Method for monitoring of slope interflow with combined geophysical sounding and soil moisture sensor network

Abstract

The application discloses a slope soil flow monitoring method combining geophysical exploration and soil moisture sensor network, and comprises the following steps: acquiring rainfall data, resistivity spatial dynamic distribution data, multi-depth and multi-slope position soil volume water content data and soil temperature data, and multi-depth runoff data of a target runoff plot; processing the resistivity data; establishing a quantitative relationship model between the resistivity and the soil water content; determining rainfall data and resistivity data for identifying the soil flow path and the dynamic change process; calculating the soil moisture spatial distribution dynamics of the monitoring range at different times; verifying the inversion result; and visually outputting based on the dynamic process. The application focuses on the multi-source data fusion and monitoring system structure innovation, breaks through the technical bottleneck of the dynamic path identification and quantitative analysis of the soil flow under the present ground conditions, and provides high-precision and non-intrusive method support for the slope rainfall runoff relationship analysis and in-situ monitoring of the mountain water and soil process.

Description

TECHNICAL FIELD

[0001] The present application relates to a slope interflow monitoring method combining geophysical exploration and soil moisture sensor network, and belongs to the field of environmental science and technology. BACKGROUND

[0002] As a key transmission carrier of slope hydrological process, interflow is not only an important component of runoff formation in mountainous areas, but also a core link of regulating water and soil loss mechanism and flood disaster evolution. In recent years, under the dual effects of global climate change leading to changes in rainfall pattern and increasing intensity of land development in mountainous areas, the coupling effect of interflow transport process and surface erosion is becoming more and more significant. Therefore, building a precise interflow monitoring technology system has become a frontier issue for the construction of ecological security barrier in mountainous areas.

[0003] The current interflow monitoring technology system has obvious observation scale fault: although traditional point-scale monitoring methods (such as time domain reflectometer (TDR) and neutron moisture meter) can obtain local soil moisture content time series data, they are difficult to reconstruct the interflow path network; and although the profile excavation dye tracing method can directly show the interflow path under a certain rainfall event, it destroys the soil structure and cannot capture the dynamic process. It is worth noting that the electrical resistivity tomography (ERT) technology can non-invasively obtain the spatial and temporal distribution data of soil resistivity through the arrangement of high-density electrode array, and its strong correlation with soil moisture content provides a new way for the inversion of interflow dynamics. However, single ERT data has the bottleneck of multiple solutions, and the resistivity anomaly area may correspond to multiple factors such as water transport, soil density change or root distribution, so it is urgent to build a verification mechanism for multi-source data fusion.

[0004] It is worth noting that the standardized runoff plot, as a classic experimental unit for hydrological research, has ideal monitoring sites for ERT technology due to its closed boundary conditions and complete outlet flow monitoring system. However, the existing technology has not yet formed a complete solution that integrates continuous monitoring of ERT with system verification of runoff plots, which restricts the depth of interflow process mechanism research and the reliability of engineering application.

[0005] Therefore, it is urgent to develop an interflow monitoring method that uses runoff plots as sites and ERT technology as the main monitoring means to accurately and reliably capture the interflow path and dynamic change process. SUMMARY

[0006] The present application aims to solve the problems existing in the prior art, and provides a slope interflow monitoring method combining geophysical exploration and soil moisture sensor network.

[0007] The technical solution provided by the present application to solve the above technical problems is: a slope interflow monitoring method combining geophysical exploration and soil moisture sensor network, comprising the following steps:

[0008] Step one, obtaining rainfall data, resistivity data spatial dynamic distribution data, multi-depth multi-slope soil volumetric water content data and soil temperature data, and multi-depth runoff data of the target runoff plot;

[0009] Step two, topographic and temperature correction of resistivity data, soil moisture inversion and other processing;

[0010] Step three, establishing a quantitative relationship model between resistivity and soil moisture content;

[0011] Step four, determining rainfall data and resistivity data for identifying the path and dynamic process of interflow;

[0012] Step five, obtaining the dynamic soil moisture spatial distribution of the monitoring range at different times through time-delay geophysical scanning and inversion calculation;

[0013] Step six, verifying the inversion results in terms of dynamic moisture content, point scale, and interflow outflow;

[0014] Step seven, visualizing the dynamic process according to the inversion results, and realizing in-situ high-precision monitoring of the path and dynamic process of interflow on the slope during rainfall events.

