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24Model的矿工怠工行为 管理模式架构及策略研究

为减少矿工怠工行为,基于24Model研究矿工怠工行为的管理模式架构及策略。首先,基于冰山模型理论分析矿工怠工行为的影响因素;其次,基于24Model和行为修正理论提出集怠工行为识别、行为原因分析、行为管理策略选择和行为修正评估于一体的矿工怠工行为管理模式架构;最后,提出了针对性的管理策略。对于提高煤矿企业矿工的工作绩效具有重要的理论意义和实践价值。     

井工煤矿建设工程对土地破坏的危险性评估

通过改变地表形态和降低土壤质量,井工煤矿生产对土地产生比较严重破坏.影响井工煤矿破坏土地的因素较多,针对所评估煤矿,煤层埋深与开采厚度、水文地质条件通常是主要因素,因此,选用土地利用类型、地表破坏程度和地面积水状况三个评估指标,采用单因素分析和叠加分析的方法进行土地破坏危险性评估.     

西部煤矿建设项目土地资源影响量化评价研究

以煤矿建设项目对土地资源影响的特点和规律为基础,满足该类项目环境影响评价要求为指导,以陕西省黄陵建庄矿区建北矿井及选煤厂新建项目为例,介绍西部煤矿建设项目土地资源影响量化评价的方法与思路,是西部煤矿项目土地资源影响评价深化的一种研究,有利于协调煤炭资源开发与土地资源保护问题.     

城市地下空间开发中的弃土管理和处置对策

城市地下空间的大规模开发利用将产生大量弃土。简要分析了弃土对环境的影响,介绍了弃土管理和处置的现状以及存在的问题,提出了加强立法和管理、优化施工方法和技术、对弃土进行资源化利用、妥善处置已污染的弃土等对策。     

喀斯特坡地地表径流系数监测初报

通过对喀斯特坡地6种不同土地利用条件下径流小区的地表径流和植被穿透雨量以及地下水出口水位变化进行野外定点连续观测研究,结果表明,6个径流小区地表径流系数均非常小,介于0.01%~12.81%。显然,喀斯特坡地的地表径流易于入渗转化为地下径流。受人为活动影响较大的径流小区,地表径流系数随降雨量的变化呈指数函数型变化特征,容易产生地表径流系数的突变式增长。     

长春市不同土地利用生境土壤昆虫幼虫群落组成与生态分布

对长春市郊区天然次生林、市内公园风景林、郊区防护林和农田等土地利用生境进行土壤昆虫幼虫调查,研究土地利用差异对土壤昆虫幼虫群落特征的影响。研究区共捕获土壤昆虫幼虫35科411只,其中鞘翅目幼虫14科319只,双翅目幼虫13科64只,鳞翅目幼虫8科28只。研究结果表明:不同土地利用生境土壤昆虫幼虫群落组成与生态分布存在差异,防护林和农田土壤昆虫幼虫的类群数和个体密度显著减少;除农田外,其他生境土壤昆虫幼虫群落个体密度和类群数主要集中分布在0～5cm土壤层,生境凋落物的移除对土壤昆虫幼虫群落组成影响不明显;此外,鞘翅目、双翅目和鳞翅目幼虫对土地利用和植被类型变化的响应也不同。     

农业生态与土地退化的土壤微形态研究

简要地介绍了土壤微形态研究方法及其在农业生态与土地退化研究中的应用,并回顾了近20a的国际土壤微形态研究的内容与进展.在过去的20a中,土壤微形态学在概念、现象的解释、分析技术'以及应用上都取得了前所未有的进展.特别在样品脱水方法、荧光分析、图象处理及定量分析技术上得到很大的发展.植物根系与土壤微结构的关系,土壤改良对结构的影响等方面研究取得一定的成就.利用土壤微形态研究农业生态系统中的根系与根际的生态过程,作物对水分和养分的吸收过程,人为活动对土壤退化,熟化的微形态指标,以及利用微形态指标评价人为因素在现代土壤过程中的作用等,是土壤微形态研究的最新动态.