Non-consistent design flood estimation method and system considering climate and human activities

Abstract

The invention discloses a non-consistent design flood estimation method and system considering climate and human activities. The method comprises the following steps: obtaining hydro meteorological data of a research basin and reservoir related data; based on the data obtained in the step 1, a GAMLSS method and a sliding window method are adopted to deduce a time-varying probability distribution model; on the basis of the annual average reliability, according to the time-varying probability distribution model deduced in the step 2, inconsistent design flood values under the two methods within the engineering design age limit T1-T2 are estimated respectively; based on the actual data acquisition condition of the drainage basin, the design flood values of the GAMLSS method and the sliding window method are compared, the inconsistent design flood value better meeting the design requirements of the research drainage basin is selected, and a reference is provided for engineering design in a changing environment. According to the method, the difference between the GAMLSS method and the sliding window method in non-consistency design flood value determination is comprehensively considered, and a more reliable non-consistency design flood value is selected according to the actual drainage basin condition, so that the design is more scientific and reasonable.

Description

Technical Field

[0001] The invention belongs to the technical field of water conservancy design, and in particular relates to a method and system for estimating floods designed with consideration of the inconsistency of climate and human activities. Background Art

[0002] Due to the influence of climate conditions and human activities, the probability distribution of hydrological variables and the design values ​​corresponding to a certain recurrence period may change over time. If the design values ​​under consistent conditions are still used, the risk of engineering construction or operation will increase. The derivation of time-varying probability distribution can be achieved by statistical model means or basin hydrological model means. There are differences in the method structure and model input information of different models. In engineering practice, it is very necessary to select reliable inconsistent design flood results to guide the design of water conservancy projects under changing environments. Considering that climate change and human activities are increasingly disturbing the flood sequence, a method and system for estimating inconsistent design floods considering climate and human activities is constructed for calculating engineering hydrological design values ​​under inconsistent conditions, which is of great significance for the design of water conservancy projects under changing environments. Summary of the invention

[0003] One purpose of the present invention is to address the shortcomings of the prior art and provide a method for estimating inconsistent design floods that takes into account climate and human activities. The method comprehensively considers the differences in different model methods in determining inconsistent design flood values, and selects more reliable inconsistent design flood values ​​based on the actual situation of the basin, thereby making the design more scientific and reasonable.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0005] A method for estimating design floods with consideration of climate and human activities, comprising the following steps:

[0006] Step 1: Obtain hydrological and meteorological data of the study basin and reservoir-related data;

[0007] Step 2: Based on the data obtained in step 1, the time-varying probability distribution model is derived using the GAMLSS method and the sliding window method respectively;

[0008] Step 3: Based on the annual average reliability, the non-consistent design flood values ​​of the two methods within the engineering design life T1 to T2 are estimated respectively according to the time-varying probability distribution model derived in step 2;

[0009] Step 4: Based on the actual data obtained in the basin, the non-consistent design flood values ​​of the GAMLSS method and the sliding window method are compared, and the non-consistent design flood value that is more in line with the situation of the study basin is selected to provide a reference for engineering design under a changing environment.

[0010] Furthermore, the data obtained in step 1 includes:

[0011] Study the basin's historical runoff, meteorological data for the entire basin's historical period, future meteorological data simulated by climate models, as well as the controlled area and total storage capacity of large and medium-sized reservoirs in the basin, and the planning and construction of reservoir projects in basin planning.

[0012] Furthermore, the method for obtaining future meteorological data in step 1 includes: using the CMIP6 global climate model GCM to simulate meteorological data, and obtaining rainfall data for future periods of the basin after downscaling.

[0013] Furthermore, the method of using the GAMLSS method to derive the time-varying probability distribution model includes:

[0014] Based on the collected basin meteorological data and reservoir information, the summer precipitation anomaly SPA, annual precipitation anomaly TPA and reservoir index RI are calculated. The calculated summer precipitation anomaly SPA and annual precipitation anomaly TPA are used as basin climate covariates, and the reservoir index RI is used as the basin human activity covariate.

