A method for detecting extreme hydrological events in a watershed and determining changes in hydrodynamic conditions.

Abstract

This invention discloses a method for detecting extreme hydrological events in a watershed and determining changes in hydrodynamic conditions, belonging to the field of water conservancy engineering technology. The specific steps include: S1, calculating the annual runoff distribution difference coefficient and characteristic flow process; S2, screening extreme flood and extreme drought events, i.e., extreme flood condition: selecting the annual maximum flow Q from the historical sequence. max The first 1% quantile value; extreme drought conditions: selecting the minimum value of the runoff distribution difference coefficient λ to characterize the minimal difference in water volume between the flood season and the non-flood season; S3, determining typical hydrological events by analyzing the relationship between characteristic flow and annual average flow; S4, analyzing the water depth-flow relationship and classifying extreme flood types; S5, deriving the physical formula of the water depth-flow relationship; S6, analyzing and verifying the differences in hydrodynamic conditions for extreme floods. This invention can solve the problems of insufficient sensitivity of traditional methods to short-duration extreme hydrological events and the lack of quantitative analysis of hydrodynamic mechanisms under extreme events.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering technology, and in particular to a method for detecting extreme hydrological events in a watershed and determining changes in hydrodynamic conditions. It is applicable to water resource management of transboundary rivers and flood and drought prevention in watersheds, especially for the accurate identification of extreme hydrological events in watersheds affected by climate change. Background Technology

[0002] Against the backdrop of global climate change and intensified human activities, the frequency of extreme hydrological events in river basins (such as extreme floods and droughts) has increased significantly, posing a major threat to water resource management, disaster prevention and mitigation, and the ecological environment. Changes in extreme hydrological conditions in the upper reaches of rivers have a profound impact on water supply security, agricultural production, and hydropower operations in the middle and lower reaches. Currently, the identification of extreme hydrological events mainly relies on traditional statistical methods and historical extreme value screening, but these methods have significant limitations.

[0003] Traditional methods typically analyze annual hydrological series, making it difficult to capture the characteristics of short-duration extreme events. For example, while the Mann-Kendall test can identify long-term trends in annual runoff, it lacks sensitivity to sudden extreme floods or seasonal droughts within the same year. Another significant drawback of existing technologies is the insufficient analysis of hydrodynamic mechanisms. Current research largely focuses on statistical analysis of hydrological indicators, lacking quantitative correlations of changes in hydrodynamic parameters under extreme events. For instance, events that both reach flood standards may exhibit fundamental differences in their depth-discharge relationships and energy transfer mechanisms, which traditional methods cannot distinguish, resulting in a lack of precise scientific basis for reservoir operation decisions.

[0004] Meanwhile, current hydrological analysis methods suffer from significant limitations in adaptability to time scales, primarily manifested in an over-reliance on annual hydrological sequences. This analytical framework struggles to effectively capture the key characteristics of short-duration extreme hydrological phenomena, severely restricting its ability to identify sudden flood peaks and seasonal anomalous droughts. Traditional statistical indicator systems also exhibit a marked lack of sensitivity when dealing with such instantaneous hydrological events.

[0005] Existing methodologies have significant shortcomings in analyzing hydrodynamic processes, particularly lacking a systematic quantitative correlation of dynamic changes in key hydraulic parameters. This lack of a theoretical framework directly leads to insufficient analytical depth of physical mechanisms under extreme hydrological scenarios, making it difficult to reveal the intrinsic dynamic differences and evolutionary patterns of different flood types.

[0006] Therefore, there is an urgent need to develop a method that can simultaneously integrate runoff temporal characteristics, hydrodynamic parameter changes, and event physical mechanisms to achieve accurate identification of extreme hydrological events in the basin and scientific analysis of hydrodynamic responses, thus providing technical support for cross-border water resources collaborative management. Summary of the Invention

[0007] The present invention aims to provide a method for detecting extreme hydrological events in a watershed and determining changes in hydrodynamic conditions, in order to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A method for detecting extreme hydrological events in a watershed and determining changes in hydrodynamic conditions, with the following specific calculation steps:

[0010] 1. Calculate the annual distribution variation coefficient of runoff and the characteristic flow process;

[0011] 1.1 Data Acquisition

[0012] Collect daily flow data from the long sequence (≥30 years) of the control hydrological stations in the target watershed.

