Chained prediction method for medium-and-long-term runoff in four-dimensional coupling of gas, land and reservoir water

Abstract

The invention provides an air-land reservoir water four-dimensional coupling medium and long-term runoff chain prediction method, which comprises the following steps of: determining a control section and a control reservoir according to hydrometeorological characteristics of a research area, constructing a rainfall-runoff production-confluence-evolution chain prediction framework, and decomposing the framework into four modules of meteorological rainfall, land runoff production, river channel confluence and runoff evolution; then, aiming at a time scale to be calculated, selecting a calculation model suitable for each module, and respectively completing rainfall, runoff production, confluence and evolution calculation through a mixed downscaling model, a land runoff production calculation formula, a river channel confluence calculation method and a runoff evolution equation considering the influence of reservoir scheduling, so as to obtain runoff processes of different control sections; and finally, runoff process calculation of different time scales is carried out successively, and the runoff process calculation is connected in series to obtain medium and long-term runoff calculation results of four-dimensional coupling of gas, land, reservoir and water in the research area. The method provided by the invention can effectively improve the medium and long term runoff prediction precision, and provides important technical support for optimal allocation and efficient utilization of drainage basin water resources.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrological water resources forecasting and prediction, and particularly to a four-dimensional coupling medium- and long-term runoff chain prediction method for atmosphere-land-reservoir-water Background Technique

[0002] Medium- and long-term runoff prediction is an important supporting tool in the fields of water resources management, flood control and drought relief, energy planning, ecological restoration, etc. With the intensification of global climate change and human activities, the contradiction between water supply and demand has become increasingly prominent, and the accuracy and reliability of medium- and long-term runoff prediction are particularly important. Accurate medium- and long-term runoff prediction can not only provide a scientific basis for reservoir operation, agricultural irrigation, power generation planning, etc., but also provide strong support for regional water resources allocation and flood control and disaster reduction decision-making. Especially in the context of frequent extreme weather events, the uncertainty analysis and risk assessment of medium- and long-term runoff prediction have important practical significance.

[0003] However, there is still much room for improvement in the accuracy and reliability of current medium- and long-term runoff prediction. Traditional statistical-based prediction methods and hydrological models often show great limitations when facing complex hydrological processes and nonlinear changes. On the one hand, traditional methods have insufficient description of the physical mechanism of hydrological processes and are difficult to capture the long-term impact of climate change and human activities on runoff; on the other hand, with the introduction of machine learning technology, although the prediction accuracy has been improved to a certain extent, its stability and applicability in complex hydrological environments still need to be verified. In addition, the existing prediction methods have weak multi-source data fusion capabilities and are difficult to make full use of multi-dimensional data such as meteorology, remote sensing, and hydrology, which further restricts the improvement of prediction accuracy.

[0004] In summary, medium- and long-term runoff prediction plays an irreplaceable role in water resources management and social and economic development, but its insufficient accuracy and method limitations are still the difficulties in current research. By improving prediction methods, optimizing model structures, enhancing data utilization efficiency, and strengthening research on the impact of climate change, it is expected to significantly improve the accuracy and reliability of medium- and long-term runoff prediction and provide more solid technical support for sustainable water resources management. Summary of the Invention

[0005] The purpose of the present invention is to provide a four-dimensional coupling medium- and long-term runoff chain prediction method for atmosphere-land-reservoir-water in view of the above-mentioned deficiencies of the existing technologies. Starting from four links of rainfall, runoff generation, confluence, and evolution, a four-dimensional coupling medium- and long-term runoff chain prediction method for atmosphere-land-reservoir-water is provided. According to the characteristics of rainfall forecasting, runoff generation calculation, confluence calculation, and runoff evolution process, the basis for calculating model selection and the result coupling method are proposed to improve the accuracy of medium- and long-term runoff prediction.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions:

[0007] The present invention provides a four-dimensional coupling medium- and long-term runoff chain prediction method for air-land-reservoir-water, including:

[0008] S1. Select a study area, determine control sections, controlled reservoirs, and collect and organize long-term historical data of meteorology and hydrology.

