A non-uniform hydrological drought attribution method based on successive runoff reduction

Abstract

The present invention provides a non-consistent hydrological drought attribution method based on the successive restoration of runoff, which includes three steps: fusing multi-source data to restore runoff successively, constructing a non-consistent drought index, and calculating the contribution of multiple influencing factors and their interactions. Specifically, multi-source data such as topography, land use, remote sensing images, global-scale published water use, and water resources bulletins and fusion algorithms are used to restore the measured runoff series successively; the measured runoff series, the first-time restored runoff series, and the second-time restored runoff series are used to calculate the corresponding non-consistent hydrological drought index; the hydrological drought index of different restoration series and periods is used, combined with the elasticity coefficient method, to calculate the contribution of reservoir regulation, water withdrawal, climate change, and the interaction of various factors to hydrological drought. The present invention solves the problem of hydrological drought attribution under the comprehensive influence of multiple factors and their interactions by fusing multi-source data to restore runoff successively and constructing a non-consistent hydrological drought index.

Description

Technical Field

[0001] The present invention relates to the technical field of extreme hydrological event analysis, and in particular to a non-uniform hydrological drought attribution method based on successive runoff reduction. Background Art

[0002] Globally, the frequency, duration, intensity, and impact of droughts are also on the rise. Driven by climate change and significant human activity, hydrological drought refers to abnormal water shortages caused by an imbalance between precipitation and surface or groundwater levels. Therefore, understanding the causes and mechanisms of hydrological drought in this changing environment is a theoretical prerequisite for improving comprehensive preparedness for extreme droughts.

[0003] Constructing a drought index to assess hydrological drought and using attribution methods to calculate the contribution of influencing factors to hydrological drought is an important approach to conducting hydrological drought attribution analysis. Commonly used hydrological drought indices include the surface water supply index (SWSI), streamflow drought index (SDI), standardized streamflow index (SSI), and standardized runoff index (SRI). Common attribution methods include the comparative test basin method, the pre- and post-disturbance method, the upstream- and downstream comparison method, and the simulation observation method.

[0004] However, existing hydrological drought attribution methods have the following shortcomings. First, most existing attribution methods rely on constructing hydrological models for the base period and the impact period to calculate the contribution of climate change and human activities. However, when the basin is large and there are many influencing factors, the cost of building the model is high; and it is difficult to calculate the interaction of multiple influencing factors. Therefore, there is an urgent need for a method suitable for calculating the contribution of multiple influencing factors and their interactions to hydrological drought; and this method should be simple and direct, and be able to make full use of multi-source data for attribution analysis without the need to build complex models. Second, the changing environment has caused inconsistency in the runoff time series. Therefore, under a changing environment, the reliability of SRI estimation based on the premise of consistency of the runoff time series has been questioned. Summary of the Invention

[0005] The purpose of the present invention is to address the deficiencies of the above-mentioned existing technologies and provide a non-uniform hydrological drought attribution method based on successive runoff reduction, so as to achieve refined and dynamic management of water resources and prevent and alleviate drought events.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] The present invention provides a non-uniform hydrological drought attribution method based on successive runoff reduction, comprising:

[0008] S1. Fusion of multi-source data to restore runoff sequentially, i.e. using multi-source data such as topography, land use, remote sensing images, global-scale water use and water resources bulletins and fusion algorithms to restore measured runoff series sequentially;

[0009] S2. Construct a non-consistent drought index, that is, use the measured runoff series and the first-time restored runoff series to calculate the corresponding non-consistent hydrological drought index, and use the second-time restored runoff series to calculate the hydrological drought index.

[0010] Furthermore, in S1, fusing multi-source data to restore runoff successively includes:

[0011] According to the measured runoff series R obs , the reservoir storage change data obtained by remote sensing inversion, using water balance, considering the impact of water conservancy projects on the runoff series, and obtaining a single-stage reduction runoff series R re1 , calculated as:

[0012] ;

[0013] Where ΔRV t is the storage variable of the upstream reservoir group in the section during time period t;

[0014] According to the single reduction runoff series R re1 , the water withdrawal data obtained by multi-source fusion, based on the primary reduction, further consider the impact of water withdrawal on the runoff series using water balance, and obtain the secondary reduction runoff series R re2 , calculated as:

[0015] ;

[0016] Among them, WU t is the water consumption upstream of the section in time period t; ζ is the water consumption rate.

