Multi-working-condition simulation method of reservoir construction on ecological environment change

Abstract

The invention relates to the technical field of ecological environment change in reservoir construction, and discloses a multi-working-condition simulation method for ecological environment change in reservoir construction, and the method comprises the steps: building a water level dynamic and downstream ecological flow regulation and control model through reservoir operation working condition data, so as to determine an ecological base flow scheme; a sediment regulation and control model is optimized by utilizing reservoir-entering sediment monitoring data and a sediment migration rule to slow down reservoir siltation, and an outflow water temperature structure regulation and control strategy is constructed by analyzing reservoir area water temperature layering characteristics to relieve the influence of a cold water effect. And a pollutant diffusion path is predicted in combination with a water quality temporal and spatial change rule, and local eutrophication treatment measures are formulated to improve the habitat connectivity. Through the scheme of the embodiment of the invention, the problem of how to regulate and control the ecological flow of the downstream reach according to the change of the water level of the reservoir so as to solve the problem of insufficient ecological base flow can be solved, and a safer and more convenient migration route network can be built by planting the scale width of the local vegetation type greening buffer zone with high adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment changes caused by reservoir construction, and particularly to a multi-condition simulation method for ecological environment changes caused by reservoir construction. Background Art

[0002] A multi-condition simulation method for ecological environment changes caused by reservoir construction mainly aims at the ecological environment problems that may be caused by reservoir construction and its operation. Through a multi-factor dynamic coupling model, it simulates the interaction between the reservoir and the surrounding ecosystem under different conditions, providing a scientific basis for optimizing reservoir management measures.

[0003] However, this method still faces many challenges in practical applications: First, how to adjust the ecological flow of the downstream reach according to the dynamic changes of the reservoir water level to solve the problem of insufficient ecological base flow; second, it is necessary to adjust the sediment distribution in the reservoir based on the sediment transport law to slow down the accelerating trend of reservoir siltation; third, it is necessary to optimize the regulation strategy of the outflow water temperature structure according to the water temperature stratification characteristics of the reservoir area to avoid the damage of the cold water effect to the downstream ecosystem; fourth, it is necessary to combine the dynamic change law of water quality to control the pollutant diffusion path and prevent local water eutrophication; fifth, it is necessary to regulate the habitat connectivity by analyzing the migration habits of animals and plants to protect biodiversity and alleviate the phenomenon of habitat fragmentation. The existence of these problems limits the synergistic effect between reservoir construction and ecological environment protection. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: A multi-condition simulation method for ecological environment changes caused by reservoir construction, including: Establishing a water level dynamic and downstream ecological flow regulation model based on reservoir operation condition data to determine the ecological base flow plan, optimizing the sediment regulation model by using the reservoir sediment monitoring data and the sediment transport law to slow down the reservoir siltation, constructing an outflow water temperature structure regulation strategy by analyzing the water temperature stratification characteristics of the reservoir area to alleviate the influence of the cold water effect, predicting the pollutant diffusion path by combining the spatio-temporal change law of water quality and formulating local eutrophication control measures to improve the habitat connectivity; The establishment of the water level dynamics and downstream ecological flow regulation model based on reservoir operation condition data further includes: calculating the basic discharge V = kH according to the daily average inflow Qi and water level H, where k is an empirical parameter and depends on the ecological sensitivity of the river section, monitoring the water demand WVe of plants in the target area, and solving whether the constraint W = (1 ± ε)WVe ≤ V ≤ (1 + ε)WVe is satisfied, where ε is the fluctuation tolerance. If V is not within the constraint interval, the adjustment strategy is triggered and ΔV = θ(W - V) is set, where θ>0 is the adjustment coefficient. The final discharge plan synthesizes the historical optimal ratio α and the real-time adjustment ratio β and determines Qfinal = αQhist + βQadj. Preferably, the optimization of the sediment regulation model using the incoming sediment monitoring data to slow down sedimentation further includes: Obtaining the sediment distribution matrix M(i,j), where i represents the reservoir stratification and j represents the time; Estimating the accumulated sediment value Si through the formula Si = Σ(Mk / Tk), where Tk is the sedimentation rate of the k-th layer; Setting an early warning index γ based on the sedimentation rate, and starting the pre-scouring mode when Si > Smax(1 - γ); Invoking the time-segmented sediment flushing algorithm to allocate the daily water discharge ratio P(k)= f(k, S(k) / Smax) for minimizing the cumulative effect. Preferably, refining the scouring intensity function Fi(d,k)= a * exp(b*(kd^2))^n within each cycle, where d is the sediment particle diameter range and n represents the morphological correction index; Defining the total sediment discharge ratio TPs to satisfy TPs = Σ[Σ(Pik)] >= Ropt to ensure that the sediment discharge efficiency is greater than the threshold; Calculating the cumulative efficiency Cw of the sediment flushing window period Cw = η * ∑(Fi - Fimin)^p, where p is positively correlated with the water flow intensity; Outputting the final optimization suggestion to clarify that the maximum allowable cumulative siltation ratio Cap < Sth / γeff. Preferably, the constructed flow regime water temperature regulation model includes: Calculating the temperature response curve Tf(z,t)= Tw_initial - λexp[δzt] based on the stratified measuring point temperature gradient ΔTw(z,i) and time t, where z is the depth variable; When the upwelling of deep cold water causes the surface temperature Tsf < Tcritical, it is determined to activate the water supply control valve; Design a step-by-step compensation temperature difference logic δi = Ti_next - Ti_current to ensure Tnext ∈ [Ttarget - σ, Ttarget + σ]; Evaluate the temperature difference fluctuation integral ΔI = σΣ(ΔTk) under continuous water replenishment cycles and ensure the stability threshold. Preferably, in the analysis of the spatio-temporal variation of water quality in the treatment of local eutrophication, establish a morphological analysis of the dissolved oxygen DO distribution gradient prediction DO(z,t)≈D_ambient(α / N)*Z + βsin(wt + kz); When the detected DO concentration is lower than the threshold, execute Ozone_release_flag = 1 if there is O_amb < Tmin_DOfree; Set the interception efficiency function Ie(x)=1 - (cx)^2 / [B + cx+(cx^2)], where B is a constant and c is the pollution input load ratio; Verify that the effect after interception meets the standard condition Dafter = Dcurrent*(1 - Ieff) ≥ Dmin_acceptance.

[0005] Preferably, the coupled simulation of habitat connectivity and migration habits includes: Extract the habitat area A_i and connection distance d_j within the habitat and calculate the connectivity weight CWj(i,dj)≥CW_min as the reference value; Use the population diffusion simulation function Pop(t,d_i)= P_initial*exp{(kd)^m} to locate the key diffusion path sections; Enhance the connection index EC(j)=λ(l_i)*d_ij / (l_ij_max + d_ij_min) by setting the length l_j of the artificial fishway or ecological corridor; After optimization, check whether the target connectivity rate EC_post ≈ α*EC_baseline + (1 - α)*EC_opt is achieved.

[0006] Preferably, the measures to alleviate the water temperature stratification structure include: The calculation model Tf(z,d) is Tf=T_f + ΔTherm_z*ln(T_top / T_bot)^n; When the thermal barrier effect is detected to be too strong, execute the additional thermal balance factor Bc=f(Cf*d_Th / Ct); Optimize the segmented air injection and temperature increase logic to set ΔTa = ξ(Surface_influx - C_depthflow); Verify the temperature rise maintenance condition Tmaxpost = Tbefore_pre*(1 + ΔCoolingEff) by combining long-term records.

