Water area hydro-fluctuation belt nitrogen release flux prediction method, system, equipment and medium

Abstract

The invention discloses a water area hydro-fluctuation belt nitrogen release flux prediction method, system, equipment and medium, and relates to the technical field of nitrogen accumulation release amount prediction. The method comprises the following steps: acquiring basic data; the basic data comprises water quality data, water level data, rainfall data, remote sensing image data and social economic data of the target water area; carrying out trend and correlation analysis on the water quality indexes by utilizing various statistical analysis methods based on the basic data, and determining spatial and temporal distribution characteristics and influence factors of the water quality; preprocessing the remote sensing image data, performing image analysis according to the preprocessed data and spatial and temporal distribution characteristics and influence factors of water quality, and determining a water-level-fluctuating zone submerging range of the target water area; and constructing a revenue-expenditure balance model, and predicting the nitrogen release flux of the water-level-fluctuating zone in the water-level-fluctuating zone submerging range according to the revenue-expenditure balance model and the Fick law to obtain a prediction result. According to the method, the nitrogen release flux of the hydro-fluctuation belt of the target water area can be accurately predicted.

Description

Technical Field

[0001] The invention relates to the technical field of nitrogen accumulation and release prediction, and in particular to a method, system, equipment and medium for predicting nitrogen release flux in a water drawdown zone. Background Art

[0002] As a transition zone between water bodies and terrestrial ecosystems, the ecological functions and environmental processes of the water drawdown zone play a vital role in maintaining the health of water bodies, water quality safety and biodiversity. However, the water drawdown zone is affected by both natural factors (such as water level fluctuations and precipitation changes) and human factors (such as agricultural activities and urbanization), and its ecological environment is prone to change, especially the circulation and release of nutrients such as nitrogen, which has a significant contribution to the eutrophication of adjacent water bodies.

[0003] Traditional nitrogen release flux prediction methods often rely on field monitoring data. Although this method is accurate, it is time-consuming and laborious, and it is difficult to fully cover a wide range of waters. Especially in the face of complex and changing hydrological conditions and ecological environments, its prediction ability and timeliness are limited. Therefore, it is particularly important to explore an efficient, accurate and widely applicable method for predicting nitrogen release flux in the drawdown zone of waters. Summary of the invention

[0004] The purpose of the present invention is to provide a method, system, equipment and medium for predicting nitrogen release flux in the drawdown zone of a water area, which can achieve accurate prediction of nitrogen release flux in the drawdown zone of a target water area.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for predicting nitrogen release flux in a water drawdown zone, comprising:

[0007] Obtaining basic information; the basic information includes water quality data, water level data, precipitation data, remote sensing image data and socio-economic data of the target water area;

[0008] Based on the basic data, various statistical analysis methods are used to analyze the trend and correlation of water quality indicators to determine the temporal and spatial distribution characteristics and influencing factors of water quality;

[0009] Preprocessing the remote sensing image data, and performing image analysis based on the preprocessed data and the spatiotemporal distribution characteristics and influencing factors of the water quality to determine the flooding range of the drawdown zone of the target water area;

[0010] A budget balance model is constructed, and the nitrogen release flux in the drawdown zone of the water area within the inundation range of the drawdown zone is predicted according to the budget balance model and Fick's law to obtain a prediction result; the prediction result includes the nitrogen accumulation and release in the sediment and the diffusion flux between the sediment and water interface; the budget balance model includes the exchange flux of nitrogen in the drawdown zone sediment during the flooding period and the exchange flux of nitrogen in the drawdown zone sediment during the drying period.

[0011] Optionally, after predicting the nitrogen release flux in the water drawdown zone, the method further includes: estimating the external pollution input load using the pollution discharge coefficient method.

[0012] Optionally, the preprocessing method includes: radiation correction and geometric correction; the radiation correction includes atmospheric correction, radiation calibration, and terrain and solar altitude angle correction.

[0013] Optionally, the image analysis method adopts the MNDWI algorithm.

[0014] Optionally, the calculation formula for the diffusion flux between the sediment and water interface is:

[0015]

[0016] Where, F is the diffusion flux at the sediment-water interface; is the porosity of surface sediments; dC / dx is the concentration gradient between overlying water and sediments, which is determined by the concentration difference between interstitial water and bottom water; D s is the diffusion coefficient in the sediment.

[0017] The present invention also provides a water drawdown zone nitrogen release flux prediction system, comprising:

[0018] A data collection unit, used to obtain basic information; the basic information includes water quality data, water level data, precipitation data, remote sensing image data and socio-economic data of the target water area;

[0019] A water quality characteristic analysis unit, for performing trend and correlation analysis of water quality indicators based on the basic data using a variety of statistical analysis methods, and determining the temporal and spatial distribution characteristics and influencing factors of water quality;

[0020] An image analysis unit is used to pre-process the remote sensing image data, and perform image analysis based on the pre-processed data and the spatiotemporal distribution characteristics and influencing factors of the water quality to determine the flooding range of the drawdown zone of the target water area;

[0021] A prediction unit is used to construct a budget balance model, and predict the nitrogen release flux of the drawdown zone in the water area within the flooded range of the drawdown zone according to the budget balance model and Fick's law to obtain a prediction result; the prediction result includes the nitrogen accumulation release in the sediment and the diffusion flux between the sediment and the water interface; the budget balance model includes the exchange flux of nitrogen in the drawdown zone sediment during the flooding period and the exchange flux of nitrogen in the drawdown zone sediment during the drying period.

[0022] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned method for predicting nitrogen release flux in the water drawdown zone.

[0023] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for predicting nitrogen release flux in a water drawdown zone as described above.

