Mountain torrent forecasting method suitable for different formation mechanisms

Abstract

The invention discloses a mountain torrent forecasting method suitable for different formation mechanisms. The mountain torrent forecasting method comprises the following steps: step 1, collecting and sorting basic data of a research area; step 2, determining a mountain torrent type partition and a water catchment topological relation of the mountain torrent type partition; 3, constructing a multi-type mountain torrent prediction and forecast integrated model; 4, determining an optimal parameter set and an evaluation index of the integrated model; and 5, forecasting the mountain torrent process of each mountain torrent type partition and exit. According to the method, the difference of mountain torrent formation mechanisms is considered, a multi-type mountain torrent prediction and forecast integrated model with a physical mechanism and a parameter optimization strategy and an evaluation index of the multi-type mountain torrent prediction and forecast integrated model are provided, and traditional single-type mountain torrent prediction and forecast is expanded to multi-type mountain torrent prediction and forecast. According to the method, the main control factors of the formation, development and disaster-causing process of the mountain torrents in any region and the prediction and forecasting process of the main control factors can be provided, the physical process of formation and development of different types of mountain torrents can be accurately predicted, the applicability is high, and making of mountain torrent disaster defense decisions can be better supported.

Description

Technical Field

[0001] The invention belongs to the technical field of hydrological and meteorological forecasting, and in particular relates to a flash flood prediction method suitable for different formation mechanisms. Background Art

[0002] Flash floods are the most devastating flood disaster in my country, causing casualties and economic losses. They are characterized by widespread occurrence, suddenness, high mortality rates, and significant regional variation. With the increasing number of extreme precipitation events, the risk of flash floods continues to increase, making accurate flash flood prediction and forecasting a crucial technical tool for improving flash flood preparedness. However, due to the dual nature of flash floods, their formation, development, and harm are the result of the combined effects of multiple factors, including rainfall, the underlying surface, and human activities. These factors exhibit high spatial and temporal variability, leading to complex flash flood formation and evolution mechanisms in small watersheds across different regions. Different types of flash floods also vary in their causes, hazard potential, and destructiveness. Ignoring these diverse flash flood causal mechanisms will inevitably lead to uncertainty in flash flood forecasting.

[0003] Currently, single hydrological or hydrodynamic models are primarily used to simulate and predict flash floods in small watersheds, focusing primarily on a single type of flash flood process, such as those caused by frequent, short-duration, heavy rainfall. However, recent flash flood disasters have demonstrated that the interplay and compounding effects of processes such as sudden flood surges, sediment erosion, and glacier melt in small watersheds are prominent, leading to the frequent occurrence of minor rainfall events leading to major disasters. Consequently, prediction methods for a single flash flood type fail to account for the differences in flash flood causal mechanisms, making it difficult to accurately reproduce the occurrence and development of different flash flood types. This results in low flash flood prediction accuracy, severely impacting decision-making regarding flash flood disaster preparedness. Therefore, there is an urgent need to develop flash flood prediction methods that are appropriate for different causal mechanisms to improve flash flood prediction accuracy and reduce flash flood risks and losses. Summary of the Invention

[0004] The purpose of the present invention is to provide a flash flood prediction and forecasting method suitable for different formation mechanisms to solve the above technical problems.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The present invention discloses a flash flood prediction method suitable for different cause mechanisms, the method comprising the following steps:

[0007] Step 1. Collect and organize basic data of the study area: Collect and organize various basic data of the study area, including multi-source precipitation, water level process, flow process, sediment load process, snowmelt process, meteorology, distribution of villages along the river, channel measurement, basic geographic information and historical mountain torrent disaster investigation data; the multi-source precipitation data include station observation precipitation, radar quantitative estimation precipitation, satellite inversion precipitation data; the historical mountain torrent disaster investigation data include the number of deaths / missing persons caused by the disaster, the number of collapsed houses, direct economic losses, flood mark point data, and inundated area; the flood mark point data include the location of the flood mark point, the occurrence of the flood mark point, and the location of the flood mark point. The flood occurrence time and flood mark elevation were calculated; the maximum values ​​were extracted from the water level process, flow process and sediment load process of each event respectively to determine the flood peak water level, flood peak flow and sediment load peak value of each event; the snowmelt process of each event was summed to determine the total snowmelt of each event; then, based on the basic geographic information data of the study area and the distribution data of villages along the river, the basic calculation units of the small watershed were divided according to the set area threshold, and the water system distribution and its water catchment topological relationship were extracted; based on the divided basic calculation units of the small watershed, the watershed units were divided according to the set catchment area threshold and the distribution data of villages along the river, and the water catchment topological relationship of each watershed unit was established;

