CN116756481B - Watershed future hydrologic drought probability analysis method based on time-varying gain model and Copula - Google Patents
Watershed future hydrologic drought probability analysis method based on time-varying gain model and Copula Download PDFInfo
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Abstract
The invention discloses a watershed future hydrological drought probability analysis method based on a time-varying gain model and Copula, which comprises the following steps: collecting hydrological weather, geography, reservoir characteristics and monthly water supply data in a research area; correcting and downscaling the future climate mode data by using a BCSD method; constructing a distributed time-varying gain model DTVGM-SOP of a standard operation strategy module of the coupling reservoir, and carrying out parameter calibration on the DTVGM-SOP by using an SCE-UA method; predicting the key section runoff process under different climate change scenes in the future based on DTVGM-SOP; calculating a history and a future period SRI based on the hydrological station runoff data and the future period runoff data predicted by the model; based on the AIC, the Copula function which is preferably suitable for the flow field different-trunk-branch hydrodrought joint probability analysis is calculated, and the flow field different-trunk-branch hydrodrought joint probability in the history and future period is calculated. The method can analyze the future hydrologic drought joint probability distribution characteristics of different main and branch flows in the river basin, and provides scientific basis for drought prevention, drought resistance and disaster reduction in the river basin and scientific implementation of drought early warning.
Description
Technical Field
The invention relates to the technical field of hydrological drought probability analysis, in particular to a basin future hydrological drought probability analysis method based on a time-varying gain model and Copula.
Background
Drought is one of the most frequent natural disasters in the world and causes the most serious loss, and has the characteristics of high frequency, wide range, large harm and the like. Drought can be divided into four types of weather drought, agricultural drought, hydrologic drought and socioeconomic drought according to different research objects and application fields. Hydrologic drought is usually represented by a long-term shortage of water, can cause serious harm to local grain safety and people's production and life, and has become an important factor for restricting the sustainable development of society. Under the background of global climate change and high-speed development of socioeconomic, accurate assessment of the future hydrologic drought change of the river basin can provide important scientific basis for drought prevention and disaster reduction of the river basin and scientific implementation of drought early warning.
The basis for accurately evaluating the future hydrological drought change of the river basin is to accurately predict the future runoff process. At present, a hydrologic model, a machine learning method and the like are mainly adopted to predict a future runoff process, such as a SWAT model, a VIC model, a DTVGM model, an LSTM model and the like. But is affected by climate change and human activity, especially the construction and operation of a large number of water-benefit projects, and the production and confluence process, river and lake relationship and the like are obviously changed. The traditional hydrologic model and other methods do not consider the influence of the factors, and it is difficult to accurately predict the future runoff process. Therefore, a hydrologic model considering the influence of hydraulic engineering needs to be constructed to accurately predict the future runoff process, and an important basis is provided for evaluating the future hydrologic drought change condition of the river basin. In addition, current hydrologic drought research focuses on descriptions of hydrologic drought characteristics (such as drought intensity, drought duration, etc.) of the river basin and contributions of climate change and human activity to hydrologic drought change, mainly, the situation of a certain area or a certain site in the river basin is studied, and dry branches, rivers and lakes and sub-river basins in the river basin are not considered as a system. In recent years, the regional, watershed and even nationwide hydrologic drought events frequently occur show that the dry tributary hydrologic drought in the watershed, the river and lake hydrologic drought and the like have obvious relevance, and the dry tributary in the watershed, the river and lake, the sub-watershed thereof and the like are required to be used as a system for analyzing the joint distribution characteristics of the future hydrologic drought in the watershed in the future, so that the internal connection and interaction of the hydrologic drought in different areas in the system are clear, and the method has important significance for further understanding the occurrence mechanism of the hydrologic drought in a large range.
