Organic carbon multi-source classification and dynamic list construction method for reservoir methane accounting

Abstract

The invention relates to an organic carbon multi-source classification and dynamic list construction method for reservoir methane accounting, and belongs to the technical field of reservoir ecology and greenhouse gas accounting. Aiming at the technical problem that the dynamic change and degradation influence of the organic carbon source cannot be accurately quantified by a traditional method, the invention proposes that the organic carbon source is refined into four types of submerged soil, sediment sediment, algae autogenesis and in-river pollution, calculation models are respectively established, and a dynamic degradation list is constructed by combining a time attenuation factor and reservoir age-month weighted calculation. According to the method, dynamic quantification of the organic carbon input-degradation process is achieved, the CH4 emission prediction error is reduced by 30% or above, and high-precision data support is provided for reservoir carbon management and emission reduction.

Description

Technical Field

[0001] The present invention belongs to the technical field of reservoir ecology and greenhouse gas accounting, and relates to a method for multi-source classification of organic carbon and construction of a dynamic inventory for reservoir methane accounting. Background Art

[0002] As an artificially regulated water ecosystem, the construction of a reservoir has significantly changed the carbon cycle process of natural rivers. According to relevant regulations, the "flooded land" formed after the reservoir is impounded becomes an important source of methane (CH4) emissions due to the degradation of organic matter in the flooded soil, vegetation, and sediment under anaerobic conditions. Existing studies have shown that the CH4 emission intensity of reservoirs is closely related to the source, transformation, and dynamic balance of organic carbon. However, traditional accounting methods have significant deficiencies in the classification of organic carbon sources, the analysis of degradation mechanisms, and the construction of dynamic inventories.

[0003] Currently, the academic community generally simplifies the sources of reservoir organic carbon into two categories: endogenous (such as algae and aquatic plants) and exogenous (such as soil, terrestrial plants, and pollutants). It is considered that exogenous organic carbon mainly consists of refractory components and is mainly deposited as a carbon sink in the long term, while endogenous organic carbon is easily decomposed by microorganisms quickly due to its simple structure. However, recent studies have found that exogenous organic carbon (such as terrestrial plant residues) can still be partially mineralized in the anaerobic environment of reservoirs and become a direct substrate for CH4 production. At the same time, a high organic carbon load accelerates the consumption of dissolved oxygen in the water body and inhibits the CH4 oxidation process, further exacerbating the emission risk. This contradiction indicates that the existing classification system fails to accurately reflect the degradation characteristics of organic carbon from different sources and their differential contributions to CH4 emissions.

[0004] In addition, existing accounting methods are mostly based on the construction of static inventories, ignoring the dynamic changes in organic carbon input and degradation during the operation of reservoirs. For example, the initial release of organic carbon from flooded soil is relatively high, but it decays exponentially with the impoundment time; the organic carbon in sediment deposits shows interannual variations due to fluctuations in upstream input; the autogenous organic carbon of algae is seasonally regulated by hydrological conditions (such as transparency and nutrients); and the organic carbon of river pollutants is directly related to the intensity of human activities. If these dynamic processes are not quantitatively modeled, it will lead to the deviation of CH4 emission prediction results from reality and weaken the scientific nature of emission reduction strategies.

[0005] The current technical bottlenecks are mainly reflected in: (1) the extensive multi-source classification system of organic carbon, lacking a refined analysis of degradation pathways; (2) the absence of a dynamic inventory construction method, making it difficult to integrate long-term hydrological, environmental, and human activity data; (3) the dependence on empirical values for key parameters (such as degradation rate and burial efficiency), and the lack of a correlation model with environmental factors such as water temperature and microbial activity. These problems seriously restrict the accuracy of reservoir CH4 accounting and hinder the formulation and implementation of carbon management policies.

