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Article

Response of Riverbed Shaping to a Flood Event in the Reach from Alar to Xinquman in the Mainstream of the Tarim River

by
Mingcheng Zhao
1,
Yujian Li
1,*,
Lin Li
1 and
Wenhong Dai
1,2
1
College of Water Resources and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
2
Geotechnical Research Institute, Hohai University, Nanjing 210098, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(7), 1092; https://doi.org/10.3390/w17071092 (registering DOI)
Submission received: 21 February 2025 / Revised: 19 March 2025 / Accepted: 29 March 2025 / Published: 6 April 2025
(This article belongs to the Special Issue Flow Dynamics and Sediment Transport in Rivers and Coasts)
Browse Figures
Review Reports Versions Notes

Abstract

:
As the largest inland river in China, the Tarim River’s flood events significantly influence its riverbed formation. This paper took the Alar to Xinquman section of the Tarim River as the study area. The study area’s digital elevation model of the river was constructed using historical Google images and Copernicus DEM 30. Six different flood events were selected, corresponding to flood events with varying sediment loads, flood volumes, and peak flow volumes. The MIKE 21 software was used to simulate and investigate the response of the riverbed shape to different flood events. The experimental findings indicate that the sand content constitutes a pivotal factor in the formation of the riverbed during flood events. Flood sediment load goes through stages linked to changes in riverbed erosion and deposition. The combination of high peak flow and bed-forming flow after the peak effectively shapes the central channel’s morphology. The fourth type of flood event had the highest sediment transport coefficient Φ among the six types of floods and caused the most significant scouring effect on the riverbed under low sediment load conditions.

1. Introduction

Floods are among the most dangerous and severe climate-related disasters. Globally, floods ranked first in the number of people affected by natural disasters from 2000 to 2019 [1,2,3]. Floods can cause widespread damage, including casualties among humans and livestock (casualties) and damage to infrastructure (e.g., collapsed houses, bridges, and roads) [4]. A study of the economic losses caused by floods in China found that the average annual loss from 1999 to 2019 was as high as USD 15.5 billion [5].
A comprehensive understanding of riverbed morphology, defined as the evolution of riverbed topography, is paramount in flood research [6]. The long-term interaction between water flow, sediment, and boundary conditions shapes riverbed morphology. The increasing influence of human activities and engineering interventions leads to chain reactions. Changes in riverbed configuration, particularly local scouring, have emerged as a pivotal research direction in river disaster prevention and control, garnering widespread attention [7].
A comprehensive review of traditional flood hazard management highlights the growing importance of alternative strategies that regulate river flow and maintain bank stability, offering advantages over conventional approaches. Researchers emphasize the need for adaptive flood management techniques that go beyond flood control to support long-term river ecosystem health [8]. One study challenges the common assumption that precipitation is the main driver of floods. Through a 101-year analysis of the Jaintia River’s riverbed topography using fuzzy logic and principal component analysis, researchers found that riverbed changes, rather than rainfall, play a key role in flood expansion [6]. This finding underscores the importance of monitoring and managing riverbed dynamics in flood mitigation.
In the lower Yellow River, sediment transport dynamics have been a focus of research. By correlating upstream sediment concentration with transport rates at Lijin Station, scientists identified critical sediment thresholds for erosion, balance, and deposition under different flood scenarios. These insights provide a scientific basis for sediment management and flood control [9]. Another study analyzed 15 years of cross-sectional data from high-concentration flood events in the braided reaches of the lower Yellow River, exploring factors influencing channel geometry adjustments at both local and regional scales [10]. Additional research evaluated the feasibility of narrowing the lower Yellow River to improve flood management, based on hydrological department data [11]. Experimental approaches have further expanded understanding. One study combined simulations with theoretical research to analyze floodwater evolution in floodplains and the role of vegetation in sediment deposition, highlighting the complex interactions between natural and engineered floodplain features [12]. However, studies on how floods shape riverbeds remain limited and require deeper investigation.
Hydrodynamic models are essential for simulating flood inundation and assessing risks, particularly in floodplains and protected areas [13]. A standardized methodology was used to construct and calibrate a hydrodynamic model for the lower Ina River, enhancing understanding of river flow under different flood conditions [14]. Similarly, a coupled model for the Ningxia section of the Yellow River integrated hydrodynamic and sediment transport analysis to assess flood risks in vulnerable areas [15]. A multi-tiered modeling approach, using HEC-HMS, WASH123D, SRH-2D, and CCHE3D, provided detailed insights into runoff, hydraulic routing, and local bed scouring [16]. Further advancements in modeling include a two-dimensional morphodynamic coupled model, tested on real flood events in the lower Yellow River, which improves flood simulation accuracy [17]. A simplified stepwise flow solver was also introduced for estimating riverbed changes, applicable to both local and large-scale flood models [18]. These innovations enhance flood prediction and risk management globally. Given the significance of mathematical modeling in flood studies, this research adopts modeling as a key tool for experimentation and analysis.
Flooding, a recurrent natural disaster in Xinjiang, China, has been responsible for significant economic and agricultural losses, amounting to 88% of the total losses experienced in various fields [19]. The Tarim River Basin, the largest arid inland river basin in China, encompasses an expanse of 1.02 × 106 km2 (996,000 km2 in Xinjiang), with its land type primarily comprising mountains (47%), plains (22%), and deserts (31%). The area dedicated to cultivation is 1.36 × 106 hm2 [20]. Currently, the primary surface water sources within the Tarim River Basin consist of the Aksu River, the Yarkant River, the Hotan River, the Kaidu Peacock River, and the Tarim River [21]. The Tarim River is a primary water source within the basin’s arid lowlands, supporting over eight million individuals residing on its banks and in the alluvial plains downstream [22]. Research has shown that since the 1960s, the changes in flood affected areas in Xinjiang have been consistent with changes in precipitation, indicating that flood disasters are intensifying and occurring more frequently [23].
The adjustment of riverbed morphology is closely related to the flood event. Studying how riverbed shaping responds to flood sedimentation helps us better understand the mechanisms and development of floods. This knowledge can optimize flood warning systems, improve emergency response strategies, and support the sustainable management of river ecosystems. In recent decades, morphological dynamics models, especially two-dimensional water and sediment mathematical models, have been widely used in studying alluvial river evolution [15]. This study, guided by the dual imperatives of research trends and practical necessities, has identified the Alar to Xinquman section of the upper Tarim River as the primary research area (see Figure 1). In this section, a digital elevation model of the river channel has been developed, employing Copernicus DEM 30 as the underlying data source. Utilizing MIKE 21 numerical simulation and meticulous data analysis, this study delved into the response of bed shaping in this particular section of the river to flood event characterized by varying sediment loads, flood volumes, and peak flow volumes. This investigation aims to elucidate the quantitative laws inherent in the response of bed shaping in this section of the river to the process of flood sediment.

