Hydrological simulation method and system based on space-ground-air integrated monitoring in frozen soil mountainous area

Abstract

The present disclosure provides a permafrost mountainous hydrological simulation method and system based on space-ground-air integrated monitoring, relating to the technical field of hydro-meteorological monitoring and water resources forecasting. The method comprises: obtaining permafrost hydrological monitoring data of a permafrost region through space-ground-air multi-monitoring equipment; the permafrost hydrological monitoring data comprises permafrost state and precipitation data; assimilating the permafrost hydrological monitoring data into a distributed hydrological model coupled with a permafrost runoff generation process to obtain a first distributed hydrological model; simulating runoff processes under different permafrost states and permafrost underlying surface hydrological characteristics through the first distributed hydrological model to dynamically quantify precipitation-runoff conversion efficiency. The present scheme can accurately simulate the runoff process in the permafrost region, and then reliably dynamically quantify the precipitation-runoff conversion efficiency.

Description

Technical Field

[0001] This disclosure relates to the field of hydrological and meteorological monitoring and water resources forecasting technology, specifically to a hydrological simulation method and system for permafrost mountainous areas based on integrated air-ground-space monitoring. Background Technology

[0002] In permafrost mountainous areas, the hydrological cycle exhibits significant seasonal characteristics. During winter, the surface freezes, resulting in extremely low soil water conductivity. In spring, as temperatures rise, the melting of snow and the thawing of the topsoil combine to easily lead to spring floods. Summer rainfall may interact with the partially thawed lower soil layers, affecting infiltration and runoff processes. Due to complex terrain and sparse human activity, many permafrost watersheds lack long-term, continuous hydrological monitoring stations, resulting in scarce measured runoff data.

[0003] Precipitation-runoff conversion efficiency is a commonly used indicator in hydrology, describing the proportion of precipitation converted into surface or subsurface runoff. Its value is influenced by various factors, including watershed underlying surface conditions, precipitation intensity, and antecedent soil moisture. In practical research, this efficiency is often obtained through observation or simulation and is used in fields such as water resource assessment, flood frequency analysis, and ecohydrological research.

[0004] Traditional methods for determining precipitation-runoff conversion efficiency in permafrost mountainous areas typically rely on direct ground observations, which makes it difficult to accurately simulate the runoff process and thus reliably quantify precipitation-runoff conversion efficiency dynamically.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this disclosure is to provide a hydrological simulation method and system for permafrost mountainous areas based on integrated air-ground-space monitoring, which can accurately simulate the runoff process in permafrost mountainous areas and reliably and dynamically quantify the precipitation-runoff conversion efficiency.

[0007] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part by practice of this disclosure.

[0008] According to a first aspect of the present disclosure, a method for simulating hydrology in permafrost mountainous areas based on integrated air-ground-space monitoring is provided, comprising:

[0009] Permafrost hydrological monitoring data is acquired through a multi-sensor system encompassing air, ground, and space monitoring devices; the permafrost hydrological monitoring data includes permafrost conditions and precipitation data.

[0010] The frozen soil hydrological monitoring data is assimilated into a distributed hydrological model coupled with the frozen soil runoff process to obtain the first distributed hydrological model.

[0011] The first distributed hydrological model is used to simulate runoff processes under different permafrost conditions and hydrological characteristics of the permafrost underlying surface, and to dynamically quantify precipitation-runoff conversion efficiency.

[0012] In some exemplary embodiments of this disclosure, based on the foregoing scheme, the multi-site monitoring equipment includes satellites, ground monitoring equipment, and aircraft;

[0013] The acquisition of permafrost hydrological monitoring data in permafrost mountainous areas through multi-site, ground, and air monitoring equipment includes:

[0014] The satellite is used to monitor the permafrost mountain area and obtain the snow water equivalent and soil moisture of the permafrost mountain area.

[0015] The ground monitoring equipment is used to monitor precipitation, obtain precipitation phase, precipitation intensity and the thickness of the active permafrost layer;

[0016] The aircraft scans the terrain and surface of the permafrost mountain area to obtain terrain and surface information of the permafrost mountain area.

[0017] In some exemplary embodiments of this disclosure, based on the foregoing scheme, after assimilating the permafrost hydrological monitoring data into a distributed hydrological model coupled with a permafrost runoff process to obtain a first distributed hydrological model, the method further includes:

[0018] The first distributed hydrological model is calibrated to obtain the second distributed hydrological model.

[0019] The process of simulating runoff processes under different permafrost conditions and hydrological characteristics of the underlying permafrost surface using the first distributed hydrological model, and dynamically quantifying precipitation-runoff conversion efficiency, includes:

[0020] The second distributed hydrological model is used to simulate runoff processes under different permafrost conditions and hydrological characteristics of the permafrost underlying surface, and to dynamically quantify precipitation-runoff conversion efficiency.

[0021] In some example embodiments of this disclosure, based on the foregoing scheme, the calibration of the first distributed hydrological model to obtain the second distributed hydrological model includes:

[0022] Obtain measured runoff data from the permafrost mountainous area;

[0023] Based on the measured runoff data, the parameters related to permafrost runoff generation in the first distributed hydrological model are calibrated to obtain a second distributed hydrological model; the fitting error between the runoff process simulated by the second distributed hydrological model and the measured runoff data during the calibration period is less than a preset error.

[0024] In some exemplary embodiments of this disclosure, based on the foregoing scheme, the step of simulating runoff processes under different permafrost conditions and hydrological characteristics of the permafrost underlying surface using the first distributed hydrological model, and dynamically quantifying precipitation-runoff conversion efficiency, includes:

[0025] Based on different permafrost conditions, the first distributed hydrological model is used to determine the corresponding hydrological characteristics of the underlying permafrost surface.

[0026] Simulate the runoff process corresponding to the precipitation data under different frozen soil conditions and corresponding hydrological characteristics of the frozen soil underlying surface;

[0027] For the aforementioned runoff process, the precipitation-runoff conversion efficiency is dynamically quantified.

[0028] In some exemplary embodiments of this disclosure, based on the foregoing scheme, the hydrological characteristics of the permafrost underlying surface include infiltration rate, water storage capacity, and flow path, wherein the flow path includes lateral interflow path and composite flow path structure;

[0029] The step of determining the corresponding hydrological characteristics of the underlying frozen soil surface based on different frozen soil states using the first distributed hydrological model includes:

[0030] When the frozen soil state indicates that the frozen soil is thawing, the effective hydrological action layer depth is dynamically expanded according to the thickness of the active frozen soil layer by the first distributed hydrological model to obtain the first infiltration rate. The first water storage capacity is updated based on the product of the effective hydrological action layer depth and the effective porosity of the soil, and the lateral interflow path of water in the unfrozen layer is obtained.

[0031] When the frozen soil state indicates frozen soil degradation, the first distributed hydrological model releases the constraints on vertical water transport based on the continuously thickening active layer or the locally connected unfrozen layer, thereby obtaining the second infiltration rate and the second water storage capacity, and obtaining the flow path structure that changes from surface-dominated to a combination of interbedded flow and shallow groundwater flow.

[0032] In some exemplary embodiments of this disclosure, based on the foregoing scheme, the dynamic quantification of precipitation-runoff conversion efficiency for the runoff process includes:

[0033] For each simulated time period of the runoff process, the runoff volume and precipitation within that simulated time period are obtained;

[0034] Based on the runoff and precipitation in each of the simulated time periods, the precipitation-runoff conversion efficiency is dynamically quantified.