[0024] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0025] The present invention discloses a method, system, equipment and medium for predicting nitrogen release flux in a water area drawdown zone. The method comprises obtaining basic data; the basic data comprises water quality data, water level data, precipitation data, remote sensing image data and socio-economic data of a target water area; based on the basic data, using a variety of statistical analysis methods to perform trend and correlation analysis of water quality indicators, and determining the spatiotemporal distribution characteristics and influencing factors of water quality; pre-processing remote sensing image data, and performing image analysis based on the pre-processed data and the spatiotemporal distribution characteristics and influencing factors of water quality, and determining the flooding range of the drawdown zone of the target water area; constructing a balance of payments model, and predicting the nitrogen release flux in the drawdown zone of the water area within the flooding range of the drawdown zone according to the balance of payments model and Fick's law, and obtaining a prediction result. The present invention can realize accurate prediction of nitrogen release flux in the drawdown zone of the target water area. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0027] Figure 1 It is a schematic flow chart of the method for predicting nitrogen release flux in the drawdown zone of water area of ​​the present invention;

[0028] Figure 2 1 is a logic flow chart of the method for predicting nitrogen release flux in the water drawdown zone in this embodiment;

[0029] Figure 3 Schematic diagram of the imaging mechanism of sunlight in water in this embodiment;

[0030] Figure 4 Schematic diagram of the final remote sensing water body extraction result of the target area in this embodiment; wherein, part (a) is the original remote sensing image; part (b) is the NDWI index extracted water body image; part (c) is the MNDWI index extracted water body image;

[0031] Figure 5 Schematic diagram of the vector boundary range in this embodiment;

[0032] Figure 6 Schematic diagram of the calculation results of the nitrogen content in various forms per unit area of ​​soil at different sampling points in the drawdown zone at different drying periods in this embodiment. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] The purpose of the present invention is to provide a method, system, equipment and medium for predicting nitrogen release flux in the drawdown zone of a water area, which can achieve accurate prediction of nitrogen release flux in the drawdown zone of a target water area.

[0035] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] like Figure 1 As shown, the present invention provides a method for predicting nitrogen release flux in a water drawdown zone, comprising:

[0037] Step 100: Obtain basic information; the basic information includes water quality data, water level data, precipitation data, remote sensing image data and socio-economic data of the target water area.

[0038] Step 200: Based on the basic data, a variety of statistical analysis methods are used to perform trend and correlation analysis of water quality indicators to determine the spatiotemporal distribution characteristics and influencing factors of water quality.

[0039] Step 300: pre-process the remote sensing image data, and perform image analysis based on the pre-processed data and the spatiotemporal distribution characteristics and influencing factors of the water quality to determine the flooding range of the drawdown zone of the target water area.

[0040] Step 400: construct a budget balance model, and predict the nitrogen release flux of the drawdown zone in the water area within the inundation range of the drawdown zone according to the budget balance model and Fick's law to obtain a prediction result; the prediction result includes the nitrogen accumulation release in the sediment and the diffusion flux between the sediment and the water interface; the budget balance model includes the exchange flux of nitrogen in the drawdown zone sediment during the flooding period and the exchange flux of nitrogen in the drawdown zone sediment during the drying period.

[0041] According to the above technical solution, taking the Danjiang Reservoir area in Henan Province as an example, an embodiment as shown below is proposed.

[0042] Through field investigation, data collection, in-situ monitoring, remote sensing interpretation and mathematical model estimation and analysis, the water environment evolution and sediment temporal and spatial distribution characteristics of the Danjiangkou Reservoir in Henan Province were analyzed, and the scope of the reservoir flooding area under different water level operation conditions was explored. Finally, the nitrogen accumulation and release in the drawdown zone of the reservoir was quantitatively estimated, and the external pollution input load was estimated. Figure 2 As shown in the figure, the main research contents are divided into the following parts:

[0043] (1) Basic data collection and processing: Collect and organize water quality data, water level data, precipitation data, remote sensing images, and social and economic information through literature review or statistical yearbooks.

[0044] (2) Research on the evolution characteristics of the water environment in the reservoir area: Based on the systematically collected data on nutrients, water level, precipitation, chlorophyll a and algae density at 11 monitoring sections of the Danjiangkou Reservoir between 2015 and 2022, a comprehensive analysis and evaluation of the Danjiangkou Reservoir was conducted in combination with two comprehensive water quality index methods. A variety of statistical analysis methods were used to conduct trend and correlation analysis on water quality indicators. On this basis, the main water quality factors affecting the nutrient level in the reservoir area were identified, and the spatiotemporal distribution characteristics and influencing factors of the water quality in the Danjiang Reservoir area were explored since the water was put into operation in the middle line project.

[0045] (3) Dynamic study of the drawdown zone based on remote sensing data: Based on the 30m resolution remote sensing image data of the Landsat sequence from 2010 to 2020, the water body of the Danjiangkou Reservoir was extracted, and the inter-annual and annual evolution characteristics of the water area in the 11 years before and after the water diversion of the middle line project were statistically analyzed; the fitting correlation between the water area and the water level data monitored during the same period was analyzed; the inundation range of the drawdown zone of the Danjiangkou Reservoir was determined, the frequency distribution of water inundation was calculated and the water body was divided; based on the land use in Xichuan County, the changes in land cover types under different inundation levels were analyzed.

[0046] (4) Study on the nitrogen release law in the drawdown zone of Danjiangkou Reservoir: By constructing a budget balance model, the absorption and release of nitrogen in sediments were explored, and the diffusion rate between the water and sediment interface was analyzed by Fick's law. Finally, the emission coefficient method was used to analyze the contribution of nitrogen load from agricultural non-point source pollution around the reservoir area.

[0047] As a further technical solution, the following specific processing process is proposed:

[0048] Acquisition and preprocessing of remote sensing data:

[0049] 1. Source and acquisition of remote sensing data: After comprehensively considering multiple factors such as timeliness, macroscopicity and economy, this embodiment selects Landsat satellite data for research.

[0050] This embodiment obtains the required Landsat series image data from the official website of the United States Geological Survey (USGS). A total of 130 synchronous images were obtained from 2010 to 2020. After manually eliminating 14 remote sensing images that were affected by thick clouds over the study area and were obviously damaged, 112 valid remote sensing images were screened out, accounting for 86.15% of the total images. These include 10 scenes of Landsat 5 TM data, 58 scenes of Landsat 7 ETM+ data, and 44 scenes of Landsat 8 OLI data. The strip number and line number of the Landsat remote sensing image in the study area are 125 and 37, respectively. Since the Landsat 7ETM+ images after 2003 have striping, this embodiment repairs them through ENVI patches. The selected Landsat satellite image data are all C1 Level-1 products that have been corrected for terrain geometry and can generally be processed directly.

