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太湖水质的动态变化及影响因子的多元分析 总被引：16，自引：11，他引：16
利用１９９１～１９９２年监测资料，分析了太湖梅梁湾和局面西太湖水域水质的动态变化。用主成分分析方法揭示出各因子间的相互关系。结果表明，水质主要因子有明显的时空变化特征，沿南北方向从湾内向湾外，ＴＰ、ＴＮ、ＣＯＤ、Ｃｈｌ－ａ和电导率明显递减，且ＴＰ和ＴＮ含量季节变化，冬季高于夏季，梁溪河口更是如此，Ｃｈｌ－ａ含量与ＴＰ、ＴＮ含量密切相关，其变化基本与水温同步，时间上略后滞。主成分分析说明，ＴＰ、ＴＮ 相似文献
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本文分析了１９９１年１月５日缅甸７．６级大震前，云南省西部水化网观测到的水化异常，这些异常不仅有水氡，还有水汞、ＣＯ２、Ｃａ（＋＋）、Ｍｇ（＋＋）和Ｃｌ－等，异常的时间不仅有中长期，还有短临异常。文中根据观测事实指出，一次强烈地震发生之前，水化学的异常范围不仅仅在距震中２００公里的范围之内，甚至在距震中５００公里的范围内也有分布。 相似文献
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震级—频度关系的非线性特征 总被引：1，自引：0，他引：1
在假设地震断裂系统具有分形特征前提下，推导出非线性震级－频度关系公式：ＬｏｇＮ＝ａ＋ｂ△Ｍ－Ｃ／△Ｍ，作为示例，用此公式对一些实际观测资料进行了最小二乘拟合，效果明显好于线性公式。 相似文献
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在滇西单拉铜矿区发现了４层硅质岩，该硅质岩具有低的ＴｉＯ２，Ａｌ２Ｏ３与高的成矿元素（Ｃｕ，Ａｕ，Ａｇ）含量特征．硅质岩稀土元素总量很低，其球粒陨石标准化配分模 式为向右倾的曲线，具有明显的负Ｅｕ异常与弱的正Ｃｅ异常，与矿区早期形成的块状硫 化物矿石、矿石矿物及脉石矿物具有一致的稀土元素球粒陨石标准化配分模式，而与 成矿中晚期形成的夕卡岩型矿石及破碎带充填交代型矿石明显不同．硅质岩的硅同位 素组成与热水沉积的硅华及硅质岩一致，它的铅同位素组成与块状硫化物矿石一致， 它的 Ｒｂ－Ｓｒ等时线年龄为 ２７２ Ｍａ± ６ Ｍａ，与赋矿地层时代一致．研究表明羊拉矿区硅 质岩为典型的热水沉积硅质岩，且与矿区块状硫化物矿体关系密切，这为该矿床块状 硫化物矿体为海底喷流热水沉积作用形成提供了直接的证据． 相似文献
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一个新的中矩期地震预报指标 总被引：1，自引：1，他引：0
通过对强震前数年一定震级档次内的地震活动进行分析,提出一个刻划地震孕育过程进入中短期阶段的指标－Ｃｒ值。它可以反映岩石本构关系曲线峰值点附近的状态。在对７次７级左右强震进行震例研究表明,震前Ｃｒ值出现明显高值异常。Ｃｒ值达最高值的时间超前地震发生１￣３ａ,多数是在１ａ左右,这对中短期地震预报有意义。 相似文献
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从原欧亚板块划出阿穆利亚微板块,并建立一个包括太平洋、北美、欧亚、鄂霍次克及菲律宾海等板块在内的６板块系统,利用地震滑移矢量,转换断层走向,洋中脊扩展速率,以及最新的ＧＰＳ观测资料,采用Ｍｏｎｔｅ－Ｃａｒｌｏ盒子法,反演得到该６板块系统的欧拉运动矢量．本文得到的有关欧拉矢量为：ＡＭ-ＥＵ（６０．４２°Ｎ,１２３．２５°Ｅ,０．０２５°／Ｍａ）,ＡＭ-ＯＫ（５３．２０°Ｎ,１４１．９５°Ｅ,０．４７６°／Ｍａ）以及ＡＭ-ＰＨ（５０．８４°Ｎ,１５８.１３°Ｅ,１.２０４°／Ｍａ）,ＡＭ－ＥＵ的欧拉运动极点位于贝加尔隆起的东北及斯塔诺夫山脉的西北,并给出在贝加尔隆起有０．４－０．７ｍｍ／ａ的扩张,与从地质学证据得到的估计结果一致;由ＡＭ－ＯＫ及ＡＭ－ＰＨ欧拉运动矢量可以分别得到日本海东缘的６-１５ｍｍ／ａ及日本南海地槽带的５１－６６ｍｍ／ａ收敛速率,与最新的ＧＰＳ观测结果并不一致．因此,东亚北部地区存在独立的南中国微板块及其它亚尺度的微板块,可以更好地描述该地区现今板块构造的运动规律． 相似文献
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Forecasting the state of large marine ecosystems is important for many economic and public health applications. However, advanced three-dimensional (3D) ecosystem models, such as the European Regional Seas Ecosystem Model (ERSEM), are computationally expensive, especially when implemented within an ensemble data assimilation system requiring several parallel integrations. As an alternative to 3D ecological forecasting systems, we propose to implement a set of regional one-dimensional (1D) water-column ecological models that run at a fraction of the computational cost. The 1D model domains are determined using a Gaussian mixture model (GMM)-based clustering method and satellite chlorophyll-a (Chl-a) data. Regionally averaged Chl-a data is assimilated into the 1D models using the singular evolutive interpolated Kalman (SEIK) filter. To laterally exchange information between subregions and improve the forecasting skills, we introduce a new correction step to the assimilation scheme, in which we assimilate a statistical forecast of future Chl-a observations based on information from neighbouring regions. We apply this approach to the Red Sea and show that the assimilative 1D ecological models can forecast surface Chl-a concentration with high accuracy. The statistical assimilation step further improves the forecasting skill by as much as 50%. This general approach of clustering large marine areas and running several interacting 1D ecological models is very flexible. It allows many combinations of clustering, filtering and regression technics to be used and can be applied to build efficient forecasting systems in other large marine ecosystems. 相似文献
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Introduction In the probability analysis method of seismic risk considering time-space inhomogeneity of seismic activity and adopted commonly in China (State Seismological Bureau, 1996) (called in-homogeneous distribution model for short), the division of seismic statistical regions, delimitation of potential seismic sources and estimation of seismicity parameters are the main links that affect significantly the estimation of ground motion parameters of a site. HUANG and WU (2005) studied … 相似文献
