A method for analyzing the impact of water resources projects in a river basin on meteorological and hydrological droughts

Abstract

The present invention discloses a method for analyzing the impact of water resources projects in a basin on meteorological-hydrological drought. First, data of the target basin are collected and preprocessed to form a database for input into the SWAT model to simulate the hydrological process and obtain simulated monthly flow data. Then, the basin is divided into sub-basins and hydrological response units, and then a sensitivity analysis of the parameters is carried out to select the parameters with higher sensitivity. Next, the parameters are calibrated and verified to determine the optimal values, and the rationality and applicability of the SWAT model at this time are evaluated to obtain a relatively preferred target SWAT model. Finally, different driving factor scenarios are constructed through the target SWAT model for attribution analysis of meteorological-hydrological drought propagation. Through the method of the present invention, the impact of water resources development and utilization on the meteorological-hydrological drought process in the basin can be effectively explored, providing a scientific basis for future comprehensive prevention and control of basin droughts.

Description

Technical Field

[0001] The present invention relates to the field of hydrological water resources applications, and particularly to a method for analyzing the impact of water resources projects in a basin on meteorological-hydrological droughts. Background Art

[0002] In the natural state, meteorological droughts usually originate from climate anomalies such as insufficient precipitation and abnormal high temperatures. If the meteorological drought situation persists and spreads to the basin water cycle process through the underlying surface medium, it may further lead to increased evaporation, reduced soil moisture, decreased runoff, and lowered groundwater levels. In severe cases, it may lead to hydrological droughts. The spatio-temporal distribution characteristics, occurrence times, and their temporal variation trends of meteorological droughts and hydrological droughts show a high degree of correlation. Apart from climate change, the construction of water conservancy projects has, to a certain extent, changed the natural rhythm of the water cycle process in the Xijiang River Basin, and the development and utilization of water resources have had a certain impact on the meteorological-hydrological drought transmission process in the Xijiang River Basin. The present invention uses the SWAT model to carry out multi-scenario simulations to further explore the impact of water resources development and utilization on the meteorological-hydrological drought process in the Xijiang River Basin. Summary of the Invention

[0003] The present invention provides a method for analyzing the impact of water resources projects in a basin on meteorological-hydrological droughts. By using the SWAT model to conduct attribution analysis on the meteorological-hydrological drought propagation process, it can effectively understand the impact of water resources development and utilization on the hydrological drought situation in the basin, provide a reference for subsequent basin governance, so as to better carry out basin governance and improve the ability to prevent disasters.

[0004] The technical solution of the present invention is as follows:

[0005] A method for analyzing the impact of water resources projects in a basin on meteorological-hydrological droughts includes the following steps:

[0006] Step 1: Select a target basin and collect DEM digital elevation data, soil data, land use data, and meteorological data of the target basin over a period of time;

[0007] Step 2: Preprocess the soil data, land use data, and meteorological data, construct a corresponding database, and input the database into the SWAT model to simulate the hydrological process of the target basin during the corresponding period, so as to obtain the simulated monthly flow data of the target basin;

[0008] Step 3: Input the DEM digital elevation data into the SWAT model, and divide the sub-basins and hydrological response units of the target basin in the SWAT model;

[0009] Step 4: Conduct a sensitivity analysis on the parameters of the SWAT model to obtain the top 10 parameters ranked by sensitivity;

[0010] Step 5: Calibrate and verify the top 10 parameters ranked by sensitivity according to the simulated monthly flow data obtained in Step 2, determine the optimal values of each parameter, and evaluate the rationality and applicability of the SWAT model simulation through the Nash efficiency coefficient and the coefficient of determination, so as to obtain the target SWAT model with well-regulated parameters;

[0011] Step 6: In the target SWAT model, by constructing different driving factor scenarios, quantitatively analyze the impact on the meteorological-hydrological drought propagation law through simulation from all aspects.

[0012] The present invention first collects data of the target basin, preprocesses the data to form a database for input into the SWAT model to simulate the hydrological process, so as to obtain the simulated monthly flow data of the target basin. Then, according to the obtained DEM data, the basin is divided into sub-basins and hydrological response units to obtain each hydrological response unit and its corresponding parameters. Then, a sensitivity analysis is performed on the parameters, and the parameters with higher sensitivity are selected. Then, the parameters are calibrated and verified through the monthly flow data to determine the optimal values, and the rationality and applicability of the SWAT model at this time are evaluated, so as to obtain a more preferred target SWAT model. Finally, different driving factor scenarios are constructed through the target SWAT model to conduct an attribution analysis of meteorological-hydrological drought propagation. Through the method of the present invention, the impact of water resource development and utilization on the meteorological-hydrological drought process in the basin can be effectively explored, providing a scientific basis for future comprehensive drought prevention and control in the basin.

[0013] Further, in Step 2, the establishment process of the soil database is as follows:

[0014] Import the soil data, calculate the soil parameters required for input into the SWAT model through the SPAW software, and complete the construction of the soil database.

[0015] Further, in Step 2, the establishment process of the land use database is as follows:

[0016] According to the collected land use data, reclassify the original land use type map, and each land use type is represented by a 4-digit code to complete the construction of the land use database.

