Methods, systems, and equipment for constructing distributed hydrological models based on feature units

Abstract

This invention relates to the field of hydrological forecasting and discloses a method, system, and device for constructing a distributed hydrological model based on feature units. The method includes: establishing a historical dataset of flood events based on collected topographic, soil, hydrological, and meteorological data; dividing the topographic data in the historical dataset into feature units; matching the remaining soil, hydrological, and meteorological data to each feature unit; constructing spatial topological relationships based on the feature units; calibrating the parameters of a runoff generation model based on the spatial topological relationships; calibrating the parameters of a confluence model based on the runoff generation model parameters; and generating and issuing flood warning information based on the parameter calibration results and various monitoring and forecasting information, according to different levels of flood warning issuance standards. This invention, based on feature units, constructs a distributed hydrological model capable of real-time flood operational forecasting.

Description

Technical Field

[0001] This invention relates to the field of hydrological forecasting technology, and in particular to a method, system, medium, and device for constructing a distributed hydrological model based on feature units. Background Technology

[0002] Due to the influence of climate change and human activities, extreme precipitation events are becoming more frequent, and floods and droughts cause enormous losses, seriously threatening the sustainable economic development of river basins. Hydrological models are an important tool for flood forecasting, providing a scientific basis for flood control command by accurately predicting future hydrological conditions.

[0003] Currently, most flood forecasting models used in watershed water resources management and flood and drought disaster prevention are lumped-type models. These models do not consider the impact of spatial distribution of rainfall on runoff generation and confluence, and their accuracy is not high in arid areas. On the other hand, grid-based distributed hydrological models are limited in production applications due to the large amount of input data and the high computational requirements. Summary of the Invention

[0004] To address the aforementioned problems, the purpose of this invention is to provide a method, system, medium, and device for constructing a distributed hydrological model based on feature units. This distributed hydrological model, constructed based on feature units, enables real-time flood operation forecasting.

[0005] To achieve the above objectives, in a first aspect, the technical solution adopted by the present invention is as follows: a method for constructing a distributed hydrological model based on feature units, comprising: establishing a historical dataset of flood events based on collected topographic data, soil data, hydrological data, and meteorological data; dividing the topographic data in the historical dataset of flood events into feature units; matching the remaining soil data, hydrological data, and meteorological data to each feature unit; constructing spatial topological relationships based on the feature units; constructing a distributed hydrological model based on the spatial topological relationships to calibrate the parameters of the runoff generation model; calibrating the parameters of the confluence model based on the results of the runoff generation model parameter calibration; and, based on the parameter calibration results and various monitoring and forecasting information, producing and issuing flood warning information according to different levels of flood warning issuance standards.

[0006] Furthermore, based on the collected topographic, soil, hydrological, and meteorological data, a historical dataset of flood events was established. The topographic data within this dataset was divided into feature units, and the remaining soil, hydrological, and meteorological data were matched to these feature units. Spatial topological relationships were then constructed based on these feature units, including:

[0007] The collected topographic data, soil data, hydrological data, and meteorological data are processed into time series data of equal time periods according to the set time step to obtain historical datasets of flood events.

[0008] Based on the principle of similarity in natural geography and hydro-meteorological characteristics, the large-scale watershed topographic map extracted from topographic data is divided into several small watersheds as feature units;

[0009] The spatial topological relationships are established for the divided feature units according to their upstream and downstream relationships, and the watershed corresponding to each feature unit is spatially associated with the watershed corresponding to the inflow feature unit.

[0010] Furthermore, the defined characteristic units include two types: one is a closed watershed with no upstream inflow characteristic units, and the outlet flow of this type of characteristic unit consists only of its own rainfall runoff; the other is a regional watershed with upstream inflow characteristic units, and the outlet flow of this type of characteristic unit consists of the outlet flow of the inflow characteristic units and its own rainfall runoff.

[0011] Furthermore, when calculating the flow generation and confluence of characteristic units, there is a one-to-one correspondence between the net rainfall of the characteristic unit and the outflow.

[0012] When performing river confluence calculations between feature units:

[0013] When the outflow feature unit code is different from the inflow feature unit code, the outflow feature unit is defined as the first feature unit, the inflow feature unit is defined as the second feature unit, and the inflow feature unit corresponding to the outflow feature unit is defined as the third feature unit. When performing river confluence calculation between feature units, the confluence calculation of the second feature unit can only be performed after the confluence calculation of the third feature unit. The confluence calculation result of the third feature unit is added to the outlet flow of the second feature unit, and so on, traversing all feature units.

[0014] When the outflowing feature unit code is the same as the inflowing feature unit code, the outflowing feature unit is the outlet feature unit of the basin, and its outlet flow rate is the basin outlet flow rate.

[0015] Furthermore, a distributed hydrological model is constructed based on spatial topological relationships, including a runoff generation model and a runoff confluence model;

[0016] The runoff generation model includes a frozen soil module, a snowmelt runoff module, a slope runoff generation module, and a small and medium-sized reservoir runoff reduction module; the runoff confluence model includes a slope confluence module and a river confluence module.

[0017] Based on measured frozen soil data, the frozen soil module analyzes the empirical relationship between frozen soil depth and air temperature and determines the freeze-thaw factor in order to calculate the freezing or thawing depth of frozen soil.

[0018] Based on the calculation results of the freezing or thawing depth of permafrost, the snowmelt runoff module uses the day-period method to calculate the snowmelt volume according to the empirical relationship between temperature and snowmelt rate.

[0019] Based on the calculated snowmelt volume, the slope runoff generation module uses the infiltration capacity watershed distribution curve to obtain the average infiltration capacity of the watershed and calculates the runoff volume for each time period.

