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.     

Optical Flash of GRB 990123: Constraints on the Physical Parameters of the Reverse Shock

The optical flash accompanying GRB 990123 is believed to be powered by the reverse shock of a thin shell. With the best-fit physical parameters for GRB 990123 and the assumption that the parameters in the optical flash are the same as in the afterglow, we show that: 1) the shell is thick rather than thin, and we have provided the light curve for the thick shell case which coincides with the observation; 2) the theoretical peak flux of the optical flash accounts for only 3×10~-4 of the observed. In order to remove this discrepancy, the physical parameters, the electron energy and magnetic ratios, εe and εB, should be 0.61 and 0.39, which are very different from their values for the late afterglow.     

Silicon limitation on primary production and its destiny in Jiaozhou Bay, China——Ⅳ：Study on cross-bay transect from estuary to ocean

The authors analyzed the data collected in the Ecological Station Jiaozhou Bay from May 1991 to November 1994, including 12 seasonal investigations, to determine the characteristics, dynamic cycles and variation trends of the silicate in the bay. The results indicated that the rivers around Jiaozhou Bay provided abundant supply of silicate to the bay. The silicate concentration there depended on river flow variation. The horizontal variation of silicate concentration on the transect showed that the silicate concentration decreased with distance from shorelines. The vertical variation of it showed that silicate sank and deposited on the sea bottom by phytoplankton uptake and death, and zooplankton excretion. In this way, silicon would endlessly be transferred from terrestrial sources to the sea bottom. The silicon took up by phytoplankton and by other biogeochemical processes led to insufficient silicon supply for phytoplankton growth. In this paper, a 2D dynamic model of river flow versus silicate concentration was established by which silicate concentrations of 0.028–0.062 μmol/L in seawater was yielded by inputting certain seasonal unit river flows (m³/s), or in other words, the silicate supply rate; and when the unit river flow was set to zero, meaning no river input, the silicate concentrations were between 0.05–0.69 μmol/L in the bay. In terms of the silicate supply rate, Jiaozhou Bay was divided into three parts. The division shows a given river flow could generate several different silicon levels in corresponding regions, so as to the silicon-limitation levels to the phytoplankton in these regions. Another dynamic model of river flow versus primary production was set up by which the phytoplankton primary production of 5.21–15.55 (mgC/m²·d)/(m³/s) were obtained in our case at unit river flow values via silicate concentration or primary production conversion rate. Similarly, the values of primary production of 121.98–195.33 (mgC/m²·d) were achieved at zero unit river flow condition. A primary production conversion rate reflects the sensitivity to silicon depletion so as to different phytoplankton primary production and silicon requirements by different phytoplankton assemblages in different marine areas. In addition, the authors differentiated two equations (Eqs. 1 and 2) in the models to obtain the river flow variation that determines the silicate concentration variation, and in turn, the variation of primary production. These results proved further that nutrient silicon is a limiting factor for phytoplankton growth. This study was funded by NSFC (No. 40036010), and the Director's Fund of the Beihai Sea Monitoring Center, the State Oceanic Administration.     

A note on the algorithm of symplectic integrators

For a Hamiltonian that can be separated into N+1(N\geq 2) integrable parts, four algorithms can be built for a symplectic integrator. This research compares these algorithms for the first and second order integrators. We found that they have similar local truncation errors represented by error Hamiltonian but rather different numerical stability. When the computation of the main part of the Hamiltonian, H ₀, is not expensive, we recommend to use S ^* type algorithm, which cuts the calculation of the H ₀ system into several small time steps as Malhotra(1991) did. As to the order of the N+1 parts in one step calculation, we found that from the large to small would get a slower error accumulation. This revised version was published online in July 2006 with corrections to the Cover Date.     

中国大别山东南缘首次发现大坝陨坑构造

简介首次在大别山东南缘安徽省境内发现的大坝陨坑构造。经初步评价,知陨坑呈椭圆形,长轴呈北北东向,长约19km,短轴长约12km,最大坑深约2km,是一个有中央隆起区的复杂型陨坑。在卫片上陨坑显示环形影像,地貌形态为一洼地。陨坑基座保存尚好,可对它直观和追索陨坑构造边界。形成于230Ma左右的各类撞击变质岩石系列齐全,其中含有柯石英等典型的撞击变质矿物及撞击碎理等超微构造,特别是在陨坑基座内壁普遍发现有鉴别陨坑构造最可靠的标志——干裂自角砾岩,都证明大坝环形影像是一个典型的陨星撞击坑构造。它的发现,具有很大的科学及经济意义,对今后褶皱山区寻找和研究陨坑构造具有示范和指导作用,同时大大丰富了建设大别山世界地质公园的地质依据。无庸置疑此发现将促进大别山旅游业的发展及陨星撞击科普知识的传播。     

南海沉积岩芯的地球化学研究

本文对南海中部陆坡区和海盆区的5个岩芯中的 Si,Al,Fe,Mn,Ca,Mg 和 CaCO_3成分做了分析,并对其分布特征与有关影响因素进行了讨论。受沉积区环境差异的影响,元素的分布具有区域性特征;受海区特定地理位置的影响,元素的分布呈现出过渡性特征(浅海到大洋的过渡);受沉积过程中环境变化的影响,元素的分布展示了周期性变化或旋回性。根据岩芯中元素的分布特征,并参考有关古地磁,氧碳同位素和钙质超微化石的测试分析结果,将本区岩芯划分为14层地球化学层,并将本区划分为两个沉积地球化学区,揭示了早更新世以来本区沉积环境的变化和存在的两种沉积模式。     

南黄海灾害性地质研究

本文研完了南黄海10种灾害地质因素的性质、危害性及分布,对其进行了分类,在此基础上对本区之灾害地质进行了区划和评价。     

海藻糖类的气相色谱分析 Ⅱ.红藻及红藻多糖中D-半乳糖和3, 6-内醚半乳糖的测定

红藻中的糖类主要以多糖形式存在(琼胶、卡拉胶等)，其中主要的单糖组分是D-半乳糖和3, 6-内醚半乳糖，除此之外还含有少晕的木糖、葡萄糖及甘露糖等成分。由于各单糖的相互干扰，难以用比色等着通化学分析方法对每一种单糖准确地定性定量。作者曾研究过褐藻中单糖的气相色谱测定法, 但该法不能测定3, 6-内醚半乳糖，囚为这种内醚糖对酸水解不稳定。据Dutton和Nozawa等人报道, 将多糖经甲醇分解后进行气相色谱分析，结果令人满意。本文用琼胶(Difco bacto-agar)作代表，研究探讨了红藻胶甲醇-HCI分解的条件，确定了红藻胶中D-半乳糖和3, 6-内醚半乳糖的气相色谱分析方法，并将其应用于红藻中D-半乳糖和3, 6-内醚半乳糖的分析。     

黄河三角洲桩西101站潮沟地貌形态及其水动力学研究

在潮沟下游，横跨且生趣与潮沟的横断面设了５个观测站，进行了２４ｈ的水动力测量。结果表明，涨落潮流速流向不对称，在潮沟内，涨落潮流流速差别很大，涨潮流流速最大为９ｃｍ／２，而落潮流流速最大可达３８ｃｍ／ｓ。据推测，潮沟内之所以落潮流流速明显大于涨潮流流速，可能是由于潮沟与岸线斜交有关。