Disclosure of Invention
The invention aims to provide a watershed future hydrologic drought probability analysis method based on a time-varying gain model and Copula, which is used for accurately predicting a future runoff process by constructing a distributed hydrologic model considering the influence of hydraulic engineering, identifying hydrologic drought by applying a standardized runoff index, calculating the probability of the watershed future hydrologic drought under a climate change background by combining with a Copula function, and has important significance for further understanding the occurrence mechanism of the large-scale hydrologic drought, and providing important scientific basis for drought prevention and disaster reduction of the watershed and drought early warning of scientific implementation.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a basin future hydrologic drought probability analysis method based on a time-varying gain model and Copula comprises the following steps:
step 1) collecting and downloading hydrological data, geographical information data, reservoir characteristic reservoir capacity data and monthly water supply data in a research area, wherein the hydrological data comprise measured runoff data and future climate pattern data.
And 2) performing deviation correction and downscaling on the future climate pattern data by adopting a deviation correction downscaling (BCSD) method.
Step 3) constructing a distributed time-varying gain model (DTVGM-SOP) coupled with a reservoir standard operation strategy module; carrying out parameter calibration on the DTVGM-SOP model by using a shuffling complex evolution algorithm (SCE-UA) method;
the coupling thought is as follows: and calculating the runoff in storage by the DTVGM, regulating and accumulating the runoff in storage by a reservoir of the SOP module to obtain the flow out of the storage, and calculating and converging the runoff to an outlet of a research area by a model to obtain the flow process at the outlet section. The input of the coupling model comprises meteorological data (precipitation, temperature, relative humidity, sunshine hours and wind speed), reservoir characteristic reservoir capacity data and month water supply data; the output is the flow of the outlet section of the drainage basin; the model-rated parameters are the confluence parameters of the DTVGM.
Step 4) predicting the important section runoff process of the research area under different climate change scenes in the future by using a DTVGM-SOP model: inputting the CMIP6 data subjected to the deviation correction in the step 2) into a DTVGM-SOP model calibrated in the step 3), and calculating the key section runoff process of a research area under the future climate change scene to obtain future period runoff data;
step 5) calculating research area history and future period Standardized Runoff Index (SRI) based on the measured runoff data of the research area hydrologic station obtained in the step 1) and the future period runoff data obtained in the step 4) model prediction;
step 6) calculating the hydrographic drought joint probabilities of different main and branch water in the historical and future period by adopting a Copula function which is preferably suitable for hydrographic drought joint probability research of different main and branch water in the river basin by adopting a red pool information criterion (AIC): providing a plurality of dry tributary systems; adopting AIC to select the optimal edge distribution of each trunk and branch flow system site SRI; determining optimal bivariate Copula functions of each main and branch flow system by adopting AIC; according to the designed main and branch flow system, constructing a combined probability distribution function of different main and branch hydrographic droughts in the historical and future period watershed; and setting a threshold value of SRI as a discrimination standard, and calculating the joint probability of hydrologic drought of each trunk and branch flow system.
Further, the hydrometeorological data described in step 1) includes daily runoff, precipitation, highest air temperature, lowest air temperature, average air temperature, relative humidity, number of sunshine hours, wind speed data, and future climate pattern data, wherein the future climate pattern data includes ACCESS-CM2, BCC-CSM2-MR, canESM5, CMCC-ESM2, FGOALS-g3, and MIROC6 pattern data in CMIP 6; the geographic data includes digital elevation model Data (DEM), soil and land use data; the reservoir characteristic reservoir capacity data comprises total reservoir capacity, flood regulating reservoir capacity, xingli reservoir capacity and dead reservoir capacity; the monthly water supply data is the monthly water supply data of each city in the study area.
Further, the BCSD method in step 2): performing deviation correction on each climate variable of the climate mode by using the actual measurement data and the climate mode history scene data; and (5) reducing the scale of each climate variable by adopting a bilinear interpolation method. The method specifically comprises the following steps: 2-1, respectively constructing a cumulative distribution function of a certain climate variable under actual measurement data and climate mode history situations; 2-2, searching the probability quantile corresponding to the variable value according to the cumulative distribution function of the variable climate mode history scene, and returning the observed value of the same probability quantile of the actually measured data as a correction value to the climate mode data; 2-3, extracting actual measurement data with low resolution and a certain climate variable value under the history scene of the corrected climate mode from grid to grid; 2-4, calculating a scale factor grid by grid: the temperature is the difference between the low-resolution measured data and the corrected climate mode history, and the rest variables are the quotient of the low-resolution measured data and the corrected climate mode history; 2-5, interpolating the scale factors to a high-resolution grid by adopting a bilinear interpolation method; 2-6, calculating the climate mode variable values with high resolution grid by grid.
