To address the complex multi-valuedness of stage-discharge relationships in mid-lower river reaches and the weak generalizability of traditional data-driven models，this paper proposes a generalized modeling approach integrating hydrodynamic mechanisms. Based on the Saint-Venant equations，a physical feature set comprising water level （Z），temporal rates of change （dZ/dt, dQ/dt），and Froude number （Fr） is coupled with a BP neural network. Nine representative cross-sections across China’s seven major river basins are selected for evaluation. Results show that Fr significantly enhances the capture of complex hydrodynamic features，achieving Nash-Sutcliffe Efficiencies （ENS） approaching 1. However，improvements from dZ/dt and dQ/dt are site-dependent，excelling in deep-channel sections but limited elsewhere by data noise. The proposed framework demonstrates robust cross-basin universality，effectively overcoming the downstream boundary limitations of traditional models and providing reliable support for real-time flood forecasting and digital twin applications.
Hydrological modeling is often constrained by small-sample problems in certain regions，leading to a significant decline in performance. To address this issue，this paper proposes a Gamma Convolutional Neural Network (GCN) that integrates hydrological prior information. It characterizes the time-lag response mechanism of rainfall-runoff using the Gamma distribution probability density function. A runoff weight allocation structure is incorporated to explicitly represent basin storage effects and multi-timescale runoff components. Moreover，GCN jointly optimizes hydrophysical parameters and network weights in an end-to-end manner to achieve adaptive parameter learning. In the test conducted at the Shijiao Station in the Beijiang River Basin，the multi-kernel GCN achieved a Nash-Sutcliffe efficiency coefficient (ENS) of over 0.90 under full-sample conditions，outperforming the Xin’anjiang Model (XAJ) and Multilayer Perceptron (MLP). For a minimal sample size of 5%，ENS maintained a value of 0.86 (XAJ: 0.67，MLP: 0.73). The obtained Gamma parameter exhibited regional adaptability，offering a plausible interpretation of the runoff’s time-lag characteristics. The study demonstrates that GCN effectively improves both data efficiency and interpretability in small-sample hydrological modeling.
Cascade reservoir development has significantly altered water and sediment sources in the Upper Yangtze River,with tributary inputs in reservoir areas becoming the dominant control on sediment supply.This study analyzed observations from 16 tributary stations and combined statistical analysis,XGBoost modeling,and scenario simulations to quantify multi-factor controls on water and sediment fluxes.Runoff showed relatively minor changes,whereas sediment loads declined sharply.During 2007-2020,tributary sediment input to the Three Gorges Reservoir was only 34.2％of that in 1970-1987.In the Lower Jinshajiang River,tributary sediment loads decreased by 13.6％—90.6％.The monthly models for specific runoff and specific sediment yield performed well(R2＞0.74).Rainfall,month,and topography explained 80.2％of runoff variability,while run off,rainfall,reservoir impact,land use,and month explained 81.0％of sediment variability.Future projections(2026-2056)suggest slightly lower mean annual rainfall than historical levels.The Beibei Station of the Jialingjiang River is projected to deliver about 35.74 Mt/a,remaining the main sediment source to the Three Gorges Reservoir.High specific sediment yields are concentrated in several tributaries of the Lower Jinshajiang River,reaching up to～829 t/(km2·a).These findings clarify the spatiotemporal evolution of tributary water and sediment inputs in the Upper Yangtze River.
This study aimed to address challenges associated with diverse dam failure triggers，complex transmission pathways，and difficulties in quantifying associated losses. By leveraging Scrapy-based web crawlers and literature analysis to collect multisource data，we developed regular expression sets to extract transmission pathways，which were categorized into four major types: meteorological，geological，engineering/management，and biological. Quantum encoding，incorporating failure potential energy and frequency factors，yielded four topological configurations applicable to risk assessment. A credibility evaluation mechanism was introduced to validate the robustness and reliability of the quantum topology analysis. The results demonstrate that the knowledge graph extracted 70 typical pathways，whereas quantum topology analysis revealed that engineering/management factors exhibited the highest topological vulnerability index，followed by meteorological and geological factors，with biological factors showing the lowest values. Path credibility aligns with node connectivity patterns，and topological vulnerability levels correspond to actual disaster mechanisms across different risk categories. The proposed “graph-topology” technical paradigm enables the structured integration and quantification of dam failure risk transmission pathways，thereby providing a novel analytical framework for risk pathway evaluation.
