XAJ Model Research Articles

Investigating Spatial Heterogeneity of Karst Water Storage Capacity and Nonclosure of Underground Watersheds in Karst Hydrological Simulation

ABSTRACTKarst landforms interfere with the runoff generation and confluence process, resulting in generally poor hydrological simulation accuracy in karst watersheds. We proposed a new karst hydrological module, which has two cores. One is the karst water storage capacity distribution curve that represents the distribution of runoff generation thresholds in karst areas, and the other is the underground nonclosure coefficient that represents the nonclosure phenomenon of underground watersheds in karst areas. The new module was further coupled with the Xinanjiang rainfall–runoff (XAJ) model to establish a complete hydrological model for karst areas (referred to as XAJ‐karst model). The sensitivity of the XAJ‐karst model parameters was analysed using the Sobol method, and applied to a typical karst watershed in Guizhou Province, China, to test the model performance on daily and hourly time scales. In addition, we also explored the impact of dynamic changes in the nonclosure coefficient of underground watershed area in karst watersheds on model results. Results showed that the average value of Kling–Gupta efficiency (KGE) of the XAJ‐karst model on the daily and hourly time scales was 0.85 and 0.77, respectively. In comparison with the XAJ model, the average KGE value of the XAJ‐karst model on both daily and hourly scales improved by 10.8% and 6.4%, respectively, demonstrating better simulation accuracy. In addition, there is a underground nonclosure phenomenon in the Xiangyang watershed, and the actual area of underground watershed expands abruptly as the antecedent‐precipitation increases to the critical value. Moreover, the water storage and hysteresis effects of the karst landform result in a certain hysteresis in water exchange between the underground watershed and adjacent watersheds.

A hybrid model coupling process-driven and data-driven models for improved real-time flood forecasting

Accurate and reliable incoming flood forecasting is an important prerequisite for flood warning, flood risk analysis and reservoir flood control operation. This paper proposes a hybrid model for real-time flood forecasting that couples process-driven hydrological models (HMs) with data-driven models (DDMs). The generic hybrid model framework adds DDMs as the post-processing procedure for residual correction to the original results of HMs, and considers multiple uncertainties in input data, parameter and model structure simultaneously. The performance of the hybrid model is evaluated comprehensively in terms of deterministic forecast accuracy, interval forecast reliability, and the reliability and sharpness of probabilistic forecast. Taking the multireservoir system at the east Pi River as a study case, the results indicate that: (1) Compared to the benchmark model (ensemble XAJ model), the hybrid model with additional residual analysis show a significant improvement. The average continuous ranked probability score (CRPS) metric values calculated by the Stacking-Hybrid model improved by 71.5 %, 67.0 % and 38.1 % in the three data samples. Furthermore, the adaptability of the Stacking-Hybrid model for residual correction during short-duration intense rainfall events has been validated, with the relative error of the peak discharge improved to within ± 10 %. (2) The Stacking-Hybrid model, which also takes into account structure uncertainty, is able to better exploit the combined advantages and improve the stability of the model performance compared to those that only apply a single DDM. (3) When the number of iterations within the BOA reaches 300, the parameter optimization process is capable to search for the hyperparameters that bring out the best performance of the DDMs. (4) When the ensemble size reaches 200, the uncertainty of HM parameters can be fully defined, and the consumed computational resources can be controlled within an acceptable bound while ensuring stable model performance. Overall, the hybrid model that takes into account multiple sources of uncertainty generates both interval and probabilistic forecast in addition to deterministic forecast, which can provide richer risk information for subsequent flood warning and reservoir operation, making the flood prevention decisions more reliable and scientific.

A Multiple Model Approach for Flood Forecasting, Simulation, and Evaluation Coupling in Zhouqu County

Flood disasters are considered to be one of the ten natural disasters that threaten the survival of mankind. They occur frequently and have a serious impact on the national economy. For quicker response to the sudden flood, in this paper, the relevant characteristics of flood forecasting and disaster assessment are comprehensively studied to establish the corresponding models, and a multi-objective culture shuffled complex differential evolution (MOCSCDE) algorithm is proposed to optimize the model parameters. It can achieve better convergence and significantly improve the model accuracy. Then, a river hydrodynamic model is established to simulate the flooding process, and the characteristics of flood evolution, such as water depth, flow speed, duration, and submerged area, are analyzed. Third, based on the above-mentioned flood forecasting and flood evolution calculations, the relative membership function (VFS) is determined via the set pair analysis method (SPA), and the variable fuzzy set model (SPAVFS) is used for flood risk assessment. Finally, through the study of flow forecasting at Zhouqu hydrological station, it is found that the accuracy of the forecast result of the built model is best compared with LSTM and XAJ model, the mean relative error is only 7.6%, and the certainty coefficient can reach 0.96, which surpass the baselines by 20% and 7.9%.

