An Adaptive Nonhydrostatic Atmospheric Dynamical Core Using a Multi-Moment Constrained Finite Volume Method

An adaptive 2 D nonhydrostatic dynamical core is proposed by using the multi-moment constrained finite-volume(MCV) scheme and the Berger-Oliger adaptive mesh refinement(AMR) algorithm. The MCV scheme takes several pointwise values within each computational cell as the predicted variables to build high-order schemes based on single-cell reconstruction. Two types of moments, such as the volume-integrated average(VIA) and point value(PV), are defined as constraint conditions to derive the updating formulations of the unknowns, and the constraint condition on VIA guarantees the rigorous conservation of the proposed model. In this study, the MCV scheme is implemented on a height-based, terrainfollowing grid with variable resolution to solve the nonhydrostatic governing equations of atmospheric dynamics. The AMR grid of Berger-Oliger consists of several groups of blocks with different resolutions, where the MCV model developed on a fixed structured mesh can be used directly. Numerical formulations are designed to implement the coarsefine interpolation and the flux correction for properly exchanging the solution information among different blocks. Widely used benchmark tests are carried out to evaluate the proposed model. The numerical experiments on uniform and AMR grids indicate that the adaptive model has promising potential for improving computational efficiency without losing accuracy.     

单双频智能手机GNSS定位精度分析

为分析单、双频手机在单点定位和动态导航中的差异,本文对单、双频手机观测卫星数、信噪比、静态/动态定位中原始观测值/卡尔曼滤波值进行了对比和分析。试验结果表明,在静态无遮挡的试验条件下,小米MI8双频手机原始观测值较OPPO Reno单频手机定位误差均值减小了1.70 m且能观测到更多的卫星数,进行卡尔曼滤波处理后,单频手机较双频手机而言精度提升更多,两者最终精度相当。而在动态有遮挡的试验条件下,双频较单频定位误差均值减小了5.24 m,进行卡尔曼滤波处理后,两者精度均有明显改善,双频相对单频误差均值减小了0.87 m。     

LOLAS: an optical turbulence profiler in the atmospheric boundary layer with extreme altitude resolution

We report the development and first results of an instrument called Low Layer SCIDAR (Scintillation Detection and Ranging) (LOLAS) which is aimed at the measurement of optical-turbulence profiles in the atmospheric boundary layer with high altitude resolution. The method is based on the Generalized SCIDAR (GS) concept, but unlike the GS instruments which need a 1-m or larger telescope, LOLAS is implemented on a dedicated 40-cm telescope, making it an independent instrument. The system is designed for widely separated double-star targets, which enables the high altitude resolution. Using a 200-arcsec-separation double star, we have obtained turbulence profiles with unprecedented 12-m resolution. The system incorporates necessary novel algorithms for autoguiding, autofocus and image stabilization. The results presented here were obtained at Mauna Kea Observatory. They show LOLAS capabilities but cannot be considered as representative of the site. A forthcoming paper will be devoted to the site characterization. The instrument was built as part of the Ground Layer Turbulence Monitoring Campaign on Mauna Kea for Gemini Observatory.     

非局部均值滤波在重力异常数据处理中的应用

根据小子域滤波原理,小子域滤波产生异常边界扭曲主要表现为高频干扰,提出小子域滤波与非局部均值滤波联合的方法以克服小子域滤波存在的异常边界扭曲问题。利用小子域滤波良好的边界增强能力和非局部均值滤波高频细节保护能力,在压制小子域滤波缺陷的同时尽可能保护小子域滤波的边界增强效果。通过模拟计算和莱州湾海域实测重力数据处理表明,本文方法在增强异常边界的同时压制小子域滤波产生的边界扭曲效应,获得了更好的重力异常处理效果。与单一小子域滤波相比,本文方法不需要考虑异常边界形态,具有更强的稳定性和普适性。     

SESTET: A spatially explicit stream temperature model based on equilibrium temperature