[0009] The meteorological data of the control section is denoted as A f ={a1, a2, a3,..., a i ,..., a N};

[0010] The hydrological data of the control section is denoted as H f ={h1, h2, h3,..., h i ,..., h N};

[0011] The meteorological data of the controlled reservoir is denoted as A r ={b1, b2, b3,..., b j ,..., b M};

[0012] The hydrological data of the controlled reservoir is denoted as H r ={l1, l2, l3,..., l j ,..., l M};

[0013] Wherein, A f represents the meteorological data of the control section; a i represents the meteorological data of the control section i; H f represents the hydrological data of the control section; h i represents the hydrological data of the control section i; N represents the number of control sections; A r represents the meteorological data of the controlled reservoir; b j represents the meteorological data of the controlled reservoir j; H r represents the hydrological data of the controlled reservoir; l j represents the hydrological data of the controlled reservoir j; M represents the number of controlled reservoirs;

[0014] S2. Establish a rainfall-runoff-confluence evolution chain prediction framework, and perform series calculations of a meteorological rainfall module R(r1, r2,..., r x ), a land surface runoff generation module Q(R, S, Pa, M), a river channel confluence module M(Q, L, G, X), and a runoff evolution module E(M, L, G, O, Y).

[0015] Wherein, R(r1, r2,..., r x ) represents a rainfall calculation equation; r x represents different factors affecting rainfall values;

[0016] Q(R, S, Pa, M) represents the runoff generation calculation equation, where R is the rainfall value; S is the catchment area of the basin; Pa is the antecedent precipitation; M is the runoff generation method, including saturation excess runoff and infiltration excess runoff;

[0017] M(Q, L, G, X) represents the confluence calculation equation; Q is the runoff generation value; L is the length of the confluence river channel; G is the slope ratio of the confluence river channel; X is the factor affecting confluence;

[0018] E(M, L, G, O, Y) is the routing calculation equation; M is the confluence value; O is the regulation and storage influence of the reservoir in the routing process; Y is the factor affecting routing;

[0019] S3. In the meteorological rainfall module, construct a hybrid downscaling model R′ to conduct medium- and long-term rainfall forecasts for the study area;

[0020] S4. In the land surface runoff generation module, using the rainfall forecast as the input, conduct runoff generation calculations for the corresponding control sections in different sub-areas of the study area using a hydrological model. Assume that S m is the m-th sub-area of the study area, and the runoff is Q m = Γ(S m ), where Γ(S m ) is the runoff generation calculation formula for the m-th sub-area;

[0021] S5. In the river channel confluence module, combine the isochrone method, the unit hydrograph method for a time period, and the instantaneous unit hydrograph method to conduct river channel confluence calculations to obtain the flow processes at different control sections;

[0022] S6. In the runoff routing module, use the Muskingum method, the synthetic flow method, and the hydrodynamic method to conduct river channel routing calculations to obtain the flow processes at different control sections after river channel routing;

[0023] S7. From upstream to downstream, each node conducts successive routing calculations at the same time scale until the runoff processes at each control section at the time scale are output;

[0024] S8. For the study area, complete the runoff process calculations at different time scales of ten-day, monthly, quarterly, and annual.

[0025] Furthermore, in S1, the method for determining the control sections and the controlling reservoirs is based on the flood control protection object situation, the sections to be predicted, and the reservoirs with large total storage capacity and regulation capacity in the study area. On the premise of ensuring the flood control safety of the basin, select the sections where important flood control protection objects are located, the sections of concern, and the reservoirs with a flood control storage capacity of not less than 100 million m 3 or the reservoirs whose total storage capacity ranks among the top 5-10% of all reservoirs in the basin as the control sections and the controlling reservoirs.

[0026] Further, in S1, the meteorological data includes: rainfall, temperature, wind speed, and radiation;

[0027] The hydrological data includes: flow rate, water level, evaporation, reservoir water level, reservoir inflow, and reservoir outflow.

[0028] Further, in S3, the medium term uses ten-day as the calculation scale and outputs the daily rainfall of the area above each control section; the long term uses month, season, and year as the calculation scale and outputs the monthly rainfall of the area above each control section.