[0051] 2. Preprocessing of remote sensing data: Since the original remote sensing images are inevitably affected by external factors during the shooting process, such as the atmosphere, water vapor, seasonal differences, radiation, etc., the remote sensing images must be preprocessed before using them. Image preprocessing operations mainly include radiation correction and geometric correction, and radiation correction includes atmospheric correction, radiation calibration, terrain and solar altitude correction. After initially obtaining the image data, this embodiment uses ENVI (The Environment for Visualizing Images) software to preprocess the Landsat series images of different periods, and uses band operation and decision tree classification methods to extract the selected images, and a preliminary reservoir water map can be obtained.

[0052] (1) Radiation calibration

[0053] When the sensor receives electromagnetic radiation from the ground, it will be affected by factors such as the atmosphere, topography, and the height of the sun, which will cause errors between the sensor's received value and the actual radiation value. Radiation calibration is the process of converting the DN value obtained by the sensor into an absolute brightness value to eliminate the error of the sensor itself. The conversion formula between the remote sensing image ND value and the radiation brightness value L(λ) is as follows:

[0054] L(λ)=Gain*DN+Bias

[0055] The calibration coefficients Gian and Bias in the above formula can be obtained from the remote sensing image metafile. ENVI supports radiometric calibration of multiple sensors such as TM, OLI, MODIS, etc. This embodiment is based on ENVI5.3 software, uses the Radiometric Calibration tool to perform radiometric calibration on Landsat images, selects multispectral images, clicks Apply FLAASH Settings to automatically set relevant parameters, and then outputs the radiometric calibration results.

[0056] (2) Atmospheric correction

[0057] Due to the presence of atmospheric molecules, aerosols, and cloud particles, the sensor will be affected by these atmospheric components when acquiring information, which will cause interference. For example, in the atmosphere, factors such as light, water vapor, carbon dioxide, and oxygen will affect the spectral reflectance of objects, causing the acquired remote sensing images to contain non-target object information. In addition, due to the constant change in the relative positions of satellite sensors, target objects, and the sun, the path length of radiation passing through the atmosphere is different, which leads to different effects of the atmosphere on different pixels and grayscale values. Atmospheric correction is to eliminate the effects of atmospheric absorption and scattering on the radiation transmission of pixels and pixel grayscale values ​​through calculation. Its goal is to convert the reflectance of the top layer of the atmosphere into the reflectance of the surface. By using the FLAASH atmospheric correction module in ENVI, the differences caused by the effects of sensors, atmosphere, and light on the reflection of objects can be effectively eliminated, and more realistic and accurate model parameters such as the emissivity and reflectivity, and the surface temperature of objects can be obtained.

[0058] This example uses the FlaashAtmospheric Correction tool for correction, which requires manual input of parameters including the latitude and longitude of the image center, sensor type, sensor flight altitude, average altitude, imaging time, aerosol model, aerosol inversion method, etc. The basic parameter settings used for correction are obtained from the image header file, and the average elevation of the image area is calculated using ENVI's built-in global DEM data. The atmospheric model is selected according to the season and longitude and latitude information of the image area, and the initial visibility is set to 40km.

[0059] (3) Geometric correction

[0060] Since the latitude and longitude, elevation, speed, etc. of the satellite sensor are changing at any time during operation, and are also affected by a variety of factors such as topography, the geometric shape, size, orientation, etc. of the objects on the original image will deviate from the actual ground objects during the remote sensing imaging process, which is also called geometric distortion. The deformation errors of remote sensing images can be roughly divided into two categories: systematic distortion caused by the remote sensing satellite system sensor itself and random distortion caused by the topography of the earth itself. Geometric correction can be used to eliminate the geometric distortion between the image objects and the actual target objects to generate a planar orthophoto. Geometric correction generally requires the conversion of pixel coordinates and the resampling of pixel grayscale values ​​after coordinate conversion. The coordinate conversion relationship between the original distorted image and the corrected image is shown in the following formula:

[0061]

[0062] In the formula, x and y are the pixel coordinates in the distorted original image space; α and β are the conjugate points of x and y, which are the corresponding pixel coordinates in the corrected image space after correction. This study uses the automatic orthorectification tool (RPCOrthorectificationWorkflow) of ENVI software to perform geometric correction on remote sensing images.

[0063] (4) Image cropping

[0064] Since the scope of the entire original remote sensing image acquired is too large, the actual preprocessing process occupies a large amount of memory space and is time-consuming and slow, so the image can be cropped according to the scope of the study area, which is also convenient for the extraction of target objects. Image cropping mainly includes block cropping and standard map cropping. Among them, block cropping can be performed according to the research content, through row and column numbers, vector boundaries, coordinates or custom polygons. Standard frame image cropping must be performed according to the frame number under the national standard scale coordinate system. This study mainly focuses on the Danjiang Reservoir area in the Henan section of the Danjiangkou Reservoir, so this embodiment uses the vector boundary of Xichuan County for image cropping.

[0065] For other data sources:

[0066] (1) Reservoir water quality data

[0067] This embodiment selects 14 water pollutant indicators of 11 surface water environmental responsibility target sections in the Danjiang Reservoir area. The monitoring data is provided by relevant testing institutions in the water source area. The monitoring period is from January 2015 to September 2022. Water samples are collected at 0.5m underwater at the beginning of each month, and water quality monitoring and analysis are carried out in accordance with the "Surface Water Environmental Quality Standard" (GB 3838-2002). The main water quality monitoring indicators include pH, water temperature, water level, conductivity, transparency (SD), dissolved oxygen (DO), total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), permanganate index (CODMn), biochemical oxygen demand (BOD5), chlorophyll a (Chl-a) and algae density (Ad). The precipitation data comes from the monthly value data set of China's ground climate data. The water quality monitoring points include multiple reservoir points and 3 tributary sections. Among them, the tributary sections include Jingziguan, Shijiawan of Danjiang and Zhangying of Laoguanhe. The confluence of the tributaries is Caowan. The main monitoring points in the reservoir area include Taocha, Songgang, Tumen, Dangzikou and Liangjiagang at the head of the channel, as well as Heijizui at the entrance and Wulongquan at the exit.

