Temporal Characteristics of Pacific Decadal Oscillation (PDO) and ENSO and Their Relationship Analyzed with Method of Empirical Mode Decomposition (EMD)

1. IntroductionPacific Decadal Oscillation (PDO) is a long-termENSO-like variability of the North Pacific. It can becharacterized by the first principal component of EOFof the North Pacific SST (Zhu and Yang, 2003; Tren-berth, 1990; Yang and Zhang, 2003). ENSO is thestrongest signal of annular change of global climatesystem (Trenberth, 1997). The spatial pattern of PDOis a wedge similar to El Nino. In the cool (warm)phases of PDO, the central and northwest Pacific is ofwarm (co…     

Variation of earthquake ground motionwith depth

Introduction The 21st century is the time for the exploitation and utilization of underground spaces. It is astrategic way to defeat the severe challenge that human are now confronting with, such as the lackof land, contamination of environment, traffic jam, waste of sources, and so on. The exploitationand utilization of underground space has become a global tendency, and a significant sign inevaluating the speed of modernization of cities. Facing the challenge, it is significantly necess…     

Study on comparison between absolute and relative input energy spectra and effects of ductility factor

Introduction Estimation of an attenuation relationship for strong ground motion parameters has been an interesting research subject in the field of engineering seismology and has played a very important role in seismic safety evaluation, seismic zoning, seismic hazard evaluation of major constructions, etc. At present, the generally used parameters include peak acceleration, peak velocity and elastic response spectrum. Such parameters mentioned above are essentially independent of the duration…     

Channel geometry of mountain streams in New Zealand

A dataset of 21 study reaches in the Porter and Kowai rivers (eastern side of the South Island), and 13 study reaches in Camp Creek and adjacent catchments (western side of the South Island) was used to examine downstream hydraulic geometry of mountain streams in New Zealand. Streams in the eastern and western regions both exhibit well-developed downstream hydraulic geometry, as indicated by strong correlations between channel top width, bankfull depth, mean velocity, and bankfull discharge. Exponents for the hydraulic geometry relations are similar to average values for rivers worldwide. Factors such as colluvial sediment input to the channels, colluvial processes along the channels, tectonic uplift, and discontinuous bedrock exposure along the channels might be expected to complicate adjustment of channel geometry to downstream increases in discharge. The presence of well-developed downstream hydraulic geometry relations despite these complicating factors is interpreted to indicate that the ratio of hydraulic driving forces to substrate resisting forces is sufficiently large to permit channel adjustment to relatively frequent discharges.     

泥石流运动衰减动力学初步研究

我国公路泥石流病害严重，泥石流淤埋公路构建筑物是一类常见的公路泥石流病害类型。泥石流衰减动力学是防治泥石流淤埋病害的重要关键技术，也是泥石流运动学、动力学研究的核心问题之一。本文作者运用泥沙运动力学及流体力学原理，初步建立了泥石流固相颗粒和液相浆体的能量衰减条件，把泥石流衰减模式概化为两类，即能量抑制衰减和能量自由衰减；通过泥石流沉积模型试验，得到了不同粘度泥石流体的沉积扇变化形态，随着泥石流体粘度的增大，沉积扇边缘变陡、扩展范围变小、纵轴线长度减小等结论与实际情况吻合；初步建立了泥石流能量衰减速率计算方法。研究成果为防治公路泥石流病害奠定了基础。     

利用可见光/近红外反射光谱估算土壤总氮含量的实验研究

利用土壤的室内反射率光谱，探讨土壤氮元素的高光谱机理。利用土壤光谱各吸收带的特征参数与总氮含量进行逐步回归运算，确定与氮元素关系比较密切的几个吸收带。计算出这几个特征吸收带内土壤反射率的变化形式：一阶导数(FDR)、倒数(1/R)、倒数之对数(log(1/R))、波段深度(Depth)，并与总氮含量进行逐步回归分析，得到比较理想的结果：建模样本的Ra^2(修正的判定系数)分别为0．789、0．753、0．736、0．699，验证样本的Ra^2分别为0．759、0．468、0．794、0．725。可见土壤的反射率光谱与氮元素含量之间存在比较明显的相关性，可见光／近红外反射光谱具有快速估算土壤中氮元素含量的潜力。     

Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach

In this study, the climate teleconnections with meteorological droughts are analysed and used to develop ensemble drought prediction models using a support vector machine (SVM)–copula approach over Western Rajasthan (India). The meteorological droughts are identified using the Standardized Precipitation Index (SPI). In the analysis of large‐scale climate forcing represented by climate indices such as El Niño Southern Oscillation, Indian Ocean Dipole Mode and Atlantic Multidecadal Oscillation on regional droughts, it is found that regional droughts exhibits interannual as well as interdecadal variability. On the basis of potential teleconnections between regional droughts and climate indices, SPI‐based drought forecasting models are developed with up to 3 months' lead time. As traditional statistical forecast models are unable to capture nonlinearity and nonstationarity associated with drought forecasts, a machine learning technique, namely, support vector regression (SVR), is adopted to forecast the drought index, and the copula method is used to model the joint distribution of observed and predicted drought index. The copula‐based conditional distribution of an observed drought index conditioned on predicted drought index is utilized to simulate ensembles of drought forecasts. Two variants of drought forecast models are developed, namely a single model for all the periods in a year and separate models for each of the four seasons in a year. The performance of developed models is validated for predicting drought time series for 10 years' data. Improvement in ensemble prediction of drought indices is observed for combined seasonal model over the single model without seasonal partitions. The results show that the proposed SVM–copula approach improves the drought prediction capability and provides estimation of uncertainty associated with drought predictions. Copyright © 2013 John Wiley & Sons, Ltd.     

Spatiotemporal estimation of snow depth using point data from snow stakes,digital terrain models,and satellite data

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.