Numerical simulation method of solid-liquid two-phase mixture on landslide under extreme climatic conditions

Abstract

The present invention is a numerical simulation method for landslide solid-liquid two-phase mixture under extreme climatic conditions, which takes into account both the previous rainfall and short-term extreme rainfall conditions to calculate the surface runoff, and is a beneficial supplement to the calculation and evaluation of landslide solid-liquid two-phase mixture. The method includes: obtaining the digital elevation model of the landslide, soil type, surface vegetation type, daily rainfall, daily temperature value, daily evapotranspiration within a time range of one month before the landslide occurs, and the extreme rainfall value pe that induces the landslide; calculating the previous rainfall infiltration value qa of the landslide, and calculating the groundwater level ha caused by the previous rainfall; according to R=CI 3 T c 2 The surface runoff value R was calculated under the combined conditions of the rainfall infiltration value qa before the landslide occurred and the extreme rainfall value pe that induced the landslide. The surface runoff value R was input into the FLO-2D software as the clear water flow data to obtain the flow velocity of the landslide solid-liquid mixture and the thickness distribution of the deposits, which were used to assess the landslide risk.

Description

Technical Field

[0001] The invention relates to the field of engineering geological research, and in particular to a numerical simulation method for a landslide solid-liquid two-phase mixture under extreme climatic conditions. Background Art

[0002] Landslides are one of the most common natural disasters in the world. In most cases, landslides are closely related to extreme rainfall conditions. Heavy rain and long-term continuous rainfall (extreme climate conditions) are the main factors that cause landslide disasters. If the rainfall is small, the rainwater infiltrates into the slope, and the state of the landslide movement can be approximately considered as pure solid; but under extreme rainfall conditions, a large amount of rainfall cannot infiltrate into the slope in time, and part of it will form surface runoff, carrying the landslide solid blocks together. At this time, the state of the landslide movement is a mixture of solid and liquid phases.

[0003] There are still two limitations in the numerical simulation of landslide solid-liquid two-phase mixture. First, few studies consider the impact of previous rainfall, and most of them only analyze the critical rainfall in a short period of time before the landslide occurs. For example, Zhang Han et al. (Zhang Han et al., Numerical simulation analysis of solid-liquid coupling process of composite landslide - taking Wushanping landslide as an example) used the smoothed particle hydrodynamics (SPH) method to analyze the coupling process of landslide solid-liquid two-phase, but only considered the heavy rainfall process that induced the landslide, without involving the previous rainfall; in addition, for solid-liquid two-phase mixture, it is very important to find out its liquid content, because it determines the viscosity of the mixture, and further determines the movement characteristics and potential harm of the mixture. However, few studies have studied the quantitative relationship between actual rainfall and the liquid content in the solid-liquid mixture. For example, Tang et al. (Tang et al., Assessing debris flow risk at a catchment scale for an economic decision based on the LiDAR DEM and numerical simulation) used FLO-2D software to analyze the movement speed and accumulation thickness of landslide solid-liquid mixture, but did not calculate the surface runoff based on rainfall. This makes it impossible to conduct effective numerical simulation and quantitative assessment of the danger of landslide solid-liquid two-phase mixtures under actual rainfall conditions.

[0004] The present invention quantitatively calculates the surface runoff under different antecedent rainfall and extreme rainfall conditions by taking into account the influence of topography, topsoil type, and surface vegetation, and then combines the FLO-2D software to realize the numerical simulation assessment of the danger of landslide solid-liquid two-phase mixture, which is of great significance for the effective implementation of landslide risk assessment and reasonable early warning. Summary of the invention

[0005] In view of the shortcomings of the prior art, the technical problem that the present invention intends to solve is to provide a numerical simulation method for solid-liquid two-phase mixtures of landslides under extreme climatic conditions, while taking into account previous rainfall and critical rainfall conditions, and being able to quantitatively calculate the conversion of extreme rainfall conditions and surface runoff under specific topsoil and vegetation coverage conditions, thereby realizing numerical simulation of solid-liquid two-phase mixtures of landslides, and the entire process is more reasonable and closer to the actual situation.

