中国区域地理、地形因子对降水分布影响的估算和分析

不同与以往基于最小二乘的多元线性回归方法,本文首次尝试将新型的第二代回归分析方法——偏最小二乘回归分析方法应用到中国区域的降水建模中．利用区域内394个气象观测站建站到2000年45年(及以上)的降水资料,建立了一个简单的年、季降水量和地理、地形因子(包括纬度、经度、地形高程、坡度、坡向和遮蔽度)的关系模型,估算了区域降水量中地理、地形的影响部分,并分析了这种影响的特征．结果表明,用此方法建立的模型能够解释70%以上的因变量的变异,相关系数基本都在0.84以上,经交叉有效性检验,模型的回归效果较显著．分析表明,在多元线性回归不适用的情况下,本文基于偏最小二乘法的简单模型能够比较准确地定性、定量地再现实际降水分布．     

起伏地形下天文辐射分布式估算模型

基于数字高程模型（DEM）,建立了起伏地形下天文辐射分布式估算模型.模型全面考虑了地形因子对天文辐射的影响,只需DEM数据作为输入项,适用于遥感图像处理、地理信息系统等数据处理平台.以1km×1km分辨率的DEM数据作为地形的综合反映,计算了我国全年各月天文辐射的空间分布.结果表明：我国年天文辐射总量有明显的纬向分布特点,随着纬度的降低,年天文辐射总量由北向南增加;由于受坡向、坡度和地形遮蔽因子影响,山区天文辐射表现出非地带性分布特征.本文所提供的我国天文辐射数据产品,可作为基础地理数据供相关研究应用.     

长江流域降水极值时间序列的分布特征

在1960-2005年长江流域147气象观测站汛期4-9月逐日降水资料基础上,通过计算逐站大于95th强降水及其间隔天数、小于1.27mm/d的持续天数,分析长江流域降水极值时间序列的时空分布特征,并建立概率分布模式.研究发现,长江上游四川盆地附近及中下游鄱阳湖流域东南部是汛期强降水中心,也是长江流域强降水最集中发生的地区.汛期降水强度小于1.27mm/d的天数,在上游干流、岷沱江流域、乌江上游地区为多.但此处干旱持续天数最短,干旱形式并不严重.而在金沙江上、下游,洞庭湖流域,鄱阳湖流域东南部支流及下游干流区干旱持续天数较长.长江流域大于95th强降水的间隔天数与小于1.27mm/d的干旱持续天数服从Weibull-Ⅱ型分布.分布参数变化的模式较准确的反映降水极值时间序列的时空变化特征.     

风垂直切变对中尺度地形对流降水影响的研究

针对长江中下游中尺度地形特点以及暴雨过程发生发展期间风垂直切变的主要观测特征,设计了一系列中尺度地形的三维理想数值试验,分析了干大气地形流和重力波特征,探讨了条件不稳定湿大气地形对流降水的模态分布,在此基础上研究了圆形、直线风垂直切变和切变厚度对中尺度地形对流降水强度和模态分布的影响.结果发现:在 Fr≈1的干大气条件下,气流遇到地形后分支、绕流和爬升现象同时存在,地形激发的重力波在水平和垂直方向上传播,其在迎风坡、背风坡、地形上游和下游的振幅不同,并组织出不同强度的垂直上升运动.在Fr > 1的条件不稳定湿大气下,地形对流降水主要存在三种模态,即迎风坡和背风坡准静止对流降水以及地形下游移动性对流降水,地形对流降水的形成与重力波在低层组织的上升运动密切相关.风垂直切变对地形对流降水的强度和模态分布有重要作用,其中圆形风垂直切变(风随高度旋转)不仅影响地形下游对流降水系统的移动方向,而且影响迎风坡和背风坡山脚处对流降水中心的分布和强度;直线风垂直切变(风随高度无旋转)主要影响地形对流降水的移动速度和强度.风随高度自下而上顺(逆)时针旋转,地形对流系统向下游传播时向右(左)偏移.风垂直切变主要通过影响地形重力波的结构和传播以及对流系统的形成、移动方向和速度,来影响地形对流降水的模态分布,其中对流层中低层的风垂直切变对地形对流降水强度和模态分布有重要影响.     

一种通过地形指数提取流域自由水蓄水容量空间分布的方法

对于分布式水文模型而言,如何获得参数的空间分布是模型应用的重点和难点问题.本文将分水源参数中的敏感参数——自由水蓄水容量为研究对象.建立地形指数与自由水蓄水容量的函数关系,以此提取流域内的自由水蓄水容量空间分布.最后利用本方法提取了陕西省陈河流域的自由水蓄水容量空间分布,并将之作为栅格型新安江模型的参数进行洪水模拟演算.应用结果表明本文提出的方法得到了理想的模拟结果.该方法以物理规律为基础能较为准确地计算出流域内自由水蓄水容量的空间分布,为分布式模型的发展奠定了坚实的基础.     

