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.     

Triple diagram method for the prediction of wave height and period

Many formulations have been developed so far to predict the wave height and period from fetch length and wind blowing duration for a constant wind speed. This study aimed to predict wave parameters from fetch length and meteorological factors by using triple diagram methodology based on Kriging principles. Proposed model results were compared with Joint North Sea Wave Project (JONSWAP) model which is used so commonly in the ocean and coastal engineering studies. For the implementation of the methodology hourly wave and wind data were obtained from a buoy located in Lake Ontario. Numerical and graphical comparisons demonstrated that the proposed method outperforms the classical formulation.     

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.     

Grey Prediction of Siltation in North Channel of Yangtze Estuary

-The North Channel in the Yangtze Estuary is one of sea-leading waterways of Shanghai Harbour, in which yearly dredging volume reaches over ten million cubic meters, and it tends to increase year by year. Based on the channel regime similarity and through the relational grade, a GM (2, 1) is set up. It reveals the course of development of channel regime similarity under the action of various factors, and predicts the siltation volume in the nearest future which is the basis of dredging planning for relevant dredging departments.     

长江中下游6月份降水的一个预报指标

根据１９６３－１９９２年嵊山海洋站２月海气感热输送和２２ａ太阳磁周期与降水的关系，提出了一个长江中下游６月降水的综合预报指标，用此指标，对１９９３年６月长江中下游降水进行回报，结果与实况一致。     

运用德尔菲调查—灰色统计法确立水库鱼产力综合评价中的指标权重体系

本文运用德尔菲方法对水库鱼产力综合评价中指标权重的合理分配问题作了专家调查,并采用灰色统计法对调查结果进行归纳处理,从而确立了一个水库鱼产力影响因素诸层次各方面的评价指标权重体系,可供今后的评价工作参考使用。     

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.     

预测陕西关中地区需水量的改进GM(1,1)模型

需水量评价与预测是水资源规划与管理一项重要的研究内容。本文采用定额法计算需水量。通过对陕西关中地区需水量要素组成的分析,认为需水量要素属平稳时间序列,且一阶累加生成数据系列满足指数规律,符合灰色预测条件。为了提高GM(1,1)模型的精度,采用一种改变背景值的新方法,即:中心逼近方法。通过精度检验,中心逼近式GM(1,1)模型平均误差百分比、误差平方和分别比传统的GM(1,1)模型提高了0.147和1.579。用中心逼近式GM(1,1)模型预测需水量各要素近期和中长期值,经检验,其中非农业人口、农业人口、耕地面积、工业总产值预测模型满足一级模型,等级为良好。牲畜头数预测模型为不合格模型,分析其原因,主要是因为时间序列数据不完全符合指数规律。通过定额法计算,预计75%保证率时2005年和2010年关中地区工农业需水量分别占总需水量的92.21%和89.75%,生活需水量分别占7%和9.04%。     

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.