Water and sediment scheduling and water quality scene prediction method for reservoir area in high-cold area

Abstract

The invention relates to the technical field of environmental governance, and discloses an alpine region reservoir area water and sediment scheduling and water quality scenario prediction method comprising the following steps: river network segmentation and node classification; embedding ice kinetic parameters; transporting and correcting low-temperature silt; carrying out low-temperature adaptation on water quality parameters; performing dynamic boundary transfer and joint solution; and carrying out synchronous real-time iterative calculation. According to the method, ice kinetic parameters are embedded in a one-dimensional Saint-Venant equation, and the coupling relation between the thickness of the ice cover and the hydraulic element is dynamically solved by correcting the effective cross-section area in a continuous equation and the friction gradient in a momentum equation and adopting an iterative algorithm. Therefore, the water level fluctuation in the ice dam forming and outburst process is accurately described, and the problem that an existing model is insufficient in response to ice period hydraulic sudden change is solved.

Description

Technical Field

[0001] The present invention relates to the field of environmental governance technology, and in particular to a method for water and sediment scheduling and water quality scenario prediction in a reservoir area in a high-altitude cold region. Background Art

[0002] In the fields of environmental governance and water conservancy engineering, water and sediment scheduling and water quality prediction in cascade reservoirs are key technologies for ensuring water resource security, preventing river siltation, and protecting the ecosystem. Traditional hydrodynamic-sediment-water quality coupling models are typically based on river network segmentation and node classification methods under normal temperature conditions. They describe water flow using the one-dimensional Saint-Venant equation and simulate water and sediment processes and pollutant migration in river networks by combining sediment transport formulas and water quality reaction equations. This technology has been widely used in river management in non-alpine regions, providing basic data support for reservoir scheduling, flood control planning, and water quality assessment.

[0003] A search of Chinese patent publication number CN109992909B discloses a method for coupling hydrodynamic, water quality, and sediment simulation of cascade reservoirs in a tree-like river network. The method comprises the following steps: based on the distribution of cascade reservoirs in the tree-like river network, the section where the dam is located in each reservoir is used as a control element to segment the entire river network, thereby obtaining control nodes of the river network and a plurality of sub-river networks; classifying the control nodes of the river network; and programming the calculation of each control node in the order in which the sub-river networks are numbered. Each sub-river network is independently calculated according to the tree-like river network algorithm. After the calculation is completed, based on the relationship between the upstream and downstream control nodes of the dam, the calculation result of the downstream control node of each sub-river network is transmitted to the next sub-river network through the upstream and downstream control nodes of the dam, and serves as the boundary condition of the upstream control node of the next sub-river network. The method is repeated in this way, thereby achieving a joint solution of the river network and the cascade reservoirs to obtain the water level and flow value of the section where the dam is located in each reservoir at each time; and achieving synchronous real-time simulation calculation through the overall hydrodynamic simulation calculation platform of the river network.

[0004] However, when the above method is applied to the reservoir area in the alpine region, many problems are exposed due to the special climate and geographical conditions of the region, especially the existence of ice.

[0005] Sudden changes in hydraulic conditions during glacial periods: In winter in high-altitude cold regions, rivers freeze over. The formation of ice sheets significantly alters the geometric boundaries and hydraulic characteristics of river channels. Ice sheets reduce the actual flow area of ​​a river channel, significantly reducing the hydraulic radius while increasing the roughness and resistance of the water flow. Furthermore, the accumulation and blockage of ice can cause a sharp drop in the flow capacity of localized river channels, triggering abnormal water level fluctuations, resulting in dangerous conditions such as backwaters and even ice dams. Existing models, based on the one-dimensional Saint-Venant equations at room temperature, fail to fully account for the dynamic impact of these glacial factors on water flow and are unable to accurately simulate the complex hydrodynamic processes during glacial periods, leading to significant deviations in predictions of key hydraulic parameters such as water level and flow. For example, in the Inner Mongolia section of the Yellow River, water levels rise abnormally each year due to ice conditions during the ice flood season. Existing models struggle to accurately predict these changes, posing significant challenges to flood control and the safety of water conservancy facilities.

