2003年淮河流域大水期间体积降水量的研究

针对 2 0 0 3年梅雨期淮河流域大水提出了体积降水量的概念及其计算方法 ,在计算出淮河流域和其各子流域逐日和总体积降水量的基础上 ,将体积降水量与水文站的水位和流量进行了对比分析。结果表明 :水位对累计体积降水量有较好的即时响应 ,流量对体积降水量有较好的延迟响应。另外 ,讨论了流域体积降水量的预报问题 ,用国内外数值天气预报产品和中央气象台指导预报产品 ,进行了流域体积降水量预报试验 ,并对预报结果进行了检验分析。结果表明 :在目前天气预报水平条件下 ,利用数值天气预报和中央气象台业务预报产品制作体积降水量是可行的 ,能够延长洪水预报的预见期 ;而且 ,数值天气预报产品在预报体积降水量方面有明显的优势 ,因此可以直接利用数值天气预报产品进行体积降水量的客观预报 ,为防汛工作提供重要的依据。     

安徽铜陵矿集区海底喷流沉积体系的流体包裹体微量元素对比

铜陵矿集区与铜-金(多金属)矿床有关的热液活动主要有两大体系。即与海西期海底喷流沉积有关的热液体系和与矽卡岩矿化有关的燕山期岩浆热液体系。查明这两类热液体系的流体包裹体特征对区域找矿和矿床成因研究都有实际意义。在包裹体岩相学研究基础上，应用ICP-MS技术和热爆提取方法，研究了新桥、冬瓜山、峙门口、铜官山、朝山等矿床具代表性的热液石英中流体包裹体的微量元素、稀土元素特征。结果表明，两类热液体系在流体包裹体特征上有较大的区别，在流体的微量元素和稀土元素特征方面也很不相同。海底喷流沉积体系的热液石英中流体包裹体与岩浆热液体系的相比。稀土总量较高，LREE／HREE比值较大，δEu不明显。且Mo／(W Sn)比值较高，反映流体中成矿物质的深源特征；Ga／T1、Rb／Cs大。Zr／Hf低，也不同于岩浆热液体系。     

Geography Education: The Glass Is Half Full and It's Getting Fuller

This article examines the recent progress of geography education as an academic subfield of geography and as an area of activity in K–12 education. Challenges to both endeavors are described. A restructuring of geography education's research program that focuses on spatial thinking and reasoning and merges applied and theoretical research is proposed.     

Correcting method of eddy covariance fluxes over non-flat surfaces and its application in ChinaFLUX

Although Eddy Covariance (EC) technique is one of the best methods for estimating the energy and mass exchanges between underlying surface and atmosphere in micrometeorology, errors and uncertainties still exist without necessary corrections. In this paper, we will focus on the effect of coordinate system on the eddy fluxes. Based on the data observed over four sites (one farmland site, one grassland site and two forest sites), the effects of three coordinate system transforming methods (Double Rotation-DR, Triple Rotation-TR and Planar Fit-PF)on the turbulent fluxes are analyzed. It shows that (i) the corrected fluxes are more or less than the uncorrected fluxes, which is related mainly to the sloping degree of surface, wind speed and wind direction; and (ii) pitch angle has a sinusoidal dependence on wind direction, especially in the regular sloping terrain; and (iii) PF method is something like the simplification of TR or DR,and there are not obvious distinctions in correction in sloping grassland and flat farmland, but PF method is not suitable for uneven and irregular forest sites.     

可解释性长短期记忆模型用于预测湖泊总磷浓度变化

对湖泊总磷的变化预测和来源识别对水资源调度和流域生态治理有着重要的意义,然而复杂的生化反应和水动力条件导致的非平稳性给湖泊总磷浓度的准确预测带来极大的困难。为克服这一挑战,本文引入了基于加权回归的季节趋势分解(seasonal and trend decomposition using Loess,STL)技术和夏普利加法(SHapley additive exPlanations,SHAP)结合长短期记忆网络(long short-term memory neural network,LSTM)和门控循环单元(gated recurrent unit,GRU)构建了一个可解释的预测框架,以增强对湖泊总磷浓度演变的预测并提高其可解释性。研究表明:(1)在骆马湖总磷浓度的预测中,该框架拥有较好的预报精度(R²=0.878),优于LSTM和卷积长短期记忆模型(convolutional neural networks and long short term memory network,CNN-LSTM)。当预测时间步长增加到8 h时,该框架有效提高了总磷浓度的预测精度,平均相对误差和均方根误差分别降低了47.1%和33.3%。从预测趋势来看,骆马湖在汛期的总磷平均浓度为0.158 mg/L,相较于非汛期的平均浓度,增加了202.1%。(2)运河来水是骆马湖总磷浓度最重要的影响因素,贡献权重为60.0%,并且不同断面(三湾、三场)的污染源受水动力、气象等因素的影响存在显著的时空差异。本文凸显了神经网络模型在预警水体污染方面的可实施性,并且为提高传统神经网络的学习能力和可解释性的开发与验证提供了重要方向。