Method and system for evaluating multi-scale drought response under vegetation change

Abstract

A method and system for evaluating multi-scale drought response under vegetation change. The method comprises: constructing a distributed time-varying gain hydrological model PML coupled with a remote sensing evapotranspiration module PML, fusing it with a traditional Palmer drought index PDSI, replacing the corresponding water flux of the traditional empirical water tank model with the hydrological simulation result of the DTVGM-PML, and generating an improved drought index PDSI-V considering vegetation dynamics; by setting different vegetation change scenarios, simulating hydrological processes under different vegetation conditions and calculating the corresponding PDSI-V time series; combining run theory and three-dimensional spatio-temporal clustering and other drought identification methods to extract multi-scale drought characteristic quantities; comparing and analyzing the differences in drought characteristics under different scenarios, quantitatively evaluating the influence of vegetation change on drought evolution, and revealing the drought response mechanism under vegetation change. The present application can provide more scientific technical support for regional drought monitoring, early warning and risk management.

Description

Technical Field

[0001] This invention relates to the field of hydrological and water resources and drought monitoring and assessment technology, specifically a method and system for assessing multi-scale drought response under vegetation change. Background Technology

[0002] Drought is driven by a complex interplay of natural and anthropogenic factors, and has long been considered one of the most poorly understood and researched areas among natural disasters. Over the past 30 years, global vegetation cover has increased significantly, enhancing terrestrial carbon sequestration and mitigating global warming to some extent. As a core component of terrestrial ecosystems, vegetation exhibits a complex two-way feedback relationship with drought processes. On the one hand, drought inhibits photosynthesis and growth through water stress, leading to decreased vegetation cover and productivity. On the other hand, vegetation changes, by regulating hydrological processes such as canopy interception, evapotranspiration, soil infiltration, and runoff, conversely influence regional water cycles and the occurrence, development, and resolution of drought. Therefore, accurately quantifying the impact of vegetation change on drought processes and revealing drought response mechanisms under the background of vegetation change is of significant scientific importance and practical application value for regional drought monitoring and early warning, risk management, and ecological restoration decision-making.

[0003] Existing research has largely focused on the statistical correlation between meteorological factors and drought, or the unidirectional impact of vegetation change on hydrological processes. The mechanisms by which vegetation change influences drought evolution remain unclear, and the relative contributions of climatic and vegetation factors to drought have not been effectively separated at the quantitative level. Regarding drought assessment methods, traditional drought indices are significantly inadequate in characterizing vegetation dynamics. Most indices only indirectly monitor drought through the physiological responses of vegetation under drought stress, failing to incorporate the influence of vegetation actively regulating hydrological processes. Therefore, they are difficult to use for analyzing the feedback effects of vegetation change on drought. In conclusion, there is an urgent need to develop a drought assessment method that considers vegetation dynamics, thereby systematically revealing the impact and feedback mechanisms of vegetation change on drought. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies and provide a multi-scale drought response assessment method and system under vegetation change. By coupling a distributed hydrological model with physical mechanisms with the Palmer drought index, an improved drought index that considers vegetation dynamics is constructed. Combined with multi-scenario simulation and multi-scale drought event identification technology, a quantitative assessment of the impact of vegetation change on drought processes is achieved, revealing the multi-scale drought response mechanism under vegetation change, and providing scientific support for regional drought monitoring and early warning, risk management, and ecological restoration decision-making.

[0005] To achieve the above objectives, this invention provides a multi-scale drought response assessment method under vegetation change, comprising the following steps: Step S1, acquiring a spatiotemporal dataset of the study area, the spatiotemporal dataset including meteorological driving data, land surface driving data, and model validation data, constructing a distributed time-varying gain hydrological model DTVGM-PML coupled with a remote sensing evapotranspiration module (PML), and calibrating and validating the model based on long-series data to obtain a calibrated and validated distributed hydrological model DTVGM-PML; Step S2, based on the calculation framework of the Palmer Drought Index (PDSI), using the hydrological simulation results of the calibrated and validated distributed hydrological model DTVGM-PML obtained in Step S1, replacing the water flux corresponding to the traditional tank model in the Palmer Drought Index (PDSI), to generate an improved drought index (PDS) considering vegetation dynamic changes. Step S3: Set a baseline scenario and a vegetation detrending scenario as different vegetation change scenarios, respectively representing the impact of considering vegetation dynamic changes and not considering interannual vegetation change trends. Run the DTVGM-PML model under each scenario and calculate the spatiotemporal sequence of the PDSI-V drought index corresponding to each scenario according to the method in step S2. Step S4: For the spatiotemporal sequence of the PDSI-V drought index under each scenario calculated in step S3, use run theory and three-dimensional spatiotemporal clustering methods to identify drought events and extract multi-scale drought features including drought frequency, drought duration, drought intensity, and drought area. Step S5: Compare and analyze the differences in drought features extracted under different vegetation change scenarios in step S4, quantitatively assess the impact of vegetation change on drought event characteristics, and output the multi-scale drought response assessment results under vegetation change.

