Extreme rainfall-vegetation regulation and storage delay response quantification method and system

Abstract

The invention relates to the technical field of hydro meteorology, in particular to an extreme rainfall-vegetation regulation and storage delay response quantification method and a system thereof.By obtaining near-infrared, mid-infrared and Raman multiband molecular spectrum data of a vegetation sample, a moisture state spectrogram containing free water, bound water and hydrogen bond network indexes is constructed; molecular hydrodynamic time-lag characteristics such as moisture response, balance and release time are extracted; the method comprises the following steps: determining regulation and storage efficiency indexes of cell and tissue scales by combining vegetation tissue microstructure parameters such as epidermis, mesophyll and vascular bundles, finally generating a regional vegetation regulation and storage response prediction result and a regulation and storage efficiency spatial distribution diagram, and distinguishing water molecules in different combination states in vegetation tissues through multi-band molecular spectrum analysis, the molecular mechanism of vegetation moisture regulation is deeply revealed, and the characterization precision is improved by 90% or above compared with that of a traditional method.

Description

Technical Field

[0001] The present invention relates to the field of hydrological and meteorological technology, and specifically to a method and system for quantifying the delayed response of extreme precipitation-vegetation regulation and storage, and more particularly to a technical solution for using molecular spectral analysis technology to study the regulation and storage mechanism of vegetation to extreme precipitation, quantify its delayed response characteristics, and provide a scientific basis for sponge city planning, watershed management, and flood prevention and disaster reduction. Background Art

[0002] As global climate change intensifies, the frequency and intensity of extreme precipitation events continue to increase. The resulting floods have become a major natural disaster threat to human life and property. Vegetation, as a crucial component of terrestrial ecosystems, significantly regulates watershed hydrological processes by intercepting precipitation and regulating infiltration and evapotranspiration. This effectively mitigates peak flood flows and reduces the risk of flood disasters.

[0003] However, existing technologies have many shortcomings in quantifying the regulation and storage effects of vegetation. Traditional hydrological analysis methods are mostly based on simple linear correlation models, which make it difficult to capture the complex nonlinear time-lag relationship between vegetation and hydrological systems under extreme precipitation conditions. Such methods usually regard vegetation as a static factor and ignore its dynamic regulation role, resulting in analysis results that are difficult to accurately reflect the actual situation. At the same time, existing research has mostly focused on the analysis of a single time scale and lacks a systematic investigation of multi-scale lag characteristics. The regulation and storage effects of vegetation vary at different time scales, from hourly interception to monthly soil moisture regulation. Single-scale analysis is difficult to fully reflect this complex effect.

[0004] While existing spectral analysis technologies (such as the "Remote Sensing Inversion Method for Ecological Water Content in Terrestrial Vegetation Canopies Based on Spectral Analysis" disclosed in CN110118742A) can invert vegetation water content through spectral indices, they focus solely on static measurements of macroscopic water content. They are unable to distinguish between water distributions in different bound states within vegetation tissues and lack the ability to monitor and analyze dynamic changes in vegetation water content before and after extreme precipitation events. Furthermore, existing technologies fail to model the temporal relationship between precipitation and vegetation water response, quantify lag effects, and fail to consider the impact of structural and physiological differences among vegetation types on water absorption, transport, and release.

[0005] Therefore, there is an urgent need to develop a method that can systematically quantify the extreme precipitation-vegetation storage hysteresis response characteristics from the perspective of molecular hydrodynamics to provide technical support for scientific disaster prevention and mitigation and ecosystem management. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for quantifying the delayed response of vegetation storage to extreme precipitation. By introducing molecular spectral analysis technology, the regulation mechanism of vegetation to extreme precipitation is studied from the perspective of molecular hydrodynamics, and the characteristics of the delayed response of vegetation storage can be accurately quantified, providing a scientific basis for sponge city planning, watershed management, and flood prevention and disaster reduction.

[0007] The present invention discloses a time-delayed response quantification method for extreme precipitation-vegetation regulation, comprising:

[0008] Acquiring multi-band molecular spectral data of a vegetation sample, wherein the multi-band molecular spectral data includes near-infrared spectral data, mid-infrared spectral data, and Raman spectral data;

[0009] Based on the multi-band molecular spectral data, constructing a water state spectrum that characterizes the state of water molecules in plant tissues, the water state spectrum including a free water index, a bound water index, and a hydrogen bond network index;

[0010] Extracting molecular water dynamics hysteresis characteristics according to the water state spectrum, wherein the molecular water dynamics hysteresis characteristics include water response time, water equilibrium time and water release time;

[0011] Acquiring vegetation tissue microstructure parameters, wherein the vegetation tissue microstructure parameters include epidermal structure parameters, mesophyll structure parameters, and vascular bundle structure parameters;

[0012] Determining a multi-scale vegetation storage efficiency index based on the molecular hydrodynamic hysteresis characteristics and the vegetation tissue microstructure parameters, wherein the multi-scale vegetation storage efficiency index includes a cell-scale storage index and a tissue-scale storage index;

[0013] Based on the multi-scale vegetation storage efficiency index, regional vegetation storage response prediction results and a storage efficiency spatial distribution map are generated.

[0014] Preferably, the obtaining of multi-band molecular spectral data of vegetation samples comprises:

[0015] Acquiring near-infrared spectral data in a wavelength range of 780-2500 nm, wherein the near-infrared spectral data includes vibration characteristics of a combination band and a harmonic band of water molecules;

[0016] Acquiring mid-infrared spectral data in a wavelength range of 2500-25000 nm, wherein the mid-infrared spectral data includes fundamental frequency vibration characteristics of water molecules;

[0017] The range of wave numbers is 200 to 4000 Raman spectral data comprising scattering characteristics of hydrogen bond structures between water molecules;

[0018] The multi-band molecular spectral data are preprocessed, and the preprocessing includes spectral calibration, noise elimination, baseline correction, spectral standardization and spectral differential transformation.

[0019] Preferably, the constructing of a water state spectrum representing the state of water molecules in plant tissue based on the multi-band molecular spectral data comprises:

[0020] Extracting water absorption peak features near 1450nm and 1940nm from the near-infrared spectral data;

[0021] Extract 3400 from the mid-infrared spectral data -OH stretching vibration characteristics near 1640 nearby HOH bending vibration characteristics;

[0022] Extract 3200~3600 from the Raman spectrum data -OH stretching vibration scattering characteristics of the range;

[0023] Based on the extracted spectral features, the free water index representing the relative content of free water, the bound water index representing the relative content of bound water, and the hydrogen bond network index representing the strength of the hydrogen bond network between water molecules were calculated;

[0024] Construct a dynamic distribution diagram of moisture status with time as the horizontal axis and the moisture ratio of different states as the vertical axis.

[0025] Preferably, extracting the molecular water dynamics hysteresis characteristics according to the water state spectrum includes:

[0026] Based on the water state spectrum, the extreme precipitation response process is divided into absorption stage, equilibrium stage and release stage;

[0027] Analyze the state conversion paths and conversion rates of water molecules in vegetation tissues at each stage;

[0028] The time interval from the peak of extreme precipitation intensity to the peak of vegetation water state parameters was determined as the water response time;

[0029] Determine the time required for the rate of change of the moisture state parameter to fall below a preset threshold as the moisture balance time;

[0030] The time required for the moisture state parameter to drop from the peak value to the half-peak value was determined as the moisture release time.

[0031] Preferably, the determining of the multi-scale vegetation storage efficiency index based on the molecular hydrodynamic hysteresis characteristics and the vegetation tissue microstructure parameters includes:

[0032] Analyzing the relationship between the epidermal structural parameters and the moisture response time to determine the effect of the epidermis on the initial absorption of moisture;

[0033] Analyzing the relationship between the mesophyll structural parameters and the water balance time to determine the effect of the mesophyll on water storage;

[0034] Analyzing the relationship between the structural parameters of the vascular bundle and the water conversion rate to determine the effect of the vascular bundle on water transport;

[0035] Determine the water capacity of a cell based on the total amount of water that can be stored per unit volume of the cell;

[0036] The cell water retention time is determined based on the sum of the water balance time and the water release time;

[0037] Based on the characteristic rate of water diffusion in tissue, the tissue water diffusion coefficient is determined;

[0038] Based on the differences in water content between different tissue parts, the tissue water content gradient is determined.

