An intelligent monitoring and assessment method and system for terrestrial carbon disturbance in permafrost areas

Abstract

The present invention discloses an intelligent monitoring and evaluation method and system for terrestrial carbon disturbance in permafrost areas, which belongs to the field of carbon cycle research in permafrost areas. The method includes: collecting 13 background element data in permafrost areas; calculating and obtaining carbon disturbance evaluation indicators in permafrost areas; conducting credibility evaluation of carbon disturbance evaluation indicators in permafrost areas; monitoring the characteristics and changes of regional background elements and river particle organic carbon output in permafrost areas, and calculating the change rate of "carbon disturbance evaluation indicators" based on monitoring data; using numerical models to identify high-risk areas and time periods of carbon disturbance in permafrost areas, identifying key disturbance factors and impacts; analyzing the change trend of carbon disturbance evaluation indicators, and using machine learning methods to establish a prediction model. The method is comprehensive, dynamic, effective, and supports carbon disturbance attribution and prediction analysis.

Description

Technical Field

[0001] The present invention relates to the research field of carbon cycle in permafrost areas, and in particular to an intelligent monitoring and evaluation method and system for terrestrial carbon disturbance in permafrost areas. Background Art

[0002] Permafrost degradation accelerates the "carbon cycle" process and intensifies its feedback to climate change, which has attracted widespread attention from scientists. Since the spatial distribution dynamics of permafrost and the law of carbon release are difficult to monitor over the long term, and the carbon cycle mechanism is not yet clear, the risk of permafrost degradation and its carbon output efficiency need to be evaluated urgently. Summary of the invention

[0003] The purpose of the present invention is to overcome the problem that it is difficult to dynamically monitor the terrestrial carbon release process in permafrost areas over a long period of time, and to provide an intelligent monitoring and evaluation method and system for terrestrial carbon disturbances in permafrost areas.

[0004] The objective of the present invention is achieved through the following technical solutions:

[0005] An intelligent monitoring and assessment method for terrestrial carbon disturbance in permafrost regions, the method comprising the following steps:

[0006] Step 1: Collect data on relevant background elements of terrestrial carbon disturbance in permafrost areas;

[0007] Step 2: Based on the collected background element data, a database of background elements related to terrestrial carbon disturbance in permafrost areas is constructed through multi-source data fusion methods, and the carbon disturbance assessment index in permafrost areas is calculated;

[0008] Step 3: Assess the credibility of carbon disturbance assessment indicators in permafrost areas;

[0009] Step 4: Monitor the regional background elements and river particulate organic carbon output dynamics in permafrost areas, calculate the changes in relevant background element data, and calculate the change rate of the "carbon disturbance assessment index" based on the monitoring data to identify high-risk areas of carbon disturbance in permafrost areas;

[0010] Step 5: Use numerical models to identify high-risk areas and periods of carbon disturbance in permafrost regions, and identify key disturbance factors and impacts;

[0011] Step 6: Use CMIP6 future scenario data to analyze the changing trends of carbon disturbance assessment indicators, use machine learning methods, based on historical data, quantify the intrinsic relationship between carbon disturbance indicators and river particulate organic carbon output, and establish a prediction model for subsequent research.

[0012] Furthermore, the background data in step 1 include: river runoff (Q, m³ / s), river particulate organic carbon concentration (POC, mg / L), river particulate organic carbon 14 isotope value (POC-Δ 14 C, ‰), river suspended sediment concentration (TSS, mg / L), river network density (Dd, km / km²), forest cover (For, %), 2-m soil organic carbon content (SOC, g / kg), soil sandstone percentage (Snd, %), terrain slope (Slp, °), high ground ice content coverage (HIGH, %), low ground ice content coverage (LOW, %), permafrost active layer thickness (ALT, m) and river length (L, millionkm) data (units are included in brackets after the data).

