Revisiting Sustainable Development of Dry Valleys in Hengduan Mountains Region

Dry valleys are a striking geographic landscape in Hengduan Mountains Region and are characterized by low rainfall, desert type of vegetation and fragile environment. Past efforts and resources have been concentrated mainly on rehabilitation of degraded ecosystem and fragile environment, particularly reforestation, while socio-economic development has been largely overlooked. Despite successes in pocket areas, the overall trend of unsustainability and environmental deterioration are continuing. It is important to understand that uplift of the Tibetan Plateau is the root cause of development of dry valleys, and development and formation of dry valleys is a natural process. Human intervention has played a secondary role in development of dry valleys and degradation of dry valleys though human intervention in many cases has speeded up environmental degradation of the dry valleys. It is important to understand that dry valleys are climatic enclaves and an integrated approach that combines rehabilitation of degraded ecosystems and socio-economic development should be adopted if the overall goal of sustainable development of dry valleys is to be achieved. Promotion of niche-based cash crops, rural energy including hydropower, solar energy, biogas and fuelwood plantation is recommended as the priority activities.     

利用Lageos 1 SLR资料解算1990-2001年的地球定向参数

利用 12年的Lageos 1卫星激光测距资料 (1990 - 2 0 0 1)解算得到了地球定向参数 (EOP) ,将该序列的结果与同期的EOP(IERS)C0 4进行比较 ,其外符精度为 :极移XP— 0 .4 0mas,YP— 0 .4 2mas ,日长变化Dr— 0 .0 35ms。     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.     

Wolf-Rayet stars

Summary Recent literature on Population I Wolf-Rayet star research extending from the Milky Way to blue compact dwarf galaxies is reviewed, broken down into inventory, basic parameters and galactic distribution, atmospheres, binaries, intrinsic variability, mass loss, enrichment and evolution. Also the incidence of Wolf-Rayet stars with variable non-thermal radio emission, excess X-ray fluxes, and episodic/periodic IR excesses is reviewed. These phenomena appear to be associated with wind-wind interaction in wide long-period WR+OB binaries and with wind-compact object interaction in WR+c binaries, with orbit sizes of the order of magnitude of the WR radio photosphere sizes or larger.     

用遗传算法对自动控制系统数学模型寻优

探讨用遗传算法对数学模型进行优化。考虑到控制系统稳健性的要求 ,用遗传算法寻找出控制系统最佳稳定域 ,实现控制系统数学模型的寻优。阐述了用遗传算法求解问题的步骤和参数的取值 ,并用仿真实验对优化结果进行了检验。结果表明 :在控制系统数学模型的优化中 ,遗传算法具有其他算法无可比拟的优越性。     

Detecting the first objects in the mid-infrared with the Next Generation Space Telescope

We calculate the expected mid-infrared (MIR) molecular hydrogen line emission from the first objects in the Universe. As a result of their low masses, the stellar feedback from massive stars is able to blow away their gas content and collect it into a cooling shell where H₂ rapidly forms and IR roto-vibrational (as for example the rest-frame 2.12 μm) lines carry away a large fraction (up to 10 per cent) of the explosion energy. The fluxes from these sources are in the range 10⁻²¹–10⁻¹⁷ erg s⁻¹ cm⁻² . The highest number counts are expected in the 20-μm band, where about 10⁵ sources deg⁻² are predicted at the limiting flux of 3×10⁻¹⁸ erg s⁻¹ cm⁻². Among the planned observational facilities, we find that the best detection perspectives are offered by the Next Generation Space Telescope ( NGST ), which should be able to reveal about 200 first objects in one hour observation time at its limiting flux in the above band. Therefore, mid-IR instruments appear to represent perfect tools to trace star formation and stellar feedback in the high ( z ≳5) redshift Universe.     

Silicon limitation on primary production and its destiny in Jiaozhou Bay, China——Ⅳ：Study on cross-bay transect from estuary to ocean

The authors analyzed the data collected in the Ecological Station Jiaozhou Bay from May 1991 to November 1994, including 12 seasonal investigations, to determine the characteristics, dynamic cycles and variation trends of the silicate in the bay. The results indicated that the rivers around Jiaozhou Bay provided abundant supply of silicate to the bay. The silicate concentration there depended on river flow variation. The horizontal variation of silicate concentration on the transect showed that the silicate concentration decreased with distance from shorelines. The vertical variation of it showed that silicate sank and deposited on the sea bottom by phytoplankton uptake and death, and zooplankton excretion. In this way, silicon would endlessly be transferred from terrestrial sources to the sea bottom. The silicon took up by phytoplankton and by other biogeochemical processes led to insufficient silicon supply for phytoplankton growth. In this paper, a 2D dynamic model of river flow versus silicate concentration was established by which silicate concentrations of 0.028–0.062 μmol/L in seawater was yielded by inputting certain seasonal unit river flows (m³/s), or in other words, the silicate supply rate; and when the unit river flow was set to zero, meaning no river input, the silicate concentrations were between 0.05–0.69 μmol/L in the bay. In terms of the silicate supply rate, Jiaozhou Bay was divided into three parts. The division shows a given river flow could generate several different silicon levels in corresponding regions, so as to the silicon-limitation levels to the phytoplankton in these regions. Another dynamic model of river flow versus primary production was set up by which the phytoplankton primary production of 5.21–15.55 (mgC/m²·d)/(m³/s) were obtained in our case at unit river flow values via silicate concentration or primary production conversion rate. Similarly, the values of primary production of 121.98–195.33 (mgC/m²·d) were achieved at zero unit river flow condition. A primary production conversion rate reflects the sensitivity to silicon depletion so as to different phytoplankton primary production and silicon requirements by different phytoplankton assemblages in different marine areas. In addition, the authors differentiated two equations (Eqs. 1 and 2) in the models to obtain the river flow variation that determines the silicate concentration variation, and in turn, the variation of primary production. These results proved further that nutrient silicon is a limiting factor for phytoplankton growth. This study was funded by NSFC (No. 40036010), and the Director's Fund of the Beihai Sea Monitoring Center, the State Oceanic Administration.