[0029] Further, in S3, the hybrid downscaling model refers to a medium- and long-term rainfall forecasting model constructed by coupling dynamic downscaling, statistical downscaling, and artificial intelligence methods. Specifically:

[0030] S301. Obtain the output data of different global climate models For the rainfall output data of the kth climate model, where K is the type of climate model participating in the calculation, and compare it with the measured data of the study area to conduct a comparative analysis and evaluate the prediction accuracy P k of different climate models, and rank the usability of different climate models from high to low according to the prediction accuracy, where where, P k is the prediction accuracy of the kth climate model; is the prediction accuracy of the control section in the kth climate model; is the prediction accuracy of the controlled reservoir in the kth climate model;

[0031] S302. According to the evaluation results, combined with the characteristics of the study area, select one or more climate models with high usability, set the initial conditions and boundary conditions respectively, and conduct simulation runs to obtain a regional climate model with good prediction effect for the study area;

[0032] If one climate model is selected, the calculation result of the regional climate model is equal to the output data of the global climate model

[0033] If multiple climate models are selected, the calculation result of the regional climate model is equal to the arithmetic mean of the calculation results of multiple models. The calculation formula is:

[0034]

[0035] S303. Analyze the simulation results, evaluate the errors in the global climate model, use the artificial intelligence algorithm of deep learning to fit the error distribution law, and obtain the error correction calculation formula e(x), where e is the error correction data; x is the matrix composed of the factors affecting the error distribution law.

[0036] S304. Use the regional climate model to conduct medium- and long-term rainfall prediction for the study area, and revise the prediction results based on the error correction calculation formula, and output them as the final rainfall prediction results, that is, the final rainfall prediction results.

[0037] Furthermore, in the above S4, the hydrological models include the Xin'anjiang model, rainfall-runoff correlation diagram, SAC model, SWAT model, TOPMODEL model, and hydrological models applicable to runoff calculation at monthly scale and above.

[0038] Among them, the SAC model is the Sacramento Basin Hydrological Model, which is a hydrological model used to simulate the hydrological processes of the basin, mainly used for predicting rainfall runoff. By simulating the changes in soil moisture, rainfall is converted into surface runoff and subsurface runoff.

[0039] The SWAT model is the Soil and Water Assessment Tool, which is a distributed basin hydrological model based on GIS. It uses the spatial information provided by remote sensing and geographic information systems to simulate a variety of different hydrological, physical and chemical processes.

[0040] The TOPMODEL model is the TOPOgraphy-based MODel of Landscape Evolution, which is a terrain-based distributed hydrological model mainly used for simulating surface runoff, water movement, and basin hydrological processes. The above three models are all well-known common sense models that are relatively mature in the field of hydrological forecasting.

[0041] For medium-term runoff calculation within dekad scale, according to the climate zone and runoff generation characteristics of the study area, the Xin'anjiang model, rainfall-runoff correlation diagram, SAC model, SWAT model, and TOPMODEL model are used for calculation, and daily runoff forecasts are output.

[0042] For long-term runoff calculation at monthly scale and above, the SWAT model, TOPMODEL model, and hydrological models applicable to runoff calculation at monthly scale and above are used for calculation, and monthly runoff forecasts are output.

[0043] Furthermore, in the above S5, when conducting river channel confluence calculation, if the rainfall surface distribution in the study area is uneven, the isochrone method is adopted.

[0044] If the spatial and temporal distribution of rainfall is uniform and the duration of net rainfall is short during the calculation period, the unit hydrograph method is adopted;

[0045] If there is an obvious runoff variation process in the forecast during the calculation period and the distribution of net rainfall intensity is uniform within the unit time period, the instantaneous unit hydrograph method is adopted.

[0046] Furthermore, in S6, when calculating the river channel evolution, if the river channel is straight and the flow rate changes linearly along the way, the Muskingum method is adopted;

[0047] If the length of the river channel evolution is short and there are many tributary inflows, the composite flow method is adopted;

[0048] If the river channel topography is complex and the variation pattern of the flow rate along the way is changeable, the hydrodynamic method is adopted.

[0049] Furthermore, it includes at least one processor and a memory communicatively connected to at least one of the processors, wherein,

[0050] the memory stores instructions executed by the processor, and the instructions are used to be executed by the processor to implement a four-dimensional coupled medium- and long-term runoff chain prediction method for atmosphere-land-reservoir-water.

