Hydrological Drought Feature Identification and Attribution Method and System Based on Water Cycle

Abstract

The present application provides a method and system for identifying and attributing hydrological drought characteristics based on the water cycle. The method includes: collecting basin meteorological and hydrological data within the study period of the study area; conducting a mutation test on the runoff data of the basin control station, and dividing the study period into a pre-mutation period and a post-mutation period; constructing a distributed hydrological model and simulating runoff sequences under different scenarios; respectively constructing hydrological drought sequences according to the runoff sequences under different scenarios, using the run theory to identify droughts in the constructed hydrological drought sequences, obtaining hydrological drought events under different scenarios and statistically analyzing hydrological drought characteristic quantities; comparing the differences in hydrological drought characteristic quantities under different scenarios, and obtaining the impacts of different driving factors on hydrological drought characteristics according to the comparison results. The present application analyzes climate change and human activities based on the distributed hydrological model DTVGM-PML model, focuses on attributing the impact of vegetation changes on hydrological drought, and provides a theoretical basis for hydrological drought monitoring and prevention.

Description

Technical Field

[0001] This application relates to the technical field of hydrological drought monitoring and identification and water resources management, and particularly to a method and system for identifying and attributing hydrological drought characteristics based on the water cycle. Background Art

[0002] Drought is one of the natural disaster events that cause a large amount of economic losses, occur most easily, and have the widest influence range. Affected by drought, the global average annual economic loss is 6-8 billion US dollars. On the one hand, driven by both climate change and human activities, the occurrence frequency, duration, and intensity of drought have all increased significantly. On the other hand, after a series of vegetation restoration processes, the vegetation coverage in China has increased significantly, which has had a certain impact on the regional local water cycle. Therefore, identifying drought characteristics and analyzing drought driving factors have become research hotspots at home and abroad.

[0003] Drought is mainly divided into meteorological drought, hydrological drought, agricultural drought, and socioeconomic drought. At present, the analysis of hydrological drought driving factors is mainly divided into three categories: paired watershed comparison method, statistical analysis method, and hydrological model-based method. Compared with the former two research methods, the hydrological model-based analysis method considers the laws of mass conservation and energy conservation, and can effectively simulate the watershed hydrological process to a certain extent. Domestic and foreign research scholars have constructed some distributed hydrological models to analyze the attribution of climate change and human activities to hydrological drought, but there are relatively few drought attribution analyses considering vegetation dynamic changes.

[0004] The constructed DTVGM-PML model considering vegetation changes is an improved distributed time-varying gain model, which couples the PML evapotranspiration module on the basis of the original DTVGM, and includes a rainfall interception module, a snowmelt module, a surface and subsurface runoff generation module, and a confluence module. Summary of the Invention

[0005] This application provides a method and system for identifying and attributing hydrological drought characteristics based on the water cycle, which can solve the technical problem in the prior art that there is relatively little consideration of the differences in hydrological drought characteristic quantities under different scenarios of drought considering vegetation dynamic changes, and obtaining the influence of different driving factors on hydrological drought characteristics according to the comparison results.

[0006] In the first aspect, this application provides a method for identifying and attributing hydrological drought characteristics based on the water cycle, including the following steps:

[0007] Collect the basin meteorological and hydrological data within the study period of the study area;

[0008] Conduct a mutation test on the basin runoff data to determine the mutation year, and divide the study period into a pre-mutation period and a post-mutation period;

[0009] Based on the basin meteorological and hydrological data during the pre-mutation period and the post-mutation period, as well as the DTVGM-PML model, a distributed hydrological model is constructed and the runoff sequences under different scenarios are simulated;

[0010] Hydrological drought sequences are constructed respectively according to the runoff sequences under different scenarios, and the run theory is used to identify droughts in the constructed hydrological drought sequences, so as to obtain hydrological drought events under different scenarios and statistically calculate hydrological drought characteristic quantities;

[0011] The differences in hydrological drought characteristic quantities under different scenarios are compared, and the impacts of different driving factors on hydrological drought characteristics are obtained according to the comparison results.

[0012] Combined with the first aspect, in one implementation manner, in the step of collecting the basin meteorological and hydrological data during the research period of the research area, the basin meteorological and hydrological data includes meteorological driving data, land surface driving data, calibration reference data, and measured data of basin hydrological stations.

[0013] Combined with the first aspect, in one implementation manner, the step of constructing a distributed hydrological model and simulating the runoff sequences under different scenarios according to the basin meteorological and hydrological data during the pre-mutation period and the post-mutation period, as well as the DTVGM-PML model, specifically includes the following steps:

[0014] Based on the basin hydrological and meteorological data during the pre-mutation period, a distributed hydrological model is constructed and the model parameters are calibrated to obtain the distributed hydrological model after parameter calibration;

[0015] In the post-mutation period, according to different scenarios, different input data including meteorological data and vegetation data are designed and input into the distributed hydrological model after parameter calibration to obtain the runoff sequences under different scenarios.

