A carbon-nitrogen water coupling simulation method based on a hydrological response unit

Abstract

The present application relates to a kind of carbon-nitrogen water coupling simulation methods based on hydrological response unit, comprising: data collection and processing;SWAT model is built;Hydrological parameter calibration;Preparation of input file;Run DNDC model;Model effect evaluation;Carbon-nitrogen water flux simulation.The present application will be HRU as the minimum simulation unit of DNDC model, compared to be divided into grid simulation, save computing resource, optimize visual effect.Through the construction of SWAT model in study area, the calibration of hydrological parameter, to obtain more accurate hydrological parameter, the hydrological parameter after calibration is used as the input of DNDC model, improves the simulation accuracy of DNDC model to hydrological variable.In general, the present application will DNDC model be HRU as the minimum simulation unit applied to watershed scale, improves the simulation accuracy of model to hydrological process, saves computing resource, improves calculation speed.

Description

Technical Field

[0001] This invention relates to a carbon-nitrogen-water coupled simulation method based on hydrological response units, which involves the intersection of eco-hydrology, hydrological models and biogeochemical process models, and is particularly suitable for the comprehensive analysis of climate change, grazing and soil carbon-nitrogen-water cycle in grassland watershed ecosystems. Background Technology

[0002] Against the backdrop of global climate change, agricultural management needs to strike a balance between environmental protection and economic benefits, especially in grazing systems where the dual considerations of carbon sequestration and carbon emissions are increasingly important. Agricultural ecosystem models, such as the DNDC (Denitrification-Decomposition) model, APSIM (Agricultural Production Systems sIMulator), DayCent (Daily Century Model), and Roth C (Rothamsted Carbon Model), are primarily used to simulate the impact of agricultural management on soil carbon and nitrogen cycles. These models are mostly point-scale driven and struggle to simulate regional-scale hydrological processes, such as surface runoff and nutrient loss. Many traditional models, due to limitations in their code structure, cannot effectively couple hydrological processes, making it difficult to simulate streams, nutrient migration, and infiltration. Therefore, integrating agricultural ecological models with watershed hydrological models is a problem that needs to be addressed in large-scale studies. Summary of the Invention

[0003] To overcome the problems of existing technologies, this invention proposes a carbon-nitrogen-water coupled simulation method based on hydrological response units. The method aims to improve the simulation accuracy of watershed carbon-nitrogen-water cycles by coupling a hydrological model and a biogeochemical process model. This is mainly reflected in the improved simulation capability of the DNDC model for watershed hydrological processes, while also enabling easy acquisition of hydrological parameter values ​​and saving computational load and resources.

[0004] The objective of this invention is achieved as follows: a carbon-nitrogen-water coupled simulation method based on hydrological response units, the steps of which are as follows:

[0005] Step 1, Data Collection and Processing: Acquire meteorological and hydrological data, soil data, land use data, elevation data, and management data for the study area. Preprocess the collected data, interpolate missing values ​​in the meteorological and hydrological data, and extract and process the remote sensing data to the same resolution.

[0006] Step 2, Construct SWAT Model: Construct a SWAT model for the study area, and reclassify land use and soil data according to the actual situation of the study area; determine the number of hydrological response units to be generated based on the land use and soil classification; during the HRU definition process, HRUs are not merged to ensure that each HRU has the same land use type, soil type and slope.

[0007] Step 3, Hydrological parameter calibration: Calibrate model parameters based on existing long series of hydrological variables. Common hydrological variables include runoff, soil water content, or evaporation. Select the Nash efficiency coefficient (NSE) and the coefficient of determination (R²). 2 Used as an objective function to evaluate the model's simulation accuracy;

[0008] Step 4, Prepare input files: Obtain hydrological response unit information from the constructed SWAT model, use the HRU generated in Step 2 as the smallest simulation unit of the DNDC model to prepare the DNDC model input file, input localized model parameters, and import the hydrological parameter values ​​calibrated in Step 3 to form multiple DNDC model input files;

[0009] Step 5, run the DNDC model: run multiple DNDC model input files generated in step 4 in batches in the DNDC model to simulate the carbon, nitrogen and water cycle in the study area, perform sensitivity analysis on the model parameters, and parameterize the model based on the measured values.

