Hydrodynamic impacts of extreme drought-to-flood transition events: insights from the Poyang Lake Open Access

With the intensification of global climate change, the frequency and severity of extreme weather events have risen significantly, and Drought–Flood Abrupt Alternation (DFAA) has emerged as a critical and increasingly common hydrological phenomenon (Bai et al. 2023; Guo et al. 2023; Zhou et al. 2023). DFAA refers to the abrupt transition from drought to flood or vice versa within a short period, a phenomenon particularly prominent in humid subtropical and monsoon climate regions (Shi et al. 2021; Zhang et al. 2023). This abrupt alternation disrupts regional hydrological cycles and poses serious threats to ecosystem stability, leading to dynamic changes in lake sedimentation and erosion, potentially altering the functions and structure of lake ecosystems and affecting their long-term health and stability (Liu et al. 2023a, b; Ren et al. 2023).

DFAA has become a global issue, triggering a chain of extreme climate-related disasters across multiple regions. For instance, California, USA, experienced nine atmospheric river events during the winter of 2022–2023, abruptly shifting from severe drought to flood conditions. This event not only replenished reservoir levels but also caused severe floods and mudslides (DeFlorio et al. 2024). In eastern Australia, the New South Wales region, after prolonged droughts, encountered heavy rainfall, with frequent DFAA events posing flood and wildfire risks that have severely impacted ecosystems, agriculture, and public health (Lee et al. 2023). Such events disrupt regional hydrological cycles and cause extensive impacts on ecosystems and socio-economic conditions, leading to agricultural damage, infrastructure destruction, and worsened living conditions (Chen et al. 2024; Liu et al. 2024; Matanó et al. 2024; Shi et al. 2024). Recent studies have applied hydrodynamic models to analyze the impacts of DFAA events. For example, Darj et al. (2024) developed an integrated approach using unmanned aerial vehicles (UAVs), satellite imagery, and hydrological simulations to predict flood scenarios in Gujarat, India. Haces-Garcia et al. (2025) used deep neural networks (DNNs) to optimize hydrodynamic flood models. Their approach significantly reduced computation time while maintaining accurate flood predictions.

The Poyang Lake, the largest freshwater lake in China, is significantly affected by DFAA events (Deng et al. 2024; Li et al. 2024; Zou et al. 2024). In recent years, the frequency and intensity of DFAA in the Poyang Lake basin have shown a continuous increase, along with distinct spatiotemporal variations. Previous studies have evaluated the characteristics of DFAA in the Poyang Lake basin using various hydrological methods. For instance, Rong et al. (2020) analyzed DFAA events between 1960 and 2012, finding that these events predominantly occurred between wet and dry seasons and exhibited notable differences in frequency and intensity across sub-basins. Wang et al. (2023) employed the Soil and Water Assessment Tool (SWAT) model to predict future DFAA trends under climate change scenarios, indicating that DFAA event frequency in humid subtropical regions will significantly increase, especially between January and October. Further, Li et al. (2024) highlighted the spatiotemporal heterogeneity of DFAA in the Poyang Lake basin, observing that drought-to-flood (DTF) events generally occur from March to June, while flood-to-drought (FTD) events are concentrated between July and September. The Poyang Lake region, particularly the southern lake area, has been identified as a high-risk zone for DFAA, with the impact range of extreme DFAA events gradually expanding over time (Li et al. 2024). In summary, DFAA in the Poyang Lake basin is influenced by a combination of climate change and regional characteristics, resulting in varying degrees of risk and distinct event characteristics across sub-basins.

The causes of DFAA in the Poyang Lake are complex, involving factors such as climate change, human activities, and hydrological characteristics (Li et al. 2023; Sun et al. 2023; Lu et al. 2024; Yu et al. 2024). Global climate change has increased the spatiotemporal variability of precipitation, leading to more frequent extreme rainfall events that directly impact hydrological processes within the basin (Ma et al. n.d.; Zhang et al. 2024). Meanwhile, the regulation function of hydraulic projects, such as the Three Gorges Dam, has altered Poyang Lake's water level characteristics, further exacerbating the risk of DFAA (Yu et al. 2023). Additionally, human activities within the basin, including land use changes and excessive water extraction, have weakened the lake's natural regulatory functions, thereby increasing the frequency and severity of DFAA events in the region (Liu et al. 2023a, b).