[0006] The technical solution adopted by the present invention to solve the technical problem is:

[0007] A numerical simulation method for a landslide solid-liquid two-phase mixture under extreme climatic conditions, the method comprising the following steps:

[0008] S1: Obtain the digital elevation model (DEM) of the landslide body, soil type (S), surface vegetation type (LULC), daily rainfall (in mm), daily temperature (in °C), daily evapotranspiration (in mm) within one month before the landslide, and the extreme rainfall value (pe, in mm) that induced the landslide;

[0009] S2: Input the daily rainfall, daily temperature and daily evapotranspiration within one month before the landslide into the Eas_Bal software to calculate the rainfall infiltration value qa before the landslide, in mm / d; at the same time, calculate the groundwater level caused by the previous rainfall, expressed in ha;

[0010] The rainfall intensity I is calculated according to formula (6):

[0011]

[0012] Among them, I d is the rainfall infiltration value before the landslide occurred, q a , unit: mm / d; Tc is the water collection time, calculated by formula (7):

[0013]

[0014] Where L is the longest distance from the watershed to the outlet of the basin, j is the average slope angle;

[0015] S3: The groundwater level he caused by the extreme rainfall value pe that induces landslide is expressed by formula (4):

[0016]

[0017] Where, CN is the runoff curve number, which is only related to the vegetation type. Different vegetation types have different CN values; n is the porosity of the soil;

[0018] The runoff coefficient C is calculated using formula (2):

[0019]

[0020] Where Ia is the initial extraction amount calculated by CN, which refers to all the parts of rainfall except the runoff generated, and is expressed by formula (5):

[0021] Ia=ha+he-z (5)

[0022] Where z is the thickness of the soil layer;

[0023] S4: Calculate the surface runoff value R under the combined conditions of the rainfall infiltration value qa before the landslide and the extreme rainfall value pe that induces the landslide according to formula (8), unit: m 3 / s:

[0024] R=CI 3 T c 2 (8);

[0025] S5: input the surface runoff value R obtained in step S4 as clean water flow data into FLO-2D software, use FLO-2D software to simulate historical landslide events to perform parameter inversion of solid-liquid mixture, obtain the flow velocity of the landslide solid-liquid mixture and the thickness distribution of the deposits, and determine the model parameters that are consistent with the actual situation;

[0026] S6: Obtain daily meteorological data in the study area for many years, including actual atmospheric rainfall, temperature, and evapotranspiration, and use Eas_Bal software to obtain the previous rainfall infiltration value qa for each month; take the maximum rainfall of two consecutive days each month as the extreme rainfall value pe of the month; calculate the previous rainfall effective infiltration qa and extreme rainfall pe in the study area under different recurrence periods;

[0027] S7: The qa and pe values ​​of different return periods obtained in step S6 are used as input, together with the digital elevation model (DEM), soil type (S), and surface vegetation type (LULC) of the study area, to calculate the surface runoff value under the return period; then the surface runoff value under the return period is used as the clear water flow value, together with the model parameters consistent with the actual situation in step S5, and input into the FLO-2D software to perform numerical simulation of the landslide solid-liquid mixture under future rainfall conditions. The numerical simulation results obtained in FLO-2D include the landslide flow velocity and the thickness of the deposit, and the landslide risk is then evaluated based on the landslide flow velocity and the thickness of the deposit.

[0028] The process of assessing landslide risk based on landslide flow velocity and deposit thickness is:

[0029] The landslide flow velocity is divided into three intervals according to whether it is less than 0.5 and whether it is less than 1.0: 0≤v<0.5, 0.5≤v<1.0, v≥1.0. The deposit thickness h is divided into three intervals according to whether it is less than 0.5 and whether it is less than 2.5: 0≤h<0.5, 0.5≤h<2.5, h≥2.5. A hazard assessment matrix is ​​constructed with the landslide flow velocity as the row and the deposit thickness as the column. By comprehensively considering the joint influence of the velocity and the deposit thickness, the entire area is divided into low-risk area, medium-risk area and high-risk area; among them, the unit of v is m / s and the unit of h is m.