[0008] Step 2: Determine the flash flood type zoning and its catchment topological relationship: First, based on the flood peak water level, flood peak flow, sediment load peak, total snowmelt volume of each event in the study area and historical flash flood disaster survey data, analyze the main controlling factors of the flash flood formation, development and disaster-causing process in each basin; then, based on the main controlling factors and their values ​​of each basin unit, use a clustering algorithm to determine the flash flood type zoning in the study area; the flash flood type zoning includes rainstorm flash flood zoning, snowmelt flash flood zoning, water-sand flash flood zoning, rain-snow composite flash flood zoning, and snow-sand composite flash flood zoning; then, based on the catchment topological relationship of each basin unit and the determined flash flood type zoning, establish the catchment topological relationship of each flash flood type zoning;

[0009] Step 3. Construct a multi-type mountain torrent prediction and forecasting integrated model: set the model time step to hours or minutes, the model spatial scale to the basic calculation unit of the small watershed, establish an immediate short-term numerical precipitation forecast model, a nonlinear runoff generation and confluence model, a snowmelt generation and confluence model, and a non-uniform flow mountain torrent and sediment one- and two-dimensional hydrodynamic model, and determine the input and output data of each model; the input data of the immediate short-term numerical precipitation forecast model are radar quantitative estimation precipitation and satellite inversion precipitation data, and the output data are short-term forecast precipitation data; the input data of the nonlinear runoff generation and confluence model are site observed precipitation and meteorological data, and the output data are channel flow process data; the input data of the snowmelt generation and confluence model are site observed precipitation and meteorological data, and the output data are channel flow and snowmelt process data; the input data of the non-uniform flow mountain torrent and sediment one- and two-dimensional hydrodynamic model are site observed precipitation and channel measurement data, and the output data are channel flow, water level, sediment load and flood depth process data;

[0010] The input and output interfaces and model structures of each established model were standardized to build a full-type flash flood model library; according to the determined flash flood type zoning, the corresponding calculation model was selected from the full-type flash flood model library, and the calculation model was combined according to the order of precipitation-runoff-flood evolution in the small watershed to establish a flash flood prediction model for each flash flood type zoning; then, based on the output data of the flash flood prediction model of each zoning, the maximum values ​​were extracted from the channel water level process, flow process, sediment load process and inundation depth process respectively to determine the predicted peak water level, peak flow, sediment load peak value and maximum inundation depth of each zoning, and the channel snowmelt process was summed to determine the total predicted snowmelt of each zoning; according to the established watershed topological relationship of each flash flood type zoning, the upstream type zoning model and the downstream type zoning model of each zoning flash flood prediction model were determined to build a multi-type flash flood prediction integrated model for the study area;

[0011] Step 4: Determine the optimal parameter set and evaluation indicators for the integrated model: Based on the established catchment topology of each flash flood type zone, calibrate the flash flood prediction model for each flash flood type zone one by one in the order of upstream first and downstream, and determine the optimal parameter set for the integrated model of multi-type flash flood prediction;

[0012] The weighted comprehensive index is used to evaluate the accuracy of the integrated model for multi-type flash flood prediction and forecasting and the flash flood prediction and forecasting model for each sub-region. The optimal value is 1, and the calculation formula is:

[0013]