[0006] Therefore, there is an urgent need to develop an inventory construction method that can systematically classify the sources of organic carbon, quantify the dynamic input-degradation process, and integrate multi-source environmental data, so as to provide technical support for the accurate accounting and scientific management of reservoir CH4 emissions. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide a method for multi-source classification of organic carbon and dynamic inventory construction for reservoir methane accounting.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for multi-source classification of organic carbon and dynamic inventory construction for reservoir methane accounting, comprising the following steps:

[0010] S1: Evaluation of the amount of organic carbon in flooded soil. By obtaining parameters such as the area of the reservoir inundation area, soil type, organic carbon content, soil density, and depth of the degradation layer, calculate the total amount of organic carbon input by the flooded soil.

[0011] S2: Determination of the amount of organic carbon in sediment. Based on the annual average sediment deposition volume of the reservoir and the organic carbon content of the deposited sediment, calculate the amount of organic carbon input by the sediment.

[0012] S3: Estimation of autochthonous organic carbon in algae. By measuring parameters such as the total nitrogen, total phosphorus, chlorophyll a concentration, transparency, water temperature, solar radiation intensity, and light duration of the water body, and combining the vertical model induction method to calculate the primary productivity of algae, obtain the amount of autochthonous organic carbon in algae.

[0013] S4: Calculation of the amount of organic carbon in the pollution load entering the river. Based on the pollution census data and the pollution discharge coefficient method, calculate the amount of organic carbon input from urban and rural domestic sewage, livestock and poultry breeding, industrial point sources, and runoff.

[0014] S5: Prediction of the amount of organic carbon degradation and inventory compilation. Comprehensive consideration of reservoir hydrological conditions, sedimentary environment, microbial activity, and temperature factors, predict the degradation rate and degradation amount of organic carbon from different sources, and integrate the evaluation results of S1-S4 to construct a dynamic organic carbon accounting inventory.

[0015] Furthermore, the calculation formula for the amount of organic carbon in flooded soil in S1 is:

[0016]

[0017] Among them, OC1 represents the organic carbon in flooded soil, A represents the area of the inundated area, D represents the depth of the degradable soil layer, t represents the month of water storage; f soliC represents the content of organic carbon in flooded soil, ρ soilc represents the soil density, and β1 represents the natural degradation coefficient of organic matter in flooded soil.

[0018] Furthermore, the calculation formula for the organic carbon content in sediment deposits in S2 is as follows:

[0019]

[0020] Among them, OC2 represents the organic carbon in sediment deposits, j represents the reservoir age, n represents the month, ΔQ represents the sediment deposition volume, ΔQ0 represents the sediment deposition volume during the construction period and the initial operation period, and ΔQ j represents the sediment deposition volume in the j-th year of the formal operation of the reservoir: ΔQ m represents the sediment deposition volume in the m-th year, ε represents the burial amount of organic carbon in the reservoir bottom sediment, and f soliC represents the content of organic carbon in the flooded soil, ρ soilc represents the soil density, and β2 represents the natural degradation coefficient of organic matter in sediment.

[0021] Furthermore, the calculation formula for the autochthonous organic carbon content of algae in S3 is as follows:

[0022] OC3 = α × PP eu × SA t

[0023]

[0024] Among them, OC3 represents the autochthonous organic carbon of algae, and PP eu represents the primary productivity in the euphotic zone, represents the maximum photosynthesis rate of the water body, E0 represents the daily photosynthetically active radiation intensity at the lake surface, C opt represents the Chla concentration at the depth where the maximum photosynthesis rate is located, D irr represents the light cycle, Z eu represents the depth of the euphotic zone; α represents the ratio of net productivity to total primary productivity; SA t represents the water area of the reservoir, α represents the proportion of net productivity, and PP eu represents the primary productivity in the euphotic zone, and SA t represents the water area of the reservoir;

[0025] The PP eu is calculated by the following formula:

[0026]

[0027] represents the maximum photosynthesis rate, E0 represents the photosynthetically active radiation intensity, Z eu is the depth of the euphotic zone, C opt represents the chlorophyll a concentration at the depth corresponding to the maximum photosynthesis rate, and D irr represents the light cycle.

[0028] Furthermore, the calculation formula for the organic carbon content of the pollution load entering the river in S4 is as follows:

[0029]

[0030] where λ represents the OC content in COD, β4 represents the natural degradation coefficient of organic matter emitted by human activities around the reservoir area, and ΔP m represents the pollution load entering the river in the m-th year, j represents the number of years of reservoir operation, and n represents the month.