[0010] Step 6, Model Performance Evaluation: Extract the DNDC carbon, nitrogen, and water cycle flux simulation results, and evaluate the model's effectiveness and performance based on multiple index values. Index selection includes the coefficient of determination R0. 2 The three metrics, Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (nRMSE), are used to evaluate the simulation accuracy and performance of the DNDC model.

[0011] Step 7, Carbon, Nitrogen and Water Flux Simulation: Simulate carbon, nitrogen and water fluxes using an HRU-based DNDC model, including fluxes of soil water, net primary productivity (NPP), total nitrogen (TN), and soil organic carbon (SOC).

[0012] The advantages and beneficial effects of this invention are as follows: This invention utilizes a method of constructing a watershed SWAT model, obtaining hydrological parameter values ​​at the HRU scale, and using the HRU as input to the DNDC model. This couples the SWAT model and the DNDC model, improving the simulation accuracy of the DNDC model for watershed hydrological processes, thereby more accurately simulating carbon, nitrogen, and water cycle fluxes. This invention uses the HRU as the smallest simulation unit of the DNDC model, saving computational resources and optimizing visualization compared to grid-based simulation. By constructing a SWAT model of the study area and calibrating the hydrological parameters, more accurate hydrological parameters are obtained. Using the calibrated hydrological parameters as input to the DNDC model improves the simulation accuracy of the DNDC model for hydrological variables. In summary, this invention applies the DNDC model with the HRU as the smallest simulation unit to the watershed scale, improving the model's simulation accuracy for hydrological processes, saving computational resources, and increasing computational speed. Attached Figure Description

[0013] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0014] Figure 1 This is a flowchart of the method described in the embodiments of the present invention;

[0015] Figure 2 This is a verification diagram of the carbon-nitrogen-water coupling simulation in the Xilin River Basin, the research area of ​​the application example described in the embodiments of the present invention;

[0016] Figure 3 This is a schematic diagram showing the changes and distribution of net primary productivity (NPP) in the Xilin River Basin, the research area of ​​the application example described in this invention. Detailed Implementation

[0017] Example:

[0018] DNDC is a biogeochemical model that simulates carbon and nitrogen dynamics on a diurnal scale. Its soil hydrothermal module assesses the diurnal variations of soil temperature, oxygen, and moisture, while the plant growth sub-model considers vegetation uptake of water and nutrients. The original version of DNDC used a one-dimensional soil water flow model to calculate soil profile moisture dynamics. The improved version introduces a hydrological module, employing SCS curves and the Musle equation to simultaneously simulate water flow processes and soil nitrogen leaching. The required hydrological parameters are generally derived from experiments or estimates. The DNDC model is driven by both point-based and regional simulations; regional simulations can be viewed as simulations of uniformly distributed points. The SWAT (Soil and Water Assessment Tool) model is widely used in hydrology, soil science, and agricultural management. Based on different soil types, land use, and slope, the model divides the study area into multiple hydrological response units (HRUs), which serve as the smallest simulation unit.

[0019] The limitation of applying the DNDC model to watershed-scale carbon, nitrogen, and water cycle simulations lies in the difficulty of obtaining accurate hydrological parameter values ​​through experiments or estimations. Given that the model is driven by point or regional simulations, improving simulation accuracy inevitably requires dividing the data into more grids, i.e., simulating a large number of points. However, the large study area and spatial heterogeneity of soil, land use, and slope bring significant difficulties and uncertainties to obtaining hydrological parameter values. The limitation of using the SWAT model for carbon, nitrogen, and water cycle simulations is that the SWAT model itself provides a simplified description of soil carbon and nitrogen cycling. A key factor in soil carbon and nitrogen cycling is the activity and role of microorganisms; however, the SWAT model does not meticulously simulate the impact of microorganisms on processes such as soil organic matter decomposition and nitrogen transformation.

