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.     

2004.7.20云南盈江滑坡泥石流山洪灾害成因及减灾对策

云南省德宏州盈江县位于横断山区西南部.2004年7月18日～19日,该县北部的的支那、盏西、芒璋等地遭受特大暴雨袭击,引起7.20滑坡泥石流山洪灾害,造成59人死亡失踪和15 857万元财产损失.60年一遇、19小时186.2mm的特大暴雨,是该次滑坡泥石流山洪灾害形成的根本原因.针对灾害的特征、现状和发展趋势,提出了拟采取的减灾措施.     

基于GIS的长江中下游地区洪灾风险分区及评价

国内外近几年的发展表明,在所有可能避免和减轻自然灾害的措施中,最简单有效的方法就是通过在科学研究基础上进行风险区划,将自然灾害管理提高到风险管理的水平.在长江流域数字化地图的基础上,选取不同重复期(20,50,100年),及包括1870年历史洪水和1931,1935,1954,1991,1995,1996,1998,1999和2002年共10次洪水,借助Arcview地理信息系统的空间分析和叠加功能,对长江中下游地区的洪水灾害危险性进行了初步评价.首先参考洪水灾害淹没图和相关历史文献记录资料,构建10次洪水受灾县(市)分布图;其次对这10次洪水受灾县(市)分布图进行叠加,得到长江中下游地区洪涝灾害风险性评价图.分析表明:长江中下游地区洪水风险的分布是有规律的,而且具有明显的地理意义.有4个明显的高危风险区,分别是洞庭湖、鄱阳湖两湖平原的湖滨地区和公安以下的长江中游河段的沿江一带,尤其是荆江河段以及两江相夹地势低洼的江汉平原;沿高危风险区外侧为高风险地区,重点在汉江下游、资、沅、澧水、清江流域、皖沿江地区以及太湖流域的部分地区;沿长江于高危风险和高风险地区两侧分别为风险较小地区;其他地区对于洪水灾害而言则为安全地区.评价结果与长江中下游的实际情况基本吻合.     

洪涝灾害评价的威布尔模型

首次将威布尔分布用于淮河流域水灾成灾面积研究,揭示了淮河流域洪涝灾害成灾面积形成的 内在规律,进而利用河南、安徽、江苏和山东4省的各自灾度对淮河流域的灾害风险建立了线性回归模型.实证分析表明,本方法切实可行,特别适用于大样本计算.     

中国水灾社会脆弱性评估方法的改进与应用--以长沙地区为例

中国是洪水灾害发生频繁的国家之一,研究水灾脆弱性对中国的灾害风险管理有重要的意义.但是,合理评估脆弱性尤其是社会脆弱性却面临着极大的挑战.论文对评估社会脆弱性指数的研究现状进行了分析,指出了传统评估方法存在的问题,并尝试改进Hoovering评估模式.选择湘江流域的长沙地区为研究区,应用改进模型对研究区进行了社会脆弱性指数的评估.结果表明,长沙地区社会脆弱性从1980年至2000年基本处于下降趋势,但是在2002年和2003年增长迅速.2003年,长沙5区4县中社会脆弱性指数最大的是开福区,其次是长沙县,而宁乡县的社会脆弱性指数最低.     

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.     

Public Perception of Flood Hazard in the Niger Delta,Nigeria

Summary Our study had the aim of understanding how floodplain dwellers regard the risk of flooding. About 500 questionnaires were administered to landowners in the selected settlements in the study area using systematic random sampling. The results of analysis show, among other things, that the population regards most important the causes of floods as heavy, prolonged rainfall and river overflow. Nevertheless, they have little knowledge of the frequency of severe floods, and flood alleviation schemes. Most flood victims do not get compensation or relief during flood disaster, and the reason why they remain in the study area is influenced by their occupations, especially fishing, subsistence agriculture, and the presence of crude oil in the region which has attracted many migrants who cannot afford the high cost of accommodation and are therefore forced to live in vulnerable areas of the floodplain. Finally, the study concludes that flood control in the region needs the cooperation of government, community efforts and an enlightenment programmes through environmental education and mass media.     

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.     

GEOMORPHOLOGY OF STEEPLAND HEADWATERS: THE TRANSITION FROM HILLSLOPES TO CHANNELS1

Headwater streams comprise 60 to 80 percent of the cumulative length of river networks. In hilly to mountainous terrain, they reflect a mix of hillslope and channel processes because of their close proximity to sediment source areas. Their morphology is an assemblage of residual soils, landslide deposits, wood, boulders, thin patches of poorly sorted alluvium, and stretches of bedrock. Longitudinal profiles of these channels are strongly influenced by steps created by sediment deposits, large wood, and boulders. Due to the combination of small drainage area, stepped shallow gradient, large roughness elements, and cohesive sediments, headwater streams typically transport little sediment or coarse wood debris by fluvial processes. Consequently, headwaters act as sediment reservoirs for periods spanning decades to centuries. The accumulated sediment and wood may be episodically evacuated by debris flows, debris floods, or gully erosion and transported to larger channels. In mountain environments, these processes deliver significant amounts of materials that form riverine habitats in larger channels. In managed steepland forests, accelerated rates of landslides and debris flows resulting from the harvest of headwater forests have the potential to seriously impact the morphology of headwater streams and downstream resources.     

“We cannot let this happen again”: reversing UK flood policy in response to the Somerset Levels floods, 2014

The Multiple Streams Framework offers a theoretical account of how policy proposals move from latent possibilities to becoming favored for implementation. We apply this framework in the context of the policy response to the 2013–2014 flooding of the Somerset Levels and Moors. Stakeholder interviews and analysis of news media coverage evidence the way in which a specific policy option that had fallen out of favor with the national Environment Agency – dredging – came to the fore and was eventually adopted during the period in which the conjunction of problem, policy, and political pressures came to a head. Local political activists mobilized a wider campaign with the help of social media and capitalized on national political sensitivities to successfully promote dredging. What is less clear is the longevity of the policy reversal, given funding constraints.