[0031] Furthermore, the organic carbon content of the submerged soil in S1 is obtained by referring to the reservoir environmental impact assessment report or by on-site sampling and measurement.

[0032] Furthermore, the data of sediment deposition in S2 comes from the annual hydrological sediment report or sediment sampling analysis, and the organic carbon content of sediment is obtained through an elemental analyzer or literature data.

[0033] Furthermore, the vertical model induction method in S3 includes: calculating the euphotic layer depth Zeu according to the water transparency, and the formula is Zeu = 4.605 / Kd, where Kd is the water extinction coefficient.

[0034] Furthermore, the degradation rate prediction in S5 includes: determining the half-life of organic carbon from different sources under anaerobic conditions through experiments, or establishing a regression model of degradation coefficients β1, β2, and β4 and environmental temperature based on historical data.

[0035] Furthermore, the results of the dynamic inventory compilation are used to generate input parameters for the reservoir CH4 emission prediction model or as a decision-making basis for carbon cycle management strategies.

[0036] The beneficial effects of the present invention are as follows:

[0037] (1) By refining the sources of reservoir organic carbon into four categories: submerged soil, sediment, algae autogenous source, and pollution entering the river, and establishing a quantitative evaluation model for each source, the limitations of the traditional endogenous / exogenous dichotomy are broken through, the degradation paths of organic carbon from different sources and their contribution differences to CH4 generation are accurately analyzed, and the scientificity and operability of the classification system are significantly improved.

[0038] (2) By introducing the time decay factor and the weighted calculation of reservoir age-month, the long-term dynamic changes of organic carbon input and degradation are quantified (such as the high release in the initial stage of submerged soil, the interannual fluctuation of sediment, and the seasonal growth of algae), the problem that the static inventory cannot reflect the characteristics of the reservoir operation stage is solved, and the CH4 emission prediction error is reduced by more than 30%.

[0039] (3) By using the vertical model induction method (such as Zeu = 4.605 / Kd) to correlate water transparency with the euphotic layer depth, and combining the experimental modeling of the degradation coefficient with water temperature and microbial activity, the defect of relying on empirical parameters in the traditional method is avoided, and the reliability of the predicted organic carbon degradation amount is increased by 40%-60%.

[0040] (4) Integrate multi-source heterogeneous data such as environmental monitoring data (chlorophyll a, transparency), annual hydrological and sediment reports, and pollution census bulletins, and be compatible with the literature data alternative scheme, so that the method can be flexibly applied to reservoirs with different data conditions (such as newly built reservoirs lacking historical data and old reservoirs with incomplete data), and the applicability covers more than % of reservoir types.

[0041] (5) The output results of the dynamic inventory are directly used as the input parameters of the CH4 emission prediction model, which can identify high-contribution sources (such as initially flooded soil, long-term river pollution), guide targeted emission reduction (such as sediment covering, algae inhibition, pollution control), and help increase the reservoir carbon sink potential by 10%-25%, providing a key technical tool for the "dual carbon" goal.

[0042] (6) By revealing the whole-chain mechanism of reservoir organic carbon source - transformation - emission, a high-resolution data interface is provided for the global freshwater ecosystem carbon cycle model, filling the method gap in the dynamic accounting of reservoirs in the IPCC inventory guidelines.

[0043] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the following specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0045] Figure 1 is the schematic diagram of the principle of the present invention;

[0046] Figure 2 is the schematic diagram of the calculation equation for the four aspects of the organic carbon source of the present invention;

[0047] Figure 3 is an example of the emissions of the four sources of organic carbon since the water storage of the Xiangjiaba Reservoir in the specific embodiment of the present invention;

[0048] Figure 4 is the correlation relationship between the methane emissions and environmental factors of the Xiangjiaba Reservoir in the specific embodiment of the present invention;

[0049] Figure 5This is the long-term trend prediction result of organic carbon from different sources in the Xiangjiaba Reservoir, a specific example of the present invention. Detailed implementation manners

[0050] The following describes the implementation manners of the present invention through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following examples only illustrate the basic concept of the present invention in a schematic manner. Without conflict, the following examples and the features in the examples can be combined with each other.

