Drainage basin extreme hydrological event detection and hydrodynamic condition change judgment method

Abstract

The invention discloses a drainage basin extreme hydrological event test and hydrodynamic condition change determination method, and belongs to the technical field of hydraulic engineering, and the method specifically comprises the steps: S1, calculating a runoff annual distribution difference coefficient and a characteristic flow process; s2, extreme flood and extreme dry water events are screened, namely, according to the extreme flood condition, the first 1% quantile of the annual maximum flow Qmax in the historical sequence is selected; an extreme low water condition: selecting a minimum value of a runoff distribution difference coefficient lambda to represent that the water quantity difference between the flood season and the non-flood season is extremely small; s3, a typical hydrological event is determined by analyzing the relation between the characteristic flow and the annual average flow; s4, analyzing a water depth-flow relationship and dividing extreme flood types; s5, deducing a water depth-flow relation physical formula; and S6, analyzing and testing the hydrodynamic condition difference of the extreme flood. By adopting the method, the problems that a traditional method is insufficient in sensitivity to short-duration extreme hydrological events and lacks quantitative analysis on a hydrodynamic mechanism under the extreme events can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydraulic engineering, in particular to a method for detecting extreme hydrological events and changes in hydrodynamic conditions in a river basin, which is suitable for cross-border river water resource management and flood control and drought resistance in a river basin, and is particularly aimed at accurately identifying extreme hydrological events in a river basin affected by climate change. BACKGROUND

[0002] Under the background of global climate change and intensified human activities, the frequency of extreme hydrological events in a river basin, such as extreme floods and droughts, has significantly increased, posing a significant threat to water resource management, disaster prevention and mitigation, and the ecological environment. Changes in extreme hydrological conditions in the upper reaches of a river have a profound impact on water supply safety, agricultural production, and the operation of hydropower 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 obvious limitations.

[0003] Traditional methods usually analyze annual hydrological sequences, making it difficult to capture the characteristics of short-duration extreme events. For example, although the Mann-Kendall test can identify long-term trends in annual runoff, it lacks sensitivity to sudden extreme floods or seasonal abnormal droughts within a year. Another major drawback of existing technology is the lack of analysis of hydrodynamic mechanisms. Current research focuses on statistical analysis of hydrological indicators, but lacks quantitative correlation of changes in hydrodynamic parameters under extreme events. For example, events that meet the flood criteria have essential differences in their water depth-flow relationship and energy transfer mechanisms, but traditional methods cannot distinguish between these differences, resulting in a lack of accurate and scientific basis for reservoir operation decisions.

[0004] At the same time, current hydrological analysis methods have significant shortcomings in terms of time scale adaptability, mainly manifested in over-reliance on annual hydrological sequences. This analysis framework cannot effectively capture the key characteristics of short-duration extreme hydrological phenomena, severely restricting the ability to identify sudden flood peaks and seasonal abnormal droughts. The traditional statistical indicator system shows a clear lack of sensitivity when dealing with such instantaneous hydrological events.

[0005] The existing method system has obvious shortcomings in the analysis of hydrodynamic processes, especially the lack of systematic quantitative correlation of changes in key hydraulic parameters. This lack of theoretical framework directly leads to a lack of depth in the analysis of physical mechanisms under extreme hydrological conditions, making it difficult to reveal the inherent dynamic differences between different flood types and their evolution laws.

[0006] Therefore, it is urgent to develop a method that can integrate runoff time series characteristics, changes in hydrodynamic parameters, and event physical mechanisms to accurately identify extreme hydrological events in a river basin and scientifically analyze hydrodynamic responses, providing technical support for cross-border water resource management. SUMMARY

[0007] The present application intends to provide a method for determining extreme hydrological events and changes in hydrodynamic conditions in a river basin, in order to solve the problems raised in the background art.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical solutions.