[0007] Preferably, the specific details of the water quality regulation include: Remove the formula TNred = ϕ*N_input - K_decayN*I_adjustment, where K_decayN is the decay coefficient; If TN exceeds the standard, start the supersaturated aeration system flag AerationFlag = T > Tref_N; Design enhanced interception measures by introducing the adsorption capacity limit Cs(max)=(Cinit*V) / ε*Cbulk, where ε is the leakage factor; Confirm that the discharge meets the standard after interception, with the constraint CTpost < Tthreshold_Stand.

[0008] Preferably, the condition limitations for introducing the improvement of the sedimentation model include: Redefine the effective packing radius of muddy particles r_eff = rsink*exp{ψ(Hmax / Dmean)}, where Hmax is the maximum water depth; If the calculated r_diff = r_sink - r_bounded has a significant step, adjust the model coefficient αs = αbase*(rs / r_mean_ref)^2; Add the inertial drift weight term Drift_factor = G_drift*t^p / G_settling; The overall mean squared error loss is minimized, L2_error_sum = sum(abs(D_pred / D_true - 1)) < δerr.

[0009] Preferably, the introduction of habitat restoration regulation includes: Define the vegetation cover restoration factor VegRecover = k_veg*D_repl*S_v_area, where D_repl is the planting density and k_veg reflects the growth acceleration effect; Start the compensation mechanism VegCompensation = sign[Veg_target - Veg_curr], where sign is the judgment symbol; Configure the logic EcOpt(j) = ξ(Connetivity_gain / Separation_dist_max)+λ*A_overlap; Verify that the overall improvement effect after restoration meets the expectation: VegFinal ≈ β*VegRef_prev + γVegTarget_plan.

[0010] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: 1. By establishing a water level dynamic and downstream ecological flow regulation model based on reservoir operation condition data to determine the ecological base flow scheme, optimizing the sediment regulation model using the incoming sediment monitoring data and sediment transport law to slow down reservoir sedimentation, constructing a flowing water temperature structure regulation strategy by analyzing the water temperature stratification characteristics in the reservoir area to mitigate the impact of the cold water effect, predicting the pollutant diffusion path in combination with the spatio-temporal variation law of water quality and formulating local eutrophication control measures to improve habitat connectivity. Through the solution of the present disclosure embodiment, the problem of how to regulate the ecological flow in the downstream reach according to the reservoir water level change to solve the insufficient ecological base flow can be solved.

[0011] 2. By deeply observing and mastering the activity track preferences and spatio-temporal conversion habit laws of local representative endangered wildlife populations, designing and constructing artificial auxiliary structures such as bridge and other hardware and software supporting service facilities and equipment dedicated for their passage through the culvert under the dam; in addition, the scale and width of the green buffer zone with strong adaptability of native vegetation types can be considered to be expanded to create a safer and more convenient migration route network, promote gene exchange and interaction among patches, increase the anti-interference toughness, and improve the ecological protection ability and efficiency of the overall area with double harvest. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic diagram of the water quality environment process of the present invention; Figure 2 It is a schematic diagram of the water discharge ratio process of the sand flushing algorithm of the present invention; Figure 3 It is a schematic diagram of the water replenishment interception process of the present invention; Figure 4 It is a schematic diagram of the process of optimizing the configuration and restoring the expectation of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0014] Please refer to Figure 1 - Figure 4, a multi-condition simulation method for the impact of reservoir construction on ecological environment changes. In the first step, a water level dynamic and downstream ecological flow regulation model is constructed based on the historical water levels, precipitation, and downstream river ecological data of reservoir operation. This model uses the data generated by the reservoir operation plan as input parameters, comprehensively analyzes the operation rules of flood discharge gates in the reservoir area and the spatial distribution characteristics of water resources in the basin, and outputs specific ecological base flow allocation plans for different seasonal demands. For example, during the dry season, the discharge flow is appropriately increased to ensure the minimum water volume required to maintain sufficient biological activity in the downstream river; while during the wet season, unnecessary waste is reasonably reduced based on rainfall prediction, which not only saves precious water resources but also prevents floods.

[0015] Combined with the sediment monitoring data of the reservoir inflow, a sediment regulation model for optimizing the sediment deposition in the reservoir is established in the second step using modern computing technologies and traditional empirical formulas. In this step, the sediment concentration samples at the water inlet are collected over a long period, supplemented by high-precision remote sensing images to identify the sediment particle sizes and their spatial layout patterns. Together with specific software simulation tools, a sediment movement trajectory map closest to the actual situation is simulated. For example, in a certain project case, it was found that due to the reduction of water flow velocity in a specific area, larger particle sizes accumulated rapidly. Therefore, measures such as changing the angle of the diversion channel or installing sediment discharge pipes were adopted to adjust the water flow direction and velocity, thereby significantly reducing the sedimentation degree in some sensitive areas, extending the effective life of the project, and maintaining good navigation capacity.

[0016] The third step mainly focuses on the research of the temperature stratification phenomenon formed in the reservoir area. By conducting real-time temperature measurements and periodic sampling of water bodies at various depths, the positional relationship of the warm and cold water intersection interface under the winter ice layer is clearly understood. Subsequently, corresponding improvement measures for the water flow structure are proposed, such as adjusting the opening height of the spillway or installing a water mixing injection device to accelerate the heat exchange between the upper and lower layers, and trying to keep the discharged river water at a relatively balanced and suitable temperature throughout the year, reducing the risk of damage to the local ecological system balance caused by extreme cold and hot water alternation impacts. Specifically, in one embodiment, the problem of a sudden drop of more than 10°C within a short period at the confluence of a small tributary and the main stream was successfully solved, protecting the breeding environment of fish populations that rely on a constant temperature difference to reproduce from being damaged.

[0017] In the fourth stage, the focus turns to assessing the changing trends of pollutant concentrations in the entire reservoir area and its surrounding areas. This process requires the use of advanced numerical calculation platforms to simulate the process trajectories of potential hazardous substances spreading to a wider water area under various conditions, especially the eutrophication crisis in lake-type areas that may be caused by the nutrient components brought by a large amount of surface runoff after heavy rain. Effective treatment measures can start from reducing the agricultural runoff pollution load at the source and be implemented with comprehensive prevention and control plans such as strengthening the construction of shoreline buffer vegetation to absorb and intercept excess fertilizer residues. Specifically, in a demonstration pilot project, a list of the sources of the largest contribution values was systematically sorted out, and targeted measures such as shutting down nearby sewage outlets and changing to low-water-consuming crop varieties were gradually taken in order of priority, resulting in a significant reduction of about 40% in the total phosphorus and nitrogen content levels, showing obvious results.

[0018] Finally, there is the consideration of maintaining the connectivity of animal migration paths. In this step, the internal connection points between the habitat conditions on both sides of the land and water need to be comprehensively considered to ensure that even with the intervention of artificial facilities, the original natural network framework function cannot be completely blocked. For example, sufficient openings are reserved at the bottom of the bridge piers for migratory birds to take temporary shelter during seasonal round trips; or a scientific and reasonable schedule for pumped storage power generation is designed to avoid the activity pattern restrictions during the peak period of fish migration, and to avoid the possibility of too much human interference disrupting the normal life behavior rhythm. All in all, a multi-condition simulation method for the impact of reservoir construction on ecological environment changes not only focuses on the overall planning guiding principles from a macro strategic perspective, but also attaches great importance to accurately grasping the execution efficiency conversion rate of each detail at the micro level to ultimately achieve the maximization of the expected target benefits.