[0006] Furthermore, the construction of the distributed time-varying gain hydrological model DTVGM-PML coupled with the remote sensing evapotranspiration module PML in step S1 refers to: replacing the original evapotranspiration module in the DTVGM model with the PML remote sensing evapotranspiration module to calculate soil evaporation and vegetation transpiration, and using the ratio of soil moisture content to soil water storage capacity calculated by the DTVGM model as the soil evaporation ratio parameter in the PML equation; at the same time, remote sensing leaf area index (LAI) data is introduced into the PML evapotranspiration module as a dynamic input to consider the dynamic constraints of vegetation change on the evapotranspiration process.

[0007] Furthermore, the vegetation detrending scenario described in step S3 is constructed as follows: the LAI time series of all grids within the study period are detrended to eliminate the annual-scale linear trend while retaining seasonal variation characteristics, so as to isolate the influence of interannual vegetation variation trend on hydrological processes and keep the meteorological driving variables completely consistent with the baseline scenario.

[0008] Furthermore, the extraction method of multi-scale drought features in step S4 specifically includes: (1) calculating the annual drought intensity S based on PDSI-V at the single grid or regional scale. y and annual drought frequency S y Drought intensity S y The cumulative value of PDSI-V during the annual drought period at each grid or regional scale, with drought frequency S y (1) The proportion of drought periods at each grid or regional scale to the total number of periods; (2) Based on run theory, independently identify one-dimensional drought events for each grid or regional scale PDSI-V time series, and count the frequency N1, average duration D1, and average intensity S1 of one-dimensional drought events; (3) Based on the three-dimensional spatiotemporal clustering method, identify three-dimensional drought events for the PDSI-V spatiotemporal grid dataset, and count the frequency S1, average duration D3, average area A3, and average intensity S3 of three-dimensional drought events; The three-dimensional drought events are identified based on the three-dimensional spatiotemporal clustering method for the PDSI-V spatiotemporal grid dataset, where the area of ​​a single three-dimensional drought event is the projected area of ​​the three-dimensional drought body on the spatial plane, and the intensity of a single event is the cumulative value of all grid PDSI-V in the three-dimensional drought body.

[0009] Furthermore, the quantitative assessment described in step S5 specifically includes: (1) for drought intensity S y With drought frequency P y (1) Calculate the linear trend of the difference sequence between the baseline scenario and the vegetation detrending scenario, and output the impact of vegetation change on the annual drought characteristics; (2) For the frequency N1, average duration D1 and average intensity S1 of one-dimensional drought events, calculate the difference between each characteristic quantity under the baseline scenario and the vegetation detrending scenario, and output the impact of vegetation change on the one-dimensional drought event characteristics; (3) For the frequency N3, average duration N3, average area A3 and average intensity S3 of three-dimensional drought events, calculate the difference between each characteristic quantity under the baseline scenario and the vegetation detrending scenario, and output the impact of vegetation change on the three-dimensional drought event characteristics.

[0010] A multi-scale drought response assessment system under vegetation change is provided to perform the multi-scale drought response assessment method under vegetation change as described above. The system includes: a data acquisition module for acquiring a spatiotemporal dataset of the study area, comprising meteorological driving data, land surface driving data, and model validation data; a model construction and calibration module for constructing a distributed time-varying gain hydrological model (DTVGM-PML) coupled with a remote sensing evapotranspiration module (PML) based on the spatiotemporal dataset, and performing calibration and validation based on long-series data, outputting the calibrated and validated distributed hydrological model DTVGM-PML; and a drought index generation module for replacing the traditional tank model in the Palmer Drought Index (PDSI) with the hydrological simulation results of the calibrated and validated distributed hydrological model DTVGM-PML, based on the Palmer Drought Index (PDSI) calculation framework. The system comprises the following modules: a water flux module, which generates an improved drought index (PDSI-V) that considers vegetation dynamics; a scenario simulation module, which sets a baseline scenario and a vegetation detrended scenario to characterize the impacts of considering vegetation dynamics and not considering interannual vegetation trends, respectively; a DTVGM-PML model for each scenario, which is then used by the drought index generation module to calculate the spatiotemporal sequence of the PDSI-V drought index for each scenario; a drought event identification module, which uses runs theory and three-dimensional spatiotemporal clustering to identify drought events in the spatiotemporal sequence of the PDSI-V drought index for each scenario, respectively characterizing the impacts of considering vegetation dynamics and not considering interannual vegetation trends; and an evaluation module, which compares and analyzes the differences in drought characteristics under different vegetation change scenarios, quantitatively evaluates the impact of vegetation change on drought event characteristics, and outputs multi-scale drought response evaluation results under vegetation change.

[0011] Furthermore, the model construction and calibration module is specifically used to: replace the original evapotranspiration module in the DTVGM model with the PML remote sensing evapotranspiration module, and introduce remote sensing leaf area index (LAI) data as dynamic input into the PML evapotranspiration module to calculate soil evaporation and vegetation transpiration; use the ratio of soil moisture content to soil water storage capacity calculated by the DTVGM model as the soil evaporation ratio parameter in the PML equation; and introduce remote sensing leaf area index (LAI) data as dynamic input into the PML evapotranspiration module to consider the dynamic constraints of vegetation change on the evapotranspiration process.

