A flood season drought risk assessment method based on composite runoff index

Abstract

The present application belongs to the technical field of flood and drought disaster identification and evaluation, and discloses a flood season reverse drought risk evaluation method based on a composite runoff index, which comprises the following steps: obtaining geographic information data, hydrological and meteorological data and water conservancy engineering data of a target region; setting a drought threshold to select a historical flood season low-flow drought event in combination with urban water supply demand; selecting an optimal probability distribution to analyze the marginal distribution characteristics of drought flow and duration of the flood season low-flow drought event; selecting a Copula function to establish the joint distribution of drought flow and duration of the flood season low-flow drought event, and to construct the composite runoff index of the flood season low-flow drought event; and considering different emission scenarios of future climate change, based on the prediction results of a distributed hydrological model of a basin, the composite runoff index is used to analyze the response characteristics of the occurrence risk of future flood season reverse drought events. The present application can accurately evaluate the sudden occurrence characteristics of flood season reverse drought and the flood season drought characteristics.

Description

Technical Field

[0001] This invention relates to the field of flood and drought disaster identification and assessment technology, and in particular to a method for assessing the risk of drought during the flood season based on a composite runoff index. Background Technology

[0002] Drought is one of the most common natural disasters globally, and in recent years, the proportion of the global population affected by drought has been gradually increasing. With the continued warming of the global climate, the intensity and frequency of extreme drought events are increasing, posing a significant challenge to economic and social development. In particular, the phenomenon of drought reversal during the flood season presents a huge challenge to flood control and drought relief, not only introducing significant uncertainty to flood control but also seriously threatening the security of water supply for economic and social development. For example, in 2022, during the flood season in the Yangtze River basin, reservoirs lowered their water levels to ensure flood control safety; however, drought reversal during the flood season led to insufficient reservoir storage, causing water shortages for economic, social, and production activities. Identifying and assessing drought reversal events during the flood season is a crucial foundation for quantifying the severity of flood and drought disasters and analyzing their impact. It is of great significance for flood and drought disaster forecasting, response, and water supply security.

[0003] According to literature review, commonly used methods for quantitatively evaluating hydrological drought characteristics include the surface water supply index, Palmer hydrological drought index, standardized runoff index, and runoff drought index. Taking the most commonly used standardized runoff index as an example, it is generally obtained by determining the optimal probability distribution of runoff and then performing normal standardization to obtain the standardized runoff index for drought characteristic analysis. Its time scale is generally monthly, quarterly, or annual. This relatively coarse temporal resolution often makes it difficult to capture the sudden characteristics of drought reversal during the flood season, and at the same time, it confuses the statistical characteristics of low water levels that originally belonged to the non-flood season with the characteristics of drought reversal during the flood season, thus affecting the predictability of drought reversal events during the flood season. In addition, in the actual drought relief process, since urban water supply infrastructure is often designed based on a certain guarantee rate standard, prolonged drought will put significant pressure on the water supply security level. Therefore, it is necessary to focus on the drought flow corresponding to low water levels and its duration and conduct joint analysis to assess the risk to water supply security. However, there is still a lack of joint characteristic analysis of drought flow and duration for drought reversal events during the flood season.

[0004] Based on this, it is necessary to further combine urban water supply requirements, consider the characteristics and patterns of flood season drought events, and improve the design of identification and assessment methods for flood season drought events. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method for assessing the risk of drought reversal during the flood season based on a composite runoff index by fully considering the combined characteristics of drought flow and drought duration during the flood season.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] This invention provides a method for assessing the risk of drought during the flood season based on a composite runoff index, the method comprising:

[0008] Acquire geographic information data, hydrological and meteorological data, and water conservancy project data for the target area;

[0009] Based on geographic information data, hydrological and meteorological data, water conservancy project data, and urban water supply demand, a drought threshold is set to select low-flow drought events during historical flood seasons.

[0010] By selecting the optimal probability distribution, we analyze the marginal distribution characteristics of drought flow and duration in low-flow drought events during the flood season.

[0011] By selecting the Copula function, the joint distribution of drought flow and duration during low-flow drought events in the flood season is established, and a composite runoff index for low-flow drought events in the flood season is constructed.

[0012] A distributed hydrological model for the watershed was constructed, taking into account different emission scenarios due to future climate change. Based on the prediction results of the distributed hydrological model, a composite runoff index was used to analyze the risk response characteristics of future flood season drought events.