[0020] Based on the calculated time period runoff, the water source module divides net rainfall into three types of water sources: surface runoff, interflow, and groundwater runoff according to the infiltration rate of the upper soil layer and the infiltration rate of the lower soil layer. Surface runoff, interflow, and groundwater runoff are calculated separately.

[0021] The slope runoff module calculates the slope runoff for surface runoff, interflow, and groundwater runoff separately. The slope runoff for interflow and groundwater runoff is calculated separately. The surface runoff runoff is calculated using the instantaneous unit hydrograph, while the interflow and groundwater runoff runoff are calculated using a linear reservoir.

[0022] Based on the calculation results of the slope confluence module, the river confluence module uses the Muskingen piecewise continuous algorithm to calculate the river network runoff, and then calculates the total watershed runoff.

[0023] Furthermore, the parameters of the flow generation model are calibrated as follows:

[0024] The parameters of the frozen soil module were calibrated. The soil freeze-thaw coefficient is the largest when the seasonal frozen soil is completely thawed in summer (June-August) and the smallest when the frozen soil depth reaches its maximum in winter (January-March). The value range of the soil freeze-thaw coefficient is 0.01-0.55.

[0025] The parameters of the snowmelt runoff module were calibrated. The degree-day factor was the largest in summer and the smallest in winter. The value range of the degree-day factor was 0.01-0.25.

[0026] The parameters of the runoff generation module are calibrated using optimization methods, hydrological analysis methods, or parameter transfer methods based on historical hydrological and meteorological data. The reservoir characteristic values ​​of the small and medium-sized reservoir group are collected, and the maximum storage capacity of the small and medium-sized reservoir group is analyzed and calculated.

[0027] The parameters of the water source module are calibrated using optimization methods, hydrological analysis methods, or parameter transfer methods, based on historical hydrological and meteorological data.

[0028] Furthermore, the parameters of the confluence model are calibrated as follows:

[0029] The time-varying slope runoff confluence module parameters were calibrated. Surface runoff confluence was represented by the whisker instantaneous unit hydrograph, while interflow and groundwater runoff confluence were represented by a linear reservoir. The peak value of the whisker instantaneous unit hydrograph is related to net rainfall intensity; in practical applications, factors influencing the peak value are considered. n, k The value is corrected, and a unit linear delay is established when applying it. nk The nonlinear relationship with surface runoff R was obtained. k ;

[0030] The parameters of the time-varying river confluence module are calibrated. The number of segments in the Muskingan method is related to the average flow velocity of the river segment, which varies with the size of the flood. When applying this method, an empirical relationship between the average flow velocity of the river segment and the peak flow should be established based on measured data.

[0031] In this process, the flow proportion factor of each flood event is first calibrated using an optimization method, and then an empirical relationship between the flow proportion factor and the peak flow is established.

[0032] Secondly, the technical solution adopted by this invention is as follows: a distributed hydrological model construction system based on feature units, comprising: a feature unit construction module, which establishes a historical dataset of flood events based on collected topographic data, soil data, hydrological data, and meteorological data, divides the topographic data in the historical dataset of flood events into feature units, and matches the remaining soil data, hydrological data, and meteorological data to each feature unit, and constructs spatial topological relationships based on the feature units; a model construction and parameter calibration module, which constructs a distributed hydrological model based on spatial topological relationships to calibrate the parameters of the runoff generation model, and then calibrates the parameters of the confluence model based on the results of the runoff generation model parameter calibration; and an early warning release module, which produces and releases flood early warning information based on the parameter calibration results and various monitoring and forecasting information, according to different levels of flood early warning release standards.

[0033] Thirdly, the technical solution adopted by the present invention is: a computer-readable storage medium for storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any of the methods described above.

[0034] Fourthly, the technical solution adopted by the present invention is: a computing device comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described above.

[0035] The present invention has the following advantages due to the adoption of the above technical solutions:

[0036] 1. This invention combines a hybrid runoff generation model, time-varying parameter slope runoff generation, slope confluence and river confluence methods, and constructs a distributed hydrological model based on feature units. It solves the problems of uneven spatial distribution of rainfall on runoff generation and confluence, rainfall intensity on runoff generation parameters, net rainfall intensity on slope confluence parameters, and flood size on river confluence parameters. It is applicable to both humid and arid regions and improves forecast accuracy.

[0037] 2. The distributed hydrological model based on feature units, constructed using a structured and modular design, is highly portable and easy to promote and apply. Attached Figure Description

[0038] Figure 1 This is an overall flowchart of the distributed hydrological model construction method based on feature units in this embodiment of the invention;

[0039] Figure 2 This is a schematic diagram of the distributed hydrological model structure based on feature units in an embodiment of the present invention;

[0040] Figure 3 This is a schematic diagram of the flow generation and confluence calculation of the characteristic unit in this embodiment of the invention; wherein, WUM represents the maximum tensile water storage capacity of the upper layer (unit: mm); E is the evapotranspiration (unit: mm); CI is the subsurface flow recession coefficient, dimensionless; CG is the groundwater runoff recession coefficient, dimensionless; Qs is the surface runoff flow rate (unit: m³ / s). 3 / s); Qg is the underground runoff flow rate (unit: m³ / s). 3 / s); CS is the river network runoff recession coefficient, dimensionless; Qo is the river network outflow (m³ / s). 3 / s); QT is the total outflow of the river network (m³ / s); 3 / s);

[0041] Figure 4 This is a schematic diagram of the parameter calibration of the slope confluence module in an embodiment of the present invention;

[0042] Figure 5 This is a schematic diagram of the parameter calibration of the river confluence module in an embodiment of the present invention. Detailed Implementation

[0043] To address the issues of low accuracy and high computational requirements of existing hydrological models, this invention provides a method, system, medium, and equipment for constructing a distributed hydrological model based on feature units. This feature-based distributed hydrological model comprehensively considers runoff generation and confluence characteristics and production application conditions, meeting the accuracy and timeliness requirements of actual production with less input data and computational load, and thus has significant application value.

