February 5, 2020

XINANJIANG MODEL DOWNLOAD

The Jiangkou basin, located in the upper of the Hanjiang River, was selected as case study. Introduction Since Crawford and Linsley developed the Stanford Watershed Model [ 1 ]; conceptual rainfall-runoff models have been widely used to tackle many practical and pressing issues in the planning, design, operation, and management of water resources. Table 5 compared the uncertainties evaluation of the Xinanjiang model with respect to different thresholds for the baseflow efficiency index. Dingzhi Peng Dingzhi Peng. View at Google Scholar J.

xinanjiang model

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It is suggested that the infiltration excess runoff mechanism should be included in rainfall—runoff models in arid and semi-arid regions. Many studies sought ways and measures to reduce prediction uncertainty in hydrological modeling by using other available information [ 34 ].

Mathematical Problems in Engineering

For example, Gallart et al. The baseflow separation procedure in the SMM method is described as follows [ 16 ].

Four indices, that is, the containing ratio CRrelative interval width RIWthe Nash-Sutcliffe efficiency index, and the baseflow efficiency index of the median value, MQ 0. Lei Wang, Yu Yun Kang. In order to compare the discharge components, an efficient index for the baseflow efficiency is defined as where, and denote the baseflow obtained by the smoothed minima method SMMthe simulated baseflow from the Xinanjiang model, and the mean baseflow from the SMM method, respectively.

February 25, Accepted: Soil moisture plays an important role in agricultural drought predicting, therefore there is an increasing demand for detailed predictions of soil moisture, especially at basin scales.

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This content is only available as a PDF. The simulation results show the correctness and feasibility of the industrial manipulators 3D model.

Sign In or Create an Account. Parameters of the Xinanjiang model and related prior ranges. In practice, it is very difficult to separate a hydrograph into three components due to the lack of the observed data for a given basin.

xinanjiang model

In order to get better predicted soil moisture information, we use two basin hydrological models, i. Mathematical Problems in Engineering.

xinanjiang model

Cited by Google Scholar. The XAJ model mdel the most popular rainfall-runoff model in China, and widely used all over the world. The mean and the standard deviation of behavior parameter sets and efficiency indices under different thresholds for the baseflow efficiency index were compared in Figure 5. Comparison of the BFI values obtained by different methods.

At annual scale, the parameters for input data adjustment are most sensitive. Firstly, the industrial manipulators 3D model is built by using 3D software. These implied that using the baseflow estimation information can reduce the uncertainty in hydrological modeling to some degree and gain more reasonable prediction bounds.

The Nash-Sutcliffe efficiency coefficient was used to assess the effectiveness of model calibration. By continuing to xijanjiang our website, you are agreeing to our privacy policy.

A Novel Soil Moisture Predicting Method Based on Artificial Neural Network and Xinanjiang Model

A systematic comparison of statistical and hydrological methods for design flood estimation. The Nash-Sutcliffe efficiency index NE and the baseflow efficiency index are used to evaluate the median values, MQ 0.

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The results also showed that uncertainty interval width decreased significantly, while containing ratio did not decrease by much and the simulated runoff with the behavioral parameter sets can fit better to the observed runoff, when threshold for the baseflow efficiency index was taken into xinanjiiang.

xinanjiang model

View xinnajiang Google Scholar Q. Shown in Figure 4 are the simulated total flows, the baseflow estimated by the SMM method, and groundwater flow QG simulated by the Xinanjiang model. To receive news and publication updates for Mathematical Problems in Engineering, enter your email address in the box below.

A Novel Soil Moisture Predicting Method Based on Artificial Neural Network and Xinanjiang Model

The sensitivities of these parameters were shown to change. Previous article Next article.

However, most of the baseflow separation methods including the physically-based digital baseflow separation algorithm are parametric methods [ 14 ], which often result in more uncertainty. View at Google Scholar Mldel. Close mobile search navigation Article navigation.

The objective of this study was to investigate the sensitivities of the parameters of the XinAnJiang model, hereinafter referred to as XAJ model.