A method for determining the drought warning flow of a river channel considering multi-dimensional water demand and phased connection

Abstract

The present invention provides a method for determining the drought warning flow of a river channel considering multi-dimensional water demand and phased connection, including three links: water demand satisfaction analysis, phased division, and threshold determination. The ecological water demand is calculated by using the 90% frequency value of the average value of the minimum continuous 5-day flow in a month, the agricultural, domestic, and industrial water demands of each month are determined, the water demand satisfaction is introduced, and the critical values of water demand satisfaction for different drought levels are determined; with the month as the minimum phased unit, a phased model based on optimized parameters of kernel principal component analysis and K-means clustering is constructed to determine the optimal phasing within a year; based on the multi-objective water demand and the critical value of water demand satisfaction, the drought warning flow threshold is determined, and the smooth transition of thresholds in different phases is realized based on the principle of water use priority and the triangular membership function. The present invention can effectively handle the non-linear relationship of hydrological characteristic data, solve the problem of threshold mutation in traditional rigid phasing, make the drought warning flow threshold more conform to the gradual change characteristics of the hydrological system, and improve the practicability and adaptability of drought warning.

Description

Technical Field

[0001] The invention belongs to the technical field of hydrology and water resources management, and particularly relates to a method for determining the dry - warning flow of a river channel considering multi - dimensional water demand and phased connection. Background Technique

[0002] The determination of the dry - warning flow needs to comprehensively consider multi - dimensional water use demands such as ecological base flow, agricultural irrigation, industrial water intake, and domestic water use, and has significant spatial heterogeneity and time dynamics. Traditional methods for determining the dry - warning flow of river channels are mostly based on single water use demand or static analysis, and do not fully consider the priority of different water use demands under drought conditions, making it difficult to effectively adapt to the dynamic changes of multi - dimensional water use demands such as ecology, agriculture, industry, and life. In addition, the annual phased division of traditional methods is too simple, mostly using rigid phasing (fixed phasing), which easily leads to unreasonable threshold mutations at the phased connection, does not conform to the gradual response characteristics of the social - hydrological system, and affects the accuracy and practicality of drought warning. The existing technologies mainly have the following deficiencies: ① Do not fully integrate the dynamic response of multi - dimensional water use demands, resulting in the lack of comprehensiveness and adaptability of the dry - warning flow threshold; ② The phasing method does not fully consider the priority of different water use demands under limited water resources; ③ Traditional rigid phasing (fixed phasing) easily leads to threshold mutations at the phased connection, making it difficult to reflect the gradual nature of drought warning.

[0003] Therefore, aiming at the deficiencies of the existing technologies, the invention proposes a method for determining the dry - warning flow of a river channel considering multi - dimensional water demand and phased connection. Compared with the existing technologies, especially compared with the methods relying on ordered segmentation, the method of the invention can more objectively and flexibly achieve non - linear phasing of the dry - warning flow, and at the same time, based on the principle of water use priority and triangular membership function, realize smooth transition of different phased thresholds, solve the problem of threshold mutation of traditional rigid phasing, make the dry - warning flow threshold more in line with the gradual characteristics of the hydrological system, improve the practicality and adaptability of drought warning, and has significant economic and social values. Summary of the Invention

[0004] The invention proposes a method for determining the dry - warning flow of a river channel considering multi - dimensional water demand and phased connection. This method comprehensively considers ecological, agricultural, industrial, and domestic water demands, quantifies the changes in dynamic water demand of each month by introducing the water demand satisfaction degree; improves the accuracy of non - linear data processing and the objectivity of phasing results based on the KPCA - K - means method; and realizes smooth transition of different phased thresholds based on the principle of water use priority and triangular membership function, solving the problem of threshold mutation of traditional rigid phasing.

[0005] To achieve the above - mentioned purpose, the invention adopts the following technical solutions: A method for determining the dry - warning flow of a river channel considering multi - dimensional water use demands and optimized phased connection, which includes three links: water demand satisfaction analysis, phased division, and threshold determination. The main steps are as follows:

[0006] Step S1, water demand satisfaction analysis: Taking a month as the minimum unit, calculate the ecological water demand for each month using the 90% frequency value of the average flow of the minimum consecutive 5-day flow in a month, and determine the agricultural, domestic, and industrial water demands for each month based on the analysis of measured data; introduce the water demand satisfaction, and set the critical values of water demand satisfaction for different drought levels.

[0007] Step S2, staging division: Adopt the method of kernel principal component analysis and K-means clustering, conduct annual staging in units of months, and determine the optimal staging scheme by optimizing the kernel parameters.

