UNCERTAINTY IN ESTIMATING BANKFULL CONDITIONS1

ABSTRACT: Bankfull depth and discharge are basic input parameters to stream planform, stream restoration, and highway crossing designs, as well as to the development of hydraulic geometry relationships and the classification of streams. Unfortunately, there are a wide variety of definitions for bankfull that provide a range of values, and the actual selection of bankfull is subjective. In this paper, the relative uncertainty in determining the bankfull depth and discharge is quantified, first by examining the variability in the estimates of bankfull and second by using fuzzy numbers to describe bankfull depth. Fuzzy numbers are used to incorporate uncertainty due to vagueness in the definition of bankfull and subjectivity in the selection of bankfull. Examples are provided that demonstrate the use of a fuzzy bankfull depth in sediment trans. port and in stream classification. Using fuzzy numbers to describe bankfull depth rather than a deterministic value allows the engineer to base designs and decisions on a range of possible values and associated degrees of belief that the bankfull depths take on each value in that range.     

基于C/S的三峡云阳-巫山段滑坡数据管理系统

长江三峡云阳-巫山段是三峡库区滑坡分布密集灾情严重的一个区域,随着库水位抬升到135 m以及今后的175 m,势必会加剧原有滑坡以及危险斜坡的不稳定性.本研究是在前期工作的基础上,开发出用以管理研究区数量繁多灾害点的管理系统.     

BSE in the UK: Why the Risk Communication Strategy Failed

The 2000 BSE Inquiry report points out that the most serious failure of the UK Government was one of risk communication. This paper argues that the government's failure to communicate the risks BSE posed to humans to a large degree can be traced back to a lack of transparency in the first risk assessment by the Southwood Working Party. This lack of transparency ensured that the working party's risk characterization and recommendations were ambiguous and thus hard to interpret. It also meant that uncertainties were not addressed in a satisfactory way. In the recommendations, the attitude to uncertainty was implicit rather than explicit.The risk communication based on the report amplified these flaws. Most importantly, it did not address the uncertainty at all. Apparently, the reason for this was fear of overreaction by the public. However, the result was counter-productive, because the risk communication did not then appear trustworthy. Later risk assessments and risk communication omitted to correct these flaws. Indeed, the fact that, following receipt of new information, advisory experts and policy makers had changed their views of the risk to humans was never clearly communicated to the public. There seemed to be little faith in the public's ability to reach a balanced judgment regarding the uncertainties.In the concluding section of the paper, this analysis is compared with the food standards agency's (FSA's) approach to BSE. The intervention of this agency was seen as one of the more important efforts to restore consumer confidence in British beef. And the agency certainly appears to be committed to openness and to addressing scientific uncertainty. However, using the risk of BSE in sheep as a case study, the paper shows that transparency – i.e., the clear presentation of factual and normative claims and assumptions underlying advice, and openness about the reasoning based on these claims and assumptions – is less than fully achieved in the FSA's work.     

NITRATE CONCENTRATIONS IN ILLINOIS RIVERS 1967 TO 1974: COMPARISON AMONG REPORTING AGENCIES1

ABSTRACT: Long term data on surface water quality can sometimes be assembled by combining data collected by different agencies at different times and assuming that between agency differences in data quality are insignificant. The objective of this paper was to assess the quality of riverine nitrate (NO₃) concentrations in Illinois measured and reported by four agencies from 1967 to 1974 by comparing median values for similar sampling locations and periods. A total of 17 river reaches were identified for which two agencies reported NO₃ concentrations during similar periods. Nonparametric comparison of median values and analysis of covariance with discharge as a covariant produced similar results. Nitrate concentrations reported by the U.S. Geological Survey (USGS) from 1967 to 1971 were not statistically (P > 0.05) different from values reported by the Illinois State Water Survey (ISWS) for two of three river reaches. Additionally, NO₃ concentrations reported by USGS from 1972 to 1974 were not statistically different than concentrations reported by the Illinois Environmental Protection Agency (IEPA) for four of five river reaches. From 1969 to 1971, NO₃ concentrations reported by the Illinois Department of Public Heath and the Illinois Environmental Protection Agency (IDPH/IEPA) were less than one‐fourth the magnitude of values reported by ISWS. The median NO₃ concentrations measured by the Central Illinois Public Service (CIPS) were significantly greater than those measured by USGS and IDPH/IEPA in the three comparable sampling locations. The use of NO₃ concentrations measured by CIPS and IDPH/IEPA prior to 1972 is not recommended.     

