洪水灾害评估体系研究

从系统论的观点出发，提出了洪水灾害系统的概念，并结合洪水灾害评估的特点，设计了洪水灾害评估体系的总体框架。     

Semi-parametric statistical approaches for space-time process prediction

The problem of estimation and prediction of a spatial-temporal stochastic process, observed at regular times and irregularly in space, is considered. A mixed formulation involving a non- parametric component, accounting for a deterministic trend and the effect of exogenous variables, and a parametric component representing the purely spatio-temporal random variation is proposed. Correspondingly, a two-step procedure, first addressing the estimation of the non- parametric component, and then the estimation of the parametric component is developed from the residual series obtained, with spatial-temporal prediction being performed in terms of suitable spatial interpolation of the temporal variation structure. The proposed model formula-tion, together with the estimation and prediction procedure, are applied using a Gaussian ARMA structure for temporal modelling to space-time forecasting from real data of air pollution concentration levels in the region surrounding a power station in northwest Spain.     

Comparison of Point Estimation Techniques in the Spatial Analysis of Radium-226, Radium-228 and Potassium-40 in Soil

Many environmental surveys require the implementation of estimation techniques to determine the spatial distribution of the variable being investigated. Traditional methods of interpolation and estimation, for example, inverse distance squared and triangulation often ignore features of the data set such as anisotropy which may have a significant impact on the quality of the estimates produced. Geostatistical techniques may offer an improved method of estimation by modelling the spatial continuity of the variable using semi-variogram analysis. The theoretical model fitted to the semi-variogram is then used in the assignation of weighting factors to the samples surrounding the location to be estimated. This paper outlines the results of a comparison between three common estimation methods, polygonal, triangulation and inverse distance squared and a geostatistical method, in the estimation of soil radionuclide activities. The geostatistical estimation method known as kriging performed best over a range of parameters used to test the performance of the methods. Kriging exhibited the best correlation between actual and estimated values, the narrowest error distribution and the lowest overall estimation error. Polygonal estimation was best at reproducing the data set distribution. Conditional bias was evident in all the methods, low values being over-estimated and high values being under-estimated.     

METHODOLOGIES FOR CALIBRATION AND PREDICTIVE ANALYSIS OF A WATERSHED MODEL1

ABSTRACT: The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.     

农业非点源污染田间模型及其应用

非点源污染的负荷定量化研究是控制、评价和管理非点源污染的基础.农业非点源污染负荷估算包括农田排水估算和排水中的污染物浓度预测2个环节.依据水量平衡原理,农田排水应用DRAINMOD模型估算;将农田的施肥和灌溉过程"合成"作为田间污染物浓度的脉冲输入,农田排水中的污染物浓度变化则视作对应于此脉冲输入的响应过程,而污染物在田间的复杂迁移转化过程以逆高斯概率密度函数隐含表达.以此为基础,构建了农田尺度农业非点源污染负荷估算模型.以青铜峡灌区典型试验区为例,对稻田排水沟中硝态氮(NO_3~--N)和总磷(TP)的负荷过程进行了模拟,结果表明,模型估算结果和实测污染物负荷过程非常接近,Nash-Suttcliffe模拟效率系数分别为0.963和0.945,表明该模型具有较高的可靠性.     

农业生产效率对农业用水量的影响

2016年中国农业用水量占用水总量的60%以上,水资源成为国家农业安全的重要保障,而影响农业用水量的因素较多。论文首先采用非期望产出的Super-SBM模型测度1998—2015年中国30个省份（西藏、香港、澳门、台湾缺少资料,未计算）的农业生产效率,再利用非参数核密度估计演示主要年份农业生产效率和农业用水量的动态变化,然后借鉴Hansen的门槛模型检验农业生产效率对农业用水量的“门槛效应”。结果表明：1）我国农业生产效率呈倒“U”型走势,省际间差异性减弱;农业用水量先下降后上升,省际间差距存在扩大的趋势。2）提高农业生产效率是降低农业用水量的有效途径,农业生产效率对农业用水量存在显著的“门槛抑制效应”,抑制强度呈“N”型走势。3）扩大粮食作物种植比例、增加农村劳动力和提高农村居民收入均能有效抑制农业用水量增加,而水资源禀赋、水利投资和耕地灌溉面积与农业用水量呈正相关性,农民受教育水平回归结果不显著。     

中国水环境非点源污染负荷的估算研究

中国已占水环境容量中,除了点源污染以外,非点源污染也占有相当比重,非点源污染负荷的定量化研究势在必行.国外对非点源污染的研究开始较早,成果也较成熟.从20世纪80年代以来,中国也逐渐认识到非点源污染的存在及其危害性,开始了非点源污染的控制研究.但是中国对非点源污染问题的研究现在尚处于初级阶段,多为借鉴国外模型.文章分析了国内外对非点源污染的研究方法和研究现状,介绍了适合中国国情的非点源污染负荷估算的方法,包括流域水文估算法、大尺度区域中非点源污染负荷估算的方法.     

黄河支流非点源污染物(N、P)排放量的估算

为了估算黄河支流域非点源污染物的排放量,为今后黄河流域的治理提供科学的依据,对黄河流域的6个子流域(渭河流域、泾河流域、洛河流域、无定河流域、窟野河流域、黄甫川流域)进行了水样、土样和泥沙样的采集分析;结合6个子流域的多年水文资料,利用已经被证明了的平均浓度法对黄河6个子流域的氮磷污染物进行了定量描述,估算出各个流域的氮磷排放量.结果表明,95%的全磷、大于53%的全氮来自于非点源污染,非点源污染是造成黄河污染的主要原因.各流域非点源污染的发生具有明显的地域分异特征.硝态氮主要来自于渭河,铵态氮主要来自于泾河,其次是渭河.从全氮看,绝大部分氮来自于渭河和泾河.全磷则主要来自于泾河,其次是渭河.泾河和渭河是6条河流中对黄河污染物排放量贡献最大的2条河流.     

湖泊水质参数空间最优估计精度分析

在湖泊研究中,往往要了解污染的空间分布状况,其核心是空间最优估计问题.本文在阐述了克立格方差特点的基础上,以太湖为例,对湖泊水质参数空间最优估计的精度问题进行了分析.结果显示,估计精度受变差函数、各采样点的空间配置关系等因素的影响.空间结构直接决定着估计精度的大小,而与各信息采样点的值的大小无关.     

Spatial Patterns of Breeding Success of Grizzly Bears Derived from Hierarchical Multistate Models

Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low‐elevation wetlands or mid‐elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy—herbaceous alpine ecotones—were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection. Patrones Espaciales del Éxito Reproductivo de Osos Pardos, Derivados de Modelos Jerárquicos Multi‐Estado