[0035] According to a second aspect of the present disclosure, a hydrological simulation system for permafrost mountainous areas based on integrated air-ground-space monitoring is provided, comprising:

[0036] The frozen soil hydrological monitoring data acquisition module is used to acquire frozen soil hydrological monitoring data in frozen soil mountainous areas through multi-monitoring equipment from the ground, air, and space; the frozen soil hydrological monitoring data includes frozen soil status and precipitation data;

[0037] The monitoring data assimilation module is used to assimilate the frozen soil hydrological monitoring data into a distributed hydrological model coupled with the frozen soil runoff process to obtain the first distributed hydrological model.

[0038] The hydrological simulation module is used to simulate runoff processes under different frozen soil conditions and hydrological characteristics of the frozen soil underlying surface through the first distributed hydrological model, and dynamically quantify precipitation-runoff conversion efficiency.

[0039] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory storing computer-readable instructions, which, when executed by the processor, implement the hydrological simulation method for permafrost mountainous areas based on integrated air-ground-space monitoring as described in the first aspect.

[0040] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the hydrological simulation method for permafrost mountainous areas based on integrated air-ground-space monitoring as described in the first aspect.

[0041] The technical solutions provided in this disclosure may have the following beneficial effects:

[0042] The hydrological simulation method for permafrost mountainous areas based on integrated air-ground-space monitoring in the exemplary embodiments of this disclosure acquires permafrost hydrological monitoring data of permafrost mountainous areas through multiple monitoring devices in air-ground-space, including permafrost conditions and precipitation data. The permafrost hydrological monitoring data is assimilated into a distributed hydrological model coupled with permafrost runoff processes to obtain a first distributed hydrological model that can characterize actual permafrost conditions. This first distributed hydrological model can accurately simulate runoff processes under different permafrost conditions and hydrological characteristics of the permafrost underlying surface, thereby reliably and dynamically quantifying precipitation-runoff conversion efficiency and improving the accuracy of precipitation-runoff conversion efficiency.

[0043] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0044] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0045] Figure 1 The illustration shows a schematic diagram of a hydrological simulation method for permafrost mountainous areas based on integrated air-ground-space monitoring according to some embodiments of the present disclosure.

[0046] Figure 2 The illustration schematically shows a flowchart of the steps for dynamically quantifying precipitation-runoff conversion efficiency by simulating runoff processes under different permafrost conditions and hydrological characteristics of the permafrost underlying surface using a first distributed hydrological model, according to some embodiments of the present disclosure.

[0047] Figure 3 The diagram illustrates a hydrological simulation system for permafrost mountainous areas based on integrated air-ground-space monitoring, according to some embodiments of the present disclosure.

[0048] Figure 4 The schematic diagram illustrates the structural schematic of a computer system of an electronic device according to some embodiments of the present disclosure.

[0049] Figure 5 A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is shown.

[0050] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0051] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this specification as detailed in the appended claims.

[0052] Furthermore, the accompanying drawings are for illustrative purposes only and are not necessarily drawn to scale. The block diagrams shown in the drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0053] In this example embodiment, a hydrological simulation method for permafrost mountainous areas based on integrated air-ground-space monitoring is first provided. This method can be applied to hydrological monitoring, water resource management and disaster early warning systems in high-altitude permafrost regions, such as watershed hydrological simulation platforms, climate change hydrological response assessment systems, flash flood disaster risk early warning models or cold region eco-hydrological research tools, etc. Figure 1 The illustration schematically shows a flowchart of a hydrological simulation method for permafrost mountainous areas based on integrated air-ground-space monitoring, according to some embodiments of this disclosure. (Reference) Figure 1 As shown, the hydrological simulation method for permafrost mountainous areas based on integrated space-ground-ground monitoring may include the following steps:

[0054] Step S110: Obtain permafrost hydrological monitoring data in permafrost mountainous areas through multi-site monitoring equipment; the permafrost hydrological monitoring data includes permafrost status and precipitation data;

[0055] Step S120: Assimilate the frozen soil hydrological monitoring data into a distributed hydrological model coupled with the frozen soil runoff process to obtain the first distributed hydrological model.

[0056] Step S130: The runoff process under different frozen soil conditions and hydrological characteristics of the frozen soil underlying surface is simulated by the first distributed hydrological model to dynamically quantify the precipitation-runoff conversion efficiency.

[0057] According to the hydrological simulation method for frozen soil mountainous areas based on integrated air-ground-space monitoring in this example embodiment, frozen soil hydrological monitoring data of frozen soil mountainous areas is acquired through multiple monitoring devices in air-ground-space, including frozen soil conditions and precipitation data. The frozen soil hydrological monitoring data is assimilated into a distributed hydrological model coupled with frozen soil runoff processes to obtain a first distributed hydrological model that can characterize actual frozen soil conditions. This first distributed hydrological model can accurately simulate runoff processes under different frozen soil conditions and hydrological characteristics of the underlying frozen soil surface, thereby reliably and dynamically quantifying precipitation-runoff conversion efficiency and improving the accuracy of precipitation-runoff conversion efficiency.

[0058] The following will further explain the hydrological simulation method for permafrost mountainous areas based on integrated air-ground-space monitoring in this example embodiment.

[0059] In step S110, permafrost hydrological monitoring data of the permafrost mountain area is acquired through multi-site monitoring equipment; the permafrost hydrological monitoring data includes permafrost status and precipitation data.

[0060] Among them, the multi-site, ground, and air monitoring equipment refers to a collaborative monitoring system composed of three types of observation platforms: space-based (satellites), ground-based (ground monitoring equipment), and air-based (aircraft, including drones, manned aircraft, etc.). It is used to achieve multi-scale, high spatiotemporal resolution, and multi-dimensional joint observation of hydrological elements in permafrost areas.

[0061] Frozen soil mountainous areas refer to watershed areas with mountainous terrain features and widespread seasonal or perennial frozen soil. Below the surface, there are soil or rock layers that are frozen due to low temperatures and undergo periodic freeze-thaw processes on an interannual scale, resulting in significant dynamic changes in the hydrological characteristics of the underlying surface (including infiltration capacity, water storage capacity, and runoff path).

[0062] Depending on the duration of freezing, permafrost mountain areas can be categorized into perennial permafrost mountain areas and seasonal permafrost mountain areas. Perennial permafrost mountain areas refer to regions where the underground soil layer maintains a temperature below or equal to 0 degrees Celsius for two consecutive years or more. They typically have a vertical structure of "active layer – permafrost layer": the upper active layer thaws in the warm season and freezes in the cold season, while the lower permafrost layer remains unchanged year-round. Seasonal permafrost mountain areas refer to regions where the soil freezes only in winter and thaws in summer, with a shorter freezing period.

[0063] In permafrost mountainous areas, the frozen layer acts as a relatively impermeable boundary, restricting the vertical transport of water. This causes precipitation or snowmelt to primarily migrate within the active layer or form surface runoff. When permafrost degrades, the active layer thickens or continuous unfrozen channels appear, allowing water to penetrate deeper layers and altering the runoff pathway. In seasonally permafrost mountainous areas, the overall freezing of the soil in winter significantly reduces infiltration capacity, causing precipitation or snowmelt to quickly convert into surface runoff. However, the hydrological characteristics rapidly recover after thawing in spring.

