三级串联人工快渗系统处理养殖废水

人工快速渗滤系统（constructed rapid infiltration,CRI）是在传统的污水快速渗透系统上发展起来的一种新的生物处理方法。采用猪粪浸泡污水模拟实际猪场处理系统的厌氧出水,研究三级串联人工快渗系统对其污染物的去除效果。试验结果表明,三级串联系统对废水COD、NH3N的去除率稳定在81%和94.5%,出水均满足了《畜禽养殖行业污染物排放标准》（GB18596-2001）的要求,同时三级串联系统还可以有效预防系统的堵塞。     

INCORPORATING NATURAL VARIABILITY,UNCERTAINTY, AND RISK INTO WATER QUALITY EVALUATIONS USING DURATION CURVES1

ABSTRACT: Quantifying natural variability, uncertainty, and risk with minimal data is one of the greatest challenges facing those engaged in water quality evaluations, such as development of total maximum daily loads (TMDL), because of regulatory, natural, and analytical constraints. Quantification of uncertainty and variability in natural systems is illustrated using duration curves (DCs), plots that illustrate the percent of time that a particular flow rate (FDC), concentration (CDC), or load rate (LDC; “TMDL”) is exceeded, and are constructed using simple derived distributions. Duration curves require different construction methods and interpretations, depending on whether there is a statistically significant correlation between concentration (C) and flow (Q), and on the sign of the C‐Q regression slope (positive or negative). Flow DCs computed from annual runoff data vary compared with an FDC developed using all data. Percent exceedance for DCs can correspond to risk; however, DCs are not composed of independent quantities. Confidence intervals of data about a regression line can be used to develop confidence limits for the CDC and LDC. An alternate expression to a fixed TMDL is suggested as the risk of a load rate being exceeded and lying between confidence limits. Averages over partial ranges of DCs are also suggested as an alternative expression of TMDLs. DCs can be used to quantify watershed response in terms of changes in exceedances, concentrations, and load rates after implementation of best management practices.     

Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande1

Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.x Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.     

THE ARMA MODELS APPLIED TO THE FLOW OF KARSTIC SPRINGS1

ABSTRACT: Autoregressive moving average (ABMA) models have been applied to study the flow series of the karstic springs of La Villa, Fuente Mayor (Spain), and Aliou (France). The theoretical meaning of the parameters involved in the model upon applying it to a simplified scheme of the emptying of a karstic aquifer is first analyzed. The types of transformations necessary to apply these models to the flow series that lack normality and have strong periodic components are also indicated, as are the advantages of this type of model and the physical significance of the parameters obtained, with respect to the standpoint of hydraulics, ranging from rather homogeneous aquifers (La Villa) to extremely karstic (Aliou), including aquifers with intermediate characteristics (Fuente Mayor).     

IMPACT OF COAL SURFACE MINING ON THREE OHIO WATERSHEDS - SURFACE-WATER HYDROLOGY1

ABSTRACT: A study was conducted to determine the effects of mining and reclaiming originally undisturbed watersheds on surface-water hydrology in three small experimental watersheds in Ohio. Approximately six years of data were collected at each site, with differing lengths of premining (Phase 1), mining and reclamation (Phase 2), and post-reclamation (Phase 3) periods. Mining and reclamation activities showed no consistent pattern iii base-flow, and caused slightly more frequent higher daily flow volumes. Phase 2 activities can cause reductions in seasonal variation in double mass curves compared with Phase 1. Restoration of seasonal variations was noticeably apparent at one site during Phase 3. The responses of the watersheds to rainfall intensities causing larger peak flow rates generally decreased due to mining and reclamation, but tended to exceed responses observed in Phase 1 during Phase 3. Natural Resources Conservation Service (NRCS) curve numbers increased due to mining and reclamation (Phase 2), ranging from 83 to 91. During Phase 3, curve numbers remained approximately constant from Phase 2, ranging from 87 to 91.     

Recent Changes in Stream Flashiness and Flooding,and Effects of Flood Management in North Carolina and Virginia

The southeastern United States has undergone anthropogenic changes in landscape structure, with the potential to increase (e.g., urbanization) and decrease (e.g., reservoir construction) stream flashiness and flooding. Assessment of the outcome of such change can provide insight into the efficacy of current strategies and policies to manage water resources. We (1) examined trends in precipitation, floods, and stream flashiness and (2) assessed the relative influence of land cover and flow‐regulating features (e.g., best management practices and artificial water bodies) on stream flashiness from 1991 to 2013. We found mean annual precipitation decreased, which coincided with decreasing trends in floods. In contrast, stream flashiness, overall, showed an increasing trend during the period of study. However, upon closer examination, 20 watersheds showed stable stream flashiness, whereas 5 increased and 6 decreased in flashiness. Urban watersheds were among those that increased or decreased in flashiness. Watersheds that increased in stream flashiness gained more urban cover, lost more forested cover and had fewer best management practices installed than urban watersheds that decreased in stream flashiness. We found best management practices are more effective than artificial water bodies in regulating flashy floods. Flashiness index is a valuable and straightforward metric to characterize changes in streamflow and help to assess the efficacy of management interventions.     

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.     

Time series in M dimensions: The one-dimensional case: Pollution in the Chicago Sanitary and Ship Canal

A nonstationary time-series model is used to examine the changes occurring at sampling stations on the Chicago Sanitary and Ship Canal. Using data from upstream sampling sites, downstream levels of dissolved oxygen, total dissolved solids, nitrates and nitrites, and ammonia are accurately predicted. The method is simple, insensitive to extreme values, and responsive to changes in the system.     

NONLINEAR MODELING AND PREDICTION OF A RIVER FLOW SYSTEM1

ABSTRACT: Model estimation and prediction of a river flow system are investigated using nonlinear system identification techniques. We demonstrate how the dynamics of the system, rainfall, and river flow can be modeled using NARMAX (Nonlinear Autoregressive Moving Average with eXogenuous input) models. The parameters of the model are estimated using an orthogonal least squares algorithm with intelligent structure detection. The identification of the nonlinear model is described to represent the relationship between local rainfall and river flow at Enoree station (inputs) and river flow at Whitmire (output) for a river flow system in South Carolina.     

A NEW LOOK AT FLOOD RISK DETERMINATION1

ABSTRACT: The probability distributions of annual peak flows used in flood risk analysis quantify the risk that a design flood will be exceeded. But the parameters of these distributions are themselves to a degree uncertain and this uncertainty increases the risk that the flood protection provided will in fact prove to be inadequate. The increase in flood risk due to parameter uncertainty is small when a fairly long record of data is available and the annual flood peaks are serially independent, which is the standard assumption in flood frequency analysis. But standard tests for serial independence are insensitive to the type of grouping of high and low values in a time series, which is measured by the Hurst coefficient. This grouping increases the parameter uncertainty considerably. A study of 49 annual peak flow series for Canadian rivers shows that many have a high Hurst coefficient. The corresponding increase in flood risk due to parameter uncertainty is shown to be substantial even for rivers with a long record, and therefore should not be neglected. The paper presents a method of rationally combining parameter uncertainty due to serial correlation, and the stochastic variability of peak flows in a single risk assessment. In addition, a relatively simple time series model that is capable of reproducing the observed serial correlation of flood peaks is presented.