[0030] In step S6, the calculation formula of Weibull distribution developed by Matlab software is used to calculate the effective infiltration amount qa of the previous rainfall and the extreme rainfall pe of the study area under different return periods;

[0031] The formula for the cumulative probability function P(x) of the Weibull distribution is:

[0032]

[0033] Among them, P(x) is the cumulative probability function of variable x, A is the scale parameter of Weibull distribution, k is the scale parameter of Weibull distribution, and the relationship between them is:

[0034]

[0035] Where Γ is the Gamma function.

[0036] In step S5, the model parameters include Manning coefficient, yield stress, viscosity coefficient and laminar flow resistance coefficient. The model parameters are assigned according to the conditions of the study area and the specification manual. The volume (m 3 ), the unit of surface runoff value is m 3 / s, and the total flow of surface runoff (m 3 );

[0037] The volume of landslide solid matter / (total flow of surface runoff+volume of landslide solid matter) is taken as the volume concentration Cv of landslide solid-liquid mixture; and the amplification factor B is determined according to B=1 / (1-Cv);

[0038] The volume concentration and amplification factor are input into FLO-2D, and then the numerical simulation of the landslide solid-liquid mixture is carried out to calculate the flow velocity of the solid-liquid mixture and the thickness distribution of the deposits;

[0039] Compare the calculated results with the actual situation. If they are consistent with the actual situation, it means that the model parameter assignment is reasonable. If they are significantly different from the actual situation, the model parameters need to be adjusted and recalculated until the accuracy requirements are met and the model parameters consistent with the actual situation are obtained.

[0040] The groundwater level rise value ha caused by the previous rainfall is expressed by formula (1):

[0041]

[0042] Where a is the upstream catchment area at a particular location, determined by terrain elevation, b is the cell size, K is the soil permeability coefficient, θ is the slope angle, and ρ w is the density of water, ρ s is the density of the soil layer.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] (1) Extreme rainfall can easily induce landslides. If the rainfall cannot penetrate into the soil layer in time, surface runoff will be formed to carry solid rocks. The surface runoff formed by extreme rainfall is related to the actual previous soil moisture content, but currently few studies have considered the impact of previous rainfall on surface runoff. The present invention considers both previous rainfall and short-term extreme rainfall conditions to calculate the surface runoff, which is a useful supplement to the calculation and evaluation of landslide solid-liquid two-phase mixtures;

[0045] (2) For the numerical simulation of solid-liquid mixtures, the volume concentration determines the viscosity of the mixture, and further determines the magnitude of its potential disaster-causing effect. The present invention calculates the surface runoff through rainfall conditions, and inputs the value as clear water flow data into the FLO-2D software to perform numerical simulation of the landslide solid-liquid two-phase mixture. The quantitatively calculated surface runoff is linked to the volume concentration and the amplification factor, which can better simulate the movement speed of the object and the thickness of the deposit.

[0046] (3) Surface runoff is the part of rainfall that flows on the surface after infiltration, evaporation, and evapotranspiration are removed. Calculating surface runoff based on rainfall requires considering the impact of previous rainfall, which can cause the groundwater level to rise by ha. It is also necessary to consider the soil permeability coefficient and vegetation type at the same time, making the calculation of surface runoff more accurate. At the same time, using accurate surface runoff for landslide stability research is a significant improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Schematic diagram of tributary area calculation using D8 algorithm.

[0048] Figure 2Movement velocity distribution diagram of the solid-liquid two-phase mixture in Chengbeigou landslide during parameter calibration.

[0049] Figure 3 Distribution map of deposit thickness during parameter calibration of solid-liquid two-phase mixture of Chengbeigou landslide.

[0050] Fig. 4 Distribution of movement velocity of solid-liquid two-phase mixture in Xiachengbeigou landslide during the 100-year rainfall return period.

