Exploring the sensitivity of coastal inundation modelling to DEM vertical error

As sea level is projected to rise throughout the twenty-first century due to climate change, there is a need to ensure that sea level rise (SLR) models accurately and defensibly represent future flood inundation levels to allow for effective coastal zone management. Digital elevation models (DEMs) are integral to SLR modelling, but are subject to error, including in their vertical resolution. Error in DEMs leads to uncertainty in the output of SLR inundation models, which if not considered, may result in poor coastal management decisions. However, DEM error is not usually described in detail by DEM suppliers; commonly only the RMSE is reported. This research explores the impact of stated vertical error in delineating zones of inundation in two locations along the Devon, United Kingdom, coastline (Exe and Otter Estuaries). We explore the consequences of needing to make assumptions about the distribution of error in the absence of detailed error data using a 1 m, publically available composite DEM with a maximum RMSE of 0.15 m, typical of recent LiDAR-derived DEMs. We compare uncertainty using two methods (i) the NOAA inundation uncertainty mapping method which assumes a normal distribution of error and (ii) a hydrologically correct bathtub method where the DEM is uniformly perturbed between the upper and lower bounds of a 95% linear error in 500 Monte Carlo Simulations (HBM+MCS). The NOAA method produced a broader zone of uncertainty (an increase of 134.9% on the HBM+MCS method), which is particularly evident in the flatter topography of the upper estuaries. The HBM+MCS method generates a narrower band of uncertainty for these flatter areas, but very similar extents where shorelines are steeper. The differences in inundation extents produced by the methods relate to a number of underpinning assumptions, and particularly, how the stated RMSE is interpreted and used to represent error in a practical sense. Unlike the NOAA method, the HBM+MCS model is computationally intensive, depending on the areas under consideration and the number of iterations. We therefore used the HBM+ MCS method to derive a regression relationship between elevation and inundation probability for the Exe Estuary. We then apply this to the adjacent Otter Estuary and show that it can defensibly reproduce zones of inundation uncertainty, avoiding the computationally intensive step of the HBM+MCS. The equation-derived zone of uncertainty was 112.1% larger than the HBM+MCS method, compared to the NOAA method which produced an uncertain area 423.9% larger. Each approach has advantages and disadvantages and requires value judgements to be made. Their use underscores the need for transparency in assumptions and communications of outputs. We urge DEM publishers to move beyond provision of a generalised RMSE and provide more detailed estimates of spatial error and complete metadata, including locations of ground control points and associated land cover.     

Assimilating data into open ocean tidal models

The problem of deriving tidal fields from observations by reason of incompleteness and imperfectness of every data set practically available has an infinitely large number of allowable solutions fitting the data within measurement errors and hence can be treated as ill-posed. Therefore, interpolating the data always relies on some a priori assumptions concerning the tides, which provide a rule of sampling or, in other words, a regularization of the ill-posed problem. Data assimilation procedures used in large scale tide modeling are viewed in a common mathematical framework as such regularizations. It is shown that they all (basis functions expansion, parameter estimation, nudging, objective analysis, general inversion, and extended general inversion), including those (objective analysis and general inversion) originally formulated in stochastic terms, may be considered as utilizations of one of the three general methods suggested by the theory of ill-posed problems. The problem of grid refinement critical for inverse methods and nudging is discussed.     

Head/Tail Breaks: A New Classification Scheme for Data with a Heavy-Tailed Distribution

This article introduces a new classification scheme—head/tail breaks—to find groupings or hierarchy for data with a heavy-tailed distribution. The heavy-tailed distributions are heavily right skewed, with a minority of large values in the head and a majority of small values in the tail, commonly characterized by a power law, a lognormal, or an exponential function. For example, a country's population is often distributed in such a heavy-tailed manner, with a minority of people (e.g., 20 percent) in the countryside and the vast majority (e.g., 80 percent) in urban areas. This new classification scheme partitions all of the data values around the mean into two parts and continues the process iteratively for the values (above the mean) in the head until the head part values are no longer heavy-tailed distributed. Thus, the number of classes and the class intervals are both naturally determined. I therefore claim that the new classification scheme is more natural than the natural breaks in finding the groupings or hierarchy for data with a heavy-tailed distribution. I demonstrate the advantages of the head/tail breaks method over Jenks's natural breaks in capturing the underlying hierarchy of the data.     

基于多源数据的中国地形海拔分级指标调整研究

使用SRTM DEM、土地覆被、冻融侵蚀、河流沟谷等多源数据,对区分海拔等级的指标地物作了具体分析,以指标地物的平均海拔为依据,通过分级指标对象(如冰川、林线)分布高程重合或贴近的多条等高线圈形成分级等高平面,对陆地地貌进行切割划分,用每组指标对象偏离等高平面的值作为控制量(控制点),插值重构分级参考曲面。该方案最大限度突出指标的地理意义,形成的海拔分级结果与中国地理系统分布特征更为贴近,可以为中国大尺度地貌基本形态的划分提供参考。     

轨迹大数据: 数据处理关键技术研究综述

针对轨迹大数据的高效点-轨迹k近邻（point to trajectory k nearest neighbor, P2T_kNN）查询处理需求,提出了一种融合时空剖分和轨迹分段的轨迹组织方法,其核心思想是在对轨迹作时间剖分的基础上,利用离散全球网格系统（discrete global grid system, DGGS）在空间上进行再次剖分,从而利用两次剖分得到的时空单元编码来索引落入其中的轨迹片段。在此基础上利用分布式列式存储技术设计了面向轨迹大数据的P2T_kNN查询处理框架,提出了一种顾及轨迹数据空间分布的自适应空间单元搜索算法,即通过分析轨迹数据在给定时间约束下的空间分异特征,动态调整空间单元的搜索步长,从而提升了轨迹稀疏区域的处理效率。针对亿级轨迹的实验结果表明,该方法适用于轨迹大数据的P2T_kNN查询处理,在轨迹稠密与稀疏区域的平均查询响应时间均小于1 s。     