[0013] Furthermore, in step 2, a database of background elements related to terrestrial carbon disturbance in permafrost areas is constructed by fusion of multi-source data, and a carbon disturbance assessment index system for permafrost areas is established by determining carbon disturbance assessment indicators. The carbon disturbance assessment index system includes 6 carbon disturbance assessment indicators, including:

[0014] Carbon migration index CMI, calculated as: CMI=Dd / For;

[0015] Carbon load ratio index ROLPI, calculated as: ROLPL=SOC×For×Snd / Dd;

[0016] Carbon age index CCI, calculated as: CCI = Slp × Snd;

[0017] The underground ice index θ is calculated as follows: θ = ICE × ( / LOW);

[0018] Freeze-thaw induced carbon disturbance index ThCD, calculated as: ThCD = ALT / θ × L;

[0019] Suspended carbon ratio index b, the fitting formula is: (POC / TSS) = b / (TSS+a)+c;

[0020] Among them, a and c are the suspended carbon ratio coefficients, and ICE is the soil ice content.

[0021] Furthermore, in step 3, the credibility of the carbon disturbance assessment index in the permafrost region is evaluated. The correlation coefficient between the river particulate organic carbon output index and the regional carbon disturbance index is obtained through linear regression analysis, and the credibility of the index under different regions and time periods is comprehensively evaluated. Specifically, the background element data and the carbon disturbance assessment index are selected for linear fitting respectively, and the corresponding determination coefficient R is obtained. 2 , root mean square error RMSE and mean absolute error MAE;

[0022] Among them, the four sets of linear fitting are:

[0023] 1) River particulate organic carbon concentration (POC) and carbon migration index (CMI);

[0024] 2) Flux of river particulate organic carbon POC The freeze-thaw induced carbon disturbance index ThCD, where the river particulate organic carbon flux Flux POC The calculation formula is: Flux POC =POC × Q;

[0025] 3) POC-Δ isotope value of organic carbon 14 in river particles 14 C and Carbon Age Index CCI;

[0026] 4) Suspended carbon ratio index b and carbon load ratio index ROLPI;

[0027] Among them, the determination coefficient R 2 , RMSE and MAE are calculated as follows:

[0028] ;

[0029] ;

[0030] ;

[0031] in, is the observed value, is the predicted value, is the mean of the observed values, n is the number of samples; is the residual sum of squares, , is the total sum of squares.

[0032] Furthermore, in step 4, the changes in river runoff Q, river particulate organic carbon concentration POC, river suspended sediment concentration TSS, and river particulate organic carbon 14 isotope value POC-Δ in permafrost areas are monitored through high-resolution remote sensing image data and measured data from hydrological stations. 14 C changes, river network density Dd changes, forest coverage rate For changes, river length L changes, and based on the monitoring data, the change rates of the carbon migration index CMI, carbon load ratio index ROLPI and freeze-thaw induced carbon disturbance index ThCD in permafrost areas are calculated.

[0033] Furthermore, in step 5, ArcGIS is used to display the spatial distribution characteristics and differences of each index in each carbon disturbance assessment index in the permafrost zone, identify high-risk areas and time periods of carbon disturbance in the permafrost zone, reveal indicators and key driving data information that have changed significantly in the carbon disturbance assessment index, and identify disturbance factors and their impact on terrestrial carbon output (different aspects such as carbon output concentration, flux and carbon component).

[0034] Furthermore, in step 6, relevant background element data in the CMIP6 future scenario data are used to analyze the future changing trends of carbon disturbance assessment indicators, and machine learning methods are used to quantify the intrinsic relationship between carbon disturbance indicators and river particulate organic carbon output based on historical data, and a prediction model is established for further research on future carbon disturbance in permafrost areas.

[0035] In some embodiments, a method executed by a computer system is disclosed for implementing one or more functions of the method.

[0036] According to some embodiments, a computing system includes a processor and a memory storing instructions, which when executed cause one or more processors to perform the methods described herein. According to some embodiments, an electronic device includes one or more processors, and a memory storing one or more programs; the one or more programs are configured to be executed by the one or more processors, and the one or more programs include instructions for performing or causing the operations of any method described herein to be performed.

