Conventional studies of vegetated shear layers have typically modelled aquatic vegetation as rigid, upright structures to simplify submerged canopy hydrodynamics. While this approach has advanced the understanding of basic flow-canopy interactions, it overlooks vegetation bending and swaying, which are critical but challenging to characterize in flexible systems. As a result, comparative analyses of turbulent flow dynamics between flexible and rigid canopies remain limited. This study employs a coupled fluid-structure interaction framework to investigate turbulent open-channel flows across four vegetation configurations: (1) rigid upright canopy, (2) rigid curved canopy, (3) flexible canopy with constrained motion, and (4) flexible canopy with unconstrained sway. The hydrodynamics are resolved using Large Eddy Simulation, while vegetation motion is computed with the Vector-Form Intrinsic Finite Element method. This integrated approach enables systematic evaluation of how vegetation compliance modifies momentum transport. Results show that dynamic sway generates additional flow resistance beyond bending reconfiguration, amplifying shear-layer turbulence relative to rigid canopies. Under strong reconfiguration, unconstrained flexible canopies exhibit mean sway amplitudes approaching half the stem diameter, producing up to 60% enhancement in Reynolds stresses and 20% increase in turbulent kinetic energy within the canopy. Streamwise bending and sway also enhance mechanical dispersion, with dispersive stresses 10-50% greater than Reynolds stresses, and near-bed stress ratios peaking at 400%. Flow visualization reveals that streamlined bending suppresses flow-around effects and promotes symmetric secondary circulation, whereas lateral sway expands turbulence propagation and intensifies mixing. Momentum exchange is dominated by coherent Kelvin-Helmholtz vortices at the canopy-water interface, which transfer momentum 5-6 times more efficiently than other turbulent mechanisms. Streamlined reconfiguration reinforces turbulence organization, while multi-directional sway shifts transport deeper into the canopy, enhancing diffusion. These findings highlight the critical role of vegetation flexibility in shaping canopy hydrodynamics and provide new insights into turbulence-vegetation interactions in aquatic environments.

The Yellow River Delta (YRD), a crucial ecotone, is becoming increasingly polluted by antibiotics, posing serious threats to aquatic ecosystems and human health. In this study, comprehensive investigations were conducted to explore the regional distribution, environmental risks, and source apportionment of antibiotics, with the aim of facilitating precise management and control of antibiotic pollution. The results show that the surge in runoff during the water–sediment regulation period (June and August) of the Yellow River drove a sharp rise in antibiotic concentrations in the surface water, peaking at 135.0 ng/L, whereas antibiotics were rarely detected in the sediments after multiple rounds of intense hydraulic scouring (0.2~12.6 ng/g in October). Furthermore, seven antibiotics (sulfadiazine, sulfamethoxazole, flumequine, ofloxacin, tetracycline, doxycycline, and lincomycin) in surface water and six antibiotics (norfloxacin, enrofloxacin, ofloxacin, doxycycline, oxytetracycline, and florfenicol) in sediments were identified as representative compounds according to the antibiotic pollution profiles. Environmental risk assessment coupled with spatial autocorrelation analysis revealed that sulfamethoxazole generally posed medium to high risk (0.12~1.27) in surface water. Sediments posed more serious ecological risks, with universally high risk levels (ranging from 1.11 to 280.00). More importantly, in both surface water and sediment, four core antibiotic sources—namely, human sewage, livestock farming, agricultural and aquaculture inputs, and hydrodynamic-driven resuspension processes—were consistently identified through the Positive Matrix Factorization model and Kriging interpolation. These findings provide crucial insights for establishing targeted antibiotic pollution control strategies in the YRD and advance the understanding of antibiotic fate in sediment-laden rivers.

