近50年中国地面气候变化基本特征

采用国家基准气候站和基本气象站的地面资料,系统地分析了中国大陆地区1951年以来近地表主要气候要素演化的时间和空间特征。结果表明,中国近50 a来年平均地表气温变暖幅度约为1.1℃,增温速率接近0.22℃/(10 a),比全球或半球同期平均增温速率明显偏高。地表气温增暖主要发生在最近的20余年,其季节和空间特征与前人分析结论基本一致。降水量变化趋势对所取时间段和区域范围敏感。1951年以来全国平均降水量变化趋势不明显,但1956年以来略有增加。降水变化的空间特征明显而相对稳定,东北北部、包括长江中下游的东南部地区和西部广大地区降水增加,而华北地区以及东北东南部和西北东部地区降水明显减少。分析还发现,近50a来全国平均的日照时数、平均风速、水面蒸发等气候要素均呈显著下降趋势,但积雪地带的最大积雪深度却有所增加。中国日照时间和水面蒸发量变化的空间特征很相似,减少最明显的地区均发生在华北和华东,新疆次之。影响中国年代以上尺度气候变化的因子错综复杂,人类活动引起的大气中温室气体浓度增高可能在一定程度上影响了中国近50 a来的气候,但考虑到尚存的不确定性,目前仍不能给出明确结论。中国东部大部分地区日照时间和水面蒸发量减少可能均起源于人为排放的气溶胶影响,平均风速减弱也有利于水面蒸发量下降,而在西部地区云量和降水量的变化可能更重要。     

塔城市一次暴雪天气的分析

2003年2月22日塔城市出现一次暴雪天气过程。文章着重分析了此次天气过程的高空环流形势、地面形势、T—Td等，指出暖湿气流是造成暴雪的主要原因。     

Effects of spatially structured vegetation patterns on hillslope erosion in a semiarid Mediterranean environment: a simulation study

A general trend of decreasing soil loss rates with increasing vegetation cover fraction is widely accepted. Field observations and experimental work, however, show that the form of the cover‐erosion function can vary considerably, in particular for low cover conditions that prevail on arid and semiarid hillslopes. In this paper the structured spatial distribution of the vegetation cover and associated soil attributes is proposed as one of the possible causes of variation in cover–erosion relationships, in particular in dryland environments where patchy vegetation covers are common. A simulation approach was used to test the hypothesis that hillslope discharge and soil loss could be affected by variation in the spatial correlation structure of coupled vegetation cover and soil patterns alone. The Limburg Soil Erosion Model (LISEM) was parameterized and verified for a small catchment with discontinuous vegetation cover at Rambla Honda, SE Spain. Using the same parameter sets LISEM was subsequently used to simulate water and sediment fluxes on 1 ha hypothetical hillslopes with simulated spatial distributions of vegetation and soil parameters. Storms of constant rainfall intensity in the range of 30–70 mm h^?1 and 10–30 min duration were applied. To quantify the effect of the spatial correlation structure of the vegetation and soil patterns, predicted discharge and soil loss rates from hillslopes with spatially structured distributions of vegetation and soil parameters were compared with those from hillslopes with spatially uniform distributions. The results showed that the spatial organization of bare and vegetated surfaces alone can have a substantial impact on predicted storm discharge and erosion. In general, water and sediment yields from hillslopes with spatially structured distributions of vegetation and soil parameters were greater than from identical hillslopes with spatially uniform distributions. Within a storm the effect of spatially structured vegetation and soil patterns was observed to be highly dynamic, and to depend on rainfall intensity and slope gradient. Copyright © 2005 John Wiley & Sons, Ltd.     

The diurnal and seasonal characteristics of urban heat island variation in Beijing city and surrounding areas and impact factors based on remote sensing satellite data

Based on the land surface temperature (LST), the land cover classification map,vegetation coverage, and surface evapotranspiration derived from EOS-MODIS satellite data, and by the use of GIS spatial analytic technique and multivariate statistical analysis method, the urban heat island (UHI) spatial distribution of the diurnal and seasonal variabilities and its driving forces are studied in Beijing city and surrounding areas in 2001. The relationships among UHI distribution and landcover categories, topographic factor, vegetation greenness, and surface evapotranspiration are analyzed. The results indicate that: (i) The significant UHI occur in Beijing city areas in the four seasons due to high heat capacity and multi-reflection of compression building, as well as with special topographic features of its three sides surrounded by mountains,especially in the summer. The UHI spatial distribution is corresponding with the urban geometry structure profile. The LST difference is approximately 4-6℃ between Beijing city and suburb areas, comparatively is 8- 10℃ between Beijing city area and outer suburb area in northwestern regions. (ii) The UHI distribution and intensity in daytime are different from nighttime in Beijing city area, the nighttime UHI is obvious. However, in the daytime, the significant UHI mainly appears in the summer, the autumn takes second place, and the UHI in the winter and the spring seem not obvious. The surface evapotranspiration in suburb areas is larger than that in urban areas in the summer, and high latent heat exchange is evident, which leads to LST difference between city area and suburb area. (iii) The reflection of surface landcover categories is sensitive to the UHI, the correlation between vegetation greenness and UHI shows obviously negative.The scatterplot shows that there is the negative correlation between NDVI and LST (R2 = 0.6481).The results demonstrate that the vegetation greenness is an important factor for reducing the UHI,and large-scale construction of greenbelts can considerably reduce the UHI effect.     

