Hanjiang River Basin Research Articles

A modified CVaR‐based interval coordination model for economic benefit and pollutant discharge

AbstractThis paper proposes a modified conditional value‐at‐risk interval two‐stage stochastic programming coordination model (MCITSP) for water allocation and illustrates its advantages in risk aversion and pollution control. We analyze its performance in maintaining the equity of water use in various sectors, which is specifically reflected in the water satisfaction of multiple users. In this paper, the MCITSP model and original ITSP model are applied to the case of the Hanjiang River Basin, and three scenarios of water availability are set up to provide theoretical support for water allocation. Our results show that the MCITSP model with a higher risk coefficient has a stronger ability to avoid risks. The MCITSP model simultaneously controls pollutant discharge and guarantees economic benefits, making it superior to the ITSP model under different scenarios. Water shortages primarily affect the agricultural sector, due to its high water demand and low economic value, and the MCITSP model plays a positive role in maintaining equity and coordinating water conflicts among multiple users. Managers can choose appropriate model parameters according to their preferences to formulate more reasonable decisions.

A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM

Satellite products have mediocre performance in precipitation estimation, while rain gauges are incapable of describing continuous spatial precipitation distributions. To obtain spatially continuous and accurate precipitation data, this paper proposes a two-step scheme incorporating environmental variables, satellite precipitation estimations, and rain gauge observations for the calibration of satellite precipitation data. First, the GPM data are downscaled from 0.1° to 0.01° based on the seasonal RF models to minimize the spatial differences between the satellite estimations and the rain gauge observations. Secondly, the fusion model combining ConvLSTM and CBAM explores the spatiotemporal correlation of downscaled satellite precipitation data with environmental co-variables and ground-based observations to correct GPM precipitation. The integrated scheme (CBAM-ConvLSTM) is applied to acquire monthly precipitation at a spatial resolution of 0.01° over Hanjiang River Basin from 2014 to 2018. Comparative analyses of model-based satellite products with in situ observations show that model-based precipitation products have a high-resolution spatial distribution along with high accuracy, which combines the advantages of in situ observations and satellite products. Compared to the original GPM product, the evaluation metric values of the merged precipitation products all improved: the RMSE decreased by 31% while the CC increased from 0.55 to 0.69, the bias decreased from about 25% to less than 1.8%, and the MAE decreased by 27.8% while the KGE increased from 0.28 to 0.52. This two-step scheme provides an effective way to derive a high-resolution and accurate monthly precipitation product for humid regions.

The relationship between mountain wetland health and water quality: A case study of the upper Hanjiang River Basin, China

This study investigates the degradation process of mountain wetlands in the upper Hanjiang River Basin (HRB) over a 30-year span from 1990 to 2020. In particular, the landscape development intensity (LDI) index was employed to conduct a comprehensive assessment of the wetland health. This was subsequently combined with the spatio-temporal changes of water quality in the basin to explore the potential correlations between the health status of mountain wetlands and the associated watershed water quality. The results show that over the past three decades, wetland ecosystems have shrunk by 18% due to conversion into farmland, grass, construction land and forest land. This was significant between 2010 and 2020, as shown by a land use dynamic index of −1.121% during 2010–2020, which was significantly higher than that in the preceding two decades (0.003%, 0.367%) (p < 0.05). LDI values for individual sub-watersheds across different years ranged from 2.39 to 4.93, demonstrating an increasing trend since 2010. This indicates a heightened level of human interference in mountain wetlands. Although the water quality within the basin generally adhered to the Class II surface water quality standard, total nitrogen (TN) (primarily from farming) was a concern. Areas with relatively more human activity were observed to exhibit increased pollution levels, as demonstrated by a positive correlation between LDI and the concentrations of total phosphorus (TP), ammonium nitrogen (NH4+-N), and chemical oxygen demand (COD) in the basin. The LDI of the mountain wetland exhibited a consistent positive correlation with the water quality comprehensive function, both during the flood (r = 0.77–0.81) and non-flood (r = 0.61–0.70) seasons (p < 0.05). This indicates the significant impact of the wetland landscape structure on the water quality within a 1000 m radius on either side of the river. Special attention should be paid to the management and allocation of wetland landscapes within this 1000 m buffer zone. Furthermore, efforts to control upstream pollutant emission should be strengthened.