[0017] Furthermore, the reservoir water storage capacity change data obtained by remote sensing inversion is obtained by:

[0018] S101. Construct a water level-area relationship curve based on the digital elevation model (DEM) data: starting from the lowest water level of the reservoir, count the number of raster pixels in the digital elevation model (DEM) that are less than or equal to the water level, and multiply the number of pixels by the pixel area to obtain the water surface area corresponding to the water level; repeat the calculation at intervals of 1 meter from the lowest water level to the highest water level of the reservoir to obtain a series of water level-area values; and use polynomial fitting based on the water level-area series values ​​to obtain a water level-area relationship curve.

[0019] S102. Extract the change process of reservoir water surface area using remote sensing images: On the Google Earth Engine (GEE) platform, use image series and median composite to reduce the impact of clouds, and calculate the improved normalized difference water index (MNDWI):

[0020] ;

[0021] Among them, ρ(GREEN) is the green band, ρ(SWIR1) is the shortwave infrared band;

[0022] According to the normalized water body index (MNDWI) and threshold, the reservoir water surface area change process is extracted.

[0023] S103. Calculate the reservoir water storage capacity change process based on the reservoir water level-area relationship curve and the reservoir water surface area change process:

[0024] ;

[0025] Among them, RA is the surface area of ​​the reservoir and RE is the water level of the reservoir.

[0026] Furthermore, the water use data obtained by multi-source fusion is obtained by:

[0027] S104. Publish water use data on a global scale R As a reference, the water resources bulletin water use data WU G The calculation for interpolation to fill in missing data is:

[0028] WU t G = [ R j − R i j − i × ( t − i ) + R i ] × WU t R ;

[0029] in, is the water consumption data of the water resources bulletin during period t, and i < t < j; R j and R i They represent the ratios of the published water consumption data to the reference water consumption data in period j and period i, respectively.

[0030] S105, assuming that the time variation of water use is an S-shaped curve, the water use data WU from the water resources bulletin G Perform forward extension to make up for the missing data, and the calculation is:

[0031] ;

[0032] Water Resources Bulletin Water Use Data WU G Backward extension is performed to make up for the missing data, and the calculation is:

[0033] ;

[0034] Among them, α is the corrected growth rate, ranging from [0.1, 0.2]; K is the corrected water use change rate.

[0035] S106: Assuming that water use only exists in construction land and cultivated land, use the land use data of each period to obtain the water use data WU from the water resources bulletin after processing in S104 and S105. G Spatial distribution data , calculated as:

[0036] ;

[0037] Among them, A LU Indicates the area of ​​construction land or cultivated land;

[0038] Based on the spatial distribution data of water use in water resources bulletin Know the water consumption WU in any section within the above period t t .

[0039] Furthermore, in S2, the calculation of the corresponding inconsistent hydrological drought index is specifically as follows:

[0040] S201, according to the measured runoff series R obs , using reservoir index RI and water intake index WUI as explanatory variables, the first model was constructed to calculate the corresponding measured runoff series R obs The first distribution function parameter μ obs and the second distribution function parameter σ obs :

[0041] ;

[0042] ;

[0043] Where g(.) represents the link function; k is the coefficient vector; X is the explanatory variable;

[0044] According to the first distribution function parameter μ obs and the second distribution function parameter σ obs , calculate the corresponding measured runoff series R obs The SRI is a non-uniform hydrological drought index. obs .

[0045] S202, according to the primary reduction runoff series R re1 , taking the water intake index WUI as the explanatory variable, the second model is constructed to calculate the corresponding first-time reduction runoff series R re1 The third distribution function parameter μ re1 and the fourth distribution function σ re1 :

[0046] ;

[0047] ;

[0048] According to the third distribution function parameter μ re1 and the fourth distribution function parameter σ re1 , calculate the corresponding one-time reduction runoff series R re1 The SRI is a non-uniform hydrological drought index. re1 .

[0049] S203, according to the secondary reduction runoff series R re2 , calculate the corresponding secondary reduction runoff series R re2 Hydrological Drought Index (SRI) re2 .