[0019] The water level dynamic and downstream ecological flow regulation model established based on the reservoir operation condition data of the present invention can be divided into multiple steps: The first step is to calculate the basic discharge amount based on the daily average inflow and water level; the second step is to judge whether there is ecological flow to meet the constraint conditions according to the water demand of plants in the target area; the third step is to set adjustment strategies through adjustment formulas when the constraint conditions are not met; the fourth step is to comprehensively form the final discharge plan using historical optimal values and real-time adjustment values.

[0020] In the first step, the basic discharge amount is calculated using the daily average inflow Qi and the current water level H of the reservoir through the basic formula V = kH. The empirical parameter k here is determined according to the ecological sensitivity of the actual river section. The value range of k is generally between 0 and 1.5, and the best value needs to be obtained through on-site actual situation tests. Its purpose is to use the water level as an important input to maintain the stable demand flow of the river section with high ecological sensitivity. The physical meaning of this formula is to convert the current water level into the corresponding minimum ecological flow, which is used to provide a preliminary reference for subsequent dynamic balance.

[0021] In the second step, monitor the actual water demand WVe of the plant community in the target area to verify whether it meets the requirements of ecological balance and ensure that the discharge volume is within the constraint range W = (1 - ε)WVe ≤ V ≤ (1 + ε)WVe. Among them, the fluctuation tolerance parameter ε represents the allowable error range. Generally, its value ranges between 0 and 0.2 (i.e., 0% - 20%). If the establishment of this formula indicates that the basic plan meets the basic requirements under environmental conditions, the initially set basic flow can be continued.

[0022] If the basic discharge volume V obtained in the first step does not meet the above constraints, then start the third step and define ΔV = θ(W - V), that is, use an adjustment function to correct the insufficient or excessive part. Among them, the adjustment coefficient θ must be positive and usually ranges from 0.5 to 3. The selection of the value of θ here needs to consider the trade-off between economic cost and ecological protection. The specific value needs to be obtained through experimental analysis, and the goal of this adjustment strategy is to minimize the deviation from the ecological water required by plants while controlling the resource usage.

[0023] For the fourth step, finally make a comprehensive decision Qfinal = αQhist + βQadj by combining the historical discharge records and the current status. Among them, α represents the historical optimal ratio, and its value range is recommended to be between 0.4 and 0.8. This step aims to draw on past successful practices; while β represents the proportion of real-time adjustment, and the range is usually set between 0.2 and 0.6. This operation makes the model closer to the current actual dynamic change requirements, thus ensuring the rationality of the result and the controllability and stability during implementation.

[0024] The present invention optimizes the sediment regulation model by using the reservoir sediment monitoring data to slow down the sedimentation. First, obtain the sediment distribution matrix; second, estimate the sediment accumulation value; third, set the warning index; fourth, call the sand flushing algorithm in different time periods to allocate the discharge ratio.

[0025] In the initial stage, by monitoring the dynamic of reservoir sediment, obtain the sediment distribution matrix M(i, j), where i represents the reservoir stratification, indicating different height areas perpendicular to the water flow direction, usually ranging from 1 to n (n is the total number of layers divided by the reservoir); j represents time, representing the sequence of continuous observation time nodes, usually days or months, ranging from 1 to t (t is the total number of time units in the entire simulation period). This matrix is used to describe the change of sediment volume in each layer and different time periods, and its purpose is to quantify the spatial and temporal distribution of sediment inside the reservoir.

[0026] Subsequently, the accumulated sediment value Si of each layer is estimated by the formula Si = Σ(Mk / Tk). In the formula, Mk represents the total sedimentation amount of the k-th layer during the specified period, and Tk represents the actual sedimentation rate corresponding to the k-th layer, usually in cubic meters per year. Si is obtained by summing the sediment data of each layer divided by its sedimentation rate, which can reflect the average accumulated sedimentation scale of each layer during the simulation period. The optimal value of Si is determined according to the reservoir design objective. For example, for the objective of less impact on the ecological environment, Si is usually set in the reservoir capacity range of 2% - 5%. The core significance of this formula setting is to quantify and balance the relationship between the sedimentation rate and the accumulation amount, thereby providing a basic basis for subsequent operations.

[0027] Based on this result, combined with the reservoir management requirements, the warning index γ is set, and its typical value is from 0.1 to 0.3, which is used to reflect the acceptable sedimentation risk range. When the estimated Si exceeds the set maximum sedimentation threshold Smax(1 - γ), the pre-scouring mode is immediately activated. The introduction of this judgment criterion aims to prevent the reservoir from reducing its efficiency due to long-term sediment accumulation.

[0028] Finally, at the actual regulation level, the time-segmented sediment flushing algorithm is called to dynamically calculate the daily water discharge ratio P(k)= f(k, S(k) / Smax), that is, to adjust the reservoir water discharge according to the ratio of Si at the current time k to the maximum allowable sedimentation amount Smax, ensuring the minimization of the cumulative effect. For example, when S(k) / Smax reaches 0.85 in a certain embodiment, the daily water discharge ratio is increased through a specific non-linear function f(k,x) to accelerate the removal of sediment deposition. In this way, both the effect of sedimentation control during the reservoir operation is guaranteed, and unnecessary water resource waste is reduced.

[0029] In one embodiment, for the multi-condition simulation requirements of a newly built reservoir, three layers of vertical zoning (n = 3) and a monthly monitoring period (t = 12) are set. The sedimentation rate Tk is obtained by recording the data of M(1,j), M(2,j), and M(3,j). After formula calculation, it shows that S2 has exceeded the maximum allowable threshold Smax(1 - γ) (assuming γ = 0.2 here). The system then enables the pre-developed flushing plan, adjusts the P(k) allocation parameters in the specific schedule, and finally achieves the goal of efficient sediment regulation in the critical time period. This proves that by reasonably setting the model and real-time feedback strategy, the deterioration of the sedimentation problem during the long-term use of the reservoir can be effectively delayed.

[0030] The steps of the present invention also include the following process: The scour intensity function within each period is refined and expressed as Fi(d,k) = a * exp(b*(kd^2))^n. d is the range of sediment particle diameters, and the parameters a, b, and n all affect the calculation result of the scour intensity. a is the initial proportionality coefficient (its range can be taken from 0 to 1, depending on environmental variables), b is a constant positively correlated with the water flow characteristics (generally a positive real number, and the recommended optimal value is between 0.5 and 1.5), and the morphological correction index n takes into account the influence of the irregularity of the particle morphology distribution on the intensity (the value range is usually the integer 2 or 3, and the best is dynamically adapted). The purpose of this formula is to accurately describe the law of the scour response intensity of sediment particles in the reservoir under different particle size ranges, so as to reflect the non-linear coupling relationship between particle size and water flow dynamic changes.

[0031] Subsequently, the total sediment discharge ratio TPs is defined to satisfy the condition constraint of TPs = Σ[Σ(Pik)] ≥ Ropt. Where Pik represents the ratio of the sediment particles with diameter k discharged within period i (the value range is [0, 1]), and Ropt represents the preset sediment discharge efficiency threshold (such as the typical value is 80% to 90%). This process evaluates whether the effectiveness of the overall drainage plan reaches or exceeds the expected threshold by statistically accumulating the output ratios of each particle size level within each time period, so as to quantify whether the sediment discharge strategy is sufficient to cope with ecological and engineering challenges.

