Artificial Rain Enhancement Methods and Systems Based on Watershed Water Storage Characteristics and Dynamic Water Storage

Abstract

This invention belongs to the field of meteorological and hydropower energy technology, and relates to a method and system for artificial rain enhancement based on watershed water storage characteristics and dynamic water storage. It includes: acquiring the movement path of cloud clusters within a target area and their impact range within a preset time period; performing spatial overlay analysis of the impact range with pre-divided sub-watersheds to generate a list of affected sub-watersheds; calculating the water storage capacity and runoff generation area ratio of each sub-watershed based on pre-calibrated static water storage parameters and real-time hydrological data; acquiring the reservoir capacity data of the corresponding reservoirs for each sub-watershed and calculating the water demand urgency; selecting the target sub-watershed for operation based on the sub-watershed list, water storage capacity, runoff generation area ratio, and water demand urgency, combined with preset matching rules; and triggering rain enhancement when cloud clusters move over the target sub-watershed and meet the meteorological conditions for operation. This invention significantly improves the conversion efficiency of transit cloud water resources into reservoir runoff and power generation benefits.

Description

Technical Field

[0001] This invention belongs to the field of meteorology and hydropower energy technology, and relates to an artificial rain enhancement method and system based on watershed water storage characteristics and dynamic water storage. Background Technology

[0002] In river basins with a high concentration of hydropower stations, rainfall is extremely unevenly distributed in time and space. During the flood season, localized, short-duration heavy rainfall is frequent, resulting in the loss of large amounts of water resources as floodwaters and threatening dam safety; conversely, the dry season is prone to periods of drought, causing reservoir water levels to drop and insufficient power generation. This combination of drought and flooding leads to a mismatch between the supply and demand of water resources needed for hydropower generation. Currently, artificial rain enhancement technologies mainly include cloud monitoring and identification technologies based on multi-source data such as radar and satellites, catalyst seeding technologies and equipment development, and physical verification and statistical evaluation methods for operational effectiveness. Regarding operational decision-making, existing technologies generally rely on real-time meteorological data, judging whether indicators such as cloud thickness, cloud top temperature, and supercooled water content meet the catalytic conditions to determine whether to implement the operation. However, existing technologies have the following major drawbacks.

[0003] First, operational decision-making is reactive and lacks a clear demand orientation. Existing technologies generally adopt a passive strategy of weather observation, meaning that operations are only considered when cloud systems with rain enhancement potential are detected, rather than proactively selecting operational targets based on the urgency of water demand in the basin. Artificial rain enhancement relies on natural cloud conditions and cannot produce rainfall in the absence of suitable cloud formations. Its timing and location are severely limited by naturally occurring catalytic clouds.

[0004] Secondly, some technologies attempt to artificially intervene to alter the movement of clouds and guide them to specific areas for rainfall. However, such active intervention methods suffer from significant instability and errors. The macroscopic movement of clouds is controlled by large-scale weather systems, and artificial intervention cannot alter their natural paths. Furthermore, cloud belts are composed of constantly evolving and regenerating cloud bodies, lacking the upstream-downstream flow relationship found in rivers. Therefore, it is impossible to achieve precise water resource allocation by changing the direction of cloud clusters.

[0005] Furthermore, while existing artificial rain enhancement technologies for reservoir storage enhancement target specific reservoir basins, they generally treat the entire basin as a homogeneous unit, neglecting the spatial heterogeneity of water storage characteristics of the underlying surface within the basin. In mountainous basins, due to differences in topography, soil type, and vegetation cover, the water storage capacity varies significantly across different sub-regions: in some areas, the soil is near saturation, and rainwater enhancement can rapidly generate runoff, even triggering soil erosion; while in other areas, the soil is severely arid, and most of the rainwater is absorbed by the soil, making it difficult to form effective inflow into the reservoir. Existing technologies only focus on the amount of rainfall enhancement without analyzing the linkage between the rainfall enhancement process and the runoff generation process, resulting in a lack of targeted selection of operational objectives.

[0006] Due to the combined effect of the aforementioned shortcomings, existing technologies have failed to effectively utilize cloud water resources passing through the region. Specifically, when abundant cloud clusters pass through saturated watersheds, rain enhancement operations may lead to rapid runoff accumulation or even small-scale flooding, not only failing to effectively increase water storage but also increasing the risk of disasters; when cloud clusters pass through arid watersheds, if the opportunity for operations is missed due to a lack of advance prediction, a large amount of cloud water resources are lost without being effectively utilized. Summary of the Invention

[0007] To address the problems in the existing technology, this invention provides an artificial rain enhancement method and system based on watershed water storage characteristics and dynamic water storage, realizing the transformation of rain enhancement operation objectives from passive response to water demand orientation and precise spatial matching, significantly improving the conversion efficiency of transit cloud water resources into inflow runoff and power generation benefits.