[0017] Further, in Step 2, the establishment process of the meteorological database is as follows:

[0018] Calculate the parameters of the weather generator through the SwatWeather software. Except for the daily rainfall, daily minimum temperature, and daily maximum temperature which are the measured data collected, other data are simulated and generated by the weather generator to complete the construction of the meteorological database.

[0019] Further, the process of dividing the sub-basins and hydrological response units in Step 3 is as follows:

[0020] Step 3.1: After loading the DEM digital elevation data, set the minimum catchment area threshold in the SWAT model to define and extract the river network. Through sub-basin parameter calculation, the entire target basin is divided into several sub-basins;

[0021] Step 3.2: After dividing the sub-basins, set the soil type, land use type, and slope area percentage thresholds within each sub-basin, input the land use type maps for each year respectively, and divide the hydrological response units by year.

[0022] Furthermore, in Step 4, the process of sensitivity analysis of the parameters of the SWAT model is as follows:

[0023] Use the SWAT-CUP program to conduct sensitivity analysis on the parameters of the SWAT model, arrange the parameters according to the sensitivity degree, calibrate the parameters, and list the top 10 parameters in terms of sensitivity degree after calibration.

[0024] Furthermore, in Step 5, the process of calibrating and validating the parameters is as follows:

[0025] First, select three periods, namely the warm-up period, calibration period, and validation period. Use the monthly flow data measured at the hydrological station to calibrate and validate the parameters, and verify whether the simulated monthly flow data and the measured monthly flow data obtained in Step 2 of the SWAT model are within the set error range. Iterate until the two results are within the set error range to determine the optimal values of each parameter.

[0026] Furthermore, in Step 5, after determining the optimal values of the parameters, the process of evaluating the rationality and applicability of the SWAT model through the Nash efficiency coefficient and the coefficient of determination is as follows:

[0027] The calculation formulas for the Nash efficiency coefficient and the coefficient of determination are as follows:

[0028]

[0029] In the formula, NS represents the Nash efficiency coefficient, and R 2 represents the coefficient of determination, Q o and Q s represent the measured flow and the simulated flow of the SWAT model at the hydrological station respectively, represent the average values of the measured flow and the simulated flow respectively, Q i,o and Q i,s represent the measured flow and the simulated flow in the i-th month respectively;

[0030] If the values of both NS and R 2 are greater than 0.5, it indicates that the simulation results of the SWAT model are ideal, and use the SWAT model at this time as the target SWAT model.

[0031] Furthermore, in step 6, different driving factor scenarios are constructed, and the process of simulating and quantitatively analyzing the impact on the meteorological-hydrological drought propagation law from various aspects is as follows:

[0032] Step 6.1: Simulation scenario design;

[0033] Select a period as the baseline period, take the hydrological drought situation in this period as the baseline scenario, and take the subsequent period as the impact period. Based on the data in the impact period, design comparison scenarios. In order to further analyze the differences in driving contribution rates across generations, divide the impact period into two time segments and design different combinations of meteorological data and land use data as the baseline scenario, climate scenario, land use scenario, and water resource development and utilization scenario;

[0034] Step 6.2: Quantify the contribution rates of different driving factors;

[0035] According to the simulation results of each scenario in the target SWAT model, calculate the drought metric indicators for each time segment respectively. On this basis, obtain the change amounts of the hydrological drought characteristics under the scenarios of climate, land use change, and water resource development and utilization; assume that the drought characteristic variable in the baseline scenario is x 0 , and the corresponding drought characteristic variables in the climate scenario, land use scenario, and water resource development and utilization scenario are x cli , x luc , x cl respectively. Define the measured drought characteristic variable considering water resource development and utilization as x cls . Then, the relative impact contribution rates of each factor on the hydrological drought drive are shown by the following formula:

[0036] Δx cli = x cli - x 0

[0037] Δx luc = x luc - x 0

[0038] Δx oper = x cls - x cl

[0039]

[0040] In the formula, θ cli , θ luc , θ oper are the driving contribution rates of climate change, land use change, and water resource development and utilization to hydrological drought respectively; Δx cli is the change amount of the drought characteristic variable in the climate scenario; Δxluc is the change amount of the drought characteristic variable in the land use plan; Δx oper is the change amount of the drought characteristic variable in the water resources development and utilization plan;

[0041] If θ cli 、θ luc 、θ oper > 0, it indicates the intensifying effect on hydrological drought;

[0042] If θ cli 、θ luc 、θ oper < 0, it indicates the mitigation effect on hydrological drought;

[0043] If θ cli 、θ luc 、θ oper = 0, it means that the driving effect is not obvious.

