CN110598315A - Uncertainty analysis method for basin non-uniformity design flood under variable conditions - Google Patents
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Abstract
The invention discloses an uncertainty analysis method for basin non-uniformity design flood under variable conditions. The method mainly comprises the following steps: by combining a watershed hydrological simulation technology, an uncertainty analysis technology and a non-uniformity hydrological frequency analysis method, uncertainty influence of climate change and human activities on non-uniformity design flood is analyzed, so that an uncertainty interval of a non-uniformity peak combined design value under a designated probability is obtained, reliability of a non-uniformity design flood result is improved, and reference is provided for decision makers for watershed water resource planning management and flood control scheduling.
Description
Technical Field
The invention relates to the field of basin design flood uncertainty research, in particular to a method for analyzing influence of non-uniform design flood uncertainty under a changing condition.
Background
At present, the study of the flood designed according to the non-uniformity of the drainage basin under the changing condition usually does not comprehensively consider the uncertain influences of time factors, climate change factors and human activity factors on hydrologic sequences and the non-uniformity of the designed flood, or does not carry out joint distribution analysis according to the multivariable of flood characteristics, so that the systematic study cannot be carried out on the aspect of comprehensively considering the uncertain influences of all the factors on the non-uniformity design flood.
Disclosure of Invention
The invention aims to provide an analysis method for uncertainty influence of a variable condition on a watershed non-uniform design flood, which integrates a non-uniform hydrological frequency analysis method and an uncertainty analysis method, takes a time factor, a climate change factor and a reservoir scheduling factor as covariates, couples the influence of the covariates into parameters of a joint distribution function, quantifies uncertainty of the influence of the factors on the watershed non-uniform design flood, and has great theoretical value and practical significance for comprehensively evaluating the influence of a variable environment on the watershed design flood, further researching the evolution rule of a hydrological extreme value event under the variable environment and making flood control adaptability decisions.
The invention provides an analysis method for uncertainty influence of a change condition on a basin non-uniform design flood, which quantifies the uncertainty influence of the change condition on basin runoff by combining a non-uniform hydrological frequency analysis method and an uncertainty analysis method.
The non-consistency flood frequency analysis is mainly carried out aiming at the non-consistency extreme flood sequence, and the mathematical description of the non-consistency extreme flood sequence is obtained.
The non-consistency extreme value flood sequence mainly comprises a history period and a future period, wherein a flood peak and a corresponding flood sequence are selected as the extreme value flood sequence in the history period AM; in the future period, the climate scenario sets output by various climate modes are obtained through a SWAT hydrological model with good coupling ratio, each climate scenario set drives the SWAT model respectively, the runoff process in the future period under different climate scenario sets is obtained, and an extreme value flood sequence is selected by using an AM method.
The SWAT hydrological model belongs to a distributed hydrological model with a strong physical foundation, and can simulate the hydrological process of a complex basin by using hydrological, meteorological and geographic information energy basic data to output a simulation result of continuous years in a daily scale.
The specific steps of the localization construction are as follows:
(1) collecting daily rainfall, runoff data and daily scale meteorological data of a research area, wherein the daily rainfall, runoff data and daily scale meteorological data comprise air temperature, relative humidity, wind speed, sunshine hours and the like; the geographic information data comprises DEM data and a soil and land utilization distribution map;
(2) in modeling, firstly, generating a water system based on a watershed DEM, and then dividing hydrological response units based on double coding results of soil and land utilization distribution maps; then converting the meteorological element sequence into data meeting the format requirement of the SWAT model and inputting the data into the meteorological element sequence; finally, parameters of the model are calibrated through parameter sensitivity analysis, and therefore the simulated runoff process is obtained.
The mathematical description of the non-uniform extreme flood sequence mainly means that the parameters of the distribution function of the non-uniform extreme flood sequence are not constants any more, but change along with covariates, and the covariates mainly comprise time factors, climate change factors and reservoir scheduling factors. And determining the distribution line type of the non-consistency extreme value flood sequence on the basis of comprehensively considering the factors.