浅埋暗挖技术在南平市天台隧道工程施工中的应用

主要介绍了在Ⅱ、Ⅲ类围岩中施工城市浅埋隧道的施工技术，以“短开挖、快封闭、强支护、勤量测”为指导，将隧道分成上、下两个台阶进行开挖，及时进行初期支护，保证施工始终处于安全状态。     

Spatiotemporal estimation of snow depth using point data from snow stakes,digital terrain models,and satellite data

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.     

Landscape controls on long‐term runoff in subhumid heterogeneous Boreal Plains catchments

We compared median runoff (R) and precipitation (P) relationships over 25 years from 20 mesoscale (50 to 5,000 km²) catchments on the Boreal Plains, Alberta, Canada, to understand controls on water sink and source dynamics in water‐limited, low‐relief northern environments. Long‐term catchment R and runoff efficiency (RP^?1) were low and varied spatially by over an order of magnitude (3 to 119 mm/year, 1 to 27%). Intercatchment differences were not associated with small variations in climate. The partitioning of P into evapotranspiration (ET) and R instead reflected the interplay between underlying glacial deposit texture, overlying soil‐vegetation land cover, and regional slope. Correlation and principal component analyses results show that peatland‐swamp wetlands were the major source areas of water. The lowest estimates of median annual catchment ET (321 to 395 mm) and greatest R (60 to 119 mm, 13 to 27% of P) were observed in low‐relief, peatland‐swamp dominated catchments, within both fine‐textured clay‐plain and coarse‐textured glacial deposits. In contrast, open‐water wetlands and deciduous‐mixedwood forest land covers acted as water sinks, and less catchment R was observed with increases in proportional coverage of these land covers. In catchments dominated by hummocky moraines, long‐term runoff was restricted to 10 mm/year, or 2% of P. This reflects the poor surface‐drainage networks and slightly greater regional slope of the fine‐textured glacial deposit, coupled with the large soil‐water and depression storage and higher actual ET of associated shallow open‐water marsh wetland and deciduous‐forest land covers. This intercatchment study enhances current conceptual frameworks for predicting water yield in the Boreal Plains based on the sink and source functions of glacial landforms and soil‐vegetation land covers. It offers the capability within this hydro‐geoclimatic region to design reclaimed catchments with desired hydrological functionality and associated tolerances to climate or land‐use changes and inform land management decisions based on effective catchment‐scale conceptual understanding.     

Diagnosing snow accumulation errors in a rain‐snow transitional environment with snow board observations

Diagnosing the source of errors in snow models requires intensive observations, a flexible model framework to test competing hypotheses, and a methodology to systematically test the dominant snow processes. We present a novel process‐based approach to diagnose model errors through an example that focuses on snow accumulation processes (precipitation partitioning, new snow density, and snow compaction). Twelve years of meteorological and snow board measurements were used to identify the main source of model error on each snow accumulation day. Results show that modeled values of new snow density were outside observational uncertainties in 52% of days available for evaluation, while precipitation partitioning and compaction were in error 45% and 16% of the time, respectively. Precipitation partitioning errors mattered more for total winter accumulation during the anomalously warm winter of 2014–2015, when a higher fraction of precipitation fell within the temperature range where partition methods had the largest error. These results demonstrate how isolating individual model processes can identify the primary source(s) of model error, which helps prioritize future research.     

Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high‐elevation catchment

In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km²). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments.     

Exploration of remotely sensed forest structure and ultrasonic range sensor metrics to improve empirical snow models

Current methods to estimate snow accumulation and ablation at the plot and watershed levels can be improved as new technologies offer alternative approaches to more accurately monitor snow dynamics and their drivers. Here we conduct a meta‐analysis of snow and vegetation data collected in British Columbia to explore the relationships between a wide range of forest structure variables – obtained from Light Detection and Ranging (LiDAR), hemispherical photography (HP) and Landsat Thematic Mapper – and several indicators of snow accumulation and ablation estimated from manual snow surveys and ultrasonic range sensors. By merging and standardizing all the ground plot information available in the study area, we demonstrate how LiDAR‐derived forest cover above 0.5 m was the variable explaining the highest percentage of absolute peak snow water equivalent (SWE) (33%), while HP‐derived leaf area index and gap fraction (45° angle of view) were the best potential predictors of snow ablation rate (explaining 57% of variance). This study reveals how continuous SWE data from ultrasonic sensors are fundamental to obtain statistically significant relationships between snow indicators and structural metrics by increasing mean r² by 20% when compared to manual surveys. The relationships between vegetation and spectral indices from Landsat and snow indicators, not explored before, were almost as high as those shown by LiDAR or HP and thus point towards a new line of research with important practical implications. While the use of different data sources from two snow seasons prevented us from developing models with predictive capacity, a large sample size helped to identify outliers that weakened the relationships and suggest improvements for future research. A concise overview of the limitations of this and previous studies is provided along with propositions to consistently improve experimental designs to take advantage of remote sensing technologies, and better represent spatial and temporal variations of snow. Copyright © 2013 John Wiley & Sons, Ltd.