[0006] Sediment transport deviation: Under low-temperature conditions, the physical and chemical properties of sediment change, and its flocculation and sedimentation characteristics differ significantly from those at normal temperatures. Furthermore, during ice ages, water flow slows and shear forces decrease, causing sediment transport capacity and sedimentation patterns to differ from conventional model predictions. Conventional models may overestimate or underestimate the amount and location of sediment accumulation, which is extremely detrimental to river channel evolution analysis, reservoir capacity maintenance, and long-term stability assessments of water conservancy projects. For example, in the Songhua River, sediment accumulation during the winter ice age differed significantly from existing model predictions, impacting the river's navigability and the normal operation of water conservancy projects.

[0007] Inaccurate water quality parameters: Low temperatures in alpine regions inhibit microbial activity, significantly reducing the degradation rate of pollutants in water bodies. Ice cover also severely hinders gas exchange between the atmosphere and water bodies, affecting the water's reoxygenation capacity and leading to a decrease in dissolved oxygen content. However, existing water quality modules do not make parameter corrections for these special conditions in alpine regions and still calculate according to conventional reaction rates and reoxygenation mechanisms, resulting in large errors in water quality predictions. For some drinking water sources and ecologically sensitive areas with strict water quality requirements, such errors may lead to incorrect water quality assessments and decisions, threatening the ecological environment and residents' health. For example, in some reservoirs in Heilongjiang, inaccurate predictions of water quality parameters during the ice age have affected the assessment of water quality in drinking water sources and the formulation of protective measures.

[0008] To this end, we propose a method for water and sediment scheduling and water quality scenario prediction in reservoir areas in alpine regions. Summary of the Invention

[0009] The present invention mainly solves the technical problems existing in the above-mentioned prior art and provides a method for water and sediment scheduling and water quality scenario prediction in reservoir areas in high-altitude and cold regions.

[0010] To achieve the above objectives, the present invention adopts the following technical solution: a method for water and sediment scheduling and water quality scenario prediction in a reservoir area in a high-altitude cold region, comprising the following steps:

[0011] S1 River network segmentation and node classification: Based on the distribution of cascade reservoirs in the tree-like river network, the section where the dam of each reservoir is located is used as the segmentation point, and the entire river network is divided into several sub-river networks and corresponding control nodes. The sub-river networks and control nodes are numbered in order from upstream to downstream; the control nodes are classified into external boundary nodes that require external boundary conditions and internal boundary nodes at river confluences that do not require boundary conditions;

[0012] S2 Ice Dynamic Parameter Embedding: Based on the glacial characteristics of high-altitude cold regions, ice parameters are dynamically embedded in the one-dimensional Saint-Venant equation. Based on the temperature module, the glacial and non-glacial states are determined in real time. The hydraulic radius calculation formula and water flow continuity equation are modified to generate a hydrodynamic model adapted to the glacial period.

[0013] S3 Low-temperature sediment transport correction: A temperature-dependent sedimentation velocity formula is introduced into the sediment module. Based on experimental data, the correlation between sediment flocculation and sedimentation characteristics at low temperatures and water shear force is established. The correction factor for the velocity distribution under the ice sheet is dynamically adjusted to generate a low-temperature sediment transport correction model.

[0014] S4 Water Quality Parameters Adaptation to Low Temperatures: Add an ice cover inhibition factor on the dissolved oxygen diffusion coefficient to the water quality module, dynamically adjust the pollutant degradation rate constant based on the Arrhenius formula, and establish a temperature-reaction rate correlation function to generate a low-temperature adapted water quality model;

[0015] S5 Dynamic Boundary Transfer and Joint Solution: Within each time step, coupled hydrodynamic-sediment-water quality calculations are performed synchronously according to the sub-river network numbering sequence. During the calculation of each sub-river network, the water level, flow, sediment concentration, ice sheet thickness, and water quality parameters of its downstream control node are transferred in real time to the upstream boundary node of the next sub-river network as input for dynamically updated boundary conditions, achieving synchronized parameter iteration across sub-river networks.

[0016] S6 synchronous real-time iterative calculation: Through the revised hydrodynamic-sediment-water quality coupling model, based on the river network topology, cross-section data, initial conditions and time step parameters, dynamic boundary conditions are called before each time step iteration to synchronously solve the water level, flow, sediment accumulation and water quality indicators of each sub-river network.