Spatiotemporal Variations of Production–Living–Ecological Space under Various, Changing Climate and Land Use Scenarios in the Upper Reaches of Hanjiang River Basin, China

Land is an important resource that supports the production, life, and ecological development of human society. The current research on production–living–ecological space (PLES) is mainly focusing on the identification of single and dominant functions of land space, and the comprehensive spatial function measurement index of PLES (PLESI) is less known in the effective quantitative evaluation of multifunctionality of different land use categories. Integrating the CMIP6 (Coupled Model Intercomparison Project phase 6) scenario data and the future land use simulation model (FLUS), this research took the upper reaches of the Hanjiang River (URHR) as an example to explore the temporal and spatial variations in land use, PLES, and PLESIs during 2000–2020, and in the SSP2-4.5 and SSP5-8.5 scenarios from 2021 to 2100. The findings were as follows: (1) Forest land is the most widely distributed type of land; correspondingly, ecological space has the widest distribution area in PLES, followed by production space. (2) The area of dry land and building land increased between 2000 and 2010, accompanied by the increase in living space. From 2010 to 2020, the growth rate of building land tended to slow down while forest land increased, and the conflict of PLES eased. (3) The transfer between forest land and dry land is projected to intensify under the SSP2-4.5 scenario, while it is projected to occur between forest land and grassland under the SSP5-8.5 scenario. As for the changes in PLES, the SSP2-4.5 scenario has a greater impact than the SSP5-8.5 scenario. Spatially, several sub-basins in the northern URHR are the main areas of land use and PLES change. (4) PLESI presents a significant downward trend from 2000 to 2020 while trending upward under the SSP5-8.5 scenario and trending downward slightly under the SSP2-4.5 scenario between 2020 and 2100. Combining climate scenarios and the future land use simulation, this research would support the effective utilization of regional land resources and ecosystem management decision-making.

Projection of future precipitation change using CMIP6 multimodel ensemble based on fusion of multiple machine learning algorithms: A case in Hanjiang River Basin, China

AbstractProjecting precipitation changes is essential for researchers to understand climate change impacts on hydrological cycle. This study projected future precipitation over the Hanjiang River Basin (HRB) based on the multimodel ensemble (ME) of six global climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). An ME method using the fusion of four machine learning (ML) algorithms (random forest [RF], K‐nearest neighbors [KNN], extra tree [ET], and gradient boosting decision tree [GBDT]) was proposed in this study. The future precipitation changes were investigated during 2023–2042 (Near‐term), 2043–2062 (Mid‐term), and 2081–2100 (Long‐term) periods, with reference to the base period 1995–2014, under three integrated scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5) of the Shared Socioeconomic Pathways (SSPs) and the representative concentration pathways (RCPs). The results show that: (1) the proposed ME method performs better than the ME mean and individual ML algorithms, with a correlation coefficient value reaching 0.88 and Taylor skill score reaching 0.764. (2) The precipitation under SSP5‐8.5 has the largest upward trend with the annual precipitation variation range of −9.27% to 112.84% from 2023 to 2100, followed by SSP2‐4.5 with −30.48% to 44.67%, and the smallest under SSP1‐2.6 with −37.19% to 37.78%, which show a significant trend of humidification over the HRB in the future. (3) The precipitation changes over the HRB are projected to increase over time, with the largest in the Long‐term, followed by Mid‐term, and the smallest in the Near‐term. (4) The northeastern parts of the HRB are projected to experience a large precipitation in the future, and the southeastern parts are smaller. (5) Uncertainties in the projected precipitation over the HRB still exist, which can be reduced by ME. The findings obtained in this study have important implications for hydrological policymakers to make adaptive strategies to reduce the risks of climate change.

The Impact Mechanism of Climate and Vegetation Changes on the Blue and Green Water Flow in the Main Ecosystems of the Hanjiang River Basin, China

Water resources management and planning traditionally focus on visible liquid or blue water. However, green water also maintains social development and ecosystem services. Therefore, blue and green water should be incorporated into the watershed management system for evaluating water resources. To analyze the water resources of the Hanjiang River Basin, the SWAT model was set up using long-term and high-precision geographic data. The methods of wavelet analysis and Pearson’s correlation analysis were used to explore the influence mechanism of climate and vegetation changes on the blue and green water flow (BWF and GWF) of the main ecosystems in the basin. The results showed that: (1) The spatial–temporal distribution of the BWF and GWF in the main ecosystems of the basin over the past 50 years was uneven. Forest ecosystems and farmland ecosystems have a greater concentration of water resources in the south, while grassland ecosystems have a greater concentration of water resources in the east. (2) Climate dominates the BWF and GWF changes in the main ecosystems of the basin. The BWF and the precipitation change cycle are synergistic, and the GWF and the temperature change cycle are synergistic. (3) The correlation between vegetation and BWF and GWF in the farmland ecosystem is significant. Vegetation affects the hydrological change process of the BWF and GWF at the microscale. This study can provide data support and scientific rules for ecosystem water resource management in the basin.

