Search Results (24)

As the largest freshwater lake in China, Poyang Lake plays a crucial role in hydrological processes. Conventional models often fail to capture the time-lagged relationships between meteorological drivers and runoff responses, while lacking regional generalization capability. To address these limitations, this study proposes a novel XAJ-LSTM-TFM hybrid model that accounts for time-lagged hydrological responses and enhances the regional applicability of the Xinanjiang model. The model innovatively integrates the physical mechanisms of the Xinanjiang model with the temporal learning capacity of LSTM networks. By incorporating intermediate hydrological variables (including interflow and groundwater flow) along with 1–3 day lagged meteorological features, the model achieves an average 15.3% improvement in Nash–Sutcliffe Efficiency (NSE) across five sub-basins, with the Ganjiang Basin attaining an NSE of 0.812 and a 25.7% reduction in flood peak errors. The results demonstrate superior runoff simulation performance and reliable generalization capability under intensive anthropogenic activities. Full article

To address the critical demand for high-precision forecasts in flood management, real-time error correction techniques are increasingly implemented to improve the accuracy and operational reliability of the hydrological prediction framework. However, developing a robust error correction scheme remains a significant challenge due to the compounded errors inherent in hydrological modeling frameworks. In this study, a Bayesian ensemble learning-based correction (BELC) scheme is proposed which integrates hydrological modeling with multiple machine learning methods to enhance real-time error correction for flood forecasting. The Xin’anjiang (XAJ) model is selected as the hydrological model for this study, given its proven effectiveness in flood forecasting across humid and semi-humid regions, combining structural simplicity with demonstrated predictive accuracy. The BELC scheme straightforwardly post-processes the output of the XAJ model under the Bayesian ensemble learning framework. Four machine learning methods are implemented as base learners: long short-term memory (LSTM) networks, a light gradient-boosting machine (LGBM), temporal convolutional networks (TCN), and random forest (RF). Optimal weights for all base learners are determined by the K-means clustering technique and Bayesian optimization in the BELC scheme. Four baseline schemes constructed by base learners and three ensemble learning-based schemes are also built for comparison purposes. The performance of the BELC scheme is systematically evaluated in the Hengshan Reservoir watershed (Fenghua City, China). Results indicate the following: (1) The BELC scheme achieves better performance in both accuracy and robustness compared to the four baseline schemes and three ensemble learning-based schemes. The average performance metrics for 1–3 h lead times are 0.95 (NSE), 0.92 (KGE), 24.25 ${\mathrm{m}}^3/\mathrm{s}$ (RMSE), and 8.71% (RPE), with a PTE consistently below 1 h in advance. (2) The K-means clustering technique proves particularly effective with the ensemble learning framework for high flow ranges, where the correction performance exhibits an increment of 62%, 100%, and 100% for 1 h, 2 h, and 3 h lead hours, respectively. Overall, the BELC scheme demonstrates the potential of a Bayesian ensemble learning framework in improving real-time error correction of flood forecasting systems. Full article

Many hydrological models incorporate vegetation-related parameters to describe hydrological processes more precisely. These parameters should adjust dynamically in response to seasonal changes in vegetation. However, due to limited information or methodological constraints, vegetation-related parameters in hydrological models are often treated as fixed values, which restricts model performance and hinders the accurate representation of hydrological responses to vegetation changes. To address this issue, a vegetation-related dynamic-parameter framework is applied on the Xinanjiang (XAJ) model, which is noted as Eco-XAJ. The dynamic-parameter framework establishes the regression between the Normalized Difference Vegetation Index (NDVI) and the evapotranspiration parameter K. Two routing methods are used in the models, i.e., the unit hydrograph (XAJ-UH and Eco-XAJ-UH) and the Linear Reservoir (XAJ-LR and Eco-XAJ-LR). The original XAJ model and the modified Eco-XAJ model are applied to the Ou River Basin, with detailed comparisons and analyses conducted under various scenarios. The results indicate that the Eco-XAJ model outperforms the original model in long-term discharge simulations, with the NSE increasing from 0.635 of XAJ-UH to 0.647 of Eco-XAJ-UH. The Eco-XAJ model also reduces overestimation and incorrect peak flow simulations during dry seasons, especially in the year 1991. In drought events, the modified model significantly enhances water balance performance. The Eco-XAJ-UH outperforms the XAJ-UH in 9 out of 16 drought events, while the Eco-XAJ-LR outperforms the XAJ-LR in 14 out of 16 drought events. The results demonstrate that the dynamic-parameter model, in regard to vegetation changes, offers more accurate simulations of hydrological processes across different scenarios, and its parameters have reasonable physical interpretations. Full article