Microtopography in low-relief plain regions exerts a significant regulatory influence on rainfall-runoff processes through dynamic ponding-detention effects. However，quantitatively describing these effects and integrating them into hydrological models remain challenging. Based on field hydrological monitoring in the Taihu Basin，an elementary microtopographic unit method was developed to provide an organized characterization of microtopographic structures. A dynamic depression-filling algorithm was then established to efficiently simulate ponding-detention processes. Furthermore，a microtopographic detention curve was formulated to quantify these dynamic effects，and a hydrological model incorporating microtopographic effects was proposed. The algorithm and model were applied and validated using high-resolution topographic data from seven typical microtopographic regions，along with field monitoring data from experimental stations. The results demonstrate that while maintaining physical consistency，the algorithm reduces the number of units involved in computations by 1-2 orders of magnitude. The microtopographic detention curve achieves a high goodness-of-fit to the ponding-detention process，with an average correlation coefficient of 0.99. The model effectively captures early-stage rainfall detention and late-stage delayed-release processes，reducing runoff simulation errors by 19%. Overall，the proposed approach provides an effective pathway for quantifying dynamic ponding-detention effects and advances the process-based framework of hydrological modeling in plain regions.
Evaporation from saturated bare soil is the basis for estimating actual evaporation rate，although it is often substituted with open-water evaporation in practical applications. Systematic monitoring and driving factor analysis on the differences in evaporation between saturated bare soil and water surfaces remain insufficient. In this study，evaporation differences between saturated bare soil and water were investigated based on high-frequency field measured data from lysimeters. Combined with the energy balance and vapor transport mechanisms of the evaporation process，we analyzed the driving factors of evaporation differences between these two water-sufficient media using sensitivity analysis and machine learning methods. The results indicated that annual evaporation from saturated fine sand was higher than that from water，with a 21% increase during the summer. The surface energy balance equation coupled with surface temperature，as well as the vapor transport model can quantitatively resolve the observed evaporation differences. It was found that the discrepancy in evaporation from the two surfaces was caused by differences in responses to temperature. Specifically，evaporation from saturated soil was dominated by rapid responses to temperature，while evaporation from water resulted from thermal inertia and heat-capacity buffering. Additionally，both temperature and heat flux were critical determinants of differences in evaporation from water sufficient media and the accurate calculation of potential evaporation rates over sub-daily scale.
Quantifying the mechanisms driving the runoff-sediment relationship under different rainfall intensities is crucial for overcoming cumulative effect limitations and accurately characterizing the evolution of watershed erosion dynamics.This study focuses on the Beiyuhe River,a tributary within the Jialingjiang River system.Utilizing meteorological,hydrological,and land surface data from 2007 to 2020,264 erosive rainfall events were identified,and a partial least squares-structural equation model(PLS-SEM)was constructed to quantitatively analyze the variation characteristics and dominant drivers of the runoff-sediment relationship across different rainfall intensities.The results indicated that the Beiyuhe River watershed exhibits a typical low runoff coefficient and high sediment concentration.High-intensity events(daily maximum precipitation＞30 mm)were the critical drivers of sediment transport,contributing to approximately 44％of the average annual total sediment load on a per-event basis.As precipitation intensity increased,the water and sediment relationship transitioned from a highly dispersed state(R2=0.07-0.09)to a significant power function relationship(R2=0.94).PLS-SEM analysis revealed that the explanatory power of runoff for sediment variation increased significantly with precipitation intensity(the path coefficient rose from 0.519 to 0.922),with sedient transport primarily regulated by the synergy of antecedent precipitation and runoff characteristics.In addition,the regulatory effects of land surface factors,including temperature,vegetation,and sediment connectivity,became more prominent in determining suspended sediment concentration during high-intensity rainfall events.These findings provide a scientific basis for the precise prevention and control of soil erosion in small mountainous watersheds.
