A method for analyzing hydrological regime change of small and medium-sized rivers based on improved hydrological variation index system

Abstract

The application discloses a kind of based on the change analysis method of small and medium-sized river hydrological regime of improved hydrological variation index system, method includes: first, for the seasonal characteristics of small and medium-sized river runoff, with ten as the calculation scale, hydrological staging is carried out, and four stages of dry period, pre-flood period, main flood period, post-flood period are divided in a year;Then, the hydrological parameters in each stage are counted, the parameters of conventional hydrological variation index system are supplemented, and the improved hydrological variation index system is proposed;Finally, based on the improved hydrological variation index system, the change range evaluation method and histogram matching method are used to calculate the change degree of small and medium-sized river hydrological regime.The improved hydrological variation index system proposed by the application can comprehensively represent the hydrological regime characteristics of small and medium-sized river, and avoids the seasonal differences of parameters in different statistical periods due to the use of single statistical scale in conventional hydrological variation index system.

Description

Technical Field

[0001] This invention relates to the field of hydrological assessment technology, specifically to a method for assessing the degree of change in the hydrological situation of small and medium-sized rivers. Background Technology

[0002] Hydrological conditions are a major driving force of river ecosystem structure and are crucial for maintaining river health and ecosystem function. However, water conservancy projects that dam rivers disrupt the exchange of information and materials between upstream and downstream areas, significantly altering the river's hydrological conditions and leading to a series of river ecological and environmental problems. Conducting hydrological condition change analysis and assessing the impact of water conservancy projects on river ecosystems is of great significance for formulating river ecological protection measures. Currently, research on hydrological condition change analysis mainly focuses on large rivers, with relatively insufficient research on small and medium-sized rivers. Small and medium-sized rivers, due to their small catchment areas and rapid flow velocities, are significantly affected by rainfall, exhibiting multi-peak runoff phenomena, especially during the flood season when extreme rainfall is frequent, making the multi-peak runoff phenomenon even more pronounced. Conventional hydrological variability index systems, which comprehensively and scientifically represent hydrological information and are easy to use, are widely used for river hydrological condition analysis. However, the method of statistically analyzing parameters on an annual basis ignores the seasonal differences of certain hydrological parameters, making it difficult to accurately reflect the hydrological characteristics of small and medium-sized rivers. Therefore, it is necessary to propose a hydrological variability index system suitable for small and medium-sized rivers, comprehensively considering the seasonal differences of various hydrological parameters to fully reflect the hydrological characteristics of rivers. Summary of the Invention

[0003] Purpose of the invention: To provide a hydrological situation analysis method based on an improved hydrological variation index system, which provides a feasible method and technology for accurately assessing the changing patterns of hydrological conditions in small and medium-sized rivers.

[0004] Technical solution: The present invention provides a hydrological situation analysis method based on an improved hydrological variability index system, comprising the following steps:

[0005] S1 collects historical runoff data and constructs a hydrological grading index set X. The CRITIC method is used to calculate the weights of each index, and the grading index set X is transformed into a comprehensive grading index set Y. A threshold L for determining the flood season and dry season is constructed from Y, and the membership degree of each ten-day period to the flood season set is calculated.

[0006] Furthermore, the hydrological staging index set X constructed in step S1 is as follows:

[0007]

[0008] Where p is the number of years of runoff data; X iis the set of indicators for year i; m is the number of indicators selected for each ten-day period (in addition to the average flow rate, indicators that can reflect runoff process information, such as minimum and maximum 1-day, 3-day, 5-day, and 7-day runoff, are added); This is the j-th indicator in the k-th ten-day period of the i-th year.

[0009] Furthermore, the step S1 of transforming the hydrological grading index set X into the grading comprehensive index set Y is as follows:

[0010]

[0011] Among them, Y i Let i be the set of comprehensive indicators for year i. W is a comprehensive indicator for the k-th ten-day period within the i-th year; j The weight of the j-th indicator is calculated using the CRITIC method.

[0012] Furthermore, the threshold L used to determine the flood season and dry season in step S1 is calculated using the following formula:

[0013]

[0014] in, Let be the mean of the j-th indicator.