[0012] Furthermore, the scenario simulation module sets up a vegetation detrended scenario, including: detrending the LAI time series of all grids within the study period, eliminating the annual-scale linear trend and retaining the seasonal variation characteristics, generating LAI input data under the vegetation detrended scenario, while keeping the meteorological driving variables consistent with the baseline scenario.

[0013] Furthermore, the drought event identification module includes: an annual-scale feature calculation unit, used to calculate the annual drought intensity S based on PDSI-V at a single grid or regional scale. y and annual drought frequency S y Drought intensity S y The cumulative value of PDSI-V during the annual drought period at each grid or regional scale, with drought frequency S y The number of drought periods at each grid or regional scale represents the proportion of the total number of periods. A one-dimensional event identification unit is used to independently identify one-dimensional drought events in the PDSI-V time series at each grid or regional scale based on runs theory, and to statistically analyze the frequency N1, average duration D1, and average intensity S1 of the one-dimensional drought events. A three-dimensional event identification unit is used to identify three-dimensional drought events in the PDSI-V spatiotemporal grid dataset based on a three-dimensional spatiotemporal clustering method, and to statistically analyze the frequency S1, average duration D3, average area A3, and average intensity S3 of the three-dimensional drought events. The three-dimensional drought events are identified based on the PDSI-V spatiotemporal grid dataset using a three-dimensional spatiotemporal clustering method, where the area of ​​a single three-dimensional drought event is the projected area of ​​the three-dimensional drought volume on the spatial plane, and the intensity of a single event is the cumulative value of all PDSI-V values ​​within that three-dimensional drought volume.

[0014] Furthermore, the assessment module includes: an interannual trend assessment unit for assessing drought intensity S. y With drought frequency P y The system calculates the linear trend of the difference sequence between the baseline scenario and the vegetation detrending scenario, and outputs the impact of vegetation change on annual-scale drought characteristics. A one-dimensional event assessment unit calculates the differences between the baseline scenario and the vegetation detrending scenario for the one-dimensional drought event frequency N1, average duration D1, and average intensity S1, and outputs the impact of vegetation change on one-dimensional drought event characteristics. A three-dimensional event assessment unit calculates the regional overall difference between the baseline scenario and the vegetation detrending scenario for the three-dimensional drought event frequency N3, average duration N3, average area A3, and average intensity S3, and outputs the impact of vegetation change on three-dimensional drought event characteristics.

[0015] The advantages and beneficial effects of this invention are as follows:

[0016] (1) The physical mechanism is clear and the impact of vegetation change can be quantitatively separated. This invention organically integrates the DTVGM-PML distributed hydrological model with the traditional PDSI drought index, replaces the empirical tank model with physical hydrological simulation, and constructs an improved drought index PDSI-V that can reflect the feedback of vegetation change by dynamically constraining the evapotranspiration process through remote sensing LAI. On this basis, a baseline scenario and a vegetation detrending scenario are set and the meteorological driving force is kept completely consistent, effectively removing climate interference and quantitatively separating the independent impact of long-term vegetation change on drought.

[0017] (3) Multi-scale comprehensive assessment with comprehensive analysis dimensions. It supports grid refinement and regional comprehensive assessment on a spatial scale, takes into account interannual trends and independent event characteristics on a temporal scale, and provides identification and statistics of one-dimensional time series events and three-dimensional spatiotemporal continuous events on an event dimension, forming a complete multi-scale drought characteristic quantification system, and fully revealing the multi-scale characteristics of drought response under vegetation change.

[0018] (4) It has strong versatility and broad application prospects. The method of this invention does not depend on specific research areas and climate conditions, and can be applied to vegetation-drought interaction studies at different climate zones and watershed scales. Its evaluation results can provide important technical support for the construction of regional drought monitoring and early warning systems, drought risk management planning, ecological restoration project benefit assessment, and sustainable water resource utilization management. Attached Figure Description

[0019] Figure 1 This is a flowchart of a multi-scale drought response assessment method under vegetation change according to Embodiment 1 of the present invention.

[0020] Figure 2 This is a model framework diagram of DTVGM-PML in an embodiment of the present invention.

[0021] Figure 3 This is a simulation diagram of the monthly runoff at the watershed outlet station according to an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] like Figure 1As shown, this embodiment of the invention provides a rolling simulation method for reservoir scheduling that couples data-driven and physical constraints, including the following steps: Step S1, acquire spatiotemporal datasets such as meteorological driving data, land surface driving data, and model validation data of the study area, construct a distributed time-varying gain hydrological model (DTVGM-PML) coupled with a remote sensing evapotranspiration module (PML equation), and calibrate and validate the model based on long-sequence data to obtain a calibrated and validated distributed hydrological model DTVGM-PML; Step S2, based on the Palmer Drought Index (PDSI) calculation framework, replace the water flux corresponding to the traditional tank model with the DTVGM-PML hydrological simulation results of the calibrated and validated distributed hydrological model DTVGM-PML obtained in Step S1 to generate an improved drought index PD considering vegetation dynamic changes. SI-V; Step S3: Set up different vegetation change scenarios, such as the baseline scenario and the vegetation detrending scenario, to represent the impact of considering vegetation dynamic changes and not considering interannual vegetation change trends, respectively. Run the DTVGM-PML model under each scenario, and calculate the spatiotemporal sequence of the PDSI-V drought index corresponding to each scenario according to the method in Step S2; Step S4: Use drought event identification methods such as runs theory and three-dimensional clustering to extract drought events from the spatiotemporal sequence of the PDSI-V drought index under each scenario, and statistically analyze multi-scale drought characteristics including drought frequency, drought duration, drought intensity, and drought area; Step S5: Compare and analyze the differences in drought characteristics extracted under different vegetation change scenarios in Step S4, quantitatively assess the impact of vegetation change on drought event characteristics, and output the multi-scale drought response assessment results under vegetation change.

