Mountainous small watershed multi-source precipitation data space-time alignment flood forecasting method and system

Abstract

The present application belongs to the technical field of hydrological forecast, and relates to a mountainous medium and small watershed multi-source precipitation data space-time alignment flood forecast method and system. The method comprises the following steps: obtaining digital elevation model data of a target watershed, dividing the target watershed into multiple sub-watersheds based on the digital elevation model data; obtaining meteorological rainfall station group data and hydrological rainfall station data of each sub-watershed; for each sub-watershed meeting the alignment feasibility condition, performing spatial downscaling processing on the meteorological rainfall station group data to obtain meteorological surface rainfall time series of each sub-watershed, and quantifying the deviation of the hydrological rainfall station data to calculate a deviation index; correcting the meteorological surface rainfall time series based on the deviation index to obtain aligned surface rainfall data; inputting the aligned surface rainfall data into a hydrological model of each sub-watershed to obtain outlet flow of each sub-watershed, and superimposing the total watershed total inflow after river channel confluence calculation. The present application significantly improves the precision and reliability of mountainous medium and small watershed flood forecast.

Description

Technical Field

[0001] This invention belongs to the field of hydrological forecasting technology, and relates to a method and system for flood forecasting based on spatiotemporal alignment of multi-source precipitation data in small and medium-sized watersheds in mountainous areas. Background Technology

[0002] Mountainous areas with small and medium-sized watersheds often feature complex terrain, deep valleys, and steep gradients, leading to frequent and significant spatiotemporal variability in rainfall. Floods can develop from initial rise to peak in just tens of minutes to several hours, posing a severe challenge to predicting inflows to hydropower stations and implementing flood control measures. The forecast accuracy of hydrological models is highly dependent on the quality of the input rainfall data.

[0003] Currently, precipitation data in small and medium-sized watersheds in mountainous areas mainly comes from meteorological rain gauges set up by meteorological departments and hydrological rain gauges set up by hydrological departments. In practice, rainfall data collected by meteorological rain gauges is often systematically higher than that from hydrological rain gauges. Directly using meteorological rainfall data to drive hydrological models can lead to an overestimation of inflow. When there is a discrepancy between the two data sources, scheduling decisions face a dilemma: relying on meteorological data may result in excessive pre-discharge, causing losses in water volume and power generation; relying on hydrological data may lead to unpreparedness due to response lag. In addition, existing flood forecasting technologies have the following shortcomings: (1) Most existing methods perform uniform downscaling and bias analysis on the entire watershed without considering the significant differences in runoff generation and confluence characteristics, location of rainstorm centers, and coverage of meteorological stations among different sub-watersheds. Uniform processing of the entire watershed will average the bias characteristics of different regions, masking the forecast bias in local areas.

[0004] (2) Most existing technologies are limited to data fusion and lack methods for quantifying systematic biases in meteorological and hydrological precipitation data. Furthermore, they fail to effectively integrate the quantified bias results with scheduling decisions. There is a lack of clear technical guidance on what correction strategies should be adopted under different rainfall intensities and different prior wet conditions.

[0005] (3) Traditional models are used for a long time once calibration is completed, and lack a self-learning update mechanism. As the underlying surface of the watershed changes and the observation equipment drifts, the forecast accuracy gradually decreases, making it difficult to guarantee long-term reliability. Summary of the Invention

[0006] To address the problems in the existing technology, this invention provides a method and system for spatiotemporal alignment of multi-source precipitation data in small and medium-sized watersheds in mountainous areas, which significantly improves the accuracy and reliability of flood forecasting in small and medium-sized watersheds in mountainous areas.

[0007] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides a method for spatiotemporal alignment of multi-source precipitation data in small and medium-sized watersheds in mountainous areas for flood forecasting, comprising the following steps: Obtain digital elevation model data of the target watershed, and divide the target watershed into multiple sub-watersheds based on the digital elevation model data; Obtain meteorological and hydrological rainfall data from each sub-basin to determine whether each sub-basin meets the conditions for alignment feasibility. For each sub-basin that meets the alignment feasibility conditions, the meteorological rainfall station group data is spatially downscaled to obtain the meteorological surface rainfall time series of each sub-basin. The deviation between the meteorological surface rainfall time series of each sub-basin and the hydrological rain gauge data is quantified, and the deviation index is calculated. The meteorological areal rainfall time series is corrected based on the deviation index to obtain aligned areal rainfall data; The aligned areal rainfall data is input into the hydrological model of each sub-basin to obtain the outlet flow of each sub-basin. After river confluence calculation, the total inflow of the entire basin is obtained by superimposing the data.

[0008] Preferably, the step of acquiring digital elevation model data of the target watershed and dividing the target watershed into multiple sub-watersheds based on the digital elevation model data includes: Hydrological analysis tools were used to fill depressions in the digital elevation model data to obtain the digital elevation model data after depression filling. The flow direction of each grid is calculated based on the digital elevation model data after the depressions are filled, and the flow direction grid data is obtained. The flow accumulation of each grid is calculated based on the flow direction grid data to obtain the flow accumulation grid data; River network data is obtained by extracting the river network raster data based on the accumulated flow raster data. Based on the river network raster data and the location of the watershed outlet, the watershed boundary is extracted, and the target watershed is divided into multiple sub-watersheds.

[0009] Preferably, the step of acquiring meteorological and hydrological rain gauge data for each sub-basin and determining whether each sub-basin meets the alignment feasibility conditions includes: Each sub-basin is spatially associated with meteorological and hydrological rain gauge stations. Historical and real-time rainfall data from meteorological rain gauge stations within each sub-basin are collected as meteorological rain gauge station data for each sub-basin. Simultaneously, rainfall data from hydrological rain gauge stations within each sub-basin are collected as hydrological rain gauge station data for each sub-basin. Determine whether the number of meteorological rain gauge stations contained in each sub-basin is greater than or equal to a first preset threshold, and whether the number of hydrological rain gauge stations contained therein is greater than or equal to a second preset threshold. If yes, the sub-basin meets the alignment feasibility condition; otherwise, the sub-basin does not meet the alignment feasibility condition.