Further, the calculation formula of the scale factor in step 2) 2-4 is as follows:
wherein CF is a scale factor, S obs S is a certain variable value in the actual measurement data mod In order to correct a certain variable value in the history scenario of the post-climate mode, the variable is other climate variables except temperature, and if the variable is temperature, the following steps are: cf=s obs -S mod 。
Further, the calculation formula of the high-resolution climate mode variable values in 2-6 in the step 2) is as follows:
G BCSD ＝CF×G mod (2)
wherein G is BCSD For the corrected climate mode variable value at high resolution, CF is the above-mentioned interpolated scale factor, G mod The original climate mode variable value under high resolution is that the variables are other climate variables except the temperature, and if the variables are the temperature, the variables are as follows: g BCSD ＝CF+G mod 。
Further, the operation scheduling rule of the SOP module in the DTVGM-SOP model in step 3) 3-1 includes the following six parts:
A. principle of water balance:
S(t+1)＝S(t)+Q in ×Δt-Q out ×Δt (3)
B. water storage capacity limitation:
S d ≤S(t)≤S m (4)
C. outflow limit:
Q e ≤Q out ≤Q s (5)
D. flood control limit in flood season: the minimized reservoir capacity is larger than the flood regulating reservoir capacity (S (t) is more than or equal to S) c ) Days of (2);
E. water demand guarantee limit: maximizing outflow greater than water demand (Q) out ≥Q d ) Days of (2);
F. water storage requirements in non-flood period: the minimized reservoir capacity is larger than the Khingan reservoir capacity (S (t) is more than or equal to S) n ) Days of (2);
in which Q in The flow is obtained by simulating DTVGM; q (Q) out Is the delivery flow; s (t) is the water storage capacity of the reservoir at the moment t; q (Q) e Is ecological flow; q (Q) s Is downstream flood control flow restriction; Δt is the time step; q (Q) d Is the water demand, calculated according to the water supply of city month; s is S m Is the total stock capacity; s is S n Is a good stock volume; s is S c Is a flood regulating reservoir capacity; s is S d Is dead stock capacity.
Further, the step 3) specifically includes the following steps:
3-1: constructing a distributed time-varying gain model (DTVGM-SOP) coupled with a reservoir standard operation strategy module;
3-2: adopting a shuffling complex evolution algorithm (SCE-UA) to rate a DTVGM-SOP model according to historical observation data;
the criterion of the DTVGM-SOP model calibration is that the Nash-Sutcliffe Efficiency (NSE) coefficient is the largest, wherein the NSE coefficient has a calculation formula as follows:
in the method, in the process of the invention,is an analog runoff sequence Q sim I-th runoff value of (a)>Is the sequence Q of the observed runoff obs I-th runoff value of (a)>Is the average value of the observed runoff, and T is the total length of the runoff sequence.
Further, the step 5) includes the following steps:
5-1: calculating a historical period Standardized Runoff Index (SRI) based on the measured runoff data of the hydrologic station in the research area collected in the step 1), wherein gamma distribution is adopted to fit the runoff data, and a maximum likelihood method is adopted to carry out parameter estimation;
5-2: calculating a future period normalized runoff index (SRI) based on the future period runoff data obtained by the model prediction in step 4), wherein the parameters used for calculation are parameters obtained by calculating the historical period normalized runoff index in step 5-1;
further, the calculation formula of the normalized runoff index in step 5) is:
wherein:
H(x)＝q+(1-q)G(x)(8)
where x is the average runoff amount per month (which may be set as desired to be 1,3,6, 12 months of average runoff amount), a and b are the shape and scale parameters, respectively, q is the probability of x=0,is a standard normal distribution. The parameters a and b are estimated by a maximum likelihood method, and the calculation formula is as follows:
wherein L (θ) is a runoff sequence x i (i=1, 2, …, n) brings in the likelihood function obtained for the selected distribution pattern g (x; θ), θ being the set of parameters to be solved, i.e. a and b, g (x) being the probability density function of the gamma distribution.