[0013] 1.2 Calculation of the coefficient of difference

[0014] Based on measured data from control hydrological stations in the target watershed, the annual runoff distribution difference coefficient is calculated, which is the ratio of runoff during the flood season (May-October) to runoff during the non-flood season (other months), characterizing the seasonal distribution difference of runoff within the watershed. A larger annual runoff distribution difference coefficient indicates a greater difference between flood season and non-flood season runoff, representing an extreme high-water scenario; conversely, a smaller coefficient indicates a smaller difference between flood season and non-flood season runoff, representing an extreme low-water scenario. When the annual runoff distribution difference coefficient varies within the range of 2-2.5, it represents a general hydrological scenario. The calculation formula is as follows:

[0015] (1)

[0016] In the formula, This is the coefficient of variation in runoff distribution within the year. The runoff at hydrological stations during the flood season (100 million m³). This refers to the runoff volume of hydrological stations during the non-flood season (100 million m³).

[0017] 1.3 Feature Flow Extraction

[0018] By statistically analyzing annual runoff, minimum annual flow, maximum annual flow, average flow during flood season and non-flood season, water volume ratio during flood season and non-flood season, and annual distribution difference coefficient, the characteristics of changes in the watershed's hydrological situation are analyzed.

[0019] 2. Screening for extreme flood and extreme drought events;

[0020] Extreme flood conditions: Select the annual maximum flow rate from the historical sequence. The top 1% quantile;

[0021] Extreme drought conditions: The minimum value of the runoff distribution difference coefficient λ is selected to characterize the minimal difference in water volume between the flood season and the non-flood season.

[0022] 3. Identify typical hydrological events by analyzing the relationship between characteristic flow rates and annual average flow rates;

[0023] 3.1 Establish the relationship between average annual flow and characteristic flow:

[0024] (2)

[0025] (3)

[0026] 3.2 Identifying Typical Hydrological Events

[0027] Extreme floods: if If the threshold is reached, it is confirmed as a typical event;

[0028] Extreme drought: if If the threshold is reached, it is confirmed as a typical event.

[0029] 4. Analyze the water depth-discharge relationship and classify extreme flood types;

[0030] 4.1 Constructing Relationship Curves: Plotting Water Depths in Different Hydrological Years -flow Relationship diagram;

[0031] 4.2 Flood Type Classification Criteria:

[0032] By comparing data from extreme flood years and typical hydrological years, flood types are classified into the following two categories:

[0033] Continuous high flow type: water depth -flow The relationship follows the power function pattern;

[0034] Short-duration maximum flow type: water depth -flow The curve shifted significantly upward.

[0035] 5. Derive the physical formula for the water depth-discharge relationship;

[0036] 5.1. The curves showing the relationship between water depth and flow rate at typical river cross-sections represent the flow capacity of the cross-section under different water depth conditions. Based on the geometric characteristics of the flow cross-section and the flow velocity conditions, a mass conservation relationship satisfying the relationship between water depth and flow rate is constructed, as shown in the following formula:

[0037] (4)

[0038] (5)

[0039] (6)

[0040] In the formula, For flow rate (m) 3 / s), The cross-sectional area of ​​the flow (m²) 2 ), The depth is in meters (m). The width of the water surface (m). It is related to the river.

[0041] 5.2 Calculate the flow velocity in an open channel using Manning's formula or Chezy's formula.

[0042] (7)

[0043] In the formula, The cross-sectional average velocity is (m / s). For the cross-sectional river channel gradient, Let be the roughness of the river channel; Substituting equations (5) to (7) into equation (4), we obtain the material conservation relationship between water depth and flow rate;

[0044] (8)

[0045] (9)

[0046] 5.3. Based on the law of cross-sectional geometric similarity, analyze the relationship between changes in flow rate and changes in water depth.

[0047] (10)

[0048] In the formula, The changed cross-sectional flow rate (m³) 3 / s), The changed cross-sectional water depth (m) is given. The roughness of the cross section after the change.

[0049] As can be seen from equation (10), the flow rate variation of the cross section is positively correlated with the water depth variation and negatively correlated with the roughness variation.