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0045] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0046] In one embodiment of the present invention, a method for constructing a distributed hydrological model based on feature units is provided. In this embodiment, as shown... Figure 1 As shown, the method includes the following steps:

[0047] 1) Based on the collected topographic data, soil data, hydrological data and meteorological data, establish a historical dataset of flood events, divide the topographic data in the historical dataset of flood events into feature units, and match the remaining soil data, hydrological data and meteorological data to each feature unit, and construct spatial topological relationships based on the feature units.

[0048] 2) Construct a distributed hydrological model based on spatial topological relationships to calibrate the parameters of the runoff generation model, and then calibrate the parameters of the confluence model based on the results of the runoff generation model parameter calibration.

[0049] 3) Based on parameter calibration results and various monitoring and forecasting information, flood warning information is produced and issued according to different levels of flood warning issuance standards.

[0050] In step 1) above, a historical dataset of flood events is established based on the collected topographic data, soil data, hydrological data, and meteorological data. The topographic data in the historical dataset is divided into feature units, and the remaining soil data, hydrological data, and meteorological data are matched to each feature unit. The spatial topological relationship is constructed based on the feature units, including the following steps:

[0051] 1.1) The collected topographic data, soil data, hydrological data and meteorological data are processed into time series data of equal time periods according to the set time step to obtain the historical dataset of flood events.

[0052] The topographic data includes digital elevation model (DEM), slope, river length, area and other water system characteristic data; soil data includes soil type, porosity and other soil characteristic data; hydrological data includes measured hydrological data such as water level, flow rate, cross-sectional area, cross-sectional average flow velocity and other measured hydrological data; meteorological data includes measured meteorological data such as precipitation and temperature.

[0053] 1.2) Based on the principle of similarity in natural geography and hydro-meteorological characteristics, the large-scale watershed topographic map extracted from the topographic data is divided into several small watersheds as feature units.

[0054] Specifically, based on topographic and geomorphological data and historical flood datasets, and according to the principle of similarity in natural geographical and hydrological meteorological characteristics, large-scale watersheds are divided into several smaller watersheds.

[0055] In this embodiment, as Figure 3 As shown, the feature units of the division include two types:

[0056] One type is a closed watershed with no upstream inflow characteristic units; the outlet discharge of this type of characteristic unit consists solely of its own rainfall runoff. For example... Figure 3 The middle feature units are R1, R4, and R7, and the thin arrows indicate slope confluence.

[0057] Another type is the interval basin, where there are inflow characteristic units upstream. The outlet discharge of this type of characteristic unit consists of the outlet discharge of the inflow characteristic unit and its own rainfall runoff. For example... Figure 3 The feature units R2, R3, R5, R6, and R8, with the coarse simplified header representing the confluence of the river channel.

[0058] 1.3) Establish spatial topological relationships between the divided feature units according to their upstream and downstream connections, and associate the watershed corresponding to each feature unit with the watershed corresponding to the inflow feature unit using spatial elements. Spatial elements include watershed length, area, average slope, and average elevation.

[0059] This embodiment sets up a data structure to represent the spatial topological relationship between feature units, as shown in Table 1.

[0060] Table 1 Spatial Topological Relationships of Feature Units

[0061]

[0062] In this embodiment, when calculating the flow generation and merging of the characteristic unit, there is a one-to-one correspondence between the net rainfall of the characteristic unit and the outlet flow rate. For example... Figure 3 As shown, net rainfall is represented by Ri (i=1,2,3,…,8), and the outlet flow rate of the characteristic unit is represented by Qi (i=1,2,3,…,8).

[0063] When performing river confluence calculations between feature units:

[0064] When the outflow feature unit code is different from the inflow feature unit code, the outflow feature unit is defined as the first feature unit, the inflow feature unit is defined as the second feature unit, and the inflow feature unit corresponding to the outflow feature unit is defined as the third feature unit. When performing river confluence calculation between feature units, the confluence calculation of the second feature unit can only be performed after the confluence calculation of the third feature unit. The confluence calculation result of the third feature unit is added to the outlet flow Qi of the second feature unit, and so on, traversing all feature units.

[0065] When the outflowing feature unit code is the same as the inflowing feature unit code, the outflowing feature unit is the outlet feature unit of the basin, and its outlet flow rate is the basin outlet flow rate.

[0066] In step 2) above, a distributed hydrological model is constructed based on spatial topological relationships. Specifically, this involves performing runoff generation and confluence calculations within characteristic units, and calculating river confluence between characteristic units based on spatial topological relationships. This invention performs calculations based on characteristic units, unlike traditional calculations based on the entire watershed. This invention provides a smaller and more precise spatial scale for hydrological simulation, thus meeting practical work requirements while significantly reducing computational load and improving the timeliness of flood forecasts.

[0067] In this embodiment, the distributed hydrological model includes a runoff generation model and a runoff confluence model. The runoff generation model includes a frozen soil module, a snowmelt runoff module, a slope runoff generation module, and a small and medium-sized reservoir runoff reduction module; the runoff confluence model includes a slope confluence module and a river confluence module.

[0068] Specifically, such as Figure 2 As shown, the specific structure of the distributed hydrological model is as follows:

[0069] 2.1) Based on measured frozen soil data, the frozen soil module analyzes the empirical relationship between frozen soil depth and air temperature and determines the freeze-thaw factor in order to calculate the freezing or thawing depth of frozen soil.