[0064] For example, permafrost mountainous areas are mostly distributed in high-latitude or high-altitude cold regions. Their common characteristics are low average annual temperature, long freezing period, and the freeze-thaw cycle has a decisive influence on surface hydrological processes.

[0065] Permafrost hydrological monitoring data refers to the set of key observational data used to characterize the state of hydrological processes in permafrost mountainous areas, which may include permafrost conditions and precipitation data.

[0066] The state of permafrost refers to the physical freezing characteristics of permafrost under specific spatiotemporal conditions. In some embodiments, the state of permafrost may include a state representing permafrost thawing and a state representing permafrost degradation. In other embodiments, the state of permafrost may also include at least one of the following: active layer thickness (i.e., the depth from the surface to the top of the frozen layer, in meters), freezing depth, location of the freeze-thaw interface, soil freezing / thawing time, and permafrost type (e.g., seasonally frozen or perennially frozen). This state of permafrost can be obtained through ground-penetrating radar (GPR), thermistor chains, automatic permafrost monitoring stations, or by inverting the freeze-thaw state through satellite microwave remote sensing (such as SMAP (Soil Moisture Active Passive) or by inferring the surface freeze-thaw boundary through airborne thermal infrared imaging.

[0067] Precipitation data refers to meteorological observation information related to precipitation events, which may include precipitation time, precipitation intensity (precipitation per unit time, mm / h), total precipitation (mm), and precipitation phase (rain, snow, sleet, etc.). This precipitation data can be acquired through ground monitoring equipment such as automatic weather stations, rain and snow gauges, or laser precipitation monitoring instruments; in areas without ground station coverage, satellite remote sensing can be used for spatial interpolation, while measured ground precipitation data should be used to ensure accuracy.

[0068] In some implementations, hydrological monitoring data of permafrost mountainous areas are acquired through multi-site monitoring equipment, including: monitoring permafrost mountainous areas by satellite to obtain snow water equivalent and soil moisture; monitoring precipitation by ground monitoring equipment to obtain precipitation phase, precipitation intensity and thickness of active permafrost layer; and scanning the topography and surface of permafrost mountainous areas by aircraft to obtain topographic and surface information.

[0069] Snow water equivalent refers to the mass of liquid water contained in a unit area of ​​snow cover. It is a key parameter characterizing the potential for snowmelt runoff and can be obtained through inversion from passive microwave remote sensing satellites. Soil moisture reflects the water content of the unfrozen surface layer and is of great significance for estimating infiltration capacity and evapotranspiration. It can be obtained by L-band or C-band microwave radiometers or optical-thermal infrared fusion algorithms. Satellite observations have a wide coverage and strong periodicity, making them suitable for continuous monitoring of large-scale permafrost regions.

[0070] Ground-based monitoring equipment may include automatic weather stations, laser rain spectrometers, weighing rain and snow gauges, and integrated permafrost observation systems. Precipitation phase refers to the form of precipitation, such as rain, snow, or sleet, which can be automatically determined by a laser rain spectrometer combined with temperature thresholds. Precipitation intensity refers to the amount of precipitation per unit time, recorded in real-time by high-precision rain gauges. The thickness of the active permafrost layer refers to the vertical depth from the surface to the top of the frozen layer, a core indicator characterizing the permafrost state. This thickness can be dynamically determined by a thermistor array deployed in observation wells based on the location of the 0°C isotherm, or obtained through periodic scanning by portable ground-penetrating radar. Ground-based equipment provides high-precision point-scale data, which is a key input for validating remote sensing products and driving hydrological models.

[0071] Aircraft can include unmanned aerial vehicles (UAVs) or manned aircraft, equipped with lidar (LiDAR), high-resolution cameras, multispectral or thermal infrared sensors, etc. LiDAR aerial surveys can generate sub-meter level digital elevation models, accurately depicting micro-topographic features (such as frost heaves, thermomelt lakes, gullies, etc.), providing a basis for confluence paths for distributed hydrological models; optical or thermal infrared imaging can acquire land cover types (such as grassland, bare soil, snow cover), surface albedo, and surface temperature distribution, helping to identify freeze-thaw boundaries and surface hydrological connectivity.

[0072] After undergoing unified spatiotemporal coordinate alignment, quality control, and multi-source fusion processing, the aforementioned space-based, ground-based, and air-based monitoring data form a set of permafrost hydrological monitoring datasets covering multiple spheres of "atmosphere-surface-shallow subsurface," fully supporting the accurate assimilation of permafrost conditions and precipitation processes by subsequent hydrological models.

[0073] Step S120: Assimilate the frozen soil hydrological monitoring data into a distributed hydrological model coupled with the frozen soil runoff process to obtain the first distributed hydrological model.

[0074] Among them, the distributed hydrological model coupled with permafrost runoff generation process refers to an improved model that explicitly incorporates the hydrological physical mechanism of permafrost on the basis of a distributed hydrological model (such as the independently developed permafrost mountain hydrological model). This model regards the frozen layer as a dynamic impermeable boundary, determines the effective hydrological action layer depth in real time according to the permafrost state (such as the thickness of the active layer), and calculates infiltration, water storage, runoff generation and other processes based on this, thereby reflecting the regulatory effect of permafrost thawing or degradation on the hydrological characteristics of the underlying surface.

[0075] Assimilation refers to the fusion of observational data with model states to optimize initial model conditions or key parameters and improve simulation accuracy.

[0076] In some embodiments, a distributed hydrological model coupled with permafrost runoff processes can be pre-constructed.

[0077] The distributed hydrological model can use grids or sub-basins as basic computational units in space, with each unit independently calculating hydrological processes and achieving spatial coupling through a confluence module. In terms of physical mechanisms, it introduces hydrological constraints unique to permafrost, treating the frozen layer as a dynamically changing impermeable or weakly permeable boundary, the location and continuity of which are determined by the state of the permafrost (such as the thickness of the active layer and whether there is a continuous unfrozen layer).

[0078] Specifically, the distributed hydrological model incorporates permafrost control logic into its runoff generation module: when the soil is frozen, the effective hydrological layer depth is limited to the active layer, and soil units below the frozen layer are excluded from water transport calculations; infiltration capacity is dynamically adjusted based on the active layer thickness and unfrozen water content, typically significantly lower than under non-frozen conditions; the runoff generation mechanism prioritizes triggering over-permeable surface runoff, while simultaneously determining whether interflow is generated based on the thawing layer thickness and hydraulic conductivity. In permafrost degradation scenarios, if the distributed hydrological model identifies the downward shift of the permafrost top plate or the formation of continuous unfrozen channels, it automatically removes the restrictions on vertical water transport, allowing water to penetrate deeper soil layers and activating the shallow groundflow module, forming a composite runoff generation structure.

[0079] The construction of this distributed hydrological model can be extended based on the existing distributed hydrological framework. Coupling of the permafrost runoff generation process can be achieved by embedding a permafrost hydrothermal coupling submodule or introducing a state-dependent parameterization scheme. For example, soil freeze-thaw calculations can be added to the energy balance module to output the daily active layer thickness. This thickness, as a key state variable, drives the real-time updates of the effective hydrological layer depth, infiltration rate, water storage capacity, and flow path type in the hydrological module, thereby achieving bidirectional feedback between thermal and hydrological processes.