In recent years, machine learning has been widely applied in sediment forecasting; however, existing research has largely focused on the model algorithms themselves, with insufficient integration of the mechanistic characteristics of sediment transport processes (such as the response mechanism of sediment peaks and time-lag effects). This has resulted in limited forecasting capabilities for sudden sediment peaks in complex river basins. To address the challenge of highly abrupt and spatio-temporally variable sediment transport processes in the typical high-sediment- concentration basin of the middle Yellow River, this study proposes a machine learning model for sediment forecasting (ML-P-EF) incorporating a structural enhancement mechanism. Building upon LSTM (Long Short-Term Memory), ANN (Artificial Neural Network) and Transformer models, systematically incorporates Sediment Process Vectorization (SPV), Dynamic Lag Encoding (DLE) and Event-Driven Features (EDF) to enhance the model’s ability to perceive and simulate sediment physical processes. Through validation in typical catchment areas of the Jing River and Beiluo River in the middle reaches of the Yellow River, taking Hongde Station as an example, for a 1-hour forecast horizon, the ANN-P-EF model achieved a Nash efficiency coefficient (NSE) of 0.97 and a Root Mean Square Error (RMSE) of 52.31 kg∙m⁻3, and the peak sediment error was –3.81 %. Under a 6-hour forecast horizon, the NSE of the LSTM-P-EF model improved by more than 40 % compared to the LSTM-P and LSTM models, the RMSE decreased by over 50 %, and the peak error was reduced by 6 %–12 %. This study innovatively integrates the mechanisms of sediment transport processes with deep learning models, enhancing the model’s ability to capture the non-linear response of sediment peaks. It provides a new technical approach to accurate sediment forecasting in high-sediment river basins, with significant implications for regional water and sediment management as well as disaster prevention and mitigation.

Floods are among the most devastating natural disasters worldwide, posing severe threats to human society and economic development. Conducting a comprehensive and scientifically grounded flood risk assessment is therefore essential for effective disaster prevention and mitigation. In this context, the rational definition of disaster-bearing elements and the establishment of a multivariate evaluation framework are key to improving assessment accuracy. This study focuses on the middle reaches of the Yellow River, where a multivariate joint distribution model of flood risk was constructed based on the Copula function, integrating human society and ecosystem as disaster-bearing bodies. Following the IPCC framework of “Risk = Hazard × Exposure × Vulnerability”, soil erosion was introduced as an indicator of ecosystem vulnerability to explore the spatiotemporal patterns of flood risk. The results reveal that the Fenhe, Qinhe, Jinghe, Weihe, and Yiluo River basins represent high-risk flood areas, while the central, western, and northern parts of the study area exhibit relatively lower flood risks. The Beiluo, Jinghe, and Fenhe River basins are more prone to extreme events where both flood peak and volume exceed design thresholds, indicating higher flood risk. Flood risk in the Jinghe, Weihe, and Fenhe River basins is primarily driven by population density and construction levels, whereas the influence of ecosystem factors such as vegetation cover and soil erosion is relatively weaker. This study integrates human-society and ecosystem disaster-bearing elements within a unified analytical framework, enriching the theoretical and methodological foundation of flood risk assessment and providing a scientific basis for regional flood prevention and mitigation planning in the Yellow River Basin.

