Search Results (16)

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

To investigate the spatiotemporal distribution and causes of blooms of Rhopilema esculentum in Hangzhou Bay during 2024, this study investigated its growth characteristics, including umbrella diameter and body weight, along with environmental factors, spatiotemporal dynamics and yield variations. The analysis was based on the 2024 monitoring data of R. esculentum resources in Hangzhou Bay, together with relevant social research data. The results showed that umbrella diameter and body weight increased over time at all monitoring points. The growth rate of the R. esculentum umbrella diameter declined gradually over time, whereas that of body weight rapidly increased. The daily growth rate of umbrella diameter in the water of Tangnao and Xiaoji Mountains was significantly higher than that in the waters of Tanxu Mountain. A sharp drop in salinity caused by Xin’anjiang Reservoir flood discharge from the 23rd to 28th June was the primary cause of the R. esculentum blooms in Hangzhou Bay. During the special R. esculentum fishing period in the summer fishing moratorium, R. esculentum was mainly distributed in the southern and eastern Hangzhou waters, with a maximum daily yield of 28,000 kg/day. After the 16th, R. esculentum production expanded across the entire bay, with blooms also occurring in Xiangshan Bay and Liuheng, reaching a production peak of 44,000 kg/day. In 2024, R. esculentum production in Hangzhou Bay totalled 250,000 tonnes, breaking historical records. This study revealed the 2024 growth and spatiotemporal dynamics of R. esculentum in Hangzhou Bay, providing a reference for the rational use and protection of the species and revealing the causes of the unprecedented blooms. Full article

Small- and medium-sized reservoirs significantly alter natural flood processes, making it essential to understand their impact on runoff for effective water resource management. However, the lack of measured data for most small reservoirs poses challenges for accurately simulating their behavior. This study proposes a novel method that utilizes readily available satellite observation data, integrating hydraulic, hydrological, and mathematical formulas to derive outflow coefficients. Based on the Grid-XinAnJiang (GXAJ) model, the enhanced GXAJ-R model accounts for the storage and release effects of ungauged reservoirs and is applied to the Tunxi watershed. Results show that the original GXAJ model achieved a stable performance with an average NSE of 0.88 during calibration, while the NSE values of the GXAJ and GXAJ-R models during validation ranged from 0.78 to 0.97 and 0.85 to 0.99, respectively, with an average improvement of 0.03 in the GXAJ-R model. This enhanced model significantly improves peak flow simulation accuracy, reduces relative flood peak error by approximately 10%, and replicates the flood flow process with higher fidelity. Additionally, the area–volume model derived from classified small-scale data demonstrates high accuracy and reliability, with correlation coefficients above 0.8, making it applicable to other ungauged reservoirs. The OTSU-NDWI method, which improves the NDWI, effectively enhances the accuracy of water body extraction from remote sensing, achieving overall accuracy and kappa coefficient values exceeding 0.8 and 0.6, respectively. This study highlights the potential of integrating satellite data with hydrological models to enhance the understanding of reservoir behavior in data-scarce regions. It also suggests the possibility of broader applications in similarly ungauged basins, providing valuable tools for flood management and risk assessment. 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

Due to the complexity of terrain and climate in the mountain–plain transition zone, it is difficult to simulate and forecast the flow discharge of river basins accurately. The poor regularity of the river thus leads to uncertain flood control scheduling. Meanwhile, reservoirs and flood detention areas are constructed to store and divert water when severe floods threaten the safety of the basin. In order to improve the accuracy of flood forecasts and the effectiveness of flood control, a hydrological and 1D/2D hydrodynamic coupling model was developed to enable the joint computation of multiple objects, including mountainous streams, plains river networks, hydraulic control structures, and flood detention areas. For the hydrological component, the Xin’anjiang model with the Muskingum module is employed to simulate mountainous flow discharge. For the hydrodynamic component, the Saint–Venant equations and shallow water equations are applied to estimate flood processes in rivers and on land surfaces, respectively. The Dongtiaoxi River Basin in Zhejiang Province, China, serves as the case study, where river flow is influenced by both upstream mountainous floods and downstream backwater effects. Using the integrated model, flood routing and scheduling are simulated and visualized. Both the Xin’anjiang model and the 1D hydrodynamic model demonstrate over 80% acceptability in calibration and validation, confirming their robustness and reliability. Meanwhile, inundation in flood detention areas can be effectively estimated by coupling the 1D and 2D hydrodynamic models with a flood diversion scheduling model. The coupled model proves capable of simulating flood routing in complex river basins that include mountains, plains, and hydraulic control structures, accounting for the interactions between hydrological elements. These findings provide a new perspective on flood simulation in other similarly complex river basins. Full article