[0010] Preferably, for each sub-basin that meets the alignment feasibility condition, the meteorological rainfall station group data is spatially downscaled to obtain the meteorological areal rainfall time series for each sub-basin, including: Each sub-basin that meets the alignment feasibility conditions is divided into multiple regular grids; The Kriging interpolation method is used, with the location coordinates of each meteorological rain gauge station in each sub-basin as known points and the center point of each grid as the interpolation point. The real-time rainfall data in the meteorological rain gauge station group data of each sub-basin is interpolated onto the multiple regular grids to obtain the interpolated rainfall of each grid at each time. The interpolated rainfall of all grids within each sub-basin at each time point is averaged by area weighting to obtain the meteorological surface rainfall of each sub-basin at each time point. The meteorological surface rainfall at each time point is then summarized to form the meteorological surface rainfall time series of each sub-basin.

[0011] Preferably, the step of quantifying the deviation between the meteorological surface rainfall time series and the hydrological rain gauge data of each sub-basin, and calculating the deviation index, includes: The meteorological surface rainfall time series of each sub-basin is aligned with the hydrological rain gauge data in time so that they correspond to the same time period, thus obtaining the meteorological surface rainfall data and the hydrological station rainfall data of the same period. Based on the concurrent meteorological areal rainfall data and the concurrent hydrological station rainfall data, independent rainfall events within each sub-basin are identified and extracted. For each independent rainfall event, the deviation rate, absolute deviation, cumulative deviation, and correlation coefficient are calculated to obtain the four-dimensional deviation index for each independent rainfall event. The four-dimensional deviation indicators were classified and statistically analyzed according to rainfall intensity level, rainfall duration and previous humidity level to obtain the deviation indicators of each sub-basin under each classification condition.

[0012] Preferably, the step of correcting the meteorological areal rainfall time series based on the deviation index to obtain aligned areal rainfall data includes: Based on the aforementioned deviation index, the time series of meteorological surface rainfall for each sub-basin at each time moment is corrected to obtain the corrected rainfall value for each sub-basin at each time moment. The corrected areal rainfall values ​​of each sub-basin at each time point are summarized to obtain the aligned areal rainfall data of each sub-basin.

[0013] Preferably, the step of inputting the aligned areal rainfall data into the hydrological model of each sub-basin to obtain the outlet discharge of each sub-basin, and then superimposing the data after river confluence calculation to obtain the total inflow discharge of the entire basin, includes: Obtain the initial hydrological model and historical runoff data for each sub-basin; Based on the aligned areal rainfall data and historical runoff data of each sub-basin, the parameters of the initial hydrological models of multiple sub-basins are calibrated to obtain the calibrated hydrological models of each sub-basin. The aligned areal rainfall data of each sub-basin are input into the calibrated hydrological model of the corresponding sub-basin to obtain the outflow of each sub-basin; The outflow of each sub-basin is calculated by the river confluence calculation to obtain the process of the outflow of each sub-basin propagating to the downstream target section. The total inflow of the entire basin is obtained by superimposing the flow processes of each sub-basin at the same time.

[0014] Preferably, after obtaining the total inflow of the entire basin, the method further includes: Historical correction coefficients for each sub-basin are constructed based on the aforementioned deviation index; During the current flood event, real-time deviation coefficients for each sub-basin are constructed based on measured hydrological and rain gauge data and concurrent meteorological rainfall data. The current operating conditions of each sub-basin are obtained, and the current operating conditions are determined based on the rainfall intensity level and the previous humidity level. Based on the current operating conditions, the real-time meteorological rainfall data is corrected using the historical correction coefficient or the real-time deviation coefficient, and a scheduling instruction is generated based on the corrected rainfall data.

[0015] Preferably, the method further includes: After each flood, the actual deviation data of the flood is obtained, the historical correction coefficients are updated, and the hydrological models of each sub-basin are incrementally updated.

[0016] Secondly, the present invention provides a spatiotemporal aligned flood forecasting system for multi-source precipitation data in small and medium-sized watersheds in mountainous areas, comprising: Sub-basin division module: used to acquire digital elevation model data of the target basin, and divide the target basin into multiple sub-basins based on the digital elevation model data; Condition judgment module: used to obtain meteorological and hydrological rain gauge data of each sub-basin and determine whether each sub-basin meets the alignment feasibility conditions; Spatial downscaling module: used to perform spatial downscaling processing on the meteorological rainfall station group data for each sub-basin that meets the alignment feasibility conditions, to obtain the meteorological surface rainfall time series of each sub-basin; Deviation quantification module: used to quantify the deviation between the meteorological surface rainfall time series of each sub-basin and the hydrological rain gauge data, and calculate the deviation index; Deviation correction module: used to correct the meteorological areal rainfall time series based on the deviation index to obtain aligned areal rainfall data; Flood forecasting module: It is used to input the aligned areal rainfall data into the hydrological model of each sub-basin to obtain the outlet flow of each sub-basin, and then superimpose them after river confluence calculation to obtain the total inflow of the entire basin.