Abstract The Yellow River Estuary is the primary depositional zone for sediments from the Yellow River Basin and supports a key wetland ecosystem in the warm temperate zone. However, pressures from river, marine and anthropogenic activities have led to challenges related to flood discharge, delta morphology and wetland ecological function. This study employed statistical analysis, remote sensing, numerical modelling and physical experiments to calculate the thresholds for water and sediment necessary to sustain channel discharge capacity, maintain delta equilibrium and meet wetland ecological water demands. A multi‐objective collaborative allocation model for water and sediment in the Yellow River Delta was developed and applied. Results indicate that lower incoming sediment coefficients during flood seasons enhance flood discharge capacity. Increased sediment load and coarser grain sizes accelerate the expansion of estuarine sand spits. The critical sediment load to maintain area balance is approximately 1.31 × 10 8 t for the Qingshuigou Nature Reserve (QSGNR) and 0.59 × 10 8 t for the Diaokouhe Nature Reserve (DKRNR). Delta imbalance continues to worsen due to erosion in the DKRNR and accretion in the QSGNR, with the imbalance coefficient reaching 0.1202 in 2020. Total annual ecological water demand for the delta wetlands is about 367.18 × 10 6 m 3 , with 288.8 × 10 6 m 3 allocated to QSGNR and 78.38 × 10 6 m 3 to DKRNR. The flood discharge capacity competes with both the delta landform equilibrium and the wetland ecological function, whereas the delta landform equilibrium and the wetland ecological function are synergistic. In low‐flow years, priority should be given to maintaining flood discharge capacity while meeting wetland water needs. In normal and high‐flow years, appropriately increasing water and sediment diversion to the Diaokou River can help mitigate delta imbalance trends and satisfy wetland ecological water needs. This research offers scientific guidance for the multi‐objective allocation of water and sediment in the Yellow River Estuary.

Accurately assessing habitat quality is crucial for ecosystem conservation. The Remote Sensing Ecological Index (RSEI), which incorporates soil moisture (WET), was widely used to assess regional habitat quality. However, in coastal wetlands, the performance of the WET index is often undermined due to high soil salinity caused by seawater intrusion. Therefore, a Water-Salt Stress (WSS) index was developed by integrating WET and salinity (SI) to quantify the combined water-salt stress on vegetation. Replacing WET with WSS, an improved index, Water-Salt Stress Remote Sensing Ecological Index (WSRSEI), was established for habitat quality assessment. Results show higher WSS (> 0.7) in tide-influenced coastal areas and lower WSS (< 0.4) near inland channels. In high-WSS zones, WSRSEI was significantly lower than RSEI. The correlation between WSS and WSRSEI (R 2 = 0.850) was substantially stronger than that between WET and RSEI (R 2 = 0.345), confirming WSS as a superior index. Long-term analysis (1985–2025) revealed a slight overall decline in the delta's WSRSEI (from 0.631 to 0.621). Habitat quality improved in the Qingshuigou Nature Reserve (WSRSEI: 0.565 to 0.627) due to sediment input and post-2008 ecological water supplementation, but declined in the Diaokou River Nature Reserve (0.621 to 0.554) due to coastal erosion and saltwater intrusion. Engineering structures such as levees and farm dikes effectively mitigated seawater intrusion. Since 1985, the expansion of aquaculture ponds, bare land, and built-up areas has been a major factor in the overall decline of habitat quality. This study provides a refined methodological framework for coastal habitat quality assessment.

Understanding how vegetation phenology affects evapotranspiration (ET) is essential for evaluating hydrological responses under climate change. However, the quantitative contribution of phenological shifts to ET remains unclear. In this study, we extracted the start of the growing season (SOS), end of the growing season (EOS), and growing season length (GSL) across China from 1982 to 2018 using the TIMESAT method, and incorporated dynamic phenological parameters into the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model. The improved model reduced ET simulation errors by 20–40

Sediment coarsening in submerged deltas is commonly attributed to seabed erosion because of insufficient sediment input. The Yellow River subaqueous delta (YRSD) has exhibited distinct coarsening patterns following both accretion and erosion events. To investigate these contrasting mechanisms, grain size distributions, elevation changes, and bottom shear stress patterns were analyzed across the delta from 1992 to 2022. The results revealed distinct sedimentary patterns among the abandoned YRSD, active YRSD, southern Laizhou Bay, and adjacent Bohai Sea, with average median grain size (D50) increases of 17, 17, 6, and 0 μm, respectively. Sediment coarsening occurred primarily from 1992 to 2000, when the river mouth position was artificially altered and fluvial sediment grain size increased from 16 to 29 μm. From 1992 to 2015, the active YRSD experienced accretion at a rate of 7.8 mm/yr. Moreover, the abandoned YRSD and southern Laizhou Bay experienced significant erosion. The erosion rates were −5.1 and −1.0 mm/yr, respectively. This led to the identification of two mechanisms of sediment coarsening: erosion-driven coarsening in sediment-deficient areas and accretion-driven coarsening where the input sediment grain size increased. Although marine processes did not intensify during this period, the bottom shear stress distribution changed substantially due to morphological evolution, with correlation coefficients between grain size and shear stress showing increasing trends in littoral zones. This strengthening relationship, coupled with the declining fluvial sediment load, demonstrates the YRSD transition from river-dominated to wave-dominated processes, providing important insight into delta evolution under changing sediment regimes. The insights gained can guide Yellow River Delta management through targeted strategies and provide essential evidence for predicting delta evolution.