[0008] Step S3, threshold determination: Based on the multi-objective water demand and the critical values of water demand satisfaction, determine the drought warning flow threshold, and achieve the smooth transition of thresholds for different stages according to the principle of water use priority and the triangular membership function, and finally obtain the dynamic drought warning flow threshold.

[0009] Furthermore, the water demand satisfaction analysis in Step S1 is specifically as follows:

[0010] Step S11, water demand calculation: The ecological water demand adopts the improved Q 10,5dmin method, that is, calculate using the 90% frequency value of the average flow of the minimum consecutive 5-day flow in a month, the agricultural water demand is determined based on the measured irrigation water use data, and the domestic and industrial water demands are determined based on the data of water plants and industrial water users respectively.

[0011] Step S12, water demand satisfaction determination: The water demand satisfaction is defined as the ratio of the actual water supply to the target water demand, which is used to characterize the satisfaction degree of water use demand under different drought levels. The value range of the water demand satisfaction is [0, 1], and the closer the value is to 1, the higher the water demand satisfaction degree; according to the industry standard and the water shortage ratio, set the critical values of water demand satisfaction corresponding to mild, moderate, severe, and extreme droughts.

[0012] Furthermore, the staging division in Step S2 is specifically as follows:

[0013] Step S21, selection of characteristic indicators: Select 6 indicators including the multi-year average flow of each month from January to December, the change in adjacent-month flow, the multi-year average precipitation, evaporation, agricultural water demand, and ecological water demand as the staging characteristic indicators; the calculation of the change in adjacent-month flow is as follows:

[0014] ;

[0015] Among them, represents the change in adjacent-month flow of the th month; represents the multi-year average flow of the th month, i represents the month within the stage, and has a value range of 1 to 12; represents the The multi-year average flow of the month; in particular, the change in the adjacent-month flow in January is equal to the difference between the multi-year average flows of January and December;

[0016] Step S22, KPCA-K-means model construction: Construct a staging model based on kernel principal component analysis and K-means clustering, where the kernel principal component analysis uses a Gaussian radial basis kernel function for data mapping, and the mathematical expression is:

[0017] ;

[0018] Among them, and are characteristic index data points, represents the kernel function similarity between the characteristic index data point and the characteristic index data point y, is the characteristic index data point and the square of the Euclidean distance between the characteristic index data point y and y, is the width parameter of the Gaussian radial basis kernel function, which controls the similarity calculation range between characteristic index data points;

[0019] Step S23, optimal staging determination: Input the staging characteristic indexes in Step S21 into the KPCA-K-means model constructed in Step S22, traverse and calculate within the effective interval of the width parameter σ of the Gaussian radial basis kernel function, use the Gaussian radial basis kernel function for kernel principal component analysis, map the high-dimensional feature data to a low-dimensional space, select the principal components that retain 90% of the information volume, and for each combination of the width parameter σ of the Gaussian radial basis kernel function and the number of clusters K, calculate the corresponding silhouette coefficient S; select the combination of the width parameter σ of the Gaussian radial basis kernel function and the number of clusters K that maximizes the silhouette coefficient S to determine the optimal staging scheme; the calculation formula of the silhouette coefficient S is:

[0020] ;

[0021] Among them, is the average distance between the sample point and other points in the same cluster, representing the compactness within the cluster; is the average distance between the sample point and all points in the nearest neighbor cluster, representing the separation between clusters.

[0022] Among them, the sample point refers to the principal component feature data of each month obtained after dimensionality reduction by kernel principal component analysis. The silhouette coefficient S measures the compactness within the cluster and the separation between clusters. The closer the value is to 1, the better the clustering quality.

[0023] Furthermore, in Step S22, the optimization process of the KPCA-K-means model is specifically as follows:

[0024] KPCA-K-means optimization process: The KPCA-K-means optimization algorithm is adopted. The KPCA-K-means optimization algorithm realizes the optimal staging of nonlinear hydrological data by iteratively optimizing the width parameter σ of the Gaussian radial basis kernel function and the number of clusters K.

[0025] The optimal staging of nonlinear hydrological data mainly includes inputting staging characteristic indexes and the initial value of the width parameter σ of the Gaussian radial basis kernel function, kernel principal component analysis for dimensionality reduction, K-means clustering, and evaluation of the silhouette coefficient S. The optimal parameter combination is determined by evaluating and selecting the maximum silhouette coefficient.

