Relevance of Hydro-Climatic Change Projection and Monitoring for Assessment of Water Cycle Changes in the Arctic

Rapid changes to the Arctic hydrological cycle challenge both our process understanding and our ability to find appropriate adaptation strategies. We have investigated the relevance and accuracy development of climate change projections for assessment of water cycle changes in major Arctic drainage basins. Results show relatively good agreement of climate model projections with observed temperature changes, but high model inaccuracy relative to available observation data for precipitation changes. Direct observations further show systematically larger (smaller) runoff than precipitation increases (decreases). This result is partly attributable to uncertainties and systematic bias in precipitation observations, but still indicates that some of the observed increase in Arctic river runoff is due to water storage changes, for example melting permafrost and/or groundwater storage changes, within the drainage basins. Such causes of runoff change affect sea level, in addition to ocean salinity, and inland water resources, ecosystems, and infrastructure. Process-based hydrological modeling and observations, which can resolve changes in evapotranspiration, and groundwater and permafrost storage at and below river basin scales, are needed in order to accurately interpret and translate climate-driven precipitation changes to changes in freshwater cycling and runoff. In contrast to this need, our results show that the density of Arctic runoff monitoring has become increasingly biased and less relevant by decreasing most and being lowest in river basins with the largest expected climatic changes.     

Sensitivity of key factors and uncertainties in health risk assessment of benzene pollutant

Predicting long-term potential human health risks from contaminants in the multimedia environment requires the use of models. However,there is uncertainty associated with these predictions of many parameters which can be represented by ranges or probability distributions rather than single value.Based on a case study with information from an actual site contaminated with benzene,this study describes the application of MMSOILS model to predict health risk and distributions of those predictions generated using Monte Carlo techniques.A sensitivity analysis was performed to evaluate which of the random variables are most important in producing the predicted distributions of health risks.The sensitivity analysis shows that the predicted distributions can be accurately reproduced using a small subset of the random variables.The practical implication of this analysis is the ability to distinguish between important versus unimportant random variables in terms of their sensitivity to selected endpoints.This directly translates into a reduction in data collection and modeling effort.It was demonstrated that how correlation coefficient could be used to evaluate contributions to overall uncertainty from each parameter.The integrated uncertainty analysis shows that although drinking groundwater risk is similar with inhalation air risk,uncertainties of total risk come dominantly from drinking groundwater route.Most percent of the variance of total risk comes from four random variables.     

矿产资源人口承载力研究

本文建立了矿产资源人口承载力分析的指标体系与计算模型,并对现有资源和预测资源的人口承载力进行了定量计算和分析;确定了２０００年、２０１０年的矿产资源人口承载数量。     

Injury severity analysis of accidents involving young male drivers in Great Britain

Introduction

Young male drivers are over-represented in traffic accidents; they were involved in 14% of fatal accidents from 1991 to 2003 while holding only 8% of all drivers licenses in the UK. In this study, a subset of the UK national road accident data from 1991 to 2003 has been analyzed. The primary aim is to determine how to best use monetary and progressive resources to understand how road safety measures will reduce the severity of accidents involving young male drivers in both London and Great Britain.

Method

Ordered probit models were used to identify specific accident characteristics that increase the likelihood of one of three categorical outcomes of accident severity: slight, serious, or fatal.

Results

Characteristics found to lead to a higher likelihood of serious and fatal injuries are generally similar across Great Britain and London but are different from those predicted to lead to a higher likelihood of slight injuries. Those characteristics predicted to lead to serious and fatal injuries include driving in darkness, between Friday and Sunday, on roads with a speed limit of 60 mph, on single carriageways, overtaking, skidding, hitting an object off the carriageway, and when passing the site of a previous accident. Characteristics predicted to lead to slight injuries include driving in daylight, between Monday and Thursday, on roads with a speed limit of 30 mph or less, at a roundabout, waiting to move, and when an animal is on the carriageway.

Impact on Industry

These results aid the selection of policy options that are most likely to reduce the severity of accidents involving young male drivers.     

Conceptual PHES-system models of the Aysén watershed and fjord (Southern Chile): testing a brainstorming strategy

The use of brainstorming techniques for the generation of conceptual models, as the basis for the integrated management of physical-ecological-social systems (PHES-systems) is tested and discussed. The methodology is applied in the analysis of the Aysén fjord and watershed (Southern Chilean Coast). Results show that the proposed methods can be adequately used in management scenarios characterized by highly hierarchical, experts/non-experts membership.     

Audiences for Science Communication in the United States

ABSTRACT

The US National Science Foundation collects biennial data on views about science and technology (S&T). Analyses have typically focused on the degree to which demographics, knowledge, and attitudes predict S&T support or views about specific technologies or issues. Published efforts do not appear to have focused on identifying latent classes within the data. Initial analyses using latent profile analysis suggests that Americans can be categorized into six substantive audience groups when it comes to views about S&T. These groups vary by demographics, including scientific background and ideology, as well as in views about S&T. Post hoc analyses explore how “class-focused” analyses may provide additional insight beyond what can be obtained from “variable-focused” analyses.     