[0017] Compared with the prior art, the present invention has the following beneficial effects: By finely dividing the target watershed into multiple sub-watershed units, all subsequent processing is based on spatial partitions with independent hydrological response characteristics, effectively avoiding the loss of spatial heterogeneous information caused by uniform processing across the entire watershed. By setting alignment feasibility conditions, it is ensured that each participating sub-watershed has statistically valid station samples, guaranteeing the reliability of the deviation quantification results from the outset. Spatial downscaling is performed on sub-watersheds that meet the conditions, converting the observation data from discrete meteorological stations into continuous meteorological surface rainfall time series, fully leveraging the high spatiotemporal resolution of the meteorological station network. Multi-dimensional deviation quantification is performed between the meteorological surface rainfall time series and hydrological rainfall station data, systematically revealing the difference between meteorological source precipitation and hydrological baseline. The study investigated the differential deviation patterns under different rainfall conditions. Based on the deviation index, the meteorological areal rainfall time series was corrected, ensuring that the corrected areal rainfall data maintained a high degree of consistency with the hydrological baseline in terms of total amount, peak value, and process morphology. This provided the hydrological model with high-quality rainfall input that combined high spatiotemporal resolution with the authority of the hydrological baseline. Finally, the aligned areal rainfall data was input into the hydrological models of each sub-basin. Through parameters independently calibrated in each sub-basin and river confluence calculations, the spatial differences in runoff generation and confluence characteristics of different sub-basins were fully respected, and the propagation process of flood waves in the river network was scientifically characterized. The total inflow of the entire basin obtained by superposition fully reflected the cumulative effect of runoff generation in each sub-region flowing into the target section through different paths, significantly improving the accuracy and reliability of flood forecasting in small and medium-sized watersheds in mountainous areas. Attached Figure Description

[0018] 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.

[0019] Figure 1 This is a flowchart of the method of the present invention; Figure 2 A diagram illustrating the alignment feasibility criteria. Detailed Implementation

[0020] 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.

[0021] 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.

[0022] The first objective of this invention is to provide a spatiotemporal aligned flood forecasting method for multi-source precipitation data in small and medium-sized watersheds in mountainous areas, such as... Figure 1 As shown, it includes the following steps: S1. Obtain digital elevation model data of the target watershed, and divide the target watershed into multiple sub-watersheds based on the digital elevation model data.

[0023] Hydrological analysis tools were used to fill depressions in the digital elevation model data to obtain the digital elevation model data after depression filling. The flow direction of each grid is calculated based on the digital elevation model data after the depressions are filled, and the flow direction grid data is obtained. The flow accumulation of each grid is calculated based on the flow direction grid data to obtain the flow accumulation grid data; River network data is obtained by extracting the river network raster data based on the accumulated flow raster data. Based on the river network raster data and the location of the watershed outlet, the watershed boundary is extracted, and the target watershed is divided into multiple sub-watersheds.

[0024] Specifically, when dividing sub-basins using hydrological analysis tools based on digital elevation model (DEM) data, the original DEM data is first filled with depressions to eliminate the interference of surface depressions on runoff path simulation and ensure the topographic continuity of the hydrological analysis. Then, based on the filled DEM, the flow direction of each grid cell is calculated using the D8 single-flow-direction algorithm or multi-flow-direction algorithm, generating flow direction raster data. This data reflects the initial spatial direction of runoff. Based on the flow direction raster data, the cumulative runoff amount raster data is obtained by statistically analyzing the number of upstream catchment grids received by each grid cell. This data quantitatively characterizes the water accumulation at various locations within the watershed. Next, based on the set runoff accumulation threshold (e.g., empirically determined by the basin area and river network density), grid cells forming permanent waterways are extracted from the runoff accumulation grid to generate river network grid data, which simulates the planar distribution of the natural river network within the basin. Finally, the river network grid data is overlaid with manually selected or automatically identified basin outlet locations, and the watershed extraction algorithm is used to trace the watershed line in the opposite direction of the runoff direction, dividing the river network into several independent sub-basin units. Each sub-basin has a clear closed boundary and a relatively independent runoff generation and collection system, and ensures that each sub-basin contains at least one hydrological and rain gauge station as a benchmark control point for subsequent deviation quantification.

[0025] S2. Obtain meteorological and hydrological rainfall data for each sub-basin and determine whether each sub-basin meets the alignment feasibility conditions.

[0026] Each sub-basin is spatially associated with meteorological and hydrological rain gauge stations. Historical and real-time rainfall data from meteorological rain gauge stations within each sub-basin are collected as meteorological rain gauge station data for each sub-basin. Simultaneously, rainfall data from hydrological rain gauge stations within each sub-basin are collected as hydrological rain gauge station data for each sub-basin. Determine whether the number of meteorological rain gauge stations contained in each sub-basin is greater than or equal to a first preset threshold, and whether the number of hydrological rain gauge stations contained therein is greater than or equal to a second preset threshold. If yes, the sub-basin meets the alignment feasibility condition; otherwise, the sub-basin does not meet the alignment feasibility condition.

[0027] Specifically, firstly, based on the vector boundaries of each sub-basin, spatial overlay analysis (such as point-area containment relationship queries) is used to assign meteorological and hydrological rain gauge stations distributed within the basin to their respective sub-basins. Then, hourly (or smaller time step) rainfall records are extracted from the historical databases and real-time data acquisition systems of each rain gauge station. The meteorological rain gauge station cluster provides high spatiotemporal resolution continuous observation sequences, while the hydrological rain gauge station cluster provides authoritative rainfall data to complement runoff observations. Together, they form the foundation of multi-source precipitation data for each sub-basin. To ensure the reliability of subsequent deviation quantification statistics and the stability of interpolation calculations, alignment feasibility criteria are set for each sub-basin, namely, the number of meteorological rain gauge stations within the sub-basin must reach a first preset threshold (e.g., ...). Figure 2 As shown, preferably there are two or more hydrological and rain gauge stations to ensure that Kriging spatial interpolation has enough sample points to characterize the spatial variability of rainfall. The number of hydrological and rain gauge stations must reach the second preset threshold (preferably one or more to ensure that each sub-basin has at least one reliable hydrological benchmark station as a deviation reference). When both conditions are met, the sub-basin is deemed to have the feasibility of independently aligning meteorological and hydrological dual-source data. Otherwise, it is marked as infeasible. In this case, the deviation characteristics of the neighboring feasible sub-basins can be used for weighted substitution or the sub-basin can be merged with the adjacent sub-basins.

[0028] S3. For each sub-basin that meets the alignment feasibility conditions, the meteorological rainfall station group data is spatially downscaled to obtain the meteorological surface rainfall time series of each sub-basin.

