Method, device and computer equipment for simulating freeze-thaw runoff in frozen soil area

Abstract

The present application provides a method, device and computer equipment for simulating freeze-thaw runoff in frozen soil areas. The method for simulating freeze-thaw runoff in frozen soil areas obtains data of the research area, and determines the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost according to the data of the research area. According to the maximum freezing depth and the maximum thawing depth, a curve of the soil hydraulic conductivity varying with the soil depth is determined. Based on the distributed hydrological model and the curve of the soil hydraulic conductivity varying with the soil depth, the freeze-thaw runoff in the frozen soil area is simulated to obtain a simulation result. By using the present application, the simulation accuracy of soil freeze-thaw runoff can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of flood forecasting in frozen soil areas, and particularly relates to a method, device and computer equipment for simulating freeze-thaw runoff in frozen soil areas. Background Art

[0002] The frozen soil phenomenon refers to the freezing of soil or rock containing moisture with a temperature below 0°C. The formation of runoff in frozen soil areas is greatly affected by air temperature. An increase in air temperature will cause the intensification of frozen soil ablation, resulting in an increase in runoff. Frozen soil ablation is one of the important sources of runoff formation in frozen soil area basins. The frozen soil hydrological model is an important means for hydrological forecasting in frozen soil areas, providing a scientific basis for flood warning and forecasting in cold regions.

[0003] According to different research purposes, the distributed frozen soil hydrological model for a basin can be divided into two categories: a process model mainly simulating the complex coupling of freeze-thaw processes and hydrological processes, and a model mainly simulating runoff. The process model mainly simulating the complex coupling of freeze-thaw processes and hydrological processes has a perfect physical mechanism and is suitable for revealing the interaction mechanism between soil freeze-thaw processes and hydrological processes. However, it has a large amount of input data, many model parameters and high operation costs, and is limited by the difficulty of obtaining basin data, resulting in low calculation efficiency.

[0004] The model mainly simulating runoff focuses on the simulation of basin runoff. Usually, on the basis of the existing distributed frozen soil hydrological model, a parameterization method is used to generalize and abstract the freeze-thaw runoff process, aiming to accurately simulate the runoff process. This type of model usually only considers frozen soil as a static frozen layer, or simply classifies it, and cannot simulate the dynamic changes of the soil freeze-thaw process. The advantage of this type of model is that the amount of input data is relatively small, the calculation efficiency is high, and it can meet the timeliness requirements of runoff simulation and forecasting at the basin scale.

[0005] However, the model mainly simulating runoff oversimplifies the description of the freeze-thaw runoff process and fails to reflect the interaction between the freeze-thaw process and the hydrological process, and may still not be able to improve the simulation effect of freeze-thaw runoff, that is, the simulation accuracy is low. Summary of the Invention

[0006] Based on this, the present application proposes a method, device and computer equipment for simulating freeze-thaw runoff in frozen soil areas to improve the simulation effect of freeze-thaw runoff, that is, to improve the runoff simulation accuracy.

[0007] In a first aspect, the present application provides a method for simulating freeze-thaw runoff in frozen soil areas. The method includes:

[0008] Obtain research area data;

[0009] Determine the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost according to the research area data;

[0010] Determine the curve of the soil hydraulic conductivity varying with the soil depth according to the maximum freezing depth and the maximum thawing depth;

[0011] Based on the distributed hydrological model and the curve of the soil hydraulic conductivity varying with the soil depth, conduct freeze-thaw runoff simulation in the permafrost region to obtain the simulation results.

[0012] In one embodiment, the research area data includes: meteorological data, topographic data, land use data, soil parameter data, vegetation data, runoff data, and the spatial distributions of the seasonal permafrost and the multi-year permafrost corresponding to the research area.

[0013] In one embodiment, the determining the maximum freezing depth of the seasonal permafrost and the maximum thawing depth of the multi-year permafrost according to the research area data includes:

[0014] Determine the maximum freezing depth of the seasonal permafrost and the maximum thawing depth of the multi-year permafrost based on the Stefan formula according to the research area data.

[0015] In one embodiment, the determining the maximum freezing depth of the seasonal permafrost and the maximum thawing depth of the multi-year permafrost according to the research area data includes:

[0016] Determine the daily negative accumulated temperature and the daily positive accumulated temperature according to the research area data;

[0017] Determine the maximum freezing depth of the seasonal permafrost according to the daily negative accumulated temperature;

[0018] Determine the maximum thawing depth of the multi-year permafrost according to the daily positive accumulated temperature.

[0019] In one embodiment, the determining the daily negative accumulated temperature and the daily positive accumulated temperature according to the research area data includes:

[0020] Determine the soil freezing start time and the soil thawing start time according to the research area data;

[0021] Obtain the daily average temperature below 0°C from the soil freezing start time to the calculation date, and accumulate and sum them to obtain the daily negative accumulated temperature;

[0022] Obtain the daily average temperature below 0°C from the soil thawing start time to the calculation date, and accumulate and sum them to obtain the daily positive accumulated temperature.

