渭河流域降雨侵蚀力时空分布特征

[目的]揭示渭河流域降雨侵蚀力的时空变化特征,为区域水土保持规划提供依据。[方法]根据渭河流域及其周边范围30个气象站点1957—2014年逐日降雨资料,采用章文波日降雨量侵蚀模型计算各站点的降雨侵蚀力,分析其空间分布规律和年内分布特征。[结果]渭河流域多年平均降雨侵蚀力值分布范围为806.25~3 510.81 MJ·mm/(hm2·h),平均值1 798.97 MJ·mm/(hm2·h),与多年平均侵蚀性降雨的空间分布基本一致,总体呈现西北低东南高的趋势。渭河流域降雨侵蚀力年内变化呈单峰型,主要集中在7—9月,占全年降雨侵蚀力的63.91%。北部黄土高原地区和关中平原发生水土流失的时期集中在7—9月,而秦岭北麓地区5—10月均有可能发生较大的水土流域,侵蚀风险由西北向东南递增。流域降雨侵蚀力年际波动较大,年际变率Cv值在34%~56%之间,整体而言,流域西北部地区的降雨侵蚀力年际变化幅度大于东南部地区。除洛川、长武、环县、平凉4个站点降雨侵蚀力在研究时段内有所增大外,其余地区降雨侵蚀侵蚀力呈不同速率的减小趋势。[结论]渭河流域降雨侵蚀力时空分布差异显著,尽管流域降雨侵蚀力呈减弱趋势,由于流域地处黄土高原,水土保持与水源涵养工作仍需高度重视。     

基于GLEAM模型的淮河流域地表蒸散量时空变化特征

蒸散发是连接地表水循环和能量循环的纽带,淮河流域地表蒸散量的时空变化分析对深入理解中国气候过渡带水循环对全球变化的响应具有重要价值。该文基于流域水量平衡原理,利用流域水文数据对淮河流域GLEAM产品进行精度验证;并利用GLEAM(global land-surface evaporation:the Amsterdam methodology)产品分析1980-2011年淮河流域地表蒸散发年际和年内的时空变化。结果表明:1)淮河流域及其水资源二级分区的降水实测值与GLEAM产品估算结果比较,平均相对偏差为8.0%,相关系数高达0.94,GLEAM产品对于淮河流域的模拟精度较高;2)淮河流域1980-2011年多年平均年地表蒸散量为673 mm;3)淮河流域多年平均年地表蒸散量空间变化范围为528~848 mm,空间差异显著,呈从西南向东北逐渐减少,淮河以南地表蒸散量大于淮河以北地表蒸散量,四个季节地表蒸散发具有类似的空间分布特征;4)近32 a淮河流域平均的年地表蒸散量变化范围为588.6~767.8 mm,且存在显著的上升趋势;地表蒸散量的季节变化大致呈单峰型分布,峰值出现在8月,最小值出现在12月;且季节变化较为明显,夏季(272.0 mm)春季(191.4 mm)秋季(144.3 mm)冬季(65.0 mm);5)基于栅格尺度年地表蒸散量的变化速率主要受春季主导,依次为夏季、秋季,冬季的影响最小,淮河流域大部分区域地表蒸散发量呈增加趋势。该研究可为淮河流域洪涝、干旱等极端水文气象事件的监测与预警提供科学依据,同时为该流域水资源管理提供参考及决策依据。     

Spatial Regression Modeling for Compositional Data With Many Zeros

Compositional data analysis considers vectors of nonnegative-valued variables subject to a unit-sum constraint. Our interest lies in spatial compositional data, in particular, land use/land cover (LULC) data in the northeastern United States. Here, the observations are vectors providing the proportions of LULC types observed in each 3 km×3 km grid cell, yielding order 10⁴ cells. On the same grid cells, we have an additional compositional dataset supplying forest fragmentation proportions. Potentially useful and available covariates include elevation range, road length, population, median household income, and housing levels. We propose a spatial regression model that is also able to capture flexible dependence among the components of the observation vectors at each location as well as spatial dependence across the locations of the simplex-restricted measurements. A key issue is the high incidence of observed zero proportions for the LULC dataset, requiring incorporation of local point masses at 0. We build a hierarchical model prescribing a power scaling first stage and using latent variables at the second stage with spatial structure for these variables supplied through a multivariate CAR specification. Analyses for the LULC and forest fragmentation data illustrate the interpretation of the regression coefficients and the benefit of incorporating spatial smoothing.     