[0026] Furthermore, the determination of the threshold in step S3 is specifically as follows:

[0027] Step S31, determination of the basic threshold: Based on the ecological, agricultural, domestic, and industrial water demands of each month within different stages, combined with the critical value of water demand satisfaction corresponding to the corresponding drought level, the drought warning flow threshold is determined. For the i-th month and drought level g, the upper limit of the drought warning flow threshold The calculation formula is:

[0028] ;

[0029] Among them, , , , are the domestic, ecological, agricultural, and industrial water demands of the i-th month respectively; , , are the critical values of water demand satisfaction for ecology, agriculture, and industry under drought level g; the critical value of domestic water demand satisfaction is fixed at 1.0;

[0030] Step S32, determination of the threshold within the stage: The average value of the flow thresholds of the same drought level within the same stage is used as the drought warning flow threshold for the corresponding stage; the drought warning flow thresholds corresponding to different drought levels are calculated as follows:

[0031] ;

[0032] ;

[0033] ;

[0034] ;

[0035] Among them, M is the number of months within the stage; is the upper limit value of the warning flow for mild drought; is the upper limit value of the warning flow for moderate drought and the lower limit value of the warning flow for mild drought; is the upper limit value of the severe drought warning flow and the lower limit value of the moderate drought warning flow; is the upper limit value of the extreme drought warning flow and the lower limit value of the severe drought warning flow; i represents the month within the sub-period; , , , respectively represent the critical values of the ecological water demand satisfaction degree under the mild, moderate, severe and extreme drought levels; , , , respectively represent the critical values of the agricultural water demand satisfaction degree under the mild, moderate, severe and extreme drought levels; , , , respectively represent the critical values of the industrial water demand satisfaction degree under the mild, moderate, severe and extreme drought levels;

[0036] Step S33, fuzzy transition processing: Introduce a triangular membership function to achieve fuzzy processing of the transition period threshold. The transition period is centered at the boundary between sub-periods and extends 15 days forward and backward in months, that is, it is defined as a 31-day buffer period between the two adjacent sub-periods;

[0037] For the transition period from the t start th day to the t end th day, define the membership degrees of the adjacent sub-periods on the tth day:

[0038] ;

[0039] ;

[0040] where, μ m (t) represents the membership degree of the tth day to the previous sub-period; μ n (t) represents the membership degree of the tth day to the subsequent sub-period. The sum of the membership degree of the tth day to the previous sub-period and the membership degree of the tth day to the subsequent sub-period is always 1, ensuring the continuity and normalization of the membership function;

[0041] Step S34, determination of the soft sub-period threshold: Expand the original drought warning flow threshold within the sub-period to a daily dynamic threshold within the transition period. The calculation formula is as follows:

[0042] ;

[0043] ;

[0044] ;

[0045] ;

[0046] Wherein: , , , are respectively the upper limits of the warning flow thresholds for mild, moderate, severe, and extremely severe droughts in the previous sub-period of membership degree μ m within; , , , are respectively the upper limits of the warning flow thresholds for mild, moderate, severe, and extremely severe droughts in the subsequent sub-period of membership degree μ n within; , , , are respectively the upper limits of the warning flow thresholds for mild, moderate, severe, and extremely severe droughts corresponding to the t-th day during the transition period;

[0047] The warning flow threshold intervals for mild, moderate, severe, and extremely severe droughts within each sub-period are respectively: ( MD , LD , ( SD , MD , ( ED , SD , (0, ED , and the daily warning flow thresholds for mild, moderate, severe, and extremely severe droughts during the transition period are respectively ( MD , LD , ( SD , MD , ( ED , SD , (0, ED .

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] First, by introducing the water demand satisfaction index, the multi-dimensional water use demands of ecology, agriculture, life, and industry are precisely quantified, making the basis for determining the drought warning flow threshold more scientific and reasonable, and better reflecting the actual water use tension under different drought degrees.

[0050] Second, the KPCA-K-means method is used for annual staging. By systematically iteratively optimizing the kernel function width parameter and the number of clusters, and using the clustering mechanism of K-means based on feature similarity, compared with traditional methods and the KPCA-Fisher optimal segmentation method, it can more objectively and flexibly capture and divide the non-linear time-varying characteristics of the drought warning flow, improving the scientificity and accuracy of staging.

[0051] Third, the fuzzy membership function is innovatively introduced to achieve a smooth transition in staging connection, effectively solving the threshold mutation problem caused by traditional rigid staging, making the drought warning flow threshold more in line with the gradual change characteristics of the hydrological system.

[0052] Fourth, the threshold is determined based on the priority principle of different water resource users, which is more in line with the actual water resource management needs, significantly improving the operability and adaptability of drought warning.

[0053] In summary, this method can effectively handle the non-linear relationship of hydrological characteristic data, achieve scientific annual staging, and solve the threshold mutation problem caused by traditional rigid staging through fuzzy transition, making the drought warning flow threshold more in line with the gradual change characteristics and management reality of the hydrological system, improving the practicality and adaptability of drought warning, and having significant economic and social value.