[0039] Preferably, generating a regional vegetation storage response prediction result and a storage efficiency spatial distribution map based on the multi-scale vegetation storage efficiency index includes:

[0040] Extract key characteristic parameters such as response delay, rising rate, peak position, falling rate and recovery degree from the moisture state change curve;

[0041] Establish a relationship model between precipitation characteristic parameters and response curve characteristic parameters;

[0042] Based on the relationship model, the regulation and storage response characteristics of vegetation under extreme precipitation scenarios of different intensities and durations are predicted;

[0043] Determine the critical threshold of vegetation storage capacity and predict the potential failure of storage function if the threshold is exceeded;

[0044] The multi-scale vegetation storage efficiency index is mapped to geographic space to generate a spatial distribution map including storage intensity, storage duration and storage efficiency.

[0045] Preferably, the extreme precipitation-vegetation regulation time-delay response quantification method is used for sponge city planning, flood risk management and ecosystem service assessment.

[0046] The time-delayed response quantitative system of precipitation-vegetation regulation at the terminal adopts the method described above, and is characterized by comprising:

[0047] Multi-band spectral data acquisition module, used to obtain near-infrared spectral data, mid-infrared spectral data and Raman spectral data of vegetation samples;

[0048] A water state spectrum construction module is used to construct a water state spectrum characterizing the state of water molecules in plant tissue based on the multi-band spectral data, wherein the water state spectrum includes a free water index, a bound water index, and a hydrogen bond network index;

[0049] A hysteresis feature extraction module, configured to extract molecular hydrodynamic hysteresis features according to the moisture state spectrum, wherein the molecular hydrodynamic hysteresis features include moisture response time, moisture equilibrium time, and moisture release time;

[0050] A microstructure analysis module is used to obtain vegetation tissue microstructure parameters, wherein the vegetation tissue microstructure parameters include epidermal structure parameters, mesophyll structure parameters and vascular bundle structure parameters;

[0051] A regulation and storage efficiency evaluation module is used to determine a multi-scale vegetation regulation and storage efficiency index based on the molecular hydrodynamic hysteresis characteristics and the vegetation tissue microstructure parameters, wherein the multi-scale vegetation regulation and storage efficiency index includes a cell-scale regulation and storage index and a tissue-scale regulation and storage index;

[0052] The prediction application module is used to generate regional vegetation storage response prediction results and a storage efficiency spatial distribution map based on the multi-scale vegetation storage efficiency index.

[0053] Preferably, the multi-band spectral data acquisition module includes:

[0054] A near-infrared spectrum acquisition unit, used to acquire near-infrared spectrum data in the wavelength range of 780-2500nm;

[0055] A mid-infrared spectrum acquisition unit, used to acquire mid-infrared spectrum data in the wavelength range of 2500-25000nm;

[0056] Raman spectrum acquisition unit, used to obtain wavenumber range of 200 to 4000 Raman spectral data of

[0057] The spectrum preprocessing unit is used to perform calibration, noise elimination, baseline correction, standardization and differential transformation on the multi-band spectrum data.

[0058] Preferably, the prediction application module includes:

[0059] Response characteristic parameterization unit, used to extract key characteristic parameters from the water state change curve and establish a precipitation-response relationship model;

[0060] A storage capacity prediction unit, configured to predict the storage response characteristics of vegetation under different extreme precipitation scenarios based on the relationship model;

[0061] Spatial distribution visualization unit, used to map the storage efficiency index to the geographic space and generate a spatial distribution map of storage efficiency;

[0062] The decision support unit is used to provide decision support for sponge city planning, flood risk management and ecosystem service assessment based on the results of storage efficiency assessment.

[0063] The beneficial effects of the present invention include:

[0064] 1. Accurate characterization of molecular-level water status has been achieved: Through multi-band molecular spectral analysis, it is possible to distinguish water molecules in different binding states in plant tissues, deeply revealing the molecular mechanism of vegetation water regulation, and the characterization accuracy is improved by more than 90% compared with traditional methods.

[0065] 2. It breaks through the limitations of static measurements and realizes continuous dynamic monitoring of the entire process of extreme precipitation: by tracking the changes in the state of water molecules in vegetation tissues before, during and after extreme precipitation, it comprehensively captures the dynamic process of water absorption, balance and release, with a time resolution of up to 15 minutes.

[0066] 3. A complete molecular hydrodynamic hysteresis analysis framework was established: The hysteresis characteristics of vegetation storage were quantified from the perspective of molecular hydrodynamics, and the time characteristics of the three stages of absorption, equilibrium and release were accurately distinguished, providing a new perspective for understanding the vegetation storage mechanism.

[0067] 4. Revealed the quantitative relationship between vegetation microstructure and water regulation function: established the correlation between vegetation microstructure and water dynamic parameters, revealed the impact of different structural characteristics on water regulation capacity, and provided a scientific basis for vegetation optimization selection.

[0068] 5. An integrated analysis of regulation and storage characteristics from the molecular scale to the regional scale has been achieved: By establishing a correlation between molecular spectral features and remote sensing hyperspectral data, the point-scale analysis method has been expanded to the regional scale, providing a new method for the study of regional hydrological processes.

[0069] 6. A complete application support system has been formed: decision support tools have been developed in areas such as sponge city planning, watershed management and flood prevention and disaster reduction, and ecosystem service assessment, transforming research results into practical application solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a flow chart of the method for quantifying the delayed response of extreme precipitation-vegetation regulation of the present invention. DETAILED DESCRIPTION

[0071] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. The present invention provides a method and system for quantifying the delayed response of vegetation to extreme precipitation, aiming to study the regulation mechanism of vegetation to extreme precipitation from the perspective of molecular hydrodynamics and quantify its delayed response characteristics.

[0072] Reference Figure 1 The method for quantifying the hysteresis response of extreme precipitation-vegetation regulation of the present invention includes the following steps: obtaining multi-band molecular spectral data of vegetation samples; constructing a water state spectrum that characterizes the state of water molecules in vegetation tissues; extracting molecular water dynamics hysteresis characteristics; obtaining vegetation tissue microstructure parameters; determining multi-scale vegetation regulation efficiency indicators; and generating regional vegetation regulation response prediction results and a regulation efficiency spatial distribution map.

[0073] The present invention first obtains multi-band molecular spectral data of vegetation samples, including near-infrared spectral data, mid-infrared spectral data, and Raman spectral data. In a preferred embodiment of the present invention, the acquisition process is as follows:

[0074] 1. Near-Infrared Spectral Data Acquisition: Use a spectrometer to collect near-infrared spectral data in the wavelength range of 780-2500 nm. Preferably, focus on the water absorption peaks near 1450 nm and 1940 nm, as these peaks primarily reflect the vibrational characteristics of the water-OH bond combination band and the harmonic band. During acquisition, maintain a distance of 3-5 cm between the spectrometer and the plant leaf, with a sampling angle of 45°. Repeat the measurement 10 times at each sample point and take the average value to eliminate the influence of random error.

[0075] 2. Mid-infrared spectral data acquisition: Use Fourier transform infrared spectrometer to collect mid-infrared spectral data with a wavelength range of 2500-25000nm. -OH stretching vibration near 1640 The HOH bending vibrations nearby can directly reflect the fundamental vibration of water molecules. The resolution is set to 4 The number of scans was 32, and air was used as a reference for background acquisition.

[0076] 3. Raman spectroscopy data collection: Use Raman spectrometer to collect 200~4000 Preferably, focus on the Raman scattering spectrum in the range of 3200 to 3600 The -OH stretching vibration scattering characteristic of the spectrum is highly sensitive to changes in intermolecular hydrogen bonding. The excitation wavelength was 785 nm, the power was kept below 10 mW to avoid sample damage, the integration time was 10 seconds, and each point was sampled five times.

[0077] In practical applications, data collection is divided into three key stages: before extreme precipitation (establishing baseline data), during extreme precipitation (collecting every 15 minutes to capture rapid response characteristics), and after extreme precipitation (collecting every hour for the first 4 hours, and every 4 hours thereafter for 24 hours to track recovery process characteristics).

[0078] The collected raw spectral data needs to go through a preprocessing step. In one embodiment of the present invention, the preprocessing process is as follows:

[0079] 1. Spectral Calibration: Use a standard reflector (such as Spectralon®) for white reference calibration to eliminate the effects of instrument response and ambient light. Perform white reference calibration before each sample measurement to ensure data accuracy.

[0080] 2. Noise Removal: For near-infrared and mid-infrared spectra, the Savitzky-Golay smoothing algorithm was used with a window size of 9 points and a polynomial order of 3 to balance noise removal and feature preservation. For Raman spectra, a wavelet denoising algorithm was used, using the Daubechies4 wavelet with a decomposition level of 5 to effectively remove random noise while preserving the fine features of water molecular vibrations.

[0081] 3. Baseline Correction: For near-infrared spectra, multi-point linear interpolation is used for baseline correction; for mid-infrared spectra, an iterative polynomial fitting method is used with a polynomial order of 5 and 10 iterations; for Raman spectra, an improved adaptive iterative baseline correction algorithm (ModPoly) is used to accurately eliminate fluorescence background interference.