[0008] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides an artificial rain enhancement method based on watershed water storage characteristics and dynamic water storage, comprising the following steps: Obtain the movement path of cloud clusters within the target area and the influence range of the cloud clusters within a preset time period in the future; The scope of impact is spatially overlaid with multiple pre-divided sub-basins to generate a list of affected sub-basins; Based on the pre-calibrated static water storage parameters of each sub-basin and the real-time hydrological data of each sub-basin, the water storage capacity and runoff area ratio of each sub-basin are calculated. Obtain the reservoir capacity data for each sub-basin, and calculate the water demand urgency of each reservoir based on the reservoir capacity data; Based on the sub-basin list, the water storage capacity of each sub-basin, the runoff area ratio of each sub-basin, and the water demand urgency of each reservoir, the target sub-basins for operation are selected by combining the preset matching rules. When the cloud cluster moves over the target sub-basin and meets the preset meteorological conditions for operation, rain enhancement operation is triggered.

[0009] Preferably, obtaining the movement path of the cloud cluster within the target area and the influence range of the cloud cluster within a preset time period includes: The system acquires real-time, continuous, multi-time satellite cloud image data of the target area using a meteorological satellite cloud image receiving device. Based on the satellite cloud image data, identify cloud clusters with precipitation potential and calculate the direction and speed of movement of the cloud clusters. Based on the direction and speed of movement, the movement path of the cloud cluster and the range of its influence are predicted within the next 2-4 hours.

[0010] Preferably, the step of spatially overlaying the affected area with multiple pre-divided sub-basins to generate a list of affected sub-basins includes: Draw the bounding box of the future impact area of ​​the cloud cluster on the electronic map; The bounding box is overlaid with a pre-built GIS watershed boundary layer to automatically identify all sub-watershed units located within the bounding box; The list of affected sub-basins is generated based on the identified sub-basin units.

[0011] Preferably, the static water storage parameters of each sub-basin that have been pre-calibrated are obtained through calibration using the Xin'anjiang model. The static water storage parameters include the average water storage capacity of the basin, the upper layer tension water capacity, the lower layer tension water capacity, the deep layer tension water capacity, and the water storage capacity curve index. The real-time hydrological data includes real-time rainfall and real-time evaporation capacity.

[0012] Preferably, the calculation of the water storage capacity and runoff generation area ratio of each sub-basin includes: Based on the real-time rainfall and the real-time evaporation capacity, the three-layer evapotranspiration mechanism of the Xin'anjiang model is used to update the upper soil moisture content of each sub-basin on a time-by-time basis. When the upper soil moisture content exceeds the upper tensile water capacity, the excess portion is used as the first infiltration amount to replenish and update the lower soil moisture content. When the lower soil moisture content exceeds the lower tensile water capacity, the excess portion is used as the second infiltration amount to replenish and update the deep soil moisture content, and the deep soil moisture content does not exceed the deep tensile water capacity. Soil water storage capacity is calculated based on the updated moisture content of the upper soil layer, lower soil layer, and deep soil layer. The formula for calculating soil water storage capacity is as follows:

[0013] In the formula, Soil water storage capacity; This refers to the moisture content of the upper soil layer. This refers to the moisture content of the lower soil layer. This refers to the moisture content of deep soil layers; The water storage capacity is calculated based on the average water storage capacity of the watershed and the soil water storage. The formula for calculating the water storage capacity is as follows:

[0014] In the formula, To reach full capacity; The average water storage capacity of the basin; Based on the average water storage capacity of the watershed, the water storage capacity curve index, and the soil water storage, the runoff-generating area ratio is calculated. The formula for calculating the runoff-generating area ratio is as follows:

[0015] In the formula, The proportion of the area producing runoff; This represents the index of the water storage capacity curve.

[0016] Preferably, the reservoir capacity data includes current reservoir capacity, dead reservoir capacity, flood control limit reservoir capacity, and target reservoir capacity; the calculation of the water demand urgency of each reservoir based on the reservoir capacity data includes: When the current reservoir capacity is less than the target reservoir capacity, the formula for calculating the water urgency is:

[0017] In the formula, The urgency of water demand; Current storage capacity; For dead storage capacity; For target storage capacity; When the current reservoir capacity is greater than the target reservoir capacity, the water urgency is 0; The target reservoir capacity is determined based on the hydropower station's power generation plan and does not exceed the flood control limit reservoir capacity.

[0018] Preferably, the step of selecting target sub-basins based on the sub-basin list, the water storage capacity of each sub-basin, the runoff-generating area ratio of each sub-basin, and the water demand urgency of each reservoir, combined with preset matching rules, includes: Based on the sub-basin list, extract the water storage capacity, runoff area ratio, and water demand urgency of the corresponding reservoir for each affected sub-basin; Sub-basins whose water volume is within a preset water volume threshold range and whose runoff area ratio is greater than a preset runoff area ratio threshold are marked as first candidate sub-basins. In the first candidate sub-basins, they are sorted from high to low according to the urgency of water demand, and the sub-basin with the highest urgency of water demand is selected as the target sub-basin for the operation. When the number of the first candidate sub-basins is zero, the sub-basins with a water storage capacity greater than the upper limit of the threshold interval or less than the lower limit of the threshold interval, and a water demand urgency higher than the preset water demand urgency threshold, are selected as the target sub-basins for the operation.