[0070] The freeze-thaw factor is the depth to which soil thaws or freezes for every 1 degree Celsius increase or decrease in daily temperature. Using the freeze-thaw factor method, the depth of soil freezing or thawing can be calculated, and the average maximum water storage capacity of the watershed can be automatically adjusted based on changes in frozen soil depth, thus solving the problem of the impact of frozen soil on the accuracy of snowmelt runoff forecasts.

[0071] Specifically, when the temperature drops to zero degrees Celsius and continues to fall, the soil enters the freezing period; when the temperature rises to zero degrees Celsius and continues to rise, the soil enters the thawing period. Based on measured frozen soil data, the empirical relationship between frozen soil depth and temperature is analyzed, and the freeze-thaw factor (the depth to which the soil thaws or freezes for every 1 degree Celsius increase or decrease in daily temperature) is determined and used for calculating the freezing or thawing depth of frozen soil. The formula is:

[0072]

[0073] In the formula, fsd The depth at which the soil freezes or thaws, expressed in cm; β Freeze-thaw factor, unit is cm. ℃ -1 d -1 ; tThe time step is expressed in hrs. T The temperature at the end of the time period is expressed in °C.

[0074] The depth of frozen soil at the end of a time period is equal to the depth of frozen soil at the beginning of the time period minus the freezing depth (negative value) or thawing depth (positive value). The calculation formula is as follows:

[0075]

[0076] In the formula, fsd n-1 The initial frost depth for that period is expressed in cm. fsd n The value represents the depth of frozen soil at the end of the time period, in cm.

[0077] The upper boundary of permafrost indicates the location of the permafrost layer, which is the distance from the top of the permafrost layer to the ground surface. The calculation formula is:

[0078]

[0079] In the formula, fsu n-1 The upper limit of the initial frozen soil in the specified period is expressed in cm. fsu n This represents the upper limit of the frozen soil at the end of the time period, in cm.

[0080] During the soil freezing period, the upper limit of the frozen soil is 0; during the soil thawing period, the upper limit of the frozen soil is greater than 0.

[0081] When the soil is in a frozen or thawing period, assuming the frozen soil is an impermeable layer, the maximum water storage capacity of the watershed needs to be corrected. The formula for calculating the correction factor is as follows:

[0082]

[0083] In the formula, K f This is a correction factor for the maximum water storage capacity of the basin. ps Soil porosity, expressed as % . fsu This represents the upper boundary of the permafrost layer, measured in cm. WM This represents the maximum water storage capacity of the basin, expressed in mm.

[0084] When performing runoff calculations, the maximum water storage capacity of the basin must be corrected for each time period. The calculation formula is as follows:

[0085]

[0086] In the formula, WWM The corrected maximum water storage capacity of the basin is expressed in mm.

[0087] 2.2) Based on the calculation results of the freezing or thawing depth of permafrost, the snowmelt runoff module uses the day-by-day method to calculate the snowmelt volume according to the empirical relationship between temperature and snowmelt rate.

[0088] Specifically, the snowmelt runoff module is established when the temperature is below the critical temperature. TC At the following times, precipitation is in the form of solid precipitation, which is snowfall. At this time, snow accumulation increases, and no snowmelt occurs. When the temperature is above the critical temperature... TC At the above time, the precipitation is in the form of liquid precipitation, i.e., rain. Simultaneously, the snow on the ground begins to melt, and the melted snow water is combined with the rainfall for runoff calculation. The snowmelt amount is calculated using the degree-day method based on the empirical relationship between temperature and snowmelt rate, with the following formula:

[0089]

[0090] In the formula, sm This represents the amount of snow melted over a given period, in mm. α The day factor is measured in cm. ℃ -1 d -1 ; TC The temperature at the monitoring boundary is expressed in °C.

[0091] The formula for calculating snow depth at the end of the time period is:

[0092]

[0093]

[0094] In the formula, sd n-1 The initial snow depth for that period is in mm. sd n This represents the snow depth at the end of the time period, in mm. ΔP The amount of precipitation during the specified period is in mm.

[0095] 2.3) Based on the calculated snowmelt volume, the slope runoff generation module uses the infiltration capacity watershed distribution curve to obtain the average infiltration capacity of the watershed and calculates the runoff volume for each time period.

[0096] Specifically, a slope runoff generation module is established, and the Horton infiltration formula is used for the slope runoff generation model. Due to the heterogeneity of the underlying surface of the watershed, the infiltration capacity varies greatly at different points. The average infiltration capacity of the watershed is obtained by using the infiltration capacity distribution curve of the watershed. After a series of mathematical derivations, the time-period runoff generation calculation formula is obtained:

[0097]

[0098] in:

[0099] In the formula, R Net rainfall depth for that period, in mm; K An index reflecting the decreasing characteristics of soil infiltration rate; P a This represents the initial soil moisture content, in mm. b The index represents the area distribution curve of infiltration capacity.

[0100] Evapotranspiration calculations employ a two-layer evapotranspiration model. The calculation formula is as follows:

[0101] ;

[0102] ;

[0103] In the formula, P Rainfall amount, in mm; E m Evaporation capacity, unit: mm; EU Evapotranspiration of the upper soil layer, in mm; EL This represents the evapotranspiration of the lower soil layer, in mm. WU This represents the water storage capacity of the upper soil layer, in mm. WL WLM represents the water storage capacity of the lower soil layer, in mm; WLM represents the maximum water storage capacity of the lower soil layer, in mm.

[0104] The water retention capacity of small and medium-sized reservoirs is closely related to their storage capacity. If the initial water storage situation of the reservoir is reflected by the rainfall in the early stage, the actual water retention capacity of the reservoir can be calculated based on the empirical relationship between the rainfall in the early stage and the actual water retention capacity of the reservoir. The formula for calculating the actual water retention capacity of the reservoir is as follows:

[0105]

[0106] In the formula, R V The reservoir's flood control capacity is expressed in mm. R cm The actual water storage capacity of the reservoir is expressed in mm.