[0029] Each sub-basin that meets the alignment feasibility conditions is divided into multiple regular grids; The Kriging interpolation method is used, with the location coordinates of each meteorological rain gauge station in each sub-basin as known points and the center point of each grid as the interpolation point. The real-time rainfall data in the meteorological rain gauge station group data of each sub-basin is interpolated onto the multiple regular grids to obtain the interpolated rainfall of each grid at each time. The interpolated rainfall of all grids within each sub-basin at each time point is averaged by area weighting to obtain the meteorological surface rainfall of each sub-basin at each time point. The meteorological surface rainfall at each time point is then summarized to form the meteorological surface rainfall time series of each sub-basin.

[0030] Specifically, firstly, based on the boundary range and computational accuracy requirements of each sub-basin, each sub-basin is regularly divided into several uniformly sized grid cells (the grid resolution can be reasonably set according to the area and topographic complexity of the sub-basin, such as 1km×1km or 500m×500m, to accurately depict the spatial variability of rainfall while ensuring computational efficiency); then, using the geographical coordinates of each meteorological rain gauge station within the sub-basin as known sample points and the center point of each grid as the location to be estimated, Ordinary Kriging or Universal Kriging is employed. The Kriging (pan-Kriging) interpolation method quantifies the spatial autocorrelation structure of rainfall variation with distance by constructing a semi-variogram model. Based on this, it assigns optimal weights to each known station and interpolates the real-time rainfall observations of each station at each time point onto all regular grids, obtaining the interpolated rainfall for each grid at that time point. Since different grid areas contribute differently to the sub-basin areal rainfall, the interpolated rainfall of all grids is finally averaged based on the proportion of each grid area to the total area of ​​the sub-basin, calculating the average areal rainfall value of the sub-basin at each time point. Arranging the calculation results of consecutive time points in chronological order forms a complete meteorological areal rainfall time series. This step scientifically transforms discrete point rainfall observations into a continuously distributed raster areal rainfall field through Kriging spatial interpolation, making full use of the spatial autocorrelation of rainfall variables to optimize interpolation accuracy. Compared with traditional arithmetic mean or Thiessen polygon methods, it can more realistically reflect the spatial non-uniform distribution of rainfall within sub-basins in areas with complex terrain.

[0031] S4. Quantify the deviation between the meteorological surface rainfall time series of each sub-basin and the hydrological rain gauge data, and calculate the deviation index.

[0032] The meteorological surface rainfall time series of each sub-basin is aligned with the hydrological rain gauge data in time so that they correspond to the same time period, thus obtaining the meteorological surface rainfall data and the hydrological station rainfall data of the same period. Based on the concurrent meteorological areal rainfall data and the concurrent hydrological station rainfall data, independent rainfall events within each sub-basin are identified and extracted. For each independent rainfall event, the deviation rate, absolute deviation, cumulative deviation, and correlation coefficient are calculated to obtain the four-dimensional deviation index for each independent rainfall event. The four-dimensional deviation indicators were classified and statistically analyzed according to rainfall intensity level, rainfall duration and previous humidity level to obtain the deviation indicators of each sub-basin under each classification condition.

[0033] Specifically, firstly, using a unified time label and equal time step (e.g., hourly or minute-level) as the matching benchmark, the meteorological surface rainfall time series and hydrological rain gauge observation series of each sub-basin are precisely aligned on the time axis. The time periods where the two completely overlap are extracted to form contemporaneous analysis data pairs, ensuring that all subsequent deviation calculations are based on the same rainfall process timeframe. Then, based on the contemporaneous data, according to the principle of dividing rainfall into continuous processes from start to end (usually using a continuous dry period exceeding a preset duration (e.g., 6 hours) as the termination boundary of a rainfall event, while setting a minimum cumulative rainfall threshold for a single rainfall event to filter out invalid micro-rain events), independent rainfall events within each sub-basin are automatically identified and extracted from the time series. Each event contains two synchronous process curves: meteorological surface rainfall and the corresponding hydrological station rainfall. For each independent rainfall event, according to... The calculations include deviation rate (reflecting the average deviation of meteorological data from hydrological station rainfall), absolute deviation (characterizing the absolute value of the total deviation for each event), cumulative deviation (tracking the cumulative change trend of deviation as rainfall progresses during a rainfall event, revealing the temporal evolution pattern of deviation), and correlation coefficient (quantifying the linear consistency of two sequences in process morphology, measuring the temporal matching degree of rainfall peak processes). This forms a four-dimensional deviation characterization system covering total deviation, absolute magnitude, cumulative dynamics, and process similarity for each event. Finally, the calculated four-dimensional indicators for each event are hierarchically classified and statistically analyzed according to rainfall intensity level (e.g., light rain, moderate rain, heavy rain and above), rainfall duration (e.g., short duration, medium duration, long duration), and pre-wetness level (e.g., dry, wet, saturated). The deviation characteristics are summarized according to the operating conditions system to form a deviation indicator library for each sub-basin under different operating condition combinations.

[0034] S5. Correct the meteorological areal rainfall time series based on the deviation index to obtain aligned areal rainfall data.

[0035] Based on the aforementioned deviation index, the time series of meteorological surface rainfall for each sub-basin at each time moment is corrected to obtain the corrected rainfall value for each sub-basin at each time moment. The corrected areal rainfall values ​​of each sub-basin at each time point are summarized to obtain the aligned areal rainfall data of each sub-basin.