[0019] Preferably, the step of triggering rain enhancement operations when the cloud mass moves over the target sub-basin and meets preset meteorological conditions includes: The location of the cloud cluster is monitored in real time by meteorological radar or satellite cloud images to determine whether the leading edge of the cloud cluster has entered the catchment area of ​​the target sub-basin; When the cloud front has entered the rain collection area, collect the cloud top temperature data and cloud thickness data of the cloud; When the cloud top temperature is within a preset catalytic temperature range and the cloud thickness exceeds a preset minimum operating thickness, rain enhancement operations are triggered for the target sub-basin.

[0020] Preferably, after triggering the rain enhancement operation, the method further includes: Obtain information on runoff changes in the target sub-basin before and after the rain enhancement operation, as well as information on changes in power generation efficiency of the corresponding reservoir in the target sub-basin; Based on the runoff change information and power generation benefit change information, the matching rules in the subsequent operation process are optimized.

[0021] Secondly, the present invention provides an artificial rain enhancement system based on watershed water storage characteristics and dynamic water storage capacity, comprising: Cloud path prediction module: used to obtain the movement path of clouds within the target area and the influence range of the clouds within a preset time period in the future; Impacted watershed identification module: used to perform spatial overlay analysis of the impact range with multiple pre-divided sub-watersheds to generate a list of affected sub-watersheds; Dynamic water storage assessment module: used to calculate the water storage capacity and runoff area ratio of each sub-basin based on the pre-calibrated static water storage parameters of each sub-basin and the real-time hydrological data of each sub-basin. Water demand urgency calculation module: used to obtain the reservoir capacity data of the corresponding reservoirs in each sub-basin, and calculate the water demand urgency of each reservoir based on the reservoir capacity data; Matching and filtering module: used to filter out target sub-basins for operation based on the sub-basin list, the storage capacity of each sub-basin, the runoff area ratio of each sub-basin, and the water demand urgency of each reservoir, combined with preset matching rules. Operational condition triggering module: used to trigger rain enhancement operations when the cloud cluster moves over the target sub-basin and meets the preset meteorological conditions for operation.

[0022] Compared with the prior art, the present invention has the following beneficial effects: By acquiring the cloud cluster's movement path and impact range, the meteorological forecast results are spatially overlaid with pre-defined sub-basins to identify the affected sub-basins. Based on this, the water storage capacity and runoff generation area ratio of each sub-basin are calculated using static water storage parameters calibrated from the Xin'anjiang model and real-time hydrological data to quantify the spatial differences in underlying surface water storage capacity and runoff generation potential. Simultaneously, the water demand urgency is calculated based on the reservoir capacity data to reflect the degree of water shortage for power generation. Finally, the sub-basin list, water storage capacity, runoff generation area ratio, and water demand urgency are combined and filtered according to preset matching rules. The target sub-basin for the operation is identified, ensuring that the target simultaneously meets the conditions of high runoff efficiency and urgent power generation demand. Finally, rain enhancement is triggered when the cloud cluster actually moves over the target sub-basin and meets the meteorological conditions for operation. This organically integrates cloud cluster path prediction, dynamic assessment of basin water storage status, and reservoir water demand urgency, overcoming the shortcomings of existing technologies such as passive response, neglect of underlying surface heterogeneity, and lack of demand orientation. It realizes the transformation of rain enhancement operations from weather-based operations to demand-oriented and spatially precise matching, significantly improving the conversion efficiency of transit cloud water resources into reservoir runoff and power generation benefits. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0025] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0026] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0027] The first objective of this invention is to provide an artificial rain enhancement method based on watershed water storage characteristics and dynamic water storage, such as... Figure 1 As shown, it includes the following steps: S1. Obtain the movement path of the cloud cluster within the target area and the influence range of the cloud cluster within a preset time period in the future.

[0028] The system acquires real-time, continuous, multi-time satellite cloud image data of the target area using a meteorological satellite cloud image receiving device. Based on the satellite cloud image data, identify cloud clusters with precipitation potential and calculate the direction and speed of movement of the cloud clusters. Based on the direction and speed of movement, the movement path of the cloud cluster and the range of its influence are predicted within the next 2-4 hours.

