The state of land subsidence and prediction approaches due to groundwater withdrawal in China

This article gives a general introduction to land subsidence with the prediction approaches due to withdrawal of groundwater in three subsided/subsiding regions in China: the deltaic plain of Yangtse River (YRDP), North China Plain (NCP), and Fenwei Plain (FP). On YRDP, Shanghai is the typical subsided/subsiding city; on NCP Tianjin is the typical subsided/subsiding city, and on FP Taiyuan is the typical subsided/subsiding city. The subsided area with subsidence over 200 mm on YRDP is about 10,000 km² and the maximum subsided value reached 2.9 m at Shanghai; on NCP the subsided area reached 60,000 km² with the maximum subsidence of 3.9 m at Tianjing; on FP the subsided area is relatively smaller than that on the other two plains and is about 1,135 km² with maximum subsidence of 3.7 m at Taiyuan city. In order to protect the civil and industrial facilities, it is necessary to predict the future development of land subsidence based on present state. Many researchers proposed several approaches to predict the land subsidence due to groundwater withdrawal according to different geological conditions and groundwater withdrawal practice. This article classifies these approaches into five categories: (i) statistical methods; (ii) 1D numerical method; (iii) quasi-3D seepage model; (iv) 3D seepage model; (v) fully coupled 3D model. In China, the former four categories are presently employed in the prediction practice and their merits and demerits are discussed. According to the prediction practice, 3D seepage model is the best method presently.     

Filters for linear sea-wave prediction

Deterministic sea-wave prediction (DSWP) models are appearing in the literature designed for quiescent interval prediction in marine applications dominated by large swell seas. The approach has focused upon spectral methods which are straightforward and intuitively attractive. However, such methods have the disadvantage that while the sea is aperiodic in nature, the standard discrete spectral processing techniques force an absolutely periodic structure onto the resulting sea surface prediction models. As it is the shape of the sea surface that is important in such applications, particularly near the end of the domain which is important, the standard windowing techniques used in signal processing work to reduce leakage artifacts cannot be employed. This has necessitated the use of end matching methods that can be both inconvenient and may reduce the fraction of the time for which legitimate predictions are available. As a result, an investigation has been undertaken of the use of finite impulse response prediction filters to provide the necessary dispersive phase shifting required in DSWP systems. The present work examines the theoretical basis for such filters and explores their properties together with their application to both long and short crested swell seas. It is shown that wide band forms of such filters are only convergent in the sense of distributions having both infinite duration impulse responses and asymptotically divergent first derivatives. However, appropriate band limitation can produce useful finite impulse responses allowing implementation via standard discrete convolution methods. It is demonstrated that despite the prediction filters having a non-causal impulse response such filters can be used in practice due to a combination of the asymmetric nature of the impulse response and the fundamental nature of the prediction process. The findings are confirmed against actual sea-wave data.     

Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.     

Debris-flow event in the Frangerello Stream-Susa Valley (Italy)—calibration of numerical models for the back analysis of the 16 October, 2000 rainstorm

Three debris-flow simulation model software have been applied to the back analysis of a typical alpine debris flow that caused significant deposition on an urbanized alluvial fan. Parameters used in the models were at first retrieved from the literature and then adjusted to fit field evidence. In the case where different codes adopted the same parameters, the same input values were used, and comparable outputs were obtained. Results of the constitutive laws used (Bingham rheology, Voellmy fluid rheology and a quadratic rheology formulation which adds collisional and turbulent stresses to the Bingham law) indicate that no single rheological model appears to be valid for all debris flows. The three applied models appear to be capable of reasonable reproduction of debris-flow events, although with different levels of detail. The study shows how different software can be used to predict the debris-flow motion for various purposes from a first screening, to predict the runout distance and deposition of the solid material and to the different behaviour of the mixtures of flows with variation of maximum solid concentration.     

