土地非农化生态价值损失估算

随着城镇化、工业化的快速推进,我国城乡建设用地快速扩展使农用地保护面临巨大挑战,从经济机制上防止土地过度非农化尤为重要.该文对土地非农化生态价值损失过程机制进行了理论探索,建立了价值损失估算方法,并以江汉平原为例进行案例分析.研究表明:单纯的市场机制没有考虑土地非农化造成的生态价值损失,无法实现土地资源最佳配置,非农化规模偏大;土地非农化生态价值损失巨大,1987-2010年江汉平原因此累计损失73 692.11万元,人均承担49.25元;不同类型土地的生态价值损失强度有所差异,每公顷的自然保留地、水域、林地、草地、耕地非农化价值损失分别为2.459 8万元/a,2.036 7万元/a,1.262 9万元/a,0.524 1万元/a,0.354 8万元/a.文章认为,发展与保护始终是土地资源优化配置必然要面对的难题,土地非农化是社会经济发展过程中的必然现象,也是一个土地生态服务功能不断减少和消亡的过程.土地非农化虽然在一定程度上提高了土地的经济效益和社会效益,但生态效益损失巨大,可能会导致总效益净减.土地过度非农化在很大程度上是由于传统市场体系仅部分考虑了土地的经济、社会功能,而几乎完全没有考虑其生态价值导致的.作为一类负外部性,土地非农化过程中的生态价值损失如果不能得到及时遏制,快速增长的城乡建设用地需求将使农地保护面临巨大威胁,这需要将生态价值纳入土地价值体系,计入征地成本,并在一定程度上提高现行土地价格体系的合理性,防止建设用地的过度扩张,减少生态价值损失.     

基于集对分析的石化企业安全投资状况动态评价

石油化工企业的安全事故频发和职业危害严重,不仅造成重大经济损失,而且对社会造成了不良影响,不合理的安全投资是造成这种状况的主要原因之一,对企业现有安全投资状况进行评价是合理进行安全投资的前提和基础。本文针对石油化工企业安全投资评价中诸多不确定性因素和动态特征进行分析,运用集对分析和马尔可夫链理论,建立了基于集对分析石化企业安全投资状况的动态评价模型,为安全投资状况评价工作提供了一个新方法。以某石油化工企业为例,通过对该企业2006-2009年这四年安全投资状况进行动态评价分析,并预测出2010年该企业的安全投资状况。评价结果表明,将集对分析和马尔可夫链理论用于石化企业安全投资状况的动态评价与预测,起到了非常好的效果,能够为企业科学合理地作出正确决策提供理论依据。     

A Multi‐Agency Nutrient Dataset Used to Estimate Loads,Improve Monitoring Design,and Calibrate Regional Nutrient SPARROW Models1

Saad, David A., Gregory E. Schwarz, Dale M. Robertson, and Nathaniel L. Booth, 2011. A Multi‐Agency Nutrient Dataset Used to Estimate Loads, Improve Monitoring Design, and Calibrate Regional Nutrient SPARROW Models. Journal of the American Water Resources Association (JAWRA) 47(5):933‐949. DOI: 10.1111/j.1752‐1688. 2011.00575.x Abstract: Stream‐loading information was compiled from federal, state, and local agencies, and selected universities as part of an effort to develop regional SPAtially Referenced Regressions On Watershed attributes (SPARROW) models to help describe the distribution, sources, and transport of nutrients in streams throughout much of the United States. After screening, 2,739 sites, sampled by 73 agencies, were identified as having suitable data for calculating long‐term mean annual nutrient loads required for SPARROW model calibration. These sites had a wide range in nutrient concentrations, loads, and yields, and environmental characteristics in their basins. An analysis of the accuracy in load estimates relative to site attributes indicated that accuracy in loads improve with increases in the number of observations, the proportion of uncensored data, and the variability in flow on observation days, whereas accuracy declines with increases in the root mean square error of the water‐quality model, the flow‐bias ratio, the number of days between samples, the variability in daily streamflow for the prediction period, and if the load estimate has been detrended. Based on compiled data, all areas of the country had recent declines in the number of sites with sufficient water‐quality data to compute accurate annual loads and support regional modeling analyses. These declines were caused by decreases in the number of sites being sampled and data not being entered in readily accessible databases.     