[0023] In one embodiment, the determining the curve of the soil hydraulic conductivity varying with the soil depth according to the maximum freezing depth and the maximum thawing depth includes:

[0024] Determine the first conversion coefficient of the soil hydraulic conductivity of the seasonal permafrost according to the maximum freezing depth;

[0025] Determine a second conversion coefficient of the hydraulic conductivity of the permafrost soil according to the maximum thawing depth;

[0026] Determine the curve of the hydraulic conductivity of the soil varying with the soil depth according to the first conversion coefficient and the second conversion coefficient.

[0027] In a second aspect, the present application also provides a freeze-thaw runoff simulation device for a frozen soil area. The freeze-thaw runoff simulation device for a frozen soil area includes: a data acquisition module, configured to acquire data of a research area; a data processing module, configured to determine the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost according to the data of the research area, and determine the curve of the hydraulic conductivity of the soil varying with the soil depth according to the maximum freezing depth and the maximum thawing depth; a data simulation module, configured to perform freeze-thaw runoff simulation for the frozen soil area based on a distributed hydrological model and the curve of the hydraulic conductivity of the soil varying with the soil depth, and obtain a simulation result.

[0028] In one embodiment, the data of the research area includes:

[0029] Meteorological data, topographic data, land use data, soil parameter data, vegetation data, runoff data corresponding to the research area, and the spatial distributions of the seasonal frozen soil and the permafrost.

[0030] In one embodiment, the data processing module is further configured to:

[0031] Determine the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost based on Stefan's formula according to the data of the research area.

[0032] In one embodiment, the data processing module is further configured to:

[0033] Determine the daily negative accumulated temperature and the daily positive accumulated temperature according to the data of the research area;

[0034] Determine the maximum freezing depth of seasonal frozen soil according to the daily negative accumulated temperature;

[0035] Determine the maximum thawing depth of permafrost according to the daily positive accumulated temperature.

[0036] In one embodiment, the data processing module is further configured to:

[0037] Determine the soil freezing start time and the soil thawing start time according to the data of the research area;

[0038] Obtain the daily average temperature below 0°C from the soil freezing start time to the calculation date, and accumulate and sum them to obtain the daily negative accumulated temperature;

[0039] Obtain the daily average temperature below 0°C from the soil thawing start time to the calculation date, and accumulate and sum them to obtain the daily positive accumulated temperature.

[0040] In one embodiment, the data processing module is further configured to:

[0041] Determine a first conversion coefficient of the soil hydraulic conductivity of the seasonal frozen soil according to the maximum freezing depth;

[0042] Determine a second conversion coefficient of the soil hydraulic conductivity of the permafrost according to the maximum thawing depth;

[0043] Determine a curve of the soil hydraulic conductivity varying with the soil depth according to the first conversion coefficient and the second conversion coefficient.

[0044] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method described in any one of the above embodiments are implemented.

[0045] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the steps of the method described in any one of the above embodiments are implemented.

[0046] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of the above embodiments are implemented.

[0047] The method, device and computer device for simulating freeze-thaw runoff in a frozen soil area provided by the present application obtain data of a research area, and determine the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost according to the data of the research area. According to the maximum freezing depth and the maximum thawing depth, a curve of the soil hydraulic conductivity varying with the soil depth is determined. Based on a distributed hydrological model and the curve of the soil hydraulic conductivity varying with the soil depth, freeze-thaw runoff simulation in the frozen soil area is carried out to obtain a simulation result. Compared with the existing distributed hydrological model that considers frozen soil as a static frozen layer, the method, device and computer device for simulating freeze-thaw runoff in a frozen soil area provided by the present application add a curve of the soil hydraulic conductivity varying with the soil depth on the basis of the distributed hydrological model during the freeze-thaw runoff simulation process. Since the curve of the soil hydraulic conductivity varying with the soil depth can reflect the dynamic changes of the soil freeze-thaw process, that is, the present application considers the dynamic changes of the soil freeze-thaw process during the freeze-thaw runoff simulation process, so the present application can perform dynamic freeze-thaw runoff simulation, and thus can improve the simulation accuracy of the runoff of the distributed hydrological model. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a schematic flowchart of a method for simulating freeze-thaw runoff in a frozen soil area according to an embodiment of the present application.

[0049] Figure 2 It is a schematic diagram of the research area in an embodiment of the present application.

[0050] Figure 3 It is a schematic diagram of the process for determining the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost in an embodiment of the present application.

[0051] Figure 4 It is a schematic diagram of the process for determining daily negative accumulated temperature and daily positive accumulated temperature in an embodiment of the present application.

[0052] Figure 5 It is a schematic diagram of the process for determining the curve of soil hydraulic conductivity varying with soil depth in an embodiment of the present application.

[0053] Figure 6 It is a schematic diagram of the measured flow time series in the research area and the flow time series obtained by simulating a distributed hydrological model without considering the variation of soil hydraulic conductivity with soil depth in an embodiment of the present application.

[0054] Figure 7 It is a schematic diagram of the measured flow time series in the research area and the flow time series obtained by the freeze-thaw runoff simulation method in the frozen soil area of the present application.

[0055] Figure 8 It is a schematic diagram of the freeze-thaw runoff simulation device in the frozen soil area in an embodiment of the present application.

