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961.
Large-scale structures within a rough-wall boundary layer generated over a cube array have recently been linked to small-scale fluctuations close to the roughness through a dynamical mechanism similar to amplitude modulation. Demonstrating the existence of this mechanism for different roughness types is a crucial step towards the development of a generic model for wind fluctuations in the urban canopy. Here the influence of the upstream roughness geometry (two-dimensional (2D) and three-dimensional (3D)) and planform packing density (\( \lambda_{p} \)) and street-canyon aspect ratio on the non-linear interactions between large-scale momentum regions and the small scales induced by the presence of the roughness is studied within a wind tunnel using combined particle-image velocimetry and hot-wire anemometry. A multi-time delay linear stochastic estimation is used to decompose the flow into large scales that participate in modulation and the remaining small scales. Using three different upstream roughness configurations composed of either 3D cubes or 2D rectangular blocks it is shown that the upstream roughness configuration has an influence on the non-linear interactions in the rough-wall boundary layer. Analysis of the turbulence skewness decomposition shows a change in the location of the maximum of the term \( \overline{{u_{L}^{\prime} u_{S}^{\prime 2}}} \), which represents the influence of the large-scale momentum regions on the small scales, whilst the temporal correlation shows a modification of the interaction located closer to the roughness with a change from 3D to 2D roughness. Furthermore, a two-point spatio–temporal correlation demonstrates that the non-linear relationship is significantly modified in the wake-interference-flow regime compared to the skimming-flow regime. Through skewness decomposition and temporal correlations the canyon aspect ratio is shown to have no influence on the non-linear interactions, indicating that the mechanism depends only on the flow developing upstream. Finally, although the upstream roughness configuration is shown to influence the non-linear interactions, the nature of the mechanism remains the same in all configurations.  相似文献   
962.
More often than not, assessments of future climate risks are based on future climatic conditions superimposed on current socioeconomic conditions only. The new IPCC-guided alternative global development trends, the shared socioeconomic pathways (SSPs), have the potential to enhance the integration of future socioeconomic conditions—in the form of socioeconomic scenarios—within assessments of future climate risks. Being global development pathways, the SSPs lack regional and sectoral details. To increase their suitability in sectoral and/or regional studies and their relevance for local stakeholders, the SSPs have to be extended. We propose here a new method to extend the SSPs that makes use of existing scenario studies, the (re)use of which has been underestimated so far. Our approach lies in a systematic matching of multiple scenario sets that facilitates enrichment of the global SSPs with regional and sectoral information, in terms of both storylines and quantitative projections. We apply this method to develop extended SSPs of human vulnerability in Europe and to quantify them for a number of key indicators at the sub-national level up to 2050, based on the co-use of the matched scenarios’ quantitative outputs. Results show that such a method leads to internally consistent extended SSPs with detailed and highly quantified narratives that are tightly linked to global contexts. This method also provides multiple entry points where the relevance of scenarios to local stakeholders can be tested and strengthened. The extended SSPs can be readily employed to explore future populations’ vulnerability to climate hazards under varying levels of socioeconomic development.  相似文献   
963.
This study applies the soil and water assessment tool (SWAT), with climate (precipitation and temperature) outputs from four general circulation models (GCMs) and a regional circulation model (PRECIS), to evaluate (1) the impacts of climate change on reservoir sedimentation and (2) the impacts of climate change and reservoir development on sediment outflow in the Nam Ou River Basin located in northern Laos. Three reservoir–density scenarios, namely one reservoir (1R), three reservoirs in series (3R), and five reservoirs in series (5R), were evaluated for both no climate change and climate change conditions. The results show that under no climate change conditions, by 2070, around 17, 14, and 15% of the existing reservoir storage volume in the basin will be lost for 1R, 3R, and 5R scenarios, respectively. Notably, under climate change scenario with highest changes in erosion and sediment outflux from the basin, the additional reduction in reservoir storage capacity due to sedimentation is estimated to be nearly 26% for 1R, 21% for 3R, and 23% for 5R. Climate change alone is projected to change annual sediment outflux from the basin by ?20 to 151%. In contrast, the development of reservoirs in the basin will reduce the annual sediment outflux from the basin varying from 44 to 80% for 1R, 44–81% for 3R, and 66–89% for 5R, considering climate change. In conclusion, climate change is expected to increase the sediment yield of the Nam Ou Basin, resulting in faster reduction of the reservoir’s storage capacity. Sediment yield from the Nam Ou River Basin is likely to decrease significantly due to the trapping of sediment by planned reservoirs. The impact of reservoirs is much more significant than the impact of climate change on the sediment outflow of the basin. Hence, it is necessary to investigate appropriate reservoir sediment management strategies.  相似文献   
964.
Bangladesh, the sixth largest rice producer in the world, has been identified as high risk from the effects of climate change. Many of the adverse impacts of climate change such as land inundation and changes in weather patterns and CO2 levels will impact the agricultural sector. This study develops a partial-equilibrium multi-regional farm household model of Bangladesh rice and non-rice agricultural markets to quantify the impacts of climate change on consumption, production, prices, and farmers’ welfare. The model is calibrated to the Bangladesh rice market using Household Income and Expenditure Survey data. The model is simulated to analyze the impact of land reduction and productivity decline resulting from climate change. The results show that the decline in production in the coastal and northern regions offsets the production increase in the central and eastern regions, and the simulation predicts that total rice production for Bangladesh falls by about 2%. As total rice consumption falls and imports rise, the net effect leads to a rise in the rice price by 5.71% and a decline in farmers’ welfare. Sensitivity analysis shows that more- (less-) effective abatement technology could play a key role in mitigating (exacerbating) the price and welfare effects. The model predicts that many farmers in regions directly impacted by climate change could leave farming in search of off-farm work. Thus, the government can ease this transition by promoting urban development to provide more job options and technical training for farmers.  相似文献   
965.
