A method and system for improving water resource benefits of arid region black carbon soil optimization

Abstract

The application discloses a kind of arid region black carbon soil optimization water resource benefit promotion method and system, belong to water and soil improvement field, the method includes: the soil characteristics of target arid region is evaluated, and arid region soil database is constructed;According to the information of the arid region soil database, an optimization target model is constructed;According to biological black carbon application model, the biological black carbon application amount of target area is obtained, the soil improvement of target arid region is carried out using the biological black carbon application amount, the data related to soil and crop growth of target arid region after improvement is completed are collected, and the collected data is constructed as monitoring database;The data in monitoring database is compared with the prediction result of optimization model, and the accuracy and actual effect of the biological black carbon application model are verified.The application can effectively guide the application amount of biological black carbon by introducing biological black carbon to influence evaporation loss model and establishing optimization target model.

Description

Technical Field

[0001] This invention relates to the field of soil and water conservation and water resource optimization in arid regions, and particularly to a method and system for improving water resource efficiency in black coal soil optimization in arid regions. Background Technology

[0002] Arid regions suffer from scarce rainfall and intense evaporation, resulting in poor soil moisture retention and a short effective retention time of water in the soil, severely impacting agricultural production and ecological restoration. Soil moisture loss in arid areas manifests in two main ways: firstly, rapid water loss from the surface soil under strong evaporation conditions; and secondly, difficulty in retaining water within the effective root distribution layer during infiltration, leading to deep seepage losses. Especially under prolonged strong evaporation, evaporite or evaporite crusts characterized by salt and mineral deposits easily form in arid soils. These crusts exhibit significant differences in spatial distribution, thickness, and composition, significantly hindering soil water infiltration and crop root growth, further exacerbating water use efficiency decline and the risk of soil salinization.

[0003] Existing technologies typically improve soil moisture conditions by increasing irrigation water volume, preventing soil erosion, and strengthening vegetation cover. However, these methods focus on engineering or management measures and are difficult to effectively control water evaporation loss and deep seepage from the perspective of soil structure and water process mechanisms. In fact, they may even exacerbate water waste and soil erosion under unreasonable application conditions.

[0004] Biochar, as a soil amendment material with a porous structure and high specific surface area, has received widespread attention in soil improvement in arid regions in recent years. Studies have shown that biochar can improve soil water use in arid areas by altering soil pore structure, enhancing water retention capacity, and regulating evaporation processes. However, existing biochar application methods still have significant shortcomings in practical applications: on the one hand, the assessment of existing biochar addition amounts relies on expert opinions and empirical judgments, lacking a systematic calculation method that combines soil planar differentiation and vertical structural characteristics; on the other hand, existing research mainly focuses on soil moisture carrying capacity assessment after artificial vegetation construction, lacking quantitative analysis of the relationship between evaporite crust characteristics, biochar input parameters, and water use efficiency in the pre-improvement stage, making it difficult to provide a scientific basis for the rational allocation of biochar in arid soils.

[0005] Therefore, there is an urgent need for a method and system that can comprehensively consider soil structure characteristics, biochar input parameters, and water use efficiency requirements in arid regions, and achieve parameter configuration and effect verification based on model calculation and monitoring evaluation, so as to improve the soil moisture retention capacity in arid regions, achieve a win-win situation for water and carbon, and promote sustainable agricultural development. Summary of the Invention

[0006] One of the objectives of this invention is to provide a method for improving the water resource efficiency of biochar soil optimization in arid areas, in order to solve the problem in the existing technology of lacking quantitative calculation and effect evaluation of biochar application parameters in combination with the soil structure characteristics of arid areas, thereby improving the soil moisture retention capacity and ecological benefits per unit of water resources in arid areas.