[0024] Taking a certain river basin as an example, the specific implementation steps are as follows:

[0025] Step S1, Hydrological Model Construction:

[0026] 1) Data collection and processing

[0027] Spatiotemporal datasets for the study area were collected, including driving and validation data. Meteorological driving data, sourced from the China Regional Surface Meteorological Elements Driving Dataset (CMFD), with a resolution of 0.1°×0.1°, includes precipitation, temperature, specific humidity, air pressure, wind speed, downflow shortwave radiation, and downflow longwave radiation. Land surface driving data, primarily leaf area index (LAI), is derived from the Global Land Surface Characteristic Parameters Product (GLASS) provided by the National Earth System Science Data Center, with a spatiotemporal resolution of 8 days and 0.05°×0.05°. The data were first interpolated to a daily scale using piecewise cubic Hermite polynomials, and then smoothed using the Savitzky-Golay filtering method. Model validation data includes grid product data, such as monthly runoff data from the China Land Surface Hydrological Flux and State Dataset (IGSNRR) and monthly evapotranspiration data from the Global Land Surface Evapotranspiration Product (GLEAM), both with a resolution of 0.25°×0.25°. Measured runoff data from a specific hydrological station was also included. The grid data used was uniformly resampled to 0.1°×0.1° using a bilinear interpolation method, dividing the watershed into 965 grids. The model was then run on a diurnal scale, spanning from 1982 to 2018.

[0028] 2) Model Building

[0029] The Distributed Time-Varying Gain Model (DTVGM) is a distributed hydrological model that characterizes nonlinear runoff generation mechanisms based on time-varying gain factors. This embodiment improves upon it by replacing the original evapotranspiration module in the DTVGM model with a PML remote sensing evapotranspiration module to calculate soil evaporation and vegetation transpiration. The ratio of soil moisture content to soil water storage capacity calculated by the DTVGM model is used as the soil evaporation ratio parameter in the PML equation, thus establishing a DTVGM-PML model that considers vegetation dynamics. Figure 2 The model structure of DTVGM-PML is demonstrated. The model first divides precipitation into liquid rainfall and solid snowfall. Rainfall is intercepted by vegetation before reaching the surface; this intercepted water evaporates directly and returns to the atmosphere, while the remainder falls to the surface and infiltrates through canopy gaps. Snowfall is converted into liquid water volume through a snowmelt module. Surface runoff is determined by hydrological nonlinear time-varying gain theory. The underground structure is generalized as soil water and groundwater reservoirs, with infiltrating water successively forming interflow and groundwater runoff.

[0030] The PML equation expresses actual evapotranspiration as plant transpiration (Em). t ) and soil evaporation (E s ) and:

[0031] (1)

[0032] (2)

[0033] (3)

[0034] Where: λ is the latent heat of vaporization, MJ / kg; air density, kg / m³ 3 ; , is the hygrometer constant, kPa / ℃, and Δ is the slope of the temperature-saturated vapor pressure curve; For saturated water vapor pressure difference, The temperature is T a Saturated vapor pressure at time, kPa, e a The actual water vapor pressure is given in kPa; c p The specific heat of air at constant pressure is MJ / kg. -1 K -1 G s G represents surface conductivity, in m / s; a aerodynamic conductivity, m / s; f is the soil evaporation rate; A is available energy, MJ / m -2 day -1 A can be divided into energy absorbed from the plant canopy (A c ) and the energy absorbed by the soil (A s ); k A G is the attenuation coefficient of available radiation; τ is the proportion of energy absorbed from the soil to the total energy, which can be expressed as a function of LAI. Therefore, LAI data can be introduced as a dynamic input into the PML evapotranspiration module to consider the dynamic constraints of vegetation change on the evapotranspiration process; c Canopy conductance, a key eco-hydrological parameter connecting vegetation physiological activities and transpiration processes, is expressed in m / s and g. sx The maximum stomatal conductance of the top leaves of the canopy, in m / s; k Q Q is the attenuation coefficient for shortwave radiation; 50 and D 50 When the pore conductance g s =g sx Photosynthetically active radiation absorbed at / 2 (MJ m -2 day -1 ) and water vapor pressure difference (kPa); Q h For photosynthetically active radiation absorbed at the top of the canopy, MJ m -2 day -1 .