[0068] (2) Water level data

[0069] Since the inundation range of the water area is different under different water levels of the Danjiangkou Reservoir, in order to accurately explore the changing relationship between different water levels of the Danjiangkou Reservoir and the water area, and to analyze the characteristics of land cover changes in the water area, this study collected real-time water level elevation monitoring data for all downloadable remote sensing images from the website of the Hubei Water Resources Department from 2010 to 2020.

[0070] (3) Land use data

[0071] The land use data in this study comes from the data platform of the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences. Four land use and cover change remote sensing monitoring data sets in Xichuan County, Henan Province in 2010, 2015, 2018 and 2020 are selected, with a data resolution of 30m×30m. According to the characteristics of different land use types, this example uses ArcGIS software to reclassify them into six common land use types, including construction land, cultivated land, forest land, grassland, water body, and unused land.

[0072] In the step "Dynamic study of the range of the drawdown zone in the reservoir area based on remote sensing data", the frequent fluctuations in water levels will cause the drawdown zone to be continuously submerged and exposed, and the submerged area shows a certain trend of change. Therefore, based on the study of the evolution characteristics of the water environment of the Danjiangkou Reservoir in the previous step, this embodiment is based on the long-term series of Landsat 5TM / 7ETM+ / 8OLI remote sensing images from 2010 to 2020, and the water body boundary of the Danjiangkou Reservoir is extracted year by year to determine the water area and the range of the drawdown zone. The inter-annual and intra-annual evolution characteristics of the drawdown zone of the Danjiangkou Reservoir in the past 11 years are analyzed, and the fitting correlation between the water area and the water level data monitored during the same period is analyzed. The inundation range of the drawdown zone of the Danjiangkou Reservoir is determined, and the frequency distribution of water inundation is calculated by overlay analysis and the water body is divided. On the basis of land use in Xichuan County, the changes in land cover types under different inundation levels are analyzed.

[0073] For the analysis of the ebb and flow zone based on remote sensing data:

[0074] 1. Water body extraction and delineation of the drawdown zone based on GIS:

[0075] The imaging mechanism of sunlight in water is as follows Figure 3 As shown in the figure, this figure describes the electromagnetic wave radiation transmission process, which mainly includes water surface reflection, scattering in water, bottom reflection, underwater refraction and absorption, etc. For water bodies, the reflectivity is very low in the whole band, there is a reflection peak in the blue-green band in the visible light band, and there is only a small reflectivity in the near-red and short-wave infrared bands, which is significantly different from the spectral characteristics of other ground objects. Therefore, the spectral characteristics of different bands can be used to extract remote sensing water bodies.

[0076] Common water indexes include the Normalized Difference Water Index (NDWI), the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), the Enhanced Water Index (EWI), the New Water Index (NWI), etc. Compared with the traditional NDVI method, the Normalized Difference Water Index (NDWI) is less sensitive to atmospheric influences and is less affected by the atmosphere. It uses near-infrared and green light bands to enhance water characteristics while eliminating the presence of soil and terrestrial vegetation characteristics. The calculation formula is:

[0077]

[0078] In the formula, R Greenis the green band reflectance, which corresponds to bands 2 and 3 in TM / ETM+ and OLI images, respectively. NIR is the near-infrared reflectivity, which corresponds to bands 4 and 5 in TM / ETM+ and OLI images, respectively.

[0079] However, in actual application, NDWI only improves phenomena such as atmospheric influence, but ignores the fact that the spectral reflectance of water bodies in the green light and near-infrared bands is almost the same as that of soil and buildings, which leads to the influence of buildings and soil when extracting the NDWI index. That is, in most cases, there are still other non-water body information affecting the accuracy. Therefore, some scholars have proposed an improved normalized difference water index method (MNDWI). Since the shadows of buildings and the spectral characteristics of water bodies in the near-infrared band are difficult to distinguish, there will be obvious differences when using the mid-infrared band instead. MNDWI is more suitable for extracting water bodies in towns, and can eliminate the shadows of buildings in water bodies to make water body extraction more accurate. This embodiment mainly uses Landsat sequence images for the dynamic study of the hydrology of Danjiangkou Reservoir. Considering its band setting, the improved normalized difference water index (MNDWI) is used to extract water bodies. The calculation formula is:

[0080]

[0081] In the formula, R Green is the reflectivity of green light band, R MIR is the mid-infrared reflectivity, corresponding to the 2nd and 5th bands in TM / ETM+ images and the 3rd and 6th bands in OLI images.

[0082] The calculated MNDWI image results are classified by decision tree and set thresholds to highlight water body information. In this embodiment, the threshold value range is 0 to 0.2. Pixels below this value are defined as water bodies, and pixels above this value are defined as non-water bodies. However, there are still a small number of other pixel information in the water body information extraction results. After converting them into vector format, ArcGIS is used to perform manual visual correction to obtain the final remote sensing water body extraction results of the target area, such as Figure 4 shown.

[0083] There are many types and numbers of water-fluctuating zones, with large differences in time and space, and complex structures and functions. Since the 1950s and 1960s, the definition of "water-fluctuating zone" has been continuously developed. Domestic and foreign researchers have carried out a lot of research on this special area, including the definition of the scope of the water-fluctuating zone, the division of its meaning, etc., from the initial "river bank zone" to the reservoir-type "fluctuating zone", and then the water-fluctuating zone is more carefully defined according to the different ecosystems, structural functions, etc. In general, the water-level-fluctuating zone refers to a special area where the land around the reservoir is submerged and exposed due to the water level fluctuation caused by the reservoir regulating water or seasonal rainfall. It is a transition zone between land and water, has special water and land ecosystem characteristics, and plays an important role in the ecosystem. Accordingly, in this embodiment, the water body index method is used to extract the water body submergence surface under different water levels based on GIS remote sensing data, and the boundaries of different water areas are superimposed and analyzed respectively. The area formed between different water levels is defined as the water-fluctuating zone. The extraction principle is mainly based on the water level height difference information.