The spatiotemporal heterogeneity of rainfall is one of the key factors affecting the accuracy of flood forecasting. In this study，a deep learning framework combining rainfall spatial features and flood process features is proposed. Based on the coupled graph convolutional neural network （GCN） and long short-term memory network （LSTM），the adjacency matrix with rainfall stations as nodes is used to improve the LSTM input module，and the runoff process vectorization method （RPV） is introduced to construct the GCN-RPV-LSTM flood process forecasting model. The model was trained and validated by 34 measured rainfall-runoff data in the the control basin of Jialuhe River at Zhongmou Station，and the results were compared with GCN-LSTM and RPV-LSTM models. The results show that: ① The GCN-RPV-LSTM model has the best flood forecasting accuracy. Under the lead time of 6 h，the Nash efficiency coefficient （ENS） in the validation period is 0.014 and 0.076 higher than that of the two comparison models，respectively. ② The model is more accurate for the forecasting of bimodal floods，and the ENS is above 0.9. It also has significant performance advantages in high-flow flood forecasting. ③ The model has stronger robustness，but there are underestimation of flood peak and delay of peak time. In the future，the model structure can be further improved to provide scientific decision-making basis for flood control safety of the basin.
Under the dual influence of climate change and human activities，the rainfall-runoff relationship in river basins exhibits non-stationary characteristics，and hydrological models based on the traditional steady-state assumption struggle to capture the structural transitions of the system，leading to a decline in runoff simulation performance. In this paper，hydrological states are used as the representation of non-stationarity，and Hidden Markov Models (HMMs) with 1 to 3 states are constructed，the Viterbi algorithm is adopted to identify the hydrological states of the river basin and their evolution paths，and the decoded hydrological state results are introduced into the runoff simulation process to address the impact of non-stationarity. Experiments are conducted based on 240 typical river basins in the CAMELS database，and the results show that: approximately 22.5% of the river basins have significant multi-state hydrological characteristics; the multi-state model with hydrological state constraints shows significant advantages in both probabilistic simulation and deterministic simulation，with the uncertainty interval coverage rate increased by an average of about 30% compared with the single-state model; the Nash-Sutcliffe efficiency coefficient increases from 0.45 to 0.83 (an increase of about 84%)，and the root mean square error decreases by an average of about 49%. Research shows that: the HMM-based hydrological state identification method can effectively capture hydrological state transitions，and incorporating state information into the simulation process helps improve runoff simulation performance under non-stationary conditions，providing a new path for hydrological simulation in a changing environment.
As an important region underpinning ecological security and water-resource balance in Northern China，the Loess Plateau’s ecohydrological regulation plays a pivotal and strategic role in ensuring water security across the Yellow River Basin. However，in the wake of large-scale ecological restoration，the region has been confronted with a complex combination of both emerging and historical challenges，such as vegetation degradation，progressive soil desiccation，and abrupt declines in runoff and sediment yield. The conventional single-objective regulation model centered on “soil conservation and greening” is now inadequate for the realities of multi-objective and coordinated management. This study demonstrated the necessity of a paradigm shift from a “soil conservation and greening” focus toward a system-equilibrium framework that integrates water，ecological，economic，and social dimensions. The core content of this paradigm innovation was proposed across five dimensions (objectives，concepts，scales，assessment，and governance)，and an enabling pathway was established based on an intelligent，closed-loop technological system encompassing “sensing-cognition-prediction-regulation.” Building on this framework，five priority frontiers in fundamental science requiring breakthrough advances were identified: deep vadose-zone water cycling，vegetation water-use adaptability，rebalancing of water-sediment regimes，coupled water-carbon-nitrogen processes，and socio-ecological system modeling. This study provides a scientific foundation and decision-making reference for integrating ecological conservation and water security on the Loess Plateau. Future progress will depend on the coordinated advancement of science and technology，policy instruments，and engineering interventions to support high-quality regional development and the long-term goal of harmonious human-water relations.