[0015] Furthermore, the steps in step S1 for calculating the membership degree of each ten-day period to the flood season set are as follows:

[0016] ① For a given runoff sequence in year i, select a comprehensive index. The starting period greater than the threshold L and the end of the ten-day period As the beginning and end of the flood season, and so on, data from p years can be statistically analyzed to obtain p flood season intervals. Where i = 1, 2, ..., p.

[0017] ② Count the number n of each ten-day period falling into p flood season intervals, and use it as its membership degree to the flood season.

[0018] λ k =n / p (4)

[0019] S2 is based on the membership sequence {λ} of each ten-day period to the flood season set. k The period is divided into dry season and flood season. Furthermore, the mean change point analysis method is used to further divide the flood season into pre-flood season, main flood season, and post-flood season.

[0020] Furthermore, the criteria for dividing the dry season and flood season in step S2 are as follows:

[0021] Selecting the membership degree λ k=0.5 is used as the cutoff level to determine whether it is the flood season. When λ k When λ > 0.5, it indicates that the tendency for the k-th ten-day period to be the flood season is stronger, so the k-th ten-day period is included in the flood season; when λ k If the value is less than 0.5, it indicates that the k-th ten-day period has a stronger tendency towards the dry season, so the k-th ten-day period is classified as the dry season.

[0022] Furthermore, the steps in step S2, which use mean change point analysis to divide the flood season into the pre-flood season, the main flood season, and the post-flood season, are as follows:

[0023] ①The ten-day period with the highest membership degree during the flood season (λ) m As the dividing point, the membership sequence {λ} k} is divided into the pre-sequence {λ f ,λ f+1 ,...,λ m} and the subsequent sequence {λ m ,λ m+1 ,...,λ f+d} Where f and d represent the start and length of the flood season, respectively.

[0024] ② Assume the previous sequence {λ f ,λ f+1 ,...,λ m The change point of} is the αth ten-day period λ α Calculate the difference β between the statistics D and D'.

[0025]

[0026] β=DD' (6)

[0027] in, Sequences {λ f ,λ f+1 ,...,λ α}、{λ α+1 ,λ α+1 ,...,λ m} and {λ f ,λ f+1 ,...,λ m The mean of}.

[0028] ③ The change point α changes sequentially from f to m, and the ten-day period s corresponding to the maximum β is selected as the dividing point between the pre-flood season and the main flood season.

[0029] ④ Similarly, following the search method for the variable points described above, the subsequent sequence {λ} is obtained. m ,λ m+1 ,...,λ f+d The variable point e of} is used to complete the division of the flood season. The pre-flood season, the main flood season, and the post-flood season correspond to [f,s], [s+1,e], and [e+1,f+d], respectively.

[0030] Based on the conventional hydrological variability index system, S3 keeps the parameters of the first and third components unchanged, and supplements the parameters of the second, fourth and fifth components to propose an improved hydrological variability index system.

[0031] Furthermore, the first set of indicators reflects the basic hydrological conditions of the river from the perspective of monthly average flow. Since the scale of the parameters is clearly defined, it remains unchanged and includes 12 monthly average flows. The third set of indicators reflects the extreme hydrological conditions of the river, conveying signals of biological migration and spawning. However, biological migration and spawning are usually infrequent and only related to extreme flows within the year. Therefore, the statistical scale of the parameters is still mainly annual, including two parameters: the time of occurrence of the annual maximum and minimum flow. The second set adds the maximum and minimum 1-day, 3-day, and 7-day flows in the four stages of dry season, pre-flood season, main flood season, and post-flood season, adding a total of 24 parameters. The fifth set adds the average increase rate of flow, the average decrease rate of flow, and the number of flow reversals in the four stages of dry season, pre-flood season, main flood season, and post-flood season, adding a total of 12 parameters. The fourth set reflects the high and low flow pulse process. The short statistical scale is prone to causing fragmentation of flow pulse events. Therefore, the flood season is no longer divided into three stages: pre-flood, main flood, and post-flood. Instead, only the number and duration of high and low flow pulses within the two stages of dry season and flood season are added, resulting in a total of eight additional parameters.

[0032] Based on the improved hydrological variability index system, S4 uses the RVA method (i.e., variation range evaluation method, the same below) and the HMA method (i.e., histogram matching method, the same below) to calculate the degree of change in the hydrological situation of small and medium-sized rivers.

