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The aim of this study is to shed light on the hydrogeochemical characteristics of karst underground waters at shallow depth in Guiyang City, Guizhou Province with an emphasis on the geochemistry of major elements. Guiyang City bears abundant underground waters and it is also an important representative of the karst areas throughout the world. Ca^2 and Mg^2 are the dominant cations, accounting for 81% -99.7% of the total, and HCO3^- and SO4^2- are the dominant anions. Weathering of limestones and dolostones is the most important factor controlling the hydrogeochemistry of underground waters, and weathering of sulfate and evaporite rocks is less important. Moreover, the precipitation and human activities also have a definite influence on the hydrogeoehemistry of underground waters in the region studied. 相似文献
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Sedimentation rates in the Wanggang salt marshes, Jiangsu 总被引:1,自引:0,他引:1
WANGAijun GAOShu JIAJianjun PANShaoming 《地理学报(英文版)》2005,15(2):199-209
1 IntroductionLand-ocean interaction in coastalzone is one of the key m atters of the International G eosphereand Biosphere Program (IG BP). The key problem s of the second phase in the next decadeinclude the m aterial cycle, the system evolution process … 相似文献
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Analysis of rock structure stability in coal mines 总被引:1,自引:0,他引:1
In this paper, the theory of limit point instability is used to analyse the stability of rock structures in coal mines. A general method of analysing stability of rock structures is put forward and a uniform instability condition of rock structures is set up. Some instability phenomena, such as rock bursts in circular roadways, pillars and long walls, and the outburst of coal and gas from circular roadways, are discussed analytically. At a later stage, the critical point of rock structure instability is determined. The influence of relative parameters (such as the mechanical properties of rock, coal, and the geometric sizes) on the stability of the rock structures is carefully analysed. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
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Using raw regional climate model outputs for quantifying climate change impacts on hydrology 下载免费PDF全文
General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions. 相似文献
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Watershed delineation is a required step when conducting any spatially distributed hydrological modelling. Automated approaches are often proposed to delineate a watershed based on a river network extracted from the digital elevation model (DEM) using the deterministic eight‐neighbour (D8) method. However, a realistic river network cannot be derived from conventional DEM processing methods for a large flat area with a complex network of rivers, lakes, reservoirs, and polders, referred to as a plain river network region (PRNR). In this study, a new approach, which uses both hydrographic features and DEM, has been developed to address the problems of watershed delineation in PRNR. It extracts the river nodes and determines the flow directions of the river network based on a vector‐based hydrographic feature data model. The river network, lakes, reservoirs, and polders are then used to modify the flow directions of grid cells determined by D8 approach. The watershed is eventually delineated into four types of catchments including lakes, reservoirs, polders, and overland catchments based on the flow direction matrix and the location of river nodes. Multiple flow directions of grid cells are represented using a multi‐direction encoding method, and multiple outflows of catchments are also reflected in the topology of catchments. The proposed approach is applied to the western Taihu watershed in China. Comparisons between the results obtained from the D8 approach, the ‘stream burning’ approach, and those from the proposed approach clearly demonstrate an improvement of the new approach over the conventional approaches. This approach will benefit the development of distributed hydrological models in PRNR for the consideration of different types and multiple inlets and outlets of catchments. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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气候系统具有非平稳特征,根本原因在于其外强迫随时间发生改变,因此外部驱动力的分析对于理解气候系统的动力学特征至关重要,而如何有效提取系统外部驱动信息是一个亟待解决的前沿科学问题。最近几年,在生物神经学领域中应用的一种提取非平稳信号中外强迫信息的方法——慢特征分析法(Slow Feature Analysis,SFA),在气象领域中也得到了初步成功的尝试,结果显示出此方法对气候系统的外强迫信息分析及有关动力学机制的探究有较好的应用前景。本文主要介绍SFA方法的理论思想及实施步骤,并通过一个理想的非平稳时间序列检验其提取外强迫信息的能力,结果证明在衰减的Logistic模型中,可利用SFA算法提取出模型中的外强迫,且与真实外强迫的相关系数可达0.99;此外,还介绍将该方法应用于Arosa臭氧时间序列,分析其提取的外强迫信息的动力学特征;并介绍了在气候时间序列建模中引入外强迫因子的预测效果。 相似文献
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