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.     

An improved method to study the impact of pesticide sprays on small song birds

Abstract

Four test groups of small songbirds (Zebra Finch, Poephila guttata) were sprayed in a chamber with varying concentrations of fenitrothion. Exposure levels were assessed by monitoring air concentrations, deposits of the active ingredient (AI) on glass plates and droplets/cm² on Kromekote® cards. All indices of exposure were linearly correlated and the mean AI deposit on glass plates for the four groups tested were equivalent to 38, 51, 139 and 255 g/ha or 14%, 18%, 50% and 91% of the highest permissible emitted rate for broadscale forest spraying in Canada. Significant depression in body weights and brain acetylcholinesterase levels were noted only for the highest exposure group. Fenitrothion residues in blood were detectable only at the highest exposure level, and in liver at the two higher levels. Carcass and feather residues were much higher than those in blood and liver, and were detectable at all exposure levels but the residues did not increase linearly with exposure. For one of the spray groups, we were able to compute an equivalent acute oral dose based on matching acetylcholinesterase inhibition.     

锶盐废渣含硫状态分析及硫含量的测定

锶盐废渣中硫的状态及含量不同其作用大不相同,有的呈现活性导致膨胀破坏,而有的则表现为惰性,因此有必要了解硫的存在状态及含量。分析表明锶盐废渣中硫主要以硫酸盐硫(SO3)和硫化物硫(S2-)两种形式存在。选择硫酸钡重量法和碘量法进行化学测定,然后用X-射线能谱仪进行元素定性分析;并将化学分析和仪器测定两种方法进行对比。结果表明,X-射线能谱法可以快捷、方便地探测锶渣所含各种元素,但不能确定元素的价态;化学分析方法可得到相对准确的元素含量;用硫酸钡重量法和碘量法联合测定锶渣中的硫含量,与X-射线能谱法相比,偏差仅为0.37%。     

Generation of Monthly Precipitation Under Climate Change for the Upper Blue Nile River Basin,Ethiopia1

Abstract: This work develops a methodology to project the future precipitation in large river basins under limited data and climate change while preserving the historical temporal and spatial characteristics. The computationally simple and reliable conditional generation method (CGM) is presented and applied to generate reliable monthly precipitation data in the upper Blue Nile River Basin of Ethiopia where rain‐fed agriculture is prevalent. The results showed that the temporal analysis with the CGM performs better to reproduce the historical long‐term characteristics than other methods, and the spatial analysis with the CGM reproduced the historical spatial structure accurately. A 100‐year time series analysis using the outcomes of the six general circulation models showed that precipitation changes by the 2050s (2040 through 2069) can be ?7 to 28% with a mean increase of about 11%. The seasonal results showed increasing wet conditions in all seasons with changes of mean precipitation of 5, 47, and 6% for wet, dry, and mild seasons, respectively.     

顶空气相色谱法测定土壤中的苯系物

利用惠普顶空进样仪作为一种处理土壤样品的手段,自动把待测物送到气相色谱进行分析。研究顶空温度、气液比、平衡时间等条件对测定结果的影响。方法操作简便,灵敏度高。苯的变异系数为1.7%和4.1%,加标回收率为95％－105％,最小检出浓度为2.0μg/kg。     

1976-2005年唐山地区降水变化规律及干旱预测

利用唐山市1976-2005年各县年降水序列,分析了该市降水的空间分布规律和时间变化特点。采用灰色系统的灾变预测方法,对各县分别建立了GM（1,1）模型,进行未来25年唐山市各站的干旱年预测。利用残差检验、后验差检验和关联度检验对各模型分别进行了精度检验。结果表明,预测模型精度较高,可以对唐山市各县未来的干旱年进行预测,从而为科学决策提供依据。     

过硫酸盐去除石灰土介质中汽油BTEX的效果及乙醇的影响

为了认识岩溶区石灰土含水介质中化学氧化联合生物降解去除燃油苯系物的效果,以汽油中的苯、甲苯、乙苯和二甲苯(BTEX)作为污染物,通过批实验研究过硫酸盐(PS)联合硝酸盐去除汽油BTEX的效果,并认识不同浓度乙醇(EtOH)存在时带来的影响及化学氧化与反硝化联合修复的可能性.结果表明,在35 d内各组的BTEX去除率均达到91.00%以上,PS化学氧化能有效地去除BTEX.在不含EtOH及含EtOH初始浓度为500和5000 mg·L^-1的情况下,BTEX浓度在第65 d时分别为未检出、0.226 mg·L^-1、0.243 mg·L^-1,去除率分别为99.98%、99.00%、98.70%,其中,苯的去除率分别为99.98%、97.01%、93.32%.随着EtOH浓度的增加,苯的去除受到抑制,EtOH对BTEX的化学氧化具有阻碍作用.石灰土介质中高铁含量对PS具有活化能力,高含量有机质能促进PS分解,并导致pH值回升,出现反硝化作用和硫酸盐还原作用,有利于生物降解作用的恢复.     

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.