[0014] Where g is the weighted comprehensive index of the integrated model for multi-type flash flood forecasting; m is the number of flash flood type zones in the study area; i is the i-th flash flood type zone with station observation data and flood trace point data in the study area, 1≤i≤m; gi and α i are the weighted comprehensive index and its weight of the i-th partition, RMSE i is the root mean square error of precipitation in the ith partition, P o,j,i and P s,j,i are the observed precipitation at the station at the jth moment in the ith subarea and the short-term forecast precipitation output by the model; M i is the sequence length of the precipitation data observed at the station in the ith partition; |Re i | is the absolute value of the relative error of flood peak flow, flood peak water level, sediment load peak, snowmelt, or flood depth in the ith subarea. Q o,p,i is the peak flow or peak water level or peak sediment load or total snowmelt or flood mark elevation of the ith zone determined by the site observation data; Q s,p,i The model predicts the peak flow or peak water level or peak sediment load or total snowmelt or maximum flooding depth for the i-th zone; NSE i is the Nash efficiency coefficient of the flow process line, the water level process line, the sediment load process line, or the snowmelt process line of the i-th partition, Q o,j,i is the observed flow, water level, sediment load, or snowmelt at the jth moment in the i-th partition; Q s,j,i The model predicts the flow or water level or sediment load or snowmelt for the jth moment in the i-th partition; is the mean flow, water level, sediment load or snowmelt observed at the station in the ith zone; N i is the length of the sequence of observed flow, water level, sediment load or snowmelt at the station in the i-th partition; γ i is the precipitation assessment index weight of the i-th partition; β i is the weight of the evaluation indicator of flow, water level, sediment load, snowmelt or flood depth in the ith zone;

[0015] Step 5. Forecast the flash flood process for each flash flood type zone and outlet: Based on the multi-source precipitation data, meteorological data, and channel measurement data collected in step 1, drive the multi-type flash flood prediction and forecasting integrated model to forecast the short-term precipitation, channel flow, water level, sediment load, snowmelt, and flooding depth for each flash flood type zone and outlet in the study area.

[0016] Furthermore, the water level, flow, sediment load and snowmelt process data in step 1 are all site observation data; the meteorological data include site observation temperature, soil moisture, water surface evaporation, sunshine hours, and wind speed data; the channel measurement data include cross-section, longitudinal section, and bridge and culvert measurement data; the basic geographic information data include DEM of no less than 1:50,000, land use type vector data, and soil texture type vector data; the set area threshold is 50km 2 The set catchment area threshold is 200km 2 .

[0017] Furthermore, the specific process of establishing the watershed topological relationship of each basin unit in step 1 is: taking the basin unit where the river source is located as the source basin, and according to the watershed topological relationship of the water system, determining the river into which the river at the outlet of the source basin converges, the basin unit where the river is located is the downstream basin of the source basin, and repeating the above process until there is no river into which the river at the outlet of the basin unit converges.

[0018] Furthermore, the specific process of analyzing the main controlling factors of the formation, development and disaster-causing processes of mountain torrents in each river basin as described in step 2 is as follows: using multivariate data mining technology, analyzing the contribution rates of the single factors of the peak water level, peak flow, peak sediment load, and total snowmelt of each mountain torrent disaster in each river basin unit to the corresponding disaster data of the number of deaths / missing persons, the number of collapsed houses, the direct economic loss, the elevation of the maximum flood mark point, and the maximum inundated area, as well as the contribution rates of multiple basin combination factors of each mountain torrent disaster in each river basin unit to the corresponding disaster data, including the combination of peak flow and total snowmelt, the combination of peak flow, peak water level and peak sediment load, and the combination of total snowmelt, peak water level and peak sediment load; if the contribution rate of a single basin factor exceeds 0.5, the single basin factor is the main controlling factor of the formation, development and disaster-causing processes of mountain torrents in each river basin; if the contribution rate of the basin combination factor is greater than the sum of the contribution rates of the single factors, the basin combination factor is the main controlling factor of the mountain torrents in the basin;

[0019] The contribution rate calculation formula is:

[0020]

[0021] In the formula, CR(X i ,Y i ) is the contribution rate of the factor X of the i-th watershed unit to the disaster data Y, 1≤i≤bsn, bsn is the number of watershed units in the study area; X i is the factor matrix of the ith watershed unit, X i ∈X bsn×c×dx , dx is the number of factors, if it is a single factor, dx = 1, if it is a combination factor, dx is the number of combination factors, c is the number of games; Y i ∈Ybsn×c×dy , dy is the number of indicators of disaster data, dy = 5; σ(·) 2 is the overall variance.