[0017] Preferably, the specific steps of embedding the ice dynamics parameters in S2 include:

[0018] Correct the water surface width in the one-dimensional Saint-Venant equation to the equivalent water surface width, and the corrected continuity equation is:

[0019]

[0020] Among them, A eff The effective cross-sectional area for considering ice cover and blockage is m 2 ; Q is the amount of water flowing through the cross section per unit time, unit is m 3 / s; t is time, in seconds; x is the position coordinate along the river;

[0021] Taking into account the ice sheet's increased drag and changed hydraulic shape, the momentum equation is modified to:

[0022]

[0023] Where Q is the flow rate, unit is m 3 / s;A eff The effective cross-sectional area corrected for ice cover is in m 2 ; h is the water surface elevation, the unit is m, g is the acceleration due to gravity, the unit is m / s 2 ; S0 is the riverbed slope of the natural terrain, S f is the friction slope.

[0024] As a preference, the coupling relationship between ice sheet thickness and hydraulic elements is solved through an iterative algorithm, and the influence of ice sheet roughness on water flow resistance is dynamically updated; an ice age discrimination threshold is set in the temperature module to trigger the calculation of ice sheet parameters and adjust the ice blockage coefficient in the water flow continuity equation.

[0025] Preferably, the specific steps of correcting low-temperature sediment transport in S3 include:

[0026] The temperature-dependent sedimentation velocity formula is used:

[0027]

[0028] where v s (T) is the sedimentation velocity at the current temperature T, v s0 is the reference value of sedimentation velocity under the reference temperature T0, k is the temperature sensitivity coefficient calibrated according to the measured data;

[0029] According to the velocity distribution characteristics under the ice sheet, the vertical velocity correction factor is introduced: where h ice is the ice cover thickness of the current section, and H is the total water depth of the section, which is used to adjust the calculated value of riverbed shear force.

[0030] Preferably, the specific steps of low temperature adaptation of water quality parameters in S4 include:

[0031] Adding an ice cover inhibition coefficient to the reoxygenation equation to correct the dissolved oxygen diffusion flux;

[0032] Based on the Arrhenius formula, the pollutant degradation rate constant is corrected to:

[0033] J DO =K2·(C s -C)·β ice

[0034] Among them J DO represents the reoxygenation flux per unit time, K2 is the reoxygenation rate constant at standard temperature, C s is the saturated dissolved oxygen concentration corresponding to the water temperature, C is the current dissolved oxygen concentration in the water body, β ice is the ice cover suppression coefficient, which ranges from 0 to 1 and is used to quantitatively describe the degree of obstruction of the ice cover to oxygen diffusion.

[0035] Preferably, the S6 further includes a data-driven calibration and verification step:

[0036] Using the measured hydrological, sediment and water quality data of the alpine region during the glacial period, the ice sheet parameters, sedimentation velocity formula and degradation rate coefficient were calibrated;

[0037] Simulate ice disaster scenarios, including ice dam formation and breach processes, to verify the model's accuracy in predicting water level fluctuations and the consistency of the spatial distribution of sediment deposition under extreme conditions.

[0038] Preferably, the specific rules for transferring the boundary conditions in S5 are:

[0039] The output parameters of the control node upstream of the dam include water level, ice cover thickness, sediment concentration, and dissolved oxygen content. The downstream control node receives them and uses them as the initial values ​​of the upstream boundary of the next sub-river network.

[0040] The boundary nodes within the river confluence are interpolated and transferred using the principles of mass conservation and momentum conservation.

[0041] Beneficial effects

[0042] The present invention provides a method for water and sediment scheduling and water quality scenario prediction in a reservoir area in a high-altitude cold region.

[0043] Beneficial effects:

[0044] (1) This method for water and sediment scheduling and water quality scenario prediction in high-altitude cold reservoirs embeds ice dynamic parameters into the one-dimensional Saint-Venant equation. By modifying the effective cross-sectional area in the continuity equation and the friction slope in the momentum equation, an iterative algorithm is used to dynamically solve the coupling relationship between ice sheet thickness and hydraulic elements. This method accurately depicts water level fluctuations during ice dam formation and breach, solving the problem that existing models are insufficiently responsive to sudden hydraulic changes during ice ages.

[0045] (2) This method for water and sediment scheduling and water quality scenario prediction in high-altitude cold reservoirs adds an ice cover inhibition coefficient to the reoxygenation equation to quantitatively describe the degree to which the ice cover hinders oxygen diffusion. It also dynamically adjusts the pollutant degradation rate constant based on the Arrhenius formula and establishes a temperature-reaction rate correlation function. This improves the accuracy of water quality assessment at drinking water sources.

