侯马市酸雨概况及其气象条件分析

通过对侯马近14a酸雨观测资料分析,发现侯马出现酸雨的概率较大,强度较强,时间变化特征明显,与气象条件关系密切。     

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。     

The eastward subtropical countercurrent on isopycnal surface in the western North Pacific

The classic Sverdrup theory suggests that the water movement in the central subtropical gyre of North Pa-cific be slowly westward or southwestward.In the late sixties of the20th century,the existence of a peculiar eastward narrow flow between20°N and25°N in spring was theoretically predicted.It was named the Subtropical Countercurrent(STCC),although direct observational evidences were not yet sufficient to con-firm whether or not such eastward flow between20°N and25°N was a persistent…     

Soil respiration in tropical seasonal rain forest in Xishuangbanna, SW China

With the static opaque chamber and gas chromatography technique, from January 2003 to January 2004 soil respiration was investigated in a tropical seasonal rain forest in Xishuangbanna, SW China. In this study three treatments were applied, each with three replicates: A (bare soil), B (soil+litter), and C (soil+litter+seedling). The results showed that soil respiration varied seasonally, low from December 2003 to February 2004, and high from June to July 2004. The annual average values of CO2 efflux from soil respiration differed among the treatments at 1% level, with the rank of C (14642 mgCO2· m-2. h-1)＞B (12807 mgCO2· m-2. h-1)＞A (9532 mgCO2· m-2. h-1). Diurnal variation in soil respiration was not apparent due to little diurnal temperate change in Xishuangbanna. There was a parabola relationship between soil respiration and soil moisture at 1% level. Soil respiration rates were higher when soil moisture ranged from 35% to 45%. There was an exponential relationship between soil respiration and soil temperature (at a depth of 5cm in mineral soil) at 1% level. The calculated Q1o values in this study,ranging from 2.03 to 2.36, were very near to those of tropical soil reported. The CO2 efflux in 2003was 5.34 kgCO2· m-2. a-1 from soil plus litter plus seedling, of them 3.48 kgCO2· m-2. a-1 from soil (accounting for 62.5%), 1.19 kgCO2· m-2. a-1 from litter (22.3%) and 0.67 kgCO2·m-2. a-1 from seedling (12.5%).     

上海市公共卫生中心空调设计

介绍了上海市公共卫生中心空调通风的设计特点,突出反映在呼吸道传染病区、呼吸道门诊、呼吸道临检、生物安全实验室的系统设计中,并介绍了空调水系统的特殊要求。     

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.     

Nine thousand years of upper montane soil/vegetation dynamics from the summit of Caratuva Peak,Southern Brazil

Biodiversity loss, climate change, and increased freshwater consumption are some of the main environmental problems on Earth. Mountain ecosystems can reduce these threats by providing several positive influences, such as the maintenance of biodiversity, water regulation, and carbon storage, amongst others. The knowledge of the history of these environments and their response to climate change is very important for management, conservation, and environmental monitoring programs. The genesis of the soil organic matter of the current upper montane vegetation remains unclear and seems to be quite variable depending on location. Some upper montane sites in the very extensive coastal Sea Mountain Range present considerable organic matter from the late Pleistocene and other from only the Holocene. Our study was carried out on three soil profiles (two cores in grassland and one in forest) on the Caratuva Peak of the Serra do Ibitiraquire (a sub-range of Sea Mountain Range – Serra do Mar) in Southern Brazil. The δ¹³C isotopic analyses of organic matter in soil horizons were conducted to detect whether C₃ or C₄ plants dominated the past communities. Complementarily, we performed a pollen analysis and ¹⁴C dating of the humin fraction to obtain the age of the studied horizons. Except for a short and probably drier period (between 6000 and 4500 cal yr BP), C₃ plants, including ombrophilous grasses and trees, have dominated the highlands of the Caratuva Peak (Pico Caratuva), as well as the other uppermost summits of the Serra do Ibitiraquire, since around 9000 cal yr BP. The Caratuva region represents a landscape of high altitude grasslands (campos de altitude altomontanos or campos altomontanos) and upper montane rain/cloud forests with soils that most likely contain some organic matter from the late Pleistocene, as has been reported in Southern and Southeastern Brazil for other sites. However, our results indicate that the studied deposits (near the summit) are from the early to late Holocene, when somewhat wetter and warmer conditions (since around 9000 cal yr BP) enabled a stronger colonization of the ridge of Pico Caratuva by mainly C₃ plants, especially grassland species. However, at the same time, even near the summit, the soil core from the forest site already presented the current physiognomy (or a shrubby/elfin or successional forest), indicating that the colonization of the neighboring uppermost saddles and valleys were probably populated mainly by upper montane forest species.     

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.