Existing studies on the channel storage increment during freeze-up periods in cold-region rivers have rarely addressed its differentiation under non-overbank and overbank conditions. Based on the self-regulation and delayed-response theory of river systems，and employing the Muskingum method while considering the phase transitions (liquid to ice) of channel storage，a delayed-response model for the channel storage increment (including liquid water and ice volume) during the freeze-up period is developed in this study. By statistically analyzing the different influencing factors and variation patterns of model parameters under non-overbank and overbank conditions，the differentiation mechanism of the channel storage increment is revealed. Taking the Inner Mongolia reach of the Yellow River as a case study，through the model parameters calibration，the variation process of the channel storage increment during the freeze-up period is simulated. The simulation results are found to be in good agreement with the measured data，with the coefficient of determination and the Nash-Sutcliffe efficiency coefficient reaching 0.95 and 0.96，respectively. The analysis of model parameter variations reveals a significant positive correlation between the liquid water storage coefficient and the ratio of initial channel storage to bank-full discharge，reflecting the effect of increased flow resistance under overbank conditions. Under non-overbank conditions，the comprehensive ice volume coefficient exhibits a negative correlation with the liquid water storage coefficient，reflecting the effects of reduced main channel velocity and decreased frazil ice accumulation thickness. Under overbank conditions，the comprehensive ice volume coefficient can be decomposed into a river ice area growth coefficient and an ice thickness growth coefficient. The former shows a positive correlation with the ratio of average discharge during freeze-up to bank-full discharge，indicating the effect of the increased overbank extent，while the latter is positively correlated with bank-full discharge，reflecting enhanced main flow velocity and increased frazil ice accumulation thickness. In the Inner Mongolia reach of the Yellow River，channel shrinkage and reduced bank-full discharge increase the likelihood of overbank flow during the freeze-up period. An increase in the liquid water storage coefficient contributes to greater liquid channel storage，while an expansion of the river ice area promotes an increase in ice volume. In contrast，a decrease in the ice thickness growth coefficient tends to reduce ice volume. The differing variation patterns and combined effects of these three factors result in complex changes to the channel storage increment.
The evaluation of the applicability of precipitation indices is a critical component in flood disaster loss analysis. Focusing on annual flood loss across 31 provinces of China from 2002 to 2023，this paper constructs both stationary and nonstationary loss models and comparatively evaluate the applicability of 13 commonly used precipitation indices using goodness-of-fit (R2)，Bayesian information criterion (BIC)，and root mean square error (ERMS). The results show that:①The 12-month standardized precipitation index (SPI-12) exhibits overall the highest applicability across the full sample，yet its performance is relatively unstable in high-loss years. Under the nonstationary model，the median R2 values for affected population and direct economic loss are 0.50 and 0.52，respectively. The simple daily intensity index and the number of days with daily precipitation ≥ 20.0 mm show slightly lower applicability than SPI-12，as they fail to account for the distribution of multi-year precipitation-their median R2 values for affected population are 0.48 and 0.46，and for direct economic loss are 0.36 and 0.38，respectively. ②Despite the inclusion of a time term in the nonstationary model，the median BIC values of the precipitation indices remain comparable to or lower than those of the stationary model. ③Compared to area weighting，the exposure-based weighting (population and GDP) helps to reduce the ERMS of some precipitation indices in fitting flood loss，especially annual total wet-day precipitation and precipitation anomaly. Overall，the rational selection of precipitation indices is important for flood impact assessment.
Plain polder areas are characterized by low-lying terrain and are highly susceptible to the combined impacts of external flooding and internal waterlogging. Insufficient hydrodynamic conditions are a key limiting factor for improving water environment in these regions. To address the subjectivity and difficulty of achieving global optimization associated with traditional regulation methods，this study takes the Dadang polder in the Taihu Basin as the study area. Joint inner-outer polder hydrological scenarios were constructed using a Copula function. The InfoWorks ICM model was employed to simulate the hydrodynamic processes within the polder area，and a surrogate model integrating CNN-LSTM-SelfAttention architecture was coupled with a genetic algorithm to perform multi-objective coordinated optimization of hydrodynamic regulation. The results show that the addition of a single diversion sluice increased the water exchange rate from 1.72% to 36.69%，which was further improved to 49.82% after coordinated optimization of gate-pump operation. Under the scenario of external flooding and heavy rainfall，the channel water level could be stably maintained below the flood warning level while remaining above the minimum control water level after water withdrawal. Furthermore，the duration guarantee rates of flow velocity and discharge in the river network were significantly improved. These findings can provide theoretical and technical support for flood control and water environment improvement in plain polder areas.
With densely distributed wetland ecosystems,the Nenjiang River Basin is a crucial major grain-producing area in China,where water,food,and wetland form a strong nexus.Studying this Water-Food-Wetland(WFW)nexus is of vital significance for the security and synergetic development of multiple resources in the basin.This study constructs a WFW evaluation indicator system covering wetland hydrological service functions and integrates the coordination degree model,structural equation model,and machine learning model to reveal the spatiotemporal evolution and driving mechanism of the WFW nexus in the Nenjiang River Basin.The results show that:① From 1990 to 2020,the WFW nexus coordination degree in the basin evolved from near imbalance to good coordination and near imbalance,with the spatial WFW nexus coordination degrees ranking as upper reaches＞middle reaches＞lower reaches.② The water and wetland subsystems in the basin exhibit a synergy effect,while the food subsystem shows significant competition effects with the water and wetland subsystems.Specifically,every 1％increase in food security level decreases water security and wetland ecological security levels by 0.52％and 0.67％,respectively.③Agricultural irrigation water consumption is the dominant factor driving the evolution of WFW nexus coordination degree in the basin,with a contribution rate of 35.2％.To maintain the synergistic development of the WFW nexus in the basin,the irrigation water consumption threshold should be 9.86 billion m3.The fierce competition for land and water between grain production and wetland ecological protection proves to be the key factor restricting the synergetic development of the WFW nexus in the basin.These findings can provide reasonable support for devising irrigation agriculture development plans and wetland ecological restoration schemes in the region.
