Visualising 30 Years of Population Density Change in Australia’s Major Capital Cities

ABSTRACT

This study uses a novel spatial approach to compare population density change across cities and over time. It examines spatio-temporal change in Australia’s five most populated capital cities from 1981 to 2011, and documents the established and emerging patterns of population distribution. The settlement patterns of Australian cities have changed substantially in the last 30 years. From the doughnut cities of the 1980s, programs of consolidation, renewal and densification have changed and concentrated population in our cities. Australian cities in the 1980s were characterised by sparsely populated, low density centres with growth concentrated to the suburban fringes. ‘Smart Growth’ and the ‘New Urbanism’ movements in the 1990s advocated higher dwelling density living and the inner cities re-emerged, inner areas were redeveloped, and the population distribution shifted towards increased inner city population densities. Policies aimed at re-populating the inner city dominated and the resultant changes are now visible in Australia’s five most populated capital cities. While this pattern has been reported in a number of studies, questions remain regarding the extent of these changes and how to analyse and visualise them across urban space. This paper reports on a spatial method which addresses the limitations of changing statistical boundaries to identify the changing patterns in Australian cities over time and space.     

Hybrid N-order Lagrangian Interpolation Eulerian?Lagrangian Method for Salinity Calculation

The Eulerian?Lagrangian method (ELM) has been used by many ocean models as the solution of the advection equation, but the numerical error caused by interpolation imposes restriction on its accuracy. In the present study, hybrid N-order Lagrangian interpolation ELM (LiELM) is put forward in which the N-order Lagrangian interpolation is used at first, then the lower order Lagrangian interpolation is applied in the points where the interpolation results are abnormally higher or lower. The calculation results of a step-shaped salinity advection model are analyzed, which show that higher order (N=3?8) LiELM can reduce the mean numerical error of salinity calculation, but the numerical oscillation error is still significant. Even number order LiELM makes larger numerical oscillation error than its adjacent odd number order LiELM. Hybrid N-order LiELM can remove numerical oscillation, and it significantly reduces the mean numerical error when N is even and the current is in fixed direction, while it makes less effect on mean numerical error when N is odd or the current direction changes periodically. Hybrid odd number order LiELM makes less mean numerical error than its adjacent even number order LiELM when the current is in the fixed direction, while the mean numerical error decreases as N increases when the current direction changes periodically, so odd number of N may be better for application. Among various types of Hybrid N-order LiELM, the scheme reducing N-order directly to 1st-order may be the optimal for synthetic selection of accuracy and computational efficiency.     

西太平洋暖池热盐结构盐度场变异的主模态

基于1950~2011年间的月平均温、盐度资料,以28℃等温线作为西太平洋暖池的定义标准,并取ΔT=-0.4℃,分别计算了暖池区(20°N~15°S,120°E~140°W)各格点混合层、障碍层和深层的平均盐度,构成了暖池热盐结构的盐度场.据此,运用EOF分解法分析了暖池热盐结构盐度距平场主要模态的变化特征及其与ENSO间的关系,并探讨了主要模态的年际变异机理.结果表明,暖池热盐结构盐度场第一模态揭示了盐度场变异的关键区位于暖池中部;该模态具有2~4a的年际变化和准10a的年代际变化,并在1977年前后经历了一次气候跃变(此外,深层盐度场第一模态还在1999年前后发生了一次气候跃变),且在跃变前后与不同类型的ENSO事件有较密切的联系.暖池中部混合层和障碍层盐度的变化比较一致,即在跃变前盐度为偏高期,而在跃变后则变为偏低期.暖池中部深层盐度在1977年以前和1999年之后皆处于偏高期,而在1978~1999年间则处于偏低期.而且,从混合层至深层,盐度的变化幅度逐渐变小.进一步分析表明,暖池中部混合层和障碍层盐度的年际变化主要是由纬向风、南赤道流(SEC)和降水共同引起的,即当东风增强(减弱)时,强(弱)SEC将携带更多(少)的高盐水进入混合层或潜沉至障碍层,同时局地降水的减少(增多),也使得混合层和障碍层的盐度增加(减少);深层盐度的年际变化主要是由SEC和赤道潜流(EUC)导致的,即当SEC增强(减弱)时,将有更多(少)的高盐水进入暖池,而当EUC增强(减弱)时则有更多(少)的低盐水流出暖池,从而使得暖池的深层盐度升高(降低).     