The non-consistency extreme value flood sequence distribution line type is determined mainly by constructing a generalized additive parameter time-varying statistical model (GALSS), and the method comprises the following steps:
(1) selecting covariate index
Respectively taking time indexes, climate indexes including rainfall, air temperature and reservoir scheduling indexes as covariates, wherein the reservoir scheduling indexes are defined as follows:
wherein A isiFor controlling the basin area of reservoirs, ATFor controlling the basin area of a hydrological station, SiFor flood-control storage capacity of reservoirs, STThe annual runoff of the hydrological station. For the future period, the annual runoff of the hydrological station is required to be obtained by simulating runoff in the future period, other variables are consistent with the historical period, and the index can reflect the influence of reservoir flood control scheduling on runoff in a drainage basin.
(2) Selecting an extremum distribution model
GEV, GLO, Gumbel, Weibull, Log-Normal, Gamma, etc. in the extreme value distribution model are selected as the distribution function.
(3) Determining parameter-as-covariate variation combination schemes
When constructing GALSS, besides fixing the shape parameter xi as a constant, setting the following schemes according to the difference of covariates for the position mu and the scale sigma parameters, only considering that the parameters are linearly changed along with the covariates, and regarding the time and reservoir scheduling covariates: the position and scale parameters are constants; only the position parameters change with covariates; only the scale parameter changes with covariate; and fourthly, the position and the scale parameters are changed along with the covariates. For the climate covariate, because two variables of rainfall and air temperature are involved, the scheme is as follows: the position parameter is constant, and the scale parameter is divided into four forms of constant, change with rainfall, change with air temperature and change with rainfall and air temperature; secondly, the position parameters only change along with rainfall, and the change of the scale parameters is the same as the above; the position parameter only changes with the temperature, and the change of the scale parameter is the same as the above; the position parameters change along with the rainfall and temperature, and the scale parameters change as above.
(4) Extremum model and corresponding covariate combination preferences
And selecting an optimal extreme value model and a covariate combination by comparing AIC (Akaike Information criterion) values and SBC (SBC Bayes criterion) values of all extreme value models and corresponding covariate combinations by adopting an AIC (Akaike Information criterion) criterion preferred model, wherein the smaller the AIC value is, the better the AIC value is, and if the AIC value is close to the SBC value, the combination with the minimum SBC value is selected.
The construction of the non-uniform Copula function to describe the correlation of the flood peak flooding amount comprises the following steps:
(1) and establishing a connection function to represent the relationship between the non-uniformity distribution parameter and the interpretation variable vector.
Wherein, gc(. h) is a linkage function of Copula, depending on Copula parameters, ifNamely the function of Frank copula,orNamely GH and Clayton copula functions,wherein, beta0，β1，...，βmIs a parameter of GALSS.
(2) Non-uniform Copula functions are preferred.
First, Kendall rank correlation coefficient and Spearman rank correlation coefficient are selected to measure bivariate dependency. Then, the degree of fitting was evaluated by K-S (Kolmogorov-Smirnov) test. And finally, selecting a Copula function fitting the optimal non-uniformity by adopting AIC and SBC criteria for the tested non-uniformity Copula function.
(3) The largest possible peak combination is obtained.
Selecting flood peak and flood volume to exceed a certain specific value:
wherein μ is the average interval time of two consecutive events, the peak amount joint recurrence period "OR JRP" is calculated by the above formula, and the Most Likely Combination value (MLC) of the peak amounts is selected by the maximum joint probability density method:
in the formula ft(Q, W) is a non-uniform joint probability density function of the flood peak Q and the flood volume W, fQt(q),fWt(w)，Non-uniform edge probability density function and edge distribution function for Q and W respectively,is a density function of the non-uniform Copula function.
