Profondeur d'extraction racinaire et signature isotopique de l'eau prélevée par les racines des couverts végétaux     

Zakaria Boujamlaoui  Thierry Bariac  Philippe Biron  Laurent Canale  Patricia Richard 《Comptes Rendus Geoscience》2005,337(6):589-598

We seek to identify the depth to which water is extracted by the roots in the soil. Indeed, in an isotopic steady-state condition of leaf water, transpiration introduces into the atmosphere a vapour whose isotopic signature is identical to that of root water. In the isotopic models of atmospheric general circulation, it is classically allowed that the signature of transpiration belongs to the meteoric water line. This supposes that the water taken by the roots has escaped with the evaporation of the soil and comes thus from the deep layers of the soil. At the time of experimentation carried out on maize plants (Nemours, Seine-et-Marne, France), this extraction depth was inferred from the comparison between the signature of the water measured on the level of the first internode of the stems of the plants and the isotopic profile of water in the soil. When the flow of transpiration reaches a maximum value, the plant uptakes water resulting from precipitations and which preserves its non-evaporating character after having quickly infiltrated in the deep layers of the soil. This relates to only 55% of the flux transpired by the canopy, the remainder presenting an evaporating character more or less marked according to ambient conditions. This experiment invalidates the classical hypothesis used in isotopic models of general atmospheric circulation in temperate regions. In fact, only half the amount of water vapour transpired by the canopy during the day presents a signature similar to that of the rainwater sampled in deep soil layers. To cite this article: Z. Boujamlaoui et al., C. R. Geoscience 337 (2005).  相似文献   

Analytical prediction of aggregates' effects on the ITZ volume fraction and Young's modulus of concrete          下载免费PDF全文

R. Zouaoui  K. Miled  O. Limam  A. Beddey 《国际地质力学数值与分析法杂志》2017,41(7):976-993

In order to evaluate analytically the ITZ volume fraction (f_ITZ) in concrete, a three phase model is proposed for the random concrete microstructure using the Voronoï tessellation. Within this model, the ITZ local thickness is a statistical variable depending on the local paste thickness available between each couple of neighbouring aggregates. The f_ITZ is found to not exceed 7% for typical concretes. Then, the concrete Young's modulus is predicted analytically using a four‐phase generalized self consistent model but in which the proposed f_ITZ is considered. It is found that the concrete Young's modulus increases when increasing aggregates volume fraction, aggregates maximum size and the proportion of coarse aggregates and when decreasing the ITZ thickness and Young's modulus. Finally, the validity of the proposed model is discussed based on a comparison between its predictions and three sets of experimental results related to normal and high strength concretes taken from literature. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)     

《Ocean Modelling》2015

In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.  相似文献   

Evaluation of NLDAS‐2 evapotranspiration against tower flux site observations          下载免费PDF全文

Youlong Xia  Michael T. Hobbins  Qiaozhen Mu  Michael B. Ek 《水文研究》2015,29(7):1757-1771

The North American Land Data Assimilation System project phase 2 (NLDAS‐2) has run four land surface models for a 30‐year (1979–2008) retrospective period. Land surface evapotranspiration (ET) is one of the most important model outputs from NLDAS‐2 for investigating land–atmosphere interaction or to monitor agricultural drought. Here, we evaluate hourly ET using in situ observations over the Southern Great Plains (Atmospheric Radiation Measurement/Cloud and Radiation Testbed network) for 1 January 1997–30 September 1999 and daily ET u‐sing in situ observations at the AmeriFlux network over the conterminous USA for an 8‐year period (2000–2007). The NLDAS‐2 models compare well against observations, with the National Centers for Environmental Prediction's Noah land surface model performing best, followed, in order, by the Variable Infiltration Capacity, Sacramento Soil Moisture Accounting, and Mosaic models. Daily evaluation across the AmeriFlux network shows that for all models, performance depends on season and vegetation type; they do better in spring and fall than in winter or summer and better for deciduous broadleaf forest and grasslands than for croplands or evergreen needleleaf forest. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

Potential seasonal calibration for palaeoenvironmental reconstruction using skeletal microstructures and strontium measurements from the cold‐water coral Lophelia pertusa          下载免费PDF全文

VINCENT MOUCHI  QUENTIN G. CROWLEY  ANDREW L. JACKSON  FRANK MCDERMOTT  XAVIER MONTEYS  MARC DE RAFÉLIS  JOSÉ LUIS RUEDA  FRANCK LARTAUD 《第四纪科学杂志》2014,29(8):803-814

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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   

A Review for the Thermophysics and the Yarkovsky and YORP Effects of Asteroidstwo     

