A biodiversity evaluation framework for restoration of aquatic macrophyte communities in shallow lakes driven by hydrological process management

Macrophyte community diversity and composition respond to ecosystem conservation and local environmental factors. In this study, we developed a multidimensional diversity framework for macrophyte communities, including the taxonomic and functional alpha and beta diversity. We used the framework to explore the relationships among water level regimes and these diversity parameters in a case study of China's Baiyangdian Lake. Analysis of indicators of hydrologic alteration divided the water level from 1959 to 2019 into four regimes (dry, <6.42 m; low, 6.42–7.23 m; medium, 7.23–8.19 m; high, >8.19 m). Alpha and beta diversity were significantly higher in the medium regime than in the low and high regimes. Redundancy analysis indicated that the maximum water depth significantly affected taxonomic alpha diversity, and total nitrogen (TN) and chemical oxygen demand (COD) concentration significantly affected functional alpha diversity, respectively. Mantel tests showed that TN, Secchi depth (SD), and water depth in the high water level regime significantly increased the total beta diversity and turnover components. TN was the main factor that increased total taxonomic beta diversity. Water level regime mainly influenced interspecific relationships by changing the TN and COD concentration. The water level should be maintained between the medium and high water level regimes to promote restoration of the macrophyte community and improve ecosystem stability. The biodiversity evaluation framework would provide a deeper insight into the hydrological process management for restoration of aquatic macrophyte communities in shallow lakes.     

Hydrological management strategies for the control of algal blooms in regulated lowland rivers

Reservoirs of lowland floodplain rivers with eutrophic backgrounds cause variations in the hydrological and hydraulic conditions of estuaries and low-dam reservoir areas, which can promote planktonic algae to proliferate and algal bloom outbreaks. Understanding the ecological effects of variations in hydrological and hydraulic processes in lowland rivers is important for algal bloom control. In this study, the middle and lower reaches of the Han River, China, a typical regulated lowland river with a eutrophic background, are selected. Based on the effect of hydrological and hydraulic variability on algal blooms, a hydrological management strategy for river algal bloom control is proposed. The results showed that (a) differences in river morphology and background nutrient levels cause significant differences in the critical threshold flow velocities for algal bloom outbreaks between natural river and low-dam reservoir sections; there is no uniform threshold flow velocity for algal bloom control. (b) There are significant differences in the river hydrological/hydraulic conditions between years with and without algal blooms. The average river flow, water level and velocity in years with algal blooms are significantly lower than those in years without algal blooms. (c) For different river sections where algal blooms occur and to meet the threshold flow velocities, the joint operation of cascade reservoirs and diversion projects is an effective method to prevent and control algal blooms in regulated lowland rivers. This study is expected to deepen our understanding of the ecological significance of special hydrological processes and guide algal bloom management in regulated lowland rivers.     

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.     

建鲤基因组DNA的RAPD分析

用 1 5个随机引物对建鲤基因组DNA进行RAPD分析 ,结果显示 ,建鲤群体内的遗传相似系数为 0 91 81± 0 0 73 8,多态位点比例为 0 41 67,平均杂合度为 0 1 884。表明建鲤种质较纯 ,群体内的遗传变异程度较小。建鲤优良性状产生的分子学基础是等位基因的杂合程度较高     

Determination of the regional deformation rates of Shanghai and Kashima VLBI stations based on ITRF97

The vertical deformation rates (VDRs) and horizontal deformation rates (HDRs) of Shanghai VLBI station in China and Kashima and Kashima34 VLBI stations in Japan were re-analysed using the baseline length change rates from Shanghai to 13 global VLBI stations, and from Kashima to 27 stations and from Kashima34 to 12 stations, based on the NASA VLBI global solution glb1123 (Ma, 1999). The velocity vectors of the global VLBI stations were referred to the ITRF97 reference frame, and the Eulerian vectors of different models of plate motion were used for comparative solutions. The VDR of Shanghai station is estimated to be −1.91±0.56 mm/yr, and those of Kashima and Kashima34 stations, −3.72±0.74mm/yr and −8.81±0.84mm/yr, respectively. The difference between the last two was verified by further analysis. Similar estimates were also made for the Kokee, Kauai and MK_VLBA VLBI stations in mid-Pacific.     

青藏高原隆升的非线性动态有限元仿真研究

根据青藏高原的地质特征建立分析模型，采用3维动态有限元方法，在计算仿真板块速度场的基础上，计算在青藏高原的隆升过程中该地区地壳岩石的等效应力和位移随时间的变化，计算仿真得到的速度场与1998年GPS观测的速度场吻合良好；与过去一贯的假设相反，计算结果反映出地壳应力场不是静态的，而是此起彼伏，不断变化的，应力值最大且变化最剧烈的地区在克什米尔地区、鄂尔多斯地区和鲜水河－小江断裂带，与地震多发区域吻合。     

中国1∶100万景观生态制图设计

中国1∶100万景观生态图系借鉴国内外传统景观制图方法和制图规范,在遥感、地理信息系统先进技术的支持下,设计研制其制图方法、制图内容、样图和技术流程,同时提出初步的景观分类系统,为今后编制中国1∶100万景观生态图奠定基础。     

观驾山—黑岛一带信息量法找金研究

本文以成矿地质模型为依据，对观驾山—黑山岛一带的化探、物探、遥感及地质等信息变量进行了详细的分析，选取了１０个信息变量２４个标志状态；在此基础上，运用信息量法对研究区内４５８个单元的信息量值进行计算，并由此确定了研究范围内的成矿远景区。事实表明，研究区信息量成矿预测是非常有效的     

双极化天气雷达方程

在回转椭球状降水粒子的假设下求出了降水粒子的散射截面，并且考虑了粒子轴线铅垂和随机取向两种重要的典型情况；推导了双极化雷达的回波方程以及降雨量公式。该方程除包含降水粒子的信息外，关于雷达与降水粒子间的几何关系都以雷达天线仰角及雷达与降水粒子间的距离表示，同时还考虑了地球的球面效应和大气折射效应。由于采用了并矢方法，使推导简捷明了     

用C#.Net实现软件系统的动态更新

为了便于大型软件系统的后续开发和用户软件系统的升级，介绍了软件系统在Internet/Intranet上更新功能或数据的原理和方法，并用C#．Net进行了编程实现。