[0080] Furthermore, those skilled in the art will understand that the coupling method for permafrost runoff generation is not limited to the above implementation. A data-driven approach can be used to establish a mapping relationship between permafrost conditions and hydrological parameters, or a hybrid modeling strategy can be employed combining physical mechanisms with machine learning modules. Regardless of the method used, as long as the model can dynamically adjust its hydrological response mechanism according to the permafrost conditions, it is considered a "distributed hydrological model coupled with permafrost runoff generation." This model provides the necessary physical foundation and computational platform for subsequent assimilation of permafrost hydrological monitoring data, simulation of runoff processes under different permafrost scenarios, and dynamic quantification of precipitation-runoff conversion efficiency.

[0081] In some embodiments, the assimilation operation may include: using satellite-retrieved snow water equivalent and soil moisture as inputs to a spatial continuous field to initialize the snow accumulation module and surface soil moisture content of the model; using precipitation phase and precipitation intensity acquired by ground monitoring equipment as high temporal resolution meteorological driving data and inputting them into the precipitation module of the distributed hydrological model; using the thickness of the active permafrost layer as a key state variable to directly constrain the effective hydrological action layer depth of each computational unit in the distributed hydrological model, that is, the distributed hydrological model only allows water to infiltrate and store vertically within the active layer, and the layer below the frozen layer is set as an impermeable layer; at the same time, high-precision terrain information (such as digital elevation models) acquired by aircraft is used to construct the model's runoff network and slope flow paths.

[0082] The assimilation process can employ deterministic methods (such as direct replacement and optimal interpolation) or stochastic methods (such as ensemble Kalman filtering), depending on actual computational resources and accuracy requirements. After assimilation, the internal state of the model remains consistent with the external observation data in time and space. The resulting first distributed hydrological model is a dynamic simulation platform capable of characterizing the current real permafrost-hydrological coupling state in permafrost mountainous areas, providing a foundation for subsequent runoff process simulations under different permafrost scenarios.

[0083] Furthermore, in other embodiments, assimilation can also be achieved through other methods, such as optimal interpolation, variational assimilation (3D-Var / 4D-Var), particle filtering, etc., or it can be combined with machine learning methods to construct an observation-model mapping relationship to assist in state updates. All of the above methods can effectively integrate permafrost hydrological monitoring data into the first distributed hydrological model without departing from the core ideas of this disclosure; therefore, the specific implementation of assimilation is not limited to these. Through this step, the first distributed hydrological model can be transformed from a general state into a customized simulation platform that can reflect the real permafrost conditions of the target area, laying the foundation for subsequent runoff process simulations under different permafrost scenarios.

[0084] Step S130: The runoff process under different frozen soil conditions and hydrological characteristics of the frozen soil underlying surface is simulated by the first distributed hydrological model to dynamically quantify the precipitation-runoff conversion efficiency.

[0085] Different permafrost states may include, but are not limited to: frozen state, seasonal thawing state, and permafrost degradation state.

[0086] The hydrological characteristics of the underlying surface of frozen soil refer to the hydrological behavior characteristics of the surface and shallow soil determined by the state of frozen soil. These characteristics can include infiltration rate, water storage capacity and runoff path (flow path), etc. These characteristics are not preset fixed values ​​in the model and can be dynamically generated according to the assimilated state of frozen soil.

[0087] For example, when the active layer is thin or completely frozen, the first distributed hydrological model can set the effective hydrological layer depth to a minimum or zero, the infiltration rate approaches zero, the water storage capacity is limited, and the runoff is mainly surface runoff. When the active layer thickens or unfrozen channels appear, the first distributed hydrological model can correspondingly expand the effective hydrological layer, improve the infiltration capacity and water storage capacity, and allow the formation of interflow or shallow groundwater.

[0088] Under the different permafrost conditions and their corresponding underlying hydrological characteristics, the precipitation data of the first distributed hydrological model is used as the driving input to simulate the surface runoff, interflow and groundwater runoff processes at time steps (such as hourly or daily scales), and finally outputs the total runoff sequence of each calculation unit and the watershed outlet.

[0089] Based on the runoff simulation results, the precipitation-runoff conversion efficiency is further dynamically quantified: for each preset simulation time period (such as a single precipitation event, day, month, or freeze-thaw season), the total runoff output by the model within that time period is extracted and the total precipitation of the corresponding time period is calculated. The ratio between the two is the precipitation-runoff conversion efficiency under a specific frozen soil condition for that time period. Since the frozen soil condition evolves over time, this efficiency also changes dynamically, thus achieving time-series and state-dependent quantification of the conversion efficiency.

[0090] Understandably, by simulating runoff processes under different permafrost conditions and hydrological characteristics of the underlying permafrost surface using the first distributed hydrological model, and dynamically quantifying precipitation-runoff conversion efficiency, it can not only reflect the regulatory role of permafrost changes on hydrological responses, but also provide quantitative support for water resource assessment, flood risk early warning, and research on the hydrological impacts of climate change in permafrost regions.

[0091] Furthermore, dynamic quantification can employ other strategies, such as calculating moving average efficiency based on a sliding window, describing the uncertainty range of efficiency using probability distributions, or introducing normalization methods to eliminate the interference of precipitation intensity on efficiency values, and is not limited to these. This step not only accurately reflects the regulatory effect of permafrost changes on hydrological responses but also provides high spatiotemporal resolution quantitative support for water resource management in cold regions, flood risk early warning, and climate change impact assessment.

[0092] The contents of steps S110 to S130 will be described in detail below.

[0093] In one example embodiment of this disclosure, after assimilating permafrost hydrological monitoring data into a distributed hydrological model coupled with permafrost runoff processes to obtain a first distributed hydrological model, the method further includes: calibrating the first distributed hydrological model to obtain a second distributed hydrological model; and simulating runoff processes under different permafrost conditions and hydrological characteristics of the permafrost underlying surface using the first distributed hydrological model to dynamically quantify precipitation-runoff conversion efficiency, including: simulating runoff processes under different permafrost conditions and hydrological characteristics of the permafrost underlying surface using the second distributed hydrological model to dynamically quantify precipitation-runoff conversion efficiency.

[0094] Among them, calibration refers to adjusting the key parameters in the model related to the permafrost runoff process based on available hydrological observation information in order to improve the consistency between the model output and the actual hydrological response.

[0095] For example, in a permafrost mountainous watershed, the initial hydrological model may overestimate the runoff during the snowmelt period because the snowmelt rate coefficient or soil infiltration capacity set in the model does not match the actual situation. Through calibration, the measured spring runoff process at the watershed's hydrological stations can be used as a reference to gradually adjust parameters such as the snowmelt coefficient and soil saturated hydraulic conductivity until the runoff process simulated by the model is highly consistent with the measured values ​​in terms of trend and magnitude. The parameter combination at this point is the calibration result, and the corresponding model is the calibrated optimized model.

[0096] The first distributed hydrological model refers to the initial model that has completed the assimilation of frozen soil hydrological monitoring data. The second distributed hydrological model is an optimized model obtained after calibration based on the first model, which has higher regional adaptability and simulation accuracy.

[0097] The hydrological characteristics of the underlying surface of permafrost can include infiltration rate, water storage capacity and runoff path, which are dynamically determined by the state of permafrost (such as freezing, seasonal thawing or perennial permafrost degradation) and can affect the process of precipitation being converted into runoff.