[0013] Preferably, the geographic information data of the target area includes digital elevation topographic data, river system and hydrological and meteorological station vector data of the target area; the hydrological and meteorological data of the target area includes long-term series of water level and flow observation data of hydrological stations in the target area, long-term series of meteorological observation data of meteorological stations, and long-term series of climate prediction data for different future emission scenarios; the water conservancy engineering data of the target area includes spatial distribution vector data of water intake projects in the target area and engineering parameters.

[0014] Preferably, based on geographic information data, hydrological and meteorological data, water conservancy project data, and urban water supply demand, a drought threshold is set to select historical low-flow drought events during the flood season, including:

[0015] Given the water intake level of the water intake project and the water level-discharge relationship at the water intake channel cross section, the drought discharge threshold is determined by the water level-discharge relationship curve.

[0016] In the absence of the above engineering parameters, the daily runoff process sequence during the flood season at the water intake section is selected, and a generalized extreme value distribution is used for fitting. The flow rate under the design frequency condition is selected as the drought threshold according to the urban water supply standard. The distribution function of the generalized extreme value distribution is as follows:

[0017]

[0018] In the formula, The distribution function, For random variables, These are the position parameter, scale parameter, and shape parameter, respectively, and exp is an empirical function;

[0019] Based on the established drought threshold, historical flood season runoff data were used to select low-flow drought events during the flood season based on run theory. The flow and duration of each low-flow drought event were analyzed and calculated to obtain drought flow and drought duration sequence data for low-flow drought events during the flood season.

[0020] Preferably, the optimal probability distribution is selected to analyze the marginal distribution characteristics of drought flow and duration during low-flow drought events in the flood season, including:

[0021] Based on drought flow and drought duration series data of low-flow drought events during the flood season, univariate probability distributions were used as candidate distribution types. The Kolmogorov-Smirnov test, Bayesian information criterion, and Akaike information content criterion were used as evaluation criteria to select the best-fit marginal distribution function of drought flow and duration for low-flow drought events during the flood season. Among them, the multiple univariate probability distributions include gamma distribution, Rayleigh distribution, normal distribution, exponential distribution, lognormal distribution, Weibull distribution, extreme value distribution, generalized Pareto distribution, generalized extreme value distribution, log-logistic distribution, logistic distribution, and Pearson type III distribution.

[0022] Based on the optimally fitted marginal distribution function of drought flow and duration during low-flow drought events in the flood season, the marginal distribution characteristics of drought flow and duration during low-flow drought events in the flood season are analyzed.

[0023] Preferably, the Copula function is selected to establish the joint distribution of drought flow and duration during low-flow drought events in the flood season, including:

[0024] Multiple two-dimensional Copula functions are compared, and the Bayesian information criterion and the Akaike information criterion are used to evaluate the fitting effect of each Copula function. The optimal Copula function is selected to establish a joint distribution function of drought flow and duration during a low-flow drought event in the flood season. The joint distribution function is expressed as follows:

[0025]

[0026] In the formula, To select the optimal Copula function, These are the low-flow drought flow and duration values ​​during the flood season. The distribution function of drought flow and duration. These are the distribution function expressions for drought flow and duration, respectively. These are the inverse function expressions for drought flow and duration distribution functions, respectively. This is the functional expression for the joint distribution.

[0027] Preferably, the composite runoff index for low-flow drought events during the flood season includes:

[0028] Based on the joint distribution function, the joint probability sequence of drought flow and drought duration during historical flood season low-flow events is calculated. The sequence is then standardized and normalized to obtain the corresponding composite runoff index. The calculation process is as follows:

[0029]

[0030]

[0031] In the formula, The composite runoff index, For joint probability, For a sign function, when hour, Take 1.0, when hour, Take -1.0, These are intermediate variables, and c0, c1, c2, d1, d2, and d3 are all constants. , , , , , ;

[0032] The flood season drought event levels are classified according to the Composite Runoff Index (CSI) as follows:

[0033] At that time, the drought event during the flood season was classified as extreme drought.

[0034] At that time, the drought reversal event during the flood season was classified as severe drought;

[0035] At that time, the drought reversal event during the flood season was classified as moderate drought;

[0036] At that time, the drought level during the flood season was classified as mild.

[0037] Preferably, a watershed-distributed hydrological model is constructed, considering different emission scenarios under future climate change. Based on the prediction results of the watershed-distributed hydrological model, a composite runoff index is used to analyze the risk response characteristics of future flood season drought events, including:

[0038] A distributed hydrological model of the target area is constructed. The target area is divided into grids using digital elevation topographic data, river system data, etc. The distributed hydrological model of the watershed is driven by historical meteorological data to simulate runoff changes, and historical hydrological data is used for model calibration.