[0046] (3) This method for water and sediment scheduling and water quality scenario prediction in high-altitude cold reservoirs introduces a temperature-dependent sedimentation velocity formula into the sediment module, corrects the sedimentation velocity through the water temperature exponential decay relationship, and dynamically adjusts the vertical velocity correction factor based on the ratio of ice sheet thickness to water depth to reflect the compression effect of ice sheet on riverbed shear force. This effectively improves the reliability of river evolution analysis and reservoir storage capacity maintenance.

[0047] (4) This method of water and sediment scheduling and water quality scenario prediction in reservoirs in high-altitude cold regions uses a dynamic boundary transfer mechanism to update the boundary parameters between sub-river networks in real time within each time step, solving the time lag problem caused by traditional static transfer and significantly improving the model's response speed and prediction accuracy to sudden events such as ice dam breaches and snowmelt floods. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0049] Figure 1 Flow chart of the method of the present invention;

[0050] Figure 2 This is a schematic diagram of river network segmentation and node classification in the present invention;

[0051] Figure 3 This is a logic diagram for the correction of the glacial hydrodynamic model of the present invention;

[0052] Figure 4 This is a flow chart of the dynamic boundary transfer mechanism of the present invention. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] A method for water and sediment scheduling and water quality scenario prediction in reservoirs in high-altitude cold regions, such as Figure 1-Figure 4 As shown,

[0055] The following steps are involved:

[0056] S1 River network segmentation and node classification: Based on the distribution of cascade reservoirs in the tree-like river network, the section where the dam of each reservoir is located is used as the segmentation point, and the entire river network is divided into several sub-river networks and corresponding control nodes. The sub-river networks and control nodes are numbered in order from upstream to downstream; the control nodes are classified into external boundary nodes that require external boundary conditions and internal boundary nodes at river confluences that do not require boundary conditions;

[0057] S2 Ice Dynamic Parameter Embedding: Based on the glacial characteristics of high-altitude cold regions, ice parameters are dynamically embedded in the one-dimensional Saint-Venant equation. Based on the temperature module, the glacial and non-glacial states are determined in real time. The hydraulic radius calculation formula and water flow continuity equation are modified to generate a hydrodynamic model adapted to the glacial period.

[0058] S3 Low-temperature sediment transport correction: A temperature-dependent sedimentation velocity formula is introduced into the sediment module. Based on experimental data, the correlation between sediment flocculation and sedimentation characteristics at low temperatures and water shear force is established. The correction factor for the velocity distribution under the ice sheet is dynamically adjusted to generate a low-temperature sediment transport correction model.

[0059] S4 Water Quality Parameters Adaptation to Low Temperatures: Add an ice cover inhibition factor on the dissolved oxygen diffusion coefficient to the water quality module, dynamically adjust the pollutant degradation rate constant based on the Arrhenius formula, and establish a temperature-reaction rate correlation function to generate a low-temperature adapted water quality model;

[0060] S5 Dynamic Boundary Transfer and Joint Solution: Based on the river network topology and time step division, the hydrodynamic-sediment-water quality coupled calculation is performed synchronously in the order of the sub-river network numbers within each time step. During the calculation process, each sub-river network outputs the dynamic parameters of its downstream control node in real time, including water level, flow, sediment concentration, ice sheet thickness, and dissolved oxygen content. These parameters are transferred to the upstream boundary nodes of the adjacent downstream sub-river network through the upstream and downstream control nodes of the dam, and serve as the boundary condition input for the sub-river network within the same time step. This dynamic transfer mechanism ensures that the boundary values ​​are updated in real time as the calculation progresses, avoiding the timing lag caused by static transfer.

[0061] S6 synchronous real-time iterative calculation: Through the revised hydrodynamic-sediment-water quality coupling model, based on the river network topology, cross-section data, initial conditions and time step parameters, dynamic boundary conditions are called before each time step iteration to synchronously solve the water level, flow, sediment accumulation and water quality indicators of each sub-river network.

[0062] During glacial conditions, river surfaces are often covered with varying degrees of ice cover, which affects the lateral distribution of water flow, hydraulic radius, and flow velocity. To improve the adaptability of the one-dimensional Saint-Venant equation under glacial conditions, its key hydraulic parameters need to be dynamically corrected. Therefore, the continuity equation is rewritten as:

[0063]

[0064] Among them, A eff The effective cross-sectional area for considering ice cover and blockage is m 2 ; Q is the amount of water flowing through the cross section per unit time, unit is m 3 / s; t is time in seconds; x is the position coordinate along the river channel.