Frequent DFAA events have had profound impacts on the ecosystem and socio-economic conditions of the Poyang Lake. Drastic fluctuations in water levels disrupt the wetland ecosystems in the lake region, intensify habitat fragmentation for aquatic species, and increase the risk of water quality degradation (Bai et al. 2023; Qian et al. 2023). Studies indicate that the frequency of DFAA events raises the vulnerability of lake ecosystems, leading to a marked decline in aquatic biodiversity and a heightened risk of algal blooms (Wang et al. 2023). Socio-economically, DFAA events exacerbate challenges in agricultural irrigation, affect crop yields, and place higher demands on water resource management (Bi et al. 2022, 2023).

However, the existing research has primarily focused on the frequency and ecological impacts of DFAA events, while a comprehensive examination of their dynamic mechanisms within the lake's hydrodynamic system remains lacking. Previous studies often concentrated on the general characteristics of these events and their impacts on the local environment, yet few have addressed the detailed hydrodynamic responses to such abrupt transitions in lake ecosystems. This study aims to fill this gap by systematically evaluating the hydrodynamic effects of extreme DFAA events, focusing on the dynamic changes of key parameters such as water level, flow velocity, flow direction, and bottom shear stress. The specific objectives of this study include the following: (1) analyzing the spatiotemporal distribution of DFAA events within the Poyang Lake basin, including the trends in frequency and intensity across sub-basins; and (2) evaluating the impact of extreme DFAA events on lake hydrodynamic parameters. By revealing the disturbance effects of DFAA on lake hydrodynamic systems, this study aims to provide scientific support and a basis for decision-making in lake management and climate change adaptation.

The water level and surface area of the Poyang Lake fluctuate significantly with seasonal changes, exhibiting strong seasonality. During the flood season, the lake's surface area can expand to over 4,000 km², while in the dry season (from October to the following March), the area contracts to approximately 1,000–2,000 km². These hydrological fluctuations are influenced by seasonal rainfall, inflow from upstream rivers, and the lake's unique topography (Xue et al. 2024). The subtropical humid monsoon climate of the Poyang Lake basin causes rainfall to concentrate from April to June each year, resulting in a rapid rise in lake water levels during this period (Qiu et al. 2024).

This study utilizes a variety of hydrological data from the Poyang Lake and its main inflowing tributaries, providing a crucial foundation for the construction and validation of the hydrodynamic model. The flow data for the five major inflowing rivers – Xiushui, Ganjiang, Fuhe, Xinjiang, and Raohe – were obtained from hydrological stations within the basin, covering the period from 1970 to 2019 and measured in cubic meters per second. This dataset reflects seasonal and hydrological variations in water inflow, offering comprehensive boundary conditions for hydrodynamic simulations of the Poyang Lake. Additionally, water level and flow data from the HK hydrological station, located at the primary connection between Poyang Lake and the Yangtze River, include multiple years of observations. The water level variations at HK, which are critical to Poyang Lake's hydrodynamic conditions, provide insights into hydrological changes between flood and dry seasons, allowing for the simulation of hydraulic interactions between the lake and the Yangtze River. Topographic data from surveys conducted in 1998, covering the entire lake and surrounding basin, were used for model grid generation.

When SDFI > 0, it indicates a DTF transition; when SDFI < 0, it indicates a FTD transition. The absolute value of SDFI reflects the strength of the DFAA event, with higher values indicating greater intensity. A threshold of ∣SDFI∣ > 1 is generally used to identify DFAA events. Additionally, this study divides the SDFI values for the Poyang Lake basin into seven levels (as shown in Table 1), to further analyze the impact of DFAA events of varying intensities on lake hydrological conditions.

