的共同影响, 50m水层Chl a质量浓度主要受制于深层富营养盐海水的向上补充, 75m水层Chl a质量浓度受水温的影响明显。  相似文献   

Population abundance and seasonal migration patterns indicated by commercial catch-per-unit-effort of hakes (Merluccius capensis and M. paradoxus) in the northern Benguela Current Large Marine Ecosystem     

JN Kathena  D Yemane  N Bahamon  T Jansen 《African Journal of Marine Science》2018,40(2):197-209

We developed generalised additive models (GAMs) to estimate standardised time-series of population abundance indices for assessment purposes and to infer ecological and behavioural information on northern Benguela hakes, Merluccius capensis and M. paradoxus, using haul-by-haul commercial trawl catch-rate data as proxies for hake densities. The modelling indicated that individual ship identifiers should be used rather than general vessel characteristics, such as vessel size. The final models explained 79% and 68% of the variability in the commercial catch rates of M. capensis and M. paradoxus, respectively. The spatial density patterns were consistent and confirmed existing knowledge about these species in the northern Benguela system. Furthermore, seasonal migration patterns were described for the first time and were found to correspond to the known spawning areas and seasons for M. capensis and M. paradoxus. Spatial density patterns were validated using the geostatistical modelling results of fisheries-independent trawl survey data. Improved understanding of the relationships between fleet dynamics and fish movement can be achieved by taking into consideration the present catch-rate model and spatial and seasonal distribution maps. We conclude that the yearly standardised CPUE time-series are problematic as proxies for total stock abundance because of spatial coverage issues. Consequently, such CPUE data should not be used for stock-size assessments and fisheries advice concerning northern Benguela hakes until this is solved. We generally recommend the exclusion of standardised CPUE time-series from stock assessments when important and changing parts of the stock distribution cannot be targeted by the fishery, such as due to closed areas or seasons.  相似文献