[0016] Combined with the first aspect, in one implementation manner, the step of constructing a distributed hydrological model based on the basin meteorological and hydrological data during the pre-mutation period, calibrating the model parameters, and obtaining the distributed hydrological model after parameter calibration specifically includes the following steps:

[0017] The pre-mutation period is divided into a model calibration period and a model verification period;

[0018] Input the basin meteorological and hydrological data of the model calibration period of the research area into the DTVGM-PML model for data simulation, construct a distributed hydrological model and calibrate the parameters to obtain the model simulation effect of the calibration period data;

[0019] Input the basin meteorological and hydrological data of the model verification period of the research area into the distributed hydrological model after parameter calibration to obtain the model simulation effect of the verification period data;

[0020] According to the model simulation effects of calibration period data and verification period data, obtain the simulation effect of the DTVGM-PML model on the water cycle process in the study area. According to the simulation effect, obtain the distributed hydrological model with verified simulation effect.

[0021] Combined with the first aspect, in one implementation, based on the basin meteorological and hydrological data in the pre-mutation period, construct a distributed hydrological model and calibrate the model parameters to obtain the distributed hydrological model after parameter calibration, which specifically includes the following steps:

[0022] Divide the pre-mutation period into a model calibration period and a model verification period;

[0023] Input the basin meteorological and hydrological data of the model calibration period in the study area into the DTVGM-PML model for data simulation, construct a distributed hydrological model and calibrate the parameters to obtain the model simulation effect of the calibration period data;

[0024] Input the basin meteorological and hydrological data of the model verification period in the study area into the distributed hydrological model after parameter calibration to obtain the model simulation effect of the verification period data;

[0025] According to the model simulation effects of calibration period data and verification period data, obtain the simulation effect of the DTVGM-PML model on the water cycle process in the study area. According to the simulation effect, obtain the distributed hydrological model with verified simulation effect. Combined with the first aspect, in one implementation, in the post-mutation period, according to different scenarios, design different input data including meteorological data and vegetation data into the distributed hydrological model after parameter calibration to obtain runoff sequences under different scenarios, which specifically includes the following steps:

[0026] In the post-mutation period, according to different scenarios, design different input data including meteorological data and vegetation data into the distributed hydrological model after parameter calibration to obtain runoff simulation results;

[0027] According to the runoff simulation results and the measured runoff sequences before and after the mutation, obtain runoff sequences under different scenarios.

[0028] Combined with the first aspect, in one implementation, construct hydrological drought sequences according to the runoff sequences under different scenarios, use the run theory to identify droughts in the constructed hydrological drought sequences, obtain hydrological drought events under different scenarios and statistically analyze hydrological drought characteristic quantities, which specifically includes the following steps:

[0029] Construct hydrological drought sequences according to the runoff simulation results of the DTVGM-PML model under different scenarios and the measured runoff sequences before and after the mutation;

[0030] The run theory is used to identify hydrological drought sequences respectively to obtain hydrological drought events under different design scenarios and actual scenarios, and the hydrological drought characteristic quantities are statistically analyzed.

[0031] Combined with the first aspect, in one implementation, to compare the differences in hydrological drought characteristic quantities under different scenarios and obtain the influence of different driving factors on hydrological drought characteristics according to the comparison results, the following steps are specifically included:

[0032] According to the hydrological drought characteristics under different design scenarios and actual scenarios, the average values of hydrological drought characteristics under different scenarios are statistically analyzed;

[0033] Compare the differences in the average values of hydrological drought characteristics under different scenarios to obtain the influence amounts of different factors on the average value of drought characteristics;

[0034] According to the influence amounts of different factors on the average value of drought characteristics, statistically analyze the proportion of the influence amount of different factors on the average value of drought characteristics in the total influence amount of the average value of drought characteristics to obtain the drought influence analysis result.

[0035] In the second aspect, the present application provides a hydrological drought characteristic identification and attribution system based on the water cycle, including:

[0036] A data collection module, configured to collect basin meteorological and hydrological data within the research period of the research area;

[0037] A time period division module, communicatively connected to the data collection module, configured to perform a mutation test on the basin runoff data to determine the mutation year and divide the research period into a pre-mutation period and a post-mutation period;

[0038] A model construction and scenario simulation module, communicatively connected to the data collection module and the time period division module, configured to construct a distributed hydrological model and simulate runoff sequences under different scenarios according to the basin meteorological and hydrological data in the pre-mutation period and the post-mutation period and the DTVGM-PML model;

[0039] A characteristic identification module, communicatively connected to the model construction and scenario simulation module, configured to respectively construct hydrological drought sequences according to the runoff sequences under different scenarios, perform drought identification on the constructed hydrological drought sequences by using the run theory, obtain hydrological drought events under different scenarios, and statistically analyze the hydrological drought characteristic quantities;

[0040] A hydrological drought attribution module, communicatively connected to the characteristic identification module, configured to compare the differences in the hydrological drought characteristic quantities under different scenarios and obtain the influence of different driving factors on the hydrological drought characteristics according to the comparison results.