[0051] Fig. 5 Distribution of the thickness of the deposits of the solid-liquid two-phase mixture of the Xiachengbeigou landslide during the 100-year rainfall return period.

[0052] Fig. 6 Hazard assessment results of solid-liquid two-phase mixture of Xiachengbeigou landslide with a rainfall return period of 100 years. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Of course, the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0054] The numerical simulation method of a landslide solid-liquid two-phase mixture under extreme climatic conditions of the present invention comprises the following steps:

[0055] S1: Obtain the digital elevation model (DEM) of the landslide body, soil type (S), surface vegetation type (LULC), daily rainfall (in mm), daily temperature (in °C), daily evapotranspiration (in mm) within one month before the landslide, and the extreme rainfall value (pe, in mm) that induced the landslide;

[0056] The daily rainfall, daily temperature, and daily evapotranspiration within one month before the landslide occurred were input into the Eas_Bal software to calculate the rainfall infiltration value qa (unit: mm / d) before the landslide occurred. Then, the surface runoff value R (unit: m) was calculated under the combined conditions of the rainfall infiltration value qa before the landslide occurred and the extreme rainfall value pe that induced the landslide using the runoff curve number (CN) method. 3 / s).

[0057] According to formula (1), the groundwater level rise value ha caused by previous rainfall can be calculated:

[0058]

[0059] Where a is the upstream catchment area at a particular location, determined by terrain elevation, and a is the tributary area, which is calculated using the standard D8 algorithm (e.g. Figure 1(shown in Figure 2) was calculated in ArcGIS; b is the cell size, K is the soil permeability coefficient, θ is the slope angle, ρ w is the density of water, ρ s is the density of the soil layer.

[0060] The runoff curve number (CN) method is used to determine the surface runoff volume under given extreme rainfall conditions, which requires rainfall intensity and runoff coefficient.

[0061] Use formula (2) to calculate the runoff coefficient C:

[0062]

[0063] Where pe is the extreme rainfall that triggers landslides, I a It is the initial extraction volume calculated by CN (unit: mm, refers to all parts of rainfall except the runoff generated, including infiltration, evapotranspiration, etc.).

[0064] The groundwater level caused by the previous rainfall is ha. At this time, the thickness of the soil layer not infiltrated by groundwater, hn, can be calculated using formula (3):

[0065] hn=z-ha (3)

[0066] Among them, z is the original soil layer thickness;

[0067] The groundwater level he caused by the extreme rainfall value pe that induces landslide is expressed by formula (4):

[0068]

[0069] Where, CN is the runoff curve number, which is only related to the vegetation type. Different vegetation types have different CN values; n is the porosity of the soil. Since the thickness of the slope soil layer is certain, the maximum value of he cannot be greater than hn. Then Ia is the part of the sum of ha and he that exceeds the thickness of the soil layer:

[0070] Ia=ha+he-z (5)

[0071] The rainfall intensity I is calculated using the rainfall intensity-duration frequency curve:

[0072]

[0073] Among them, I d is the rainfall infiltration value before the landslide occurred, q a , unit: mm / d; Tc is the water collection time, calculated by the following formula:

[0074]

[0075] Where L is the longest distance from the watershed to the outlet of the basin, and j is the average slope angle.

[0076] According to formula (8), the surface runoff value R under the combined conditions of the rainfall infiltration value qa before the landslide and the extreme rainfall value pe that induced the landslide was calculated (in m2). 3 / s:

[0077] R=CI 3 T c 2 (8).

[0078] In the above, formula (8) is the surface runoff model, which is used to calculate the surface runoff value under given rainfall conditions.