[0065] When a river is frozen, the end surface of the ice layer can be regarded as a rectangular end surface, so the effective cross-sectional area can be expressed by multiplying the equivalent width by the water depth:

[0066] A eff =B eff H=BW ice H

[0067] During the freezing period, ice blockage needs to be considered, so the blockage rate β is introduced, and

[0068] A eff =(1-β)BW ice H

[0069] Where B is the width of the water surface under ice-free conditions; W ice is the ice cover shielding coefficient, that is, the ice cover reduces the effective water width, and its value range is 0 to 1; H is the water depth; β represents the ice blockage ratio, and its value range is 0 to 1. After correction, the continuity equation is obtained through A eff This reflects the compression of the channel flow area during the ice age.

[0070] Accordingly, considering that the ice sheet increases resistance and changes the hydraulic shape, the momentum equation is rewritten as

[0071]

[0072] Where Q is the flow rate, unit is m 3 / s;A eff The effective cross-sectional area corrected for ice cover is in m 2 ; h is the water surface elevation, the unit is m, g is the acceleration due to gravity, the unit is m / s 2 ; S0 is the riverbed slope of the natural terrain, S f is the friction slope.

[0073] The friction slope S f Calculated using the modified Manning formula:

[0074]

[0075] The ice cover increases the wetted perimeter, reduces the hydraulic radius, and increases the roughness, resulting in a significant increase in resistance. eff =BW ice , R h The perimeter is the base width B eff The water depths on both sides are added. The momentum equation remains in standard form, but all cross-sectional areas A, water widths B, and friction coefficients n are replaced with corrected values ​​to reflect the flow path contraction and increased friction caused by the ice sheet.

[0076] In the hydrodynamic simulation of rivers in alpine areas, there is a complex interaction between the thickness of the ice sheet and the hydraulic elements. In order to reflect this coupling effect, an iterative algorithm is required for dynamic solution. In each time step, the thickness of the ice sheet is first preliminarily estimated based on the current water velocity, water level, water temperature and historical ice sheet status, and the ice sheet roughness is calculated by substituting it into the empirical formula. The roughness value will directly affect the value of the Manning coefficient in the momentum equation, and then correct the flow velocity and water level. The ice sheet thickness and hydrodynamic response are gradually updated through iteration until the difference between the two converges to the preset accuracy. The Manning roughness coefficient n in the water flow resistance term during the iteration process is eff The update formula is

[0077] n eff =n0+α·h ice

[0078] Where n0 is the reference roughness under ice-free conditions, h ice is the current ice sheet thickness, and α is a coefficient representing the growth rate of the ice sheet's roughness. A threshold for identifying an ice age is set in the temperature module. When the water temperature falls below 0°C for 24 hours, the ice age is considered to have begun, activating the dynamic update of the ice sheet parameters. An ice blockage correction term is also introduced into the continuity equation. Its impact is indirectly reflected by adjusting the cross-sectional area and flow calculation formulas, thereby comprehensively characterizing the weakening effect of ice sheet formation on water transport capacity.

[0079] In the simulation of river sediment in cold regions, the low temperature environment has a significant impact on the floc structure and sedimentation behavior. It is necessary to introduce a temperature-dependent sedimentation velocity model to correct the traditional sediment transport calculation. In the specific operation, the sedimentation velocity is regarded as a function of water temperature, and the correction formula in the form of exponential decay is used.

[0080]

[0081] where v s (T) is the sedimentation velocity at the current temperature T, v s0is the baseline settling velocity at reference temperature T0, and k is the temperature sensitivity coefficient calibrated based on measured data. This formula indicates that as water temperature decreases, sediment settling velocity slows exponentially, reflecting the inhibitory effect of low temperatures on the flocculation and sedimentation process. In the model implementation, water temperature data for each calculation section is first acquired through the temperature module, and the settling velocity is updated in real time. This data is then substituted into the sediment continuity equation to correct the vertical transport term. This processing effectively captures the changes in sediment deposition intensity during sediment flow under the ice sheet during glacial or near-glacial conditions, thereby improving the accuracy and responsiveness of sediment simulation in low-temperature environments.

