区域营养盐管理模型在沙河流域的应用

应用基于经典广义流域负荷函数(GWLF)模式开发的区域营养盐管理模型,以天津市饮用水水源地于桥水库上游沙河流域为研究对象开展模拟实践研究.本研究基于我国可获得数据量的一般水平,实现了对沙河流域氮污染物环境行为过程的评估,包括对产流量和溶解态氮通量在月尺度上的模拟,以及对溶解态氮污染物的来源进行源解析,模拟精度可靠,结果能够满足管理需求,可作为该模型在我国的进一步推广应用的例证与参考.     

Uncertainty Analysis in Sediment Load Modeling Using ANN and SWAT Model

Sediment load estimation is essential in many water resources projects. In this study, the capability of two different types of model including SWAT as a process-based model and ANNs as a data-driven model in simulating sediment load were evaluated. The issue of uncertainty in the simulated outputs of the two models which stems from different sources was also investigated. Calibration and uncertainty analysis of SWAT were performed using monthly observed discharge and sediment load values and through the application of SUFI-2 procedure. The issue of uncertainty in the ANN model was also accounted for by training a network several times with different initial weights and bias values as well as randomly-selected training and validation sets, each time a network trained. Trying different input variables to find the best and most efficient network structure, it was found that in the forested watershed of Kasilian, adding average daily rainfall or previous values of discharge dose not change the performance of the ANN model significantly. Comparing the results of SWAT and ANN, it was found that SWAT model has a superior performance in estimating high values of sediment load, whereas ANN model estimated low and medium values more accurately. Moreover, prediction interval for the results of ANN was narrower than that of SWAT which suggests that ANN outputs are with less uncertainty.     

Applications of Artificial Neural Networks in integrated water management: fiction or future?

An Artificial Neural Network (ANN) is nowadays recognized as a very promising tool for relating input data to output data. It is said that the possibilities of artificial neural networks are unlimited. Here we focus on the potential role of neural networks in integrated water management. An Artificial Neural Network (ANN) is a mathematical methodology which describes relations between cause (input data) and effects (output data) irrespective of the process laying behind and without the need for making assumptions considering the nature of the relations. The applications are widespread and vary from optimization of measuring networks, operational water management, prediction of drinking water consumption, on-line steering of wastewater treatment plants and sewage systems, up to more specific applications such as establishing a relationship between the observed erosion of groyne field sediments and the characteristics of passing vessels on the river Rhine. Especially where processes are complex, neural networks can open new possibilities for understanding and modelling these kinds of complex processes. Besides explaining the method of ANN this paper shows different applications. Three examples have been worked out in more detail. An intelligent monitoring system is shown for the on-line prediction of water consumption, ANN are successfully used for sludge cost monitoring and optimizing wastewater treatment and the usage of ANN is shown in optimizing and monitoring water quality measuring networks. An ANN appears to be a multiuse and powerful tool for modelling complex processes.     

Impact of Normalization and Input on ARMAX-ANN Model Performance in Suspended Sediment Load Prediction

The suspended sediment load in rivers is an important parameter in watershed planning and management. Since daily suspended sediment time series contain linear and nonlinear components, existing prediction models are associated with limitations. Therefore, this study introduces a new hybrid model comprising two commonly used stochastic and nonlinear models. The sediment load is first modeled by an autoregressive-moving average with exogenous terms (ARMAX) model. Subsequently, the ARMAX residuals are modeled with an artificial neural network (ANN). For this purpose, discharge (Q) and sediment (S) are considered as model input parameters. Three modeling scenarios are defined to investigate the impact of data normalization on the hybrid model. The exponential and Box-Cox transformation methods are combined into a new data normalization method called mixed transformation. The performance of these methods is then compared. In addition, the impact of the type and number of input combinations on ARMAX-ANN model accuracy is evaluated. To this end, 12 input combinations and 1331 ARMAX and ANN models are verified. The ARMAX model inputs include S, Q and the white noise disturbance term (e), while the ANN model inputs include the ARMAX model results and residuals. Moreover, the hybrid model’s accuracy is compared with the ARMAX and ANN models.     

The Improvement in GWLF Model Simulation Performance in Watershed Hydrology by Changing the Transport Framework

The correct and reasonable delineation of actual hydrologic processes is a footstone for the effective simulation of pollutants in watershed models. In this study, a simple but comprehensive semidistributed modeling approach based on the generalized watershed loading function (GWLF) was modified to enable the accurate simulation of hydrology in watersheds. The frame of the original GWLF model (ORM), with a lumped hydrological parameter, was modified by adding channel routing processes, which made it possible to introduce the concept of subbasins. Then, the revised GWLF model was applied to the Luanhe watershed (30,000 km²) on a monthly bias in comparison with the ORM and the previously revised version. The sensitivity analysis and generalized likelihood uncertainty estimation (GLUE) uncertainty analysis were individually conducted to evaluate these modifications. Eventually, we compared four extreme conditions for the daily streamflow simulations of the three model versions in the Tunxi watershed but without calibration. All of the results indicated that the stability and accuracy of the model and the validity of the parameters were all enhanced and improved by the new revised version of the model, which provided reliable simulation results and indicated that it is a prospective tool to support watershed management.