Nonpoint Source Pollution in Plot-Scale and Small Watershed Studies under Natural Rainfall: An Analysis of the China Experience

Agricultural nonpoint source (NPS) pollution is still the main factor that affects the quality of rivers, lakes, and reservoirs. Starting from the study of the mechanism of NPS, taking the Yangliu small watershed in the Shaanxi section of the Hanjiang River Basin in China as an example, and based on the combination of measured data and models, this paper focused on the analysis of the characteristics of NPS between the runoff plot and small watershed. The results showed that with the rainfall and surface runoff, the nutrient concentration increased first and then decreased, and the highest occurred in the first half of the whole rainfall process. Rainfall, maximum rainfall intensity at 60 min (I60), runoff, and suspended sediment (SS) were all related to nutrient diversion loss. However, the correlation among rainfall, I60, and runoff and the content of various forms of nitrogen and phosphorus were significantly higher than that of SS. Crop interplanting and terrace planting could play a significant role in reducing sediment and nutrient loss. For the analysis of NPS pollution in small watersheds, the time-variant gain model (TVGM) was used to simulate the missing flow in some months of the basin during the flood season. The average concentration method was used to calculate the NPS pollution load in flood season. It was found that total nitrogen (TN) and total phosphorus (TP) accounted for more than 92% and 69%, respectively. This study is expected to provide scientific basis for NPS pollution control in areas both regionally and worldwide.

Nonstationary flood and its influencing factors analysis in the Hanjiang River Basin, China

Due to the impact of climate change and human activities, flood disasters have occurred frequently in the Hanjiang River Basin (HRB) in recent years, and the stationary behavior of its flood fluctuation has been disrupted. In this study, we used Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to analyze the nonstationary changes of annual maximum runoff in the HRB and its influencing factors. It has also been complemented with a stationary model for comparative analysis. We found that the annual maximum runoff has a significant decreasing trend. The top four factors influencing this runoff change are the local changes in precipitation and temperature, and the regional climate oscillations as reflected by the North Atlantic Oscillation index (NAO) and the Pacific North American Index (PNA). The nonstationary model had better simulation effect than the stationary model. The stationary model could not reflect the impact of explanatory variables on annual maximum runoff, while the nonstationary model could well analyze the impact of single and dual explanatory variables. The results of this study provide some new insights in support of flood prevention and control in the HRB.

Distribution and relationship of antibiotics, heavy metals and resistance genes in the upstream of Hanjiang River Basin in Shiyan, China.

The upstream basin of Hanjiang River is an important water source for the middle route of China's South-to-North Water Diversion Project. The quality of water and soil in the Hanjiang River have enormous biological and environmental impacts, and resistant genetic contamination has emerged, but only few studies are concerned the correlation between heavy metals and metal resistance genes (MRGs). In this study, 8 antibiotics and 19 heavy metals were analyzed, the results showed that the highest antibiotic content was tetracycline, with mean concentrations of 43.201µg/kg and 0.022µg/L. Mn was the highest heavy metal in soil with a content of 1408.284µg/kg, and in water was Zn with a content of 10.611µg/L. We found that the most abundant antibiotic resistance genes (ARGs) and metal resistance genes (MRGs) in the study area were bacA and arsT genes, coding for resistance mechanisms to bacitracin and arsenic, respectively. The data showed that heavy metals had a greater impact on antibioticresistance genes than antibiotics, and the correlation between resistance genes was significantly positive. This work expands our understanding of the correlations of antibiotics, heavy metals, and resistance genes in the Hanjiang River, indicating that more attention should be paid to the effects of resistance genes and the quality of water.