Further, step 6) comprises the steps of: 6-1: setting a plurality of dry branch flow systems, including a plurality of dry branch flow systems, such as downstream-midstream, midstream-upstream, downstream-upstream, upstream and branch flows thereof, midstream and branch flows thereof, downstream and branch flows thereof, and the like; 6-2: selecting optimal edge distribution of SRI of each site, wherein the candidate distribution comprises P-III type distribution, gumbel distribution, LN3 distribution and GEV distribution, and determining the optimal edge distribution of the SRI by adopting AIC; 6-3: selecting an optimal bivariate Copula function, wherein the Copula function to be selected comprises a Gaussian Copula, a Student t Copula (t-Copula), a Clayton Copula, a Frank Copula, a BB 1Copula, a BB8 Copula, a Tawn type 1Copula and a Tawn type 2Copula, and determining the optimal bivariate Copula function by adopting AIC; 6-4: according to the designed main and branch flow system, constructing a combined probability distribution function of different main and branch hydrographic droughts in the historical and future period watershed; 6-5: and setting a threshold value of SRI as a discrimination standard, and calculating the joint probability of hydrologic drought of each trunk and branch flow system.
Further, the formulas of the P-III distribution, gumbel distribution, LN3 distribution and GEV distribution of the candidate distribution in the step 6) 6-2 are respectively as follows:
P-III type distribution:
wherein alpha, beta and a 0 Respectively a shape parameter, a scale parameter and a position parameter, and gamma (alpha) is a gamma function;
gumbel distribution:
wherein, alpha and u are scale parameters and position parameters respectively;
LN3 distribution:
wherein a is y 、σ y And b are logarithmic mean, logarithmic standard deviation, and positional parameters, respectively;
GEV distribution:
where μ, σ, and ζ are the position parameter, the scale parameter, and the shape parameter, respectively.
Further, the formula of the discriminant criterion AIC described in step 6-2) is:
in the method, in the process of the invention,is the maximum likelihood estimate of the parameter β, p is the number of parameters.
Further, the hydrologic drought in the step 6) is divided into five types of non-drought SRI > -0.5, light drought-0.5 not less than SRI > -1.0, medium drought-1.0 not less than SRI > -1.5, heavy drought-1.5 not less than SRI > -2.0 and extreme drought-2.0 not less than SRI according to the SRI value. The invention focuses on the drought in the middle and above degree of hydrologic drought, the set criterion is SRI less than or equal to-1.0, and the joint probability of the main and branch hydrologic drought in the river basin system is the probability when the SRI of two stations in the main and branch flow system is less than-1.0.
The invention has the beneficial effects that:
according to the invention, a distributed time-varying gain model DTVGM-SOP of the coupling reservoir standard operation strategy module is constructed, and the future runoff process of the key section of the river basin is estimated based on the future climate mode data. On the basis, a Copula function is utilized to construct a historical and future period drainage basin different main and branch hydrologic drought joint probability distribution function, and the joint probability of different main and branch hydrologic drought in a drainage basin system is calculated. The method can analyze the future hydrologic drought joint probability distribution characteristics of different main and branch flows in the river basin, reveal the internal association and interaction mechanisms of the different main and branch hydrologic drought in the river basin system, help to reveal the occurrence mechanism of the large-scale drought, and can provide important scientific basis for drought prevention and disaster reduction in the river basin and scientific implementation of drought early warning.
The invention will be described in further detail with reference to the drawings and the detailed description.
Drawings
FIG. 1 is a flow chart of a method for analyzing the probability of future hydrologic drought in a river basin based on a time-varying gain model and Copula;
FIG. 2 is a graph showing the cumulative distribution function of measured data and precipitation for a climate pattern history scenario in example 1;
FIG. 3 is a schematic diagram of the downscaling process of climate pattern data in example 1; wherein: CF is a scale factor, G BCSD G is the corrected climate mode variable value at high resolution mod The original climate mode variable value is used under high resolution, N is the total grid number under low resolution, and M is the total grid number under high resolution;
FIG. 4 is a graph showing the joint probability of hydrologic drought in the dry tributary system of example 1.