[0098] The calibration process can be implemented in various ways. In some implementations, hydrological monitoring information of the target area or adjacent watersheds (such as runoff time series retrieved from remote sensing or historical runoff data from analogous watersheds) can be obtained as a reference benchmark. An automatic optimization algorithm (such as Bayesian optimization) is used to iteratively adjust parameters such as soil saturated hydraulic conductivity, effective porosity, impermeability threshold of frozen layer, and snowmelt rate coefficient in the first distributed hydrological model. The Nash-Sutcliffe Efficiency (NSE) or Relative Error (RE) is used as the objective function. When the fitting error between the simulated runoff and the observed information meets a preset threshold (such as the Nash efficiency coefficient being greater than or equal to 0.7 and the absolute value of the relative error being less than or equal to 10%), the optimization stops, the final parameter set is output, and it is solidified as the second distributed hydrological model.

[0099] In other implementations, when direct runoff observations are lacking, multivariate constraint calibration is performed using the consistency of multi-source permafrost hydrological monitoring data. For example, the soil moisture retrieved from satellites, the rate of change of active layer thickness measured on the ground, and the surface water accumulation range identified by aerial platforms are used as joint constraints to construct a multi-objective function. A multi-objective optimization algorithm is then used to search for a parameter combination that is balanced across multi-dimensional hydrological characteristics, thereby obtaining a second distributed hydrological model.

[0100] In other embodiments, the first distributed hydrological model is calibrated to obtain a second distributed hydrological model, including: acquiring measured runoff data in permafrost mountainous areas; calibrating the parameters related to permafrost runoff generation in the first distributed hydrological model based on the measured runoff data to obtain a second distributed hydrological model; the fitting error between the runoff process simulated by the second distributed hydrological model and the measured runoff data during the calibration period is less than a preset error.

[0101] Among them, measured runoff data refers to the actual runoff time series data reflecting the surface water flow process, which is directly obtained through on-site hydrological observation in the target watershed or a specific cross-section. This data can be recorded in units of time (such as hours or days) to record the flow value at the corresponding moment or time period, or it can be converted into equivalent depth units (such as millimeters, mm) according to the watershed area, and used for hydrological model calibration, verification or hydrological process analysis.

[0102] Measured runoff data can be obtained through monitoring facilities deployed at key hydrological control sections such as river, stream, or lake outlets. Observation methods can include: water level gauges with water level-discharge curves, flow meters (such as electromagnetic and ultrasonic Doppler current profilers), radar flow meters, and weir and flume measuring devices (such as Parshall flumes). In permafrost mountainous areas, due to special hydrological phenomena such as freezing, bottom ice, or flow interruption in winter, high-quality measured runoff data also need to have the ability to monitor low flow or intermittent flow during the freezing period. For example, pressure water level gauges combined with sub-ice flow measurement technology can be used, or continuous year-round observation can be achieved through telemetry systems.

[0103] Parameters related to permafrost runoff generation can include at least one of the following: soil saturated hydraulic conductivity (controlling the infiltration capacity of the thawing layer), effective porosity (affecting water storage capacity), the threshold for determining the impermeable boundary of the frozen layer (determining the depth of the effective hydrological action layer), snowmelt rate coefficient (regulating snowmelt runoff generation), and critical water storage capacity or surface roughness in the runoff generation module, etc., and are not limited to these.

[0104] During the calibration process, measured runoff data can be used as a target reference. Parameter optimization algorithms (such as the Bayesian Markov chain Monte Carlo method) can be used to iteratively adjust the parameters related to permafrost runoff generation, so that the runoff process simulated by the second distributed hydrological model gradually approaches the measured values ​​during the calibration period.

[0105] Fitting error can be quantified using various hydrological model evaluation indicators, such as Nash efficiency coefficient, relative error, and root mean square error. When the simulation results meet the error requirements, calibration is considered complete, and the model parameter set is solidified, forming the second distributed hydrological model.

[0106] It should be noted that the above calibration method is merely an illustrative example. Those skilled in the art can also use other methods to optimize the model, such as introducing a machine learning surrogate model to accelerate parameter search, combining ensemble Kalman filtering for dynamic parameter estimation, using hydrological response patterns under historical climate scenarios for analog calibration, or setting prior parameter distributions based on expert experience and performing Bayesian updates, etc. These methods can all achieve the calibration of the first distributed hydrological model without departing from the technical concept of this disclosure. Therefore, the specific implementation of calibration is not limited to this.

[0107] Accordingly, the calibrated second distributed hydrological model was used to drive hydrological process simulations under different permafrost conditions, and the precipitation-runoff conversion efficiency was dynamically calculated on a time-by-time basis based on high-precision runoff output and corresponding precipitation data. By introducing a calibration step, the second distributed hydrological model further improved the reliability and regional applicability of the conversion efficiency quantification results while preserving the rationality of the physical mechanism of permafrost runoff generation.

[0108] Through the above process, the second distributed hydrological model not only retains the physical mechanisms of permafrost runoff generation (such as the dynamic control of infiltration depth by the active layer and the frozen layer as an impermeable boundary), but also significantly improves its simulation accuracy and reliability in the target permafrost region by constraining measured runoff data, providing a solid foundation for subsequent high-precision dynamic quantification of precipitation-runoff conversion efficiency.

[0109] In one example embodiment of this disclosure, it can be achieved through Figure 2 The implementation of step S130, which involves simulating runoff processes under different permafrost conditions and hydrological characteristics of the underlying permafrost surface using a first distributed hydrological model, and dynamically quantifying precipitation-runoff conversion efficiency, may specifically include:

[0110] Step S210: Based on different permafrost conditions, determine the corresponding hydrological characteristics of the underlying permafrost surface using the first distributed hydrological model.

[0111] Step S220: Simulate the runoff process corresponding to precipitation data under different frozen soil conditions and corresponding hydrological characteristics of the frozen soil underlying surface;

[0112] Step S230: For the runoff process, dynamically quantify the precipitation-runoff conversion efficiency.

[0113] In some embodiments, the hydrological characteristics of the permafrost underlying surface may include infiltration rate, water storage capacity, and flow path, with the flow path including lateral interflow path and composite flow path structure. A first distributed hydrological model is used to determine the corresponding hydrological characteristics of the permafrost underlying surface based on different permafrost states, including: when the permafrost state indicates permafrost thawing, the first distributed hydrological model dynamically expands the effective hydrological layer depth based on the thickness of the active permafrost layer to obtain a first infiltration rate; the first water storage capacity is updated based on the product of the effective hydrological layer depth and the effective porosity of the soil; and the lateral interflow path formed by water within the unfrozen layer is obtained. When the permafrost state indicates permafrost degradation, the first distributed hydrological model removes the constraints on vertical water transport based on the continuously thickening active layer or locally connected unfrozen layer to obtain a second infiltration rate and a second water storage capacity; and the flow path changes from surface-dominated to a composite flow path structure combining interflow and shallow groundwater flow.

[0114] The active permafrost layer refers to the seasonally frozen and thawed soil layer covering the permafrost. Its thickness fluctuates dynamically with temperature changes, reaching its maximum value in summer and freezing completely or partially in winter.

[0115] The effective hydrological depth refers to the vertical depth range within the soil that allows liquid water to migrate and be stored under the current frozen soil conditions. Its upper limit is the surface, and its lower limit is usually the freezing front or the top of the permafrost. It can dynamically expand downwards when the permafrost thaws or degrades.