[0050] Furthermore, it also includes calculating the contribution of multiple influencing factors and their interactions, specifically:

[0051] S301, secondary reduction runoff series R re2 Perform mutation point analysis and divide the analysis period into two periods before and after the impact of climate change, namely the first period and the second period, and then obtain SRI obs_1 、SRI obs_2 、SRI re1_1 、SRI re1_2 、SRI re2_1 and SRI re2_2 ;

[0052] Among them, SRI obs_1 and SRI obs_2 Represents the SRI of the first and second periods respectively obs , SRI obs_1 Affected by water conservancy projects and water use, SRI obs_2 Affected by water conservancy projects, water withdrawal, climate change, the interaction between water conservancy projects and climate change, and the interaction between water withdrawal and climate change;

[0053] SRI re1_1 and SRI re1_2 Represents the SRI of the first and second periods respectively re1 , SRI re1_1 Affected by water withdrawal, SRI re1_2 Affected by water withdrawal, climate change, and the interaction between water withdrawal and climate change;

[0054] SRI re2_1 and SRI re2_2 Represents the SRI of the first and second periods respectively re2 , SRI re2_1 Nearly natural state, SRI re2_2Affected by climate change.

[0055] S302. Calculate the contribution of water conservancy project operation to hydrological drought α R , Contribution of water use to hydrological drought α W , the contribution of climate change to hydrological drought α C , the contribution of the interaction between water conservancy projects and climate change to hydrological drought α RC And the contribution of the interaction between water withdrawal and climate change to hydrological drought α WC :

[0056] ;

[0057] Among them, ΔSRI R , ΔSRI W , ΔSRI C , ΔSRI RC and ΔSRI WC They represent the changes in the inconsistent hydrological drought index (SRI) caused by water conservancy project operation, water withdrawal, climate change, the interaction between water conservancy projects and climate change, and the interaction between water withdrawal and climate change.

[0058] S303. Calculate the variation of the non-uniform hydrological drought index SRI, including ΔSRI R , ΔSRI W , ΔSRI C , ΔSRI RC and ΔSRI WC :

[0059] ;

[0060] Among them, ΔSRI R1 and ΔSRI R2 represents the changes in the hydrological drought index caused by water conservancy projects in the first and second periods, and ΔSRI R1 = ΔSRI R2 ;

[0061] ΔSRI W1 and ΔSRI W2 represents the change of hydrological drought index caused by water withdrawal in the first and second periods, respectively, and ΔSRI W1 = ΔSRI W2 ;

[0062] f is SRI re2_1 About atmospheric circulation factor a i The expression f is constructed by regression equation, proxy model or machine learning, where A is the set of atmospheric circulation factors, Δai is the atmospheric circulation factor a affected by climate change i The mean and atmospheric circulation factor a before the impact of climate change i Difference of means.

[0063] The beneficial effects of the present invention are: using multi-source data such as topography, land use, remote sensing images, global-scale published water use, and water resources bulletins and a fusion algorithm to gradually restore the measured runoff series and construct a non-consistent hydrological drought index, thus solving the problem of hydrological drought attribution under the comprehensive influence of multiple factors and their interactions;

[0064] Constructing an inconsistency index under a changing environment is conducive to improving the reliability of hydrological drought attribution and reducing the error of hydrological drought attribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a structural block diagram of a non-uniform hydrological drought attribution method based on successive runoff reduction according to an embodiment of the present invention.

[0066] Figure 2 This chart shows the changes in water storage capacity at the four large reservoirs in the lower reaches of the Jinsha River (Wudongde, Baihetan, Xiluodu, and Xiangjiaba).

[0067] Figure 3 This is the water use process above the Yichang section.

[0068] Figure 4 This is the spatial distribution of water consumption above the Yichang section in 2020.

[0069] Figure 5 2 is a comparison diagram of the consistency and inconsistency distribution functions of spring natural runoff according to an embodiment of the present invention.

[0070] Figure 6 This is a dynamic change diagram of the non-uniform hydrological drought index according to an embodiment of the present invention.

[0071] Table 1 shows the optimal distribution function types of the annual runoff in each season according to an embodiment of the present invention.