[0051] Among them, the drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as a limitation on the present invention; in order to better illustrate the embodiments of the present invention, some components in the drawings will be omitted, enlarged or reduced, and do not represent the dimensions of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0052] In the drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc. indicating the orientation or positional relationship, they are based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the drawings are only for illustrative purposes and should not be construed as a limitation on the present invention. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0053] As one of the important ways for humans to develop and utilize water resources, reservoir dam construction and water storage have changed the biogeochemical cycle of the original river ecosystem. Comparing the operation before and after reservoir dam construction, the factors affecting or changing the organic carbon cycle of the water ecosystem and causing CH4 emissions are mainly summarized in the following four aspects:

[0054] (1) Submerged soil organic carbon (S1): Building a reservoir will permanently or temporarily submerge some soil, resulting in the degradation of the submerged soil and vegetation to produce CH4 under anaerobic or anoxic conditions;

[0055] (2) Sediment organic carbon (S2): Building a reservoir changes the connectivity of the original river, reduces the water flow velocity, and causes sediment deposition. At the same time, some sediment input from the upstream is intercepted in front of the dam, and the organic matter in the sediment undergoes anaerobic degradation to produce CH4;

[0056] (3) Autochthonous organic carbon of algae (S3): The impoundment of the reservoir reduces the flow velocity, forming a lake area in front of the dam, which causes a large proliferation of algae. The dominant population changes from river-type algae in the initial stage of impoundment to lake-type algae, altering the carbon cycle and carbon flux.

[0057] (4) Organic carbon from river pollution load (S4): As the reservoir receives point-source and non-point-source pollution from surrounding towns, the discharged pollutants such as N and P promote the growth of algae. Meanwhile, the organic pollutants entering the river also support carbon emissions.

[0058] The above four aspects jointly determine the source and transformation intensity of organic carbon in the reservoir ecosystem. However, different environmental factors and human activity intensities constitute the characteristics of the reservoir, affecting the relative contributions of the four effects. Therefore, it is necessary to establish an accounting list of organic carbon emissions in the reservoir ecosystem to clarify the impact of the composition of endogenous and exogenous organic carbon on the organic matter mineralization process in the complex aquatic environment.

[0059] Table 1 is an example of the preparation of the original environmental dataset in the specific embodiment of the present invention.

[0060] Table 1

[0061] WL SAt Inflow V HRT T TP Chla TN DO DH CH4 375.05 93.63 2692.5 44.62 19.18 15.37 0.06 1.32 1.68 8.89 10.55 0.0211 376.95 96.75 2480.08 46.86 21.87 14.66 0.05 13.61 0.84 10.62 12 0.0349 377.09 96.99 3168.55 47.03 17.18 17.16 0.04 2 2.09 8.91 13.57 0.0468 372.12 89 9283.75 41.37 5.16 21.51 0.11 0.98 1.62 8.6 13.75 0.0635 374.73 93.11 9297.92 44.25 5.51 22.39 0.13 0.2 2.97 9.26 12.34 0.0636 376.76 96.43 3607.92 46.63 14.96 18.69 0.08 0.2 2.7 9.02 10.75 0.0431 377.47 97.63 2741.21 47.5 20.05 14.75 0.06 0.47 2.74 9.51 10.55 0.0363 371.52 88.08 5690 40.74 8.29 23.57 0.02 3.02 2.04 10.86 13.75 0.0528 376.16 95.43 7592 45.91 7 23.77 0.04 4.82 1 11.59 12.34 0.0498 378.07 98.64 3591 48.23 15.55 19.04 0.04 0.43 1.2 9.64 10.75 0.0324 376.84 96.56 2558.39 46.72 21.14 16.91 0.02 0.43 0.58 9.3 10.55 0.0441 374.71 93.08 2394.52 44.23 21.38 16.33 0.02 2.75 0.81 9.86 12 0.0508 374.81 93.23 3644.84 44.34 14.08 17.9 0.03 5.06 1.19 10.51 13.57 0.0477 372.51 89.6 6362.26 41.79 7.6 21.28 0.03 6.72 1.1 12.83 13.75 0.0462 377.67 97.96 3062.33 47.74 18.04 21.58 0.02 1.91 0.92 8 10.75 0.0525 373.17 90.62 2316 42.5 21.24 18.7 0.03 3.27 1.06 9.66 12.85 0.0478 376.23 95.55 2511.29 46 21.2 22.61 0.03 1.67 0.88 9.18 13.57 0.0471 373.47 91.09 3619.68 42.83 13.7 22.81 0.01 0.39 0.77 9.18 13.75 0.0612 376.58 96.13 5786.33 46.41 9.28 23.95 0.02 3.99 1.27 8.76 12.34 0.0582 377.35 97.41 3399.33 47.34 16.12 20.25 0.02 0.48 0.77 8.33 10.75 0.0467 375.42 90.34 3880.97 45.04 13.43 17.74 0.02 0.52 0.89 8.46 10.55 0.0458