[0054] It should be noted that when determining the drought warning flow by the method of the present invention, it mainly focuses on the four key water use demands of ecology, agriculture, life, and industry. In specific applications, other specific in-river or out-of-river water use demands may need to be further considered according to the functional positioning (such as shipping, power generation, etc.) and management objectives of a specific river, and their impacts on the drought warning flow threshold should be evaluated. In addition, the reliability of the calculation results of this method depends on the accuracy and representativeness of the input historical hydrometeorological data and various water demand data, and the quality of the basic data should be ensured during application. Description of the Drawings

[0055] Figure 1 It is the method flow chart of the present invention.

[0056] Figure 2 It is the flow chart of the KPCA-K-means optimization algorithm of the present invention.

[0057] Figure 3 It is the daily drought warning flow threshold (soft staging threshold) diagram in the embodiment of the present invention. Detailed Embodiment

[0058] The following describes the technical solutions in the embodiments of the present invention clearly and completely in conjunction with specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0059] As Figure 1 shown, a method for determining the drought warning flow of a river channel considering multi-dimensional water demand and phased connection provided by the present invention includes three links: water demand satisfaction analysis, phased division, and threshold determination. The main steps are as follows:

[0060] Step S1, water demand satisfaction analysis: Taking a month as the smallest unit, calculate the ecological water demand of each month using the 90% frequency value of the average flow of the minimum consecutive 5 days in a month, and determine the agricultural, domestic, and industrial water demands of each month based on the analysis of measured data; introduce the water demand satisfaction degree, and set the critical values of water demand satisfaction degrees for different drought levels.

[0061] Step S2, phased division: Adopt the method of kernel principal component analysis and K-means clustering to conduct annual phased division on a monthly basis, and determine the optimal phased plan by optimizing the kernel parameters.

[0062] Step S3, threshold determination: Based on the multi-objective water demand and the critical value of water demand satisfaction degree, determine the drought warning flow threshold, and realize the smooth transition of thresholds in different phases according to the water use priority principle and the triangular membership function, and finally obtain the dynamic threshold of the drought warning flow.

[0063] Further, the water demand satisfaction analysis in step S1 is specifically as follows:

[0064] Step S11, water demand calculation: The ecological water demand adopts the improved Q 10,5dmin method, that is, calculate it using the 90% frequency value of the average flow of the minimum consecutive 5 days in a month. The agricultural water demand is determined based on the measured irrigation water use data, and the domestic and industrial water demands are determined based on the data of water plants and industrial water users respectively.

[0065] Step S12, water demand satisfaction determination: The water demand satisfaction degree is defined as the ratio of the actual water supply to the target water demand, which is used to characterize the satisfaction degree of water use demand under different drought levels. The value range of the water demand satisfaction degree is [0, 1], and the closer the value is to 1, the higher the water demand satisfaction degree; according to industry standards and the water shortage ratio, set the critical values of water demand satisfaction degrees corresponding to mild, moderate, severe, and extreme droughts.

[0066] As Figure 2 shown, further, the phased division in step S2 is specifically as follows:

[0067] Step S21, Feature Index Selection: Select six indicators, namely the multi-year average monthly flow from January to December, the change in flow between adjacent months, the multi-year average precipitation, evaporation, agricultural water demand, and ecological water demand, as the staging feature indicators; the change in flow between adjacent months is calculated as follows:

[0068] ;

[0069] where represents the change in flow between adjacent months of the th month; represents the multi-year average flow of the th month, i represents the month within the staging period, and ranges from 1 to 12; represents the multi-year average flow of the th month; in particular, the change in flow between adjacent months in January is equal to the difference between the multi-year average flows of January and December;

[0070] Step S22, KPCA-K-means Model Construction: Construct a staging model based on kernel principal component analysis and K-means clustering, where kernel principal component analysis uses the Gaussian radial basis kernel function for data mapping, and the mathematical expression is:

[0071] ;

[0072] where and are feature index data points, represents the kernel function similarity between the feature index data point and the feature index data point y, is the square of the Euclidean distance between the feature index data point and the feature index data point y, and is the width parameter of the Gaussian radial basis kernel function, which controls the similarity calculation range between feature index data points.