[0056] Figure 9 It is an internal structure diagram of a computer device in an embodiment of the present application. Detailed implementation manners

[0057] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] In one embodiment, as Figure 1 shown, a freeze-thaw runoff simulation method in a frozen soil area is provided. In this embodiment, the method is exemplified by being applied to a terminal. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0059] Step 101, obtain research area data.

[0060] Among them, the research area is the area where technicians need to simulate freeze-thaw runoff for flood forecasting, and this research area includes a permafrost area. The research area data is data related to the simulation of freeze-thaw runoff in the research area, which can be obtained from various data websites, and the embodiments of the present application do not specifically limit the research area data.

[0061] Step 103: Determine the maximum freezing depth of seasonal permafrost and the maximum thawing depth of permafrost according to the research area data.

[0062] Among them, seasonal permafrost and permafrost are two types of permafrost areas in the research area. The maximum freezing depth of seasonal permafrost and the maximum thawing depth of permafrost can be determined respectively according to the research area data.

[0063] Among them, the maximum freezing depth is the maximum value of the freezing depth when seasonal permafrost freezes, and the maximum thawing depth is the maximum value of the thawing depth when permafrost thaws. The embodiments of the present application do not specifically limit the determination methods of the maximum freezing depth and the maximum thawing depth. For example: the maximum freezing depth of seasonal permafrost and the maximum thawing depth of permafrost can be determined by using corresponding calculation formulas, that is, substituting the research area data into the calculation formulas of the maximum freezing depth of seasonal permafrost and the maximum thawing depth of permafrost respectively to calculate the maximum freezing depth of seasonal permafrost and the maximum thawing depth of permafrost; or the maximum freezing depth of seasonal permafrost and the maximum thawing depth of permafrost can be estimated by using the hydrothermal coupling physical equation method; or the maximum freezing depth of seasonal permafrost and the maximum thawing depth of permafrost can be predicted by using a pre-trained neural network.

[0064] Step 105: Determine the curve of the soil hydraulic conductivity varying with the soil depth according to the maximum freezing depth and the maximum thawing depth.

[0065] In the embodiments of the present disclosure, in the seasonal permafrost area, the soil layers above the maximum freezing depth and the soil layers below the maximum freezing depth in the soil have different soil hydraulic conductivities. In the permafrost area, the soil layers above the maximum thawing depth and the soil layers below the maximum thawing depth in the soil have different soil hydraulic conductivities. The soil hydraulic conductivities of seasonal permafrost and permafrost are also different. Therefore, the curve of the soil hydraulic conductivity varying with the soil depth in the seasonal permafrost area can be determined according to the maximum freezing depth, and the curve of the soil hydraulic conductivity varying with the soil depth in the permafrost area can be determined according to the maximum thawing depth.

[0066] Step 107: Based on the distributed hydrological model and the curve of the soil hydraulic conductivity varying with the soil depth, conduct freeze-thaw runoff simulation in the permafrost area to obtain the simulation results.

[0067] Exemplarily, a distributed hydrological model (GBHM, Geomorphology Based Hydrological Model) can be constructed using the data of the study area. The distributed hydrological model can calculate the coupled soil water-heat transfer process using physical equations, emphasizing the accurate simulation of the basin water cycle. This distributed hydrological model is a process model for simulating hydrological processes. In the embodiments of the present application, the runoff simulation method based on the distributed hydrological model and the curve of soil hydraulic conductivity varying with soil depth mainly focuses on simulating runoff. The parametric method is used to generalize and abstract the freeze-thaw runoff process to obtain the curve of soil hydraulic conductivity varying with soil depth, and the freeze-thaw runoff is simulated based on the curve of soil hydraulic conductivity varying with soil depth.

[0068] That is, in the embodiments of the present disclosure, a mathematical model for determining the curve of soil hydraulic conductivity varying with soil depth can be embedded on the basis of the distributed hydrological model. The curve of soil hydraulic conductivity varying with soil depth can be used to characterize the process of land freeze-thaw. That is, in the embodiments of the present disclosure, the calculation steps of the land freeze-thaw process are added during the freeze-thaw runoff simulation process, and a new distributed frozen soil hydrological model (including the distributed hydrological model and the mathematical model for determining the curve of soil hydraulic conductivity varying with soil depth) is established. Technicians can obtain the simulation results by simulating the freeze-thaw runoff of the study area using the distributed frozen soil hydrological model.

[0069] For the above-mentioned freeze-thaw runoff simulation method in the frozen soil area, the data of the study area is obtained, and according to the data of the study area, the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost are determined. According to the maximum freezing depth and the maximum thawing depth, the curve of soil hydraulic conductivity varying with soil depth is determined. Based on the distributed hydrological model and the curve of soil hydraulic conductivity varying with soil depth, the freeze-thaw runoff in the frozen soil area is simulated to obtain the simulation results. Compared with the existing distributed hydrological model that considers frozen soil as a static frozen layer, during the freeze-thaw runoff simulation process, on the basis of the distributed hydrological model, the curve of soil hydraulic conductivity varying with soil depth is added. Since the curve of soil hydraulic conductivity varying with soil depth can reflect the dynamic changes of the soil freeze-thaw process, that is, in the present application, the dynamic changes of the soil freeze-thaw process are considered during the freeze-thaw runoff simulation process. Therefore, the freeze-thaw runoff can be simulated dynamically, and thus the simulation accuracy of the runoff of the distributed hydrological model can be improved.