[0079] S2: Input the surface runoff value R obtained in step S1 as the clean water flow data into the FLO-2D software, use the FLO-2D software to simulate the historical landslide event to perform parameter inversion of the solid-liquid mixture, obtain the flow velocity of the landslide solid-liquid mixture and the thickness distribution of the deposits, and determine the model parameters that are consistent with the actual situation; specifically:

[0080] The surface runoff value R obtained in step S1 is input into FLO-2D software as the clean water flow data. The model parameters such as Manning coefficient, yield stress, viscosity coefficient, laminar flow resistance coefficient, etc. are assigned according to the study area conditions and the specification manual. The surface runoff value is taken as liquid. The unit of surface runoff value is m 3 / s, combined with the time of landslide movement, the total flow of surface runoff (m 3 ), and determine the volume of landslide solid matter through engineering survey (m 3 ), the volume of landslide solid matter / (total flow of surface runoff + volume of landslide solid matter) is taken as the volume concentration Cv of landslide solid-liquid mixture; and the amplification factor B is determined according to formula (9);

[0081] B=1 / (1-Cv) (9)

[0082] The volume concentration and amplification factor are input into FLO-2D, and then the numerical simulation of the landslide solid-liquid mixture is carried out to calculate the flow velocity of the solid-liquid mixture and the thickness distribution of the deposits;

[0083] Compare the calculated results with the actual situation. If they are consistent with the actual situation, it means that the model parameter assignment is reasonable. If they are significantly different from the actual situation, the model parameters need to be adjusted and recalculated until the accuracy requirements are met and the model parameters consistent with the actual situation are obtained.

[0084] S3: Obtain daily meteorological data (actual atmospheric rainfall, temperature, and evapotranspiration) in the study area for many years, and use Eas_Bal software to obtain the previous rainfall infiltration value qa for each month; the maximum rainfall of two consecutive days per month is taken as the extreme rainfall value pe for that month. The calculation formula of Weibull distribution developed by Matlab software is used to calculate the previous rainfall infiltration value qa and extreme rainfall pe in the study area under different recurrence periods.

[0085] In S3, the formula of the cumulative probability function P(x) of the Weibull distribution is as follows:

[0086]

[0087] Among them, P(x) is the cumulative probability function of variable x, A is the scale parameter of Weibull distribution, k is the scale parameter of Weibull distribution, and the relationship between them is as follows:

[0088]

[0089] Where Γ is the Gamma function.

[0090] Specifically: set the recurrence period, use the monthly previous rainfall infiltration values ​​obtained above as input, obtain the previous rainfall infiltration values ​​of the study area under the recurrence period, and use the monthly extreme rainfall as input to obtain the extreme rainfall in the study area under the recurrence period; the recurrence period is the influence of the probability of rainfall events, and different recurrence periods refer to once in 5 years, once in 20 years or once in 100 years, etc. The probability of once in 5 years is 1 / 5=0.2, and the probability of once in 20 years is 1 / 20=0.05.

[0091] S4: Take the qa and pe values ​​of different return periods obtained in S3 as input, together with the digital elevation model (DEM), soil type (S), and surface vegetation type (LULC) of the study area, and calculate the surface runoff value under the return period. Take the surface runoff value under the return period as liquid, and calculate the volume concentration Cv and amplification factor of the landslide solid-liquid mixture at this time.

[0092] The surface runoff value under the recurrence period is taken as the clear water flow value, together with the model parameters consistent with the actual situation in step S2, the volume concentration Cv and the amplification factor of the landslide solid-liquid mixture obtained in step S4, and input into the FLO-2D software to carry out the numerical simulation of the landslide solid-liquid mixture under future rainfall conditions, and obtain the distribution map of the landslide flow velocity and the deposit thickness. The numerical simulation results obtained in FLO-2D include the landslide flow velocity and the deposit thickness.

[0093] S5: According to the data of landslide flow velocity and deposit thickness in various parts of the area obtained in S4, the landslide flow velocity is divided into three intervals according to whether it is less than 0.5 and whether it is less than 1.0: 0≤v<0.5, 0.5≤v<1.0, v≥1.0; the deposit thickness h is divided into three intervals according to whether it is less than 0.5 and whether it is less than 2.5: 0≤h<0.5, 0.5≤h<2.5, h≥2.5. A hazard assessment matrix is ​​constructed with landslide flow velocity as rows and deposit thickness as columns (as shown in Table 1). By comprehensively considering the joint influence of velocity and deposit thickness, the entire area is divided into low-risk area, medium-risk area and high-risk area.