Vegetation restoration is widely regarded as a key measure for mitigating soil erosion in the middle reaches of the Yellow River. However, the regulation of flood sediment transport by forest-grass vegetation coverage (Ve) shows strong nonlinear characteristics and threshold effects. This makes it difficult for traditional physical models to accurately describe sediment production responses across different vegetation stages. To address this issue, this study proposes a stage-specific water-sediment simulation framework that integrates Ve threshold identification with machine learning, namely a threshold-aware modeling framework. Four typical basins in the middle reaches of the Yellow River, namely the Kuye, Wuding, Fen, and Wei River basins, were selected as the study areas. Based on multi-source data from 1979 to 2025, including flood events, rainfall, Ve, and land use, random forest was first used to identify the dominant factors controlling sediment production. The exponential function, piecewise linear regression, and Copula model were then combined to identify Ve threshold bands from three perspectives: functional form, structural breakpoint, and probability dependence. Based on the identified thresholds, threshold information was embedded into LSTM, XGBoost, and SVR models to construct overall and stage-specific simulation scenarios.The results show that: (1) Ve and rainfall are the key factors controlling sediment production; (2) clear Ve threshold bands exist in all basins, with recommended ranges of 31%∼35% for the Kuye River Basin, 27%∼32% for the Wuding River Basin, 23%∼28% for the Fen River Basin, and 20%∼23% for the Wei River Basin; (3) model performance improved significantly after introducing threshold information. Taking the Kuye River Basin as an example, the NSE values of the LSTM, XGBoost, and SVR models increased to 0.858, 0.861, and 0.830, respectively; and (4) vegetation exerted a strong regulatory effect on suspended sediment concentration in the pre-threshold stage, whereas the system gradually shifted to a sediment-supply-limited state in the post-threshold stage, with a markedly weakened marginal vegetation effect. This study reveals the stage-specific regulatory mechanism of vegetation restoration on water-sediment processes. It also provides new theoretical and methodological support for intelligent modeling of complex water-sediment systems and ecological management of river basins.

Study region The Jiahetan to Gaocun section floodplain of the Lower Yellow River (LYR). Study focus The LYR exhibits a typical wide-shallow compound channel morphology, with frequent floodplain flooding driven by geographical location and upstream hydrological variations. As roughness is a critical parameter governing floodplain flood evolution, this study developed a 2D hydrodynamic model using the finite volume method and unstructured triangular grids. The model incorporated Yellow River sediment effects and introduced frictional thickness in roughness calculations. Three roughness values were assigned based on floodplain land use, combined with five flood peaks to design 15 scenarios, aiming to quantify the mechanism by which roughness influences water depth, velocity, and their spatial distributions during floods. New hydrological insights for the region Concave banks are prone to overflow under terrain and scouring effects. When flood discharge <= 10,000 m(3) /s, roughness significantly impacts water-depth distribution at 0-2.0 m. When discharge > 10,000 m(3) /s, its influence on water-depth distribution above 2.0 m becomes prominent. Overbank velocities concentrate at 0-0.5 m/s, showing nonlinear relationships with roughness. Introducing frictional thickness improves roughness calculation accuracy for sediment-laden rivers and flood inundation modeling. Regulating hydrodynamic distributions based on roughness-flood relationships reduces flood losses, supporting coordinated flood control and socioeconomic development in the LYR floodplain.