[0009] A method for determining extreme hydrological events and changes in hydrodynamic conditions in a river basin, the specific calculation steps are as follows:

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

[0011] 1.1. Data acquisition

[0012] Collect the long sequence (≥30 years) of daily flow data of the target basin control hydrological station.

[0013] 1.2. Difference coefficient calculation

[0014] Based on the measured data of the target basin control hydrological station, the difference coefficient of runoff annual distribution is calculated, that is, the ratio of flood season (May-October) runoff to non-flood season (other months) runoff, which represents the seasonal distribution difference of basin runoff. The greater the difference coefficient of runoff annual distribution, the greater the difference between flood season runoff and non-flood season runoff, representing an extreme wet scenario; on the contrary, the smaller the difference coefficient of runoff annual distribution, the smaller the difference between flood season runoff and non-flood season runoff, representing an extreme dry scenario; when the difference coefficient of runoff annual distribution varies within the range of 2-2.5, it represents a general hydrological scenario; the calculation formula is as follows:

[0015] λ = q flood / q non-flood (13)

[0016] In the formula, λ is the difference coefficient of runoff annual distribution, q flood is the flood season hydrological station runoff (billion m 3 ), and q non-flood is the non-flood season hydrological station runoff (billion m 3 ).

[0017] 1.3. Feature flow extraction

[0018] Calculate the annual runoff, annual minimum flow, annual maximum flow, average flow in flood season and non-flood season, flood season and non-flood season water volume ratio, and difference coefficient of annual distribution, and analyze the characteristics of changes in basin hydrological regime.

[0019] 2. Screen extreme flood and extreme dry events;

[0020] Extreme flood condition: select the top 1% quantile value of the annual maximum flow Q max in the historical sequence;

[0021] Extreme dry condition: the minimum value of runoff distribution difference coefficient λ is selected to represent the minimum difference between flood season and non-flood season.

[0022] 3. Determine the typical hydrological event by analyzing the relationship between characteristic flow and annual average flow.

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

[0024] Q max / Q 年均 = k1 (14)

[0025] Q min / Q 年均 = k2 (15)3.2. Determine the typical hydrological event

[0026] Extreme flood: if k1 > K threshold, it is confirmed as a typical event;

[0027] Extreme dry condition: if k2 < K threshold, it is confirmed as a typical event.

[0028] 4. Analyze the water depth-flow relationship and classify extreme flood types;

[0029] 4.1. Construct the relationship curve: draw the water depth H-flow Q relationship graph of different hydrological years;

[0030] 4.2. Flood type classification criteria:

[0031] Compare the data of extreme flood years and general hydrological years to classify the flood types into the following two types:

[0032] Continuous high flow type: the water depth H-flow Q relationship meets the power function law;

[0033] Short-time maximum flow type: the water depth H-flow Q curve moves significantly upward.

[0034] 5. Derive the physical formula of water depth-flow relationship;

[0035] 5.1. The water depth-flow relationship curve of the typical cross section of the river represents the flow capacity of the cross section under different water depth conditions; according to the geometric characteristics of the flow cross section and the flow velocity conditions, the material conservation relationship satisfied by water depth and flow is constructed, and the formula is as follows:

[0036] Q = Au (16)

[0037] A = H × B (17)

[0038]

[0039] In the formula, Q is the flow (m 3 / s), A is the flow cross section area (m2 H is the water depth (m), B is the water surface width (m), and η is the river relationship.

[0040] 5.2 Calculate the flow velocity in an open channel using Manning's formula or Chezy's formula.

[0041] u = H 2 / 3 J 1 / 2 / n (19)

[0042] In the formula, u is the average flow velocity of the cross section (m / s), J is the channel gradient of the cross section, and n is the channel roughness. Substituting equations (5) to (7) into equation (4), we obtain the material conservation relationship between water depth and flow rate.

[0043] Q = m × H 11 / 3 (20)

[0044] m = η 2 ×J 1 / 2 / n (21)5.3. Based on the cross-sectional geometric similarity law, analyze the relationship between flow rate change and water depth change.