The effects of global warming and precipitation changes on water temperature and thermocline parameters, such as thermocline depth, thickness, and strength, were assessed. A catchment model, coupled with a reservoir thermal model with meteorological input calculated by a downscaled general circulation model (GCM) projection under three representative concentration pathways (RCPs), was applied to the Xin’anjiang Reservoir, located in southeast China. The results indicate that water temperature in each layer increased (decreased) with the rise (decline) in air temperature, especially the surface water temperature. There was a significant negative (positive) correlation between thermocline depth (strength) and air temperature during the period of stratification weakness. The most sensitive phenomenon of water temperature-to-precipitation changes occurred in the middle layer (depth = 30 m). Additionally, the thermocline depth and thickness increased with decreases in hydraulic residence time, which were caused by precipitation increases. According to the simulation experiments driven by RCP outputs, mean water temperature in each water layer in the future (2096–2100) has a strong response to increases in air temperature, which is projected to increase by 0.11–0.62 °C for RCP2.6, 0.76–1.19 °C for RCP4.5, and 1.50–2.35 °C for RCP8.5, compared to the baseline (2012–2016). However, mean water temperature in each water layer from 2096 to 2100 underwent a slight decrease caused by precipitation changes, with a 0.03–0.25 °C decrease for RCP2.6, 0.07–0.40 °C for RCP4.5, and 0.04–0.29 °C for RCP8.5, compared to 2012–2016. The mean thermocline depth in the future (2096–2100) will be significantly decreased, while the mean thermocline thickness will be slightly increased. Over a multiyear timescale, the impacts of air temperature changes are stronger than those induced by precipitation variations. However, the effects of hydraulic residence time changes caused by precipitation changes (especially rainstorm) should be considered in the management of deep reservoirs. Full article

This research addresses the separate and combined impacts of changes in climate and land use/land cover on the hydrological processes and sediment yield in the Xin’anjiang Reservoir Basin (XRB) in the southeast of China by using the soil and water assessment tool (SWAT) hydrological model in combination with the downscaled general circulation model (GCM) projection outputs. The SWAT model was run under a variety of prescribed scenarios including three climate changes, two land use changes, and three combined changes for the future period (2068–2100). The uncertainty and attribution of the sediment yield variations to the climate and land use/land cover changes at the monthly and annual scale were analyzed. The responses of the sediment yield to changes in climate and land use/land cover were considered. The results showed that all scenarios of climate changes, land use/land cover alterations, and combined changes projected an increase in sediment yield in the basin. Under three representative concentration pathways (RCP), climate change significantly increased the annual sediment yield (by 41.03–54.88%), and deforestation may also increase the annual sediment yield (by 1.1–1.2%) in the future. The comprehensive influence of changes in climate and land use/land cover on sediment yield was 97.33–98.05% (attributed to climate change) and 1.95–2.67% (attributed to land use/land cover change) at the annual scale, respectively. This means that during the 2068–2100 period, climate change will exert a much larger influence on the sediment yield than land use/land cover alteration in XRB if the future land use/land cover remains unchanged after 2015. Moreover, climate change impacts alone on the spatial distribution of sediment yield alterations are projected consistently with those of changes in the precipitation and water yield. At the intra-annual scale, the mean monthly transported sediment exhibits a significant increase in March–May, but a slight decrease in June–August in the future. Therefore, the adaptation to climate change and land use/land cover change should be considered when planning and managing water environmental resources of the reservoirs and catchments. Full article

Frequent flood hazards in the Raoyang River Basin in western Liaoning, China, have posed serious threats to people’s lives and property. In an effort to study the simulation efficiencies of hydrological models in this arid and semi-arid catchment, this study examined the performance of the Xin’anjiang model, the Liaoning unsaturated model, and the DHF model in the Dongbaichengzi station watershed in the upper reaches of the Raoyang River, China. Additionally, this paper proposed an improved DHF model, which considers the impoundment and regulation of small- and medium-sized reservoirs in the upper reaches of the basin. The flood simulation results demonstrated that the Xin’anjiang model was difficult to apply in this area because the average value of its Nash–Sutcliffe efficiency (NSE) was as low as 0.31. Meanwhile, the simulation efficiencies of the Liaoning unsaturated model and the DHF model were higher than that of the Xin’anjiang model, but the relative error of flood peak discharge and runoff depth for most floods were still high and could not meet the actual forecast requirements by the Reservoir Administration Bureau of Liaoning Province. Overall, the improved DHF model showed the best efficiency, and the mean value of the NSE reached 0.79. Therefore, the improved DHF model has good applicability in the Dongbaichengzi station watershed in the upper reaches of the Raoyang River, China. Full article

Under a changing environment, the current hydrological design values derived from historical flood data for the Three Gorges Reservoir (TGR) might be no longer applicable due to the newly-built reservoirs upstream from the TGR and the changes in climatic conditions. In this study, we perform a multivariate dam-site flood frequency analysis for the TGR considering future reservoir regulation and summer precipitation. The Xinanjiang model and Muskingum routing method are used to reconstruct the dam-site flood variables during the operation period of the TGR. Then the distributions of the dam-site flood peak and flood volumes with durations of 3, 7, 15, and 30 days are built by Pearson type III (PIII) distribution with time-varying parameters, which are expressed as functions of both reservoir index and summer precipitation anomaly (SPA). The multivariate joint distribution of the dam-site flood variables is constructed by a 5-D C-vine copula. Finally, by using the criteria of annual average reliability (AAR) associated with the exceedance probabilities of OR, AND and Kendall, we derive the multivariate dam-site design floods for the TGR from the predicted flood distributions during the future operation period of the reservoir. The results indicate that the mean values of all flood variables are positively linked to SPA and negatively linked to RI. In the future, the flood mean values are predicted to present a dramatic decrease due to the regulation of the reservoirs upstream from the TGR. As the result, the design dam-site floods in the future will be smaller than those derived from historical flood distributions. This finding indicates that the TGR would have smaller flood risk in the future. Full article