2. Study Area and Data

2.1. Selection of Typical River Sections

Rivers themselves possess feedback regulation mechanisms, and the adjustment of river systems is primarily manifested in alterations to the riverbed boundary and the shaping of cross-sectional morphology [24]. The dominance of braided channels characterizes the upper reaches of the Tarim River. In contrast, meandering channels develop in the middle and lower reaches, where the river bends to a greater extent. The meandering index of these channels exhibits significant spatial variability, ranging from 1.6 to 2.2, indicating high levels of lateral transport [25]. The mainstream of the Tarim River is characterized by a gradually gentler longitudinal slope from top to bottom, a typical upstream cross-section wide and shallow, and a typical downstream cross-section narrower and deeper [26]. The river bank and bed are composed primarily of coarse silt and fine sand, with minimal clay content, resulting in a low bank strength [27]. A high degree of curvature, a complex cross-sectional shape, and significant local silting characterize the river’s geometry. During periods of flooding, the volume of sediment transported by the river increases substantially, resulting in a complex and dynamic relationship between water and sediment.
Considering geographical and historical constraints, the availability of measured river elevation and hydrological data in this area is limited. The selection of the Alar to Xinquman Hydrological Station as the study area was made based on existing measured data and data such as DEM. The reach under consideration is approximately 189 km long and is characterized by a gently undulating riverbed and a predominantly “U”-shaped cross-section [28].
To explore the role of floods in shaping riverbeds in-depth, this paper used Google Historical Maps to obtain and analyze historical image data from multiple periods of the Alar to Xinquman River section. The paper focuses mainly on the significance and representativeness of the evolution of river erosion and deposition and compares the characteristics of changes in river morphology in different periods. Six typical reaches along the course that can reflect the characteristics and main trends of dynamic adjustment of the river in the study area were selected uniformly as the research objects. The locations of the selected typical reaches are shown in Figure 2.

2.2. Selection of Different Flood Event

The sediment load of the Tarim River is mainly concentrated during the flood season and even more so during the several significant floods of the flood season. Most sediment transport occurs during flood events [9,29]. From 1981 to 2020, two significant high flood periods were observed in the Tarim River Basin, from 1994 to 2002 and 2006 to 2011 [30]. A study of the frequency of significant floods in the Tarim River revealed that 95% of these occur in July and August, with August being the period with more frequent major floods [31]. The flood flow rates for the two-year, five-year, and ten-year recurrence intervals at the Aral Hydrological Station are 1249 m3/s, 1613 m3/s, and 1821 m3/s, respectively. To explore the response mechanism of riverbed formation to flood processes under different flood volumes and peak flow conditions for these three common flood frequencies in the main stream of the Tarim River, this study analyzed flood events from 2000 to 2024. Six typical flood events from 2001, 2005, 2006, 2010, and 2022 were selected, and the MIKE 21 software was used for unsteady flow flood numerical simulations.
The duration of a flood on the main channel of the Tarim River is approximately 30 days, as established by research on the characteristics of river floods in the upper reaches of the Tarim River. The number of days in the flood event is controlled and is uniformly selected as 32 days [32]. The simulated floods are divided into six types (see Figure 3): The first type of flood has a volume of approximately 20,000 m3 and a peak flow of around 1250 m3/s, which is analogous to a flood with a return period of roughly two years (1249 m3/s). The second type of flood has a volume of approximately 20,000 m3 and a peak discharge of around 1420 m3/s. The third type of flood, which is characterized by a volume of approximately 27,000 m3 and a peak discharge of around 14,900 m3/s, is also considered. The period from 27 July 2005 to 27 August 2005 was selected as a typical example; the fourth type of flood has a discharge of approximately 31,000 m3 and a peak discharge of about 18,100 m3/s, which is similar to the flood with a return period of approximately one in ten years (18,210 m3/s). The period from 20 July 2006 to 20 August 2006 is a typical example; the fifth type flood has a flood volume of about 37,000 m3 and a peak discharge of about 1870 m3/s. The period from 19 July 2010 to 19 August 2010 is a typical example; the sixth type flood has a flood volume of about 37,000 m3 with a peak discharge of about 1820 m3/s. A typical example is the period from 4 August 2022 to 4 September 2022.
Sediment concentration in rivers refers to the total mass of suspended and bedload sediments per unit volume of water. It is a key parameter in characterizing sediment transport capacity, erosion-deposition dynamics, and water–sediment interactions. Sediment concentration is closely related to total flood volume and peak discharge. Generally, there is a positive correlation between total flood volume and sediment concentration: higher precipitation leads to increased flood discharge, which in turn results in greater sediment influx into the river and a rise in sediment concentration. Similarly, an increase in peak discharge accelerates flow velocity, enabling the river to transport more sediment. As a result, sediment concentration typically reaches its maximum during the flood peak. As flow velocity decreases, sediment gradually settles, leading to a decline in sediment concentration.
This study analyzed daily measured sediment concentrations from six types of flood events and found that they follow the above pattern. The “peak sediment concentration” in the study reach generally occurs within one day before or after the flood peak. When the research focus is on the independent effect of sediment concentration on specific hydraulic factors (e.g., velocity distribution and sediment deposition patterns), a fixed sediment concentration allows for effective isolation of its impact on hydraulic parameters (such as discharge fluctuations and sediment gradation changes) through the controlled variable method. This method facilitates a direct analysis of sediment concentration influences while reducing model complexity and improving computational efficiency.
Through the analysis of daily measured sediment concentration data from six flood events, the minimum recorded sediment concentration was found to be less than 2 kg/m3, while the maximum reached approximately 10 kg/m3, with average daily values ranging between 5 and 62 kg/m3. To simplify the inflow boundary conditions, sediment concentration is categorized into three representative values: 2 kg/m3, 6 kg/m3, and 10 kg/m3. These conditions are used to investigate the impact of different flood events on riverbed morphology within the study reach. The specific data for the six types of flood events are shown in Table 1.