Integrating soil carbon cycling with that of nitrogen and phosphorus in the watershed model SWAT: Theory and model testing

In this paper we describe and test a sub-model that integrates the cycling of carbon (C), nitrogen (N) and phosphorus (P) in the Soil Water Assessment Tool (SWAT) watershed model. The core of the sub-model is a multi-layer, one-pool soil organic carbon (S_C) algorithm, in which the decomposition rate of S_C and input rate to S_C (through decomposition and humification of residues) depend on the current size of S_C. The organic N and P fluxes are coupled to that of C and depend on the available mineral N and P, and the C:N and N:P ratios of the decomposing pools. Tillage explicitly affects the soil organic matter turnover rate through tool-specific coefficients. Unlike most models, the turnover of soil organic matter does not follow first order kinetics. Each soil layer has a specific maximum capacity to accumulate C or C saturation (S_x) that depends on texture and controls the turnover rate. It is shown in an analytical solution that S_x is a parameter with major influence in the model C dynamics. Testing with a 65-yr data set from the dryland wheat growing region in Oregon shows that the model adequately simulates the S_C dynamics in the topsoil (top 0.3 m) for three different treatments. Three key model parameters, the optimal decomposition and humification rates and a factor controlling the effect of soil moisture and temperature on the decomposition rate, showed low uncertainty as determined by generalized likelihood uncertainty estimation. Nonetheless, the parameter set that provided accurate simulations in the topsoil tended to overestimate S_C in the subsoil, suggesting that a mechanism that expresses at depth might not be represented in the current sub-model structure. The explicit integration of C, N, and P fluxes allows for a more cohesive simulation of nutrient cycling in the SWAT model. The sub-model has to be tested in forestland and rangeland in addition to agricultural land, and in diverse soils with extreme properties such high or low pH, an organic horizon, or volcanic soils.     

Risk analysis and assessment methodologies in the work sites: On a review, classification and comparative study of the scientific literature of the period 2000-2009

The objective of this work is to determine and study, analyze and elaborate, classify and categorize the main risk analysis and risk-assessment methods and techniques by reviewing the scientific literature. The paper consists of two parts: a) the investigation, presentation and elaboration of the main risk-assessment methodologies and b) the statistical analysis, classification, and comparative study of the corresponding scientific papers published by six representative scientific journals of Elsevier B.V. covering the decade 2000-2009. The scientific literature reviewing showed that the risk analysis and assessment techniques are classified into three main categories: (a) the qualitative, (b) the quantitative, and (c) the hybrid techniques (qualitative-quantitative, semi-quantitative). The qualitative techniques are based both on analytical estimation processes, and on the safety managers-engineers ability. According to quantitative techniques, the risk can be considered as a quantity, which can be estimated and expressed by a mathematical relation, under the help of real accidents’ data recorded in a work site. The hybrid techniques, present a great complexity due to their ad hoc character that prevents a wide spreading. The statistical analysis shows that the quantitative methods present the highest relative frequency (65.63%) while the qualitative a lower one (27.68%). Furthermore the hybrid methods remain constantly at a very low level (6.70%) during the entire processing period.     

景区动态承载力估算方法及其应用

景区承载力是旅游学研究的重要领域,由于现有景区承载力估算模型以景区由“均质的可游览空间”构成为假设前提,通过面积和游客单位游览面积进行景区承载力的估算,未涉及游客进入景区游览行为的动态性。由于游客不能瞬时填满整个景区,景区承载力具有时空特性,故建立景区动态承载力计算的概念模型,并构建以停留区、通道以及出入口等要素为空间实体的网络结构逻辑模型,从而提出基于网络结构的景区动态承载力估算方法,选取南京总统府景区作为实验区域,验证所提出的模型和算法。将地理信息科学应用于旅游管理,有利于确定景区承载力的具体量化水平,使景区管理分时管控措施得到有效发挥,进而促进旅游景区实现更高质量的经济社会效益。     

确立灾害评估标准是我国“国际减灾十年”目标的重要问题

本文通过许多实例.从测、报、抗、防、救等几个方面论述灾害评估标准的重要性,进而说明了灾害评估与减灾管理以及灾情统计的密切关系.文中还根据我国近几年灾害经济损失的情况对90年代的减灾目标提出了看法.     

