琼东南盆地北礁地区梅山组丘形反射特征

目前琼东南盆地北礁凹陷中中新统梅山组顶部丘形反射引起广泛关注,但对其成因有不同认识。本文通过高精度二维、三维地震、钻井资料,研究丘形反射的特征。研究表明北礁地区梅山组顶部发育近东西向展布的长条形丘体,丘间为水道,丘内为中-弱振幅的地震反射,与西南部强振幅水道砂岩形成鲜明的对比,波阻抗反演揭示丘内为低波阻抗,属泥岩范畴。梅山组塑性丘内地层发生重力扩展,在其上覆的脆性地层（强振幅砂岩和弱振幅泥岩）发育多边形断层,反推出梅山组形成于深水环境,丘为泥丘,沉积环境分析也认为北礁凹陷中中新世为半深海沉积,梅山组的丘-谷分别对应上覆地层的谷-丘,认为是底流剥蚀/沉积成因。本文的研究对南海北部丘形反射的认识有重要意义,并可降低油气探勘风险。     

辽河口邻近海域小型底栖生物的空间分布及季节变化

本文研究了辽河口邻近海域2013年8月、10月和2014年5月3个航次小型底栖生物的种类及其空间分布,分析了小型底栖生物丰度和生物量的季节变化。结果表明,3个航次（夏季、秋季和春季）小型底栖生物的平均丰度分别为（264±83） ind/（10 cm2）、（216±85） ind/（10 cm2）和（227±67） ind/（10 cm2）,平均生物量分别为（272±125）μg/（10 cm2）、（207±89）μg/（10 cm2）和（244±103）μg/（10 cm2）。与其他研究海域相比,辽河口小型底栖的丰度和生物量处于较低水平。共鉴定出了14个小型生物类群,按照丰度排序,线虫是最优势的类群,夏季、秋季和春季3个航次占总丰度的比例分别为94.0%、92.5%和90.8%;其他优势类群为多毛类、桡足类和双壳类。小型底栖生物量的优势类群则为多毛类（41.1%~44.0%）,高于线虫（33.8%~36.5%）,其次是双壳类（2.6%~6.7%）。水平分布的研究表明,调查海域近岸入海口小型底栖生物的丰度和生物量普遍低于近海海域,但是秋季时近岸分布与近海差距不大。垂直分布的研究表明,95.9%的小型底栖生物分布于0~5 cm的表层沉积物中。小型底栖生物的丰度和生物量在夏季时都达到高峰值。与环境因子的相关分析表明,小型底栖生物的数量分布与盐度和水深呈极显著正相关（P<0.01）,与叶绿素a呈显著正相关（P<0.05）。     

基于风廓线雷达观测的台风“天兔”边界层演变特征研究

垂直探测台风边界层特征对于认清台风结构具有重要意义。基于1319号台风“天兔”途经的三个边界层风廓线雷达站的观测资料,结合探空数据,本文分析了“天兔”的边界层径向、切向风特征,结果表明：1）最大切向风速出现在眼区附近,在“天兔”稳定维持为强台风级别期间,最大切向风速基本稳定在1800 m高度左右,随着登陆后强度的迅速减弱,最大切向风速减弱、最大切向风速垂直范围缩小;2）最大风速所在高度和台风入流层顶高基本相近,大于依据理查森数或位温梯度所判断出的边界层高度,而基于信噪比（SNR）或其梯度所判断的混合层高度时常偏低;3）“天兔”登陆前边界层高度可以达到2100 m以上,在台风级别及以上时,各站观测到的边界层高度变化不大,基本在1200～1600 m之间,登陆后随着台风强度的减弱,边界层高度迅速减小。     