Integration of the generalized complementary relationship into a lumped hydrological model for improving water balance partitioning: A case study with the Xinanjiang model

Lumped hydrological models (LHMs) are useful tools for simulating catchment runoff (Q) owing to their low data requirements, high computational efficiency, and robust performance. However, most LHMs are purposely conceptualized for rainfall-runoff modeling with over-simplified consideration of other water balance components, especially evapotranspiration (E). The generalized complementary relationship (GCR) can reliably estimate the regional E using only routine meteorological observations. Here, a lumped hydrological model (Xinanjiang model, XAJ) was integrated with the GCR (XAJ-GCR) to improve the accuracy of the water balance partitioning of LHMs. Long-term daily observations of Q and E in the Seolmacheon experimental watershed (8.54 km2) in South Korea were collected to test the capability of the XAJ-GCR model to capture Q and E dynamics with three different integration schemes and three different calibration strategies (single or multi-objective). Results show that XAJ-GCR model obtained excellent results in simulating Q (NSEQ = 0.91 ± 0.02) and markedly improves simulating E with NSEE from − 0.35 ± 0.27 (mean ± standard deviation) to 0.56 ± 0.10. The multi-objective calibration strategy achieves better simulation results for Q and E simultaneously with NSEQ = 0.93 and NSEE = 0.69 ± 0.01, in which the built-in structure of the XAJ model is the optimal scheme. This study demonstrated that the integration of GCR into LHMs is feasible and applicable for improving water balance partitioning. The integration maintains all the advantages of LHM and does not increase the model complexity and data requirements.

Improving the flood prediction capability of the Xin’anjiang model by formulating a new physics-based routing framework and a key routing parameter estimation method

The well-known conceptual Xin'anjiang model (XAJ) has limited capability for application in the basins with limited data due to two major runoff routing problems: relying on the observed flow data to estimate the parameters of the Muskingum method and lack of physics-based representation of spatial heterogeneity in key routing parameters such as the river network recession constant (Cs). To tackle these issues, this study first developed a new hybrid rainfall-runoff model named the Xin’anjiang Digital CHannel (XAJ-DCH) model by coupling the XAJ model with the Muskingum-Cunge-Todini (MCT) and diffusion wave methods to improve the river network routing. Then a physics-based Cs estimation method was developed to conquer the data limitation by deriving the quantitative relationships of the Cs parameter with basin characteristics such as sub-catchment area, average river slope, average rainfall intensity, and average river width based on the mass conservation. The flood prediction capabilities of the XAJ and XAJ-DCH models were further tested and compared in the Tunxi Catchment with two nested sub-catchments. The results show that the XAJ-DCH model can predict stream flows well at both catchment outlet and internal channel grid cells without recalibration. Moreover, the derived Cs values based on the proposed method enable both XAJ and XAJ-DCH models to gain good results with the averaged Nash-Sutcliffe efficiency ranging between 0.91 and 0.95.

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

Abstract 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.

Transferability of a lumped hydrologic model, the Xin'anjiang model based on similarity in climate and geography

Abstract Hydrological experiments are essential to understanding 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 that 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 XAJ model and the transferred model parameters can capture the main features of the hydrological processes in both uncalibrated catchments (western catchments and the entire watershed). This transferability of the model is useful for a scarce data region to simulate the hydrological process and its forecasting.

Multi-source error correction for flood forecasting based on dynamic system response curve method

Real-time correction is the key to reducing hydrological forecasting errors. Methods of real-time correction can be classified into terminal error correction (TEC) and process error correction (PEC) methods. A state-of-the-art PEC method is the dynamic system response curve (DSRC) method which has been developed to improve flood forecasting, but its underlying assumption of error source limits the correction accuracy. This study developed a multi-source error DSRC method (MSE-DSRC) to partition the total error of forecast discharge into input-caused error and model-caused error. Based on the known quantitative relationship between rain gauge density and error division ratio, two different sources of error are corrected simultaneously. A synthetic case study was done to evaluate the ability of the MSE-DSRC method to correct the variables and parameters of the hydrological model (XAJ model). The MSE-DSRC method was found to overcome the limitations of the traditional DSRC method and have a high accuracy indexed with the Nash-Sutcliffe (NS) efficiency increasing above 0.9. Then, real case studies in three basins of the Huaihe River (China) demonstrated that the MSE-DSRC method achieved higher accuracy and stability than did the traditional DSRC method and can be easily applied in practice, with the average improvement of NS above 50%. Overall, the error division of the MSE-DSRC method opens the possibility for multi-variable updating and multi-source error correction, which may further improve the accuracy of flood forecasting.