[0041] Combined with the second aspect, in one implementation, the model construction and scenario simulation module includes:

[0042] The time period division unit, which is communicatively connected to the data collection module and the time period division module, is used to divide the time period before mutation into a model calibration period and a model verification period;

[0043] The model simulation effect acquisition unit for calibration period data, which is communicatively connected to the time period division unit, is used to input the basin meteorological and hydrological data of the model calibration period in the study area into the DTVGM-PML model for data simulation, construct a distributed hydrological model and perform parameter calibration, and obtain the model simulation effect of the calibration period data;

[0044] The model simulation effect acquisition unit for verification period data is used to input the basin meteorological and hydrological data of the model verification period in the study area into the distributed hydrological model after parameter calibration, and obtain the model simulation effect of the verification period data;

[0045] The model acquisition unit is used to obtain the simulation effect of the DTVGM-PML model on the water cycle process in the study area according to the model simulation effect of the calibration period data and the model simulation effect of the verification period data, and obtain a distributed hydrological model with the simulation effect verified and passed according to the simulation effect.

[0046] The beneficial effects brought by the technical solutions provided in the embodiments of the present application at least include:

[0047] Based on the distributed hydrological model DTVGM-PML model, this application analyzes the attribution of hydrological drought caused by climate change and human activities (with a focus on vegetation changes), provides a theoretical basis for hydrological drought monitoring and prevention, and provides a scientific basis and decision-making support for water resource management and related policy formulation. Description of the Drawings

[0048] Figure 1 It is a schematic flow chart of the method for identifying and attributing hydrological drought characteristics based on water cycle in this application;

[0049] Figure 2 It is another schematic flow chart of the method for identifying and attributing hydrological drought characteristics based on water cycle in this application;

[0050] Figure 3 It is a functional module block diagram of the system for identifying and attributing hydrological drought characteristics based on water cycle in this application. Detailed Embodiments

[0051] In order to enable those skilled in the art to better understand the solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0052] In the description of the specification, claims and the above-mentioned drawings of the present application, the terms "comprising", "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices. Descriptions such as "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequential order, nor do they limit that "first", "second" and "third" are of different types.

[0053] In the description of the embodiments of the present application, words such as "exemplary", "for example" or "for illustration" are used to indicate examples, illustrations or explanations. Any embodiment or design solution described as "exemplary", "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "for example" or "for illustration" is intended to present related concepts in a specific manner.

[0054] In the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B may mean A or B; "and / or" in the text is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B may mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present application, "a plurality of" means two or more than two.

[0055] In some processes described in the embodiments of the present application, a plurality of operations or steps appear in a specific order. However, it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or may be executed in parallel. The serial numbers of the operations are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0056] To make the objectives, technical solutions and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the drawings.

[0057] In a first aspect, please refer to Figure 1 - Figure 2 , the present application provides a method for identifying and attributing hydrological drought characteristics based on water cycle, including the following steps:

[0058] Step S1: Collect basin meteorological and hydrological data within the study period of the study area;

[0059] Step S2: Conduct a mutation test on the basin runoff data to determine the mutation years of drought events, and divide the research period into the pre-mutation period and the post-mutation period.

[0060] Step S3: Based on the basin meteorological and hydrological data of the pre-mutation period and the post-mutation period, and the DTVGM-PML model, construct a distributed hydrological model and simulate the runoff sequences under different scenarios.

[0061] Step S4: Construct hydrological drought sequences based on the runoff sequences under different scenarios, use the run theory to identify droughts in the constructed hydrological drought sequences, obtain hydrological drought events under different scenarios, and statistically analyze the hydrological drought characteristic variables.

[0062] Step S5: Compare the differences in hydrological drought characteristic variables under different scenarios, and obtain the impacts of different driving factors on hydrological drought characteristics according to the comparison results.

[0063] This application conducts an attribution analysis of hydrological drought based on the distributed hydrological model - DTVGM-PML model to analyze the impacts of climate change and human activities (with a focus on vegetation changes), provides a theoretical basis for hydrological drought monitoring and prevention, and provides a scientific basis and decision-making support for water resource management and related policy formulation.

[0064] In one embodiment, in the step S1 of collecting basin meteorological and hydrological data during the research period of the research area, the basin meteorological and hydrological data includes:

[0065] A. Meteorological driving data, including precipitation, temperature, specific humidity, air pressure, wind speed, downward shortwave radiation, and downward longwave radiation;

[0066] B. Land surface driving data, including leaf area index and albedo;

[0067] C. Calibration reference data, including runoff and evapotranspiration grid product data;

[0068] D. Hydrological measured data, including runoff data of hydrological stations and soil moisture content grid product data.

[0069] In one embodiment, in order to collect high-quality basin meteorological and hydrological data during the research period in the research area, the runoff data of the hydrological station is the continuous flow data of the outlet section or control section that can be collected in the basin where the hydrological station is located, and the runoff data of the hydrological station is calculated based on the hydrological control area from the continuous flow data.

[0070] Among them, the research area is the area that needs or awaits analysis of the causes of hydrological drought.