拖曳声源深海传播损失算法改进

为实现水声传播损失高效准确计算,以满足工程应用要求,基于南海某深水海域水声调查数据研究改进拖曳声源深海传播损失算法。首先,对信号时间序列分析发现:深海多途传播结构显著,可分为直达波、第一二次海底反射波,信号幅度逐渐减小,海面反射波与直达波重叠;目标信号中心频率产生多普勒频移,与声源拖曳速度对应较好;"单频"正弦信号并非单一频点上的声信号,为一窄带功率谱,谱峰对应信号中心频率。进而,从信号识别及多途效应处理两方面对算法进行改进:基于功率谱频带分布,设计Butterworth滤波器带通截止频率,从频域上滤除噪声信号;依据环境噪声电压幅值,制定其判定标准,从时域剔除与目标信号同频带噪声信号。算法改进后可较好适应低信噪比环境下多途拖曳声源信号能量计算,快速高精度得到传损失数据。     

拖曳声源深海脉冲多途效应及直达波传播特征

深海脉冲传播多途效应显著,直达波受海洋环境影响较大。基于南海某海域深水试验数据,采用Butterworth带通滤波器识别目标信号,进而分析近、中、远距离处VLA接收到的信号特征,并根据射线理论解释多途效应、直达波特征规律。结果表明:近距离目标信号可分为直达波及两次海底反射波;中距离可分为直达波与三次海底反射波;远距离目标信号弱,反射波特征不明显。其中,直达波声强显著低于第一次海底反射波,受夏季海面波导的影响,近表层深度处的直达波强度最大;50~200m深度层在强跃层控制下,声线向下弯曲,直达波信号随深度增加逐渐减弱;随传播距离增加,直达波逐渐减弱消失。     

Distributed estimation and analysis of precipitation recharge coefficient in strongly-exploited Beijing plain area,China

The precipitation recharge coefficient(PRC), representing the amount of groundwater recharge from precipitation, is an important parameter for groundwater resources evaluation and numerical simulation. It was usually obtained from empirical knowledge and site experiments in the 1980 s. However, the environmental settings have been greatly modified from that time due to land use change and groundwater over-pumping, especially in the Beijing plain area(BPA). This paper aims to estimate and analyze PRC of BPA with the distributed hydrological model and GIS for the year 2011 with similar annual precipitation as long-term mean. It is found that the recharge from vertical(precipitation + irrigation) and precipitation is 291.0 mm/yr and 233.7 mm/yr, respectively, which accounts for 38.6% and 36.6% of corresponding input water. The regional mean PRC is 0.366, which is a little different from the traditional map. However, it has a spatial variation ranging from –7.0% to 17.5% for various sub-regions. Since the vadose zone is now much thicker than the evaporation extinction depth, the land cover is regarded as the major dynamic factor that causes the variation of PRC in this area due to the difference of evapotranspiration rates. It is suggested that the negative impact of reforestation on groundwater quantity within BPA should be well investigated, because the PRC beneath forestland is the smallest among all land cover types.     

基于相对风险模型的土地利用变化生态风险定量评价——以石羊河流域为例

土地利用是否合理是推进国民经济发展的关键,也是生态环境变化的风向标。通过理论研究和实地调研,构建了土地利用生态风险定量评价体系,借助相对风险模型（RRM）对石羊河流域的生态风险及其影响因素进行研究。结果表明：（1）在划分的3个评价单元中,中部绿洲生态保护区在土地利用中面临的生态风险最大,北部盆地防沙治沙区次之,南部山地水源涵养区生态风险最小;（2）在3大风险单元的6类不同风险源中,工程建设对石羊河流域的生态风险影响最大,其次是作物种植和畜牧养殖,且在自然环境相对较好的区域表现得尤为明显。     