[0082] 4. Spectral Normalization: Standard Normal Variate (SNV) and Multi-Scattering Correction (MSC) methods are used to eliminate the influence of physical factors such as sample thickness, density, and surface scattering. In addition, spectra collected at different time points are normalized using maximum normalization to ensure data comparability.

[0083] 5. Spectral Differential Transformation: Calculate the first and second derivatives of the spectrum to enhance the fine features of water molecular vibration. The first derivative is used to enhance absorption edges and peak position changes, while the second derivative is used to enhance peak shape and width changes. Derivative calculations use the Savitzky-Golay differential smoothing method with an 11-point window size and a polynomial order of 3.

[0084] Through the above preprocessing steps, the present invention can effectively eliminate various interference factors and extract high-quality spectral information reflecting the state of water molecules in vegetation tissues.

[0085] Based on the pre-processed multi-band spectral data, the present invention constructs a water state spectrum that characterizes the state of water molecules in plant tissues. The specific implementation process is as follows:

[0086] 1. Extraction of water molecule vibration features: The present invention first extracts key vibration features that can characterize the state of water molecules in plant tissues from multi-band spectral data.

[0087] Preferably, characteristic water absorption peaks near 1450 nm and 1940 nm are extracted from near-infrared spectral data. The 1450 nm peak primarily corresponds to the frequency harmonic of the first -OH stretching vibration of water molecules, while the 1940 nm peak corresponds to the combined frequency band of -OH stretching and HOH bending. Parameters such as the intensity, position, half-peak width, and peak shape of these two peaks accurately reflect the binding state and distribution of water molecules in plant tissues. During the extraction process, Gaussian peak fitting is used to determine characteristic parameters such as peak position, peak height, peak area, and half-peak width.

[0088] Extract 3400 from mid-infrared spectral data -OH stretching vibration characteristics near 1640 These fundamental vibration characteristics are extremely sensitive to the binding state of water molecules and can directly reflect the interaction between water molecules and plant tissue macromolecules. In particular, free water and bound water show obvious differences in these characteristic peaks: the -OH stretching vibration peak of free water is relatively narrow and has a higher position (about 3600 ), while the peak of bound water is broad and located at a lower position (about 3300 During the extraction process, peak decomposition was used to separate the contributions of water in different binding states.

[0089] Extract 3200~3600 from Raman spectrum data Raman spectroscopy is extremely sensitive to the hydrogen bond network structure of water molecules, and the changes in the profile of the -OH stretching vibration region reflect the strength and order of the hydrogen bond network between water molecules. During the extraction process, a multi-component Gaussian-Lorentzian mixture model is used to fit this region, isolating the contributions of water molecules with different hydrogen bond strengths.

[0090] 2. Calculation of water state parameters: Based on the extracted vibration characteristics, the present invention calculates a series of key parameters that characterize the state of water molecules.

[0091] Preferably, the free water index (FWI) is calculated, which is defined as the high frequency (3600 Nearby) and low frequency (3200 The ratio of the -OH stretching vibration intensity near:

[0092] ,

[0093] in, 3600cm -1The Raman scattering intensity near ,reflects the contribution of weak hydrogen bonds or free OH; 3200 The Raman scattering intensity near the surface of the water reflects the contribution of a strong hydrogen bond network. Higher FWI values ​​indicate a greater proportion of free water. In practical applications, the FWI for broadleaf forests normally ranges from 0.4 to 0.7, but can rise to 0.8 to 1.2 after extreme rainfall, indicating a significant increase in free water.

[0094] The bound water index (BWI) was calculated as the bending (1640 ) and telescopic (3400 ) Ratio of vibration intensity:

[0095] BWI= ,

[0096] in 1640 The mid-infrared absorption intensity near , reflects the HOH bending vibration; 3600 The mid-infrared absorption intensity near the BWI reflects the -OH stretching vibration. Higher BWI values ​​indicate a greater proportion of water bound to biomolecules. In practical applications, the normal BWI range for broadleaf forest vegetation is 0.3-0.5. Following extreme rainfall, it can initially drop to 0.2-0.3, then gradually increase to 0.4-0.6, indicating a gradual transition of water from a free to a bound state.

[0097] Calculate the hydrogen bond network index (HBNI), defined as the shift of the -OH stretching vibration peak:

[0098] ,

[0099] in, is the standard vibration frequency of free -OH (about 3650 ） ; The central frequency of the measured -OH stretching vibration peak. The higher the HBNI value, the stronger the hydrogen bond network between water molecules. In practical applications, the normal range of HBNI for broad-leaved forest vegetation is 150 to 250. , after extreme rainfall, it can increase to 250-350 , indicating that the hydrogen bond network between water molecules is enhanced.

[0100] Based on spectral decomposition technology, the relative proportions of free water, weakly bound water and strongly bound water in plant tissues are quantitatively calculated. Multi-component fitting is performed in the region, and the typical proportion distribution is: under normal conditions, strongly bound water accounts for 40% to 50%, weakly bound water accounts for 30% to 40%, and free water accounts for 10% to 20%; in the early stage after extreme precipitation, the proportion of free water can increase to 40% to 60%, and then gradually return to normal proportion.

[0101] 3. Moisture state spectrum drawing: Based on the calculated moisture state parameters, the present invention constructs a moisture state spectrum to intuitively characterize the comprehensive state of water molecules in vegetation tissues.

[0102] Preferably, a dynamic distribution diagram of water states is constructed, with time as the horizontal axis and the proportion of water in different states as the vertical axis, to show the changing trends of the proportions of water in various states before and after extreme precipitation. This diagram can clearly show the rapid increase in free water during the initial precipitation period and the subsequent gradual decrease, as well as the corresponding changes in the proportion of bound water.

[0103] A hydrogen bond strength dynamics graph was constructed, with time as the horizontal axis and the hydrogen bond network index as the vertical axis, to show the dynamic evolution of interactions between water molecules. This graph can reveal the formation, reorganization, and stabilization of the hydrogen bond network of water molecules after extreme rainfall.

[0104] A water state transition diagram was constructed to depict the interconversion relationships between different water states, reflecting the dynamic process of water state transitions in vegetation tissues under extreme precipitation conditions. This diagram is presented as a state transition matrix, where the matrix elements represent the probability or rate of transition from one state to another.

[0105] The water state spectrum constructed by the above method can comprehensively characterize the dynamic changes of water molecules in vegetation tissues and lay the foundation for the extraction of hysteresis features.

[0106] Based on the water state spectrum, the present invention extracts the molecular water dynamics hysteresis characteristics. The specific implementation process is as follows:

[0107] 1. Segmentation of moisture dynamic process: The present invention first divides the extreme precipitation response process into clear dynamic stages.

[0108] Preferably, the absorption phase is identified, which is the period from the onset of precipitation to the point where vegetation reaches maximum water uptake. This phase is characterized by a rapid rise in the Free Water Index (FWI), indicating a rapid increase in total water content. By monitoring the rate of change of the FWI, the absorption phase is identified as beginning when its growth rate exceeds 0.01 / minute and ending when the FWI reaches its peak (the rate of change approaches 0). For broadleaf forest vegetation, the absorption phase typically lasts 30 to 90 minutes, depending on precipitation intensity and vegetation characteristics.

[0109] Identify the equilibrium phase, which is the period of water redistribution within the vegetation. This phase is characterized by a gradual decrease in the free water index (FWI), an increase in the bound water index (BWI), and relatively stable total water content. The equilibrium phase begins when the FWI begins to decrease (the rate of change becomes negative) and the BWI begins to increase (the rate of change is greater than 0.005 / hour). The equilibrium phase ends when the absolute values ​​of the rates of change of both the FWI and BWI are less than 0.002 / hour. For broadleaf forests, the equilibrium phase typically lasts 2 to 8 hours.

[0110] Identify the release phase, the period of excess water release and transpiration. This phase is characterized by a gradual decline in all moisture indices, approaching pre-precipitation levels. The release phase begins when the FWI, BWI, and HBNI begin to decline simultaneously and ends when these indices return to 90% of their pre-precipitation baseline levels. For broadleaf forests, the release phase typically lasts 12 to 48 hours and is significantly influenced by environmental conditions (such as temperature, humidity, and wind speed).

[0111] 2. Molecular water migration path analysis: The present invention analyzes the migration path and conversion rules of water molecules in vegetation tissues during extreme precipitation.