[0037] The beneficial effects of the present invention are:

[0038] (1) Further develop previous research and use future scenario data to more clearly predict the development trend of carbon disturbance;

[0039] (2) Help solve the problem of difficulty in monitoring and quantifying carbon release from permafrost, and effectively use the available data to assess carbon disturbance in permafrost areas;

[0040] (3) It should be comprehensive, dynamic, and effective, and support carbon disturbance attribution and prediction analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flowchart of the method is shown in FIG.

[0042] Figure 2 (a) The relationship between regional %POC (POC / TSS) and TSS concentration;

[0043] Figure 3 (b) The relationship between regional %POC (POC / TSS) and TSS concentration;

[0044] Figure 4 (c) is a graph showing the relationship between regional %POC (POC / TSS) and TSS concentration;

[0045] Figure 5 (d) is the relationship curve between regional %POC (POC / TSS) and TSS concentration;

[0046] Figure 6 (e) is a graph showing the relationship between regional %POC (POC / TSS) and TSS concentration;

[0047] Figure 7 (f) is the relationship curve between regional %POC (POC / TSS) and TSS concentration;

[0048] Figure 8 The linear fitting diagram of the river particulate organic carbon concentration POC and carbon migration index CMI in step 3 of each region;

[0049] Fig. 9 It is a linear fitting diagram of the suspended carbon ratio index b and the carbon load ratio index ROLPI in step 3 of each region;

[0050] Fig.10 The linear fitting diagram of the river particle organic carbon 14 isotope value POC-Δ14C and the carbon age index CCI in step 3 of each region;

[0051] Fig.11 Linear fitting plot of river particulate organic carbon flux FluxPOC and freeze-thaw induced carbon disturbance index ThCD in step 3 for each region. DETAILED DESCRIPTION

[0052] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1 The present invention provides an intelligent monitoring and assessment method and system for terrestrial carbon disturbance in permafrost regions, including:

[0054] Step 1: Collect data on relevant background elements of terrestrial carbon disturbance in permafrost areas;

[0055] Step 2: Based on the collected background element data, a database of background elements related to terrestrial carbon disturbance in permafrost areas is constructed through multi-source data fusion methods, and the carbon disturbance assessment index in permafrost areas is calculated;

[0056] Step 3: Assess the credibility of carbon disturbance assessment indicators in permafrost areas;

[0057] Step 4: Monitor the regional background elements and river particulate organic carbon output dynamics in permafrost areas, calculate the changes in relevant background element data, and calculate the change rate of the "carbon disturbance assessment index" based on the monitoring data to identify high-risk areas of carbon disturbance in permafrost areas;

[0058] Step 5: Use numerical models to identify high-risk areas and periods of carbon disturbance in permafrost regions, and identify key disturbance factors and impacts;

[0059] Step 6: Use CMIP6 future scenario data to analyze the changing trends of carbon disturbance assessment indicators, use machine learning methods, based on historical data, quantify the intrinsic relationship between carbon disturbance indicators and river particulate organic carbon output, and establish a prediction model for subsequent research. Example 1

[0060] Take the following six regions as examples: (a), (b), (c), (d), (e) and (f) are located in high-latitude areas and are prone to forming permafrost environments:

[0061] Step 1: First, collect background data for the selected six regions within one year. As shown in Table 1, Table 1 lists the data for the spring, summer and winter seasons for region (d) for the next step of data calculation.

[0062] Table 1 lists the values ​​of river suspended sediment concentration TSS, river particulate organic carbon concentration POC and carbon isotope in spring, summer and winter in region (d);

[0063]

[0064] Table 1;

[0065] Step 2: Through multi-source data fusion, a database of background elements related to terrestrial carbon disturbance in permafrost areas is constructed. By determining the carbon disturbance assessment indicators, a carbon disturbance assessment index system for permafrost areas is established. The carbon disturbance assessment indicators include 6 indicators, namely:

[0066] Carbon migration index (CMI=Dd / For);

[0067] Carbon load ratio index, calculated as: ROLPL = SOC × For × Snd / Dd;

[0068] Carbon age index, calculated as: CCI = Slp × Snd;

[0069] Ground ice index, calculated as: θ = ICE × (HIGH / LOW);

[0070] Freeze-thaw induced carbon disturbance index, calculated as: ThCD = ALT / θ × L;

[0071] The suspended carbon ratio index b is calculated as follows: (POC / TSS) = b / (TSS+a)+c;

[0072] Where a and c are the suspended carbon ratio coefficients, and ICE is the soil ice content.