Detailed Description
Example 1
The invention provides a time-varying gain model and Copula-based watershed future hydrological drought probability analysis method, which takes a certain watershed in China as a case area to further explain the specific application of the technical scheme of the invention, wherein the specific application comprises the following steps:
step 1) collecting and downloading hydrological data, geographical information data, reservoir characteristic reservoir capacity data and monthly water supply data in a research area: the daily runoff data of hydrologic stations in the research area are derived from the hydrologic annual book of the people's republic of China; weather data such as precipitation, highest air temperature, lowest air temperature, average air temperature, relative humidity, sunshine hours, wind speed and the like come from the national weather science data center (http:// data. Cma. Cn); soil type and land utilization data for DEM at 90m resolution and 1km resolution were obtained from the national academy of sciences resource science and data center (https:// www.resdc.cn /); the information of total reservoir capacity, flood regulating reservoir capacity, xingli reservoir capacity, dead reservoir capacity and the like is obtained from various reservoir authorities and the existing researches; the urban monthly water supply data comes from the national urban construction statistics annual book; CMIP6 data, such as ACCESS-CM2, BCC-CSM2-MR, canESM5, CMCC-ESM2, FGOALS-g3, and MIROC6 modes, were obtained from the world climate research program website (https:// kgf-node. Llnl. Gov/projects/CMIP6 /).
And 2) performing deviation correction and downscaling on the CMIP6 data by adopting a deviation correction downscaling method, taking precipitation data of the river basin as an example, downscaling the CMIP6 data with 1.88 degrees multiplied by 1.25 degrees to 0.5 degrees multiplied by 0.5 degrees, wherein the specific algorithm is as follows:
2-1, taking a certain grid point under the river basin as an example, respectively constructing actual measurement data and a cumulative distribution function (figure 2) of the precipitation amount under the climate mode history scene;
2-2, searching probability quantiles corresponding to the precipitation according to a cumulative distribution function of the historical scene of the precipitation climate mode, for example, 0.2, 0.4, 0.6, 0.8 and the like, and returning the observed values of the same probability quantiles of the measured data as correction values to the climate mode data;
2-3, extracting actual measurement data and precipitation under the history scene of the corrected climate mode from grids (1.88 degrees multiplied by 1.25 degrees resolution);
2-4, calculating a scale factor from grid to grid (1.88 degrees multiplied by 1.25 degrees resolution), wherein the scale factor is the precipitation amount of the actual measurement data divided by the precipitation amount of the corrected climate mode history (the temperature is the difference between the actual measurement data and the corrected climate mode history, and the rest variables are quotient), and the calculation formula refers to formula (1);
2-5, interpolating the scale factors to a high resolution grid (0.5 degree by 0.5 degree resolution) by bilinear interpolation;
2-6, calculating climate mode variable values (figure 3) grid by grid (0.5 degree x 0.5 degree resolution), and calculating formula reference formula (2).
And 3) constructing a distributed time-varying gain model (DTVGM-SOP) of the coupling reservoir standard operation strategy module, and calibrating parameters of the DTVGM-SOP model by using a shuffling complex evolution algorithm (SCE-UA) method. The operation scheduling rule of the SOP module in the DTVGM-SOP model comprises the six parts, which relate to formulas (3) to (5); the specific algorithm is as follows:
3-1: constructing a distributed time-varying gain model (DTVGM-SOP) coupled with a reservoir standard operation strategy module;
3-2: and (3) adopting a shuffling complex evolution algorithm (SCE-UA), calibrating a DTVGM-SOP model according to the measured runoff data, and enabling an objective function to be the maximum Nash efficiency coefficient (NSE), as shown in a formula (6). Model rating results for 7 hydrologic sites within the basin are shown in table 1:
TABLE 1DTVGM-SOP model calibration results
Step 4) measuring the important section runoff process of the research area under different climate change scenes in the future by using a DTVGM-SOP model: inputting the CMIP6 data subjected to the deviation correction in the step 2) into the DTVGM-SOP model calibrated in the step 3), and calculating the basin runoff process under the future climate change scene.
