Uncertainty Analysis on Hybrid Double Feedforward Neural Network Model for Sediment Load Estimation with LUBE Method

Water Resources Management - The assessment of uncertainty prediction has become a necessity for most modeling studies within the hydrology community. This paper addresses uncertainty analysis on a...     

Suitability of the SWAT Model for Simulating Water Discharge and Sediment Load in a Karst Watershed of the Semiarid Mediterranean Basin

Water Resources Management - This paper presents a case study conducted in the Upper Argos River, in southeast Spain, to verify the applicability of the SWAT model for prediction of the water...     

基于SWAT模型多站点不确定性评价方法的比较

分布式水文模型在拥有众多优点的同时,也面临着参数过多难以率定的问题。SWAT模型作为典型的分布式水文模型,同样存在着参数率定难的问题。基于SWAT模型,选取美国佛罗里达州中部Peace河流域为研究区,采用SUFI-2、GLUE、PARASOL和PSO共4种评价方法进行了SWAT模型参数的敏感性分析、校准、验证以及不确定性研究,通过对4种不确定性方法的模拟结果、难易程度、运行次数以及各方法的理论基础进行对比,总结了4种方法的适用情况。结果表明:4种方法具有各自的优缺点和适用性,SUFI-2方法是半自动的,可以结合分析者的主观和认知,对于较复杂的模型更具有优势;GLUE方法相对简单,要优于SUFI-2方法;PARASOL方法适用于需要找到全局最优纳什系数的模型;PSO方法既适用于较简单的模型,也适用于相对复杂的模型,与PARASOL方法相比,两者得到的纳什系数、相关系数等均差别不大,但PSO方法运行次数大大减少,故PSO方法的整体性能优于PARASOL方法的。     

Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression

The forecast of the sediment yield generated within a watershed is an important input in the water resources planning and management. The methods for the estimation of sediment yield based on the properties of flow and sediment have limitations attributed to the simplification of important parameters and boundary conditions. Under such circumstances, soft computing approaches have proven to be an efficient tool in modelling the sediment yield. The focus of present study is to deal with the development of decision tree based M5 Model Tree and wavelet regression models for modeling sediment yield in Nagwa watershed in India. A comparison is also performed with the artificial neural network (ANN) model for streamflow forecasting. The root mean square errors (RMSE), Nash-Sutcliff efficiency index (N-S Index), and correlation coefficient (R) statistics are used for the statistical criteria. A comparative evaluation of the performance of M5 Model Tree and wavelet regression versus ANN clearly shows that M5 Model Tree and wavelet regression can prove more useful than ANN models in estimation of sediment yield. Further, M5 model tree offers explicit expressions for use by design engineers.     

Middle- and Long-Term Streamflow Forecasting and Uncertainty Analysis Using Lasso-DBN-Bootstrap Model

Middle-term and long-term streamflow forecasting is of great significance for water resources planning and management, cascade reservoirs optimal operation, agriculture and hydro-power generation. In this work, a framework was proposed which integrates least absolute shrinkage and selection operator (lasso), DBN and bootstrap to improve the performance and the stability of streamflow forecasting with the lead-time of one month. Lasso helps to screen the appropriate predictors for the DBN model, and the DBN model simulates the complex relationship between the selection predictors and streamflow, and then bootstrap with the DBN model contributes to evaluate the uncertainty. The Three-River Headwaters Region (TRHR) was taken as a case study. The results indicated that lasso-DBN-bootstrap model produced significantly more accurate forecasting results than the other three models and provides reliable information on the forecasting uncertainty, which will be valuable for water resources management and planning.

     

基于SWAT模型的白洋淀流域总氮负荷模拟研究

在白洋淀水质现状评价的基础上,以营养元素总氮为指示因子,用SWAT模型分析白洋淀流域污染负荷影响因素,模拟各类污染源的贡献值及分布。结果表明,相对于环境背景值,添加面源或点源排放后,总氮负荷分别增加了64%和116%。白洋淀流域总氮多年平均面源负荷贡献率为33.8%,点源负荷贡献率为66.2%,且汛期总氮负荷量远高于非汛期。     

山地小流域降雨及输沙过程的计算模型及其数值模拟

本文依据山地小流域产流及汇流特征，采用数理分析方法，探讨了其降雨强度，出口流量及输沙率随时间变化过程的数学表达关系，建立了从降雨至径流输沙的系统模型，经编程计算，模拟值与某小流域降雨和汇流资料吻合较好。     