[0029] Specifically, firstly, meteorological satellite cloud image receiving devices (such as medium-scale utilization stations or HRPT / FY-3 series satellite data receiving systems) deployed in the target area are used to acquire real-time visible light, infrared, and water vapor channel cloud image data of the target area and its surroundings over multiple consecutive time intervals (typically 15 to 30 minutes). Then, based on the acquired satellite cloud image sequence, cloud clusters with precipitation potential are identified. Specific criteria include: cloud top brightness temperature below -20℃ (infrared channel), dense cloud body with clear boundaries (visible light channel), and relatively uniform internal texture structure of the cloud cluster (water vapor channel), while excluding high clouds or cirrus clouds. The cloud system has no precipitation potential. Next, by comparing the positional changes of the same cloud cluster in adjacent cloud images, the direction and speed of cloud cluster movement can be quantitatively calculated using manual interpretation and overlay grid method or automatic centroid tracking algorithm (such as centroid coordinate difference of connected domains after threshold segmentation). Finally, based on the current position of the cloud cluster center and the calculated direction and speed of movement, the continuous movement path of the cloud cluster and the geographical impact range to be covered by the path can be predicted in the next 2-4 hours through linear extrapolation or extrapolation model after considering weather system guiding airflow correction. The output is a series of geographic coordinate strings or rasterized impact areas with time labels.

[0030] S2. Perform spatial overlay analysis on the affected area and the pre-divided multiple sub-basins to generate a list of affected sub-basins.

[0031] Draw the bounding box of the future impact area of ​​the cloud cluster on the electronic map; The bounding box is overlaid with a pre-built GIS watershed boundary layer to automatically identify all sub-watershed units located within the bounding box; The list of affected sub-basins is generated based on the identified sub-basin units.

[0032] Specifically, firstly, based on the cloud cluster's movement path and impact range data predicted by S1 (usually a set of geographic coordinate strings or rasterized areas with time labels), the impact range bounding the cloud cluster will cover in the next 2-4 hours is accurately drawn on a GIS (Geographic Information System) electronic map in a polygonal manner. The boundary of this bounding box can be appropriately expanded according to the cloud cluster's extrapolated diffusion trend (e.g., expanding 10-20 kilometers to each side of the path centerline) to cover prediction uncertainties. Then, this bounding box is spatially overlaid with a pre-built GIS watershed boundary layer stored in a spatial database. The watershed boundary layer is pre-divided into multiple sub-watershed units based on the target area's digital elevation model (DEM) using hydrological analysis tools (such as the watershed extraction module in ArcGIS). Each sub-watershed unit has a unique identifier and its corresponding reservoir, hydrological station, and other attribute information. By calling the GIS spatial query function (such as "intersection" or "inside" analysis), the system automatically identifies all sub-watershed units that intersect with or are completely contained within this bounding box. Finally, based on the identified sub-basin units, a list of affected sub-basins is generated according to the expected arrival time of the cloud cluster front in each sub-basin.

[0033] S3. Based on the pre-calibrated static water storage parameters of each sub-basin and the real-time hydrological data of each sub-basin, calculate the water storage capacity and runoff area ratio of each sub-basin.

[0034] First, for each pre-divided sub-basin within the target watershed, static water storage parameters are calibrated based on the Xin'anjiang model. During the calibration process, historical hydrological and meteorological data (such as long-sequence rainfall, evaporation, and runoff data) of the sub-basin are used. The static water storage parameters of each sub-basin are determined through parameter optimization methods (such as manual trial and error or the SCE-UA global optimization algorithm). Specifically, these parameters include the average watershed water storage capacity, upper layer tension water capacity, lower layer tension water capacity, deep layer tension water capacity, and water storage capacity curve index. Rainfall is collected in real time at each time period (typically 1 hour or daily) by rain gauge networks deployed in each sub-basin, and the water surface evaporation capacity at the same time is obtained through meteorological station observations as real-time hydrological data.

[0035] The calculation of the water storage capacity and runoff generation area ratio of each sub-basin includes: Based on the real-time rainfall and the real-time evaporation capacity, the three-layer evapotranspiration mechanism of the Xin'anjiang model is used to update the upper soil moisture content of each sub-basin on a time-by-time basis. When the upper soil moisture content exceeds the upper tensile water capacity, the excess portion is used as the first infiltration amount to replenish and update the lower soil moisture content. When the lower soil moisture content exceeds the lower tensile water capacity, the excess portion is used as the second infiltration amount to replenish and update the deep soil moisture content, and the deep soil moisture content does not exceed the deep tensile water capacity. Soil water storage capacity is calculated based on the updated moisture content of the upper soil layer, lower soil layer, and deep soil layer. The formula for calculating soil water storage capacity is as follows:

[0036] In the formula, Soil water storage capacity; This represents the water content of the upper soil layer, used to characterize vegetation interception and surface soil water retention; This represents the water content of the lower soil layer, used to characterize the water storage capacity of the root zone soil. This represents the water content of deep soil layers, used to characterize the water storage capacity of deep soil layers and fissures. The water storage capacity is calculated based on the average water storage capacity of the watershed and the soil water storage. The formula for calculating the water storage capacity is as follows:

[0037] In the formula, The water saturation level reflects the current soil saturation level in the sub-basin. The closer it is to 100%, the more likely the watershed is to be full, and any further rainfall will primarily be converted into surface runoff. The lower the value, the more severe the soil water shortage, and most of the rainfall will be absorbed by the soil. The average water storage capacity of the basin; Based on the average water storage capacity of the watershed, the water storage capacity curve index, and the soil water storage, the runoff-generating area ratio is calculated. The formula for calculating the runoff-generating area ratio is as follows:

[0038] In the formula, This represents the proportion of the watershed area that, under current soil moisture conditions, has reached its maximum water storage capacity and is generating runoff. The larger the value, the more areas will directly contribute to surface or groundwater runoff; It is a water storage capacity curve index used to describe the degree of unevenness in the distribution of point water storage capacity within a watershed.