[0045] Q' / Q=(H' / H) 11 / 3 / (n' / n) (22)

[0046] In the formula, Q' is the changed cross-sectional flow rate (m³ / s). 3 / s), H' is the changed cross-sectional water depth (m), and n' is the changed cross-sectional roughness.

[0047] 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.

[0048] 6. Analyze and verify the differences in hydrodynamic conditions during extreme floods;

[0049] 6.1 Analysis of the variability of channel roughness and frictional velocity

[0050] 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.

[0051]

[0052] In the formula, u * 'with u * 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.

[0053] 6.2, Difference of water dynamic condition of extreme flood

[0054] If the variation law of river roughness is the same as that in general hydrological years, it indicates that the river resistance mechanism of extreme flood and dry years has not changed; if the variation law of river roughness is rightwardly translated, that is, when the daily water depth changes greatly, the river roughness changes less, and the flow velocity increases rapidly.

[0055] Compared with the prior art, the present application has the following beneficial effects:

[0056] 1. Accurate identification of extreme events: through the relationship between the lambda coefficient and the characteristic flow, the problem of insufficient sensitivity of traditional MK test to extreme events is solved;

[0057] 2. Flood mechanism analysis: combining with the water dynamic parameters (roughness, friction velocity) to quantify the difference of dynamic mechanism of extreme flood;

[0058] 3. Support for disaster prevention and control: provide short-term flood warning and dry period ecological flow regulation basis for reservoir regulation. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 It is a flow chart for the determination method of basin extreme hydrological event test and water dynamic condition change;

[0060] Figure 2 It is a hydrological index change process chart of the upper Mekong River;

[0061] Figure 3 It is a relationship chart of annual flow and annual average runoff;

[0062] Figure 4 It is a variation law chart of extreme hydrological condition of the upper Mekong River

[0063] Figure 5 It is a relationship chart of friction velocity variation rate and river roughness variation rate. DETAILED DESCRIPTION

[0064] The present application will be further described in detail below in combination with the drawings and embodiments:

[0065] As shown in the drawings, Figure 1 A determination method of basin extreme hydrological event test and water dynamic condition change, the specific calculation steps are as follows:

[0066] 1. Calculate the runoff annual distribution difference coefficient and the characteristic flow process;

[0067] 1.1, Data acquisition

[0068] Collect the long sequence of daily runoff data of the control hydrological station in the target basin (≥30 years).

[0069] 1.2, difference coefficient calculation

[0070] Based on the measured data of the control hydrological station in the target basin, the difference coefficient of runoff distribution within a year is calculated, that is, the ratio of runoff in the flood season (May-October) to that in the non-flood season (the remaining months), which represents the seasonal distribution difference of runoff in the basin. The greater the difference coefficient of runoff distribution within a year, the greater the difference between runoff in the flood season and that in the non-flood season, representing an extreme wet scenario; on the contrary, the smaller the difference coefficient of runoff distribution within a year, the smaller the difference between runoff in the flood season and that in the non-flood season, representing an extreme dry scenario; when the difference coefficient of runoff distribution within a year varies in the range of 2-2.5, it represents a general hydrological scenario; the calculation formula is as follows:

[0071] λ = q flood / q non-flood (25)

[0072] In the formula, λ is the difference coefficient of runoff distribution within a year, q flood is the runoff of the hydrological station in the flood season (billion m 3 ), and q non-flood is the runoff of the hydrological station in the non-flood season (billion m 3 ).

[0073] 1.3, extraction of characteristic flow

[0074] The annual runoff, annual minimum flow, annual maximum flow, average flow in the flood season and non-flood season, water volume proportion in the flood season and non-flood season, and difference coefficient of runoff distribution within a year are calculated to analyze the characteristics of the change of the hydrological regime of the basin.

[0075] 2, screening of extreme flood and extreme dry events;

[0076] Extreme flood condition: select the top 1% quantile value of the annual maximum flow Q max in the historical sequence;

[0077] Extreme dry condition: select the minimum value of the runoff distribution difference coefficient λ, representing the minimum difference of water volume between the flood season and the non-flood season.