[0033] Furthermore, in step S4, the RVA method is used to calculate the degree of change in the hydrological situation of small and medium-sized rivers based on the improved hydrological variability index system, as follows:

[0034] ① Determine the abrupt change point in the runoff sequence, and calculate the parameter values ​​of each component under the improved hydrological variation index system based on the historical daily flow data before the runoff change.

[0035] ② Select the characteristic values ​​of each parameter at 25% and 75% frequencies to form the target range I of RVA, which is the acceptable range for each hydrological parameter.

[0036] ③ Based on the changed historical daily flow data, calculate the parameter values ​​of each component under the improved hydrological variation index system after the runoff is affected, and calculate the hydrological change degree of individual parameters and the overall hydrological change degree according to the following formula.

[0037]

[0038] Among them, D iD represents the hydrological change of the i-th parameter; D represents the overall hydrological change; m represents the number of parameters in the improved hydrological variability index system; N io N represents the number of years that the i-th parameter actually falls within the RVA target range I; ie The number of years in which the i-th parameter is expected to fall within the RVA target range can be taken as half of the total number of years after the impact. The classification criteria for the overall hydrological change of the river include slight change (0-33%), moderate change (33%-67%), and significant change (67%-100%).

[0039] Furthermore, in step S4, the following steps are taken to calculate the degree of change in the hydrological situation of small and medium-sized rivers using the HMA method based on the improved hydrological variability index system:

[0040] ① Determine the number of classes n in the histogram. c That is, how many intervals should the parameter be divided into.

[0041]

[0042] Where r is the difference between the maximum and minimum values; n is the number of data points; r iq It is the difference between the 3 / 4 quantile and the 1 / 4 quantile.

[0043] ② Draw frequency histograms H and K of hydrological parameters before and after the change, and use quadratic distance d Q (H,K) measures the difference between two histograms.

[0044]

[0045] Where h = (h1, h2, ..., h nc ), k = (k1, k2, ..., k nc ) are the histogram frequency vectors of the parameters before and after the change; A = [a ij ] is a similarity matrix, a ij The similarity between class i and class j in the histogram is calculated as follows:

[0046]

[0047] Where, d ij This represents the distance between the i-th and j-th classes in the histogram.

[0048] ③ Calculate the degree of hydrological change D for each parameter. Q,p .

[0049]

[0050] Where, d Q,p This represents the difference in frequency histograms before and after the change of the p-th hydrological parameter.

[0051] ④ Calculate the overall hydrological change.

[0052]

[0053] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0054] (1) Traditional staging methods often use average flow rate as the staging index. However, a single staging index does not comprehensively consider the runoff process within a ten-day period and ignores information such as extreme flow rates and high and low flow pulses within the ten-day period. This invention uses a ten-day period as the calculation scale and adds indicators that reflect runoff process information, such as minimum and maximum 1-day, 3-day, 5-day, and 7-day runoff, to the average flow rate. The constructed hydrological staging index set has an appropriate scale and reasonable indicators, resulting in more accurate staging results.

[0055] (2) Conventional hydrological variation index systems use annual statistics for each parameter, which may overlook the seasonal differences of some hydrological parameters and make it difficult to accurately reflect the hydrological characteristics of small and medium-sized rivers. This invention supplements and improves the conventional hydrological variation index system by statistically analyzing hydrological parameters in different periods. The proposed improved hydrological variation index system can comprehensively reflect the hydrological characteristics of rivers. Attached Figure Description

[0056] Figure 1 This is a flowchart of the method of the present invention;

[0057] Figure 2 This is a diagram showing the hydrological grading results of the Liujiaping River in an embodiment of the present invention;

[0058] Figure 3 This invention illustrates the degree of change in some hydrological parameters of the Liujiaping River before and after reservoir construction, under both the original statistical scale (annual) and the newly added statistical scale (dry season, pre-flood season, main flood season, post-flood season).

[0059] Figure 4 In this embodiment of the invention, based on conventional and improved hydrological variation index systems, the RVA method and HMA method are used to quantitatively calculate the degree of change in the overall hydrological situation of the Liujiaping River before and after the reservoir construction. Detailed Implementation

[0060] The technical solution of the present invention will be described in detail below, but the scope of protection of the present invention is not limited to the embodiments described.

