应用GMS5多通道云图估计降水技术的研究

对近两年来5～8月中国东部地区120多个测站雨量观测资料和对应4400多幅GMS-5四个通道云图的研究发现：红外亮温的时间、空间变率、红外和水汽通道亮温差等云图衍生资料对消除卷云，弥补夜间缺少可见光云图的不足起到明显作用。从而，应用双判据双重MOS多元回归法建立四通道云图因子、衍生因子与降水量之间的回归方程，进行6小时分级降水估计。为了进一步消除厚卷云和特殊地形的影响，提出使用逐日实时资料自动建立多元回归方程估计降水量，从而对双判据双重MOS多元回归法估计结果进行校正。上海中心气象台的业务使用表明，总体估计的准确率达70％左右。     

国家气象中心客观要素预报--MOS系统

面向全国2000多个台站，应用数值预报产品释用MOS技术制作温度、降水、相对湿度、风、云量及能见度等要素预报，并实现了预报业务运行。通过建立MOS预报系统，表明预报因子和预报对象的处理、建方程前的参数选择以及预报因子的选取都会影响要素预报的质量，需要做大量的细致工作。预报检验结果显示，降水预报尚未达到可用程度．温度和相对湿度的短期预报在大多数情况下是可用的或是可参考的，但还有待进一步改进。降水预报尚需在预报因子和充分运用多种探测信息方面加以改进。     

夏季降水量与气温资料的恢复试验

该文对我国汛期降水量和气温缺测资料及资料均一性恢复的方法进行试验研究。对单个气候地区（东北区），以及全国6个气候地区用逐步回归和逐步判别方法进行缺测1～5年恢复试验。对东北地区内所有站点的试验表明，逐步判别分析方法比逐步回归分析方法恢复效果好些；选择邻站用判别分析方法建立的恢复模型，效果更好。全国气候地区试验表明，选择邻站的判别分析方法有较好的恢复效果。     

泰斯模型的统计分析求解

本文利用统计分析来求解水文地质参数,原理简单,解是唯一的。其基本思想是利用非稳定流抽水试验获得的s-t系列资料,以泰斯公式为参考模型,建立试验系列的非线性统计模型,求解导水系数T和贮水系数μ 。统计模型既可利用目前先进的软件辅助求解,亦可利用一台可编程计算器完成计算。本文借助一个实例,应用MATLAB语言的统计分析工具的多元回归分析模块进行求解,获得了理想的结果。     

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.     

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.