[0112] Preferably, the transformation pathways of water molecules from free water to weakly bound water, to strongly bound water, and vice versa are identified based on the temporal changes in water state parameters. This is achieved by analyzing the changing trends of FWI, BWI, and water state ratio. A typical transformation pathway involves the following: during the initial precipitation phase, external water first enters plant tissues as free water (increase in FWI); subsequently, some free water is converted to weakly bound water (decrease in FWI, increase in BWI); and finally, some weakly bound water is further converted to strongly bound water (BWI remains high). The release phase exhibits the opposite process.

[0113] The conversion rate between different water states is calculated to characterize the ability of vegetation tissue to regulate water. The conversion rate is calculated by the time derivative of the water state parameter:

[0114] ,

[0115] in, is the rate of conversion from state A to state B, expressed in % / hour; [A] and [B] are the water ratios in state A and state B, respectively. In practical applications, for broad-leaved forest vegetation, the conversion rate of free water to bound water is typically 5% to 15% / hour.

[0116] Combining spectral data from different tissue locations, we analyze the migration patterns of water molecules between different tissues, such as the epidermis, mesophyll, and vascular bundles. This is achieved by performing micro-spectral measurements of different parts of plant leaves. Typical migration patterns include: water first enters the leaf through the epidermal stomata or directly penetrates the cuticle, then diffuses through the intercellular spaces of the mesophyll as free water, is subsequently absorbed by the cells and becomes bound water, and excess water is transported to other parts of the plant through the vascular system.

[0117] 3. Quantification of hysteresis characteristics: Based on the analysis of molecular water dynamic processes, the present invention quantifies key hysteresis characteristic parameters.

[0118] Preferably, the molecular water response time is determined ( ), defined as the time interval from the peak of extreme precipitation intensity to the peak of vegetation water state parameters:

[0119] ,

[0120] in, ) is the time when the water status parameter (such as FWI) reaches its peak value; The time when the precipitation intensity reaches its peak. Reflects the response speed of vegetation to absorb water. In practical applications, for broad-leaved forest vegetation, Usually 15 to 45 minutes.

[0121] Determine the molecular water equilibrium time ( ), defined as the time required for the rate of change of the moisture state parameter to fall below a preset threshold:

[0122] ,

[0123] in, is the start time of the equilibrium phase; is the end time of the equilibrium phase; is the water status parameter (such as FWI or BWI); is the absolute value of its rate of change. e Reflects the speed at which the water content in vegetation reaches equilibrium. In practical applications, for broad-leaved forest vegetation, Usually 2 to 8 hours.

[0124] Determine the molecular water release time ( ), defined as the time required for the moisture state parameter to drop from the peak value to the half-peak value:

[0125] ,

[0126] in, It is the time when the moisture state parameter drops to half peak value. Reflects the ability of vegetation to release excess water. In practical applications, for broad-leaved forest vegetation, It usually takes 6 to 24 hours and is significantly affected by environmental conditions.

[0127] Determine the state transition time ( ), defined as the characteristic time for water to transform from a free state to a bound state:

[0128] ,

[0129] in, is the first-order rate constant for the conversion of free water to bound water, which is obtained by fitting the exponential decay curve of the free water ratio. Reflects the ability of vegetation tissue to fix water. In practical applications, for broad-leaved forest vegetation, Usually 2 to 6 hours.

[0130] Through the above-mentioned hysteresis characteristic quantification method, the present invention can accurately describe the response dynamics of vegetation to extreme precipitation, providing a quantitative basis for subsequent regulation and storage efficiency evaluation.

[0131] The present invention obtains vegetation tissue microstructure parameters and analyzes their relationship with water dynamic characteristics. The specific implementation process is as follows:

[0132] 1. Acquisition of vegetation tissue microstructure parameters: The present invention acquires key tissue microstructure parameters related to vegetation water regulation.

[0133] Preferably, epidermal structural parameters are obtained, including epidermal thickness (μm), stomatal density (stomata / mm²), and cuticle thickness (μm). These parameters are obtained through optical microscopy or scanning electron microscopy. In practical applications, for broadleaf forest vegetation, typical epidermal thickness is 15-30 μm, stomatal density is 100-500 / mm², and cuticle thickness is 2-8 μm.

[0134] Mesophyll structural parameters, including mesophyll cell arrangement (palisade / spongy tissue ratio), intercellular space ratio (%), and cell wall thickness (μm), are obtained through tissue sectioning and microscopic image analysis. In practice, for broadleaf forests, typical palisade / spongy tissue ratios are 0.8-1.5, intercellular space ratios are 15%-30%, and cell wall thicknesses are 0.1-0.5 μm.

[0135] Obtain vascular bundle structural parameters, including vascular bundle density (lines / mm²), vessel diameter (μm), and sieve tube characteristics. These parameters are obtained through tissue sectioning and microscopic image analysis. In practical applications, for broadleaf forest vegetation, typical vascular bundle densities range from 3 to 8 lines / mm², and vessel diameters range from 20 to 60 μm.

[0136] 2. Microstructure-water relationship analysis: The present invention analyzes the relationship between vegetation microstructure characteristics and water dynamic parameters.

[0137] Preferably, the skin structure parameters and the moisture response time are analyzed ( ) relationship, revealing the influence of microstructural characteristics on the initial absorption of water. The study found that the pore density is related to There was a significant negative correlation (r=-0.75, p<0.01), that is, the greater the stomatal density, the shorter the response time; the thickness of the cuticle was significantly There was a significant positive correlation (r=0.68, p<0.01), indicating that the thicker the cuticle, the longer the response time. This suggests that stomata are the main channel for water to enter vegetation under extreme precipitation conditions, while the cuticle is an obstacle.

[0138] Analysis of mesophyll structural parameters and water balance time ( ), the relationship between water state distribution, revealing the influence of microstructural characteristics on water storage. The study found that the ratio of intercellular space and There was a significant negative correlation (r=-0.62, p<0.01), indicating that a larger interstitial ratio was associated with a shorter equilibrium time. There was also a significant positive correlation (r=0.71, p<0.01) between cell wall thickness and the bound water index (BWI), indicating that thicker cell walls were associated with a higher bound water ratio. This suggests that interstitial spaces primarily influence the rate of water diffusion in tissues, while the cell wall is the primary storage location for bound water.

[0139] The relationship between vascular bundle structural parameters and water migration path and conversion rate was analyzed to reveal the influence of microstructural characteristics on water transport. The study found that vascular bundle density and water release time ( ) showed a significant negative correlation (r=-0.58, p<0.01), indicating that greater vascular bundle density was associated with shorter release times. Vessel diameter also showed a significant negative correlation with the rate of conversion of free water to bound water (r=-0.64, p<0.01), indicating that larger vessel diameters were associated with lower conversion rates. This suggests that the vascular system primarily influences the efficiency of excess water discharge, while vessel characteristics influence long-distance water transport and distribution.

[0140] 3. Analysis of dynamic characteristics of hydraulic conductivity: Based on the changes in spectral characteristics, the present invention analyzes the dynamic regulation characteristics of hydraulic conductivity of vegetation tissue.

[0141] Preferably, the water conductivity of vegetation tissue is evaluated based on the rate of change of water molecule state, and its dynamic changes under extreme precipitation conditions are analyzed. The water conductivity (K) can be estimated by the following formula:

[0142] ,

[0143] in, is the water flux, estimated by the rate of change of the free water index (FWI); is the water potential gradient, estimated from the spatial distribution of water state parameters.

[0144] The study found that in the early stages of extreme precipitation, the water conduction capacity of vegetation increased significantly (the increase could reach 200% to 300%), and then gradually decreased to normal levels. This dynamic change reflects the ability of vegetation to actively regulate water absorption and transport.

[0145] Identify the main water transport pathways within vegetation and analyze their functional regulatory mechanisms under extreme precipitation conditions. By comparing the rates of water change in different tissue locations, it was found that water is primarily transported along the following pathway: epidermal stomata / cuticle → mesophyll intercellular spaces → cytoplasm / cell walls → vascular system. Under extreme precipitation conditions, vegetation can control water transport efficiency by regulating stomatal aperture, cell membrane permeability, and vascular hydraulic conductivity.

[0146] Analyze the changing patterns of vegetation tissue hydraulic resistance during extreme precipitation and reveal the mechanism by which vegetation actively regulates water transport. Hydraulic resistance (R) is the inverse of water conductivity:

[0147] ,

[0148] The study found that the change in hydraulic resistance during extreme precipitation follows a U-shaped curve: a rapid decrease in the early stages, a low level in the middle stages, and a gradual increase in the later stages. This pattern reflects the adaptive strategy of vegetation: reducing resistance during the absorption phase to promote water absorption, and increasing resistance during the release phase to control water loss.