[0061] like Figure 1 As shown, the present invention provides a method for analyzing hydrological situation changes in small and medium-sized rivers based on an improved hydrological variability index system, comprising the following steps:

[0062] S1 collects historical runoff data and constructs a hydrological grading index set X. The CRITIC method is used to calculate the weights of each index, and the grading index set X is transformed into a comprehensive grading index set Y. A threshold L for determining the flood season and dry season is constructed from Y, and the membership degree of each ten-day period to the flood season set is calculated.

[0063] The constructed hydrological staging index set X is as follows:

[0064]

[0065] Where p is the number of years of runoff data; X i is the set of indicators for year i; m is the number of indicators selected for each ten-day period (in addition to the average flow rate, indicators that can reflect runoff process information, such as minimum / maximum 1-day, 3-day, 5-day, and 7-day runoff, are added). This is the j-th indicator in the k-th ten-day period of the i-th year.

[0066] The steps to transform the hydrological stage index set X into a stage comprehensive index set Y are as follows:

[0067]

[0068] Among them, Y i Let i be the set of comprehensive indicators for year i. W is a comprehensive indicator for the k-th ten-day period within the i-th year; j The weight of the j-th indicator is calculated using the CRITIC method.

[0069] The threshold L used to determine the flood season and the dry season is calculated using the following formula:

[0070]

[0071] in, Let be the mean of the j-th indicator.

[0072] The steps for calculating the membership degree of each ten-day period to the flood season set are as follows:

[0073] ① For a given runoff sequence in year i, select a comprehensive index. The starting period greater than the threshold L and the end of the ten-day period As the beginning and end of the flood season, and so on, data from p years can be statistically analyzed to obtain p flood season intervals. Where i = 1, 2, ..., p.

[0074] ② Count the number n of each ten-day period falling into p flood season intervals, and use it as its membership degree to the flood season.

[0075] λ k =n / p (4)

[0076] S2 is based on the membership sequence {λ} of each ten-day period to the flood season set. k The period is divided into dry season and flood season. Furthermore, the mean change point analysis method is used to further divide the flood season into pre-flood season, main flood season, and post-flood season.

[0077] Selecting the membership degree λ k =0.5 is used as the cutoff level to determine whether it is the flood season. When λ k When λ > 0.5, it indicates that the tendency for the k-th ten-day period to be the flood season is stronger, so the k-th ten-day period is included in the flood season; when λ k If the value is less than 0.5, it indicates that the k-th ten-day period has a stronger tendency towards the dry season, so the k-th ten-day period is classified as the dry season.

[0078] The steps for dividing the flood season into the pre-flood season, main flood season, and post-flood season using the mean change point analysis method are as follows:

[0079] ①The ten-day period with the highest membership degree during the flood season (λ) m As the dividing point, the membership sequence {λ k} divided into the pre-sequence {λ f ,λ f+1 ,...,λ m} and the subsequent sequence {λ m ,λ m+1 ,...,λ f+d} Where f and d represent the start and length of the flood season, respectively.

[0080] ② Assume the previous sequence {λ f ,λ f+1 ,...,λ m The change point of} is the αth ten-day period λ α Calculate the difference β between the statistics D and D'.

[0081]

[0082] β=DD' (6)

[0083] in, Sequences {λ f ,λ f+1 ,...,λ α}、{λ α+1 ,λ α+1 ,...,λ m} and {λ f ,λ f+1 ,...,λ m The mean of}.

[0084] ③ The change points α are successively from f to m. The ten-day period s corresponding to the maximum β is selected as the change point of the previous sequence, which serves as the dividing point between the pre-flood season and the main flood season.

[0085] ④ Similarly, following the search method for the variable points described above, the subsequent sequence {λ} is obtained. m ,λ m+1 ,...,λ f+d The variable point e of} finally completes the division of the flood season, with the pre-flood season, main flood season and post-flood season corresponding to [f,s], [s+1,e] and [e+1,f+d] respectively.

[0086] Based on the conventional hydrological variability index system, S3 keeps the parameters of the first and third components unchanged, and supplements the parameters of the second, fourth and fifth components to propose an improved hydrological variability index system.

[0087] The second component adds the maximum and minimum 1-day, 3-day, and 7-day flow rates for four stages: dry season, pre-flood season, main flood season, and post-flood season, for a total of 24 additional parameters.

