VELOCITY EQUATION FOR WATER QUALITY MODELING IN GEORGIA1

ABSTRACT: Tabletop water quality modeling still plays an important role in the water pollution control activities of the Georgia Environmental Protection Division. Tabletop models are those developed with out the aid of extensive field data. One important component of GEORGIA DOSAG, our basic water quality model, is the equation used to predict flow through velocity. However, Georgia is characterized by wide physiographic diversity which reduces the effectiveness of uncalibrated velocity equations. Using 15 years of accumulated time-of-travel studies, a series of empirical velocity equations were developed and calibrated to various physiographic conditions in Georgia. Equations are available for each major soil province and for three stream flow ranges within each province - Q<100 cfs, 100<Q<1000 cfs, and Q>1000 cfs. Now, in the absence of extensive field data, we have data based velocity equations which can be tailored to each site under study.     

ESTIMATION OF PRE- AND POST-DEVELOPMENT NONPOINT WATER QUALITY LOADINGS1

ABSTRACT: A simple procedure for estimating pre- and post-development water quality loadings from residential communities is discussed. The procedure deals with: (a) gathering basic water quality loading numbers observed by others at several watersheds with various land uses; (b) obtaining the breakdown of proposed land uses at various phases of the community development; and (c) estimating pre- and post-development water quality loading numbers by taking the weighted average of the basic loading numbers in terms of areal coverages of different land uses at various phases of development. Results of this simplified procedure have been verified indirectly by comparing them with the estimates derived independently through a more fundamental but time-consuming approach. The procedure was used to evaluate the anticipated water quality impact of two future residential communities in South Florida by analyzing four water quality parameters: Suspended Soils (SS), Total Nitrogen (TN), Total Phosphorus (TP), and Biochemical Oxygen Demand (BOD₅). Although computation of loading numbers with mixed land uses is not an exact science at the present time, the recommended approach appears to be the best available technique to analyze quantitatively the water quality-quantity-land use interactions.     

PROBLEMS IN FORECASTING WATER REQUIREMENTS1

ABSTRACT. Methodological problems associated with forecasting water requirements by use of regression analysis are examined. Problems occurring when long-range forecasts are based on linear and nonlinear extrapolation of time series models include possible changes in socioeconomic conditions, water allocation system structure, and limits to growth. Problems arising in forecasting based on multiple regression models are likely to involve serially dependent errors, multicollinear explanatory variables, and difficulties inherent to the presence of explanatory variables that must themselves be predicted.     

An Evaluation of Daumod Model in Estimating Urban Background Concentrations

This article presents an evaluation of the performance of the urban atmospheric dispersion model (DAUMOD) in estimating nitrogen oxides (NO_x) background concentrations in Copenhagen. Estimations of hourly average (averaged over a year), mean daily and mean monthly concentrations of NO_x are compared with observed values for two years of data. The model slightly underestimates low hourly average values and overestimates high values. The cumulative frequency distribution of mean daily concentration obtained from model estimations is in good agreement with the obtained from observed data. We performed a statistical analysis to determine the agreement between estimated and observed concentration values. The results show that 95.8% of hourly average estimations, 86.8% of mean daily and 100% of monthly average concentrations are within a factor of two of the observed values. The normalised mean square error of predictions is +0.13 for hourly average estimations, +0.22 for mean daily values and +0.02 for monthly mean concentrations. The fractional bias values are: –0.049 for hourly mean estimations, –0.047 for mean daily values and –0.053 for monthly average estimations. The values of the statistical parameters allow us to consider that though estimations are lightly larger than the observed values, the model performance is acceptable.     

