Crystallisation conditions (T, P, fO2) from mineral chemistry of Cu- and Au-mineralised alkaline intrusions in the Red River–Jinshajiang alkaline igneous belt, western Yunnan Province, China

The Oligocene Yao’an syenite porphyry, associated with gold mineralisation, and the Machangqing alkali granite porphyry–monzonite porphyry, associated with Cu mineralisation, belong to the Red River–Jinshajiang alkaline igneous belt that formed in a continental setting in southwestern China. A study of the mineral chemistry of major silicate minerals in these two mineralised intrusions provides insights into their overall crystallisation conditions. The temperature and pressure conditions, derived from amphibole–plagioclase and perthite–plagioclase geothermometry and Al-in-amphibole barometry, suggest that the Yao’an intrusion crystallised at around 820?±?50°C and 0.9–1.3 kbar, whereas the Machangqing intrusion crystallised at around 730?±?50°C and 2.2–2.8 kbar. The higher temperature and lower pressure of crystallisation for the Yao’an intrusion relative to the Machangqing intrusion indicates that it was emplaced at a shallower crustal level. Based on biotite composition, the two intrusions formed under imposed oxygen fugacities above the Ni–NiO buffer (NNO), and the Yao’an intrusion crystallised under more oxidising conditions than the Machangqing intrusion. The results show that the intrusions associated with Cu–Au mineralisation in the Red River–Jinshajiang alkaline igneous belt were emplaced at a relatively high fO₂, which, together with the weakly fractionated magma, favoured Au enrichment relative to Cu in the belt.     

Annual variations in the surface radiation budget and soil water and heat content in the Upper Yellow River area

Measurements taken between July 2006 to May 2007 at the Maqu station in the Upper Yellow River area were used to study the surface radiation budget and soil water and heat content in this area. These data revealed distinct seasonal variations in downward shortwave radiation, downward longwave radiation, upward longwave radiation and net radiation, with larger values in the summer than in winter because of solar altitudinal angle. The upward shortwave radiation factor is not obvious because of albedo (or snow). Surface albedo in the summer was lower than in the winter and was directly associated with soil moisture and solar altitudinal angle. The annual averaged albedo was 0.26. Soil heat flux, soil temperature and soil water content changed substantially with time and depth. The soil temperature gradient was positive from August to February and was related to the surface net radiation and the heat condition of the soil itself. There was a negative correlation between soil temperature gradient and net radiation, and the correlation coefficient achieved a significance level of 0.01. Because of frozen state of the soil, the maximum soil thermal conductivity value was 1.21 W m⁻¹°C⁻¹ in January 2007. In May 2007, soil thermal conductivity was 0.23 W m⁻¹°C⁻¹, which is the lowest value measured in the study, likely due to the fact that the soil was drier then than in other months. The soil thermal conductivity values for the four seasons were 0.27, 0.38, 0.55 and 0.83 W m⁻¹°C⁻¹, respectively.     

富营养化对海洋生态系统的影响及其围隔实验研究

陆源的营养盐输入引起海洋中的富营养化,提高了水体中营养盐含量,改变了水体中营养盐比例,引起浮游植物水华甚至赤潮。水华发生时由于沿传统食物链的能流在浮游植物环节阻塞,能量过多地分配到微生物环,细菌大量繁殖分解有机物,导致水体缺氧,造成鱼虾贝类窒息死亡。围隔系统是物质相对守恒的系统,围隔与自然海水没有水交换,利用现场围隔实验可以定量地、系统地研究海洋生态系统对富营养化的响应。围隔实验现已成为全球海洋生态系统动力学研究的重要手段。     

用NOAA/AVHRR资料遥感土壤水分时风速的影响

以热惯量法为基础,在地理信息系统（ＧＩＳ）的支持下,通过计算地形参数Ｒ与Ｆ,间接考虑了风速对用ＮＯＯＡＡ／ＡＶＨＲＲ资料遥感土壤水分的影响。结果表明：考虑风速后,遥感土壤水分的精度比热惯量法有所提高;风速对遥感土壤水分的影响主要限于土壤浅层,到３０ｃｍ深度以下时可以不考虑其影响;遥感土壤水分的最佳深度并不在土壤表层,而在２０ｃｍ深度左右。     

SiteTesting with IRA-935 Hygrometer and Statistical Analyses

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Parameterization of the Ångström–Prescott formula based on machine learning benefit estimation of reference crop evapotranspiration with missing solar radiation data

