Information Network Topologies for Enhanced Local Adaptive Management

We examined the principal effects of different information network topologies for local adaptive management of natural resources. We used computerized agents with adaptive decision algorithms with the following three fundamental constraints: (1) Complete understanding of the processes maintaining the natural resource can never be achieved, (2) agents can only learn by experimentation and information sharing, and (3) memory is limited. The agents were given the task to manage a system that had two states: one that provided high utility returns (desired) and one that provided low returns (undesired). In addition, the threshold between the states was close to the optimal return of the desired state. We found that networks of low to moderate link densities significantly increased the resilience of the utility returns. Networks of high link densities contributed to highly synchronized behavior among the agents, which caused occasional large-scale ecological crises between periods of stable and high utility returns. A constructed network involving a small set of experimenting agents was capable of combining high utility returns with high resilience, conforming to theories underlying the concept of adaptive comanagement. We conclude that (1) the ability to manage for resilience (i.e., to stay clear of the threshold leading to the undesired state as well as the ability to re-enter the desired state following a collapse) resides in the network structure and (2) in a coupled social–ecological system, the systemwide state transition occurs not because the ecological system flips into the undesired state, but because managers lose their capacity to reorganize back to the desired state. An erratum to this article can be found at .     

A multi-objective optimisation approach to water management

The management of river basins is complex especially when decisions about environmental flows are considered in addition to those concerning urban and agricultural water demand. The solution to these complex decision problems requires the use of mathematical techniques that are formulated to take into account conflicting objectives. Many optimization models exist for water management systems but there is a knowledge gap in linking bio-economic objectives with the optimum use of all water resources under conflicting demands. The efficient operation and management of a network of nodes comprising storages, canals, river reaches and irrigation districts under environmental flow constraints is challenging. Minimization of risks associated with agricultural production requires accounting for uncertainty involved with climate, environmental policy and markets. Markets and economic criteria determine what crops farmers would like to grow with subsequent effect on water resources and the environment. Due to conflicts between multiple goal requirements and the competing water demands of different sectors, a multi-criteria decision-making (MCDM) framework was developed to analyze production targets under physical, biological, economic and environmental constraints. This approach is described by analyzing the conflicts that may arise between profitability, variable costs of production and pumping of groundwater for a hypothetical irrigation area.     

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.     

基于GIS的上海市嘉定区暴雨积涝灾害风险区划研究

根据2006-2012年上海嘉定区9个气象站点的小时降水资料,结合嘉定区的社会经济与自然地理要素,构建一个集致灾因子、孕灾环境、承灾体及防灾减灾能力为一体的区域暴雨积涝风险评价模型。通过GIS技术实现各指标的栅格化,并利用指数模型对各因子基于500 m×500 m栅格的基本单元进行计算,编制嘉定区的暴雨积涝灾害风险区划图,构成嘉定区五个等级的风险,分别为高、次高、中等、次低和低。区划结果表明嘉定区的暴雨积涝灾害危险性指数从中心向外围成递减的趋势,且嘉定镇、新城和工业南区相对风险较高,华亭、徐行、南航和江桥风险相对较低。经嘉定区历年暴雨积涝灾情资料、典型暴雨积涝案例和专家的验证,均表明风险区划的结果和实际灾情符合度较高,对嘉定暴雨积涝的防灾减灾具有重要的现实指导意义。     

区域洪水灾害风险评估体系(Ⅰ)——原理与方法

区域洪水灾害风险评估是一个新的研究领域。从洪水灾害系统的概念出发,首先阐述了洪水灾害风险管理系统、洪水风险评估及风险类型划分;然后从指标选取原则、指标层次结构、指标量化、风险等级、风险指数等方面构建了洪水灾害风险评估指标体系;接着分析了矢量面状及栅格点状的洪水灾害风险评估单元;最后,介绍了适合区域洪水灾害风险评估的层次分析法、模糊综合评价法及空间分析方法。     

矿渣与工业废渣改良黄土的性能与机理研究进展

黄土在世界上分布面积较广,在我国也有约60×104 km2的分布,具有湿陷性是其区别于其它区域性土的最显著特点,并由此引发造成各种地质灾害和工程问题.采用石灰、水泥或高分子材料等对其进行改性以达到消除湿陷性或提高黄土强度的目的,是岩土工程领域的重要课题.选用矿渣和工业废渣作为改性参料,用以研究改性黄土的力学特性变化和改...     

环境污染事故风险预测评估模式研究

从环境风险到环境污染事故演变的一般规律入手,提出了环境污染事故风险预测评估模式,为环境污染事故风险评估、预防与应急响应提供科学手段。环境污染事故风险预测评估模式的提出,一方面丰富和发展了环境安全与应急响应理论;另一方面,对公共安全、企业安全管理与应急响应等工作具有一定指导意义。     

厦门湾海域表层沉积物重金属和多环芳烃污染特征及生态风险评价

研究了厦门湾海域表层沉积物中Cu,Pb,Zn,Cr,Cd,Hg和As和16种多环芳烃的分布、来源及潜在的生态风险.结果表明:(1)Cu,Pb和As平均含量均介于效应浓度区间低值(ERL)和效应浓度区间中值(ERM)之间,而大多数站位Zn,Cr,Cd和Hg平均含量则低于ERL值;(2)沉积物多环芳烃(PAHs)以石油或油料燃烧来源为主,PAHs的总量远低于ERL值(4000μg·kg~(-1));(3)重金属与低环PAHs相关性显著,易发生交互作用;(4)利用沉积物平均效应区间中值商法(mERM-Q)进行沉积物生态风险评价发现,筼笃湖表层沉积物对海洋底栖生物可能存在较强的生物毒性风险.     

加油站地下储油罐压力管理系统的相关研究

在当前普遍采用的加油站真空辅助式第二阶段油气回收系统中,为了避免回收到地下储油罐中的油气再次通过P/V阀排空,必须对其中气相空间的压力实施有效控制。从国外的发展情况来看,地下储油罐的压力管理系统主要分为基于膜分离技术、基于弹性气囊和基于活性炭吸附三大类,其中膜法油气回收装置在国内已经得到了较多关注。首次系统介绍了气囊式压力管理系统的工作原理及其主要结构设计,具体包括管线布局、钢制压力容器壳体、位于容器顶部封头上的接口组件和全部位于容器内的气囊等;同时对另外一种可行方案进行了分析。     

基于信息熵理论的泥石流沟谷危险度评价

泥石流沟谷是一个复杂的地貌系统,有其发生、发展和衰退的过程.文章从系统科学和地貌学的角度出发,运用了能综合表述地貌演化特征的因子-地貌系统信息熵对泥石流沟谷进行危险度评价,研究以昆明市东川城区后山深沟、石羊沟和尼姑拉沟为试点,对各泥石流沟的信息熵进行了计算.结果表明,深沟和石羊沟的地貌信息熵值分别为0.124和0.174,泥石流沟谷正由发育的中年末期向老年期演化;尼古拉沟的信息熵值为0.246,正处于沟谷发育的旺盛时期,计算结果与当地实际情况有较好的一致性.