Rainfall erosivity map for Brazil

Rainfall erosivity is the potential ability for rainfall to cause soil loss. Erosivity can be quantified by means of the R factor calculation of the universal soil loss equation (USLE). The purpose of this study was to investigate the spatial distribution of annual rainfall erosivity in Brazil. For each of eight Brazilian regions covering the whole of the territory of Brazil, one adapted equation was applied using pluviometric records obtained from 1600 weather stations. A geographic information system (GIS) was used to interpolate the values and to generate a map showing spatial variations of erosivity. The annual values of erosivity ranged from 3116 to 20,035 MJ mm ha⁻¹ h⁻¹ year⁻¹. The region with highest annual values was the extreme northwestern, while the northeastern region showed the lowest annual values of erosivity. For the most part of the Brazilian territory, December and January revealed the highest erosivity values, while the lowest values were observed from June to September.     

Non-Stationary Dependence Structures for Spatial Extremes

Max-stable processes are natural models for spatial extremes because they provide suitable asymptotic approximations to the distribution of maxima of random fields. In the recent past, several parametric families of stationary max-stable models have been developed, and fitted to various types of data. However, a recurrent problem is the modeling of non-stationarity. In this paper, we develop non-stationary max-stable dependence structures in which covariates can be easily incorporated. Inference is performed using pairwise likelihoods, and its performance is assessed by an extensive simulation study based on a non-stationary locally isotropic extremal t model. Evidence that unknown parameters are well estimated is provided, and estimation of spatial return level curves is discussed. The methodology is demonstrated with temperature maxima recorded over a complex topography. Models are shown to satisfactorily capture extremal dependence.     

A hierarchical model for the analysis of spatial rainfall extremes

In this article, we propose a spatial model for analyzing extreme rainfall values over the Triveneto region (Italy). We assess the existence of a long-term trend in the extremes. To integrate data coming from the different stations, we propose a hierarchical model. At the first level, for each monitoring station we model data by making use of a generalized extreme value distribution; at the second level, we combine results from the first stage by exploiting recent advances in modeling nonstationary spatial random fields.     

A Non-Gaussian Spatial Generalized Linear Latent Variable Model

We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents.     

试验研究三峡库区大宁河流域降雨侵蚀力的时空变化

[目的]分析流域降雨侵蚀力时空变化规律,为水土流失预报及水土保持措施科学配置提供依据。[方法]以三峡库区大宁河流域内13个雨量站41 a 日降雨资料为基础,采用侵蚀力简易模型,分析了该流域降雨侵蚀力的年内分配和年际变化规律,并在软件 ArcGIS 10．2支持下,探讨流域降雨侵蚀力时空变化特征。[结果]大宁河流域年均降雨侵蚀力为7245．55 MJ ? mm／（hm2? h ? a）,它在空间上与流域降雨分布特征基本一致,呈现由东、西向流域中部逐渐减小的趋势,而南北差异较小;最大和最小降雨侵蚀力分别位于流域西北部的建楼站和南部的巫山站;降雨侵蚀力多年变化范围为3619．55～11109．14 MJ ? mm／（hm2? h ? a）。降雨侵蚀力的年内分布呈双峰型,集中程度高,4—10月占全年的95％。[结论]大宁河流域降雨侵蚀力和降雨变化年内分配一致,侵蚀力时空特征除与流域降雨量分布密切相关外,还与区域降雨格局及地形地貌等因素有关。     

Spatial stochastic volatility for lattice data

Spatial heteroscedasticity may arise jointly with spatial autocorrelation in lattice data collected from agricultural trials and environmental studies. This leads to spatial clustering not only in the level but also in the variation of the data, the latter of which may be very important, for example, in constructing prediction intervals. This article introduces a spatial stochastic volatility (SSV) component into the widely used conditional autoregressive (CAR) model to capture the spatial clustering in heteroscedasticity. The SSV component is a mean zero, conditionally independent Gaussian process given a latent spatial process of the variances. The logarithm of the latent variance process is specified by an intrinsic Gaussian Markov random field. The SSV model relaxes the traditional homoscedasticity assumption for spatial heterogeneity and brings greater flexibility to the popular spatial statistical models. The Bayesian method is used for inference. The full conditional distribution of the heteroscedasticity components can be shown to be log-concave, which facilitates an adaptive rejection sampling algorithm. Application to the well-known wheat yield data illustrates that incorporating spatial stochastic volatility may reveal the spatial heteroscedasticity hidden from existing analyses.     