[0039] This step introduces the Xin'anjiang model from the field of hydrology into the artificial rainfall enhancement decision-making system. By calibrating the static water storage parameters of each sub-basin in a zonal and hierarchical manner, the spatial non-uniformity of the underlying surface water storage capacity of the mountainous watershed is quantified. Through real-time updates of three-layer evapotranspiration driven by rainfall and evaporation, the real-time evolution of soil moisture in each sub-basin is tracked. The calculated water storage capacity is then used to determine the water storage level. and the ratio of runoff area It can provide runoff potential criteria for subsequent cloud-ground matching decisions, thereby overcoming to some extent the problems caused by existing technologies treating watersheds as homogeneous units, such as rain enhancement in saturated areas easily aggravating runoff and rain enhancement in arid areas having low runoff generation efficiency.

[0040] S4. Obtain the reservoir capacity data of each sub-basin and calculate the water demand urgency of each reservoir based on the reservoir capacity data.

[0041] First, a real-time connection is established with the hydropower station reservoir scheduling system or the hydrological automatic monitoring system through a data interface to obtain the reservoir capacity data corresponding to each sub-basin. Specifically, this includes: current reservoir capacity (obtained from the measured water level through the water level-capacity relationship curve), dead reservoir capacity (i.e., the reservoir capacity that cannot be used below the reservoir's minimum design operating water level), flood limit reservoir capacity (the reservoir capacity corresponding to the highest water level that is restricted during the flood season to reserve flood control capacity), and target reservoir capacity. Among them, the target reservoir capacity is determined according to the hydropower station's power generation plan and scheduling scheme. It is usually the expected water storage capacity to meet the planned power generation output, and its value should not exceed the flood limit reservoir capacity in principle, so as to take into account flood control safety.

[0042] When the current reservoir capacity is less than the target reservoir capacity, the formula for calculating the water urgency is:

[0043] In the formula, Due to the urgency of water demand, The larger the value, the greater the gap between the water supply and the target capacity, and the more urgent the water need. Current storage capacity; For dead storage capacity; For target storage capacity; When the current reservoir capacity is greater than the target reservoir capacity, it indicates that the reservoir's water storage has met or exceeded the power generation demand. At this time, there is no need for additional rainfall to replenish the water supply, so the water demand urgency is 0.

[0044] S5. Based on the sub-basin list, the water storage capacity of each sub-basin, the runoff area ratio of each sub-basin, and the water demand urgency of each reservoir, the target sub-basins for operation are selected in combination with the preset matching rules.

[0045] Based on the sub-basin list, extract the water storage capacity, runoff area ratio, and water demand urgency of the corresponding reservoir for each affected sub-basin; Sub-basins whose water volume is within a preset water volume threshold range and whose runoff area ratio is greater than a preset runoff area ratio threshold are marked as first candidate sub-basins. In the first candidate sub-basins, they are sorted from high to low according to the urgency of water demand, and the sub-basin with the highest urgency of water demand is selected as the target sub-basin for the operation. When the number of the first candidate sub-basins is zero, the sub-basins with a water storage capacity greater than the upper limit of the threshold interval or less than the lower limit of the threshold interval, and a water demand urgency higher than the preset water demand urgency threshold, are selected as the target sub-basins for the operation.

[0046] Specifically, firstly, based on the generated list of affected sub-basins, the current water storage level of each sub-basin is extracted one by one. and the ratio of runoff area And the corresponding urgency of water demand in reservoirs This forms a three-dimensional decision attribute vector for each sub-basin. , , Then, the task targets are filtered according to the preset matching rules: the first priority rule will accumulate to full capacity. The proportion of areas within a preset threshold range (e.g., a lower bound of 40% and an upper bound of 70%, which represents the "optimal runoff generation window" where the watershed soil is neither saturated nor drought-stricken, resulting in minimal rainfall infiltration loss and high runoff generation efficiency) and generating runoff Greater than a preset threshold (e.g.) A value >0.3 indicates that a significant proportion of the watershed area has reached full storage capacity, and the surface runoff generated by increased rainfall can effectively flow into the river network. Sub-watersheds with a value >0.3 are marked as first-candidate sub-watersheds. If the set of first-candidate sub-watersheds is not empty, then the water urgency is considered. Sort by highest to lowest, and select first. The sub-basin with the highest value (i.e., the most urgent water shortage for power generation) is selected as the target sub-basin for this operation. When the number of first-candidate sub-basins is zero, it indicates that no sub-basin currently meets both the optimal water storage capacity window and the runoff generation area ratio conditions. In this case, the second priority rule is activated: the sub-basin with the highest water storage capacity is selected. Exceeding the threshold range (i.e.) <40% or >70%) but water urgency Higher than the preset water urgency threshold (e.g.) Sub-basins with a water shortage rate greater than 50% (indicating that the reservoir's water shortage has exceeded the acceptable range) were selected as candidates, and then a selection was made from these sub-basins. The highest sub-basin is the target of the operation. In this case, although the runoff efficiency may be low (too dry or too wet), the operation can still be carried out as appropriate in order to meet the urgent power generation needs.