[0022] Furthermore, the specific process of establishing the watershed topological relationship of each flash flood type partition described in step 2 is: starting from the source basin, analyze according to the watershed order from the upstream basin to the downstream basin. If the flash flood type partition of a basin unit is recorded as partition A and the flash flood type partition of its downstream basin unit is recorded as partition B, then the downstream type partition of partition A is determined to be partition B, and the upstream type partition of partition B is partition A; if the two are consistent, partition A and partition B are merged into a virtual partition, and continue to search the downstream basin unit until the downstream basin unit that is inconsistent with its flash flood type partition is found; traverse all basin units until the basin unit at the exit of the study area, and finally determine the upstream type partition and downstream type partition of each flash flood type partition.

[0023] Furthermore, the specific process of standardizing the input and output interfaces and model structures of each established model described in step 3 is as follows: standardizing the site-scale input data according to the data format of site code, site longitude, site latitude, time step, site-observed precipitation, meteorology, snowmelt, and sediment load; standardizing the radar quantitative estimation precipitation and satellite inversion precipitation data according to the MICAPS Class 4 grid data format; standardizing the channel measurement data according to the data format of section number, longitude, latitude, and elevation; standardizing the output short-term forecast precipitation data according to the MICAPS Class 4 grid data format; standardizing the output channel flow process data according to the data format of model time step, current time step, model code, channel code, flow, water level, snowmelt, sediment load, and submerged depth; standardizing the model file according to basic information, parameter information, and model information; using a unified input and output interface to form a dynamic integration framework for various models, and performing model registration, deployment, packaging, release, and call.

[0024] Furthermore, the specific process of constructing the integrated model for multi-type flash flood prediction and forecasting in the study area described in step 3 is: for flash flood type partitions with multiple upstream type partitions, the inundation water depth process of all upstream basin partitions is taken as the upper boundary of the flash flood type partition, and the sum of the remaining model calculation results of all upstream type partitions except the inundation water depth is taken as the channel input value of the flash flood type partition; for flash flood type partitions with multiple downstream type partitions, the inundation water depth process of the flash flood type partition is taken as the lower boundary of all downstream type partitions, and the remaining channel output values ​​of the flash flood type partition except the inundation water depth are determined according to the channel water-passing cross-sectional area weight of the downstream type partition.

[0025] Furthermore, the specific process of determining the optimal parameter set of the integrated model for multi-type flash flood prediction and forecasting described in step 4 is: using the flow process data collected in step 1 to optimize the parameters of the flash flood prediction and forecasting model for the rainstorm type flash flood zoning, using the flow and snowmelt process data to optimize the parameters of the flash flood prediction and forecasting model for the snowmelt type and rain-snow composite flash flood zoning, using the flow, water level and sediment load process data and flood mark data to optimize the parameters of the flash flood prediction and forecasting model for the water-sand type flash flood zoning, using the flow, water level, snowmelt and sediment load process data and flood mark data to optimize the parameters of the flash flood prediction and forecasting model for the snow-sand composite flash flood zoning, and determining the model optimal parameters of the corresponding zoning respectively; according to the determined model optimal parameters of each zoning, using the parameter regionalization analysis method to determine the model parameters of the flash flood zoning that lacks station observation data and flood mark data; and determining the optimal parameter set of the integrated model with the flash flood type zoning as the unit.

[0026] Furthermore, the parameter regionalization analysis method is a spatial proximity method, an attribute similarity method, a parameter regression method or a classification and regression method.