[0050] 6. Analyze and verify the differences in hydrodynamic conditions during extreme floods;

[0051] 6.1 Analysis of the variability of channel roughness and frictional velocity

[0052] According to equation (10), the variability of water depth under varying flow conditions is mainly affected by the variability of channel roughness. The influence of the channel boundary on water flow is characterized by frictional velocity. By analyzing the relationship between frictional velocity variability and roughness variability, the laws governing unsteady water flow are revealed.

[0053] (11)

[0054] (12)

[0055] In the formula, and The frictional velocity of the river channel before and after the change is respectively. The roughness rate and frictional velocity rate are calculated using equations (11) and (12) respectively, and the quantitative relationship between the two is analyzed. The frictional velocity rate and the roughness rate satisfy a nonlinear positive correlation. When the frictional velocity rate or water depth rate is larger, the roughness rate of the river channel is larger.

[0056] 6.2 Testing the differences in hydrodynamic conditions during extreme floods

[0057] By comparing the variation patterns of river roughness in extreme hydrological years, if the variation pattern of river roughness is the same as that in ordinary hydrological years, it indicates that the river resistance mechanism has not changed during extreme floods and dry years; if the variation rate of river roughness shifts to the right, that is, when the daily water depth changes greatly, the change in river roughness is small, and the water flow velocity increases rapidly.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. Accurately identify extreme events: By analyzing the relationship between the λ coefficient and characteristic flow rate, the problem of insufficient sensitivity of the traditional MK test to extreme events is solved;

[0060] 2. Flood Mechanism Analysis: Quantifying the differences in the dynamic mechanisms of extreme floods by combining hydrodynamic parameters (roughness, frictional velocity);

[0061] 3. Supporting disaster prevention and control: Providing short-term flood warnings and ecological flow regulation during the dry season for reservoir operation. Attached Figure Description

[0062] Figure 1 The flowchart shows the calculation process for the method of determining the impact of extreme hydrological events and changes in hydrodynamic conditions in a watershed.

[0063] Figure 2 A graph showing the changes in hydrological indicators in the upper Mekong River;

[0064] Figure 3 This is a graph showing the relationship between annual flow and average annual runoff.

[0065] Figure 4 A diagram showing the variation patterns of extreme hydrological conditions in the upper Mekong River.

[0066] Figure 5 This is a graph showing the relationship between the frictional velocity variation and the channel roughness variation. Detailed Implementation

[0067] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments:

[0068] like Figure 1 The method for verifying extreme hydrological events and determining changes in hydrodynamic conditions in a watershed, as shown, includes the following specific calculation steps:

[0069] 1. Calculate the annual distribution variation coefficient of runoff and the characteristic flow process;

[0070] 1.1 Data Acquisition

[0071] Collect daily flow data from the long sequence (≥30 years) of the control hydrological stations in the target watershed.

[0072] 1.2 Calculation of the coefficient of difference

[0073] Based on measured data from control hydrological stations in the target watershed, the annual runoff distribution difference coefficient is calculated, which is the ratio of runoff during the flood season (May-October) to runoff during the non-flood season (other months), characterizing the seasonal distribution difference of runoff within the watershed. A larger annual runoff distribution difference coefficient indicates a greater difference between flood season and non-flood season runoff, representing an extreme high-water scenario; conversely, a smaller coefficient indicates a smaller difference between flood season and non-flood season runoff, representing an extreme low-water scenario. When the annual runoff distribution difference coefficient varies within the range of 2-2.5, it represents a general hydrological scenario. The calculation formula is as follows:

[0074] (1)

[0075] In the formula, This is the coefficient of variation in runoff distribution within the year. The runoff at hydrological stations during the flood season (100 million m³). This refers to the runoff volume of hydrological stations during the non-flood season (100 million m³).

[0076] 1.3 Feature Flow Extraction

[0077] By statistically analyzing annual runoff, minimum annual flow, maximum annual flow, average flow during flood season and non-flood season, water volume ratio during flood season and non-flood season, and annual distribution difference coefficient, the characteristics of changes in the watershed's hydrological situation are analyzed.