[0035] The runoff component of DTVGM includes surface runoff, interflow, and groundwater runoff, and is calculated using the following formula:

[0036] (4)

[0037] (5)

[0038] (6)

[0039] (7)

[0040] (8)

[0041] (9)

[0042] In the formula: R s Surface runoff, mm; W, soil moisture content, mm; W M Soil water storage capacity, mm; W / W M The relative soil moisture content can be used as a substitute for the soil evaporation ratio f, i.e., f = W / W M ;P t G is the rainfall amount that penetrates the canopy, in mm; g1 is the amount of rainfall when the soil is saturated (i.e., W / W). M =1) is the runoff coefficient; g2 is the runoff parameter; I is the infiltration rate, mm; R ss For the middle flow of soil, mm; k s R is the outflow coefficient from the soil; r Groundwater recharge, mm; kJ r R is the groundwater recharge ratio coefficient. g Base flow rate, mm; kJ g denoted as the drainage coefficient; G is the groundwater storage capacity (mm); R is the total production flow (mm).

[0043] The watershed runoff process was simulated using the Lohmann runoff model. The grid runoff output from the DTVGM-PML simulation was gradually channeled to the watershed outlet grid-controlled hydrological stations to obtain the watershed outlet cross-sectional flow process.

[0044] 3) Model calibration and validation

[0045] Monthly runoff and evaporation data were used to calibrate the model parameters grid-by-grid, with 1998–2012 serving as the calibration period and 1982–1997 as the validation period. The SCE-UA (Shuffled Complex Evolution) optimization algorithm was employed for multivariate calibration. The objective function aimed to minimize the Euclidean distance between monthly runoff and the monthly evaporation Nash efficiency coefficient (NSE); a smaller distance indicates better model simulation performance.

[0046] (10)

[0047] (11)

[0048] In the formula: F is the objective function; NSE R NSE is an indicator of the accuracy of flow simulation. ET w1 and w2 are the weights assigned to the accuracy of the evaporation simulation, respectively, and are both set to 1 in this paper. X s These are the measured sequence and the simulated sequence, respectively. This represents the mean of the measured sequence.

[0049] Model simulation accuracy evaluation results: such as Figure 3 As shown, the monthly runoff NSE and KGE at the watershed outlet station reached 0.91, while PBIAS was only -0.8%, indicating that DTVGM-PML can accurately simulate the hydrological processes of the watershed.

[0050] Step S2, Improve the drought index:

[0051] By integrating DTVGM-PML with PDSI, an improved drought index PDSI-V considering vegetation dynamics is obtained. The calculation steps of PDSI-V are as follows: ① First, the water flux corresponding to the traditional PDSI tank model is replaced by the hydrological process simulation results of DTVGM-PML to obtain the water balance components based on the DTVGM-PML model, thereby establishing a hydrological account; ② Based on the statistics of the actual and possible values ​​of each water balance component, each climate constant is calculated, and then the climate characteristic value k representing the ratio of water demand to water supply is obtained. This value is the first approximation of the final climate characteristic value K, and the climate-suitable precipitation is calculated from it to obtain the water anomaly d and the water anomaly index z; ③ The basic relationship between the water anomaly z and the duration t is established, and the impact of the previous water conditions on the later drought conditions is considered to obtain the PDSI calculation formula. ; ④ By establishing a relationship between the average climate characteristic value of extreme drought and the average water contribution, the climate characteristic value K is corrected to improve the spatial comparability of PDSI and obtain the final PDSI-V calculation formula.

[0052] Step S3, Scenario Simulation:

[0053] To quantitatively separate the independent impact of vegetation change on drought processes, two core comparative scenarios were set up. The DTVGM-PML model was run with the LAI sequence of each scenario and the same meteorological driving data to obtain the daily grid-scale hydrological simulation results for each scenario. Then, the spatiotemporal sequence of the PDSI-V drought index corresponding to each scenario was calculated according to the method in step S2.

[0054] ① Baseline Scenario (S0): The model input is the actual observed LAI time series, and the meteorological driving data retains the original observation values. This scenario simulates hydrological processes under real vegetation dynamics, reflecting the drought evolution under the combined effects of actual vegetation changes and climatic factors.

[0055] ② Vegetation Detrending Scenario (S1): The LAI time series of all grids within the study area are detrended one by one to eliminate the annual-scale linear trend while preserving seasonal variation characteristics. This aims to isolate the influence of interannual vegetation variation trends on hydrological processes, while ensuring that meteorological driving variables remain completely consistent with the baseline scenario S0. The specific steps of the detrending method are as follows:

[0056] a) First, process the actual daily-scale LAI data into an annual mean series;

[0057] b) Perform linear fitting on the actual annual LAI sequence, and calculate the predicted value for each year based on the fitted linear equation;

[0058] (12)

[0059] In the formula: The year is y i The predicted annual LAI; a is the slope of the fitted linear equation; b is the intercept of the linear equation.

[0060] c) Eliminate the trend of the actual annual LAI sequence to obtain the detrended annual LAI sequence;

[0061] (13)

[0062] In the formula: and The year is y i The actual and detrended annual LAI; This represents the predicted annual LAI for the first year. This term is added to ensure that the value of the first year in the detrended annual LAI series is equal to the actual value. .

[0063] d) Convert the actual daily LAI of the year into the detrended daily LAI based on the ratio of the detrended LAI to the actual LAI each year;

[0064] (14)

[0065] e) Perform steps 1 to 4 on each grid to obtain the detrended daily LAI sequence for each grid.