2.3. Model Selection

In recent years, with the advancement of numerical methods, the speed of solving hydrodynamic equations has greatly accelerated the study of hydrodynamics. Hydrodynamic mathematical models are generally classified into one-dimensional, two-dimensional, and three-dimensional models. Among them, two-dimensional models overcome the limitations of traditional one-dimensional models in simulating the lateral and vertical variations of water flow. Compared to three-dimensional models, two-dimensional models also offer higher computational efficiency. Currently, commonly used commercial and free models include MIKE 21 FM, Delft 3D, FESWMS, Hec Ras 2D, and CCHE 2D, among others.
FESWMS is the most efficient model in terms of operational performance, providing hydrodynamic indicators quickly, but it has lower simulation accuracy near the shoreline. The results of Hec Ras 2D show significant differences when compared to other models, especially in bend studies, where this model cannot be relied upon alone [33]. CCHE 2D is a highly descriptive model that requires more data input and has lower accuracy in regions with sharp variations [34]. Sara Ansarifard et al. [35] determined the flood extent in the Khalkai Basin, Gilan Province, Iran, using Sentinel-1 images. The results showed that the MIKE 21 model had a better overlap with the actual flood area map (determined by 686 images), demonstrating its high accuracy in simulations. Unlike HEC-RAS, MIKE 21 also considers factors such as wind speed, wave radiation, infiltration, and eddy viscosity, which help more accurately reproduce flood events—features that HEC-RAS cannot provide. Delft 3D and MIKE 21 are considered the most complex long-term integrated modeling tools, supporting comprehensive multi-objective optimization, while SWAN and XBeach excel in specific functions [36]. Compared to MIKE 21, the Delft 3D model may require higher grid resolution to achieve the same depth-averaged velocity [37]. Additionally, Delft 3D grids can be manually constructed, while MIKE 21 does not support this feature, but MIKE 21 has an advantage in computational efficiency [38]. Although MIKE 21 is relatively more difficult in terms of result acquisition, interpretation, and presentation, its overall effectiveness is still widely recognized [39].
MIKE 21 is a powerful two-dimensional hydrodynamic model with strong pre-processing, post-processing, and computational capabilities. It can simulate water level and flow changes caused by various forces, providing reliable technical support and important references for surface water research. The model has been continuously developed and improved in practical applications [40]. Therefore, this study selected the MIKE 21 model for numerical simulation experiments.

3. Methodologies

3.1. DEM Construction

Due to the excessively large scale of the study river section, there is an issue of incomplete remote sensing imagery information and partial lack of measured terrain elevation data. In this study, historical images from Google Earth were overlapped with Copernicus DEM 30 data in ArcGIS 10.8.2 software. Through proportional scaling, the precision was unified to distinguish the high and low beaches of the river section. The waterline and outer edge of the new Tarim River channel from Manji to Alar were delineated, and the floodplain DEM data were extracted. Based on the elevation data of the main channel cross-sections, a quadratic interpolation algorithm was used to interpolate the underwater cross-section elevation data of the main channel. The elevation data of the main channel cross-sections were corrected by the average difference method, and the smooth algorithm was applied to smooth the cross-sections along the main channel.
The main channel DEM was generated in the DHI MIKE Zero 2014 software and converted to ArcGIS. It was then resampled and rasterized with the floodplain DEM, and both were imported into MIKE 21 software to reconstruct a complete river channel DEM, as shown in Figure 4. The Copernicus DEM is publicly available in two horizontal resolutions: 90 m resolution (3 arc-seconds) and 30 m resolution (1 arc-second). This study selected the 1 arc-second version (Copernicus DEM 30, version v2022). The Copernicus DEM and its data based on TanDEM-X are currently the most recent and precise global DEM, regarded as the “gold standard” for global DEM [41,42]. Michael Meadows [43] selected five commonly used DEM datasets to represent changes in global flood-prone areas and used different high-precision reference datasets (from airborne LiDAR surveys) to evaluate vertical accuracy. They found that the best performing DEMs were the latest Copernicus DEM and FAB DEM (Copernicus DEM with forest and buildings removed). The normalized median absolute deviation (NMAD) of the overall vertical accuracy of Copernicus DEM was 1.27 m, while the NMAD of SRTM was 3.65 m.