沂河流域1961-2010年降雨侵蚀力时空分布特征

[目的]分析沂河流域近50 a的降雨量和降雨侵蚀力的时空变化特征,为流域水土流失防治及土地利用合理规划等工作提供参考.[方法]利用沂河流域及周边12个气象站1961-2010年的日降雨数据,基于日降雨信息的月降雨侵蚀力模型计算流域多年平均降雨侵蚀力,采用Mann-Kendall非参数检验法及析取Kriging内插法分析流域降雨量和降雨侵蚀力的时空变化特征.[结果]沂河流域降雨量和降雨侵蚀力空间分布上呈现出由西南向北逐级递减的变化趋势.多年平均降雨量为789.41 mm,多年平均降雨侵蚀力为2 626.09(MJ·mm)/(hm2·h·a),两者都在1965年产生突变;降雨量和降雨侵蚀力年内分布主要集中在夏季(6-8月),分别占全年比例的63.02％和71.22％,二者最大值都出现在7月,且秋季对流域多年降雨量的减少趋势贡献最多,夏季的降雨侵蚀力上升幅度最大.[结论]沂河流域的降雨量和降雨侵蚀力空间分布趋势相似,不同月份的降雨量与降雨侵蚀力差异不同.     

南汀河流域土壤侵蚀的时空分异

[目的]分析南汀河流域坡面土壤侵蚀的时空分异特征,为流域水土保持和边疆生态环境建设提供科学参考。[方法]基于通用土壤流失方程(USLE),运用RS和GIS技术计算南汀河流域1990,2000及2010年3个时段的土壤侵蚀模数。[结果]3个时段内研究区侵蚀模数呈现先升后降的趋势,年均侵蚀模数从24.75t/(hm2·a)升到30.05t/(hm2·a),然后降为25.87t/(hm2·a)。3个时段内,流域内强烈侵蚀及其以上的侵蚀面积仅占总侵蚀面积的19.94%,但对流域总侵蚀量的贡献高达73.56%。1990—2000年,强烈及强烈以下侵蚀面积减少了1 059.85km2,强烈侵蚀以上的侵蚀面积则增加了112.29km2;2000—2010年,微度侵蚀面积有小幅增加,其余侵蚀等级的侵蚀面积都有所下降。当坡度小于20°时,侵蚀模数随着坡度的增加而增加,坡度超过20°后,侵蚀模数有降低的趋势;从海拔上看,高侵蚀模数区域主要位于海拔500~2 000m范围。[结论]流域内的土壤侵蚀治理已初见成效,但在局部地区,土壤侵蚀仍有加剧现象。     

Nonparametric Estimation of Spatial and Space-Time Covariance Function

Covariance structure modeling plays a key role in the spatial data analysis. Various parametric models have been developed to accommodate the idiosyncratic features of a given dataset. However, the parametric models may impose unjustified restrictions to the covariance structure and the procedure of choosing a specific model is often ad hoc. To avoid the choice of parametric forms, we propose a nonparametric covariance estimator for the spatial data, as well as its extension to the spatio-temporal data based on the class of space-time covariance models developed by Gneiting (J. Am. Stat. Assoc. 97:590–600, 2002). Our estimator is obtained via a nonparametric approximation of completely monotone functions. It is easy to implement and our simulation shows it outperforms the parametric models when there is no clear information on model specification. Two real datasets are analyzed to illustrate our approach and provide further comparison between the nonparametric estimator and parametric models.     

Modelling spatial uncertainty of soil erodibility factor using joint stochastic simulation

Soil erosion varies greatly over space and is commonly estimated using the revised universal soil loss equation (RUSLE). Neglecting information about estimation uncertainty, however, may lead to improper decision‐making. One geostatistical approach to spatial analysis is joint stochastic simulation, which draws alternative, equally probable, joint realizations of a regionalized variable. Differences between the realizations provide a measure of spatial uncertainty and allow us to carry out an error propagation analysis. The objective of this paper was to assess spatial uncertainty of a soil erodibility factor (K) model resulting from the uncertainties in the input parameters (texture and organic matter). The 500 km² study area was located in central‐eastern Sardinia (Italy) and 152 samples were collected. A Monte Carlo analysis was performed where spatial cross‐correlation information through joint turning bands simulation was incorporated. A linear coregionalization model was fitted to all direct and cross‐variograms of the input variables, which included three different structures: a nugget effect, a spherical structure with a shorter range (3500 m) and a spherical structure with a longer range (10 000 m). The K factor was then estimated for each set of the 500 joint realizations of the input variables, and the ensemble of the model outputs was used to infer the soil erodibility probability distribution function. This approach permitted delineation of the areas characterized by greater uncertainty, to improve supplementary sampling strategies and K value predictions. Copyright © 2007 John Wiley & Sons, Ltd.     