[0116] The first and second infiltration rates are not fixed parameters, but rather infiltration capacity values ​​calculated by the model under the frozen soil thawing and frozen soil degradation states, respectively, reflecting the efficiency of frozen soil in receiving precipitation or meltwater at different stages of frozen soil evolution.

[0117] A composite flow path structure refers to a combination of multiple flow paths that coexist during the runoff generation process, which can include the coexistence and coupling of surface runoff, lateral interflow, and shallow groundwater runoff.

[0118] A continuous unfrozen layer refers to a continuous unfrozen channel formed vertically between the surface and the deep unfrozen aquifer in permafrost regions due to continuous thermal disturbance. Its existence can significantly change the traditional barrier effect of permafrost on vertical water transport.

[0119] When the frozen soil condition indicates that the frozen soil is thawing, the first distributed hydrological model obtains the current active layer thickness of the frozen soil (e.g., through ground monitoring or remote sensing inversion) and sets this current active layer thickness as the effective hydrological layer depth. Based on this, the first distributed hydrological model calculates the first water storage capacity of the soil within this depth range (i.e., the product of the effective hydrological layer depth and the effective porosity of the soil) and determines the first infiltration rate according to the water conductivity characteristics of the unfrozen layer. At the same time, combined with topographic and soil parameters, the model simulates the lateral interflow path of water along the slope within the active layer.

[0120] When the permafrost state indicates permafrost degradation, the first distributed hydrological model identifies situations where the active permafrost layer continues to thicken (e.g., the average thickness increases year by year) or where a localized unfrozen layer appears. In this case, the effective hydrological layer depth is no longer limited by the original permafrost top plate and can be further extended downwards. The first distributed hydrological model can be updated to obtain a higher second infiltration rate and a larger second water storage capacity, and the runoff path is adjusted from a single surface-dominated mode to a composite flow path structure with the combined action of interflow and shallow groundwater.

[0121] In some implementations, one or more target permafrost states can be identified as simulation scenarios; the same set of precipitation data is used as meteorological driving input and applied to the different permafrost state scenarios; the first distributed hydrological model performs confluence calculations on surface and groundwater runoff components in various locations through distributed hydrological calculation modules (such as kinematic waves, diffusion waves, etc.), and finally outputs a time-continuous runoff process line at the watershed outlet or a designated section.

[0122] The target permafrost state can be based on actual monitoring data (such as the continuous observation sequence of active layer thickness in permafrost regions), climate years (such as extreme warm or cold years), or preset theoretical scenarios (such as active layer thicknesses of 0.2 m, 0.6 m, and 1.5 m, representing shallow freezing, seasonal thawing, and significant degradation states, respectively). For each permafrost state, the first distributed hydrological model automatically matches or calculates the corresponding hydrological characteristics of the underlying permafrost surface, including the effective hydrological action layer depth, infiltration rate, water storage capacity, and dominant flow path type (such as surface flow, interflow, or combined flow path).

[0123] Precipitation data can include precipitation event sequences with a time resolution of at least hour, including precipitation phase (rain, snow, or a mixture), intensity, duration, and cumulative amount. Spatial interpolation has been performed based on factors such as regional elevation and slope aspect, and the data has been distributed to each computational unit of the model. The first distributed hydrological model performs water distribution calculations at each time step based on the underlying surface hydrological characteristics determined by the current permafrost state.

[0124] For example, firstly, it is determined whether the precipitation exceeds the current infiltration rate. The excess infiltration portion forms surface runoff; the non-excess infiltration portion enters the effective hydrological layer and fills the soil water storage capacity; when the water storage capacity is saturated, the excess water triggers interflow or shallow groundwater flow, the path and rate of which are determined by the current flow path structure (such as whether there is a through unfrozen layer, slope, hydraulic conductivity, etc.).

[0125] The first distributed hydrological model uses distributed hydrological calculation modules (such as kinematic waves, diffused waves, or the complete Saint-Venant equation) to perform confluence calculations on surface and subsurface runoff components in various locations, ultimately outputting a time-continuous runoff hydrograph at the watershed outlet or a designated cross-section. This process operates independently under each permafrost condition, ensuring consistent precipitation input while maintaining unique underlying surface conditions, thereby isolating the influence of permafrost conditions on the runoff response.

[0126] For example, in a frozen state with an active layer thickness of 0.2 m, the model assumes an extremely shallow effective hydrological layer with low infiltration rate (e.g., 1 mm / h), where most precipitation quickly converts into surface runoff, forming sharp, short-duration flood peaks. In contrast, in a degraded state with an active layer thickness of 1.2 m, the infiltration rate is high (e.g., 10 mm / h) and the water storage capacity is large, resulting in a significant amount of precipitation being intercepted and infiltrated. Runoff is mainly composed of slowly released interflow, leading to delayed flood peaks and a significantly reduced peak value.

[0127] Through the above process, the first distributed hydrological model, under the premise of strictly controlling precipitation input variables, can simulate the regulatory effect of different permafrost states and their corresponding underlying surface hydrological characteristics on the runoff formation mechanism and process, providing an accurate and comparable runoff output basis for subsequent dynamic quantification of precipitation-runoff conversion efficiency.

[0128] In some implementations, for a runoff process, the precipitation-runoff conversion efficiency is dynamically quantified, including: for each simulated time period of the runoff process, obtaining the runoff volume and precipitation within the simulated time period; and dynamically quantifying the precipitation-runoff conversion efficiency based on the runoff volume and precipitation within each simulated time period.

[0129] The simulation time period refers to the time unit used for model operation or analysis, which can be set to an hour, day, single precipitation event, or a sliding window of multiple consecutive days according to actual needs. After completing the simulation of runoff processes under different permafrost conditions, the results can be summarized according to this time period: on the one hand, the total cumulative precipitation during this period is calculated, including all forms of liquid input such as rainfall and snowmelt; on the other hand, the total runoff output by the model during the same period is accumulated, covering effective runoff components such as surface runoff, interflow, and shallow groundwater. Subsequently, the ratio of the total runoff to the total precipitation during this simulation time period is calculated, and the result is the precipitation-runoff conversion efficiency corresponding to this simulation time period, which is used to characterize the proportion of precipitation converted into runoff under specific permafrost conditions.

[0130] Understandably, this process supports continuous, time-period-based dynamic output. For example, in daily simulations, the first distributed hydrological model can generate a conversion efficiency value daily, forming an efficiency sequence that changes over time. This clearly reflects the dynamic regulation of hydrological response by seasonal freeze-thaw cycles in permafrost (such as high efficiency in spring and low efficiency in summer). In analyses based on single precipitation events, efficiency can be calculated independently for each effective precipitation event, allowing for comparison of runoff differences under different permafrost conditions for the same precipitation event, supporting attribution analysis of extreme hydrological events.

[0131] In some implementations, the above calculations can be automatically performed by the post-processing module of the first distributed hydrological model. After each simulation, precipitation and runoff data are extracted according to a preset time period, the corresponding conversion efficiency is calculated and stored, and the permafrost state parameters at that time (such as the thickness of the active layer) are correlated to facilitate subsequent spatiotemporal analysis or visualization. In other implementations, users can customize the analysis time period, for example, only performing efficiency calculations on events where precipitation exceeds a certain threshold. The system dynamically selects time periods that meet the conditions and performs quantification accordingly, improving the relevance and practicality of the analysis.