An accurate quantification of flow components and an understanding of water source dynamics are essential for effective water resource and quality management. However, the complexity of hydrological processes and the interference of intensive human activities pose significant challenges in precisely separating water discharge into distinct components such as surface runoff, interflow, and groundwater. The Xinanjiang (XAJ) model, a conceptual watershed hydrological model, has been developed and successfully implemented for rainfall–runoff simulations and hydrograph separations across various Chinese watersheds. While the model framework is robust, it fails to account for agricultural irrigation water withdrawals and the variations in in-stream water travel times across different hydrological regimes, introducing considerable uncertainty in simulating low-flow conditions. This study introduced modifications to the XAJ model by allowing parameter adjustments across different flow regimes and incorporating irrigation withdrawals into the runoff routing process. Utilizing a decade of hydrometeorological data (2013–2022) from the Yongan River watershed in eastern China, the modified model demonstrated improved efficiency metrics in low- and medium-flow regimes compared to the original model, with a Nash–Sutcliffe coefficient improvement from −4.43~−0.49 to 0.40~0.46, R² from 0.21~0.36 to 0.53~0.63, and BIAS reduction from 7.60~89.08% to 2.06~12.71%. Furthermore, the modified XAJ model provided a more accurate estimation of the spatial and temporal distribution of streamflow components across sub-watersheds. The original model tended to overestimate groundwater contributions (13%) and underestimate interflow (14%), particularly in low-flow conditions. The enhanced XAJ model, thus, offers a more effective tool for identifying streamflow components, providing essential insights into hydrological processes for better management decisions. Full article

Data scarcity plays the crucial role in hydrological modeling, causing the uncertainties in hydrological model calibration and parameterization. Therefore, while considering the sensitivity of the parameter optimization, it is essential to determine which parameters have the most significant implications on model performance, especially when there is limited hydro-climatological information. Previous studies have underscored the significance of data adjustment parameter sensitivity and its consequential influence on both Xinanjiang (XAJ) model performance and the determination of the acceptable minimum data length, particularly in data-scarce regions. Nevertheless, it is essential to consider the recession constant sensitivity as it has been identified as the most sensitive parameter on an annual scale while keeping the data adjustment parameters constant during a period of data scarcity. Hence, the objective of this study is to extend the previous research by examining the relationship between recession constant sensitivity and data adjustment parameters in shorter datasets leading to more reliable parameter estimation for data-scarce basins. Five U.S. river basins were analyzed using the 28-year dataset and shorter subsets to highlight the impacts of recession constant sensitivities with different data lengths. This study explores the impact of recession constant sensitivities over the hydrological parameter estimation using two approaches (c_g): (i) assessing the relationship between the recession constant (c_g) and the data adjustment parameter (C_ep), for the 28-year dataset, and (ii) investigating the significant impacts of the sensitivity of c_g over C_ep in shorter datasets, which can affect the estimation of the acceptable minimum data length in the data-scarce basins. The polynomial regression analysis was applied to compare and evaluate the model results, varying over the recession constant with different data lengths. The findings indicated that the influence of the recession constant over the data adjustment parameters in the 28-year dataset is limited in the annual scale. However, there is a significant impact of recession constant sensitivity over the model performance while calibrating the model with subsets, particularly in the worst-case scenario. This study underscores the importance of the recession constant sensitivity for reliable continuous hydrological model predictions, especially in data-scarce areas. Full article