[0149] Through the above-mentioned microstructure-water coupling analysis, the present invention reveals the mechanism by which vegetation microstructure characteristics influence water regulation capacity, and provides a scientific basis for understanding the structural basis of vegetation storage efficiency.

[0150] Based on the molecular hydrodynamic characteristics and vegetation tissue microstructure parameters, the present invention determines the multi-scale vegetation storage efficiency index. The specific implementation process is as follows:

[0151] 1. Determination of cell-scale storage index: The present invention constructs a cell-scale storage efficiency index based on the molecular water dynamics characteristics.

[0152] Preferably, the cell water capacity (CWC) is determined, defined as the total amount of water that can be stored per unit volume of the cell, calculated by integrating the free water index (FWI) over time:

[0153] ,

[0154] in, and are the start and end time of the absorption phase, respectively; is the free water index at time t. CWC reflects the ability of cells to store water, and the unit is arbitrary unit·time (such as AU In practical applications, for broad-leaved forest vegetation, typical The value is 0.5-2.0 AU·h, and the cellular water retention time (CRT) is determined, which is defined as the sum of the water equilibrium time and the water release time: ,in, It is the water balance time; CRT is the water release time. It reflects the cell's ability to retain water and is expressed in hours. In practical applications, for broad-leaved forest vegetation, the typical CRT value is 8-32 hours.

[0155] Determine the cellular water state stability (CSS), defined as the inverse of the standard deviation of the water state parameter fluctuations:

[0156] ,

[0157] in, is the standard deviation of the FWI at equilibrium. CSS reflects the ability of cells to maintain a stable water state and is dimensionless. In practice, typical CSS values ​​for broadleaf forests are 5-20.

[0158] 2. Determination of tissue-scale water storage index: The present invention constructs a tissue-scale water storage efficiency index based on the dynamic characteristics of water in different tissue parts.

[0159] Preferably, the tissue diffusion coefficient (TDC) is determined, defined as the characteristic rate of diffusion of water in tissue:

[0160] ,

[0161] in, is the characteristic diffusion distance, estimated by tissue thickness; TDC is the water balance time. TDC reflects the efficiency of water migration in tissues and is expressed in mm2 / h. In practical applications, for broad-leaved vegetation, the typical TDC value is 0.02-0.2 mm 2 / h. Determine the tissue water content gradient (TCG), defined as the difference in water content between different tissue sites:

[0162] ,

[0163] in, is the difference in bound water index between different tissue sites; The distance between tissue parts. TCG reflects the uneven spatial distribution of water in vegetation tissues, and its unit is mm. -1In practical applications, for broad-leaved forest vegetation, the typical TCG value is 0.05 to 0.5 mm. -1 .

[0164] Determine the tissue water synergy coefficient (TCS), defined as the degree of correlation between changes in water status at different tissue sites:

[0165] ,

[0166] in, is the FWI covariance of tissue sites i and j; and are the corresponding standard deviations. TCS reflects the ability of different vegetation components to coordinate water management. It is dimensionless and ranges from -1 to 1. In practice, for broadleaf forests, a typical TCS value is 0.6-0.9.

[0167] 3. Evaluation of overall vegetation storage efficiency: The present invention integrates cell- and tissue-scale indicators to evaluate the overall vegetation storage efficiency.

[0168] Preferably, the regulation intensity (RI) is determined, which is defined as the maximum change in vegetation water state parameters before and after extreme precipitation:

[0169] ,

[0170] in, is a water status parameter (such as FWI or BWI). RI reflects the capacity of vegetation to respond to extreme precipitation and is dimensionless. In practical applications, typical RI values ​​for broadleaved forest vegetation are 0.3-0.8. Determine the regulation duration (RD), which is defined as the sum of the water response time, equilibrium time, and release time: ,

[0171] in, is the moisture response time; It is the water balance time; is the water release time. RD reflects the total duration of the vegetation storage process, expressed in hours. In practice, typical RD values ​​for broadleaved forests range from 9 to 36 hours. The storage efficiency (RE) is determined as the ratio of the storage intensity to the storage duration:

[0172] ,

[0173] RE reflects the vegetation's storage capacity per unit time, and its unit is h -1 In practical applications, for broad-leaved forest vegetation, the typical RE value is 0.01 to 0.05h -1Determine regulatory resilience (RR), defined as the ability of vegetation water status parameters to return to baseline levels, through recovery rate and recovery completeness assessment:

[0174] ,

[0175] in, is the recovery rate; To restore integrity; is the baseline level; Peak level The final level. RR reflects the ability of vegetation to recover from extreme precipitation disturbances. It is dimensionless and ranges from [0, 1]. In practice, for broadleaf forest vegetation, the typical RR value is 0.7-0.95.

[0176] Through the above-mentioned multi-scale vegetation storage efficiency evaluation method, the present invention can comprehensively quantify the vegetation's storage capacity for extreme precipitation, providing a scientific basis for subsequent regional applications.

[0177] Based on multi-scale vegetation storage efficiency indicators, the present invention generates regional vegetation storage response prediction results and storage efficiency spatial distribution maps. The specific implementation process is as follows:

[0178] 1. Response characteristic parameterization: The present invention parameterizes the vegetation water molecule state change curve to establish a precipitation-response model.

[0179] Preferably, key characteristic parameters are extracted from the water state change curve, including response delay (minutes), rise rate (% / minute), peak position (dimensionless), fall rate (% / hour), recovery degree (%), etc. These parameters are obtained through curve fitting methods and can concisely characterize the complex response process.

[0180] Extreme precipitation events are parameterized, including characteristics such as precipitation intensity (mm / h), duration (h), cumulative amount (mm), and temporal distribution (e.g., uniform, front-peak, and back-peak). These parameters can be extracted from meteorological observation data.

[0181] Establish a relationship model between the characteristic parameters of precipitation and the characteristic parameters of the response curve. Preferably, the model is constructed using multiple regression or support vector regression methods:

[0182] ,

[0183] in, is the response characteristic parameter vector; is the precipitation characteristic parameter vector; 、 and is the regression coefficient; is the error term. Model training is based on historical extreme precipitation events and corresponding vegetation response data. Model validation uses cross-validation. Typical model prediction accuracy (R²) can reach 0.75-0.85.

[0184] 2. Prediction of vegetation storage capacity: Based on historical response data and parameter relationship models, the present invention predicts the storage response of vegetation to future extreme precipitation events.

[0185] Preferably, the vegetation's response characteristics are predicted for extreme precipitation scenarios of varying intensity and duration. For example, for a 30 mm / h precipitation scenario lasting two hours, the predicted response characteristics of broadleaf forest vegetation might be: a response delay of 20 minutes, a rise rate of 2.5% / minute, a peak FWI of 0.9, a fall rate of 15% / hour, and a recovery degree of 95%.

[0186] Determine the critical threshold of vegetation's storage capacity and predict the potential failure of this function if it is exceeded. This critical threshold was determined by analyzing historical cases of vegetation storage failure during extreme events. For example, for broad-leaved forests, when precipitation intensity exceeds 50 mm / h and lasts for more than three hours, storage efficiency can drop by more than 50%, leading to partial failure of the storage function.

[0187] Predict the time and conditions required for vegetation to return to normal water conditions after extreme precipitation. This recovery process is predicted based on a model that relates the Resilience to Resilience (RR) metric to environmental conditions (such as temperature, humidity, and sunlight). For example, under summer conditions of high temperatures (30°C) and low humidity (50%), it may take 24 to 36 hours for broadleaf forest vegetation to fully recover from moderate extreme precipitation (30 mm / h for 2 hours).

[0188] 3. Regional scale application: This invention extends the point scale analysis method to the regional scale to support practical applications.

[0189] Preferably, molecular spectral signatures are correlated with remote sensing hyperspectral data to enable rapid assessment of vegetation water storage capacity at the regional scale. By developing regression models linking spectral indices with water storage efficiency indicators, laboratory analysis results can be generalized to the regional scale. For example, by analyzing the 1450nm and 1940nm bands of satellite hyperspectral data, the water response time (τᵣ) and water storage intensity (RI) of regional vegetation can be estimated.

[0190] Generates a spatial distribution map of regional vegetation storage capacity, including the spatial distribution of parameters such as storage intensity (RI), storage duration (RD), and storage efficiency (RE). With a spatial resolution of up to 30m x 30m, it clearly shows the spatial differences in storage capacity across different vegetation types and terrain conditions.

[0191] Based on the results of the water storage efficiency assessment, a sponge city planning support system was developed. This system optimizes the layout and vegetation configuration of urban green spaces based on their distribution of water storage efficiency, thereby improving a city's ability to cope with extreme precipitation. For example, it can increase high-efficiency vegetation cover in areas with low water storage efficiency, or adjust the vegetation type mix to improve overall efficiency.

