Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River,China

This paper presents the application of autoregressive integrated moving average(ARIMA),seasonal ARIMA(SARIMA),and Jordan-Elman artificial neural networks(ANN)models in forecasting the monthly streamflow of the Kizil River in Xinjiang,China.Two different types of monthly streamflow data(original and deseasonalized data)were used to develop time series and Jordan-Elman ANN models using previous flow conditions as predictors.The one-month-ahead forecasting performances of all models for the testing period(1998-2005)were compared using the average monthly flow data from the Kalabeili gaging station on the Kizil River.The Jordan-Elman ANN models,using previous flow conditions as inputs,resulted in no significant improvement over time series models in one-month-ahead forecasting.The results suggest that the simple time series models(ARIMA and SARIMA)can be used in one-month-ahead streamflow forecasting at the study site with a simple and explicit model structure and a model performance similar to the Jordan-Elman ANN models.     

Integration of time series forecasting in a dynamic decision support system for multiple reservoir management to conserve water sources

In most of arid and semi-arid regions, there are limited sources of available fresh water for different domestic and environmental demands. Strategic and parsimonious fresh water-use in water-scarce areas such as Southern New Mexico is crucially important. Elephant Butte and Caballo reservoirs are two integrated reservoirs in this region that provide water supply for many water users in downstream areas. Since Elephant Butte Reservoir is in a semi-arid region, it would be rational to utilize other energy sources such as wind energy to produce electricity and use the water supply to other critical demands in terms of time and availability. This study develops a strategy of optimal management of two integrated reservoirs to quantify the savable volume of water sources through optimal operation management. To optimize operations for the Elephant Butte and Caballo reservoirs as an integrated reservoir operation in New Mexico, the authors in this case study utilized two autoregressive integrated moving average models, one non-seasonal (daily, ARIMA model) and one seasonal (monthly, SARIMA model), to predict daily and monthly inflows to the Elephant Butte Reservoir. The coefficient of determination between predicted and observed daily values and the normalized mean of absolute error (NMAE) were 0.97 and 0.09, respectively, indicating that the daily ARIMA prediction model was significantly reliable and accurate for a univariate based streamflow forecast model. The developed time series prediction models were incorporated in a decision support system, which utilizes the predicted values for a day and a month ahead and leads to save significant amount of water volume by providing the optimal release schedule from Elephant Butte into the Caballo Reservoir. The predicted daily and monthly values from the developed ARIMA prediction models were integrated successfully with the dynamic operation model, which provides the optimal operation plans. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. The saved volume of the water would be considered as a significant water supply for environmental conservation actions in downstream of the Caballo Reservoir. Providing an integrated optimal management plan for two reservoirs led to save significant water sources in a region that water shortage has led to significant environmental consequences. Finally, since the models are univariate, they demonstrate an approach for reliable inflow prediction when information is limited to only streamflow values. We find that hydroelectric power generation forces the region to lose significant amount of water to evaporation and therefore hinder the optimal use of freshwater. Based on these findings, we conclude that a water scarce region like Southern New Mexico should gain independence from hydroelectric power and save the freshwater for supporting ecosystem services and environmental purposes.     

Application of snowmelt runoff model (SRM) in mountainous watersheds: A review

The snowmelt runoff model (SRM) has been widely used in simulation and forecast of streamflow in snow-dominated mountainous basins around the world. This paper presents an overall review of worldwide applications of SRM in mountainous watersheds, particularly in data-sparse watersheds of northwestern China. Issues related to proper selection of input climate variables and parameters, and determination of the snow cover area (SCA) using remote sensing data in snowmelt runoff modeling are discussed through extensive review of literature. Preliminary applications of SRM in northwestern China have shown that the model accuracies are relatively acceptable although most of the watersheds lack measured hydro-meteorological data. Future research could explore the feasibility of modeling snowmelt runoff in data-sparse mountainous watersheds in northwestern China by utilizing snow and glacier cover remote sensing data, geographic information system (GIS) tools, field measurements, and innovative ways of model parameterization.     