Ensemble precipitation forecasts (EPFs) can help to extend lead times and provide reliable probabilistic forecasts, which have been widely applied for streamflow predictions by driving hydrological models. Nonetheless, inherent biases and under-dispersion in EPFs require post-processing for accurate application. It is imperative to explore the skillful lead time of post-processed EPFs for summer streamflow predictions, particularly in mountainous regions. In this study, four popular EPFs, i.e., the CMA, ECMWF, JMA, and NCEP, were post-processed by two state of art methods, i.e., the Bayesian model averaging (BMA) and generator-based post-processing (GPP) methods. These refined forecasts were subsequently integrated with the Xin’anjiang (XAJ) model for summer streamflow prediction. The performances of precipitation forecasts and streamflow predictions were comprehensively evaluated before and after post-processing. The results reveal that raw EPFs frequently deviate from ensemble mean forecasts, particularly underestimating torrential rain. There are also clear underestimations of uncertainty in their probabilistic forecasts. Among the four EPFs, the ECMWF outperforms its peers, delivering skillful precipitation forecasts for 1–7 lead days and streamflow predictions for 1–4 lead days. The effectiveness of post-processing methods varies, yet both GPP and BMA address the under-dispersion of EPFs effectively. The GPP method, recommended as the superior method, can effectively improve both deterministic and probabilistic forecasting accuracy. Moreover, the ECMWF post-processed by GPP extends the effective lead time to seven days and reduces the underestimation of peak flows. The findings of this study underscore the potential benefits of adeptly post-processed EPFs, providing a reference for streamflow prediction over mountain river basins. Full article

Minimum data length is vital to guarantee accuracy in hydrological analysis. In practice, it is sometimes determined by the experiences of hydrologists, leading the selection of the acceptable minimum data length to an arguable issue among hydrologists. Therefore, this study aims to investigate the impact of data length on parameter estimation and hydrological model performance, especially for data-scarce regions. Using four primary datasets from river basins in Japan and USA, subsets were generated from a 28-year dataset and used to estimate data adjustment parameters based on the aridity index approach to improve the parameter estimation. The influence of their length on hydrological analysis is evaluated using the Xinanjiang (XAJ) model; also, the effectiveness of outlier removal on the parameter estimation is checked using regression analysis. Here, we present the estimation of the most acceptable minimum data length in parameter estimation for assessing the XAJ model and the effectiveness of parameter adjustment by removing the outliers in observed datasets. The results show that between 10-year to 13-year datasets are generally sufficient for the robust estimate of the most acceptable minimum data length in the XAJ model. Moreover, removing outliers can improve parameter estimation in all study basins. Full article

The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin’anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models. Full article

The study of runoff under the influence of human activities is a research hot spot in the field of water science. Land-use change is one of the main forms of human activities and it is also the major driver of changes to the runoff process. As for the relationship between land use and the runoff process, runoff yield theories pointed out that the runoff yield capacity is spatially heterogeneous. The present work hypothesizes that the distribution of the runoff yield can be divided by land use, which is, areas with the same land-use type are similar in runoff yield, while areas of different land uses are significantly different. To prove it, we proposed a land-use-based framework for runoff yield calculations based on a conceptual rainfall–runoff model, the Xin’anjiang (XAJ) model. Based on the framework, the modified land-use-based Xin’anjiang (L-XAJ) model was constructed by replacing the yielding area (f/F) in the water storage capacity curve of the XAJ model with the area ratio of different land-use types (L/F; L is the area of specific land-use types, F is the whole basin area). The L-XAJ model was then applied to the typical cultivated–urban binary land-use-type basin (Taipingchi basin) to evaluate its performance. Results showed great success of the L-XAJ model, which demonstrated the area ratio of different land-use types can represent the corresponding yielding area in the XAJ model. The L-XAJ model enhanced the physical meaning of the runoff generation in the XAJ model and was expected to be used in the sustainable development of basin water resources. Full article