[0015] Constructing an inconsistent probability distribution model based on GAMLSS model And the maximum likelihood method is used to estimate its distribution parameters;

[0016] Assuming that each observation is independent of each other, the expressions of the log-likelihood function under consistent and inconsistent conditions are as follows:

[0017]

[0018] Where n is the length of the measured flood sequence; is the flood variable Y t The probability density function of y t represents the observed value of flood variable at a certain time t; X k is the covariate, including the climate covariate X1 and the human activity covariate X2; θ is the model parameter vector, under the consistency condition θ is a constant, under the inconsistency condition θ t =(μ t ,σ t ,ξ t ), θ t represents the model distribution parameter at a certain time t, μ t represents the location parameter at a certain time t, σ t represents the scale parameter at a certain time t, ξ t Represents the shape parameter at a certain time t;

[0019] That is, the functional relationship between the distribution parameter and the covariate is: t =g(X 1,t ,X2,t );

[0020] Substituting the future climate covariates and human activity covariates into the functional relationship between the above distribution parameters and covariates, we can obtain the probability distribution parameter θ for future years. t , the obtained probability distribution parameter θ t Substitute into the above probability distribution model The time-varying probability distribution of flood within the design life of the project is obtained as F Q (Q|θ t ).

[0021] Furthermore, the method of using the sliding window method to derive the time-varying probability distribution model includes:

[0022] The basins with significant variations in flood sequences were divided into two periods: natural period and variation period, and the hydrological model was calibrated and verified using the hydrological runoff data of the two periods.

[0023] The meteorological data output by the climate model GCM is downscaled and input into the hydrological model verified during the above-mentioned change period to simulate the future daily runoff sequence of the basin;

[0024] Select an m-year sliding window length, take each year within the engineering design period as the window center for the future daily runoff sequence, select the m / 2-year sequences before and after the window center to form the flood samples within the window, and then obtain the probability distribution corresponding to each window by distribution fitting. Then we can get the time-varying probability distribution F of flood within the design life of the project: Q (Q|θ t ).

[0025] Furthermore, the calculation formula of the non-consistent design flood value in step 3 is:

[0026]

[0027] Where p is the probability of exceeding, T1 and T2 are the start and end years of the project, and F Q (Q 1-p |θ t ) represents the time-varying probability distribution derived in step 2.

[0028] Furthermore, in step 4, when determining the inconsistent design flood value of the study area, first consider whether there are flood data and inconsistent influencing factor data in the historical survey period in the study basin. If so, the design value result of the GAMLSS method is selected as the inconsistent design flood value; if not, by comparing the uncertainties of the inconsistent design values ​​corresponding to different exceedance probabilities of the GAMLSS method and the sliding window method, the design value result with the smaller uncertainty of the two methods is selected.

[0029] Another object of the present invention is to provide a system for implementing the above-mentioned inconsistency design flood estimation method considering climate and human activities, comprising:

[0030] Data acquisition module, used to obtain hydrological and meteorological data of the study basin and reservoir-related data;

[0031] The time-varying probability distribution model derivation module is used to derive the time-varying probability distribution model based on the acquired data using the GAMLSS method and the sliding window method;

[0032] The non-consistent design flood value calculation module is used to estimate the non-consistent design flood values ​​under two methods within the engineering design period T1 to T2 based on the annual average reliability and the derived time-varying probability distribution model;

[0033] The module for determining the non-consistent design flood value of the study basin is used to compare the design flood values ​​of the GAMLSS method and the sliding window method based on the actual data obtained in the basin, and select the non-consistent design flood value that better meets the design requirements of the study basin.

[0034] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0035] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention comprehensively considers the influence of the structure and input information of different models on the estimated value of design flood, and compares the difference between the GAMLSS method and the sliding window method in determining the inconsistent design flood value, wherein the GAMLSS method has good applicability in processing the nonlinear relationship and inconsistency of the sequence, and if the covariate is selected appropriately, the model construction based on the long sequence data will also be more reliable; the sliding window method analyzes the changing trend of the data by continuously moving the time window, and can better adapt to the dynamic changes of the data, but both methods have certain uncertainties in the model structure and parameter estimation, so in order to provide the accuracy of the results, the present invention compares the two methods, and selects a more suitable method to calculate the inconsistent design flood value according to the actual situation of the basin, which can provide more reliable design results for the design flood estimation and water conservancy project design under the changing environment; in addition, in the context of increasingly significant impacts of climate change and human activities, the calculated design flood results are very important for flood prevention and disaster reduction, and can be used for flood estimation and risk management of different regions and different types of rivers. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1It is the Wujiang River basin distribution map and reservoir generalization map in the example of the present invention;