[0078] 2. Screening for extreme flood and extreme drought events;

[0079] Extreme flood conditions: Select the annual maximum flow rate from the historical sequence. The top 1% quantile;

[0080] Extreme drought conditions: The minimum value of the runoff distribution difference coefficient λ is selected to characterize the minimal difference in water volume between the flood season and the non-flood season.

[0081] 3. Identify typical hydrological events by analyzing the relationship between characteristic flow rates and annual average flow rates;

[0082] 3.1 Establish the relationship between average annual flow and characteristic flow:

[0083] (2)

[0084] (3)

[0085] 3.2 Identifying Typical Hydrological Events

[0086] Extreme floods: if If the threshold is reached, it is confirmed as a typical event;

[0087] Extreme drought: if If the threshold is reached, it is confirmed as a typical event.

[0088] 4. Analyze the water depth-discharge relationship and classify extreme flood types;

[0089] 4.1 Constructing Relationship Curves: Plotting Water Depths in Different Hydrological Years -flow Relationship diagram;

[0090] 4.2 Flood Type Classification Criteria:

[0091] By comparing data from extreme flood years and typical hydrological years, flood types are classified into the following two categories:

[0092] Continuous high flow type: water depth -flow The relationship follows the power function pattern;

[0093] Short-duration maximum flow type: water depth -flow The curve shifted significantly upward.

[0094] 5. Derive the physical formula for the water depth-discharge relationship;

[0095] 5.1. The curves showing the relationship between water depth and flow rate at typical river cross-sections represent the flow capacity of the cross-section under different water depth conditions. Based on the geometric characteristics of the flow cross-section and the flow velocity conditions, a mass conservation relationship satisfying the relationship between water depth and flow rate is constructed, as shown in the following formula:

[0096] (4)

[0097] (5)

[0098] (6)

[0099] In the formula, For flow rate (m) 3 / s), The cross-sectional area of ​​the flow (m²)2 ), The depth is in meters (m). The width of the water surface (m). It is related to the river.

[0100] 5.2 Calculate the flow velocity in an open channel using Manning's formula or Chezy's formula.

[0101] (7)

[0102] In the formula, The cross-sectional average velocity is (m / s). For the cross-sectional river channel gradient, Let be the roughness of the river channel; Substituting equations (5) to (7) into equation (4), we obtain the material conservation relationship between water depth and flow rate;

[0103] (8)

[0104] (9)

[0105] 5.3. Based on the law of cross-sectional geometric similarity, analyze the relationship between changes in flow rate and changes in water depth.

[0106] (10)

[0107] In the formula, The changed cross-sectional flow rate (m³) 3 / s), The changed cross-sectional water depth (m) is given. The roughness of the cross section after the change.

[0108] As can be seen from equation (10), the flow rate variation of the cross section is positively correlated with the water depth variation and negatively correlated with the roughness variation.

[0109] 6. Analyze and verify the differences in hydrodynamic conditions during extreme floods;

[0110] 6.1 Analysis of the variability of channel roughness and frictional velocity

[0111] According to equation (10), the variability of water depth under varying flow conditions is mainly affected by the variability of channel roughness. The influence of the channel boundary on water flow is characterized by frictional velocity. By analyzing the relationship between frictional velocity variability and roughness variability, the laws governing unsteady water flow are revealed.

[0112] (11)

[0113] (12)

[0114] In the formula, and The frictional velocity of the river channel before and after the change is respectively. The roughness rate and frictional velocity rate are calculated using equations (11) and (12) respectively, and the quantitative relationship between the two is analyzed. The frictional velocity rate and the roughness rate satisfy a nonlinear positive correlation. When the frictional velocity rate or water depth rate is larger, the roughness rate of the river channel is larger.

[0115] 6.2 Testing the differences in hydrodynamic conditions during extreme floods

[0116] By comparing the variation patterns of river roughness in extreme hydrological years, if the variation pattern of river roughness is the same as that in ordinary hydrological years, it indicates that the river resistance mechanism has not changed during extreme floods and dry years; if the variation rate of river roughness shifts to the right, that is, when the daily water depth changes greatly, the change in river roughness is small, and the water flow velocity increases rapidly.

[0117] Based on the above steps, a method for detecting extreme hydrological events in a watershed and determining changes in hydrodynamic conditions is constructed.