[0039] We selected future low-emission, medium-emission, and high-emission scenarios, and used the quantile mapping method to downscale the meteorological elements output by the model based on historical measured data, to obtain long-series data of future precipitation, temperature, wind speed, humidity, and radiation predicted by different climate models under the low-emission, medium-emission, and high-emission scenarios.

[0040] Climate prediction data from different climate models under various future emission scenarios are used to drive a calibrated distributed watershed hydrological model to predict future runoff evolution in the watershed. A constructed composite runoff index is then used to analyze the risk response characteristics of future flood season drought events. The calculation formula is as follows:

[0041]

[0042] In the formula, To mitigate the risk of drought during the flood season, This represents the total number of low-flow drought events during the flood season. For the year, For the first The composite runoff index value of a low-flow drought event during the flood season. These are the upper and lower limits of the drought composite runoff index, corresponding to the level of drought reversal events during the flood season.

[0043] The beneficial effects of the present invention through the above technical solution are as follows:

[0044] 1. This invention fully considers the urban water supply demand and constructs a composite runoff index that couples drought flow and duration during low-flow drought events in the flood season. This index can more accurately assess the sudden characteristics of drought reversal in the flood season and the characteristics of low water levels during the flood season, and can provide a more scientific basis for early warning, forecasting, response and disposal of drought reversal disasters in the flood season and impact assessment.

[0045] 2. The nonlinear low-flow drought composite runoff index for the flood season, constructed based on the Copula function, overcomes the subjectivity problem of linear weighting of multivariate indicators in the past. It can more objectively reflect the risk response characteristics of flood season drought under different emission scenarios of future climate change, and provide scientific and technological support for ensuring the water supply security needs of sustainable urban economic and social development. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0047] Figure 1This is a flowchart of a flood season drought risk assessment method based on a composite runoff index according to an embodiment of the present invention.

[0048] Figure 2 This is a characteristic map of the historical composite runoff index variation in the target area according to an embodiment of the present invention.

[0049] Figure 3 This is a risk response feature map of the target area during the future flood season drought in an embodiment of the present invention. Detailed Implementation

[0050] The following examples are merely illustrative of the invention, and the scope of the invention is not limited to the embodiments described. Therefore, any non-essential modifications and adjustments made by those skilled in the art based on the above description to other embodiments are still within the scope of protection of this invention.

[0051] The invention will now be further described with reference to the accompanying drawings.

[0052] Figure 1 This is a flowchart illustrating a flood season drought risk assessment method based on a composite runoff index according to an embodiment of the present invention. The present invention provides a flood season drought risk assessment method based on a composite runoff index, such as... Figure 1 As shown, the method includes the following steps:

[0053] Step 1: Obtain geographic information data, hydrological and meteorological data, and water conservancy project data for the target area.

[0054] In this embodiment, the collected and organized geographic information data within the target area includes digital elevation topographic data, river system and hydrological and meteorological station vector data; hydrological and meteorological data includes long-term series of water level and flow observation data of major hydrological stations in the target area, long-term series of meteorological observation data of meteorological stations, and long-term series of climate prediction data for different future emission scenarios; water conservancy engineering data includes spatial distribution vector data of major water intake projects in the target area and engineering parameters, etc.

[0055] In one exemplary embodiment, taking a city in the middle reaches of the Yangtze River as the target area, basic geographic information data above the cross-section of the city in the middle reaches of the Yangtze River are collected and organized, including 30m resolution raster data, river system vector data of the Yangtze River, and spatial distribution vector data of hydrological and meteorological stations; hydrological and meteorological data include water level and flow observation data of the city's cross-section of the Yangtze River, daily data such as precipitation, temperature, wind speed, humidity, and sunshine duration of the meteorological station, and daily data such as precipitation, temperature, wind speed, humidity, and radiation predicted under different future emission scenarios output by the climate model; water conservancy project data include spatial distribution vector data of the main water intake projects of the city in the middle reaches of the Yangtze River, and parameters of the water conservancy projects include water intake water level of the water intake projects.

[0056] Step 2: Based on geographic information data, hydrological and meteorological data, water conservancy project data, and urban water supply demand, a drought threshold is set to select low-flow drought events during historical flood seasons.