[0094] Table 1 Hazard matrix of landslide solid-liquid two-phase mixture

[0095]

[0096] According to the combined relationship between the landslide flow velocity and the deposit thickness calculated in Table 1, the landslide hazard level of a specific location is determined, which is divided into low-risk area, medium-risk area and high-risk area.

[0097] Example

[0098] 1. Based on the Eas_Bal software, the previous rainfall infiltration value one month before the occurrence of the embodiment is calculated, the DEM, land use, soil type and rainfall data when the landslide occurs are obtained, and the surface runoff value under the rainfall conditions is calculated.

[0099] The embodiment is located in Chengbeigou, Ji County, Linfen City, Shanxi Province, and the Luliang Mountain area in the eastern part of the Loess Plateau of China. On July 3, 2013, the area recorded a heavy rainfall event with a cumulative value of more than 100 mm, with a maximum hourly rainfall of 54.6 mm. After the rain, rainwater mixed with soil particles, gravel, and stones, moved along the downstream of the ditch, destroying courtyards and residential houses. The event observed in Chengbeigou this time is considered to be a landslide caused by extreme rainfall. Long-term monitoring data of meteorological stations in the study area were obtained from the local meteorological department, covering a total of 20 years from 2001 to 2020. The daily atmospheric rainfall, daily temperature average, and daily evapotranspiration for one month before the landslide occurred were entered in the EASY_BAL software, and the previous rainfall infiltration value for this one-month period was calculated, and the cumulative previous rainfall infiltration value was obtained to be 0.15 mm / d. The DEM of the study area comes from the Geospatial Data Cloud ( https: / / www.gscloud.cn / ); the land use types include 4 categories, namely forest, shrub, grassland and village; the soil types mainly include 3 types, namely Quaternary loess, red soil and brown soil; the previous rainfall infiltration value adopts the calculated numerical result, and a unified value is adopted in the whole area; the extreme rainfall adopts the rainfall raster file that induces the Chengbeigou landslide, and the surface runoff value under this rainfall condition is obtained by using the path curve number method, which is 710m 3 / s.

[0100] 2. Use the surface runoff value to calculate the volume concentration and amplification factor of the solid-liquid mixture of the Chengbeigou landslide, and simulate it in the FLO-2D software. After comparing the simulation output results (velocity and deposit thickness) with the actual situation, calibrate the calculation model parameters. The adjusted model parameter results will be used as the basic parameters when applying FLO-2D in subsequent steps.

[0101] After obtaining the surface runoff value when the landslide occurred, the volume concentration and amplification factor of the solid-liquid mixture of the Chengbeigou landslide were calculated. In the confluence model of FLO-2D, the intersection of the two branches in the upstream area is used as the water collection point. The obtained surface runoff value is imported into the grid where the water collection point is located in the form of HYD as the clean water flow data. According to the conditions of the study area, the Manning coefficient, yield stress, viscosity coefficient, laminar resistance coefficient and other parameters are assigned according to the table in the specification manual, and the rainfall duration similar to the real event is set. Then the movement process of the Chengbeigou landslide is simulated in the FLO-2D software. The calculated velocity distribution and deposit thickness of the landslide solid-liquid mixture are shown as follows: Figure 2 and Figure 3 As shown in the figure, it is consistent with the actual situation, indicating that the model parameters at this time are accurate. Therefore, the model parameters such as the Manning coefficient, yield stress, viscosity coefficient, and laminar flow resistance coefficient used at this time will also be used as subsequent model parameters for numerical simulation.

[0102] 3. The calculation formula of Weibull distribution developed by Matlab software was used to calculate the previous rainfall infiltration value and extreme rainfall in the study area under different return periods.