[0044] The present invention also provides a system for analyzing the impact of watershed water resources projects on meteorological-hydrological droughts, including the following modules:

[0045] A data collection module, which is used to select a target watershed and collect DEM digital elevation data, soil data, land use data, and meteorological data of the target watershed within a certain period;

[0046] A preprocessing module, which is used to preprocess the soil data, land use data, and meteorological data, construct a corresponding database, input the database into the SWAT model to simulate the hydrological process of the target watershed during the corresponding period, and thus obtain the simulated monthly flow data of the target watershed;

[0047] A division module, which is used to input the DEM digital elevation data into the SWAT model and divide the target watershed into sub-watersheds and hydrological response units in the SWAT model;

[0048] A verification and analysis module, which is used to perform sensitivity analysis on the parameters of the SWAT model, obtain the top 10 parameters ranked by sensitivity, calibrate and verify the top 10 parameters ranked by sensitivity according to the obtained simulated monthly flow data, determine the optimal values of each parameter, and evaluate the rationality and applicability of the SWAT model simulation through the Nash efficiency coefficient and the determination coefficient, so as to obtain the target SWAT model with well-regulated parameters;

[0049] An analysis and demonstration module, which is used to simulate and quantitatively analyze the impact on the meteorological-hydrological drought propagation law from various aspects by constructing different driving factor schemes in the target SWAT model;

[0050] The data collection module, preprocessing module, partitioning module, verification and analysis module, and analysis and demonstration module are all communicatively connected to the central processing unit to achieve information exchange among them through the central processing unit.

[0051] The beneficial effects of the present invention are as follows:

[0052] The present invention provides a method for analyzing the impact of basin water resources projects on meteorological-hydrological droughts. Through numerical simulation of hydrological processes under different scenarios, attribution analysis of the meteorological-hydrological drought process in the basin is carried out, and the impact of water resources development and utilization on the meteorological-hydrological drought process in the basin is analyzed, so as to provide a scientific basis for future comprehensive prevention and control of basin droughts. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flowchart of the method for analyzing the impact of basin water resources projects on meteorological-hydrological droughts according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The drawings are only for illustrative purposes and should not be construed as limitations on this patent; for better illustration of this embodiment, some components in the drawings will be omitted, enlarged or reduced, and do not represent the dimensions of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted. The positional relationships described in the drawings are only for illustrative purposes and should not be construed as limitations on this patent.

[0055] Embodiment 1:

[0056] As Figure 1 shown, a method for analyzing the impact of basin water resources projects on meteorological-hydrological droughts includes the following steps:

[0057] Step 1: Select a target basin and collect DEM digital elevation data, soil data, land use data, and meteorological data of the target basin over a period of time.

[0058] Step 2: Preprocess the soil data, land use data, and meteorological data, construct a corresponding database, and input the database into the SWAT model to simulate the hydrological process of the target basin during the corresponding period, so as to obtain the simulated monthly flow data of the target basin.

[0059] Step 3: Input the DEM digital elevation data into the SWAT model, and divide the sub-basins and hydrological response units of the target basin in the SWAT model.

[0060] Step 4: Conduct a sensitivity analysis on the parameters of the SWAT model to obtain the top 10 parameters ranked by sensitivity.

[0061] Step 5: Calibrate and validate the top 10 parameters ranked by sensitivity based on the simulated monthly flow data obtained in Step 2, determine the optimal values of each parameter, and evaluate the rationality and applicability of the SWAT model simulation through the Nash efficiency coefficient and the coefficient of determination, so as to obtain the target SWAT model with well-regulated parameters;

[0062] Step 6: In the target SWAT model, by constructing different driving factor scenarios, quantitatively analyze the impact on the meteorological-hydrological drought propagation law from various aspects through simulation.

[0063] The present invention first collects data of the target basin, preprocesses the data to form a database for input into the SWAT model to simulate the hydrological process, so as to obtain the simulated monthly flow data of the target basin. Then, according to the obtained DEM data, the basin is divided into sub-basins and hydrological response units to obtain each hydrological response unit and its corresponding parameters. Then, a sensitivity analysis is carried out on the parameters, the parameters with higher sensitivity are selected, and then the parameters are calibrated and validated through the monthly flow data to determine the optimal values, and the rationality and applicability of the SWAT model at this time are evaluated, so as to obtain a relatively preferred target SWAT model. Finally, different driving factor scenarios are constructed through the target SWAT model for attribution analysis of meteorological-hydrological drought propagation. Through the method of the present invention, the impact of water resource development and utilization on the meteorological-hydrological drought process of the basin can be effectively explored, providing a scientific basis for the future comprehensive prevention and control of basin drought.

[0064] In Step 1 of this embodiment, the spatial resolution of the DEM digital elevation data is 30 m, which is sourced from the Geospatial Cloud. The soil data is sourced from the Nanjing Institute of Soil Science, Chinese Academy of Sciences. The land use data is sourced from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences. The meteorological data is sourced from the China Meteorological Data Sharing Service Network.

[0065] In Step 2 of this embodiment:

[0066] The process of establishing the soil database is as follows:

[0067] Import the soil data, and calculate the soil parameters required for input into the SWAT model through the SPAW software to complete the construction of the soil database.

[0068] The process of establishing the land use database is as follows:

[0069] According to the collected land use data, reclassify the original land use type map, and each land use type is represented by a 4-digit code to complete the construction of the land use database.

[0070] The process of establishing the meteorological database is as follows:

[0071] The parameters of the weather generator are calculated by the SwatWeather software. Except for the daily rainfall, daily minimum temperature, and daily maximum temperature which are the measured data collected, other data are generated by the weather generator simulation to complete the construction of the meteorological database.

[0072] In step 3 of this embodiment, the process of dividing the sub-basins and hydrological response units is as follows:

[0073] Step 3.1: After loading the DEM digital elevation data, set the minimum catchment area threshold in the SWAT model to define and extract the river network. Through the calculation of sub-basin parameters, the entire target basin is divided into several sub-basins.