Based on the above available history and future time periods, the designated JRP lower peak value MLC combination is
The non-uniformity Bootstrap analysis method based uncertainty interval of the combined design value of the quantization history and the peak value in the future period comprises the following specific steps:
(1) obtaining a peak volume joint design value of a non-uniform flood sequence under a designated JRP based on the research
(2) Time-varying parameters in obtaining non-uniform joint distribution functionOn the basis of the above-mentioned data, calculating the standard normal residual error of distribution functionFrom residual sequence riSampling with put-back mode to obtain new residual sequence, i is 1,2, … nUsing formula in new residual sequenceCalculating a sample sequence of flood flow, and recalculating under the appointed JRP according to the mode of obtaining the peak combined design value in the step 1
(3) Is repeated toThe above steps obtain N groups of sampling design valuesThe confidence interval at a given confidence level is finally obtainedAnd the uncertainty interval is used as a non-uniformity peak combined design value.
Based on the above, by combining the non-uniformity hydrological frequency analysis technology and the uncertainty qualitative analysis technology, the confidence interval of the non-uniformity peak combined design value under a certain confidence level is obtained and is the uncertainty interval, so that a reasonable reference is provided for the adaptability of watershed water resource planning management and flood control decision.
The invention has the beneficial effects that:
climate change and human activities further change the hydrological laws of consistency, and there is a great degree of uncertainty in the study of designing floods for non-consistency, however, no systematic consideration for this has been studied. The method for analyzing the influence of the change conditions on the uncertainty of the basin non-uniformity design flood can be used for solving the uncertainty of the basin non-uniformity design flood in the future period of the system, and has great reference value and practical significance for mastering the evolution rule of the basin non-uniformity extremum flood event under the change environment and the adaptive decision of water resource planning management and flood control scheduling.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the drawings and examples, but is not limited to the following examples.
Example 1:
fig. 1 shows a flow chart of a non-uniform design flood uncertainty analysis method under varying conditions according to the present invention, comprising the steps of:
step 1, collecting and arranging basic data of a research area, wherein the basic data mainly comprises daily scale long-series rainfall runoff data and daily scale temperature, relative humidity, wind speed, sunshine time and other meteorological data; in addition, there are also spatial geographic information Data (DEM) data, a map of land utilization and a map of soil distribution type in the study area.
And 2, locally constructing a SWAT hydrological model, inputting the collected hydrological meteorological and spatial information data into the SWAT model, calibrating relevant parameters of the SWAT model on the basis of the sensitivity analysis of model parameters, and simulating the runoff process of the research area in the future period.
And 3, selecting the annual maximum peak and the corresponding maximum 7-day flood volume in the history and future periods by adopting an AM method. As an extremum flood sequence.
And 4, constructing a GALSS model and carrying out non-consistency hydrological frequency analysis on the sequence.
And 5, constructing a non-uniform Copula function to describe the correlation of the flood peak and the flood volume, and obtaining the maximum possible combination of the flood peak and the flood volume.
And 6, quantifying uncertainty intervals of the historical and future period peak combined design values based on a non-uniformity Bootstrap analysis method.
Constructing a GALSS model in the step 4 to perform non-uniform hydrologic frequency analysis on the flood extreme value sequence, and performing the following steps of:
(1) selecting covariate index
Respectively taking time indexes, climate indexes including rainfall, air temperature and reservoir scheduling indexes as covariates, wherein the reservoir scheduling indexes are defined as follows:
wherein A isiFor controlling the basin area of reservoirs, ATFor controlling the basin area of a hydrological station, SiFor flood-control storage capacity of reservoirs, STThe annual runoff of the hydrological station. For the future period, the annual runoff of the hydrological station is required to be obtained by simulating runoff in the future period, other variables are consistent with the historical period, and the index can reflect the influence of reservoir flood control scheduling on runoff in a drainage basin。
(2) Selecting an extremum distribution model
GEV, GLO, Gumbel, Weibull, Log-Normal, Gamma, etc. in the extreme value distribution model are selected as the distribution function.
