A modified Xinanjiang model and its application in northern China

Hu, Caihong; Xiong, Lihua; Peng, Danling

doi:10.2166/nh.2005.0013

Cited by 53 publications

(34 citation statements)

References 22 publications

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“…It has been widely used for flood forecasting, water resource evaluation, hydrological station design and water quality accounting [38][39][40][41]. The XAJ assumes that the net rainfall complements the soil water without producing runoff until the soil water reaches the field capacity.…”

Section: Rainfall-runoff Modelmentioning

confidence: 99%

Tracing Temporal Changes of Model Parameters in Rainfall-Runoff Modeling via a Real-Time Data Assimilation

Meng

Xie

2016

Water

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Abstract:Watershed characteristics such as patterns of land use and land cover (LULC), soil structure and river systems, have substantially changed due to natural and anthropogenic factors. To adapt hydrological models to the changing characteristics of watersheds, one of the feasible strategies is to explicitly estimate the changed parameters. However, few approaches have been dedicated to these non-stationary conditions. In this study, we employ an ensemble Kalman filter (EnKF) technique with a constrained parameter evolution scheme to trace the parameter changes. This technique is coupled to a rainfall-runoff model, i.e., the Xinanjiang (XAJ) model. In addition to a stationary condition, we designed three typical non-stationary conditions, including sudden, gradual and rotational changes with respect to two behavioral parameters of the XAJ. Synthetic experiments demonstrated that the EnKF-based method can trace the three types of parameter changes in real time. This method shows robust performance even for the scenarios of high-level uncertainties within rainfall input, modeling and observations, and it holds an implication for detecting changes in watershed characteristics. Coupling this method with a rainfall-runoff model is useful to adapt the model to non-stationary conditions, thereby improving flood simulations and predictions.

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Section: Rainfall-runoff Modelmentioning

confidence: 99%

Tracing Temporal Changes of Model Parameters in Rainfall-Runoff Modeling via a Real-Time Data Assimilation

Meng

Xie

2016

Water

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“…The slope and river network convergence processes were then integrated to obtain the streamflow series of the hydrologic station. The Hybrid rainfall-runoff model (HYB) is a modified version of the XAJ model (Hu et al 2005). Numerous field studies show that runoff within a basin is mainly generated by two mechanisms: infiltration excess (Horton) runoff and saturation excess (Dunne) runoff.…”

Section: Hydrological Modelsmentioning

confidence: 99%

“…The HYB model combines the two runoff mechanisms by means of combining the spatial distribution curves of soil tension water storage capacity and infiltration capacity. For a detailed description of the mechanisms and application of the HYB model used here see Hu et al (2005). HYMOD is simple conceptual lumped hydrological model developed by Moore in the 1980s (Moore 1985; hereinafter referred to as HYM).…”

Section: Hydrological Modelsmentioning

confidence: 99%

Multi-model ensemble hydrologic prediction and uncertainties analysis

et al. 2014

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Modelling uncertainties (i.e. input errors, parameter uncertainties and model structural errors) inevitably exist in hydrological prediction. A lot of recent attention has focused on these, of which input error modelling, parameter optimization and multi-model ensemble strategies are the three most popular methods to demonstrate the impacts of modelling uncertainties. In this paper the Xinanjiang model, the Hybrid rainfallrunoff model and the HYMOD model were applied to the Mishui Basin, south China, for daily streamflow ensemble simulation and uncertainty analysis. The three models were first calibrated by two parameter optimization algorithms, namely, the Shuffled Complex Evolution method (SCE-UA) and the Shuffled Complex Evolution Metropolis method (SCEM-UA); next, the input uncertainty was accounted for by introducing a normally-distributed error multiplier; then, the simulation sets calculated from the three models were combined by Bayesian model averaging (BMA). The results show that both these parameter optimization algorithms generate good streamflow simulations; specifically the SCEM-UA can imply parameter uncertainty and give the posterior distribution of the parameters. Considering the precipitation input uncertainty, the streamflow simulation precision does not improve very much. While the BMA combination not only improves the streamflow prediction precision, it also gives quantitative uncertainty bounds for the simulation sets. The SCEM-UA calculated prediction interval is better than the SCE-UA calculated one. These results suggest that considering the model parameters' uncertainties and doing multi-model ensemble simulations are very practical for streamflow prediction and flood forecasting, from which more precision prediction and more reliable uncertainty bounds can be generated.

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“…Conceptual models include the Xinanjiang model, the Tank model, and the Sacramento model, etc. The Xinanjiang model is proposed by Renjun Zhao in 1973 [9] and widely used in China [10][11][12]. Tank model is another conceptual model developed in Japan [13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Flood Forecasting Method Based on Initial State Variable Correction

Kuang

Kan

Ding

et al. 2017

Water

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Abstract:The influence of initial state variables on flood forecasting accuracy by using conceptual hydrological models is analyzed in this paper and a novel flood forecasting method based on correction of initial state variables is proposed. The new method is abbreviated as ISVC (Initial State Variable Correction). The ISVC takes the residual between the measured and forecasted flows during the initial period of the flood event as the objective function, and it uses a particle swarm optimization algorithm to correct the initial state variables, which are then used to drive the flood forecasting model. The historical flood events of 11 watersheds in south China are forecasted and verified, and important issues concerning the ISVC application are then discussed. The study results show that the ISVC is effective and applicable in flood forecasting tasks. It can significantly improve the flood forecasting accuracy in most cases.

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A modified Xinanjiang model and its application in northern China

Cited by 53 publications

References 22 publications

Tracing Temporal Changes of Model Parameters in Rainfall-Runoff Modeling via a Real-Time Data Assimilation

Tracing Temporal Changes of Model Parameters in Rainfall-Runoff Modeling via a Real-Time Data Assimilation

Multi-model ensemble hydrologic prediction and uncertainties analysis

A Novel Flood Forecasting Method Based on Initial State Variable Correction

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