大数据视角下的商业中心和热点区分布特征及其影响因素分析——以乌鲁木齐主城区为例

商业空间结构是城市经济活动的重要载体,识别商业中心和商业热点区以及探究其影响因素对于商业资源优化配置显得尤为必要,进而指导城市有序发展。论文以乌鲁木齐主城区为例,利用开放平台大数据兴趣点(point of interest, POI),采用地理学空间统计方法定量识别商业中心和商业热点区,对商业分布和空间集聚特征进行分类和解读,并利用地理探测器方法探寻其影响因素。主要结论如下：① 乌鲁木齐市商业高值区分布在吐乌大高速—和平渠沿线地带,大型商业中心主要有南湖商圈、中山路商圈、友好商圈、会展商圈、米东商圈、铁路局商圈。② 商业热点区呈现“T型”双轴分布,北部新城商业地带与南部传统商业地带共同构成乌鲁木齐市最具活力的商业地带;6类商业热点区的分布可归纳为3种类型,商务和金融服务类为单一点状型,住宿和餐饮服务类为带状延伸型,生活与购物服务类为带状双核型。③地价、集聚效应、路网密度等是影响商业宏观分布的主要因素,其次为人口密度和中心可达性,自然因素如高程、地形起伏度等对商业布局影响有限;各因素对不同类型商业业态的影响程度各异,如人口密度、路网密度对购物类影响较大,中心可达性和地价对于商务、金融类影响较大;就各业态类型网点间的关系而言,商务和金融类协同作用强,餐饮与购物类协同效应较强,共同影响城市商业空间。     

利用奇异谱分析提高极移中长期预报精度

由于空间大地观测数据传输耗时及处理过程复杂, 导致极移测量值的获取存在时延, 无法满足对高精度的极移预报值有重大需求的应用领域. 针对极移复杂的时变特性, 提出一种基于奇异谱分析(singular spectrum analysis, SSA)的预报方法. 首先用SSA分离提取极移时序中的高频组分与低频组分; 其次建立最小二乘(least square, LS)外推与自回归(AutoreGressive, AR)模型对极移高频和低频组分进行组合预报. 结果表明, SSA方法能够准确地分离和提取极移低频和高频组分, 对低频和高频组分组合预报可以显著改善极移的中长期(30--365d)预报精度, 与国际地球自转和参考系服务局(International Earth Rotation and Reference Systems Service, IERS)提供的A公报中的极移预报值相比, SSA方法对极移PMX分量(本初子午线方向)和PMY分量(西90$^\circ$子午线方向)的中长期预报精度改进最高分别可达45.97%和62.44%. 研究结果验证了SSA方法对极移中长期预报改进的有效性.     

大型射电望远镜主反射面调整方法研究

随着毫米波天文学和空间通信的重要性日益提高, 对天线性能提出了越来越高的要求, 而天线性能往往受到其反射器表面精度的限制. 微波全息技术是一种快速有效的检测反射面天线表面轮廓的测量技术. 通过微波全息测量得到天线口径场, 计算天马65m射电望远镜反射面与理想抛物面的偏差. 天马65m射电望远镜的主反射面板是放射状的, 有14圈. 面板的每个角都固定在面板下方促动器的螺栓上进行上下移动, 且相邻面板交点处的拐角共用一个促动器. 采用平面拟合的方法可以计算各块面板拐角处的调整值, 但是同一个促动器会得到4个不同的调整量. 通过平面拟合, 同时以天线照明函数为权重的平差计算方法得到相邻面板拐角的一个平差值, 即天马65m射电望远镜1104个促动器的最佳调整值. 通过多次调整和新算法的应用, 天马65m射电望远镜反射面的面形精度逐渐提高到了0.24mm.     

高层大气密度的午夜极大值特征分析与建模

利用CHAMP (CHAllenging Minisatellite Payload)、GRACE-A (Gravity Recovery and Climate Experiment-A)、SWARM-C (The Earth''s Magnetic Field and Environment Explorers-C)等3颗极轨卫星的资料, 研究360—480km高层大气密度在低纬度区域的午夜极大值(Midnight Density Maximum, MDM)现象. MDM一般出现在23:00- 02:00 LT (Local Time)之间,峰值位置在低纬度15°以内,谷值位置在中纬度35°-45°附近,整体略偏向南半球,振幅约为平均密度的26%.随着高度增大以及太阳辐射水平的增强,MDM振幅呈减小趋势;冬至和夏至日附近的季节效应会减弱MDM振幅,在春秋分日的振幅最大.用3个主流大气模型DTM2000 (Drag Temperature Model 2000), NRLMSISE00 (US Naval Research Laboratory, Mass Spectrom-eter and Incoherent Scatter radar)和JB2008 (Jacchia- Bowman 2008 model)对MDM进行模拟,JB2008没有刻画出MDM现象;另两个模型低估了MDM效应,在360km和480km两个高度DTM2000模型的振幅仅为观测的46%和53%, NRLMSISE00模型仅为观测的33%和26%;模型没有准确刻画出MDM与高度、辐射水平和季节的关系.联合3颗卫星的资料,研究了-种基于地理纬度的6阶勒让德多项式,同时融合地方时和高度因素的经验函数,在振幅和相位上可以较好地刻画MDM特征,相关系数达到0.923,可为大气密度模型的修正提供借鉴,服务于低轨道航天器高精度轨道预报.