Assessing the applicability of conceptual hydrological models for design flood estimation in small-scale watersheds of northern China

The estimation of design flood is mainly focused on the peak flow and the volume, ignoring the underlying surface factor and flood rising and falling process. Three basic conceptual hydrological models, XAJ, TANK and SCS, are selected and applied for design flood estimation in two small-scale basins of northern China. Model parameter calibration is based on both the optimization algorithm SCE-UA and artificial adjusting, by using a combined objecting function of flood peak, volume and process. Each model singles out a set of optimal parameters as input to simulate the design flood process. The simulation results are compared with original engineering design standards and instantaneous unit hydrograph method. The results show that the XAJ model has the best performance in simulating the 100-year design flood in study basins. The SCS model also gives acceptable results, but the TANK model on the other hand in an underestimated flood peak with a prolonged recession period, which is not likely to be applicable. This study is to test the applicability of the conceptual hydrological models in simulating the design flood process in small-scale watersheds and should be a supplement to the traditional methods and further deliberation to a ungauged basin. Starting from the most basic models with simple structures, it is hoped that the methodology can be transferred to more complicated and physically based models with more realistic description of the rainfall-runoff transformation mechanism and dynamic mechanism for climate change.

The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China

Flow forecasting is an essential topic for flood prevention and mitigation. This study utilizes a data-driven approach, the Long Short-Term Memory neural network (LSTM), to simulate rainfall–runoff relationships for catchments with different climate conditions. The LSTM method presented was tested in three catchments with distinct climate zones in China. The recurrent neural network (RNN) was adopted for comparison to verify the superiority of the LSTM model in terms of time series prediction problems. The results of LSTM were also compared with a widely used process-based model, the Xinanjiang model (XAJ), as a benchmark to test the applicability of this novel method. The results suggest that LSTM could provide comparable quality predictions as the XAJ model and can be considered an efficient hydrology modeling approach. A real-time forecasting approach coupled with the k-nearest neighbor (KNN) algorithm as an updating method was proposed in this study to generalize the plausibility of the LSTM method for flood forecasting in a decision support system. We compared the simulation results of the LSTM and the LSTM-KNN model, which demonstrated the effectiveness of the LSTM-KNN model in the study areas and underscored the potential of the proposed model for real-time flood forecasting.

Long-Term Hydropower Generation of Cascade Reservoirs under Future Climate Changes in Jinsha River in Southwest China

In this paper, the impact of future climate changes on long-term hydropower generation (LTHG) of cascade hydropower stations in the lower reaches of the Jinsha River is discussed. Global climate models (GCM) were used to estimate the impacts of future climate changes, the Xinanjiang model (XAJ) was applied to project the streamflow of the hydropower stations, and then gravitational search algorithm (GSA) was adopted to solve the LTHG problem. In case studies, the validation of the XAJ model shows that it perform well in the projection of streamflow in the Jinsha River. Moreover, the future hydropower generation is simulated based on five different GCMs under three climate change scenarios. Finally, the GSA algorithm is used to obtain a set of schemes under the influence of climate change. The results show that future climate changes are expected to have different impact on power generation of cascade reservoirs in the downstream of the Jinsha River when the climate change scenarios are different. These findings can provide decision support for future water resources management of the Jinsha River.

Real-time updating of XAJ model by using Unscented Kalman Filter

Real-time updating of XAJ model by using Unscented Kalman Filter

Could operational hydrological models be made compatible with satellite soil moisture observations?

AbstractSoil moisture is a significant state variable in flood forecasting. Nowadays more and more satellite soil moisture products are available, yet their usage in the operational hydrology is still limited. This is because the soil moisture state variables in most operational hydrological models (mostly conceptual models) are over‐simplified—resulting in poor compatibility with the satellite soil moisture observations. A case study is provided to discuss this in more detail, with the adoption of the XAJ model and the Soil Moisture and Ocean Salinity (SMOS) level‐3 soil moisture observation to illustrate the relevant issues. It is found that there are three distinct deficiencies existed in the XAJ model that could cause the mismatch issues with the SMOS soil moisture observation: (i) it is based on runoff generation via the field capacity excess mechanism (interestingly, such a runoff mechanism is called the saturation excess in XAJ while in fact it is clearly a misnomer); (ii) evaporation occurs at the potential rate in its upper soil layer until the water storage in the upper layer is exhausted, and then the evapotranspiration process from the lower layers will commence – leading to an abrupt soil water depletion in the upper soil layer; (iii) it uses the multi‐bucket concept at each soil layer – hence the model has varied soil layers. Therefore, it is a huge challenge to make an operational hydrological model compatible with the satellite soil moisture data. The paper argues that this is possible and some new ideas have been explored and discussed. Copyright © 2016 John Wiley & Sons, Ltd.