Application of coupling mechanism and data-driven models in the Hanjiang river basin

Widely confirmed and applied, data-driven models are an important method for watershed runoff predictions. Since decomposition methods such as time series decomposition cannot automatically handle the decomposition process of date changes and less consideration of influencing factors before decomposition, resulting in insufficient correlation analysis between influencing factors and forecast objects, we propose a method based on hydrological model decomposition to generate time series state variables (broadening the range of influencing factors to be considered). In this study, we constructed hydrological models wherein rainfall and other hydrological elements are decomposed into hydrological and hydrodynamic characteristic state variables to expand the range of the prediction factors. A data-driven model was then built to perform runoff predictions in the Han River Basin. The results showed that compared with the single prediction model, the prediction results based on the coupling model were superior, the performance evaluation grade of the coupling model was high, and the coupling model had a higher stability.

Spatial Association Network and Driving Factors of Agricultural Eco-Efficiency in the Hanjiang River Basin, China

Reducing agricultural emissions and promoting carbon sequestration are vital for China to achieve its dual carbon goals. Achieving the green transformation of agricultural watersheds requires a thorough understanding of the internal transmission relationships within the watersheds and the underlying spatial correlation structures. This paper used the SBM-3E model to calculate the agricultural ecological efficiency of 17 prefecture-level cities in the Hanjiang River Basin (HRB) from 2010 to 2020, taking agricultural carbon emissions and a comprehensive non-point source pollution index as the unexpected output. The Gravity model and social network analysis methods were used to analyze the evolution characteristics of the network structure of agricultural ecological efficiency, and the secondary assignment procedure method was used to identify the driving factors from the planting structure, water use structure, and resource endowment. First, from 2010 to 2020, the overall agricultural ecological efficiency in the HRB demonstrates a declining trend, with efficiency values of 12.15, 9.40, and 6.67 in the upper, middle, and lower reaches, respectively. Second, the spatial correlation network density of agricultural ecological efficiency in the HRB is 0.17, with a network efficiency of 0.89. The correlation among units within the basin is relatively low, but stability is high. Moreover, the individual network spillover absorption capacity exhibits heterogeneity, and the status of each subject within the watershed follows a “core-edge” structure. Third, total water consumption and corn cultivation have a positive impact on the agricultural ecological efficiency network in the HRB, whereas agricultural water use and rice cultivation negatively influence the network. We propose policy recommendations to facilitate the advancement of green development in China’s agricultural watersheds and the achievement of the dual carbon goals.

Reconstruction and characterization of droughts and floods in the Hanjiang River Basin, China, 1426–2017

Reconstruction and characterization of droughts and floods in the Hanjiang River Basin, China, 1426–2017

Construction of precipitation index based on ensemble forecast and heavy precipitation forecast in the Hanjiang River Basin, China

Construction of precipitation index based on ensemble forecast and heavy precipitation forecast in the Hanjiang River Basin, China

Tourism Ecological Efficiency and Sustainable Development in the Hanjiang River Basin: A Super-Efficiency Slacks-Based Measure Model Study

The Hanjiang River is one of the major tributaries of the Yangtze River, and the Hanjiang River Basin serves as an important link connecting the western and eastern regions of China. As a significant tourist destination, the development of the tourism industry in the Hanjiang River Basin is of great significance for promoting local economic, social, and environmental sustainability. The study of the spatiotemporal evolution of the ecological efficiency of tourism in the Hanjiang River Basin is beneficial for optimizing the investment of tourism resource elements and promoting ecological protection and high-quality development in the area. In this paper, we used the Super-Efficiency Slacks-Based Measure Model to calculate the tourism ecological efficiency of 12 cities in the Hanjiang River Basin from 2010 to 2019 and measured its spatiotemporal evolution and spatial agglomeration using ArcGIS software. The results show that during the study period, the ecological efficiency of tourism in the Hanjiang River Basin presented a phased characteristic of first rising and then falling over time and a heterogeneity characteristic of decreasing in a downward-middle-upward staircase pattern spatially. In addition, the distribution of tourism ecological efficiency exhibited obvious spatial clustering and dependence, with significant low-level homogenization phenomena, which requires strengthening cooperation and coordination among neighboring cities to achieve more efficient resource utilization and higher-quality tourism product development.

Study on non-point source pollution characteristics under different spatial scales: a case study of Hanjiang River Basin, China.