[0192] Integrate vegetation storage information to optimize flood warning and reservoir operation strategies. By considering the storage capacity of upstream vegetation, the accuracy of flood forecasts and warning times can be improved, and reservoir operation decisions can be optimized. For example, when the storage capacity of upstream vegetation is nearing saturation due to previous rainfall, reservoirs can pre-discharge water in advance to increase flood control storage capacity.

[0193] Quantifying the ecosystem service value of vegetation regulation supports the design of eco-compensation mechanisms. Based on regulation efficiency indicators, the regulation service value of vegetation in different vegetation types and regions is assessed to provide a scientific basis for the development of eco-compensation standards. For example, forest areas with high regulation efficiency (RE) could receive higher eco-compensation standards.

[0194] Through the above-mentioned regional vegetation storage response prediction and application method, the present invention transforms the research results at the molecular scale into practical applications at the regional scale, providing scientific support for flood prevention and disaster reduction and ecosystem management.

[0195] The present invention also provides a time-delayed response quantification system for extreme precipitation-vegetation regulation and storage, which includes the following modules: a multi-band spectral data acquisition module, a moisture state spectrum construction module, a time-delayed feature extraction module, a microstructure analysis module, a regulation and storage efficiency evaluation module, and a prediction application module.

[0196] The multi-band spectral data acquisition module is used to obtain near-infrared spectral data, mid-infrared spectral data and Raman spectral data of vegetation samples.

[0197] Preferably, the module includes a near-infrared spectrum acquisition unit for acquiring near-infrared spectrum data in the wavelength range of 780-2500nm. The unit is equipped with a high-precision near-infrared spectrometer (such as ASDFieldSpec4Hi-Res) with a spectral resolution of 1nm and a reflectance accuracy of 0.1%.

[0198] The module also includes a mid-infrared spectrum acquisition unit for acquiring mid-infrared spectrum data in the wavelength range of 2500-25000nm. The unit is equipped with a Fourier transform infrared spectrometer (such as Bruker VERTEX70) with 4 Excellent spectral resolution and high signal-to-noise ratio (>10000:1).

[0199] The module also includes a Raman spectrum acquisition unit for acquiring the wave number range of 200 to 4000 cm -1The unit is equipped with a high-sensitivity Raman spectrometer (such as Horiba LabRAMHREvolution) with <1 Excellent spectral resolution and high spatial resolution (<1 μm).

[0200] The module also includes a spectral preprocessing unit for calibrating, noise removal, baseline correction, standardization, and differential transformation of multi-band spectral data. This unit integrates multiple spectral preprocessing algorithms and can efficiently process large amounts of spectral data.

[0201] The water state spectrum construction module is used to construct a water state spectrum that characterizes the state of water molecules in vegetation tissues based on the preprocessed multi-band spectral data.

[0202] Preferably, this module implements the function of extracting water molecular vibrational characteristics from near-infrared, mid-infrared, and Raman spectral data. Through peak fitting and decomposition algorithms, spectral features related to the binding state of water molecules are accurately extracted.

[0203] The module also calculates water status parameters such as the Free Water Index (FWI), Bound Water Index (BWI), and Hydrogen Bond Network Index (HBNI). It quantifies the distribution of different water states in vegetation tissues through the ratio relationship between spectral features.

[0204] This module also implements the function of drawing the dynamic distribution diagram of water state, the dynamic change diagram of hydrogen bond strength, and the water state transition diagram. Through visual expression, the dynamic change process of water state can be intuitively displayed.

[0205] The hysteresis feature extraction module is used to extract the molecular water dynamics hysteresis features based on the water state spectrum.

[0206] Preferably, the module divides the extreme precipitation response process into absorption, equilibrium, and release phases, and automatically identifies the start and end times of each phase by analyzing the changing trends and rates of change of water state parameters.

[0207] This module also analyzes the state transition paths and conversion rates of water molecules within plant tissues. By tracking the dynamic changes in the proportion of water in different states, it reveals the migration patterns of water molecules within plant tissues.

[0208] This module also calculates the moisture response time (τᵣ), moisture balance time (τ e ) and moisture release time (τᵣ e ) and other time-delay characteristic parameters. Through time series analysis, the temporal characteristics of vegetation storage can be accurately quantified.

[0209] The microstructure analysis module is used to obtain the microstructure parameters of vegetation tissue and analyze their relationship with the dynamic characteristics of water.

[0210] Preferably, the module realizes the function of obtaining epidermal structural parameters, mesophyll structural parameters and vascular bundle structural parameters, and automatically extracts key structural parameters by importing microscopic image data.

[0211] This module also analyzes the relationship between microstructural parameters and water dynamics, revealing the mechanisms by which structural characteristics influence water regulation through correlation analysis and regression models.

[0212] The module also evaluates the water transport capacity of vegetation tissue and its dynamic changes. It quantifies the water transport efficiency of vegetation by calculating water flow and water potential gradient.

[0213] The regulation and storage efficiency evaluation module is used to determine multi-scale vegetation regulation and storage efficiency indicators based on molecular hydrodynamic hysteresis characteristics and vegetation tissue microstructure parameters.

[0214] Preferably, this module calculates cell-scale water regulation indicators such as cell water capacity (CWC), cell water retention time (CRT), and cell water state stability (CSS). Through integral calculation and statistical analysis, the cell water regulation capacity is quantified.

[0215] This module also calculates tissue-scale water regulation indicators such as the tissue water diffusion coefficient (TDC), tissue water capacity gradient (TCG), and tissue water synergy coefficient (TCS). Through spatial analysis and related calculations, it quantifies the ability of tissue water synergy regulation.

[0216] The module also calculates overall vegetation storage performance indicators such as storage intensity (RI), storage duration (RD), storage efficiency (RE), and storage resilience (RR). Through comprehensive analysis, the vegetation's storage capacity can be comprehensively assessed.

[0217] The prediction application module is used to generate regional vegetation storage response prediction results and storage efficiency spatial distribution maps based on multi-scale vegetation storage efficiency indicators.

[0218] Preferably, the module includes a response characteristic parameterization unit for extracting key characteristic parameters from the water state change curve and establishing a precipitation-response relationship model. This unit integrates curve fitting and multivariate regression algorithms to efficiently process large amounts of response data.

[0219] The module also includes a water storage capacity prediction unit, which uses a relational model to predict the water storage response characteristics of vegetation under different extreme precipitation scenarios. This unit can simulate multiple precipitation scenarios, providing a scientific basis for decision-making.

[0220] The module also includes a spatial distribution visualization unit, which maps storage efficiency indicators to geographic space and generates a spatial distribution map of storage efficiency. This unit supports multiple map projections and spatial interpolation methods to generate high-quality distribution maps.

[0221] The module also includes a decision support unit, which uses the results of storage and regulation performance assessment to support decisions on sponge city planning, flood risk management, and ecosystem service assessment. This unit integrates multiple decision support tools to facilitate practical applications.

[0222] Through the above system architecture, the present invention realizes the automation of the entire process from data collection, processing and analysis to application decision-making, providing systematic technical support for the study of extreme precipitation-vegetation storage and regulation lag response.

[0223] In this example, typical broad-leaved forest vegetation (such as Liquidambar formosana) is selected for detailed storage and regulation lag response analysis.

[0224] 1. Multi-band molecular spectral data acquisition and preprocessing: ASD FieldSpec 4 Hi-Res spectrometer was used to collect near-infrared spectral data (780-2500 nm), Bruker VERTEX 70 spectrometer was used to collect mid-infrared spectral data (2500-25000 nm), and Horiba Lab RAM HREvolution spectrometer was used to collect Raman spectral data (200-4000 nm). The data were collected before extreme precipitation (baseline), every 15 minutes during extreme precipitation, and every hour for the first four hours and every four hours thereafter for 24 hours. The raw spectral data were preprocessed using calibration, noise removal, baseline correction, normalization, and differential transformation.

[0225] 2. Construction and analysis of water state spectrum: Extract key water vibration characteristics from pre-processed spectral data. The intensity and position changes of the 1450nm and 1940nm peaks in the near-infrared spectrum reflect the changes in the overall water content; the 3400nm peak in the mid-infrared spectrum reflects the changes in the water content. and 1640 The change of the peak reflects the water binding state; 3200~3600 The changes in the contour of the region reflect the hydrogen bond network structure. Based on these characteristics, the free water index (FWI), bound water index (BWI), and hydrogen bond network index (HBNI) were calculated to construct a water state spectrum. The results showed that after extreme rainfall, the FWI increased rapidly (from 0.5 to 1.1) and then slowly decreased; the BWI first decreased (from 0.4 to 0.25) and then increased (to 0.5); the HBNI continued to increase (from 180 Increased to 320 ) and then slowly recover.

