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

Shuhan Li  Tao Sun  Wei Yang  Baoshan Cui  Xinan Yin 《水文研究》2021,35(1):e13983

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.  相似文献   

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

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

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.  相似文献   

An approximate method for evaluating the shear band thickness in saturated soils     

X. B. Lu  Wang Shuyun  Wang Yihua  Peng Cui 《国际地质力学数值与分析法杂志》2004,28(15):1533-1541

A formula for the thickness of a shear band formed in saturated soils under a simple shear or a combined stress state has been proposed. It is shown that the shear band thickness is dependent on the pore pressure properties of the material and the dilatancy rate, but is independent of the details of the combined stress state. This is in accordance with some separate experimental observations. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

GPS/LEO掩星观测的变分同化技术   总被引：6，自引：0，他引：6  

刘敏  郭鹏 《天文学进展》2006,24(1):27-42

在简单介绍GPS/LEO掩星探测大气的发展历史和科学意义之后,详细阐述了反演的基本原理;分析了标准反演中存在的问题,并说明一维变分同化(1DVAR)在反演方法中的重要性;给出了一维变分同化中价值函数的求解,以及各种同化因子;简单介绍了对当前气象学中普遍使用的四维变分同化(4DVAR);重点讨论了各种同化方法,以及使用各种同化因子的优缺点。最后,通过CHAMP卫星的观测实例分析,验证了GPS数据在数值天气预报(NWP)中的作用,以及相对于标准反演法一维变分对气象要素的改进。  相似文献   

Approach to Mountain Hazards in Tibet, China   总被引：1，自引：1，他引：0  

MADongtao TUJianjun CUIPeng LURuren 《山地科学学报》2004,1(2):143-154

Tibet is located at the southwest boundary of China. It is the main body of the Qinghai-Tibet Plateau, the highest and the youngest plateau in the world. Owing to complicated geology, Neo-tectonic movements, geomorphology, climate and plateau environment, various mountain hazards, such as debris flow, flash flood, landslide, collapse, snow avalanche and snow drifts, are widely distributed along the Jinsha River (the upper reaches of the Yangtze River), the Nu River and the Lancang River in the east, and the Yarlungzangbo River, the Pumqu River and the Poiqu River in the south and southeast of Tibet. The distribution area of mountain hazards in Tibet is about 589,000 km^2, 49.3% of its total territory. In comparison to other mountain regions in China, mountain hazards in Tibet break out unexpectedly with tremendously large scale and endanger the traffic lines, cities and towns, farmland, grassland, mountain environment, and make more dangers to the neighboring countries, such as Nepal, India, Myanmar and Bhutan. To mitigate mountain hazards, some suggestions are proposed in this paper, such as strengthening scientific research, enhancing joint studies, hazards mitigation planning, hazards warning and forecasting, controlling the most disastrous hazards and forbidding unreasonable human exploring activities in mountain areas.  相似文献   

SDSS J143030.22-001115.1： A Misclassified Narrow-line Seyfert 1 Galaxy with Flat X-ray Spectrum     

Wei-Hao Bian  Quan-Ling Cui  Li-Hua Chao Department of Physics  Institute of Theoretical Physics  Nanjing Normal University  Nanjing Key Laboratory for Particle Astrophysics  Institute of High Energy Physics  Chinese Academy of Sciences  Beijing 《中国天文和天体物理学报》2006,6(3):281-286

We used multi-component profiles to model the Hβ and [OⅢ]λλ4959,5007 lines of SDSS J143030.22-001115.1, a narrow-line Seyfert 1 galaxy (NLS1) in a sample of 150 NLS1 candidates selected from the Sloan Digital Sky Survey (SDSS), Early Data Release (EDR). After subtracting the Hβ contribution from narrow line regions (NLRs), we found that its full width half maximum (FWHM) of broad Hβ line is nearly 2900km s~(-1) , significantly larger than the customarily adopted criterion of 2000 km s~(-1) . With its weak Fe Ⅱ multiples, we believe that SDSS J143030.22-001115.1 should not be classified as a genuine NLS1. When we calculate the virial black hole masses of NLS1s, we should use the Hβ linewidth after subtracting the NLR component.  相似文献   

Optical monitoring of the quasar 4C 38.41     

B. Peng  J. Wu  X. Zhou 《Monthly notices of the Royal Astronomical Society》2003,346(2):483-488

The results of a photometric monitoring of the quasar 4C 38.41, performed at the optical R and B bands in 2002 February–March, are presented. With a 60/90 cm Schmidt telescope at the Xinglong station of the National Astronomical Observatories of China, we observed the source exhibiting amplitude variations of up to 0.78 mag in both bands during the whole campaign. Intraday and even intranight variations are detected as well. A typical variability time-scale of about 36 d is derived from our 2-month observations at the optical bands, which is identical to that found at a radio wavelength of 92 cm, suggesting a common origin for the variations in 4C 38.41 from optical to radio bands.  相似文献   

SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK     

WANGXie-kang HUANGEr CUIPeng 《中国地理科学(英文版)》2003,13(3):262-266

Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.  相似文献   

Characters of the Pco2 and CO2 Flux in the East China Sea in Autumn     

纪雷  崔鹤  辛淑平  王军  张龙军  陆贤昆 《中国海洋湖沼学报》2003,21(2):180-186

Pco2 of air and seawater samples from the East China Sea(ECS) were measured in situ in autumn, 1994,Ocean currents,terrestrial fluviation,biological activities,etc.,Pco2 char-acters in air and seawater were investigated,CO2 flux and its character in the East China Sea are discussed on the basis of the Pco2 profiles of air and seawater,It was clear that the nearshore was the source of CO2;and tht the oulter sea area was the sink of CO2; and that the shelf area of the EXS is a net sink for atmospheric CO2 in autumn.  相似文献   

对黄河水资源可持续利用的认识          下载免费PDF全文

崔庆瑞 《山东国土资源》2006,22(6):60-61

水资源可持续利用是经济社会发展的重大战略问题。近年来，黄河下游水量持续偏枯，用水量不断增加，水污染范围逐步扩大，污染程度日益加重，黄河水资源管理面临水资源短缺和水污染程度加剧两大难题。当前，采取多种措施确保水资源可持续利用势在必行。  相似文献