Using this approach, the study identified multiple DFAA events in the Poyang Lake basin from 1960 to 2019, thus determining typical years of DFAA events. The data from these typical years will be used to set boundary conditions for the hydrodynamic model, allowing for an assessment of hydrodynamic variations in the Poyang Lake under different hydrological scenarios.

The Z value is used to determine the trend in the time series. When∣Z∣exceeds a critical value at a specified significance level (e.g., 95 or 99%), the series is considered to exhibit a significant upward or downward trend.

In this study, the MK test is employed to analyze the trend of the SDFI in the Poyang Lake basin from 1970 to 2019, to determine whether there is a significant upward or downward trend. Additionally, mutation point analysis is used to identify significant changes in DFAA events within the basin for specific years.

This study employs the Finite-Volume Coastal Ocean Model (FVCOM) to simulate the hydrodynamic processes in the Poyang Lake. FVCOM, based on an unstructured grid, is well-suited for adapting to the complex topography of lakes, making it an effective tool for simulating hydrodynamic processes in lake and coastal environments (Chen et al. 2006). The model setup was guided by relevant previous studies to ensure the scientific rigor and validity of the simulations (Li et al. 2014; Li et al. 2017b; Tang et al. 2023).

The model's initial conditions include inflow data from the five main tributaries, while boundary conditions incorporate the inflow from the five rivers upstream and water levels at HK. To assess the sensitivity of the model, three grid sizes were tested, namely fine (20–100 m), medium (50–400 m), and coarse grids (200–1,000 m), with grid sizes ranging from 20 to 1,000 m. The sensitivity analysis was performed to evaluate the impact of grid resolution on model accuracy, particularly in capturing flow velocity and water levels under varying conditions.

To assess the impact of DFAA events on the hydrodynamics of the Poyang Lake, four simulation scenarios were designed (Table 2), including two extreme scenarios and two baseline scenarios. The first set of scenarios (S1 and S2) is based on historical observations, representing typical DTF conditions from 1998 and FTD conditions from 1977, aiming to simulate the effects of extreme DFAA events on lake hydrodynamic characteristics. The second set of scenarios (S3 and S4) uses multi-year daily average flow rates as baseline conditions to contrast against the extreme scenarios, thereby revealing the differences in lake hydrodynamics under extreme versus typical flow conditions. By comparing these four scenarios, this study clarifies the independent and combined effects of extreme upstream inflow and water level variations on flow velocity, shear stress, and water level fluctuations within the lake.

To further explore the trend of SDFI values in the Poyang Lake basin, an MK test was conducted on the annual maximum and minimum SDFI values. Figure 3(c) and 3(d) shows the MK test results for the maximum and minimum SDFI values, respectively. In Figure 3(c), the Forward Trend Statistic (UF) and Backward Trend Statistic (UB) curves intersect around 1984 and exceed the significance level, indicating a significant change point for the annual maximum SDFI in that year. Prior to 1984, the UF curve exhibited minimal fluctuations, suggesting an overall stable trend in the intensity and frequency of extreme drought events. However, after 1984, the UF curve rose sharply, particularly from the 1990s to the early 2000s, indicating an increase in the frequency and intensity of extreme DTF events during this period.

In Figure 3(d), the MK test results show that the UF and UB curves intersect around 1988, also exceeding the significance level, suggesting a significant shift in the annual minimum SDFI around that year. Before 1988, the UF curve displayed no significant trend, with extreme flood events showing cyclical variability. However, following the change point in 1988, especially from the mid-1990s to 2010, the UF curve exhibited a pronounced upward trend, indicating an intensifying frequency and severity of extreme FTD events, with droughts becoming more frequent and intense.

Overall, the MK test results reveal a critical turning point in the mid-to-late 1980s in the Poyang Lake basin. After this period, both the intensity and frequency of extreme DTF and FTD events have shown significant increases.