[0118] The specific implementation process is as follows:

[0119] Using the method for testing extreme hydrological events and determining changes in hydrodynamic conditions proposed in this invention, the variation patterns of extreme hydrological scenarios in the upper Mekong River are calculated.

[0120] The Mekong River is the longest river in Southeast Asia, flowing through China, Laos, Myanmar, Vietnam, and Cambodia. It is 4,880 km long with a total drop of approximately 5,060 m and a drainage basin of 795,000 km². 2 The Mekong River is known as the Lancang River in China, with a length of approximately 2,151 km, accounting for 44.1% of the total Mekong River length; its drainage area is approximately 165,000 km². 2 The Mekong River basin covers 20.7% of the total drainage area. The upper and middle reaches of the Mekong River are divided by the Chiang Saen hydrological station. Located in Thailand, the Chiang Saen station controls a drainage area of ​​189,000 km². 2 It accounts for 23.8% of the total basin area.

[0121] The hydrological situation changes in the upper Mekong River were calculated using equation (1). The results are shown in [the table below]. Figure 2 Based on the differences in average flow and runoff distribution between the flood season and non-flood season, the seasonal distribution characteristics of the upper Mekong River basin were analyzed.

[0122] From 1960 to 2023, the average flow during the flood season in the upper Mekong River basin was 4719.7 m³ / s, and the average flow during the non-flood season was 2178.7 m³ / s. As the annual runoff of the upper Mekong River has continued to decrease, its average flow during the flood season has been declining, while the average flow during the non-flood season has not changed significantly. This indicates that the reduction in runoff in the upper Mekong River is mainly due to the reduction in runoff during the flood season.

[0123] The statistical analysis of the proportion of runoff during the flood season and the non-flood season shows that from 1960 to 2023, the proportion of water volume during the flood season continuously decreased, while the proportion of water volume during the non-flood season continuously increased. Around 2009, the average annual proportion of water volume during the flood season decreased from 69.6% to 60.1%. Under the influence of extreme drought conditions, the proportion of water volume during the flood season in the upper Mekong River further decreased to 54.9% after 2019. Comparing the annual runoff distribution difference coefficients in different periods, the average annual runoff distribution difference coefficient was 2.3 before 2009, decreased to 1.8 from 2009 to 2018, and further decreased to 1.3 after 2019.

[0124] To examine extreme hydrological events under different hydrological year conditions, the relationships between annual average flow and intra-annual extreme flow, and between flood season and flood season flow, were analyzed sequentially. The results are as follows: Figure 3 As shown, from 1960 to 2023, the annual runoff of the upper Mekong River showed a linear positive correlation with both the maximum and minimum annual flow rates. The average ratio of the maximum flow rate to the average annual flow rate was 3.7, and the average ratio of the minimum flow rate to the average annual flow rate was 0.3. Figure 3 In (a), the maximum flow rates in 1966 and 2006 were significantly higher than in other years, with the ratios of maximum flow rate to annual average flow rate being 5.8 and 10.1, respectively. The peak flow rate in 2006 was 1.2 times that in 1996, while the annual runoff was only 72.2% of that in 1996, indicating a significant difference in the physical mechanisms underlying the extreme flood events in the two years.

[0125] From 1960 to 2023, there was a linear positive correlation between the average flow during the flood season and the average annual flow in the upper Mekong River, with average ratios of 1.8 and 0.7 respectively to the average annual flow. Under the same annual runoff conditions, the flood season flow was consistently greater than the non-flood season flow. However, in 2019, the flood season flow of 1417.0 m³ / s was less than the non-flood season flow of 2094.2 m³ / s, with a ratio of 0.7 between the average flood season flow and the average annual flow, while the ratio between the average non-flood season flow and the average annual flow was 1.1. Extreme hydrological events were mainly determined by the hydrological conditions during the flood season, with less influence from the hydrological conditions during the non-flood season. Extreme floods corresponded to the maximum peak flow during the flood season, while extreme droughts corresponded to the smallest proportion of water volume during the flood season.

[0126] A comparative analysis of the intra-annual flow processes and hydrodynamic differences in extreme hydrological years in the upper Mekong River is presented in the following figures. Figure 4 Analysis of the extreme low water levels in the upper Mekong River revealed that the non-flood season flow rate in 2019 was higher than in typical hydrological years, while the flood season flow rate showed significant flattening and the flood peak disappeared. Under extreme low water conditions, the channel depth-discharge relationship did not differ significantly from that in typical hydrological years.