[0062] Table 2 shows the contents and log values of four different sources of organic carbon since the impoundment of the Xiangjiaba Reservoir in the specific embodiment of the present invention.

[0063] Table 2

[0064] Year S1 S2 S3 S4 S1 S2 S3 S4 2013 307429.2 1246669.5 574.1 34560.2 5.49 6.10 2.76 4.54 2014 224233.1 1098826.0 574.1 81398.3 5.35 6.04 2.76 4.91 2015 121149.2 780791.2 574.1 109206.5 5.08 5.89 2.76 5.04 2016 61854.7 595344.8 574.1 125921.6 4.79 5.77 2.76 5.10 2017 30936.5 512416.1 574.1 136084.3 4.49 5.71 2.76 5.13 2018 15321.6 489869.2 574.1 142306.3 4.19 5.69 2.76 5.15 2019 7546.9 499090.8 574.1 146109.8 3.88 5.70 2.76 5.16 2020 3705.0 522998.4 574.1 148371.8 3.57 5.72 2.76 5.17 2021 1814.9 551945.7 574.1 149488.6 3.26 5.74 2.76 5.17 2022 887.7 580721.4 574.1 149844.6 2.95 5.76 2.76 5.18 2023 433.7 606673.3 574.1 149702.4 2.64 5.78 2.76 5.18

[0065] Please refer to Figures 1-5 , and the specific calculation formulas for the four types of organic carbon are as follows:

[0066] The emission of submerged soil organic carbon (OC1) mainly depends on the submerged area (A), the depth of the degradable soil layer (D), and the impoundment month (t):

[0067]

[0068] Among them, f soliC represents the content of organic carbon in the submerged soil (%); ρ soilc is the soil density; β1 represents the natural degradation coefficient of the organic matter in the submerged soil.

[0069] The generation of sediment organic carbon (OC2) mainly depends on the reservoir age (j), the month (n), and the sediment deposition volume (ΔQ). Among them, ΔQ0 is the sediment deposition volume during the construction period and the initial operation period, and ΔQ j is the sediment deposition volume in the jth year of the formal operation of the reservoir:

[0070]

[0071] In the above formula, ε represents the burial amount (%) of organic carbon in the reservoir bottom sediment; f sediC represents the organic carbon content (%) in sediment; ρ sediC is the sediment density; β2 represents the natural degradation coefficient of organic matter in sediment.

[0072] The production of autochthonous organic carbon (OC3) by algae depends on the primary productivity of the reservoir:

[0073] OC3 = α × PP eu × SA t

[0074]

[0075] Among them, PP eu represents the primary productivity in the euphotic zone [mg / (m 2 ·d)]; is the maximum photosynthesis rate of the water body [mg / (m 3 ·h)]; E0 is the daily photosynthetically active radiation intensity on the lake surface [mg / (m 2 ·d)]; C opt is the Chl a concentration (mg / m 3 ) at the depth where the maximum photosynthesis rate is located; D irr is the light period (h); Z eu represents the depth of the euphotic zone (m); α represents the ratio (%) of net productivity to total primary productivity; SA t represents the water area of the reservoir (km 2 ).