[0078] 3, determine the typical hydrological event by analyzing the relationship between the characteristic flow and the annual average flow;

[0079] 3.1, establish the relationship between the annual average flow and the characteristic flow:

[0080] Q max / Q 年均 = k1 (26)

[0081] Q min / Q 年均= k2 (27) 3.2, Determination of typical hydrological events

[0082] Extreme flood: if k1>Kthreshold, it is confirmed as a typical event;

[0083] Extreme drought: if k2

[0084] 4, Analysis of water depth-flow relationship and classification of extreme flood type;

[0085] 4.1, Construction of relationship curve: draw the water depth H-flow Q relationship graph of different hydrological years;

[0086] 4.2, Flood type classification standard:

[0087] Compare the data of extreme flood year and general hydrological year, divide the flood type into the following two types:

[0088] Sustained high flow type: water depth H-flow Q relationship meets the power function law;

[0089] Short-time maximum flow type: water depth H-flow Q curve moves up significantly.

[0090] 5, Derivation of water depth-flow relationship physical formula;

[0091] 5.1, River typical section water depth and flow relationship curve, which represents the flow capacity of the section under different water depth conditions; according to the geometric characteristics of the flow section and the flow velocity conditions, the material conservation relationship satisfied by water depth and flow is constructed, and the formula is as follows:

[0092] Q=Au (28)

[0093] A=HxB (29)

[0094]

[0095] In the formula, Q is the flow (m 3 / s), A is the flow section area (m 2 ), H is the water depth (m), B is the water surface width (m), and η is the river correlation.

[0096] 5.2, Calculate the flow velocity of open channel by using Manning formula or Chezy formula

[0097] u=H 2 / 3 J 1 / 2 / n (31)

[0098] In the formula, u is the average flow velocity of the section (m / s), J is the section river gradient, and n is the river roughness; formula (5) to formula (7) are brought into formula (4) to obtain the material conservation relationship of water depth and flow;

[0099] Q = m × H 11 / 3 (32)

[0100] m = η 2 ×J 1 / 2 / n (33)5.3. Based on the cross-sectional geometric similarity law, analyze the relationship between flow rate change and water depth change.

[0101] Q' / Q=(H' / H) 11 / 3 / (n' / n) (34)

[0102] In the formula, Q' is the changed cross-sectional flow rate (m³ / s). 3 / s), H' is the changed cross-sectional water depth (m), and n' is the changed cross-sectional roughness.

[0103] 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.

[0104] 6. Analyze and verify the differences in hydrodynamic conditions during extreme floods;

[0105] 6.1 Analysis of the variability of channel roughness and frictional velocity

[0106] 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.

[0107]

[0108] In the formula, u * 'with u * 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.

[0109] 6.2 Testing the differences in hydrodynamic conditions during extreme floods

[0110] 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.

[0111] Based on the above steps, a method for detecting extreme hydrological events in a watershed and determining changes in hydrodynamic conditions is constructed.

[0112] The specific implementation process is as follows:

[0113] The application provides a determination method for extreme hydrological event test and water dynamic condition change of a river basin.

[0114] The Mekong River is the longest river in Southeast Asia, and the main stream flows through China, Laos, Myanmar, Vietnam and Cambodia. The river is 4880 km long, with a total drop of about 5060 m, and a drainage area of 795,000 km 2 . The Mekong River in China is called Lancang River, which is about 2151 km long, accounting for 44.1% of the total length of the Mekong River; the drainage area is about 165,000 km 2 , accounting for 20.7% of the total drainage area. The Mekong River Basin is divided into upper and lower reaches by the Qingsheng Hydrological Station. The Qingsheng Hydrological Station is located in Thailand, and its control drainage area is 189,000 km 2 , accounting for 23.8% of the total drainage area.