[0127] Analyzing the patterns of extreme flood events, the extreme peak flows in 1966 and 2006 mainly occurred in August and September. Excluding the peak flows, the remaining flow patterns largely coincided with the multi-year average flow patterns, indicating that the annual flow patterns of extreme flood years are consistent with the basic hydrological year. The timescale of extreme flood events is approximately 15 days. Considering the continuous characteristics of upstream and downstream river flow, the short duration and rapid flow increases of extreme flood events are mainly influenced by extreme rainfall events. In August and September 1996, the daily rainfall at Qingsheng station repeatedly exceeded 140.0 mm, coinciding with the timing of the peak flows. On August 7, 2006, the daily peak flow at Qingsheng station reached 223.6 mm, corresponding to a single-day extreme peak flow of 29300.0 m³ / s.

[0128] Comparing the differences in rainfall conditions between 1966 and 2006, the rainfall process in August and September of 1996 was characterized by large daily rainfall and relatively long duration, while the extreme rainfall in 2006 was characterized by extremely high daily rainfall and short duration. The dynamic mechanisms leading to extreme floods under different rainfall conditions differ significantly. The river flood peak process in 1996 followed a continuous variation pattern, thus its depth-discharge relationship curve was relatively stable; however, the river flood peak process in 2006 exhibited discontinuous and non-constant characteristics, with the channel roughness having a smaller moderating effect on the flood peak discharge, and the flow velocity increasing rapidly. Figure 4 (b) The water depth-discharge curve rises rapidly.

[0129] According to equation (10), the water depth variability under varying flow conditions is mainly affected by the variability of the channel roughness. By using frictional velocity to characterize the influence of the channel boundary on the flow motion, and by analyzing the relationship between frictional velocity variability and roughness variability, the law of non-steady flow motion is revealed.

[0130] Figure 5 The variability of frictional velocity and the variability of roughness exhibit a nonlinear positive correlation; the greater the variability of frictional velocity or water depth, the greater the variability of channel roughness. Comparing the variation patterns of channel roughness in extreme hydrological years, the patterns in 1966 and 2019 are the same as in typical hydrological years, indicating that the channel resistance mechanism remains unchanged during extreme floods and dry years. In 2006, when the variability of frictional velocity was relatively small, the variation pattern of channel roughness did not show significant differences. However, when the variability of frictional velocity exceeded 1.1, the variability of channel roughness shifted to the right, meaning that when the daily water depth variation was extremely large, the channel roughness variation was small, and the flow velocity increased rapidly.

[0131] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for detecting extreme hydrological events in a watershed and determining changes in hydrodynamic conditions, characterized in that: The specific calculation steps are as follows: S1. Calculate the annual runoff distribution difference coefficient and characteristic flow process; S2. Screening for extreme flood and extreme drought events, namely: Extreme flood conditions: Select the annual maximum flow rate from the historical sequence. The top 1% quantile; Extreme drought conditions: The minimum value of the runoff distribution difference coefficient λ is selected to characterize the minimal difference in water volume between the flood season and the non-flood season; S3. Identify typical hydrological events by analyzing the relationship between characteristic flow and annual average flow. S4. Analyze the water depth-discharge relationship and classify extreme flood types; S5. Derive the physical formula for the water depth-discharge relationship; S6. Analyze and verify the differences in hydrodynamic conditions during extreme floods; Step S1 specifically includes: S101, Data Acquisition Collect daily flow data of the target watershed control hydrological stations over a long sequence (≥30 years); S102, Calculation of Difference Coefficient Based on measured data from the control hydrological stations in the target watershed, the annual runoff distribution difference coefficient is calculated, which is the ratio of runoff during the flood season from May to October to runoff during the non-flood season, representing the seasonal distribution difference of runoff in the watershed throughout the year. A larger annual runoff distribution difference coefficient indicates a greater difference between flood season and non-flood season runoff, representing an extreme high-water scenario; conversely, a smaller annual runoff distribution difference coefficient indicates a smaller difference between flood season and non-flood season runoff, representing an extreme low-water scenario; when the annual runoff distribution difference coefficient varies within the range of 2-2.5, it represents a general hydrological scenario, and the calculation formula is as follows: In the formula, This is the coefficient of variation in runoff distribution within the year. The runoff at hydrological stations during the flood season (100 million m³). The runoff at hydrological stations during the non-flood season (100 million m³). S103, Feature Flow Extraction By statistically analyzing annual runoff, minimum annual flow, maximum annual flow, average flow during flood season and non-flood season, water volume ratio during flood season and non-flood season, and annual distribution difference coefficient, the characteristics of changes in the watershed's hydrological situation are analyzed.