[0007] This invention is achieved through the following technical solution: a method for improving water resource efficiency through optimization of biogenic black coal soil in arid regions, comprising the following steps: S100, acquiring soil characteristics of the target arid region, wherein the soil characteristic data includes at least planar differentiation characteristics and vertical structural characteristics, and identifying the spatial distribution, thickness, and composition information of the evaporite crust layer formed by strong evaporation, and constructing an arid region soil database; S200, constructing an arid region soil optimization target model based on the arid region soil database, wherein the optimization target model comprehensively considers the structural characteristics of the evaporite crust layer, biogenic black coal input parameters, and the need to improve water use efficiency; S300, calculating the preferential ablation space range of the evaporite crust layer and its impact on water use efficiency through the optimization target model. S400: Based on the parameter configuration scheme, calculate the input parameter configuration results of biochar and output the parameter configuration scheme for soil optimization; S500: Establish a target area monitoring database based on the parameter configuration scheme, and receive historical monitoring data of the target arid area before the implementation of improvement measures and the concurrent monitoring data of the comparison area that is spatially adjacent to the target arid area, has similar soil characteristics, and has not implemented improvement measures, and construct a comparison database; S500: Compare and analyze the data in the monitoring database and the comparison database with the prediction results of the optimization target model to verify the accuracy and actual effect of the biochar input model, evaluate the change in ecological benefits per unit of water resources after the optimization of biochar soil in arid areas, and output the results of improved water resource utilization efficiency.

[0008] Furthermore, the arid zone soil database includes: soil type, soil texture or pore structure parameters, soil salinity or electrical conductivity, soil pH, organic matter content, soil water holding characteristics, and spatial distribution, thickness, and composition information of evaporite crust.

[0009] Furthermore, the optimized target model includes: a soil moisture dynamic model to describe the temporal and spatial variation of soil moisture; a modified evaporation loss model (BEMEL) for quantifying biochar; a compaction layer parameter calculation module for calculating the preferred ablation space range and parameter scheme of the evaporite compaction layer; and a parameter optimization module for outputting the biochar input parameter configuration results under the constraints of the soil moisture dynamic model.

[0010] Furthermore, the soil moisture dynamic model is as follows: ,in, S ( i) is the input function for water in the soil, representing the amount of water flowing into the soil per unit time and per unit area; E ( i ) represents the amount of water lost from the soil due to evaporation; θ represents the moisture content. K ( i ) represents the hydraulic conductivity of the soil, which is a function of the water content θ; P Soil water potential is used to reflect the driving force of water, and it typically varies with changes in water content θ. Ψ represents the gradient of soil water potential; It is a divergence operator used to describe the spatial variation of a scalar field.

[0011] Furthermore, the model of the impact of biochar on evaporation loss is investigated by introducing a function. f ( B The amount of biochar applied per unit area of ​​arid soil is expressed as ). B The effect of improving moisture retention. The new water evaporation rate after applying biochar is E B ( i ): E B ( i )= E ( i ) (1— f ( B )), Among them, (1— f ( B )) is the correction factor.

[0012] Furthermore, the biochar application model uses a soil moisture dynamics model as a constraint, specifically: .

[0013] Furthermore, the function f ( B Based on the content of biochar. B The effect of evaporation rate was constructed as follows: f ( B )= α B / ( B+β ), where α is the soil type parameter, β These are climatic condition parameters.

[0014] Furthermore, the monitoring database includes: soil moisture monitoring data, crop growth monitoring data, evaporation and water loss monitoring data, and environmental factor monitoring data.

[0015] Further, step S500 also includes: S510: comparing the results predicted by the optimized target model with the actual observation data in the monitoring database to quantitatively evaluate the prediction accuracy of the optimized target model and obtain the effect evaluation results; S520: when there is a large deviation between the optimized target model and the actual effect, adjusting the parameters or assumptions in the optimized target model to correct the model; S530: based on the data in the monitoring database and the effect evaluation results, determining the application amount of biochar. B Adjustments will be made.

[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. By constructing a soil database for arid regions and identifying the structural characteristics of evaporite slabs, a refined description of soil moisture processes in arid regions was achieved, providing a data foundation for the scientific configuration of input parameters for biochar.