[0074] Step 3.2: After dividing the sub-basins, set the soil type, land use type, and slope area percentage thresholds within each sub-basin, input the land use type maps of each year respectively, and divide the hydrological response units by year.

[0075] In step 4 of this embodiment, the process of performing a sensitivity analysis on the parameters of the SWAT model is as follows:

[0076] Use the SWAT-CUP program to perform a sensitivity analysis on the parameters of the SWAT model, arrange the parameters according to the sensitivity degree, calibrate the parameters, and list the top 10 parameters in terms of sensitivity degree after calibration.

[0077] In step 5 of this embodiment, the process of calibrating and validating the parameters is as follows:

[0078] First, select three periods, namely the warm-up period, calibration period, and validation period. Use the monthly flow data measured at the hydrological station to calibrate and validate the parameters, and verify whether the simulated monthly flow data and the measured monthly flow data obtained in step 2 of the SWAT model are within the set error range. Iterate until the two results are within the set error range to determine the optimal values of each parameter.

[0079] In step 5 of this embodiment, after determining the optimal values of the parameters, the process of evaluating the rationality and applicability of the SWAT model through the Nash-Sutcliffe efficiency coefficient (NS) and the coefficient of determination (R2) is as follows:

[0080] The calculation formulas for the Nash-Sutcliffe efficiency coefficient and the coefficient of determination are as follows:

[0081]

[0082] In the formula, NS represents the Nash-Sutcliffe efficiency coefficient, and R 2 represents the coefficient of determination, and Q o , Q s represent the measured flow at the hydrological station and the simulated flow of the SWAT model respectively. respectively represent the average values of the measured flow and the simulated flow, Q i,o , Q i,s respectively represent the measured flow and the simulated flow in the i-th month;

[0083] If both the values of NS and R 2 are greater than 0.5, it indicates that the simulation result of the SWAT model is ideal, and the SWAT model at this time is used as the target SWAT model.

[0084] In step 6 of this embodiment, different driving factor schemes are constructed, and the process of quantitatively analyzing the impact on the meteorological-hydrological drought propagation law from various aspects is as follows:

[0085] Step 6.1: Simulation scheme design;

[0086] Select a period as the reference period, take the hydrological drought situation in this period as the reference scheme, and take the subsequent period as the impact period. Based on the data in this impact period, design comparison schemes. In order to further analyze the differences in driving contribution rates across generations, divide the impact period into two time periods, and design different combinations of meteorological data and land use data as the reference scheme, climate scheme, land use scheme, and water resources development and utilization scheme;

[0087] Step 6.2: Quantify the contribution rates of different driving factors;

[0088] According to the simulation results of each scheme in the target SWAT model, calculate the drought metric indicators for each time period respectively. On this basis, obtain the change amounts of the hydrological drought characteristics in the scenarios of climate, land use change, and water resources development and utilization; assume that the drought characteristic variable in the reference scheme is x 0 , and the corresponding drought characteristic variables in the climate scheme, land use scheme, and water resources development and utilization scheme are x cli , x luc , x cl respectively. Define the measured drought characteristic variable considering water resources development and utilization as x cls . Then the relative impact contribution rates of each factor on the hydrological drought driving effect are shown in the following formula:

[0089] Δx cli =x cli -x 0

[0090] Δx luc =x luc -x 0

[0091] Δx oper =x cls -x cl

[0092]

[0093] Wherein, θ cli and θ luc and θ oper are the driving contribution rates of climate change, land use change, and water resources development and utilization to hydrological drought, respectively; Δx cli is the change amount of the drought characteristic variable in the climate scenario; Δx luc is the change amount of the drought characteristic variable in the land use scenario; Δx oper is the change amount of the drought characteristic variable in the water resources development and utilization scenario;

[0094] If θ cli and θ luc and θ oper > 0, it indicates the intensifying effect on hydrological drought;

[0095] If θ cli and θ luc and θ oper < 0, it indicates the alleviating effect on hydrological drought;

[0096] If θ cli and θ luc and θ oper = 0, it means the driving effect is not obvious.

[0097] The present invention provides a method for analyzing the impact of basin water resources projects on meteorological-hydrological drought. Through numerical simulation of hydrological processes under different scenarios, attribution analysis of the meteorological-hydrological drought process in the basin is carried out, and the impact of water resources development and utilization, etc. on the meteorological-hydrological drought process in the basin is analyzed, so as to provide a scientific basis for future comprehensive prevention and control of basin drought.

[0098] Example 2:

[0099] In this example, the Xijiang River Basin is taken as an example to illustrate a method for analyzing the impact of basin water resources projects on meteorological-hydrological drought in Example 1.

[0100] In step 1, the spatial resolution of the DEM digital elevation data is 30 m, which is sourced from the Geospatial Cloud; the scale of the soil data is 1:1,000,000, which is sourced from the Nanjing Institute of Soil Science, Chinese Academy of Sciences; the land use data includes Landsat TM image remote sensing data for the three years of 1980, 1990, and 2000, with a scale of 1:100,000, all sourced from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences; the meteorological data includes daily-scale meteorological data of 41 meteorological stations such as Liupanshui, Wuzhou, Yuxi, Baise, and Pingguo in the Xijiang River Basin from 1978 to 2018, mainly including daily average rainfall, daily maximum temperature, etc., and the data is sourced from the China Meteorological Science Data Sharing Service Network.