[0038] Figure 2 is a flow chart of a method and system for estimating floods by considering the inconsistency of climate and human activities in an example of the present invention;

[0039] Figure 3 It is a quantile curve diagram of the annual maximum daily flow corresponding to the consistency and optimal inconsistency probability distribution model in the example of the present invention;

[0040] Figure 4 is the future climate covariate sequence of the Wujiang River Basin in the example of the present invention;

[0041] Figure 5 is the maximum daily flow sequence of the Wujiang River Basin in the future years in the example of the present invention;

[0042] Figure 6 It is the estimation result of the GAMLSS method and the sliding window method in the example of the present invention corresponding to the non-consistent design flood value and the consistent design flood value;

[0043] Figure 7 It is the 5% to 95% quantile interval of the non-consistent design flood value corresponding to the different exceeding probabilities of the GAMLSS method and the sliding window method in the example of the present invention. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present invention will be described clearly and completely below in combination with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0046] The present invention will be further described below in conjunction with specific embodiments, but the present invention is not limited thereto.

[0047] The embodiment of the present invention discloses a method for estimating design floods that takes into account inconsistency in climate and human activities, comprising the following steps:

[0048] Step 1: Obtain hydrological and meteorological data of the study basin and reservoir-related data; in this step, the collected data include:

[0049] Collect and organize the basin's historical runoff and meteorological data, as well as the controlled area and total storage capacity of large and medium-sized reservoirs built in the basin;

[0050] The simulated meteorological data of CMIP6 global climate model (GCM) (including three shared socioeconomic pathways: SSP1-2.6, SSP2-4.5, and SSP5-8.5) were downloaded and downscaled to obtain the daily rainfall in the future period of the basin to calculate the future climate covariates of the basin;

[0051] The information on the construction and operation of future reservoir projects and the corresponding reservoirs in the basin planning is collected to calculate the covariates of future human activities in the basin.

[0052] Step 2: Based on the data obtained in step 1, the time-varying probability distribution model is derived using the GAMLSS method and the sliding window method respectively;

[0053] Among them, the method of using the GAMLSS method to deduce the time-varying probability distribution model includes:

[0054] (1) First, based on the collected basin meteorological data and reservoir information, the precipitation anomaly factors are calculated: summer precipitation anomaly (SPA), annual precipitation anomaly (TPA) and reservoir impact factor index: reservoir index (RI):

[0055] SPA=P SP -μ SP

[0056] TPA=P TP -μ TP

[0057]

[0058] Where P SP is the average precipitation in summer (May-September), mm / day; μ SP is the multi-year average of summer precipitation, mm / day; P TP is the annual average precipitation, mm / day; μ TP is the multi-year average of annual precipitation, mm / day; N is the total number of reservoirs upstream of the hydrological station; A i is the controlled area of ​​the upstream reservoir, km 2 ; A T is the controlled area of ​​the hydrological station, km 2 ; V i is the total storage capacity of the ith reservoir, m 3 ; V T is the sum of the total storage capacity of all reservoirs upstream of the hydrological station, m 3 ;

[0059] The calculated summer precipitation anomaly (SPA) and annual precipitation anomaly (TPA) were used as basin climate covariates, and the reservoir index (RI) was used as basin human activity covariate;

[0060] (2) Based on the GAMLSS model, a non-uniform probability distribution model is constructed (the distribution types include six two-parameter distributions: Weibull distribution, Gumbel distribution, Gamma distribution, Logistic distribution, Normal distribution, Lognormal distribution, and two three-parameter distributions: Generalized extreme value distribution and Pearson type III distribution). Taking the Lognormal distribution as an example, its probability density function is as follows:

[0061]