Improving the flood prediction capability of the Xinanjiang model in ungauged nested catchments by coupling it with the geomorphologic instantaneous unit hydrograph

Summary Improving the predictive capabilities of rainfall-runoff models in ungauged catchments is a challenging task but has important theoretical and practical significance. In this study, we investigated the predictive capabilities of the conceptual Xinanjiang model (XAJ), which is widely used for flood forecasting and simulation in China, in ungauged catchments. We further produced a hybrid rainfall-runoff model (named XAJ-GIUH) by coupling the XAJ model with the geomorphologic instantaneous unit hydrograph (GIUH) to achieve improved flood predictions in ungauged catchments. The flood prediction capabilities of the original XAJ model and the XAJ-GIUH model were investigated and compared at an hourly time scale in a mountainous catchment with six nested catchments located in the south of Anhui province, China. The two models were first calibrated for each individual catchment by comparing with the observed streamflows. Then, the nested catchments were treated as ungauged and modeled using the parameter values regionalized by transposition from the downstream catchments. The results show that the performance of both models is comparable and satisfactory on different catchment scales in the case that model parameters are calibrated in each catchment. However, the models perform differently in the case that model parameters are transposed from the downstream catchments. The XAJ-GIUH model produced markedly improved estimates of peak discharge and peak time as compared to the original XAJ model in the latter case, indicating that the runoff routing is a major uncertainty source for the application of the XAJ model in this case. Coupling XAJ with topography-based GIUH has the potential to substantially improve the flood prediction capability of the XAJ model in ungauged catchments. Our analysis further reveals that the models do not necessarily perform better when the parameter values are transposed from closer donor catchment. Instead, adopting the parameter values from the catchment with more similar topographic characteristics is more likely to produce better model performance. This study implicates the necessity of including remotely sensed geomorphologic characteristics of ungauged catchments to improve flood predictions in these regions.

Parameter identification and calibration of the Xin’anjiang model using the surrogate modeling approach

Practical experience has demonstrated that single objective functions, no matter how carefully chosen, prove to be inadequate in providing proper measurements for all of the characteristics of the observed data. One strategy to circumvent this problem is to define multiple fitting criteria that measure different aspects of system behavior, and to use multi-criteria optimization to identify non-dominated optimal solutions. Unfortunately, these analyses require running original simulation models thousands of times. As such, they demand prohibitively large computational budgets. As a result, surrogate models have been used in combination with a variety of multi-objective optimization algorithms to approximate the true Pareto-front within limited evaluations for the original model. In this study, multi-objective optimization based on surrogate modeling (multivariate adaptive regression splines, MARS) for a conceptual rainfall-runoff model (Xin’anjiang model, XAJ) was proposed. Taking the Yanduhe basin of Three Gorges in the upper stream of the Yangtze River in China as a case study, three evaluation criteria were selected to quantify the goodness-of-fit of observations against calculated values from the simulation model. The three criteria chosen were the Nash-Sutcliffe efficiency coefficient, the relative error of peak flow, and runoff volume (REPF and RERV). The efficacy of this method is demonstrated on the calibration of the XAJ model. Compared to the single objective optimization results, it was indicated that the multi-objective optimization method can infer the most probable parameter set. The results also demonstrate that the use of surrogate-modeling enables optimization that is much more efficient; and the total computational cost is reduced by about 92.5%, compared to optimization without using surrogate modeling. The results obtained with the proposed method support the feasibility of applying parameter optimization to computationally intensive simulation models, via reducing the number of simulation runs required in the numerical model considerably.