[0066] Step S4, Drought Event Identification

[0067] 1) Calculation of annual-scale drought characteristics

[0068] Based on the monthly PDSI-V sequences at grid or regional scales for each scenario, calculate the annual drought intensity and drought frequency:

[0069] drought intensity S y For each grid, the cumulative PDSI-V values ​​for each year during periods when PDSI-V is below a given drought threshold (e.g., -1.0) are statistically analyzed, forming an n×m data matrix, where n is the number of years and m is the number of grids. Regional-scale S y The PDSI-V is obtained by aggregating all grid hydrological simulation results into a regional average, with a data dimension of n×1.

[0070] drought frequency P y For each grid, the proportion of months with PDSI-V below the drought threshold is counted year by year, forming an n×m or n×1 data matrix.

[0071] 2) One-dimensional drought event identification and feature extraction

[0072] One-dimensional drought event identification is performed on PDSI-V time series at grid or regional scales under various scenarios using run theory:

[0073] Set a drought threshold (e.g., -1.0), and for each grid's PDSI-V time series, identify periods where the PDSI-V is continuously below the threshold. If the continuous period below the threshold lasts for only one month and the PDSI-V for that month is extremely low (e.g., ≤ -2.0), it is considered an independent drought event. If multiple periods are spaced only one month apart and the PDSI-V for that month is < 0, then adjacent periods are merged into a single drought event. Small drought events (e.g., PDSI-V only slightly below the threshold and short duration) can be excluded as needed for the research.

[0074] For each identified drought event, the following features are extracted: drought duration (number of consecutive months from start to end); drought intensity (cumulative PDSI-V value during the event, taking the absolute value).

[0075] For each grid, the frequency N1 (total number of drought events), average duration D1 (arithmetic mean of the durations of all events), and average intensity S1 (arithmetic mean of the intensities of all events) of one-dimensional drought events are statistically analyzed, with a data dimension of m. The regional-scale N1, D1, and S1 are based on regional-scale PDSI-V sequence statistics, with a data dimension of 1.

[0076] 3) Three-dimensional drought event identification and feature extraction

[0077] A three-dimensional spatiotemporal clustering method is used to identify three-dimensional drought events in the PDSI-V spatiotemporal grid dataset. This algorithm combines spatial clustering and temporal concatenation to merge spatially adjacent and temporally continuous drought grid cells into three-dimensional drought events. First, the drought grid is spatially clustered month by month to extract independent drought patches, and patches with excessively small areas are removed. Then, between adjacent months, the degree of spatial overlap of patches is used to determine whether they belong to the same event. Finally, through temporal concatenation, a series of spatiotemporally continuous drought events are formed.

[0078] For each identified 3D drought event, the following features are extracted: duration, the length of the 3D drought volume on the time axis, in months; area, the area of ​​the 3D drought volume projected onto the spatial plane, in km² or grid number; and intensity, the cumulative PDSI-V value of all grids within the 3D drought volume.

[0079] For all three-dimensional drought events identified during the study period, the statistical frequency N3 (total number of events), average duration D3, average area A3, and average intensity S3 are all regional statistics with a data dimension of 1.

[0080] Step S5, Drought Response Assessment:

[0081] By comparing various drought characteristics of the baseline scenario S0 and the vegetation detrending scenario S1, the impact of vegetation change on drought events is quantitatively assessed.

[0082] 1) For annual-scale drought characteristics S y and P y The method calculates the difference sequence between the corresponding values ​​of scenario S0 and S1 at each grid or regional scale, and then performs linear regression analysis on the difference sequence to obtain the degree and statistical significance of the impact of vegetation change on the interannual variation trend of drought. The resulting data has a dimension of m or 1, and spatial distribution maps can be drawn to show the regional differences in the impact of vegetation change on drought.

[0083] 2) For one-dimensional drought event features N1, D1 and S1: Calculate the absolute difference and relative rate of change of the corresponding values ​​of scenario S0 and S1 at each grid or region scale to obtain the degree of influence of vegetation change on one-dimensional drought event features. The resulting data dimension is m or 1.

[0084] 3) For the three-dimensional drought event features N3, D3, A3 and S3: calculate the absolute difference and relative rate of change of the corresponding values ​​of scenario S0 and S1 to obtain the degree of influence of vegetation change on the three-dimensional drought event features. The result data dimension is 1.