[0073] See also Figure 2-Figure 7 , the relationship curve between %POC (POC / TSS) and TSS concentration in the six regions was drawn, and the b value corresponding to each region was calculated according to the fitting formula of each region, as shown in Table 2.

[0074]

[0075] Table 2;

[0076] Table 2 shows the relationship curve between %POC (POC / TSS) and TSS concentration in six regions. The value of suspended carbon ratio b is obtained by fitting calculation. Then, the background data and carbon disturbance assessment index are selected to perform four sets of linear fitting to obtain the determination coefficient R according to the obtained value of suspended carbon ratio b. 2 , the values ​​of root mean square error RMSE and mean absolute error MAE;

[0077] Step 3: Conduct credibility assessment of carbon disturbance assessment indicators in permafrost areas. Through linear regression analysis, obtain the correlation coefficient between river particulate organic carbon output indicators and regional carbon disturbance indicators, and comprehensively evaluate the credibility of the indicator under different regional and time conditions. Specifically, select background element data and carbon disturbance assessment indicators for linear fitting, and obtain the corresponding determination coefficient R 2 , root mean square error RMSE and mean absolute error MAE;

[0078] The four groups that perform the linear fitting algorithm are:

[0079] River particulate organic carbon concentration (POC, mg / L) and carbon migration index Carbon Migration Index (CMI=Dd / For);

[0080] Flux POC =POC×Q) and freeze-thaw induced carbon disturbance index Thaw-InducedCarbonDisturbanceIndex (ThCD=ALT / θ×L);

[0081] The isotopic values ​​of organic carbon 14 in river particles (POC-Δ14 C) and Carbon Chronology Index, the calculation formula is: CCI = Slp × Snd;

[0082] Suspended carbon ratio index b and carbon load ratio index Riverine Organic Load Proportion Index (ROLPL=SOC×For×Snd / Dd).

[0083] See Figure 8-Figure 11 ,Through linear regression analysis, the correlation coefficient between the river particulate organic carbon output index and the regional carbon disturbance index was obtained, and the credibility of the index under different regional and time conditions was comprehensively evaluated. Specifically, the background element data and carbon disturbance assessment index were selected for linear fitting respectively;

[0084] The four sets of linear fits performed in this embodiment are:

[0085] 1) River particulate organic carbon concentration (POC) and carbon migration index (CMI);

[0086] 2); Suspended carbon ratio index b and carbon load ratio index ROLPI

[0087] 3) POC-Δ isotope value of organic carbon 14 in river particles 14 C and Carbon Age Index CCI;

[0088] 4) Flux of river particulate organic carbon POC The freeze-thaw induced carbon disturbance index ThCD, where the river particulate organic carbon flux Flux POC The calculation formula is: Flux POC =POC × Q;

[0089] like Figure 8 The figure shows the linear fitting of river particulate organic carbon concentration POC and carbon migration index CMI;

[0090] Among them, the determination coefficient (R2) = 0.93; (a): river network density (Dd) = 0.21, forest coverage rate (For) = 47; (b): river network density (Dd) = 0.18, forest coverage rate (For) = 67; (c): river network density (Dd) = 0.18, forest coverage rate (For) = 72; (d): river network density (Dd) = 0.20, forest coverage rate (For) = 50; (e): river network density (Dd) = 0.20, forest coverage rate (For) = 24; (f): river network density (Dd) = 0.20, forest coverage rate (For) = 50;