[0047] S6. When the cloud cluster moves over the target sub-basin and meets the preset meteorological conditions for operation, rain enhancement operation is triggered.

[0048] The location of the cloud cluster is monitored in real time by meteorological radar or satellite cloud images to determine whether the leading edge of the cloud cluster has entered the catchment area of ​​the target sub-basin; When the cloud front has entered the rain collection area, collect the cloud top temperature data and cloud thickness data of the cloud; When the cloud top temperature is within a preset catalytic temperature range and the cloud thickness exceeds a preset minimum operating thickness, rain enhancement operations are triggered for the target sub-basin.

[0049] Specifically, the real-time location of cloud clusters is continuously tracked using Doppler weather radar or high spatiotemporal resolution meteorological satellite cloud images deployed in the target area. Spatial matching analysis is then performed between the real-time observed cloud cluster front boundary and the pre-mapped target sub-basin catchment area (i.e., the entire catchment area within the sub-basin capable of generating runoff and flowing into the reservoir outlet section) to determine whether the cloud cluster front has entered the catchment area boundary. If the determination is yes, further information on the cloud top temperature and cloud thickness is acquired through radar echo inversion or satellite infrared channel brightness temperature data. If the cloud top temperature is within a preset range... If the catalytic temperature range is within the specified range (typically -20℃ lower limit and -5℃ upper limit, this temperature range indicates the presence of abundant supercooled water in the cloud, i.e., possessing the physical basis for artificially catalyzing precipitation), and the cloud thickness exceeds the preset minimum working thickness (e.g., cloud vertical thickness greater than 2 kilometers, ensuring sufficient time and space for the catalyst to complete the nucleation growth process in the cloud), then the meteorological conditions for operation are deemed met, and ground seeding equipment (such as rain-enhancing rocket launch systems) pre-positioned in the upwind direction of the target sub-basin or in the catchment area is activated to carry out fixed-point and timed catalytic operations.

[0050] S7. Obtain information on runoff changes in the target sub-basin before and after the rain enhancement operation, as well as information on changes in power generation benefits of the corresponding reservoir in the target sub-basin. Optimize the matching rules in subsequent operations based on the runoff change information and power generation benefit change information.

[0051] Specifically, by comparing the measured inflow process at the hydrological station at the outlet of the target sub-basin with the natural runoff prediction process constructed based on historical data of similar cloud systems (the latter representing background runoff without rain enhancement), the runoff increment between the two is calculated. Simultaneously, the actual water level changes and power generation data of the reservoir over a period of time after the operation are obtained, and then a formula is used based on the hydropower station's operating parameters. Calculate the increase in power generation caused by increased rainfall (in the formula) For unit efficiency, The density of water, It is the acceleration due to gravity. For average power generation head, (For power generation time); the predicted cloud path, decision-making basis (such as the storage level of the selected sub-basin, water demand urgency, etc.), actual operation parameters (such as catalyst dosage, operation duration) and final evaluation results (runoff increment, power generation increment) of this operation are correlated to form a complete case record and stored in the database; by regularly analyzing multiple sets of case data, the statistical law of rain enhancement runoff efficiency under different storage level windows, runoff area ratio thresholds and water demand urgency ranking rules can be extracted, and the matching rule parameters such as storage level threshold range, runoff area ratio lower limit and water demand urgency ranking weight in S5 can be gradually optimized.

[0052] A second objective of this invention is to provide an artificial rain enhancement system based on watershed water storage characteristics and dynamic water storage capacity, comprising: Cloud path prediction module: used to obtain the movement path of clouds within the target area and the influence range of the clouds within a preset time period in the future; Impacted watershed identification module: used to perform spatial overlay analysis of the impact range with multiple pre-divided sub-watersheds to generate a list of affected sub-watersheds; Dynamic water storage assessment module: used to calculate the water storage capacity and runoff area ratio of each sub-basin based on the pre-calibrated static water storage parameters of each sub-basin and the real-time hydrological data of each sub-basin. Water demand urgency calculation module: used to obtain the reservoir capacity data of the corresponding reservoirs in each sub-basin, and calculate the water demand urgency of each reservoir based on the reservoir capacity data; Matching and filtering module: used to filter out target sub-basins for operation based on the sub-basin list, the storage capacity of each sub-basin, the runoff area ratio of each sub-basin, and the water demand urgency of each reservoir, combined with preset matching rules. Operational condition triggering module: used to trigger rain enhancement operations when the cloud cluster moves over the target sub-basin and meets the preset meteorological conditions for operation.