[0027] The beneficial effects of the present invention are: the method of the present invention takes into account the differences in the causal mechanisms of flash floods, adopts a method combining multivariate data mining technology and multiple numerical simulation technologies, and proposes a multi-type flash flood prediction and forecasting integrated model with physical mechanisms and its parameter optimization strategy and evaluation indicators, and expands the traditional single-type flash flood prediction and forecasting to multi-type flash flood prediction and forecasting, and can provide the main controlling factors of the formation, development and disaster-causing process of flash floods in any area and their prediction and forecasting process, accurately predict the physical process of the formation and development of different types of flash floods, and the analysis is more comprehensive and highly applicable, which can better support the decision-making of flash flood disaster prevention.

[0028] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Schematic diagram of the process of the present invention;

[0030] Figure 2 Schematic diagram of the process of determining flash flood type zoning and its catchment topology;

[0031] Figure 3 Schematic diagram of the process of building an integrated model for multi-type flash flood prediction and forecasting. DETAILED DESCRIPTION

[0032] The present invention discloses a flash flood prediction method suitable for different cause mechanisms, such as Figures 1 to 3 As shown, the method includes the following steps:

[0033] Step 1: Collect and organize basic data of the study area.

[0034] Various basic data of the study area were collected and sorted, mainly including multi-source precipitation, water level process, flow process, sediment load process, snowmelt process, meteorology, distribution of villages along the river, channel measurement, basic geographic information and historical mountain torrent disaster investigation data. The maximum values ​​were extracted from the water level process, flow process and sediment load process of each session, and the peak water level, peak flow and sediment load peak value of each session were determined. The snowmelt process of each session was summed to determine the total snowmelt amount of each session. Among them, multi-source precipitation data included station observation precipitation, radar quantitative estimation precipitation and satellite inversion precipitation data; water level, flow process and sediment load process of each session were extracted, and the peak values ​​of flood peak, flood peak and sediment load of each session were determined. The process data of water volume, sediment load and snowmelt are all station observation data; meteorological data include station-observed temperature, soil moisture, water surface evaporation, sunshine hours, wind speed, etc.; channel measurement data include cross-section, longitudinal section, bridge and culvert measurement data, etc.; basic geographic information data include DEM of no less than 1:50,000, land use type vector data, soil texture type vector data, etc.; historical mountain torrent disaster investigation data include the number of deaths / missing persons caused by the disaster, the number of collapsed houses, direct economic losses, flood mark point data, inundated area, etc.; flood mark point data include flood mark point location, flood mark point occurrence time, and flood mark point elevation.

[0035] Then, based on the basic geographic information data of the study area and the distribution data of villages along the river, the 2 As the area threshold, the basic calculation unit of the small watershed is divided to extract the water system distribution and its catchment topological relationship; based on the divided basic calculation unit of the small watershed, according to the 200km 2 The catchment area threshold and the distribution data of villages along the river are used to divide the watershed units, and the watershed topology relationship of each watershed unit is established. Specifically, the watershed unit where the river source is located is the source watershed. According to the watershed topology relationship of the water system, the river into which the river at the outlet of the source watershed flows is determined. The watershed unit where the river is located is the downstream watershed of the source watershed. The above process is repeated until there is no river into which the river at the outlet of the watershed unit flows.

[0036] Step 2: Determine the flash flood type zones and their catchment topology.

[0037] First, based on the flood peak water level, flood peak flow, sediment load peak, total snowmelt volume and historical mountain torrent disaster survey data in the study area, the main controlling factors of the formation, development and disaster-causing process of mountain torrents in each basin are analyzed. The specific process is: using multivariate data mining technologies such as geographic detectors and sorting analysis, the contribution rates of the single factors of peak water level, peak flow, sediment load peak, and total snowmelt of each flash flood disaster in each watershed unit to the corresponding disaster data such as the number of deaths / missing persons, number of collapsed houses, direct economic losses, maximum flood mark elevation, and maximum inundated area are analyzed, as well as the contribution rates of multiple basin combination factors of each flash flood disaster in each watershed unit to the corresponding disaster data, including the combination of peak flow and total snowmelt, the combination of peak flow, peak water level and sediment load peak, and the combination of total snowmelt, peak water level and sediment load peak; if the contribution rate of a single basin factor exceeds 0.5, the basin single factor is the main controlling factor in the formation, development, and disaster-causing process of flash floods in each basin; if the contribution rate of the basin combination factor is greater than the sum of the contribution rates of the single factors, the basin combination factor is the main controlling factor of the flash floods in the basin. The contribution rate calculation formula is shown as follows:

[0038]

[0039] In the formula, CR(X i ,Y i ) is the contribution rate of the factor X of the i-th watershed unit to the disaster data Y, 1≤i≤bsn, bsn is the number of watershed units in the study area; X i is the factor matrix of the ith watershed unit, X i ∈X bsn×c×dx , dx is the number of factors, if it is a single factor, dx = 1, if it is a combination factor, dx is the number of combination factors, c is the number of games; Y i ∈Y bsn×c×dy , dy is the number of indicators of disaster data, dy = 5; σ(·) 2 is the overall variance.

[0040] Then, according to the main controlling factors and their values ​​of each watershed unit, a clustering algorithm is used to determine the main types of mountain torrents in the study area and their divisions. The main mountain torrent types include: rainstorm type, snowmelt type, water-sand type, rain-snow composite type, and snow-sand composite type; the corresponding mountain torrent type divisions are: rainstorm type mountain torrent division, snowmelt type mountain torrent division, water-sand type mountain torrent division, rain-snow composite type mountain torrent division, and snow-sand composite type mountain torrent division; then, according to the watershed topological relationship of each watershed unit and the determined mountain torrent type division, the watershed topological relationship of each mountain torrent type division is established, specifically: starting from the source basin, according to the direction from the upstream basin to the downstream, the watershed topological relationship of each mountain torrent type division is established. The order of water collection in the upstream basin is analyzed. If the flash flood type partition of a basin unit (denoted as partition A) is inconsistent with the flash flood type partition of its downstream basin unit (denoted as partition B), the downstream type partition of partition A is determined to be partition B, and the upstream type partition of partition B is partition A. If the two are consistent, partition A and partition B are merged into a virtual partition, and the search continues to the downstream basin unit until a downstream basin unit that is inconsistent with its flash flood type partition is found. All basin units are traversed until the basin unit at the exit of the study area, and finally the upstream type partition and downstream type partition of each flash flood type partition are determined.

[0041] Step 3: Build an integrated model for multi-type flash flood prediction and forecasting.

[0042] Set the model time step to hours or minutes, the model spatial scale to the basic calculation unit of the small watershed, establish a near-term numerical precipitation forecast model, a nonlinear runoff generation and confluence model, a snowmelt runoff generation and confluence model, and a one- and two-dimensional hydrodynamic model of non-uniform flow and mountain floods and sediments, and determine the input and output data of each model; among them, the input data of the near-term numerical precipitation forecast model are radar quantitative estimated precipitation and satellite inversion precipitation data, and the output data are short-term forecast precipitation data; the input data of the nonlinear runoff generation and confluence model are station observed precipitation and meteorological data, and the output data are channel flow process data; the input data of the snowmelt runoff generation and confluence model are station observed precipitation and meteorological data, and the output data are channel flow and snowmelt process data; the input data of the one- and two-dimensional hydrodynamic model of non-uniform flow and mountain floods and sediments are station observed precipitation and channel measurement data, and the output data are channel flow, water level, sediment load, and flood depth process data.

[0043] The input and output interfaces and model structures of each established model are standardized. Specifically, the site-scale input data are standardized according to the data formats of site code, site longitude, site latitude, time step, site-observed precipitation, meteorology, snowmelt, and sediment load; the radar quantitative estimation of precipitation and satellite inversion precipitation data are standardized according to the MICAPS Class 4 grid data format; the channel measurement data are standardized according to the data formats of section number, longitude, latitude and elevation; the output short-term forecast precipitation data are standardized according to the MICAPS Class 4 grid data format; the output channel flow process data are standardized according to the data formats of model time step, current time step, model code, channel code, flow, water level, snowmelt, sediment load, and inundation depth; the model files are standardized according to basic information, parameter information, and model information; a unified input and output interface is used to form a dynamic integration framework for various models, and model registration, deployment, packaging, release, and calling are carried out; and then a full-type flash flood model library is constructed.