[0032] The cumulative efficacy of the scour window period is measured and expressed as Cw = η * ∑(Fi - Fimin)^p. Here, Fi is obtained from the aforementioned scour intensity function; Fimin is the base value of the background noise level; η is the efficacy weight parameter (by default, it is a positive value approximately equal to 1); p depends on the actual water flow impact force and is positively correlated with it (the recommended numerical range is between 2 and 4). The construction of this formula aims to capture the actual net benefit contribution value during the high-intensity scour period within a specific time span, so as to provide reliable support data for subsequent optimization judgment.

[0033] The final step involves defining and restricting the maximum allowable cumulative siltation ratio to be Cap < Sth / γeff, that is, the amount of sediment allowed to accumulate in the reservoir should not exceed the specific ecological carrying capacity upper limit Sth (selected according to the characteristics of the basin) divided by its equivalent effectiveness factor γeff (theoretically greater than 1 and adjusted to be close to but not exceed 2 according to the model accuracy). This step integrates the above various indicators into the system evaluation criteria, generating a decision-making basis for maintaining the health during the long-term operation of the reservoir.

[0034] In one embodiment, if a new multi-gate control scheme is to be adopted for a reservoir to improve its discharge performance and reduce the risk of downstream wetland shrinkage, Fi(d,k) may first be used to make a preliminary estimation of the particle size distribution corresponding to each potential regulation point; then, by comparing the historical experimental observation records, the most appropriate combination of a, b, and n values is selected to verify whether its simulation effect meets the requirements, such as whether it can effectively reflect the difference in scouring force caused by particle size differences. Then, according to the target design requirements, the appropriate value of the Ropt parameter is determined, and the existing operation specifications are reexamined for deviations, and the plan is modified accordingly until it is ensured that the TP reaches above the specified interval. At the same time, combined with the Cw results, more economical and environmentally friendly work cycle configuration options are selected. Specifically, when Sth is assumed to be the storage limit per unit volume corresponding to the base area multiplied by the height increment, the above method is further used for fine-tuning until the balance Cap does not violate the safety warning boundary, so as to achieve the strategic policy of protecting the balance of the natural system and extending the project life.

[0035] The constructed flow state water temperature regulation model of the present invention includes the following: First, based on the temperature gradient ΔTw(z,i) and time t at the stratified measurement points, the dynamic fitting of the water temperature distribution law of the reservoir is realized by calculating the temperature response curve Tf(z,t) = Tw_initial λexp[δzt]. Here, z represents the depth variable, λ and δ are empirical parameters, Tw_initial is the initial temperature at the starting depth, and Tf(z,t) represents the predicted value of the actual water temperature changing with time at a certain depth. The value range of λ is between [0.8,1], and the default optimal value is 0.95; δ depends on the thermophysical properties of the specific reservoir, and it is recommended to take [0.01,0.03]. The purpose of this formula setting is to accurately quantify the water temperature fluctuations under different depths and time conditions for more accurate regulation prediction.

[0036] Subsequently, it is determined whether the water supply control valve needs to be activated. If the upwelling of deep cold water causes the surface temperature Tsf of the reservoir to be lower than the pre-set critical threshold Tcritical, the control logic is activated. Tsf represents the measured value of the real-time water surface temperature, and its unit is degree Celsius. Tcritical is usually the minimum limit of the local ecological suitable interval. For example, in waters with high fish growth requirements, Tcritical is set to 12°C to avoid the influence of low temperature environment on the normal metabolism of aquatic species.

[0037] Furthermore, a step-by-step temperature difference compensation logic δi = Ti_next - Ti_current is designed to ensure that the simulated target temperature Tnext in the next state is always maintained within the predetermined range [Ttarget - σ, Ttarget + σ]. Here, Ttarget is the target equilibrium temperature control level, and its typical value is to reduce the peak summer water temperature to 22°C. The allowable error range σ can be set to ±1.5°C. This method aims to gradually approach the ideal water temperature condition while reducing the system burden caused by frequent operations.

[0038] Finally, the temperature difference fluctuations in consecutive water replenishment cycles are evaluated and controlled. By introducing the fluctuation integral expression ΔI = σΣ(ΔTk), the absolute values of temperature differences in all cycles are accumulated, and it is ensured that the integral result does not exceed a specific stability threshold. In this formula, ΔTk represents the single temperature difference, and σ maintains the aforementioned error tolerance. Its core role is to detect the overall water temperature stability and verify the effect of the water replenishment regulation scheme.

[0039] In one embodiment, the above process can be applied to a large reservoir project. The reservoir is located in a temperate region, and its ecosystem has a strong temperature sensitivity. It is necessary to predict the impact of deep cold water upwelling in winter or insufficient water replenishment in summer through modeling. For example, in an experimental scenario in the cold season, through the formula Tf(z,t), it is found that the water temperature at a depth of 20 meters in the reservoir is about 4°C lower than the surface. After activating the water replenishment system according to the determination rule, the outlet flow rate is adjusted in combination with the temperature difference compensation logic, and finally the surface temperature is restored to the target range, and the overall regulation is confirmed to meet the stability standard by using the fluctuation integral.

[0040] In the analysis of the spatio-temporal variation of water quality in the treatment of local eutrophication of the present invention: a prediction model for the distribution gradient of dissolved oxygen DO is established, and threshold judgment is performed according to the prediction results to determine corresponding treatment measures; pollutants are intercepted by zoning, and an interception efficiency function is defined to quantitatively control the pollutant diffusion process; the water quality change after pollution interception is verified to ensure that the treatment effect meets the minimum standard.

[0041] First, list the steps. The first step is to establish a prediction model of the dissolved oxygen (DO) distribution gradient in water, DO(z,t) ≈ D_ambient (α / N)*Z + βsin(wt+kz), based on the method of claim 4 using a mathematical analytical formula. The second step is to trigger the flag variable Ozone_release_flag = 1 when the dissolved oxygen concentration is lower than the set lower threshold and evaluate whether an additional external environmental oxygen source needs to be injected, while considering that the background oxygen concentration is less than a certain minimum free survival critical condition. The third step is to set up a partition interception measure and introduce an interception efficiency formula expressed as Ie(x) = 1(c*x)^2 / [B + c*x + (c*x)^2]. The fourth step is to check whether the actual effect of the interception measure reaches the pre-specified water quality acceptance standard after the simulation method is implemented.

[0042] The first step aims to construct an analytical formula for the spatial and temporal evolution of dissolved oxygen to characterize the possible local hypoxia risk situation in the water body during reservoir construction or use. D_ambient refers to the background environmental dissolved oxygen, representing the average dissolved oxygen level in the normal non-interference state. α and N are the proportion and normalization constant of the eutrophication factor respectively, and the optimal value of α / N usually ranges between [0.5, 3.0]. β, the periodic amplitude parameter, is used to describe the degree of dissolved oxygen fluctuation; its value can be calibrated from the observed data. wt and kz respectively represent the influence relationship variables of time and depth. The purpose of this formula is to provide an early warning mechanism for the possible DO decline risk in the reservoir area under dynamic conditions.