[0088] The fourth component adds the number and duration of high and low flow pulses during the dry and flood seasons, for a total of 8 parameters.

[0089] The fifth component adds 12 parameters, including the average increase rate of flow, the average decrease rate of flow, and the number of flow reversals during the four stages of dry season, pre-flood season, main flood season, and post-flood season.

[0090] S4 uses an improved hydrological variability index system and employs the RVA and HMA methods to calculate the degree of change in the hydrological situation of small and medium-sized rivers.

[0091] The steps for calculating the degree of change in hydrological conditions of small and medium-sized rivers using the RVA method are as follows:

[0092] ① Determine the abrupt change point in the runoff sequence, and calculate the parameter values ​​of each component under the improved hydrological variation index system based on the historical daily flow data before the runoff change.

[0093] ② Select the characteristic values ​​of each parameter at 25% and 75% frequencies to form the target range I of RVA, which is the acceptable range for each hydrological parameter.

[0094] ③ Based on the changed historical daily flow data, calculate the parameter values ​​of each component under the improved hydrological variation index system after the runoff is affected, and calculate the hydrological change degree of individual parameters and the overall hydrological change degree according to the following formula.

[0095]

[0096] Among them, D i D represents the hydrological change of the i-th parameter; D represents the overall hydrological change; m represents the number of parameters in the improved hydrological variability index system; N io N represents the number of years that the i-th parameter actually falls within the RVA target range I; ieThe number of years in which the i-th parameter is expected to fall within the RVA target range can be taken as half of the total number of years after the impact. The classification criteria for the overall hydrological change of the river include slight change (0-33%), moderate change (33%-67%), and significant change (67%-100%).

[0097] The steps for calculating the degree of change in hydrological conditions of small and medium-sized rivers using the HMA method are as follows:

[0098] ① Determine the number of classes n in the histogram. c That is, how many intervals should the parameter be divided into.

[0099]

[0100] Where r is the difference between the maximum and minimum values; n is the number of data points; r iq It is the difference between the 3 / 4 quantile and the 1 / 4 quantile.

[0101] ② Draw frequency histograms H and K of hydrological parameters before and after the change, and use quadratic distance d Q (H,K) measures the difference between two histograms.

[0102]

[0103] Where h = (h1, h2, ..., h nc ), k = (k1, k2, ..., k nc ) are the histogram frequency vectors of the parameters before and after the change; A = [a ij ] is a similarity matrix, a ij The similarity between class i and class j in the histogram is calculated as follows:

[0104]

[0105] Where, d ij is the distance between the i-th class and the j-th class in the histogram.

[0106] ③ Calculate the hydrological variability D of each parameter. Q,p .

[0107]

[0108]

[0109] Where, d Q,p This represents the difference in frequency histograms before and after the change of the p-th hydrological parameter.

[0110] ④ Calculate the overall hydrological change.

[0111]

[0112] Example:

[0113] This embodiment uses the Liujiaping River in Hunan Province as an example for illustration. This river is a typical small and medium-sized river. By applying the improved hydrological variation index system proposed in this invention, the changes in the river's hydrological situation before and after the construction of the reservoir are analyzed, demonstrating the effectiveness and rationality of this invention.

[0114] The Liujiaping River is located in Xupu County, Hunan Province, at longitudes of 110°39'~110°43' east and latitudes of 27°27'~27°30' north, with a total drainage area of ​​72 km². 2 The river is 30 km long with a significant gradient and an average annual flow of 1.51 m³ / h. 3 The Liujiaping Hydropower Station is located on the river. Construction began in 1984, and the station began storing water and generating electricity in 1987. The construction of the reservoir and the operation of the power station are important factors influencing changes in the river's hydrological conditions. Therefore, using the daily runoff from 1962 to 1981 as the natural runoff and the daily discharge from the power station from 1999 to 2018 as the altered runoff, this study analyzes the changes in the hydrological conditions of the Liujiaping River before and after the reservoir's construction.