Scenario‐Based and Scenario‐Neutral Assessment of Climate Change Impacts on Operational Performance of a Multipurpose Reservoir

Scenario‐based and scenario‐neutral impacts assessment approaches provide complementary information about how climate change‐driven effects on streamflow may change the operational performance of multipurpose dams. Examining a case study of Cougar Dam in Oregon, United States, we simulated current reservoir operations under scenarios of plausible future hydrology. Streamflow projections from the CGCM3.1 general circulation model for the A1B emission scenario were used to generate stochastic reservoir inflows that were then further perturbed to simulate a potentially drier future. These were then used to drive a simple reservoir model. In the scenario‐based analysis, we found reservoir operations are vulnerable to climate change. Increases in fall and winter inflow could lead to more frequent flood storage, reducing flexibility to store incoming flood flows. Uncertainty in spring inflow volume complicates projection of future filling performance. The reservoir may fill more or less often, depending on whether springs are wetter or drier. In the summer, drawdown may occur earlier to meet conservation objectives. From the scenario‐neutral analysis, we identified thresholds of streamflow magnitude that can predict climate change impacts for a wide range of scenarios. Our results highlight projected operational challenges for Cougar Dam and provide an example of how scenario‐based and scenario‐neutral approaches may be applied concurrently to assess climate change impacts.     

Evaluation of various global solar radiation models for Nigeria

This study assessed the performance of six solar radiation models. The objective was to determine the most accurate model for estimating global solar radiation on a horizontal surface in Nigeria. Twenty-two years meteorological data sets collected from the Nigerian Meteorological agency and the National Aeronautics and Space Administration for the three regions, covering the entire climatic zones in Nigeria were utilized for calibrating and validating the selected models for Nigeria. The accuracy and applicability of various models were determined for three locations (Abuja, Benin City, and Sokoto), which spread across Nigeria using seven viable statistical indices. This study found that the estimation results of considered models are statistically significant at the 95% confidence level, but their accuracy varies from one location to another. However, the multivariable regression relationship deduced in terms of sunshine ratio, air temperature ratio, maximum air temperature, and cloudiness performs better than other relationships. The multivariable relationship has the least root mean square error and mean absolute bias error, not exceeding 1.0854 and 0.8160 MJ m^?2 day^?1, respectively, and monthly relative percentage error in the range of ± 12% for the study areas.     

Noise annoyance from industrial and road traffic combined noises: A survey and a total annoyance model comparison

The literature on noise annoyance emphasizes that acoustical factors cannot totally explain the annoyance felt by a population exposed to community noises, and that some non-acoustical factors have an effect on annoyance. This research paper deals with the annoyance caused by combined noises coming from road traffic and an industrial site. This type of combination has been very little studied to date. An in situ study was conducted in an area of a French town exposed to these two noise sources. To investigate the annoyance caused by these combined noises, the work involved both a mapping of the industrial and road traffic noises present in the survey area and a questionnaire designed to evaluate the noise annoyance experienced by residents and to identify the factors that probably influence noise annoyance. The results highlight the link between the noise levels measured and the annoyance felt by the respondents. They also show that certain non-acoustical factors have an impact on annoyance felt. Indeed, the results highlight a positive correlation between fear of industrial sites and the annoyance expressed. They also show correlations between some items to evaluate noise sensitivity and the annoyance expressed. No significant correlation has been found between annoyance and the other non-acoustical factors such as age, sex and length of residence. Finally, several total annoyance models were tested for this noise combination. Two of them, namely the strongest component model and a proposed perceptual version of the mixed model, were able to better predict total annoyance than the other tested models.     

Observations and Modeling of Stream Plunging in an Urban Lake1

Owens, Emmet M., Steven W. Effler, Anthony R. Prestigiacomo, David A. Matthews, and Susan M. O’Donnell, 2012. Observations and Modeling of Stream Plunging in an Urban Lake. Journal of the American Water Resources Association (JAWRA) 48(4): 707‐721. DOI: 10.1111/j.1752‐1688.2012.00646.x Abstract: The plunging behavior of two tributaries in Onondaga Lake, New York, is quantified based on a program of monitoring, process studies, and modeling. The dynamics of buoyancy of the tributaries are resolved with hourly measurements of temperature (T), specific conductance (SC), and turbidity (Tn) at the mouths, and observations every 6 h in the lake. Negative buoyancy of the tributaries is found to diminish and change rapidly during runoff events compared to dry periods. In‐lake patterns of the transport of plunging inflow are resolved for dry weather conditions using a dye tracer, and following a runoff event through measurements of T, SC, and Tn. The hydrodynamic/transport model ELCOM (Estuary Lake and Coastal Ocean Model) is demonstrated to perform well in simulating these patterns. Analyses conducted with the model establish the importance of diurnal effects and in‐lake mixing mediated by wind, the need for temporally detailed measurements during runoff events, and modifications of the plunging behavior of the urban tributary as it passes through a harbor. The model provides critical information to support rehabilitation programs for the lake by quantifying the transport of the two largest tributaries, particularly the distribution of the loads between the upper waters and stratified layers. The model predicts that 10% of the urban tributary load enters the upper waters of the lake within 24 h for a dry weather period; this portion increases to 30% for a runoff event.