[0015] Further technical solutions are that in step one, rainfall data, resistivity data, soil volumetric water content data, soil temperature data, and runoff data are obtained by rain gauge, electrical resistivity tomography (ERT), time domain reflectometry (TDR) system, and flowmeter.

[0016] Further technical solutions are that in step one, the triangular linear interpolation method is used to interpolate the inversion resistivity values at the location of the TDR sensor; the measurement results of the TDR are linearly interpolated according to the end time of each ERT measurement; through this process, the inversion resistivity, soil volumetric water content, and soil temperature data at the same spatial location and the same time point are obtained,

[0017] Further technical solutions are that in step two, the resistivity data are processed by the commercial software Res2DInv for topographic and temperature correction, soil moisture inversion, and other processing.

[0018] Further technical solutions are that the specific processing process of step two is:

[0019] Step 1, first add topographic elevation data: open the original data file with the suffix "rd.dat" and input the elevation information after the last line of resistivity data;

[0020] Step 2, then open the Res2DInv software and set the inversion parameters;

[0021] Step 3, after the inversion parameter setting is successful, the software is used to start the inversion;

[0022] Step 4, after the inversion is completed, the inversion file is opened, and the data with a data mismatch percentage exceeding 100 is manually removed, the new resistivity data file is saved after the removal, and then secondary inversion is performed with the same inversion parameters to obtain the final inversion resistivity distribution result.

[0023] Further technical solutions are that the step 3 adopts a least square inversion algorithm, and a mathematical expression is as follows:

[0024] (J T J+λF)Δq k =J T g-λFq k

[0025]

[0026] In the formula, C x is a horizontal roughness filtering coefficient, C z is a vertical roughness filtering coefficient, J is a Jacobian matrix of a partial derivative, J T is a transpose matrix of J, λ is a damping coefficient, q is a model change vector, and g is a data mismatch vector.

[0027] Further technical solutions are that the step three has the following specific process:

[0028] Step S1, first, the inversion resistivity is temperature corrected;

[0029] Step S2, based on the inversion resistivity and soil volume water content data of a long time sequence, a power function fitting method is adopted to construct a quantitative relationship model between the temperature-corrected inversion resistivity and the soil volume water content.

[0030] Further technical solutions are that the temperature correction formula in the step S1 is as follows:

[0031] ρ0=ρ[1+δ(T-25)]

[0032] In the formula, ρ0 is a 25℃ standard temperature correction value of the inversion resistivity; ρ is an inversion resistivity value at soil temperature T; and δ is a temperature correction coefficient, and the value is 0.025.

[0033] The application has the beneficial effects that: the application discloses the coupling rule of the soil flow dynamic transport process and the surface runoff-erosion under specific rainfall events by continuously monitoring and analyzing the space-time correlation of the runoff plot outlet runoff data through ERT, breaks through the static and fragmented constraints of the traditional method on the mechanism research, focuses on the structural innovation of the multi-source data fusion and monitoring system, overcomes the technical bottleneck of the soil flow dynamic path identification and quantitative analysis, and provides high-precision and non-invasive monitoring support for the mechanism research of the mountain water and soil loss and the ecological security barrier construction. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 is a schematic diagram of a runoff plot monitoring system;

[0035] Figure 2 is an instrument layout diagram;

[0036] Figure 3 is an inversion resistivity distribution result. DETAILED DESCRIPTION

[0037] The technical solutions of the application will be described clearly and completely in combination with the drawings. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0038] The slope soil flow monitoring method provided by the application combines geophysical exploration and a soil moisture sensor network, and comprises the following steps:

[0039] Step 1: a monitoring system as shown in Figure 1 is used to monitor a target runoff plot, and rainfall data, resistivity spatial dynamic distribution data, multi-depth and multi-slope soil volume moisture content data and soil temperature data, and multi-depth runoff data of the target runoff plot are obtained.

[0040] The monitoring system comprises four kinds of instruments.

[0041] The first kind is a rain gauge, which is located Figure 1 at the upper left, and is used to measure the rainfall in the runoff plot. A tipping bucket rain gauge is installed in the runoff plot.

[0042] The second kind is an electrical resistivity tomography instrument (ERT), which is used to monitor the soil resistivity by burying an electrode measuring line on the ground (the red dots are the electrode positions). Two ERT measuring lines are installed in the runoff plot, each of which is 14.5 m long and contains 30 electrodes, the electrode spacing is 0.5 m, and a Wenner-Schlenz device is used.