(3) Determining parameter-as-covariate variation combination schemes
When constructing GALSS, besides fixing the shape parameter xi as a constant, setting the following schemes according to the difference of covariates for the position mu and the scale sigma parameters, only considering that the parameters are linearly changed along with the covariates, and regarding the time and reservoir scheduling covariates: the position and scale parameters are constants; only the position parameters change with covariates; only the scale parameter changes with covariate; and fourthly, the position and the scale parameters are changed along with the covariates. For the climate covariate, because two variables of rainfall and air temperature are involved, the scheme is as follows: the position parameter is constant, and the scale parameter is divided into four forms of constant, change with rainfall, change with air temperature and change with rainfall and air temperature; secondly, the position parameters only change along with rainfall, and the change of the scale parameters is the same as the above; the position parameter only changes with the temperature, and the change of the scale parameter is the same as the above; the position parameters change along with the rainfall and temperature, and the scale parameters change as above.
(4) Extremum model and corresponding covariate combination preferences
And selecting an optimal extreme value model and a covariate combination by comparing AIC (Akaike Information criterion) values and SBC (SBC Bayes criterion) values of all extreme value models and corresponding covariate combinations by adopting an AIC (Akaike Information criterion) criterion preferred model, wherein the smaller the AIC value is, the better the AIC value is, and if the AIC value is close to the SBC value, the combination with the minimum SBC value is selected.
Constructing a non-uniform Copula function in the step 5 to describe the correlation of the flood peak and the flood volume, obtaining the maximum possible combination of the flood peak and the flood volume, and realizing the method through the following steps:
(1) and establishing a connection function to represent the relationship between the non-uniformity distribution parameter and the interpretation variable vector.
Wherein the content of the first and second substances,gc(. h) is a linkage function of Copula, depending on Copula parameters, ifNamely the function of Frank copula,orNamely GH and Clayton copula functions,wherein, beta0，β1，...，βmIs a parameter of GALSS.
(2) Non-uniform Copula functions are preferred.
First, Kendall rank correlation coefficient and Spearman rank correlation coefficient are selected to measure bivariate dependency. Then, the degree of fitting was evaluated by K-S (Kolmogorov-Smirnov) test. And finally, selecting a Copula function fitting the optimal non-uniformity by adopting AIC and SBC criteria for the tested non-uniformity Copula function.
(3) The largest possible peak combination is obtained.
Selecting flood peak and flood volume to exceed a certain specific value:
wherein μ is the average interval time of two consecutive events, the peak amount joint recurrence period "OR JRP" is calculated by the above formula, and the Most Likely Combination value (MLC) of the peak amounts is selected by the maximum joint probability density method:
in the formula ft(Q, W) is a non-uniform joint probability density function of the flood peak Q and the flood volume W, fQt(q),fWt(w)，Non-uniform edge probability density function and edge distribution function for Q and W respectively,is a density function of the non-uniform Copula function.
Based on the above available history and future time periods, the designated JRP lower peak value MLC combination is
In the step 6, the uncertainty interval of the history and future period peak value joint design value is quantified based on the non-uniformity Bootstrap analysis method, and the method is realized by the following steps:
(1) obtaining a peak volume joint design value of a non-uniform flood sequence under a designated JRP based on the research
(2) Time-varying parameters in obtaining non-uniform joint distribution functionOn the basis of the above-mentioned data, calculating the standard normal residual error of distribution functionFrom residual sequence riSampling with put-back mode to obtain new residual sequence, i is 1,2, … nUsing formula in new residual sequenceCalculating a sample sequence of flood flow, and recalculating under the appointed JRP according to the mode of obtaining the peak combined design value in the step 1
(3) Repeating the above steps to obtain N groups of sampling design valuesThe confidence interval at a given confidence level is finally obtainedAnd the uncertainty interval is used as a non-uniformity peak combined design value.
The confidence interval of the non-uniform combined design value under the designated confidence level, namely the uncertainty interval, can be obtained through the steps.
Claims (9)
1. The uncertainty analysis method for the basin non-uniformity design flood under the changing condition is characterized by comprising the following steps: the method comprises the steps of analyzing mathematical description of a historical and future period non-uniformity extreme value flood sequence under a single factor and different factor combination scheme by using a non-uniformity hydrological frequency analysis method, researching non-uniformity edge distribution line types and corresponding covariate combinations of the historical and future period flood peaks and the flood sequence on the basis of considering peak correlation, and giving an uncertainty interval of peak joint distribution under a specified joint recurrence period.