[0132] Furthermore, those skilled in the art will understand that the calculation of precipitation-runoff conversion efficiency can be adjusted according to actual needs. For example, evaporation losses can be deducted from precipitation, time-matching corrections can be made for snow accumulation and delayed meltwater, or a confidence range for the efficiency can be given by combining uncertainty analysis. All of the above implementation methods can be applied without departing from the technical concept of this disclosure; therefore, the specific quantification method is not limited to these. Through this step, precipitation-runoff conversion efficiency is no longer a fixed empirical value, but a dynamic indicator that changes in real time with the state of permafrost and meteorological conditions, significantly improving the precision and timeliness of hydrological process assessment in cold regions.

[0133] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0134] Furthermore, this example embodiment also provides a hydrological simulation system for permafrost mountainous areas based on integrated air-ground-space monitoring. (Refer to...) Figure 3 As shown, the permafrost mountain hydrological simulation system 300 based on integrated air-ground-space monitoring includes: a permafrost hydrological monitoring data acquisition module 310, a monitoring data assimilation module 320, and a hydrological simulation module 330. Among them:

[0135] The frozen soil hydrological monitoring data acquisition module 310 is used to acquire frozen soil hydrological monitoring data in frozen soil mountainous areas through multi-site monitoring equipment; the frozen soil hydrological monitoring data includes frozen soil status and precipitation data;

[0136] The monitoring data assimilation module 320 is used to assimilate the frozen soil hydrological monitoring data into a distributed hydrological model coupled with the frozen soil runoff process to obtain the first distributed hydrological model.

[0137] The hydrological simulation module 330 is used to simulate runoff processes under different permafrost conditions and hydrological characteristics of the permafrost underlying surface through the first distributed hydrological model, and dynamically quantify precipitation-runoff conversion efficiency.

[0138] In some example embodiments of this disclosure, based on the aforementioned scheme, the above-mentioned hydrological simulation system 300 for frozen soil mountainous areas based on integrated air-ground-space monitoring further includes a model calibration module, which is used to calibrate the first distributed hydrological model to obtain the second distributed hydrological model.

[0139] The aforementioned hydrological simulation module 330 is also used to simulate runoff processes under different permafrost conditions and hydrological characteristics of the permafrost underlying surface through a second distributed hydrological model, and to dynamically quantify precipitation-runoff conversion efficiency.

[0140] In some example embodiments of this disclosure, based on the aforementioned scheme, the model calibration module is also used to obtain measured runoff data in permafrost mountainous areas; based on the measured runoff data, the parameters related to permafrost runoff generation in the first distributed hydrological model are calibrated to obtain the second distributed hydrological model; the fitting error between the runoff process simulated by the second distributed hydrological model and the measured runoff data during the calibration period is less than a preset error.

[0141] In some example embodiments of this disclosure, based on the foregoing scheme, the hydrological simulation module 330 is further configured to determine the corresponding hydrological characteristics of the underlying frozen soil surface based on different frozen soil states using the first distributed hydrological model; simulate the runoff process corresponding to precipitation data under different frozen soil states and corresponding hydrological characteristics of the underlying frozen soil surface; and dynamically quantify the precipitation-runoff conversion efficiency for the runoff process.

[0142] In some example embodiments of this disclosure, based on the aforementioned scheme, the hydrological characteristics of the permafrost underlying surface include infiltration rate, water storage capacity, and flow path; the aforementioned hydrological simulation module 330 is further used to obtain a first infiltration rate by dynamically expanding the effective hydrological action layer depth according to the thickness of the active layer of permafrost through a first distributed hydrological model when the permafrost state indicates permafrost thawing, and to update the first water storage capacity based on the product of the effective hydrological action layer depth and the effective porosity of the soil, and to obtain the lateral interflow path of water in the unfrozen layer; when the permafrost state indicates permafrost degradation, the first distributed hydrological model removes the constraint on vertical water transport according to the continuously thickening active layer or the locally connected unfrozen layer, to obtain a second infiltration rate and a second water storage capacity, and to obtain the composite flow path structure of the runoff path changing from surface-dominated to a combination of interflow and shallow groundwater flow.

[0143] In some example embodiments of this disclosure, based on the foregoing scheme, the hydrological simulation module 330 is further configured to obtain the runoff volume and precipitation within each simulation time period of the runoff process; and dynamically quantify the precipitation-runoff conversion efficiency based on the runoff volume and precipitation within each simulation time period.

[0144] In some example embodiments of this disclosure, based on the aforementioned scheme, the multi-site monitoring equipment includes satellites, ground monitoring equipment, and aircraft; the aforementioned permafrost hydrological monitoring data acquisition module 310 is also used to monitor permafrost mountainous areas via satellites to obtain snow water equivalent and soil moisture in permafrost mountainous areas; to monitor precipitation conditions via ground monitoring equipment to obtain precipitation phase, precipitation intensity, and thickness of the active permafrost layer; and to scan the topography and surface of permafrost mountainous areas via aircraft to obtain topographic and surface information of permafrost mountainous areas.

[0145] The specific details of each module of the hydrological simulation system for frozen soil mountainous areas based on integrated space-ground-air-ground monitoring have been described in detail in the corresponding hydrological simulation method for frozen soil mountainous areas based on integrated space-ground-air-ground monitoring, so they will not be repeated here.

[0146] It should be noted that although several modules or units of the hydrological simulation system for permafrost mountainous areas based on integrated air-ground monitoring have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units for embodiment.

[0147] Furthermore, in an exemplary embodiment of this disclosure, an electronic device is also provided that can implement the above-described hydrological simulation method for permafrost mountainous areas based on integrated air-ground-space monitoring.

[0148] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be embodied in the following forms: a completely hardware embodiment, a completely software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0149] The following reference Figure 4 To describe an electronic device 400 according to such an embodiment of the present disclosure. Figure 4 The electronic device 400 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0150] like Figure 4 As shown, the electronic device 400 is manifested in the form of a general-purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one storage unit 420, a bus 430 connecting different system components (including storage unit 420 and processing unit 410), and a display unit 440.

[0151] The storage unit stores program code that can be executed by the processing unit 410, causing the processing unit 410 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 410 can perform actions such as... Figure 1In step S110, permafrost hydrological monitoring data of the permafrost mountain area is acquired through multi-site monitoring equipment; the permafrost hydrological monitoring data includes permafrost status and precipitation data; in step S120, the permafrost hydrological monitoring data is assimilated into a distributed hydrological model coupled with permafrost runoff generation process to obtain the first distributed hydrological model; in step S130, the runoff process under different permafrost status and hydrological characteristics of the permafrost underlying surface is simulated through the first distributed hydrological model to dynamically quantify the precipitation-runoff conversion efficiency.

[0152] Storage unit 420 may include readable media in the form of volatile storage units, such as random access memory (RAM) 421 and / or cache memory 422, and may further include read-only memory (ROM) 423.