[0073] Furthermore, the specific KPCA-K-means optimization process is as follows:

[0074] KPCA-K-means Optimization Process: Adopt the KPCA-K-means optimization algorithm, which iteratively optimizes the width parameter σ of the Gaussian radial basis kernel function and the number of clusters K to achieve the optimal staging of non-linear hydrological data;

[0075] The optimal staging of non-linear hydrological data mainly includes inputting the initial values of the staging feature indicators and the width parameter σ of the Gaussian radial basis kernel function, kernel principal component analysis for dimensionality reduction, K-means clustering, and evaluation of the silhouette coefficient S. The optimal parameter combination is determined by selecting the largest silhouette coefficient through evaluation;

[0076] Step S23, Optimal staging determination: Input the staging characteristic indicators in Step S21 into the KPCA-K-means model constructed in Step S22, traverse and calculate within the effective range of the width parameter σ of the Gaussian radial basis kernel function, perform kernel principal component analysis using the Gaussian radial basis kernel function, map the high-dimensional feature data to a low-dimensional space, select the principal components that retain 90% of the information volume, and calculate the corresponding silhouette coefficient S for each combination of the width parameter σ of the Gaussian radial basis kernel function and the number of clusters K; Select the combination of the width parameter σ of the Gaussian radial basis kernel function and the number of clusters K that maximizes the silhouette coefficient S to determine the optimal staging plan; The calculation formula for the silhouette coefficient S is:

[0077] ;

[0078] where, is the average distance from the sample point to other points within the same cluster, representing the compactness within the cluster; is the average distance from the sample point to all points within the nearest neighboring cluster, representing the separation between clusters.

[0079] The above sample points refer to the principal component feature data of each month obtained after dimensionality reduction through kernel principal component analysis. The silhouette coefficient S measures the compactness within the cluster and the separation between clusters. The closer the value is to 1, the better the clustering quality.

[0080] Furthermore, in Step S3, the threshold determination is specifically as follows:

[0081] Step S31, Basic threshold determination: Based on the ecological, agricultural, domestic, and industrial water demands of each month within different stages, combined with the critical values of water demand satisfaction corresponding to the corresponding drought levels, determine the drought warning flow threshold; For the i-th month and drought level g, the upper limit of the drought warning flow threshold The calculation formula is:

[0082] ;

[0083] where, , , , are respectively the domestic, ecological, agricultural, and industrial water demands of the i-th month; , , are the critical values of water demand satisfaction for ecology, agriculture, and industry under drought level g; The critical value of domestic water demand satisfaction is fixed at 1.0.

[0084] Step S32, Threshold determination within the stage: To reduce the interference of single-month extreme values and ensure the consistency of the thresholds within the stage, use the average value of the flow thresholds of the same drought level within the same stage as the corresponding stage's drought warning flow threshold; The drought warning flow thresholds corresponding to different drought levels are calculated as follows:

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] where M is the number of months within the M staging; is the upper limit value of the warning flow for mild drought; is the upper limit value of the warning flow for moderate drought and the lower limit value of the warning flow for mild drought; is the upper limit value of the warning flow for severe drought and the lower limit value of the warning flow for moderate drought; is the upper limit value of the warning flow for extreme drought and the lower limit value of the warning flow for severe drought; i represents the month within the staging; , , , respectively represent the critical values of the satisfaction degree of ecological water demand under the levels of mild, moderate, severe, and extreme drought; , , , respectively represent the critical values of the satisfaction degree of agricultural water demand under the levels of mild, moderate, severe, and extreme drought; , , , respectively represent the critical values of the satisfaction degree of industrial water demand under the levels of mild, moderate, severe, and extreme drought.

[0090] Step S33, fuzzy transition processing: Introduce a triangular membership function to implement the fuzzy processing of the transition period threshold. The transition period is centered at the boundary between periods and extends 15 days forward and backward in months, that is, it is defined as a 31-day buffer period between the two adjacent periods;

[0091] For the transition period from the t start th day to the t end th day, define the membership degrees of the adjacent periods on the tth day:

[0092] ;

[0093] ;

[0094] where μ m (t) represents the membership degree of the tth day to the previous period; μ n (t) represents the membership degree of the tth day to the next period. The sum of the membership degree of the tth day to the previous period and the membership degree of the tth day to the next period is always 1, ensuring the continuity and normalization of the membership function;

[0095] Step S34, determination of soft staging threshold: Expand the drought warning flow threshold within the original stage into a daily dynamic threshold during the transition period. The calculation formula is as follows:

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] Where: , , , are the upper limits of the warning flow thresholds for mild, moderate, severe, and extreme droughts within the membership degree μ m of the previous stage; , , , are the upper limits of the warning flow thresholds for mild, moderate, severe, and extreme droughts within the membership degree μ n of the next stage; , , , are the upper limits of the warning flow thresholds for mild, moderate, severe, and extreme droughts corresponding to the t-th day during the transition period;

[0101] The warning flow threshold intervals for mild, moderate, severe, and extreme droughts within each stage are respectively: ( MD , LD , ( SD , MD , ( ED , SD , (0, ED , and the warning flow thresholds for mild, moderate, severe, and extreme droughts on a daily basis during the transition period are respectively ( MD , LD , ( SD , MD , ( ED , SD , (0, ED .

