Xinanjiang Rainfall-runoff Model Research Articles

A novel fast and efficient adaptive shuffled complex evolution algorithm for model parameter calibration

The research and optimization of hydrological forecasting models are among the most crucial components in the scope of water management and flood protection. Optimizing the calibration of hydrological forecasting models is crucial for forecasting performance. A rapid adaptive Shuffled Complex Evolution (SCE) method called Fast Adaptive SCE (FASCE) is proposed for calibrating model parameters. It builds upon the previously established SCE-UA, known for its effectiveness and robustness in the same calibration context. The robustness of the original SCE-UA is expanded upon, introducing a revised adaptive simplex search to bolster efficiency. Additionally, a new strategy for setting up the initial population base enhances explorative capacities. FASCE’s performance has been assessed alongside numerous methods from prior studies, demonstrating its effectiveness. Initial tests were conducted on a set of functions to assess FASCE’s efficacy. Findings revealed that FASCE could curtail the failure rate by a minimum of 80%, whereas the requirement for function evaluations fell between 30% and 60%. Two hydrological models - Support Vector Machine (SVM) and Xinanjiang rainfall-runoff model were employed to estimate the new algorithm’s performance. No failures were reported, and there was a reduction of at least 30% in function evaluations using FASCE. The outcomes from these studies affirm that FASCE can considerably reduce both the number of failures and the count of function evaluations required to reach the global maximum. Hence, FASCE emerges as a viable substitute for model parameter calibration.

Real-time flood forecast using the coupling support vector machine and data assimilation method

An accurate and real-time flood forecast is a crucial nonstructural step to flood mitigation. A support vector machine (SVM) is based on the principle of structural risk minimization and has a good generalization capability. The ensemble Kalman filter (EnKF) is a proven method with the capability of handling nonlinearity in a computationally efficient manner. In this paper, a type of SVM model is established to simulate the rainfall–runoff (RR) process. Then, a coupling model of SVM and EnKF (SVM + EnKF) is used for RR simulation. The impact of the assimilation time scale on the SVM + EnKF model is also studied. A total of four different combinations of the SVM and EnKF models are studied in the paper. The Xinanjiang RR model is employed to evaluate the SVM and the SVM + EnKF models. The study area is located in the Luo River Basin, Guangdong Province, China, during a nine-year period from 1994 to 2002. Compared to SVM, the SVM + EnKF model substantially improves the accuracy of flood prediction, and the Xinanjiang RR model also performs better than the SVM model. The simulated result for the assimilation time scale of 5 days is better than the results for the other cases.

Uncertainty analysis of downscaling methods in assessing the influence of climate change on hydrology

Five downscaling techniques, namely the statistical downscaling model, the automated statistical downscaling method, the change factor (CF) method, the advanced CF method, the Weather generator (LarsWG5) method, are applied to the upstream basin of the Huaihe River. Changes in regional climate scenarios and hydrology variables are compared in future periods to investigate the uncertainty associated with the downscaling techniques. Paired-sample T test is applied to evaluation the significant of the difference of the means between the observed data and the downscaled data in the future. The Xinanjiang rainfall–runoff model is employed to simulate the rainfall–runoff relation. The results demonstrate that the downscaling techniques utilized herein predict an increased tendency in the future. The increases range of maximum temperature (Tmax) is between 3.7 and 4.7 °C until the time period of 2070–2099 (2080s). While, the increases range of minimum temperature (Tmin) is between 2.8 and 4.9 °C until 2080s. The research presented herein determined that there is an increase predicted for the peaks over threshold (discussed in the paper) and a decrease predicted for the peaks below the threshold (discussed in the paper) in the future, which illustrates that the temperature would rise gradually in the future. Precipitation changes are not as obvious as temperatures changes and tend to be influence by the season. Most downscaling techniques predict increases, and others indict decreases. The annual mean precipitation range changes between 3.2 and 53.3 %, and moreover, these changes vary from season to season.

Sensitivity analysis of Xinanjiang rainfall–runoff model parameters: a case study in Lianghui, Zhejiang province, China

The Xinanjiang rainfall–runoff model has been successfully applied in many humid and sub-humid areas in China since 1973. The wide application is due to the simple model structure, the clear physical meaning of the parameters and the well-defined model calibration procedure. However, due to a data scarcity problem and short runoff concentration time, its applications to small drainage basins are difficult. Therefore, we investigate the model application in Lianghui, a small drainage basin of Zhejiang province in China. By using generalized likelihood uncertainty estimation (GLUE) methodology, the sensitivity of parameters of Xinanjiang model was investigated. The data clearly showed that equifinality phenomenon was evident in both water balance parameter calibration and runoff routing parameter calibration procedures. The results showed that K (evapotranspiration conversion coefficient), Cs (recession constant in channel system) and Sm (areal free water storage capacity of surface soil) are the most sensitive parameters for the water balance parameter calibration while Cs, Sm and Wm (mean area tension water capacity) are the most sensitive parameters for runoff routing parameter calibration. The conclusion is favourable for understanding parameters of Xinanjiang model in order to provide valuable scientific information for simulating hydrological processes in small drainage basins.