Testing a Conceptual Lumped Model in Karst Area, Southwest China

Karst aquifers are known for their heterogeneous physical properties and irregular complex flow patterns which make it a challenge to describe the hydrological behavior and to quantitatively define the distribution of river flow components using hydrologic models. In this paper, a conceptual lumped hydrologic model, Xin’anjiang model (XAJ), was applied in Sancha River, which is a karst basin in southwest China, for the simulation of streamflow. The performance of XAJ model was evaluated based on the model’s ability to reproduce the streamflow and baseflow. Percentage of bias (PBIAS), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2), and standard deviation (RSR) were calculated between the simulated and measured flow for both calibration and validation period. The low PBIAS and RSR (2.7% and 0.367 for calibration period, 1.3% and 0.376 for validation period) and the high NSE andR2(0.866 and 0.866 for calibration period, 0.858 and 0.860 for validation period) indicate that the model structure and parameters are of reasonable validity. Furthermore, streamflow was separated to baseflow and surface flow using the “baseflow programme,” and the calculated results indicate that the model could also reproduce the response of baseflow in such karst system.

Uncertainty Intercomparison of Different Hydrological Models in Simulating Extreme Flows

A growing number of investigations on uncertainty quantification for hydrological models have been widely reported in past years. However, limited studies are found on uncertainty assessment in simulating streamflow extremes so far. This article presents an intercomparison of uncertainty assessment of three different well-known hydrological models in simulating extreme streamflows using the approach of generalized likelihood uncertainty estimation (GLUE). Results indicate that: (1) The three modified hydrological models can reproduce daily streamflow series with acceptable accuracy. When the threshold value used to select behavioral parameter sets is 0.7, XAJ model generates the best GLUE estimates in simulating daily flows. However, the percentage of observations contained in the calculated 95 % confidence intervals (P-95CI) is low (<50 %) when simulating the high-flow index (Q10). (2) Decreasing average relative length (ARIL), P-95CI and increasing average asymmetry degree (AAD) are found, when the threshold value increases for both daily-flows and high-flows. However, there is a significant inconsistence between sensitivity of daily-flows and high-flows to various threshold values of the likelihood function. Uncertainty sources from parameter sets, model structure and inputs collectively accounts for above sensitivity. (3) The best hydrological model in simulating daily-flows is not identical under different threshold values. High P-95CIs of GLUE estimate for high-flows (Q10 and Q25) indicate that TOPMODEL generally performs best under different threshold values, while XAJ model produces the smallest ARIL under different threshold values. The results are expected to contribute toward knowledge improvement on uncertainty behaviors in simulating streamflow extremes by a variety of hydrological models.

Estimating Selected Parameters for the XAJ Model under Multicollinearity among Watershed Characteristics

Because the problem of prediction in ungauged basins (PUB) has become a central theme of hydrological research, there is a clear need to have an effective and efficient method that can help to transfer information from gauged basins to ungauged ones. Previous research relating the parameters of a hydrologic model to physical drainage basin characteristics has had limited success. Conventional regression procedures do not take into account multicollinearity among the basin characteristics, which have a great effect on the stability of regression equations. This study presents an unbiased ridge regression estimate (URR) that can provide robust regression equations in the presence of multicollinearity. The proposed method is applied to estimate parameters of the Xinanjiang model in 20 watersheds located in southeastern China. Compared with conventional estimations, such as ordinary least squares estimate and ordinary ridge regression estimate, the URR was reduced by 78% for root mean square error, 47% for variance, and 13.1% for Akaike’s Information Criterion. The coefficient of determination R2 was increased by 49%.

Comparison of Runoff Parameterization Schemes with Spatial Heterogeneity across Different Temporal Scales in Semihumid and Semiarid Regions

Due to the spatial variability of precipitation, topography, soil properties, and antecedent soil moisture, runoff is generated by more than one kind of mechanism. Based upon digital elevation model-derived subcatchments and river network, the Xinanjiang Model (XAJ, saturation excess mechanism of rainfall-runoff partitioning), the Shaanbei Model (SAB, infiltration excess mechanism), and the Hybrid Model (HYB, two mixed runoff mechanisms by combining spatial distribution curves of soil tension water storage capacity and infiltration capacity) were applied for streamflow modeling at four hydrological observational stations in the Laohahe River basin, and compared at different time intervals ranging from 1 to 24 h. The results show that the daily (24 h) HYB Model or XAJ Model performs better than the SAB Model over semihumid and semiarid areas. It is revealed that there is less possibility that rain intensity is larger than infiltration rate in semihumid and semiarid regions within a duration of 24 h. Uniform distribution of precipitation within a duration of 24 h makes the physical concept of infiltration excess fuzzy. In the event-based flood modeling at time intervals of 1, 2, 3, 4, 6, and 12 h, HYB is superior to XAJ and SAB in semiarid areas where the mean annual precipitation is less than 400 mm. It is concluded that it is more meaningful to use infiltration excess mechanisms for shorter-duration flood modeling than for daily streamflow simulation in semihumid and semiarid regions.