[0053] The artificial rain enhancement system provided by this invention, based on watershed water storage characteristics and dynamic water storage, actively tracks the movement trajectory of clouds through a cloud path prediction module. Combined with an impact watershed identification module, it achieves precise superposition of meteorological targets and underlying surface spatial units. Then, a dynamic water storage assessment module quantifies the water storage capacity and runoff generation area ratio of each sub-watershed, and a water demand urgency calculation module quantifies the water shortage degree of reservoir power generation. Finally, a matching and screening module automatically selects the target sub-watershed with the highest runoff generation efficiency and the most urgent water demand according to preset optimization rules. Rain enhancement is initiated when the operation condition triggering module determines that the cloud has passed through and the catalytic window is met. Thus, cloud path, watershed water storage status, and reservoir water demand are organically coupled into a closed-loop decision-making system. This overcomes the shortcomings of existing technologies, such as passive response, neglect of underlying surface non-uniformity, and lack of demand orientation for operation targets. It realizes the transformation from weather-based operations to demand-oriented and precise matching, significantly improving the conversion efficiency of transit cloud water resources into effective inflow into reservoirs and power generation benefits.

[0054] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or function. The processor described in this embodiment of the present invention can be used for the operation of artificial rain enhancement methods based on watershed water storage characteristics and dynamic water storage.

[0055] This invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the artificial rain enhancement method based on watershed water storage characteristics and dynamic water storage in the above embodiments.

[0056] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0057] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0058] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0059] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for artificial rain enhancement based on watershed water storage characteristics and dynamic water storage, characterized in that, Includes the following steps: Obtain the movement path of cloud clusters within the target area and the influence range of the cloud clusters within a preset future time period; The scope of impact is spatially overlaid with multiple pre-divided sub-basins to generate a list of affected sub-basins; Based on the pre-calibrated static water storage parameters of each sub-basin and the real-time hydrological data of each sub-basin, the water storage capacity and runoff area ratio of each sub-basin are calculated. Obtain the reservoir capacity data for each sub-basin, and calculate the water demand urgency of each reservoir based on the reservoir capacity data; Based on the sub-basin list, the water storage capacity of each sub-basin, the runoff area ratio of each sub-basin, and the water demand urgency of each reservoir, the target sub-basins for operation are selected by combining the preset matching rules. When the cloud cluster moves over the target sub-basin and meets the preset meteorological conditions for operation, rain enhancement operation is triggered.

2. The artificial rain enhancement method based on watershed water storage characteristics and dynamic water storage capacity according to claim 1, characterized in that, The acquisition of the movement path of the cloud cluster within the target area and the influence range of the cloud cluster within a preset time period includes: The system acquires real-time, continuous, multi-time satellite cloud image data of the target area using a meteorological satellite cloud image receiving device. Based on the satellite cloud image data, identify cloud clusters with precipitation potential and calculate the direction and speed of movement of the cloud clusters. Based on the direction and speed of movement, the movement path of the cloud cluster and the range of its influence are predicted within the next 2-4 hours.

3. The artificial rain enhancement method based on watershed water storage characteristics and dynamic water storage capacity according to claim 1, characterized in that, The step involves spatially overlaying the affected area with multiple pre-divided sub-basins to generate a list of affected sub-basins, including: Draw the bounding box of the future impact area of ​​the cloud cluster on the electronic map; The bounding box is overlaid with a pre-constructed GIS watershed boundary layer to automatically identify all sub-watershed units located within the bounding box; The list of affected sub-basins is generated based on the identified sub-basin units.

4. The artificial rain enhancement method based on watershed water storage characteristics and dynamic water storage capacity according to claim 1, characterized in that, The pre-calibrated static water storage parameters of each sub-basin are obtained through calibration using the Xin'anjiang model. The static water storage parameters include the average water storage capacity of the basin, the upper tension water capacity, the lower tension water capacity, the deep tension water capacity, and the water storage capacity curve index. The real-time hydrological data include real-time rainfall and real-time evaporation capacity.