In recent years, the research on non-point source (NPS) pollution has been deepening, but it is focused on the large-scale watershed or region. There are a few studies on the scales of small watershed and runoff plot, and it is even less to analyze the characteristics and mechanism of non-point source pollution in certain watershed by combining three different scales. Based on the combination of natural rainfall monitoring and MIKE model simulation, the Shaanxi section of Hanjiang River Basin in China was taken as an example to study the characteristics of NPS pollution at different spatial scales. The results showed that there was an obvious correlation between rainfall and runoff/sediment yield. The order of runoff yield/sediment yield per unit area was as follows: woodland > forested and grassy land > arable land. There was a significant relationship between the loss of total phosphorus and the sediment yield in the runoff plots. The total nitrogen pollution was serious, with an average concentration of 3.8mg/L. The nutrient loss was in the form of nitrate nitrogen, with an average proportion of 63.06%. For small watershed scale, the characteristics of rainfall runoff pollution generation were like runoff plot scale, both have obvious initial scour phenomenon. However, compared with runoff plot scale, the pollutant loss concentration increases with a strong lag. The MIKE model based on the coupling of hydrology, hydrodynamics, and pollution load had a strong applicability in the basin. The critical source areas of NPS pollution were identified, and five scenarios were laid out in the areas for the control of NPS pollution. Centralized livestock and poultry farming had the best reduction effect.

Exploring the impacts of climate change and human activities on future runoff variations at the seasonal scale

Most watersheds around the world have been changing their natural flow patterns because of the coupled effects of climate change and human activities. Understanding the quantitative impacts of projected climate and human factors on future runoff variations is essential for adaptability assessment of water resource management, especially at the seasonal scale where their intra-annual changes are detected. In this study, an extended Budyko framework that combines traditional elasticity and decomposition methods is developed to analyze future seasonal runoff variations. The upper reach of the Hanjiang River basin (UHRB) in China is used as a case study. The case results are quantified and compared with the monthly ABCD model using historical hydrometeorological observations over 1961–2020 and near-future climate projections over 2031–2060 from the multi-model ensemble of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). We find that: (1) human activities including the operation of the Han-to-Wei inter-basin water transfer (IBWT) and reforestation projects have a substantial negative impact on runoff reduction in this region, accounting for a proportion of ∼70% in energy-limited seasons and ∼65% in water-limited seasons; and (2) the role of climate change projected by ISIMIP3b will intensify in energy-limited seasons, especially under the high-emission scenario with the increasing trend of effective precipitation,. Additionally, the performance of our extended Budyko framework is sufficiently robust to parameter disturbance experiments, indicating its applicability as an alternative for exploring future seasonal runoff variations.

Vegetation Dynamics and Their Response to Climate Changes and Human Activities: A Case Study in the Hanjiang River Basin, China

The Hanjiang River Basin (HJRB) is an important water conservation and ecological barrier area for the South–North Water Transfer Central Project. The quantitative analysis of regional differences in vegetation changes and their main drivers is important for the monitoring of the ecological environment of the basin and formulation of ecological protection measures. Based on MODIS13Q1 data from 2000 to 2020, spatiotemporal variation characteristics of vegetation in the HJRB were analyzed using Theil–Sen + Mann–Kendall, the Hurst index, and correlation analysis. Then, we detected the drivers using an optimal parameter geographic detector. The results showed that from 2000 to 2020, the average NDVI value increased from 0.651 to 0.737, with a spatial distribution pattern of “high in the northwest and low in the southeast”, and 88.68% of the study area showed an increase in vegetation cover, while 5.80% showed a significant degradation. The positive persistence of future vegetation changes is stronger than the negative. It may show a slowdown or degradation trend, among which the vegetation restoration along the Han River and urbanized areas need to be strengthened. The factor detector indicated that the main factors influencing vegetation change were topography and climate, for which the most influential variables, respectively, were elevation (0.1979), landform (0.1720), slope (0.1647), and soil type (0.1094), with weaker influence from human activity factors. The interaction test results showed that the interaction of various geographic factors enhanced the explanatory power of vegetation changes and showed mainly nonlinear and two-factor enhancements. The dominant factor varies between sub-basins; for example, the interaction between wind speed and land use conversion was the dominant factor in the middle reaches of the HJRB; the dominant factor in the lower reaches of the HJRB was expressed as the interaction between land use conversion and temperature. Finally, the effects of the range or category of different drivers on vegetation growth were systematically analyzed. The results of the study contribute to the understanding of the dynamic changes of vegetation based on a comprehensive consideration of the interaction of topography, climate, and human activities, taking into account the totality and variability of the geographical environment, and provide a reference for the ecological restoration and rational use of vegetation resources in the HJRB.