2. The method of claim 1, wherein the method comprises the steps of: the non-consistency hydrological frequency analysis method is carried out aiming at a non-consistency extreme value flood sequence, and a maximum annual value method AM is adopted to select a flood peak and a corresponding flood sequence as the non-consistency extreme value flood sequence;
the non-consistency extreme value flood sequence of the future period is obtained by integrating climate situation sets output by various climate modes into a well-calibrated SWAT hydrological model, each climate situation set drives the SWAT model respectively to obtain the runoff process of the future period under different climate situation sets, and the extreme value flood sequence is selected by using an AM method;
the mathematical description of the historical and future period non-consistency extreme value flood sequence mainly refers to that the distribution parameters of the hydrological sequence under the changing condition are not constant any more but change along with covariates, the covariates comprise time factors, meteorological factors and reservoir scheduling factors, and the distribution line type of the non-consistency extreme value flood sequence is determined under the condition of comprehensively considering the factors.
3. The method of claim 2, wherein the method comprises the steps of: the specific steps of the local construction of the SWAT model are as follows:
(1) collecting daily rainfall, runoff data and daily scale meteorological data of a research area, wherein the daily rainfall, runoff data and daily scale meteorological data comprise air temperature, relative humidity, wind speed and sunshine hours; the geographic information data comprises DEM data and a soil and land utilization distribution map;
(2) in modeling, firstly, generating a water system based on a watershed DEM, and then dividing hydrological response units based on double coding results of soil and land utilization distribution maps; then converting the meteorological element sequence into data meeting the format requirement of the SWAT model and inputting the data into the meteorological element sequence; finally, parameters of the model are calibrated through parameter sensitivity analysis, and therefore the simulated runoff process is obtained.
4. The method of claim 1, wherein the method comprises the steps of: the determination of the distribution line type of the non-consistency extreme value flood sequence comprises the following steps: selecting a distribution function in an extreme value distribution model, constructing a generalized additive parameter time-varying statistical model GALSS, setting a scheme that shape, position and scale parameters are changed along with covariates, calculating AIC values and SBC values of different covariate combinations corresponding to the extreme value models by adopting AIC and SBC rules, finally selecting the extreme value model with the minimum AIC value and the covariate combination corresponding to the extreme value model as the best, and selecting the combination with the minimum SBC value if the AIC is closer, thereby obtaining the best extreme value model and covariate combination.
5. The method of claim 4, wherein the method comprises the steps of: the GALSS model is constructed, and the method comprises the following steps:
(1) selecting covariate index
Respectively according to the time index; weather indexes, namely indexes of rainfall and air temperature; and the reservoir dispatching index is a covariate, and the reservoir dispatching index is defined as:
wherein A isiFor controlling the basin area of reservoirs, ATFor controlling the basin area of a hydrological station, SiFor flood-control storage capacity of reservoirs, STAnnual runoff of a hydrological station; for the future period, the annual runoff of the hydrological station is obtained by simulating runoff in the future period, other variables are consistent with the historical period, and the index can reflect the influence of reservoir flood control scheduling on runoff in a drainage basin;
(2) selecting an extremum distribution model
Selecting GEV, GLO, Gumbel, Weibull, Log-Normal and Gamma in an extreme value distribution model as a distribution function;
(3) determining parameter-as-covariate variation combination schemes
When constructing GALSS, besides fixing the shape parameter xi as a constant, setting the following schemes according to the difference of covariates for the position mu and the scale sigma parameters, only considering that the parameters are linearly changed along with the covariates, and regarding the time and reservoir scheduling covariates: the position and scale parameters are constants; only the position parameters change with covariates; only the scale parameter changes with covariate; position and scale parameters are changed along with covariates;
for the climate covariate, because two variables of rainfall and air temperature are involved, the scheme is as follows: the position parameter is constant, and the scale parameter is divided into four forms of constant, change with rainfall, change with air temperature and change with rainfall and air temperature; secondly, the position parameters only change along with rainfall, and the change of the scale parameters is the same as the above; the position parameter only changes with the temperature, and the change of the scale parameter is the same as the above; the position parameters change along with the rainfall and temperature, and the scale parameters change as above;
(4) extremum model and corresponding covariate combination preferences
And selecting the optimal extreme value model and covariate combination by comparing the extreme value models with the AIC and SBC values of the corresponding covariate combination by adopting an AIC and SBC criterion optimization model, wherein the smaller the AIC value is, the better the AIC value is, and if the AIC value is close to the SBC value, the combination with the minimum SBC value is selected.