[0051] The beneficial effects of the present invention are as follows: First, clarify the control sections and controlled reservoir (group) according to the hydrological and meteorological characteristics of the research area, construct a rainfall-runoff generation-confluence-evolution chain prediction framework, and decompose it into four modules: meteorological rainfall, land surface runoff generation, river channel confluence, and runoff evolution; then, for the time scale to be calculated, select the applicable calculation models for each module, and complete the rainfall, runoff generation, confluence, and evolution calculations respectively through the hybrid downscaling model, land surface runoff generation calculation formula, river channel confluence calculation method, and runoff evolution equation considering the influence of reservoir (group) operation, so as to obtain the runoff processes of different control sections; finally, carry out the runoff process calculations of different time scales successively and concatenate them into the medium- and long-term runoff calculation results of the four-dimensional coupling of atmosphere-land-reservoir-water in the research area. Through this technology, the accuracy of medium- and long-term runoff prediction can be effectively improved, providing an important technical support for the optimal allocation and efficient utilization of water resources in the basin. Description of the Drawings

[0052] Figure 1 It is a flowchart of a four-dimensional coupled medium- and long-term runoff chain prediction method for atmosphere-land-reservoir-water;

[0053] Figure 2 It is a schematic diagram of the system structure of the upper reaches of the Han River in the embodiment, including a distribution map of the water system in the upper reaches of the Han River and a schematic diagram of the controlled reservoirs in the upper reaches of the Han River;

[0054] Figure 3 It is a schematic diagram of the rainfall zoning of the system in the upper reaches of the Han River in the embodiment;

[0055] Figure 4 Schematic diagram of the runoff process of the Yangxian Station section in the embodiment;

[0056] Figure 5 Schematic diagram of the runoff process of the Ankang Station section in the embodiment;

[0057] Figure 6 Schematic diagram of the inflow and outflow processes of the Shiquan Reservoir section in the embodiment;

[0058] Figure 7 Schematic diagram of the inflow and outflow processes of the Ankang Reservoir section in the embodiment;

[0059] Figure 8 Schematic diagram of the inflow and outflow processes of the Danjiangkou Reservoir section in the embodiment. Detailed implementation manners

[0060] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0061] Please refer to Figure 1 , a four-dimensional coupling medium- and long-term runoff chain prediction method for air-land-reservoir-water, including:

[0062] S1. Select a research area, determine control sections, controlled reservoirs, and collect and collate long-term historical data of meteorology and hydrology.

[0063] The meteorological data of the control section is denoted as A f ={a1, a2, a3,..., a i ,..., a N};

[0064] The hydrological data of the control section is denoted as H f ={h1, h2, h3,..., h i ,..., h N};

[0065] The meteorological data of the controlled reservoir is denoted as A r ={b1, b2, b3,..., b j ,..., b M};

[0066] The hydrological data of the controlled reservoir is denoted as H r ={l1, l2, l3,..., l j ,..., l M};

[0067] Among them, A f represents the meteorological data of the control section; a iRepresent the meteorological data of control section i; H f Represent the hydrological data of the control section; h i Represent the hydrological data of control section i; N represents the number of control sections; A r Represent the meteorological data of the controlled reservoir; b j Represent the meteorological data of controlled reservoir j; H r Represent the hydrological data of the controlled reservoir; l j Represent the hydrological data of controlled reservoir j; M represents the number of controlled reservoirs;

[0068] S2. Establish a chain - type prediction framework for rainfall - runoff - confluence evolution, and conduct series calculations of the meteorological rainfall module R(r1, r2,..., r x ), the land surface runoff generation module Q(R, S, Pa, M), the river channel confluence module M(Q, L, G, X) and the runoff evolution module E(M, L, G, O, Y);

[0069] Among them, R(r1, r2,..., r x ) represents the rainfall calculation equation; r x represents different factors affecting rainfall values;

[0070] Q(R, S, Pa, M) represents the runoff generation calculation equation, where R is the rainfall value; S is the catchment area of the basin; Pa is the antecedent precipitation index; M is the runoff generation mode, including full - storage runoff generation and infiltration - excess runoff generation;

[0071] M(Q, L, G, X) represents the confluence calculation equation; Q is the runoff generation value; L is the length of the confluence river channel; G is the slope of the confluence river channel; X is the factor affecting confluence;

[0072] E(M, L, G, O, Y) is the evolution calculation equation; M is the confluence value; O is the regulation influence of the reservoir during the evolution process; Y is the factor affecting evolution;