[0076] The production of organic carbon (OC4) from pollutants entering the river is defined by the human pollution load (ΔP) entering the river, the reservoir age (j), and the month (n):

[0077]

[0078] Among them, λ is the OC content in COD; β4 is the natural degradation coefficient of organic matter discharged from human activities around the reservoir area.

[0079] Example 1: Assessment of the amount of organic carbon in flooded soil

[0080] 1.1 Data acquisition: Extract the flooded area A = 95.7939 km from the "Environmental Impact Assessment Report" of the reservoir 2 , soil type (such as clay), soil organic carbon content f soliC = 1.1%, soil density ρ soilc = 1.0 g / cm 3, the depth D of the degradation layer = 2 cm.

[0081] 1.2 Parameter determination: The degradation coefficient β1 = 0.001975408 / day was measured through laboratory anaerobic culture experiments, and the water storage time t = 5 years.

[0082] 1.3 Calculate OC1: Apply the formula

[0083] OC1 = 95.7939×10 6 m 2 ×2 cm×0.011×1000 kg / m 3 ×e -0.001975408×365×5 = 5.728×10 6 kg

[0084] 1.4 Result application: Input OC1 into the dynamic inventory. It is found that the contribution ratio of the flooded soil in the initial stage of water storage (the 1st year) reaches 19%, and drops to 5% in the 5th year, guiding the priority control of the initial soil carbon release.

[0085] Example 2: Determination of the organic carbon content in sediment

[0086] 2.1 Data collection: Obtain the annual average sediment deposition volume ΔQ0 = 2.24×10 8 t (during the construction period), and the annual average ΔQ j = 4.13×10 4 t during the operation period; f soliC = 1% and ρ soilc = 1.16 g / cm 3 were measured by sediment sampling, and the burial rate ε = 67%.

[0087] 2.2 Calculate OC2: The reservoir age j = 10 years, the current month n = 6, β2 = 0.0025 / day, substitute into the formula:

[0088]

[0089] It is calculated that OC2 = 3.73×10 7 kg, accounting for 78% of the total annual organic carbon input.

[0090] 2.3 Dynamic analysis: The sediment contribution rate decreases from 78% (the 1st year) to 75% (the 5th year) with the decrease of the reservoir age, and the change of sediment input from the upstream needs to be continuously monitored.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the present technical solution, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for multi-source classification of organic carbon and construction of dynamic inventory for reservoir methane accounting, characterized in that: It includes the following steps: S1: Evaluation of the amount of soil organic carbon inundated. By obtaining parameters such as the area of the reservoir inundation area, soil type, organic carbon content, soil density, and depth of the degradation layer, calculate the total amount of organic carbon input by the inundated soil; S2: Determination of the amount of organic carbon in sediment. Based on the annual average sediment deposition amount in the reservoir and the organic carbon content of the deposited sediment, calculate the amount of organic carbon input by the sediment; S3: Estimation of autochthonous organic carbon of algae. By measuring parameters such as the total nitrogen, total phosphorus, chlorophyll a concentration, transparency, water temperature, solar radiation intensity, and light duration of the water body, and combining the vertical model induction method to calculate the primary productivity of algae, obtain the amount of autochthonous organic carbon of algae; S4: Calculation of the amount of organic carbon in the pollution load entering the river. Based on the pollution census data and the pollution discharge coefficient method, calculate the amount of organic carbon input from urban and rural life, livestock and poultry breeding, industrial point sources, and runoff; S5: Prediction of the amount of organic carbon degradation and compilation of the inventory. Comprehensively considering the reservoir hydrological conditions, sedimentary environment, microbial activity, and temperature factors, predict the degradation rate and degradation amount of organic carbon from different sources, integrate the evaluation results of S1 - S4, and construct a dynamic organic carbon accounting inventory.

2. The method for constructing an organic carbon multi-source classification and dynamic inventory for reservoir methane accounting according to claim 1, characterized in that: The formula for calculating the amount of soil organic carbon inundated in S1 is: Among them, OC1 represents the submerged soil organic carbon, A represents the area of the submerged area, D represents the depth of the degradable soil layer, t represents the water storage month; f soliC represents the content of organic carbon in the submerged soil, ρ soilc represents the soil density, and β1 represents the natural degradation coefficient of the submerged soil organic matter.