[0102] A hydrological station is located in the river basin of Jiangxi hilly mountainous area. The river section where the hydrological station is located has the water demand for ecology, agriculture, industry and domestic use, and has no water demand for shipping, power generation, etc. Taking the river section where the hydrological station is located as an example, the implementation steps of determining the drought warning flow threshold of the section where the hydrological station is located by using the above method are introduced. Collect the daily runoff from January 1, 1990 to December 31, 2022, the monthly precipitation from 1990 to 2022, and the monthly evaporation from 1990 to 2022 at this station. According to the on-site investigation and statistics, collect the data of industrial water use, domestic water use and agricultural irrigation water use from January to December from 2018 to 2022 at this station. These data comprehensively reflect the runoff situation, climate impact and the time-series change characteristics of the main water demand of the research section.

[0103] Step S1, water demand satisfaction analysis: Adopt the improved Q 10,5dmin method, that is, calculate the ecological water demand of each month from January to December respectively with the 90% frequency value of the average of the minimum continuous 5-day flow in a month, and determine the agricultural water demand, industrial water demand and domestic water demand of each month respectively with the average value of the data of industrial water use, domestic water use and agricultural irrigation water use from January to December from 2018 to 2022. See the following table for details.

[0104] Statistical table of different types of water demand in each month

[0105]

[0106] The above unit: cubic meters per second. According to the "Regional Drought Level: GB / T 32135-2015" and the water shortage ratio, set the critical values of water demand satisfaction corresponding to mild, moderate, severe and extreme drought. Among them, the critical values of ecological water demand satisfaction corresponding to mild, moderate, severe and extreme drought are taken as 0.8, 0.6, 0.4 and 0.2 respectively; the critical values of agricultural water demand satisfaction corresponding to mild, moderate, severe and extreme drought are taken as 0.95, 0.8, 0.65 and 0.5 respectively; the critical values of domestic and industrial water demand satisfaction corresponding to mild, moderate, severe and extreme drought are taken as 0.95, 0.9, 0.8 and 0.7 respectively. See the following table for the range of water demand satisfaction under different drought levels.

[0107] Range of water demand satisfaction under different drought levels

[0108]

[0109] Step S2, staging division: Select six indicators, namely the multi-year average flow of each month from January to December, the change in flow between adjacent months, the multi-year average precipitation, evaporation, agricultural water demand, and ecological water demand, as the staging characteristic indicators. Among them, the agricultural water demand and ecological water demand of each month are obtained from Step S1. The staging characteristic indicators are shown in the following table:

[0110] Staging index values of drought warning flow

[0111]

[0112] Input the above staging characteristic values into the constructed KPCA-K-means model. The value range of the width parameter σ of the Gaussian radial basis kernel function is set to 0.01 - 10. Set the traversal step size of the width parameter σ of the Gaussian radial basis kernel function to 0.01, and calculate 1000 times in total. Through the optimization of the width parameter σ of the Gaussian radial basis kernel function, when σ = 5.84 finally, the silhouette coefficient S of the Kmeans clustering result (K = 3) reaches the maximum value of 0.56. The contribution rates of the corresponding six principal components are 0.516, 0.384, 0.04, 0.025, 0.017, and 0.018 in sequence. Among them, the cumulative contribution rate of the first two reaches 90%, that is, extract the characteristic data of two principal components and input them into K-means clustering analysis. At this time, the corresponding optimal staging is divided into three categories: April - June, July - October, November - March.

[0113] Step S3, threshold determination: Fix the critical value of domestic water demand satisfaction at 1.0, and use the average value of the flow thresholds of the same drought level within the same stage as the drought warning flow threshold of this stage and this level. The drought warning flow thresholds corresponding to different drought levels are calculated as follows:

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] Among them, M is the number of months within the stage; is the upper limit value of the warning flow for mild drought; is the upper limit value of the warning flow for moderate drought and the lower limit value of the warning flow for mild drought; is the upper limit value of the warning flow for severe drought and the lower limit value of the warning flow for moderate drought; is the upper limit value of the warning flow for extreme drought and the lower limit value of the warning flow for severe drought; i represents the month within the stage.

[0119] The drought warning flow thresholds (hard staging thresholds) for each stage are obtained from the above formula, as shown in the following table.

[0120] Drought warning flow thresholds (hard staging thresholds) for different periods

[0121]

[0122] Note: The above table shows the hard staging thresholds of drought warning flow corresponding to different drought levels, and the above unit is cubic meters per second.