Assessing the impact of biological control of Plutella xylostella through the application of Lotka–Volterra model

The Lotka–Volterra model was applied to the population densities of diamondback moth (DBM), Plutella xylostella (L.) and its exotic larval parasitoid Diadegma semiclausum (Hellen) data that was collected earlier by icipe's DBM biological control team. The collections were done for 15 months before the release and 36 months after release of the parasitoid in two areas; in Werugha, Coast Province of Kenya and Tharuni, Central Province of Kenya, respectively. For each area in pre- and post-release periods, we estimated Lotka–Volterra model parameters from the minimization of the loss function between the theoretical and experimental time-series datasets following the Nelder-Mead multidimensional method. The model estimated a reduction in the value of the steady state of DBM population from 4.86 to 2.17 in Werugha and from 6.11 to 3.76 and 3.45 (with and without exclusion of the time before D. semiclausum recovery) in Tharuni when transiting from the pre- and post-release periods, respectively. This change was a consequence of the newly introduced parasitoid, in the areas. The study presented a successful and detailed technique for non-linear model parameters restoration which was demonstrated by the correct mimicking of empirical datasets from the classical biological control with D. semiclausum, in different areas of Kenya. The applied model has measured the parasitoids impact on the DBM biological control through a quantitative estimate of the effectiveness of the newly introduced species D. semiclausum. These equations may therefore be used as tool for decision making in the implementation for such pests’ management system strategy.     

Impact of bias in predicted height on tree volume estimation: A case-study of intrinsic nonlinearity

Bias originating from intrinsic nonlinearity in nonlinear models is caused by excess curvature in the solution locus of parameter estimates derived from least squares procedures. Bias due to intrinsic nonlinearity varies according to sample size as well as model specification. This paper analyses consequences of fractionising data into smaller sub-samples. Based on measurements of stem diameter and total tree height from the first Danish national forest inventory, it is demonstrated how data splitting at random may cause the intrinsic nonlinear curvature to exceed the critical F-value. Application of a Taylor-series expansion shows that, for all practical purposes, the bias in predictions of individual tree volume (based on stem diameter and tree height) is negligible. To minimize residual variance, intrinsic curvature and, in turn, prediction bias, it is recommended that data be stratified according to site conditions, stand characteristics or other relevant criteria. Finally, the preferred model should exhibit close-to-linear behaviour.     

Evaluation of discrete host–parasitoid models for diamondback moth and Diadegma semiclausum field time population density series

The performance of discrete mathematical models to describe the population dynamics of diamondback moth (DBM) (Plutella xylostella L.) and its parasitoid Diadegma semiclausum was investigated. The parameter values for several well-known models (Nicholson–Bailey, Hassell and Varley, Beddington, Free and Lawton, May, Holling type 2, 3 and Getz and Mills functional responses) were estimated. The models were tested on 20 consecutive sets of time series data collected at 14 days interval for pest and parasitoid populations obtained from a highland cabbage growing area in eastern Kenya. Model parameters were estimated from minimized squared difference between the numerical solution of the model equations and the empirical data using Powell's method. Maximum calculated DBM growth rates varied between 0.02 and 0.07. The carrying capacity determined at 16.5 DBM/plant by the Beddington et al. model was within the range of field data. However, all the estimated parameter values relating to the parasitoid, including the instantaneous searching rate (0.07–0.28), per capita searching efficiency (0.20–0.27), search time (5.20–5.33), handling time (0.77–0.90), and parasitism aggregation index (0.33), were well outside the range encountered empirically. All models evaluated for DBM under Durbin–Watson criteria, except the May model, were not autocorrelated with respect to residuals. In contrast, the criteria applied to the parasitoid residuals showed strong autocorrelations. Thus, these models failed to estimate parasitoid dynamics. We conclude that the interactions of the DBM with its parasitoid cannot be explained by any of the models tested. Two factors may be associated with this failure. First, the parasitoid in this integrated biological control system may not be playing a major role in regulating DBM population. Second, and perhaps more likely, poor correlations reflect gross inadequacies in the theoretical assumptions that underlie the existing models.