DETERMINATION OF SEDIMENT YIELD BY TRANSFERRING RAINFALL DATA1

ABSTRACT: A methodology for obtaining the optimal design value to allow for sediment storage in a reservoir is presented for the situation where no data on sediment loads in the incoming streams are available. Information concerning the amount of sediment delivered to the reservoir over its life-time is obtained by a sediment yield model which uses data on rainfall amount and duration obtained from a nearby experimental watershed. Bayesian Decision Theory is used to obtain the optimal storage requirements in order to consider the natural variation of rainfall and the sampling error due to the short rainfall record available. The normally difficult calculations involved were made tractable by the use of simplifications and approximations valid in the context of the problem. Results show that sediment storage requirements can be calculated in this manner and that consideration of the uncertainties involved leads to a storage requirement substantially larger than that calculated without such consideration.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

气相分子吸收光谱法测定氨氮校准曲线斜率、截距参考值研究

主要阐述了气相分子吸收光谱法测定氨氮的校准曲线斜率、截距参考值的分析.文章收集了临安市环境监测站实验室自2012年以来气相分子吸收光谱法氨氮校准曲线制作的一系列原始数据,并通过数理统计方法对校准曲线、残余标准偏差、斜率扩展不确定度、截距扩展不确定度开展了详细讨论,得出气相分子吸收光谱法氨氮校准曲线截距a值、斜率b值实验室参考值.此结果对实验室分析人员判定氨氮项目校准曲线制作是否合格具有一定的参考价值.     

关于重量法测定硫酸盐化速率的不确定度分析

碳酸钾溶液浸渍过的玻璃纤维滤膜曝露于空气中,与空气中的二氧化硫、硫酸雾、硫化氢等发生反应,生成硫酸盐。测定生成的硫酸盐含量,计算硫酸盐化速率。不确定度的来源主要包括:样品重复性分析、电子天平称量等。评定结果为硫酸盐化速率的不确定度(2.90±0.06)(SO3),mg/(100cm2碱片·d),k=2。     

随机不确定性影响下某航炮发射动力学仿真

目的研究随机不确定性对航炮发射动力学响应的影响,提高其射击密集度。方法建立某航炮刚柔耦合动力学模型,利用ADAMS软件进行发射动力学仿真分析。考虑航炮射击过程中的不确定因素,采用非概率凸集模型进行量化,揭示缓冲器弹簧刚度、平衡机弹簧刚度、炮膛合力等参数不确定性对航炮射击密集度的影响规律。结果炮口振动在不确定性因素的影响下,出现振动幅值增大,在区间范围内波动的现象。结论航炮射击时的炮口振动严重影响了射击密集度,因此在设计时必须考虑不确定性因素,所得结果对于提高航炮系统射击密集度具有一定的指导意义。     

A Recourse‐Based Interval Fuzzy Programming Model for Point‐Nonpoint Source Effluent Trading under Uncertainty

In this study, a recourse‐based interval fuzzy programming (RIFP) model is developed for tackling uncertainties expressed as fuzzy, interval, and/or probabilistic forms in an effluent trading program. It can incorporate preregulated water‐pollution control policies directly into its optimization process, such that an effective linkage between environmental regulations and economic implications (i.e., penalties) caused by improper policies due to uncertainty existence can be provided. The RIFP model is applied to point‐nonpoint source effluent trading of the Xiangxi River in China. The efficiency of trading efforts between water quality improvement and net system benefit under different degrees of satisfying discharge limits is analyzed. The results are able to help support (1) formulation of water‐pollution control strategies under various economic objectives and system‐reliability constraints, (2) selection of the desired effluent trading pattern for point and nonpoint sources, and (3) generation of tradeoffs among system benefit, satisfaction degree, and pollutant mitigation under multiple uncertainties. Compared with the traditional regulatory approaches, the results demonstrate that the water‐pollution control program can be performed more cost‐effectively through trading than nontrading.     

Effects of Land Use and Climate Change on Stream Temperature I: Daily Flow and Stream Temperature Projections

Freshwater mussels (order Unionida) are a highly imperiled group of organisms that are at risk from rising stream temperatures (T). There is a need to understand the potential effects of land use (LU) and climate change (CC) on stream T and have a measure of uncertainty. We used available downscaled climate projections and LU change simulations to simulate the potential effects on average daily stream T from 2020 to 2060. Monte Carlo simulations were run, and a novel technique to analyze results was used to assess changes in hydrologic and stream T response. Simulations of daily mean T were used as input to our stochastic hourly T model. CC effects were on average two orders of magnitude greater than LU impacts on mean daily stream T. LU change affected stream T primarily in headwater streams, on average up to 2.1°C over short durations, and projected CC affected stream T, on average 2.1‐3.3°C by 2060. Daily mean flow and T ratios from Monte Carlo simulations indicated greater variance in the response of streamflow (up to 55%) to LU change than in the response of stream T (up to 9%), and greater variance in headwater stream segments compared to higher order stream segments for both streamflow and T response. Simulations indicated that combined effects of climate and LU change were not additive, suggesting a complex interaction and that forecasting long‐term stream T response requires simulating CC and LU change simultaneously.     

An Adaptive‐Management Framework for Optimal Control of Hiking Near Golden Eagle Nests in Denali National Park

Abstract: Unintended effects of recreational activities in protected areas are of growing concern. We used an adaptive‐management framework to develop guidelines for optimally managing hiking activities to maintain desired levels of territory occupancy and reproductive success of Golden Eagles (Aquila chrysaetos) in Denali National Park (Alaska, U.S.A.). The management decision was to restrict human access (hikers) to particular nesting territories to reduce disturbance. The management objective was to minimize restrictions on hikers while maintaining reproductive performance of eagles above some specified level. We based our decision analysis on predictive models of site occupancy of eagles developed using a combination of expert opinion and data collected from 93 eagle territories over 20 years. The best predictive model showed that restricting human access to eagle territories had little effect on occupancy dynamics. However, when considering important sources of uncertainty in the models, including environmental stochasticity, imperfect detection of hares on which eagles prey, and model uncertainty, restricting access of territories to hikers improved eagle reproduction substantially. An adaptive management framework such as ours may help reduce uncertainty of the effects of hiking activities on Golden Eagles.     

基于水质模拟的不确定条件下两阶段随机水资源规划模型

针对流域内不同企业的水资源分配及企业生产污染排放导致的水环境问题,运用区间两阶段随机规划的方法,耦合区间两阶段模型（ITSP）和区间水质模型（IS-P）,建立不确定两阶段随机水质-水量耦合规划模型（ITSP-SP）.该模型以流域内系统利益最大为目标函数,模拟了流域内各个企业的水量分配及排污过程中河道水质变化,并在保证河流水质达标前提下优化预计分配水量,调整企业生产规模.通过模型运算得到区间解,为管理者提供了多样的决策方案.并且,该模型充分考虑不确定因素对系统利益的影响,能够有效的规避系统决策失误及方案缺失现象.