Stream-water temperature is a key variable controlling chemical, biological, and ecological processes in freshwater environments. Most models focus on a single river cross-section; however, temperature gradients along stretches and tributaries of a river network are crucial to assess ecohydrological features such as aquatic species suitability, growth and feeding rates, or disease transmission. We propose SESTET, a deterministic, spatially explicit stream temperature model for a whole river network, based on water and energy budgets at a reach scale and requiring only commonly available spatially distributed datasets, such as morphology and air temperature, as input. Heat exchange processes at the air–water interface are modelled via the widely used equilibrium temperature concept, whereas the effects of network structure are accounted for through advective heat fluxes. A case study was conducted on the prealpine Wigger river (Switzerland), where water temperatures have been measured in the period 2014–2018 at 11 spatially distributed locations. The results show the advantages of accounting for water and energy budgets at the reach scale for the entire river network, compared with simpler, lumped formulations. Because our approach fundamentally relies on spatially distributed air temperature fields, adequate spatial interpolation techniques that account for the effects of both elevation and thermal inversion in air temperature are key to a successful application of the model. SESTET allows the assessment of the magnitude of the various components of the heat budget at the reach scale and the derivation of reliable estimates of spatial gradients of mean daily stream temperatures for the whole catchment based on a limited number of conveniently located (viz., spanning the largest possible elevation range) measuring stations. Moreover, accounting for mixing processes and advective fluxes through the river network allows one to trust regionalized values of the parameters controlling the relationship between equilibrium and air temperature, a key feature to generalize the model to data-scarce catchments.     

An optimized baseflow separation method for assessment of seasonal and spatial variability of baseflow and the driving factors

Baseflow is an important component of river or streamflow. It plays a vital role in water utilization and management. An improved Eckhardt recursive digital filter(IERDF) is proposed in this study. The key filter parameter and maximum baseflow index(BFImax) were estimated using the minimum smoothing method to improve baseflow estimation accuracy. The generally considered BFImax of 0.80, 0.50 and 0.25 according to the drainage basin's predominant geological characteristics often leads to significant errors in the regions that have complex subsurface and hydrologic conditions. The IERDF improved baseflow estimation accuracy by avoiding arbitrary parameter values. The proposed method was applied for baseflow separation in the upstream of Yitong River, a tributary of the Second Songhua River, and its performance was evaluated by comparing the results obtained using isotope-tracer data. The performance of IERDF was also compared with nine baseflow separation techniques belonging to filter, BFI and HYSEP methods. The IERDF was also applied for baseflow separation and calculation of rainfall infiltration recharge coefficient at different locations along the Second Songhua River's mainstream for the period 2000–2016. The results showed that the minimum smoothing method significantly improved BFImax estimation accuracy. The baseflow process line obtained using IEDRF method was consistent with that obtained using isotope 18 O. The IERDF estimated baseflow also showed stability and reliability when applied in the mainstream of the Second Songhua River. The BFI alone in the river showed an increase from the upstream to the downstream. The proportion of baseflow to total flow showed a decrease with time. The intra-annual variability of BFI was different at different locations of the river due to varying climatic conditions and subsurface characteristics. The highest BFI was observed at the middle reaches of the river in summer due to a water surplus from power generation. The research provided valuable information on baseflow characteristics and runoff mode determination, which can be used for water resources assessment and optimization of economic activity distribution in the region.     

