Evaluation of methods of spatial interpolation for monthly rainfall data over the state of Rio de Janeiro,Brazil

Five deterministic methods of spatial interpolation of monthly rainfall were compared over the state of Rio de Janeiro, southeast Brazil. The methods were the inverse distance weight (IDW), nearest neighbor (NRN), triangulation with linear interpolation (TLI), natural neighbor (NN), and spline tension (SPT). A set of 110 weather stations was used to test the methods. The selection of stations had two criteria: time series longer than 20 years and period of data from 1960 to 2009. The methods were evaluated using cross-validation, linear regression between values observed and interpolated, root mean square error (RMSE), coefficient of determination (r ²), coefficient of variation (CV, %), and the Willmott index of agreement (d). The results from different methods are influenced by the meteorological systems and their seasonality, as well as by the interaction with the topography. The methods presented higher precision (r ²) and accuracy (d, RMSE) during the summer and transition to autumn, in comparison with the winter or spring months. The SPT had the highest precision and accuracy in relation to other methods, in addition to having a good representation of the spatial patterns expected for rainfall over the complex terrain of the state and its high spatial variability.     

Coupling statistical and dynamical methods for spatial downscaling of precipitation

The resolution of General Circulation Models (GCMs) is too coarse for climate change impact studies at the catchment or site-specific scales. To overcome this problem, both dynamical and statistical downscaling methods have been developed. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. This paper evaluates the improvement in statistical downscaling (SD) predictive power when using predictors from a Regional Climate Model (RCM) over a GCM for downscaling site-specific precipitation. Our approach uses mixed downscaling, combining both dynamic and statistical methods. Precipitation, a critical element of hydrology studies that is also much more difficult to downscale than temperature, is the only variable evaluated in this study. The SD method selected here uses a stepwise linear regression approach for precipitation quantity and occurrence (similar to the well-known Statistical Downscaling Model (SDSM) and called SDSM-like herein). In addition, a discriminant analysis (DA) was tested to generate precipitation occurrence, and a weather typing approach was used to derive statistical relationships based on weather types, and not only on a seasonal basis as is usually done. The existing data record was separated into a calibration and validation periods. To compare the relative efficiency of the SD approaches, relationships were derived at the same sites using the same predictors at a 300km scale (the National Center for Environmental Prediction (NCEP) reanalysis) and at a 45km scale with data from the limited-area Canadian Regional Climate Model (CRCM) driven by NCEP data at its boundaries. Predictably, using CRCM variables as predictors rather than NCEP data resulted in a much-improved explained variance for precipitation, although it was always less than 50?% overall. For precipitation occurrence, the SDSM-like model slightly overestimated the frequencies of wet and dry periods, while these were well-replicated by the DA-based model. Both the SDSM-like and DA-based models reproduced the percentage of wet days, but the wet and dry statuses for each day were poorly downscaled by both approaches. Overall, precipitation occurrence downscaled by the DA-based model was much better than that predicted by the SDSM-like model. Despite the added complexity, the weather typing approach was not better at downscaling precipitation than approaches without classification. Overall, despite significant improvements in precipitation occurrence prediction by the DA scheme, and even going to finer scales predictors, the SD approach tested here still explained less than 50?% of the total precipitation variance. While going to even smaller scale predictors (10–15?km) might improve results even more, such smaller scales would basically transform the direct outputs of climate models into impact models, thus negating the need for statistical downscaling approaches.     

Summer temperature variability across four urban neighborhoods in Knoxville,Tennessee, USA

The urban heat island (UHI) is a well-documented effect of urbanization on local climate, identified by higher temperatures compared to surrounding areas, especially at night and during the warm season. The details of a UHI are city-specific, and microclimates may even exist within a given city. Thus, investigating the spatiotemporal variability of a city’s UHI is an ongoing and critical research need. We deploy ten weather stations across Knoxville, Tennessee, to analyze the city’s UHI and its differential impacts across urban neighborhoods: two each in four neighborhoods, one in more dense tree cover and one in less dense tree cover, and one each in downtown Knoxville and Ijams Nature Center that serve as control locations. Three months of temperature data (beginning 2 July 2014) are analyzed using paired-sample t tests and a three-way analysis of variance. Major findings include the following: (1) Within a given neighborhood, tree cover helps negate daytime heat (resulting in up to 1.19 ^°C lower maximum temperature), but does not have as large of an influence on minimum temperature; (2) largest temperature differences between neighborhoods occur during the day (0.38–1.16 ^°C difference), but larger differences between neighborhoods and the downtown control occur at night (1.04–1.88 ^°C difference); (3) presiding weather (i.e., air mass type) has a significant, consistent impact on the temperature in a given city, and lacks the differential impacts found at a larger-scale in previous studies; (4) distance from city center does not impact temperature as much as land use factors. This is a preliminary step towards informing local planning with a scientific understanding of how mitigation strategies may help minimize the UHI and reduce the effects of extreme weather on public health and well-being.     

红壤丘陵区农业开发中的生态环境效应及其调控

文章以江西余江县为例。通过实验研究，历史资料对比分析及邻近参照等方法。研究红壤区农业开发中的生态环境效应。农业开发引起的微观环境效应在于改变土壤水分及其运动状况，加速土壤物质迁移及降低土壤肥力质量；区域性效应是植被减少，农业面源污染，水土流失扩大及气候异常；全球性影响除区域集聚效应外，则是农业活动中产生的温室气体对全球气候的影响等，采取的调控对策主要是：保持合理开发，适当减轻土地压力，优化农业开发模式，更新农业耕作技术。强化环境整治与生态建设。     

山东长岛特色旅游资源研究

文章从海岛角度出发 ,在分析山东长岛旅游资源条件及开发现状的基础上 ,提出了该岛发展特色旅游应坚持独特性、时代性和系统性的原则及若干建议。     

Influencing domain of peripheral sources in the urban heavy pollution process of Beijing

Multiple cities in a diveloped economic area may consist of a city cluster,and the difusion and mixing of its pollutants result in the effect of pollutants plume between cities and the large-scale regional pollution diffusion phenomenon.The distant transfer and diffusion of pollutants occurs when massive aerosols are affected by the dynamic porcess of large-scale circulations.Research suggesten that the life span of aerosol particles whose diameters are about 1 um is the longest.The longevity …     

Late Mesozoic crust-mantle interaction and lower crust components in South China: A geochemical study of mafic granulite xenoliths from Cenozoic basalts

Mafic granulite xenoliths have been discovered in many volcanoes (especially alkali basalt and kimberlite) all over the world. They formed generally in lower crust, and recorded lots of in- formation on the lithosphere formation and crust-mantle interacti…     

拟合水工设计反应谱的人工地震波的生成与Huang变换校正

根据非平稳输入下建立的功率谱与均值反应谱之间的关系,合成基于水工设计反应谱的人工地震波,并对其幅值进行修正,降低了高频区误差作用.为了解决加速度时程积分后的速度、位移时程的零线漂移现象,利用Huang变换得到加速度时程的固有模态函数,这种最低频率固有模态函数分量通常情况下代表原始信号的趋势或均值;再对去掉均值后的加速度时程进行积分,得到的速度、位移时程不存在零线漂移问题.