[0107] The actual water storage capacity of the reservoir is calculated using the watershed filling formula. The formula is as follows:

[0108]

[0109] In the formula, R r The actual water storage capacity of the reservoir is expressed in mm.

[0110] The actual net rainfall is calculated by subtracting the actual amount of water stored in the reservoir from the net rainfall.

[0111] 2.4) Based on the calculated time period runoff, the water source module divides net rainfall into three types of water sources: surface runoff, interflow, and groundwater runoff according to the infiltration rate of the upper soil layer and the infiltration rate of the lower soil layer, and calculates surface runoff, interflow, and groundwater runoff respectively.

[0112] Specifically, the water source module is established, and net rainwater is divided into three water sources—surface runoff, interflow, and groundwater—based on the stable infiltration rates of the upper and lower soil layers. The calculation formula is as follows:

[0113]

[0114]

[0115]

[0116] In the formula, R Net rainfall, measured in mm; F CA The infiltration rate of the upper soil layer is expressed in mm / hr. F CB The infiltration rate of the underlying soil is expressed in mm / hr. R s Surface runoff, in mm; R i Interflow in soil, unit: mm; R g This refers to underground runoff, measured in mm.

[0117] 2.5) The slope runoff module calculates the slope runoff of surface runoff, interflow, and groundwater runoff respectively. The slope runoff of interflow and groundwater runoff is calculated separately. The surface runoff runoff is calculated using the instantaneous unit hydrograph, while the interflow and groundwater runoff runoff are calculated using a linear reservoir.

[0118] Specifically, the slope runoff confluence module is established, with surface runoff confluence using the whisker instantaneous unit hydrograph, and interflow and groundwater runoff confluence using a linear reservoir. The peak value of the whisker instantaneous unit hydrograph is related to net rainfall intensity; in practical applications, factors affecting the peak value must be considered. n, k The value is corrected.

[0119] The slope runoff of surface runoff is represented by the instantaneous unit hydrograph of the stubble, and the mathematical equation for the instantaneous unit hydrograph is:

[0120]

[0121] In the formula, Γ For gamma function; n The number of linear reservoirs is a positive integer. k Let be the storage and release coefficient of a linear reservoir.

[0122] When the instantaneous unit line parameter of Nash n When the integer is positive, the integral is converted to a time-interval unit line, and the calculation formula is:

[0123]

[0124] The formula for calculating surface runoff runoff from slopes is:

[0125]

[0126] In the formula, Q s,t for t Surface runoff at any time, in m³ 3 / s; A The drainage area is expressed in km². 2 ; R s,i for t Surface runoff at any time, in mm; q t-i+1 is the vertical coordinate value of the unit line for the runoff period; m is the number of runoff periods.

[0127] The slope runoff from interflow and groundwater runoff is calculated using a linear reservoir algorithm, with the following formula:

[0128]

[0129]

[0130] In the formula, Q i,t The interflow discharge at time t is expressed in m³. 3 / s; Q i,t-1 The interflow discharge at time t-1 is expressed in m³. 3 / s; R i,t-1 The interflow in the soil at time t-1 is expressed in mm. Q g,t The groundwater runoff at time t is expressed in cubic meters per second (m³). 3 / s; Q g,t-1 The underground runoff at time t-1 is expressed in cubic meters per second (m³). 3 / s; R g,t-1 The underground runoff at time t-1 is expressed in mm.

[0131] 2.6) Based on the calculation results of the slope confluence module, the river confluence module uses the Muskingen piecewise continuous algorithm to calculate the river network runoff, and then calculates the total watershed runoff.

[0132] Specifically, the river confluence module is established, and the river confluence uses the Muskingen piecewise continuous algorithm, with the calculation formula as follows:

[0133]

[0134]

[0135] in:

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142] In the formula, Q(t) The outlet cross-sectional flow rate is expressed in cubic meters per second (m³). 3 / s; t Number of time periods; N is the number of river sections; x This is the flow proportion factor; K The unit for stable flow convergence time is hr; L The length of the river section is expressed in kilometers. V The average flow velocity of the river section is expressed in m / s.

[0143] In step 2) above, the parameter calibration of the flow generation model is specifically as follows:

[0144] (1) Calibration of frozen soil module parameters, soil freeze-thaw coefficient β The freeze-thaw coefficient reflects the ability of permafrost to thaw and freeze as temperatures rise and fall. It exhibits seasonal variation, reaching its maximum during the summer months of June to August when seasonally frozen soil completely thaws, and its minimum during the winter months of January to March when the frozen soil depth reaches its maximum. The range of the freeze-thaw coefficient is 0.01–0.55. It can be determined based on historical temperature and permafrost data.

[0145] (2) Snowmelt runoff module parameter calibration: The degree-day factor α reflects the ability of snow to melt as the temperature rises. The degree-day factor also has seasonal variation characteristics. The degree-day factor is the largest in summer and the smallest in winter; the value range of the degree-day factor is 0.01-0.25. It can be determined based on historical temperature, snow cover, and runoff data.

[0146] (3) Calibration of runoff generation module parameters: Based on historical hydrological and meteorological data, the parameters of the runoff generation module are calibrated using optimization methods, hydrological analysis methods, or parameter transfer methods; collect reservoir characteristic values ​​of small and medium-sized reservoir groups, and analyze and calculate the maximum storage capacity of small and medium-sized reservoir groups.

[0147] (4) Calibration of water source module parameters: Based on historical hydrological and meteorological data, the parameters of the water source module are calibrated using optimization methods, hydrological analysis methods, or parameter transfer methods.