[0036] Specifically, firstly, for each sub-basin, the current rainfall event belongs to a specific operational condition category (i.e., a combination of rainfall intensity, rainfall duration, and prior humidity). Based on a deviation index (preferably using the multiplicative reciprocal of the deviation rate as the correction coefficient, i.e., using hydrological station measurements as a benchmark to infer the correction factor from the meteorological data; for operational conditions with a deviation rate close to zero, additive correction is directly used to ensure the rationality of the values ​​under low rainfall conditions), this correction coefficient is applied to the original meteorological areal rainfall values ​​at each time point under the same operational condition, and the corrected areal rainfall values ​​are calculated moment by moment. If the cumulative deviation characteristics of a rainfall event show a trend of gradually increasing over time, a dynamic correction mechanism is further introduced, based on the time sequence of the cumulative deviation. The evolution pattern is used to adjust the correction intensity in stages, so that the correction coefficient changes adaptively with the rainfall process, thereby refining the cumulative effect of the correction deviation. For rainfall events with low correlation coefficients (indicating poor consistency between meteorological and hydrological data process morphology), total event correction is preferred over point-by-point correction. That is, the total rainfall is matched first, and then the morphology is scaled according to the actual process of the hydrological station to avoid over-correction and the introduction of new morphological distortions. After completing the correction of all times within each sub-basin, the corrected rainfall values ​​of each sub-basin are summarized and arranged in chronological order to form a complete aligned rainfall time series for each sub-basin. This series is highly consistent with the hydrological station benchmark in terms of total amount, peak value and process morphology.

[0037] S6. Input the aligned areal rainfall data into the hydrological model of each sub-basin to obtain the outlet flow of each sub-basin. After river confluence calculation, the total inflow of the entire basin is obtained by superimposing the data.

[0038] Obtain the initial hydrological model and historical runoff data for each sub-basin; Based on the aligned areal rainfall data and historical runoff data of each sub-basin, the parameters of the initial hydrological models of multiple sub-basins are calibrated to obtain the calibrated hydrological models of each sub-basin. The aligned areal rainfall data of each sub-basin are input into the calibrated hydrological model of the corresponding sub-basin to obtain the outflow of each sub-basin; The outflow of each sub-basin is calculated by the river confluence calculation to obtain the process of the outflow of each sub-basin propagating to the downstream target section. The total inflow of the entire basin is obtained by superimposing the flow processes of each sub-basin at the same time.

[0039] Specifically, the initial hydrological model framework for each sub-basin is first collected (the Xin'anjiang model, API model, tank model, or LSTM-based hybrid hydrological model can be selected according to the characteristics of the basin; for small and medium-sized watersheds in mountainous areas, due to the short rainfall-runoff response time and strong nonlinearity, the Kriging-LSTM hybrid model, which combines physical mechanisms and data-driven capabilities, is preferred to capture rapid hydrological response characteristics) and historical runoff data (including hourly inflow or measured flow data from hydrological stations) for each sub-basin; then, the aligned historical areal rainfall sequence for each sub-basin is used as the model input. The measured runoff processes for the corresponding time periods were used as training targets. An automatic optimization algorithm (such as the SCE-UA global optimization algorithm or Bayesian parameter inference method) was combined with manual fine-tuning to independently calibrate the initial hydrological model parameters for each sub-basin. Historical data were divided into a calibration period (e.g., the first 70%) and a validation period (e.g., the last 30%). The Nash-Sutcliffe efficiency coefficient and water balance error were used as comprehensive evaluation indicators. Iterative optimization was employed to ensure that the fitting accuracy between the simulated and measured flow processes met preset requirements (efficiency coefficient not less than 0.7 and water balance error not less than 0.7). To obtain calibrated hydrological models for each sub-basin (within ±5%), the aligned rainfall time series (hourly or smaller time steps) of the current rainfall events for each sub-basin are input into their respective calibrated hydrological models. Through embedded runoff calculations (such as full-storage runoff or infiltration-excess runoff mechanisms) and slope runoff calculations (such as the unit hydrograph method or linear reservoir method), the flow process lines at the outlet sections of each sub-basin are simulated and generated time-by-time. Then, for the river runoff paths from the outlet of each sub-basin to the reservoir dam site (or downstream target control section), the Muskingan method or time-delay algorithm is used to further analyze the flow. The river channel confluence calculation is performed. Based on the river length, cross-sectional hydraulic characteristics, and flood wave propagation velocity, the calculation parameters are determined. The outflow process lines of each sub-basin are propagated downstream along the river channel. Considering the river channel regulation effect and propagation time lag, the confluence flow process of each sub-basin outflow propagating to the target cross-section is calculated. Finally, the confluence flow processes of all sub-basins are algebraically superimposed at the target cross-section using the same time reference (that is, the contribution flows of each sub-basin at the same time are added together) to obtain the total inflow process line of the entire basin. This process line fully reflects the cumulative effect of runoff from each sub-region entering the reservoir through different paths within the entire basin.

[0040] For example, after obtaining the total inflow of the entire basin, the process further includes: Historical correction coefficients for each sub-basin are constructed based on the aforementioned deviation index; During the current flood event, real-time deviation coefficients for each sub-basin are constructed based on measured hydrological and rain gauge data and concurrent meteorological rainfall data. The current operating conditions of each sub-basin are obtained, and the current operating conditions are determined based on the rainfall intensity level and the previous humidity level. Based on the current operating conditions, the real-time meteorological rainfall data is corrected using the historical correction coefficient or the real-time deviation coefficient, and a scheduling instruction is generated based on the corrected rainfall data.

[0041] Specifically, after completing the prediction of the total inflow into the entire basin, this invention further extends the deviation quantification results to real-time scheduling decisions, constructing a bridge mechanism connecting deviation quantification with actual operation. This step first defines two core coefficients, and then combines them with rainfall intensity (level 3) and pre-congestion humidity (level 3) to form nine typical operating conditions, providing emergency scheduling guidance strategies for each, including: First, historical correction coefficients, i.e. long-term statistical coefficients, are constructed for each sub-basin: based on measured data from hydrological and rain gauge stations, historical data (at least 10 years) from meteorological and rain gauge stations in each sub-basin are statistically analyzed, and the average deviation characteristics are calculated according to different operating conditions. The formula for calculating the historical correction coefficients is as follows:

[0042] In the formula, This is the historical correction factor. A factor less than 1 indicates that the meteorological data is systematically overestimated. For example, the historical correction factor for a certain sub-basin under heavy rainfall and prior wet conditions is 0.70, which means that when the weather forecast is 100 mm, the historical pattern corresponds to an actual rainfall of about 70 mm.