[0072] Table 2 shows the attribution analysis results of various influencing factors of the embodiment of the present invention. DETAILED DESCRIPTION

[0073] The present embodiment is described in detail below, and examples of the embodiment are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0074] As mentioned in the background technology, existing attribution methods have the following shortcomings:

[0075] (1) Most existing attribution methods rely on constructing hydrological models for the baseline and impact periods to calculate the contribution of climate change and human activities to hydrological drought. When the basin is large and the influencing factors are numerous, the cost of constructing the model is high; and it is difficult to calculate the interaction of multiple influencing factors. (2) The consistency assumptions underlying most existing hydrological indices are detrimental to the reliability of hydrological drought attribution in a changing environment.

[0076] To this end, this embodiment provides a non-uniform hydrological drought attribution method based on sequential runoff restoration. This method utilizes multi-source data, including topography, land use, remote sensing imagery, global-scale published water use data, and water resources bulletins, along with a fusion algorithm, to sequentially restore measured runoff series and construct a non-uniform hydrological drought index. This method addresses the issue of hydrological drought attribution under the combined influence of multiple factors and their interactions, improving the reliability of hydrological drought attribution under changing environments. This approach facilitates a deeper understanding of the evolution of hydrological drought under non-uniform conditions, enabling refined and dynamic management of water resources and preventing and mitigating drought events.

[0077] Attachment Figure 1 This is a structural diagram of the non-uniform hydrological drought attribution method based on successive runoff reduction according to an embodiment of the present invention. Figure 1 Taking the hydrological drought attribution analysis of Yichang Station as an example, the non-consistent hydrological drought attribution method based on successive runoff reduction is described.

[0078] (1) Fusion of multi-source data to restore runoff successively:

[0079] (1.1) Primary runoff reduction:

[0080] The large and medium-sized reservoirs above the Yichang section were identified as Liyuan, Ahai, Jin'anqiao, Longkaikou, Ludila, Guanyinyan, Wudongde, Baihetan, Xiluodu, Xiangjiaba, Lianghekou, Jinping I, Ertan, Zipingpu, Houziyan, Changheba, Dagangshan, Pubugou, Goupitan, Silin, Shatuo, Pengshui, Bikou, Baozhusi, Tingzikou, Caojie, Jiangkou, Wudu, and Sanxia. For each reservoir, starting from the lowest water level, the number of SRTM-DEM raster pixels that were equal to or less than the water level was counted. This number of pixels was multiplied by the pixel area (i.e., 30 m × 30 m) to obtain the corresponding water surface area. This calculation process was repeated at 1 m intervals from the lowest to the highest water level of the reservoir to obtain a series of water level-area values. Based on these water level-area values, a polynomial fit was used to construct a water level-area relationship curve.

[0081] On the GEE platform, the Modified Normalized Difference Water Index (MNDWI) is calculated using the Landsat5, Landsat7, and Landsat8 image series (using median composite to reduce cloud effects). The calculation is as follows:

[0082]

[0083] Where ρ(Green) is the green band and ρ(SWIR1) is the shortwave infrared band. Based on MNDWI and threshold, the water surface area change process of each of the above reservoirs was extracted.

[0084] According to the reservoir water level area relationship curve and the reservoir water surface area change process, the water storage capacity change process of each reservoir is calculated as follows:

[0085]

[0086] Among them, RA is the surface area of ​​the reservoir, and RE is the water level of the reservoir. Taking the four reservoirs in the lower reaches of the Jinsha River as an example, the calculation results are shown in the attached Figure 2

[0087] According to the long series of measured flow R at Yichang Station from 1956 to 2023 obs As well as the change process of water storage capacity of the control reservoir above Yichang Station, the water balance method is used to consider the impact of water conservancy projects on the runoff series, and the primary reduction runoff series R is obtained. re_1 , calculated as follows:

[0088]

[0089] Where ΔRV t is the total storage variable of the Yichang upstream reservoir group during period t.

[0090] (1.2) Secondary reduction:

[0091] The water use data published by the Food and Agriculture Organization of the United Nations (WU R ) as a reference, and the water resources bulletin water use data (WU G ) to interpolate and fill in the missing data, and the calculation is as follows:

[0092] WU t G = [ R j − R i j − i × ( t − i ) + R i ] × WU t R

[0093] in, is the water consumption data of the water resources bulletin during period t, and i < t < j; Rj and R i They represent the ratios of the water use data published in the bulletin to the water use data released by the Food and Agriculture Organization of the United Nations in period j and period i, respectively.