Accurately estimated reference evapotranspiration (ET₀) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P-M) model with the Ångström–Prescott (A-P) formula as the standard method for ET₀ estimation with missing R_s measurements. However, its application is usually restricted by the two fundamental coefficients (a and b) of the A-P formula. This paper proposes a new method for estimating ET₀ with missing R_s by combining machine learning with physical-based P-M models (PM-ET₀). The benchmark values of the A-P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in R_s and ET₀ estimates at 80 national R_s measuring stations. Then, three empirical models and four machine-learning methods were evaluated in estimating the A-P coefficients. Machine learning methods were also used to estimate ET₀ (ML-ET₀) to compare with the PM-ET₀. Finally, the optimal estimation method was used to estimate the A-P coefficients for the 839 regular weather stations for ET₀ estimation without R_s measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a. The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest R_s and ET₀ estimation errors. Additionally, the A-P coefficients calibrated at the daily scale obtained the best estimation accuracy for both R_s and ET₀, and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A-P coefficients, especially for the sites with extreme values. Further, ML-ET₀ outperformed the PM-ET₀ with yearly A-P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET₀. However, we found that using the A-P coefficients with finer time scales is unnecessary to deal with the missing R_s measurements.     

海面温度变化影响台风"海棠"强度的数值研究

通过对台风"海棠"5 d的数值模拟,研究海表温度(SST)变化对台风强度的影响。与NCEP月平均海表温度相对比,在中尺度大气模式中引入热带测雨卫星(TRMM)微波成像仪(TMI)/先进微波扫描辐射计(AMSR-E)来考察SST对台风"海棠"路径和强度的影响。研究结果表明,每天变化SST的试验模拟的台风强度和路径整体效果不错;模拟的台风路径不敏感于SST的变化,而台风强度的变化不仅取决于由于台风移动引发的SST冷却的幅度大小,而且取决于SST冷却区域的相对位置。在台风"海棠"强烈发展过程中,台风中心右侧冷却区对台风中心气压影响很小;台风强烈发展过后,SST冷却区开始影响台风强度,但造成台风中心气压下降幅度不大,6 h内台风中心气压减弱约3.9 hPa。海面热量通量和海面风速与SST的分布都有良好的相关性:在SST变化为正值的暖水区,感热通量和潜热通量都是一个正的通量分布的极值区,并有风速极大值区域存在;在台风右侧相应的冷却区,则存在着负的通量异常和风速极小值区域。     

中国数字地震台网记录的苏门答腊-安达曼地震激发的地球球型自由振荡的检测

利用中国数字地震台网 （CDSN）改造后的7个台站的VHZ波形资料, 提取了2004年12月26日苏门答腊-安达曼地震激发的0S2~0S54地球球型自由振荡,并与地球初步参考模型(PREM)的理论自由振荡频率进行了对比,发现与PREM模型预测的球型自由振荡周期符合得很好. 观测的该地震激发的0S2,0S3,0S4, 2S1和1S2振型自由振荡有明显的频谱分裂现象. 特别是2S1振型,是继Rosat等之后的第二次报道不叠加的情况下观测到的这种振型. 根据该地震的总体震源机制,试算了不同的地震矩,模拟了该地震0S2振型在CDSN 7个台上的自由振荡振幅, 并与记录资料进行了比较,得到该地震的地震矩可达1023Nmiddot;m;本文发现地球自由振荡记录包含了大量震源机制和地震位置的信息, 可用于地震震源破裂参数的精细研究.       

1961—2010年东北地区夏季降水时空变化的研究

东北地区地处中高纬欧亚大陆东岸,处在东亚季风区的边缘,其降水主要集中在夏季,并且降水量具有显著的地域差异和年际变化。研究东北夏季降水特点,可为降水预报、气候预测和农业生产提供可靠依据。本文利用东北地区27个测站1961—2010年夏季逐月降水资料,通过E-OF和小波分析的方法研究东北地区夏季降水的时空变化规律,结果表明：东北地区夏季降水量主要表现为三种空间分布型：总体一致型、南-北反位相型以及东南-西北反位相型;东北地区夏季降水量存在明显的年际变化,降水变化周期为8—11a;东北地区夏季各月降水量的差异,主要是由地形原因和纬线跨度差异造成的。     

地震灾害脆弱性分析--以河北省张北-尚义地震灾害为例

河北省位于环太平洋地震带,是我国地震灾害多发,灾害死伤人数最多,损失最重的省份。地震灾害的伤亡和损失不仅与地震强度有关,而且与人类和社会经济财产的脆弱性有关。通过对张北－尚义地震灾害脆弱性的分析,指出理解和减轻地震灾害的关键在于减轻镶嵌于日常生活中的地震灾害脆弱性。在目前人类尚不能准确预报、控制和改变地震过程的情况下,通过提高对地震灾害的认识,调整日常生活和生产中与震害有关的各项活动,将脆弱性降到