Figure 3(b) illustrates the monthly variability in the maximum (purple area) and minimum (red area) values of the SDFI in the Poyang Lake basin from 1970 to 2019, reflecting the intensity of DFAA events across different months of the year. The results indicate that both maximum (DTF) and minimum (FTD) SDFI values exhibit pronounced seasonal fluctuations. Notably, the maximum SDFI values peak from May to June, suggesting that DTF events in the Poyang Lake basin are especially intense at the onset of the rainy season, a period characterized by a rapid increase in precipitation and a heightened flood risk. Simultaneously, the minimum SDFI values reach a pronounced low (approaching −20) from April to June, indicating strong FTD events within this period.

From January to March, the SDFI values remain relatively low, signifying fewer occurrences of DFAA and relatively stable hydrological conditions within the basin. In autumn and winter (August to December), the SDFI values gradually approach zero, indicating a reduction in precipitation and lower temperatures, leading to stable hydrological conditions and fewer DFAA events in the Poyang Lake basin. Overall, Figure 3(b) highlights the seasonal characteristics of the hydrological cycle in the Poyang Lake basin, with particularly intense DFAA events from May to June.

Figure 3(e) and 3(f) illustrates the interannual percentage changes in DTF and FTD events of varying intensities in the Poyang Lake basin from 1970 to 2019. By analyzing the proportions of mild, moderate, and severe DFAA events, the evolving trends of extreme hydrological events over different decades are revealed. Severe DTF events were particularly prevalent during the 1970s, accounting for 15% of events, but this proportion declined to 10% in the 1980s, 5% in the 1990s, and reached its lowest point in the 2000s at only 3%. However, the proportion of severe DTF events rose significantly to 12% during 2010–2019, the highest level over the study period. This suggests an increase in the frequency and intensity of flood events in the Poyang Lake basin over the past decade, likely reflecting intensified extreme precipitation events in the region that may be linked to climate change. Mild DTF events peaked at 12% in the 1990s but declined to 5% in the 2010s, indicating a reduced occurrence frequency. Moderate DTF events remained relatively stable throughout the study period, with minimal fluctuation and a consistently low proportion.

Regarding FTD events, severe FTD events were most common in the 1970s and 1980s, with proportions of 10 and 8%, respectively, indicating a high drought risk during these decades. The proportion of severe FTD events declined sharply to 5% in the 2000s but saw a slight increase to 8% in the 2010s, though it did not reach the peak levels observed in the 1970s and 1980s. The proportions of moderate and mild FTD events remained relatively stable across the study period, particularly with minimal fluctuations after the 1990s.

In summary, the Poyang Lake basin experienced a higher frequency of severe DFAA events in the 1970s and 1980s, reflecting frequent climate fluctuations and the impact of extreme weather events during this period. However, the incidence of severe DFAA events declined markedly in the 2000s, suggesting a period of relative hydrological stability in the basin. In contrast, the proportion of severe DTF events rose sharply to 12% from 2010 to 2019, indicating an increase in flood events over the past decade.

Figure 4(a) shows the water level changes in the central and coastal regions of Poyang Lake under low water conditions during a DTF scenario (#1 DTF), where water levels rise sharply, reaching over 2 m in some areas. This increase reflects the lake's rapid water storage response to substantial upstream inflows, particularly in the relatively flat lake areas where water levels fluctuate significantly. In Figure 5(d), the water levels at the Kangshan (KS), DC, and XZ stations are significantly higher under the actual scenario S1 (DTF) compared to the baseline scenario S3, especially during periods when water levels are below 14 m, indicating a strong influence of upstream flooding on lake levels.

Figure 4(d) illustrates the water level changes during an FTD scenario under low water conditions (#1 FTD). Water levels decrease markedly in the central and northern regions of the lake, with shallow areas experiencing the most substantial drops. This pattern reflects the rapid recession of water when flood inflows decrease abruptly. The water level trends in Figure 5(a) also indicate a sharp decline at the KS, DC, and XZ stations under S1, contrasting with the relatively stable conditions observed in the baseline scenario S3, highlighting the significant impact of FTD on lake water levels.