[0020] To simulate the carbon, nitrogen, and water cycle in a watershed more accurately and efficiently, this embodiment proposes a carbon, nitrogen, and water coupled simulation method based on hydrological response units (HRUs). This method involves constructing a watershed SWAT model, obtaining hydrological parameter values ​​at the HRU scale, and using the HRU as input to a DNDC model. This couples the SWAT and DNDC models, improving the DNDC model's accuracy in simulating watershed hydrological processes and thus more accurately predicting carbon, nitrogen, and water cycle fluxes. The following detailed description, using an application example, illustrates this embodiment. The method includes the following steps:

[0021] Step 1, Data Collection and Processing: Acquire meteorological and hydrological data, soil data, land use type data, elevation data, and management measure data of the study area. Preprocess the collected data, interpolate missing values ​​in the meteorological and hydrological data, and extract the remote sensing data to the same resolution.

[0022] Application examples:

[0023] This application example uses the Xilin River Basin in Inner Mongolia as the study area. It collects daily meteorological and hydrological data for the Xilin River Basin from 1960 to 2019, including runoff data and temperature data (precipitation, air temperature, wind speed, radiation, humidity, etc.). Missing values ​​are interpolated and formatted into the standard meteorological data input format for SWAT and DNDC models. Soil type data, land use type data, and elevation data are collected and extracted to the same resolution. Grazing and mowing parameters for the Xilin River Basin from 1980 to 2019 are also collected, including time and intensity, as well as relevant livestock attribute parameters.

[0024] Step 2, Construct the SWAT Model: Construct a SWAT model for the study area, reclassifying land use and soil data according to the actual conditions of the study area. Determine the number of hydrological response units to be generated based on the land use and soil classifications. During the HRU definition process, no HRU merging is performed, ensuring that each HRU has the same land use type, soil type, and slope.

[0025] Based on the collected basic data, a SWAT model for the Xilin River Basin was constructed. Land use and soil data were reclassified according to the actual conditions of the Xilin River Basin: land use data was divided into nine categories: water bodies, wetlands, urban land, wasteland, agricultural land, shrubland, low-cover grassland, medium-cover grassland, and high-cover grassland. Soil data was divided into five categories: calcareous black soil, leached chestnut soil, calcareous red sandy soil, gleyed black soil, and calcareous chestnut soil. Based on these classifications, a total of 466 hydrological response units were generated, of which 262 were grassland. During the hydrological response unit definition process, HRU merging was not performed to ensure that the final hydrological response units all shared the same land use type, soil type, and slope.

[0026] Step 3, Hydrological Parameter Calibration: Based on the existing long series of hydrological variables, calibrate the model parameters. The calibration of hydrological parameter values ​​can be achieved automatically using SWATCUP or other software, or manually. Common hydrological variables include runoff, soil water content, or evaporation. Select the Nash efficiency coefficient (NSE) and the coefficient of determination (R²). 2 It is used as an objective function to evaluate the accuracy of model simulation.

[0027] The Nash efficiency coefficient (NSE) is calculated as follows:

[0028]

[0029] Coefficient of determination R 2 The calculation formula is as follows:

[0030]

[0031] Among them, y i These are observed values; These are simulated values; It is the average of the observed values; is the average of the simulated values; n is the number of samples (1, 2, ..., i, ..., n).

[0032] The daily runoff data from the Xilinhot hydrological station, the main outlet of the Xilin River basin, were used to calibrate the SWAT model. The SWATCUP software was used to automatically calibrate the model parameters at the main outlet of the basin. During the calibration process, the steps of changing the model parameter ranges and running the program were repeated until the evaluation indicators met the requirements.