[0070] In one embodiment, the data of the study area includes the meteorological data, topographic data, land use data, soil parameter data, vegetation data, runoff data, and the spatial distribution of seasonal frozen soil and permafrost corresponding to the study area.

[0071] Exemplarily, see Figure 2, the study area can be the source region of the Yangtze River. Meteorological data can include daily precipitation, daily temperature (including daily average temperature, daily maximum temperature and daily minimum temperature), daily average wind speed, daily average relative humidity, and sunshine hours, etc. The daily-scale soil freezing depth data and soil temperature data can be provided by two meteorological stations in the source region of the Yangtze River. The above data can be obtained from the "National Meteorological Science Data Sharing Service Platform" (http: / / data.cma.cn) of the China Meteorological Administration Meteorological Data Center.

[0072] Topographic data can be obtained using the digital elevation model provided by the SRTM (shuttle radar topography mission) dataset. Land use data can be the thematic dataset of the national 1:100,000 land use information system provided by the Cold and Arid Regions Science Data Center. The land use data can be downloaded from the website of the Cold and Arid Regions Science Data Center (http: / / westdc.westgis.ac.cn).

[0073] Soil parameter data includes soil composition ratio data and soil hydraulic parameters. The soil composition ratio data uses the Harmonized World Soil Database (HWSD) released by the Food and Agriculture Organization of the United Nations. The spatial accuracy of this data is 0.00833°. Soil hydraulic parameters include the saturated water content, residual water content, saturated hydraulic conductivity of the soil, and the parameters α and n required for the soil water-heat coupling transport calculation in the distributed hydrological model. The soil hydraulic parameter data can come from the China Soil Hydrology Dataset, and the spatial accuracy of this data is 0.00833°.

[0074] Vegetation data can include the leaf area index of the vegetation. The leaf area index of the vegetation can use the LAI3g data with a spatial accuracy of 0.0833° provided by Boston University, USA. The temporal accuracy of this data is one scene every 15 days.

[0075] The runoff data of the Zhimenda Hydrological Station at the outlet of the source region of the Yangtze River can be obtained from the Hydrology Bureau of the Ministry of Water Resources of the People's Republic of China.

[0076] The spatial distribution of permafrost and seasonal frozen soil can be simulated using the Distributed Geomorphology-Based Ecohydrological Model (GBEHM), combined with the above daily-scale soil freezing depth data, to obtain the spatial distribution of permafrost and seasonal frozen soil in the source region of the Yangtze River.

[0077] It should be noted that in this application, there is no specific limitation on the acquisition method of the data of each research area. Any method that can obtain the research area data for realizing the freeze-thaw runoff simulation method in the permafrost area of this application is applicable to the embodiments of this application.

[0078] In one embodiment, in step 103, according to the research area data, determining the maximum freezing depth of seasonal permafrost and the maximum thawing depth of permafrost for many years may include: based on the Stefan formula according to the research area data, determining the maximum freezing depth of seasonal permafrost and the maximum thawing depth of permafrost for many years.

[0079] In the embodiments of the present disclosure, traditional distributed hydrological models use the method of coupling water and heat physical equations to estimate the soil freezing and thawing depths. Although this method has a complete physical mechanism, it has a high degree of complexity and high computational cost. In the embodiments of the present disclosure, the Stefan formula can be used to calculate the maximum freezing depth of seasonal permafrost and the maximum thawing depth of permafrost for many years respectively, which can simplify the complexity of the distributed hydrological model calculation and improve the computational efficiency. The calculation of the heat and water transfer processes in the soil by the Stefan formula is mainly based on the following three simplified assumptions: (1) The acquisition and dissipation of heat in the soil are both in the form of heat conduction, that is, heat radiation, heat convection and other forms are not considered; (2) The sensible heat of the soil near the freezing front can be ignored, and the latent heat of phase change is the main heat source for soil freezing and thawing; (3) The soil temperature in the frozen soil layer changes linearly with depth, that is, the soil temperature profile line in the frozen layer is a straight line.

[0080] In one embodiment, referring to Figure 3 , in step 103, according to the research area data, determining the maximum freezing depth of seasonal permafrost and the maximum thawing depth of permafrost for many years includes:

[0081] Step 301, determining the daily negative accumulated temperature and the daily positive accumulated temperature according to the research area data.

[0082] Among them, the research area data includes meteorological data such as daily precipitation, daily air temperature (including daily average air temperature, daily maximum air temperature and daily minimum air temperature), daily average wind speed, daily average relative humidity, and sunshine hours. The daily negative accumulated temperature and the daily positive accumulated temperature can be obtained from the daily average air temperature. The daily negative accumulated temperature is the sum of the daily average air temperatures below 0°C from the start date of land freezing to the calculation date in a year. The daily positive accumulated temperature is the sum of the daily average air temperatures below 0°C from the start date of land thawing to the calculation date in a year.

[0083] Step 303, determining the maximum freezing depth of seasonal permafrost according to the daily negative accumulated temperature.

[0084] In the embodiments of the present disclosure, the maximum freezing depth of seasonal permafrost is positively correlated with the daily negative accumulated temperature.