Floods are one of the main natural disaster threats to the safety of people’s lives and property. Flood hazards intensify as the global risk of flooding increases. The control of flood disasters on the basin scale has always been an urgent problem to be solved that is firmly associated with the sustainable development of water resources. As important nonengineering measures for flood simulation and flood control, the hydrological and hydraulic models have been widely applied in recent decades. In our study, on the basis of sufficient remote-sensing and hydrological data, a hydrological (Xin’anjiang (XAJ)) and a two-dimensional hydraulic (2D) model were constructed to simulate flood events and provide support for basin flood management. In the Chengcun basin, the two models were applied, and the model parameters were calibrated by the parameter estimation (PEST) automatic calibration algorithm in combination with the measured data of 10 typical flood events from 1990 to 1996. Results show that the two models performed well in the Chengcun basin. The average Nash–Sutcliffe efficiency (NSE), percentage error of peak discharge (PE), and percentage error of flood volume (RE) were 0.79, 16.55%, and 18.27%, respectively, for the XAJ model, and those values were 0.76, 12.83%, and 11.03% for 2D model. These results indicate that the models had high accuracy, and hydrological and hydraulic models both had good application performance in the Chengcun basin. The study can a provide decision-making basis and theoretical support for flood simulation, and the formulation of flood control and disaster mitigation measures in the basin. Full article

The amount of water taken from groundwater for agricultural irrigation is often not observed, while hydrological models have been extensively proposed to investigate the irrigation dynamics and impacts in agricultural areas. In this work, we propose an agro-hydrological model that integrates agricultural irrigation with the traditional Xin’anjiang (XAJ) hydrological model. In particular, the proposed model incorporates the FAO guidelines on crop evapotranspiration into hydrological routing of water balance and flow fluxes in unsaturated and saturated zones. The model was used to calibrate the groundwater irrigation amounts in terms of both the observed river discharge and the groundwater depth in the Xuanwu plain area of the Huaihe River Basin in China. The calibration and sensitivity analyses were performed by the shuffled complex evolution (SCE-UA) method. This method can be applied to a single-objective optimization of model parameters, based on either the river discharge or the groundwater depth, or to a multi-objective optimization of model parameters based on both of these objectives. The results show that the multi-objective calibration is more efficient than the single-objective method for capturing dynamics of the river discharge and the groundwater depth. The estimated means of the annual groundwater withdrawal for wheat and maize irrigations were found to be about 140.5 mm and 13.7 mm, respectively. The correlation between the groundwater withdrawal and the change in groundwater depth during crop growing seasons demonstrated that the groundwater withdrawal is the dominant factor for the groundwater depth change in the river basin, particularly in the winter wheat season. Moreover, model simulations show that the combined effects of the reduced precipitation and the increased groundwater withdrawal would lead to a decrease of the average annual runoff and an increase of the average groundwater depth. These estimates can greatly help in understanding the irregular changes in the groundwater withdrawal and offer a quantitative basis for studying future groundwater demands in this area. Full article

