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Stream–subsurface exchange plays a significant role in the fate and transport of contaminants in streams. It has been modelled explicitly by considering fundamental processes such as hydraulic exchange, colloid filtration, and contaminant interactions with streambed sediments and colloids. The models have been successfully applied to simulate the transport of inorganic metals and nutrients. In this study, laboratory experiments were conducted in a recirculating flume to investigate the exchange of a hydrophobic organic contaminant, p,p′‐dichloro‐diphenyl‐dichloroethane (DDE), between a stream and a quartz sand bed. A previously developed process‐based multiphase exchange model was modified by accounting for the p,p′‐DDE kinetic adsorption to and desorption from the bed sediments/colloids and was applied to interpret the experimental results. Model input parameters were obtained by conducting independent small‐scale batch experiments. Results indicate that the immobilization of p,p′‐DDE in the quartz sand bed can occur under representative natural stream conditions. The observed p,p′‐DDE exchange was successfully simulated by the process‐based model. The model sensitivity analysis results show that the exchange of p,p′‐DDE can be sensitive to either the sediment sorption/desorption parameters or colloidal parameters depending on the experimental conditions tested. For the experimental conditions employed here, the effect of colloids on contaminant transport is expected to be minimal, and the stream–subsurface exchange of p,p′‐DDE is dominated by the interaction of p,p′‐DDE with bed sediment. The work presented here contributes to a better mechanistic understanding of the complex transport process that hydrophobic organic contaminants undergo in natural streams and to the development of reliable, predictive models for the assessment of impacted streams. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   
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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   
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Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.  相似文献   
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[Translated by the editorial staff] Simulating the precipitation regime of Northern Africa is challenging for regional climate models, particularly because of the strong spatial and temporal variability of rain events in the region. In this study we evaluate simulations conducted with two recent versions of regional climate models (RCM) developed in Canada: the CRCM5 and CanRCM4. Both are also used in the COordinated Regional Climate Downscaling EXperiment (CORDEX)-Africa. The assessment is based on the occurrence, duration, and intensity indices of daily precipitation in Maghreb during the fall and spring seasons from 1998 to 2008. We also examine the links between the North-Atlantic Oscillation (NAO) index, weather systems, and the precipitation regime over the region. During the rainy season (September to February), the CRCM5 reproduces the frequency and intensity of extreme precipitation adequately, as well as the occurrence of days with rain, while the CanRCM4 underestimates precipitation extremes. The study of links between weather systems and the precipitation regime shows that, along the Atlantic coast, precipitation (occurrence, intensity, and wet sequences) increases significantly with storm frequency in the fall. In winter, these links grow stronger going east, from the Atlantic coast to the Mediterranean coast. The negative phases of the NAO index are statistically associated with the increase in rain intensity, extremes, and accumulation along the Atlantic coast in the fall. However, the link weakens in winter over these regions and strengthens along the Mediterranean coast as the precipitation frequency rises during negative phases of the NAO. Both RCMs generally reproduce the links between the NAO and the precipitation regime well, regardless of location.  相似文献   
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青藏高原和亚洲夏季风动力学研究的新进展   总被引:4,自引:1,他引:3  
亚洲夏季风环流受海陆和伊朗高原—青藏高原大地形的热力作用调控.亚洲季风所释放的巨大潜热又对大气环流形成反馈.这种相互反馈过程十分复杂,揭示其物理过程对理解气候变化格局的形成和变化以及提高天气预报及气候预测的准确率十分重要.夏季北半球副热带对流层上层环流的主要特征是存在庞大的南亚高压(SAH)以及强大的对流层上层温度暖中心(UTTM).本文介绍了温度—加热垂直梯度(T-QZ)理论的发展,并用以揭示SAH和UTTM的形成机制.指出沿副热带欧亚大陆东部的季风对流潜热加热及其中西部的表面感热加热和高层长波辐射冷却是导致SAH和UTTM在南亚上空发展的原因.文中还介绍了Gill模型用于上部对流层研究的局限性及解决的办法.  相似文献   
120.
贵州西北部发育多处基性侵入岩,总体规模较小(约0.25 Km2),多沿深大断裂侵位,出露于铅锌矿点外围,本文对猫猫厂、凉山两处矿点附近的儿马冲和白岩庆两地小型基性侵入岩进行了重点研究。侵入岩主要岩性为细粒辉长岩,造岩矿物主要为拉长石、普通辉石。SiO2范围为49.60-51.09 wt%,MgO从3.88-4.27 wt %,TiO2为3.69-3.85 wt %。LA-ICP-MS锆石U-Pb定年结果为268.3±7.4 Ma,显示岩浆侵位于二叠纪。基性侵入岩的微量元素蛛网图呈OIB型特征,富集大离子亲石元素(LILE)、轻稀土元素(LREE),亏损重稀土元素(HFEE),相对亏损高场强元素(Nb,Ta),有Sr、Y亏损,Pb富集。(87Sr/86Sr)i范围0.706749~0.707069,(143Nd/144Nd)i范围0.512313~0.512363,εNd(t)范围0.2~1.2;源区熔融深度处于石榴石橄榄岩相深度,可能经历了1-3 %的部分熔融,处于亏损石榴石二辉橄榄岩相向原始石榴石二辉橄榄岩相的过渡区。成岩过程中发生了单斜辉石、斜长石等矿物分离结晶,受到了有限的地壳混染作用,未经历明显的AFC过程。地壳物质在地幔源区中的加入可能是造成地幔富集的主要原因。侵入岩与成矿作用之间的关系,主要通过两方面所表现。一方面是二者间构造活动上的耦合性。另一方面是基性岩在成矿过程中可能发挥了重要的化学屏障层作用。  相似文献   
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