[0043] Meanwhile, during the current flood evolution, the real-time deviation coefficient is calculated using measured rainfall data from hydrological stations and meteorological rainfall data for the corresponding time periods:

[0044] In the formula, This is the real-time deviation coefficient. This coefficient fluctuates greatly in the early stages of rainfall. When the cumulative rainfall exceeds 10 mm and the rate of change is less than 5% for 3 consecutive hours, it is considered to have converged and can be used for subsequent correction.

[0045] Then, the current operating conditions of each sub-basin were obtained, and the two factors that most significantly affect the rainfall-runoff relationship were combined. The results are shown in Table 1: Table 1. Classification Standards for Rainfall Intensity and Pre-Rainfall Moisture Level

[0046] In the above classification, rainfall intensity is divided into three levels based on daily rainfall, from light to heavy: light rain, moderate rain, and heavy rain or above. Anterior humidity is divided into three levels based on the amount of preceding rainfall, from low to high: dry, humid, and saturated. These three levels of rainfall intensity are paired with the three levels of anterior humidity (i.e., 3×3), resulting in 3×3=9 typical operating conditions. Historical correction coefficients are calculated for each operating condition, and different real-time deviation coefficient application strategies are developed.

[0047] Differentiated emergency dispatch guidance strategies were formulated for the above nine operating conditions. The core principle is: when the real-time deviation coefficient is not yet reliable, the historical correction coefficient should be used as the primary factor; when the real-time deviation coefficient is reliable, the real-time deviation coefficient should be used first, but the switching threshold can be adjusted according to the differences in operating conditions, as shown in Table 2. Table 2 Emergency Dispatch Guidance Strategies for 9 Operating Conditions

[0048] For example, the present invention further includes a post-scheduling evaluation and self-learning upgrade mechanism to ensure that the system can continuously optimize as operational data accumulates and maintain forecast accuracy and the effectiveness of scheduling guidance over the long term.

[0049] Specifically, for each rainfall event, dispatch instructions are generated according to the following process: The first step is to determine the current operating conditions, namely the rainfall intensity level and the previous humidity level, and then look up the historical correction coefficients for this sub-basin in the table. ; The second step is to determine whether the real-time deviation coefficient reliability zone has been entered. If the cumulative meteorological rainfall is less than 10 mm, enter scenario A and use the historical coefficient; if the cumulative meteorological rainfall is greater than or equal to 10 mm and If the change rate is less than 5% for 3 consecutive hours, it is considered reliable and can proceed to scenario B. In particular, for operating conditions 1 to 3 (light rain condition), since the deviation itself is very small, historical coefficients can be used directly without waiting for real-time coefficients. The third step is the scheduling strategy for scenario A, where the real-time coefficient is unreliable: using historical correction coefficients. The remaining rainfall in the weather forecast will be revised, and the pre-discharge flow will be implemented according to the lower limit of the warning level corresponding to the revised rainfall to leave sufficient safety margin. The warning notification will be implemented according to the revised level, and the downstream initial forecast will be informed that it is pending update. The fourth step, the scheduling strategy for scenario B, where the real-time coefficients are reliable, involves calculating the real-time deviation coefficient. Historical correction coefficient The relative deviation D=| - | / If D is less than 10%, it indicates consistency, and the following approach is adopted. Correct the remaining rainfall allocation to normal; if D is greater than or equal to 10%, it indicates a deviation, and K is set to min( , This means using a smaller coefficient, because a smaller coefficient implies a more severe weather forecast, a smaller corrected rainfall, and greater safety. Simultaneously, it triggers a manual check of data quality; if the check confirms no anomalies, the data continues to be used. If a weather station is found to be malfunctioning, the malfunctioning data will be removed and the calculation will be recalculated. The fifth step is to output the final dispatch instructions, including: the corrected total areal rainfall forecast, the predicted inflow and warning level, the pre-discharge start time and pre-discharge flow curve, the downstream notification level, and the next dispatch instruction refresh time (usually 1 hour later).

[0050] After each flood, the average deviation rate actually occurring in that event is recorded to update the historical correction coefficient for that condition. Specifically, a combination of sliding window and exponentially weighted moving average is used. The sliding window ensures that the sample used in the calculation is always data from the most recent few years (e.g., the last 10 years), while the exponentially weighted moving average assigns a higher weight to recent data. The two work together to maintain the timeliness of the statistical sample and avoid instability caused by parameter mutations. At the same time, the time when the real-time deviation coefficient reaches stability and the final stable value are stored in the database as a basis for optimizing the switching threshold. For example, if it is found that the real-time coefficient under a certain condition has been stable for a long time when the cumulative rainfall is much less than the preset threshold, the cumulative rainfall threshold for switching under that condition can be appropriately lowered to enable real-time correction earlier. Conversely, the threshold is raised to ensure statistical reliability.

[0051] Regarding the self-learning upgrade of the model and bias parameters, this invention further designs a multi-level incremental learning and adaptive adjustment mechanism: For bias parameters, a sliding window and exponentially weighted moving average are used for rolling updates, enabling historical correction coefficients to smoothly track the slow changes in watershed underlying surface conditions, meteorological station network layout, and equipment accuracy over time; For the LSTM hydrological forecasting model, an incremental learning strategy is implemented, freezing the bottom layer network to retain the learned general hydrological response characteristics, fine-tuning the top layer network to adapt to the latest watershed runoff dynamics, and introducing an experience playback mechanism to periodically reproduce historical typical samples during incremental learning to prevent catastrophic forgetting. At the same time, the model is periodically (e.g., annually or quarterly) fully retrained using all accumulated data to ensure the global optimality of the model structure; For sub-watershed division, an adaptive assessment is conducted every 3 years based on the latest topographic data, changes in hydrological station layout, and watershed underlying surface evolution. If it is found that the hydrological response characteristics of a certain sub-watershed have significantly differentiated or the representativeness of the stations has changed, the sub-watershed boundaries are adjusted accordingly or the sub-watersheds are re-merged or split.

