Nonlinear ensemble prediction of chaotic daily rainfall

The significance of treating rainfall as a chaotic system instead of a stochastic system for a better understanding of the underlying dynamics has been taken up by various studies recently. However, an important limitation of all these approaches is the dependence on a single method for identifying the chaotic nature and the parameters involved. Many of these approaches aim at only analyzing the chaotic nature and not its prediction. In the present study, an attempt is made to identify chaos using various techniques and prediction is also done by generating ensembles in order to quantify the uncertainty involved. Daily rainfall data of three regions with contrasting characteristics (mainly in the spatial area covered), Malaprabha, Mahanadi and All-India for the period 1955–2000 are used for the study. Auto-correlation and mutual information methods are used to determine the delay time for the phase space reconstruction. Optimum embedding dimension is determined using correlation dimension, false nearest neighbour algorithm and also nonlinear prediction methods. The low embedding dimensions obtained from these methods indicate the existence of low dimensional chaos in the three rainfall series. Correlation dimension method is done on the phase randomized and first derivative of the data series to check whether the saturation of the dimension is due to the inherent linear correlation structure or due to low dimensional dynamics. Positive Lyapunov exponents obtained prove the exponential divergence of the trajectories and hence the unpredictability. Surrogate data test is also done to further confirm the nonlinear structure of the rainfall series. A range of plausible parameters is used for generating an ensemble of predictions of rainfall for each year separately for the period 1996–2000 using the data till the preceding year. For analyzing the sensitiveness to initial conditions, predictions are done from two different months in a year viz., from the beginning of January and June. The reasonably good predictions obtained indicate the efficiency of the nonlinear prediction method for predicting the rainfall series. Also, the rank probability skill score and the rank histograms show that the ensembles generated are reliable with a good spread and skill. A comparison of results of the three regions indicates that although they are chaotic in nature, the spatial averaging over a large area can increase the dimension and improve the predictability, thus destroying the chaotic nature.     

Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter

Catchment scale hydrological models are critical decision support tools for water resources management and environment remediation. However, the reliability of hydrological models is inevitably affected by limited measurements and imperfect models. Data assimilation techniques combine complementary information from measurements and models to enhance the model reliability and reduce predictive uncertainties. As a sequential data assimilation technique, the ensemble Kalman filter (EnKF) has been extensively studied in the earth sciences for assimilating in-situ measurements and remote sensing data. Although the EnKF has been demonstrated in land surface data assimilations, there are no systematic studies to investigate its performance in distributed modeling with high dimensional states and parameters. In this paper, we present an assessment on the EnKF with state augmentation for combined state-parameter estimation on the basis of a physical-based hydrological model, Soil and Water Assessment Tool (SWAT). Through synthetic simulation experiments, the capability of the EnKF is demonstrated by assimilating the runoff and other measurements, and its sensitivities are analyzed with respect to the error specification, the initial realization and the ensemble size. It is found that the EnKF provides an efficient approach for obtaining a set of acceptable model parameters and satisfactory runoff, soil water content and evapotranspiration estimations. The EnKF performance could be improved after augmenting with other complementary data, such as soil water content and evapotranspiration from remote sensing retrieval. Sensitivity studies demonstrate the importance of consistent error specification and the potential with small ensemble size in the data assimilation system.     

海域使用活动对海湾生态环境的压力评估——以莱州湾为例

以莱州湾为例,基于GIS和地理空间模拟框架,结合海洋生态红线区分布,建立了海域使用活动对海洋生态环境的潜在压力评估模型,空间量化评估多种海域使用活动对海洋生态环境的潜在影响。结果显示,多种海域使用活动的潜在压力总体呈近岸高于远岸、湾顶>东部>西部的分布特征;压力高值区集中于距岸10 km以内海域和5 m水深以内海域;海洋特别保护区、海洋自然保护区及重要河口生态系统等类型的海洋生态红线区受到开放式养殖、围海养殖、盐业用海活动的压力较大,需要对其进行重点监控。研究揭示了莱州湾海域使用活动对海洋生态环境潜在压力的空间分布格局,为海洋生态红线落地实施和海域使用管理提供决策依据。     