5. The artificial rain enhancement method based on watershed water storage characteristics and dynamic water storage capacity according to claim 4, characterized in that, The calculation of the water storage capacity and runoff generation area ratio of each sub-basin includes: Based on the real-time rainfall and the real-time evaporation capacity, the three-layer evapotranspiration mechanism of the Xin'anjiang model is used to update the upper soil moisture content of each sub-basin on a time-by-time basis. When the upper soil moisture content exceeds the upper tensile water capacity, the excess portion is used as the first infiltration amount to replenish and update the lower soil moisture content. When the lower soil moisture content exceeds the lower tensile water capacity, the excess portion is used as the second infiltration amount to replenish and update the deep soil moisture content, and the deep soil moisture content does not exceed the deep tensile water capacity. Soil water storage capacity is calculated based on the updated moisture content of the upper soil layer, lower soil layer, and deep soil layer. The formula for calculating soil water storage capacity is as follows: In the formula, Soil water storage capacity; This refers to the moisture content of the upper soil layer. This refers to the moisture content of the lower soil layer. This refers to the moisture content of deep soil layers; The water storage capacity is calculated based on the average water storage capacity of the watershed and the soil water storage. The formula for calculating the water storage capacity is as follows: In the formula, To reach full capacity; The average water storage capacity of the basin; Based on the average water storage capacity of the watershed, the water storage capacity curve index, and the soil water storage, the runoff-generating area ratio is calculated. The formula for calculating the runoff-generating area ratio is as follows: In the formula, The proportion of the area producing runoff; This represents the index of the water storage capacity curve.

6. The artificial rain enhancement method based on watershed water storage characteristics and dynamic water storage capacity according to claim 1, characterized in that, The reservoir capacity data includes current reservoir capacity, dead reservoir capacity, flood limit reservoir capacity, and target reservoir capacity; The calculation of the water demand urgency of each reservoir based on the reservoir capacity data includes: When the current reservoir capacity is less than the target reservoir capacity, the formula for calculating the water urgency is: In the formula, The urgency of water demand; Current storage capacity; For dead storage capacity; For target storage capacity; When the current reservoir capacity is greater than the target reservoir capacity, the water urgency is 0; The target reservoir capacity is determined based on the hydropower station's power generation plan and does not exceed the flood control limit reservoir capacity.

7. The artificial rain enhancement method based on watershed water storage characteristics and dynamic water storage capacity according to claim 1, characterized in that, The process involves selecting target sub-basins based on the sub-basin list, the water storage capacity of each sub-basin, the runoff-generating area ratio of each sub-basin, and the water demand urgency of each reservoir, combined with preset matching rules. These sub-basins include: Based on the sub-basin list, extract the water storage capacity, runoff area ratio, and water demand urgency of the corresponding reservoir for each affected sub-basin; Sub-basins whose water volume is within a preset water volume threshold range and whose runoff area ratio is greater than a preset runoff area ratio threshold are marked as first candidate sub-basins. In the first candidate sub-basins, they are sorted from high to low according to the urgency of water demand, and the sub-basin with the highest urgency of water demand is selected as the target sub-basin for the operation. When the number of the first candidate sub-basins is zero, the sub-basins with a water storage capacity greater than the upper limit of the threshold interval or less than the lower limit of the threshold interval, and a water demand urgency higher than the preset water demand urgency threshold, are selected as the target sub-basins for the operation.

8. The artificial rain enhancement method based on watershed water storage characteristics and dynamic water storage capacity according to claim 1, characterized in that, When the cloud mass moves over the target sub-basin and meets the preset meteorological conditions for operation, the rain enhancement operation is triggered, including: The location of the cloud cluster is monitored in real time by meteorological radar or satellite cloud images to determine whether the leading edge of the cloud cluster has entered the catchment area of ​​the target sub-basin; When the cloud front has entered the rain collection area, collect the cloud top temperature data and cloud thickness data of the cloud; When the cloud top temperature is within a preset catalytic temperature range and the cloud thickness exceeds a preset minimum operating thickness, rain enhancement operations are triggered for the target sub-basin.

9. The artificial rain enhancement method based on watershed water storage characteristics and dynamic water storage capacity according to claim 1, characterized in that, After triggering the rain enhancement operation, the following are also included: Obtain information on runoff changes in the target sub-basin before and after the rain enhancement operation, as well as information on changes in power generation efficiency of the corresponding reservoir in the target sub-basin; Based on the runoff change information and power generation benefit change information, the matching rules in the subsequent operation process are optimized.

10. An artificial rain enhancement system based on watershed water storage characteristics and dynamic water storage capacity, characterized in that, include: Cloud path prediction module: used to obtain the movement path of clouds within the target area and the influence range of the clouds within a preset time period in the future; Impacted watershed identification module: used to perform spatial overlay analysis of the impact range with multiple pre-divided sub-watersheds to generate a list of affected sub-watersheds; Dynamic water storage assessment module: used to calculate the water storage capacity and runoff area ratio of each sub-basin based on the pre-calibrated static water storage parameters of each sub-basin and the real-time hydrological data of each sub-basin. Water demand urgency calculation module: used to obtain the reservoir capacity data of the corresponding reservoirs in each sub-basin, and calculate the water demand urgency of each reservoir based on the reservoir capacity data; Matching and filtering module: used to filter out target sub-basins for operation based on the sub-basin list, the storage capacity of each sub-basin, the runoff area ratio of each sub-basin, and the water demand urgency of each reservoir, combined with preset matching rules. Operational condition triggering module: used to trigger rain enhancement operations when the cloud cluster moves over the target sub-basin and meets the preset meteorological conditions for operation.

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