[0052] The second objective of this invention is to provide a spatiotemporally aligned flood forecasting system based on multi-source precipitation data in small and medium-sized watersheds in mountainous areas, comprising: Sub-basin division module: used to acquire digital elevation model data of the target basin, and divide the target basin into multiple sub-basins based on the digital elevation model data; Condition judgment module: used to obtain meteorological and hydrological rain gauge data of each sub-basin and determine whether each sub-basin meets the alignment feasibility conditions; Spatial downscaling module: used to perform spatial downscaling processing on the meteorological rainfall station group data for each sub-basin that meets the alignment feasibility conditions, to obtain the meteorological surface rainfall time series of each sub-basin; Deviation quantification module: used to quantify the deviation between the meteorological surface rainfall time series of each sub-basin and the hydrological rain gauge data, and calculate the deviation index; Deviation correction module: used to correct the meteorological areal rainfall time series based on the deviation index to obtain aligned areal rainfall data; Flood forecasting module: It is used to input the aligned areal rainfall data into the hydrological model of each sub-basin to obtain the outlet flow of each sub-basin, and then superimpose them after river confluence calculation to obtain the total inflow of the entire basin.

[0053] This system significantly improves the accuracy and reliability of flood forecasting in small and medium-sized watersheds in mountainous areas, providing scientific and efficient technical support for flood control scheduling of hydropower stations. Specifically, the sub-watershed division module achieves refined decomposition of watershed spatial units, ensuring that all subsequent processing is based on partitioned units with independent hydrological response characteristics, avoiding the loss of spatial heterogeneous information caused by uniform processing across the entire watershed; the condition judgment module ensures that each participating sub-watershed has statistically valid sample support through station quantity quantification constraints, guaranteeing the reliability of deviation quantification results from the source; the spatial downscaling module uses Kriging interpolation to convert discrete station observations into continuous surface rainfall fields, fully leveraging the high spatiotemporal resolution of the meteorological station network and providing high-quality input for deviation quantification; the deviation quantification module comprehensively calculates multi-dimensional deviation indicators and classifies and statistically analyzes them according to different operating conditions. The study revealed the differential deviation patterns of meteorological precipitation relative to the hydrological baseline under different rainfall conditions, providing a precise data foundation for subsequent correction. The deviation correction module, by calling differentiated correction strategies according to different working conditions, ensured that the corrected areal rainfall sequence maintained a high degree of consistency with the hydrological baseline in terms of total amount, peak value, and process morphology, providing the hydrological model with high-quality rainfall input that combines high spatiotemporal resolution with the authority of the hydrological baseline. The flood forecasting module, through independent calibration of model parameters in each sub-basin and river confluence calculation, fully respected the spatial differences in runoff generation and confluence characteristics of different sub-basins and scientifically characterized the propagation process of flood waves in the river network. The final superimposed inflow process line of the entire basin fully reflects the cumulative effect of runoff generation in each region flowing into the reservoir through different paths.

[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 in the operation of a spatiotemporally aligned flood forecasting method based on multi-source precipitation data in small and medium-sized watersheds in mountainous areas.

[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 spatiotemporally aligned flood forecasting method for multi-source precipitation data in small and medium-sized watersheds in mountainous areas described 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 1 The 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 flood forecasting using spatiotemporal alignment of multi-source precipitation data in small and medium-sized watersheds in mountainous areas, characterized in that, Includes the following steps: Obtain digital elevation model data of the target watershed, and divide the target watershed into multiple sub-watersheds based on the digital elevation model data; Obtain meteorological and hydrological rainfall data from each sub-basin to determine whether each sub-basin meets the conditions for alignment feasibility. For each sub-basin that meets the alignment feasibility conditions, the meteorological rainfall station group data is spatially downscaled to obtain the meteorological surface rainfall time series of each sub-basin. The deviation between the meteorological surface rainfall time series of each sub-basin and the hydrological rain gauge data is quantified, and the deviation index is calculated. The meteorological areal rainfall time series is corrected based on the deviation index to obtain aligned areal rainfall data; The aligned areal rainfall data is input into the hydrological model of each sub-basin to obtain the outlet flow of each sub-basin. After river confluence calculation, the total inflow of the entire basin is obtained by superimposing the data.

2. The method for spatiotemporal alignment of multi-source precipitation data in small and medium-sized watersheds in mountainous areas according to claim 1, characterized in that, The process of acquiring digital elevation model data for the target watershed, and dividing the target watershed into multiple sub-watersheds based on the digital elevation model data, includes: Hydrological analysis tools were used to fill depressions in the digital elevation model data to obtain the digital elevation model data after depression filling. The flow direction of each grid is calculated based on the digital elevation model data after the depressions are filled, and the flow direction grid data is obtained. The flow accumulation of each grid is calculated based on the flow direction grid data to obtain the flow accumulation grid data; River network data is obtained by extracting the river network raster data based on the accumulated flow raster data. Based on the river network raster data and the location of the watershed outlet, the watershed boundary is extracted, and the target watershed is divided into multiple sub-watersheds.

3. The method for spatiotemporal alignment of multi-source precipitation data in small and medium-sized watersheds in mountainous areas according to claim 1, characterized in that, The process of acquiring meteorological and hydrological rainfall station data for each sub-basin and determining whether each sub-basin meets the alignment feasibility conditions includes: Each sub-basin is spatially associated with meteorological and hydrological rain gauge stations. Historical and real-time rainfall data from meteorological rain gauge stations within each sub-basin are collected as meteorological rain gauge station data for each sub-basin. Simultaneously, rainfall data from hydrological rain gauge stations within each sub-basin are collected as hydrological rain gauge station data for each sub-basin. Determine whether the number of meteorological rain gauge stations contained in each sub-basin is greater than or equal to a first preset threshold, and whether the number of hydrological rain gauge stations contained therein is greater than or equal to a second preset threshold. If yes, the sub-basin meets the alignment feasibility condition; otherwise, the sub-basin does not meet the alignment feasibility condition.