[0226] 3. Molecular water dynamics hysteresis feature extraction: Based on the water state spectrum, the three phases of extreme precipitation response are identified: absorption phase (lasting approximately 60 minutes), equilibrium phase (lasting approximately 5 hours), and release phase (lasting approximately 18 hours). By analyzing the conversion relationship between different water states, it is found that the conversion rate of free water to bound water is approximately 8% / hour. Key hysteresis parameters are calculated: water response time τᵣ = 25 minutes, water equilibrium time τ e = 5 hours, moisture release time τᵣ e = 12 hours, state transition time τ t =3.5 hours.

[0227] 4. Vegetation tissue microstructure-water coupling analysis: Obtain tissue microstructure parameters of Liquidambar formosana leaves: epidermal thickness 20 μm, stomatal density 350 / mm 2 , cuticle thickness 5μm, palisade / spongy tissue ratio 1.2, intercellular space ratio 22%, cell wall thickness 0.3μm, vascular bundle density 5 / mm 2 , with a vessel diameter of 35 μm. Analysis results showed a significant negative correlation between stomatal density and water response time (r=-0.78), intercellular space ratio and water balance time (r=-0.65), and vascular bundle density and water release time (r=-0.62). Analysis of water conductivity showed that water conductivity increased by approximately 250% during the initial phase of extreme rainfall, then gradually returned to normal within 12 hours.

[0228] 5. Multi-scale vegetation storage efficiency assessment: Calculate storage efficiency indicators based on hysteresis characteristics and microstructural parameters. Cell-scale indicators: Cell water capacity (CWC) = 1.2 AU·h, Cell water retention time (CRT) = 17 hours, Cell water state stability (CSS) = 12. Tissue-scale indicators: Tissue water diffusion coefficient (TDC) = 0.08 mm 2 / h, tissue water capacity gradient TCG = 0.2mm -1 , tissue water synergy coefficient TCS = 0.82. Overall storage efficiency: storage intensity RI = 0.6, storage duration RD = 24 hours, storage efficiency RE = 0.025h -1 , storage resilience RR=0.88.

[0229] 6. Prediction and application of regional vegetation storage response: A precipitation-response relationship model was established based on experimental data to predict the response characteristics of liquidambar formosana forests to extreme precipitation of varying intensities. For example, for extreme precipitation of 50 mm / h lasting 3 hours, the predicted response delay is 30 minutes, the storage intensity is 0.7, the storage duration is 36 hours, and the storage efficiency is reduced to 0.019 h. -1Determining the critical threshold of storage capacity: When precipitation intensity exceeds 60 mm / h and lasts for more than four hours, storage efficiency drops by more than 40%, leading to partial functional failure. By correlating laboratory analysis results with remote sensing hyperspectral data, a spatial distribution map of storage efficiency was generated for the study area, identifying key storage areas (accounting for 28% of the total area) and weak areas (accounting for 15% of the total area).

[0230] This example selects different ecological vegetation types (broad-leaved forest, coniferous forest, herbaceous plants, shrubs) for comparative analysis.

[0231] 1. Multi-band molecular spectral data acquisition and preprocessing: Spectral data were collected and preprocessed for four different vegetation types (Liquidambar formosana representing broadleaf forests, Pinus massoniana representing coniferous forests, Setaria viridis representing herbaceous plants, and Azalea representing shrubs). Three samples from each type were selected as replicates.

[0232] 2. Construction and Comparison of Water Status Spectra: Water status spectra were constructed for each vegetation type and their characteristic differences were compared. Results showed that coniferous forests had a smaller FWI variation than broadleaf forests (maximum value 0.8 vs. 1.1), but a larger BWI variation than broadleaf forests (0.3-0.6 vs. 0.25-0.5). Herbaceous plants had a rapid FWI change (peaking in 15 minutes vs. 25 minutes for broadleaf forests) but also a rapid recovery (total duration 12 hours vs. 24 hours for broadleaf forests). Shrubs had a HBNI variation between broadleaf and coniferous forests.

[0233] 3. Analysis of differences in hysteresis characteristic parameters among vegetation types: Calculate and compare the hysteresis characteristic parameters of each vegetation type. Water response time τᵣ: herbaceous (15 minutes) < shrubs (20 minutes) < broad-leaved forests (25 minutes) < coniferous forests (35 minutes); water balance time τ e : Herbs (2 hours) < Shrubs (3.5 hours) < Broadleaf forest (5 hours) < Coniferous forest (7 hours); Water release time τᵣ e : Herbs (6 hours) < Shrubs (9 hours) < Broad-leaved forests (12 hours) < Coniferous forests (16 hours).

[0234] 4. Vegetation-Type Specific Analysis of Microstructure-Water Relationships: Microstructural characteristics of different vegetation types and their relationship to water dynamics were compared. Coniferous forests had the thickest epidermis and cuticle (30 μm and 8 μm, respectively), corresponding to the longest response time. Herbs had the highest intercellular space ratio (30%), corresponding to the shortest equilibrium time. Broadleaf forests had a high vascular bundle density (5 / mm²) but a medium vessel diameter (35 μm), indicating balanced water transport capacity.

[0235] 5. Comparison of storage efficiency index among vegetation types: Calculate and compare the storage efficiency index of each vegetation type. Storage intensity RI: coniferous forest (0.5) < herbaceous forest (0.55) < shrub (0.58) < broad-leaved forest (0.6); storage duration RD: herbaceous forest (12 hours) < shrub (18 hours) < broad-leaved forest (24 hours) < coniferous forest (30 hours); storage efficiency RE: coniferous forest (0.017h -1） <Broad-leaved forest (0.025h -1） <Shrubs (0.032h -1） < Herbal (0.046h -1） ; RR of storage resilience: herb (0.75) < shrub (0.82) < broad-leaved forest (0.88) < coniferous forest (0.92).

[0236] 6. Classification of Vegetation Types by Storage Characteristics: Based on hysteresis characteristics and storage efficiency indicators, different vegetation types are classified to establish a classification system for vegetation storage capacity. Vegetation is divided into four categories: Category I (e.g., coniferous forests)—long-lasting vegetation, characterized by slow response, long duration, low efficiency, and high resilience; Category II (e.g., broad-leaved forests)—balanced vegetation, characterized by moderate response, moderate duration, moderate efficiency, and high resilience; Category III (e.g., shrubs)—intermediate vegetation, with indicators between categories I / II and IV; and Category IV (e.g., herbaceous vegetation)—fast-response vegetation, characterized by fast response, short duration, high efficiency, and low resilience.

[0237] Through this comparative analysis of vegetation types, the present invention reveals the differences in storage mechanisms and efficiencies among different vegetation types, providing a scientific basis for optimizing vegetation configuration.

[0238] This example extends the analysis method to the regional scale.

[0239] 1. Selection of typical sample plots: Select typical sample plots (approximately 10 km²) that contain a variety of vegetation types, including urban parks, suburban woodlands, riparian zones, and other different ecosystem types.

[0240] 2. Remote sensing hyperspectral data acquisition: Use an airborne hyperspectral imager (such as AVIRIS) to obtain hyperspectral image data of the sample area with a spatial resolution of 5m, a spectral range covering 400-2500nm, and 224 continuous bands.

[0241] 3. Point-to-surface scaling: Based on laboratory analysis results, a correlation between spectral indices and water storage efficiency indicators was established. For example, by analyzing the characteristics of the 1450nm and 1940nm bands (such as location, depth, area, and asymmetry), a regression model (R²=0.82) was established to predict water response time (τᵣ). By analyzing the first-order derivative characteristics in the 950-1050nm range, a regression model (R²=0.79) was established to predict water storage intensity (RI).

[0242] 4. Regional storage capacity assessment: Based on the established model, remote sensing images were processed to generate a spatial distribution map of regional vegetation storage capacity. The analysis results showed that approximately 25% of the areas in the sample area had high storage efficiency (RE>0.03h -1） , mainly distributed in mixed forests and healthy broad-leaved forests; about 18% of the area showed low storage efficiency (RE < 0.015h -1） , mainly distributed in degraded forests and artificial green spaces. Areas with high resilience (RR>0.85) accounted for approximately 30%, mainly distributed in mature forest areas; areas with low resilience (RR<0.7) accounted for approximately 15%, mainly distributed in newly built green spaces and grasslands.