Space and time variability of uncertainty in morphodynamic simulations

The uncertainty associated with simulations of process-based coastal area morphodynamic models is assessed through numerical experimentation. Appropriate metrics of uncertainty are defined based on the standard deviation of the model results at each location and each time step. Uncertainty is examined using a set of realistic one year morphodynamic simulations of the evolution of a highly dynamic tidal inlet. Results indicate that uncertainty increases linearly with time, and suggest that its rate grows with increasing sediment fluxes. Hence, the limits of predictability of morphodynamic model applications are higher for slowly varying systems. Attempts to reduce uncertainty by aggregating model results at larger spatial scales met with limited success. Ensemble simulations are suggested as a possible avenue to investigate the long-term evolution of tidal inlets using process-based models.     

矿产预测信息化问题的研究

地质科学中地球物理信息数据属于矢量，可用位场变换法对其进行数据处理，化探、物探中的能谱信息数据属于标量，可用多元统计分析法进行数据处理，地质信息数据也是标量，但属二态变量，可用数量化理论等定性数据分析进行处理。在GIS平台上建立自变量、因变量图层组，应用相应的数学模型，来实现综合信息矿产预测。综合信息矿产预测数据库要在按国家统一标准建立的原始数据库(元数据库)和用于在GIS平台上开展决策分析的知识数据库的基础上建立。     

黑龙江省高松山浅成低温热液金矿床围岩蚀变元素迁移特征、定量计算与形成机制

高松山浅成低温热液金矿床赋存于下白垩统安山岩等中基性火山岩中。矿体受围岩中断裂破碎带的控制,呈脉状产出。矿区围岩蚀变主要发育硅化、黄铁矿化、冰长石化、伊利石化、绢云母化、碳酸盐化和绿泥石化等。根据蚀变矿物组合和蚀变岩地球化学特征,可将围岩蚀变由近矿至远矿分为3个带:强硅化-冰长石化带、硅化-伊利石化-冰长石化带、伊利石化-青磐岩化带。对各蚀变带进行了全岩元素地球化学分析,利用元素迁移定量计算和图解等方法详细估算和拟定了蚀变过程中元素的迁移规律及其与蚀变矿物学之间的联系。结果表明,安山岩蚀变过程与金矿化密切相关,特别是硅化、冰长石化和黄铁矿化蚀变阶段。根据围岩蚀变形成过程与成矿之间的联系,探讨了高松山金矿床围岩蚀变的形成机制。     

地矿勘查工作信息化的理论与方法问题

实现地矿勘查工作信息化的有效途径与方法是根据地矿勘查工作自身的特点, 建立以主题式地矿点源数据库(包括空间数据库和属性数据库)为基础的共用数据平台; 利用信息系统技术对地矿勘查工作主流程进行充分改造, 实现全程计算机辅助化; 进行“多S”的技术集成、网络集成、数据集成和应用集成, 同时实现勘查数据的三维可视化.为此, 需要加强地质信息科学和地矿勘查工作信息化的理论框架、技术体系和方法论研究, 重视与地矿勘查工作相适应的集成化信息技术开发.      

中国南方寒武纪岩相古地理

中国南方是指西至金沙江-元江断裂、西北至龙门山断裂、北至城口-房县-襄樊-广济断裂、东北至郯庐断裂、东至黄海和东海、南至南海的我国南方广大地区。在各露头剖面和钻井剖面地层学和岩石学研究所取得的各种定量及定性资料的基础上,采用单因素分析综合作用法,编制出了中国南方下寒武统下部和中部、下寒武统上部、中寒武统和上寒武统的各种单因素图以及相应的早寒武世早期和中期、早寒武世晚期、中寒武世和晚寒武世岩相古地理图。这些古地理图的最主要特征是定量,即每个古地理单元的划分和确定都有确切的定量的单因素图为依据。这种定量的岩相古地理图在中国南方寒武纪还是首次出现。中国南方寒武纪有7个主要的古地理单元,即滇西台地、康滇陆、扬子台地、斜坡、江南盆地、东南台地和华夏陆。滇西台地、康滇陆、扬子台地、斜坡和江南盆地属康滇古地理体系,东南台地和华夏陆属华夏古地理体系。这两个古地理体系拼合在一起,就构成了中国南方寒武纪的两陆、三台、一盆和一坡和古地理的基本格局。是寒武世早期和中期的岩相古地理特征与早寒武世晚期、中寒武世和晚寒武世岩相古地理特征有明显的不同。这是两个大不相同的演化阶段。这种定量的岩相古地理图在古地理学中是个重大的进展,对石油、天然气以及其他各种矿产的预测和勘探有重要的指导作用。     