The hydrological and carbon cycles are two fundamental biogeochemical processes of the Earth system，and they co-evolve through water-carbon coupling at the watershed scale. As a natural unit in terms of water and material balance，the watershed provides an appropriate scale for understanding this coupling and forms an important basis for addressing climate change and watershed water security through nature-based solutions. This review clarifies the water-carbon coupling processes in natural watersheds，analyzes the underlying coupling mechanisms，and synthesizes observational techniques and modeling frameworks for watershed water-carbon interactions. A bidirectional coupling framework for the watershed water-carbon cycle is proposed. The influences of climatic conditions on watershed water-carbon coupling are further discussed. In addition，major knowledge gaps are identified in terms of bidirectional coupling processes，quantification of interface processes，data support，and the impacts of extreme events，and future research priorities are highlighted. This review is expected to provide a scientific basis for watershed carbon neutrality，water security，ecological security，and food security，as well as for climate change mitigation and adaptation based on nature-based solutions.
To address governance challenges in the implementation of China’s national river strategy，namely，difficulties in cross- sectoral coordination，limited multi-stakeholder participation，and inability to foresee complex system risks，a novel paradigm that bridges the physical watershed and human-social domain is urgently needed. By integrating theories and technologies such as the metaverse，artificial intelligence，and watershed sciences，this paper proposes the theoretical framework and technical system of the Meta-Watershed. The definition，connotation，and characteristics are elucidated. A "Six Bases and Three Elements" basic model is constructed，featuring a high-fidelity digital twin watershed as the digital foundation，integrating with virtual society，immersive interaction，and symbiotic intelligence hub. The operational mechanisms and core capabilities of the model are analyzed，and the directions for key technological breakthroughs are identified. The Meta-Watershed upgrades governance from "humans governing watersheds through tools" to "humans and intelligent agents co-governing watershed," establishing a parallel and interactive，bidirectional closed-loop collaborative governance mechanism between the physical watershed and the virtual society. This evolution drives core governance tasks-including water security，water resource allocation，and ecological protection-toward a new governance paradigm. With its novel concepts and technological innovations，the Meta-Watershed propels watershed governance from the data-driven fourth paradigm toward the intelligence-driven fifth paradigm，providing a theoretical framework and practical blueprint for modernizing China’s river governance system and capacity，ultimately achieving harmonious coexistence between human and water.
Extreme precipitation，severe flooding，widespread droughts，and compound disasters occur more frequently as the "non-stationary" features of the global water cycle become more obvious. When confronted with "low-probability，high-impact" natural disasters，the traditional approach of "defense based on historical patterns," which assumes climate stationarity，suffers from a lack of adaptation. By investigating the conceptual chain of "flood-drought-disaster-impact-prevention-resilience," this study points out a systemic bias in the present approach. This bias emphasizes structural prevention over adaptive resilience and disaster control over damage mitigation. We establish a new paradigm of "intelligent adaptation to uncertain extreme" in response，along with its practical applications. According to the study，a modernized flood and drought defense system should: apply the National Water Network as a strategic carrier to improve spatiotemporal water reallocation capacity; use smart water management as the main driver for establishing a closed-loop system that combines "perception-forecasting-simulation-decision making"; with ecological measures as a resilience foundation to promote "grey-green synergy" in systemic governance. Additionally，the perspectives of energy dynamics and social psychology expand the theoretical bounds of disaster comprehension and defense evaluation. The aim of this study is to offer a theoretical foundation and workable solutions to establish a new-generation flood and drought mitigation system that will withstand unpredictable conditions in the future.