[0085] Exemplarily, the maximum frost depth of seasonal frozen soil satisfies the following formula (1).

[0086]

[0087] Wherein, Z1 is the maximum frost depth of the soil (m), and k f is the thermal conductivity of unsaturated soil (W m -1 K -1 ), n f is the conversion coefficient between air temperature and ground temperature, τ is the daily time step (8.64×10 4 s day -1 ), L f is the latent heat of fusion per unit mass of water (3.34×10 5 J kg -1 ), ρ w is the density of liquid water (1.00×10 3 kg m -3 ), θ liq is the volumetric water content of liquid water in the soil (m 3 m -3 ), I f is the daily negative accumulated temperature (K day).

[0088] Step 305, determine the maximum thaw depth of permafrost according to the daily positive accumulated temperature.

[0089] In the embodiments of the present disclosure, the maximum thaw depth of permafrost is positively correlated with the daily positive accumulated temperature.

[0090] Exemplarily, the maximum thaw depth of permafrost satisfies the following formula (2).

[0091]

[0092] Wherein, Z2 is the maximum thaw depth of the soil (m), and I t is the daily positive accumulated temperature (K day).

[0093] The conversion coefficient n f between air temperature and ground temperature is one of the important parameters for estimating the frost and thaw depths of soil, and is also an empirical coefficient widely used in the field of frozen soil engineering in cold regions. The conversion coefficient n f between air temperature and ground temperature is defined as the freezing index of the soil surface temperature divided by the freezing index of the air temperature. The conversion coefficient between air temperature and ground temperature at the point scale can be derived by using the long-term observation data of the surface temperature and air temperature observed at meteorological stations. Furthermore, according to the land use data in the research area data, a conversion coefficient between air temperature and ground temperature corresponding to each land use type can be obtained.

[0094] The thermal conductivity k fIt directly affects the estimation of the maximum frost depth of the soil and is one of the sensitive parameters in the Stefan formula. The Johansen solution can be used to estimate the thermal conductivity k of unsaturated soil f The thermal conductivity k of unsaturated soil f satisfies the following formula (III).

[0095] k f =(k sat -k dry )K e +k dry Formula (III)

[0096] where k sat is the thermal conductivity of saturated soil (W m -1 K -1 ), k dry is the thermal conductivity of dry soil (W m -1 K -1 ). K e is the Kostyakov number and is usually estimated using soil saturation. K e satisfies the following formula (IV).

[0097]

[0098] where S r is defined as the degree of saturation of the soil, θ liq is the volumetric water content of liquid water in the soil (m 3 m -3 ), θ sat is the saturated water content of the soil (m 3 m -3 ).

[0099] The thermal conductivity k of saturated soil sat can be estimated by weighted average according to the thermal conductivity of each component in the frozen soil and its volume ratio. The thermal conductivity k of saturated soil sat satisfies the following formula (V).

[0100]

[0101] where λ w is the thermal conductivity of liquid water (0.57 W m -1 K -1 ), λ i is the thermal conductivity of ice (2.2 W m -1 K -1 ), λ s is the thermal conductivity of solid phase particles in the soil. λ s satisfies the following formula (VI).

[0102] λ s = λ q δ ·λ o 1-δ Formula (VI)

[0103] Where λ q is the thermal conductivity of quartz particles in the soil (7.7 W m -1 K -1 ), λ o is the average thermal conductivity of other components in the soil (2.0 W m -1 K -1 ), and δ is the volume fraction content of quartz in the soil. δ can be estimated by the gravel content of the soil and taken as 50% of the gravel content.

[0104] The thermal conductivity k of dry soil dry can be empirically estimated based on the dry density of the soil. The thermal conductivity k of dry soil dry satisfies the following Formula (VII).

[0105]

[0106] Where ρ d is the dry density of the soil (kg m -3 ). ρ d can be determined according to the soil type dataset.

[0107] In one embodiment, referring to Figure 4 , in step 301, determining the daily negative accumulated temperature and the daily positive accumulated temperature according to the research area data includes:[[]]

[0108] Step 401, determining the soil freezing start time and the soil melting start time according to the research area data.

[0109] In the embodiments of the present disclosure, for the maximum freezing depth of seasonal frozen soil, the soil freezing start time is an important variable for determining the daily negative accumulated temperature. The soil freezing start time can be defined as the time when the temperature is lower than 0°C for three consecutive days for the first time in the second half of the year. The soil freezing start time can be determined through the meteorological data in the research area data.

[0110] For the maximum melting depth of permafrost, the soil melting start time is an important variable for determining the daily positive accumulated temperature. The soil melting start time can be defined as the time when the temperature is higher than 0°C for three consecutive days for the first time in the first half of the year. The soil melting start time can be determined through the meteorological data in the research area data.

[0111] Step 403, obtaining the daily average temperature below 0°C from the soil freezing start time to the calculation date and accumulating them to obtain the daily negative accumulated temperature.

[0112] In an embodiment of the present disclosure, the daily negative accumulated temperature can be the sum of the daily average temperatures below 0°C from the start time of soil freezing to the calculation date. Exemplarily, I f The daily negative accumulated temperature satisfies the following formula (VIII).