[0073] S3. In the meteorological rainfall module, construct a hybrid downscaling model R′ to carry out medium - and long - term rainfall prediction in the study area;

[0074] S4. In the land surface runoff generation module, taking the rainfall prediction as the input, use the hydrological model to carry out runoff generation calculation for the corresponding control sections in different sub - regions of the study area. Assume that S m is the m - th sub - region of the study area, and the runoff volume is Q m =Γ(S m ), where Γ(S m ) is the runoff generation calculation formula for the m - th sub - region;

[0075] S5. In the river channel confluence module, combine the isochrone method, the unit hydrograph method for a time interval and the instantaneous unit hydrograph method to carry out river channel confluence calculation and obtain the flow process of different control sections;

[0076] S6. In the runoff evolution module, the Muskingum method, synthetic flow method, and hydrodynamic method are used to calculate the river channel evolution, and the flow processes at different control sections after the river channel evolution are obtained.

[0077] S7. From upstream to downstream, each node is evolved and calculated successively at the same time scale until the runoff processes at each control section under the time scale are output.

[0078] S8. For the study area, the runoff processes at different time scales of ten-day, monthly, quarterly, and annual are calculated respectively.

[0079] In the above-mentioned S1, the method for determining the control sections and controlling reservoirs is based on the flood control protection objects in the study area, the sections to be predicted, and the reservoirs with large total storage capacity and regulation ability in the basin. On the premise of ensuring the flood control safety of the basin, the sections where important flood control protection objects are located, the concerned sections, and the reservoirs with flood control storage capacity greater than or equal to 100 million m 3 or the reservoirs whose total storage capacity ranks among the top 5-10% of all reservoirs in the basin are selected as the control sections and controlling reservoirs.

[0080] In the above-mentioned S1, the meteorological data includes: rainfall, temperature, wind speed, and radiation.

[0081] The hydrological data includes: flow, water level, evaporation, reservoir water level, reservoir inflow, and reservoir outflow.

[0082] In the above-mentioned S3, the medium-term calculation scale is ten-day, and the daily rainfall of the area above each control section is output; the long-term calculation scales are monthly, quarterly, and annual, and the monthly rainfall of the area above each control section is output.

[0083] In the above-mentioned S3, the hybrid downscaling model refers to a medium- and long-term rainfall forecasting model constructed by coupling dynamic downscaling, statistical downscaling, and artificial intelligence methods. Specifically:

[0084] S301. Obtain the output data of different global climate models is the rainfall output data of the kth climate model, where K is the type of climate model participating in the calculation, and is compared with the measured data in the study area for comparative analysis to evaluate the prediction accuracy P k of different climate models, and sort the usability of different climate models from high to low according to the prediction accuracy, where where, P k is the prediction accuracy of the kth climate model; is the prediction accuracy of the control section in the kth climate model; is the prediction accuracy of the controlling reservoir in the kth climate model;

[0085] S302. According to the evaluation results and in combination with the characteristics of the research area, select one or more climate models with high availability, set the initial conditions and boundary conditions respectively, and conduct simulation runs to obtain a regional climate model with good prediction effect for the research area;

[0086] If one climate model is selected, the calculation result of the regional climate model is equal to the output data of the global climate model

[0087] If multiple climate models are selected, the calculation result of the regional climate model is equal to the arithmetic mean of the multi-model calculation results. The calculation formula is:

[0088]

[0089] S303. Analyze the simulation results, evaluate the errors in the global climate model, and use the artificial intelligence algorithm of deep learning to fit the error distribution law to obtain the error correction calculation formula e(x), where e is the error correction data; x is the matrix composed of the factors affecting the error distribution law;

[0090] S304. Use the regional climate model to conduct medium- and long-term rainfall prediction for the research area, and revise the prediction results based on the error correction calculation formula, and output them as the final rainfall prediction results, that is, the final rainfall prediction results

[0091] In the above-mentioned S4, the hydrological models include the Xin'anjiang model, rainfall-runoff correlation diagram, SAC model, SWAT model, TOPMODEL model, and hydrological models applicable to runoff calculation at monthly scale and above;

[0092] For medium-term runoff calculation within dekad scale, according to the climate zone and runoff generation characteristics of the research area, use the Xin'anjiang model, rainfall-runoff correlation diagram, SAC model, SWAT model, TOPMODEL model for calculation, and output daily runoff forecasts;

[0093] For long-term runoff calculation at monthly scale and above, use the SWAT model, TOPMODEL model, and hydrological models applicable to runoff calculation at monthly scale and above for calculation, and output monthly runoff forecasts.