AN EVALUATION OF THE AVAILABLE TECHNIQUES FOR ESTIMATING MISSING FECAL COLIFORM DATA1

ABSTRACT: This paper presents the findings of a study aimed at evaluating the available techniques for estimating missing fecal coliform (FC) data on a temporal basis. The techniques investigated include: linear and nonlinear regression analysis and interpolation functions, and the use of artificial neural networks (ANNs). In all, seven interpolation, two regression, and one ANN model structures were investigated. This paper also investigates the validity of a hypothesis that estimating missing FC data by developing different models using different data corresponding to different dynamics associated with different trends in the FC data may result in a better model performance. The FC data (counts/100 ml) derived from the North Fork of the Kentucky River in Kentucky were employed to calibrate and validate various models. The performance of various models was evaluated using a wide variety of standard statistical measures. The results obtained in this study are able to demonstrate that the ANNs can be preferred over the conventional techniques in estimating missing FC data in a watershed. The regression technique was not found suitable in estimating missing FC data on a temporal basis. Further, it has been found that it is possible to achieve a better model performance by first decomposing the whole data set into different categories corresponding to different dynamics and then developing separate models for separate categories rather than developing a single model for the composite data set.     

污染物排放相关指标计算方法的研究

针对传统的污染排放数据指标计算方法存在的问题,提出污染物排放指标应根据排污强度和经济指标确定的新思路,同时提出污染物排放指标数据的快速测算模型设计.建议通过环境监测数据或物料衡算数据确定每个排污单位的平均排污强度,作为计算机处理的参数,再由该排污单位申报每月的经济指标(产量或产值),输入计算机计算其产污量、排污量和去除量.实施计算机管理的关键是选取适当的数据计算模型.新思路应该能够规范计算程序,建立污染源动态数据库,对排污数据实行有效的统计分析.     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

Implications of different population model structures for management of threatened plants

Population viability analysis (PVA) is a reliable tool for ranking management options for a range of species despite parameter uncertainty. No one has yet investigated whether this holds true for model uncertainty for species with complex life histories and for responses to multiple threats. We tested whether a range of model structures yielded similar rankings of management and threat scenarios for 2 plant species with complex postfire responses. We examined 2 contrasting species from different plant functional types: an obligate seeding shrub and a facultative resprouting shrub. We exposed each to altered fire regimes and an additional, species‐specific threat. Long‐term demographic data sets were used to construct an individual‐based model (IBM), a complex stage‐based model, and a simple matrix model that subsumes all life stages into 2 or 3 stages. Agreement across models was good under some scenarios and poor under others. Results from the simple and complex matrix models were more similar to each other than to the IBM. Results were robust across models when dominant threats are considered but were less so for smaller effects. Robustness also broke down as the scenarios deviated from baseline conditions, likely the result of a number of factors related to the complexity of the species’ life history and how it was represented in a model. Although PVA can be an invaluable tool for integrating data and understanding species’ responses to threats and management strategies, this is best achieved in the context of decision support for adaptive management alongside multiple lines of evidence and expert critique of model construction and output.     

Connecting the Dots: Public Engagement with Environmental Data

Air pollution and other environmental hazards are often imperceptible and need to be made publicly visible. The paper argues for the importance of visualizations in drawing public attention to imperceptible hazards and in providing the public with access to empirical data describing the risks. It also argues for critical inquiry into hazards’ selective visibility as it is produced by visualizations. The impact of visualizations and their selective visibility are considered through the example of a public art project called Particle Falls installed in 2014 in Pittsburgh, a city with a long history of both ignoring air pollution and working to ameliorate this problem. I examine the impact and selective visibility of Particle Falls by considering the underlying production of data, as well as context and support systems for this visualization, and by comparing it with other visualizations of local air quality.     

Environmental cost of using poor decision metrics to prioritize environmental projects

Conservation decision makers commonly use project‐scoring metrics that are inconsistent with theory on optimal ranking of projects. As a result, there may often be a loss of environmental benefits. We estimated the magnitudes of these losses for various metrics that deviate from theory in ways that are common in practice. These metrics included cases where relevant variables were omitted from the benefits metric, project costs were omitted, and benefits were calculated using a faulty functional form. We estimated distributions of parameters from 129 environmental projects from Australia, New Zealand, and Italy for which detailed analyses had been completed previously. The cost of using poor prioritization metrics (in terms of lost environmental values) was often high—up to 80% in the scenarios we examined. The cost in percentage terms was greater when the budget was smaller. The most costly errors were omitting information about environmental values (up to 31% loss of environmental values), omitting project costs (up to 35% loss), omitting the effectiveness of management actions (up to 9% loss), and using a weighted‐additive decision metric for variables that should be multiplied (up to 23% loss). The latter 3 are errors that occur commonly in real‐world decision metrics, in combination often reducing potential benefits from conservation investments by 30–50%. Uncertainty about parameter values also reduced the benefits from investments in conservation projects but often not by as much as faulty prioritization metrics.