[0148] In step 2) above, the parameter calibration of the confluence model is specifically as follows:

[0149] The time-varying slope runoff confluence module parameters are calibrated as follows: surface runoff confluence uses a nanometer instantaneous unit hydrograph, while interflow and groundwater runoff confluence uses a linear reservoir. In this embodiment, as... Figure 4 As shown, the peak value of the instantaneous unit hydrograph is related to the net rainfall intensity. In practical applications, factors affecting the peak value are... n, k The value is corrected, and a unit linear delay is established when applying it. nk The nonlinear relationship with surface runoff R was obtained. k (Pick n=Δt The formula is:

[0150]

[0151] In the formula, R Net rainfall, in mm; α The coefficient reflects the influence of watershed characteristics and is calibrated from historical data; β It is an index that reflects nonlinear effects and is calibrated using historical data.

[0152] In this embodiment, the parameters of the time-varying river confluence module are calibrated as follows: the river confluence uses the Muskingan piecewise continuous algorithm. In this embodiment, as... Figure 5 As shown, the number of segments in the Muskingan method is related to the average flow velocity of the river segment, which varies with the size of the flood. In application, an empirical relationship between the average flow velocity of the river segment and the peak flow is established based on measured data. The relationship is as follows:

[0153]

[0154] In the formula, Q m Peak flow rate, in cubic meters per second (m³). 3 / s; a The intercept of the relationship line on a log-log coordinate system; b The slope of the relationship line on a log-log coordinate system; V The average flow velocity of the river section is expressed in m / s.

[0155] The Muskingan method has two parameters: steady-state flow convergence time and the flow weight factor. Since the flow weight factor varies with flow rate, in application, an optimization method is first used to calibrate the flow weight factor for a given flood event. Then, an empirical relationship between the flow weight factor and the peak flow is established, expressed as:

[0156]

[0157] In the formula, c The intercept of the relationship line on a log-log coordinate system; d The slope of the relationship line on a log-log coordinate system; x This is the flow rate weighting factor.

[0158] In step 3) above, flood warning and forecasting are based on feature units. According to steps 1) and 2), using various monitoring and forecasting information, and based on different levels of flood warning issuance standards, flood warning information is produced and issued, including the following sub-steps:

[0159] 3.1) Collect and organize real-time rainfall and water information, which can be obtained from the real-time rainfall and water database.

[0160] 3.2) Precipitation forecast information for the foreseeable period can be obtained from a dedicated database for hydrological forecasting.

[0161] 3.3) Prepare for-the-term flood forecasts.

[0162] 3.4) Combine human experience to correct the flood forecast calculation results and obtain the final flood forecast results.

[0163] In summary, this invention employs a structured and modular software design approach to construct a distributed hydrological model based on feature units, which is highly portable and easy to promote and apply.

[0164] In one embodiment of the present invention, a distributed hydrological model construction system based on feature units is provided, comprising:

[0165] The feature unit construction module establishes a historical dataset of flood events based on the collected topographic data, soil data, hydrological data, and meteorological data. It then divides the topographic data in the historical dataset of flood events into feature units and matches the remaining soil data, hydrological data, and meteorological data to each feature unit, and constructs spatial topological relationships based on the feature units.

[0166] The model building and parameter calibration module constructs a distributed hydrological model based on spatial topological relationships to calibrate the parameters of the runoff generation model, and then calibrates the parameters of the confluence model based on the results of the runoff generation model parameter calibration.

[0167] The early warning release module generates and releases flood warning information based on parameter calibration results, various monitoring and forecasting information, and different flood warning release standards.

[0168] In the above embodiments, a historical dataset of flood events is established based on the collected topographic data, soil data, hydrological data, and meteorological data. The topographic data in the historical dataset is divided into feature units, and the remaining soil data, hydrological data, and meteorological data are matched to each feature unit. Spatial topological relationships are constructed based on the feature units, including:

[0169] The collected topographic data, soil data, hydrological data, and meteorological data are processed into time series data of equal time periods according to the set time step to obtain historical datasets of flood events.

[0170] Based on the principle of similarity in natural geography and hydro-meteorological characteristics, the large-scale watershed topographic map extracted from topographic data is divided into several small watersheds as feature units;

[0171] The spatial topological relationships are established for the divided feature units according to their upstream and downstream relationships, and the watershed corresponding to each feature unit is spatially associated with the watershed corresponding to the inflow feature unit.

[0172] In the above embodiments, the segmented feature units include two types:

[0173] One type is a closed watershed with no upstream inflow characteristic units. The outflow of this type of characteristic unit consists only of its own rainfall runoff.

[0174] Another type is the interval basin, which has an inflow characteristic unit upstream. The outlet flow of this type of characteristic unit consists of the outlet flow of the inflow characteristic unit and its own rainfall runoff.

[0175] In the above embodiments, when performing the generation and confluence calculation of the characteristic unit, there is a one-to-one correspondence between the net rainfall of the characteristic unit and the outlet flow rate.

[0176] When performing river confluence calculations between feature units:

[0177] When the outflow feature unit code is different from the inflow feature unit code, the outflow feature unit is defined as the first feature unit, the inflow feature unit is defined as the second feature unit, and the inflow feature unit corresponding to the outflow feature unit is defined as the third feature unit. When performing river confluence calculation between feature units, the confluence calculation of the second feature unit can only be performed after the confluence calculation of the third feature unit. The confluence calculation result of the third feature unit is added to the outlet flow of the second feature unit, and so on, traversing all feature units.

[0178] When the outflowing feature unit code is the same as the inflowing feature unit code, the outflowing feature unit is the outlet feature unit of the basin, and its outlet flow rate is the basin outlet flow rate.