华北平原典型冬小麦农田生态系统能量平衡与闭合研究

准确量化分析地气之间的物质和能量交换对于水资源管理和农业可持续发展是十分重要的。能量平衡闭合是评估观测数据准确性和分析地表能量平衡的一个重要的评价指数。本研究利用开路涡度相关系统和全要素自动气象站对华北平原典型冬小麦农田生态系统2013—2014年度的能量通量及常规气象要素进行了连续观测,分析了冬小麦农田各能量通量的日变化和年变化特征,计算冬小麦在4个生育时期(出苗期、越冬期、拔节期和灌浆期)的能量闭合和波文比。结果表明:在日尺度上,选取的4个生育时期净辐射和各能量分量的日变化趋势均为单峰二次曲线,净辐射、显热通量和潜热通量的峰值出现在12:00—13:00,土壤热通量的峰值出现在14:00—15:00。在年尺度上,净辐射和潜热通量的变化趋势较为一致,均在越冬期达到最低值114.51 W·m~(-2)和13.47 W·m~(-2),而在灌浆期达到最大值327.02 W·m~(-2)和116.56 W·m~(-2)。选取的4个生育时期的代表性观测日期能量闭合良好,能量闭合率分别为0.49、0.77、0.81和0.76。4个生育时期内波文比值日变化趋势均呈倒"U"型,出苗期波文比在14:00达到最大值2.12;越冬期、拔节期和灌浆期在10:00左右达到最大值,分别为1.48、0.31和0.58。本文的定量化结果可为华北平原农田生态系统水热通量等研究提供依据。     

Point Pattern Analysis of Spatial Deformation and Blurring Effects on Exceedances

Structural characteristics of random field threshold exceedance sets (e.g., size, connectivity, and boundary regularity) are used in practice for definition of different indicators in spatial and spatio-temporal risk analysis. In this work, point process techniques are applied to study the structural changes derived from random field deformations and blurring transformations, meaningful from both physical and methodological points of view in a variety of contexts. Specifically, based on simulations from a flexible random field model class, features such as aggregation/inhibition of patterns defined by centroids of connected components, as well as by boundary A-exit points, are investigated in relation to the local contraction/dilation effects of deformation and the smoothing properties of blurring. Supplementary materials accompanying this paper appear online.     

基于CSLE-TLSD耦合模型的近40年洞庭湖流域土壤侵蚀时空分异及归因分析

为了精确模拟洞庭湖流域土壤侵蚀动态过程并定量评估其影响因素贡献,在中国水土流失方程(CSLE)的基础上耦合泥沙输送限制模型(TLSD),实现对土壤分离-搬运-沉积全动态过程分析。在此基础上,采用趋势分析和地理探测器方法探究洞庭湖流域1980-2020年土壤侵蚀时空分异特征与影响机制。结果表明:(1)CSLE-TLSD耦合模型在各水文站的年输沙量模拟值和实测值拟合结果较好(R²=0.56);(2)1980-2020年洞庭湖流域年均土壤侵蚀模数为5.09 t/(hm²·a),侵蚀模数和面积在整体上均呈下降趋势,其中侵蚀显著改善区域占流域总面积的22.60%,而显著加剧区域仅占2.69%;(3)年侵蚀总量以2005年为突变点,呈现出先下降后上升的趋势;(4)2005年前后导致土壤侵蚀变化的主导因子从地类变化转变为年降雨量变化。综上,洞庭湖流域土壤侵蚀总体呈现侵蚀面积收缩、局地恶化的趋势,其中极端降雨事件和经果林开发是侵蚀加剧的主要原因。     

Capture-Recapture Smooth Estimation of Age-Specific Survival Probabilities in Animal Populations