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Variation of particulate organic carbon and its relationship with bio-optical properties during a phytoplankton bloom in the Pearl River estuary 总被引：2，自引：0，他引：2
Wang G Zhou W Cao W Yin J Yang Y Sun Z Zhang Y Zhao J 《Marine pollution bulletin》2011,62(9):1939-1947
In this study, variations in the particulate organic carbon (POC) were monitored during a phytoplankton bloom event, and the corresponding changes in bio-optical properties were tracked at one station (114.29°E, 22.06°N) located in the Pearl River estuary. A greater than 10-fold increase in POC (112.29-1173.36 mg m−3) was observed during the bloom, with the chlorophyll a concentration (Chl-a) varying from 0.984 to 25.941 mg m−3. A power law function is used to describe the relationship between POC and Chl-a, and the POC:Chl-a ratio tends to change inversely with Chl-a. Phytoplankton carbon concentration is indirectly estimated using the conceptual model proposed by Sathyendranath et al. (2009), and this carbon is found to contribute 47.21% (±10.65%) to total POC. The estimated carbon-to-chlorophyll ratio of phytoplankton in diatom-dominated waters is found to be comparable with results reported in the literature. Empirical algorithms for determining the concentrations of Chl-a and POC were developed based on the relationships of these variables with the blue-to-green reflectance ratio. With these bio-optical models, the levels of particulate organic carbon and Chl-a could be predicted from the radiometric data measured by a marine optical buoy, which showed much more detailed information about the variability in biogeochemical parameters during this bloom event. 相似文献
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基于高频次GOCI数据的太湖悬浮物浓度短期动态和驱动力分析 总被引：2，自引：0，他引：2
总悬浮物是水体中重要的光学敏感物质之一,很大程度上决定了水柱中光的吸收、散射和衰减,同时吸附营养盐、重金属和有毒有害物,对水体物质生物地球化学过程、沉积物埋藏动力和湖泊环境演化具有重要的意义.基于星地同步实验和静止水色成像仪GOCI(Geostationary Ocean Color Imager)构建了太湖悬浮物浓度估算模型,并分析了典型风浪过程中太湖悬浮物浓度短期动态变化过程.研究表明:对太湖水体悬浮物浓度较为敏感的波段为GOCI的第7波段(745nm)和第8波段(865 nm),悬浮物浓度与对应波段遥感反射率线性相关决定系数分别为0.72和0.55;基于GOCI第7波段的悬浮物浓度单波段遥感估算模型能较为准确地估算太湖的悬浮物浓度,模型相对均方根误差和平均绝对百分误差分别为28.3%和24.4%.通过研究典型风浪过程前后太湖悬浮物浓度变化发现其短期动态变化显著,风速、风向是悬浮物浓度短期动态变化的重要驱动因素,悬浮物浓度与风速呈正比,并随着风向扩散;高频连续GOCI影像结果显示悬浮物浓度短期动态变化对风浪扰动的响应有一定的滞后性,滞后时间为数小时到1天,悬浮物沉降与沉积物再悬浮的临界风速约为3.4 m/s. 相似文献
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In the paper, we have discovered the abnormal area distribution features of maximum variation values of ground motion parameter uncertainty with different probabilities of exceedance in 50 years within the range of 100°～120°E,29°～42°N for the purpose to solve the problem that abnormal areas of maximum variation values of ground motion parameter uncertainties emerge in a certain cities and towns caused by seismicity parameter uncertainty in a seismic statistical region in an inhomogeneous distribution model that considers tempo-spatial nonuniformity of seismic activity. And we have also approached the interrelation between the risk estimation uncertainty of a site caused by seismicity parameter uncertainty in a seismic statistical region and the delimitation of potential sources, as well as the reasons for forming abnormal areas. The results from the research indicate that the seismicity parameter uncertainty has unequal influence on the uncertainty of risk estimation at each site in a statistical region in the inhomogeneous distribution model, which relates to the scheme for delimiting potential sources. Abnormal areas of maximum variation values of ground motion parameter uncertainty often emerge in the potential sources of Mu≥8 (Mu is upper limit of a potential source) and their vicinity. However, this kind of influence is equal in the homogeneous distribution model. The uncertainty of risk estimation of each site depends on its seat. Generally speaking, the sites located in the middle part of a statistical region are only related to the seismicity parameter uncertainty of the region, while the sites situated in or near the juncture of two or three statistical regions might be subject to the synthetic influences of seismicity parameter uncertainties of several statistical regions. 相似文献