Infilling Missing Daily Evapotranspiration Data Using Neural Networks

This study used artificial neural networks (ANNs) computing technique for infilling missing daily saltcedar evapotranspiration (ET) as measured by the eddy-covariance method. The study site was at Bosque del Apache National Wildlife Refuge in the Middle Rio Grande Valley, New Mexico. Data was collected from 2001 to 2003. Several ANN models were evaluated for infilling of different combinations of missing data percentages and different gap sizes. The ANN model using daily maximum and minimum temperature, daily solar radiation, day of the year, and the calendar year as inputs showed the best estimation performance. Results showed coefficient of determination (R2) of 0.96, root-mean-square error (RMSE) of 0.4 mm/day for 10% missing data and a maximum of half-month gap size data set. Missing data greater than 30% and maximum data gap size greater than 3 months resulted in R2 less than 0.90 and RMSE greater than 0.6 mm/day. The results from this study suggest that infilling of daily saltcedar ET using ANN and readily available weather data where the ET observations exist before and after the gap is a reliable and convenient method. It could be used to obtain continuous ET data for modeling and water management practices.     

Reconstruction of the hemipelvis after the excision of malignant tumours. Complications and functional outcome of prostheses

We treated 35 patients with primary malignant tumours of the periacetabular area by resection and prosthetic reconstruction of the defect. At a mean follow-up of 84 months, 15 patients (43%) were free from disease. The most common complications were deep infection (26%), local recurrence (24%) and recurrent dislocation of the hip (17%). The surviving patients achieved an average of 70% of their premorbid function. This method of reconstruction has a high morbidity and should be performed only at specialist centres, but the functional and oncological outcomes are satisfactory.     

合成4,4''-联苯二甲酸的新方法

以4-氨基苯甲酸为原料,重氮化后在Cu+/NH3的催化下偶联得到标题化合物,收率82%.通过IR、NMR和元素分析对产物的结构进行了表征.该合成路线的优点是:原料易得、反应条件温和、操作简便、收率较高、对环境污染较小.     

合成4,4′-联苯二甲酸的新方法

以4-氨基苯甲酸为原料,重氮化后在Cu^＋/NH3的催化下偶联得到标题化合物,收率82%。通过IR、NMR和元素分析对产物的结构进行了表征。该合成路线的优点是：原料易得、反应条件温和、操作简便、收率较高、对环境污染较小。     

解吸液中尿素测定方法的改进

采用分光光度法测定解吸液中微量尿素含量，与滴定法相比较，分析数据的准确性和重现性得到了提高，简化了操作步骤，达到生产控制的要求。     

A study of the anaerobic bacterial flora of the female genital tract in health and disease

Semi-quantitative and qualitative bacterial assessment of the vaginal and cervical flora of a total of 202 women was carried out over a period of six months to determine the bacterial flora in three groups of women and changes caused by prior use of antibiotics. The number was made up of 32 healthy volunteers, 80 women with gynaecological problems and 90 women with gynaecological infections who had had antibiotic treatment prior to this study. Standard methods were used for the investigations. Five main genera of anaerobic bacteria were isolated from all patients. They included, the Bacteroides spp., Prevotella spp., Porphyromonas spp., Peptostreptococcus spp. and Clostridium spp. Five non-sporing gram negative anaerobic bacteria constituted the bulk of the flora including Prevotella bivia, P. disiens, P. melanogenica, P. asaccharolytica and B. fragilis. The predominant flora was P. bivia occurring in 61 pc of cervical swab specimens of the 80 women with proven gynaecological infections who had not used antibiotics and accounting for 27 pc of the total number of Gram-negative anaerobic bacteria isolated. Escherichia coli and Staphylococcus epidermidis were the most frequently encountered aerobic bacteria. The semi-quantitative counts of the different bacterial species in the patient group were significantly higher than in the control group of healthy individuals (p < 0,025). Similarly, prior antibiotic administration significantly reduced the population and quantitative count of the anaerobic bacteria.