     

小清河流域非点源污染负荷与降水关系研究

流域非点源污染造成了严重的水环境问题,为准确核算典型流域非点源污染负荷及为流域水环境治理提供支持和依据,以山东省小清河流域为研究区,采用SWAT模型及相关分析方法,研究了流域总氮(TN)、总磷(TP)营养物输出负荷时空变化规律以及营养物输出负荷与降水间的关系。结果表明:SWAT模型对小清河流域非点源污染模拟具有较好的适用性。汛期(7—9月)营养物输出负荷最高,占全年比重的50%以上,而TN是其中主要非点源污染物。TN、TP输出负荷空间分布相似,负荷较高的区域都主要集中在流域南部;该区域TN、TP输出负荷分别在69.72～235.30 kg/hm~2和0.93～4.73 kg/hm~2范围内。不同的土地利用氮输出负荷较高的依次为林地、耕地和草地,磷输出负荷较高的依次为林地、草地和耕地。流域氮输出强度与降水相关性强的区域主要集中在流域中上游,而磷输出强度与降水相关性强的区域则主要集中在流域中游。     

Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series

In this study, a nonparametric technique to set up a river stage forecasting model based on empirical mode decomposition (EMD) is presented. The approach is based on the use of the EMD and artificial neural networks (ANN) to forecast next month’s monthly streamflows. The proposed approach is applied to a real case study. The data from station on the Kizilirmak River in Turkey was used. The mean square errors (MSE), mean absolute errors (MAE) and correlation coefficient (R) statistics were used for evaluating the accuracy of the EMD-ANN model. The accuracy of the EMD-ANN model was then compared to the artificial neural networks (ANN) model. The results showed that EMD-ANN approach performed better than the ANN in predicting stream flows. The most accurate EMD-ANN model had MSE?=?0.0132, MAE?=?0.0883 and R?=?0.8012 statistics, respectively.     

Simulation of Agricultural Management Alternatives for Watershed Protection

The Bosque River Watershed in Texas is facing a suite of water quality issues including excess sediment, nutrient, and bacteria. The sources of the pollutants are improperly managed cropland and grazing land, dairy manure application, and effluent discharge from wastewater treatment facilities. Several best management practices (BMPs) have been proposed for pollution reduction and watershed protection. The overall objectives of this study were to demonstrate a modeling approach using Soil and Water Assessment Tool (SWAT) model to simulate various BMPs and assess their long-term impacts on sediment and nutrient loads at different spatial levels. The SWAT model was calibrated and validated for long-term annual and monthly flows at Valley Mills and for monthly sediment, total nitrogen (TN) and total phosphorus (TP) at Hico and Valley Mills monitoring locations. The BMPs including streambank stabilization, gully plugs, recharge structures, conservation tillage, terraces, contour farming, manure incorporation, filter strips, and PL-566 reservoirs were simulated in the watershed areas that met the respective practice’s specific criteria for implementation. These BMPs were represented in the pre- and post-conditions by modifying one or more channel parameters (channel cover, erodibility, Manning’s n), curve number (CN), support practice factor (P-factor), filter strip width, and tillage parameters (mixing efficiency, mixing depth). The BMPs were simulated individually and the resulting Hydrologic Response Units (HRUs), subwatershed, and watershed level impacts were quantified for each BMP. Sensitivity of model output values to input parameters used to represent the BMPs was also evaluated. Implementing individual BMPs reduced sediment loads from 3% to 37% and TN loads from 1% to 24% at the watershed outlet; however, the changes in TP loads ranged from 3% increase to 30% decrease. Higher reductions were simulated at the subwatershed and HRU levels. Among the parameters analyzed for sensitivity, P-factor and CN were most sensitive followed by Manning’s n. The TN and TP outputs were not sensitive to channel cover. This study showed that the SWAT modeling approach could be used to simulate and assess the effectiveness of agricultural best management practices.     

基于GWLF模型的新安江上游练江流域面源污染特征解析

应用通用流域污染负荷模型(GWLF),对新安江上游练江流域2005-2012年的水文化学过程进行了模拟,在月尺度上评估了流域把口断面的水量及溶解性总氮污染物通量,并解析了其负荷来源分配。结果表明:GWLF模型能够作为有效的决策支持工具对目标流域开展有效评估,模拟结果纳氏效率系数在0.75以上。从年均水平上看,流域溶解态总氮主要通过地表径流和地下水传输,分别占到全部负荷量的42%和40%,且与流域水文关系密切。水质较差的风险敏感期主要出现在水量较少的时段,且该期间内农村生活源(37%)和点源(9%)贡献相对显著,应予以特别关注。上述流域面源污染特征解析结果能够为实施有针对性的管理措施提供参考。     

Joint Application of Artificial Neural Networks and Evolutionary Algorithms to Watershed Management

Artificial neural networks (ANNs) have become common data driven tools for modeling complex, nonlinear problems in science and engineering. Many previous applications have relied on gradient-based search techniques, such as the back propagation (BP) algorithm, for ANN training. Such techniques, however, are highly susceptible to premature convergence to local optima and require a trial-and-error process for effective design of ANN architecture and connection weights. This paper investigates the use of evolutionary programming (EP), a robust search technique, and a hybrid EP–BP training algorithm for improved ANN design. Application results indicate that the EP–BP algorithm may limit the drawbacks of using local search algorithms alone and that the hybrid performs better than EP from the perspective of both training accuracy and efficiency. In addition, the resulting ANN is used to replace the hydrologic simulation component of a previously developed multiobjective decision support model for watershed management. Due to the efficiency of the trained ANN with respect to the traditional simulation model, the replacement reduced the overall computational time required to generate preferred watershed management policies by 75%. The reduction is likely to improve the practical utility of the management model from a typical user perspective. Moreover, the results reveal the potential role of properly trained ANNs in addressing computational demands of various problems without sacrificing the accuracy of solutions.