[0113]

[0114] where T i is the daily average temperature on the i-th day, and m is the number of days when the daily average temperature is below 0°C during the period from the start time of soil freezing to the calculation date.

[0115] Step 405: Obtain the daily average temperatures below 0°C from the start time of soil thawing to the calculation date, and accumulate and sum them to obtain the daily positive accumulated temperature.

[0116] In an embodiment of the present disclosure, the daily positive accumulated temperature can be the sum of the daily average temperatures below 0°C from the start time of soil thawing to the calculation date. Exemplarily, I t The daily positive accumulated temperature satisfies the following formula (IX).

[0117]

[0118] where T i is the daily average temperature on the i-th day, and m is the number of days when the daily average temperature is above 0°C during the period from the start time of soil thawing to the calculation date.

[0119] In one embodiment, referring to Figure 5 , in step 105, determining the curve of the soil hydraulic conductivity varying with the soil depth according to the maximum freezing depth and the maximum thawing depth includes:

[0120] Step 501: Determine the first conversion coefficient of the soil hydraulic conductivity of the seasonal frozen soil according to the maximum freezing depth.

[0121] Among them, in the seasonal frozen soil area, for the soil layer above the maximum freezing depth in the soil, the first conversion coefficient of the soil hydraulic conductivity is 0.05. For the soil layer below the maximum freezing depth in the soil, the first conversion coefficient is calculated according to the proportion of the freezing depth in the soil layer thickness.

[0122] Step 503: Determine the second conversion coefficient of the soil hydraulic conductivity of the permafrost according to the maximum thawing depth.

[0123] Among them, in the permafrost area, for the soil layer above the maximum thawing depth in the soil, the second conversion coefficient of the soil hydraulic conductivity is 1. For the soil layer below the maximum thawing depth in the soil, the second conversion coefficient is calculated according to the proportion of the freezing depth in the soil layer thickness.

[0124] Both the first conversion coefficient and the second conversion coefficient satisfy the following formula (X).

[0125] fice = W+(1 - W)*0.05 Formula (X)

[0126] Where, when fice is the first conversion coefficient, W is the ratio of the freezing depth to the soil layer thickness; or when fice is the second conversion coefficient, W is the ratio of the thawing depth to the soil layer thickness.

[0127] Step 505: Obtain the curve of the soil hydraulic conductivity varying with the soil depth according to the first conversion coefficient and the second conversion coefficient.

[0128] In the embodiments of the present disclosure, the soil hydraulic conductivity at different depths of the seasonal frozen soil can be obtained through the first conversion coefficient. By multiplying the saturated soil hydraulic conductivity at different depths of the seasonal frozen soil by the first conversion coefficient of the soil hydraulic conductivity at different depths, the soil hydraulic conductivity at different depths of the seasonal frozen soil can be obtained, and then the curve of the soil hydraulic conductivity of the seasonal frozen soil varying with the soil depth can be obtained. Similarly, through the second conversion coefficient, the soil hydraulic conductivity at different depths of the permafrost soil can be obtained. By multiplying the saturated soil hydraulic conductivity at different depths of the permafrost soil by the second conversion coefficient of the soil hydraulic conductivity at different depths, the soil hydraulic conductivity at different depths of the permafrost soil can be obtained, and then the curve of the soil hydraulic conductivity of the permafrost soil varying with the soil depth can be obtained.

[0129] In the embodiments of the present application, a distributed frozen soil hydrological model with a spatial resolution of 2.5 km can be established in the Yangtze River source area. The distributed frozen soil hydrological model can be obtained by embedding a mathematical model for determining the curve of the soil hydraulic conductivity varying with the soil depth on the basis of the distributed hydrological model. Technicians perform freeze-thaw runoff simulation on the Yangtze River source area through the distributed frozen soil hydrological model to obtain the simulation results.

[0130] The evaluation indexes for the freeze-thaw runoff simulation effect of the distributed frozen soil hydrological model are the Nash efficiency coefficient NSE, the logarithmic Nash efficiency coefficient LNSE, and the percentage bias PBIAS. The Nash efficiency coefficient NSE, the logarithmic Nash efficiency coefficient LNSE, and the percentage bias PBIAS respectively satisfy the following Formulas (XI), (XII), and (XIII).

[0131]

[0132] Where is the measured daily freeze-thaw runoff flow of the Yangtze River source area on the t-th day is the simulated daily freeze-thaw runoff flow of the Yangtze River source area on the t-th day through the distributed frozen soil hydrological model is the average measured daily flow value within the T time period.

[0133] In the source region of the Yangtze River, the entire distributed frozen soil hydrological model is calibrated using the measured runoff data of the Zhimenda Hydrological Station. The calibration period of the distributed frozen soil hydrological model is from 1981 to 1990, and the verification period is from 1991 to 2000. See Figure 6 , at the Zhimenda Hydrological Station in the source region of the Yangtze River, the runoff simulation of the GBHM model shows that: during the calibration period, PBIAS is -19.03%, NSE is 0.56, and LNSE is 0.65; during the verification period, PBIAS is -22.97%, NSE is 0.57, and LNSE is 0.69. See Figure 7 , the runoff simulation of the distributed frozen soil hydrological model with the addition of freeze-thaw runoff simulation (i.e., the mathematical model of the curve of soil hydraulic conductivity varying with soil depth) shows that: during the calibration period, PBIAS is -4.91%, NSE is 0.68, and LNSE is 0.77; during the verification period, PBIAS is -9.77%, NSE is 0.84, and LNSE is 0.77.