One-dimensional hydrodynamic modeling approaches are useful for flood simulations; however, most studies often neglect intermediate discharges due to difficulties in obtaining the associated data. Herein, we produced the XAJ-H1DM model by coupling the Xinanjiang (XAJ) model, without the Muskingum module, with a one-dimensional hydrodynamic (H1DM) model, using regionalization approaches to test their practicality. Another model, named H1DM-XAJ, was also produced by orderly calibrating the H1DM and XAJ models to achieve improved flood simulations in poorly gauged catchments. The flood simulation capabilities of the four models (including the single XAJ and H1DM models) were investigated and compared at a daily time scale in the Three Gorges Reservoir Region, China. The results show that the regionalization approaches can be successfully used in the application of the integrated hydrologic and hydrodynamic model in ungauged intermediate catchments. Further, the coupled models produced markedly improved estimates of peak discharge and runoff volume compared to the single models. Moreover, the ability of the coupled models to simulate the peak water level and hydrograph, which hydrological models lack, is significantly better than that of the single H1DM model. The framework presented can be applied in other data-scarce catchments worldwide for better understanding of the hydrodynamic processes. Full article

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. Full article

Conducting water resource assessment and forecasting at a basin scale requires effective and accurate simulation of the hydrological process. However, intensive, complex human activities and environmental changes are constraining and challenging the hydrological modeling development and application by complicating the hydrological cycle within its local contexts. Six sub-catchments of the Yellow River basin, the second-largest river in China, situated in a semi-arid climate zone, have been selected for this study, considering hydrological processes under a natural period (before 1970) and under intensive human disturbance (2000–2013). The study aims to assess the capacity and performance of the hydrological models in simulating the discharge under a changing environment. Four well-documented and applied hydrological models, i.e., the Xin’anjiang (XAJ) model, GR4J model, SIMHYD model, and RCCC-WBM (Water Balance Model developed by Research Center for Climate Change) model, were selected for this assessment. The results show that (1) the annual areal temperature of all sub-catchments presented a significant rising trend, and annual precipitation exhibited insignificant decline trend; (2) as a result of climate change and intensive human activities, the annual runoff series showed a declining trend with abrupt changes mostly occurring in the 1980s with the exception of the Tangnaihai station; (3) the four hydrological models generally performed well for runoff simulation for all sub-catchments under the natural period. In terms of Nash–Sutcliffe efficiency coefficient, the XAJ model worked better in comparison to other hydrological models due to its detailed representations and complicated mechanism in runoff generation and flow-routing scheme; (4) environmental changes have impacted the performance of the four hydrological models under all sub-catchments, in particularly the Pianguan River catchment, which is could be attributed to the various human activities that in turn represent more complexity for the regional hydrological cycle to some extent, and reduce the ability to predict the runoff series; (5) the RCCC-WBM model, well known for its simple structure and principles, is considered to be acceptable for runoff simulation for both natural and human disturbance periods, and is recommended for water resource assessment under changing environments for semi-arid regions. Full article

The geomorphologic instantaneous unit hydrograph (GIUH) is an applicable approach that simulates the runoff for the ungauged basins. The nash model is an efficient tool to derive the unit hydrograph (UH), which only requires two items, including the indices n and k. Theoretically, the GIUH method describes the process of a droplet flowing from which it falls on to the basin outlet, only covering the flow concentration process. The traditional technique for flood estimation using GIUH method always uses the effective rainfall, which is empirically obtained and scant of accuracy, and then calculates the convolution of the effective rainfall and GIUH. To improve the predictive capability of the GIUH model, the Xin’anjiang (XAJ) model, which is a conceptual model with clear physical meaning, is applied to simulate the runoff yielding and the slope flow concentration, integrating with the GIUH derived based on Nash model to compute the river network flow convergence, forming a modified GIUH model for flood simulation. The average flow velocity is the key to obtain the indices k, and two methods to calculate the flow velocity were compared in this study. 10 flood events in three catchments in Fujian, China are selected to calibrate the model, and six for validation. Four criteria, including the time-to-peak error, the relative peak flow error, the relative runoff depth error, and the Nash–Sutcliff efficiency coefficient are computed for the model performance evaluation. The observed runoff value and simulated series in validation stage is also presented in the scatter plots to analyze the fitting degree. The analysis results show the modified model with a convenient calculation and a high fitting and illustrates that the model is reliable for the flood estimation and has potential for practical flood forecasting. Full article