3.2. Model Settings

An unstructured grid is more appropriate for simulating undulating terrain and complex boundaries; consequently, an unstructured grid is employed to divide the study reach. Considering this model’s research characteristics and the calculation speed optimization, a triangular grid with a side length of 50 m ± and an angle of 40°~60° was utilized by adjusting the river terrain, generating 97,925 grids. The roughness of the model was set to the roughness field file, with the roughness of the river channel set to 0.027 and the roughness of the beach set to 0.05, according to the selected result table of the roughness of each section of the Tarim River mainstream in Xinjiang.

3.3. Model Test

The 2011 flood event was selected for numerical simulation, focusing on the flow velocity, cross-sectional elevation deformation at the major cross-section of the new channel, and the flow-discharge relationship at three major cross-sections along the Aral to the new channel reach. The simulated flow velocity (see Figure 5), cross-sectional elevation deformation (see Figure 6), and flow-discharge relationship at the three major cross-sections (see Figure 7) were compared with the observed data from the Hydrological Yearbook of the People’s Republic of China [44]. The results show a good agreement with a minimal error, meeting the requirements outlined in the Technical Specifications for Hydrodynamic and Sediment Transport Simulation of Inland Waterways and Ports (JTS/T231-4-2018) [45]. Additionally, the flow–discharge relationship of the three major cross-sections was consistent with the observed flow–discharge relationship in the Feasibility Study Report on Flood Control and Management Project for the Tarim River Mainstream, Shaya Section, Xinjiang Uygur Autonomous Region [46]. Moreover, the deviation between the sedimentation–erosion volume in the study reach in 2011 and the sedimentation–erosion volume calculated by the 1D mathematical model [47] was within 30%. The flood simulation results, including the riverbed dynamics, floodplain inundation characteristics, and water surface distribution, effectively reflect the features of the historical flood event.

4. Results and Discussion

The impact of fluvial processes on the formation of riverbeds is predominantly influenced by three factors: the flow rate, shear force, and sediment transport capacity. The relationship between shear force and water depth (h), as well as slope (S), can be expressed as follows:
τ = ρ ghS
where ρ is the density of water and g is the acceleration due to gravity.
Sediment transport capacity can be expressed as the relationship between flow rate V and sediment concentration C:
Qs = αVC
Qs is the sediment transport rate (mass of sediment transported per unit of time), V is the flow velocity, C is the sediment concentration, and α is a scaling factor related to flow characteristics and sediment properties.
Peak flow rates are typically associated with elevated water depths and shear forces, which can transport sediment of larger particle sizes and induce strong scouring effects. Conversely, low peak flow rates exhibit reduced sediment transport capacity due to lower flow velocities; however, their cumulative sediment transport effect is frequently more pronounced due to their protracted duration. The influence of distinct flood peaks on the shaping of the riverbed can exhibit notable variations in temporal scales, with the shaping process being more characterized by stages of scouring. The significance of sediment load and discharge in shaping riverbeds is evident from Equations (1) and (2). The evolution of the riverbed is influenced by more than just the action of discharge and sediment load; it is also the result of the interaction of multiple factors, including water and sediment conditions, channel morphology, and flood events. By reasonably optimizing the peak discharge and its timing, a dynamic balance can be achieved between channel morphology and sediment transport. This, in turn, promotes the stability and sustainability of the river system.
This paper aims to ascertain how bed shaping responds to various variables in the Tarim River from Alar to Xinquman. To address this, the paper regulated variables such as sediment load, total flood volume, and peak flow during floods.

4.1. Channel Formation in Response to a Flood Event with Different Sediment Loads

The quantity of sediment in a flood plays a pivotal role in shaping the riverbed during the flood event. The volume of sediment has been shown to significantly impact the magnitude of sediment transported and the sediment discharge ratio [48]. The analysis and simulation results demonstrate that during the rising stage of the flood, the siltation rate in the main channel increases substantially, exhibiting a positive correlation with the amount of sediment. The scouring action narrows and deepens the river channel, concentrating the water flow energy. This enhances the sediment transport capacity and achieves optimal synergy between water and sediment transport. However, when the flood volume exceeds the capacity of the floodplain flow rate, the equilibrium between erosion and deposition in the main channel is disrupted. Consequently, the floodplain flood increases, leading to an escalation in sedimentation within the floodplain area. This, in turn, weakens the main channel’s capacity for erosion and deposition. During the falling stage, as the water level declines and the flow rate slows, sedimentation gradually becomes dominant. The riverbed morphology stabilizes, and the sediment deposition thickness in the floodplain area continues to increase. The higher the sediment content, the greater the thickness of the sedimentation. After the floodplain, the water disperses, and the resistance of the beach is high, thereby constraining the velocity of the main channel and weakening the bed-forming effect [49].
Sediment erosion and deposition are typically calculated using various methods, including cross-sectional morphology, sediment budget, and grid topography [50]. This paper employed the grid topography method to calculate the river erosion and deposition amount, as shown in Figure 8. In conditions of high sediment content (10 kg/m3), high sediment content flows significantly reduced the erosion efficiency of floods. Sediment settled rapidly when the flow rate slowed down. The floodplain underwent substantial siltation, while the main channel experienced limited erosion and deposition [51]. Moderate sediment conditions (6 kg/m3) altered the water flow energy and sediment deposition. During the flood peak period, the main channel underwent erosion and deposition. The sedimentation effect gradually increased after entering the falling stage, and the riverbed morphology stabilized. In conditions of low sediment load (2 kg/m3), the sediment transport capacity exceeded the sediment concentration, resulting in rapid vertical scouring in the main channel during the flood peak period. The sedimentation effect was minimal during the falling stage, and the riverbed exhibited a net-scouring continuous impact. During periods of high-sediment-load water flow, the river typically undergoes significant siltation [52].
The peak flow and total flood volume of the fourth type of flood event were lower than those of the fifth and sixth types of flood event, but it had a lower bed sedimentation rate under different sediment conditions. The underlying principle can be attributed to the enhanced sediment-carrying capacity of the flow at this rate, which effectively mitigates rapid sediment settlement under conditions of high sediment load. The synergy between flow rate and peak flow reduced the exacerbation of erosion and deep channel erosion triggered by higher flows. Consequently, this dynamic equilibrium fostered a river channel structure characterized by enhanced dynamic balance and morphological adaptability. In wandering rivers, where sediment influx matches the channel’s sediment-carrying capacity, the efficiency of transporting sediment and water improved.