[0094] In the above-mentioned S5, when conducting river channel flow concentration calculation, if the rainfall surface distribution in the research area is uneven, the isochrone method is adopted;

[0095] If the rainfall spatio-temporal distribution is uniform and the net rainfall duration is short during the calculation period, the unit hydrograph method is adopted;

[0096] If there is an obvious runoff change process in the forecast during the calculation period and the net rainfall intensity distribution is uniform within the unit time period, the instantaneous unit hydrograph method is adopted.

[0097] In S6, when performing river channel evolution calculation, if the river channel is straight and the flow changes linearly along the way, the Muskingum method is adopted;

[0098] If the river channel evolution length is short and there are many tributary inflows, the combined flow method is adopted;

[0099] If the river channel topography is complex and the flow change pattern along the way is variable, the hydrodynamic method is adopted.

[0100] It includes at least one processor and a memory communicatively connected to at least one of the processors. Among them,

[0101] The memory stores instructions executed by the processor, and the instructions are used to be executed by the processor to implement a four-dimensional coupling medium- and long-term runoff chain prediction method for air-land reservoir water.

[0102] Embodiment

[0103] Taking the river basin system with the Danjiangkou Reservoir as the core in the upper reaches of the Hanjiang River as an example, medium- and long-term ten-day scale runoff forecasting calculations are carried out to verify the feasibility and effectiveness of the method of the present invention.

[0104] Figure 2 、 Figure 3 The river basin system in the upper reaches of the Hanjiang River and the rainfall forecast sub-regions are respectively drawn. From Figure 2 and Figure 3 it can be seen that the medium- and long-term runoff prediction of the river basin system in the upper reaches of the Hanjiang River can be decomposed into rainfall forecast modules in 3 sub-regions, multi-sub-region runoff generation modules and confluence modules composed of different main and tributaries, and a river channel evolution module with Shiquan Reservoir, Ankang Reservoir, Pankou Reservoir, Huanglongtan Reservoir, and Danjiangkou Reservoir as the controlled reservoir nodes. When there is a rainfall process in the river basin, using the method of the present invention, first use the hybrid downscaling model to calculate the areal rainfall in 3 sub-regions, and then carry out runoff generation, confluence and evolution calculations respectively. For the 5 reservoirs, their operation rules are constructed and called to achieve continuous calculation of the river system until the inflow runoff process at the section of the Danjiangkou Reservoir is output as the runoff prediction result for the ten-day scale (i.e., the next 10 days) in the upper reaches of the Hanjiang River.

[0105] Table 1 shows the runoff results of the main control sections of Yangxian and Ankang stations and the controlled reservoir section of the Danjiangkou Reservoir. The specific runoff processes of each section are as Figures 4 to 7 shown.

[0106] Table 1 Specific operation process information table of Unit 1 to Unit 3

[0107]

[0108]

[0109] As can be seen from Figures 4 to 7 Figures 4 to 7 It can be seen that the method of the present invention adopts a four-dimensional coupling medium- and long-term runoff chain prediction method of gas-land-reservoir-water, which can quickly realize the calculation of rainfall, runoff generation, confluence and evolution in the study area, obtain the medium- and long-term runoff process of the required section, and the prediction result has a good fitting effect with the actual process, and the prediction accuracy is significantly improved, which also proves the feasibility and effectiveness of this method. It can be seen from this that this method has excellent application effects in medium- and long-term runoff prediction.

[0110] According to the above analysis, it can be seen that the method of the present invention has strong practicability and can effectively solve the problem of insufficient accuracy existing in medium- and long-term runoff prediction.

[0111] To sum up, the present invention has the advantages of strong practicability and operability, can quickly realize the medium- and long-term runoff prediction in the study area, obtain the runoff prediction results of important sections, and provide more scientific and efficient technical support for basin hydrological water resources forecasting and reservoir operation.