Yielding sound estimates of survival according to age in wild populations where senescence or other age-related variations may occur is very important to management decision makers, and remains challenging. This paper proposes to use penalized maximum likelihood to obtain smooth estimates of annual survival probabilities across age in populations of wild animals followed by capture–recapture. We propose to use two different types of smoothing penalties, and we use ν-fold cross-validation to select the best value of the tuning parameter for the intensity of smoothing. We then assess the accuracy of the method by a simulation study with two different shapes of the relationship between age and survival, and we conclude that a careful use of this method provides reliable noise-free estimates of age-specific annual survival. We apply this procedure to the motivating data from a population of roe deer known to exhibit a marked decrease of survival with age, and we compare our results with those previously published on this population.     

Spatial designs and properties of spatial correlation: Effects on covariance estimation

In a spatial regression context, scientists are often interested in a physical interpretation of components of the parametric covariance function. For example, spatial covariance parameter estimates in ecological settings have been interpreted to describe spatial heterogeneity or “patchiness” in a landscape that cannot be explained by measured covariates. In this article, we investigate the influence of the strength of spatial dependence on maximum likelihood (ML) and restricted maximum likelihood (REML) estimates of covariance parameters in an exponential-with-nugget model, and we also examine these influences under different sampling designs—specifically, lattice designs and more realistic random and cluster designs—at differing intensities of sampling (n=144 and 361). We find that neither ML nor REML estimates perform well when the range parameter and/or the nugget-to-sill ratio is large—ML tends to underestimate the autocorrelation function and REML produces highly variable estimates of the autocorrelation function. The best estimates of both the covariance parameters and the autocorrelation function come under the cluster sampling design and large sample sizes. As a motivating example, we consider a spatial model for stream sulfate concentration.     

基于USLE的甘南川西北土壤侵蚀研究

甘南川西北位于黄河和长江上游源区,量化研究该区土壤侵蚀对河源区生态安全保障和地方经济可持续发展具重要意义。论文采用多种数据方法,基于USLE就甘南川西北2000—2015年间土壤侵蚀的时空分布特征及变化规律进行量化评估。结果表明:(1)降雨侵蚀力因子R值介于65~411(MJ·mm)/(hm²·h·a),高值区主要分布在东南部,空间分布与该区降雨格局基本一致;(2)土壤可蚀性因子K值介于0.19~0.41(t·hm²·h)/(hm²·MJ·mm),高值呈斑块状零星分布,与地带性土壤物化性状有关;(3)坡长坡度因子LS值介于0~8.24,高值主要分布在中北部高山地带,低值分布在东北部和西南部地形较平缓区域;(4)植被覆盖管理因子C值介于0~1,高值集中分布在研究区的西北部与西南部,与该区植被覆盖稀疏有关;(5)基于USLE的甘南川西北年侵蚀量为3.3×108 t/a,总体表现为轻度侵蚀;(6)2000—2015年间,研究区土壤侵蚀呈减弱态势,与增温背景下植被活动增强有关。     

SWAT模型土壤空间离散化对径流和输沙模拟结果的影响

以东江源为研究区,采用SWAT （Soil and Water Assessment Tool）模型,通过设置11种子流域划分方案,并保持土地利用和地形2个要素不变,以定量分析子流域划分引起的土壤离散化对产流产沙过程的影响。结果表明：（1）随着子流域数量的增加,土壤类型空间离散化趋势越明显,其中面积最大的泥红壤面积显著减少（P<0.05）,面积第2和第3的渗育质水稻土和黄壤显著增加（P<0.05）,面积最小的石质土在子流域数量少（11~267个）时该地类被概化完全消失,子流域数量增加到524个后,该地类面积呈增加趋势（P<0.05）。（2）土壤空间离散化导致年均径流量（1.32%）和输沙量减少（P<0.05）,且对输沙量影响更显著（18.07%,P<0.01）。（3）随着土壤离散程度增加,年平均最大1天、连续最大5天、连续最大7天输沙量均显著减少（16.64%,17.54%,17.34%,P<0.01）,而径流量变化不显著（0.59%,0.89%,0.83%,P>0.05）。（4）土壤空间离散化导致洪水过程输沙量峰值显著减少,而对洪峰径流量影响并不显著。子流域划分引起的土壤离散化主要通过土壤侵蚀因子K值变化而引起输沙量变化。研究结果将为分布式水文模型不确定性研究和提高模拟精度提供参考。