4.2. Channel Morphology Responded to Flood Events with Different Flood Volumes

Different flood events exhibit significant differences in temporal and spatial distributions. Even if the peak flow is the same, the difference in total flood volume may lead to entirely different results regarding the fluvial bed erosion and sedimentation characteristics. The present study employed the fourth and fifth flood events, which possess equivalent sediment loads, as a foundation for analyzing the impact of floods with differing volumes on shaping riverbeds under identical peak flow conditions (as shown in Table 2). This study aimed to elucidate the role of total flood volume and temporal distribution on the evolution of riverbed morphology.
High-volume floods exhibited a heightened capacity for erosion and depositing the riverbed in flood peak conditions. This phenomenon was particularly evident in the main channel area. Numerical simulation results demonstrate that as flood volume increased, erosion and deposition depth in the main channel exhibited a linear relationship. Concurrently, the area and thickness of sedimentation in the beach area experienced an increase. The erosion and deposition effects of the riverbed by floods with different volumes exhibited a significant stage stemming from the dynamic adaptation mechanism between the riverbed morphology and the water and sediment conditions. The analysis revealed that these stages can be divided into two distinct phases: pre-flood and post-flood.

4.2.1. Pre-Flood Stage: Positive Correlation Between Siltation Intensity and Flood Volume

Before the arrival of the flood peak, the riverbed morphology had not yet fully adapted to the gradually increasing water flow intensity and sediment input. At this time, the scouring effect of the main channel significantly increased with the increase in the flood volume. High-intensity floods exhibited more substantial riverbed shaping capabilities, and their impact on the adjustment of the central channel depth and morphological reconstruction was significantly more significant than those of low-intensity floods. As can be seen from Table 2, during the pre-flood stage, the cumulative total flow of the fifth flood event was approximately 14,400 m3, with a duration of 14 days; in contrast, the cumulative total flow of the fourth flood event was only 7300 m3, with a duration of 10 days.
An analysis of the riverbed elevation change in cloud maps of six typical river sections (as shown in Figure 9 and Figure 10) shows that the scouring effect of the fifth type of flood event was significantly superior to that of the fourth type of flood event. Specifically, as the flow rate increased, the flow velocity in the main channel also increased accordingly, and the flow rate and river area decreased, narrowing and deepening the main channel as it is scoured by sediment [10]. Simultaneously, the sediment redistribution within the floodplain gave rise to a depositional feature. Notably, the fifth flood type engendered a more profound and narrower riverbed morphology within the main channel, characterized by a substantially elevated range and intensity of erosion and deposition compared to the fourth flood type. This disparity is primarily attributable to the elevated flow velocity and pronounced hydrodynamic effects of the fifth flood type, which is capable of transporting fine-grained sediments downstream and inducing downstream deposition.
Floods within the floodplain are influenced by the resistance offered by this terrain, resulting in comparatively modest flow velocities. Sedimentation of fine particles occurs beneath the floodplain, forming localized, stable sedimentation zones [53]. The fourth type of flood event exerts only a shallow modification of the riverbed surface due to its brief duration, and the capacity to significantly alter the riverbed morphology is limited.
Research has demonstrated that, in the pre-peak stage, high-volume floods significantly influenced the overall evolution of the riverbed morphology. This is achieved by shaping the main channel’s depth and adjusting the sediment distribution in the floodplain.

4.2.2. Post-Peak Stage: Differential Cumulative Sedimentation Effect

In the post-peak stage, the effects of flood events with different flood volumes on the main channel’s erosion and the beach’s deposition were still significant and reflected in the differential river shaping. As can be seen from the changes in the erosion and deposition of typical reaches 1, 2, 3, 4, and 6 in Figure 11 and Figure 12, during the post-peak stage, the fourth flood event had a weaker sedimentation effect due to the lower daily average flow and longer duration, with a large amount of suspended sediment being washed away. The low daily average flow transported sediment more in line with the main channel formed by the previous flood peak. In the fifth typical reach, the main channel formed by the fifth flood event was more likely to maintain high-intensity scouring after the flood peak. The fourth flood event cannot continue the stable development of the primary channel morphology in the retreat stage due to insufficient scouring before the flood peak.
As demonstrated in Table 1, the maximum discharge, total discharge, and peak time of the sixth and fifth flood event were comparable, and the magnitudes of erosion and deposition on the riverbed under high (10 kg/m3) and medium (6 kg/m3) sediment load conditions were also analogous. However, under low sediment load conditions (2 kg/m3), the sixth flood event, which exhibited a higher average daily discharge, demonstrated a superior scouring effect compared to the fifth flood event during the falling stage.
In essence, the phase succeeding the flood peak exhibited substantial disparities in scouring capabilities and sediment transport conditions during the falling stage. The flooding process with a lower average daily flow rate shaped a narrower and deeper riverbed form through a more stable flow rate change, more adapted to the main channel form shaped by the previous flood peak for sediment transport.

