ANALYSIS AND MODELING OF LONG-TERM STREAM TEMPERATURES ON THE STEAMBOAT CREEK BASIN,OREGON: IMPLICATIONS FOR LAND USE AND FISH HABITAT1

ABSTRACT: Steamboat Creek basin is an important source of timber and provides crucial spawning and rearing habitat for anadromous steelhead trout (Oncorhynchus mykiss). Because stream temperatures are near the upper limit of tolerance for the survival of juvenile steelhead, the possible long-term effect of clear-cut logging on stream temperatures was assessed. Twenty-year (1969–1989) records of summer stream temperature and flow from four tributaries and two reaches of Steamboat Creek and Boulder Creek (a nearby unlogged watershed) were analyzed. Logging records for the Steamboat Creek basin and air temperature records also were used in the analysis. A time-series model of the components of stream temperature (seasonal cycle of solar radiation, air temperature, streamflow, an autoregressive term of order 1, and a linear trend variable) was fitted to the water-temperature data. The linear trend variable was significant in all the fitted models except Bend Creek (a tributary fed by cool ground-water discharge) and Boulder Creek. Because no trends in either climate (i.e., air temperature) or streamflow were found in the data, the trend variable was associated with the pre-1969 loss and subsequent regrowth of riparian vegetation and shading canopies.     

MFAM模型在河流水质污染模拟及预测中的应用

文中以时间序列分析为基础，介绍了均值生成函数这一崭新概念，并且经成份因子提取分析推导建立了模拟序列的数字模型（简记为MFAM），经对黄河下游花园口断面的1988-1989年实测水质污染指标溶解氧（DO），氨氧，化学耗氧量（COD），五日生化需氧量（BOD5）等序列模拟，结果表明MFAM模型能较好地模拟河流水质污染指标的变化趋势，拟合平均误差只有5.2-6.4%，MFAM模型应用于预测1990-1991年水质污染指标变化，结果表明预测精度达85%以上，文中最后得出结论：MFAM模型应用于河流污染模拟和预测，是完全可行且十分方便。     

论减灾效益的显现类型与减灾心理

减灾效益的获得离不开民众的参与,而其参与意识又与减灾效益的可见度有关。减灾效益越能迅速、明确地被人们见到或预见到,人们的减灾参与意识越高。在客观现实中,减灾效益的显现时间具有各种类型,而人们主观意识中“见”到的类型却又可能有质的差异。本文划分了减灾效益的显现类型并提出与论述了与其有关的一些问题,分析了其与主观意识之间的复杂关系,以及影响人们的减灾参与心理的作用机制。进而探讨了如何通过这些关系、机制的认识与把握,效益意识引导,对减灾见效时间意识的人为控制等途径提高民众的减灾参与意识与减灾效益。     

BAYESIAN MODELS OF FORECASTED TIME SERIES1

Bayesian Processor of Forecasts (BPF) combines a prior distribution, which describes the natural uncertainty about the realization of a hydrologic process, with a likelihood function, which describes the uncertainty in categorical forecasts of that process, and outputs a posterior distribution of the process, conditional upon the forecasts. The posterior distribution provides a means of incorporating uncertain forecasts into optimal decision models. We present fundamentals of building BPF for time series. They include a general formulation, stochastic independence assumptions and their interpretation, computationally tractable models for forecasts of an independent process and a first-order Markov process, and parametric representations for normal-linear processes. An example is shown of an application to the annual time series of seasonal snowmelt runoff volume forecasts.     

FRACTIONAL DIFFERENCING MODELING IN HYDROLOGY1

ABSTRACT: Fractional differencing is a tool for modeling time series which have long-term dependence; i.e., series in which the correlation between distant observations, though small, is not negligible. Fractionally differenced ARIMA models are formed by permitting the differencing parameter d in the familiar Box-Jenkins ARIMA(p, d, q) models to take nonintegral values; they permit the simultaneous modeling of the long-term and short-term behavior of an observed time series. This paper discusses the usefulness of fractional differencing to time-series modeling, with emphasis on hydrologic applications. A methodology for fitting fractionally differenced ARIMA models is described, and examples are presented.     