[0091] like Fig. 9As shown in the figure, it is the linear fit of the suspended carbon ratio index b and the carbon load ratio index ROLPI; among them, the determination coefficient (R 2 ) = 0.84; (a): river network density (Dd) = 0.21, forest coverage (For) = 47, 2-meter soil organic carbon content (Soc) = 18.55, soil sandstone percentage (Snd) = 44; (b): river network density (Dd) = 0.18, forest coverage (For) = 67, 2-meter soil organic carbon content (Soc) = 20.77, soil sandstone percentage (Snd) = 43; (c): river network density (Dd) = 0.18, forest coverage (For) = 72, 2-meter soil organic carbon content (Soc) = 33.07, soil sandstone percentage (Snd) =43; (d): river network density (Dd) = 0.20, forest coverage (For) = 40, 2-meter soil organic carbon content (Soc) = 47.41, soil sandstone percentage (Snd) = 43; (e): river network density (Dd) = 0.20, forest coverage (For) = 24, 2-meter soil organic carbon content (Soc) = 41.69, soil sandstone percentage (Snd) = 44; (f): river network density (Dd) = 0.20, forest coverage (For) = 36, 2-meter soil organic carbon content (Soc) = 60.92, soil sandstone percentage (Snd) = 45;

[0092] like Fig.10 The figure shows the carbon 14 isotope value of river particles POC-Δ 14 C and the linear fit of carbon age index CCI; where the coefficient of determination (R 2 ) = 0.97; (a): terrain slope (Sld) = 2.6, soil sandstone percentage (Snd) = 44; (b): terrain slope (Sld) = 6.3, soil sandstone percentage (Snd) = 43; (c): terrain slope (Sld) = 6.9, soil sandstone percentage (Snd) = 43; (d): terrain slope (Sld) = 9.2, soil sandstone percentage (Snd) = 43; (e): terrain slope (Sld) = 9.8, soil sandstone percentage (Snd) = 44; (f): terrain slope (Sld) = 5.2, soil sandstone percentage (Snd) = 45;

[0093] like Fig.11 As shown, it is the flux of river particulate organic carbon Flux POC Linear fitting with freeze-thaw induced carbon disturbance index ThCD; where the determination coefficient (R 2) = 0.75; (a): permafrost active layer thickness (ALT) = 1.25, ground ice index (θ) = 0.25, river length (L) = 0.65; (b): permafrost active layer thickness (ALT) = 1.44, ground ice index (θ) = 0.22, river length (L) = 0.45; (c): permafrost active layer thickness (ALT) = 1.22, ground ice index (θ) = 0.50, river length (L) = 0.46; (d): permafrost active layer thickness (ALT) = 1.25, ground ice index (θ) = 0.25, river length (L) = 0.65; (b): permafrost active layer thickness (ALT) = 1.44, ground ice index (θ) = 0.22, river length (L) = 0.45; (c): permafrost active layer thickness (ALT) = 1.22, ground ice index (θ) = 0.50, river length (L) = 0.46 Thickness (ALT) = 0.86, ground ice index (θ) = 2.29, river length (L) = 0.13; (e): permafrost active layer thickness (ALT) = 1.21, ground ice index (θ) = 0.05, river length (L) = 0.17; (f): permafrost active layer thickness (ALT) = 1.19, ground ice index (θ) = 0.07, river length (L) = 0.35; Combining the four groups of linear fitting, the final carbon disturbance index determination coefficient is 0.82.

[0094] After the final data has passed four sets of linear fitting, the next step of analysis is carried out.

[0095] Step 4: Monitor the changes in river runoff, river particulate organic carbon concentration, river suspended sediment concentration, river particulate organic carbon 14 isotope value, river network density, forest coverage, and river length in permafrost areas through high-resolution remote sensing image data and measured data from hydrological stations. Calculate the change rate of the carbon migration index, carbon load ratio index, and freeze-thaw induced carbon disturbance index in permafrost areas based on the monitoring data.

