Gastropods on submersed macrophytes in Yangtze lakes: Community characteristics and empirical modelling

Epiphytic gastropods in Yangtze lakes have suffered from large-scale declines of submersed macrophytes during past decades. To better understand what controls gastropod community, monthly investigations were carried out in four Yangtze lakes during December, 2001-March, 2003. Composed of 23 species belonging to Pulmonata and Prosobranchia, the community is characterized by the constitution of small individuals. The average density and biomass were 417 +/- 160 ind/m(2) and 18.05 +/- 7.43 g/m(2), with maxima a-round August. Submersed macrophyte biomass is shown to be the key factor affecting species number, density, and biomass of gastropods. Accordingly, a series of annual and seasonal models yielding high predictive powers were generated. Preference analyses demonstrated that pulmonates and prosobranchs with different respiratory organs prefer different macrophyte functional groups.

Pressure Drop of Bubbly Two-Phase Flow in a Square Channel at Reduced Gravity

A new set of experimental pressure drop data, collected aboard the Russian IL-76MDK, is reported for bubbly airwater two-phase flow in a square channel with a cross-sectional area of 12x 12mm(2). The present data are compared to several frequently used empirical models, e.g. homogeneous model, Lockhart-Martinelli-Chisholm correlation and Friedel's model. It is shown that the predictions of the models mentioned above are generally not satisfied. A new homogeneous model is developed based on the analysis of the characteristics of bubbly two-phase flow at reduced gravity. It is tested with the present data and other data collected by other researchers in circular pipes. Some questions related to the present model are also discussed. (C) 2002 COSPAR. Published by Elsevier Science Ltd. All rights reserved.

Nutrient dynamics and the eutrophication of shallow lakes Kasumigaura (Japan), Donghu (PR China), and Okeechobee (USA)

We compared the nutrient dynamics of three lakes that have been heavily influenced by point and non-point source pollution and other human activities. The lakes, located in Japan (Lake Kasumigaura), People's Republic of China (Lake Donghu), and the USA (Lake Okeechobee), all are relatively large(>30 km(2)), very shallow (<4 m mean depth), and eutrophic. In all three lakes we found strong interactions among the sediments, water column, and human activities. Important processes affecting nutrient dynamics included nitrogen fixation, light limitation due to resuspended sediments, and intense grazing on algae by cultured fish. As a result of these complex interactions, simple empirical models developed to predict in-lake responses of total phosphorus and algal biomass to external nutrient loads must be used with caution. While published models may provide 'good' results, in terms of model output matching actual data, this may not be due to accurate representation of lake processes in the models. The variable nutrient dynamics that we observed among the three study lakes appears to be typical for shallow lake systems. This indicates that a greater reliance on lake-specific research may be required for effective management, and a lesser role of inter-lake generalization than is possible for deeper, dimictic lake systems. Furthermore, accurate predictions of management impacts in shallow eutrophic lakes may require the use of relatively complex deterministic modeling tools. (C) 2000 Elsevier Science Ltd. All rights reserved.