Figure 4(b) and 4(c) depict the DTF scenarios under high water conditions (#2 DTF and #3 DTF), where the water level fluctuations are notably reduced compared to low water periods, appearing predominantly light blue, indicating milder changes. In the central part of the lake, the changes are nearly negligible, suggesting that the lake's strong water storage capacity under high water conditions effectively buffers upstream inflow variations. Figure 5(d)–5(f) supports this observation, showing that water level fluctuations at the KS, DC, and XZ stations during the high water DTF scenario (S2) are similar to those in the baseline scenario S4, indicating a subdued response to external hydrological variations when water levels are high.

Figure 4(e) and 4(f) presents the FTD scenarios under high water conditions (#2 FTD and #3 FTD), where water level fluctuations are minimal, with nearly the entire area shaded in light blue, close to zero. This stability indicates that even during FTD scenarios, the overall lake level remains relatively stable under high water conditions. The deep water depth mitigates the impact of water level declines, with the lake's large water volume diluting the effect of external water level changes, resulting in a dampened response. Figure 5(d)–5(f) further confirms that water levels at the KS, DC, and XZ stations in scenario S2 align closely with those in baseline scenario S4, showcasing the lake's strong water level regulation capacity.

In summary, Poyang Lake's water level variations during DFAA events exhibit significant differences across hydrological scenarios. Under low water conditions, the lake shows a pronounced response to DFAA, with substantial internal water level fluctuations reflecting a high sensitivity to changes in hydrological input. Conversely, under high water conditions, the lake demonstrates a strong water storage and buffering capacity, leading to stabilized water levels.

Figure 6(a) and 6(b) compares the flow rate at HK for scenarios S1 versus S3 and S2 versus S4, respectively. In Figure 6(a), the red curve represents the flow rate variation at HK under scenario S1 (DTF), while the blue curve shows the flow rate under the baseline scenario S3. During S1, the flow rate at HK increases sharply between days 50 and 73, reaching a pronounced peak. This indicates that during the transition from drought to flood, the lake undergoes rapid inflow, creating a strong flood process. In contrast, under S3, the flow rate around day 73 turns negative, indicating a backflow at HK where Yangtze River water reverses into the Poyang Lake. Similarly, in the flow rate comparison between S2 and S4 (Figure 6(b)), a backflow phenomenon is also observed, indicating that without extreme DFAA events, the water level fluctuations of the Yangtze River significantly impact HK, making reverse flows more likely.

Figure 6(c) and 6(d) shows the internal flow direction distributions in the Poyang Lake on day 73 under scenarios S1 (DTF) and S3 (multi-year average), respectively. In Figure 6(c), the flowlines in scenario S1 reveal a dominant north-to-south drainage pattern, especially pronounced in the lake's central and main river channel areas, highlighting a strong drainage capacity. This pattern demonstrates that when upstream river inflows surge, the lake forms a consistent drainage direction to mitigate flood risks, reflecting a clear hydrodynamic tendency toward discharge. In contrast, Figure 6(d) displays the flow direction in scenario S3, which differs markedly from S1. Under S3, some Yangtze River water backflows into the lake, particularly when Yangtze water levels exceed those in the Poyang Lake, causing the reverse flow to extend into the lake's central and southern areas. This phenomenon indicates that during FTD conditions or when Yangtze River levels are high, the Poyang Lake and its internal areas are significantly influenced by the Yangtze River, resulting in a hydrodynamic process opposite to the usual drainage pattern.

The analysis of flow rate and direction under different DFAA scenarios highlights the complexity of Poyang Lake's hydrodynamics. During DTF scenarios, the lake's flow rate increases rapidly, displaying a strong drainage tendency, reflecting Poyang Lake's rapid response mechanism to flood conditions. Conversely, in FTD scenarios, the Yangtze River's influence causes backflow, adding further complexity to the lake's hydrodynamic processes.

Flow velocity is a key indicator of lake hydrodynamics, as its variation directly reflects the lake's response to external hydrological inputs. This section compares the flow velocity characteristics of the Poyang Lake under different scenarios to explore the patterns and spatial heterogeneity of flow velocity changes under typical DTF and FTD conditions.