[0123] As Figure 3 shown, based on the above hard staging thresholds, a triangular membership function is introduced to achieve the fuzzy processing of the transition period thresholds. According to the staging results determined in step S2 and the above hard staging thresholds, there are sudden changes in the drought warning flow thresholds at the junctions of March / April, June / July, and October / November in this embodiment. The transition period extends 15 days forward and backward from the cross-period boundary as the center, that is, the three transition periods are from March 17th to April 16th, from June 16th to July 16th, and from October 17th to November 16th. The daily drought warning flow thresholds for the transition period are obtained respectively using the triangular membership function, and combined with the above hard staging thresholds, the annual daily drought warning flow thresholds (soft staging thresholds) of this hydrological station are obtained.

Claims

1. A method for determining the dry - warning flow of a river channel considering multi - dimensional water demand and phased connection, characterized in that: It includes the following steps: Step S1, water demand satisfaction analysis: Taking a month as the minimum unit, calculate the ecological water demand for each month using the 90% frequency value of the average flow of the minimum continuous 5-day flow in a month, and determine the agricultural, domestic and industrial water demands for each month based on the measured data analysis; Introduce the water demand satisfaction degree, and set the critical values of water demand satisfaction degrees for different drought levels; Step S2, stage division: Adopt the method of kernel principal component analysis and K-means clustering to conduct annual stage division on a monthly basis, and determine the optimal stage division plan by optimizing the kernel parameters; Step S3, threshold determination: Based on the multi-objective water demand and the critical value of water demand satisfaction degree, determine the drought warning flow threshold, and realize the smooth transition of thresholds for different stages according to the principle of water use priority and the triangular membership function, and finally obtain the dynamic drought warning flow threshold; In the stage division in Step S2, specifically: Step S21, selection of characteristic indicators: Select 6 indicators including the multi-year average flow of each month from January to December, the change in flow between adjacent months, the multi-year average precipitation, evaporation, agricultural water demand, and ecological water demand as the stage division characteristic indicators; Step S22, construction of the KPCA-K-means model: Construct a stage division model based on kernel principal component analysis and K-means clustering, Step S23, determination of the optimal stage division: Input the stage division characteristic indicators in Step S21 into the KPCA-K-means model constructed in Step S22, traverse and calculate within the effective interval of the width parameter σ of the Gaussian radial basis kernel function, perform kernel principal component analysis using the Gaussian radial basis kernel function, map the high-dimensional characteristic data to a low-dimensional space, select the principal components that retain 90% of the information volume, and calculate the corresponding silhouette coefficient S for each combination of the width parameter σ of the Gaussian radial basis kernel function and the number of clusters K; Select the combination of the width parameter σ of the Gaussian radial basis kernel function and the number of clusters K that maximizes the silhouette coefficient S to determine the optimal stage division plan; In the threshold determination in Step S3, specifically: Step S31, determination of the basic threshold: Based on the ecological, agricultural, domestic and industrial water demands of each month within different stages, combined with the critical value of water demand satisfaction degree corresponding to the corresponding drought level, determine the drought warning flow threshold; Step S32, determination of the threshold within the stage: Use the average value of the flow thresholds of the same drought level within the same stage as the drought warning flow threshold for the corresponding stage; Step S33, fuzzy transition processing: Introduce the triangular membership function to realize the fuzzy processing of the threshold during the transition period. The transition period is centered at the boundary between stages and extends 15 days forward and backward for each month, that is, it is defined as a 31-day buffer period between the two previous and next stages; Step S34, determination of the soft stage threshold: Expand the drought warning flow threshold within the original stage into a daily dynamic threshold during the transition period.

2. The method for determining the dry warning flow of a river channel considering multi-dimensional water demand and phased connection according to claim 1, wherein: In the water demand satisfaction analysis in Step S1, specifically: Step S11, water demand calculation: The ecological water demand adopts the improved Q 10,5dmin method, that is, it is calculated based on the 90% frequency value of the average monthly minimum continuous 5-day flow, the agricultural water demand is determined based on the measured irrigation water use data, and the domestic water demand and industrial water demand are determined based on the data of water plants and industrial water users respectively; Step S12, determination of water demand satisfaction degree: The water demand satisfaction degree is defined as the ratio of the actual water supply to the target water demand, which is used to characterize the satisfaction degree of water use demand under different drought levels. The value range of the water demand satisfaction degree is [0,1], and the value closer to 1 indicates a higher degree of water demand satisfaction; Set the critical values of water demand satisfaction degrees corresponding to mild, moderate, severe and extreme drought according to industry standards and the water shortage ratio.