Development and application of a nitrogen simulation model in a data scarce catchment in South China

Abstract The Xitiaoxi catchment is one of the most important catchments in the Taihu Basin in southeastern China. It contributes a significant amount of surface runoff and nutrient to Taihu Lake. Understanding the nutrient cycling and identification of critical non-point source pollution in this catchment are therefore of primary importance. In this paper, the Xinanjiang-Nitrogen (XAJ-N) model, a conceptual model of nutrient mobilization and transport is developed by integration of the Xinanjiang rainfall–runoff model, the Integrated Nitrogen CAtchment (INCA) model and the Modified Universal Soil Loss Equation (MUSLE). It is implemented with the environmental modelling language PCRaster and estimates the water fluxes and nutrient loadings on a cell-by-cell basis in daily time steps. The model includes the nitrogen cycling processes of mineralization, leaching, fixation, volatilization, nitrification, denitrification and plant uptake. Nitrogen is assumed to be mobilized by surface runoff and groundwater. The model performance was verified by comparing simulated and measured daily discharge and nutrient loadings. The results showed a fairly good relationship between predicted and observed values. Due to the scarce observed data, the simulation results were also validated using an internal mass balance method and values from the literature. It showed that the modelling approach can be used as a tool to estimate the export of nutrient with a daily resolution at a catchment scale.

Predicting runoff in ungauged catchments by using Xinanjiang model with MODIS leaf area index

Vegetation processes are seldom considered in lumped conceptual rainfall–runoff (RR) models although they have significant impacts on runoff via the control of evapotranspiration. This paper incorporates the remotely-sensed the moderate resolution imaging spectrometer mounted on the polar-orbiting terra satellite-leaf area index (MODIS-LAI) data into Xinanjiang rainfall–runoff model and assesses the model performance on 210 catchments in south-east Australia. The results show that the inclusion of LAI data improves both the model calibration results as well as the daily runoff prediction in ungauged catchments. It is likely that more significant improvements to the model structure to integrate the remotely-sensed vegetation and other data can further reduce the uncertainty in runoff prediction in ungauged catchments.

Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure

Genetic Algorithm (GA) is globally oriented in searching and thus useful in optimizing multiobjective problems, especially where the objective functions are ill-defined. Conceptual rainfall–runoff models that aim at predicting streamflow from the knowledge of precipitation over a catchment have become a basic tool for flood forecasting. The parameter calibration of a conceptual model usually involves the multiple criteria for judging the performances of observed data. However, it is often difficult to derive all objective functions for the parameter calibration problem of a conceptual model. Thus, a new method to the multiple criteria parameter calibration problem, which combines GA with TOPSIS (technique for order performance by similarity to ideal solution) for Xinanjiang model, is presented. This study is an immediate further development of authors' previous research (Cheng, C.T., Ou, C.P., Chau, K.W., 2002. Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration. Journal of Hydrology, 268, 72–86), whose obvious disadvantages are to split the whole procedure into two parts and to become difficult to integrally grasp the best behaviors of model during the calibration procedure. The current method integrates the two parts of Xinanjiang rainfall–runoff model calibration together, simplifying the procedures of model calibration and validation and easily demonstrated the intrinsic phenomenon of observed data in integrity. Comparison of results with two-step procedure shows that the current methodology gives similar results to the previous method, is also feasible and robust, but simpler and easier to apply in practice.

Development of a Distributed Snowmelt Analysis System Using AMeDAS Data and Digital Geographic Information

The objective of this study is to develop an distributed snowmelt analysis system using conventional hydrometeorological data. This system consists of two subsystem: one for runoff generation and the other for channel routing. In this system, watershed is devided into grid blocks connected by a channel network which is derived from altitude data at central points of grid blocks. In runoff generation subsystem, the runoff from each grid block is calculated by using a snowmelt model based on radiation budget, and the XinAnJiang rainfall runoff model. The runoff is then routed to the study points using the kinematic wave model in routing subsystem. Sample calculations with respect to the Uono River basin resulted in a good correspondence between predicted and observed hydrographs due to snowmelt.