A Quantitative Climate-Match Score for Risk-Assessment Screening of Reptile and Amphibian Introductions

Assessing climatic suitability provides a good preliminary estimate of the invasive potential of a species to inform risk assessment. We examined two approaches for bioclimatic modeling for 67 reptile and amphibian species introduced to California and Florida. First, we modeled the worldwide distribution of the biomes found in the introduced range to highlight similar areas worldwide from which invaders might arise. Second, we modeled potentially suitable environments for species based on climatic factors in their native ranges, using three sources of distribution data. Performance of the three datasets and both approaches were compared for each species. Climate match was positively correlated with species establishment success (maximum predicted suitability in the introduced range was more strongly correlated with establishment success than mean suitability). Data assembled from the Global Amphibian Assessment through NatureServe provided the most accurate models for amphibians, while ecoregion data compiled by the World Wide Fund for Nature yielded models which described reptile climatic suitability better than available point-locality data. We present three methods of assigning a climate-match score for use in risk assessment using both the mean and maximum climatic suitabilities. Managers may choose to use different methods depending on the stringency of the assessment and the available data, facilitating higher resolution and accuracy for herpetofaunal risk assessment. Climate-matching has inherent limitations and other factors pertaining to ecological interactions and life-history traits must also be considered for thorough risk assessment. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Ensemble Streamflow Forecast: A GLUE‐Based Neural Network Approach1

Abstract: While training a Neural Network to model a rainfall‐runoff process, generally two aspects are considered: its capability to be able to describe the complex nature of the processes being modeled and the ability to generalize so that novel samples could be mapped correctly. The general conclusion is that, the smallest size network capable of representing the sample distribution is the best choice, as far as generalization is concerned. Oftentimes input variables are selected a priori in what is called an explanatory data analysis stage and are not part of the actual network training and testing procedures. When they are, the final model will have only a “fixed” type of inputs, lag‐space, and/or network structure. If one of these constituents was to change, one would obtain another equally “optimal” Neural Network. Following Beven and others' generalized likelihood uncertainty estimate approach, a methodology is introduced here that accounts for uncertainties in network structures, types of inputs, and their lag‐space relationships by looking at a population of Neural Networks rather than target in getting a single “optimal” network. It is shown that there is a wide array of networks that provide “similar” results, as seen by a likelihood measure, for different types of inputs, lag‐space, and network size combinations. These equally optimal networks expose the range of uncertainty in streamflow predictions and their expected value results in a better performance than any of the single network predictions.     

A Stochastic Modeling Approach to Address Hydrogeologic Uncertainties in Modeling Wellhead Protection Boundaries in Karst Aquifers1

Abstract: Development of any numerical ground‐water model is dependent on hydrogeologic data describing the subsurface. These data are obtained from geologic core analyses, stratigraphic analyses, aquifer performance tests, and geophysical studies. But typically in remote areas, these types of data are very sparse and site‐specific in terms of the aerial extent of the resource to be modeled. Uncertainties exist as to how well the available data from a few locations defines a heterogeneous surficial aquifer such as the Biscayne Aquifer in Miami‐Dade County, Florida. This is particularly the case when an exceptionally conductive horizontal flow zone is detected at one site due to specialized testing that was not historically conducted at the other at sites that provided data for the model. Not adequately accounting for the potential effect of the high flow zone in the aquifer within a ground‐water numerical model, even though the zone may be of very limited thickness, might underpredict the well field protection capture boundaries. Applied Stochastic ground‐water modeling in determining well field protection zones is steadily becoming important in addressing the uncertainty of the hydrogeologic subsurface parameters, specifically in karstic heterogeneous aquifers. This is particularly important in addressing the uncertainty of a 60‐day travel time capture zone in the Northwest Well Field, Miami‐Dade County, where a predominantly high flow zone controls much of the flow in the production wells. A stochastic ground‐water modeling application along with combination of pilot points and regularization technique is presented to further consolidate the uncertainty of the subsurface.     

Regional Scale Stressor‐Response Models in Aquatic Ecosystems1

Abstract: A systematic method for identification and estimation of regional scale stressor‐response models in aquatic ecosystems will be useful in monitoring and assessment of aquatic resources, determination of regional nutrient criteria and for increased understanding of the differences between regions. The model response variable is chlorophyll a, a measure of algal density, while the stressors include nutrient concentrations from the USEPA Nutrient Criteria Database (NCD) for lakes/ponds and reservoirs of the continental United States. The NCD has observations for both stressors and biological responses determined using methods that are not consistently available at the continental scale. To link multiple environmental stressors to biological responses and quantify uncertainty in model predictions, we take a multilevel modeling approach to the estimation of a linear model for prediction of log Chlorophyll a using predictors log TP and log TN. The multilevel modeling approach allows us to adjust the impact of covariates at all levels (observation, higher level groups) for the simultaneous operation of contextual and individual variability in the outcome. Here, we wish to allow separate regression coefficients for inference regarding similarities and differences between each of 14 ecoregions, and between the two water‐body types, lakes/ponds and reservoirs. We are also interested in the nuisance effects of the categorical variables indicating the type of nitrogen measurements (three levels) and the type of chlorophyll a measurements (four levels) used. Model‐based determination of nutrient criteria points to an apparent incompatibility of criteria developed for nutrient stressors and eutrophication responses using current Environmental Protection Agency’s guidance.     