基于SWAT模型的延河流域月径流量模拟分析

近年来SWAT模型在我国南方湿润半湿润流域以及众多大尺度流域取得了良好的应用效果,但在水文资料缺乏、气候相对干旱的流域应用不多。以黄土高原典型流域陕西省延河流域为例,结合国内外已有的研究和GIS技术,根据延河流域现有的数据成功构建了基于SWAT的水文模型,其中1980—1985年为模型的率定期,1986—1989年为模型的验证期。通过对模型的参数进行敏感性分析以及一系列率定,最终得到结果为率定期的Nash-Sutcliffe效率系数NS和相关系数R²分别是0.644和0.832;验证期的Nash-Sutcliffe效率系数NS和相关系数R²分别为0.63和0.872,均达到模型模拟的要求,表明SWAT模型基本上能够模拟延河流域月径流量水文过程,为SWAT模型在黄土高原典型流域的进一步应用提供了科学依据。     

Evaluating the SWAT Model for Hydrological Modeling in the Xixian Watershed and a Comparison with the XAJ Model

Already declining water availability in Huaihe River, the 6th largest river in China, is further stressed by climate change and intense human activities. There is a pressing need for a watershed model to better understand the interaction between land use activities and hydrologic processes and to support sustainable water use planning. In this study, we evaluated the performance of SWAT for hydrologic modeling in the Xixian River Basin, located at the headwaters of the Huaihe River, and compared its performance with the Xinanjiang (XAJ) model that has been widely used in China. Due to the lack of publicly available data, emphasis has been put on geospatial data collection and processing, especially on developing land use-land cover maps for the study area based on ground-truth information sampling. Ten-year daily runoff data (1987?C1996) from four stream stations were used to calibrate SWAT and XAJ. Daily runoff data from the same four stations were applied to validate model performance from 1997 to 2005. The results show that both SWAT and XAJ perform well in the Xixian River Basin, with percentage of bias (PBIAS) less than 15%, Nash-Sutcliffe efficiency (NSE) larger than 0.69 and coefficient of determination (R²) larger than 0.72 for both calibration and validation periods at the four stream stations. Both SWAT and XAJ can reasonably simulate surface runoff and baseflow contributions. Comparison between SWAT and XAJ shows that model performances are comparable for hydrologic modeling. For the purposes of flood forecasting and runoff simulation, XAJ requires minimum input data preparation and is preferred to SWAT. The complex, processes-based SWAT can simultaneously simulate water quantity and quality and evaluate the effects of land use change and human activities, which makes it preferable for sustainable water resource management in the Xixian watershed where agricultural activities are intensive.     

新疆尼雅河悬移质泥沙分析

尼雅河是新疆和田地区民丰县境内的主要河流,文章采用3个水文站的实测资料对尼雅河悬移质含沙量和输沙量的年内及年际变化进行分析。结果表明,河流含沙量年内分配与径流同时集中在春季和夏季,含沙量分布与径流相一致,最大年与最小年含沙量极值变化较大;泥沙输移与径流的变化关系密切,输沙量季节变化极大。     

Suspended Sediment Modeling Using Neuro-Fuzzy Embedded Fuzzy c-Means Clustering Technique

The assessment of the suspended sediment (SS) amount in rivers has an importance because it specifically affects the design and operation of numerous hydraulic structures such as dams, bridges, etc. This paper proposes an adaptive neuro-fuzzy embedded fuzzy c-means clustering (ANFIS-FCM) approach for estimating SS concentration. The accuracy of ANFIS-FCM models was compared with classical ANFIS, artificial neural networks (ANNs) and sediment rating curve (SRC). Daily streamflow and SS data from two stations, Muddy Creek near Vaughn and Muddy Creek at Vaughn, operated by the United States Geological Survey were used in the study. Applied models were compared with each other based on root mean square errors and correlation coefficient. Based on comparison, ANFIS-FCM performed superior to the other two models for modeling complex non-linear behavior of the suspended sediment concentration. The ANFIS-FCM model increased the performance (RMSE) of the optimal MLP model by 10 % and 16 % in estimating SSC for the downstream and upstream stations, separately. ANFIS-FCM model provided improvements in performance and parsimonious and took lesser time in calibration than the classical ANFIS model.     

Comparison of Process-Based and Temperature-Index Snowmelt Modeling in SWAT

Snowmelt hydrology is an important part of hydrological analyses where significant proportion of precipitation is expected to fall in a snow form. Many models have long been introduced to enable the simulation of snowmelt processes in the watershed ranging from simple temperature based equations to complex and sophisticated process-based equations. Usually, mixed results have been reported whether or not the difference between results achieved by incorporating data demanding models vis-à-vis simple temperature-index models is justifiable. In this study, we compared the performances of physically based energy budget and simpler temperature-index based snowmelt calculation approaches within the SWAT model at three sites in two different continents. The results indicate insignificant differences between the two approaches. The temperature-index based snowmelt computation method had the overall model efficiency coefficients ranging from 0.49 to 0.73 while the energy budget based approach had efficiency coefficients ranging from 0.33 to 0.59 only. The magnitude of the differences varied based on where the models were applied. However, comparison between two process-based snowmelt estimation procedures (with and without the inclusion of aspect and slope as factors dictating the incoming solar energy) indicate that accounting for ground surface slope and aspect in the snowmelt model slightly improved the results. We conclude that for most practical applications where net solar radiation, not turbulent heat flux, dominates the snowmelt dynamics, a simpler temperature-index snowmelt estimation model is sufficient.     