[0062] In the formula, μ, σ are the location parameter and scale parameter respectively, and y represents the measured sequence;

[0063] The maximum likelihood method is used to estimate the distribution parameters; assuming that each observation is independent of each other, the expressions of the log-likelihood function under consistent and inconsistent conditions are as follows:

[0064]

[0065] Where n is the length of the measured flood sequence; is the flood variable Y t The probability density function of y t represents the observed value of flood variable at a certain time t; X k are covariates, including climate covariates X1 (such as SPA, TPA) and human activity covariates X2 (such as RI); θ is the model parameter vector, which is a constant under consistent conditions and a constant under inconsistent conditions. t =(μ t ,σ t ,ξ t ), θ t represents the model distribution parameter at a certain time t, μ t represents the location parameter at a certain time t, σ t represents the scale parameter at a certain time t, ξ t Represents the shape parameter at a certain time t;

[0066] Based on historical data, the distribution parameters change with the covariate: θ t =g(X 1,t ,X 2,t );

[0067] (3) Substituting the future climate covariates and human activity covariates into the functional relationship between the distribution parameters and covariates constructed based on historical data, we can obtain the probability distribution parameter θ for future years. t , the obtained probability distribution parameter θ t Substituting into the above probability density function The time-varying probability distribution of flood within the design life of the project is obtained as F Q (Q|θ t The characteristic of the GAMLSS method is that it makes full use of the hydrological and meteorological information of each year in the future, while making the length of the engineering design life unlimited within the scope of data availability. At the same time, it can capture the occurrence sequence and magnitude of future extreme events, and is therefore easily affected by individual extreme values.

[0068] Methods for deriving time-varying probability distribution models using the sliding window method include:

[0069] (1) Using inconsistent diagnostic methods such as Mann-Kendall trend test or Pettitt change point test to diagnose the variation of flood series, the basin with a significant variation (such as 10% significance level) is divided into two periods: natural period (assuming that it is not affected by climate change and human activities) and change period (affected by climate change and human activities); the natural period must be determined before the inconsistent change point and the construction of the first large reservoir in the basin; the change period must be determined after the inconsistent change point, and the sequence lengths of the natural period and the change period should be as similar as possible; in this embodiment, the Pettitt (PT) change point test method is used to diagnose the variation of flood series to determine the inconsistent change point, specifically:

[0070] For a sequence y1, y2, ..., y with n observations n , Pettitt test statistic K t The definition is as follows:

[0071]

[0072] In the formula,

[0073]

[0074] In the formula,

[0075]

[0076] Pettitt test corresponding probability value (p PT )for:

[0077]

[0078] The null hypothesis of the PT test is that there is no significant mutation in the measured sample sequence. PT >α, the original hypothesis is accepted, and it is believed that there is no significant mutation point at position t in the sequence; when p PT When <α, the null hypothesis is rejected and it is believed that there is a significant mutation point in the series at position t. α represents the significance level.

[0079] The hydrological runoff data in the natural period and the changing period were used to calibrate and verify the hydrological models (Xin'anjiang model, GR4J model, etc.), and the maximum KGE efficiency coefficient was used as the objective function. The calculation formula is as follows

[0080]

[0081] In the formula, Q obs and Q sim are the measured runoff and the model simulated runoff series, r(Q obs , Q sim ) is the correlation coefficient between the measured runoff sequence and the simulated runoff sequence, μ(Q obs ) and μ(Q sim ) are the average values ​​of the measured runoff sequence and the simulated runoff sequence, σ(Q obs ) and σ(Q sim ) are the standard deviations of the measured runoff sequence and the simulated runoff sequence, respectively. The range of the KGE efficiency coefficient is (-∞, 1). The closer the KGE value is to 1.0, the better the runoff simulation effect of the model is.