[0085] This invention also provides a multi-scale drought response assessment system under vegetation change, used to execute the multi-scale drought response assessment method under vegetation change as described above, comprising: a data acquisition module for acquiring a spatiotemporal dataset of the study area, the spatiotemporal dataset including meteorological driving data, land surface driving data, and model validation data; a model construction and calibration module for constructing a distributed time-varying gain hydrological model DTVGM-PML coupled with a remote sensing evapotranspiration module (PML) based on the spatiotemporal dataset, and performing calibration and validation based on long-series data, outputting a calibrated and validated distributed hydrological model DTVGM-PML; and a drought index generation module for replacing the data transmitted in the Palmer Drought Index (PDSI) with the hydrological simulation results of the calibrated and validated distributed hydrological model DTVGM-PML, based on the calculation framework of the Palmer Drought Index (PDSI). The system generates an improved drought index (PDSI-V) that considers vegetation dynamics based on the water flux of a water tank model. A scenario simulation module sets a baseline scenario and a vegetation detrended scenario, representing the impacts of considering vegetation dynamics and not considering interannual vegetation trends, respectively. It runs the DTVGM-PML model under each scenario and calls the drought index generation module to calculate the spatiotemporal sequence of the PDSI-V drought index for each scenario. A drought event identification module uses runs theory and three-dimensional spatiotemporal clustering to identify drought events in the spatiotemporal sequences of the PDSI-V drought index under each scenario, representing the impacts of considering vegetation dynamics and not considering interannual vegetation trends, respectively. An evaluation module compares and analyzes the differences in drought characteristics under different vegetation change scenarios, quantitatively assesses the impact of vegetation change on drought event characteristics, and outputs multi-scale drought response evaluation results under vegetation change.

[0086] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-scale drought response assessment method under vegetation change, characterized in that, Includes the following steps: Step S1: Obtain the spatiotemporal dataset of the study area, which includes meteorological driving data, land surface driving data and model validation data. Construct a distributed time-varying gain hydrological model DTVGM-PML coupled with remote sensing evapotranspiration module PML, and calibrate and validate the model based on long sequence data to obtain the calibrated and validated distributed hydrological model DTVGM-PML. Step S2: Based on the calculation framework of the Palmer Drought Index PDSI, the hydrological simulation results of the calibrated and verified distributed hydrological model DTVGM-PML obtained in step S1 are used to replace the water flux corresponding to the traditional tank model in the Palmer Drought Index PDSI, and an improved drought index PDSI-V that takes into account the dynamic changes of vegetation is generated. Step S3: Set the baseline scenario and the vegetation detrending scenario as different vegetation change scenarios, respectively representing the impact of considering vegetation dynamic changes and not considering the interannual vegetation change trend. Run the DTVGM-PML model under each scenario, and calculate the spatiotemporal sequence of PDSI-V drought index corresponding to each scenario according to the method in step S2. Step S4: For the PDSI-V drought index spatiotemporal sequences under each scenario calculated in step S3, drought events are identified using run theory and three-dimensional spatiotemporal clustering methods, and multi-scale drought features including drought frequency, drought duration, drought intensity, and drought area are extracted. Step S5: Compare and analyze the differences in drought characteristic quantities extracted in step S4 under different vegetation change scenarios, quantitatively assess the impact of vegetation change on drought event characteristics, and output the multi-scale drought response assessment results under vegetation change. The construction of the distributed time-varying gain hydrological model DTVGM-PML coupled with the remote sensing evapotranspiration module PML in step S1 refers to: replacing the original evapotranspiration module in the DTVGM model with the PML remote sensing evapotranspiration module to calculate soil evaporation and vegetation transpiration, and using the ratio of soil moisture content to soil water storage capacity calculated by the DTVGM model as the soil evaporation ratio parameter in the PML equation; at the same time, remote sensing leaf area index (LAI) data is introduced into the PML evapotranspiration module as a dynamic input to consider the dynamic constraints of vegetation change on the evapotranspiration process.

2. The method according to claim 1, characterized in that, The vegetation detrending scenario described in step S3 is constructed as follows: the LAI time series of all grids within the study period are detrended to eliminate the annual linear trend while retaining seasonal variation characteristics, so as to isolate the influence of interannual vegetation variation trend on hydrological processes and keep the meteorological driving variables completely consistent with the baseline scenario.

3. The method according to claim 1, characterized in that, The extraction method of multi-scale drought features described in step S4 specifically includes: (1) Calculate the annual drought intensity S based on PDSI-V at the single grid or regional scale. y and annual drought frequency S y Drought intensity S y The cumulative value of PDSI-V during the annual drought period at each grid or regional scale, with drought frequency S y This represents the proportion of drought periods at each grid or regional scale to the total number of periods. (2) Based on run theory, one-dimensional drought events are independently identified in PDSI-V time series at each grid or regional scale, and the frequency N1, average duration D1, and average intensity S1 of one-dimensional drought events are statistically analyzed. (3) Based on the three-dimensional spatiotemporal clustering method, three-dimensional drought events are identified in the PDSI-V spatiotemporal grid dataset, and the frequency S1, average duration D3, average area A3 and average intensity S3 of the three-dimensional drought events are statistically analyzed. The three-dimensional drought events are identified in the PDSI-V spatiotemporal grid dataset based on the three-dimensional spatiotemporal clustering method. The area of ​​a single three-dimensional drought event is the projected area of ​​the three-dimensional drought body on the spatial plane, and the intensity of a single event is the cumulative value of all PDSI-V grids in the three-dimensional drought body.

4. The method according to claim 3, characterized in that, The quantitative assessment described in step S5 specifically includes: (1) For drought intensity S y With drought frequency P y Calculate the linear trend of the difference sequence between the baseline scenario and the vegetation detrending scenario, and output the impact of vegetation change on annual-scale drought characteristics. (2) For the frequency N1, average duration D1 and average intensity S1 of one-dimensional drought events, calculate the difference between each characteristic quantity under the baseline scenario and the vegetation detrending scenario, and output the impact of vegetation change on the characteristics of one-dimensional drought events. (3) For the frequency N3, average duration N3, average area A3 and average intensity S3 of three-dimensional drought events, calculate the difference between each characteristic quantity under the baseline scenario and the vegetation detrending scenario, and output the impact of vegetation change on the characteristics of three-dimensional drought events.