3. The method for constructing an organic carbon multi-source classification and dynamic inventory for reservoir methane accounting according to claim 1, characterized in that: The formula for calculating the amount of organic carbon in sediment in S2 is: Among them, OC2 represents the organic carbon in sediment deposits, j represents the age of the reservoir, n represents the month, ΔQ represents the sediment deposition volume, ΔQ0 represents the sediment deposition volume during the construction period and the initial operation period, and ΔQ j represents the sediment deposition volume in the jth year of the official operation of the reservoir: ΔQ m represents the sediment deposition volume in the mth year, ε represents the burial amount of organic carbon in the bottom sediment of the reservoir, and f soliC represents the content of organic carbon in the flooded soil, and ρ soilc represents the soil density, and β2 represents the natural degradation coefficient of organic matter in sediment.

4. The method for constructing an organic carbon multi-source classification and dynamic inventory for reservoir methane accounting according to claim 1, characterized in that: The formula for calculating the amount of autochthonous organic carbon of algae in S3 is: OC3 = α × PP eu × SA t Among them, OC3 represents autochthonous organic carbon of algae, and PP eu represents the primary productivity in the euphotic zone, represents the maximum photosynthesis rate of the water body, E0 represents the daily photosynthetically active radiation intensity at the lake surface, and C opt represents the Chl a concentration at the depth where the maximum photosynthesis rate occurs, and D irr represents the light period, and Z eu represents the euphotic zone depth; α represents the ratio of net productivity to total primary productivity; SA t represents the water area of the reservoir, α represents the proportion of net productivity, and PP eu represents the primary productivity in the euphotic zone, and SA t represents the water area of the reservoir; The PP eu is calculated by the following formula: represents the maximum photosynthetic rate, E0 represents the photosynthetically active radiation intensity, Z eu is the euphotic layer depth, C opt represents the chlorophyll a concentration at the depth corresponding to the maximum photosynthetic rate, D irr represents the light period.

5. The method for constructing an organic carbon multi-source classification and dynamic inventory for reservoir methane accounting according to claim 1, wherein: The formula for calculating the amount of organic carbon in the pollution load entering the river in S4 is: Among them, λ represents the OC content in COD, β4 represents the natural degradation coefficient of organic matter discharged from human activities around the reservoir area, and ΔP m represents the pollution load entering the river in the m-th year, j represents the number of years of reservoir operation, and n represents the month.

6. The method for constructing an organic carbon multi-source classification and dynamic inventory for reservoir methane accounting according to claim 1, characterized in that: The organic carbon content of the inundated soil in S1 is obtained by referring to the reservoir environmental impact assessment report or by on-site sampling and measurement.

7. The method for constructing an organic carbon multi-source classification and dynamic inventory for reservoir methane accounting according to claim 1, wherein: The sediment deposition amount data in S2 comes from the annual hydrological sediment report or sediment sampling analysis, and the sediment organic carbon content is obtained by an elemental analyzer or literature data.

8. The method for constructing a multi-source classification and dynamic inventory of organic carbon for reservoir methane accounting according to claim 1, characterized in that: The vertical model induction method in S3 includes: calculating the euphotic layer depth Zeu according to the water body transparency, and the formula is Zeu = 4.605 / Kd, where Kd is the water body extinction coefficient.

9. The method for constructing an organic carbon multi-source classification and dynamic inventory for reservoir methane accounting according to claim 1, characterized in that: The degradation rate prediction in S5 includes: experimentally determining the half-life of organic carbon from different sources under anaerobic conditions, or establishing a regression model of the degradation coefficients β1, β2, β4 and environmental temperature based on historical data.

10. The method for constructing an organic carbon multi-source classification and dynamic inventory for reservoir methane accounting according to any one of claims 1 to 9, characterized in that: The results of the dynamic inventory compilation are used to generate input parameters for the reservoir CH4 emission prediction model or as a decision-making basis for the carbon cycle management strategy.

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