4.3. Riverbed Shaping Response to Flood Events with Different Peak Flows

Most extant studies concentrate on the impact of flood events on riverbed shaping under conditions of total flood volume or a single peak flow. There is, however, less exploration of the driving mechanism of the characteristics of riverbed erosion and deposition under different peak flow rates with the same flood volume. It is evident that under the same flood volume, the flooding process with different peak flows will show significant differences in flow velocity, shear force, and sediment transport capacity over time. This variation can directly impact riverbed erosion and sedimentation’s dynamic process and spatial distribution. The present study compared and analyzed two typical flood events with a different sediment-laden flood event, with a total flood volume of 20,000 m3 as a basis. The first type of flood event had a peak flow of approximately 1250 m3/s, and the second type of flood event had a peak flow of about 1420 m3/s (as shown in Table 3). The response of bed shaping to the flooding process was simulated and analyzed under high, medium, and low sediment load conditions for the two flood events. The different characteristics and scientific principles of bed shaping in the two stages before and after the flood peak are discussed, and the impact of varying flood peak flow rates on bed shaping is explored.

4.3.1. Pre-Crash Stage: Impact of Water Flow Strength

As can be seen from Table 3, in the 15 days before the flood peak, the mean daily flow rates of the two flood categories were recorded as 585 m3/s (type I flood event) and 513 m3/s (type II flood event), respectively. As Figure 13 and Figure 14 demonstrated, the silting effect caused by the type I flood event was more pronounced before the flood peak, resulting in the river’s main channel becoming broader and shallower as the flow rate increased.
Bernoulli’s equation demonstrates that the hydrodynamic effect is proportional to the flow rate square; thus, higher flow rates are associated with more incredible kinetic energy. An increase in flow rate can enhance the transport capacity of sediment, leading to an observable increase in siltation. However, a limit exists on the flow strength required to transport high- or low-sediment-laden water, a condition that can be readily achieved in the main channel of a natural river [54]. A high daily average flow rate characterizes the first type of flood event but a low peak flow rate, resulting in insufficient flow intensity and a failure to reach the critical shear force. Consequently, this leads to the absence of observable siltation characteristics. The second type of flood event is distinguished by a low daily average flow rate but a high peak flow rate. When the flow intensity exceeds the critical value for transporting sediment particles, the deposited sediment particles re-enter the suspended state, are re-transported or washed away, and shape a narrower and deeper main channel. Therefore, the channel erosion and filling effect is not simply linearly related to the flow rate but also to the flow strength. Exceeding the critical value for transporting sediment particles can cause scouring of the riverbed.

4.3.2. Post-Flood Stage: Differences in Flushing Effects

The flow rate gradually declined in the post-peak phase, and the scouring capability exhibited substantial disparities between the two flood events. As can be seen from Table 3, the mean daily flow rate of the first flood event was 617 m3/s, whereas the second process was 666 m3/s. The experimental results show that the higher average daily flow rate in the second type of flood event significantly increased the water flow strength, making the riverbed scouring effect more noticeable, especially when there was a low sediment load. The cloud maps illustrating the changes in the erosion and filling of the lower reaches of the river during the first and second flood event are shown in Figure 15 and Figure 16.
After the flood peak, the silting effect on the riverbed decreased for both flood events, but the silting from the second flood event was slower than that from the first. Under conditions of low sediment load, the second flood event caused scouring of the study reach of the river. During the flood peak stage, the second flood event, characterized by a higher peak flow, had already caused some riverbed scouring due to the shear force of the water flow, and the high water flow energy was maintained during the post-peak stage. The first flood event showed an increase in flow rate during the post-peak stage, but both the flow rate and kinetic energy were lower than those of the second flood event, resulting in a relatively weak scouring effect. In high and medium sediment load conditions, the initial flood type led to continuous siltation, while a trend towards reduced siltation was observed under low-sediment load conditions.
In conditions of low sediment load, the initial peak flow of the first type of flood occurred earlier, extending the flow energy’s action time before the peak flow’s arrival. This resulted in the erosion and deposition of the riverbed before the peak flow arrived. In contrast, the peak discharge of the second type of flood increased relatively late, and the sediment transport capacity was insufficient to cause significant riverbed erosion before the peak. Erosion and deposition depend on the peak discharge, with the second flood event having higher peak shear stress and discharge than the first. As a result, the reduction in sedimentation from the second flood event was not noticeable until after the peak, and the riverbed depth formed afterward was greater, improving water and sediment transport efficiency.
The experimental findings demonstrate that peak flow’s magnitude and temporal distribution significantly impacted the bed shaping during the flood peak stage. Furthermore, they determined the equilibrium between bed erosion and sedimentation after the flood peak.