[0103] The EASY_BAL software and meteorological station monitoring data were used to calculate the monthly rainfall infiltration value qa in the study area from 2001 to 2020. At the same time, the maximum rainfall for two consecutive days per month during this period was counted and regarded as the short-term extreme rainfall for that month. a and p e Time series (480 q a Data, 480p e Data), and the calculation code of Weibull distribution developed by Matlab software was used to calculate the previous rainfall infiltration value (qa) and extreme rainfall (pe) in the study area under the return period of 2-100 years. The results showed that when the return period was 100 years, the previous rainfall infiltration value was 4.2mm / d and the extreme rainfall was 306mm.

[0104] 4. Using the rainfall data obtained under the 100-year return period, calculate the surface runoff in this case again, which is 908m 3 / s. Then recalculate the volume concentration and amplification factor of the landslide solid-liquid two-phase mixture at this time. Use the surface runoff value as the clear water flow data, and together with the calibrated calculation parameters, calculate the landslide flow velocity distribution and deposit thickness at a return period of 100 years in FLO-2D, respectively. Figure 4 and Figure 5 shown.

[0105] 5. Comprehensively consider the velocity distribution of the solid-liquid mixture and the thickness of the deposit, and use the hazard matrix to determine its hazard level.

[0106] According to Table 1, the landslide flow velocity v is divided into 3 levels: 0≤v<0.5, 0.5≤v<1.0, v≥1.0; the accumulation thickness h is divided into 3 levels: 0≤h<0.5, 0.5≤h<2.5, h≥2.5. Then, according to Table 1, the final landslide hazard zoning map is calculated in ArcGIS according to the logical relationship of the grid, as shown in the figure. Figure 6 As shown in Figure 1, where levels 1, 2, and 3 represent low-risk areas, medium-risk areas, and high-risk areas, respectively. Figure 6 Can show which places are dangerous and which places are safer.

[0107] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

[0108] Any matters not described in the present invention are applicable to the prior art.

Claims

1. A numerical simulation method for solid-liquid two-phase mixture of landslide under extreme climatic conditions, characterized in that: The method comprises the following steps: S1: Obtain the digital elevation model of the landslide body, soil type, surface vegetation type, daily rainfall, daily temperature, daily evapotranspiration within one month before the landslide, and the extreme rainfall value pe that induces the landslide; where the unit of daily rainfall is mm, the unit of daily temperature is ℃, the unit of daily evapotranspiration is mm, and the unit of extreme rainfall value pe that induces the landslide is mm; S2: Input the daily rainfall, daily temperature and daily evapotranspiration within one month before the landslide into the Eas_Bal software to calculate the rainfall infiltration value q before the landslide occurred. a , unit: mm / d; meanwhile, h is used to calculate the groundwater level caused by previous rainfall. a express; Calculate the rainfall intensity I according to formula (6): Among them, I d is the rainfall infiltration value q before the landslide occurred a , unit: mm / d; T c is the water collection time, calculated by formula (7): Where L is the longest distance from the watershed to the outlet of the basin, j is the average slope angle; S3: The groundwater level h caused by the extreme rainfall value pe that induces landslide e It can be expressed as: Where, CN is the runoff curve number, which is only related to the vegetation type. Different vegetation types have different CN values; n is the porosity of the soil; The runoff coefficient C is calculated using formula (2): Among them, I a is the initial extraction amount calculated by CN, which refers to all the parts of rainfall except the generated runoff, expressed by formula (5): I a =h a +h e -z(5) Where z is the thickness of the soil layer; The groundwater level rise value caused by the previous rainfall h a Expressed by formula (1): Where a is the upstream catchment area at a particular location, determined by terrain elevation, b is the cell size, K is the soil permeability coefficient, θ is the slope angle, and ρ w is the density of water, ρ s is the density of the soil layer; S4: Calculate the rainfall infiltration value q before the landslide according to formula (8) a The surface runoff value R under the combined conditions of the extreme rainfall value pe that induces landslide, unit: m 3 / s: R=CI 3 T c 2 (8); Where C is the runoff coefficient, I is the rainfall intensity, T c It is the water collection time; S5: input the surface runoff value R obtained in step S4 as clean water flow data into FLO-2D software, use FLO-2D software to simulate historical landslide events to perform parameter inversion of solid-liquid mixture, obtain the flow velocity of the landslide solid-liquid mixture and the thickness distribution of the deposits, and determine the model parameters that are consistent with the actual situation; S6: Obtain daily meteorological data for the study area over the past few years. Daily meteorological data include actual atmospheric rainfall, temperature, and evapotranspiration. Use Eas_Bal software to obtain the previous rainfall infiltration value q for each month. a ; The maximum rainfall of two consecutive days in a month is taken as the extreme rainfall value pe of that month; Calculate the previous rainfall infiltration value q of the study area under different return periods a and extreme rainfall values ​​pe; S7: The q with different return periods obtained in step S6 is a and pe as input, together with the digital elevation model, soil type, and surface vegetation type of the study area, the surface runoff value under the return period is calculated; then the surface runoff value under the return period is used as the clear water flow value, together with the model parameters in step S5 that are consistent with the actual situation, and is input into the FLO-2D software to perform a numerical simulation of the landslide solid-liquid mixture under future rainfall conditions. The numerical simulation results obtained in FLO-2D include the landslide flow velocity and the thickness of the deposit, and the landslide risk is then evaluated based on the landslide flow velocity and the thickness of the deposit.