Intercomparison of CRA-Interim Precipitation Products with ERA5 and JRA-55

By using the hourly data from surface meteorological stations in China, the 3-hour precipitation data from CRA-Interim (Chinese Reanalysis-Interim), ERA5 (ECMWF Reanalysis 5) and JRA-55 (Japanese Reanalysis-55) are compared, both on the spatial-temporal distributions and on bias with observation precipitation in China. The results show that: (1) The three sets of reanalysis datasets can all reflect the basic spatial distribution characteristics of annual average precipitation in China. The simulation of topographic forced precipitation in complex terrain by CRA-interim is more detailed, while CRA-interim has larger negative bias in central and East China, and larger positive bias in southwest China. (2) In terms of seasonal precipitation, the three sets of reanalysis datasets overestimate the precipitation in the heavy rainfall zone of spring and summer, especially in southwest China. CRA interim’s location of the rain belt in the First Rainy Season in South China is west by south, the summer precipitation has positive bias in southwest and South China. (3) All of the reanalysis datasets can basically reflect the distribution difference of inter-annual variation of drought and flood, but the overall the CRA-Interim generally shows negative bias, while the ERA5 and JRA-55 exhibit positive bias. (4) For the diurnal variation of precipitation in summer, all the reanalysis datasets perform better in simulating the daytime precipitation than in the night, and bias of CRA-interim is less in southeast and northeast than elsewhere. (5) ERA5 generally performs the best on the evaluation of quantitative precipitation forecast, the JRA-55 is the next, followed by the CRA-Interim. CRA-Interim has higher missing rate and lower threat score for heavy rains; however, at the level of downpour, the CRA-Interim performs slightly better.     

南部非洲花岗岩型与伟晶岩型钽矿床地质特征

钽作为一种重要的稀有金属矿产,广泛应用于各种工业领域。世界上的钽矿床成因主要为内生成矿,尤以花岗岩型和伟晶型最为重要,碰撞造山过程导致的多期次岩浆活动是有利的钽矿成矿环境。南部非洲钽矿资源丰富且品位高,主要为花岗岩型和伟晶岩型钽矿床,空间上主要分布在卡普瓦尔克拉通、刚果克拉通、津巴布韦克拉通以及基巴拉、泛非、达马拉等碰撞环境下形成的同造山—后造山构造带内,时间上主要集中在太古宙(2.85~2.58 Ga)、古元古代(2.48~2.0 Ga)、晚中元古代—早新元古代(1026~880 Ma)以及泛非活动期(500~440 Ma),且不同钽矿带内含钽矿物稀有及稀土元素分布特征差异较大。南部非洲发育大量与钽矿形成密切相关的花岗岩及伟晶岩岩体,资源潜力巨大,未来有望成为世界上主要的钽资源接续基地。     

基于非均质地层参数表征的颗粒流模型构建

非均质地层参数的获取与表征方法是浅层地质体改造的重要依据。基于非均质地层的地质建模,表征具有随机不确定性和模糊不确定性的土体表观参数和物理力学参数。利用 Weibull 分布统计描述局部地层信息,并应用Diamond-Square分形插值方法,结合差分盒子维计算非均质地层分形维数,进一步合理演算整体地层参数。采用PFC3D数值模拟软件,结合马尔科夫链蒙特卡洛方法采样数据,对非均质地层块石颗粒及土颗粒进行建模。通过非均质地层模拟结果,发现其破坏过程与应力应变曲线要比均质土体复杂得多,由于块石大颗粒的含量、分布特性对土样力学性质影响显著。本文建模方法有助于非均质地层参数表征研究。     

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index(BI)from basic suites of well logs is desir-able as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation(Texas).This transparent open box(TOB)algorithm matches data records by calculating the sum of squared errors be-tween their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error(RMSE)between calculated and predicted(BI).The prediction accuracy achieved by TOB using just five well logs(Gr,pb,Ns,Rs,Dt)to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE～0.056 and R2～0.790.At a sampling density of about one sample per 0.1 ft BI is predicted with RMSE～0.008 and R2～0.995.Adding a stratigraphic height index as an additional(sixth)input variable method improves BI prediction accuracy to RMSE～0.003 and R2～0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of＞±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measure-ments but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.     