[0101] In Step 2, since the SWAT (Soil and Water Assessment Tool) model is a watershed hydrological model with a strong physical mechanism, the hydrological processes corresponding to different periods can be simulated by inputting meteorological data and land use data of different periods, so as to obtain the simulated monthly flow data. However, before running the model, some input data need to be preprocessed and the corresponding database needs to be constructed.

[0102] a) Land use database

[0103] The original land use type map is reclassified, and each land use type is represented by a 4-digit code to complete the construction of the land use database. The land use types in the Xijiang River Basin are shown in Table 1.

[0104] Table 1 Reclassification of land use types in the Xijiang River Basin

[0105]

[0106] b) Soil database

[0107] The soil data used is the Harmonized World Soil Database (HWSD). The soil parameters required for model input are calculated by SPAW software. The soil types and spatial distribution maps in the Xijiang River Basin are shown in Table 2.

[0108] Table 2 Soil type table in the Xijiang River Basin

[0109]

[0110] c) Meteorological database

[0111] The parameters of the weather generator are calculated by SwatWeather software. Except for the daily rainfall, daily minimum temperature, and daily maximum temperature which are measured data, other data are simulated by the weather generator.

[0112] In Step 3, the sub-watersheds and hydrological response units in the Xijiang River Basin are divided as follows:

[0113] The minimum catchment area threshold is set to 800,000 ha for the definition and extraction of the river network. Through the calculation of sub-watershed parameters, the entire Xijiang River Basin is divided into 17 sub-watersheds. After dividing the sub-watersheds, the thresholds of soil type, land use type, and slope area percentage within each sub-watershed are all set to 0%. By inputting the land use type maps of 1980, 1990, and 2000 respectively, 1887, 1876, and 1879 hydrological response units are divided respectively.

[0114] In Step 4, a sensitivity analysis is carried out on the relevant parameters;

[0115] The SWAT-CUP program was used to conduct a sensitivity analysis of relevant parameters, select the parameters with higher sensitivity among them and calibrate them. The top 10 parameters ranked by sensitivity are listed in Table 3.

[0116] Table 3 Ranking of SWAT model parameter sensitivity

[0117]

[0118] In step 5, the top 10 parameters ranked by the above sensitivity were calibrated to determine the optimal values of each parameter and verify whether they are reasonable.

[0119] After completing the parameter sensitivity analysis, the above 10 sensitive parameters were calibrated to determine the optimal values of each parameter. In this study, 1978 was used as the warm-up period, 1979 - 1984 as the calibration period, and 1985 - 1989 as the verification period. The measured monthly flow data in the hydrological station was used to calibrate and verify the parameters, and verify whether the simulated monthly flow data obtained in step 2 by the SWAT model and the measured monthly flow data are within the set error range, and iterate until the two results are within the set error range. The measured monthly flow data includes the monthly flow data of three stations, namely Qianjiang, Dahuangjiangkou, and Wuzhou hydrological stations in the Xijiang River Basin from 1980 to 2013. After iterative calculation, the optimal values of each parameter are shown in Table 4.

[0120] Table 4 Results of parameter values in the runoff simulation process

[0121]

[0122]

[0123] After obtaining the optimal values of the parameters, the Nash efficiency coefficient (NS) and the coefficient of determination (R 2 ) were selected to evaluate the rationality and applicability of the SWAT model; after calculation, during the calibration period, the R 2 of Qianjiang Station was 0.82 and the NS was 0.61; the R 2 of Dahuangjiangkou Station was 0.87 and the NS was 0.87; the R 2 of Wuzhou Station was 0.90 and the NS was 0.89. During the verification period, the R 2 of Qianjiang Station was 0.87 and the NS was 0.86; the R 2 of Dahuangjiangkou Station was 0.89 and the NS was 0.81; the R 2 of Wuzhou Station was 0.91 and the NS was 0.88. The above NS and R 2 are both greater than 0.5, indicating that the simulation results are relatively reasonable, that is, the results of the parameter values in the runoff simulation process of the target SWAT model are as shown in Table 4 above.

[0124] In step 6, different driving factor scenarios are constructed, and the process of quantitatively analyzing the impact on the meteorological-hydrological drought propagation law through simulation from various aspects is as follows:

[0125] Simulation scenario design:

[0126] In this embodiment, the period from 1980 to 1989 is taken as the baseline period, and the hydrological drought situation during this period is used as the baseline scenario. The period from 1990 to 2009 is taken as the impact period, and the comparison scenarios are designed based on the data of this period. In order to further analyze the differences in driving contribution rates across generations, the period from 1990 to 2009 is divided into two time periods: 1990 - 1999 (1990s) and 2000 - 2009 (2000s). The specific simulation design scenarios are shown in Table 5.