[0084] 2. Changes in land use in the ebb and flow zone:

[0085] Affected by seasonal precipitation and engineering regulation, the water level of Danjiangkou Reservoir is constantly changing, and the water area is also changing accordingly. It is precisely because of this fluctuation in the water level that the Danjiang Reservoir area is frequently flooded. The designed dead water level of the Danjiangkou Reservoir before and after the dam was raised was 139m and 150m respectively. According to the real-time monitoring water level data of the reservoir, the lowest water level before the middle line project was put into operation in 2014 was only 135.11m (May 5, 2011). After the water was put into operation, the lowest water level was 150.42m on March 31, 2016, which was close to the designed dead water level of the reservoir. On October 10, 2022, the Danjiangkou Reservoir was filled to the normal water level of 170m. According to the scheduling and operation characteristics of the Danjiangkou Reservoir, this embodiment uses the digital elevation model (DEM) data with a resolution of 30m to extract the water level characteristic inundation lines of the Danjiangkou Reservoir at 150m, 160m and 170m respectively, and establishes vector data based on the extracted water level elevation inundation lines. Based on the land use data of Xichuan County in 2010, 2015, 2018 and 2020, the land use type division under different flooding elevations is obtained, and spatial analysis and geographic statistical methods are used to analyze the land change characteristics in the reservoir drawdown zone.

[0086] The vector boundary ranges extracted at 150m, 160m and 170m elevations are as follows: Figure 5As shown, the calculated drop areas between the elevation differences of 150-160m and 160-170m are 121.93km2 and 113.84km2 respectively. It can be seen from the figure that as the water level rises, the flooded area is mainly concentrated in the area with gentle slopes on the east bank of the reservoir and at both ends of the tributaries entering the reservoir. This embodiment counts the types of land use patterns between the two water level elevation differences of 150-160m and 160-170m. According to the land cover classification system of Xichuan County and the actual situation of the drawdown zone, the flooded area includes five land types: cultivated land, forest land, grassland, water area and construction land. According to statistics, the area between 150 and 160 m elevation difference is dominated by cultivated land, accounting for 70-80% of the total area of ​​the interval; followed by grassland and construction land, which account for 10-11% in total; and the area between 160 and 170 m elevation difference is similar to 150-160 m, with cultivated land as the main land cover type, accounting for about 72%, followed by grassland, construction land and forest land. It can be seen that cultivated land area is absolutely dominant in the land cover types of the drawdown zone, followed by grassland and construction land, and forest land is the least. As the water level rises to 170 m, the proportion of submerged grassland and construction land area gradually increases, while the proportion of cultivated land area gradually decreases, indicating that there are more urban construction land in the areas far from the reservoir on both sides of the reservoir. From the perspective of years, the cultivated land area in the 150-160m water level drawdown zone is constantly decreasing, and the water area is constantly increasing after the dam is raised. The forest land and grassland showed low values ​​in 2018, and the construction land showed high values ​​in 2018; the cultivated land in the 160-170m water level drawdown zone showed low values ​​in 2018, and the forest grassland and construction land showed high values ​​in 2018, and there was no obvious trend of change in other areas. In general, the cultivated land in the drawdown zone within 170m before the dam was raised and the water was released accounted for the largest proportion, supplemented by grassland and construction land. The grassland was mainly distributed in the coastal area of ​​the reservoir area, and the construction land was scattered in the eastern cultivated land and near the tributaries entering the reservoir, and was relatively concentrated in the Laoguan River; after the water was released from the middle line project in December 2014, the water area increased compared with 2010 and 2015 as the water storage increased. As the grassland area along the eastern coast was almost covered by the water body, the cultivated land and grassland areas decreased.

[0087] 3. Calculation of water flooding frequency:

[0088] The internal action mechanism among ecological elements in the water-level-fluctuation zone is more or less affected by the differences in the duration and frequency of flooding. To further subdivide the extracted water body state types, the water immersion frequency around the Danjiangkou Reservoir area was calculated in this step, and then the water bodies were classified into permanent water bodies and seasonal water bodies. The water immersion frequency refers to the ratio of the number of times a certain area is submerged in water to the total number of times within a time series. In this embodiment, the non-water body area of the extracted vector data was first supplemented, then the water body and non-water body areas were binarized, and finally, the multi-temporal water body binarized images were spatially superimposed to calculate the water immersion frequency of each pixel in the total time series.

[0089] Water Immersion Frequency calculation index:

[0090]

[0091] In the formula, N is the number of available water body image layers within the observation period, and w is a binary variable (w = 1 for water body, w = 0 for non-water body). The value range of WIF is 0 - 100%. According to the definition of WIF and the research methods of other scholars, WIF greater than 75% is defined as a permanent water body, 25% < WIF ≤ 75% is defined as a seasonal water body, and WIF less than 25% is defined as the maximum water body.

[0092] Regarding the distribution characteristics of soil nitrogen flux in the water-level-fluctuation zone:

[0093] 1. Construction of the sediment nitrogen flux model during the flooding-drying period

[0094] (1) Construction of the nitrogen flux model during the flooding period

[0095] There are various nitrogen input sources in the sediment during the flooding period, and its specific expression is as follows:

[0096] SN in = SN de + SN me + SN se + SN ad

[0097] In the formula, SN in is the total amount of nitrogen input per unit time; SN de is the input amount from the decomposition of plant and animal residues and microorganisms; SN me is the nitrogen amount produced by the survival metabolism of plants, animals, and microorganisms; SN se is the nitrogen amount deposited by zooplankton, algae, suspended matter, etc. in the water; SN ad is the nitrogen amount adsorbed by the soil in the water-level-fluctuation zone.

[0098] The output of nitrogen in the sediments of the drawdown zone during the flooding period mainly includes nitrogen consumption by solidification or mineralization, nitrogen intake by plants, animals and microorganisms in the soil, nitrogen loss during denitrification, and nitrogen release from the soil in the drawdown zone. The specific expression is as follows:

[0099] SN out =SN dec +SN upt +SN den +SN re

[0100] Where, SN out SN is the total amount of nitrogen output from the soil in the drawdown zone per unit time; dec Nitrogen consumed for fixation or mineralization; SN upt The amount of nitrogen ingested and decomposed by animals, plants and microorganisms; SN den SN is the amount of nitrogen lost during denitrification under anaerobic conditions; re It is the amount of nitrogen released by the soil in the drawdown zone to the overlying water body.