River deltas are highly vulnerable coastal areas, particularly susceptible to human interference and environmental changes. Due to reduced sediment supply and strong coastal dynamics, deltaic coasts are struggling to maintain shoreline progradation, necessitating urgent needs for integrated and data-enriched vulnerability assessments to address erosion risks. However, previous studies have often been constrained by observational datasets and lacked a systematic analysis of erosion vulnerability in deltaic coasts during varying periods under combined human activities and climatic changes. The modern Yellow River Delta (YRD) serves as a typical riverdominated delta system, currently undergoing geomorphic transition and facing erosion risks due to changing environmental conditions. This study integrates methods of numerical modeling, Analytic Hierarchy Process (AHP), and Coastal Vulnerability Index (CVI) to develop a comprehensive framework for assessing coastal erosion vulnerability. We then apply this framework to evaluate the erosion vulnerability of different coastal segments of the YRD, using multi-year hydro-geomorphic and human-interfered indicators. The results reveal that the overall erosion vulnerability of the YRD has decreased during 1992-2015, characterized by pronounced spatial and temporal disparities. The abandoned northern YRD exhibits high erosion vulnerability, primarily due to the absence of fluvial sediment supply, strong coastal currents, and the additional impact of wave climate. In contrast, the artificially protected coasts and western Laizhou Bay show relatively lower vulnerability marked by coastal protection measures and weak hydrodynamic influences. The segment of Qingshuigou delta lobe shows high vulnerability in sediment-starved areas, attributable to both engineered diversions and natural channel migration. Future management strategies should integrate coastal protection measures with wetland restoration efforts, as well as optimize riverine water and sediment regulation within the river basin and deltaic channels to enhance riverine sediment delivery to severe erosion zones.

Land subsidence in river deltas, particularly in the Yellow River Delta (YRD), represents an urgent environmental concern driven by both human activities and natural factors. This study provides a comprehensive analysis of land subsidence in the YRD region from 2019 to 2022 using multi-temporal InSAR data from Sentinel-1A. Results reveal that the maximum annual subsidence rate in the YRD exceeds 200mm/a, with the primary subsidence area located in the northeastern part of the delta, forming a subsidence funnel of approximately 200 km2 and displaying distinct spatial heterogeneity. Human activities, especially saltwater extraction and oil exploitation, are the main drivers of land subsidence. Areas heavily influenced by human activities show significantly greater subsidence than well-protected ecological zones. The study reveals pronounced seasonal variations in land subsidence across the YRD, with subsidence rates in summer being substantially lower than those in spring, autumn, and winter. By introducing the concept of equivalent precipitation, the research confirms that runoff exerts a regulatory effect on land subsidence, although its impact is considerably weaker than that of precipitation. This study proposes a novel explanatory mechanism: the expansion-contraction properties of surface soil explain how seasonal hydrological conditions influence subsidence patterns. During rainy summers, surface soil absorbs water and expands, partially offsetting subsidence caused by deep extraction. These findings provide valuable insights into the interactions between human activities and natural factors in complex deltaic systems, offering a scientific basis for subsidence monitoring and sustainable resource management in the YRD region.

