Calibration of the forest vegetation simulator (FVS) model for the main forest species of Ontario,Canada

The forest vegetation simulator (FVS) model was calibrated for use in Ontario, Canada, to predict the growth of forest stands. Using data from permanent sample plots originating from different regions of Ontario, new models were derived for dbh growth rate, survival rate, stem height and species group density index for large trees and height and dbh growth rate for small trees. The dataset included black spruce (Picea mariana (Mill.) B.S.P.) and jack pine (Pinus banksiana Lamb.) for the boreal region, sugar maple (Acer saccharum Marsh.), white pine (Pinus strobus L.), red pine (Pinus resinosa Ait.) and yellow birch (Betula alleghaniensis Britton) for the Great Lakes-St. Lawrence region, and balsam fir (Abies balsamea (L.) Mill.) and trembling aspen (Populus tremuloides Michx.) for both regions. These new models were validated against an independent dataset that consisted of permanent sample plots located in Quebec. The new models predicted biologically consistent growth patterns whereas some of the original models from the Lake States version of FVS occasionally did not. The new models also fitted the calibration (Ontario) data better than the original FVS models. The validation against independent data from Quebec showed that the new models generally had a lower prediction error than the original FVS models.     

Determination of plate waste in primary school lunches by weighing and visual estimation methods: A validation study

The aim of this study was to validate the visual estimation method for aggregated plate waste of main dish at Portuguese primary school canteens. For this purpose plate waste at school lunch was measured for 505 individual servings, using weighing individual servings and plate waste and visual estimation method by a 6-point scale, as developed by Comstock et al. (1981). A high variability of initial serving weights was found with serving sizes ranging from 88.9 to 283.3 g and with a coefficient of variation ranging from 5.5% to 24.7%. Mean plate waste was 27.5% according to the weighing method. There was a significant bias in the conversion of the visual waste estimations to actual waste, being overestimated by an average of 8.0 g (ranging from ?12.9 g to 41.4 g). According to Bland and Altman plot, the mean difference between methods was of 8.0 g and the amplitude interval was 102.6 g. The study showed that the visual estimation method is not as accurate as the weighing method in assessing nonselective aggregated plate waste at primary school canteens. Our findings are thus very important on considering plate waste assessment, since the wide variation on initial servings introduces a relevant bias when considering standard portions or a random sample of initial servings. Although, greater convenience, time-saving and the possibility to monitor plate waste of large groups, make the visual estimation method an important method to assess plate waste at school canteens, these results highlighted the need of portions standardization and control of initial servings to allow for its use.     

Reliability of groundwater vulnerability maps obtained through statistical methods

Statistical methods are widely used in environmental studies to evaluate natural hazards. Within groundwater vulnerability in particular, statistical methods are used to support decisions about environmental planning and management. The production of vulnerability maps obtained by statistical methods can greatly help decision making. One of the key points in all of these studies is the validation of the model outputs, which is performed through the application of various techniques to analyze the quality and reliability of the final results and to evaluate the model having the best performance. In this study, a groundwater vulnerability assessment to nitrate contamination was performed for the shallow aquifer located in the Province of Milan (Italy). The Weights of Evidence modeling technique was used to generate six model outputs, each one with a different number of input predictive factors. Considering that a vulnerability map is meaningful and useful only if it represents the study area through a limited number of classes with different degrees of vulnerability, the spatial agreement of different reclassified maps has been evaluated through the kappa statistics and a series of validation procedures has been proposed and applied to evaluate the reliability of the reclassified maps. Results show that performance is not directly related to the number of input predictor factors and that is possible to identify, among apparently similar maps, those best representing groundwater vulnerability in the study area. Thus, vulnerability maps generated using statistical modeling techniques have to be carefully handled before they are disseminated. Indeed, the results may appear to be excellent and final maps may perform quite well when, in fact, the depicted spatial distribution of vulnerability is greatly different from the actual one. For this reason, it is necessary to carefully evaluate the obtained results using multiple statistical techniques that are capable of providing quantitative insight into the analysis of the results. This evaluation should be done at least to reduce the questionability of the results and so to limit the number of potential choices.     

The MONICA model: Testing predictability for crop growth, soil moisture and nitrogen dynamics

A fundamentally revised version of the HERMES agro-ecosystem model, released under the name of MONICA, was calibrated and tested to predict crop growth, soil moisture and nitrogen dynamics for various experimental crop rotations across Germany, including major cereals, sugar beet and maize. The calibration procedure also included crops grown experimentally under elevated atmospheric CO₂ concentration. The calibrated MONICA simulations yielded a median normalised mean absolute error (nMAE) of 0.20 across all observed target variables (n = 42) and a median Willmott's Index of Agreement (d) of 0.91 (median modelling efficiency (ME): 0.75). Although the crop biomass, habitus and soil moisture variables were all within an acceptable range, the model often underperformed for variables related to nitrogen. Uncalibrated MONICA simulations yielded a median nMAE of 0.27 across all observed target variables (n = 85) and a median d of 0.76 (median ME: 0.30), also showing predominantly acceptable results for the crop biomass, habitus and soil moisture variables. Based on the convincing performance of the model under uncalibrated conditions, MONICA can be regarded as a suitable simulation model for use in regional applications. Furthermore, its ability to reproduce the observed crop growth results in free-air carbon enrichment experiments makes it suited to predict agro-ecosystem behaviour under expected future climate conditions.     