[0044] Based on the determined flash flood type zones, corresponding computational models were selected from the full flash flood model library. These models were combined according to the sequence of precipitation, runoff generation, and flood evolution in the small watershed to establish flash flood prediction and forecasting models for each flash flood type zone, as shown in Table 1. Based on the output data from the flash flood prediction and forecasting models for each zone, the maximum values ​​were extracted from the channel water level, flow, sediment load, and inundation depth processes to determine the predicted peak flood level, peak flow, sediment load peak, and maximum inundation depth for each zone. The total predicted snowmelt volume for each zone was then determined by summing the channel snowmelt volume processes.

[0045] Table 1 List of calculation model combinations involved in different flash flood type zoning

[0046]

[0047]

[0048] Based on the established watershed topological relationship of each flash flood type zone, the upstream type zone model and downstream type zone model of the flash flood prediction model of each zone are determined, and a multi-type flash flood prediction integrated model for the study area is constructed. The integration strategy is: for flash flood type zones with multiple upstream type zones, the submerged water depth process of all upstream basin zones is taken as the upper boundary of the flash flood type zone, and the sum of the model calculation results of all upstream type zones except the submerged water depth is taken as the channel input value of the flash flood type zone; for flash flood type zones with multiple downstream type zones, the submerged water depth process of the flash flood type zone is taken as the lower boundary of all downstream type zones, and the channel output values ​​of the flash flood type zone except the submerged water depth are determined according to the channel water-passing cross-sectional area weight of the downstream type zone.

[0049] Step 4: Determine the optimal parameter set and evaluation indicators for the integrated model.

[0050] According to the established water catchment topological relationship of each flash flood type zone, the flash flood prediction model of each flash flood type zone is calibrated one by one in the order of upstream first and downstream, and the parameter optimization strategy of the integrated model of multi-type flash flood prediction and forecasting in the study area is formed. Specifically, the station observation flow process data collected in step 1 is used to optimize the parameters of the flash flood prediction and forecast model for the rainstorm type flash flood zone, the station observation flow and snowmelt process data is used to optimize the parameters of the flash flood prediction and forecast model for the snowmelt type and rain-snow composite flash flood zone, and the station observation flow, water level and sediment load process data and flood mark point data are used to optimize the parameters of the flash flood prediction and forecast model for the snowmelt type and rain-snow composite flash flood zone. Parameters of flash flood prediction and forecasting models for water-sand type flash floods are optimized, and parameters of flash flood prediction and forecasting models for snow-sand composite flash floods are optimized using site observation flow, water level, snowmelt and sediment load process data as well as flood mark data, and the model optimization parameters of the corresponding zones are determined respectively; based on the determined model optimization parameters of each zone, parameter regionalization analysis methods such as spatial proximity method, attribute similarity method, parameter regression method or classification and regression number method are used to determine the model parameters of flash flood zones that lack site observation data and flood mark data; and the integrated model optimization parameter set is determined with the flash flood type zone as the unit.

[0051] The weighted comprehensive index is used to evaluate the accuracy of the integrated model and the partition model. The optimal value is 1. The calculation formula is as follows:

[0052]