[0017] 2. This invention introduces a model of the impact of biochar on evaporation loss and establishes an optimized target model, which can effectively guide the application amount and method of biochar, thereby minimizing soil moisture loss and evaporation in arid areas, improving water resource utilization efficiency, and solving the problem that existing technologies cannot specifically control water loss and evaporation.

[0018] 3. By jointly analyzing the monitoring database and the comparison database, a closed-loop evaluation mechanism of parameter configuration, effect verification and model calibration was constructed, which improved the adaptability of the method and the reliability of decision-making. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the method provided in Embodiment 1 of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Example 1 This embodiment provides a method for improving water resource efficiency through optimization of black coal soil in arid areas. Figure 1 A flowchart illustrating the steps in this embodiment is shown. The flowchart shows that this embodiment includes the following steps: Step 1: First, obtain the soil characteristics of the target arid region to understand the basic situation of the soil, especially the compacted layer. The soil characteristic data should include at least the planar differentiation characteristics and vertical structural characteristics. Focus on identifying the spatial distribution, thickness and composition information of the evaporite compacted layer formed by strong evaporation, and construct a soil database for the arid region to provide basic data for subsequent improvement design.

[0022] Specifically, the arid zone soil database includes: soil type, used to assess soil texture (sand, loam, clay, etc.), soil texture or pore structure parameters, as different soil types have different requirements and effects on biochar; water retention capacity, used to assess soil water holding capacity, soil salinity or electrical conductivity, to understand its water evaporation rate and water permeability under arid conditions; soil pH, since soils in arid regions are often alkaline or acidic, biochar can adjust the pH; organic matter content, as soils in arid regions are usually low in organic matter, understanding the existing organic matter content helps determine the optimal proportion of biochar for application; and spatial distribution, thickness, and composition information of evaporite crust layers, understanding the distribution patterns of underground crust layers in arid regions helps determine specific scheme parameter settings and calibrations.

[0023] Step 2: Select suitable biochar based on the information in the arid zone soil database. At the same time, combine the information in the arid zone soil database with the soil characteristics and water resource requirements to design the application method of biochar to ensure that it achieves maximum benefits.

[0024] In this embodiment, based on data from the database, partial differential equations (PDEs) and integral equations (IEs) are used to accurately calculate and optimize the application rate of biochar. This enables the description of soil moisture dynamics and the impact of biochar on water retention, thereby improving the utilization rate of biochar.

[0025] Specifically, the design of a method for applying biochar includes the following sub-steps: 1) To conserve soil water resources in arid regions, it is necessary to consider the factors influencing soil moisture flow and retention. In arid areas, the water loss mechanism is usually soil moisture evaporation. Therefore, we can construct a dynamic soil moisture model based on the specific characteristics of soil moisture flow.

[0026] For a single soil volume unit, the rate of change of moisture is equal to the difference between inflow and outflow. In other words, the rate of change of moisture content over time within a small soil unit is determined by three factors: Water inflow: mainly from sources such as precipitation and irrigation (denoted as S(θ)).

[0027] Water outflow: Water outflow in arid regions mainly includes evaporation loss (denoted as E(θ)) and water absorption by plants.

[0028] Water flow: the diffusion or infiltration of water caused by water potential gradient.

[0029] Therefore, the water conservation equation can be obtained as follows: , For water flow, the movement of water in the soil is caused by the water potential gradient, and the flow rate is directly proportional to the water potential gradient; this proportionality constant is the hydraulic conductivity. Therefore, the water flow term can be written as: , Where q is the vector of water flow, i.e., the flow rate of water per unit volume of soil, and K(θ) is the hydraulic conductivity of the soil, a function of the water content θ. P Soil water potential is used to reflect the driving force of water, and it typically varies with changes in water content θ. Ψ represents the gradient of soil water potential.

[0030] The above formula represents the flow of water in the soil. The direction of the flow is determined by the water potential gradient, and the flow rate is related to the soil's hydraulic conductivity. K (θ) is proportional to the water potential gradient.