卫星高度计资料在海洋潮汐研究中的应用

本文依据收集到的392个地面验潮站8个主要分潮（M2、S2、K1、O1、N2、K2、P1及Q1）的调和常数，对现有7个全球大洋潮汐模式的准确度进行了检验，结果显示各模式在深海区域均达到了比较高的准确度，相互之间差别也不大。经验模式GOT00和CSR4.0、同化模式NAO99、反演同化模式TPXO7.0、数值同化模式FES2002和FES2004的M2分潮均方根偏差在3 cm左右，其它分潮（S2、K1、O1、N2、K2、P1及Q1）大约在1～2 cm。本文还依据中国近海18个岛屿的调和常数对其中的5个大洋潮汐模式的准确度进行了检验，结果表明，M2分潮均方根偏差在6～14 cm，明显高于大洋部分的偏差，其中日本国家天文台的潮汐模式NAO99在中国近海的结果相对较准确。 我们利用1992年8月至2008年8月的TOPEX/POSEIDON和JASON-1(T/P-J)卫星高度计资料，对沿卫星轨道的302816个站点进行了14个分潮的潮汐调和分析，得到了全球大洋潮汐的8个主要分潮以及2个气象分潮Sa、Ssa的经验同潮图。主要结果有：（1）各分潮在卫星上升轨道与下降轨道的交叉点(约7000个)相关性分析表明：M2分潮的振幅和迟角的相关系数很高（分别为0.9965和0.9961）；S2，K1，O1和Sa分潮也有较好的相关性（相关系数为0.94～0.99）；（2）该结果与392地面个验潮站吻合较好，其中M2分潮的振幅、迟角和向量的均方根偏差分别为：1.73 cm，2.340和2.93 cm；S2，K1和O1分潮的振幅、迟角和向量的均方根偏差为1 cm左右，5.250～7.270和1.5～2.1 cm，该精度与最近几年国际上的主要大洋潮汐模式的准确度相近；（3）首次通过卫星资料获得了Sa、Ssa分潮的同潮图。周期为1年的Sa分潮与大洋105个地面站相比，振幅、迟角和向量的均方根偏差分别为1.50 cm、18.360和2.16 cm。在此基础上，进一步分析了构成Sa、Ssa气象分潮的两个主要因素（海水密度以及海面气压）在全球的分布。 在T/P-J等卫星资料无法覆盖到南大洋和北冰洋，本文利用Princeton Ocean Model（POM）进行了数值模拟，模拟结果与162个地面实测站（其中南大洋30个，北冰洋132个）的观测比较一致。基于卫星资料分析的结果和数值模拟结果合并得到了全球大洋的8个主要分潮同潮图。在此基础上通过全球潮汐能量耗散的计算得到潮能通量的分布，并得到全球M2、S2、K1和O1分潮的潮汐能量耗散率为2.431TW、0.401TW、0.336TW和0.176TW。 本文还利用卫星资料对南海潮汐进行了研究，在中国南海，获得了主要的半日潮、全日潮、四分日分潮和长周期分潮（M2，S2，N2，K2，K1，O1，P1，Q1，M4, MS4，Sa, Ssa）的经验同潮图。与南海沿岸94个地面验潮站的数据符合得比较好，M2，S2，K1及O1等4个主要分潮的平均振幅差为2～4 cm，均方根偏差分别是9～11 cm.其它4个主要分潮N2，K2，P1，Q1的平均振幅差为1～2 cm，均方根偏差为2～4 cm。此外，本文还利用卫星高度计资料潮汐分析结果沿卫星轨道进行高通滤波，分离得出中国近海的M2，S2，K1及O1分潮的内潮信息。