[0082] During ice age conditions, ice sheets significantly affect the vertical distribution of river flow velocity, especially near the riverbed, where the velocity gradient increases, causing the shear force calculation to deviate from reality. To correct this error, a vertical velocity correction factor is introduced into the sediment transport model to dynamically adjust the calculated value of riverbed shear force. The specific method is to define the correction factor based on the velocity distribution characteristics under the ice sheet. where h ice is the ice sheet thickness at the current section, and H is the total water depth at that section. This factor reflects the degree to which the ice sheet compresses the underlying water velocity by reducing the proportion of ice sheet thickness in the vertical scale, thereby adjusting the intensity of the bottom water's impact on the riverbed. In numerical implementation, this correction factor directly applies to the velocity-dependent portion of the riverbed shear force formula, dynamically reducing the effective shear force during ice cover periods, thereby affecting the threshold for sediment initiation and transport. This treatment more accurately characterizes the hydrodynamic structure beneath the ice sheet and improves the physical consistency and accuracy of sediment simulations during ice cover periods.

[0083] In cold seasons, under ice cover conditions, the gas exchange between water bodies and the atmosphere is significantly inhibited, especially the reoxygenation process of dissolved oxygen. In order to accurately simulate the trend of dissolved oxygen changes in low-temperature water bodies, it is necessary to make ice cover adaptation corrections to the diffusion flux in the reoxygenation equation. The specific method is to introduce an ice cover inhibition coefficient into the reoxygenation term and dynamically adjust the traditional gas-liquid interface diffusion flux. The correction formula is:

[0084] J DO =K2·(C s -C)·β ice

[0085] Among them J DO represents the reoxygenation flux per unit time, K2 is the reoxygenation rate constant at standard temperature, C s is the saturated dissolved oxygen concentration corresponding to the water temperature, C is the current dissolved oxygen concentration in the water body, β iceis the ice sheet suppression coefficient, which ranges from 0 to 1 and is used to quantitatively describe the degree to which the ice sheet hinders oxygen diffusion. This coefficient is determined by the ice sheet thickness, water temperature, and ice sheet integrity factor. The smaller the value, the thicker or more complete the ice sheet, and the weaker the reoxygenation capacity. In the numerical simulation, β is dynamically updated at each time step based on the temperature module and ice sheet parameters. ice , ensuring that dissolved oxygen flux decreases reasonably under glacial conditions and improving the prediction accuracy and physical consistency of water quality models in low-temperature environments;

[0086] Under glacial and near-glacial conditions, low temperatures have a significant inhibitory effect on the degradation rate of organic pollutants in water. In order to accurately simulate the dynamic changes of pollutants at different temperatures, it is necessary to correct the degradation rate constant based on the influence of temperature on the reaction rate. This correction establishes a temperature response relationship based on the Arrhenius formula, and converts the degradation constant at standard temperature into the rate under actual water temperature conditions through an empirical model. The expression is K(T) = K 20 ·θ (T-20) , where K(T) represents the pollutant degradation rate constant under the current water temperature T, K 20 The baseline degradation rate at 20 degrees Celsius is represented by the temperature correction factor, θ, which ranges from 1.02 to 1.08 and represents the multiple by which the reaction rate increases with each degree Celsius increase in temperature. In actual simulations, the model dynamically reads the cross-section water temperature at each time step, updates the degradation rate in real time using this formula, and substitutes it into the pollutant transport reaction equation to calculate dissolved oxygen consumption and organic matter decay. This ensures that the water quality model's response to pollutant evolution under low-temperature cover or ice conditions has a realistic physical basis and consistent timescale.

[0087] The S6 also includes a data-driven calibration and validation step: First, by collecting measured hydrological data from typical watersheds in alpine regions during the ice age, including water level and flow at each control section, ice sheet thickness, ice-age sediment concentration, pollutant indicators, and temperature, the key parameters in the model are systematically calibrated. The calibration process uses a multi-objective optimization method to adjust the ice sheet roughness, the temperature coefficient of sedimentation velocity, and the temperature response coefficient of the degradation rate in the water quality model, so that the deviation between the simulation results and the measured values ​​in multiple sections and multiple time periods is minimized. After the calibration is completed, the model is validated by setting a typical ice disaster scenario, including simulating the gradual formation process of an ice dam in a certain tributary section, the water level surge response at the moment of breach, and the spatial distribution of rapid sediment deposition and scour transfer, to examine the model's response to extreme hydrodynamic events. For example, in a tributary in a certain alpine region, measured data indicates that ice dams naturally burst during the spring melt period each year. The simulation model must accurately reproduce the rapid rise in upstream water levels and the propagation of short-term scour waves downstream, matching the measured peak times with water levels and verifying the consistency of silt thickness at key sections. This process not only verifies the model's adaptability to extreme events but also provides reliable support for its application in disaster warning and water resource scheduling.