The watershed of the Yellow River is an important water conservation area in the Yellow River Basin. Its fragile ecological environment, climate change and unreasonable human activities have led to the continuous degradation of plant community structure in the watershed. This study only considers environmental factors, based on MaxEnt, Garp and other niche models and spatial-temporal analysis methods such as Mess and MoD analysis, to explore the suitable areas of Salix oritrepha Schneid. (First published in C.S.Sargent, Pl. Wilson. 3: 113 (1916)) and Picea crassifolia Kom. (First published in Bot. Mater. Gerb. Glavn. Bot. Sada R.S.F.S.R. 4: 177 (1923)) in the watershed of the Yellow River under different emission scenarios in the future. The results show that the MaxEnt model has a good simulation effect. In terms of spatial distribution, the suitable areas of the two species are mainly concentrated in the southeastern part of the Yellow River source area. Compared with the current period (1970–2000), by 2070, the suitable areas of the two species in each scenario showed a distribution of high in the east and low in the west, with an obvious expansion trend in the area and moving to high altitude and high latitude. According to the analysis of Mess and MoD, the annual average temperature (Bio_1) may be the most important variable affecting the future distribution of the two vegetation types.

The Qingshuigou Channel, as the current tail channel of the Yellow River, formed by the diversion of the Diaokou River in 1976, has undergone a particularly dramatic spatio-temporal evolution, and its evolution processes and the underlying mechanisms are still unclear. On the basis of the flood season cross section data for the river downstream of the Lijin Hydrological Station from 1976 to 2017, the current study calculated the main channel morphological characteristics of the tail channel in different reaches using a reach-scale morphological parameter calculation method and K-means clustering analysis. An elevated riverbed index was proposed to identify the elevated riverbed situation of the river channel. The results show that from 1976 to 2017, the bankfull area experienced repeated processes of decrease and increase, and the main channel morphology gradually changed from wide and shallow to narrow and deep over time. For most of the time period, the conveyance capacity of the main channel gradually decreased from upstream to downstream. The elevated riverbed situation gradually became more severe along the river reach from 0 to 85 km away from Lijin, but was less severe in the reach more than 85 km downstream of Lijin. The most severe elevated riverbed situation appeared mainly in the range of 71–83 km below Lijin in 1991–1995. When the sediment-carrying capacity of the water flow was strong, the bankfull area of the main channel increased, and the elevated riverbed situation was alleviated. River channel projects have helped to maintain the narrow and deep shape of the main channel, but the installation of farm dikes have aggravated the elevated riverbed situation. At the same time, extension and diversion of the tail channel have changed the erosion base level, greatly affecting the evolution of the channel morphology. The current study has provided a typical case for exploring the processes and mechanisms of tail channel evolution.

为综合评价黄河口及毗邻海域的生态系统健康状况,借鉴驱动力-压力-状态-影响-响应(DPSIR)模型,选取人口增长率、年径流量、富营养化指数、浮游植物多样性指数、生态环境治理投资等28个反映生态系统健康状况的因子建立评价指标体系.基于实测和统计数据等基础资料,采用层次分析法和综合指数法对2011—2020年黄河口及毗邻海域生态系统健康状况和变化趋势进行评价.结果表明:2011—2020年黄河口及毗邻海域生态系统基本处于亚健康与健康状态,2018年以来,随着黄河口生态水量调度力度加大,生态系统健康状况显著向好.

[Objective] The ecological carrying capacity of water resources in the lower reaches of the Yellow River basin was evaluated in order to provide a theoretical basis for regional water resource management and planning. [Methods] The spatial and temporal distribution characteristics and driving mechanism of the ecological footprint of water resources in the lower reaches of the Yellow River basin from 2007 to 2020 were calculated and analyzed using the theory of ecological footprint of water resources and the logarithmic mean Divisia index method (LMDI), The grey forecasting model GM(1,1) was applied to predict the change trend of the ecological footprint of water resources from 2021 to 2030. [Results] The ecological footprint of water resources in the lower reaches of the Yellow River over the study years was much greater than the ecological carrying capacity, and the ecological deficit of water resources was serious. Both the ecological footprint of water resources and the ecological deficit showed a fluctuating and decreasing trend over years, and the efficiency of water use was gradually improving. Agricultural water consumption was the largest factor accounting for the ecological footprint of water resources. The Yellow River Delta was the area with the greatest ecological pressure on water resources in the lower reaches of the Yellow River basin. Zibo, Jinan, Zhengzhou, and Tai’an City had relatively little ecological pressure. The economic effect had a major positive role in the change of the ecological footprint of water resources in the lower reaches of the Yellow River basin, and the technical effect had a major negative role. The forecast results indicated that the ecological deficit of water resources per capita in the lower reaches of the Yellow River basin would decrease from 0.387 to 0.359 hm2/person from 2021 to 2030. [Conclusion] The water use efficiency in the lower reaches of the Yellow River basin has gradually increased over time, and the ecological pressure on water resources has been relieved to a certain extent under the comprehensive effects of rapid development of productivity and optimization and adjustment of water-using structures. However, the sustainable utilization of water resources in the future is still under very serious pressure due to the large deficit in the ecological base of water resources in this region. It is therefore urgent to further strengthen the overall management of water resources in order to help the lower reaches of the Yellow River basin achieve high-quality sustainable development.