[0043] In one embodiment, it is assumed that the detected dissolved oxygen level is significantly low, lower than the preset safety lower limit. At this time, the system built-in variable will be automatically set to Ozone_release_flag = 1. And if it is also detected that the environmental background value has dropped to the critical point Tmin_DOfree that cannot support biological life activities, a further prompt for a more serious alarm situation will be given, and human intervention is required as soon as possible to restore the ecological system balance. Here, Tmin_DOfree is usually selected according to the survival range of the target species and generally remains at about 2 to 4 mg / L.

[0044] The effectiveness of the interception device is quantified by introducing an interception efficiency expression Ie(x)=1(cx)^2 / [B+cx+(cx)^2]. x represents the spatial coordinate or distance, and c*x measures the pollution intensity or input ratio at this coordinate; the constant B is used as a balance adjustment term to adapt to different actual scenarios. The non-linear form of this expression reflects that as the pollution load increases, its interception efficiency may show a saturation trend rather than a monotonically increasing characteristic. This specific structure helps to reasonably allocate resource inputs and avoid cost waste caused by overkill.

[0045] The last part is a verification process for checking the overall compliance after all the above-mentioned processes are completed: that is, to confirm whether the final remaining dissolved oxygen content Dafter is not lower than the minimum acceptance threshold Dmin_acceptance. Dcurrent * (1 - Ieff) is used to reflect the degree of change in the current water quality state after applying the interception technology compared with before. Specifically, for example, assuming that the original measured value Dcurrent = 7 mg / L and the bottom value of the expected target range is 6 mg / L, then if the estimated total removal ratio exceeds approximately 17%, it should be able to meet the qualified acceptance requirements.

[0046] The coupled simulation of habitat connectivity and migration habits in the present invention includes the following defined steps: extracting the habitat area $A_i$ and connection distance $d_j$ within the habitat and calculating the connectivity weight $CW_{j}(i,dj)≥CW_{min}$ as the reference value; using the population diffusion simulation function $Pop(t,d_i) = P_{initial} \times \exp{(k \cdot d)^m}$ to locate the key diffusion path sections; enhancing the connection index $EC(j) = \lambda(l_i) \cdot d_{ij} / (l_{ij\_max} \cdot d_{ij\_min})$ by setting the length $l_j$ of the fishway or ecological corridor; checking whether the target connectivity rate $EC_{post} ≈ \alpha \cdot EC_{baseline} + (1 - \alpha) \cdot EC_{opt}$ is achieved after the optimal configuration.

[0047] The step of extracting the habitat area $A_i$ and connection distance $d_j$ within the habitat is the basic work for quantitatively measuring the relationship between the existing habitat quality and spatial distribution. This process digitizes the actual environmental parameters, thereby enabling the establishment of a preliminary model of the physical connection between habitats. Here, $A_i$ represents the inhabitable area within each habitat, and the range varies from several square meters to thousands of square meters according to species requirements. $d_j$ represents the straight-line distance between adjacent habitats or the distance index of water body flow. The connectivity weight formula $CW_{j}(i,dj)$ is an algorithm for judging the importance of a specific channel, where $CW_{min}$ is set as a predefined threshold to exclude irrelevant nodes, and the optimal value is usually set based on ecological experimental data. For example, in the migration of a certain fish species, if its perceived effective connectivity is less than a certain threshold, it cannot achieve cross-reservoir upstream and downstream communication.

[0048] In the second step, a population diffusion simulation function is used to evaluate potential migration trajectories and frequencies, and to locate priority protection or restoration paths. Specifically, $Pop(t,d_i) = P_{initial} \times \exp{(k \cdot d)^m}$ simulates the extent of organism expansion in a specific path over time, where the initial population size $P_{initial}$ sets the baseline condition, the constant $k$ determines the migration rate (constrained by factors such as water flow and slope), and $m$ adjusts the influence factor of the diffusion acceleration effect. The setting of this formula conforms to the actual characteristic law of diffusion phenomena, that is, it is relatively slow in the initial stage and becomes more active in the later stage. In one embodiment, if it is found that the channels under certain reservoir dams are particularly frequented by fish, these locations need to be given extra attention for protection or improved design.

[0049] In the stage of setting artificial structure measures, the connectivity efficiency is further improved by introducing artificial intervention. The parameter $l_j$ here defines the scale size of the fishway or land corridor, and the actual value contribution ratio of each newly added path is determined by means of the improved mathematical model $EC(j)=\lambda(l_i)\cdot d_{ij} / (l_{ij\_max} \cdot d_{ij\_min})$. The function λ describes the non-linear relationship between length and ecological benefits, and the denominator part of $d_{ij}$ standardizes the comparison criteria between different schemes. For example, during the planning process of a reservoir construction, various combinations of artificial auxiliary facilities may be tried and the best-performing one is selected for actual application.

[0050] The last step of result review aims to confirm that the foregoing treatments achieve the predetermined goal, that is, to improve the overall ecological environment network connectivity level close to the established target ratio relationship expression $EC_{post} ≈ α·EC_{baseline}+(1-α )·EC_{opt}$, where α controls the weight ratio of the two parties and the generally recommended value is in the range of [0.3, 0.7] to balance the two tendencies of improving the current situation in the short term and pursuing the maximum long-term efficiency. The rationality of the engineering design scheme is judged by comparing before and after correction, and it is iteratively improved according to the feedback until a satisfactory result appears. Specifically, when analyzing a set including the existing bridge bottom empty transformation project and the completely new bridge construction scheme, this criterion is used to select the best one for final implementation.

[0051] The present invention supplements the mitigation measures for the water temperature stratification structure, and the specific steps include the following four parts: expanding the water depth heat conduction calculation model, setting additional heat balance factors under the heat insulation effect, optimizing the segmented air injection and temperature increase logic, and evaluating the long-term verified temperature rise maintenance conditions.

[0052] First, expand the water depth heat conduction calculation model from the original single variable to the form of Tf=T_f + ΔTherm_z*ln(T_top / T_bot)^n. Here, T_f is the basic heat distribution parameter; ΔTherm_z represents the heat difference correction coefficient that varies with depth z; T_top and T_bot are the temperature values of the water surface and bottom respectively, and their ratio is calculated by natural logarithm and exponentiated to reflect the non-linear characteristics; n can be adjusted according to the characteristics of the simulation area within the range [1,2], and its optimal value is usually set to 1.5. This formula takes into account the heat conduction differences between different layers of water and uses logarithms and power functions to characterize the complex gradient relationship, making the overall dynamics of the water body more in line with the water temperature stratification characteristics in nature.

[0053] Second, when it is monitored that there is a strong heat barrier effect in the reservoir due to human activities (such as dam construction) or environmental factors, introduce an additional heat balance factor Bc=f(Cf*d_Th / Ct) as an adjustment tool to correct local extreme situations. In this formula, Cf represents the heat conduction performance correction parameter, and its default value is adjusted within the range of 0.1 to 0.4; d_Th is the actual distance compensation term caused by the temperature gradient, ranging from a few meters to hundreds of meters; Ct represents the overall heat transfer capacity constant, ranging from dozens to hundreds of MJ / m³, and generally the theoretical intermediate value such as 80MJ / m³ is selected to standardize the results. This factor ensures that the heat distribution of the water body tends to be stable.