[0031] According to the divergence theorem for water flow, the rate of change of water flow (i.e., the diffusion of water) is the divergence of the flow vector field. Therefore, the change in water flow can be written as: This item describes the diffusion or infiltration process of water caused by the water potential gradient.

[0032] water inflow S ( i This represents the input of water, such as precipitation, irrigation, or groundwater rise. It is typically a function of soil moisture content, representing the amount of water flowing into a unit area per unit time. This term signifies an increase in the water content of the soil.

[0033] Water outflow E ( i This represents the amount of water lost due to evaporation. In arid regions, evaporation is the primary mechanism of water loss. The evaporation rate typically increases as soil moisture decreases and is closely related to factors such as temperature and humidity.

[0034] Combining the above, we can conclude that: .

[0035] Because biochar effectively increases soil porosity and reduces soil evaporation rate, thereby improving soil water retention capacity and enabling better water retention, the effect of biochar on water retention can be quantified by modifying the water conductivity and evaporation terms.

[0036] By solving this dynamic model of soil moisture, a dynamic model of soil moisture and water flow can be obtained.

[0037] 2) Construct a Biomass Black Charcoal (BEMEL) model to assess the impact of biomass black charcoal on evaporation loss, assuming the application rate of biomass black charcoal per unit area of ​​arid soil is [missing information]. B (Unit: kg / m³) 2 Then a function can be introduced. f ( B The value is used to represent its effect on improving moisture retention. The new rate of moisture evaporation after applying biochar is... E B ( i This can be represented as: E B ( i )= E ( i ) (1— f ( B ), where (1— f ( B )) is the correction factor.

[0038] In this embodiment, the function f ( B )= α B / ( B+β ), where α is the soil type parameter, β This refers to climatic condition parameters. In other words, under the influence of biochar, the evaporation rate will vary depending on the biochar content. B Adjustments will be made. Specifically, with the content of biochar... B As the evaporation rate increases, the evaporation rate will decrease accordingly.

[0039] 3) Construct a parameter calculation module for the slab layer, which is used to calculate the preferred ablation space range and corresponding parameter scheme of the evaporite slab layer based on the spatial distribution, thickness and composition information of the evaporite slab layer.

[0040] 4) In the first three sub-steps, the changes in soil moisture were first described using a soil moisture dynamics model, and then a new water evaporation rate was introduced after the application of biochar. In this sub-step, an optimization objective is obtained by combining the aforementioned three sub-steps, thereby guiding the application of biochar based on the final optimization objective model.

[0041] First, it's important to clarify that improving water resource utilization efficiency in arid regions requires managing soil moisture. For arid areas, the primary concern is water loss, as evaporation is the most significant form of water loss due to the region's characteristics. The goal is to reduce this water loss through the application of biochar, thereby optimizing water resource use.

[0042] Soil moisture dynamics models describe how soil moisture changes over time and space. Therefore, the optimization objective (i.e., minimizing water loss) must take into account the water content. i The evolution throughout the entire optimization cycle. We need to incorporate the water dynamics equation as a constraint into the optimization objective.

[0043] Therefore, in the optimization objective, it is necessary to calculate the water loss (including evaporation) over the entire time period. E B ( i ) and other water loss (WaterLoss) i The calculation of water loss is based on the water content. i ( t , x )of, i ( t , x As time goes by t and spatial location x The change in soil moisture is calculated by solving a dynamic model of soil moisture.

[0044] Due to the evaporation rate after applying biochar E B ( i If time is variable in both time and space, then at any given moment... t and spatial location x evaporation rate is E B ( i ( t , x This refers to the moisture content, which depends on location and time. i ( t , x Therefore, the evaporation loss over the entire time and space can be expressed as: .