华北地区地震地面危害分布的定量估算

根据1500年以来的地震活动,用确定性方法对华北地区地震地面危害分布图进行了定量计算。震源和研究区域均用网格单元表示,网格点的间距取为0.2°。计算中主要的输入参量为该地区的地震目录、震源机制、地震活动水平、深部结构模型等。各个单元上模拟的理论地震图考虑到了来自不同地点的震源和传播路径的影响。计算获得了华北地区最大地面运动和设计地面加速度(DGA)的分布图。最大DGA值位于北京以东,达0.75g。该结果可对未来华北地区地震危害的分布特征研究和该地区的防震减灾工作提供依据     

模型扩展和地形平化起伏地形处理方案在地震走时层析成像中的对比研究

中国大陆中西部普遍具有强烈的地形起伏,起伏地形会对地震资料的处理分析产生严重干扰.精细处理起伏地形成为高精度地震成像的必然要求.传统方法通过填充低速介质将不规则模型扩展为规则模型来处理起伏地形.近年来,借助坐标变换将物理空间不规则模型转换为计算空间规则模型的地形平化方法,为解决起伏地形问题提供了新思路.本文基于经典的模型扩展和新发展的地形平化方法分别处理起伏地形,从走时正演、射线追踪和反演成像三个方面,全面细致地评判了两种地形处理方法在起伏地形层析成像中的适用性和有效性.结果表明,模型扩展中阶梯状近似和填充介质速度参与计算,会造成起伏地形走时计算精度损失,出现虚假射线路径和错误出射角,导致反演分辨率降低,成像结果模糊甚至失真;地形平化中采用贴体网格参数化,能够保证离散模型完全匹配起伏地形,并且保持起伏地形在物理空间和计算空间中均为自由表面.在此基础上发展的层析成像技术具有高度的保真性,有效地处理了地形起伏效应,为起伏地形区域精细速度成像提供了有力的技术保障.

     

层状半空间中周期分布凸起地形对平面SH波的散射

提出了一种新的以层状半空间中周期分布斜线荷载动力格林函数为基本解的间接边界元方法,研究了周期分布凸起地形对平面SH波的散射问题.方法将散射波场分解为凸起内部散射波场和凸起外部散射波场.凸起内部散射波场通过在凸起闭合边界上施加虚拟斜线荷载产生的动力响应来模拟,而凸起外部散射波场则通过在凸起与半空间交界面上施加虚拟周期分布斜线荷载产生的动力响应来模拟.周期分布斜线荷载动力格林函数的引入,使得本文方法仅需针对一个凸起进行边界单元的离散和求解,便可完成问题的求解,避免了通过截断无限边界求解而引入的误差,方法具有较高精度的同时显著降低了求解自由度.文中通过与已有结果的比较,验证了方法的正确性,并以均匀半空间和基岩上单一土层中周期分布凸起为例进行了数值计算分析.研究表明,凸起间距对凸起地形间的动力相互作用有着显著的影响,同时层状半空间中周期分布凸起地形对SH波的散射与均匀半空间情况也有着显著的差别.

     

山西省人口空间公里格网分布模型研究

精准的人口分布信息是震后灾害快速评估、应急救援决策的重要依据。本文应用居民地这一与人口分布密切相关的基础数据,结合居民地建筑物给出不同区域人口权重,以人口统计数据为总量控制,完成人口空间公里格网数据模型建立,并与夜间灯光数据、道路数据、实际调研数据等进行对比分析。结果表明,该人口分布模型可靠性高,可为震后灾害快速评估、应急救援决策提供可参考的人口数据。     

中国大陆地震空间分布模型检验

本文以中国大陆地震目录为基础资料,以泊松模型为零假设模型,并将Neyman-Scott空间丛集过程的各子模型设为检验模型,采用K-function 点过程分析法和最大似然估计法计算各模型参数,并以AIC准侧判定模型的拟合优度,来检验中国大陆地震空间分布模型.检验结果表明：泊松模型的拟合优度最差,说明地震在空间的分布不是完全随机的;广义Thomas模型的拟合优度最好,说明地震的空间分布是丛集的,可用由两个高斯核组成的广义Thomas模型较好地描述.研究结果还表明,同一研究区内,采用不同时段具有不同最小完整起始震级的地震目录计算得到的地震空间分布的丛集尺度几乎不变,这意味着地震空间丛集尺度不受小地震的控制,且可能与研究区的断层规模有关.     