[0096] Step 5: Use ArcGIS to display the spatial distribution characteristics and differences of various carbon disturbance indices in permafrost areas, identify high-risk areas and periods of carbon disturbance in permafrost areas, analyze indicators and key driving data information that have changed significantly in carbon disturbance assessment indicators, and identify the main disturbance factors and their impact on terrestrial carbon output (carbon output concentration, flux, carbon components, etc.);

[0097] The ArcGIS system is the infrastructure for mapping and geographic information, which can be used within departments, across the enterprise, between multiple different organizations and user communities, and external networks for everyone to access. For example, workers using mobile devices can update measurement data in real time in the field, while at the same time, they can use desktop computers to analyze this information, and planners can conduct impact assessments on the results of the analysis through web-based applications. Ultimately, the maps and data produced by the project can be published to the web so that anyone can access them through web browsers and applications on smartphones and tablets. Not only can you view the results of the project, but you can also merge the data with other available data to create more maps and apply geographic information in a whole new way.

[0098] Step 6: Analyze the collected data using CMIP6 future scenario data and machine learning algorithms, and establish a prediction model based on the relationship between the permafrost carbon disturbance index and river particulate organic carbon output in historical data for subsequent analysis and research.

[0099] CMIP6 is the implementation of the Climate Model Intercomparison Project, and its main purpose is to understand past, present and future climate change by collecting and comparing simulation results from various global climate models (GCMs). Compared with previous versions, CMIP6 has a larger number of participating models, richer numerical experiments and huge simulation data, which will support global climate research in the next 5 to 10 years and form the basis for future climate assessments and climate negotiations. In addition, CMIP6 also includes multiple scientific sub-programs, such as the High-Resolution Model Intercomparison Project (HighResMIP) and the Detection and Attribution Model Intercomparison Project (DAMIP), which focus on studying specific scientific issues, such as the impact of human activities on climate change.

[0100] Machine learning methods have been successfully applied to downscaling techniques due to their powerful ability to handle complex pattern recognition and high-dimensional data problems. In the field of climatology, machine learning has been successfully used to associate coarse-scale climate model outputs (e.g., temperature and precipitation) with other environmental variables (e.g., topography and soil type) to obtain higher-resolution climate predictions.

[0101] Through the above steps, the carbon disturbance related data in permafrost areas are obtained, and combined with the relationship between river particulate organic carbon output and permafrost area carbon disturbance assessment indicators, a prediction model is obtained based on future scenario data for subsequent research. This method is conducive to further development of previous research and uses future scenario data to more clearly predict the development trend of carbon disturbance; it helps solve the problem of permafrost carbon release being difficult to monitor and quantify, effectively uses monitorable data, and realizes carbon disturbance assessment in permafrost areas; it is comprehensive, dynamic, and effective, and supports carbon disturbance attribution and predictive analysis.