[0054] Furthermore, for segmented gas supply and local heating operations, add the formula ΔTa = ξ(Surface_influxC_depthflow) to the design logic for quantitative control. In the formula, ξ is the flow velocity conversion coefficient, and the optional value range is [-1,1], which is used to adapt the action intensity between the surface flow rate change and the deep replenishment flow demand; Surface_influxC_depthflow is used to evaluate the influence degree of the product of the surface input flow rate and the deep flow rate, so as to achieve precise control of the supplementary air temperature difference.

[0055] Finally, complete the verification link of Tmaxpost=Tbefore_pre*(1+ΔCoolingEff) based on long-term actual observation data. In this formula, Tbefore_pre involves the estimated comparison reference value before and after a certain fixed moment or state in the historical record; ΔCoolingEff is defined as the comprehensive cooling efficiency change rate, which is set according to the tolerance of the target ecological system, and generally ranges from less than or equal to -0.05 to +0.02, aiming to ensure that the final temperature control value is within the controllable deviation.

[0056] For example, in a case study of reservoir ecological environment research, if the monitoring data shows that the thermal stratification caused by increased sunlight in summer is significant, the extended heat conduction model can be used to calculate the potential thermal gradient adjustment at different vertical levels. If an abnormally obvious state of upper-layer heat and lower-layer cold isolation occurs, the above-mentioned thermal balance factor correction strategy is further triggered to reduce the influence of interface differences. At the same time, gas injection equipment is used in specific areas to increase the support of mixing dynamics, and appropriate injection parameters are selected with reference to the segmented optimization model. By integrating the above improvement measures and regularly comparing the consistency between the actual detection records and the theoretical estimated values, the healthy operation of the water circulation system around the newly built reservoir can be ensured, thereby achieving the purpose of ecological protection. Specifically, when the initial model shows that the temperature at the bottom of a certain reservoir is more than 8°C lower than that at the top layer, this multi-dimensional method is gradually adopted to improve the water temperature difference, reducing the later stable value to only about 3°C.

[0057] The present invention incorporates the specific details of water quality regulation: in the multi-condition simulation method for the impact of reservoir construction on ecological environment changes, multiple key steps are added to achieve the water quality regulation goal.

[0058] First formula: TNred = ϕ*N_input*K_decayN*I_adjustment, where TNred is the reduction of total nitrogen, ϕ is the removal efficiency (usually in the range of 0.5 to 0.9), N_input represents the nitrogen content entering the system (in mg / L), K_decayN is the decay coefficient (generally taking values of 0.01 - 0.03 d - ¹), and I_adjustment is an adjustment factor used to consider the influence of external conditions (such as temperature, light). The purpose of this formula is to quantify the process of removing total nitrogen from the water body through natural biodegradation processes or assisted technologies, to ensure that the calculation results are close to the data performance in the real world, and setting the optimal value of the decay coefficient K_decayN = 0.02 can obtain higher prediction accuracy.

[0059] Second, when the total nitrogen (TN) in the water is monitored to exceed the reference standard T > Tref_N, the supersaturated aeration system flag AerationFlag = T > Tref_N is activated. This step aims to prevent the risk of eutrophication caused by high-concentration nutrients. The Tref_N reference standard depends on the environmental requirements of different water areas and can generally be set at about 1mg / L to control the needs of low-concentration discharge areas. After being triggered, the oxygen level in the water will be enhanced, promoting the acceleration of nitrification and denitrification processes, thereby more effectively decomposing excess nutrients.

[0060] Thirdly, strengthening the interception measures needs to be combined with the adsorption capacity limit condition, i.e., Cs(max)=(Cinit*V)ε*Cbulk. Here, the parameter Cs(max) is the maximum adsorption capacity allowed by the system, Cinit is the initial concentration, V is the water volume (measured in cubic meters), Cbulk represents the stable concentration value of the components in the main solution, and the leakage factor ε reflects the residual rate that is not fully captured, and its value range is approximately 0 to 0.2. The setting of this formula mainly considers the potential and limitation of fixing pollutants on the barrier through physical and chemical adsorption methods to determine the appropriate material filling ratio and the design parameters of the filter layer structure. For example, setting an ε value of approximately 0.1 in a better state can effectively balance the economic cost and technical effectiveness.

[0061] Fourthly, confirm the interception effect and set the emission threshold constraint as CTpost<Tthreshold_Stand, where CTpost refers to the measured concentration at the outlet after treatment, and Tthreshold_Stand sets the regulatory baseline that must be met. For example, if it is stipulated that the total phosphorus concentration before the lake or reservoir intake should be controlled to be less than or equal to 0.05 mg / L, then Tthreshold_Stand is set to this value.

[0062] In one embodiment, consider the scenario of a small reservoir project for a newly built urban water supply. Specifically, first, calculate the average input nitrogen load as 50 kg / day based on the historical data of the incoming river water and substitute it into TNred to calculate the daily removal amount; then assume that a short-term abnormal reading at the actual detection point exceeds the Tref_N threshold, and quickly respond to switch to the artificial aeration state to improve the local habitat condition; then screen through experiments to select a sandy medium suitable for the local geological characteristics as the adsorption medium and check whether it meets the preset adsorption force limit Cs(max) to ensure that the interception device operates normally without seepage and leakage; finally, when all the purification links are completed and the output flow reaches or is lower than the limit specified by Tthreshold_Stand, safe clear water can be transported downstream to complete the full process of cyclic feedback management.

[0063] The improved conditions for the deposition model incorporated in the present invention include the following: The effective packing radius of the muddy particles is redefined as \(r_{eff} = r_{sink} \cdot \exp\{psi(H_{max} / D_{mean})}\), where \(H_{max}\) is the depth of the highest water level and \(D_{mean}\) is the average particle size. By combining the water level depth and particle characteristics, this formula dynamically adjusts the actual deposition range of the particles. The parameter \(r_{sink}\) represents the maximum possible diffusion range during the sinking process of the particles, and its value usually ranges from 0.1 to 5 meters, depending on the water flow regime. The function \(\psi\) is a correction factor that reflects the disturbance effect under unsteady water flow, and its optimal form is obtained through experiments. The common value range in reservoir environment research is 0.2 to 0.6. The formula is intended to introduce environmental factors to adjust the packing radius, thereby improving the simulation accuracy. For example, in a high-water-level environment during floods, the formula can predict more particles to deposit at farther edges.

[0064] If there is a step change in the calculated \(r_{diff} = r_{sink} - r_{bounded}\), then adjust the model coefficient \(\alpha_s = \alpha_{base} \cdot (r_s / r_{mean\_ref})^2\). In this formula, \(r_{bounded}\) is the radius boundary value of the restricted area, and its typical range is between 0 and 3 meters, representing the upper limit of the probability of the settling particles reaching a specific position. \(\alpha_{base}\) is the basic weight coefficient of the model, and its initial value range is 0.5 to 1.2 to ensure the stability of the model. The formula dynamically adjusts the coefficient ratio relationship by comparing the difference between the theoretical radius and the actual effective radius, and is used to correct the particle motion state at different scales. For example, in deep water areas where the particles are subject to greater resistance, the error accumulation problem can be improved by reducing this parameter.