6. The method of claim 2, wherein the method comprises the steps of: the non-uniformity hydrological frequency analysis considering the peak correlation is realized by constructing a non-uniformity Copula function, and the specific steps are as follows: firstly, the optimized GAMLSS model of the annual maximum peak and the corresponding 7-day maximum flood is taken as edge distribution, then the selected covariate combination is reflected on the parameters of the Copula function, and finally the optimal non-uniform Copula function is selected by adopting a goodness-of-fit criterion.
7. The method of claim 6, wherein the method comprises the steps of: constructing a non-uniform Copula function to describe the correlation of flood peak flooding quantity, comprising the following steps:
(1) establishing a connection function to represent the relationship between the non-uniformity distribution parameter and the interpretation variable vector:
wherein, gc(. h) is a linkage function of Copula, depending on Copula parameters, ifNamely the function of Frank copula,orNamely GH and Clayton copula functions,wherein, beta0，β1，...，βmIs a parameter of GALSS;
(2) the non-uniform Copula function is preferred:
firstly, measuring the dependency of bivariables by selecting Kendall rank correlation coefficients and Spearman rank correlation coefficients; then, the fitting degree is evaluated by K-S test; finally, selecting a Copula function which is fitted with the optimal non-uniformity by adopting AIC and SBC criteria for the tested non-uniformity Copula function;
(3) obtaining the maximum possible peak combination:
selecting flood peak and flood volume to exceed a certain specific value:
wherein mu is the average interval time of two continuous events, the peak quantity joint recurrence period 'OR JRP' is calculated by the formula, and the most possible combination value MLC of the peak quantity is selected by adopting a maximum joint probability density method:
in the formula ft(Q, W) is a non-uniform joint probability density function of the flood peak Q and the flood volume W, fQt(q),fWt(w)，Non-uniform edge probability density function and edge distribution function for Q and W respectively,a density function which is a non-uniform Copula function;
based on the obtained MLC combination of JRP lower peak value in the appointed joint reappearing period of the history and the future period, the method comprises the steps of
8. The method of claim 1, wherein the method comprises the steps of: the uncertainty interval of peak amount joint distribution is mainly realized based on a non-uniform Bootstrapping method, and the method comprises the following specific steps: firstly, obtaining a peak amount combined design value under a specified combined reappearance period based on the parts, then calculating a standard normal residual error of a distribution function on the basis of obtaining a non-consistency time-varying parameter, obtaining a new residual error sequence from the residual error sequence through a sample with a return, recalculating the peak amount combined design value under the specified reappearance period, and finally repeating the steps for N times to finally obtain a confidence interval under a given confidence level as an uncertainty interval of the non-consistency peak amount combined design value.
9. The method of claim 8, wherein the method comprises the steps of: based on the uncertainty interval of the non-uniform Bootstrapping analysis method quantitative history and future period peak value combined design value, the method comprises the following specific steps:
(1) obtaining a peak value joint design value of a non-uniform extreme value flood sequence under the appointed JRP based on the research
(2) Time-varying parameters in obtaining non-uniform joint distribution functionOn the basis of the above-mentioned data, calculating the standard normal residual error of distribution functionFrom residual sequence riSampling with put-back mode to obtain new residual sequence, i is 1,2, … nUsing formula in new residual sequenceCalculating a sample sequence of flood flow, and recalculating the designated JRP according to the mode of obtaining the peak combined design value in the step (1)
(3) Repeating the above steps to obtain N groups of sampling design valuesThe confidence interval at a given confidence level is finally obtainedAnd the uncertainty interval is used as a non-uniformity peak combined design value.
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