内蒙古呼包鄂地区近56年来大气环境容量变化特征分析

为探讨内蒙古呼包鄂地区大气对污染物承载能力的变化,科学评价气候变化对大气环境的影响,利用1961—2016年呼包鄂地区8个国家气象站降水量、风速、云量资料,分析了该地区气象条件的变化特征及大气环境容量的时空分布特征,并对大气环境容量与气象要素之间的相关性进行了分析。结果显示:呼包鄂地区大气环境容量1月的最小、4月的最大,春季的>秋季的>夏季的>冬季的,年平均大气环境容量为62.4 t/d/km^2;月、季、年平均大气环境容量呈波动减小趋势,特别是20世纪70年代末期之后减小趋势更为显著;有明显的空间分布特征,由城区向郊区逐渐增大,城郊差异冬季最为明显,夏季差异最小;大气环境容量与降水相关性差,与风速和日最大混合层高度呈显著的正相关关系,特别是与风速相关性更显著,与小风日数呈显著的负相关关系。     

三江源区大气可降水量时空特征及其与降水关系

利用1979—2016年欧洲中期天气预报中心(ECMWF) ERA-Interim (1°×1°)再分析资料中的经、纬向水汽通量和大气可降水量(precipitation water vapor,PWV)数据,采用相关性分析、趋势分析法、累积距平、IDW等方法,分析三江源地区PWV与水汽通量的时空分布特征、降水转化率(precipitati-on conversion efficiency,PCE)变化规律。结果表明:过去的38 a,经、纬向多年平均水汽通量分别为50. 2、196. 7 kg·m-1·s^(-1),纬向水汽通量气候倾向率比经向大。南边界为纬向主要水汽输入边界,东边界为经向主要水汽输出边界,纬向水汽输送大于经向输送。多年平均PWV为1998. 3 mm,近38 aPWV呈现微弱增加趋势,1979—1997年,PWV呈下降趋势,1998年后PWV呈增加趋势,同期降水也在增加,说明该时段三江源地区气候转湿。PWV与水汽通量的年际变化趋势和转折年相一致。三江源区多年平均PCE为24. 57%,1989年PCE最高,达32. 76%,各季节平均PCE空间分布与年平均PCE分布一致,均表现出南部、东南部高,西部、东北部低的变化特征,各季节PCE大小差异明显,春季多年平均PCE为15. 92%,夏季25. 67%,秋季21. 01%,冬季仅7. 03%。     

青海高原致灾性对流天气时空分布特征

利用2005-2016年青海高原地面观测、灾情和卫星云图等资料,对青海高原致灾性对流天气进行筛选和分类,在此基础上分析了各类致灾性对流天气的时空分布特征及与地形的关系。结果表明:(1)青海高原致灾性对流主要有雷暴、短时强降水、冰雹以及混合类四种,集中分布于高原东部。(2)地形对致灾性对流的落区、频次和强度起关键作用。雷暴多产生于山区,短时强降水和冰雹主要产生在迎风坡、河谷和地势较开阔的低地。其中,青东农区以混合类和冰雹居多,青南牧区以混合类居多,环湖与祁连地区和柴达木盆地以短时强降水居多。(3)近12 a青海高原致灾性对流整体呈波动式减少,2005-2010年(前期)致灾性对流日数和次数较多,2011-2016年(后期)显著减少,但不同类型年际变化特征略有差异。其中,冰雹和雷暴日数前期较大,后期显著减少;混合类和短时强降水日数无明显变化趋势,但前者年际波动幅度较后者大。(4)致灾性对流主要产生于5-9月,各类型均呈现典型的单峰型月分布,混合类和冰雹日数及次数的峰值均在8月,雷暴日数和次数的峰值均在6月,而短时强降水日数和次数的峰值分别在8月、7月。(5)致灾性对流集中产生于13:00至次日01:00,高峰时段(16:00-20:00)以冰雹和混合类居多,而夜间时段以短时强降水居多。