[0071] In one embodiment, in step S2, a mutation test is performed on the basin runoff to determine the mutation year, and the research period is divided into a pre-mutation period and a post-mutation period, which specifically includes the following steps:

[0072] The MK test method and the heuristic analysis method are used to perform a mutation test on the runoff data in the study area to obtain the mutation test results;

[0073] Based on the mutation test results, a reasonable runoff mutation year is determined through comprehensive analysis;

[0074] According to the determined reasonable runoff mutation year, the research period is divided into a pre-mutation period and a post-mutation period, which are respectively denoted as the T1 period and the T2 period.

[0075] In one embodiment, in step S3, a distributed hydrological model considering vegetation changes is constructed. This improved distributed time-varying gain model is coupled with the PML evapotranspiration module on the basis of the original DTVGM, and includes a rainfall interception module, a snowmelt module, a surface and subsurface runoff generation module, and a confluence module.

[0076] In one embodiment, in step S3, according to the basin meteorological and hydrological data in the pre-mutation period and the post-mutation period, and the DTVGM-PML model, a distributed hydrological model is constructed and the runoff sequences under different scenarios are simulated, which specifically includes the following steps:

[0077] Step S31: Based on the basin meteorological and hydrological data in the pre-mutation period, a distributed hydrological model is constructed and model parameter calibration is performed to obtain the distributed hydrological model after parameter calibration;

[0078] Step S32: In the post-mutation period, according to different scenarios, different input data including meteorological data and vegetation data are designed and input into the distributed hydrological model after parameter calibration to obtain the runoff sequences under different scenarios.

[0079] In one embodiment, in step S31, based on the basin meteorological and hydrological data in the pre-mutation period, a distributed hydrological model is constructed and model parameter calibration is performed to obtain the distributed hydrological model after parameter calibration, which specifically includes the following steps:

[0080] Step S311: The pre-mutation period is divided into a model calibration period and a model verification period; more specifically, according to a time ratio of 7:3, the pre-mutation period is divided into a model calibration period and a model verification period;

[0081] Step S312: Input the basin meteorological and hydrological data in the model calibration period of the study area into the DTVGM-PML model for data simulation, construct a distributed hydrological model and perform parameter calibration to obtain the model simulation effect of the calibration period data;

[0082] Step S313: Input the basin meteorological and hydrological data during the model verification period of the study area into the distributed hydrological model after parameter calibration, and obtain the model simulation effect of the verification period data.

[0083] Step S314: According to the model simulation effect of the calibration period data and the model simulation effect of the verification period data, obtain the simulation effect of the DTVGM-PML model on the water cycle process of the study area. According to the simulation effect, obtain the distributed hydrological model that passes the simulation effect verification.

[0084] In one embodiment, the step S312: Input the basin meteorological and hydrological data during the model calibration period of the study area into the DTVGM-PML model for data simulation, construct a distributed hydrological model and perform parameter calibration, and obtain the model simulation effect of the calibration period data, specifically includes the following steps:

[0085] Input the meteorological driving data and land surface driving data in the historical data during the model calibration period in the study area into the DTVGM-PML model, and obtain the simulation data during the model calibration period output by the model, including runoff generation, evaporation, runoff, and soil moisture content.

[0086] Compare the simulation data during the model calibration period with the reference data and measured data during the model verification period, and use KGE and PBIAS as evaluation indicators for the model simulation effect to evaluate the model simulation effect, and obtain the model simulation effect of the calibration period data.

[0087] Construct a distributed hydrological model with parameter calibration according to the model simulation effect of the calibration period data.

[0088] In one embodiment, the step S313: Input the basin meteorological and hydrological data during the model verification period of the study area into the distributed hydrological model after parameter calibration, and obtain the model simulation effect of the verification period data, specifically includes the following steps:

[0089] Input the meteorological driving data and land surface driving data in the historical data during the model verification period in the study area into the DTVGM-PML model after parameter calibration, and obtain the simulation data during the model calibration period output by the model, including runoff generation, evaporation, runoff, and soil moisture content.

[0090] Compare the simulation data during the model verification period with the reference data and measured data during the model calibration period, and use KGE and PBIAS as evaluation indicators for the model simulation effect to evaluate the model simulation effect, and obtain the model simulation effect of the verification period data.

[0091] In one embodiment, the step S32: In the post-mutation period, according to different scenarios, design different input data including meteorological data and vegetation data into the distributed hydrological model after parameter calibration, and obtain runoff sequences under different scenarios, specifically includes the following steps:

[0092] Step S321: In the post-mutation period, according to different scenarios, design different input data including meteorological data and vegetation data into the calibrated distributed hydrological model to obtain runoff simulation results.