[0088] Among them, the implementation of dynamic boundary transfer and joint solution of S5 is based on the unified time step division mechanism of the river network topology structure. At each time step, all sub-river networks start the hydrodynamic sediment and water quality coupling calculation synchronously in the order of numbering from upstream to downstream. The calculation process of each sub-river network outputs the dynamic parameter set of its downstream control node in real time, including water level h, flow Q, sediment concentration S, ice cover thickness h, etc. ice and dissolved oxygen content DO. These parameters are immediately input to the upstream boundary nodes of the adjacent downstream sub-river network through the transmission link formed by the upstream and downstream control nodes of the dam, and serve as the real-time boundary condition input of the sub-river network within the same time step. The core of this mechanism is to break the sequence barriers of traditional static transmission, embed boundary updates into the calculation process, and realize the spatiotemporal synchronization of parameter transmission and sub-network solution. Specifically, when the upstream sub-river network n completes the hydraulic calculation of a certain section within the time step t, its downstream boundary value directly drives the initial conditions of the corresponding section of the adjacent downstream sub-river network n+1, so that the entire river network system always maintains the instantaneous linkage of boundary conditions during the advancement of time step t. This process is iteratively coordinated through a unified global time step Δt, and the formula is expressed as the boundary parameter transfer function:

[0089]

[0090] in is the boundary flow of sub-river network n at time step t (unit: m 3 / s), is the boundary water level (unit: m), is the sediment concentration (unit: mg / L), is the dissolved oxygen content (unit: mg / L), is the thickness of the ice sheet (unit: mm). The downstream sub-river network n+1 is directly adopted As the input value of its upstream boundary, it eliminates the inherent defect of the boundary condition lag Δt in traditional methods. This dynamic transmission mechanism ensures the phase synchronization of water level fluctuations and the spatiotemporal continuity of pollutant migration in sudden ice dam burst or snowmelt flood events.

[0091] Traditional static boundary transfer requires waiting for the full computation of a sub-river network before transferring results, resulting in a one-time-step lag in the downstream sub-river network boundary conditions. This solution utilizes a dynamic boundary transfer mechanism to synchronously update boundary parameters within each time step, enabling the downstream sub-river network to respond in real time to changes in upstream hydraulics, sedimentation, and water quality. Dynamic boundaries can significantly improve the accuracy of water level fluctuation and pollutant migration predictions, particularly for sudden events such as ice dam breaches and snowmelt floods.

[0092] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for water and sediment scheduling and water quality scenario prediction in a reservoir area in a high-altitude cold region, characterized by: The following steps are involved: S1 River network segmentation and node classification: Based on the distribution of cascade reservoirs in the tree-like river network, the section where the dam of each reservoir is located is used as the segmentation point, and the entire river network is divided into several sub-river networks and corresponding control nodes. The sub-river networks and control nodes are numbered in order from upstream to downstream; the control nodes are classified into external boundary nodes that require external boundary conditions and internal boundary nodes at river confluences that do not require boundary conditions; S2 Ice Dynamic Parameter Embedding: Based on the glacial characteristics of high-altitude cold regions, ice parameters are dynamically embedded in the one-dimensional Saint-Venant equation. Based on the temperature module, the glacial and non-glacial states are determined in real time. The hydraulic radius calculation formula and water flow continuity equation are modified to generate a hydrodynamic model adapted to the glacial period. S3 Low-temperature sediment transport correction: A temperature-dependent sedimentation velocity formula is introduced into the sediment module. Based on experimental data, the correlation between sediment flocculation and sedimentation characteristics at low temperatures and water shear force is established. The correction factor for the velocity distribution under the ice sheet is dynamically adjusted to generate a low-temperature sediment transport correction model. S4 Water Quality Parameters Adaptation to Low Temperatures: Add an ice cover inhibition factor on the dissolved oxygen diffusion coefficient to the water quality module, dynamically adjust the pollutant degradation rate constant based on the Arrhenius formula, and establish a temperature-reaction rate correlation function to generate a low-temperature adapted water quality model; S5 Dynamic Boundary Transfer and Joint Solution: Within each time step, coupled hydrodynamic-sediment-water quality calculations are performed synchronously according to the sub-river network numbering sequence. During the calculation of each sub-river network, the water level, flow, sediment concentration, ice sheet thickness, and water quality parameters of its downstream control node are transferred in real time to the upstream boundary node of the next sub-river network as input for dynamically updated boundary conditions, achieving synchronized parameter iteration across sub-river networks. S6 synchronous real-time iterative calculation: Through the revised hydrodynamic-sediment-water quality coupling model, based on the river network topology, cross-section data, initial conditions and time step parameters, dynamic boundary conditions are called before each time step iteration to synchronously solve the water level, flow, sediment accumulation and water quality indicators of each sub-river network.