Figure 7(a) shows the flow velocity changes during a DTF event (#1 DTF) under low water conditions. Under the actual scenario S1, the increase in flow velocity is concentrated in the northern part of the lake and the narrow inflow areas, with velocities reaching nearly 1 m/s. This indicates that, when upstream inflows surge, the topographic constraints of the lake cause concentrated flow in the northern and inlet regions, resulting in a significant increase in flow velocity. Figure 7(d) illustrates the flow velocity during an FTD event (#1 FTD). As water levels drop sharply, water within the lake decreases, causing flow to accelerate through narrow channels. However, unlike DTF events, the central and southern lake areas experience relatively smaller changes in flow velocity, reflecting stable hydrodynamic conditions. This suggests that during FTD events, the most pronounced hydrodynamic changes occur at the outlet, while other areas within the lake are less affected.

In contrast, under high water conditions, the lake's hydrodynamic system demonstrates a greater buffering capacity. Figure 8(b) and 8(c) shows the flow velocity changes under high water DTF scenarios, with overall fluctuations significantly smaller than under low water conditions. The central and downstream regions, in particular, exhibit stable flow, as the increased water storage capacity at high water levels effectively moderates external water input fluctuations, reducing dramatic changes in flow velocity. Similarly, in high water FTD scenarios (Figure 8(e) and 8(f)), flow velocity variations decrease further. The increased lake depth under high water conditions contributes to spatial uniformity in flow velocity, especially in the central and downstream regions, resulting in an overall stable hydrodynamic system. This contrasts sharply with the rapid fluctuations observed during low water periods.

During DFAA events, significant differences in hydrodynamic responses are observed across stations in the Poyang Lake, including KS, TY, DC, and XZ, highlighting the spatial heterogeneity in flow velocity changes.

Figure 8(a)–8(c) shows the flow velocity changes at these stations during three typical DTF events. Under low water conditions, flow velocity at the upstream KS station increases notably, reaching up to 0.15 m/s, while changes at the other stations are relatively minor, with velocities below 0.07 m/s. In contrast, flow velocity variations at TY, DC, and XZ stations are modest. Under high water conditions, overall flow velocity variations at all stations are below 0.09 m/s.

Figure 8(d)–8(f) depicts flow velocity differences during three FTD events at the same stations under both low and high water conditions. During low water periods, velocity differences between stations range from 0.07 to 0.29 m/s, indicating more significant flow velocity changes. This reflects the rapid outflow of water through narrow channels due to the sharp drop in lake levels, resulting in intensified hydrodynamic activity in these regions. Under high water conditions, station-to-station velocity differences remain substantial, ranging from 0.04 to 0.28 m/s, indicating that significant hydrodynamic fluctuations persist during FTD events, though moderated by the lake's depth and diffusion effects. Overall, flow velocity increases across stations range from 18 to 170%, demonstrating the considerable variability and spatial heterogeneity in hydrodynamic disturbances under extreme hydrological scenarios.

In summary, during DTF scenarios, upstream flow velocity changes are limited. However, during FTD scenarios, the rapid drop in lake water levels causes water to discharge through HK and narrow channels, resulting in intense flow velocity variations in these areas. These rapid discharge-induced fluctuations significantly influence the lake's hydrodynamic processes, with the most pronounced disturbances occurring at the outlet and in narrow channels.

Figure 9 illustrates the bottom shear stress distribution and station responses in the Poyang Lake under extreme DFAA scenarios. Under the extreme drought-to-flood (DTF) scenario (S1) (Figure 9(a)), bottom shear stress significantly increases in the northern and inlet regions of the lake, with localized values reaching 1.0–1.5 N/m², indicating the concentrated hydrodynamic effects in the inlet area due to the substantial upstream inflow. In contrast, under the baseline scenario (S3 DTF) (Figure 9(b)), shear stress values are notably lower, particularly in the northern inlet area, suggesting that hydrodynamic disturbances and sediment erosion risk are reduced under average flow conditions.