[0093] Step S322: According to the runoff simulation results and the measured runoff sequences before and after the mutation, obtain runoff sequences under different scenarios. The design results for different scenarios are shown in Table 1:

[0094] Table 1 Design of Different Scenarios

[0095] Scenario Simulation period Meteorological data Vegetation data S1 <![CDATA[Time period T1 before mutation]]> <![CDATA[Measured meteorological data during T1 period]]> <![CDATA[Measured vegetation data during T1 period]]> S2 <![CDATA[Post-mutation time period T2]]> <![CDATA[Measured meteorological data during T2 period]]> <![CDATA[Measured vegetation data during T2 period]]> S3 <![CDATA[Post-mutation time period T2]]> <![CDATA[Measured meteorological data during T2 period]]> <![CDATA[Detrended vegetation data during T2 period]]>

[0096] In one embodiment, in step S4: According to the runoff sequences under different scenarios, construct hydrological drought sequences respectively, and use the run theory to identify droughts in the constructed hydrological drought sequences, obtain hydrological drought events under different scenarios and count hydrological drought characteristic quantities, which specifically include the following steps:

[0097] According to the runoff simulation results of the DTVGM-PML model under different scenarios and the measured runoff sequences before and after the mutation, construct hydrological drought sequences (SIR) respectively.

[0098] Use the run theory to identify hydrological drought events in the hydrological drought sequences respectively to obtain hydrological drought events in different design scenarios and actual scenarios and count hydrological drought characteristic quantities. Specifically, when the SRI sequence is higher than the set threshold R (for example, -0.5) within a period of time, this event is identified as a hydrological drought event. Among them, the hydrological drought characteristics include drought duration, drought intensity and drought frequency.

[0099] In one embodiment, in step S5: Compare the differences in hydrological drought characteristic quantities under different scenarios, and obtain the influence of different driving factors on hydrological drought characteristics according to the comparison results, which specifically include the following steps:

[0100] Step S51: According to the hydrological drought characteristics under different design scenarios and actual scenarios, count the average values of hydrological drought characteristics under different scenarios. In this step, mainly consider three average values of drought characteristics, including average drought frequency, average drought duration and average drought intensity; the average hydrological drought characteristic value (except the average hydrological drought frequency) refers to taking the average of all hydrological drought events occurring within the period, that is, dividing the sum of all hydrological drought event characteristics by the total number of hydrological drought events occurring, and the average hydrological drought frequency refers to the sum of all drought events of hydrological drought occurring within the period divided by the total length of the period.

[0101] Step S52: Compare the differences in the average values of hydrological drought characteristics under different scenarios to obtain the influence amount of different factors on the average values of drought characteristics.

[0102] Step S53: According to the influence amounts of different factors on the average value of drought characteristics, count the proportions of the influence amounts of different factors on the average value of drought characteristics in the total influence amount of the average value of drought characteristics, and obtain the drought influence analysis results.

[0103] In a more specific embodiment, the step S52 of comparing the differences in the average values of hydrological drought characteristics under different scenarios to obtain the influence amounts of different factors on the average value of drought characteristics specifically includes the following steps:

[0104] Compare the average hydrological drought characteristic quantity in the S2 scenario with the average hydrological drought characteristic quantity in the S1 scenario to obtain the influence amount a0 of climate change and vegetation change on the hydrological drought characteristics;

[0105] Compare the average hydrological drought characteristic quantity in the S2 scenario with the average hydrological drought characteristic quantity in the S3 scenario to obtain the influence amount a1 of vegetation change on the hydrological drought characteristics, and further obtain the influence amount a2 of climate change on the hydrological drought characteristics, where a2 = a0 - a1;

[0106] Compare the average hydrological drought characteristic quantity in the T2 measured scenario with the average hydrological drought characteristic quantity in the S2 scenario to obtain the influence amount a3 of human activities other than vegetation change on the hydrological drought characteristics;

[0107] In an embodiment, the step S53 of, according to the influence amounts of different factors on the average value of drought characteristics, counting the proportions of the influence amounts of different factors on the average value of drought characteristics in the total influence amount of the average value of drought characteristics to obtain the drought influence analysis results specifically includes the following steps:

[0108] Calculate respectively the proportions of the influence amounts of climate change, vegetation change, and human activities other than vegetation change on the hydrological drought characteristics in the total influence amount a = a1 + a2 + a3 of the hydrological drought characteristics;

[0109] Obtain the contribution rates of different factors to the influence on the hydrological drought characteristics. The contribution rates of vegetation change, climate change, and human activities other than vegetation change to the influence on the hydrological drought characteristics are respectively

[0110] This application is based on a distributed hydrological model to simulate the water cycle process and provides relatively accurate runoff simulation results;

[0111] This application considers the runoff differences under different scenarios and comprehensively considers the influences of climate change, vegetation change, and human activities other than vegetation change on hydrological drought;

[0112] The method of this application includes fields such as meteorology and hydrology, involves multi-disciplinary cross-integration, has a clear model structure, can simultaneously provide a theoretical basis for hydrological drought monitoring and prevention, and provides a scientific basis and decision-making support for water resource management and relevant policy formulation.

[0113] In summary, the present application proposes a method for identifying and attributing hydrological drought characteristics based on the physical process of water cycle, which has a clear structure and simple calculation. Through this model, the hydrological drought characteristics under different scenarios can be accurately simulated.