[0057] In this embodiment, the drought flow threshold is determined based on the city's water supply security requirements. This can be achieved through the following methods: If the water intake level of the water intake project and the water level-flow relationship at the water intake cross-section are known, the drought flow threshold is determined using the water level-flow relationship curve. If the above engineering parameters are lacking, a daily-scale runoff process sequence during the flood season at the water intake cross-section is selected, and a generalized extreme value distribution (GEV) is used for fitting. The flow value under the design frequency conditions is selected as the drought threshold according to the city's water supply standards. Its distribution function is as follows:

[0058]

[0059] In the formula, The distribution function, For random variables, These are the position parameter, scale parameter, and shape parameter, respectively.

[0060] Based on the established drought threshold, historical flood season runoff data were used to select low-flow drought events during the flood season based on run theory. The flow and duration of each low-flow drought event were analyzed and calculated to obtain drought flow and drought duration sequence data for low-flow drought events during the flood season.

[0061] In one exemplary embodiment, based on a long-term historical series of daily flow data from 1960 to 2018 at a control section of a city in the middle reaches of the Yangtze River, a daily-scale runoff process sequence during the flood season is selected. Then, a GEV model is used for distribution fitting. To analyze the characteristics of historical drought variations, a five-year return period flow is calculated as the drought threshold. Based on the drought threshold, run-length analysis is applied to the historical daily runoff data to obtain low-flow drought events during the flood season from 1960 to 2018, and time-series data of flow and duration are calculated.

[0062] Step 3: Select the optimal probability distribution and analyze the marginal distribution characteristics of drought flow and duration during low-flow drought events in the flood season.

[0063] In this embodiment, based on the drought flow and duration sequence determined from the low-flow drought event during the flood season selected in step 2, the univariate probability distribution widely used in hydrological frequency analysis is adopted as the candidate distribution type, including Gamma distribution, Rayleigh distribution, Normal distribution, Exponential distribution, Lognormal distribution, Weibull distribution, Extreme Value distribution, Generalized Pareto distribution (GPD), Generalized Extreme Value distribution (GEV), Log-logistic distribution, Logistic distribution, Pearson Type III distribution, etc. The Kolmogorov-Smirnov test, Bayesian Information Criterion (BIC), and Akaike Information Criterion (AIC) are used as evaluation criteria to select the marginal distribution function that best fits the drought flow and duration of the low-flow drought event during the flood season.

[0064] In an exemplary embodiment, the drought flow and drought duration of low-flow drought events during the flood season in a city in the middle reaches of the Yangtze River from 1960 to 2018 were fitted with probability distributions to obtain the optimal marginal distribution functions as follows: the optimal marginal distribution of drought duration is the generalized Pareto distribution (GPD), and the optimal marginal distribution of drought flow is the generalized extreme value distribution (GEV).

[0065] Step 4: Select the Copula function to establish the joint distribution of drought flow and duration during low-flow drought events in the flood season, and construct the composite runoff index for low-flow drought events in the flood season.

[0066] After obtaining the marginal distribution functions of drought flow and duration for low-flow drought events during the flood season in step 3, the joint distribution is further constructed using Copula functions. By comparing various two-dimensional Copula functions, including Gaussian Copula, Student t Copula, Clayton Copula, Gumbel Copula, and Frank Copula, the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AlC) are used to evaluate the fitting effect of each Copula function. The optimal Copula model is selected to establish the joint distribution function of drought flow and duration for low-flow drought events during the flood season, as shown below:

[0067]

[0068] In the formula, For Copula functions, These are the low-flow drought flow and duration values ​​during the flood season. The distribution function of drought flow and duration. These are the distribution function expressions for drought flow and duration, respectively. These are the inverse function expressions for drought flow and duration distribution functions, respectively. This is the functional expression for the joint distribution.

[0069] Based on the joint distribution function, the joint probability sequence of drought flow and drought duration during historical flood season low flow events is calculated. The sequence is then standardized and normalized to obtain the corresponding composite streamflow index (CSI).

[0070]

[0071]

[0072] In the formula, The composite runoff index, For joint probability, For a sign function, when hour, Take 1.0, when hour, Take -1.0, The intermediate variable is used as the reference, and the remaining parameters are as follows: , , , , , The CSI index can be used to classify flood season drought events into the following levels: It is an extreme drought. It is a severe drought. It is a moderate drought. It is a mild drought.

[0073] In an exemplary embodiment, Gaussian Copula, Student t Copula, Clayton Copula, Gumbel Copula, and Frank Copula are used to fit a joint distribution. From these two-dimensional Copula functions, the optimal Copula function is selected as Frank Copula using the AIC and BIC criteria. Based on the preferred Copula function, the joint probability sequence of low-flow-drought flow and drought duration during the flood season is calculated and standardized to obtain a historical composite runoff index sequence. The results clearly reflect historical flood season drought events such as 1972, 1977, 1984, 2011, and 2018. (See...) Figure 2 .