[0115] Using daily runoff data from 1962 to 1981 (a total of 20 years), 720 ten-day samples were generated, with ten-day periods as the unit. Five indicators were then selected: ten-day average runoff, maximum and minimum one-day runoff, and maximum and minimum three-day runoff, to form the period-specific indicator set X. (720×5) The weights of five indicators—ten-day average runoff, ten-day maximum and minimum 1-day runoff, and ten-day maximum and minimum 3-day runoff—were calculated using the CRITIC method, with values ​​of 0.1982, 0.1011, 0.2859, 0.1331, and 0.2816, respectively. The comprehensive index value of each ten-day sample was calculated according to equation (2), the period threshold L = 5.503 was calculated according to equation (3), and the membership degree of each ten-day period to the flood season set was calculated according to equation (4). The results are shown in Table 1.

[0116] Table 1. Membership degree of each ten-day period to the flood season set.

[0117]

[0118]

[0119] Figure 2 The calculation process for hydrological periods is shown, using the membership degree λ of each ten-day period to the flood season. k=0.5 is used as the cutoff level for flood season division, with the flood season from late April to mid-August, and the remaining time period being the dry season. Further, using the maximum membership degree within the flood season as the dividing point, the ordered membership degree sequence of the flood season is divided into a pre-flood season (late April to early June) and a post-flood season (mid-June to mid-August). According to equations (5) and (6), the turning points of the pre-flood season and post-flood season are found, which are late May and early July, respectively. Therefore, the result of the flood season division is: late April to late May belongs to the pre-flood season, early June to early July belongs to the main flood season, and mid-July to mid-August belongs to the post-flood season.

[0120] Using each hydrological period as a statistical scale, parameters were supplemented to the conventional hydrological variability index system. Component 2 added the maximum and minimum 1-day, 3-day, and 7-day flows for the four stages: dry season, pre-flood season, main flood season, and post-flood season, totaling 24 parameters. Component 4 added the number and duration of high and low flow pulses for the two stages: dry season and flood season, totaling 8 parameters. Component 5 added the average flow increase rate, average flow decrease rate, and number of flow reversals for the four stages: dry season, pre-flood season, main flood season, and post-flood season, totaling 12 parameters. The parameters for both the conventional and improved hydrological variability index systems are shown in Table 2.

[0121] Table 2 Conventional and Improved Hydrological Variation Index Systems

[0122]

[0123]

[0124] To verify the necessity of improving the hydrological variability index system, the changes in parameters under the original statistical scale (annual) and the newly added statistical scale (dry season, pre-flood season, main flood season, post-flood season) before and after reservoir construction were compared. The results are shown in […]. Figure 3 Among them, arrows represent the trend of parameter change, and double arrows represent a significant trend of change.

[0125] from Figure 3 As can be seen, the trends of the parameters maximum and minimum 1-day flow, maximum and minimum 3-day flow, and minimum 7-day flow in the second component, under the newly added statistical scale (dry season, pre-flood season, main flood season, and post-flood season), are basically consistent with those under the original statistical scale (annual), all showing a downward trend. Furthermore, except for the maximum 3-day flow in the post-flood season, all other parameters passed the 95% significance test, indicating a significant downward trend. For the parameter of maximum 7-day flow, a significant downward trend was observed on the annual statistical scale, but this pattern was only retained during the pre-flood and main flood seasons. While there was a downward trend in the post-flood season, it was not significant, and in the dry season, there was the opposite upward trend.

[0126] In component 4, the trends of high / low flow pulse counts differ significantly across statistical scales. As the flow process flattens, both high and low flow pulse counts show a significant decreasing trend throughout the year. However, the high flow pulse count primarily decreases during the flood season, while it tends to increase during the dry season. Similarly, the low flow pulse count primarily decreases during the dry season, while it tends to increase during the flood season.

[0127] In component 5, the average rate of decrease in flow, the average rate of increase in flow, and the number of flow reversals generally showed consistent trends across different scales. Except for the average rate of decrease in flow during the dry season / post-flood season, the average rate of increase in flow before the flood season / main flood season / post-flood season, and the number of flow reversals during the post-flood season, all other parameters passed the 95% significance test. However, the magnitude of change in these parameters varied considerably across different statistical scales.

[0128] In summary, a single statistical scale ignores the seasonal differences in parameters across different statistical periods, leading to biased trend judgments and affecting the results of hydrological situation analysis. Therefore, it is necessary to add different statistical scales and improve the conventional hydrological variation index system to reflect the seasonal characteristics of each parameter.