Step 5) calculating a research area history and a future period Standardized Runoff Index (SRI) based on the measured runoff data of the research area hydrologic station and future period runoff data obtained by model prediction, wherein the related formulas are (7) to (11), and the method specifically comprises the following steps:
5-1: and 3) calculating a normalized runoff index (SRI 3) of a 3 month scale in a historical period based on the measured runoff data of the hydrologic station in the research area collected in the step 1), wherein gamma distribution is adopted to fit the runoff data, and a maximum likelihood method is adopted to carry out parameter estimation.
5-2: calculating a normalized runoff index (SRI 3) of 3 months of the future period based on the future period runoff data obtained by predicting the model in step 4), wherein the parameter used for calculation is the parameter obtained by calculating the historical period normalized runoff index in step 5-1.
Step 6) calculating the hydrographic drought joint probabilities of different main and branch water in the historical and future period by adopting a Copula function which is preferably suitable for hydrographic drought joint probability research of different main and branch water in the river basin by adopting a red pool information criterion (AIC):
6-1: a plurality of dry branch flow systems are arranged, including downstream-midstream, midstream-upstream, downstream-upstream, upstream and branch flow thereof, midstream and branch flow thereof, downstream and branch flow thereof and the like: 7 main and branch flow systems were set up according to the hydraulic link as shown in table 2:
table 2 Main and branch flow system
6-2: selecting optimal edge distribution of SRI of each site, wherein the candidate distribution comprises P-III type distribution, gumbel distribution, LN3 distribution and GEV distribution, such as formulas (12) - (15), determining the optimal edge distribution of the SRI by adopting AIC, and formula (16), wherein the P-III type distribution calls a PPEArsonIII function in an R language PearsonDS package, the Gumbel distribution calls a pgumbel function in an R language ismev package, the LN3 distribution writes corresponding R language code according to the formula (14) to realize, the GEV distribution calls a pgev function in an R language VGAM package, AIC calculation calls an AIC function in an R language Envcpt package, and the optimal edge distribution of each site is shown in Table 3:
TABLE 3 optimal edge distribution for SRI for each site
6-3: selecting an optimal bivariate Copula function, wherein the Copula function to be selected comprises a Gaussian Copula, a Student t Copula (t-Copula), a Clayton Copula, a Frank Copula, a BB 1Copula, a BB8 Copula, a Tawn type 1Copula and a Tawn type 2Copula, determining the optimal bivariate Copula function by adopting AIC, wherein the BiCopSelect function in an R language Vinecopula package is called to determine the optimal bivariate Copula function, and the optimal bivariate Copula function of each main and branch flow system is shown in Table 4:
TABLE 4 optimal bivariate Copula function for each main and sub flow system
6-4: according to the set main and branch flow system, constructing a combined probability distribution function of different main and branch hydrographic droughts in the historical and future period drainage basin: calling Gausian Copula, student t Copula (t-Copula), clayton Copula, frank Copula, BB 1Copula, BB8 Copula, tawn type 1Copula and Tawn type 2Copula functions in the R language Copula package, the VC2Copula package and the coppic package, and constructing a combined probability distribution function of different trunk and branch hydrologic drought of a historical and future period drainage basin by combining the trunk and branch flow system, the optimal bivariate Copula functions and the optimal edge distribution of each site SRI set by the steps.
6-5: the joint probability of the hydrodrought of each main and branch flow system is calculated, the embodiment focuses on the hydrodrought of the medium drought and above degree, the set discrimination standard is SRI less than or equal to-1.0, the joint probability of the hydrodrought of the main and branch flow system is the probability when the SRI of two stations in the main and branch flow system is less than-1.0 (figure 4), and the joint probability of the hydrodrought of each main and branch flow system is shown in the table 5:
TABLE 5 joint probability of hydrographic drought for main and sub-stream systems
The calculation result of the hydrologic drought joint distribution probability shows that the joint probability of the hydrologic drought of each trunk and branch flow system in the historical period is 4.00% -11.95%, the joint probability of the hydrologic drought of each trunk and branch flow system in the future is 1.60% -8.55%, and the change rate of the hydrologic drought of each trunk and branch flow system in the future is-69.06% -15.95% compared with the change rate of the hydrologic drought of each trunk and branch flow system in the historical period. Under the SSP1-2.6 scene, the joint probability of the hydrologic drought of each trunk and branch flow system is 2.40-8.55%, and compared with the historical period, the joint probability is reduced by 15.95-65.95%; under the SSP2-4.5 scene, the joint probability of hydrologic drought of each trunk and branch flow system is 2.30-6.52%, and compared with the historical period, the joint probability is reduced by 28.41-62.14%; under the SSP5-8.5 scene, the joint probability of the hydrologic drought of each trunk and branch flow system is 1.60% -7.25%, and compared with the historical period, the joint probability is reduced by 22.61% -69.06%.
