Predicting quantiles of water quality from catchment characteristics

Water quality is often highly variable both in space and time, which poses challenges for modelling the more extreme concentrations. This study developed an alternative approach to predicting water quality quantiles at individual locations. We focused on river water quality data that were collected over 25 years, at 102 catchments across the State of Victoria, Australia. We analysed and modelled spatial patterns of the 10th, 25th, 50th, 75th and 90th percentiles of the concentrations of sediments, nutrients and salt, with six common constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NO_x), and electrical conductivity (EC). To predict the spatial variation of each quantile for each constituent, we developed statistical regression models and exhaustively searched through 50 catchment characteristics to identify the best set of predictors for that quantile. The models predict the spatial variation in individual quantiles of TSS, TKN and EC well (66%–96% spatial variation explained), while those for TP, FRP and NO_x have lower performance (37%–73% spatial variation explained). The most common factors that influence the spatial variations of the different constituents and quantiles are: annual temperature, percentage of cropping land area in catchment and channel slope. The statistical models developed can be used to predict how low- and high-concentration quantiles change with landscape characteristics, and thus provide a useful tool for catchment managers to inform planning and policy making with changing climate and land use conditions.     

A multiscale work-analysis approach for geotechnical structures

This paper presents a second-order work analysis in application to geotechnical problems by using a novel effective multiscale approach. To abandon complicated equations involved in conventional phenomenological models, this multiscale approach employs a micromechanically-based formulation, in which only four parameters are involved. The multiscale approach makes it possible a coupling of the finite element method (FEM) and the micromechanically-based model. The FEM is used to solve the boundary value problem (BVP) while the micromechanically-based model is utilized at the Gauss point of the FEM. Then, the multiscale approach is used to simulate a three-dimensional triaxial test and a plain-strain footing. On the basis of the simulations, material instabilities are analyzed at both mesoscale and global scale. The second-order work criterion is then used to analyze the numerical results. It opens a road to interpret and understand the micromechanisms hiding behind the occurrence of failure in geotechnical issues.     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

自然和自然贡献情景模型研究综述

本文回顾了自然和自然贡献情景模型发展的背景、历史和内容,概括总结了自然和自然贡献情景模型的发展进程以及联合国生物多样性与生态系统服务政府间科学—政策平台(IPBES)情景模型的概念框架,讨论了自然和自然贡献情景模型存在的问题和发展方向。为了在全球层面解决现有综合集成模型存在的问题,根据地球表层建模基本定理和生态环境曲面建模基本定理,提出了具有中国原创特点的自然与自然贡献情景模型概念框架。     

Pulsar magnetic field for an infinitely conductive accretion disk

The magnetic-field distribution outside a flat, infinitely conductive unbounded disk in the field of a point magnetic dipole is determined. A relationship is established between the problem of magnetic-field determination and the problem of the flow of an ideal incompressible fluid around an infinitely thin disk.     

景观生态分类与制图浅议

本文在查阅分析大量文献和前人研究的基础上 ,对目前景观生态分类和景观制图作了详细的对比分析 ,认为景观分类需要结合实际区域现状 ,采用逐级分类的方法 ;同时利用 ETM遥感影像为数据源 ,以天山北麓为示范区.研制其土地利用土地覆盖变化的景观类型图。     

平衡剖面的制作流程及其地质意义

平衡剖面技术是地质思维和计算机技术的结晶,使对断层构造的研究提高到定量阶段,其依据是在垂直构造走向的剖面上,地层长度和面积(2D)或体积(3D)是均衡的。在此原理基础上利用数学手段对盆地的构造发育史进行正演和反演模拟,直观地再现地下构造的原始几何形态,迅速提供地震剖面的构造解释方案,并对解释结果进行检验(不平衡的剖面其解释一般有问题),为深刻认识构造发育史、分析油气运移及聚集规律提供依据,提高了工作效率。其结果也为盆地模拟、油藏模拟、定量计算构造伸缩量等地质研究打下了坚实的基础[1]。     

Laboratory kinetics of C2H radical reactions with ethane, propane, and n-butane at T = 96-296 K: implications for Titan

The kinetics of the reactions of C₂H radical with ethane (k₁), propane (k₂), and n-butane (k₃) are studied over the temperature range of T = 96-296 K with a pulsed Laval nozzle apparatus that utilizes a pulsed laser photolysis-chemiluminescence technique. The C₂H decay profiles in the presence of both the alkane reactant and O₂ are monitored by the CH(A²Δ) chemiluminescence tracer method. The results, together with available literature data, yield the following Arrhenius expressions: k₁(T) = (0.51 ± 0.06) × 10⁻¹⁰ exp[(−76 ± 30)K/T] cm³ molecule⁻¹ s⁻¹ (T = 96-800 K), k₂(T) = (0.98 ± 0.32) × 10⁻¹⁰exp[(−71 ± 60)K/T] cm³ molecule⁻¹ s⁻¹ (T = 96-361 K), and k₃(T) = (1.23 ± 0.26) × 10⁻¹⁰ cm³ molecule⁻¹ s⁻¹ (T = 96-297 K). At T = 296 K, k₁ is measured as a function of total pressure and has little or no pressure dependence. The results from this work support a direct hydrogen abstraction mechanism for the title reactions. Implications to the atmospheric chemistry of Titan are discussed.