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.     

Implementation of a coupled plastic damage distinct lattice spring model for dynamic crack propagation in geomaterials

This paper studies dynamic crack propagation by employing the distinct lattice spring model (DLSM) and 3‐dimensional (3D) printing technique. A damage‐plasticity model was developed and implemented in a 2D DLSM. Applicability of the damage‐plasticity DLSM was verified against analytical elastic solutions and experimental results for crack propagation. As a physical analogy, dynamic fracturing tests were conducted on 3D printed specimens using the split Hopkinson pressure bar. The dynamic stress intensity factors were recorded, and crack paths were captured by a high‐speed camera. A parametric study was conducted to find the influences of the parameters on cracking behaviors, including initial and peak fracture toughness, crack speed, and crack patterns. Finally, selection of parameters for the damage‐plasticity model was determined through the comparison of numerical predictions and the experimentally observed cracking features.     

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     

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     

江汉盆地钻孔测井资料确定地应力最大水平主压应力方向

利用钻孔测井资料并运用地层倾角测量信息分析法，给出了江汉盆地地应力最大水平主压应力方向为ＮＥ６０～６５°     

The efifect of wave breaking on surface wave imaging by Synthetic Aperture Radar

The effect of ocean wave breaking as a non-Bragg mechanism on backscattering cross-section and modulation transfer functions (MTF) of radar was investigated based on Bragg resonance theory and parametric method. The result showed that the additional effect of wave breaking on backscattering cross-section is not more than 20% except for the small incident angle of VV polarized electromagnetic (e.m.) wave but is significant for HH polarized e.m. wave. Breaking waves lead to increase in the modulus of tilt modulation MTF and the larger the wind speed, the faster the increase. For large incident angle, the modulus of tilt modulation MTF with wave breaking decreases quickly with incident angle for HH polarization and approach to that without wave breaking for VV polarization. The hydrodynamic MTF increases 30%-60% when considering wave breaking and the increase is larger for HH polarization than for VV polarization.     

DEVELOPMENT STRATEGIES OF WATER AND LAND RESOURCES IN THE HEXI REGION,CHINA

ＤＥＶＥＬＯＰＭＥＮＴＳＴＲＡＴＥＧＩＥＳＯＦＷＡＴＥＲＡＮＤＬＡＮＤＲＥＳＯＵＲＣＥＳＩＮＴＨＥＨＥＸＩＲＥＧＩＯＮ，ＣＨＩＮＡ肖洪浪，高前兆，李福兴ＤＥＶＥＬＯＰＭＥＮＴＳＴＲＡＴＥＧＩＥＳＯＦＷＡＴＥＲＡＮＤＬＡＮＤＲＥＳＯＵＲＣＥＳＩＮＴＨＥ...     

法库地区大型韧性剪切带特征及成因机制

通过对辽北法库地区构造岩的观察和研究，确定了本区存在一大型推覆韧性剪切带。根据同构造侵入岩的同位素年龄，确定该韧性剪切带形成时期为海西－印支期，这与天山－兴安褶皱系最终形成时间相一致     

Tidal effects on temperature front in the Yellow Sea

Temperature front (TF) is one of the important features in the Yellow Sea, which forms in spring, thrives in summer, and fades in autumn as thermocline declines. TF intensity ⋎S _T ⋎ is defined to describe the distribution of TF. Based on the MASNUM wave-tide-circulation coupled model, temperature distribution in the Yellow Sea was simulated with and without tidal effects. Along 36°N, distribution of TF from the simulated results are compared with the observations, and a quantitative analysis is introduced to evaluate the tidal effects on the forming and maintaining processes of the TF. Tidal mixing and the circulation structure adapting to it are the main causes of the TF. Supported by the National Basic Research Program of China (No. G1999043809) and the National Science Foundation of China (No. 49736190).