[0115] The change characteristics of the hydrological regime of the upper reaches of the Mekong River are calculated by formula (1), and the calculation results are shown in Figure 2 According to the difference coefficients of average flow and runoff distribution in flood season and non-flood season, the seasonal distribution change characteristics of the upper reaches of the Mekong River Basin are analyzed.

[0116] The average flow of the upper reaches of the Mekong River in flood season from 1960 to 2023 is 4719.7 m 3 / s, and the average flow in non-flood season is 2178.7 m 3 / s. With the continuous decrease of the annual runoff of the upper reaches of the Mekong River, the average flow in flood season decreases, while the average flow in non-flood season does not change significantly, indicating that the decrease of runoff in the upper reaches of the Mekong River is mainly caused by the decrease of runoff in flood season.

[0117] The proportion of runoff in flood season and non-flood season is calculated. From 1960 to 2023, with the continuous decrease of the proportion of flood season water, the proportion of non-flood season water increases. Before and after 2009, the average annual flood season water proportion decreases from 69.6% to 60.1%. Under the influence of extreme dry conditions, the proportion of flood season water in the upper reaches of the Mekong River further decreases to 54.9% after 2019. By comparing the difference coefficients of runoff distribution in different periods, the average difference coefficient of runoff distribution in different periods is 2.3 before 2009, the difference coefficient of runoff distribution decreases to 1.8 from 2009 to 2018, and further decreases to 1.3 after 2019.

[0118] In order to test the extreme hydrological events under different hydrological conditions, the relationship between annual average flow and annual extreme value flow, flood season and flood season flow is analyzed in sequence, and the results are shown in Figure 3The linear positive correlation between the annual runoff and the annual maximum and minimum flow in the upper Mekong River from 1960 to 2023 was found. The average ratio of the maximum flow to the annual average flow was 3.7, and the average ratio of the minimum flow to the annual average flow was 0.3. Figure 3 The maximum flow in 1966 and 2006 was much higher than that in other years, and the ratio of the maximum flow to the annual average flow was 5.8 and 10.1, respectively. The peak flow in 2006 was 1.2 times that in 1996, while the annual runoff was only 72.2% of that in 1996, indicating that the physical mechanisms of extreme flood events were significantly different.

[0119] From 1960 to 2023, the average flow in the flood season and non-flood season in the upper Mekong River had a linear positive correlation with the annual average flow, and the average ratio of the two to the annual average flow was 1.8 and 0.7, respectively. Under the same annual runoff conditions, the flood season flow was always greater than the non-flood season flow, and in 2019, the flood season flow was 1417.0 m 3 / s, which was less than the non-flood season flow of 2094.2 m 3 / s. In that year, the ratio of the average flood season flow to the annual average flow was 0.7, and the ratio of the average non-flood season flow to the annual average flow was 1.1. Extreme hydrological events were mainly determined by the hydrological conditions in the flood season, and had little relation to the hydrological conditions in the non-flood season. Extreme floods corresponded to the maximum value of the flood peak flow in the flood season, while extreme droughts corresponded to the smallest proportion of water in the flood season.

[0120] Comparative analysis of the annual flow process and water dynamics of the upper Mekong River in extreme hydrological years showed that Figure 4 The non-flood season flow process line in 2019 was higher than that in general hydrological years, while the flood season flow process was significantly flattened and the flood peak disappeared. Under extreme drought conditions, there was no significant difference in the relationship between river depth and flow compared to general hydrological years.

[0121] The analysis of the change rule of extreme flood process showed that the extreme flood peak flow in 1966 and 2006 mainly occurred in August and September. Except for the flood peak process, the rest of the flow process was basically consistent with the average flow process in many years, indicating that the annual flow process in extreme flood years was consistent with that in general hydrological years. The time scale of the extreme flood process was about 15 days, and considering the continuity of the water flow movement in the upstream and downstream river channels, the short time and sudden increase of the extreme flood event were mainly affected by the extreme rainfall event. From August to September 1996, the daily rainfall at the Qingsheng Station exceeded 140.0 mm several times, which was consistent with the time of the flood peak flow. On August 7, 2006, the daily flood peak flow at the Qingsheng Station was as high as 223.6 mm, corresponding to a daily extreme flood peak flow of 29300.0 m 3 / s.