中国省际人口迁移短期预测分析

人口迁移是一个时空路径依赖过程,同时受迁移存量和周边迁移状况影响。当前人口迁移预测大多建立在时间序列模型之上,重点考虑迁移流在时间维度上的联系,忽视了其中的时空关联。该文将特征向量时空滤波方法与普通泊松模型相结合,考虑迁移流中可能存在的时空滞后和同期两种结构,对1985-2015年不同时段的中国省际人口迁移流数据进行建模和估计,并利用拟合程度较优的模型预测2015-2025年省际人口迁移的发展趋势。结果表明:1)特征向量时空滞后和同期滤波泊松模型均能较好地模拟研究时段省际人口迁移过程,自1985年以来我国省际人口迁移流不仅受迁出地和迁入地经济、社会等因素影响,也与过去迁移存量及周边迁移流密切相关;2)区域人口规模和GDP对迁移流的“推—拉”作用符合预期,地区人口规模较高和经济发展水平较低会促进人口外迁,反之则有利于吸引外来人口;3)与特征向量时空滞后滤波泊松模型相比,时空同期模型更便于捕捉省际人口迁移过程中的时空路径依赖特性,意味着当前人口迁移流的发展更易受到同时期周边迁移流的影响,表现出明显的羊群效应;4)预计2015-2025年我国省际迁移总量持续增加,呈现更集聚的空间模式,高迁入与高迁出区域在空间上相连,形成一条南北贯通的“高密度迁移地带”。将特征向量时空滤波模型拓展到人口迁移这一空间相互作用领域,可为当前构建更加完善的要素市场化配置体制机制等提供科学参考。     

人地系统适应性研究进展：概念、理论框架与方法

适应性研究旨在通过主体对外部环境变化的调整,以削减其负面影响并改善适应能力,是实现人地系统可持续发展的重要途径。鉴于目前对适应性理论体系缺乏统一认识和系统梳理,论文通过对国内外人地系统适应性研究文献总结,从概念内涵、理论框架和方法对适应性研究进展进行了总结,并提出了适应性研究的科学范式。研究发现：① 学术界对适应性概念的理解并未达成科学共识,阻碍了通用的适应性研究理论体系的构建。② 现有适应性分析框架多是基于全球变化领域理论框架基础的延伸和修订。③ 适应性研究缺乏具有代表性的方法,以借用脆弱性、恢复力评估方法和指标体系为主。因此,统一的适应性概念和理论体系的构建亟需加强,而国外既有积累的理论经验并不通用,中国化的适应性分析框架、方法是未来的重要研究方向。同时,应强化人类社会和自然环境“综合”的人地系统适应性研究,注重多尺度结合的适应性动态分析。     

基于相关分析和自适应遗传算法的盐渍化建模变量和参数优选

机器学习结合遥感等其他数据反演土壤盐分含量（Soil Salt Content, SSC）较少关注对模型精度影响较大的建模特征变量和模型参数的优选。本文基于自适应遗传算法（Adaptive Genetic Algorithm, AGA）同步优选建模特征变量和模型参数的支持向量回归（Support Vector Regression, SVR）算法反演三工河流域2016年SSC,并分析其在不同土地利用类型的分布特征。建模特征变量和模型参数的同步优选及实验设计如下：首先基于Landsat 8 OLI和SRTM高程数据提取7类共40个盐渍化相关因子,经相关分析初步筛选出候选特征变量,分别代入AGA、遗传算法（Genetic Algorithm, GA）和格网搜索算法（Grid Search, GS）同步优选SVR的建模特征变量和模型参数,并建立盐渍化监测模型（AGA-SVR、GA-SVR、GS-SVR）。结果表明：① AGA-SVR精度最优,GA-SVR次之,GS-SVR最差,相较于GS-SVR,AGA-SVR的R²/RMSE提高了44.65%;② 三工河流域非、轻度、中度、重度盐渍地和盐土的面积占比分别为42.83%、11.02%、15.88%、9.22%、21.05%;③ 草地和未利用地主要以非盐渍地和盐土为主,耕地和林地中非盐渍地分布比例均为最大;不同土地利用类型的SSC均值和标准差均呈现未利用地>草地>耕地>林地的规律。本研究的建模特征变量和模型参数的优选方法可在一定程度上提高盐渍化监测的精度。关键词：盐渍化;遗传算法;机器学习;特征优选;参数优化;土壤盐分含量;土地利用;相关分析