Changes in rainfall and streamflow due to climate change have an adverse impact on hydropower generation reliability and scheduling of cascade hydropower stations. To estimate the impact of climate change on hydropower, a combination of climate, hydrological, and hydropower scheduling models is needed. Here, we take the Jinsha River as an example to estimate the impact of climate change on total power generation of the cascade hydropower stations and residual load variance of the power grid. These two goals are solved by applying an improved multi-objective cuckoo search algorithm, and a variety of strategies for the optimal dispatch of hydropower stations are adopted to improve the efficiency of the algorithm. Using streamflow prediction results of CMIP5 climate data, in conjunction with the Xinanjiang model, the estimated results for the next 30 years were obtained. The results indicated that the negative correlation between total power generation and residual load variance under the RCP 2.6 scenario was weaker than that under the RCP 8.5. Moreover, the average power generation and the average residual load variance in RCP 2.6 was significantly larger than that in RCP 8.5. Thus, reducing carbon emissions is not only beneficial to ecological sustainability, but also has a positive impact on hydropower generation. Our approaches are also applicable for cascade reservoirs in other river catchments worldwide to estimate impact of climate change on hydropower development. 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

A groundwater model is needed to describe the complex groundwater confluence process of the groundwater system in karst areas. This is because surface water flows through dolines, grikes, and by other means and is directly exchanged with the groundwater. In this study, using the Xin’anjiang model, the conversion of surface water into groundwater and the influence of multiple series-parallel underground reservoirs on groundwater confluence through the generalization of dolines in karst areas were simulated. The water cycle process in the Sancha River Basin was simulated with measured data using multiobjective particle swarm optimization. Then, model parameters were validated with measured runoff data and compared with simulation results obtained using the traditional Xin’anjiang model based on its optimal parameters. The results showed that the determination coefficients of all hydrological stations over the study period were >0.76, and the Nash efficiency coefficient was >0.76, which were better than those for the improved Xin’anjiang model. Next, the simulation accuracy of the flood period in the karst area was analyzed. The model achieved a high fitting rate for the main flood peaks in a year, and the passing rate for the worst hydrological stations was 53%. Finally, the influence of karst development on the runoff was examined. The results indicate that different karst development stages and the heterogeneity of the karst in the basin have different effects on runoff. Full article

Various regression models are currently applied to derive functional forms of operating rules for hydropower reservoirs. It is necessary to analyze and evaluate the model selecting uncertainty involved in reservoir operating rules for efficient hydropower generation. Moreover, selecting the optimal input variables from a large number of candidates to characterize an output variable can lead to a more accurate operation simulation. Therefore, this paper combined the Grey Relational Analysis (GRA) method and the Bayesian Model Averaging (BMA) method to select input variables and derive the monthly optimal operating rules for a hydropower reservoir. The monthly input variables were first filtered according to the relationship between the preselected output and input variables based on the reservoir optimal deterministic trajectory using GRA. Three models, Particle Swarm Optimization-Least Squares Support Vector Machine (PSO-LSSVM), Adaptive Neural Fuzzy Inference System (ANFIS), and Multiple Linear Regression Analysis (MLRA) model, were further implemented to derive individual monthly operating rules. BMA was applied to determine the final monthly operating rules by analyzing the uncertainty of selecting individual models with different weights. A case study of Xinanjiang Reservoir in China shows that the combination of the two methods can achieve high-efficiency hydropower generation and optimal utilization of water resources. Full article

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. Full article

Possible risks in reservoir flood control and regulation cannot be objectively assessed by deterministic flood forecasts, resulting in the probability of reservoir failure. We demonstrated a risk analysis of reservoir flood routing calculation accounting for inflow forecast uncertainty in a sub-basin of Huaihe River, China. The Xinanjiang model was used to provide deterministic flood forecasts, and was combined with the Hydrologic Uncertainty Processor (HUP) to quantify reservoir inflow uncertainty in the probability density function (PDF) form. Furthermore, the PDFs of reservoir water level (RWL) and the risk rate of RWL exceeding a defined safety control level could be obtained. Results suggested that the median forecast (50th percentiles) of HUP showed better agreement with observed inflows than the Xinanjiang model did in terms of the performance measures of flood process, peak, and volume. In addition, most observations (77.2%) were bracketed by the uncertainty band of 90% confidence interval, with some small exceptions of high flows. Results proved that this framework of risk analysis could provide not only the deterministic forecasts of inflow and RWL, but also the fundamental uncertainty information (e.g., 90% confidence band) for the reservoir flood routing calculation. Full article