[0101] According to the input and output expressions of nitrogen in the sediments of the drawdown zone during flooding, the nitrogen exchange flux expression of the sediments of the drawdown zone during the flooding period was constructed using the budget balance method:

[0102] F SN淹水 =(SN in -SN out )×Δt×A

[0103] In the formula, F SN淹水 is the nitrogen exchange flux in the sediment of the drawdown zone during the flooding period; A is the flooded area of ​​the soil in the drawdown zone.

[0104] (2) Construction of nitrogen flux model during dry season

[0105] The nitrogen input in the drawdown zone during the dry season is slightly different from that during the flood season. The specific expression is as follows:

[0106] SN' in =SN' de +SN' me +SN' bo +SN' ex

[0107] Where, SN' in is the total amount of nitrogen input per unit time; SN' de The amount of nitrogen input for the decay and decomposition of plant, animal and microbial residues; SN' me The amount of nitrogen produced for the survival and metabolism of animals, plants and microorganisms; SN' bo The amount of nitrogen input by plant roots after they die; SN' exThe amount of nitrogen input through external pollution such as surface runoff.

[0108] The output of nitrogen in the drawdown zone during the dry season is mainly nitrogen consumed by solidification or mineralization, nitrogen lost by ammonia volatilization, nitrogen taken up by plant and animal microorganisms, and nitrogen lost during denitrification. The specific expression is as follows:

[0109] SN' out =SN' dec +SN' upt +SN' den +SN' vol

[0110] Where, SN' out SN' is the total amount of nitrogen output from the soil in the drawdown zone per unit time; dec Nitrogen consumed for fixation or mineralization; SN' upt The amount of nitrogen ingested and decomposed by animals, plants and microorganisms; SN' den is the amount of nitrogen lost during denitrification under anaerobic conditions; SN' vol is the amount of nitrogen lost due to ammonia volatilization.

[0111] According to the input and output expressions of nitrogen in the drawdown zone during the dry period, the nitrogen exchange flux expression of the drawdown zone sediment during the dry period was constructed using the budget balance method:

[0112] F SN落干 =(SN' in -SN' out )×Δt×A

[0113] In the formula, F SN落干 is the nitrogen exchange flux in the sediments of the drawdown zone during the dry-down period; the other symbols have the same meanings as before.

[0114] 2. Estimation of nitrogen flux in sediments during flooding period:

[0115] It is difficult to obtain the relevant data in the above-mentioned balance model. Therefore, based on the existing monitoring data of this paper, without considering the loss of nitrogen element during migration and transformation, the above model is simplified based on the material balance theory, and the following formula can be obtained:

[0116]

[0117] In the formula, F 淹水期 is the nitrogen flux in the sediment of the drawdown zone during the flooding period, t; α i is the average nitrogen content per unit area of ​​soil in the i-th land use type in the water-fluctuation zone, g / m 2 ; A i The land use type area of ​​the drawdown zone, km 2, the specific calculation results of each land use area within the flooded range during the monitoring period have been obtained from the previous calculation; n is the number of land use types used for calculation. According to this model, the soil nitrogen exchange flux in the drawdown zone during the flooding period is calculated. When the calculation result is positive, it means that the nitrogen content in the sediment increases during the flooding period, otherwise it decreases.

[0118] The average nitrogen content per unit area of ​​different land use types is calculated as follows:

[0119]

[0120] Where k is the number of monitoring points; λ is the nitrogen content per unit area in the drawdown zone, g / m 2 ; a and b represent the end and beginning of flooding in the drawdown zone, respectively.

[0121] The nitrogen content per unit area in the drawdown zone is calculated as follows:

[0122] λ=mρh×10 -3

[0123] Where m is the soil nitrogen content in the drawdown zone, mg / g; ρ is the soil bulk density, g / m 3 According to relevant research, its value is 1.40g / cm 3 ; h is the soil depth of the drawdown zone, m. In this embodiment, May to October is the flood season of the drawdown zone of the reservoir, and November to April is the dry season of the drawdown zone of the reservoir. The soil nitrogen content in the drawdown zone at the beginning and end of flooding is calculated using the data collected by the aforementioned Professor Han Yuping's team in July and September 2018, respectively. The data in September and November are used as the content calculations at the end of flooding and the beginning of drying up, respectively, and the flux calculations in the flooding-drying alternation period are used. The depth of the surface soil at the time of sampling, 0.2m, is used as the soil depth for calculation. The nitrogen content of each form per unit area in the soil at different sampling points in the drawdown zone at each flooding period is calculated according to the above-mentioned formula of λ.

[0124] 3. Estimation of nitrogen flux in sediments during the dry period:

[0125] Similar to the flux estimation method during the flooding period, the above model was simplified based on the material balance theory. The simplified formula is as follows, where the calculation method for the average nitrogen content per unit area of ​​soil is the same as the formula for estimating nitrogen flux in sediments during the flooding period.

[0126]

[0127] In the formula, F 落干期 is the nitrogen flux of sediments in the drawdown zone during the drawdown period, t; α i is the average nitrogen content per unit area of ​​soil in the i-th land use type in the water-fluctuation zone, g / m 2 ; A iThe land use type area of ​​the drawdown zone, km 2 , the value is the same as before; n is the number of land use types used for calculation.

[0128] The soil sample data collected from the above sampling points in November 2018, January and May 2019 were used as the soil nitrogen content data of the water-fluctuation zone at the early, middle and late stages of dry-down. The soil depth and soil bulk density were taken as the same as the flooding period. When the calculation result is positive, it means that the nitrogen content in the sediment of the water-fluctuation zone during the dry-down period increases, and vice versa. The nitrogen content of each form per unit area of ​​soil at different sampling points in the water-fluctuation zone at each stage of dry-down is calculated, and the calculation results are as follows: Figure 6 shown.