[0114] In a second aspect, please refer to Figure 3 , the present application provides a system for identifying and attributing hydrological drought characteristics based on water cycle, including a data collection module 100, a time period division module 200, a model construction and scenario simulation module 300, a feature identification module 400, and a hydrological drought attribution module 500. The data collection module 100 is used to collect meteorological, hydrological, and basin data within the study period of the study area; the time period division module 200 is communicatively connected to the data collection module 100, and is used to perform a mutation test on the basin runoff data to determine the mutation year, and divide the study period into a pre-mutation period and a post-mutation period; the model construction and scenario simulation module 300 is communicatively connected to the data collection module 100 and the time period division module 200, and is used to construct a distributed hydrological model and simulate runoff sequences under different scenarios according to the meteorological, hydrological, and basin data of the pre-mutation period and the post-mutation period, as well as the DTVGM-PML model; the feature identification module 400 is communicatively connected to the model construction and scenario simulation module 300, and is used to construct hydrological drought sequences respectively according to the runoff sequences under different scenarios, and use the run theory to identify droughts in the constructed hydrological drought sequences, obtain hydrological drought events under different scenarios, and statistically calculate hydrological drought characteristic quantities; the hydrological drought attribution module 500 is communicatively connected to the feature identification module 400, and is used to compare the differences in hydrological drought characteristic quantities under different scenarios, and obtain the influence of different driving factors on hydrological drought characteristics according to the comparison results.

[0115] In one embodiment, the model construction and scenario simulation module includes:

[0116] A time period division unit, communicatively connected to the data collection module and the time period division module, and is used to divide the pre-mutation period into a model calibration period and a model verification period;

[0117] A unit for obtaining the model simulation effect of the calibration period data, communicatively connected to the time period division unit, and is used to input the meteorological, hydrological, and basin data of the model calibration period of the study area into the DTVGM-PML model for data simulation, construct a distributed hydrological model, and perform parameter calibration to obtain the model simulation effect of the calibration period data;

[0118] A unit for obtaining the model simulation effect of the verification period data, and is used to input the meteorological, hydrological, and basin data of the model verification period of the study area into the distributed hydrological model after parameter calibration to obtain the model simulation effect of the verification period data;

[0119] A model acquisition unit is configured to obtain the simulation effect of the DTVGM-PML model on the water cycle process in the study area based on the model simulation effects of the calibration period data and the verification period data, and obtain a distributed hydrological model whose simulation effect passes the verification according to the simulation effect.

[0120] Among them, the functional implementation of each module in the above hydrological drought feature identification and attribution system based on the water cycle corresponds to each step in the above embodiment of the hydrological drought feature identification and attribution method based on the water cycle, and its functions and implementation processes will not be elaborated here one by one.

[0121] In a third aspect, an embodiment of the present application provides a hydrological drought feature identification and attribution device based on the water cycle. The hydrological drought feature identification and attribution device based on the water cycle can be a device with data processing functions such as a personal computer (PC), a laptop computer, a server, etc.

[0122] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces, etc., which are used to implement the interconnection of components inside the hydrological drought feature identification and attribution device based on the water cycle, and interfaces for implementing the interconnection of the hydrological drought feature identification and attribution device based on the water cycle with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display screen (Display), a keyboard (Keyboard), etc.

[0123] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical memory, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0124] The processor may be a general-purpose processor, which can call the program for identifying and attributing hydrological drought characteristics based on the water cycle stored in the memory and execute the method for identifying and attributing hydrological drought characteristics based on the water cycle provided in the embodiments of the present application. For example, the general-purpose processor may be a central processing unit (CPU). Among them, the method executed when the program for identifying and attributing hydrological drought characteristics based on the water cycle is called may refer to the various embodiments of the method for identifying and attributing hydrological drought characteristics based on the water cycle in the present application, which will not be elaborated here.

[0125] Fourthly, the embodiments of the present application further provide a readable storage medium.

[0126] The readable storage medium of the present application stores a program for identifying and attributing hydrological drought characteristics based on the water cycle. When the program for identifying and attributing hydrological drought characteristics based on the water cycle is executed by a processor, the steps of the method for identifying and attributing hydrological drought characteristics based on the water cycle as described above are implemented.

[0127] Among them, the method implemented when the program for identifying and attributing hydrological drought characteristics based on the water cycle is executed may refer to the various embodiments of the method for identifying and attributing hydrological drought characteristics based on the water cycle in the present application, which will not be elaborated here.

[0128] It should be noted that the serial numbers of the embodiments of the present application above are only for description and do not represent the advantages or disadvantages of the embodiments.

[0129] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium as described above (such as ROM / RAM, magnetic disk, optical disc) and includes several instructions for causing a terminal device to execute the methods described in the various embodiments of the present application.