2. The method for detecting extreme hydrological events in a watershed and determining changes in hydrodynamic conditions according to claim 1, characterized in that, Step S3 specifically includes: S301. Establish the relationship between average annual flow and characteristic flow: S302, Identifying Typical Hydrological Events Extreme floods: if If the threshold is reached, it is confirmed as a typical event; Extreme drought: if If the threshold is reached, it is confirmed as a typical event.

3. The method for detecting extreme hydrological events in a watershed and determining changes in hydrodynamic conditions according to claim 1, characterized in that, Step S4 specifically includes: S401. Constructing Relationship Curves: Plotting Water Depths in Different Hydrological Years -flow Relationship diagram; S402. Flood Type Classification Standards: By comparing data from extreme flood years and typical hydrological years, flood types are classified into the following two categories: Continuous high flow type: water depth -flow The relationship follows the power function pattern; Short-duration maximum flow type: water depth -flow The curve shifted significantly upward.

4. The method for detecting extreme hydrological events in a watershed and determining changes in hydrodynamic conditions according to claim 1, characterized in that, Step S5 specifically includes: S501, the curves showing the relationship between water depth and flow rate at typical river cross-sections, representing the cross-sectional flow capacity under different water depth conditions; based on the geometric characteristics of the flow cross-section and the flow velocity conditions, the material conservation relationship satisfied by water depth and flow rate is constructed, as shown in the following formula: In the formula, For flow rate (m) 3 / s), The cross-sectional area of ​​the flow (m²) 2 ), The depth is in meters (m). The width of the water surface (m). For the relationship between rivers; S502. Calculate the flow velocity in an open channel using Manning's formula or Chezy's formula. In the formula, The cross-sectional average velocity is (m / s). For the cross-sectional river channel gradient, Let be the roughness of the river channel; Substituting equations (5) to (7) into equation (4), we obtain the material conservation relationship between water depth and flow rate; S503. Based on the cross-sectional geometric similarity law, analyze the relationship between flow rate change and water depth change; In the formula, The changed cross-sectional flow rate (m³) 3 / s), The changed cross-sectional water depth (m) is given. Let be the cross-sectional roughness after the change; as can be seen from equation (10), the flow rate variation of the cross-section is positively correlated with the water depth variation and negatively correlated with the roughness variation.

5. The method for detecting extreme hydrological events in a watershed and determining changes in hydrodynamic conditions according to claim 1, characterized in that, Step S6 specifically includes: S601, Analysis of the variation in river channel roughness and frictional velocity According to equation (10), the variability of water depth under varying flow conditions is mainly affected by the variability of channel roughness. The influence of the channel boundary on water flow is characterized by frictional velocity. By analyzing the relationship between frictional velocity variability and roughness variability, the laws governing unsteady water flow are revealed. In the formula, and The frictional velocity of the river channel before and after the change is respectively. The roughness rate and frictional velocity rate are calculated using Equations (11) and (12) respectively, and the quantitative relationship between the two is analyzed. The frictional velocity rate and the roughness rate satisfy a nonlinear positive correlation. When the frictional velocity rate or water depth rate is larger, the roughness rate of the river channel is larger. S602, Examining the differences in hydrodynamic conditions during extreme floods By comparing the variation patterns of river roughness in extreme hydrological years, if the variation pattern of river roughness is the same as that in ordinary hydrological years, it indicates that the river resistance mechanism has not changed during extreme floods and dry years; if the variation rate of river roughness shifts to the right, that is, when the daily water depth changes greatly, the change in river roughness is small, and the water flow velocity increases rapidly.

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