[0129] Based on conventional and improved hydrological variability index systems, the RVA and HMA methods were used to quantitatively calculate the changes in the overall hydrological situation of the Liujiaping River before and after the reservoir was built. The results are shown in […]. Figure 4 .

[0130] Under the RVA method, among the 33 hydrological parameters in the conventional hydrological variability index system, only IHA3 (March average flow), IHA8 (August average flow), and IHA... 19 (Maximum 30-day flow), IHA 26 (Time of minimum flow occurrence), IHA 33 Of the 5 parameters (number of flow reversals) showing low-level changes, 21 parameters showing high-level changes, with 13 parameters showing a change of 100%. The overall hydrological change rate reached 80.26%, classifying it as a high-level change. Of the 44 new hydrological parameters added to the improved IHA indicator system, only IIHA... 25 (Maximum 3-day flow during dry season), IIHA 61 (Low flow pulse count during flood season), IIHA 68 (Number of dry season flow reversals), IIHA 70 (Average increase rate of pre-flood season flow), IIHA 73 (Average increase rate of flow during the main flood season), IIHA 74 (Number of flow reversals during the main flood season), IIHA 77(Number of flow reversals after the flood season) 7 parameters showed low-level changes, while 32 parameters showed high-level changes. Among them, 19 parameters showed a change of 100%, and the overall hydrological change reached 81.10%, which also belonged to high-level changes.

[0131] In addition, the parameter IHA in the second component of the conventional hydrological variability index system 13 ~IHA 18 The changes in (maximum and minimum 1-day, 3-day, and 7-day flow rates) are all above 90%, according to the newly added corresponding hydrological parameter IIHA. 24 ~IIHA 46 In addition to IIHA 45 Except for the (maximum 7-day flow rate after the flood season) which showed moderate changes, all other parameters also showed significant changes. The original parameter IHA from component 4... 27 and IHA 28 The changes in (number and duration of high-flow pulses) were 90% and 100%, respectively, with the corresponding newly added hydrological parameter IIHA. 53 ~IIHA 56 All of them have also changed significantly; parameter IHA 29 and IHA 30 (Low flow pulse number and duration) represent a significant change, but the corresponding hydrological parameter IIHA has been added. 59 ~IIHA 62 In the middle, there is a period of low flow during the dry season IIHA 60 and low flow pulse number IIHA during the flood season 61 The two parameters represent moderate and low changes, respectively. The original parameter IHA from component 5... 31 ~IHA 33 The changes in (average rate of decrease in flow, average rate of increase in flow, and number of flow reversals) were relatively small, all falling below moderate levels. Meanwhile, the corresponding newly added hydrological parameter IIHA... 66 ~IIHA 77 In addition to the average reduction rate of pre-flood season flow IIHA 69 and the average reduction rate of post-flood season flow IIHA 75 Two parameters showed a high degree of change, while the remaining parameters showed a relatively small degree of change.

[0132] Overall, the degree of change of the newly added hydrological parameters is basically the same as that of the original hydrological parameters due to their similar construction methods. However, the degree of change of some parameters is still inconsistent. Therefore, in order to fully characterize the changes in the hydrological situation of Liujiaping River before and after the reservoir was built, it is necessary to improve the conventional IHA index system.

[0133] Under the HMA method, the parameter change value is significantly smaller than that under the RVA method. Of the 33 hydrological parameters in the conventional hydrological variability index system, 7 parameters show low-level changes, 13 show high-level changes, and the number of parameters showing high-level changes is significantly reduced. Only 6 parameters show a change exceeding 90%, and only the IHA method shows such a change. 23 The change in the baseflow index reached 100%, and the overall hydrological change was 64.55%. Of the 44 new hydrological parameters added to the improved IHA index system, 15 parameters showed low change, and 16 parameters showed high change. Ten parameters showed a change exceeding 90%, and no parameter showed a change of 100%. The overall hydrological change was 62.82%.

[0134] Because the RVA method calculates the degree of change solely based on whether the parameter falls within the target interval, a slight perturbation causing the parameter to jump outside the interval significantly increases the degree of change value. Therefore, the degree of change calculated by the RVA method tends to be large, and it produces a relatively large number of parameters with high degree of change. In contrast, the HMA method comprehensively considers the parameter's variation across different intervals, making it difficult to encounter a 100% degree of change. The calculated degree of change value is smaller than that of the RVA method, and the number of parameters with high degree of change is also reduced to some extent. However, regardless of the method used, both indicate that the construction and operation of the reservoir have significantly altered the hydrological conditions of the downstream river of the power station.