[0130] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A method for identifying and attributing hydrological drought characteristics based on the water cycle, characterized in that It includes the following steps: Collect the basin meteorological and hydrological data within the study period of the study area; Conduct a mutation test on the runoff data of the basin control station to determine the mutation year, and divide the study period into the pre-mutation period and the post-mutation period; Based on the basin meteorological and hydrological data of the pre-mutation period and the post-mutation period, and the DTVGM-PML model, construct a distributed hydrological model and simulate the runoff sequences under different scenarios; Construct hydrological drought sequences according to the runoff sequences under different scenarios, use the run theory to identify droughts in the constructed hydrological drought sequences, obtain hydrological drought events under different scenarios, and statistically analyze the hydrological drought characteristic quantities; Compare the differences in hydrological drought characteristic quantities under different scenarios, and obtain the impacts of different driving factors on hydrological drought characteristics according to the comparison results. Specifically, it includes the following steps: According to the hydrological drought characteristics under different design scenarios and actual scenarios, statistically analyze the average values of hydrological drought characteristics under different scenarios; Compare the differences in the average values of hydrological drought characteristics under different scenarios to obtain the impact amounts of different factors on the average values of drought characteristics. Specifically, it includes the following steps: Compare the average hydrological drought characteristic quantity in Scenario S2 with that in Scenario S1 to obtain the impact amount a0 of climate change and vegetation change on hydrological drought characteristics; the meteorological data in Scenario S1 are the measured meteorological data before the mutation, and the vegetation data are the measured vegetation data before the mutation; Compare the average hydrological drought characteristic quantity in Scenario S2 with that in Scenario S3 to obtain the impact amount a1 of vegetation change on hydrological drought characteristics, and then obtain the impact amount a2 of climate change on hydrological drought characteristics, where a2 = a0 - a1; the meteorological data in Scenario S2 are the measured meteorological data after the mutation, and the vegetation data are the measured vegetation data after the mutation; the meteorological data in Scenario S3 are the measured meteorological data after the mutation, and the vegetation data are the detrended vegetation data after the mutation; Compare the average hydrological drought characteristic quantity in the measured scenario in the post-mutation period with that in Scenario S2 to obtain the impact amount a3 of human activities other than vegetation change on hydrological drought characteristics; According to the impact amounts of different factors on the average values of drought characteristics, statistically analyze the proportion of the impact amounts of different factors on the average values of drought characteristics in the total impact amount of drought characteristics, and obtain the drought impact analysis results. Specifically, it includes the following steps: Calculate the total impact amount a = a1 + a2 + a3 of climate change, vegetation change, and human activities other than vegetation change on hydrological drought characteristics respectively; The contribution rates of vegetation change, climate change, and human activities other than vegetation change to the characteristics of hydrological drought are respectively 2. The method for identifying and attributing hydrological drought characteristics based on water cycle as claimed in claim 1, wherein, In the step of collecting the basin meteorological and hydrological data within the study period of the study area, the basin meteorological and hydrological data include meteorological driving data, land surface driving data, calibration reference data, and measured flow data of hydrological stations.

3. The method for identifying and attributing hydrological drought characteristics based on water cycle as claimed in claim 1, wherein Based on the basin meteorological and hydrological data of the pre-mutation period and the post-mutation period, and the DTVGM-PML model, construct a distributed hydrological model and simulate the runoff sequences under different scenarios. Specifically, it includes the following steps: Based on the basin meteorological and hydrological data of the pre-mutation period, construct a distributed hydrological model and conduct model parameter calibration to obtain the distributed hydrological model after parameter calibration; In the post-mutation period, according to different scenarios, different input data including meteorological data and vegetation data are designed and input into the calibrated distributed hydrological model to obtain runoff sequences under different scenarios.

4. The method for identifying and attributing hydrological drought characteristics based on water cycle according to claim 3, wherein, Based on the basin meteorological and hydrological data in the pre-mutation period, a distributed hydrological model is constructed and model parameters are calibrated to obtain the calibrated distributed hydrological model, which specifically includes the following steps: The pre-mutation period is divided into a model calibration period and a model verification period; The basin meteorological and hydrological data in the model calibration period of the study area are input into the DTVGM-PML model for data simulation, a distributed hydrological model is constructed and parameters are calibrated to obtain the model simulation effect of the calibration period data; The basin meteorological and hydrological data in the model verification period of the study area are input into the calibrated distributed hydrological model to obtain the model simulation effect of the verification period data; According to the model simulation effects of the calibration period data and the verification period data, the simulation effect of the DTVGM-PML model on the water cycle process in the study area is obtained, and based on the simulation effect, a distributed hydrological model with verified simulation effect is obtained.

5. The method for identifying and attributing hydrological drought characteristics based on water cycle as claimed in claim 3, wherein, Based on the basin meteorological and hydrological data in the pre-mutation period, a water cycle simulation emphasizing dynamic vegetation changes is carried out, a distributed hydrological model is constructed and model parameters are calibrated to obtain the calibrated distributed hydrological model, which specifically includes the following steps: The pre-mutation period is divided into a model calibration period and a model verification period; The basin meteorological and hydrological data and dynamic vegetation data (LAI) in the model calibration period of the study area are input into the DTVGM-PML model for data simulation, a distributed hydrological model is constructed and parameters are calibrated to obtain the model simulation effect of the calibration period data; The basin meteorological and hydrological data in the model verification period of the study area are input into the calibrated distributed hydrological model to obtain the model simulation effect of the verification period data; According to the model simulation effects of the calibration period data and the verification period data, the simulation effect of the DTVGM-PML model on the water cycle process in the study area is obtained, and based on the simulation effect, a distributed hydrological model with verified simulation effect is obtained.