A Fully Distributed Implementation of Mean Annual StreamFlow Regional Regression Equations1

Abstract: Estimates of mean annual streamflow are needed for a variety of hydrologic assessments. Away from gage locations, regional regression equations that are a function of upstream area, precipitation, and temperature are commonly used. Geographic information systems technology has facilitated their use for projects, but traditional approaches using the polygon overlay operator have been too inefficient for national scale applications. As an alternative, the Elevation Derivatives for National Applications (EDNA) database was used as a framework for a fully distributed implementation of mean annual streamflow regional regression equations. The raster “flow accumulation” operator was used to efficiently achieve spatially continuous parameterization of the equations for every 30 m grid cell of the conterminous United States (U.S.). Results were confirmed by comparing with measured flows at stations of the Hydro‐Climatic Data Network, and their applications value demonstrated in the development of a national geospatial hydropower assessment. Interactive tools at the EDNA website make possible the fast and efficient query of mean annual streamflow for any location in the conterminous U.S., providing a valuable complement to other national initiatives (StreamStats and the National Hydrography Dataset Plus).     

大气污染物扩散模式的应用研究综述

应用大气污染物扩散模式可以模拟不同尺度、气象、地形条件下工业污染物在大气中的输送与扩散特征,为大气监测、城市环境规划和空气质量预报等工作提供科学依据.归纳了目前广泛应用于模拟工业污染物扩散的模式,着重介绍了近年来国内外对这些模式的主要应用研究进展,比较了各模式在应用上的优缺点,并对大气污染物扩散模式的应用研究前景进行了讨论.     

OSPM - A Parameterised Street Pollution Model

For many practical applications, as e.g. in support of air pollution management, numerical models based on solution of the basic flow and dispersion equations are still too complex. Alternative are models that are basically parameterised semi-empirical models making use of a priori assumptions about the flow and dispersion conditions. However, these models must, be thoroughly tested and their performance and limitations carefully documented. The Danish Operational Street Pollution Model (OSPM) belongs to this category of parameterised models. In the OSPM, concentrations of exhaust gases are calculated using a combination of a plume model for the direct contribution and a box model for the recirculating part of the pollutants in the street. Parameterisation of flow and dispersion conditions in street canyons was deduced from extensive analysis of experimental data and model tests. Results of these tests were used to further improve the model performance, especially with regard to different street configurations and a variety of meteorological conditions.     

A Simple Model for Urban Background Pollution

A simple urban background pollution model is presented. Contributions from the individual area sources, subdivided into a grid net of a resolution of 2km × 2km, are integrated along the wind direction path assuming linear dispersion with the distance to the receptor point. Horizontal dispersion is accounted for by averaging the calculated concentrations over a certain, wind speed dependent wind direction sector, centred on the average wind direction. Formation of the nitrogen dioxide due to oxidation of nitrogen monoxide by ozone is calculated using a simple chemical model based on assumption of a photochemical equilibrium on the time scale of the pollution transport across the city area. The rate of entrainment of fresh rural ozone is governed by this time scale. The model is suitable for calculations of urban background when the dominating source is the road traffic. For this source the emissions take place at ground level, and a good approximation is to treat the emissions as area sources, but with an initial vertical dispersion determined by the height of the buildings.     

Exploring the use of computer models in participatory integrated assessment – experiences and recommendations for further steps

Integrated assessment (IA) can be defined as a structured process of dealing with complex issues, using knowledge from various scientific disciplines and/or stakeholders, such that integrated insights are made available to decision makers (J. Rotmans, Enviromental Modelling and Assessment 3 (1998) 155). There is a growing recognition that the participation of stakeholders is a vital element of IA. However, only little is known about methodological requirements for such participatory IA and possible insights to be gained from these approaches. This paper summarizes some of the experiences gathered in the ULYSSES project, which aims at developing procedures that are able to bridge the gap between environmental science and democratic policy making for the issue of climate change. The discussion is based on a total of 52 IA focus groups with citizens, run in six European and one US city. In these groups, different computer models were used, ranging from complex and dynamic global models to simple accounting tools. The analysis in this paper focuses on the role of the computer models. The findings suggest that the computer models were successful at conveying to participants the temporal and spatial scale of climate change, the complexity of the system and the uncertainties in our understanding of it. However, most participants felt that the computer models were less instrumental for the exploration of policy options. Furthermore, both research teams and participants agreed that despite considerable efforts, most models were not sufficiently user-friendly and transparent for being accessed in an IA focus group. With that background, some methodological conclusions are drawn about the inclusion of the computer models in the deliberation process. Furthermore, some suggestions are made about how given models should be adapted and new ones developed in order to be helpful for participatory IA. This revised version was published online in July 2006 with corrections to the Cover Date.     

生命线网络系统抗震拓扑优化的Benchmark模型

生命线系统拓扑优化问题的Benchmark模型是评测新型算法正确性和适用性的重要手段。基于此,首先以生命线网络系统抗震拓扑优化分析模型为背景,建立了该优化问题的三个Benchmark模型,并在Visual Compaq Fortran开发环境下,通过穷举法统计出解空间的所有网络,进而甄选出不同节点可靠度约束下的最优网络和若干次优网络,最后利用上述Benchmark模型对生命线网络系统抗震拓扑优化中的蚁群算法进行测试。结果表明,当网络规模较小时,蚁群算法能精确地搜索到最优解;当网络规模增大后,蚁群算法也能以较大概率搜索到最优解或次优解。