[0112] The above-described embodiments only express the implementation manners of the present invention, and their descriptions are relatively specific and detailed, but should not be construed as limiting the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

Claims

1. A medium- and long-term runoff chain prediction method for the four-dimensional coupling of gas-land-reservoir-water, characterized in that Including: S1. Select the research area, determine the control section and the controlling reservoir, and collect and collate the long-term historical data of meteorology and hydrology. The meteorological data of the control section is denoted as A f ={a1, a2, a3,..., a i ,..., a N}, The hydrological data of the control section is denoted as H f ={h1, h2, h3, ..., h i , ..., h N}; The meteorological data of the controlled reservoir is denoted as A r ={b1, b2, b3,..., b j ,..., b M}; The hydrological data of the controlled reservoir is denoted as H r ={l1, l2, l3, ..., l j , ..., l M}; Among them, A f represents the meteorological data of the control section; a i represents the meteorological data of control section i; H f represents the hydrological data of the control section; h i represents the hydrological data of control section i; N represents the number of control sections; A r represents the meteorological data of the controlling reservoir; b j represents the meteorological data of controlling reservoir j; H r represents the hydrological data of the controlling reservoir; l j represents the hydrological data of controlling reservoir j; M represents the number of controlling reservoirs; S2. Establish a chain-based forecasting framework for rainfall runoff and confluence evolution, and conduct serial calculations of the meteorological rainfall module R(r1, r2,..., r x ), the land surface runoff generation module Q(R, S, Pa, M), the river channel confluence module M(Q, L, G, X), and the runoff evolution module E(M, L, G, O, Y); Among them, R(r1, r2,..., r x ) represents the rainfall calculation equation; r x represents different factors affecting rainfall values; Q(R, S, Pa, M) represents the runoff calculation equation, where R is the rainfall value; S is the catchment area of the basin; Pa is the antecedent precipitation; M is the runoff generation method, including full storage runoff generation and excess infiltration runoff generation. M(Q, L, G, X) represents the confluence calculation equation; Q is the runoff generation value; L is the length of the confluence river channel; G is the slope of the confluence river channel; X is the factor affecting confluence. E(M, L, G, O, Y) is the routing calculation equation; M is the confluence value; O is the regulation influence of the reservoir in the routing process; Y is the factor affecting routing. S3. In the meteorological rainfall module, construct a hybrid downscaling model R', and carry out medium- and long-term rainfall forecasting for the research area. S4. In the land surface runoff generation module, using rainfall forecast as the input and a hydrological model, conduct runoff calculation for the corresponding control sections in different sub - regions of the study area. Assume that S m is the m - th sub - region of the study area, and the runoff volume is Q m = Γ(S m ), where Γ(S m ) is the runoff generation calculation formula for the m - th sub - region; S5. In the river confluence module, combine the isochrone method, the unit hydrograph method for a time period, and the instantaneous unit hydrograph method to carry out river confluence calculation and obtain the flow process of different control sections. S6. In the runoff routing module, use the Muskingum method, the synthetic flow method, and the hydrodynamic method to carry out river routing calculation and obtain the flow process of different control sections after river routing. S7. From upstream to downstream, each node performs successive routing calculations at the same time scale until the runoff process at the time scale of each control section is output. S8. For the research area, complete the runoff process calculations at different time scales of ten-day, monthly, quarterly, and annual.

2. A medium- and long-term runoff chain prediction method for the four-dimensional coupling of gas, land, reservoir and water according to claim 1, characterized in that: In S1, the method for determining the control section and the controlling reservoir is based on the flood control protection objects in the study area, the section to be predicted, and the reservoirs in the basin with large total storage capacity and regulation capacity. On the premise of ensuring the flood control safety of the basin, select the section where the important flood control protection object is located, the section of concern, and the reservoir with a flood control capacity greater than or equal to 100 million m 3 or the reservoir whose total storage capacity ranks among the top 5-10% of all reservoirs in the basin as the control section and the controlling reservoir.

3. A four-dimensional coupling medium- and long-term runoff chain prediction method for air-land-reservoir-water according to claim 2, characterized in that: In the S1, the meteorological data includes: rainfall, temperature, wind speed, and radiation. The hydrological data includes: flow rate, water level, evaporation, reservoir water level, reservoir inflow, and reservoir outflow.

