藏南卡鲁雄峰枪勇冰川新冰期冰川发育探讨

山地冰川对气候变化响应敏感,全新世以来,冰川呈加速退缩趋势。冰川地貌是冰川变化最直接的证据。枪勇冰川发源于卡鲁雄峰,位于喜马拉雅山中段北坡雨影区,雅鲁藏布江以南,冰川末端在海拔5000 m附近,冰川面积约为6.63 km2。枪勇沟从外至内共保存有4期冰碛垄,其中以"大枪勇错"外侧的新冰期冰碛垄最为高大,终碛垄高约30 m,且形态清晰。文章基于野外观察及谷歌影像（Google Earth）上冰碛垄的分布范围,恢复并计算了新冰期时枪勇冰川面积约为10.0 km2,较现代冰川扩张了1.5倍。采用VOLTA模型计算了现代冰川的体积为0.21±0.04 km3,平均厚度约为31.7 m。基于纵剖线模型模拟了新冰期冰川表面高程,新冰期冰川平均厚度约为58.5 m,冰川体积约0.59±0.12 km3,是现代冰川体积的2.8倍,体积减小较面积退缩更为剧烈。利用积累区面积比率法（Accumulation Area Ratio Method,简称AAR）,采用AAR值为0.7±0.05,基于现代冰川表面高程和模拟的新冰期冰川表面高程,计算现代冰川和新冰期冰川的物质平衡线高度（Equilibrium Line Altitude,简称ELA）分别为5780±70 m和5660±100 m,新冰期时平衡线高度降低了约120 m。而不利用模型恢复新冰期表面高程时,平衡线降低值为240 m,可能高估约120 m。新冰期时,枪勇冰川扩张受区域降温控制,冰川前进可能是对4.2 ka冷事件的响应。冰碛垄形成时代和大枪勇错孢粉数据表明,冰川可能在2.5 ka左右已经退缩,2.2 ka时退缩加剧。     

异龙湖流域湿地生态系统健康评价

湿地生态系统健康状况评价为湿地资源的合理开发、生态系统的维护提供了重要的借鉴。在充分了解异龙湖流域湿地资源环境的基础上,结合湖泊湿地生态系统的概念及内涵,采用PSR(压力-状态-响应)框架模型和生态系统健康指数计算方法对研究区湿地生态系统健康状况进行评价。结果表明:(1)异龙湖流域湿地生态系统健康评价综合分值(Y)为2.8494,处于[2,3]之间,属于脆弱状态;(2)从三大指标状况来看,压力指标(P)、状态指标(S)和响应指标(R)在评价等级中的得分及表现情况分别为0.7368(脆弱状态)、1.1620(亚健康状态)和0.9506(亚健康状态);(3)人口密度、人类活动强度、单位面积化肥和农药负荷、水土流失强度、湖泊水富营养化、人们的环保意识等指标是影响流域内湿地生态系统健康状况的关键因素,人为因素占主导地位。     

广东新会市人口,耕地,粮食问题及灰色模型预测

在我国改革开放政策实行较早的广东珠江三角洲地区，我们对地处西南部的新会市进行了为期两年的全面、典型抽样调查。发现全市的耕地面积和播种面积呈下降趋势，单产虽有较大幅度的增长但粮食总产量的增长缓慢，速度低于全市总人口的自然增长率，因此人均粮食产量呈下降趋势。根据建立的灰色ＧＭ（１，１）模型预测，新会市２０１０年人均耕地面积只有０．０２５ｈｍ＾２，在“人均４分保命田”之下。因此，本文对该地区作出的粮食及     

Remote forcing of Indian Ocean warming on Northwest Pacific during E1 Nino decaying years： a FOAM model approach

This paper attempts to analyze in detail the remote influence of the Indian Ocean Basin warming on the Northwest Pacific （NWP） during the year of decaying E1 Nifio. Observation data and the Fast Ocean- Atmosphere coupled Model 1.5 were used to investigate the triggering conditions under which the remote influence is formed between the positive sea surface temperature （SST） anomaly in the North Indian Ocean and the Anomalous Northwest Pacific anticyclone （ANWPA）. Our research show that it is only when there is a contributory background wind field over the Indian Ocean, i,e., when the Indian Summer Monsoon （ISM） reaches its peak, that the warmer SST anomaly in the North Indian Ocean incites significant easterly wind anomalies in the lower atmosphere of the Indo-West tropical Pacific. This then produces the remote influence on the ANWPA. Therefore, the SST anomaly in the North Indian Ocean might interfere with the prediction of the East Asia Summer Monsoon in the year of decaying E1 Nifio. Both the sustaining effect of local negative SST anomalies in the NWP, and the remote effect of positive SST anomalies in the North Indian Ocean on the ANWPA, should be considered in further research.     