Investigating the relationships between climate extremes and crop yield can help us understand how unfavourable climatic conditions affect crop production. In this study, two statistical models, multiple linear regression and random forest, were used to identify rainfall extremes indices affecting wheat yield in three different regions of the New South Wales wheat belt. The results show that the random forest model explained 41–67% of the year-to-year yield variation, whereas the multiple linear regression model explained 34–58%. In the two models, 3-month timescale standardized precipitation index of Jun.–Aug. (SPIJJA), Sep.–Nov. (SPISON), and consecutive dry days (CDDs) were identified as the three most important indices which can explain yield variability for most of the wheat belt. Our results indicated that the inter-annual variability of rainfall in winter and spring was largely responsible for wheat yield variation, and pre-growing season rainfall played a secondary role. Frequent shortages of rainfall posed a greater threat to crop growth than excessive rainfall in eastern Australia. We concluded that the comparison between multiple linear regression and machine learning algorithm proposed in the present study would be useful to provide robust prediction of yields and new insights of the effects of various rainfall extremes, when suitable climate and yield datasets are available.  相似文献   
966.
The performance of an atmospheric single-column model (SCM) is studied systematically for stably-stratified conditions. To this end, 11 years (2005–2015) of daily SCM simulations were compared to observations from the Cabauw observatory, The Netherlands. Each individual clear-sky night was classified in terms of the ambient geostrophic wind speed with a \(1\hbox { m} \hbox { s}^{-1}\) bin-width. Nights with overcast conditions were filtered out by selecting only those nights with an average net radiation of less than \(-\,30\hbox { W }\hbox {m}^{-2}\). A similar procedure was applied to the observational dataset. A comparison of observed and modelled ensemble-averaged profiles of wind speed and potential temperature and time series of turbulent fluxes showed that the model represents the dynamics of the nocturnal boundary layer (NBL) at Cabauw very well for a broad range of mechanical forcing conditions. No obvious difference in model performance was found between near-neutral and strongly-stratified conditions. Furthermore, observed NBL regime transitions are represented in a natural way. The reference model version performs much better than a model version that applies excessive vertical mixing as is done in several (global) operational models. Model sensitivity runs showed that for weak-wind conditions the inversion strength depends much more on details of the land-atmosphere coupling than on the turbulent mixing. The presented results indicate that in principle the physical parametrizations of large-scale atmospheric models are sufficiently equipped for modelling stably-stratified conditions for a wide range of forcing conditions.  相似文献   
967.
Theoretical and Applied Climatology - Multi-decadal regional projections of future climate change are introduced into a linear statistical model in order to produce an ensemble of austral...  相似文献   
968.
Results-based funding (RBF) is a governance concept that is rapidly becoming the mainstream paradigm for international collaborations in the environmental sector. While portrayed as a compromise solution between market-based mechanisms and unconditional donations, the implementation of RBF is revealing new conflicts and contradictions of its own. This paper explores the application of RBF for Reducing Emissions from Deforestation and Forest Degradation (REDD+) by describing the discursive conflicts between recipient (i.e., Brazil) and donor (i.e., Norway and Germany) countries of the Amazon Fund about what constitutes “results” or “performance.” Although all parties agree that the financial transfers to RBF should be based on past emission reductions in relation to a historical baseline, they hold clashing interpretations about temporal (i.e., past or future) and epistemological (i.e., how to measure) aspects of the results these payments are intended for. Firstly, while Brazil emphasizes that it deserves a reward of USD 21 billion for results achieved between 2006 and 2016, donor countries have indicated an interest in paying only for most recent results as a way to incentivize further reductions. Secondly, while all parties believe that Amazon Fund should support policies to reduce deforestation, donor countries have revealed concerns that the performance of the Amazon Fund projects in generating further reductions has not been measured in a rigorous manner. This suggests that donor countries may consider making changes to current RBF mechanisms or getting involved in new forms of finance.  相似文献   
969.
970.
The standard dual-component and two-member linear mixing model is often used to quantify water mixing of different sources. However, it is no longer applicable whenever actual mixture concentrations are not exactly known because of dilution. For example, low-water-content (low-porosity) rock samples are leached for pore-water chemical compositions, which therefore are diluted in the leachates. A multicomponent, two-member mixing model of dilution has been developed to quantify mixing of water sources and multiple chemical components experiencing dilution in leaching. This extended mixing model was used to quantify fracture-matrix interaction in construction-water migration tests along the Exploratory Studies Facility (ESF) tunnel at Yucca Mountain, Nevada, USA. The model effectively recovers the spatial distribution of water and chemical compositions released from the construction water, and provides invaluable data on the matrix fracture interaction. The methodology and formulations described here are applicable to many sorts of mixing-dilution problems, including dilution in petroleum reservoirs, hydrospheres, chemical constituents in rocks and minerals, monitoring of drilling fluids, and leaching, as well as to environmental science studies.  相似文献   
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