2. The numerical simulation method according to claim 1, characterized in that: The process of assessing landslide risk based on landslide flow velocity and deposit thickness is: The landslide flow velocity is divided into three intervals according to whether it is less than 0.5 and whether it is less than 1.0: 0≤v<0.5, 0.5≤v<1.0, v≥1.

0. The deposit thickness h is divided into three intervals according to whether it is less than 0.5 and whether it is less than 2.5: 0≤h<0.5, 0.5≤h<2.5, h≥2.

5. A hazard assessment matrix is ​​constructed with the landslide flow velocity as the row and the deposit thickness as the column. By comprehensively considering the joint influence of the velocity and the deposit thickness, the entire area is divided into low-risk area, medium-risk area and high-risk area; among them, the unit of v is m / s and the unit of h is m.

3. The numerical simulation method according to claim 1, characterized in that: In step S6, the calculation formula of Weibull distribution developed by Matlab software is used to calculate the previous rainfall infiltration value q of the study area under different return periods. a and extreme rainfall values ​​pe; The formula for the cumulative probability function P(x) of the Weibull distribution is: Among them, P(x) is the cumulative probability function of variable x, A is the scale parameter of Weibull distribution, k is the scale parameter of Weibull distribution, and the relationship between them is: Where Γ is the Gamma function.

4. The numerical simulation method according to claim 1, characterized in that: In step S5, the model parameters include Manning coefficient, yield stress, viscosity coefficient and laminar flow resistance coefficient. The model parameters are assigned according to the study area conditions and the specification manual. The volume of landslide solid matter is determined by engineering survey means. The unit of surface runoff value is m 3 / s, combined with the time of landslide movement to determine the total flow of surface runoff; the unit of volume of landslide solid matter is m 3 The unit of total flow of surface runoff is m 3 ; The volume of landslide solid matter / (total flow of surface runoff+volume of landslide solid matter) is taken as the volume concentration Cv of landslide solid-liquid mixture; and the amplification factor B is determined according to B=1 / (1-Cv); The volume concentration and amplification factor are input into FLO-2D, and then the numerical simulation of the landslide solid-liquid mixture is carried out to calculate the flow velocity of the solid-liquid mixture and the thickness distribution of the deposits; Compare the calculated results with the actual situation. If they are consistent with the actual situation, it means that the model parameter assignment is reasonable. If they are significantly different from the actual situation, the model parameters need to be adjusted and recalculated until the accuracy requirements are met and the model parameters consistent with the actual situation are obtained.

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