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Diatoms are identified and enumerated from the surface sediments of 100 lakes of Truelove Lowland, Devon Island, N.W.T., Canada. These lakes range from large oligotrophic lakes, to small tundra ponds, to coastal marine lagoons which are diverse in terms of ionic concentration and composition. The relationship between diatoms and 15 limnological variables is examined using Canonical Correspondence Analysis (CCA). Specific conductivity is identified as the most important variable influencing the distribution of diatoms in the Truelove lakes. A Weighted Averaging (WA) calibration model is developed to predict diatom-inferred specific conductivity. The reliability of the model is tested by evaluating the correlation between observed and diatom-inferred values and determining the error of prediction by bootstrapping. The applicability of the predictive conductivity equation is demonstrated by reconstructing the paleoconductivity history of Fish Lake. 相似文献
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不同平均时间对LOPEX10资料涡动相关湍流通量计算结果影响的探讨 总被引：1，自引：0，他引：1
通过处理涡动相关系统观测的近地层湍流脉动量可以获取地—气间感热和潜热通量,然而选择不同平均时间对通量计算的结果有较大影响。采用黄土高原陆面过程野外观测试验(LOPEX10)期间获得的涡动相关系统观测资料,分析了不同平均时间对湍流通量计算的影响,并采用雷诺平均和分解方法推导了平均时间引起的通量差值的数学表达式(Flux Compensation,FC)。结果表明:(1)FC公式可以说明采用不同平均时间数据之间的关系,也可以直接计算低频涡旋对湍流通量的贡献。FC公式计算的结果与直接计算的不同平均时间通量计算之差的相关系数在0.95以上,并可以确定计算湍流通量的最佳平均时间。(2)通过采用Ogive函数确定了计算LOPEX10期间通量的最佳平均时间长度为30min,印证了利用雷诺平均和分解方法计算湍流通量补偿的准确性。(3)通过进一步的数学变换,证明了平均时间对湍流通量计算的影响直接与湍流低频变化相关,FC公式可以用来确定涡动相关观测数据的最佳平均时间,并且在获得较高时间分辨率的湍流通量数据的同时,补偿因平均时间过短而遗漏的低频信息。 相似文献
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Assessing the uncertainties of hydrologic model selection in climate change impact studies 总被引：3，自引：0，他引：3
The uncertainties associated with atmosphere‐ocean General Circulation Models (GCMs) and hydrologic models are assessed by means of multi‐modelling and using the statistically downscaled outputs from eight GCM simulations and two emission scenarios. The statistically downscaled atmospheric forcing is used to drive four hydrologic models, three lumped and one distributed, of differing complexity: the Sacramento Soil Moisture Accounting (SAC‐SMA) model, Conceptual HYdrologic MODel (HYMOD), Thornthwaite‐Mather model (TM) and the Precipitation Runoff Modelling System (PRMS). The models are calibrated based on three objective functions to create more plausible models for the study. The hydrologic model simulations are then combined using the Bayesian Model Averaging (BMA) method according to the performance of each models in the observed period, and the total variance of the models. The study is conducted over the rainfall‐dominated Tualatin River Basin (TRB) in Oregon, USA. This study shows that the hydrologic model uncertainty is considerably smaller than GCM uncertainty, except during the dry season, suggesting that the hydrologic model selection‐combination is critical when assessing the hydrologic climate change impact. The implementation of the BMA in analysing the ensemble results is found to be useful in integrating the projected runoff estimations from different models, while enabling to assess the model structural uncertainty. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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基于贝叶斯模式平均方法(Bayesian Model Averaging),发展了一个NINO3.4指数的多模式客观权重集合预报方法(简称OBJ)。该方法基于训练期内单个模式的预报结果,用线性回归订正单个预报的偏差,依据模式的预报效果估计单个模式的权重。利用2002年2月—2015年10月美国哥伦比亚大学国际气候与社会研究所(IRI)提供的7个单一模式对NINO3.4指数的预报结果进行OBJ试验,并采用均方根误差对多模式集合平均预报(简称ENS)和OBJ的预报结果进行检验和评估。结果表明,ENS的预报效果优于7个单一模式的预报效果,而OBJ预报效果优于ENS预报效果,其NINO3.4指数的均方根误差比ENS方法降低了4%。将单一模式预报结果按时间划分为训练期和预报期,利用独立样本估计OBJ的参数并进行预报试验,这些试验也表明,OBJ能进一步提高预报精度。 相似文献
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TUTORIAL TO ROBUST STATISTICS 总被引：1，自引：0，他引：1
PETER J.ROUSSEEUW 《地理学报(英文版)》1991,(1)
The least squares method 15 eurrently the most PoPular aPProaeh to estimation beeause oftradition and ease of eomPutation,However,real data sets frequently eontain outliers,whiehmay be mistakes or exeePtional observations.In this situation least squares beeomesunreliable.Two things often haPPen:the estimates become 相似文献
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We formulate a method for determining the smallest time interval Tover which a turbulence time series can be averaged to decompose it intoinstantaneous mean and random components. From the random part the method defines the optimal interval (or averaging window) AW over which this part should be averaged to obtain the instantaneous spectrum. Both T and AW vary randomly with time and depend on physical properties of the turbulence. T also depends on the accuracy of the measurements and is thus independent of AW. Interesting features of the method are its real-time capability and the non-equality between AW and T. 相似文献