[0134] The percentage bias PBIAS is used to evaluate the simulation effect of the model on the overall water volume. The closer PBIAS is to 0, the better the simulation effect; the Nash efficiency coefficient NSE is used to evaluate the simulation effect of the model on the hydrograph. The closer NSE is to 1, the better the model effect; the logarithmic Nash efficiency coefficient LNSE focuses on the simulation effect of the low-flow hydrograph. Similarly, the closer LNSE is to 1, the better the model effect. Therefore, the distributed frozen soil hydrological model with the addition of freeze-thaw runoff simulation (i.e., the mathematical model of the curve of soil hydraulic conductivity varying with soil depth) has improved the simulation of the overall water volume, hydrograph, and low flow in the study area.

[0135] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least some of the steps or stages in other steps or other steps.

[0136] In one embodiment, see Figure 8 , a freeze-thaw runoff simulation device 800 in a frozen soil area is provided, including: a data acquisition module 801, a data processing module 802, and a data simulation module 803, where:

[0137] The data acquisition module 801 is used to acquire data of the study area.

[0138] A data processing module 802, configured to determine the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost according to the research area data, and determine the curve of the soil hydraulic conductivity varying with the soil depth according to the maximum freezing depth and the maximum thawing depth.

[0139] A data simulation module 803, configured to perform freeze-thaw runoff simulation in the frozen soil area based on the distributed hydrological model and the curve of the soil hydraulic conductivity varying with the soil depth, and obtain the simulation result.

[0140] For the freeze-thaw runoff simulation device in the frozen soil area provided by the embodiment of the present application, the data acquisition module acquires the research area data. The data processing module determines the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost according to the research area data, and determines the curve of the soil hydraulic conductivity varying with the soil depth according to the maximum freezing depth and the maximum thawing depth. The data simulation module performs freeze-thaw runoff simulation in the frozen soil area based on the distributed hydrological model and the curve of the soil hydraulic conductivity varying with the soil depth, and obtains the simulation result. Compared with the existing distributed hydrological model that considers frozen soil as a static frozen layer, in the process of freeze-thaw runoff simulation, the freeze-thaw runoff simulation device provided by the present application adds the curve of the soil hydraulic conductivity varying with the soil depth on the basis of the distributed hydrological model. Since the curve of the soil hydraulic conductivity varying with the soil depth can reflect the dynamic changes in the soil freeze-thaw process, that is, the above-mentioned freeze-thaw runoff simulation device in the process of freeze-thaw runoff simulation considers the dynamic changes in the soil freeze-thaw process, so the freeze-thaw runoff simulation can be performed dynamically, and thus the simulation accuracy of the runoff of the distributed hydrological model can be improved.

[0141] In one embodiment, the research area data includes: meteorological data, topographic data, land use data, soil parameter data, vegetation data, runoff data, and the spatial distribution of seasonal frozen soil and permafrost corresponding to the research area.

[0142] In one embodiment, the data acquisition module 801 is further configured to determine the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost based on the Stefan formula according to the research area data.

[0143] In one embodiment, the data acquisition module 801 is further configured to determine the daily negative accumulated temperature and the daily positive accumulated temperature according to the research area data, determine the maximum freezing depth of seasonal frozen soil according to the daily negative accumulated temperature, and determine the maximum thawing depth of permafrost according to the daily positive accumulated temperature.

[0144] In one embodiment, the data acquisition module 801 is further configured to determine the soil freezing start time and the soil thawing start time according to the research area data, obtain the daily average temperature below 0°C from the soil freezing start time to the calculation date, and accumulate and sum them to obtain the daily negative accumulated temperature; obtain the daily average temperature below 0°C from the soil thawing start time to the calculation date, and accumulate and sum them to obtain the daily positive accumulated temperature.

[0145] In one embodiment, the data acquisition module 801 is further configured to determine the first conversion coefficient of the soil hydraulic conductivity of seasonal frozen soil according to the maximum freezing depth, determine the second conversion coefficient of the soil hydraulic conductivity of permafrost according to the maximum thawing depth, and determine the curve of the soil hydraulic conductivity varying with the soil depth according to the first conversion coefficient and the second conversion coefficient.

[0146] Each module in the above-mentioned freeze-thaw runoff simulation device in the frozen soil area can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the computer device in the form of hardware or be independent of it, or can be stored in the memory of the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules.

[0147] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 9 shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a freeze-thaw runoff simulation method in the frozen soil area. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0148] Those skilled in the art can understand that Figure 9 the structure shown in

[0149] In one embodiment, a computer device is provided. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the freeze-thaw runoff simulation method in any of the above embodiments are implemented.

[0150] In one embodiment, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the steps of the freeze-thaw runoff simulation method in any of the above embodiments are implemented.

[0151] In one embodiment, a computer program product is provided. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the freeze-thaw runoff simulation method in any of the above embodiments are implemented.