[0094] Assuming that the time variation of water use is an S-shaped curve, G Perform forward extension to make up for missing data, and the calculation is as follows:

[0095]

[0096] About WU G Backward extension is performed to make up for the missing data, and the calculation is as follows:

[0097]

[0098] Among them, α is used to correct the growth rate and its value range is [0.1, 0.2].

[0099] Assuming that water withdrawal only exists in construction land and cultivated land, the global land cover type product (Climate Change Initiative-Land Cover, CCI-LC) released by ESA every year is used to obtain the corresponding year after time interpolation. Spatial distribution data of Since the CCI-LC time range is from 1992 to 2020, the The spatial discretization adopts the CCI-LC of 1992, and the CCI-LC after 2020. The spatial discretization adopts the CCI-LC of 2020. The calculation is as follows:

[0100]

[0101] Among them, A LU Indicates the area of ​​construction land or cultivated land. It can be seen that the water consumption WU in the Yichang section above the period t t The water usage changes above the Yichang section are shown in the attached Figure 3 The spatial distribution of water use above the Yichang section in 2020 is shown in the attached Figure 4 .

[0102] According to the single reduction runoff series R re1 Based on the water use data of Yichang section and above, the secondary reduction runoff series R is obtained by using water balance to further consider the impact of water use on the runoff series. re2 , calculated as follows:

[0103]

[0104] Among them, WU t is the water consumption above the Yichang section in time period t, and ζ is the water consumption rate.

[0105] (2) Constructing a non-consistent drought index:

[0106] To construct the non-uniform SRI, the semi-parametric additive formula of the GAMLSS model was used to estimate the parameters of the runoff distribution function at the Yichang station, which was calculated as follows:

[0107]

[0108] Among them, θ k and β k is a vector of length n, θ k is the runoff distribution function parameter of Yichang station. g k (.) is a known link function used to connect the distribution function parameters and the explanatory variables. k For a known n Design matrix, The length is The explanatory variable vector, Z jk For a known n m kj The design matrix, γ jk is m kj A random variable of dimension X. k η k is a parameter component composed of linear or nonlinear explanatory variables. jk γ jk is the additive component representing the random effect.

[0109] θ k Take the parameters μ and σ in the GAMLSS model. η k There are differences for different GAMLSS models. Specifically, for the measured runoff series, η k Including reservoir index and water withdrawal index; for the single reduction runoff series, η k Includes water withdrawal index.

[0110] In different seasons, for the measured annual runoff series R obs and the single-reduction annual runoff series R re_1 , respectively fitting GAMLSS model 1 and GAMLSS model 2, estimating the distribution function parameter μ of the corresponding annual runoff obs , σ obs 、μ re_1 and σ re_1The fitting method used was the Rigby and Stasinopoulos (RS) algorithm, with the objective function of minimizing global deviation. Five possible distributions for annual runoff were pre-set: Gumbel (GU), Gamma (GA), Weibull (WEI), Logistic (LO), and Lognormal (LOGNO). The distribution that minimized the Akaike information criterion (AIC) was ultimately selected, as shown in Table 1.

[0111] Table 1

[0112] spring summer Autumn winter GAMLSS Model 1 LOGNO WEI WEI GA GAMLSS Model 2 LOGNO WEI WEI GA

[0113] Taking the spring natural runoff at Yichang Station as an example, the non-uniform distribution obtained by fitting based on GAMLSS model 1 and its comparison with the uniform distribution are shown in the attached figure. Figure 5 The five curves from top to bottom represent the 95th, 75th, 50th, 25th, and 5th percentiles, respectively, and the red dots represent the measured runoff series. The results show that the non-uniform distribution better fits the measured runoff series than the consistent distribution. In particular, after the measured runoff series undergoes a sudden change, i.e., after 2000, the non-uniform distribution can capture the upward trend in runoff and thus better fit the extreme values ​​of the runoff series.