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Weighted averaging is widely used for inferring environmental conditions from an observed species assemblage. However, weighted average inferences are known to be systematically biased, and linear corrections (i.e., deshrinking functions) are commonly applied to adjust for this bias. In this analysis, the magnitude of the biases in weighted average inferences (and therefore the values of the deshrinking coefficients) are shown to depend upon the range of conditions sampled in the calibration data set and the true optima and niche breadths of the species observed in the calibration data set. Since the range of conditions and the observed species can differ between the calibration data set and the new data set for which environmental conditions are inferred, the coefficients for the deshrinking function derived using the calibration data may not be applicable to inferences computed using a new data set. Thus, environmental inferences may still exhibit systematic errors even after application of the linear correction. The findings from the theoretical analysis are demonstrated using stream temperature and macroinvertebrate data collected from wadeable streams in the western United States. 相似文献
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O. L. L. Moraes O. C. Acevedo R. Da Silva R. Magnago A. C. Siqueira 《Boundary-Layer Meteorology》2004,112(1):159-177
Nighttime data collected at the bottom of a valley locatedin southern Brazil are studied. The analyzed quantities includemomentum and sensible heat fluxes, turbulence statistics andintermittency factors, and their relationship to the stability parameter.The proper interval for averaging and flux calculations is foundto be 20 min, based on the comparison of the fluxes and therandom error associated in their determination. Each data seriesis classified as calm or windy, according to the mean windspeed. A much better dependency of the variables in terms of thestability parameter is found under windy conditions. Analysis ofthe wind direction and topographic scales indicates that distortioneffects under calm conditions affect the flow, while the flow is inlocal equilibrium for windy conditions. In this case, the sensible heatflux shows a minimum for a value of z/L=0.08, while the momentumflux and the normalized scales of the wind components andtemperature decrease monotonically as the stability increases. Anintermittency factor is proposed and shown to be larger (moreintermittency) for more stable conditions. 相似文献
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In this work we propose an approach for mapping flooded areas from Sentinel-2 MSI (Multispectral Instrument) data based on soft fuzzy integration of evidence scores derived from both band combinations (i.e. Spectral Indices - SIs) and components of the Hue, Saturation and Value (HSV) colour transformation. Evidence scores are integrated with Ordered Weighted Averaging (OWA) operators, which model user’s decision attitude varying smoothly between optimistic and pessimistic approach. Output is a map of global evidence degree showing the plausibility of being flooded for each pixel of the input Sentinel-2 (S2) image. Algorithm set up and validation were carried out with data over three sites in Italy where water surfaces are extracted from stable water bodies (lakes and rivers), natural hazard flooding, and irrigated paddy rice fields. Validation showed more than satisfactory accuracy for the OR-like OWA operators (F-score > 0.90) with performance slightly decreased (F-score < 0.75) over heterogeneous conditions (e.g. rice fields). The algorithm was applied with no changes and/or tuning to independent sites from the Copernicus Emergency Management Service (EMS) activations to simulate operational conditions. Over these sites, the proposed approach achieved greater, more consistent and robust mapping accuracy compared to traditional approaches based on the segmentation of single input features. Moreover, OWA operators offer an appealing way of combining and aggregating multiple information in decision making by modelling uncertainty in decision process. 相似文献