At the typical monitoring stations (Figure 9(e)), KS and DC exhibit significantly higher shear stress values under S1 (0.46 and 0.44 N/m², respectively) compared to the baseline scenario S3 (0.07 and 0.06 N/m²). This stark difference indicates that rapid increases in upstream inflows lead to intensified hydrodynamic activity and a heightened risk of sediment resuspension at these stations. Conversely, shear stress changes are minimal at TY and XZ, suggesting that the impact of increased upstream inflows on these stations is weaker.

In the extreme FTD scenario (S1) (Figure 9(c)), shear stress also rises substantially in the northern inlet area, with localized values exceeding 1.5 N/m², indicating increased shear stress due to concentrated outflows as upstream inflows sharply decrease. In the baseline scenario (S3 FTD) (Figure 9(d)), the shear stress distribution is more uniform, with overall lower hydrodynamic intensity. For station responses, Figure 9(f) shows that shear stress at DC station reaches 0.45 N/m² under the FTD scenario, compared to only 0.09 N/m² in S3, indicating that rapid water level drops lead to concentrated hydrodynamic effects, increasing sediment resuspension risk.

Figure 9(g) and 9(h) present shear stress differences under the third DFAA scenario (#3 DTF and #3 FTD) for scenarios S2 and S4. Figure 9(g) shows that under the DTF scenario in S2, shear stress at the KS station reaches 0.11 N/m², compared to only 0.03 N/m² in S4, highlighting the significant impact of DTF conditions on hydrodynamics at these stations. Under the FTD scenario (Figure 9(h)), shear stress at the KS station is 0.11 N/m², well above the 0.03 N/m² observed in the baseline scenario, suggesting that the hydrodynamic disturbance resulting from sharp upstream flow reductions significantly impacts sediment mobilization at these sensitive stations.

Experimental data indicate that the critical shear stress required to initiate sediment movement in the Poyang Lake is between 0.011 and 0.024 N/m² (Li et al. 2017a; Wang et al. 2020). In the actual DTF and FTD scenarios (S1 and S2), shear stress values in the inlet and northern lake regions far exceed this threshold, indicating that sediments in these areas are highly susceptible to disturbance and resuspension, especially under conditions of rapid increases or decreases in upstream inflows. Conversely, in the baseline scenarios (S3 and S4), shear stress values remain relatively low, resulting in limited sediment disturbance and minimal impact on water quality and the lake ecosystem.

Overall, bottom shear stress in the Poyang Lake increases significantly in the northern regions during extreme DFAA scenarios, with intensified hydrodynamic activity leading to an elevated risk of sediment resuspension and erosion. This effect is particularly pronounced at sensitive stations such as KS and DC. In contrast, shear stress remains low in the baseline scenarios, indicating a more stable hydrodynamic system with reduced sediment disturbance. These findings suggest that extreme DFAA events induce substantial changes in the bottom hydrodynamic environment of the Poyang Lake, with far-reaching implications for sediment erosion and ecosystem stability.

This study conducts an in-depth hydrodynamic analysis of DFAA events in the Poyang Lake basin, building on previous research to yield several important new insights. Notably, it provides detailed discussions on spatiotemporal distribution characteristics, intensity trends, and the impacts of DFAA events against the backdrop of climate change.

Previous studies have highlighted specific months when DFAA events are concentrated in the Poyang Lake basin (Li et al. 2024). This study further quantifies the occurrence frequency of DFAA events of different intensities, finding that while mild events dominate the basin, moderate and severe events have shown a clear increase in frequency over the past few decades, especially in the eastern regions of the Xinjiang and Raohe sub-basins. This suggests that the impact of climate change is not uniform across the basin, a finding supported by Liu et al. (2023a, b) and Zhang et al. (2023). Such insights provide precise spatial guidance for water resource management and zoned regulation within the basin.