Characteristics and Driving Mechanism of Water Resources Trend Change in Hanjiang River Basin.

Studying the historical and future trends of water resources in a basin and explaining the causes of water resource changes is very important, which is key to the management of water resources in a basin. The Hanjiang River Basin is an important water supply source for southwestern Fujian and eastern Guangdong, but it has an uneven spatial and temporal distribution of water resources and an outstanding conflict between supply and demand. In this study, the SWAT model was used to simulate the conditions of the Hanjiang River Basin in the last 50 years, using long time series climate data to study the characteristics and driving mechanism of water resources trend change. The results show that the water resources in the basin have not increased significantly in the last 50 years, but evapotranspiration has increased significantly. The forecast results for water resources in the future are reduced. The water resource changes in the basin have been unevenly distributed in the last 50 years. Climate change has been the main factor in total water resource change in the basin, while the difference in water resource change trends within the basin is caused by land use. The key reason for the decrease in water resources in the Hanjiang River Basin is the significant increase in evapotranspiration due to the significant increase in temperature. If this situation continues, the available water resources in the basin will continue to decline. In fact, many basins around the world are currently likely to have such problems, such as the 2022 summer drought in the Danube River Basin in Europe and the Yangtze River Basin in China, so this article is informative and representative of future water resources management in the basin.

A Three-Parameter Hydrological Model for Monthly Runoff Simulation—A Case Study of Upper Hanjiang River Basin

Monthly hydrological models are useful tools for runoff simulation and prediction. This study proposes a three-parameter monthly hydrological model based on the proportionality hypothesis (TMPH) and applies to the Upper Hanjiang River Basin (UHRB) in China. Two major modules are involved in the TMPH: the actual evapotranspiration and runoff, in which the coupled water–energy balance equation and the proportionality hypothesis are used for calculation, respectively. It is worth mentioning that the proportionality hypothesis was extended to the partitioning of the available water into water loss and runoff at the monthly scale, which demonstrates that the ratio of runoff to its potential value is equal to the ratio of continuing water loss to its potential value. Results demonstrate that the TMPH model performs well when the NSE values are 0.79 and 0.83, and the KGE values are 0.86 and 0.78 for calibration period and validation period, respectively. The widely used two-parameter monthly water balance (TWBM) model and ABCD model are compared with the proposed model. Results show that TMPH performs better than TWBM model with NSE increased by 0.07 and 0.11, and KGE increased by 0.02 and 0.16, respectively, whereas the TMPH performs similarly as the ABCD model in the calibration period, and performs slightly better in the validation period, with NSE increased by 0.02, and KGE increased by 0.03. Sensitivity analysis show that the simulation result is most sensitive to parameter n, followed by SC and λ. In summary, the proposed model has strong applicability to the study area.

Application of Soil Moisture Data Assimilation in Flood Forecasting of Xun River in Hanjiang River Basin

Accurate projection of floods is of great significance to safeguard economic and social development as well as people’s life and property. The development of hydrological models can improve the level of flood projection, however, the numerous uncertainties in the models limit the projection accuracy. By adding observations to correct the operation of prediction models, the accuracy can be improved to some extent. In this paper, taking the Xun River, of the Hanjiang River Basin in China, as the research object, combined with the soil moisture satellite data obtained by the soil moisture active and passive satellite (SMAP), the ensemble Kalman filter (EnKF) algorithm was used to assimilate the upper soil water content (WU) of the Xinanjiang model. In addition, based on the simultaneous assimilation of state variables and parameters, two improved assimilation schemes were proposed here, namely, the augmented ensemble Kalman filter (AEnKF) scheme and the dual ensemble Kalman filter (DEnKF) scheme. The results showed that compared with the WU assimilation scheme, the simultaneous assimilation of parameters and WU improved the prediction ability of the Xinanjiang model to a greater extent. The two improved schemes had similar effects on flood prediction accuracy, and improved the overall Nash–Sutcliffe efficiency coefficient (NSE) from 0.725 for non-assimilated, and 0.758 for assimilated WU, to 0.781. Among them, AEnKF and DEnKF schemes, respectively, improved the NSE by 10.1% and 11% at maximum. This study demonstrated that the application of data assimilation for the Xun River effectively improved the flood forecast accuracy of the Xinanjiang model, which will provide a reference basis and technical support for future flood prevention and mitigation and flood projection in this basin.