[0122] Comparing the rainfall conditions in 1966 and 2006, the rainfall process from August to September in 1996 showed large single-day rainfall and relatively long duration, while the extreme rainfall in 2006 showed extremely large single-day rainfall and short duration. Different rainfall conditions led to significant differences in the dynamic mechanism of extreme floods. In 1996, the river flood peak process met the continuous change rule, so the water depth-flow relationship curve was relatively stable; while in 2006, the river flood peak process met the non-continuous and non-constant characteristics, and the flood peak flow regulation effect of the river roughness was small, and the flow velocity increased rapidly, corresponding to Figure 4 (b) The water depth-flow curve rises rapidly.

[0123] According to formula (10), the water depth variation rate is mainly affected by the river roughness variation rate under the condition of flow variation. The influence of the river boundary on the water flow is characterized by the friction velocity, and by analyzing the relationship between the friction velocity variation rate and the roughness variation rate, the non-constant water flow movement rule is revealed.

[0124] Figure 5 The friction velocity variation rate and the roughness variation rate meet a nonlinear positive correlation relationship, and when the friction velocity variation rate or the water depth variation rate is larger, the river roughness variation rate is larger. Comparing the river roughness variation rule in extreme hydrological years, the river roughness variation rules in 1966 and 2019 are the same as those in general hydrological years, indicating that the river resistance mechanism in extreme flood and dry years has not changed; when the friction velocity variation rate is small, the river roughness variation rule in 2006 does not show obvious differences, but when the friction velocity variation rate exceeds 1.1, the river roughness variation rate shifts to the right, that is, when the daily water depth variation is large, the river roughness variation is small, and the flow velocity increases rapidly.

[0125] The above is only an embodiment of the present application, and the specific technical solutions and / or characteristics of the scheme are not described in detail. It should be noted that for those skilled in the art, without departing from the technical solutions of the present application, some modifications and improvements can be made, which should also be considered as the protection scope of the present application, which will not affect the effect and practicality of the patent. The protection scope of the present application should be subject to the content of its claims, and the specific implementation mode and the like in the specification can be used to explain the content of the claims.

Claims

1. A method for detecting changes in hydrodynamic conditions and extreme hydrological events in a river basin, characterized in that: The specific calculation steps are as follows: S1, calculating the runoff annual distribution difference coefficient and the characteristic flow process; S2, screening extreme flood and extreme dry events, namely: Extreme flood conditions: the 1% quantile of the annual maximum flow Q max in the historical series is selected. Extreme dry condition: select the minimum value of runoff distribution difference coefficient λ, representing the minimum difference between flood season and non-flood season water quantity; S3, determining the typical hydrological event by analyzing the relationship between the characteristic flow and the annual average flow; S4, analyzing the water depth-flow relationship and dividing the extreme flood type; S5, deriving the physical formula of water depth-flow relationship; S6, analyzing and testing the difference of hydrodynamic conditions of extreme flood.

2. The method of claim 1, wherein, The step S1 specifically comprises: S101, data acquisition Collecting long sequence (≥30 years) daily flow data of the target basin control hydrological station; S102, difference coefficient calculation Based on the measured data of the target basin control hydrological station, the runoff annual distribution difference coefficient is calculated, that is, the ratio of flood season runoff to non-flood season runoff, representing the seasonal distribution difference of basin runoff in a year; The greater the runoff annual distribution difference coefficient, the greater the difference between flood season runoff and non-flood season runoff, representing an extreme flood scenario; on the contrary, the smaller the runoff annual distribution difference coefficient, the smaller the difference between flood season runoff and non-flood season runoff, representing an extreme dry scenario; when the runoff annual distribution difference coefficient varies in the range of 2-2.5, it represents a general hydrological scenario, and the calculation formula is as follows: λ = q flood / q non-flood (1) In the formula, λ is the runoff annual distribution difference coefficient, q flood is the flood season hydrological station runoff (hm 3 ), q non-flood is the non-flood season hydrological station runoff (hm 3 ); S103, characteristic flow extraction Statistical annual runoff, annual minimum flow, annual maximum flow, average flow in flood season and non-flood season, water quantity proportion in flood season and non-flood season, annual distribution difference coefficient, and analyze the characteristics of hydrological regime change of the basin.