[0074] Step 5: Construct a distributed hydrological model for the watershed, considering different emission scenarios under future climate change. Based on the prediction results of the distributed hydrological model, use the composite runoff index to analyze the risk response characteristics of future flood season drought events.

[0075] In this embodiment, firstly, a distributed hydrological model of the target area is constructed. Digital elevation topography data and river system data are used to divide the target area into grids. Historical meteorological data drives the distributed hydrological model to simulate runoff changes, and historical hydrological data is used for model calibration. Secondly, different future emission scenarios are selected, including low-emission, medium-emission, and high-emission scenarios. Based on historical measured data, the quantile mapping method is used to downscale the meteorological elements output by the model, obtaining long-term data on future precipitation, temperature, wind speed, humidity, radiation, etc., predicted by different climate models under low-emission, medium-emission, and high-emission scenarios. Thirdly, the climate prediction data output by different climate models under different future emission scenarios drives the calibrated distributed hydrological model of the watershed to predict the future runoff evolution process of the watershed. The composite runoff index constructed in step 4 is used to analyze the risk response characteristics of future flood season drought events. The calculation formula is as follows:

[0076]

[0077] In the formula, To mitigate the risk of drought during the flood season, This represents the total number of low-flow drought events during the flood season. For the year, For the first The composite runoff index value of a low-flow drought event during the flood season. These represent the upper and lower limits of the drought composite runoff index at this level.

[0078] In an exemplary embodiment, a distributed time-varying gain hydrological model of the target area is constructed, and the model parameters are calibrated using an optimization algorithm based on historical hydrological observation data from control section hydrological stations. On this basis, the downscaled time-varying gain hydrological model is used to drive down-scale climate predictions from four future global climate models, including ACCESS-CM2, EC-Earth3, MRI-ESM2-0, and NorESM2-LM, to predict the risk response characteristics of future flood season drought events under three scenarios: low emissions, medium emissions, and high emissions. The risk response characteristics of flood season drought events in the target area from the middle of this century (horizontal year 1) to the end of this century (horizontal year 2) are shown in the figure. Figure 3 As shown in the results, except for mild drought under the low emission scenario and severe drought under the medium emission scenario, drought during the flood season tends to decrease, while severe drought under the medium emission scenario will increase significantly by the end of this century.

[0079] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for assessing the risk of drought during the flood season based on a composite runoff index, characterized in that, The method includes: Acquire geographic information data, hydrological and meteorological data, and water conservancy project data for the target area; Based on geographic information data, hydrological and meteorological data, water conservancy project data, and urban water supply demand, a drought threshold is set to select low-flow drought events during historical flood seasons. By selecting the optimal probability distribution, we analyze the marginal distribution characteristics of drought flow and duration in low-flow drought events during the flood season. By selecting the Copula function, the joint distribution of drought flow and duration during low-flow drought events in the flood season is established, and a composite runoff index for low-flow drought events in the flood season is constructed. A distributed hydrological model for the watershed was constructed, taking into account different emission scenarios due to future climate change. Based on the prediction results of the distributed hydrological model, the composite runoff index was used to analyze the risk response characteristics of future flood season drought events. Select the Copula function to establish the joint distribution of drought flow and duration during low-flow drought events in the flood season, including: Multiple two-dimensional Copula functions are compared, and the Bayesian information criterion and the Akaike information criterion are used to evaluate the fitting effect of each Copula function. The optimal Copula function is selected to establish a joint distribution function of drought flow and duration during a low-flow drought event in the flood season. The joint distribution function is expressed as follows: In the formula, To select the optimal Copula function, These are the low-flow drought flow and duration values ​​during the flood season. The distribution function of drought flow and duration. These are the distribution function expressions for drought flow and duration, respectively. These are the inverse function expressions for drought flow and duration distribution functions, respectively. The function expression for the joint distribution; The composite runoff index for low-flow drought events during the flood season is constructed using the following method: Based on the joint distribution function, the joint probability sequence of drought flow and drought duration during historical flood season low-flow events is calculated. The sequence is then standardized and normalized to obtain the corresponding composite runoff index. The calculation process is as follows: In the formula, The composite runoff index, For joint probability, For a sign function, when hour, Take 1.0, when hour, Take -1.0, These are intermediate variables, and c0, c1, c2, d1, d2, and d3 are all constants. , , , , , .