[0179] In the above embodiments, a distributed hydrological model is constructed based on spatial topological relationships, including a runoff generation model and a runoff confluence model;

[0180] The runoff generation model includes a frozen soil module, a snowmelt runoff module, a slope runoff generation module, and a small and medium-sized reservoir runoff reduction module; the runoff confluence model includes a slope confluence module and a river confluence module.

[0181] Based on measured frozen soil data, the frozen soil module analyzes the empirical relationship between frozen soil depth and air temperature and determines the freeze-thaw factor in order to calculate the freezing or thawing depth of frozen soil.

[0182] Based on the calculation results of the freezing or thawing depth of permafrost, the snowmelt runoff module uses the day-period method to calculate the snowmelt volume according to the empirical relationship between temperature and snowmelt rate.

[0183] Based on the calculated snowmelt volume, the slope runoff generation module uses the infiltration capacity watershed distribution curve to obtain the average infiltration capacity of the watershed and calculates the runoff volume for each time period.

[0184] Based on the calculated time period runoff, the water source module divides net rainfall into three types of water sources: surface runoff, interflow, and groundwater runoff according to the infiltration rate of the upper soil layer and the infiltration rate of the lower soil layer. Surface runoff, interflow, and groundwater runoff are calculated separately.

[0185] The slope runoff module calculates the slope runoff for surface runoff, interflow, and groundwater runoff separately. The slope runoff for interflow and groundwater runoff is calculated separately. The surface runoff runoff is calculated using the instantaneous unit hydrograph, while the interflow and groundwater runoff runoff are calculated using a linear reservoir.

[0186] Based on the calculation results of the slope confluence module, the river confluence module uses the Muskingen piecewise continuous algorithm to calculate the river network runoff, and then calculates the total watershed runoff.

[0187] In the above embodiments, the parameters of the flow generation model are calibrated as follows:

[0188] The parameters of the frozen soil module were calibrated. The soil freeze-thaw coefficient is the largest when the seasonal frozen soil is completely thawed in summer (June-August) and the smallest when the frozen soil depth reaches its maximum in winter (January-March). The value range of the soil freeze-thaw coefficient is 0.01-0.55.

[0189] The parameters of the snowmelt runoff module were calibrated. The degree-day factor was the largest in summer and the smallest in winter. The value range of the degree-day factor was 0.01-0.25.

[0190] The parameters of the runoff generation module are calibrated using optimization methods, hydrological analysis methods, or parameter transfer methods based on historical hydrological and meteorological data. The reservoir characteristic values ​​of the small and medium-sized reservoir group are collected, and the maximum storage capacity of the small and medium-sized reservoir group is analyzed and calculated.

[0191] The parameters of the water source module are calibrated using optimization methods, hydrological analysis methods, or parameter transfer methods, based on historical hydrological and meteorological data.

[0192] In the above embodiments, the parameters of the merge model are calibrated as follows:

[0193] The time-varying slope runoff confluence module parameters were calibrated. Surface runoff confluence was represented by the whisker instantaneous unit hydrograph, while interflow and groundwater runoff confluence were represented by a linear reservoir. The peak value of the whisker instantaneous unit hydrograph is related to net rainfall intensity; in practical applications, factors influencing the peak value are considered. n, k The value is corrected, and a unit linear delay is established when applying it. nk The nonlinear relationship with surface runoff R was obtained. k ;

[0194] The parameters of the time-varying river confluence module are calibrated. The number of segments in the Muskingan method is related to the average flow velocity of the river segment, which varies with the size of the flood. When applying this method, an empirical relationship between the average flow velocity of the river segment and the peak flow should be established based on measured data.

[0195] In this process, the flow proportion factor of each flood event is first calibrated using an optimization method, and then an empirical relationship between the flow proportion factor and the peak flow is established.

[0196] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0197] In one embodiment of the present invention, a computing device is provided. This computing device can be a terminal and may include a processor, a communication interface, memory, a display screen, and an input device. The processor, communication interface, and memory communicate with each other via a communication bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. When the computer programs are executed by the processor, they implement the methods described in the above embodiments. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, a management network, NFC (Near Field Communication), or other technologies. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad mounted on the casing of the computing device, or an external keyboard, touchpad, or mouse. The processor can call logical instructions stored in the memory.

[0198] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0199] In one embodiment of the present invention, a computer program product is provided, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer is able to perform the methods provided in the above-described method embodiments.

[0200] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided in the above embodiments.