Dual Carbon Goals and resource constraints make it important to understand the evolutionary characteristics and driving mechanisms of the Water-Energy-Food-Carbon（WEFC） nexus. We propose a spatially adaptive coupling evaluation（SPACE） model that combines spatial Durbin model（SDM） and the panel threshold model to measure the coupling coordination degree in the WEFC nexus，then analyze the spatial spillover effects and nonlinear influence pathways of 30 provinces in China during 2000-2023. We report the coupling coordination degree in the WEFC nexus to trend upwards，and to fluctuate over this time，with higher levels in the eastern and western regions and lower levels in the central and some inland regions. Science and technology investment reveals a significant positive direct effect and a negative indirect effect，and environmental regulation presents a negative direct and positive spillover effect. Significant threshold effects are apparent for population scale，ecological conditions，and climatic factors，with the direction or intensity of the influence of a variable changing after having crossed a threshold. These findings reveal the spatial differentiation and driving mechanisms of the coordinated evolution of the WEFC nexus，and provide a reference for its regional differentiated governance and high-quality coordinated development.
This study focuses on the braided reach of the Lower Yellow River (LYR)，which features abrupt and random river regime evolution. Studying such river regime stability variations and dominant controlling factors is crucial for addressing challenges such as channel braiding and flood control. Using the cusp catastrophe theory，this study takes thalweg migration intensity and flow scouring intensity as control variables to quantitatively analyze river regime stability in a typical braided reach before and after the operation of the Xiaolangdi Reservoir. The results show that river regime stability exhibited an overall increasing trend from 1992 to 2020. Before 2006，the river regime had been in a persistently unstable state，whereas after 2006，it shifted markedly to a stable state under water-sediment regulation，demonstrating the remarkable effect of altered water-sediment regimes on enhancing channel stability following the operation of the Xiaolangdi Reservoir. It was also found that the stability coefficient was significantly positively correlated with average scouring intensity and negatively correlated with thalweg migration intensity，where the discharge and sediment conditions constituted the dominant controlling factors. The results also indicate that after reservoir operation，average scouring intensity continuously increased，while thalweg migration width and intensity decreased significantly，leading to a substantial reduction in channel braiding. In addition，sediment concentration in flood seasons was the key factor affecting river regime stability as higher sediment concentration led to lower scouring intensity，greater thalweg migration intensity，and thus decreased river regime stability. Based on these findings，this study reveals the mechanism of stability variations in braided channel evolution driven by water-sediment regime changes，and provides theoretical support for water-sediment regulation and systematic governance of the LYR.
The construction of large reservoirs warrants adjustments to the channel morphology downstream of the dam.Existing studies provide a relatively systematic understanding of the overall characteristics of channel adjustment but lack quantitative criteria for distinguishing the adjustment patterns of different cross-sections downstream of the dam.This study investigates the morphological adjustment patterns to the cross-sections of the Middle Yangtze River following the impoundment of the Three Gorges Reservoir and establishes identification criteria based on fixed-section topographic data.Analysis is conducted from the perspective of the cross-sectional centroid.The main conclusions are as follows:① In the temporal dimension,the longitudinal coordinates(Hy)of the cross-sectional centroid generally decreased.The transverse coordinates(Hx)of the centroid in straight river types varied slightly,whereas those in meandering river types shifted toward the convex bank.Meanwhile,in bifurcated river types,Hx shifted toward the main channel.In the spatial dimension,the greatest adjustment to the cross-sectional morphology in straight-river types occurred in the lower reaches of the Lower Jingjiang reach and the Jiepai reach.Hy decreased the least in the gravel-sand river section near the dam,whereas Hy.changed the most significantly in the Upper Jingjiang reach.For both meandering and bifurcated river types,the greatest relative change in centroid coordinates occurred in the Jingjiang reach and gradually decreased with increasing distance from the dam.② Based on the intrinsic relationship between shape center migration and the oscillation of the hydrodynamic axis,it has been clarified from a dynamic perspective that changes in the shape center can reflect the characteristics of cross-sectional adjustment.③ By introducing the relative amplitude of the centroid coordinates,three cross-sectional adjustment patterns—global downward-cutting,translational downward-cutting,and translational adjustment—along with their identification criteria are proposed.Straight-river types primarily exhibit global downward-cutting,meandering-river types display all three patterns,and bifurcated-river types primarily exhibit global downward-cutting and translational downward-cutting.Compared with previous approaches,this method quantitatively distinguishes differences among river types and demonstrates that the relative amplitude of the centroid can capture the coupled variation between lateral cross-sectional displacement and vertical scouring-deposition processes.