[0114] In different seasons, according to the distribution function parameter μ of annual runoff obs , σ obs 、μ re_1 and σ re_1 , calculate the distribution function F of annual runoff respectively obs (x) and F re_1 (x), and the corresponding non-uniform hydrological drought index SRI is obtained obs and SRI re_1 For the secondary reduction annual runoff series, the conventional method is used to calculate its distribution parameter μ re_2 , σ re_2 、F re_2 (x) and SRI re_2 The formula for calculating SRI based on F(x) is as follows:

[0115]

[0116] Where, C0= 2.516, C1= 0.803, C2= 0.010, d1= 1.433, d2= 0.189, d3= 0.001, and x is the annual runoff. The calculated non-uniform hydrological drought index SRI of Yichang station obs and SRI re_1and the Hydrological Drought Index (SRI) re_2 See attached Figure 6 Based on the calculated SRI, hydrological drought levels can be divided into: normal (0.5 ≥ SRI ≥ -0.5), mild drought (-1 ≤ SRI < 0.5), moderate drought (-1.5 ≤ SRI < -1), severe drought (-2 ≤ SRI < -1.5) and extreme drought (SRI < -2).

[0117] (3) Calculate the contribution of multiple influencing factors and their interactions:

[0118] For the secondary reduction series R re2 A mutation point analysis was conducted, and the analysis period was divided into two periods: before the impact of climate change (1956-1995) and after the impact (1996-2022), and the SRI was obtained. obs_1 、SRI obs_2 、SRI re1_1 、SRI re1_2 、SRI re2_1 and SRI re2_2 , among which SRI re2_1 Nearly natural state, SRI re2_2 Affected by climate change, SRI re1_1 Affected by water withdrawal, SRI re1_2 Affected by water withdrawal, climate change, and the interaction between water withdrawal and climate change, SRI obs_1 Affected by water conservancy projects and water use, SRI obs_2 Affected by water conservancy projects, water use, climate change, the interaction between water conservancy projects and climate change, and the interaction between water use and climate change, the results are shown in the attached Figure 6 Attached Figure 6 The vertical line before and after the middle line represents period 1 and period 2. The above analysis is based on the assumption that there is no interaction between reservoir storage and water withdrawal.

[0119] SRI obs_1 With SRI re1_1 The absolute value of the difference represents the change in the hydrological drought index ΔSRI caused by the operation of the water conservancy project in period 1. R1 , see formula ; and SRI obs_2 With SRI re1_2 The absolute value of the difference represents the change in hydrological drought index caused by the water conservancy project in period 2 and the interaction between the water conservancy project and climate change (respectively represented by ΔSRI R2 and ΔSRI RC ), see formula Since the inconsistency of water conservancy projects has been taken into account by constructing the inconsistency hydrological drought index, it can be considered that ΔSRI R1 = ΔSRI R2 = ΔSRI R In summary, ΔSRI can be calculated R = 0.303, ΔSRI RC =0.056.

[0120]

[0121]

[0122] SRI re1_1 With SRI re2_1 The absolute value of the difference represents the change in the hydrological drought index ΔSRI caused by water withdrawal in period 1. W1 , see formula ; and SRI re1_2 With SRI re2_2 The absolute value of the difference represents the change in hydrological drought index caused by water withdrawal in period 2 and the interaction between water withdrawal and climate change (respectively represented by ΔSRI W2 and ΔSRI WC ), see formula Since the inconsistency of water withdrawal has been taken into account by constructing the inconsistency hydrological drought index, it can be considered that ΔSRI W1 = ΔSRI W2 = ΔSRI W In summary, ΔSRI can be calculated W = 0.256, ΔSRI WC = 0.020.

[0123]

[0124]

[0125] Through the atmospheric circulation factor a iTo describe the impact of climate change, we selected the commonly used Western Pacific Subtropical High Intensity Index (WPSHII), South China Sea Subtropical High Intensity Index (SCSSHII), North Pacific Pattern (NP), North Atlantic Oscillation Index (NAO), El Niño-Southern Oscillation Index (ENSO), Total Sunspot Number Index (TSNI) and Southern Oscillation Index (SOI) as atmospheric circulation factors. Taking a relatively simple linear regression as an example, we constructed the SRI. re2_1 About atmospheric circulation factor a i The expression of :

[0126]

[0127] Calculate the change in hydrological drought index ΔSRI caused by climate change by referring to the elasticity coefficient method C , see formula :

[0128]

[0129] Among them, A is the set of atmospheric circulation factors, Δa i a in period 2 i Mean and a in period 1 i The difference between the means is used to calculate ΔSRI C = 0.249.