Evaluation of precipitation products over complex mountainous terrain: A water resources perspective

The availability of in situ measurements of precipitation in remote locations is limited. As a result, the use of satellite measurements of precipitation is attractive for water resources management. Combined precipitation products that rely partially or entirely on satellite measurements are becoming increasingly available. However, these products have several weaknesses, for example their failure to capture certain types of precipitation, limited accuracy and limited spatial and temporal resolution. This paper evaluates the usefulness of several commonly used precipitation products over data scarce, complex mountainous terrain from a water resources perspective. Spatially averaged precipitation time series were generated or obtained for 16 sub-basins of the Paute river basin in the Ecuadorian Andes and 13 sub-basins of the Baker river basin in Chilean Patagonia. Precipitation time series were generated using the European Centre for Medium Weather Range Forecasting (ECMWF) 40 year reanalysis (ERA-40) and the subsequent ERA-interim products, and the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset 1 (NCEP R1) hindcast products, as well as precipitation estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The Tropical Rainfall Measurement Mission (TRMM) 3B42 is also used for the Ecuadorian Andes. These datasets were compared to both spatially averaged gauged precipitation and river discharge. In general, the time series of the remotely sensed and hindcast products show a low correlation with locally observed precipitation data. Large biases are also observed between the different products. Hydrological verification based on river flows reveals that water balance errors can be extremely high for all evaluated products, including interpolated local data, in basins smaller than 1000 km². The observations are consistent over the two study regions despite very different climatic settings and hydrological processes, which is encouraging for extrapolation to other mountainous regions.     

A cluster-optimizing regression-based approach for precipitation spatial downscaling in mountainous terrain

Precipitation temporal and spatial variability often controls terrestrial hydrological processes and states. Common remote-sensing and modeling precipitation products have a spatial resolution that is often too coarse to reveal hydrologically important spatial variability. A statistical algorithm was developed for downscaling low-resolution spatial precipitation fields. This algorithm auto-searches precipitation spatial structures (rain-pixel clusters), and orographic effects on precipitation distribution without prior knowledge of atmospheric setting. It is composed of three components: rain-pixel clustering, multivariate regression, and random cascade. The only required input data for the downscaling algorithm are coarse-pixel precipitation map and a topographic map. The algorithm was demonstrated with 4 km × 4 km Next Generation Radar (NEXRAD) precipitation fields, and tested by downscaling NEXRAD-aggregated 16 km × 16 km precipitation fields to 4 km × 4 km pixel precipitation, which was then compared to the original NEXRAD data. The demonstration and testing were performed at both daily and hourly temporal resolutions for the northern New Mexico mountainous terrain and the central Texas Hill Country. The algorithm downscaled daily precipitation fields are in good agreement with the original 4 km × 4 km NEXRAD precipitation, as measured by precipitation spatial structures and the statistics between the downscaling and the original NEXRAD precipitation maps. For three daily precipitation events, downscaled precipitation map reproduces precipitation variance of the disaggregation field, and with Pearson correlation coefficients between the downscaled map and the NEXRAD map of 0.65, 0.71, and 0.80. The algorithm does not perform as well on downscaling hourly precipitation fields at the examined scale range (from 16 km to 4 km), which underestimates precipitation variance of the disaggregation field. For a scale range from 4 km to 1 km, the algorithm has potential to perform well at both daily and hourly precipitation fields, indicated from good regression performance.     

Evaluation of a spatial rainfall generator for generating high resolution precipitation projections over orographically complex terrain

Space–time variability of precipitation plays a key role as driver of many environmental processes. The objective of this study is to evaluate a spatiotemporal (STG) Neyman–Scott Rectangular Pulses (NSRP) generator over orographically complex terrain for statistical downscaling of climate models. Data from 145 rain gauges over a 5760-km² area of Cyprus for 1980–2010 were used for this study. The STG was evaluated for its capacity to reproduce basic rainfall statistical properties, spatial intermittency, and extremes. The results were compared with a multi-single site NRSP generator (MSG). The STG performed well in terms of average annual rainfall (+1.5 % in comparison with the 1980–2010 observations), but does not capture spatial intermittency over the study area and extremes well. Daily events above 50 mm were underestimated by 61 %. The MSG produced a similar error (+1.1 %) in terms of average annual rainfall, while the daily extremes (>50-mm) were underestimated by 11 %. A gridding scheme based on scaling coefficients was used to interpolate the MSG data. Projections of three Regional Climate Models, downscaled by MSG, indicate a 1.5–12 % decrease in the mean annual rainfall over Cyprus for 2020–2050. Furthermore, the number of extremes (>50-mm) for the 145 stations is projected to change between ?24 and +2 % for the three models. The MSG modelling approach maintained the daily rainfall statistics at all grid cells, but cannot create spatially consistent daily precipitation maps, limiting its application to spatially disconnected applications. Further research is needed for the development of spatial non-stationary NRSP models.     