The above description is only exemplary of the present invention, and is not intended to limit the present invention, and the selection of the frequency distribution function, copula function, and future climate pattern in the present invention may be set according to the needs and the specific study area. Any modification, equivalent replacement, improvement, etc. made within the scope of the claims of the present invention should fall within the protection scope of the present invention.
Claims (4)
1. A watershed future hydrological drought probability analysis method based on a time-varying gain model and Copula is characterized by comprising the following steps of: the method comprises the following steps:
step 1) collecting hydrological weather data, geographical information data, reservoir characteristic reservoir capacity data and monthly water supply data in a research area; the hydrographic meteorological data comprise measured runoff data and future climate pattern data;
step 2) adopting a deviation correction downscaling BCSD method to carry out deviation correction and downscaling on future climate mode data; the BCSD method: performing deviation correction on each climate variable of the climate mode by using the actual measurement data and the climate mode history scene data; the scale of each climate variable is reduced by adopting a bilinear interpolation method;
step 3) constructing a distributed time-varying gain model DTVGM-SOP of the coupling reservoir standard operation strategy module, and carrying out parameter calibration on the DTVGM-SOP model by using a shuffling complex evolution algorithm SCE-UA method;
the DTVGM-SOP model is coupled with the SOP module, the DTVGM is used for simulating to obtain the warehouse-in flow, the SOP module is used for calculating to obtain the reservoir warehouse-out flow, and the warehouse-out flow is calculated to the drainage basin outlet through the confluence, wherein the operation scheduling rule of the SOP module comprises the following six parts:
A. balance of water:
S(t+1)＝S(t)+Q in ×Δt-Q out ×Δt (3)
B. water storage capacity limitation:
S d ≤S(t)≤S m (4)
C. outflow limit:
Q e ≤Q out ≤Q s (5)
D. flood control limit in flood season: minimizing reservoir capacity greater than flood regulating reservoir capacity S c Days of (2);
E. water demand guarantee limit: maximizing outflow greater than water demand Q d Days of (2);
F. water storage requirements in non-flood period: minimizing reservoir capacity greater than Kghui capacity S n Days of (2);
in which Q in The flow is obtained by simulating DTVGM; q (Q) out Is the delivery flow; s (t) is the water storage capacity of the reservoir at the moment t; q (Q) e Is ecological flow; q (Q) s Is downstream flood control flow restriction; Δt is the time step; q (Q) d The water demand is calculated by adopting month water supply data; s is S m Is the total stock capacity; s is S n Is a good stock volume; s is S c Is a flood regulating reservoir capacity; s is S d Is dead stock capacity;
constructing a DTVGM-SOP model, inputting the DTVGM-SOP model into hydrological data, reservoir characteristic reservoir capacity data and monthly water supply data, and outputting the DTVGM-SOP model into the outlet section flow of a river basin; the model is rated by a discrimination standard with the maximum NSE coefficient, wherein the calculation formula of the NSE coefficient is as follows:
in the method, in the process of the invention,is an analog runoff sequence Q sim I-th runoff value of (a)>Is the sequence Q of the observed runoff obs I-th runoff value of (a)>Is the average value of the observed runoff, T is the total length of the runoff sequence;
step 4) predicting the important section runoff process of the research area under different climate change scenes in the future by using a DTVGM-SOP model: inputting the future climate pattern data subjected to deviation correction and downscaling in the step 2) into a DTVGM-SOP model calibrated in the step 3), calculating the key section runoff process of a research area under the future climate change scene, and obtaining future period runoff data;
step 5) calculating a research area history and a future period standardized runoff index SRI based on the research area hydrological station measured runoff data obtained in the step 1) and the future period runoff data obtained by model prediction in the step 4);
the method comprises the following steps:
5-1: calculating a historical period standardized runoff index SRI based on the measured runoff data of the hydrologic station in the research area collected in the step 1), wherein gamma distribution is adopted to fit the runoff data, and a maximum likelihood method is adopted to carry out parameter estimation;