AN EVALUATION OF WEEKLY AND MONTHLY TIME SERIES FORECASTS OF MUNICIPAL WATER USE1

ABSTRACT: A cascade model for forecasting municipal water use one week or one month ahead, conditioned on rainfall estimates, is presented and evaluated. The model comprises four components: long term trend, seasonal cycle, autocorrelation and correlation with rainfall. The increased forecast accuracy obtained by the addition of each component is evaluated. The City of Deerfield Beach, Florida, is used as the application example with the calibration period from 1976–1980 and the forecast period the drought year of 1981. Forecast accuracy is measured by the average absolute relative error (AARE, the average absolute value of the difference between actual and forecasted use, divided by the actual use). A benchmark forecast is calculated by assuming that water use for a given week or month in 1981 is the same as the average for the corresponding period from 1976 to 1980. This method produces an AARE of 14.6 percent for one step ahead forecasts of monthly data and 15.8 percent for weekly data. A cascade model using trend, seasonality and autocorrelation produces forecasts with AARE of about 12 percent for both monthly and weekly data while adding a linear relationship of water use and rainfall reduces the AARE to 8 percent in both cases if it is assumed that rainfall is known during the forecast period. Simple rainfall predictions do not increase the forecast accuracy for water use so the major utility of relating water use and rainfall lies in forecasting various possible water use sequences conditioned on sequences of historical rainfall data.     

SHORT-TERM FORECASTING OF SNOWMELT RUNOFF USING ARMAX MODELS1

ABSTRACT: Time series models of the ARMAX class were investigated for use in forecasting daily riverflow resulting from combined snowmelt/rainfall. The Snowmelt Runoff Model (Martinec-Rango Model) is shown to have a form similar to the ARMAX model. The advantage of the ARMAX approach is that analytical model identification and parameter estimation techniques are available. In addition, previous forecast errors can be included to improve forecasts and confidence limits can be estimated for the forecasts. Diagnostic checks are available to determine if the model is performing properly. Finally, Kalman filtering can be used to allow the model parameters to vary continuously to reflect changing basin runoff conditions. The above advantages result in improved flow forecasts with fewer model parameters.     

Utilization of cement kiln dust (CKD) in cement mortar and concrete—an overview

Solid waste management is one of the major environmental concerns around the world. Cement kiln dust (KKD), also known as by-pass dust, is a by-product of cement manufacturing. The environmental concerns related to Portland cement production, emission and disposal of CKD is becoming progressively significant. CKD is fine-grained, particulate material chiefly composed of oxidized, anhydrous, micron-sized particles collected from electrostatic precipitators during the high temperature production of clinker. Cement kiln dust so generated is partly reused in cement plant and landfilled. The beneficial uses of CKD are in highway uses, soil stabilization, use in cement mortar/concrete, CLSM, etc.Studies have shown that CKD could be used in making paste/mortar/concrete. This paper presents an overview of some of the research published on the use of CKD in cement paste/mortar/concrete. Effect of CKD on the cement paste/mortar/concrete properties like compressive strength, tensile strength properties (splitting tensile strength, flexural strength and toughness), durability (Freeze–thaw), hydration, setting time, sorptivity, electrical conductivity are presented. Use of CKD in making controlled low-strength materials (CLSM), asphalt concrete, as soil stabilizer, and leachate analysis are also discussed in this paper.     

紫外光谱结合正交试验设计同时测定水中苯系物

利用正交试验设计,以光谱预处理、特征筛选及多元校正方法为考察因素,每个因素的4种不同方法为水平,确定了水中3种苯系物（苯酚、苯胺及苯甲酸）紫外光谱数据的最佳分析方法,从而建立了其定量校正模型。对于苯酚、苯胺及苯甲酸,其光谱预处理、特征筛选及多元校正分别采用一阶导数+无信息变量消除法（UVE）+偏最小二乘回归（PLSR）,Savitzky-Golay平滑+变量结合种群分析法（VCPA）+PLSR,Savitzky-Golay平滑+移动窗口偏最小二乘法（MWPLS）+PLSR。在独立测试集上3组分的预测误差均方根（RMSEP）分别为0.809 4、0.796 3和0.945 4。水样加标回收实验的回收率为97.79%~103.84%,相对标准偏差（RSD）小于3%。该方法可作为一种同时测定水中苯系物的简便有效方法。     

基于证书权威(CA)中心的时间戳服务系统的实现

介绍了数字签名不具有抗抵赖的问题 ,以及数据抗抵赖在信息安全中的重要性。在对原有的时间戳协议缺乏可靠的身份认证和可信性分析后 ,提出了一种新的基于证书权威 (CA)中心的时间戳服务协议。利用CA中心的信任原理和数字证书的身份认证作用 ,使提供时间戳服务的服务方具备了可靠的身份鉴别和可信性。并利用XML标记对时间戳的数据内容进行描述 ,形成简单、直观的时间戳 ,且无需复杂的编解码过程。通过正确和可信的时间戳可以判定用户数据产生的时间 ,防止用户事后的抵赖行为 ,为网络应用提供更为安全的数据。