[0082] (2) The meteorological data (daily rainfall, evaporation, etc.) output by the climate model GCM are downscaled and input into the hydrological model verified based on the change period to simulate the future daily runoff sequence of the basin under non-consistent conditions;

[0083] (3) A 30-year sliding window is selected to process the future daily runoff sequence (e.g., the runoff sequence from 2021 to 2100). Each year within the project design life (e.g., the project design life is 66 years from 2021 to 2086) is taken as the window center. The annual maximum flow sequence 15 years before and after the window center is selected to form the flood sample within the window. The probability distribution corresponding to each window can be obtained by distribution fitting. Then we can get the time-varying probability distribution F of flood within the design life of the project: Q (Q|θ t ). The sliding window method obtains the probability distribution of each year in the future through sliding window fitting. The sliding window allows a large amount of overlapping information between adjacent years, which can avoid the extreme impact of outliers in a short period of time on the distribution fitting. At the same time, the use of the sliding window limits the length of the engineering design life. The length of the sliding window can be adjusted appropriately according to research needs, but there are also problems such as a shorter window will lead to insufficient sample size when fitting the sequence distribution within the window, and a longer window will reduce the specificity of the results of each year within the engineering design life.

[0084] Step 3: Based on the annual average reliability, the non-consistent design flood values ​​of the two methods within the engineering design life T1 to T2 are estimated respectively according to the time-varying probability distribution model derived in step 2;

[0085] In this step, the calculation method of the inconsistent design flood value within the design life of the project based on the annual average reliability is:

[0086]

[0087] Where p is the probability of exceeding; T1 and T2 are the start and end years of the project.

[0088] Step 4: Based on the actual data obtained in the basin, the uncertainty of the design flood values ​​of the GAMLSS method and the sliding window method is compared, and the inconsistent design flood value that better meets the design requirements of the study basin is selected to provide a reference for engineering design under a changing environment;

[0089] When selecting the non-consistent design flood value, first consider whether there are flood data in the historical survey period in the studied basin (the major floods that occurred in a certain river section in history obtained through field investigation and literature verification, such as the flood flow in 1560, 1613, 1788, 1796, 1860, and 1870 since 1470 at the Yichang Hydrological Station) and the non-consistent influencing factor data of the corresponding period (such as the rainfall reconstruction data from 1470 to 1950 in the control basin of the Yichang Hydrological Station). If there are, the historical survey data increases the representativeness of hydrological data, making the calculation of the non-consistent design flood value obtained based on the GAMLSS method more reliable, while the sliding window method cannot establish a model due to the difficulty in collecting detailed and continuous historical flood data and meteorological data. Therefore, in this case, the design value result of the GAMLSS method is selected as the non-consistent design flood value. If the data does not meet this condition, that is, the above data does not exist, then compare the uncertainty of the non-consistent design value corresponding to the different exceedance probabilities of the GAMLSS method and the sliding window method, and select the design value result with the smaller uncertainty of the two methods. In this embodiment, the size of uncertainty is quantified using the size of the quantile interval (such as the 5% to 95% quantile interval) of the design values ​​calculated by multiple climate models, that is: one climate model can calculate a design value corresponding to a certain exceedance probability, and multiple climate models can obtain multiple design values ​​corresponding to a certain exceedance probability, and the quantile interval of the design value can be obtained.

[0090] <Specific example>:

[0091] like Figure 1As shown, the Wujiang River is the largest tributary on the right bank of the upper reaches of the Yangtze River. The basin has a large elevation difference and is affected by the alternation of the East Asian monsoon and the South Asian monsoon. The basin has abundant rainfall and rich water resources, which provides conditions for the development and utilization of large-scale cascade reservoirs. The impact of human activities, mainly climate change and reservoir group construction, has caused significant changes in the flood sequence in the basin, posing a threat to the flood control safety of the basin and the safety of people’s lives and property. Since the Wujiang River Basin has abundant rainfall and there are many cascade reservoirs developed in the basin, the present invention considers the inconsistency of climate and human activities to design a flood estimation method and system. Taking the Wujiang River Basin as an example, by comparing the uncertainties of the non-consistent design values ​​corresponding to different exceedance probabilities of the GAMLSS method and the sliding window method, the design value results with smaller uncertainty in the two methods are selected to guide the design of basin water conservancy projects under a changing environment. The specific process is as follows:

[0092] like Figure 2 As shown in the figure, firstly, the historical runoff and meteorological data of Wulong Station, as well as the controlled area and total storage capacity of 11 large and medium-sized reservoirs in the basin are collected and sorted. The meteorological data simulated by the CMIP6 global climate model (GCM) (including three shared socioeconomic pathways: SSP1-2.6, SSP2-4.5, and SSP5-8.5) are downloaded, and the daily meteorological data of the basin in the future period are obtained after downscaling. According to the basin planning situation, the future new construction projects in the Wujiang River Basin are not considered for the time being. Then, the time-varying probability distribution model is derived from two aspects: statistical model means (GAMLSS method) and basin hydrological model means (sliding window method). The GAMLSS method calculates the summer precipitation anomaly (SPA), annual precipitation anomaly (TPA) and reservoir index (RI). Based on the GAMLSS model, an inconsistent probability distribution model is constructed. The future climate covariates and human activity covariates are substituted into the optimal inconsistent probability distribution model selected based on the SBC criterion to obtain the probability distribution parameter θ for future years. t , thus obtaining the time-varying probability distribution F of flood within the design life of the project Q (Q|θ t ); The sliding window method divides the basin into two periods: the natural period (assuming that it is not affected by climate change and human activities) and the changing period (affected by climate change and human activities) according to the inconsistency diagnosis results of the flood sequence, and calibrates and verifies the Xin'anjiang hydrological model. In this example, the Wujiang River basin with significant variations in flood sequences is divided into two periods: the natural period (assuming that it is not affected by climate change and human activities) and the changing period (affected by climate change and human activities). The results are shown in Table 1. The effect of the model is judged based on the KGE coefficient, and the future meteorological data is input into the calibrated hydrological model to simulate the future daily runoff of the basin. Each year within the engineering design period is taken as the window center, and the sequences of 15 years before and after the window center are selected to constitute the flood samples in the window. The distribution fitting obtains the probability distribution corresponding to each window Then we can get the time-varying probability distribution F of flood within the design life of the project: Q (Q|θ t ). Next, the annual average reliability method is used to estimate the inconsistent design flood values ​​within the engineering design life corresponding to the GAMLSS method and the sliding window method. Finally, considering that it is impossible to collect flood data and more accurate inconsistent influencing factor data for a long historical survey period in the Wujiang River Basin, the uncertainty of the inconsistent design values ​​corresponding to different exceeding probabilities of the GAMLSS method and the sliding window method is compared, and the design value results with smaller uncertainty in the two methods are selected to guide the design of basin water conservancy projects under changing environments.

[0093] Table 1 Division of natural and variable periods in basins with significant flood sequence variations

[0094]

[0095] The correlation coefficients of the annual maximum daily flow and precipitation anomaly factors and reservoir influencing factors were calculated. It can be seen from Table 2 that the correlation between the annual maximum daily flow Q of Wulong Station and the summer precipitation anomaly SPA is greater than the correlation between Q and the annual precipitation anomaly TPA. Q has a high negative correlation with the reservoir index RI. Therefore, SPA and RI were selected as the climate covariate and human activity covariate affecting the flood flow of Wulong Station, respectively. The inconsistency probability distribution model of the annual maximum daily flow was constructed based on the GAMLSS model, and the maximum likelihood method was used to estimate the model parameters. The optimal inconsistency probability distribution model was selected based on the SBC criterion. Figure 3 As a result, the inconsistency model is significantly better than the consistency model in fitting flood samples. Figure 4 , the future SPA sequence of Wulong station shows an upward trend, the sequence changes of SSP1-2.6 and SSP2-4.5 are similar, and the upward trend of the sequence under the SSP5-8.5 scenario in the far future period is significantly greater than that of the SSP1-2.6 and SSP2-4.5 scenarios.

[0096] Table 2 Correlation coefficients between annual maximum daily flow and SPA, TPA and RI

[0097]

[0098] As shown in Table 3, the Xin'anjiang model has a better runoff simulation effect in the Wujiang River Basin. Figure 5 The future flood magnitude of Wulong Station is significantly reduced compared with the historical period. This is because the flood sequence in the basin during the change period is greatly disturbed by human activities (especially the construction of large reservoirs), so the magnitude is reduced. The changes in the flood sequence under the three scenarios of SSP1-2.6, SSP2-4.5 and SSP5-8.5 are not much different.