3. The method for determining the dry - warning flow of a river channel considering multi - dimensional water demand and phased connection according to claim 2, characterized in that: In the stage division in Step S2, specifically: Step S21, the calculation of the adjacent-month flow change amount is as follows: ； Among them, represents the change in adjacent-month flow in the th month; represents the multi-year average flow in the th month, i represents the month within the staging period, and its value range is 1 to 12; represents the multi-year average flow in the th month; Step S22, the construction of the KPCA-K-means model, the mathematical expression is: ； Among them, and are characteristic index data points, represents the kernel function similarity between the characteristic index data point and the characteristic index data point y, is the Euclidean distance square between the characteristic index data point and the characteristic index data point y, is the width parameter of the Gaussian radial basis kernel function, which controls the similarity calculation range between characteristic index data points; Step S23, the calculation formula of the silhouette coefficient S is: ； Among them, is the average distance between a sample point and other points within the same cluster, representing the compactness within the cluster; is the average distance between a sample point and all points within the nearest neighbor cluster, representing the separation between clusters.

4. A method for determining the drought warning flow of a river channel considering multi-dimensional water demand and phased connection according to claim 3, characterized in that: In Step S22, the specific process of optimizing the KPCA-K-means model is as follows: KPCA-K-means optimization process: The KPCA-K-means optimization algorithm is adopted. The KPCA-K-means optimization algorithm realizes the optimal staging of non-linear hydrological data by iteratively optimizing the width parameter σ of the Gaussian radial basis kernel function and the number of clusters K. The optimal staging of non-linear hydrological data mainly includes inputting staging characteristic indexes and the initial value of the width parameter σ of the Gaussian radial basis kernel function, kernel principal component analysis for dimensionality reduction, K-means clustering, and silhouette coefficient S evaluation. The optimal parameter combination is determined by selecting the largest silhouette coefficient through evaluation.

5. A method for determining the drought warning flow of a river channel considering multi-dimensional water demand and phased connection according to claim 4, characterized in that: In Step S3, the determination of the threshold is specifically as follows: Step S31, for the i month and drought level g, the upper limit of the drought warning flow threshold The calculation formula is: ； Among them, , , , are the domestic, ecological, agricultural, and industrial water demands in the i-th month, respectively; , , is the drought level g The critical values of the satisfaction degrees of ecological, agricultural, and industrial water demands under [drought level]; the critical value of the satisfaction degree of domestic water demand is fixed at 1.0; Step S32, the calculation of the drought warning flow threshold corresponding to different drought levels is as follows: ； ； ； ； Among them, M is the number of months within the staging period; is the upper limit value of the warning flow for mild drought; is the upper limit value of the warning flow for moderate drought and the lower limit value of the warning flow for mild drought; is the upper limit value of the warning flow for severe drought and the lower limit value of the warning flow for moderate drought; is the upper limit value of the warning flow for extreme drought and the lower limit value of the warning flow for severe drought; i represents the month within the staging period; , , , respectively represent the critical values of the satisfaction degree of ecological water demand under the levels of mild, moderate, severe, and extreme drought; , , , respectively represent the critical values of the satisfaction degree of agricultural water demand under the levels of mild, moderate, severe, and extreme drought; , , , respectively represent the critical values of the satisfaction degree of industrial water demand under the levels of mild, moderate, severe, and extreme drought; Step S33, for the transition period from the t start th day to the t end th day, define the membership degree of adjacent stages on the t th day: ； ； Among them, µ m ( t ) represents the membership degree of the t th day to the previous sub-period; µ n ( t ) represents the membership degree of the t th day to the next sub-period. The sum of the membership degree of the t th day to the previous sub-period and the membership degree of the t th day to the next sub-period is always 1, ensuring the continuity and normalization of the membership function; Step S34, the determination of the soft staging threshold, the calculation formula is as follows: ； ； ； ； Among them: , , , are respectively the membership degrees of the previous sub-period µ m upper limits of the warning flow thresholds for mild, moderate, severe, and extreme droughts , , , are respectively the membership degrees of the next sub-period µ n upper limits of the warning flow thresholds for mild, moderate, severe, and extreme droughts , , , are respectively the upper limits of the warning flow thresholds for mild, moderate, severe, and extreme droughts corresponding to the t th day during the transition period The flow threshold ranges for mild, moderate, severe, and extreme drought warning during each stage are as follows: ( MD , LD , ( SD , MD , ( ED , SD , (0, ED , and the daily flow thresholds for mild, moderate, severe, and extreme drought warning during the transition period are ( MD , LD , ( SD , MD , ( ED , SD , (0, ED .

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