长埔——西岭锡矿资源定量评价模型

本文以长埔——西岭锡矿带的76个矿床为基础从中选取17个已知成型矿床,分别用矢量长度法和最优分割法进行地质变量的优化选择与储量等级规模的划分;采用特征分析与逻辑信息法建立锡矿资源半定量和定量评价广义模型;并对该区59个矿化点进行含矿性及可能矿床规模的定量预测评价,筛选出6个找矿靶区。     

Preventing the Spread of Invasive Species: Economic Benefits of Intervention Guided by Ecological Predictions

Abstract:  Preventing the invasion of freshwater aquatic species is the surest way to reduce their impacts, but it is also often expensive. Hence, the most efficient prevention programs will rely on accurate predictions of sites most at risk of becoming invaded and concentrate resources at those sites. Using data from Vilas County, Wisconsin (U.S.A.), collected in the 1970s, we constructed a predictive occurrence model for rusty crayfish ( Orconectes rusticus ) and applied it to an independent data set of 48 Vilas County lakes to predict which of these were most likely to become invaded between 1975 and 2005. We nested this invasion model within an economic framework to determine whether targeted management, derived from our quantitative predictions of likely invasion sites, would increase the economic value of lakes in the independent data set. Although the optimum expenditure on lake protection was high, protecting lakes at this level would have produced net economic benefits of at least $6 million over the last 30 years. We did not attempt to determine the value of nonmarket benefits of protection; thus, our results are likely to underestimate the total benefits from preventing invasions. Our results demonstrate that although few data are available early in an invasion, these data may be sufficient to support targeted, effective, and economically rational management. In addition, our results show that ecological predictions are becoming sufficiently accurate that their application in management can produce net economic benefits.     

PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS1

ABSTRACT: The cascade correlation neural network was used to predict the two-year peak discharge (Q₂) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q₂ by regression. Our research shows Q₂ was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds.     

AERMOD与估算模式在环境影响评价中的应用与比较研究

简单描述了大气环境影响评价中的AERMOD模型及估算模式,采用这两种预测模式对同一生活垃圾填埋场无组织排放的NH3、H2S进行了预测。比较两种模式的预测结果可以看出,估算模式仅能预测小时值,AERMOD模式可同时预测小时值、日平均值、年平均值。在污染源、预测范围、运行周期相同的情况下,不论哪种预测模式,污染物的最大落地浓度均与排放源强呈正比,敏感点处的落地浓度均与污染物源强呈正比、与距离呈反比。预测结果同时说明,估算模式是一种保守的预测模式,其预测结果比AERMOD模式大,可作为进一步预测模式的有效补充。     

A Probabilistic,Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems

This study contributes a bathtub‐style inundation prediction model with abstractions of coastal processes (i.e., storm surge and wave runup) for flood forecasting at medium‐range (weekly to monthly) timescales along the coastline of large lakes. Uncertainty from multiple data sources are propagated through the model to establish probabilistic bounds of inundation, providing a conservative measure of risk. The model is developed in a case study of the New York Lake Ontario shoreline, which has experienced two record‐setting floods over the course of three years (2017–2019). Predictions are developed at a parcel‐level and are validated using inundation accounts from an online survey and flyover imagery taken during the recent flood events. Model predictions are compared against a baseline, deterministic model that accounts for the same processes but does not propagate forward data uncertainties. Results suggest that a probabilistic approach helps capture observed instances of inundation that would otherwise be missed by a deterministic inundation model. However, downward biases are still present in probabilistic predictions, especially for parcels impacted by wave runup. The goal of the tool is to provide community planners and property owners with a conservative, parcel‐level assessment of flood risk to help inform short‐term emergency response and better prepare for future flood events.     

USE OF CLIMATE PREDICTIONS TO DECIDE A WATER MANAGEMENT PROBLEM1

ABSTRACT: Seasonal precipitation predictions were utilized in a water management decision with major economic, societal, and political ramifications. A summer (1984) drought had created a situation calling for possible fall season use of state waters from two major multipurpose reservoirs with an ensuing effect on water price negotiations. Choices facing management and use of water from the reservoirs were to invoke expensive water restrictions with a 33 percent chance of being right, do nothing (66 percent chance of wrong outcome), or use the precipitation predictors (for above normal fall rain) having a 50 percent chance of error. Hydrologists chose to follow the precipitation predictions, which proved to be accurate for the fall of 1984, helping to reveal the long-term value of using well understood climate predictions in water management.