TheROSAT survey of extreme UV sources: a new window on the universe

This paper discusses the first all-sky suvey of cosmic extreme ultra-violet sources, discovered by theROSAT Wide Field Camera. Details of the instrument and the survey are presented, with comparisons made to previous selected surveys in the X-ray regime. The subsequent optical identification program is described, and the major results summarized. I then discuss the main classes of EUV emitters: active chromosphere stars and hot white dwarfs, and describe the importance of EUV obserevations in understanding the astrophysics of these objects. Many bright, and relatively nearby, sources have been identified with hitherto unrecognized active stars, representing the extremes in chromospheric and coronal activity, be it binary or age related. Many new hot DA white dwarfs have also been indentified, and the most exciting result in this area is the discovery that significant traces of heavier elements (e.g. C, N, O, Si, Fe & Al) exist in their atmospheres, substantially increasing their EUV opacities. The importance of hot white dwarfs as standard candles in probing the local interstellar medium is also discussed. Miscellaneous counterparts (AGN, PNN, O-B stars and CVs) that make up the rest of the sample of EUV sources are also briefly mentioned. I conclude the paper with a discussion of the new magnetic cataclysmic variables discovered at SAAO, which have been the subject of intensive follow-up observations.     

The circumstellar shells of carbon miras

The relation between mass loss rate and pulsation period in carbon Miras is discussed. The dust mass loss rate is very low (about 2 × 10^–10 M/yr) up to aboutP = 380 days, where there is a sudden increase. ForP > 400 days there is a linear relation between logM andP. The change in the mass loss rate near 380 days may be related to radiation pressure on dust becoming effective in driving the outflow.     

GIS支持下青海湖地区草地蝗虫发生的地形分析

草地蝗虫发生的地形分析是建立草地蝗虫发生预报模型的基础，以青海湖地区为实验区，在Arc/Info和ArcView地理信息系统的支持下，进行草地蝗虫发生与各地形变量（包括海拔高度、坡度与坡向）的叠置分析，提取草地蝗虫发生的地形信息数据库，然后，分别采用T－检验（对连续变量－海拔高度和坡度）和卡方（Chisquare)检验（对类变量－坡向类型）进行各地形变量对草地蝗虫发生的差异显著性检验，结果表明，海拔高度和坡度对草地蝗虫发生的影响极为显著（显著性水平p=0.000)，而坡向地草地蝗虫发生的影响不是特别明显（显著性水平p=0.039)。     

塔里木河中游天然植被的数量分类与排序研究

通过塔里木河中下游天然植被带的调查研究，应用数量分类(TWINSPAN)和排序(CCA)方法，对塔里木河中下游地区天然植被类型进行了划分，并探讨了决定该地区天然植物群落类型的主要环境因子。该地区天然植被可分为4个植被型组，4个植被型，6种植被亚型，9个群系，15个群丛。通过对8个环境因子的CCA排序分析，结果表明制约塔里木河中下游天然植被组成和结构的主导环境因子为地下水位、地下水矿化度、地下水酸碱度。通过CCA二维排序图将16种植物对干旱、盐碱的适应性划分5类型。21个样地在CCA二维排序图上可聚集成9个植物群落类群，即胡杨(Populus euphratica)群落、铃铛刺(Halimodendron halodendron)群落、库尔勒沙拐枣(Calligonum Kuerlese)群落、多枝柽柳(Tamarix ramosissima)群落、黑果枸杞(Lycium ruthenicum)群落、盐穗木(Halostashys caspica)群落、花花柴(Karelinia caspica)群落、疏叶骆驼剂(Alhagi sparsifolia)群落、罗布麻(Apocynum venetum)群落，与TWINSPAN结果中的群系分类单位一致。CCA连续排序与TWINSPAN分类结果吻合较好。9种植物群落类型中，能耐受最大盐胁迫的为盐穗木群落，能耐受最大干旱胁迫的为铃铛刺群落，能耐受最大地下水碱胁迫的为黑果枸杞群落。     

SH波对无限介质土中衬砌结构的动应力集中

运用复变函数法和保角映射法对无限介质土中的衬砌结构进行分析.通过引入两个势函数将弹性介质的控制方程解耦成两个Helmholtz方程.利用所得势函数的通解,得到无限土介质和衬砌结构的位移和应力的表达式,利用无限介质与衬砌结构之间的连续性条件和衬砌结构内边界上的边界条件,可确定波函数展开式中的末知系数,并给出动应力集中系数的数值结果.     