太阳辐射对热层和电离层变化性的影响

Solar ultraviolet (UV) radiation at wavelengths less than 400 nm is an important source of energy for aeronomic processes throughout the solar system. Solar UV photons are absorbed in planetary atmospheres, as well as throughout the heliosphere, via photodissociation of molecules, photoionization of molecules and atoms, and photoexcitation toexcitation including resonance scattering. In this paper, the solar irradiances data measured by TIMED SEE, as well as the solar proxies such as F10.7 and Mg II, thermosphere neutral density of CHAMP measurements and topside ionospheric plasmas densities from DMSP, are used to analyze solar irradiance effects on the variabilities of the thermosphere and the ionosphere. First, thermosphere densities near 410 km altitude are analyzed for solar irradiance variability effects during the period 2002-2004. Correlations between the densities and the solar irradiances for different spectral lines and wavelength ranges reveal significantly different characteristics. The density correlates remarkably well with all the selected solar irradiances except the lower chromospheric O I (130.4 nm) emission. Among the chosen solar proxies, the Mg II core-to-wing ratio index, EUV (30-120 nm) and F10.7 show the highest correlations with the density for short-term (< ~27 days) variations. For both long- (> ~27 days) and short-term variations, linear correlation coefficients exhibit a decreasing trend from low latitudes towards high latitudes. The density variability can be effectively modeled (capturing 71% of the variance) using multiple solar irradiance indices, including F10.7, SEUV (the EUV 30-120 nm index), and SFUV (the FUV 120-193 nm index), in which a lag time of 1 day was used for both F10.7 and SEUV, and 5 days for SFUV. In our regression formulation SEUV has the largest contribution to the density variation (40%), with the F10.7 having the next largest contribution (32%) and SFUV accounting for the rest (28%). Furthermore, a pronounced period of about 27.2 days (mean period of the Sun's rotation) is present in both density and solar irradiance data of 2003 and 2004, and a pronounced period of about 54.4 days (doubled period of the solar rotation) is also revealed in 2004. However, soft X-ray and FUV irradiances did not present a pronounced 54.4 day period in 2004, in spite of their high correlation with the densities. The Ap index also shows 54-day periodicities in 2004, and magnetic activity, together with solar irradiance, affects the 54-day variation in density significantly. In addition, NRLMSISE00, DTM-2000 and JB2006 model predictions are compared with density measurements from CHAMP to assess their accuracy, and the results show that these models underestimate the response of the thermosphere to variations induced by solar rotation. Next, the equatorial topside ionospheric plasmas densities Ni are analyzed for solar irradiance variability effects during the period 2002-2005. Linear correlations between Ni and the solar irradiances for different wavelength ranges reveal significantly different characteristics. XUV (0-35 nm) and EUV (115-130 nm) show higher correlation with Ni for the long-term variations, whereas EUV (35-115 nm) show higher correlation for the short-term variations. Moreover, partial correlation analysis shows that the long-term variations of Ni are affected by both XUV (0-35 nm) and EUV (35-115 nm), whereas XUV (0-35 nm) play a more important role; the short-term variations of Ni are mostly affected by EUV (35-115 nm). Furthermore, a pronounced period of about 27 days is present in both Ni and solar irradiance data of 2003 and 2004, and a pronounced period of about 54 days is also revealed in 2004. Finally, prompted by previous studies that have suggested solar EUV radiation as a means of driving the semiannual variation, we investigate the intra-annual variation in thermosphere neutral density near 400 km during 2002-2005. The intra-annual variation, commonly referred to as the ‘semiannual variation’, is characterized by significant latitude structure, hemispheric asymmetries, and inter-annual variability. The magnitude of the maximum yearly difference, from the yearly minimum to the yearly maximum, varies by as much as 60% from year to year, and the phases of the minima and maxima also change by 20-40 days from year to year. Each annual harmonic of the intra-annual variation, namely, annual, semiannual, ter-annual and quatra-annual, exhibits a decreasing trend from 2002 through 2005 that is correlated with the decline in solar activity. In addition, some variations in these harmonics are correlated with geomagnetic activity, as represented by the daily mean value of Kp. Recent empirical models of the thermosphere are found to be deficient in capturing most of the latitude dependencies discovered in our data. In addition, the solar flux and geomagnetic activity proxies that we have employed do not capture some latitude and inter-annual variations detected in our data. It is possible that these variations are partly due to other effects, such as seasonal-latitudinal variations in turbopause altitude (and hence O/N2 composition) and ionosphere coupling processes that remain to be discovered in the context of influencing the intra-annual variations depicted here. Our results provide a new dataset to challenge and validate thermosphere-ionosphere general circulation models that seek to delineate the thermosphere intra-annual variation and to understand the various competing mechanisms that may contribute to its existence and variability. We furthermore suggest that the term “intra-annual” variation be adopted to describe the variability in thermosphere and ionosphere parameters that is well-captured through a superposition of annual, semiannual, ter-annual, and quatra-annual harmonic terms, and that “semiannual’ be used strictly in reference to a pure 6-monthly sinusoidal variation. Moreover, we propose the term “intra-seasonal” to refer to those shorter-term variations that arise as residuals from the above Fourier representation.

电离层参量M(3000)F2和hmF2的经验正交函数方法建模研究

The ionospheric parameter M(3000)F2 (the so-called transmission factor or the propagation factor) is important not only in practical applications such as frequency planning for radio-communication but also in ionospheric modeling. This parameter is strongly anti-correlated with the ionospheric F2-layer peak height hmF2，a parameter often used as a key anchor point in some widely used empirical models of the ionospheric electron density profile (e.g., in IRI and NeQuick models). Since hmF2 is not easy to obtain from measurements and M(3000)F2 can be routinely scaled from ionograms recorded by ionosonde/digisonde stations distributed globally and its data has been accumulated for a long history, usually the value of hmF2 is calculated from M(3000)F2 using the empirical formula connecting them. In practice, CCIR M(3000)F2 model is widely used to obtain M(3000)F2 value. However, recently some authors found that the CCIR M(3000)F2 model has remarkable discrepancies with the measured M(3000)F2, especially in low-latitude and equatorial regions. For this reason, the International Reference Ionosphere (IRI) research community proposes to improve or update the currently used CCIR M(3000)F2 model. Any efforts toward the improvement and updating of the current M(3000)F2 model or newly development of a global hmF2 model are encouraged. In this dissertation, an effort is made to construct the empirical models of M(3000)F2 and hmF2 based on the empirical orthogonal function (EOF) analysis combined with regression analysis method. The main results are as follows: 1. A single station model is constructed using monthly median hourly values of M(3000)F2 data observed at Wuhan Ionospheric Observatory during the years of 1957–1991 and compared with the IRI model. The result shows that EOF method is possible to use only a few orders of EOF components to represent most of the variance of the original data set. It is a powerful method for ionospheric modeling. 2. Using the values of M(3000)F2 observed by ionosondes distributed globally, data at grids uniformly distributed globally were obtained by using the Kriging interpolation method. Then the gridded data were decomposed into EOF components using two different coordinates: (1) geographical longitude and latitude; (2) modified dip (Modip) and local time. Based on the EOF decompositions of the gridded data under these two coordinates systems, two types of the global M(3000)F2 model are constructed. Statistical analysis showed that the two types of the constructed M(3000)F2 model have better agreement with the observational M(3000)F2 than the M(3000)F2 model currently used by IRI. The constructed models can represent the global variations of M(3000)F2 better. 3. The hmF2 data used to construct the hmF2 model were converted from the observed M(3000)F2 based on the empirical formula connecting them. We also constructed two types of the global hmF2 model using the similar method of modeling M(3000)F2. Statistical analysis showed that the prediction of our models is more accurate than the model of IRI. This demonstrated that using EOF analysis method to construct global model of hmF2 directly is feasible. The results in this thesis indicate that the modeling technique based on EOF expansion combined with regression analysis is very promising when used to construct the global models of M(3000)F2 and hmF2. It is worthwhile to investigate further and has the potential to be used to the global modeling of other ionospheric parameters.