5-2: calculating a future period standardized runoff index SRI based on the future period runoff data obtained by predicting the model in the step 4), wherein the parameters used for calculation are parameters obtained by calculating the historical period standardized runoff index in the step 5-1;
when calculating the standardized runoff index, adopting gamma distribution to fit the runoff data, wherein the calculation formulas of the probability density function and the cumulative distribution function are as follows:
wherein x is the average runoff of the month, a and b are the shape parameter and the scale parameter respectively, the maximum likelihood method is adopted for estimation, and the calculation formula of the maximum likelihood function is as follows:
wherein L (θ) is a runoff sequence x i I=1, 2, …, n, bringing into the likelihood function obtained by the selected distribution pattern g (x; θ), θ being the set of parameters to be solved, i.e. a and b; when calculating the standardized runoff index of the historical period, the runoff sequence x is used i Actually measuring runoff data for the hydrologic station in the research area; calculating the normalized runoff index for future time period, the runoff sequence x is used i Predicting runoff data of the future period for the model, wherein the parameters are obtained by calculating a standardized runoff index of the historical period;
step 6) adopting a red pool information criterion AIC to select Copula functions suitable for the hydrographic drought joint probability research of different main and branch in the river basin, and calculating the hydrographic drought joint probabilities of different main and branch in the river basin in the history and future period: providing a plurality of dry tributary systems; adopting AIC to select the optimal edge distribution of each trunk and branch flow system site SRI; determining optimal bivariate Copula functions of each main and branch flow system by adopting AIC; according to the designed main and branch flow system, constructing a combined probability distribution function of different main and branch hydrographic droughts in the historical and future period watershed; setting a threshold value of SRI as a discrimination standard, and calculating the joint probability of hydrologic drought of each trunk and branch flow system;
the discriminant criterion of the optimal bivariate Copula function is AIC minimum, wherein the calculation formula of AIC is:
in the method, in the process of the invention,is the maximum likelihood estimate of the parameter β, p is the number of parameters.
2. The method for analyzing the watershed future hydrologic drought probability based on the time-varying gain model and Copula according to claim 1, wherein the method comprises the following steps of: the hydrometeorological data in the step 1) comprise daily runoff, precipitation, highest air temperature, lowest air temperature, average air temperature, relative humidity, sunshine hours, wind speed data and future climate pattern data, wherein the future climate pattern data adopts ACCESS-CM2, BCC-CSM2-MR, canESM5, CMCC-ESM2, FGOALS-g3 and MIROC6 pattern data in CMIP 6; the geographic information data comprise digital elevation model data and soil and land utilization data; the reservoir characteristic reservoir capacity data comprises total reservoir capacity, flood regulating reservoir capacity, xingli reservoir capacity and dead reservoir capacity; the monthly water supply data is the monthly water supply data of each city in the study area.
3. The method for analyzing the watershed future hydrologic drought probability based on the time-varying gain model and Copula according to claim 1, wherein the method comprises the following steps of: the selection range of the optimal bivariate Copula function in the step 6) includes: gaussian copula, student t copula (t-copula), clayton copula, frank copula, BB 1copula, BB8 copula, tawn type 1copula, and Tawn type 2 copula.
4. The method for analyzing the watershed future hydrologic drought probability based on the time-varying gain model and Copula according to claim 1, wherein the method comprises the following steps of: the hydrologic drought in the step 6) is divided into five categories of drought-free SRI > -0.5, drought-free SRI > -0.5 not less than SRI > -1.0, drought-neutral-1.0 not less than SRI > -1.5, drought-heavy-1.5 not less than SRI > -2.0 and drought-specific SRI not less than 2.0 according to the SRI value.
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