2. The flood season drought risk assessment method based on composite runoff index according to claim 1, characterized in that, The geographic information data of the target area includes digital elevation topographic data, river system and hydrological and meteorological station vector data of the target area; the hydrological and meteorological data of the target area includes long-term series of water level and flow observation data of hydrological stations, long-term series of meteorological observation data of meteorological stations, and long-term series of climate prediction data for different future emission scenarios; the water conservancy engineering data of the target area includes spatial distribution vector data of water intake projects in the target area and engineering parameters.

3. The flood season drought risk assessment method based on composite runoff index according to claim 1, characterized in that, Based on geographic information data, hydrological and meteorological data, water conservancy project data, and urban water supply demand, a drought threshold is set to select low-flow drought events during historical flood seasons, including: Given the water intake level of the water intake project and the water level-discharge relationship at the water intake channel cross section, the drought discharge threshold is determined by the water level-discharge relationship curve. In the absence of the above engineering parameters, the daily runoff process sequence during the flood season at the water intake section is selected, and a generalized extreme value distribution is used for fitting. The flow rate under the design frequency condition is selected as the drought threshold according to the urban water supply standard. The distribution function of the generalized extreme value distribution is as follows: In the formula, The distribution function, For random variables, These are the position parameter, scale parameter, and shape parameter, respectively, and exp is an empirical function; Based on the established drought threshold, historical flood season runoff data were used to select low-flow drought events during the flood season based on run theory. The flow and duration of each low-flow drought event were analyzed and calculated to obtain drought flow and drought duration sequence data for low-flow drought events during the flood season.

4. The flood season drought risk assessment method based on composite runoff index according to claim 3, characterized in that, By selecting the optimal probability distribution, we analyze the marginal distribution characteristics of drought flow and duration in low-flow drought events during the flood season, including: Based on drought flow and drought duration series data of low-flow drought events during the flood season, univariate probability distributions were used as candidate distribution types. The Kolmogorov-Smirnov test, Bayesian information criterion, and Akaike information content criterion were used as evaluation criteria to select the best-fit marginal distribution function of drought flow and duration for low-flow drought events during the flood season. Among them, the multiple univariate probability distributions include gamma distribution, Rayleigh distribution, normal distribution, exponential distribution, lognormal distribution, Weibull distribution, extreme value distribution, generalized Pareto distribution, generalized extreme value distribution, log-logistic distribution, logistic distribution, and Pearson type III distribution. Based on the optimally fitted marginal distribution function of drought flow and duration during low-flow drought events in the flood season, the marginal distribution characteristics of drought flow and duration during low-flow drought events in the flood season are analyzed.

5. The flood season drought risk assessment method based on composite runoff index according to claim 1, characterized in that, The flood season drought event levels are classified according to the Composite Runoff Index (CSI) as follows: At that time, the drought event during the flood season was classified as extreme drought. At that time, the drought reversal event during the flood season was classified as severe drought; At that time, the drought event during the flood season was classified as moderate drought. At that time, the drought level during the flood season was classified as mild.

6. The flood season drought risk assessment method based on composite runoff index according to claim 5, characterized in that, A distributed hydrological model for the watershed was constructed, considering different emission scenarios due to future climate change. Based on the prediction results of the distributed hydrological model, a composite runoff index was used to analyze the risk response characteristics of future flood season drought events, including: A distributed hydrological model of the target area is constructed. The target area is divided into grids using digital elevation topographic data, river system data, etc. The distributed hydrological model of the watershed is driven by historical meteorological data to simulate runoff changes, and historical hydrological data is used for model calibration. We selected future low-emission, medium-emission, and high-emission scenarios, and used the quantile mapping method to downscale the meteorological elements output by the model based on historical measured data, to obtain long-series data of future precipitation, temperature, wind speed, humidity, and radiation predicted by different climate models under the low-emission, medium-emission, and high-emission scenarios. Climate prediction data from different climate models under various future emission scenarios are used to drive a calibrated distributed watershed hydrological model to predict future runoff evolution in the watershed. A constructed composite runoff index is then used to analyze the risk response characteristics of future flood season drought events. The calculation formula is as follows: In the formula, To mitigate the risk of drought during the flood season, This represents the total number of low-flow drought events during the flood season. For the year, For the first The composite runoff index value of a low-flow drought event during the flood season. These are the upper and lower limits of the drought composite runoff index, corresponding to the level of drought reversal events during the flood season.

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