[0043] The third is time domain reflectometry (TDR), which monitors soil volumetric water content and temperature through sensors buried at different depths in the soil. Three sets of TDR are installed in the runoff plot, on the upper, middle and lower slopes, respectively. The probes for monitoring soil volumetric water content and soil temperature are installed at depths of 10, 20, 30, 40 and 50 cm.

[0044] The fourth is a flow meter, located Figure 1 on the lower right. This instrument is a standard instrument for the runoff plot and is used to measure runoff at different depths of the outlet section. The flow meter in the runoff plot is installed at depths of 20, 60 and 100 cm of the outlet section.

[0045] Figure 2 The specific layout position of the TDR sensor in the soil profile is presented. The present application uses a triangular linear interpolation method to interpolate the inverted resistivity value at the position of the TDR sensor. According to the end time of each measurement by ERT, the measurement results (soil volumetric water content and soil temperature) of the TDR are linearly interpolated. Through this process, the inverted resistivity, soil volumetric water content and soil temperature data at the same spatial position and the same time point are obtained, thereby laying a foundation for subsequent establishment of the quantitative relationship between inverted resistivity and soil volumetric water content.

[0046] Step two, processing the inverted resistivity data;

[0047] First, add the terrain elevation data. Open the original data file with the ending "rd.dat", and start entering the elevation information after the last line of resistivity data: ① The first line: 1 represents horizontal coordinates as horizontal distance, and 2 represents horizontal coordinates as slope distance; ② The second line: the number of terrain elevation points; ③ The third line and the following: input each terrain elevation point in the order of "distance" and "elevation", and input four lines of "0" as a termination symbol after the end. Save the above operation.

[0048] Then open the Res2DInv software and set the inversion parameters.

[0049] In terms of terrain correction, the twisted finite element method with a terrain attenuation factor of 0.75 is selected. Since the resistivity data has already input the elevation information, the inversion numerical method of the software will automatically select the finite element method, which is more suitable for the mountainous environment with terrain undulations than the finite difference method. In terms of the constraint type of the inversion data, the enhanced constraint is selected instead of the smooth constraint, because the enhanced constraint adopts L1 norm regularization, allows part of the parameters to be zero through sparsity constraint, preserves the edge and mutation characteristics of the geological body, and makes the generated resistivity distribution more clear. Due to the broken surface of the mountain slope, the soil resistivity distribution is highly heterogeneous, so the model refinement setting is selected, that is, when the inversion model is established, the width of the model is half of the electrode spacing. This setting can optimize the inversion result of the resistivity near the surface and improve the data reliability.

[0050] After the above settings are successful, the software starts to perform inversion. The software uses the least square inversion algorithm, and the mathematical expression is as follows:

[0051] (J T J+λF)Δq k =J T g-λFq k

[0052]

[0053] In the formula: C x is the horizontal roughness filtering coefficient, C z is the vertical roughness filtering coefficient, J is the Jacobian matrix of the partial derivative, J T is the transpose matrix of J, λ is the damping coefficient, q is the model change vector, and g is the data mismatch vector.

[0054] After the inversion is completed, the data mismatch percentage exceeding 100 is manually removed, the new resistivity data file is saved after the removal, and then the same inversion parameters are used for secondary inversion to obtain the final inversion resistivity distribution result (as shown in Figure 3 ).

[0055] Step three, establishing a quantitative relationship model between resistivity and soil moisture content according to the processed inversion resistivity data and soil volume moisture content data;

[0056] First, the inversion resistivity is temperature corrected;

[0057] ρ0=ρ[1+δ(T-25)]

[0058] In the formula: ρ0 is the inversion resistivity correction value at a standard temperature of 25℃; ρ is the inversion resistivity value at soil temperature T; and δ is the temperature correction coefficient, which is 0.025;

[0059] Based on the long-term inversion resistivity and soil volume water content data, the power function fitting method is used to build the quantitative relationship model between the temperature corrected inversion resistivity and soil volume water content. On this basis, the soil volume water content is calculated based on the temperature corrected inversion resistivity.

[0060] In order to verify the reliability of the model, the measured soil water data outside the modeling period is selected as the independent verification set. The accuracy and effect of the model are evaluated by calculating the determination coefficient and root mean square error.