6. The method for identifying and attributing hydrological drought characteristics based on water cycle as claimed in claim 5, wherein In the post-mutation period, according to different scenarios, different input data including meteorological data and vegetation data are designed and input into the calibrated distributed hydrological model to obtain runoff sequences under different scenarios, which specifically includes the following steps: In the post-mutation period, according to different scenarios, different input data including meteorological data and vegetation data are designed and input into the calibrated distributed hydrological model to obtain runoff simulation results; According to the runoff simulation results and the measured runoff sequences before and after the mutation, runoff sequences under different scenarios are obtained.

7. The method for identifying and attributing hydrological drought characteristics based on water cycle as claimed in claim 1, wherein Hydrological drought sequences are respectively constructed according to the runoff sequences under different scenarios, and the run theory is used to identify droughts in the constructed hydrological drought sequences to obtain hydrological drought events under different scenarios and statistical hydrological drought characteristic quantities, which specifically includes the following steps: Hydrological drought sequences are respectively constructed according to the runoff simulation results of the DTVGM-PML model under different scenarios and the measured runoff sequences before and after the mutation; The run - length theory is adopted to identify the hydrological drought sequences respectively, obtain the hydrological drought events under different design scenarios and actual scenarios, and statistically analyze the hydrological drought characteristic quantities.

8. The method for identifying and attributing hydrological drought characteristics based on water cycle as claimed in claim 1, wherein The differences in hydrological drought characteristic quantities under different scenarios are compared, the impacts of different driving factors on hydrological drought characteristics are obtained according to the comparison results, and the impact of vegetation change on hydrological drought characteristics is separated from the impact of human activities on hydrological drought characteristics. The specific steps are as follows: According to the hydrological drought characteristics under different design scenarios and actual scenarios, the average values of hydrological drought characteristics under different scenarios are statistically analyzed. The differences in the average values of hydrological drought characteristics under different scenarios are compared to obtain the influence amounts of different factors on the average values of drought characteristics. According to the influence amounts of different factors on the average values of drought characteristics, the proportions of the influence amounts of different factors on the average values of drought characteristics in the total influence amount of drought characteristics are statistically analyzed to obtain the drought impact analysis results.

9. A system applied to the method for identifying and attributing hydrological drought characteristics based on water cycle as described in any one of claims 1-8, characterized in that, Including: A data collection module for collecting the basin meteorological and hydrological data within the study period of the study area. A time - period division module, which is communicatively connected to the data collection module, for conducting a mutation test on the runoff data of the basin control station to determine the mutation year and dividing the study period into a pre - mutation period and a post - mutation period. A model construction and scenario simulation module, which is communicatively connected to the data collection module and the time - period division module, for constructing a distributed hydrological model and simulating the runoff sequences under different scenarios according to the basin meteorological and hydrological data of the pre - mutation period and the post - mutation period and the DTVGM - PML model. A characteristic identification module, which is communicatively connected to the model construction and scenario simulation module, for respectively constructing hydrological drought sequences according to the runoff sequences under different scenarios, using the run - length theory to identify droughts in the constructed hydrological drought sequences, obtaining the hydrological drought events under different scenarios and statistically analyzing the hydrological drought characteristic quantities. A hydrological drought attribution module, which is communicatively connected to the characteristic identification module, for comparing the differences in hydrological drought characteristic quantities under different scenarios, obtaining the impacts of different driving factors on hydrological drought characteristics according to the comparison results, and separating the impact of vegetation change on hydrological drought characteristics from the impact of human activities.

10. The system according to claim 9, wherein, The model construction and scenario simulation module includes: A time - period division unit, which is communicatively connected to the data collection module and the time - period division module, for dividing the pre - mutation period into a model calibration period and a model verification period. A unit for obtaining the model simulation effect of the calibration - period data, which is communicatively connected to the time - period division unit, for inputting the basin meteorological and hydrological data of the model calibration period of the study area into the DTVGM - PML model for data simulation, constructing a distributed hydrological model and conducting parameter calibration to obtain the model simulation effect of the calibration - period data. A unit for obtaining the model simulation effect of the verification - period data, which is used for inputting the basin meteorological and hydrological data of the model verification period of the study area into the distributed hydrological model after parameter calibration to obtain the model simulation effect of the verification - period data. A model acquisition unit, which is used for obtaining the simulation effect of the DTVGM - PML model on the water cycle process of the study area according to the model simulation effects of the calibration - period data and the verification - period data, and obtaining the distributed hydrological model with the simulation effect verified to be passed according to the simulation effect.

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