4. The method for spatiotemporal alignment of multi-source precipitation data in small and medium-sized watersheds in mountainous areas according to claim 1, characterized in that, For each sub-basin that meets the alignment feasibility condition, the meteorological rainfall station data is spatially downscaled to obtain the meteorological areal rainfall time series for each sub-basin, including: Each sub-basin that meets the alignment feasibility conditions is divided into multiple regular grids; The Kriging interpolation method is used, with the location coordinates of each meteorological rain gauge station in each sub-basin as known points and the center point of each grid as the interpolation point. The real-time rainfall data in the meteorological rain gauge station group data of each sub-basin is interpolated onto the multiple regular grids to obtain the interpolated rainfall of each grid at each time. The interpolated rainfall of all grids within each sub-basin at each time point is averaged by area weighting to obtain the meteorological surface rainfall of each sub-basin at each time point. The meteorological surface rainfall at each time point is then summarized to form the meteorological surface rainfall time series of each sub-basin.

5. The method for spatiotemporal alignment of multi-source precipitation data in small and medium-sized watersheds in mountainous areas according to claim 1, characterized in that, The step of quantifying the deviation between the meteorological surface rainfall time series and the hydrological rain gauge data of each sub-basin, and calculating the deviation index, includes: Align the meteorological surface rainfall time series of each sub-basin with the hydrological rain gauge data in time so that they correspond to the same time period, and obtain the meteorological surface rainfall data and the hydrological station rainfall data of the same period. Based on the concurrent meteorological areal rainfall data and the concurrent hydrological station rainfall data, independent rainfall events within each sub-basin are identified and extracted. For each independent rainfall event, the deviation rate, absolute deviation, cumulative deviation, and correlation coefficient are calculated to obtain the four-dimensional deviation index for each independent rainfall event. The four-dimensional deviation indicators were classified and statistically analyzed according to rainfall intensity level, rainfall duration and previous humidity level to obtain the deviation indicators of each sub-basin under each classification condition.

6. The method for spatiotemporal alignment of multi-source precipitation data in small and medium-sized watersheds in mountainous areas according to claim 1, characterized in that, The step of correcting the meteorological areal rainfall time series based on the deviation index to obtain aligned areal rainfall data includes: Based on the aforementioned deviation index, the time series of meteorological surface rainfall for each sub-basin at each time moment is corrected to obtain the corrected rainfall value for each sub-basin at each time moment. The corrected areal rainfall values ​​of each sub-basin at each time point are summarized to obtain the aligned areal rainfall data of each sub-basin.

7. The method for spatiotemporal alignment of multi-source precipitation data in small and medium-sized watersheds in mountainous areas according to claim 1, characterized in that, The aligned areal rainfall data is input into the hydrological models of each sub-basin to obtain the outflow of each sub-basin. After river confluence calculation, the total inflow of the entire basin is obtained by superimposing the data, including: Obtain the initial hydrological model and historical runoff data for each sub-basin; Based on the aligned areal rainfall data and historical runoff data of each sub-basin, the parameters of the initial hydrological models of multiple sub-basins are calibrated to obtain the calibrated hydrological models of each sub-basin. The aligned areal rainfall data of each sub-basin are input into the calibrated hydrological model of the corresponding sub-basin to obtain the outflow of each sub-basin; The outflow of each sub-basin is calculated by the river confluence calculation to obtain the process of the outflow of each sub-basin propagating to the downstream target section. The total inflow of the entire basin is obtained by superimposing the flow processes of each sub-basin at the same time.

8. The method for spatiotemporal alignment of multi-source precipitation data in small and medium-sized watersheds in mountainous areas according to claim 1, characterized in that, After obtaining the total inflow of the entire basin, the process also includes: Historical correction coefficients for each sub-basin are constructed based on the aforementioned deviation index; During the current flood event, real-time deviation coefficients for each sub-basin are constructed based on measured hydrological and rain gauge data and concurrent meteorological rainfall data. The current operating conditions of each sub-basin are obtained, and the current operating conditions are determined based on the rainfall intensity level and the previous humidity level. Based on the current operating conditions, the real-time meteorological rainfall data is corrected using the historical correction coefficient or the real-time deviation coefficient, and a scheduling instruction is generated based on the corrected rainfall data.

9. A method for spatiotemporal alignment of multi-source precipitation data in small and medium-sized watersheds in mountainous areas according to claim 8, characterized in that, Also includes: After each flood, the actual deviation data of the flood is obtained, the historical correction coefficients are updated, and the hydrological models of each sub-basin are incrementally updated.

10. A spatiotemporally aligned flood forecasting system based on multi-source precipitation data in small and medium-sized watersheds in mountainous areas, characterized in that, include: Sub-basin division module: used to acquire digital elevation model data of the target basin, and divide the target basin into multiple sub-basins based on the digital elevation model data; Condition judgment module: used to obtain meteorological and hydrological rain gauge data of each sub-basin and determine whether each sub-basin meets the alignment feasibility conditions; Spatial downscaling module: used to perform spatial downscaling processing on the meteorological rainfall station group data for each sub-basin that meets the alignment feasibility conditions, to obtain the meteorological surface rainfall time series of each sub-basin; Deviation quantification module: used to quantify the deviation between the meteorological surface rainfall time series of each sub-basin and the hydrological rain gauge data, and calculate the deviation index; Deviation correction module: used to correct the meteorological areal rainfall time series based on the deviation index to obtain aligned areal rainfall data; Flood forecasting module: It is used to input the aligned areal rainfall data into the hydrological model of each sub-basin to obtain the outlet flow of each sub-basin, and then superimpose them after river confluence calculation to obtain the total inflow of the entire basin.

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