[0045] Besides evaporation, other forms of water loss in the soil also depend on moisture content. i ( t , xThese water losses typically occur through seepage and runoff, and are defined as: WaterLoss( i Then, the temporal and spatial integrals of other forms of water loss are: , Total water loss refers to the total amount of water lost over a period of time; it is the sum of evaporation loss and other forms of water loss. Therefore, combining the two, we can conclude that: , By incorporating the dynamic soil moisture model as a constraint into the optimization problem, the final optimization objective model can be obtained: , In summary, the optimization objective is to minimize water loss, including the impact of biochar on evaporation and other forms of water loss. This optimization model can optimize the application rate of biochar, thereby minimizing water loss and evaporation and improving water use efficiency. By incorporating dynamic changes in water content and the influence of biochar into the optimization objective, the final model can find the optimal application strategy while ensuring reasonable water dynamics, thus improving biochar utilization and achieving the goals of reducing water loss, enhancing water retention capacity, and promoting sustainable agricultural development.

[0046] Step 3: After optimizing the target model and obtaining the application rate of biochar, soil improvement operations are carried out in the target arid area according to the application rate. After the improvement is completed, data related to soil and crop growth in the target area are continuously collected, and the collected data are used to build a monitoring database.

[0047] Specifically, soil moisture content at different depths is monitored in real time using soil moisture sensors. By comparing the data with data from arid zone soil databases, the changes in water retention capacity before and after the application of biochar are analyzed, thereby assessing whether biochar effectively improves soil water retention capacity.

[0048] Regularly measure crop growth in the target area, including plant height, leaf area index (LAI), and biomass. Use this data to compare crop growth between the treated and untreated control groups and assess its impact on crop growth.

[0049] An evaporometer was used to monitor soil evaporation loss. Evaporation loss after applying biochar was recorded and compared with that without application to determine the inhibitory effect of biochar application on evaporation rate.

[0050] Monitoring environmental factors, such as temperature, precipitation, wind speed, and humidity, can be used as variables to assess the changes in the effectiveness of applying biochar under different climatic conditions.

[0051] Step 4: Compare the data in the monitoring database with the prediction results of the optimization model to verify the accuracy and actual effect of the optimization model.

[0052] Compare the predictions from the optimized target model (e.g., changes in water content, evaporation loss, crop growth, etc.) with actual observational data. Quantitatively evaluate the predictive accuracy of the optimized target model.

[0053] When there is a significant discrepancy between model predictions and actual results, it may be necessary to adjust the parameters or assumptions in the model. For example, the soil moisture transfer coefficient can be corrected based on field data. K (θ) or moisture loss due to evaporation after applying charcoal E B ( i By refining the model, the optimization results are made closer to the actual situation, thereby improving the reliability of the model.

[0054] Finally, based on the monitoring data and the results of the effect evaluation, the application rate of biochar can be determined. B Adjustments and optimizations will be made to achieve better soil and water conservation and crop yield increases.

[0055] If the effect evaluation reveals that too much or too little black charcoal was applied, the optimization model can be adjusted based on the actual results.

[0056] For example, if excessive application of biochar leads to excessive soil moisture, which restricts the growth of certain crops, the amount of biochar applied can be reduced.

[0057] By continuously monitoring and evaluating under different growing seasons and environmental conditions, the application rate of biochar can be further adjusted to cope with climate change and changes in soil conditions. Simultaneously, the model's dynamic optimization function allows for dynamic adjustments based on real-time monitoring data.

[0058] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for improving water resource efficiency in black coal soil optimization in arid areas, characterized in that, The methods for improving water resource efficiency include: S100. Obtain soil characteristic data of the target arid region. The soil characteristic data includes at least planar differentiation characteristics and vertical structural characteristics. Identify the spatial distribution, thickness and composition information of the evaporite slab layer formed by strong evaporation and construct a soil database for the arid region. S200. Construct an optimized target model based on the arid zone soil database. The optimized target model shall at least comprehensively consider the structural characteristics of evaporite slabs, biochar input parameters, and the need to improve water use efficiency. S300. The preferred ablation space range and corresponding parameter scheme of the evaporite slab layer are calculated through the optimization target model, and the input parameter configuration results of biochar are calculated. The parameter configuration scheme for soil optimization is output. S400. Based on the parameter configuration scheme, establish a target area monitoring database, and receive historical monitoring data of the target arid area before the implementation of improvement measures and concurrent monitoring data of the comparison area that is spatially adjacent to the target arid area, has similar soil characteristics, and has not implemented improvement measures to construct a comparison database. S500. Compare and analyze the data in the monitoring database and the comparison database with the prediction results of the optimized target model to verify the accuracy and actual effect of the biochar input model, evaluate the ecological benefit changes per unit of water resources, and output the results of improved water resource utilization efficiency.