[0033] After calibration, in the Xilin River Basin of the study area, when the Nash efficiency coefficient (NSE) > 0.5, the coefficient of determination (R0.5) is... 2 When the value is greater than 0.6, the SWAT model is considered to have a good simulation effect on the Xilin River Basin, and the optimal values ​​of each model parameter are recorded at this time.

[0034] Step 4, Prepare input files: Obtain hydrological response unit information from the constructed SWAT model, use the HRU generated in Step 2 as the smallest simulation unit of the DNDC model to prepare the DNDC model input file, input localized model parameters, and import the hydrological parameter values ​​calibrated in Step 3 to form multiple DNDC model input files.

[0035] In the Xilin River Basin study area, the focus is on simulating the fluxes of carbon, nitrogen, and water cycles in grassland areas. Therefore, only the 262 hydrological response units with grassland land use type generated in step 2 need to be extracted for preparing the DNDC input file. Based on the DNDC model's point simulation input file template, soil parameters, meteorological parameters, input parameters, vegetation parameters, and management measure parameters are set sequentially. The hydrology module is then enabled, and the hydrological parameter values ​​for each hydrological response unit are imported accordingly. Specifically, CN2, OV_N, CH_N2, HRU_SLP, and SLSUBBSN correspond to SCS_curve_number, land_surface_roughness, channel_surface_roughness, channel_slope, and channel_length, respectively.

[0036] Step 5, Run the DNDC model: Run multiple DNDC model input files generated in Step 4 in batches to simulate the carbon, nitrogen and water cycle in the study area. At the same time, perform sensitivity analysis on the model parameters and parameterize the model based on the measured values.

[0037] In the Xilin River Basin of the study area, the DNDC model simulated the period from 1960 to 2019. The 262 DNDC input files prepared in step 4 were run in batches to simulate the carbon, nitrogen, and water cycle in the Xilin River Basin. During the simulation, parameter sensitivity analysis and parameterization were performed on the model. This is because the DNDC model lacks dedicated tools for parameter tuning; therefore, model parameterization relies on sensitivity analysis to adjust model parameters. Specifically, this involves adjusting model parameters based on the differences between simulated and measured values.

[0038] Step 6, Model Performance Evaluation: Extract the DNDC carbon, nitrogen, and water cycle flux simulation results, and evaluate the model's effectiveness and performance based on multiple index values. Index selection includes the coefficient of determination R0. 2 The three metrics, Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (nRMSE), are used to evaluate the simulation accuracy and performance of the DNDC model.

[0039] Coefficient of determination R 2 The calculation formula is as follows:

[0040]

[0041] The root mean square error (RMSE) is calculated as follows:

[0042]

[0043] The formula for calculating the normalized root mean square error nRMSE is as follows:

[0044]

[0045] Among them, y i These are observed values; These are simulated values; It is the average of the observed values; is the average of the simulated values; n is the number of samples (1, 2, ..., i, ..., n).

[0046] In the Xilin River Basin of the study area, soil water content (SW) from 2000 to 2019 and net primary productivity (NPP) from 2001 to 2019 were extracted from the DNDC output to validate the model accuracy (the selected time period was limited by the observation data duration). The soil water dataset used for validation was SMCI1.0, and the net primary productivity dataset used was MOD17A3HGF Version 6.1 (https: / / lpdaac.usgs.gov / products / mod17a3hgfv061 / ). Net primary productivity was validated both spatially and temporally. Due to the conversion between soil water content and soil moisture involving soil texture, only soil water was validated temporally. The three indicators combined show that the DNDC model simulates net primary productivity and soil water well. Spatially, for the NPP simulation validation, the coefficient of determination R0 was [value missing]. 2 The RMS value reached 0.89, the root mean square error (RMSE) was 310, and the normalized root mean square error (nRMSE) was 0.17. In terms of time, the RMS value of NPP was... 2 Between 0.4 and 0.8, the R of soil water 2 Between 0.5 and 0.7, see Figure 2 . Figure 2 middle:

[0047] a: The selection of verification points in space ensures their uniform distribution and avoids water areas.