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Pettine M Casentini B Fazi S Giovanardi F Pagnotta R 《Marine pollution bulletin》2007,54(9):1413-1426
The trophic status classification of coastal waters at the European scale requires the availability of harmonised indicators and procedures. The composite trophic status index (TRIX) provides useful metrics for the assessment of the trophic status of coastal waters. It was originally developed for Italian coastal waters and then applied in many European seas (Adriatic, Tyrrhenian, Baltic, Black and Northern seas). The TRIX index does not fulfil the classification procedure suggested by the WFD for two reasons: (a) it is based on an absolute trophic scale without any normalization to type-specific reference conditions; (b) it makes an ex ante aggregation of biological (Chl-a) and physico-chemical (oxygen, nutrients) quality elements, instead of an ex post integration of separate evaluations of biological and subsequent chemical quality elements. A revisitation of the TRIX index in the light of the European Water Framework Directive (WFD, 2000/60/EC) and new TRIX derived tools are presented in this paper. A number of Italian coastal sites were grouped into different types based on a thorough analysis of their hydro-morphological conditions, and type-specific reference sites were selected. Unscaled TRIX values (UNTRIX) for reference and impacted sites have been calculated and two alternative UNTRIX-based classification procedures are discussed. The proposed procedures, to be validated on a broader scale, provide users with simple tools that give an integrated view of nutrient enrichment and its effects on algal biomass (Chl-a) and on oxygen levels. This trophic evaluation along with phytoplankton indicator species and algal blooms contribute to the comprehensive assessment of phytoplankton, one of the biological quality elements in coastal waters. 相似文献
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叶面积指数(LAI)是分析冠层结构最常用的参数之一,它控制着植被的生物物理过程,如光合、呼吸、蒸腾、碳循环和降水截获等,因此快速、可靠和客观地评价LAI非常重要.本文发展了激光穿透指数(LPI)的简化计算方法,首次利用校正后的回波强度计算出LPI,以LPI为变量基于Beer-Lambert定律实现了甘肃大野口研究区森林LAI反演,并且与原始回波强度和回波数反演LAI的精度进行对比,结果发现通过距离和角度校正后的回波强度值能提高LAI反演精度.为了评价模型的可靠性和泛化性能,用留一法交叉验证程序(LOOCV)对最佳反演模型进行了验证,表明该模型没有过度拟合,具有很好的泛化能力.最后,用没有参加建模的16个实测LAI对预测值进行精度验证(R2=0.810,RMSE=0.198),发现校正后的回波强度反演山区森林LAI精度较高.本文还对激光雷达LAI反演结果与传统光学TM影像的反演结果进行了对比分析,结果表明机载激光雷达反演LAI精度(R2=0.825,RMSE=0.165)高于光学TM遥感数据(R2=0.605,RMSE=0.257),因此,可用激光雷达数据实现研究区的高精度LAI反演,为生态环境研究提供可靠的基础数据. 相似文献
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《Limnologica》2017
Eutrophication has become a crucial issue for water resource management in recent years. In addition, reservoir trophic states are varied with environmental and water quality variables. The objectives of this study were to apply the DFA model to examine which water quality variables significantly affect variations of trophic state index (TSI) factors (i.e. total phosphorus (TP), chlorophyll-a (Chl-a), and Secchi disk transparency (SD)) and use classification and regression tree (CART) to determine the trophic states of the Shinmen Reservoir based on the levels of TSI factors during spring 2001–winter 2009. Results showed that the optimal DFA model contained one common trend (the underlying processes influencing