4.4. Optimization of the Water and Sediment Transport Capacity of Rivers

The sediment transport process in rivers substantially influences the formation of riverbeds, which exhibit varied responses to floods characterized by distinct sediment loads, varying flood volumes, and disparate peak flow volumes. These responses manifest as stage characteristics during the pre-peak and post-peak stages. The correlation between the sediment-carrying capacity of the water and the sediment transport capacity of the flood can be discerned through the alterations in the width and depth of the river’s main channel. This relationship is closely related to the sediment transport capacity characteristic of the sediment coefficient (S/Q) [55].
As demonstrated in the preceding analysis, when the flooding process and the river channel morphology achieve an optimal match, water and sediment transport efficiency can be significantly enhanced, reaching its maximum effect. To quantitatively characterize this correlation, a comprehensive form and sediment transport coefficient, denoted as Φ = (B/H) (S/Q)0.5, was introduced [56]. Utilizing the measured data from the Hydrological Yearbook, the comprehensive coefficient values of the Alar and Xinquman hydrological stations during the initial to sixth flood event were calculated. It was ascertained that for a flood peak discharge of Q = 6000 m3/s, the flood volume of the fourth flood event exhibited the highest comprehensive coefficient, mainly when the sediment load was 2 kg/m3, where the comprehensive coefficient attained a maximum value. This finding is consistent with the numerical simulation results, indicating that the cross-sectional shape of the river channel formed by floods of this magnitude has the best sediment transport efficiency and cumulative scouring effect. The water and sediment conditions corresponding to this discharge satisfy the coordinated relationship between sediment transport and riverbed shape formation and are in a state of efficient sediment transport. For flood regulation in the upper reaches of the Tarim River, using the flood volume and peak flow that align with the maximum form and sediment transport coefficient can serve as key indicators. This approach helps optimize the flooding process and improve the efficiency of water and sediment transport. This provides a theoretical foundation for the scientific management and utilization of river training.

5. Conclusions

In this paper, a two-dimensional hydrodynamic-sediment model, designated MIKE 21, was constructed, with the Alar–Xinquman section of the Tarim River mainstream serving as the research object. The influence of different flood events on riverbed shaping was analyzed, and the coupled effects of other factors, including sediment load, flood volume, and peak flow during the flooding process, are discussed. This revealed the key influencing mechanism of the flooding process on riverbed shaping. The study’s findings, as outlined below, are of particular significance:
1.
Changes directly influenced the distribution of erosion and deposition in the riverbed in sediment load, which is a pivotal factor in shaping the riverbed during floods. Under conditions of high sediment load (10 kg/m3), the scouring capacity of the main channel was suppressed, leading to substantial deposition within the floodplain area. Under moderate sediment load conditions (6 kg/m3), the characteristics of bed erosion and deposition differed across various flood events. In contrast, under low sediment load conditions (2 kg/m3), the scouring effect on the main channel of the riverbed was significantly increased, and the riverbed continued to exhibit scouring characteristics even after the flood peak. Conversely, as the sediment load increased, the magnitude of sedimentation in the floodplain also rose. Under equivalent sediment load conditions, the magnitude of flood flow directly influenced the extent of sedimentation in the floodplain.
2.
The role of flood volume and peak flow in shaping the riverbed varied significantly at different flood stages. Through rigorous analysis, it has been determined that during the pre-peak phase of a flood, an elevated flood volume, attributable to its extended duration and substantial cumulative flow, can substantially augment the scouring capacity of the primary channel. This, in turn, fosters the adjustment and reconstruction of the riverbed in depth. In the post-peak phase, the average daily flow rate of the fourth type of flood event was 1060 m3/s, and the corresponding bed-forming flow rate was 1100 m3/s, which had the most significant scouring effect under low-sediment load conditions. By combining high peak flow with a flow that approximates bed-forming conditions after the peak, the central channel’s morphology can be more effectively shaped, enhancing the river’s efficiency in water and sediment transport.
3.
A multifaceted interaction between water and sediment conditions, flood volume, peak flow, and river channel morphology determined the riverbed shaping process. Moderate water and sediment conditions, coupled with an optimal balance of flood volume and peak flow, can achieve a dynamic equilibrium in riverbed morphology. The results indicate that the morphological and sediment transport comprehensive coefficient, denoted by Φ, of the fourth type of flood event was the most substantial among the six types of flood event, and this flow level exhibited superior flood transport capacity. This finding is significant for optimizing water and sediment transport in the Tarim River mainstream from Alar to the Xinquman River section. It provides a reliable basis for flood regulation and management.
4.
Through a comparative analysis of existing research, we believe that the pattern discovered in this study is not only applicable to the Tarim River but may also be applicable to other similar wandering river systems. For instance, Sun, D.P. et al. [56] conducted an in-depth exploration of the scour and deposition patterns in the wandering reaches of the lower Yellow River during flood events. Their findings revealed that it is not the floods with the highest discharge that have the greatest scouring effect, but rather the interaction between riverbed morphology and flood discharge that predominantly governs the processes of erosion and deposition. This aligns with the findings of our study, where the erosion and deposition patterns in the wandering reaches of the lower Yellow River also support the principle of “large floods travel straight, small floods meander, large floods scour, small floods deposit.” Given the similarities in geomorphological characteristics and river types between the study reach and the lower Yellow River, both being wandering reaches, we speculate that the pattern discovered in this study possesses strong universality and can be applied to other similar wandering river systems. Future research may further validate the applicability of this pattern in different wandering rivers and investigate its performance under various hydrological conditions and geological backgrounds.

Author Contributions

Conceptualization, M.Z. and Y.L.; methodology, M.Z.; software, M.Z.; validation, M.Z., L.L. and W.D.; formal analysis, Y.L. and L.L.; investigation, L.L.; resources, M.Z. and Y.L.; data curation, M.Z.; writing—original draft preparation, M.Z.; writing—review and editing, Y.L.; visualization, M.Z.; supervision, L.L. and W.D.; project administration, Y.L., L.L. and W.D.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key R & D project of Xinjiang Uygur Autonomous Region, grant number 2022B03024-2.