2. The method for improving water resource efficiency in arid zone black coal soil optimization according to claim 1, characterized in that, The arid zone soil database includes: soil type, soil texture or pore structure parameters, soil salinity or electrical conductivity, soil pH, organic matter content, soil water holding characteristics, and spatial distribution, thickness and composition information of evaporite crust.

3. The method for improving water resource efficiency in arid zone black coal soil optimization according to claim 1, characterized in that, The optimization objective model includes: A dynamic model of soil moisture used to describe the spatiotemporal variation of soil moisture; BEMEL, a biochar-corrected evaporation loss model used to quantify the correction of evaporation loss by biochar; A module for calculating the preferential ablation space range and corresponding parameter schemes of evaporite slabs; And a parameter optimization module for outputting the input parameter configuration results of biochar under the constraints of the soil moisture dynamic model.

4. The method for improving water resource efficiency in arid zone black coal soil optimization according to claim 3, characterized in that, The soil moisture dynamic model is as follows: ， in, S ( θ ) is the input function for water in the soil, representing the amount of water flowing into the soil per unit time and per unit area; E ( θ ) represents the amount of water lost from the soil due to evaporation; θ represents the moisture content. K ( θ ) represents the hydraulic conductivity of the soil, which is a function of the water content θ; Ψ Soil water potential is used to reflect the driving force of water, and it typically varies with changes in water content θ. Ψ represents the gradient of soil water potential; It is a divergence operator used to describe the spatial variation of a scalar field.

5. The method for improving water resource efficiency in arid zone black coal soil optimization according to claim 3, characterized in that, The BEMEL model, which describes the impact of biochar on evaporation loss, introduces a function... f ( B The amount of biochar applied per unit area of ​​arid soil is expressed as ). B The effect of improving moisture retention. The new water evaporation rate after applying biochar is E B ( θ ): E B ( θ )= E ( θ ) (1— f ( B ))， Among them, (1— f ( B )) is the correction factor.

6. The method for improving water resource efficiency in arid zone black coal soil optimization according to claim 3, characterized in that, The biochar application model uses a soil moisture dynamics model as a constraint, specifically: 。 7. The method for improving water resource efficiency in arid zone black coal soil optimization according to claim 5, characterized in that, The function f ( B Based on the content of biochar. B The effect of evaporation rate was constructed as follows: f ( B )= α B / ( B+β ), where α is the soil type parameter, β These are climatic condition parameters.

8. The method for improving water resource efficiency in arid zone black coal soil optimization according to claim 1, characterized in that, The monitoring database includes: soil moisture monitoring data, soil salinity or electrical conductivity monitoring data, crop growth monitoring data, evaporation and water loss monitoring data, and environmental factor monitoring data; the comparison database includes historical monitoring data of the target arid area before improvement and concurrent monitoring data of the comparison area.

9. The method for improving water resource efficiency in arid zone black coal soil optimization according to claim 1, characterized in that, Step S500 further includes: S510: Compare the prediction results of the optimized target model with the observation data in the monitoring database, quantitatively evaluate the prediction accuracy of the optimized target model, and obtain the effect evaluation results; S520: When there is a large deviation between the optimization target model and the actual effect, adjust the parameters or assumptions in the optimization target model and calibrate the model. S530: Based on the data in the monitoring database and the results of the effect evaluation, recalculate and output the configuration results of the input parameters for the biochar.

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