PRECIS模拟系统对中国地面气温变化的QUMP集成预估

利用英国Hadley中心QUMP模式(Quantifying Uncertainties in Model Projections)集合的5组敏感性试验产生的全球气候背景场驱动区域气候模拟系统PRECIS(Providing Regional Climates for Impacts Studies)产生的降尺度数据,分析PRECIS对中国地面气温变化的模拟能力,同时对SRES A1B温室气体排放情景下21世纪中期(2021~2050年)中国区域的温度做出预估。模拟能力分析结果显示:PRECIS在5组背景场驱动下都可以较好地模拟出气候基准时段(1961~1900年)中国区域气温的年变化和时空分布特征,但存在暖偏差,高敏感度模拟实验的暖偏差幅度要大于中低敏感度。预估结果显示:5组敏感性试验降尺度模拟的温度均呈增加趋势,其中最低温度的变暖幅度高于平均温度和最高温度。高敏感度试验Q10模拟的升温幅度介于低敏感度模拟和中敏感度模拟之间,其他敏感性试验表现出高敏感度模拟的升温幅度高于中敏感度模拟,而中敏感度模拟高于低敏感度模拟。从模拟的升温空间分布上看,西北地区升温幅度最显著,可达2.08~2.61°C,华南地区升温幅度相对较小,为1.33~1.84°C,但不同敏感度模拟的升温幅度具有一定的区域差异。     

CMIP5模式对21世纪全球和中国年平均地表气温变化和2℃升温阈值的预估

基于参加国际耦合模式比较计划第5阶段（CMIP5）的29个全球气候模式开展的历史气候模拟和3种典型浓度路径（RCP2.6、RCP4.5、 RCP8.5）下21世纪气候预估的结果,分析了单个模式和多模式集合平均（MME）的21世纪全球与中国年平均地表气温（ASAT）变化特征及2℃升温阈值的出现时间。多模式集合平均的结果显示：全球和中国年平均地表气温均将继续升高,21世纪末的升温幅度随着辐射强迫的增大而增大。RCP2.6情景下,年平均地表气温增幅先升高后降低,全球（中国）年平均地表气温在2056年（2049年）达到升温峰值,21世纪末升温1.74℃（2.12℃）;RCP4.5情景下,年平均地表气温在21世纪前半叶逐渐升高,之后升温趋势减缓,21世纪后期趋于平稳,21世纪末全球（中国）年平均地表气温增幅为2.60℃（3.39℃）;RCP8.5情景下,21世纪年平均地表气温快速升高,21世纪末全球（中国）年平均地表气温增幅为4.75℃（6.55℃）。全球平均的年平均地表气温增幅,在RCP2.6情景下没有超过2℃,RCP4.5和RCP8.5情景下分别在2047和2038年达到2℃。RCP2.6、RCP4.5和RCP8.5情景下中国年平均地表气温增幅连续5 a不低于2℃的时间分别在2032、2033和2027年,明显早于全球平均。任一典型浓度路径情景下,达到2℃升温的时间,北半球同纬度地区早于南半球,同半球高纬度地区早于低纬度地区,同纬度地区陆地早于海洋。3种不同典型浓度路径情景下21世纪全球和中国年平均地表气温将继续升高这一结果是可信的,RCP4.5和RCP8.5情景下全球和中国年平均地表气温增幅超过2℃的结果模式之间有较高的一致性。多模式预估的全球和中国年平均地表气温升幅和不同幅度升温的出现时间均存在一定的不确定性,预估结果的不确定性随预估时间的延长而增大;相同情景下,中国年平均地表气温预估的不确定性大于全球。