Optimal Filtering Algorithm for Stochastic 2-D FMM Ⅱ with Multiplicative Noise

1　Introduction　Inrecentyearstherehasbeengrowingresearchin terestinthetwo dimensional (2 D )systemtheory .The 2 Dsystemsmayfindapplicationsinareassuchasmarineseismicdataprocessingandimageprocessing .Althoughmoreandmorevaluableresultshavebeengained ,mos…     

The price for subway access: spatial econometric modelling of office rental rates in London

ABSTRACT

The relative location of commercial properties as the distance to the metro station as well as its absolute location as the address in the Central Business District (CBD) is expected to influence its valuation. We apply the advanced spatial econometric methodology, based on micro geo-located data, to the office rental transaction data from London in 2015. We use different spatial information on the local and global neighbourhood in order to estimate the spatial hedonic valuation model and test the strength of spatial spillovers and location effects on office fees. For offices in London in 2015, geographical factors may raise the price by 50%, as every next 100 m to the metro cost an additional 0.7 ? per ft² per year and the CBD location raises the cost for ca. 20 ? per ft² per year.     

胶州湾高分辨率三维风暴潮漫滩数值模拟

基于海表气压项改进的FVCOM(Finite-Volume Coastal Ocean Model)海洋模式,研发胶州湾高分辨率三维风暴潮漫滩数值模式(JS-FVCOM).利用 JS-FVCOM 模式通过对天文潮、台风强度和径流3要素的不同组合,共设计了5个试验,分别进行风暴潮漫滩模拟实验.分析各试验结果得到如下结论:(1)随着台风最大风速的增加,风暴潮增水迅速增加,当综合水位超过防潮堤高程后增水速度明显减慢.海水淹没范围和淹没深度受综合水位超防潮堤高程时间影响明显.(2)在入海河流的河口区,当洪水位与高潮位相遇时,由于高潮位的顶托作用,洪水下泄不畅,造成综合水位上升明显,极易发生海水漫溢现象.JS-FVCOM 的模拟结果清楚地再现了海水漫堤的淹没过程,可为紧急情况下的人员疏散提供科学的基础数据.     

Remote estimation of surficial seafloor properties through the application Angular Range Analysis to multibeam sonar data

The variation of the backscatter strength with the angle of incidence is an intrinsic property of the seafloor, which can be used in methods for acoustic seafloor characterization. Although multibeam sonars acquire backscatter over a wide range of incidence angles, the angular information is normally neglected during standard backscatter processing and mosaicking. An approach called Angular Range Analysis has been developed to preserve the backscatter angular information, and use it for remote estimation of seafloor properties. Angular Range Analysis starts with the beam-by-beam time-series of acoustic backscatter provided by the multibeam sonar and then corrects the backscatter for seafloor slope, beam pattern, time varying and angle varying gains, and area of insonification. Subsequently a series of parameters are calculated from the stacking of consecutive time series over a spatial scale that approximates half of the swath width. Based on these calculated parameters and the inversion of an acoustic backscatter model, we estimate the acoustic impedance and the roughness of the insonified area on the seafloor. In the process of this inversion, the behavior of the model parameters is constrained by established inter-property relationships. The approach has been tested using a 300 kHz Simrad EM3000 multibeam sonar in Little Bay, NH. Impedance estimates are compared to in situ measurements of sound speed. The comparison shows a very good correlation, indicating the potential of this approach for robust seafloor characterization.     

基于EEMD和ARIMA的海温预测模型研究

类型丰富、时空分辨率高的海洋探测数据,为信号分解和机器学习算法的应用提供了可能。本文针对如何建立有效的海温预测模型这一问题,使用高时空分辨率的海表温度(SST)融合产品,引入信号处理领域的集合经验模态分解(EEMD)和机器学习领域的自回归积分滑动平均模型(ARIMA)。首先利用最适于分解自然信号的EEMD方法,将海温数据分解成多个确定频率的序列;再利用ARIMA分别对各个频率的序列进行预测,最后将各个序列的预测结果进行组合。该方法在丰富数据的支撑下,比以往直接使用海温数据所建立的预测模型精度更高,为更好地进行海温预测提供了新方法。