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.     

红水河输沙量变化分析

通过红水河都安水文站和天峨水文站多年实测径流量和输沙量资料分析,找出红水河输沙量变化特性及其主要影响因素,结果表明,人类活动因素和径流因素均对红水河输沙量变化产生重要影响,其中人类活动是影响红水河输沙量变化的主要因素。     

Development of an Improved Pollution Source Identification Model Using Numerical and ANN Based Simulation-Optimization Model

The identification of unknown pollution sources is an important and challenging task for the engineers working on pollution management of a groundwater aquifer. The locations and transient magnitude of unknown contaminant sources can be identified using inverse optimization technique. In this approach, the absolute difference between the simulated and the observed contaminant concentration at the observation locations of the aquifer is minimized by using an optimization algorithm. The simulated concentrations is calculated using the aquifer simulation model. As such, there is a need to incorporate the aquifer simulation model with the optimization model. Thus the performance of the model is highly related to the aquifer simulation model. The incorporation of the sophisticated numerical simulation model will give better performance, but the model will be computationally expensive. On the other hand, the model will be computationally less expensive if an approximate simulation model is used in place of the numerical simulation model. However, in this case, the predictive performance of the model will decline. For achieving efficiency in both computational time as well as in predicting the performance, this study presents a new genetic algorithms based simulation-optimization method incorporating both the numerical and the approximate simulation models. The efficiency and field applicability of the model is demonstrated using illustrative study areas. The performance evaluation of the model shows that the proposed model has the potential for real-world field applications.     

不确定负荷下小干扰稳定的区间分析方法

计及负荷的不确定性,提出了区间负荷下的小干扰稳定区间分析方法。基于增广矩阵的灵敏度求解,推导了振荡模式的阻尼比对负荷有功的灵敏度指标。以负荷灵敏度指标为指导,通过迭代搜索求解了典型负荷水平下关键振荡模式的阻尼比区间极值,进而可以确定区间负荷下的阻尼比区间分布,并据此可推断特征值阻尼比随负荷变化的规律,提供更加全面的稳定性信息。通过 4机系统和新英格兰39节点系统的算例实现,验证了该方法的有效性,与蒙特卡罗模拟法的结果对比,表明了该方法计算结果的正确性和计算速度上的优越性。     

英金河流域水沙特性分析

作者对内蒙南部多沙河流英金河的来水来沙特性进行了系统的分析，从流域水沙来源及输沙特性，到上下游各站及本站的水沙关系，都一一进行了分析，最后分析了阴河拟建水库三座店综合水利枢纽的入库水沙特性，并对运用方式进行了初步探讨。水沙特性的分析给英金河流域拟建水库排沙运用方式的确定提供了初步依据。     

感潮河段悬沙数学模型——以长江口为例

感潮河段的水流、泥沙运动有其独特的性质。本文针对其特点 ,水流的挟沙力由水动力因素、泥沙因素共同决定 ,以此建立非均匀悬沙的二维数学模式。模式用于模拟长江口感潮河段的悬沙含量的分布和变化 ,计算的结果与 1996年 9月实测资料拟合的较好 ,并在此基础上分析了感潮河段流速、含沙量与水流挟沙力的确定之间的关系。     

黄河流域主要泥沙汇堆积过程及输沙量的长期变化

黄河流域是地表巨大的输沙系统之一,受气候变化和人类活动的影响,其输沙量经历了显著的变化。探讨了全新世以来黄河流域3个主要泥沙沉积汇的堆积变化过程以及黄河输沙量的变化过程,揭示出在人类活动逐渐增强的主要影响下,宋代以后黄河输沙量经历了加速增加的过程,近几十年来在气候变化以及水利水保措施作用下黄河输沙量又经历了逐渐减少的过程。与历史时期相比,近年来黄土高原坡面和沟道的侵蚀产沙量仍较大,仍需长期开展水土保持工作。20世纪中期以前,由于黄河输沙量逐渐增加,因此黄河下游与渭河盆地及小北干流年沉积量也都呈现一个逐渐加速的过程,两者沉积量年增加的比例比较接近,与黄河输沙量年增加的比例一致。近几十年来,下游主要受大堤防洪能力及来沙变化影响、渭河盆地及小北干流主要受三门峡水库水位和来沙变化影响,年沉积量起伏变化,2003年以来两泥沙汇都发生了持续冲刷过程。河套平原是黄河上游最大的泥沙汇,1949年以前400多a的平均沉积量接近0.9亿t/a,1950年以来年均沉积量较之前长期平均值高,受来沙系数变化影响,经历了两个起伏周期,近期进入低谷。