5. A multi-scale drought response assessment system under vegetation change, used to perform the multi-scale drought response assessment method under vegetation change as described in any one of claims 1-4, characterized in that, include: The data acquisition module is used to acquire spatiotemporal datasets of the study area, which include meteorological driving data, land surface driving data, and model validation data. The model building and calibration module is used to build a distributed time-varying gain hydrological model DTVGM-PML coupled with the remote sensing evapotranspiration module PML based on the spatiotemporal dataset, and to perform calibration and verification based on long-sequence data, and output the calibrated and verified distributed hydrological model DTVGM-PML. The drought index generation module is used to replace the water flux of the traditional tank model in the Palmer Drought Index PDSI with the hydrological simulation results of the calibrated and verified distributed hydrological model DTVGM-PML on the basis of the calculation framework of the Palmer Drought Index PDSI, and generate an improved drought index PDSI-V that takes into account the dynamic changes of vegetation. The scenario simulation module is used to set the baseline scenario and the vegetation detrending scenario, respectively representing the impact of considering vegetation dynamic changes and not considering the interannual variation trend of vegetation. It runs the DTVGM-PML model under each scenario and calls the drought index generation module to calculate the spatiotemporal sequence of the PDSI-V drought index corresponding to each scenario. The drought event identification module is used to identify drought events in the spatiotemporal sequences of the PDSI-V drought index under various scenarios using run theory and three-dimensional spatiotemporal clustering methods, respectively, to characterize the impact considering vegetation dynamic changes and not considering interannual vegetation change trends. The assessment module is used to compare and analyze the differences in drought characteristics under different vegetation change scenarios, quantitatively assess the impact of vegetation change on drought event characteristics, and output multi-scale drought response assessment results under vegetation change.

6. The system according to claim 5, characterized in that, The model building and calibration module is specifically used for: The PML remote sensing evapotranspiration module replaces the original evapotranspiration module in the DTVGM model, and remote sensing leaf area index (LAI) data is introduced into the PML evapotranspiration module as a dynamic input to calculate soil evaporation and vegetation transpiration. The ratio of soil moisture content to soil water storage capacity calculated by the DTVGM model is used as the soil evaporation ratio parameter in the PML equation. Remote sensing leaf area index (LAI) data is introduced as a dynamic input into the PML evapotranspiration module to account for the dynamic constraints of vegetation change on the evapotranspiration process.

7. The system according to claim 5, characterized in that, The scenario simulation module sets up a vegetation detrended scenario, including: detrending the LAI time series of all grids within the study period, eliminating the annual-scale linear trend and retaining the seasonal variation characteristics, generating LAI input data under the vegetation detrended scenario, while keeping the meteorological driving variables consistent with the baseline scenario.

8. The system according to claim 5, characterized in that, The drought event identification module includes: Annual-scale characteristic calculation unit, used to calculate annual drought intensity S based on a single grid or regional scale PDSI-V. y and annual drought frequency S y Drought intensity S y The cumulative value of PDSI-V during the annual drought period at each grid or regional scale, with drought frequency S y This represents the proportion of drought periods at each grid or regional scale to the total number of periods. The one-dimensional event identification unit is used to independently identify one-dimensional drought events in PDSI-V time series at each grid or region scale based on run theory, and to count the frequency N1, average duration D1, and average intensity S1 of one-dimensional drought events. The three-dimensional event recognition unit is used to identify three-dimensional drought events in the PDSI-V spatiotemporal grid dataset based on the three-dimensional spatiotemporal clustering method, and to count the frequency S1, average duration D3, average area A3, and average intensity S3 of the three-dimensional drought events. The three-dimensional drought events are identified based on the PDSI-V spatiotemporal grid dataset using the three-dimensional spatiotemporal clustering method. The area of ​​a single three-dimensional drought event is the projected area of ​​the three-dimensional drought body on the spatial plane, and the intensity of a single event is the cumulative value of all PDSI-V grids within the three-dimensional drought body.

9. The system according to claim 5, characterized in that, The evaluation module includes: Interannual trend assessment unit, used to assess drought intensity S y With drought frequency P y Calculate the linear trend of the difference sequence between the baseline scenario and the vegetation detrending scenario, and output the impact of vegetation change on annual-scale drought characteristics. The one-dimensional event assessment unit is used to calculate the difference between the baseline scenario and the vegetation detrending scenario for the frequency N1, average duration D1 and average intensity S1 of one-dimensional drought events, and output the impact of vegetation change on the characteristics of one-dimensional drought events. The three-dimensional event assessment unit is used to calculate the regional overall difference of each characteristic quantity between the baseline scenario and the vegetation detrending scenario for the frequency N3, average duration N3, average area A3 and average intensity S3 of three-dimensional drought events, and output the impact of vegetation change on the characteristics of three-dimensional drought events.

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