trophic states, which can be rainfall intensity or runoff volume) and 7 explanatory variables. Turbidity (TB), pH, and dissolved oxygen (DO) influence concentrations of TP, while ammonium nitrogen (NH3-N), organic nitrogen (O-N), and nitrate nitrogen (NO3-N) control variations of Chl-a, and TB is related to SD. The CART model can specify trophic states only using two dominant driving factors, i.e. TP and Chl-a. The results of the CART illustrated that eutrophication could be occurred in the Shihmen Reservoir if TP is greater than 31.65 μg/L or if Chl-a is greater than 5.95 μg/L while TP concentration is less than 31.65 μg/L. Runoff nonpoint source pollution resulted from heavy storms may be the important factor affecting reservoir trophic states. Establishing vegetative filter strips along the riparian zone may able to effectively reduce this pollution in a reservoir. The integrated DFA and CART serves as good-fit relationships among trophic states, TSI factors, and water quality variables and provide control strategies for managing water quality in the Shihmen Reservoir. 相似文献
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Soil moisture is an integral quantity in hydrology that represents the average conditions in a finite volume of soil. In this paper, a novel regression technique called Support Vector Machine (SVM) is presented and applied to soil moisture estimation using remote sensing data. SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach. SVM has been used to predict a quantity forward in time based on training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. SVM model is applied to 10 sites for soil moisture estimation in the Lower Colorado River Basin (LCRB) in the western United States. The sites comprise low to dense vegetation. Remote sensing data that includes backscatter and incidence angle from Tropical Rainfall Measuring Mission (TRMM), and Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) are used to estimate soil water content (SM). Simulated SM (%) time series for the study sites are available from the Variable Infiltration Capacity Three Layer (VIC) model for top 10 cm layer of soil for the years 1998–2005. SVM model is trained on 5 years of data, i.e. 1998–2002 and tested on 3 years of data, i.e. 2003–2005. Two models are developed to evaluate the strength of SVM modeling in estimating soil moisture. In model I, training and testing are done on six sites, this results in six separate SVM models – one for each site. Model II comprises of two subparts: (a) data from all six sites used in model I is combined and a single SVM model is developed and tested on same sites and (b) a single model is developed using data from six sites (same as model II-A) but this model is tested on four separate sites not used to train the model. Model I shows satisfactory results, and the SM estimates are in good agreement with the estimates from VIC model. The SM estimate correlation coefficients range from 0.34 to 0.77 with RMSE less than 2% at all the selected sites. A probabilistic absolute error between the VIC SM and modeled SM is computed for all models. For model I, the results indicate that 80% of the SM estimates have an absolute error of less than 5%, whereas for model II-A and II-B, 80% and 60% of the SM estimates have an error less than 10% and 15%, respectively. SVM model is also trained and tested for measured soil moisture in the LCRB. Results with RMSE, MAE and R of 2.01, 1.97, and 0.57, respectively show that the SVM model is able to capture the variability in measured soil moisture. Results from the SVM modeling are compared with the estimates obtained from feed forward-back propagation Artificial Neural Network model (ANN) and Multivariate Linear Regression model (MLR); and show that SVM model performs better for soil moisture estimation than ANN and MLR models. 相似文献