[0130] The contributions of water conservancy project operation, water withdrawal, climate change, the interaction between water conservancy projects and climate change, and the interaction between water withdrawal and climate change to hydrological drought are calculated as follows:

[0131]

[0132] Calculated: α R = 34.3%, α W = 30.0%, α C = 28.2%, α RC = 6.3%, αWC = 2.3%. The results are summarized in Table 2.

[0133] Table 2

[0134] <![CDATA[α R ]]> <![CDATA[α W ]]> <![CDATA[α C ]]> <![CDATA[α RC ]]> <![CDATA[α WC ]]> 34.3% 30.0% 28.2% 6.3% 2.3%

[0135] In summary, the present invention solves the problem of hydrological drought attribution under the combined influence of multiple factors and their interactions by fusing multi-source data to successively restore measured runoff and construct a non-uniform hydrological drought index, thereby contributing to a deeper understanding of the evolution mechanism of hydrological drought under non-uniform conditions, achieving refined and dynamic management of water resources, and preventing and alleviating drought events.

Claims

1. A non-uniform hydrological drought attribution method based on successive runoff reduction, characterized in that: include: S1. Fusion of multi-source data to restore runoff sequentially, i.e. using multi-source data such as topography, land use, remote sensing images, and global-scale published water use and water resources bulletins and fusion algorithms to sequentially restore the measured runoff series; S2. Constructing a non-consistent drought index, i.e., using the measured runoff series and the primary reduction runoff series to calculate the corresponding non-consistent hydrological drought index, and using the secondary reduction runoff series to calculate the hydrological drought index; In S1, fusing multi-source data to restore runoff successively includes: According to the measured runoff series R obs , the reservoir storage change data obtained by remote sensing inversion, using water balance, considering the impact of water conservancy projects on the runoff series, and obtaining a single-stage reduction runoff series R re1 , calculated as: ； Where ΔRV t is the storage variable of the upstream reservoir group in the section during time period t; According to the single reduction runoff series R re1 , the water withdrawal data obtained by multi-source fusion, based on the primary reduction, further consider the impact of water withdrawal on the runoff series using water balance, and obtain the secondary reduction runoff series R re2 , calculated as: ； Among them, WU t is the water consumption of the upstream section in time period t;ζ is the water consumption rate; In S2, the calculation of the corresponding inconsistent hydrological drought index is specifically as follows: S201, according to the measured runoff series R obs , using reservoir index RI and water intake index WUI as explanatory variables, the first model is constructed to calculate the corresponding measured runoff series R obs The first distribution function parameter μ obs and the second distribution function parameter σ obs : ； ； Where g(.) represents the link function; k is the coefficient vector; X is the explanatory variable; According to the first distribution function parameter μ obs and the second distribution function parameter σ obs , calculate the corresponding measured runoff series R obs The SRI is a non-uniform hydrological drought index. obs ; S202, according to the primary reduction runoff series R re1 , taking the water intake index WUI as the explanatory variable, the second model is constructed to calculate the corresponding first-time reduction runoff series R re1 The third distribution function parameter μ re1 and the fourth distribution function σ re1 : ； ； According to the third distribution function parameter μ re1 and the fourth distribution function parameter σ re1 , calculate the corresponding one-time reduction runoff series R re1 The SRI is a non-uniform hydrological drought index. re1 ; S203, according to the secondary reduction runoff series R re2 , calculate the corresponding secondary reduction runoff series R re2 Hydrological Drought Index (SRI) re2 .

2. The non-uniform hydrological drought attribution method based on successive runoff reduction according to claim 1, characterized in that: The method for obtaining the reservoir water storage change data obtained by remote sensing inversion is: S101. Constructing a water level-area relationship curve based on digital elevation model (DEM) data: Starting from the lowest water level of the reservoir, counting the number of raster pixels in the digital elevation model (DEM) that are less than or equal to the water level, multiplying the number of pixels by the pixel area to obtain the water surface area corresponding to the water level; repeating the calculation at intervals of 1 meter from the lowest water level to the highest water level of the reservoir to obtain a series of water level-area values; and using a polynomial fit based on the series of water level-area values ​​to obtain a water level-area relationship curve; S102. Extract the change process of reservoir water surface area using remote sensing images: On the Google Earth Engine (GEE) platform, use image series and median composite to reduce the impact of clouds, and calculate the improved normalized difference water index (MNDWI): ； Among them, ρ(GREEN) is the green band, ρ(SWIR1) is the shortwave infrared band; According to the normalized water body index MNDWI and threshold, the reservoir water surface area change process is extracted; S103. Calculate the reservoir water storage capacity change process based on the reservoir water level-area relationship curve and the reservoir water surface area change process: ； Among them, RA is the surface area of ​​the reservoir and RE is the water level of the reservoir.

