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.     

2016—2020年全国化工和危险化学品事故分析研究*

为分析“十三五”期间（2016—2020年）我国化工和危险化学品事故发生情况，统计“十三五”期间化工和危险化学品事故数量及死亡人数，从发生月份、发生区域、行业分布等方面分析事故特征，从事故类别、事故环节、关键作业等方面分析事故原因，总结2011—2020年我国化工和危险化学品事故发展趋势，按照全国4大经济区域研究2013—2020年事故特征。结果表明:“十三五”期间我国化工和危险化学品事故数量呈下降趋势，死亡人数出现波动，总体形势较为平稳。2011—2020年化工和危险化学品事故风险向中西部转移趋势明显，东北、西部地区事故死亡率普遍高于东部地区。研究结果可为相关行业从业者及监管部门提供决策参考。     

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.     

The effect of protected areas on forest disturbance in the Carpathian Mountains 1985–2010

Protected areas are a cornerstone for forest protection, but they are not always effective during times of socioeconomic and institutional crises. The Carpathian Mountains in Eastern Europe are an ecologically outstanding region, with widespread seminatural and old‐growth forest. Since 1990, Carpathian countries (Czech Republic, Hungary, Poland, Romania, Slovakia, and Ukraine) have experienced economic hardship and institutional changes, including the breakdown of socialism, European Union accession, and a rapid expansion of protected areas. The question is how protected‐area effectiveness has varied during these times across the Carpathians given these changes. We analyzed a satellite‐based data set of forest disturbance (i.e., forest loss due to harvesting or natural disturbances) from 1985 to 2010 and used matching statistics and a fixed‐effects estimator to quantify the effect of protection on forest disturbance. Protected areas in the Czech Republic, Slovakia, and the Ukraine had significantly less deforestation inside protected areas than outside in some periods; the likelihood of disturbance was reduced by 1–5%. The effectiveness of protection increased over time in these countries, whereas the opposite was true in Romania. Older protected areas were most effective in Romania and Hungary, but newer protected areas were more effective in Czech Republic, and Poland. Strict protection (International Union for Conservation of Nature [IUCN] protection category Ia‐II) was not more effective than landscape‐level protection (IUCN III‐VI). We suggest that the strength of institutions, the differences in forest privatization, forest management, prior distribution of protected areas, and when countries joined the European Union may provide explanations for the strikingly heterogeneous effectiveness patterns among countries. Our results highlight how different the effects of protected areas can be at broad scales, indicating that the effectiveness of protected areas is transitory over time and space and suggesting that generalizations about the effectiveness of protected areas can be misleading.     

Quantifying population declines based on presence‐only records for red‐list assessments

Accurate trend estimates are necessary for understanding which species are declining and which are most in need of conservation action. Imperfect species detection may result in unreliable trend estimates because this may lead to the overestimation of declines. Because many management decisions are based on population trend estimates, such biases could have severe consequences for conservation policy. We used an occupancy‐modeling framework to estimate detectability and calculate nationwide population trends for 14 Swiss amphibian species both accounting for and ignoring imperfect detection. Through the application of International Union for Conservation of Nature Red List criteria to the different trend estimates, we assessed whether ignoring imperfect detection could affect conservation policy. Imperfect detection occurred for all species and detection varied substantially among species, which led to the overestimation of population declines when detectability was ignored. Consequently, accounting for imperfect detection lowered the red‐list risk category for 5 of the 14 species assessed. We demonstrate that failing to consider species detectability can have serious consequences for species management and that occupancy modeling provides a flexible framework to account for observation bias and improve assessments of conservation status. A problem inherent to most historical records is that they contain presence‐only data from which only relative declines can be estimated. A move toward the routine recording of nonobservation and absence data is essential if conservation practitioners are to move beyond this toward accurate population trend estimation.     

The Role of Disaggregation of Asset Values in Flood Loss Estimation: A Comparison of Different Modeling Approaches at the Mulde River,Germany

In loss estimation there is a spatial mismatch of hazard data that are commonly modeled on an explicit raster level and exposure data that are often available only for aggregated administrative units. Usually disaggregation methods that use ancillary information to distribute lumped exposure data in a finer spatial resolution help to bridge this gap. However, the actual influence of different mapping techniques and ancillary data on the final loss estimation has not been analyzed yet. In this paper three methods are applied to disaggregate residential building assets using two kinds of land use/land cover (LULC) data. The resulting disaggregated assets are validated and compared using census data of the residential building number on the community and constituency level. In addition, the disaggregated assets are taken to estimate residential building losses due to the flood in August 2002 in 21 municipalities on the River Mulde in Saxony, Germany. Losses are calculated with the help of four loss models. In general, disaggregation helps to decrease the error variance within the loss estimation. It must, however, be stated that the application of sophisticated disaggregation methods does not lead to significant improvements compared to the straightforward binary method. Therefore more effort should instead be put into the provision of high-resolution LULC data. Finally, the remaining uncertainties in loss estimation are high and demand further improvements in all modeling aspects.     

Predicting vehicle fuel consumption patterns using floating vehicle data

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers'' information and vehicles'' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.     

CEMS比对监测数据偏差研究

简述CEMS比对监测技术背景,总结实际监测工作中导致数据偏差的各方面因素,分析问题产生原因并提出解决措施和建议。     

从经济学角度对比中国与美国的能源消费

分析了中关两国能源消费总量和能源结构的差异,探讨了中关两国能源消费差异的经济学内涵,认为经济指标GDP并不能完全反映人们生活水平的提高,而其带来的环境问题却很可能影响后续的经济发展。因此,在发展经济的同时,应该充分考虑能源需求给环境带来的压力。适当调整能源结构、提高能源效率和发展替代能源是未来能源消费战略的重中之重,也是经济、环境和社会协调发展的重要保障。     

基于遥感监测的黄海绿潮漂移路径及分布面积特征分析

本文利用2008～2012年的多源卫星遥感序列图像,动态监测各年绿潮发生的漂移路径、影响海域面积,得到各年绿潮漂移趋势、分布面积和最大分布面积出现时间。根据监测结果,划分出影响绿潮漂移路径的关键区域,并分析了2008、2009和2010年关键区域内各年绿潮漂移方向和面积变化情况。