传统的插值方法在采样河道地形横断面分布稀疏情况下,无法有效获得弯曲河道高精度插值结果,亟须研究有效的弯曲河道地形插值新方法,对河道演变及其规律进行定量分析.为此,提出了一种基于正交曲线网格的河道地形插值方法(OCGI),首先沿横断面进行线性插值,然后沿纵向网格线进行插值,弥补了纵向采样点空间分布不足的缺陷,且考虑了河势变化,将插值范围控制在网格分布的区域.应用实例表明:该方法比反距离加权法(IDW)和普通克里金法(KG)能得出更合理的结果,可应用于黄河口尾闾等弯曲河道的地形插值.

The estuarine sandspit is in the river-sea interaction zone, where it shows fast responses to changes in water and sediment volumes.High-resolution satellite and hydrological data were used to analyze the current Yellow River water and sediment regime and the evolution of the estuarine sandspit.The results show that: ① The water and sediment input into the sea have continuously decreased since 1999, and its transport has changed from linear to periodic fluctuations, with a fluctuation period of 6—8 a.It is in a rising period from 2018 to 2021.From 2018 to 2021, the water and sediment input have increased.② Since 2018, the northern and eastern branches of the estuarine sandspit have alternately become the main body of siltation with an average river extension length of 0.7 km, and an average land creation zone of the sandspit with an area of 16.9 km2.③ Even though less water and sediment entering the delta are not conducive to the seaward deposition of the delta, the area of estuarine the sandspit will still increase due to the strong sediment transport into the sea brought on by extreme runoff.④ The branching pattern slows down along the length of the river extension, but the land creation in sandspit is speeding up.These factors positively affect the long-term stability of the Qingshui channel.However, it is not conducive to the ecological security of the estuary wetland.

The East China Sea is an ocean region with frequent typhoons, typhoons are also the main reason for inducing typhoon waves. The complexity of typhoon waves is closely related to the complexity of typhoon wind field. In previous studies, symmetrical wind field models or superimposed wind field models were usually used to simulate typhoon waves. However, the actual wind fields are asymmetric, and the asymmetry is affected by many factors. Therefore, three wind field models are used to simulate the wind field of Typhoon Muifa that moved through the East China Sea. Moreover, the abovementioned wind field models are used to drive the third-generation wave model SWAN to simulate the wave field and wave spectrum of Typhoon Muifa. Studies show that the values generated by the asymmetric wind field model are most consistent with the actual measurement data. This is especially the case if the typhoon center is closer to the station. The accuracy of the typhoon waves simulated using the asymmetric wind field model is better than that of the other two wind field models. The asymmetric wind field can reflect the asymmetric characteristics of the typhoon well. The substantial wave heights on the right side of typhoon’s path are substantially higher than those on the left side of path. Additionally, the maximum wave spectral density and total energy of waves as simulated by the asymmetric wind field are both larger than those of the other two wind field models. Thus, the asymmetric wind field model is more suitable for the numerical simulation of typhoon waves in the East China Sea.