[0127] Table 5 Hydrological drought driving simulation scenarios

[0128]

[0129]

[0130] Quantifying the contribution rates of different driving factors:

[0131] According to the simulation results of each scenario, the drought metric indicators for each time period are calculated respectively. On this basis, the change amounts of the hydrological drought characteristics under the scenarios of climate, land use change, and water resources development and utilization are obtained. Assume that a certain drought characteristic variable (duration, intensity, or frequency) in Scenario 1 is x 0 , and the corresponding drought characteristic variables in Scenario 2 / 3, Scenario 4 / 5, and Scenario 6 / 7 simulation scenarios are x cli , x luc , x cl respectively. Define the hydrological drought characteristic variable considering water resources development and utilization in the 1990s / 2000s as x cls . Then, the relative impact contribution rates of each factor on the hydrological drought driving effect are shown by the following formula:

[0132] Δx cli = x cli - x 0

[0133] Δx luc = x luc - x 0

[0134] Δx oper = x cls - x cl

[0135]

[0136] In the formula, θ cli 、θluc , θ oper are the driving contribution rates of climate change, land use change, and water resource development and utilization to hydrological drought, respectively; Δx cli is the change in the drought characteristic variable in the climate scenario; Δx luc is the change in the drought characteristic variable in the land use scenario; Δx oper is the change in the drought characteristic variable in the water resource development and utilization scenario;

[0137] If θ cli , θ luc , θ oper > 0, it indicates an intensifying effect on hydrological drought;

[0138] If θ cli , θ luc , θ oper < 0, it indicates a mitigating effect on hydrological drought;

[0139] If θ cli , θ luc , θ oper = 0, it means that the driving effect is not obvious.

[0140] In summary, the attribution results are analyzed as follows:

[0141] The relative driving impact contribution rates of climate change, land use change, and water resource utilization and development in the 1990s and 2000s to meteorological-hydrological drought are shown in Table 6. It can be seen that the driving effect of land use change on meteorological-hydrological drought is 0% in both cases, indicating that the driving effect of land use change on meteorological-hydrological drought is extremely not obvious. Climate change and water resource development and utilization are the main driving factors in the meteorological-hydrological drought process in the Xijiang River Basin, and their contributions to the evolution of the meteorological-hydrological drought process show certain spatio-temporal heterogeneity.

[0142] Table 6 Relative driving effects of climate and land use changes on the hydrological drought situation

[0143]

[0144] Specifically, in the 1990s, the relative driving contribution rates of climate change to the duration and intensity of meteorological - hydrological droughts in the Xijiang River Basin, especially in the middle reaches, were both negative, indicating that under climate change, the duration and intensity of meteorological - hydrological drought events in the Xijiang River Basin decreased. However, its driving contribution rate to the drought frequency was distributed in the range of 30% - 50%, indicating that the meteorological - hydrological drought frequency increased under climate change. In other words, the climate change in the 1990s increased the occurrence frequency of meteorological - hydrological droughts but alleviated the severity of droughts. For water resource development and utilization, its driving contribution rates to the duration, intensity, and frequency of hydrological droughts in each region of the Xijiang River Basin were all positive, that is, it played an aggravating role. Among them, the contribution rates to the duration and intensity of meteorological - hydrological droughts in the upper reaches were 88% and 57% respectively, and the contribution rates to the duration and intensity of meteorological - hydrological droughts in the lower reaches were 81% and 18% respectively, significantly exacerbating the meteorological - hydrological drought conditions in the Xijiang River Basin, especially in the upper and lower reaches.

[0145] In the 2000s, the contributions of climate change to the duration, intensity, and frequency of meteorological - hydrological droughts in the upper reaches of the Xijiang River Basin were all positive, being 61%, 20%, and 67% respectively, indicating that climate change exacerbated the meteorological - hydrological drought in the upper reaches. For the middle and lower reaches of the Xijiang River Basin, climate change increased the drought duration and occurrence frequency, but at the same time alleviated the intensity of drought events. Generally speaking, the climate change in the 2000s exacerbated the meteorological - hydrological drought situation in the upper reaches, increased the occurrence frequency of hydrological droughts in the middle and lower reaches, but alleviated the severity of droughts. For water resource development and utilization, its contribution rates to the drought intensity in each region were all negative, being - 80%, - 65%, and - 52% respectively, indicating that it significantly alleviated the drought situation in the Xijiang River Basin, especially in the upper reaches. This may be because during the 2000s, several water conservancy projects were built in the upper reaches of the Xijiang River Basin, such as the Tianhengqiao First - stage Water Control Project on the Nanpan River in the upper reaches (December 2000), the Guangzhao Hydropower Station on the Beipan River in the upper reaches (August 2008), etc., effectively regulating and dispatching water resources, increasing the runoff during the dry season, and alleviating the drought situation. However, the results show that the contribution rates of water resource development and utilization to the occurrence frequency of drought events in each river basin were all positive, being 33%, 36%, and 72% respectively, indicating that water resource development and utilization increased the number of meteorological - hydrological droughts in the Xijiang River Basin, especially in the lower reaches. This may be because with the rapid economic development and the advancement of urbanization, the water demand and water intake for production and living in the densely populated urban areas in the lower reaches of the Xijiang River Basin increased, leading to an increase in water supply pressure and an increase in the occurrence frequency of hydrological droughts.