[0243] 5. Extreme Precipitation Response Prediction: Historical extreme precipitation events were selected and vegetation responses were predicted based on regional water storage capacity distribution maps. The predictions were compared and verified with actual observational data (obtained through ground monitoring stations and satellite remote sensing), achieving an average prediction accuracy of 83%. Based on the validated model, regional response prediction maps were constructed for different extreme precipitation intensities (20 mm / h, 40 mm / h, and 60 mm / h, with durations of 1 to 4 hours) to identify potential high-risk areas.

[0244] This study, by incorporating molecular spectroscopy technology, has constructed a comprehensive methodology for quantifying the delayed response of vegetation to extreme precipitation. This method reveals the vegetation regulation mechanism from the perspective of molecular hydrodynamics and accurately quantifies the delayed response characteristics, providing a scientific basis for sponge city planning, watershed management, and flood prevention and disaster reduction. Compared with existing technologies, this study achieves precise characterization of molecular-level water states, transcending the limitations of static measurements. It establishes a comprehensive molecular hydrodynamic delayed analysis framework, reveals the quantitative relationship between vegetation microstructure and its water regulation function, and integrates analysis of regulation characteristics from the molecular to the regional scale, forming a comprehensive application support system with significant theoretical and practical value.

[0245] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A quantitative method for the delayed response of extreme precipitation to vegetation storage, characterized by: include: Acquiring multi-band molecular spectral data of a vegetation sample, wherein the multi-band molecular spectral data includes near-infrared spectral data, mid-infrared spectral data, and Raman spectral data; Based on the multi-band molecular spectral data, constructing a water state spectrum that characterizes the state of water molecules in plant tissues, the water state spectrum including a free water index, a bound water index, and a hydrogen bond network index; Extracting molecular water dynamics hysteresis characteristics according to the water state spectrum, wherein the molecular water dynamics hysteresis characteristics include water response time, water equilibrium time and water release time; Acquiring vegetation tissue microstructure parameters, wherein the vegetation tissue microstructure parameters include epidermal structure parameters, mesophyll structure parameters, and vascular bundle structure parameters; Determining a multi-scale vegetation storage efficiency index based on the molecular hydrodynamic hysteresis characteristics and the vegetation tissue microstructure parameters, wherein the multi-scale vegetation storage efficiency index includes a cell-scale storage index and a tissue-scale storage index; Based on the multi-scale vegetation storage efficiency index, regional vegetation storage response prediction results and a storage efficiency spatial distribution map are generated.

2. The method according to claim 1, characterized in that The obtaining of multi-band molecular spectral data of vegetation samples comprises: Acquiring near-infrared spectral data in a wavelength range of 780-2500 nm, wherein the near-infrared spectral data includes vibration characteristics of a combination band and a harmonic band of water molecules; Acquiring mid-infrared spectral data in a wavelength range of 2500-25000 nm, wherein the mid-infrared spectral data includes fundamental frequency vibration characteristics of water molecules; The range of wave number is 200~4000 Raman spectral data comprising scattering characteristics of hydrogen bond structures between water molecules; The multi-band molecular spectral data are preprocessed, and the preprocessing includes spectral calibration, noise elimination, baseline correction, spectral standardization and spectral differential transformation.

3. The method according to claim 1, characterized in that The step of constructing a water state spectrum representing the state of water molecules in plant tissue based on the multi-band molecular spectral data includes: Extracting water absorption peak features near 1450nm and 1940nm from the near-infrared spectral data; Extract 3400 from the mid-infrared spectral data -OH stretching vibration characteristics near 1640 nearby HOH bending vibration characteristics; Extract 3200~3600 from the Raman spectrum data -OH stretching vibration scattering characteristics of the range; Based on the extracted spectral features, the free water index representing the relative content of free water, the bound water index representing the relative content of bound water, and the hydrogen bond network index representing the strength of the hydrogen bond network between water molecules were calculated; Construct a dynamic distribution diagram of moisture status with time as the horizontal axis and the moisture ratio of different states as the vertical axis.

4. The method according to claim 1, wherein The extracting of molecular water dynamics hysteresis characteristics according to the water state spectrum includes: Based on the water state spectrum, the extreme precipitation response process is divided into absorption stage, equilibrium stage and release stage; Analyze the state conversion paths and conversion rates of water molecules in vegetation tissues at each stage; The time interval from the peak of extreme precipitation intensity to the peak of vegetation water state parameters was determined as the water response time; Determine the time required for the rate of change of the moisture state parameter to drop below a preset threshold as the moisture balance time; The time required for the moisture state parameter to drop from the peak value to the half-peak value was determined as the moisture release time.

5. The method according to claim 1, characterized in that Determining the multi-scale vegetation storage efficiency index based on the molecular hydrodynamic hysteresis characteristics and the vegetation tissue microstructure parameters includes: Analyzing the relationship between the epidermal structural parameters and the moisture response time to determine the effect of the epidermis on the initial absorption of moisture; Analyzing the relationship between the mesophyll structural parameters and the water balance time to determine the effect of the mesophyll on water storage; Analyzing the relationship between the vascular bundle structural parameters and the water conversion rate to determine the impact of the vascular bundle on water transport; Determine the water capacity of a cell based on the total amount of water that can be stored per unit volume of the cell; The cell water retention time is determined based on the sum of the water balance time and the water release time; Based on the characteristic rate of water diffusion in tissue, the tissue water diffusion coefficient is determined; Based on the differences in water content between different tissue parts, the tissue water content gradient is determined.

6. The method according to claim 1, wherein Generating a regional vegetation storage response prediction result and a storage efficiency spatial distribution map based on the multi-scale vegetation storage efficiency index includes: Extract key characteristic parameters such as response delay, rising rate, peak position, falling rate and recovery degree from the moisture state change curve; Establish a relationship model between precipitation characteristic parameters and response curve characteristic parameters; Based on the relationship model, the regulation and storage response characteristics of vegetation under extreme precipitation scenarios of different intensities and durations are predicted; Determine the critical threshold of vegetation storage capacity and predict the potential failure of storage function if the threshold is exceeded; The multi-scale vegetation storage efficiency index is mapped to geographic space to generate a spatial distribution map including storage intensity, storage duration and storage efficiency.

7. The method according to claim 1, characterized in that The proposed method for quantifying the delayed response of extreme precipitation to vegetation storage is used for sponge city planning, flood risk management, and ecosystem service assessment.

8. A time-delayed response quantitative system for extreme precipitation-vegetation regulation, using the method according to any one of claims 1 to 7, characterized in that: include: Multi-band spectral data acquisition module, used to obtain near-infrared spectral data, mid-infrared spectral data and Raman spectral data of vegetation samples; A water state spectrum construction module is used to construct a water state spectrum characterizing the state of water molecules in plant tissue based on the multi-band spectral data, wherein the water state spectrum includes a free water index, a bound water index, and a hydrogen bond network index; A hysteresis feature extraction module, configured to extract molecular hydrodynamic hysteresis features according to the moisture state spectrum, wherein the molecular hydrodynamic hysteresis features include moisture response time, moisture equilibrium time, and moisture release time; A microstructure analysis module is used to obtain vegetation tissue microstructure parameters, wherein the vegetation tissue microstructure parameters include epidermal structure parameters, mesophyll structure parameters and vascular bundle structure parameters; A regulation and storage efficiency evaluation module is used to determine a multi-scale vegetation regulation and storage efficiency index based on the molecular hydrodynamic hysteresis characteristics and the vegetation tissue microstructure parameters, wherein the multi-scale vegetation regulation and storage efficiency index includes a cell-scale regulation and storage index and a tissue-scale regulation and storage index; The prediction application module is used to generate regional vegetation storage response prediction results and a storage efficiency spatial distribution map based on the multi-scale vegetation storage efficiency index.

9. The system according to claim 8, characterized in that The multi-band spectral data acquisition module includes: A near-infrared spectrum acquisition unit, used to acquire near-infrared spectrum data in the wavelength range of 780-2500nm; A mid-infrared spectrum acquisition unit, used to acquire mid-infrared spectrum data in the wavelength range of 2500-25000nm; Raman spectrum acquisition unit, used to obtain wavenumber range of 200 to 4000 Raman spectral data of The spectrum preprocessing unit is used to perform calibration, noise elimination, baseline correction, standardization and differential transformation on the multi-band spectrum data.

10. The system according to claim 8, wherein: The prediction application module includes: Response characteristic parameterization unit, used to extract key characteristic parameters from the water state change curve and establish a precipitation-response relationship model; A storage capacity prediction unit, configured to predict the storage response characteristics of vegetation under different extreme precipitation scenarios based on the relationship model; Spatial distribution visualization unit, used to map the storage efficiency index to the geographic space and generate a spatial distribution map of storage efficiency; The decision support unit is used to provide decision support for sponge city planning, flood risk management and ecosystem service assessment based on the results of storage efficiency assessment.

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