Previous studies have shown that the frequency of DFAA events in the Poyang Lake basin peaked in the 1990s and declined to a low point in the 2000s (Rong et al. 2020). Using MK tests and Ensemble Empirical Mode Decomposition, this study identifies 1984 and 1988 as significant turning points for the intensity of DFAA events. These findings are consistent with Li et al. (2023) and Yu et al. (2023), who noted similar changes in hydrological dynamics during this period. This study improves upon prior research by pinpointing specific temporal shifts, offering a clearer understanding of DFAA trends over time.

This study also reveals the hydrodynamic response characteristics of the Poyang Lake under various DFAA scenarios. Increased flow velocity and bottom shear stress during low water conditions lead to significant sediment resuspension, particularly in sensitive areas like the northern inlet and coastal regions. This finding aligns with previous research (Chen et al. 2024) that emphasized the ecological risks associated with sediment disturbance. The fluctuations in hydrodynamics may cause redistribution of sediment and pollutants, affecting benthic habitats and nutrient cycling, which could threaten the lake's ecological stability.

In particular, understanding the spatiotemporal variations in flow velocity and bottom shear stress is critical for designing targeted management actions in sensitive areas, such as the northern inlet and coastal regions of the Poyang Lake, where sediment resuspension and habitat degradation are most likely to occur.

For practical applications, this research can inform lake restoration efforts by prioritizing areas where sediment disturbance poses the greatest risk to water quality and ecosystem stability. Furthermore, flood and drought management strategies can be improved by integrating hydrodynamic analyses into decision-making processes, especially for managing water levels and sediment transport during extreme weather events. Targeted actions, such as adjusting water levels or implementing sediment control measures in high-risk zones, can mitigate the impact of extreme DFAA events on both water quality and the lake ecosystem. This study underscores the need for proactive management in response to changing hydrodynamic conditions, ensuring the lake's resilience to future climate-induced disturbances.

While the numerical model used in this study provides valuable insights into the hydrodynamic responses of the Poyang Lake under extreme DFAA events, it has certain limitations that should be considered. One key limitation is that the model does not account for wind stress, which can influence the hydrodynamics of large lakes like the Poyang Lake, especially during extreme weather events. The omission of wind stress may lead to underestimations of certain hydrodynamic effects, particularly in shallow areas or during periods of strong winds.

This study systematically analyzed the hydrodynamic responses of the Poyang Lake under various DFAA scenarios, uncovering significant impacts of extreme DFAA events on the lake's hydrodynamic environment. The main conclusions are as follows:

Characteristics and trends of DFAA: The DFAA events in the Poyang Lake primarily occur from March to October, with DTF transitions dominating from March to June and FTD transitions from July to October. While mild events are predominant, the frequency of moderate and severe events has increased, particularly in the eastern sub-basins of Xinjiang and Raohe, indicating the expanding influence of extreme climate events in the basin.

Water level variability: DFAA events significantly influence water level fluctuations in the Poyang Lake, with rapid water level rises under DTF conditions and sharp declines during FTD scenarios. These fluctuations highlight the potential impacts of intense water level changes on the lake ecosystem and coastal environments.

Flow direction and velocity variability: DFAA events lead to changes in flow direction and significant increases in flow velocity, particularly during FTD scenarios when rapid water level declines lead to flow velocities exceeding 1.7 times the average. These changes demonstrate the lake's strong hydrodynamic response to extreme hydrological events.

Bottom shear stress distribution: Bottom shear stress increases significantly during extreme DFAA events, especially in narrow channels and the outlet regions, indicating a heightened risk of sediment disturbance, erosion, and resuspension that could affect water quality and the ecosystem.

This study provides underscores the importance of considering the effects of extreme hydrological events on hydrodynamics and sediment processes when developing lake management strategies.

All authors contributed to the research conception and design. Material preparation, data collection and analysis were performed by Y.Y., Y.Y., C.L., and X.Q. Y.Y. wrote the first draft of the manuscript and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

This research was funded by the National Key R&D Program of China (2022YFC3004400), National Natural Science Foundation of China Youth Science Fund Project (52309029), Henan Provincial Science and Technology Research Project (242102321035).

All authors confirm their co-authorship.

The authors give their consent for the publication of this manuscript.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.