河南内乡-淅川-带的虎睛石矿床及开发利用

研究区虎睛石矿床在地质特征、矿物组合、化学成分及琢雕效果等方面与国外虎睛石矿床是完全不同的，属于一种特殊的罕见类型。在分析虎睛石地质特征、开发利用现状的基础上，认为该区的虎睛石资源丰富，琢雕效果好，在国内外市场上具有一定的竞争力和良好的开发利用前景。     

Environmental variables related to wheat yields in the semiarid pampa region of Argentina

The current paper analyses various environmental parameters in relation to wheat yields in Bordenave, Province of Buenos Aires, Argentina. The variables used are: precipitation (ppt), maximum (Tx) and minimum temperature (Tmi) as well as those obtained by applying the Palmer model. Decadic and phenological scales are used for data corresponding to the period 1977–2000. The stepwise method is used to obtain a multi-variate equation to calculate yield taking into account environmental variables only. For a five variates model the coefficient of determination, R² equals 95.79% and the standard error of estimation is 129.0 kg ha⁻¹. In the sample yields, the incidence of total variability for thermal variables is 42.7% and for hydrological variables, 53%. The value and sign of the correlation coefficients were analysed throughout the cultivation cycle. The α coefficient is mainly responsible for yield variance during tillering and stem elongation. There is good correlation with the values of Palmer's Drought Severity Index (PDSI) for the flowering and grain filling stages.     

Spatiotemporal estimation of snow depth using point data from snow stakes,digital terrain models,and satellite data

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.     

Quantifying the impact of landscape changes on hydrological variables in the alpine and cold region using hydrological model and remote sensing data

Quantifying the impact of landscape on hydrological variables is essential for the sustainable development of water resources. Understanding how landscape changes influence hydrological variables will greatly enhance the understanding of hydrological processes. Important vegetation parameters are considered in this study by using remote sensing data and VIC-CAS model to analyse the impact of landscape changes on hydrology in upper reaches of the Shule River Basin (URSLB). The results show there are differences in the runoff generation of landscape both in space and time. With increasing altitude, the runoff yields increased, with approximately 79.9% of the total runoff generated in the high mountains (4200–5900 m), and mainly consumed in the mid-low mountain region. Glacier landscape produced the largest runoff yields (24.9% of the total runoff), followed by low-coverage grassland (LG; 22.5%), alpine cold desert (AL; 19.6%), mid-coverage grassland (MG; 15.6%), bare land (12.5%), high-coverage grassland (HG; 4.5%) and shrubbery (0.4%). The relative capacity of runoff generation by landscapes, from high to low, was the glaciers, AL, LG, HG, MG, shrubbery and bare land. Furthermore, changes in landscapes cause hydrological variables changes, including evapotranspiration, runoff and baseflow. The study revealed that HG, MG, and bare land have a positive impact on evapotranspiration and a negative impact on runoff and baseflow, whereas AL and LG have a positive impact on runoff and baseflow and a negative impact on evapotranspiration. In contrast, glaciers have a positive impact on runoff. After the simulation in four vegetation scenarios, we concluded that the runoff regulation ability of grassland is greater than that of bare land. The grassland landscape is essential since it reduced the flood peak and conserved the soil and water.     

Cascades of green: A review of ecosystem-based adaptation in urban areas

Climate change impacts increase pressure on challenges to sustainability and the developmental needs of cities. Conventional, “hard” adaptation measures are often associated with high costs, inflexibility and conflicting interests related to the dense urban fabric, and ecosystem-based adaptation (EbA) has emerged as a potentially cost-efficient, comprehensive, and multifunctional approach. This paper reviews and systematises research on urban EbA. We propose an analytical framework that draws on theory from ecosystem services, climate change adaptation and sustainability science. It conceptualises EbA in terms of five linked components: ecological structures, ecological functions, adaptation benefits, valuation, and ecosystem management practices.Our review identified 110 articles, reporting on 112 cities, and analysed them using both quantitative statistical and qualitative content analysis. We found that EbA research in an urban context is fragmented due to different disciplinary approaches and concepts. Most articles focus on heat or flooding, and the most studied ecological structures for reducing the risk of such hazards are green space, wetlands, trees and parks. EbA is usually evaluated in bio-geophysical terms and the use of economic or social valuations are rare. While most articles do not mention specific practices for managing ecological structures, those that do imply that urban EbA strategies are increasingly being integrated into institutional structures. Few articles considered issues of equity or stakeholder participation in EbA.We identified the following challenges for future EbA research. First, while the large amount of data generated by isolated case studies contributes to systems knowledge, there is a lack of systems perspectives that position EbA in relation to the wider socio-economic and bio-geophysical context. Second, normative and ethical aspects of EbA require more thought, such as who are the winners and losers, especially in relation to processes that put people at risk from climate-related hazards. Third, there is room for more forward-looking EbA research, including consideration of future scenarios, experimentation in the creation of new ecological structures and the role of EbA in transformative adaptation.