[0048] b: Spatial validation results of net primary productivity (NPP) in 2019.

[0049] c: Validation results of net primary productivity (NPP) at the HRU scale from 2001 to 2019.

[0050] d: Validation results of soil water SW at the hydrological response unit (HRU) scale from 2000 to 2019.

[0051] Step 7, Carbon, Nitrogen and Water Flux Simulation: Simulate carbon, nitrogen and water fluxes using an HRU-based DNDC model, including but not limited to fluxes such as soil water (SW), net primary productivity (NPP), total nitrogen (TN), and soil organic carbon (SOC).

[0052] In the Xilin River Basin, the study area, the trends and characteristics of net primary productivity from 1985 to 2019 were simulated and analyzed using the DNDC model (1960-1980 was the model warm-up period). See [link to DNDC model simulation]. Figure 3 The northeastern and southeastern parts of the Xilin River Basin have higher net primary productivity, while the central part has lower net primary productivity. This is consistent with the actual situation in the Xilin River Basin, where the northeastern and southeastern parts are typical grassland areas with dominant species such as Leymus chinensis and Stipa grandis, while the central part is characterized by desert steppe with dominant species such as Caragana microphylla. Chronologically, the net primary productivity of the Xilin River Basin reached its peak around 1990, decreased significantly after 2000, and gradually increased again after 2010.

[0053] Finally, it should be noted that the above is only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention (such as data acquisition methods, processing methods of different software, application of various formulas, and the order of steps) without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A carbon-nitrogen-water coupled simulation method based on hydrological response units, characterized in that, The steps of the method are as follows: Step 1, Data Collection and Processing: Acquire meteorological and hydrological data, soil data, land use type data, elevation data, and management measure data of the study area. Preprocess the collected data, interpolate missing values ​​of meteorological and hydrological data, and extract remote sensing data to the same resolution. Step 2, Construct SWAT Model: Construct a SWAT model for the study area, and reclassify land use and soil data according to the actual situation of the study area; determine the number of hydrological response units to be generated based on the land use and soil classification; during the HRU definition process, HRUs are not merged to ensure that each HRU has the same land use type, soil type and slope. Step 3, Hydrological parameter calibration: Calibrate model parameters based on existing long series of hydrological variables. Common hydrological variables include runoff, soil water content, or evaporation. Select the Nash efficiency coefficient. NSE and coefficient of determination R 2 Used as an objective function to evaluate the model's simulation accuracy; Step 4, Prepare input files: Obtain hydrological response unit information from the constructed SWAT model, use the HRU generated in Step 2 as the smallest simulation unit of the DNDC model to prepare the DNDC model input file, input localized model parameters, and import the hydrological parameter values ​​calibrated in Step 3 to form multiple DNDC model input files; Step 5, run the DNDC model: run multiple DNDC model input files generated in step 4 in batches in the DNDC model to simulate the carbon, nitrogen and water cycle in the study area, perform sensitivity analysis on the model parameters, and parameterize the model based on the measured values. Step 6, Model Performance Evaluation: Extract the DNDC carbon, nitrogen, and water cycle flux simulation results, and evaluate the model's effectiveness and performance based on multiple index values. The index selection includes the coefficient of determination. R 2 Root mean square error RMSE Normalized root mean square error nRMSE These three metrics are used to evaluate the simulation accuracy and performance of the DNDC model; Step 7, Carbon, Nitrogen and Water Flux Simulation: Simulate carbon, nitrogen and water fluxes using an HRU-based DNDC model, including: soil water, net primary productivity (NPP), total nitrogen (TN), and soil organic carbon (SOC) fluxes.

Images