[0065] Under non - steady settlement conditions, an expression of \( Drift_{factor} = G_{drift} \cdot t^p / G_{settling} \) is added to the inertial drift weight term. In this formula, \( G_{drift} \) indicates the intensity of the lateral inertial migration of particles, generally in the range of \( 10^{-3} \sim 10^{-1} \); in \( t^p \), the optimized value of the parameter \( p \) usually ranges from 0.8 to 1.2, reflecting the degree of change in time - dependence; and the denominator \( G_{settling} \) represents the natural sinking rate, usually in the range of \( 0.001 \sim 0.02 \). This formula captures the non - ideal dynamic deviation during the settlement process and enhances the calculation accuracy in the non - steady state stage. In one embodiment, when the water flow disturbance frequency suddenly increases, this drift weight can timely reflect the new trend of particle movement.

[0066] To optimize the result of minimizing the overall mean squared error, \( L2\_error\_sum=\sum(abs(D_{pred} / D_{true1})) < \delta_{err} \) is used as the iterative optimization objective, where the error limit threshold \( \delta_{err} \) is set to a value between 0.01 and 0.1 according to the accuracy requirements. Specifically, when analyzing the ecological impact of a newly built hydropower station, based on this criterion, the gap between the predicted value and the observed sediment data is gradually reduced to ensure the high unity of the practical effect and scientific basis of the model.

[0067] The steps of the habitat restoration regulation of the present invention involve: including operation processes such as calculating the vegetation cover restoration factor, triggering the vegetation density deficiency compensation mechanism, setting the dynamic habitat optimization configuration logic, and verifying the overall improvement effect, and explaining the meaning, parameters, their ranges, and application values of each step one by one.

[0068] The first step is to calculate the vegetation cover restoration factor. This step uses the formula VegRecover = k_veg * D_repl * S_v_area to evaluate the acceleration of plant growth in a specific area. D_repl represents the planting density, usually with a value range of 1 - 100 plants per 100 square meters per unit area; k_veg reflects the promotion degree under external conditions such as water supply or fertilizer support, and its optimal value may be set in [0.8, 1.2] according to the actual situation. The setting of this formula aims to measure the contribution of the newly planted area to environmental improvement, so as to assist in judging the ecological benefits of planting activities.

[0069] Next, if the current vegetation density is found to be low during the monitoring process, the compensation mechanism VegCompensation = sign[Veg_target - Veg_curr] is activated. The judgment function sign here outputs a plus or minus sign to indicate whether vegetation resources need to be supplemented by comparing the target vegetation density Veg_target and the actual density Veg_curr. For example, when the target value is lower than the actually measured value, the sign function will return a value of 0 indicating no need for adjustment, and when it is greater than zero, it will prompt additional investment in resources for replanting actions to ensure that the restoration work progresses as planned.

[0070] Subsequently, the dynamic habitat optimization configuration logic stage is introduced: EcOpt(j) = ξ(Connection_gain / Separation_dist_max) + λ * A_overlap. Here, ξ represents the connectivity performance gain coefficient, and its optional values in different regions generally range from 0 to 2; A_overlap describes the proportional term of the overlapping area effect; Separation_dist_max is the maximum separation distance used as the denominator to control the speed of weight change, and λ is used to reconcile the relative importance between the two. The default recommended value is about 0.5 to balance the relationship between ecological index consideration factors. This setting comprehensively considers the two aspects of the smoothness of the biological migration path and the rationality of the spatial layout, and realizes the optimization design of the refined management and allocation strategy.

[0071] The last step is to confirm whether the preset standard level can be achieved after the implementation of the restoration plan through comparative tests. The specific expression is VegFinal ≈ β * VegRef_prev + γ * VegTarget_plan. Here, the parameters β and γ are both empirical correction factors used to combine the original baseline state data VegRef_prev and the planned target data VegTarget_plan to generate an estimated range of the predicted value of the final evaluation result. The recommended reference values are close to [0.9, 1.1] and [0.7, 1.3], and the best fitting points are selected inside to determine the reliability of the result. It emphasizes the closed-loop assessment of the entire restoration work to ensure that the expected ecological effects are scientifically verified.

[0072] In one embodiment, if the construction of a reservoir in a certain area causes about 30% of the original forest area in the upstream area to be damaged, the above measures need to be taken to restore the integrity of the local ecosystem. Assuming that the vegetation restoration task in this area preferentially selects fast-growing broad-leaved tree types to fill the empty space with a spacing of 80%, that is, the initial planting parameter is to plant 50 seedlings per 100 square meters, and k_veg = 1, then it is expected to obtain a relatively high VegRecover score approximately equal to 50 * 1 * the total area to be revegetated. In addition, considering that the existing tree age is not sufficient to form a dense enough protective barrier, the replanting module is automatically activated to increase the quantity until it meets the target value. At the same time, with the help of an optimization algorithm, a reasonable animal corridor reconstruction connection network layout plan is arranged, and the λ value is adjusted in a timely manner to balance the contradictions and constraints of various interests. After the cycle is completed, by comparing the measured coverage rate data with the previous statistics, it is proved that the project has successfully reached the pre-specified performance acceptance threshold range, and all work process links can be completed.

[0073] A multi-condition simulation method for the impact of reservoir construction on ecological environment changes according to the present invention includes: First, based on the reservoir operation condition data, a water level dynamic model and a downstream ecological flow regulation strategy are established to determine an ecological base flow plan that meets the needs of the ecosystem. In this step, by analyzing the periodic fluctuation law of the reservoir water level and the minimum ecological base flow required for the survival of the biological community in the downstream river section, accurate water replenishment adjustment measures are proposed to solve the problem of downstream river drying caused by human water storage, and to ensure the stability and health of the river ecosystem.

[0074] Secondly, aiming at the problem of accelerated reservoir sedimentation caused by incoming sediment, the present invention optimizes the sediment regulation model by using long-term incoming sediment monitoring data combined with the sediment flow dynamics law. This enables the maximum reduction of sediment accumulation in key areas while ensuring the reservoir capacity, and realizes the sand-carrying and sand-discharging of floods or uses mechanical means to remove sediment deposits through reasonable scheduling, extending the service life of the reservoir while maintaining the balance and clarity of the surrounding water environment.

[0075] Thirdly, since the release of deep low-temperature cold water may change the thermal characteristics of the natural river water body and thus affect the biological reproduction conditions, etc., a scientific flowing water temperature structure adjustment plan is constructed according to the characteristics of the different-season layered structure in the flowing water area revealed by the temperature tomography image of the reservoir area. For example, by choosing the appropriate time to discharge the water source at the appropriate depth and other methods to mitigate the impact of the supercooling phenomenon and maintain a stable range within the requirements of fish spawning and hatching as well as plant growth and metabolism, the operation process is smoothly transitioned and connected without obstacles, and the possibility of negative effects is greatly reduced or even eliminated.

[0076] Finally, regarding the persistent problem of damaged habitat connectivity, solutions are also provided under comprehensive consideration of upstream, downstream, and even horizontal correlation factors: By deeply observing and mastering the activity trajectory preferences and spatio-temporal conversion habits of local representative endangered wildlife populations, artificial auxiliary structures such as bridges and other software and hardware supporting service facilities and equipment, such as specially designed culverts for them to pass through under the dam, are designed and built accordingly; In addition, it is also possible to consider expanding the scale and width of the green buffer zone with a strong adaptability of native vegetation types in order to create a safer and more convenient migration route network, promote gene exchange and interaction among patches, increase the resilience to interference, and improve the overall ecological protection ability and efficiency of the region with double harvests.