[0061] Step four, determine the rainfall data and resistivity data for identifying the path and dynamic change process of interflow;

[0062] The rain gauge data (taking hourly data as an example) is viewed, and the starting time and ending time of the rainfall event are selected, with the following standards: during the period without rainfall, the rain gauge reading is always 0 mm, and after the rainfall occurs, the rain gauge reading is obviously greater than 0 mm. The rain gauge reading at the starting time of the rainfall event is not 0 mm, and the rain gauge reading is always 0 mm for at least 12 hours before this time; the rain gauge reading at the ending time of the rainfall event is not 0 mm, and the rain gauge reading is always 0 mm for at least 12 hours after this time; the duration of the entire rainfall event is at least 4 hours.

[0063] Since it is assumed that the rain gauge data is hourly and the TDR data is 10 minutes, the TDR data (soil water content and soil temperature) within 1 hour before and after the starting time of the rainfall event is viewed, and the time when the soil water content starts to rise is taken as the actual starting time of the rainfall event. The TDR data within 1 hour before and after the ending time is viewed, and the time when the soil water content starts to decline is taken as the actual ending time of the rainfall event.

[0064] The ERT data is hourly, and the ERT data at the time of 6 hours before the actual starting time of the rainfall event is selected as the t0 data (for example: the actual starting time is 12:10 or 12:50, and the time of the t0 data is 6:00), and the ERT data at the time of 1 hour is selected as the t1 data. The data at all times within the rainfall event is taken in this way. The ERT data at the time of 1 hour after the actual ending time of the rainfall event is selected as the t n The data at the time of 6 hours and 24 hours after the actual ending time is taken as the t n+1 and the t n+2 data.

[0065] The runoff gauge data from 12 hours before the starting time of the rainfall event to 48 hours after the ending time is selected as auxiliary reference.

[0066] Step five, obtain the soil moisture spatial distribution dynamics of monitoring range at different times through time delay geophysical survey and inversion calculation;

[0067] Set the inversion parameters: select the terrain attenuation factor as 0.75 of the twisted finite element method for terrain correction, the iteration number is 3, select the finite element method for the inversion numerical method, select the enhanced constraint for the inversion data constraint type, enable the model refinement setting, and disable the extended model setting.

[0068] Perform inversion calculation: generate the inversion result of the relative change amount of resistivity △ρ / ρ0, wherein ρ0 corresponds to the inversion resistivity value of t0 data.

[0069] Through the inversion result display function of Res2DInv, establish the percentage change model of the inversion result resistivity, identify the resistivity drop area, and take the drop threshold value of △ρ / ρ0≤-12% as the potential soil flow path;

[0070] Step six, dynamic verification of water content, point scale verification, and soil flow outflow verification of the inversion result;

[0071] Dynamic verification of water content: substitute the resistivity change amount △ρ obtained by inversion into the quantitative relationship model between resistivity and soil water content established in step three, calculate the water content change rate θ(t) of each spatial node, construct a two-dimensional soil water content change model, and verify the soil flow path.

[0072] Point scale verification: extract the model calculated water content θ of the spatial coordinate point corresponding to the TDR sensor model , and perform error analysis with the TDR measured value θTDR, adopt the root mean square error RMSE≤0.05 cm 3 ·cm -3 as the verification standard.

[0073] Soil flow outflow verification: compare the total runoff Q obs monitored by the flowmeter with the soil flow flux Q cal calculated by the model, and require the absolute value of the relative error |(Q obs -Q cal ) / (Q obs |≤10%.

[0074] Step seven, dynamic process visualization output according to the inversion result, realize in-situ high-precision monitoring of the slope soil flow path and dynamic change process in the rainfall event;

[0075] Construct a spatio-temporal visualization model: fuse and display the resistivity change, water content distribution, and soil flow path dynamic change parameters in the selected rainfall event through the drawing of the inversion result.

[0076] The generated dynamic process characteristic parameter set includes, but is not limited to, the soil interflow starting time (the time difference from the start of rainfall to the appearance of the first path), the soil interflow path development rate, the maximum infiltration depth, the lateral expansion range, etc.

[0077] According to the three verification effects of step six, the resistivity change, the water content distribution, and the visualization drawing of the soil interflow path, the soil interflow path and the dynamic change process identified in the selected rainfall event are evaluated.