地震综合效应场函数及其在地震预报中的应用──（一）地震综合效应场函数及其计算方法

在系统地分析了目前各种测震学地震预报方法科学思路的基础上,认为测震学地震预报方法基本上可以分为两大类。一类是以已经发生的一些地震作为未来可能发生的地震的“因”,即由于已经发生的地震对区域应力场的影响,导致未来发生较强地震。这一类包括的预报方法较多,如空区、条带、ｂ值、地震迁移、相关地震等等及其由此衍生出来的各种方法。另一类是把已经发生的一些地震作为区域应力场增强的“果”,即已经发生的地震是区域应力场增强过程中的一种反映,而未来地震不一定是已经发生的地震所导致的结果。这一类包括“地震窗口”、小震群活动等方法。针对第一类方法,各种预报方法都是力图从地震三要素中提取未来地震的信息,而具体作法又都是利用地震三要素这个多维空间的某个剖面。为了从地震活动诸要素的多维空间提取综合信息,我们对每个地震加入了破裂面方位,构成了地震第四要素,并依据地震４要素建立了地震综合效应场函数。地震综合效应场函数概括了多种测震学地震预报方法的科学思路和预报经验,从而可以形成测震学的综合预报方法。     

Testing earthquake prediction methods: «The West Pacific short-term forecast of earthquakes with magnitude MwHRV ≥ 5.8»

Analyzing the tables and probability maps posted by Yan Y. Kagan and David D. Jackson in April 2002–September 2004 at http://scec.ess.ucla.edu/~ykagan/predictions_index.html and the catalog of earthquakes for the same period, the conclusion is drawn that the underlying method could be used for prediction of aftershocks, while it does not outscore random guessing when main shocks are considered.     

长效钾肥产业化与农业可持续发展

我国钾肥需求量逐年增加，80％需要进口，但同时又有大量的低品位非水溶性钾矿资源没有利用，利用非水溶性钾矿生产长效钾肥既能缓解钾肥短缺的矛盾，又可以解决普通钾肥对环境的污染和破坏。长效钾肥原料分布广，生产和施用技术简单，成本低，易于推广普及形成产业化，其对农业可持续发展将会产生重要的促进作用。     

水库地震主震发生时间的预测

水利工程对水库地震的抗震设防要求事先对水为地震进行预测。预测内容包括蓄水后诱发地震的危险性（可能性、）最大震级、最大震的地占紧及不库地震对场址地震动参数的影响。除主震时间的预测外，本文第一作者都曾提出过初步的预测方法。本文认为，水库地震前震波速比异常、非线性灰色模型以及前震系列回归等方法，是预测主震时间的有前途的方法。     

玛纳斯县城房屋建筑地震损失预测及抗震设防效益分析

以玛纳斯县城现有房屋建筑调查资料为基础，对该县城的房屋建筑进行了分类。作了抗震分析，建立了相应的易损性矩阵。根据地震危险性分析结果及县城房屋建筑的发展情况，给出了未来１０年房屋建筑的震害预测结果，并对房屋建筑抗震设防和加固的经济效益进行了初步分析。     

一个有限区域中尺度模式的设计及预报试验

以一个６层原始方程模式的基本动力学框架为基础，设计了一个对物理过程考虑得较全面的中尺度原始方程模式。该模式采用（ｘ，ｙ，σ）坐标系；大气上界取为１０ｈＰａ，提供多种水平边界条件；水平和垂直分辨率均可调；降水方案包括大尺度降水和深厚积云对流降水；地面温度的计算采用地面热量收支方程；考虑了地气和海气交换作用；垂直交换系数的计算采用Ｌｉｏｕｉｓ格式；水平扩散采用二阶形式和四阶形式相结合的方案，扩散系数是网格点位置和风场的函数；积分方案采用经济的中央差格式。在水平格距取８０ｋｍ，垂直方向不等距地分为１６层的分辨率条件下，利用该模式进行逐个个例预报试验。结果表明，模式计算稳定，能较好地报出主要的天气形势，预报的降水也较接近实际。文中给出了一些检验指标的统计结果。还在预报能力及模式特性方面与原６层模式进行了对比。