[0129] 4. Nitrogen flux distribution characteristics at the sediment-water interface:

[0130] The first is to construct a nitrogen flux model at the sediment-water interface: water eutrophication is the focus and difficulty of environmental issues. While strictly preventing exogenous input to the reservoir, it is also necessary to study the endogenous release of the reservoir water body. When there is less exogenous input, sediments often play the role of nitrogen and phosphorus source and sink. Under certain conditions, nutrients such as nitrogen and phosphorus in sediments or bottom mud are released into the water body through diffusion, convection, resuspension, etc., causing the water quality of the overlying water to deteriorate. Exploring the adsorption or release process of nutrients at the sediment-water interface has important theoretical and practical significance for the comprehensive management of the reservoir water environment. In this study, combined with monitoring data, the interstitial water concentration diffusion model method was used to estimate the exchange flux of nitrogen at the sediment-water interface in the Danjiang Reservoir. The interstitial water concentration diffusion model method uses the difference in nutrient concentration between the interstitial water on the surface of the sediment and the overlying water body to estimate the exchange rate and flux between the interfaces using Fick's first law. Fick's first law states that the flow rate of the diffusing substance per unit cross-sectional area perpendicular to the diffusion direction per unit time is proportional to the concentration gradient at the cross-section. A positive value of the calculation result indicates a desorption process, and a negative value indicates an adsorption process. The diffusion flux calculation formula is as follows:

[0131]

[0132] Where F is the diffusion flux at the sediment-water interface, mg / (m 2 d); is the porosity of surface sediments, %; dC / dx is the concentration gradient between overlying water and sediments, mg / (L·cm), which is determined by the concentration difference between interstitial water and bottom water; D s is the diffusion coefficient in the sediment (10 -6 cm 2 / s). s According to the porosity of the sediment Calculate (when hour, when hour, ), where D0 is the ideal diffusion coefficient of the solute in an infinitely dilute solution (10 -6 cm 2 / s), which is a function of the bottom water temperature. When the water temperature is 18℃ and 25℃, the D0 value of the nitrate nitrogen diffusion flux is 16.1×10 -6 cm 2 / s and 19.0×10 -6 cm 2 / s, and the D0 value of the nitrite nitrogen diffusion flux is 15.3×10 -6 cm 2 / s and 19.1×10 -6 cm 2 / s, and the D0 value of ammonia nitrogen diffusion flux is 16.8×10 -6 cm 2 / s and 19.8×10 -6 cm 2 / s (Taking into account the randomness of the actually measured water temperature, this embodiment uses the normal temperature of 25°C for calculation).

[0133] Porosity of sediments Measured by moisture content, the calculation formula is as follows:

[0134]

[0135] Where, d s is the average density of surface sediments, generally 2.5 g / cm 3 ;d w is the average density of water, d w Value 1g / cm 3 ; W is the moisture content of sediment, %.

[0136] Furthermore, the emission characteristics of non-point source pollution in the watershed surrounding the reservoir are calculated.

[0137] High population density and nutrient replenishment lead to the accumulation of nutrients such as nitrogen and phosphorus, which will cause lakes to be easily eutrophic. According to the ways in which pollutants such as nitrogen and phosphorus enter the water body, pollution sources can be divided into endogenous and exogenous types. Endogenous pollution refers to the enrichment of nutrients such as nitrogen and phosphorus in water sediments through adsorption, precipitation, etc., and release into the water body under different conditions, while exogenous pollution refers to external pollutants input through atmospheric precipitation, surface runoff, other point or non-point sources. According to previous studies, the overall water quality of the reservoir area is good, but the total nitrogen in the water body has exceeded the Class IV water standard, and the total phosphorus is also close to Class II water, but the total nitrogen concentration of the tributaries entering the reservoir is close to the Class IV water standard. Pollutants such as nitrogen and phosphorus may enter the water body through endogenous or exogenous sources. Therefore, reducing agricultural non-point source pollution in the reservoir area is of great significance to ensuring the water quality safety of the water source area. By deeply understanding the nitrogen and phosphorus pollution load around the reservoir area, the main driving factors and contribution of the pollution source can be clarified, so as to carry out targeted governance and protection. This section investigates and analyzes the current status of non-point source pollution in small watersheds, and estimates the external input pollution load around the reservoir area using the emission coefficient model and the equal standard pollution load method, in order to provide a theoretical basis and technical guidance for the control of agricultural non-point source pollution and the prevention and control of water eutrophication around the reservoir area.

[0138] 1. Pollution emission estimation model:

[0139] The more mature methods for estimating non-point source pollution loads currently include the output coefficient method, runoff segmentation method, discharge coefficient method, and model method. The discharge coefficient method is relatively simple, has low parameter requirements, and is highly applicable. Based on the review of relevant yearbooks, annual reports, literature, and related update reports, this study used the discharge coefficient method to calculate the agricultural non-point source loads in Xichuan County and Xixia County in Henan Province, and calculated the nitrogen and phosphorus loads in various pollution sources such as planting (fertilizer and crop straw), livestock and poultry breeding, and rural life. The specific calculation formula is as follows:

[0140] L i =∑A ij α ij β ij ·λ ij

[0141] Where, L i is the total load of the i-th pollution source; A ij is the total number of pollutants of the jth type in the i-th pollution source; α ij is the output coefficient of the jth pollutant in the i-th pollution source; β ij is the average content of the jth pollutant in the i-th pollution source; ij is the coefficient of the jth pollutant entering the river from the ith pollution source. The specific calculation formulas for each pollution source are shown in Table 1.

[0142] Table 1 Calculation of pollution source input load

[0143]

[0144] This step is mainly analyzed from four aspects: crop straw, rural life, livestock and poultry breeding, and farm fertilizer. The above four are respectively calculation formulas. Wherein, the contribution of TN and TP is calculated respectively with the pure amount of nitrogen fertilizer and phosphate fertilizer consulted in the statistical yearbook. The utilization rate of general nitrogen fertilizer is 30%-35%, and the utilization rate of phosphate fertilizer is 10%-20%. Therefore, the mean is taken in the calculation of this embodiment to calculate. According to the monitoring results of the Hubei Provincial Agricultural Environment Monitoring Station, the river entry coefficients of nitrogen and phosphorus are taken as 0.1007 and 0.0599 respectively. Since Nanyang City is adjacent to Hubei Province and agricultural farming methods are roughly similar, this embodiment adopts these data to calculate.