[0099] Table 3 Evaluation of runoff simulation effect of Xin'anjiang model in Wujiang River Basin

[0100]

[0101] according to Figure 6 , the design flood values ​​of some scenarios under the non-consistent conditions of Wulong Station are greater than the consistent design values ​​under very rare probability conditions, and the 5% to 95% quantile intervals of the non-consistent design values ​​corresponding to some scenarios are large, indicating that there are differences in the response of flood design values ​​to different climate models. Comparing the design values ​​corresponding to the 0.1% exceedance probability, it can be seen that the non-consistent design values ​​corresponding to the GAMLSS method and the sliding window method are both smaller than the consistent design values. The design values ​​corresponding to the two methods differ except for the SSP1-2.6 scenario, and the design values ​​corresponding to the SSP2-4.5 scenario and the SSP5-8.5 scenario are relatively close. As the SPA sequence shows an upward trend in the future, it shows that human activities, mainly reservoir construction, have a more obvious impact on the reduction of downstream design floods.

[0102] according to Figure 7 , the 5% to 95% quantile interval of the non-consistent design value corresponding to the sliding window method with the probability of exceeding 0.001, 0.01, 0.1 and 0.2 is significantly larger than that of the GAMLSS method. Therefore, the non-consistent design flood value results of the GAMLSS method are selected as the theoretical values ​​to guide the design of water conservancy projects in the Wujiang River Basin under a changing environment.

[0103] The GAMLSS method explores the impact of climate change and human activities through covariates, while the sliding window method considers the impact of climate change and human activities on flood runoff in a relatively general way by dividing the natural period and the change period. Since the model methods and hydrological and meteorological information used by the GAMLSS method and the sliding window method are different, the results of the two methods are not strictly comparable, but the mutual comparison of the results of the two methods can increase the persuasiveness of the calculation results of inconsistent design flood values.

[0104] The present invention also provides a system for implementing the above-mentioned inconsistency design flood estimation method considering climate and human activities, comprising:

[0105] Data acquisition module, used to obtain hydrological and meteorological data of the study basin and reservoir-related data;

[0106] The time-varying probability distribution model derivation module is used to derive the time-varying probability distribution model based on the acquired data using the GAMLSS method and the sliding window method;

[0107] The non-consistent design flood value calculation module is used to estimate the non-consistent design flood values ​​under two methods within the engineering design period T1 to T2 based on the annual average reliability and the derived time-varying probability distribution model;

[0108] The module for determining the non-consistent design flood value of the study basin is used to compare the design flood values ​​of the GAMLSS method and the sliding window method based on the actual data obtained in the basin, and select the non-consistent design flood value that better meets the design requirements of the study basin.

[0109] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0110] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0111] The above are only preferred embodiments of the present invention, and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the contents of the specification of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for estimating design floods that takes into account the inconsistency of climate and human activities, characterized in that: The following steps are involved: Step 1: Obtain hydrological and meteorological data of the study basin and reservoir-related data; Step 2: Based on the data obtained in step 1, the time-varying probability distribution model is derived using the GAMLSS method and the sliding window method respectively; Step 3: Based on the annual average reliability, the non-consistent design flood values ​​of the two methods within the engineering design life T1 to T2 are estimated respectively according to the time-varying probability distribution model derived in step 2; Step 4: Based on the actual data obtained in the basin, the non-consistent design flood values ​​of the GAMLSS method and the sliding window method are compared, and the non-consistent design flood value that is more in line with the situation of the study basin is selected to provide a reference for engineering design under a changing environment.

2. The method for estimating design floods in accordance with claim 1, wherein: The data obtained in step 1 include: Study the basin's historical runoff, meteorological data for the entire basin's historical period, future meteorological data simulated by climate models, as well as the controlled area and total storage capacity of large and medium-sized reservoirs in the basin, and the planning and construction of reservoir projects in basin planning.

3. The method for estimating design floods in accordance with the inconsistency of climate and human activities according to claim 2, characterized in that: The method for obtaining future meteorological data in step 1 includes: using the CMIP6 global climate model GCM to simulate meteorological data, and obtaining rainfall data for future periods of the basin after downscaling.