Claims

1. A method for analyzing hydrological situation changes in small and medium-sized rivers based on an improved hydrological variability index system, characterized in that, The process includes the following steps: S1: Based on the seasonal characteristics of runoff in small and medium-sized rivers, hydrological periods are divided using ten-day periods as the calculation scale, dividing the year into four periods: dry season, pre-flood season, main flood season, and post-flood season. S2: Statistically analyze hydrological parameters in each period, supplement the conventional hydrological variation index system with parameters, and propose an improved hydrological variation index system. S3: Based on the improved hydrological variation index system, the change range evaluation method and histogram matching method are used to calculate the degree of change in the hydrological situation of small and medium-sized rivers. Among them, the hydrological grading adopted by S1 includes the construction of grading index set, membership degree calculation and flood season division; The construction of the phased index set includes determining the phased scale and selecting phased indicators; the phased scale is determined on a ten-day period; the phased indicator selection, based on the average runoff value, adds indicators for the minimum and maximum 1-day, 3-day, 5-day, and 7-day runoff, thus constructing the hydrological phased index set. The weights of each period indicator were calculated using the CRITIC method. Set up phased indicators Transformed into a set of phased comprehensive indicators ; ， ， ， （1） in, The number of years for runoff data; For the first The set of indicators for the year; The number of indicators selected for each ten-day period includes the average runoff within the ten-day period, and the minimum and maximum runoff over 1, 3, 5, and 7 days. For the first The first in the year The first ten days of the month One indicator; For the first A comprehensive set of indicators for the year; For the first The first in the year Comprehensive indicators for the ten-day period; （2） The membership calculation includes threshold construction and membership definition; the threshold construction is achieved by weighting the mean of each indicator to serve as the boundary between the flood season and the dry season. （3） in, For the first The average of the indicators; The membership degree is defined in the following steps: For a given first Annual runoff series, selecting comprehensive indicators Greater than the threshold The beginning of the ten-day period and the end of the ten-day period As the beginning and end of the flood season, The series of data from that year can be statistically obtained. Each flood season interval ,in ; Statistics on each ten-day period Number of flood season intervals This serves as its degree of affiliation with the flood season; （4） The division of the flood season includes the division between the flood season and the dry season, and the division within the flood season; The division between the flood season and the dry season is selected =0.5 as the cutoff level, when When >0.5, the ten-day period will be... Classified as part of the flood season; when When <0.5, the ten-day period will be [missing information]. Classified as dry season; The flood season is divided into pre-flood, main flood, and post-flood periods using the mean change point analysis method. The steps are as follows: Based on the ten-day period with the highest degree of affiliation to the flood season As the dividing point, the membership sequence Divided into pre-sequence and post-sequence ;in, and This indicates the start and length of the flood season; Assuming the previous sequence The variable point is the first Ten-day period Calculate the statistic and The difference ; ， （5） （6） in, , , Sequences , as well as The mean; Change point Traversal arrive Select the largest Corresponding ten-day period This serves as the dividing point between the pre-flood season and the main flood season. Change point Traversal arrive Select the largest Corresponding ten-day period This serves as the dividing point between the main flood season and the post-flood season; the pre-flood season, main flood season, and post-flood season correspond to... , as well as .

2. The method for analyzing hydrological situation changes in small and medium-sized rivers based on an improved hydrological variation index system as described in claim 1, characterized in that, The improved hydrological variability index system includes parameters of 5 components. The differences between the parameters of each component and those of the conventional hydrological variability index system are as follows: (1) The second component adds the maximum and minimum flow rates for 1 day, 3 days and 7 days in the four stages of dry season, pre-flood season, main flood season and post-flood season, for a total of 24 parameters; (2) The fourth component adds the number of high and low flow pulses and their durations during the dry and flood seasons, for a total of 8 parameters; (3) The fifth component adds the average increase rate of flow, the average decrease rate of flow, and the number of flow reversals in the four stages of dry season, pre-flood season, main flood season, and post-flood season, for a total of 12 parameters; (4) The first and third components remain unchanged.

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