[0146] Example 3:

[0147] This example provides a system for analyzing the impact of water resources projects in a river basin on meteorological - hydrological droughts, including the following modules:

[0148] The data collection module is used to select the target watershed and collect DEM digital elevation data, soil data, land use data, and meteorological data of the target watershed over a period of time;

[0149] The preprocessing module is used to preprocess soil data, land use data, and meteorological data, and to build a corresponding database. The database is input into the SWAT model to simulate the hydrological process of the target basin in the corresponding period, so as to obtain the simulated monthly flow data of the target basin;

[0150] The partitioning module is used to input the DEM digital elevation data into the SWAT model and divide the target watershed into sub-basins and hydrological response units in the SWAT model;

[0151] The verification and analysis module is used to conduct sensitivity analysis on the parameters of the SWAT model, obtain the top 10 parameters in terms of sensitivity, calibrate and verify the top 10 parameters in terms of sensitivity based on the obtained simulated monthly flow data, determine the optimal value of each parameter, and evaluate the rationality and applicability of the SWAT model simulation through the Nash efficiency coefficient and determination coefficient, so as to obtain the target SWAT model with well-regulated parameters;

[0152] The analysis and demonstration module is used to simulate and quantitatively analyze the impact of meteorological-hydrological drought propagation laws from all aspects by constructing different driving factor scenarios in the target SWAT model;

[0153] The data collection module, preprocessing module, division module, verification and analysis module, and analysis and demonstration module are all connected in communication with the central processing unit to achieve information exchange between them through the central processing unit.

[0154] Obviously, the above embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for analyzing the impact of basin water resources projects on meteorological - hydrological drought, characterized in that, it includes the following steps: Step 1: Select the target basin, and collect the DEM digital elevation data, soil data, land use data, and meteorological data of the target basin over a period of time; Step 2: Pre - process the soil data, land use data, and meteorological data, and construct the corresponding database. Input the database into the SWAT model to simulate the hydrological process of the target basin during the corresponding period, so as to obtain the simulated monthly flow data of the target basin; Step 3: Input the DEM digital elevation data into the SWAT model, and divide the sub - basins and hydrological response units of the target basin in the SWAT model; including: Step 3.1: After loading the DEM digital elevation data, set the minimum catchment area threshold in the SWAT model to define and extract the river network. Through the calculation of sub - basin parameters, divide the entire target basin into several sub - basins; Step 3.2: After dividing the sub - basins, set the soil type, land use type, and slope area percentage thresholds within each sub - basin, input the land use type maps of each year respectively, and divide the hydrological response units by year; Step 4: Conduct a sensitivity analysis on the parameters of the SWAT model to obtain the top 10 parameters ranked by sensitivity; including: Use the SWAT - CUP program to conduct a sensitivity analysis on the parameters of the SWAT model, arrange the parameters according to sensitivity and calibrate the parameters, and list the top 10 parameters ranked by sensitivity after calibration; Step 5: Calibrate and verify the top 10 parameters ranked by sensitivity according to the simulated monthly flow data obtained in Step 2, determine the optimal values of each parameter, and evaluate the rationality and applicability of the SWAT model simulation through the Nash efficiency coefficient and determination coefficient, so as to obtain the target SWAT model with well - regulated parameters; The process of calibrating and verifying the parameters is as follows: First, select three periods as the warm - up period, calibration period, and verification period respectively. Use the monthly flow data measured by the hydrological station to calibrate and verify the parameters, and verify whether the simulated monthly flow data and the measured monthly flow data obtained in Step 2 of the SWAT model are within the set error range. Iterate until the two results are within the set error range, so as to determine the optimal values of each parameter; After determining the optimal values of the parameters, the process of evaluating the rationality and applicability of the SWAT model through the Nash efficiency coefficient and determination coefficient is as follows: The calculation formulas of the Nash efficiency coefficient and determination coefficient are as follows: where NS represents the Nash efficiency coefficient, and R 2 represents the coefficient of determination, Q o and Q s represent the measured flow of the hydrological station and the simulated flow of the SWAT model, respectively, and represent the average values of the measured flow and the simulated flow, respectively. Q i,o and Q i,s represent the measured flow and the simulated flow in the i-th month, respectively; If NS and R 2 are both greater than 0.5, it indicates that the simulation results of the SWAT model are ideal, and the SWAT model at this time is used as the target SWAT model; Step 6: In the target SWAT model, by constructing different driving factor scenarios, simulate and quantitatively analyze the impact on the propagation law of meteorological - hydrological drought from various aspects.

2. A method for analyzing the impact of basin water resources projects on meteorological - hydrological drought according to claim 1, characterized in that, in Step 2, the establishment process of the soil database is as follows: Import the soil data, and calculate the soil parameters required for input into the SWAT model through the SPAW software to complete the construction of the soil database.

3. A method for analyzing the impact of basin water resources projects on meteorological - hydrological drought according to claim 1, It is characterized in that In step 2, the process of establishing the land use database is as follows: According to the collected land use data, the original land use type map is reclassified, and each land use type is represented by a 4-digit code to complete the construction of the land use database.

4. A method for analyzing the impact of watershed water resources projects on meteorological-hydrological droughts according to claim 1, It is characterized in that In step 2, the process of establishing the meteorological database is as follows: The parameters of the weather generator are calculated by the SwatWeather software. Except for the daily rainfall, daily minimum temperature, and daily maximum temperature which are the measured data collected, other data are simulated and generated by the weather generator to complete the construction of the meteorological database.