[0153] Storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0154] Bus 430 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0155] Electronic device 400 can also communicate with one or more external devices 470 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 400, and / or with any device that enables electronic device 400 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 450. Furthermore, electronic device 400 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 460. As shown, network adapter 460 communicates with other modules of electronic device 400 via bus 430. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0156] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0157] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of this disclosure may also be implemented as a program product including program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0158] refer to Figure 5 As shown, a program product 500 for implementing the above-described hydrological simulation method for permafrost mountainous areas based on integrated air-ground-space monitoring, according to embodiments of the present disclosure, is described. It can be stored in a portable compact disk read-only memory (CD-ROM) and includes program code, and can run on a terminal device, such as a personal computer. However, the program product of this disclosure is not limited thereto. In this document, the readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0159] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0160] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0161] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0162] Program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0163] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0164] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0165] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0166] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A hydrological simulation method for permafrost mountainous areas based on integrated air-ground-space monitoring, characterized in that, include: Permafrost hydrological monitoring data in permafrost mountainous areas were obtained through multi-site monitoring equipment from the ground, air, and space. The frozen soil hydrological monitoring data includes frozen soil condition and precipitation data, and the frozen soil condition includes the thickness of the active frozen soil layer. The frozen soil hydrological monitoring data is assimilated into a distributed hydrological model coupled with a frozen soil runoff process to obtain a first distributed hydrological model; wherein, assimilating the frozen soil hydrological monitoring data into the distributed hydrological model coupled with a frozen soil runoff process includes: using the frozen soil active layer thickness as the state variable of each computational unit in the distributed hydrological model coupled with a frozen soil runoff process, and determining the effective hydrological action layer depth of each computational unit based on the frozen soil active layer thickness corresponding to each computational unit; Using the first distributed hydrological model, the infiltration rate and water storage capacity of each computing unit are determined based on the effective hydrological action layer depth of each computing unit, and the flow path of each computing unit is determined based on the effective hydrological action layer depth of each computing unit and the frozen soil state. Within the effective hydrological layer depth corresponding to each of the aforementioned calculation units, the runoff process corresponding to the precipitation data is simulated based on the infiltration rate, water storage capacity, and flow path of each calculation unit, and the precipitation-runoff conversion efficiency is dynamically quantified based on the runoff process.

2. The method according to claim 1, characterized in that, The multi-site monitoring equipment includes satellites, ground monitoring equipment, and aircraft; The acquisition of permafrost hydrological monitoring data in permafrost mountainous areas through multi-site, ground, and air monitoring equipment includes: The satellite is used to monitor the permafrost mountain area and obtain the snow water equivalent and soil moisture of the permafrost mountain area. The ground monitoring equipment is used to monitor precipitation, obtain precipitation phase, precipitation intensity and the thickness of the active permafrost layer; The aircraft scans the terrain and surface of the permafrost mountain area to obtain terrain and surface information of the permafrost mountain area.

3. The method according to claim 1, characterized in that, After assimilating the frozen soil hydrological monitoring data into a distributed hydrological model coupled with the frozen soil runoff process to obtain the first distributed hydrological model, the process further includes: The first distributed hydrological model is calibrated to obtain the second distributed hydrological model. The step of determining the infiltration rate and water storage capacity of each computing unit based on the effective hydrological layer depth of each computing unit using the first distributed hydrological model, and determining the flow path of each computing unit based on the effective hydrological layer depth of each computing unit and the frozen soil state, includes: The second distributed hydrological model determines the infiltration rate and water storage capacity of each computing unit based on the effective hydrological layer depth of each computing unit, and determines the flow path of each computing unit based on the effective hydrological layer depth of each computing unit and the frozen soil state.

4. The method according to claim 3, characterized in that, The calibration of the first distributed hydrological model to obtain the second distributed hydrological model includes: Obtain measured runoff data from the permafrost mountainous area; Based on the measured runoff data, the parameters related to permafrost runoff generation in the first distributed hydrological model are calibrated to obtain a second distributed hydrological model; the fitting error between the runoff process simulated by the second distributed hydrological model and the measured runoff data during the calibration period is less than a preset error.

5. The method according to claim 1, characterized in that, The step of determining the infiltration rate and water storage capacity of each computing unit based on the effective hydrological layer depth of each computing unit using the first distributed hydrological model, and determining the flow path of each computing unit based on the effective hydrological layer depth of each computing unit and the frozen soil state, includes: Based on the effective hydrological layer depth of each computing unit, the soil layer range in each computing unit that allows for vertical water infiltration and storage is determined using the first distributed hydrological model. The infiltration rate and water storage capacity of each calculation unit are determined based on the soil layer range, and the flow path of each calculation unit is determined based on the soil layer range and the frozen soil state.

6. The method according to claim 5, characterized in that, The hydrological characteristics of the underlying frozen soil surface include infiltration rate, water storage capacity, and flow path, wherein the flow path includes lateral inter-soil flow path and composite flow path structure; The process of determining the infiltration rate and water storage capacity of each calculation unit based on the soil layer range, and determining the flow path of each calculation unit based on the soil layer range and the frozen soil state, includes: When the frozen soil state indicates that the frozen soil is thawing, the effective hydrological action layer depth is dynamically expanded according to the thickness of the active frozen soil layer by the first distributed hydrological model to obtain the first infiltration rate. The first water storage capacity is updated based on the product of the effective hydrological action layer depth and the effective porosity of the soil, and the lateral interflow path of water in the unfrozen layer is obtained. When the frozen soil state indicates frozen soil degradation, the first distributed hydrological model releases the constraints on vertical water transport based on the continuously thickening active layer or the locally connected unfrozen layer, thereby obtaining the second infiltration rate and the second water storage capacity, and obtaining the flow path structure that changes from surface-dominated to a combination of interbedded flow and shallow groundwater flow.

7. The method according to claim 5, characterized in that, The dynamic quantification of precipitation-runoff conversion efficiency based on the runoff process includes: For each simulated time period of the runoff process, the runoff volume and precipitation within that simulated time period are obtained; Based on the runoff and precipitation in each of the simulated time periods, the precipitation-runoff conversion efficiency is dynamically quantified.

8. A hydrological simulation system for permafrost mountainous areas based on integrated air-ground-space monitoring, characterized in that, The system includes: The frozen soil hydrological monitoring data acquisition module is used to acquire frozen soil hydrological monitoring data in frozen soil mountainous areas through multi-monitoring equipment from the ground, air, and space. The frozen soil hydrological monitoring data includes frozen soil status and precipitation data, and the frozen soil status includes the thickness of the active frozen soil layer. The monitoring data assimilation module is used to assimilate the frozen soil hydrological monitoring data into a distributed hydrological model coupled with the frozen soil runoff process to obtain a first distributed hydrological model; wherein, the monitoring data assimilation module is also used to use the frozen soil active layer thickness as the state variable of each calculation unit in the distributed hydrological model coupled with the frozen soil runoff process, and to determine the effective hydrological action layer depth of each calculation unit based on the frozen soil active layer thickness corresponding to each calculation unit. The hydrological simulation module is used to determine the infiltration rate and water storage capacity of each computing unit based on the effective hydrological layer depth of each computing unit through the first distributed hydrological model, and to determine the flow path of each computing unit based on the effective hydrological layer depth of each computing unit and the frozen soil state; within the range corresponding to the effective hydrological layer depth of each computing unit, it simulates the runoff process corresponding to the precipitation data based on the infiltration rate, water storage capacity and flow path of each computing unit, and dynamically quantifies the precipitation-runoff conversion efficiency based on the runoff process.

9. An electronic device, characterized in that, include: processor; as well as The memory stores computer-readable instructions, which, when executed by the processor, implement the hydrological simulation method for permafrost mountainous areas based on integrated air-ground-space monitoring as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the hydrological simulation method for permafrost mountainous areas based on integrated air-ground-space monitoring as described in any one of claims 1 to 7.

Images