Jiang Hao-xuan  Ji Jiang-hui 《Chinese Astronomy and Astrophysics》2019,43(1):1-22

The thermophysics of asteroids has become an important frontier for the research of asteroids in recent years. In this paper, we have introduced the thermophysical models commonly used in this field, by using these thermophysical models and combining with the data observed by the space or ground-based IR telescopes, some thermophysical parameters of asteroids, such as the thermal inertia, geometric albedo, effective diameter, surface roughness, and surface temperature, etc., can be derived. We have mentioned also the shape model and IR observation of asteroids, as well as the obtained thermophysical parameters for a part of asteroids. These thermophysical parameters can be further applied to studying the asteroids’ Yarkovsky effect, YORP effect, and so on, even can provide the relevant information for the spacecraft landing on the asteroid surface and the return mission of a spacecraft after the asteroid sampling.  相似文献   

2016与1998年春季北大西洋海表温度异常的差异及成因     

薛文璟  余锦华  陈林 《热带海洋学报》2020,39(3):19-30

利用再分析资料以及混合层海温诊断方程, 研究1997—1998与2015—2016年超级厄尔尼诺次年北大西洋海表温度异常(sea surface temperature anomalies, SSTA)的差异及成因。结果显示, 北大西洋SSTA在1998年春季呈明显正负正三极型式分布, 而在2016年呈弱的负正负型态。诊断热带北大西洋SSTA的影响因素表明, 1998年春季暖SSTA除了之前研究强调的海洋表面向大气的潜热输送异常减少, 以及吸收太阳辐射的增加外, 海洋动力过程即Ekman纬向漂流也起着重要的作用。热力过程与厄尔尼诺峰值后出现的北大西洋涛动(North Atlantic Oscillation, NAO)负位相有关, 其可引起亚速尔高压减弱, 产生西南风异常, 通过风-蒸发-海表温度(sea surface temperature, SST)反馈机制使热带北大西洋蒸发减弱, 海表增暖, 沃克环流下沉支的东移对这一增暖也有贡献。与1997—1998厄尔尼诺事件不同, 2015—2016厄尔尼诺事件没有强迫出负位相NAO, 而是出现弱NAO正位相, 热带北大西洋为弱的东风异常, 使海表发生一定的冷却, 形成2016春季北大西洋SSTA与1998年的明显差异。  相似文献   

2010-2018年北极夏季中国北极科学考察航行期间被动微波遥感海冰密集度与船基目视观测资料的比较   总被引：2，自引：0，他引：2  

修苑人  李志军  雷瑞波  王庆凯  卢鹏  Lepp&#;ranta Matti 《海洋学报(英文版)》2020,39(9):38-49

为了更有效地将卫星数据应用于北极航行导航，被动微波（PM）产品的海冰密集度（SIC）与从中国北极科学考察中收集到的船基目视观测（OBS）资料进行了比较。在2010、2012、2014、2016和2018年的北极夏季总共收集了3667组目测数据。PM SIC取自基于SSMIS传感器的NASA-Team（NT）、Bootstrap（BT）以及Climate Data Record（CDR）算法和基于AMSR-E/AMSR-2传感器的BT、enhanced NT（NT2）以及ARTIST Sea Ice（ASI）算法。使用PM SIC的日算术平均值和OBS SIC的日加权平均值进行比较。比较了PM SIC和OBS SIC之间的相关系数，偏差和均方根偏差，包括总体趋势以及在轻度/普通/严重冰况下的情况。使用OBS数据，浮冰尺寸和冰厚对不同PM产品SIC反演的影响可以通过计算浮冰尺寸编码和冰厚的日加权平均值来评估。我们的结果显示相关系数的范围为0.89（AMSR-E/AMSR-2 NT2）到0.95（SSMIS NT），偏差的范围为-3.96%（SSMIS NT）到12.05%（AMSR-E/AMSR-2），均方根偏差的范围为10.81%（SSMIS NT）到20.15%（AMSR-E/AMSR-2 NT2）。浮冰尺寸对PM产品的SIC反演有显著的影响，大多数PM产品倾向于在小浮冰尺寸情况下低估SIC，而在大浮冰尺寸情况下高估SIC。超过30 cm的冰厚对于PM产品的SIC反演没有明显影响。总体来看，在北极夏季，SSMIS NT SIC与OBS SIC之间有着最好的一致性，而AMSR-E/AMSR-2 NT2 SIC与OBS SIC的一致性最差。  相似文献   

GPS精密卫星钟差估计研究     

孟范伟 《测绘与空间地理信息》2016,(9):77-79

随着精密单点定位技术的发展,对于精确的卫星坐标以及卫星钟差改正精度的要求越来越高,精密卫星星历以及精密卫星钟差的求解成为制约精密单点定位技术发展的瓶颈。本文基于修复周跳的载波相位观测值与相位平滑伪距观测值,采用无电离层延迟星间单差精密卫星钟差估计模型,在先估计出整周模糊度后,进行了精密卫星钟差的估计,并采用与IGS事后精密钟差作二次差的方法进行精度分析,这对于提高精密单点定位精度具有一定的意义。  相似文献