3. The non-uniform hydrological drought attribution method based on successive runoff reduction according to claim 2, characterized in that: The method for obtaining the water use data obtained by multi-source fusion is: S104. Publish water use data on a global scale R As a reference, the water resources bulletin water use data WU G The calculation for interpolation to fill in missing data is: ； in, is the water consumption data of the water resources bulletin during period t, and i < t < j; R j and R i They represent the ratios of the reported water use data to the reference water use data in period j and period i, respectively; S105, the time change process of water use is an S-shaped curve, and the water use data WU G Perform forward extension to make up for the missing data, and the calculation is: ； Water Resources Bulletin Water Use Data WU G Backward extension is performed to make up for the missing data, and the calculation is: ； Wherein, α is the corrected growth rate, ranging from [0.1, 0.2]; K is the corrected water use change rate; S106: Water use only exists in construction land and cultivated land. Using the land use data of each period, obtain the water use data WU from the water resources bulletin after processing in S104 and S105. G Spatial distribution data , calculated as: ； Among them, A LU Indicates the area of ​​construction land or cultivated land; Based on the spatial distribution data of water use in water resources bulletin Know the water consumption WU in any section within the above period t t .

4. The non-uniform hydrological drought attribution method based on successive runoff reduction according to claim 1, characterized in that: It also includes calculating the contribution of multiple influencing factors and their interactions, specifically: S301, secondary reduction runoff series R re2 Perform mutation point analysis and divide the analysis period into two periods before and after the impact of climate change, namely the first period and the second period, and then obtain SRI obs_1 、SRI obs_2 、SRI re1_1 、SRI re1_2 、SRI re2_1 and SRI re2_2 ; Among them, SRI obs_1 and SRI obs_2 Represents the SRI of the first and second periods respectively obs , SRI obs_1 Affected by water conservancy projects and water use, SRI obs_2 Affected by water conservancy projects, water withdrawal, climate change, the interaction between water conservancy projects and climate change, and the interaction between water withdrawal and climate change; SRI re1_1 and SRI re1_2 Represents the SRI of the first and second periods respectively re1 , SRI re1_1 Affected by water withdrawal, SRI re1_2 Affected by water withdrawal, climate change, and the interaction between water withdrawal and climate change; SRI re2_1 and SRI re2_2 Represents the SRI of the first and second periods respectively re2 , SRI re2_1 In its natural state, SRI re2_2 affected by climate change; S302. Calculate the contribution of water conservancy project operation to hydrological drought α R , Contribution of water use to hydrological drought α W , the contribution of climate change to hydrological drought α C , the contribution of the interaction between water conservancy projects and climate change to hydrological drought α RC And the contribution of the interaction between water withdrawal and climate change to hydrological drought α WC : ； Among them, ΔSRI R , ΔSRI W , ΔSRI C , ΔSRI RC and ΔSRI WC They represent the changes in the inconsistent hydrological drought index (SRI) caused by water conservancy project operation, water withdrawal, climate change, the interaction between water conservancy projects and climate change, and the interaction between water withdrawal and climate change; S303. Calculate the variation of the non-uniform hydrological drought index SRI, including ΔSRI R , ΔSRI W , ΔSRI C , ΔSRI RC and ΔSRI WC : ； Among them, ΔSRI R1 and ΔSRI R2 represents the changes in the hydrological drought index caused by water conservancy projects in the first and second periods, and ΔSRI R1 = ΔSRI R2 ; ΔSRI W1 and ΔSRI W2 represents the change of hydrological drought index caused by water withdrawal in the first and second periods, respectively, and ΔSRI W1 = ΔSRI W2 ; f is SRI re2_1 About atmospheric circulation factor a i The expression f is constructed by regression equation, proxy model or machine learning, where A is the set of atmospheric circulation factors, Δa i is the atmospheric circulation factor a affected by climate change i The mean and atmospheric circulation factor a before the impact of climate change i Difference of means.

Images