A generalized additive model for the spatial distribution of snowpack in the Spanish Pyrenees

A generalized additive model (GAM) was used to model the spatial distribution of snow depth in the central Spanish Pyrenees. Statistically significant non‐linear relationships were found between distinct location and topographical variables and the average depth of the April snowpack at 76 snow poles from 1985 to 2000. The joint effect of the predictor variables explained more than 73% of the variance of the dependent variable. The performance of the model was assessed by applying a number of quantitative approaches to the residuals from a cross‐validation test. The relatively low estimated errors and the possibility of understanding the processes that control snow accumulation, through the response curves of each independent variable, indicate that GAMs may be a useful tool for interpolating local snow depth or other climate parameters. Copyright © 2005 John Wiley & Sons, Ltd.     

A modified global model for predicting the tritium distribution in precipitation, 1960–2005

A modified global model for predicting the tritium concentration in precipitation has been developed using the dataset of International Atomic Energy Agency/the World Meteorological Organization (IAEA/WMO) over the period from 1960 to 2005. The tritium concentration in precipitation and its history can be estimated at any location using the model. The modified global model of tritium in precipitation (MGMTP) here presented has higher accuracy than the global model of tritium in precipitation (GMTP) developed by Doney et al. ( 1992 ). The new model is not only more appropriate for a particular station but also applicable for the un‐normalized observations directly. Another advantage of MGMTP is that it can estimate a longer history (from 1960 to 2005) of tritium content in precipitation than GMTP (from 1960 to 1986). The seasonal cycle of tritium in precipitation has also been modelled in the form of a simple cosine function with five parameters. Copyright © 2011 John Wiley & Sons, Ltd.     

中国降水年际和年代际变率对空间尺度的敏感性

利用中国740站45年降水资料按5种分辨率分气候区计算了降水年际和年代际变率. 降水年际和年代际变率对空间尺度的敏感性分析表明,中国各气候区降水年际变率对空间尺度的敏感性都随空间尺度的增加而逐渐减小,且存在明显的季节变化,而年代际变率对空间尺度的敏感性却随空间尺度的增加而增大,但不存在季节变化;由于中国各气候区降水的特殊性,各气候区降水年际和年代际变率对空间尺度的敏感程度存在不可忽视的差异.在年际和年代际尺度上,西南地区降水变率对空间尺度都是最敏感的,因而该区域降水年际和年代际变率信号的检测最困难.而华南地区在年际尺度上比较敏感,年代际尺度却不敏感,但华南地区在年际和年代际尺度上区域内降水分布的非均匀程度对空间尺度的敏感性都最大.     

Variability of the spatial structure of intense Mediterranean precipitation

Intense Mediterranean precipitation can generate devastating flash floods. A better understanding of the spatial structure of intense rainfall is critical to better identify catchments that will produce strong hydrological responses. We focus on two intense Mediterranean rain events of different types that occured in 2002. Radar and rain gauge measurements are combined to have a data set with a high spatial (1 × 1 km²) and temporal (5 min) resolution. Two thresholds are determined using the quantiles of the rain rate values, corresponding to the precipitating system at large and to the intense rain cells. A method based on indicator variograms associated with the thresholds is proposed in order to automatically quantify the spatial structure at each time step during the entire rain events. Therefore, its variability within intense rain events can be investigated. The spatial structure is found to be homogeneous over periods that can be related to the dynamics of the events. Moreover, a decreasing time resolution (i.e., increasing accumulation period) of the rain rate data will stretch the spatial structure because of the advection of rain cells by the wind. These quantitative characteristics of the spatial structure of intense Mediterranean rainfall will be useful to improve our understanding of the dynamics of flash floods.     

A spatial disaggregation procedure for precipitation

Abstract

A disaggregation procedure is presented to render forecast values of precipitation from an atmospheric model with spatial resolution of 11 × 11 km suitable as input for a distributed hydrological model with spatial resolution of 1.1 × 1.1 km. Statistical and morphological properties of the input field, such as spatial mean, variance, correlation structure and intermittency, are respected in the disaggregated field. The adopted approach is a combination of interpolation and simulation. The four nodal points of the atmospheric model grid cell are used both for determining the parameters of the exponential distribution for simulating precipitation values, and in a simple interpolation procedure to determine the spatial location of the precipitation values. A shifted distribution with two parameters is used in the case of full coverage of the grid cell, and a one-parameter distribution with a theoretically derived intermittency parameter is used if intermittency is present. The results are promising with respect to the statistical and morphological properties of the disaggregated field.