[0102] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. An intelligent monitoring and assessment method for terrestrial carbon disturbance in permafrost regions, characterized in that the steps include: Step 1: Collect data on relevant background elements of terrestrial carbon disturbance in permafrost areas; Step 2: Based on the collected background element data, a database of background elements related to terrestrial carbon disturbance in permafrost areas is constructed through multi-source data fusion methods, and the carbon disturbance assessment index in permafrost areas is calculated; Step 3: Assess the credibility of carbon disturbance assessment indicators in permafrost areas; Step 4: Monitor the regional background elements and river particulate organic carbon output dynamics in permafrost areas, calculate the changes in relevant background element data, and calculate the change rate of the "carbon disturbance assessment index" based on the monitoring data to identify high-risk areas for carbon disturbance in permafrost areas; Step 5: Use numerical models to identify high-risk areas and periods of carbon disturbance in permafrost regions, and identify key disturbance factors and impacts; Step 6: Analyze the changing trend of carbon disturbance assessment indicators using CMIP6 future scenario data, quantify the intrinsic relationship between carbon disturbance indicators and river particulate organic carbon output based on historical data using machine learning methods, and establish a prediction model for subsequent research; The background element data in step 1 include: river runoff Q, river particulate organic carbon concentration POC, river particulate organic carbon 14 isotope value POC-Δ 14 C, river suspended sediment concentration TSS, river network density Dd, forest coverage For, 2-meter soil organic carbon content SOC, soil sandstone percentage Snd, terrain slope Slp, high ground ice content coverage HIGH, low ground ice content coverage LOW, permafrost active layer thickness ALT and river length L; In the step 2, a database of background elements related to terrestrial carbon disturbance in permafrost areas is constructed by fusion of multi-source data, and a carbon disturbance assessment index system for permafrost areas is established by determining carbon disturbance assessment indicators. The carbon disturbance assessment index system includes 6 carbon disturbance assessment indicators, including: Carbon migration index CMI, calculated as: CMI=Dd / For; Carbon load ratio index ROLPI, calculated as: ROLPL=SOC×For×Snd / Dd; Carbon age index CCI, calculated as: CCI = Slp × Snd; The underground ice index θ is calculated as follows: θ = ICE × (HIGH / LOW); Freeze-thaw induced carbon disturbance index ThCD, calculated as: ThCD = ALT / θ × L; Suspended carbon ratio index b, the fitting formula is: (POC / TSS) = b / (TSS+a)+c; Among them, a and c are the suspended carbon ratio coefficients, and ICE is the soil ice content; In step 3, the credibility of the carbon disturbance assessment index in the permafrost region is evaluated. The correlation coefficient between the river particulate organic carbon output index and the regional carbon disturbance index is obtained through linear regression analysis, and the credibility of the index under different regional and time conditions is comprehensively evaluated. Specifically, the background element data and the carbon disturbance assessment index are selected for linear fitting respectively, and the corresponding determination coefficient R is obtained. 2 , root mean square error RMSE and mean absolute error MAE; Among them, the four sets of linear fitting are: 1) River particulate organic carbon concentration (POC) and carbon migration index (CMI); 2) Flux of river particulate organic carbon POC The freeze-thaw induced carbon disturbance index ThCD, where the river particulate organic carbon flux Flux POC The calculation formula is: Flux POC =POC × Q; 3) POC-Δ isotope value of organic carbon 14 in river particles 14 C and Carbon Age Index CCI; 4) Suspended carbon ratio index b and carbon load ratio index ROLPI; Among them, the determination coefficient R 2 , RMSE and MAE are calculated as follows: ； ； ； in, is the observed value, is the predicted value, is the mean of the observed values, n is the number of samples; is the residual sum of squares, , is the total sum of squares.

2. The intelligent monitoring and assessment method for terrestrial carbon disturbance in permafrost regions according to claim 1 is characterized in that: In step 4, high-resolution remote sensing image data and measured data from hydrological stations are used to monitor changes in river runoff Q in permafrost areas, changes in river particulate organic carbon concentration POC, changes in river suspended sediment concentration TSS, and river particulate organic carbon 14 isotope value POC-Δ 14 C changes, river network density Dd changes, forest coverage rate For changes, river length L changes, and based on the monitoring data, the change rates of the carbon migration index CMI, carbon load ratio index ROLPI and freeze-thaw induced carbon disturbance index ThCD in permafrost areas are calculated.

3. The intelligent monitoring and assessment method for terrestrial carbon disturbance in permafrost regions according to claim 1 is characterized in that: In step 5, ArcGIS is used to display the spatial distribution characteristics and differences of each index in each carbon disturbance assessment index in the permafrost zone, identify the high-risk areas and time periods of carbon disturbance in the permafrost zone, and identify the disturbance factors and their impact on terrestrial carbon output based on the carbon disturbance assessment index and key driving data information.

4. The intelligent monitoring and assessment method for terrestrial carbon disturbance in permafrost regions according to claim 1 is characterized in that: In step 6, the relevant background element data in the CMIP6 future scenario data are used to analyze the future change trend of the carbon disturbance assessment index, and the intrinsic relationship between the carbon disturbance index and the river particulate organic carbon output is quantified based on historical data using machine learning methods to establish a prediction model.

5. An intelligent monitoring and assessment system for terrestrial carbon disturbance in permafrost regions, characterized in that: The system is used to perform the method according to any one of claims 1-4.

6. A readable storage medium storing instructions, characterized in that: The instructions, when executed by one or more processors of a machine, cause the processor to perform a method according to any one of claims 1-4.

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