3. The method of claim 1, wherein, The step S3 specifically comprises: S301, establishing the relationship between the annual average flow and the characteristic flow: Q max / Q 年均 = k1 (2) Q min / Q 年均 = k2 (3) S302, determining the typical hydrological event Extreme flood: if k1>K threshold value, it is confirmed as a typical event; Extreme dry: if k2 4. The method of claim 1, wherein, The step S4 specifically comprises: S401, constructing relationship curve: drawing water depth H-flow Q relationship graph of different hydrological years; S402, flood type division standard: Comparing the data of extreme flood year and general hydrological year, the flood type is divided into the following two types: Continuous high flow type: the water depth H-flow Q relationship meets the power function law; Short-time maximum flow type: the water depth H-flow curve moves up significantly.

5. The method of claim 1, wherein, The step S5 specifically comprises: S501, the relationship curve of water depth and flow of the typical section of the river, representing the cross section flow capacity under different water depth conditions; according to the geometric characteristics and flow velocity conditions of the flow section, the material conservation relationship satisfied by water depth and flow is constructed, and the formula is as follows: Q=Au (4) A=H×B (5) where Q is the flow rate (m 3 / s), A is the cross-sectional area of flow (m 2 ), H is the water depth (m), B is the water surface width (m), and η is the river correlation. S502, calculating the flow velocity of open channel by using Manning formula or Chezy formula u = H 2 / 3 J 1 / 2 / n (7) In the formula, u is the average flow velocity of the section (m / s), J is the section river bed slope, and n is the river bed roughness; formula (5) to formula (7) are brought into formula (4) to obtain the material conservation relationship of water depth and flow; Q = m x H 11 / 3 (8) m = η 2 x J 1 / 2 / n (9) S503, analyzing the relationship between flow change and water depth change according to the geometric similarity law of the section. Q' / Q = (H' / H) 11 / 3 (n' / n) (10) In the formula, Q' is the changed cross-sectional flow (m 3 / s), H' is the changed cross-sectional water depth (m), and n' is the changed cross-sectional roughness. As can be seen from formula (10), the change rate of the flow cross-section is positively correlated with the change rate of the water depth and negatively correlated with the change rate of the roughness.

6. The method of claim 1, wherein, The step S6 specifically comprises: S601, analyzing the variation rate of river bed roughness and frictional flow velocity According to equation (10), the water depth variation rate is mainly affected by the variation rate of the river roughness under the condition of flow variation; the influence of the river boundary on the water flow movement is characterized by the friction velocity, and the relationship between the variation rate of the friction velocity and the variation rate of the roughness is analyzed to reveal the law of unsteady water flow movement: where u * and u * are the friction velocities before and after the change, respectively. The roughness variation and the friction velocity variation are calculated using equations (11) and (12), respectively, and the quantitative relationship between them is analyzed. The friction velocity variation and the roughness variation satisfy a nonlinear positive correlation relationship. The greater the friction velocity variation or the water depth variation, the greater the roughness variation. S602, the difference of the hydrodynamic conditions of the extreme flood By comparing the variation law of the river roughness in the extreme hydrological year, if the variation law of the river roughness is the same as that in the general hydrological year, it indicates that the river resistance mechanism does not change in the extreme flood and dry year; if the variation rate of the river roughness is rightward shifted, that is, when the daily water depth variation is maximum, the variation of the river roughness is small, and the flow velocity increases rapidly.

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