5. A method for analyzing the impact of watershed water resources projects on meteorological-hydrological droughts according to claim 1, It is characterized in that In step 6, the process of constructing different driving factor scenarios and simulating and quantitatively analyzing the impact on the propagation law of meteorological-hydrological droughts from all aspects is as follows: Step 6.1: Simulation scenario design; Select a period as the reference period, take the hydrological drought situation in this period as the reference scenario, and take the subsequent period as the impact period. Based on the data in the impact period, design a comparison scenario, divide the impact period into two time periods, and design different combinations of meteorological data and land use data as the reference scenario, climate scenario, land use scenario, and water resources development and utilization scenario; Step 6.2: Quantify the contribution rate of different driving factors; According to the simulation results of each scenario in the target SWAT model, the drought metrics for each time period are calculated respectively. Based on this, the change amounts of the hydrological drought characteristics under the scenarios of climate, land use change, and water resources development and utilization are obtained; assuming that the drought characteristic variable in the baseline scenario is x 0 , the corresponding drought characteristic variables in the climate scenario, land use scenario, and water resources development and utilization scenario are x cli , x luc , x cl , and the measured drought characteristic variable considering water resources development and utilization is defined as x cls . Then the relative impact contribution rates of each factor to the hydrological drought driving effect are shown in the following formula: Δx cli = x cli - x 0 Δx luc = x luc - x 0 Δx oper = x cls - x cl where θ cli , θ luc , θ oper are the driving contribution rates of climate change, land use change, and water resource development and utilization to hydrological drought, respectively; Δx cli is the change in the drought characteristic variable in the climate scenario; Δx luc is the change in the drought characteristic variable in the land use plan; Δx oper is the change in the drought characteristic variable in the water resources development and utilization plan; If θ cli , θ luc , θ oper > 0, it indicates the intensifying effect on hydrological drought; If θ cli , θ luc , θ oper < 0, it indicates the alleviating effect on hydrological drought; If θ cli , θ luc , θ oper = 0, it means that the driving effect is not obvious.

6. A system for analyzing the impact of watershed water resources projects on meteorological-hydrological droughts, It is characterized in that It includes the following modules: A data collection module for selecting a target watershed and collecting DEM digital elevation data, soil data, land use data, and meteorological data of the target watershed for a period of time; A preprocessing module for preprocessing the soil data, land use data, and meteorological data, constructing corresponding databases, inputting the databases into the SWAT model to simulate the hydrological process of the target watershed in the corresponding period, and obtaining the simulated monthly flow data of the target watershed; A division module for inputting the DEM digital elevation data into the SWAT model and dividing the target watershed into sub-watersheds and hydrological response units in the SWAT model; Dividing the target watershed into sub-watersheds and hydrological response units in the SWAT model includes: setting the minimum catchment area threshold in the SWAT model after loading the DEM digital elevation data to define and extract the river network, calculating the sub-watershed parameters, and dividing the entire target watershed into several sub-watersheds; after dividing the sub-watersheds, set the soil type, land use type, and slope area percentage thresholds within each sub-watershed, input the land use type maps of each year respectively, and divide the hydrological response units by year; A verification analysis module is used to perform sensitivity analysis on the parameters of the SWAT model to obtain the top 10 parameters ranked by sensitivity. Based on the obtained simulated monthly flow data, the top 10 parameters ranked by sensitivity are calibrated and verified to determine the optimal values of each parameter, and the rationality and applicability of the SWAT model simulation are evaluated through the Nash efficiency coefficient and the coefficient of determination, so as to obtain the target SWAT model with well-regulated parameters. Among them, the SWAT-CUP program is used to perform sensitivity analysis on the parameters of the SWAT model, arrange the parameters according to the sensitivity degree and calibrate the parameters, and list the top 10 parameters ranked by sensitivity after calibration; The process of calibrating and verifying the parameters is as follows: First, three periods are selected as the warm-up period, the calibration period, and the verification period respectively. The monthly flow data measured by the hydrological station are used to calibrate and verify the parameters, and it is verified whether the simulated monthly flow data and the measured monthly flow data obtained in the preprocessing module of the SWAT model are within the set error range, and the iteration is continued until the two results are within the set error range, so as to determine the optimal values of each parameter; After determining the optimal values of the parameters, the process of evaluating the rationality and applicability of the SWAT model through the Nash efficiency coefficient and the coefficient of determination is as follows: The calculation formulas of the Nash efficiency coefficient and the coefficient of determination are as follows: where NS represents the Nash efficiency coefficient, and R 2 represents the coefficient of determination, and Q o , Q s represent the measured flow of the hydrological station and the simulated flow of the SWAT model respectively, respectively represent the average values of the measured flow and the simulated flow, and Q i,o , Q i,s represent the measured flow and the simulated flow in the i-th month respectively; If NS and R 2 are both greater than 0.5, it indicates that the simulation results of the SWAT model are ideal, and the SWAT model at this time is used as the target SWAT model; An analysis and demonstration module is used to simulate and quantitatively analyze the impact on the meteorological-hydrological drought propagation law from various aspects by constructing different driving factor schemes in the target SWAT model; The data collection module, the preprocessing module, the division module, the verification analysis module, and the analysis and demonstration module are all communicatively connected to the central processing unit to realize information exchange among them through the central processing unit.

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