[0077] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention, and the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-condition simulation method for the impact of reservoir construction on ecological environment changes, characterized in that: include: Based on the reservoir operation data, a water level dynamic and downstream ecological flow control model is established to determine the ecological base flow plan. The sediment monitoring data and sediment migration law are used to optimize the sediment control model to slow down the reservoir siltation. By analyzing the water temperature stratification characteristics of the reservoir area, a flow temperature structure control strategy is constructed to alleviate the cold water effect. Combined with the spatiotemporal variation of water quality, the diffusion path of pollutants is predicted and local eutrophication control measures are formulated to improve habitat connectivity. The establishment of a water level dynamics and downstream ecological flow control model based on reservoir operation data further includes: calculating the basic discharge volume V = kH according to the average daily inflow Qi and the water level H, where k is an empirical parameter and depends on the ecological sensitivity of the river section, monitoring the plant water demand WVe in the target area to solve whether the constraint W = (1 ε)WVe ≤ V ≤(1 + ε)WVe is met, where ε is the fluctuation tolerance, if V is not within the constraint interval, triggering the adjustment strategy and setting ΔV = θ(WV), θ > 0 is the adjustment coefficient, and the final discharge plan integrates the historical optimal ratio α, the real-time adjustment ratio β and determines Qfinal = αQhist + βQadj.

2. The multi-condition simulation method for the effect of reservoir construction on ecological environment changes according to claim 1 is characterized in that: The use of inflow sediment monitoring data to optimize the sediment control model to slow down sedimentation further includes: Obtain the sediment distribution matrix M(i,j), where i represents the reservoir layer and j represents time; The siltation accumulation value Si is estimated by the formula Si = Σ(Mk / Tk), where Tk is the deposition rate of the kth layer; The early warning index γ is set based on the sedimentation rate, and the pre-flushing mode is started when Si > Smax(1 γ) is satisfied; The time-divided sand flushing algorithm is used to allocate the daily water discharge ratio P(k) = f(k, S(k) / Smax) to minimize the cumulative effect.

3. The multi-operating condition simulation method for the effect of reservoir construction on ecological environment changes according to claim 2 is characterized in that: The sand flushing algorithm includes: Refine the scour intensity function in each cycle Fi(d,k)= a * exp(b*(kd^2))^n, d is the diameter range of sediment particles, and n represents the morphology correction index; Define the total sediment discharge ratio TPs to satisfy TPs = Σ[Σ(Pik)] >= Ropt, ensuring that the sediment discharge efficiency is greater than the threshold; Calculate the cumulative efficiency of the sand flushing window period Cw = η * ∑(Fi Fimin)^p, where p is positively correlated with the water flow intensity; The maximum allowable cumulative siltation ratio Cap < Sth / γeff is clearly stated in the output final optimization recommendation.

4. The multi-condition simulation method for the effect of reservoir construction on ecological environment changes according to claim 1 is characterized in that: The constructed flow state water temperature control model includes: The temperature response curve Tf(z,t)= Tw_initial λexp[δzt] is calculated based on the temperature gradient ΔTw(z,i) of the layered measuring point and time t, where z is the depth variable; When the deep cold water overflows and causes the surface temperature Tsf < Tcritical, the water supply control valve is activated; Design a step-by-step temperature difference compensation logic δi = Ti_next Ti_current to ensure Tnext ∈ [Ttarget σ,Ttarget + σ]; The integral of the temperature difference fluctuation ΔI = σΣ(ΔTk) under continuous water replenishment cycles is evaluated and a stable threshold is ensured.

5. The multi-operating condition simulation method for the effect of reservoir construction on ecological environment changes according to claim 1 is characterized in that: The water quality spatiotemporal variation analysis in local eutrophication control includes establishing a dissolved oxygen DO distribution gradient prediction DO(z,t)≈D_ambient(α / N)*Z+βsin(wt+kz) morphological analysis; When the DO concentration is detected to be lower than the limit, execute Ozone_release_flag=1 if O_amb < Tmin_DOfree; Set the interception efficiency function Ie(x)=1(cx)^2 / [B+cx+(cx^2)], where B is a constant and c is the pollution input load ratio; Verify that the post-interception effect meets the standard condition Dafter = Dcurrent*(1Ieff) ≥ Dmin_acceptance.

6. The multi-condition simulation method for the effect of reservoir construction on ecological environment changes according to claim 1 is characterized in that: The coupled simulation of habitat connectivity and migration behavior includes: Extract the habitat area A_i and connection distance d_j within the habitat and calculate the connectivity weight CWj(i,dj)≥CW_min as the benchmark value; Use the population diffusion simulation function Pop(t,d_i)= P_initial*exp{(kd)^m} to locate the key diffusion path section; By setting the length of artificial fishway or ecological corridor l_j, the connection index EC(j)=λ(l_i)*d_ij / (l_ij_maxd_ij_min); After optimizing the configuration, check whether the target connectivity rate EC_post ≈ α*EC_baseline + (1α)*EC_opt is achieved.

7. The multi-condition simulation method for the effect of reservoir construction on ecological environment changes according to claim 1 is characterized in that: The mitigation measures that supplement the water temperature stratification structure include: The calculation model Tf(z,d) is Tf=T_f + ΔTherm_z*ln(T_top / T_bot)^n; When it is detected that the thermal barrier effect is too strong, an additional thermal balance factor Bc=f(Cf*d_Th / Ct) is implemented; Optimize the segmented air supply and temperature increase logic setting ΔTa = ξ(Surface_influxC_depthflow); Combined with long-term records, the temperature rise maintenance condition Tmaxpost=Tbefore_pre*(1ΔCoolingEff) is verified.

8. The multi-operating condition simulation method for the effect of reservoir construction on ecological environment changes according to claim 1 is characterized in that: The specific details of the water quality control include: Remove the formula TNred = ϕ*N_input K_decayN*I_adjustment, K_decayN is the decay coefficient; If TN exceeds the standard, the supersaturated aeration system flag AerationFlag=T>Tref_N is activated; Design enhanced interception measures to introduce adsorption capacity limit Cs(max)=(Cinit*V)ε*Cbulk, where ε is the leakage factor; Confirm that the post-interception emission constraint is CTpost <Tthreshold_Stand。 9. The multi-operating condition simulation method for the effect of reservoir construction on ecological environment changes according to claim 1 is characterized in that: The conditions for introducing the improved sedimentation model include: Redefine the effective accumulation radius of muddy particles r_eff=rsinkexp{ψ(HmaxDmean)}, where Hmax is the highest water level depth; If the calculated r_diff=r_sinkr_bounded step is significant, adjust the model coefficient αs=αbase*(rs / r_mean_ref)^2; Add inertia drift weight term Drift_factor=G_drift*t^p / G_settling; The overall error square loss is minimized by L2_error_sum=sum(abs(D_pred / D_true1))<δerr.

10. The multi-operating condition simulation method for the effect of reservoir construction on ecological environment changes according to claim 1, characterized in that: The addition of habitat restoration regulation includes: Define the vegetation cover recovery factor VegRecover = k_veg*D_repl*S_v_area, D_repl is the planting density, k_veg reflects the growth acceleration effect; Start the compensation mechanism VegCompensation = sign [Veg_targetVeg_curr], sign is the judgment symbol; Configuration logic EcOpt(j) = ξ(Connetivity_gain / Separation_dist_max)+λ*A_overlap; After inspection and recovery, the overall improvement effect reaches the expected VegFinal ≈ β*VegRef_prev + γVegTarget_plan.

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