A new Xin'anjiang and Sacramento combined rainfall-runoff model and its application

The Xin'anjiang model and the Sacramento model are two widely used short-term watershed rainfall-runoff forecasting models, each with their own unique model structure, strengths, weaknesses and applicability. This paper introduces a weight factor to integrate the two models into a combined model, and uses the cyclic coordinate method to calibrate the weight factor and the parameters of the two models to explore the possibility of the complementarity between the two models. With application to the Yuxiakou watershed in Qingjiang River, it is verified that the cyclic coordinate method, although simple, can converge rapidly to a satisfactory calibration accuracy, mostly after two iterations. Also, the results in case studies show that the forecast accuracy of the new combined rainfall-runoff model can improve the forecast precision by 4.3% in a testing period, better in runoff process fitting than the Xin'anjiang model that performs better than the Sacramento model. HIGHLIGHT This paper introduces a weight factor to integrate the two models into a combined model, and uses the cyclic coordinate method to calibrate the weight factor. it is verified that the cyclic coordinate method can converge fast to a satisfactory calibration accuracy. The results show that the forecast accuracy of the new combined rainfall-runoff model can improve the forecast precision.

Lake Inflow Simulation Using the Coupled Water Balance Method and Xin’anjiang Model in an Ungauged Stream of Chaohu Lake Basin, China

Water resources are crucial for maintaining daily life and a healthy ecological environment. In order to gain a harmonious development among water resources and economic development in Lake Watershed, it is urgent to quantify the lake inflow. However, the calculation of inflow simulations is severely limited by the lack of information regarding river runoff. This paper attempts calculated inflow in an ungauged stream through use of the coupling water balance method and the Xin’anjiang model, applying it to calculate the inflow in the Chaohu Lake Basin, China. Results show that the coupled model has been proved to be robust in determining inflow in an ungauged stream. The error of daily inflow calculated by the water balance method is between 1.4 and −19.5%, which is within the standard error range (±20%). The calibration and verification results of the coupled model suggest that the simulation results are best in the high inflow year (2016), followed by the normal inflow year (2007) and the low inflow year (1978). The Nash-Sutcliffe efficiencies for high inflow year, normal inflow year, and low inflow year are 0.82, 0.72, and 0.63, respectively, all of which have reached a satisfactory level. Further, the annual lake inflow simulation in the normal inflow year is 19.4 × 108 m3, while the annual average land surface runoff of the study area is 18.9 × 108 m3, and the relative error is −2.6% by the two ways. These results of the coupled model offer a new way to calculate the inflow in lake/reservoir basins.

Application of Xin'anjiang Model and Wetspa Model in the Inflow Forecasting of Shiquan Reservoir

<p>In order to improve the accuracy of the inflow forecasting of Shiquan Reservoir in the Han River Basin, this paper compared the application effects of Xin'anjing model and Wetspa model. The study collected the rainfall and runoff data from 2009 to 2015, as well as the DEM, land use and soil data with 1000´1000m grid size. The model calibration and verification periods were from 2009 to 2012 and from 2013 to 2015, respectively. In addition to using the runoff depth and the determination coefficient to evaluate the accuracy of the two models, the flow relative error CR1, model confidence coefficient CR2, Nash-Sutcliffe efficiency CR3, logarithmic version of Nash-Sutcliffe efficiency CR4 for low flow, improved Nash-Sutcliffe efficiency CR5 for high flow were adopted to analyze the simulation results of the two models. The results showed that the simulation results of the Wetspa model could be used as a supplement to the simulation results of the Xin'anjiang model, providing high-precision flood forecasting results for the scheduling decisions of Shiquan Reservoir in terms of time and space.</p>

Transferability of the Xin'anjiang model based on similarity in climate and geography

Hydrological experiments are essential to understand the hydrological cycles and promoting the development of hydrologic models. Model parameter transfers provide a new way of doing hydrological forecasts and simulations in ungauged catchments. To study the transferability of model parameters for hydrological modelling and the influence of parameter transfers on hydrological simulations, the Xin'anjiang model (XAJ model), which is a lumped hydrologic model based on a saturation excess mechanism, and has been widely applied in different climate regions of the world, was applied to a low hilly catchment in eastern China, the Chengxi Experimental Watershed (CXEW). The suitability of the XAJ model was tested in the eastern branch catchment of CXEW and the calibrated model parameters of the eastern branch catchment were then transferred to the western branch catchment and the entire watershed of the CXEW. The results show that the XAJ model performs well for the calibrated eastern branch catchment at both daily and monthly scales on hydrological modelling with the NSEs over 0.6 and the REs less than 2.0%. Besides, the uncalibrated catchments of the western branch catchment and the entire watershed of the CSEW share similarities in climate (the precipitation) and geography (the soil texture and vegetation cover) with the calibrated catchment, the XAJmodel and the transferred model parameters can capture the main features of the hydrological processes in both uncalibrated catchments (western catchments and entire watershed). This transferability of the model is useful for a scarce data region to simulate the hydrological process and its forecasting.

Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods

Ensemble hydrologic forecasting which takes advantages of multiple hydrologic models has made much contribution to water resource management. In this study, four hydrological models (the Xin’anjiang model (XAJ), Simhyd, GR4J, and artificial neural network (ANN) models) and three ensemble methods (the simple average, black box-based, and binomial-based methods) were applied and compared to simulate the hydrological process during 1979–1983 in three representative catchments (Daixi, Hengtangcun, and Qiaodongcun). The results indicate that for a single model, the XAJ model and the GR4J model performed relatively well with averaged Nash and Sutcliffe efficiency coefficient (NSE) values of 0.78 and 0.83, respectively. For the ensemble models, the results show that the binomial-based ensemble method (dynamic weight) outperformed with water volume error reduced by 0.8% and NSE value increased by 0.218. The best performance on runoff forecasting occurs in the Hengtang catchment by integrating four hydrologic models based on binomial ensemble method, achieving the water volume error of 2.73% and NSE value of 0.923. Finding would provide scientific support to water engineering design and water resources management in the study areas.

A New Runoff Routing Scheme for Xin’anjiang Model and Its Routing Parameters Estimation Based on Geographical Information

The Xin’anjiang model is a conceptual hydrological model, which has an essential application in humid and semi-humid regions. In the model, the parameters estimation of runoff routing has always been a significant problem in hydrology. The quantitative relationship between parameters of the lag-and-route method and catchment characteristics has not been well studied. In addition, channels in Muskingum method of the Xin’anjiang model are assumed to be virtual channels. Therefore, its parameters need to be estimated by observed flow data. In this paper, a new routing scheme for the Xin’anjiang model is proposed, adopting isochrones method for overland flow and the grid-to-grid Muskingum–Cunge–Todini (MCT) method for channel routing, so that the routing parameters can be estimated according to the geographic information. For the new routing scheme the average overland flow velocity can be determined through the land cover and overland slope, and the channel routing parameters can be determined through channel geometric characteristic, stream order and channel gradient. The improved model was applied at a 90 m grid scale to a nested watershed located in Anhui province, China. The parent Tunxi watershed, with a drainage area of 2692 km2, contains four internal points with available observed streamflow data, allowing us to evaluate the model’s ability to simulate the hydrologic processes within the watershed. Calibration and verification of the improved model were carried out for hourly time scales using hourly streamflow data from 1982 to 2005. Model performance was assessed by comparing simulated and observed flows at the watershed outlet and interior gauging stations. The performance of both original and new runoff routing schemes were tested and compared at hourly scale. Similar and satisfactory performances were achieved at the outlet both in the new runoff routing scheme using the estimated routing parameters and in the original runoff routing scheme using the calibrated routing parameters, with averaged Nash-Sutcliffe efficiency (NSE) of 0.92 and 0.93, respectively. Moreover, the new runoff routing scheme is also able to reproduce promising hydrographs at internal gauges in study catchment with the mean NSE ranging from 0.84 to 0.88. These results indicate that the parameter estimation approach is efficient and the developed model can satisfactorily simulate not only the streamflow at the parent watershed outlet, but also the flood hydrograph at the interior gauging points without model recalibration. This study can provide some guidance for the application of the Xin’anjiang model in ungauged areas.

Ensemble flood simulation for the typical catchment in humid climatic zone by using multiple hydrological models

A good performance of hydrological model for flood simulation is of critical importance for flood forecasting. Taking Yandu River catchment, as the study area, three hydrological models (i.e. Xin'anjiang model, TOPMODEL, artificial neural network model) and a multi-model ensemble simulation method (i.e. entropy-based method) were applied to simulate the hydrological processes of 30 flood events occurring in 1981–1987. The performance of the ensemble members and multi-model ensemble simulation method was evaluated by comparing indicators of Nash-Efficiency coefficient, errors in root mean square, peak occurrence time, and relative errors of flood peak discharge, event runoff depth. Results show that the three hydrological models perform well for hydrological simulation of all 30 storm floods with Nash and Sutcliffe Efficiency coefficient of above 0.75 and relative error of less than 10 %. However, different model exhibits a difference in simulation errors of peak discharge and peak occurrence time. For example, BP model has the smallest error of 3.78 % for peak discharge simulation while that of Xin'anjiang model and TOPMODEL are 20.9 % and 24.7 % respectively. The entropy-based ensemble simulation method improved flood simulation accuracy to some extent for all evaluation criteria comparing to the three hydrological models. It is feasible to apply entropy-based ensemble approach for improving accuracy of flood forecasting in humid regions of China.