Long-range automaton models of earthquakes: Power-law accelerations, correlation evolution, and mode-switching

We introduce a conceptual model for the in-plane physics of an earthquake fault. The model employs cellular automaton techniques to simulate tectonic loading, earthquake rupture, and strain redistribution. The impact of a hypothetical crustal elastodynamic Green's function is approximated by a long-range strain redistribution law with a r(-p) dependance. We investigate the influence of the effective elastodynamic interaction range upon the dynamical behaviour of the model by conducting experiments with different values of the exponent (p). The results indicate that this model has two distinct, stable modes of behaviour. The first mode produces a characteristic earthquake distribution with moderate to large events preceeded by an interval of time in which the rate of energy release accelerates. A correlation function analysis reveals that accelerating sequences are associated with a systematic, global evolution of strain energy correlations within the system. The second stable mode produces Gutenberg-Richter statistics, with near-linear energy release and no significant global correlation evolution. A model with effectively short-range interactions preferentially displays Gutenberg-Richter behaviour. However, models with long-range interactions appear to switch between the characteristic and GR modes. As the range of elastodynamic interactions is increased, characteristic behaviour begins to dominate GR behaviour. These models demonstrate that evolution of strain energy correlations may occur within systems with a fixed elastodynamic interaction range. Supposing that similar mode-switching dynamical behaviour occurs within earthquake faults then intermediate-term forecasting of large earthquakes may be feasible for some earthquakes but not for others, in alignment with certain empirical seismological observations. Further numerical investigation of dynamical models of this type may lead to advances in earthquake forecasting research and theoretical seismology.

Empirical modelling of submersed macrophytes in Yangtze lakes

Submersed macrophytes in Yangtze lakes have experienced large-scale declines due to the increasing human activities during past decades. To seek the key factor that affects their growth, monthly investigations of submersed macrophytes were conducted in 20 regions of four Yangtze lakes during December, 2001-March, 2003. Analyses based on annual values show that the ratio of Secchi depth to mean depth is the key factor (50% of macrophyte biomass variability among these lakes is statistically explained). Further analyses also demonstrate that the months from March to June are not only the actively growing season for most macrophytes, but the key time the factor acts. Five key-time models yielding higher predictive power (r(2) reaches 0.75,0.76,0.77,0.69 and 0.81) are generated. A comparison between key-time models and traditional synchronic ones indicates that key-time models have higher predictive power. Analyses of transparency thresholds during macrophyte growing season and the limitations of the models are presented. The models and other results may benefit the work concerning submersed macrophyte recovery in Yangtze lakes. (c) 2005 Elsevier B.V. All rights reserved.

A new empirical model for retrieving sea surface salinity in L band

Sea surface salinity is a key physical parameter in ocean science. It is important in the ocean remote sensing to retrieve sea surface salinity by the microwave probe technology. Based on the in situ measurement data and remote sensing data of the Yellow Sea, we have built a new empirical model in this paper, which can be used to retrieve sea surface salinity of the Yellow Sea by means of the brightness temperature of the sea water at L-band. In this model, the influence of the roughness of the sea surface is considered, and the retrieved result is in good agreement with the in situ measurement data, where the mean absolute error of the retrieved sea surface salinity is about 0.288 psu. This result shows that our model has greater retrieval precision compared with similar models.