[0078] The above description is not intended to limit the present application in any form, although the present application has been disclosed by the above examples, however, it is not intended to limit the present application, any person skilled in the art, without departing from the technical solution range of the present application, can make some changes or modifications to the above disclosed technical content as equivalent examples, but any simple modification, equivalent change and modification made to the above examples according to the technical essence of the present application, as long as it does not deviate from the technical solution of the present application, belongs to the range of the technical solution of the present application.

Claims

1. A method for monitoring interflow on slopes using a combination of geophysical exploration and a soil moisture sensor network, characterized in that, Includes the following steps: Step 1: Obtain rainfall data, resistivity data, spatial dynamic distribution data, multi-depth and multi-slope soil volumetric water content data, soil temperature data, and multi-depth runoff data for the target runoff plot; Step 2: Perform topographic and temperature corrections and soil moisture content inversion on the resistivity data; Step 3: Establish a quantitative relationship model between resistivity and soil moisture content; Step 4: Determine the rainfall and resistivity data used to identify the flow path and dynamic changes in the soil. Step 5: Obtain the spatial distribution dynamics of soil moisture at different times within the monitoring range through time-delayed geophysical scanning and inversion calculation; Step 6: Verify the inversion results for dynamic water content, point scale, and soil outflow. Step 7: Based on the inversion results, output the dynamic process visualization to achieve in-situ high-precision monitoring of the slope interflow path and dynamic changes during rainfall events.

2. The method for monitoring interflow on slopes using a combined geophysical exploration and soil moisture sensor network as described in claim 1, characterized in that, In step one, rainfall data, resistivity data, soil volumetric water content data, soil temperature data, and runoff data are obtained through rain gauges, resistivity tomography, time domain reflectometer systems, and flow meters.

3. The method for monitoring interflow on slopes using a combined geophysical exploration and soil moisture sensor network as described in claim 2, characterized in that, In step one, the trigonometric linear interpolation method is used to interpolate the inversion resistivity value at the location of the TDR sensor; and the measurement results of the TDR are linearly interpolated based on the end time of each ERT measurement. This process allows us to obtain inversion resistivity, soil volumetric water content, and soil temperature data at the same spatial location and time point.

4. The method for monitoring interflow on slopes using a combined geophysical exploration and soil moisture sensor network as described in claim 1, characterized in that, In step two, the resistivity data is post-processed by performing terrain and temperature correction and soil moisture content inversion on the commercial software Res2DInv.

5. The method for monitoring interflow on slopes using a combined geophysical exploration and soil moisture sensor network according to claim 4, characterized in that, The specific processing procedure for step two is as follows: Step 1: First, add terrain elevation data: Open the raw data file ending with "rd.dat" and start entering elevation information after the last line of resistivity data; Step 2: Then open the Res2DInv software and set the inversion parameters; Step 3: After successfully setting the inversion parameters, start the inversion process using the software; Step 4: After the inversion is complete, open the inversion file and manually remove data with a mismatch percentage exceeding 100%. After removal, you need to save a new resistivity data file and then perform a second inversion with the same inversion parameters to obtain the final inverted resistivity distribution result.

6. The method for monitoring interflow on slopes using a combined geophysical exploration and soil moisture sensor network as described in claim 5, characterized in that, Step 3 employs the least squares inversion algorithm, the mathematical expression of which is as follows: (J T J+λF)Δq k =J T g-λFq k In the formula: C x C represents the horizontal roughness filter coefficient. z Here, J represents the vertical roughness filter coefficients, and J is the Jacobian matrix of the partial derivatives. T Let J be the transpose of J, λ be the damping coefficient, q be the model variation vector, and g be the data mismatch vector.

7. The method for monitoring interflow on slopes using a combined geophysical exploration and soil moisture sensor network according to claim 1, characterized in that, The specific process of step three is as follows: Step S1: First, perform temperature correction on the inverted resistivity; Step S2: Based on long-term inversion resistivity and soil volumetric water content data, a quantitative relationship model between temperature-corrected inversion resistivity and soil volumetric water content is constructed using the power function fitting method.

8. The method for monitoring interflow on slopes using a combined geophysical exploration and soil moisture sensor network as described in claim 7, characterized in that, The temperature correction formula in step S1 is: ρ0=ρ[1+δ(T-25)] In the formula: ρ0 is the inversion resistivity correction value at the standard temperature of 25℃; ρ is the inversion resistivity value at soil temperature T; δ is the temperature correction coefficient, with a value of 0.025.

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