[0201] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0202] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0203] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0204] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a distributed hydrological model based on feature units, characterized in that, include: Based on the collected topographic, soil, hydrological, and meteorological data, a historical dataset of flood events is established. The topographic data in the historical dataset of flood events is divided into feature units, and the remaining soil, hydrological, and meteorological data are matched to each feature unit. Spatial topological relationships are constructed based on the feature units. Based on spatial topological relationships, a distributed hydrological model is constructed to calibrate the parameters of the runoff generation model, and then the parameters of the confluence model are calibrated based on the results of the runoff generation model parameter calibration. Based on parameter calibration results, as well as various monitoring and forecasting information, flood warning information is produced and issued according to different levels of flood warning issuance standards. Distributed hydrological models are constructed based on spatial topological relationships, including runoff generation models and runoff confluence models; The runoff generation model includes a frozen soil module, a snowmelt runoff module, a slope runoff generation module, and a small and medium-sized reservoir runoff reduction module; the runoff confluence model includes a slope confluence module and a river confluence module. Based on measured frozen soil data, the frozen soil module analyzes the empirical relationship between frozen soil depth and air temperature and determines the freeze-thaw factor in order to calculate the freezing or thawing depth of frozen soil. Based on the calculation results of the freezing or thawing depth of permafrost, the snowmelt runoff module uses the day-period method to calculate the snowmelt volume according to the empirical relationship between temperature and snowmelt rate. Based on the calculated snowmelt volume, the slope runoff generation module uses the infiltration capacity watershed distribution curve to obtain the average infiltration capacity of the watershed and calculates the runoff volume for each time period. Based on the calculated time period runoff, the water source module divides net rainfall into three types of water sources: surface runoff, interflow, and groundwater runoff according to the infiltration rate of the upper soil layer and the infiltration rate of the lower soil layer. Surface runoff, interflow, and groundwater runoff are calculated separately. The slope runoff module calculates the slope runoff for surface runoff, interflow, and groundwater runoff separately. The slope runoff for interflow and groundwater runoff is calculated separately. The surface runoff runoff is calculated using the instantaneous unit hydrograph, while the interflow and groundwater runoff runoff are calculated using a linear reservoir. Based on the calculation results of the slope confluence module, the river confluence module uses the Muskingen piecewise continuous algorithm to calculate the river network runoff, and then calculates the total watershed runoff. When calculating the runoff generation and runoff of a feature unit, there is a one-to-one correspondence between the net rainfall and the outflow of the feature unit. When calculating the runoff between feature units: when the outflow feature unit code is different from the inflow feature unit code, the outflow feature unit is defined as the first feature unit, the inflow feature unit is defined as the second feature unit, and the inflow feature unit corresponding to the outflow feature unit is defined as the third feature unit. When calculating the runoff between feature units, the runoff of the second feature unit can only be calculated after the runoff of the third feature unit. The runoff calculation result of the third feature unit is added to the outflow of the second feature unit, and this process is repeated for all feature units. When the outflow feature unit code is the same as the inflow feature unit code, the outflow feature unit is the outlet feature unit of the watershed, and its outlet flow is the watershed outlet flow. The runoff model parameters were calibrated as follows: Time-varying slope runoff module parameters were calibrated; surface runoff runoff was calibrated using the whisker instantaneous unit hydrograph, while interflow and groundwater runoff runoff were calibrated using a linear reservoir; the peak value of the whisker instantaneous unit hydrograph is related to net rainfall intensity, and in practical applications, factors affecting the peak value are considered. n, k The value is corrected, and a unit linear delay is established when applying it. nk The nonlinear relationship with surface runoff R was obtained. k The parameters of the time-varying river confluence module are calibrated. The number of segments in the Muskingan method is related to the average flow velocity of the river segment, which varies with the size of the flood. When applying the method, an empirical relationship between the average flow velocity of the river segment and the peak flow is established based on measured data. Specifically, when applying the method, the flow proportion factor of the flood is first calibrated using the optimization method, and then an empirical relationship between the flow proportion factor and the peak flow is established.

2. The method for constructing a distributed hydrological model based on feature units as described in claim 1, characterized in that, Based on collected topographic, soil, hydrological, and meteorological data, a historical dataset of flood events was established. The topographic data within this dataset was divided into feature units, and the remaining soil, hydrological, and meteorological data were matched to these feature units. Spatial topological relationships were then constructed based on these feature units, including: The collected topographic data, soil data, hydrological data, and meteorological data are processed into time series data of equal time periods according to the set time step to obtain historical datasets of flood events. Based on the principle of similarity in natural geography and hydro-meteorological characteristics, the large-scale watershed topographic map extracted from topographic data is divided into several small watersheds as feature units; The spatial topological relationships are established for the divided feature units according to their upstream and downstream relationships, and the watershed corresponding to each feature unit is spatially associated with the watershed corresponding to the inflow feature unit.

3. The method for constructing a distributed hydrological model based on feature units as described in claim 2, characterized in that, The feature units of the division include two types: One type is a closed watershed with no upstream inflow characteristic units. The outflow of this type of characteristic unit consists only of its own rainfall runoff. Another type is the interval basin, which has an inflow characteristic unit upstream. The outlet flow of this type of characteristic unit consists of the outlet flow of the inflow characteristic unit and its own rainfall runoff.

4. The method for constructing a distributed hydrological model based on feature units as described in claim 1, characterized in that, The parameters of the runoff generation model are calibrated as follows: The parameters of the frozen soil module were calibrated. The soil freeze-thaw coefficient is the largest when the seasonal frozen soil is completely thawed in summer (June-August) and the smallest when the frozen soil depth reaches its maximum in winter (January-March). The value range of the soil freeze-thaw coefficient is 0.01-0.

55. The parameters of the snowmelt runoff module were calibrated. The degree-day factor was the largest in summer and the smallest in winter. The value range of the degree-day factor was 0.01-0.

25. The parameters of the runoff generation module are calibrated using optimization methods, hydrological analysis methods, or parameter transfer methods based on historical hydrological and meteorological data. The reservoir characteristic values ​​of the small and medium-sized reservoir group are collected, and the maximum storage capacity of the small and medium-sized reservoir group is analyzed and calculated. The parameters of the water source module are calibrated using optimization methods, hydrological analysis methods, or parameter transfer methods, based on historical hydrological and meteorological data.

5. A distributed hydrological model construction system based on feature units, used to implement the distributed hydrological model construction method based on feature units as described in any one of claims 1 to 4, characterized in that, include: The feature unit construction module establishes a historical dataset of flood events based on the collected topographic data, soil data, hydrological data, and meteorological data. It then divides the topographic data in the historical dataset of flood events into feature units and matches the remaining soil data, hydrological data, and meteorological data to each feature unit, and constructs spatial topological relationships based on the feature units. The model building and parameter calibration module constructs a distributed hydrological model based on spatial topological relationships to calibrate the parameters of the runoff generation model, and then calibrates the parameters of the confluence model based on the results of the runoff generation model parameter calibration. The early warning release module generates and releases flood warning information based on parameter calibration results, various monitoring and forecasting information, and different flood warning release standards.

6. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 4.

7. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 4.

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