4. The method for estimating design floods in accordance with the inconsistency of climate and human activities according to claim 1, characterized in that: Methods for deriving time-varying probability distribution models using the GAMLSS method include: Based on the collected basin meteorological data and reservoir information, the summer precipitation anomaly SPA, annual precipitation anomaly TPA and reservoir index RI are calculated, and the calculated summer precipitation anomaly SPA and annual precipitation anomaly TPA are used as basin climate covariates, and the reservoir index RI is used as the basin human activity covariate; Constructing an inconsistent probability distribution model based on GAMLSS model And the maximum likelihood method is used to estimate its distribution parameters; Assuming that each observation is independent of each other, the expressions of the log-likelihood function under consistent and inconsistent conditions are as follows: Where n is the length of the measured flood sequence; is the flood variable Y t The probability density function of y t represents the observed value of flood variable at a certain time t; X k is the covariate, including the climate covariate X1 and the human activity covariate X2; θ is the model parameter vector, under the consistency condition θ is a constant, under the inconsistency condition θ t =(μ t ,σ t ,ξ t ), θ t represents the model distribution parameter at a certain time t, μ t represents the location parameter at a certain time t, σ t represents the scale parameter at a certain time t, ξ t Represents the shape parameter at a certain time t; That is, the functional relationship between the distribution parameter and the covariate is: t =g(X 1,t ,X 2,t ); Substituting the future climate covariates and human activity covariates into the functional relationship between the above distribution parameters and covariates, we can obtain the probability distribution parameter θ for future years. t , the obtained probability distribution parameter θ t Substitute into the above probability distribution model The time-varying probability distribution of flood within the design life of the project is obtained as F Q (Q|θ t ).

5. The method for estimating design floods in accordance with the inconsistency of climate and human activities according to claim 1, characterized in that: Methods for deriving time-varying probability distribution models using the sliding window method include: The basins with significant variations in flood sequences were divided into two periods: natural period and variation period, and the hydrological model was calibrated and verified using the hydrological runoff data of the two periods. The meteorological data output by the climate model GCM is downscaled and input into the hydrological model verified during the above-mentioned change period to simulate the future daily runoff sequence of the basin; Select an m-year sliding window length, take each year within the engineering design period as the window center for the future daily runoff sequence, select the m / 2-year sequences before and after the window center to form the flood samples within the window, and then obtain the probability distribution corresponding to each window by distribution fitting. Then we can get the time-varying probability distribution F of flood within the design life of the project: Q (Q|θ t ).

6. The method for estimating design floods in accordance with the inconsistency of climate and human activities according to claim 1, characterized in that: The calculation formula for the non-consistent design flood value in step 3 is: Where p is the probability of exceeding, T1 and T2 are the start and end years of the project, and F Q (Q 1-p |θ t ) represents the time-varying probability distribution derived in step 2.

7. The method for estimating design floods in accordance with claim 1, wherein: In step 4, when determining the inconsistent design flood value of the study area, first consider whether there are flood data and inconsistent influencing factor data in the historical survey period in the study basin. If so, the design value result of the GAMLSS method is selected as the inconsistent design flood value; if not, by comparing the uncertainties of the inconsistent design values ​​corresponding to different exceedance probabilities of the GAMLSS method and the sliding window method, the design value result with the smaller uncertainty of the two methods is selected.

8. A system for implementing the method for estimating the inconsistency design flood considering climate and human activities as described in any one of claims 1 to 7, characterized in that: include: Data acquisition module, used to obtain hydrological and meteorological data of the study basin and reservoir-related data; The time-varying probability distribution model derivation module is used to derive the time-varying probability distribution model based on the acquired data using the GAMLSS method and the sliding window method; The non-consistent design flood value calculation module is used to estimate the non-consistent design flood values ​​under two methods within the engineering design period T1 to T2 based on the annual average reliability and the derived time-varying probability distribution model; The module for determining the non-consistent design flood value of the study basin is used to compare the design flood values ​​of the GAMLSS method and the sliding window method based on the actual data obtained in the basin, and select the non-consistent design flood value that better meets the design requirements of the study basin.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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