2. The method for water and sediment scheduling and water quality scenario prediction in a reservoir area in a high-altitude cold region according to claim 1 is characterized by: The specific steps of embedding the ice dynamics parameters in S2 include: Correct the water surface width in the one-dimensional Saint-Venant equation to the equivalent water surface width, and the corrected continuity equation is: Among them, A eff The effective cross-sectional area for considering ice cover and blockage is m 2 ; Q is the amount of water flowing through the cross section per unit time, unit is m 3 / s; t is time, in seconds; x is the position coordinate along the river; Taking into account the ice sheet's increased drag and changed hydraulic shape, the momentum equation is modified to: Where Q is the flow rate, unit is m 3 / s;A eff The effective cross-sectional area corrected for ice cover is in m 2 ; h is the water surface elevation, the unit is m, g is the acceleration due to gravity, the unit is m / s 2 ; S0 is the riverbed slope of the natural terrain, S f is the friction slope.

3. The method for water and sediment scheduling and water quality scenario prediction in a reservoir area in a high-altitude cold region according to claim 2 is characterized by: An iterative algorithm is used to solve the coupling relationship between ice sheet thickness and hydraulic elements, and the influence of ice sheet roughness on water flow resistance is dynamically updated; an ice age discrimination threshold is set in the temperature module to trigger the calculation of ice sheet parameters and adjust the ice blockage coefficient in the water flow continuity equation.

4. The method for water and sediment scheduling and water quality scenario prediction in a reservoir area in a high-altitude cold region according to claim 1 is characterized by: The specific steps of the low-temperature sediment transport correction in S3 include: The temperature-dependent sedimentation velocity formula is used: where v s (T) is the sedimentation velocity at the current temperature T, v s0 is the reference value of sedimentation velocity under the reference temperature T0, k is the temperature sensitivity coefficient calibrated according to the measured data; According to the velocity distribution characteristics under the ice sheet, the vertical velocity correction factor is introduced: where h ice is the ice cover thickness of the current section, and H is the total water depth of the section, which is used to adjust the calculated value of riverbed shear force.

5. The method for water and sediment scheduling and water quality scenario prediction in a reservoir area in a high-altitude cold region according to claim 1 is characterized by: The specific steps of low temperature adaptation of water quality parameters in S4 include: Adding an ice cover inhibition coefficient to the reoxygenation equation to correct the dissolved oxygen diffusion flux; Based on the Arrhenius formula, the pollutant degradation rate constant is corrected to: J DO =K2·(C s -C)·b ice Among them J DO represents the reoxygenation flux per unit time, K2 is the reoxygenation rate constant at standard temperature, C s is the saturated dissolved oxygen concentration corresponding to the water temperature, C is the current dissolved oxygen concentration in the water body, β ice is the ice cover suppression coefficient, which ranges from 0 to 1 and is used to quantitatively describe the degree of obstruction of the ice cover to oxygen diffusion.

6. The method for water and sediment scheduling and water quality scenario prediction in a reservoir area in a high-altitude cold region according to claim 1 is characterized by: The S6 also includes data driven calibration and verification steps: Using the measured hydrological, sediment and water quality data of the alpine region during the glacial period, the ice sheet parameters, sedimentation velocity formula and degradation rate coefficient were calibrated; Simulate ice disaster scenarios, including ice dam formation and breach processes, to verify the model's accuracy in predicting water level fluctuations and the consistency of the spatial distribution of sediment deposition under extreme conditions.

7. The method for water and sediment scheduling and water quality scenario prediction in a reservoir area in a high-altitude cold region according to claim 1 is characterized by: The specific rules for transferring the boundary conditions in S5 are: In each time step, the upstream control node of the dam outputs a set of dynamic parameters to the downstream control node in real time, including water level, flow, sediment concentration, ice cover thickness and dissolved oxygen content. The downstream control node synchronously receives the dynamic parameter set and uses it as the real-time input boundary condition of the upstream boundary node of the next sub-river network in the same time step. Based on the principles of conservation of mass and momentum, the boundary nodes at the river confluence perform synchronous interpolation calculations and transmit the input multi-tributary parameters.

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