Data Availability Statement

The data presented in this study are all available in the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Tarim River basin rivers and hydrological stations.
Figure 1. Tarim River basin rivers and hydrological stations.
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Figure 2. Schematic diagram showing the locations of the six typical river sections selected.
Figure 2. Schematic diagram showing the locations of the six typical river sections selected.
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Figure 3. Six flood flow processes at the Aral Hydrological station.
Figure 3. Six flood flow processes at the Aral Hydrological station.
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Figure 4. Two−dimensional river topography elevation map (left) and local three−dimensional river topography elevation map (right).
Figure 4. Two−dimensional river topography elevation map (left) and local three−dimensional river topography elevation map (right).
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Figure 5. Simulation and measured flow velocity at the full hydrological section of Xinquman.
Figure 5. Simulation and measured flow velocity at the full hydrological section of Xinquman.
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Figure 6. Changes in Riverbed Elevation at the Large Hydrological Section of the Xinquman.
Figure 6. Changes in Riverbed Elevation at the Large Hydrological Section of the Xinquman.
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Figure 7. The flow–water level relationship curves of numerical simulation data and measured dynamic measurement data of large sections 1, 2, and 3 of the river channel.
Figure 7. The flow–water level relationship curves of numerical simulation data and measured dynamic measurement data of large sections 1, 2, and 3 of the river channel.
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Figure 8. Six types of flood event, and the amount of silt under three conditions.
Figure 8. Six types of flood event, and the amount of silt under three conditions.
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Figure 9. Variation of river channel erosion and deposition before flood peak in the fourth flood event.
Figure 9. Variation of river channel erosion and deposition before flood peak in the fourth flood event.
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Figure 10. The variation of river erosion and deposition before the flood peak in the fifth flood event.
Figure 10. The variation of river erosion and deposition before the flood peak in the fifth flood event.
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Figure 11. The amount of erosion and deposition of the river channel at the end of the fourth type of footfall.
Figure 11. The amount of erosion and deposition of the river channel at the end of the fourth type of footfall.
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Figure 12. Variation of river channel erosion and deposition at the end of the fifth type of flood fallback.
Figure 12. Variation of river channel erosion and deposition at the end of the fifth type of flood fallback.
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Figure 13. Variation of river channel erosion and deposition before the arrival of the first type of flood peak.
Figure 13. Variation of river channel erosion and deposition before the arrival of the first type of flood peak.
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Figure 14. Variation of river channel erosion and deposition before the second type of flood peak came.
Figure 14. Variation of river channel erosion and deposition before the second type of flood peak came.
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Figure 15. The variation of erosion and deposition of the river channel at the end of the first type of footfall.
Figure 15. The variation of erosion and deposition of the river channel at the end of the first type of footfall.
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Figure 16. The amount of erosion and deposition of the river channel at the end of the second type of footfall.
Figure 16. The amount of erosion and deposition of the river channel at the end of the second type of footfall.
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Table 1. Data table of six types of flood event at Alar Hydrological Station.
Table 1. Data table of six types of flood event at Alar Hydrological Station.
Flood EventOccurrence TimePeak Flow (m3/s)Flood Volume (m3)Sediment
Concentration (kg/m3)
The first type4 August 2001–4 September 2001125020,0002, 6, 10
The second type13 July 2001–13 August 2001142020,0002, 6, 10
The third type27 July 2005–27 August 2005149027,0002, 6, 10
The fourth type20 July 2006–20 August 2006181031,0002, 6, 10
The fifth type19 July 2010–19 August 2010187037,0002, 6, 10
The sixth type4 August 2022–4 September 2022182037,0002, 6, 10
Table 2. Flow data table for the fourth and fifth types of flood events.
Table 2. Flow data table for the fourth and fifth types of flood events.
Flood EventTotal Flow Before Peak Stage (m3)Total Flow After Peak Stag (m3)Average Daily Flow Before Peak Stage (m3/s)Average Daily Flow After Peak Stage (m3/s)
The fourth type730021,0007301083
The fifth type14,40021,00010301235
Table 3. Flow data table for the first and second types of flood events.
Table 3. Flow data table for the first and second types of flood events.
Flood EventTotal Flow Before Peak Stage (m3)Total Flow After Peak Stag (m3)Average Daily Flow Before Peak Stage (m3/s)Average Daily Flow After
Peak Stage (m3/s)
The first type87739867585617
The second type769811,000513666
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Zhao, M.; Li, Y.; Li, L.; Dai, W. Response of Riverbed Shaping to a Flood Event in the Reach from Alar to Xinquman in the Mainstream of the Tarim River. Water 2025, 17, 1092. https://doi.org/10.3390/w17071092

AMA Style

Zhao M, Li Y, Li L, Dai W. Response of Riverbed Shaping to a Flood Event in the Reach from Alar to Xinquman in the Mainstream of the Tarim River. Water. 2025; 17(7):1092. https://doi.org/10.3390/w17071092

Chicago/Turabian Style

Zhao, Mingcheng, Yujian Li, Lin Li, and Wenhong Dai. 2025. "Response of Riverbed Shaping to a Flood Event in the Reach from Alar to Xinquman in the Mainstream of the Tarim River" Water 17, no. 7: 1092. https://doi.org/10.3390/w17071092

APA Style

Zhao, M., Li, Y., Li, L., & Dai, W. (2025). Response of Riverbed Shaping to a Flood Event in the Reach from Alar to Xinquman in the Mainstream of the Tarim River. Water, 17(7), 1092. https://doi.org/10.3390/w17071092

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