黄土高原地区土地覆盖变化对气候的反馈作用数值模拟

区域尺度土地覆盖变化是自然变化和人类活动共同驱动的结果,同时又对区域气候环境产生反馈。利用欧洲中值数值预报中心（ECMWF） ERA-Interim再分析资料,驱动RegCM4.5区域气候模式,进行了两个时间段（1980-2014、2005-2014）的数值模拟试验。以1：250 000土地利用图为基础,结合植被图、土壤图制作具有高精度、极强现势性的土地覆盖资料,区域模式中陆面过程模式采用BATS,模拟了现实的土地覆盖改变对气候要素的影响,分析了黄土高原地区土地覆盖变化对气候的反馈作用。结果表明：（1） RegCM4.5不但能够较好描述黄土高原气温、降水的时间空间分布特征,也能够模拟土地覆盖变化对黄土高原局地气候变化的反馈。（2）不同土地覆盖变化特征对气候反馈作用不同,荒漠化会引起局部地区气温升高和降水减少,并通过正反馈机制,致使自然植被生长发育受阻;水域旱化会导致夏季气温升高和降水增加,从而加剧干旱洪涝灾害风险;草地覆盖增加会导致春夏季降水量与气温的降低,秋冬季降水量与气温升高。（3）土地覆盖变化对气温与降水的影响在夏季较强。该研究可促进对黄土高原生态治理环境效应的理解,也能深化对土地利用-气候变化之间互馈作用的过程认识,并为区域生态建设对策选择提供参考。     

Influencing mechanism of energy-related carbon emissions in Xinjiang based on the input-output and structural decomposition analysis

Analysis of carbon emission mechanism based on regional perspectives is an important research method capable of achieving energy savings and emission reductions. Xinjiang, an important Chinese energy production base, is currently going through a period of strategic opportunities for rapid development. Ensuring stable socio-economic development while achieving energy savings and meeting emission reductions targets, is the key issue currently facing the region. This paper is based on the input-output theory, and conducts a structural decomposition analysis on the factors affecting energy-related carbon emissions in Xinjiang from 1997 to 2007; this analysis employs a hybrid input-output analysis framework of “energy - economy - carbon emissions”. (1) Xinjiang’s carbon emissions from energy consumption increased from 20.70 million tons in 1997 to 40.34 million tons in 2007; carbon emissions growth was mainly concentrated in the production and processing of energy resources, the mining of mineral resources, and the processing industry. (2) The analysis of the direct effects of the influencing factors on carbon emissions showed that the change in per capita GDP, the final demand structure, the population scale, and the production structure were the important factors causing an increase in carbon emissions, while the decrease in carbon emission intensity during this period was the important influencing factor in stopping the growth of carbon emissions. This showed that while the sizes of Xinjiang’s economy and population were growing, the economic structure had not been effectively optimized and the production technology had not been efficiently improved, resulting in a rapid growth of carbon emissions from energy consumption. (3) The analysis of the indirect effects of the influencing factors of carbon emission showed that the inter-provincial export, fixed capital formation, and the consumption by urban residents had significant influence on the changes in carbon emissions from energy consumption in Xinjiang. (4) The growth of investments in fixed assets of carbon intensive industry sectors, in addition to the growth of inter-provincial exports of energy resource products, makes the transfer effect of inter-provincial “embodied carbon” very significant.     

Groundwater depth prediction in a shallow aquifer in north China by a quantile regression model

There is a close relationship between groundwater level in a shallow aquifer and the surface ecological environment; hence, it is important to accurately simulate and predict the groundwater level in eco-environmental construction projects. The multiple linear regression (MLR) model is one of the most useful methods to predict groundwater level (depth); however, the predicted values by this model only reflect the mean distribution of the observations and cannot effectively fit the extreme distribution data (outliers). The study reported here builds a prediction model of groundwater-depth dynamics in a shallow aquifer using the quantile regression (QR) method on the basis of the observed data of groundwater depth and related factors. The proposed approach was applied to five sites in Tianjin city, north China, and the groundwater depth was calculated in different quantiles, from which the optimal quantile was screened out according to the box plot method and compared to the values predicted by the MLR model. The results showed that the related factors in the five sites did not follow the standard normal distribution and that there were outliers in the precipitation and last-month (initial state) groundwater-depth factors because the basic assumptions of the MLR model could not be achieved, thereby causing errors. Nevertheless, these conditions had no effect on the QR model, as it could more effectively describe the distribution of original data and had a higher precision in fitting the outliers.