Implications of ecological data constraints for integrated policy and livelihoods modelling: An example from East Kalimantan, Indonesia

Policy and human livelihoods modelling increasingly demands integrated research which requires ecological expertise. However, contributions from ecologists are often based on sparse data. Rather than discounting such data, in this paper we demonstrate how ecological modelling can effectively contribute to the development of policy recommendations in spite of data constraints. In a petrol subsidy analysis in East Kalimantan, Indonesia, we accounted for ecological data uncertainty by (a) assuming large parameter value ranges and (b) conducting a robustness test for policy recommendations. In addition to data scarcity, counter-intuitive results emerged emphasising the need for model validation. These counter-intuitive results indicated that decreasing petrol prices led to increased poverty. This informed a policy recommendation to prevent the reduction of petrol prices below IDR 5500 per litre. Using two key livelihood resources (fish and honey), we found that while a traditional sensitivity analysis suggested highly robust results, a robustness test indicated that policy recommendations would change if the incorrectness of parameter values approached 50%. The results show that ecological modelling can contribute effectively in spite of sparse data to guide policy, as well as identifying future research priorities.     

Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event

This paper develops a framework for regional scale flood modeling that integrates NEXRAD Level III rainfall, GIS, and a hydrological model (HEC-HMS/RAS). The San Antonio River Basin (about 4000 square miles, 10,000 km2) in Central Texas, USA, is the domain of the study because it is a region subject to frequent occurrences of severe flash flooding. A major flood in the summer of 2002 is chosen as a case to examine the modeling framework. The model consists of a rainfall-runoff model (HEC-HMS) that converts precipitation excess to overland flow and channel runoff, as well as a hydraulic model (HEC-RAS) that models unsteady state flow through the river channel network based on the HEC-HMS-derived hydrographs. HEC-HMS is run on a 4 x 4 km grid in the domain, a resolution consistent with the resolution of NEXRAD rainfall taken from the local river authority. Watershed parameters are calibrated manually to produce a good simulation of discharge at 12 subbasins. With the calibrated discharge, HEC-RAS is capable of producing floodplain polygons that are comparable to the satellite imagery. The modeling framework presented in this study incorporates a portion of the recently developed GIS tool named Map to Map that has been created on a local scale and extends it to a regional scale. The results of this research will benefit future modeling efforts by providing a tool for hydrological forecasts of flooding on a regional scale. While designed for the San Antonio River Basin, this regional scale model may be used as a prototype for model applications in other areas of the country.     

高层建筑人员疏散模型及其验证

介绍了我们开发的高层建筑人员疏散的计算机模型BuildGEM,并对人员疏散建模方法进行了深入的分析,同时利用教学楼中的学生疏散演习对模型进行了验证,证明了模型具有较好的准确性。     

Predicting aquaculture-derived benthic organic enrichment: Model validation

A sediment trap validation study was conducted near the commercial sea bass and sea bream fish farm in order to assess the predictive capability of a particle tracking deposition model. The validation procedure consisted of two distinct phases. First, the deposition of particulate waste (i.e. fecal pellets and excess feed) was measured near a single net pen containing 19 tons of sea bass. Afterwards, the model quality was determined by statistical comparison of predicted and observed values.Goodness of fit analysis indicates that the model successfully accounts for more than 75% of variance in the observed deposition. At 5% significance level, predictions do not underestimate or overestimate observations and there is no bias. Mean absolute relative error of ±48.9% compares favorably to other published deposition models. Obtained results affirm the reliability of particle tracking techniques in modeling the aquaculture-derived benthic organic enrichment.     

Validation of a hydride generation atomic absorption spectrometry methodology for determination of mercury in fish designed for application in the Brazilian national residue control plan

In the present study, a method for the determination of mercury (Hg) in fish was validated according to ISO/IEC 17025, INMETRO (Brazil), and more recent European recommendations (Commission Decision 2007/333/EC and 2002/657/EC) for implementation in the Brazilian Residue Control Plan (NRCP) in routine applications. The parameters evaluated in the validation were investigated in detail. The results obtained for limit of detection and quantification were respectively, 2.36 and 7.88 μg kg^?1 of Hg. While the recovery varies between 90–96%. The coefficient of variation was of 4.06–8.94% for the repeatability. Furthermore, a comparison using an external proficiency testing scheme was realized. The results of method validated for the determination of the mercury in fish by Hydride generation atomic absorption spectrometry were considered suitable for implementation in routine analysis.     

Modelling radionuclide distribution and transport in the environment

Mathematical models of radionuclide distribution and transport in the environment have been developed to assess the impact on people of routine and accidental releases of radioactivity from a variety of nuclear activities, including: weapons development, production, and testing; power production; and waste disposal. The models are used to estimate human exposures and doses in situations where measurements have not been made or would be impossible or impractical to make. Model results are used to assess whether nuclear facilities are operated in compliance with regulatory requirements, to determine the need for remediation of contaminated sites, to estimate the effects on human health of past releases, and to predict the potential effects of accidental releases or new facilities. This paper describes the various applications and types of models currently used to represent the distribution and transport of radionuclides in the terrestrial and aquatic environments, as well as integrated global models for selected radionuclides and special issues in the fields of solid radioactive waste disposal and dose reconstruction. Particular emphasis is placed on the issue of improving confidence in the model results, including the importance of uncertainty analysis and of model verification and validation.