4. A medium- and long-term runoff chain prediction method for the four-dimensional coupling of gas, land, reservoir, and water according to claim 3, characterized in that: In the S3, the medium term uses ten-day as the calculation scale and outputs the daily rainfall of the area above each control section; the long term uses monthly, quarterly, and annual as the calculation scale and outputs the monthly rainfall of the area above each control section.

5. A medium- and long-term runoff chain prediction method for the four-dimensional coupling of gas, land, reservoir and water according to claim 4, characterized in that: In the S3, the hybrid downscaling model refers to a medium- and long-term rainfall forecasting model constructed by coupling dynamic downscaling, statistical downscaling, and artificial intelligence methods. Specifically: S301. Obtain the output data of different global climate models Let be the rainfall output data of the k-th climate model, where K is the type of climate model participating in the calculation, and compare it with the measured data in the study area to conduct a comparative analysis and evaluate the prediction accuracy P of different climate models k , and rank the usability of different climate models according to the prediction accuracy from high to low, where Among them, P k is the prediction accuracy of the k-th climate model; is the prediction accuracy of the control section in the k-th climate model; is the prediction accuracy of the controlled reservoir in the k-th climate model; S302. According to the evaluation results, combined with the characteristics of the research area, select one or more climate models with high availability, set the initial conditions and boundary conditions respectively, and perform simulation runs to obtain a regional climate model with good prediction effect for the research area. If a climate model is selected, the regional climate model calculation results equal the output data of the global climate model If multiple climate models are selected, the calculation results of the regional climate model are equal to the arithmetic mean of the calculation results of multiple models, and the calculation formula is: S303. Analyze the simulation results, evaluate the errors in the global climate model, use the artificial intelligence algorithm of deep learning to fit the error distribution law, and obtain the error correction calculation formula e(x), where e is the error correction data; x is the matrix composed of the factors affecting the error distribution law. S304. Use a regional climate model to conduct medium- and long-term rainfall prediction for the study area, and revise the prediction results based on the error correction calculation formula for output as the final rainfall prediction result, i.e., the final rainfall prediction result 6. A medium- and long-term runoff chain prediction method for the four-dimensional coupling of gas storage and water, according to claim 5, characterized in that: In the S4, the hydrological models include the Xin'anjiang model, the rainfall-runoff correlation diagram, the SAC model, the SWAT model, the TOPMODEL model, and the hydrological models applicable to runoff calculation at a monthly scale or above. For the mid-term runoff calculation within dekad-scale, the Xin'anjiang model, rainfall-runoff correlation diagram, SAC model, SWAT model, and TOPMODEL model are used for calculation according to the climate zone where the study area is located and the runoff generation characteristics, and the daily runoff forecast is output. For the long-term runoff calculation at monthly scale and above, the SWAT model, TOPMODEL model, and hydrological models applicable to runoff calculation at monthly scale and above are used for calculation, and the monthly runoff forecast is output.

7. A medium- and long-term runoff chain prediction method for the four-dimensional coupling of gas, land, reservoir, and water according to claim 6, characterized in that In S5, when conducting river channel confluence calculation, if the rainfall surface distribution in the study area is uneven, the isochrone method is adopted. If the rainfall is evenly distributed in space and time during the calculation period and the net rainfall duration is short, the unit hydrograph method is adopted. If there is an obvious runoff change process in the forecast during the calculation period and the net rainfall intensity distribution is uniform within the unit time period, the instantaneous unit hydrograph method is adopted.

8. A medium- and long-term runoff chain prediction method for the four-dimensional coupling of gas, land, reservoir and water according to claim 7, characterized in that: In S6, when conducting river channel evolution calculation, if the river channel is straight and the flow rate changes linearly along the way, the Muskingum method is adopted. If the river channel evolution length is short and there are many tributary inflows, the composite flow method is adopted. If the river channel terrain is complex and the flow rate changes in various forms along the way, the hydrodynamic method is adopted.

9. A medium and long-term runoff chain prediction method for the four-dimensional coupling of gas, land, reservoir and water according to claim 8, characterized in that: It includes at least one processor and a memory communicatively connected to at least one of the processors, wherein the memory stores instructions executed by the processor, and the instructions are used to be executed by the processor to implement a four-dimensional coupling medium and long-term runoff chain prediction method for air-land reservoir water according to any one of claims 1 to 8.

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