[0145] Calculation of crop straw pollutant load requires parameters such as crop yield, crop straw output coefficient, crop straw utilization rate and nutrient content in straw. Crop yield can be obtained by consulting the statistical yearbook, the conversion ratio of crop grains to straw can be referred to the "China Organic Fertilizer Resources", and the nutrient content in crop straw can be referred to the "China Organic Fertilizer Nutrients". Combined with the actual crop planting situation in Henan Province, the various coefficients are shown in Table 2. According to the Henan Provincial Department of Agriculture and Rural Affairs, the comprehensive utilization rate of straw in Henan Province reached 90% in 2020 and 89% in 2019. The straw inflow coefficient is 0.01 based on the Hanfeng Lake Basin in the Three Gorges Reservoir Area.

[0146] Table 2 Conversion ratio and nutrient content of crop straw

[0147]

[0148] The nitrogen, phosphorus and organic matter contained in the feces and urine produced by livestock and poultry breeding cause pollution to agricultural non-point sources. The total amount of breeding is calculated using the number of livestock and poultry on hand in a year. Referring to the "Pollutant Emission Standards for Livestock and Poultry Breeding Industry" (GB 18596-2001) and the "National Survey on Pollution in Large-scale Livestock and Poultry Breeding and Prevention Measures" issued by the Ministry of Environmental Protection of China, the livestock and poultry feces excretion coefficient (Table 3) and the average content of pollutants in feces and the river entry coefficient (Table 4) are finally determined.

[0149] Table 3 Excretion coefficient of livestock and poultry feces

[0150]

[0151] Due to unreasonable discharge and treatment, rural domestic sewage is prone to cause agricultural non-point source pollution. According to the "Handbook of Pollution Emission Coefficients of Domestic Pollution Sources", the country is divided into different regions according to geographical climate environment, economic level and drainage characteristics. It is found that the sewage discharge coefficient of Nanyang City, Henan Province is 23.92L / (person / day), among which the average mass concentrations of domestic source water pollutants TN and TP are 73.8mg / L and 5.76mg / L respectively. Due to the lack of treatment facilities for rural domestic sewage, most of it is discharged into the water environment through ditches or surface runoff, and the river entry coefficient is 0.30.

[0152] Table 4 Average pollutant content and river entry coefficient in livestock and poultry manure

[0153]

[0154] In the pollution load evaluation, the equivalent pollution load method refers to the amount of medium required to dilute the pollutant emissions to the corresponding emission standards, which is used to evaluate the relative harm of each pollution source and pollutant. The calculation formula is as follows:

[0155]

[0156] Where P i is the equivalent pollution load of pollutant i, m 3 / a;c oi is the water quality control category standard value of pollutant i based on water environment functional zoning; Q i is the contribution of pollutant i, t / a; K ij is the equivalent pollution load ratio of pollutant i. According to the objectives of the Plan, the water quality management of the Danjiangkou Reservoir adopts Class II water quality indicators, so TN and TP are calculated as 0.5 mg / L and 0.025 mg / L respectively.

[0157] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0158] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for predicting nitrogen release flux in a water drawdown zone, characterized in that: include: Obtaining basic information; the basic information includes water quality data, water level data, precipitation data, remote sensing image data and socio-economic data of the target water area; Based on the basic data, various statistical analysis methods are used to analyze the trend and correlation of water quality indicators to determine the temporal and spatial distribution characteristics and influencing factors of water quality; Preprocessing the remote sensing image data, and performing image analysis based on the preprocessed data and the spatiotemporal distribution characteristics and influencing factors of the water quality to determine the flooding range of the drawdown zone of the target water area; A budget balance model is constructed, and the nitrogen release flux in the drawdown zone of the water area within the inundation range of the drawdown zone is predicted according to the budget balance model and Fick's law to obtain a prediction result; the prediction result includes the nitrogen accumulation and release in the sediment and the diffusion flux between the sediment and water interface; the budget balance model includes the exchange flux of nitrogen in the drawdown zone sediment during the flooding period and the exchange flux of nitrogen in the drawdown zone sediment during the drying period.

2. The method for predicting nitrogen release flux in the water ebb and flow zone according to claim 1 is characterized in that: After predicting the nitrogen release flux in the water drawdown zone, it also includes: estimating the external pollution input load using the pollution coefficient method.

3. The method for predicting nitrogen release flux in the water ebb and flow zone according to claim 1, characterized in that: The preprocessing method includes: radiation correction and geometric correction; the radiation correction includes atmospheric correction, radiation calibration, and terrain and solar altitude angle correction.

4. The method for predicting nitrogen release flux in the water ebb and flow zone according to claim 1, characterized in that: The image analysis method adopts the MNDWI algorithm.

5. The method for predicting nitrogen release flux in the water ebb and flow zone according to claim 1, characterized in that: The calculation formula for the diffusion flux between the sediment and water interface is: Where, F is the diffusion flux at the sediment-water interface; is the porosity of surface sediments; dC / dx is the concentration gradient between overlying water and sediments, which is determined by the concentration difference between interstitial water and bottom water; D s is the diffusion coefficient in the sediment.

6. A nitrogen release flux prediction system for a water area drawdown zone, characterized in that: include: A data collection unit, used to obtain basic information; the basic information includes water quality data, water level data, precipitation data, remote sensing image data and socio-economic data of the target water area; A water quality characteristic analysis unit, for performing trend and correlation analysis of water quality indicators based on the basic data using a variety of statistical analysis methods, and determining the temporal and spatial distribution characteristics and influencing factors of water quality; An image analysis unit is used to pre-process the remote sensing image data, and perform image analysis based on the pre-processed data and the spatiotemporal distribution characteristics and influencing factors of the water quality to determine the flooding range of the drawdown zone of the target water area; A prediction unit is used to construct a budget balance model, and predict the nitrogen release flux of the drawdown zone in the water area within the flooded range of the drawdown zone according to the budget balance model and Fick's law to obtain a prediction result; the prediction result includes the nitrogen accumulation release in the sediment and the diffusion flux between the sediment and the water interface; the budget balance model includes the exchange flux of nitrogen in the drawdown zone sediment during the flooding period and the exchange flux of nitrogen in the drawdown zone sediment during the drying period.

7. An electronic device, characterized in that: It comprises a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the method for predicting nitrogen release flux in the water drawdown zone according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the method for predicting nitrogen release flux in the water drawdown zone as described in any one of claims 1 to 5.

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