[0152] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above-mentioned embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., and are not limited thereto.

[0153] The technical features of the above-mentioned embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0154] The above-mentioned embodiments only represent several implementation manners of the present application, but should not be construed as limiting the scope of the patent of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A simulation method for freeze-thaw runoff in frozen soil regions, characterized in that, including: obtaining research area data, where the research area data at least includes daily average temperature; determining the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost according to the research area data; determining a first conversion coefficient of the soil hydraulic conductivity at different depths of the seasonal frozen soil according to the maximum freezing depth; determining a second conversion coefficient of the soil hydraulic conductivity at different depths of the permafrost according to the maximum thawing depth; determining a curve of the soil hydraulic conductivity varying with the soil depth according to the product of the first conversion coefficient at different depths of the seasonal frozen soil and the saturated soil hydraulic conductivity at different depths, and the product of the second conversion coefficient at different depths of the permafrost and the saturated soil hydraulic conductivity at different depths; performing freeze-thaw runoff simulation in the frozen soil area based on a distributed hydrological model and the curve of the soil hydraulic conductivity varying with the soil depth to obtain a simulation result; wherein, for the soil layer above the maximum freezing depth in the seasonal frozen soil, the first conversion coefficient is 0.05; for the soil layer above the maximum thawing depth in the permafrost, the second conversion coefficient is 1; for the soil layer below the maximum freezing depth in the seasonal frozen soil, or for the soil layer below the maximum thawing depth in the permafrost, the first conversion coefficient or the second conversion coefficient is calculated by the following formula: fice = W + (1 - W) * 0.05; wherein, when fice is the first conversion coefficient, W is the ratio of the freezing depth to the soil layer thickness; or when fice is the second conversion coefficient, W is the ratio of the thawing depth to the soil layer thickness.

2. The freeze-thaw runoff simulation method in the permafrost region according to claim 1, characterized in that, The research area data further includes: meteorological data, topographic data, land use data, soil parameter data, vegetation data, runoff data, and the spatial distributions of the seasonal frozen soil and the permafrost corresponding to the research area.

3. The method for simulating freeze-thaw runoff in frozen soil areas according to claim 1, characterized in that The determining the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost according to the research area data includes: determining the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost based on Stefan's formula according to the research area data.

4. The simulation method of freeze-thaw runoff in frozen soil areas according to any one of claims 1 to 3, characterized in that, The determining the maximum freezing depth of seasonal frozen soil and the maximum thawing depth of permafrost according to the research area data includes: determining the daily negative accumulated temperature and the daily positive accumulated temperature according to the research area data; determining the maximum freezing depth of seasonal frozen soil according to the daily negative accumulated temperature; determining the maximum thawing depth of permafrost according to the daily positive accumulated temperature.

5. The method for simulating freeze-thaw runoff in frozen soil areas according to claim 4, wherein, The determining the daily negative accumulated temperature and the daily positive accumulated temperature according to the research area data includes: determining the soil freezing start time and the soil thawing start time according to the research area data; obtaining the daily average temperature below 0°C from the soil freezing start time to the calculation date and accumulating them to obtain the daily negative accumulated temperature; obtaining the daily average temperature below 0°C from the soil thawing start time to the calculation date and accumulating them to obtain the daily positive accumulated temperature.

6. A freeze-thaw runoff simulation device in a frozen soil area, characterized in that, including: a data acquisition module for obtaining research area data, where the research area data at least includes daily average temperature; A data processing module, configured to determine the maximum frost depth of seasonal frozen soil and the maximum thaw depth of permafrost according to the research area data, and determine the first conversion coefficient of the soil hydraulic conductivity at different depths of the seasonal frozen soil according to the maximum frost depth; determine the second conversion coefficient of the soil hydraulic conductivity at different depths of the permafrost according to the maximum thaw depth; and determine the curve of the soil hydraulic conductivity varying with the soil depth according to the product of the first conversion coefficient and the soil saturated hydraulic conductivity at different depths of the seasonal frozen soil, and the product of the second conversion coefficient and the soil saturated hydraulic conductivity at different depths of the permafrost. A data simulation module, configured to perform freeze-thaw runoff simulation in the frozen soil area based on a distributed hydrological model and the curve of the soil hydraulic conductivity varying with the soil depth, and obtain a simulation result. Wherein, for the soil layer above the maximum frost depth in the seasonal frozen soil, the first conversion coefficient is 0.05; for the soil layer above the maximum thaw depth in the permafrost, the second conversion coefficient is 1. For the soil layer below the maximum frost depth in the seasonal frozen soil, or for the soil layer below the maximum thaw depth in the permafrost, the first conversion coefficient or the second conversion coefficient is calculated by the following formula: fice = W + (1 - W) * 0.05; Wherein, when fice is the first conversion coefficient, W is the ratio of the frost depth to the soil layer thickness; or when fice is the second conversion coefficient, W is the ratio of the thaw depth to the soil layer thickness.

7. The device according to claim 6, wherein The research area data further includes: The meteorological data, topographic data, land use data, soil parameter data, vegetation data, runoff data corresponding to the research area, and the spatial distributions of the seasonal frozen soil and the permafrost.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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