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.     

EFFECTS OF NO-FLOW RIVER CONDITIONS ON THE PLATTE RIVER WELL FIELD1

ABSTRACT: Effects of no-flow river conditions on the quantity and quality of water in the Platte River well field of the City of Grand Island, Nebraska, were examined utilizing a finite-difference computer simulation model specifically developed for this well field. Results suggest that the effects of these no-flow periods on water quality may be most important. In particular, the no-flow periods eliminate the hydraulic barrier between the well field and an area north of the River that is contaminated with nitrate (concentrations in the 20 to 40 mg/1 NO₃-N range). They also change the direction and velocity of movement of the contaminated ground water. Simulation results indicate that contaminated ground water moves toward the well field with a velocity of 0.42 ft/d after 30 days of no-flow and 1.43 ft/d after 180 days of no-flow. Limiting no-flow conditions to 10 consecutive days would protect the well field.     

绵阳市大气降水pH值时空分布及酸雨成因研究

通过对绵阳市9个测点1998－1999年大气降水监测数据的统计分析，并结合历史数据，对大气降水pH值的时空分布及酸雨成因进行了研究。结果表明：绵阳市大气降水酸化严重，除主要受本地区大气污染影响外，还受大气中、长距离输送的影响。     

快速断电安全技术中半导体中性点开关结构性能的研究

对我国目前半导体中性点开关的结构 ,工作过程 ,性能进行了分析 ;现行中性点开关所存在的问题为电路拓扑结构不合理 ,关断时间过长或过短 ,过长不满足快速断电安全技术的要求 ,过短又易引起过电压 ,使中性点开关失控。笔者提出了采用绝缘门极双极性晶体管 (IGBT) ,构成半导体中性点开关的可行方案。同时指出 ,开发新型快速断电安全技术装置 ,将大大提高我国煤矿安全技术水平     

高层建筑火灾人员疏散和人员伤亡的模拟

基于建筑火灾全风网网络模型的基础上 ,结合紧急疏散模型 ,模拟火灾时期疏散时间和建筑物内人员滞留的情况 ,同时也可以模拟可能出现的伤亡情况 ,有助于高层建筑的疏散系统设计 ,为火灾的再现提供了一个有效的分析工具 ,也为高层建筑火灾的风险评价提供了理论数据     

ERODIBILITY OF URBAN BEDROCK AND ALLUVIAL CHANNELS,NORTH TEXAS1

ABSTRACT: Major erosion of urban stream channels is found in smaller basins in the North Texas study area with contributing drainage areas of less than ten square miles. Within these basins, four basic channel types are identified based on bed and bank lithologies: alluvial banks and bottoms, alluvial banks and gravel bottoms, alluvial banks with rock bottoms, and rock banks with rock bottoms. Most channels (75 percent) have alluvial banks with gravel or rock bottoms. Channel slopes are steep (.38 to.76 percent). Rock consists predominantly of shale and limestone. Channel cross sections are divided into the following four zones based on weathering, scour and entrainment mechanisms: soil zone, slake zone, rock zone and bed material zone. Erodibility of the channels is determined using multiple techniques including reach hydraulics and stream power computations, submerged jet testing, slab entrainment thresholds, and slake durability rates. Procedures are based on both empirical and modeled time series estimates of channel erosion. Field and modeled results support rates of erosion of up to four inches per year. Rates are tied to flow regime, climate, and type of channel bed and banks.