[0053] Where g is the weighted comprehensive index of the integrated model for multi-type flash flood forecasting; m is the number of flash flood type zones in the study area; i is the i-th flash flood type zone with station observation data and flood trace point data in the study area, 1≤i≤m; g i and α i are the weighted comprehensive index and its weight of the i-th partition, RMSE i is the root mean square error of precipitation in the ith partition, P o,j,i and P s,j,i are the observed precipitation at the station at the jth moment in the ith subarea and the short-term forecast precipitation output by the model; M i is the sequence length of the precipitation data observed at the station in the ith partition; |Re i | is the absolute value of the relative error of flood peak flow, flood peak water level, sediment load peak, snowmelt, or flood depth in the ith subarea. Q o,p,iis the peak flow or peak water level or peak sediment load or total snowmelt or flood mark elevation of the ith zone determined by the site observation data; Q s,p,i The model predicts the peak flow or peak water level or peak sediment load or total snowmelt or maximum flooding depth for the i-th zone; NSE i is the Nash efficiency coefficient of the flow process line, the water level process line, the sediment load process line, or the snowmelt process line of the i-th partition, Q o,j,i is the observed flow, water level, sediment load, or snowmelt at the jth moment in the i-th partition; Q s,j,i The model predicts the flow or water level or sediment load or snowmelt for the jth moment in the i-th partition; is the mean flow, water level, sediment load or snowmelt observed at the station in the ith zone; N i is the length of the sequence of observed flow, water level, sediment load or snowmelt at the station in the i-th partition; γ i is the precipitation assessment index weight of the i-th partition; β i is the weight of the evaluation index of flow, water level, sediment load, snowmelt or flood depth in the i-th zone.

[0054] Step 5: Forecast the flash flood process for each flash flood type zone and outlet.

[0055] Based on the multi-source precipitation data, meteorological data, and channel measurement data collected in step 1, the multi-type flash flood prediction and forecasting integrated model is driven to forecast the short-term forecast precipitation, channel flow, water level, sediment load, snowmelt, and flooding depth for each flash flood type zone in the study area and the outlet of the study area.

[0056] Finally, it should be noted that the above description is only used to illustrate the technical solution of the present invention and is not intended to limit it. Although the present invention has been described in detail with reference to the preferred arrangement scheme, those skilled in the art should understand that the technical solution of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A flash flood prediction method suitable for different cause mechanisms, characterized by: The method comprises the following steps: Step 1. Collect and organize basic data of the study area: Collect and organize various basic data of the study area, including multi-source precipitation, water level process, flow process, sediment load process, snowmelt process, meteorology, distribution of villages along the river, channel measurement, basic geographic information and historical mountain torrent disaster investigation data; the multi-source precipitation data include station observation precipitation, radar quantitative estimation precipitation, satellite inversion precipitation data; the historical mountain torrent disaster investigation data include the number of deaths / missing persons caused by the disaster, the number of collapsed houses, direct economic losses, flood mark point data, and inundated area; the flood mark point data include the location of the flood mark point, the occurrence of the flood mark point, and the location of the flood mark point. The flood occurrence time and flood mark elevation were calculated; the maximum values ​​were extracted from the water level process, flow process and sediment load process of each event respectively to determine the flood peak water level, flood peak flow and sediment load peak value of each event; the snowmelt process of each event was summed to determine the total snowmelt of each event; then, based on the basic geographic information data of the study area and the distribution data of villages along the river, the basic calculation units of the small watershed were divided according to the set area threshold, and the water system distribution and its water catchment topological relationship were extracted; based on the divided basic calculation units of the small watershed, the watershed units were divided according to the set catchment area threshold and the distribution data of villages along the river, and the water catchment topological relationship of each watershed unit was established; Step 2: Determine the flash flood type zoning and its catchment topological relationship: First, based on the flood peak water level, flood peak flow, sediment load peak, total snowmelt volume of each event in the study area and historical flash flood disaster survey data, analyze the main controlling factors of the flash flood formation, development and disaster-causing process in each basin; then, based on the main controlling factors and their values ​​of each basin unit, use a clustering algorithm to determine the flash flood type zoning in the study area; the flash flood type zoning includes rainstorm flash flood zoning, snowmelt flash flood zoning, water-sand flash flood zoning, rain-snow composite flash flood zoning, and snow-sand composite flash flood zoning; then, based on the catchment topological relationship of each basin unit and the determined flash flood type zoning, establish the catchment topological relationship of each flash flood type zoning; Step 3. Construct a multi-type mountain torrent prediction and forecasting integrated model: set the model time step to hours or minutes, the model spatial scale to the basic calculation unit of the small watershed, establish an immediate short-term numerical precipitation forecast model, a nonlinear runoff generation and confluence model, a snowmelt generation and confluence model, and a non-uniform flow mountain torrent and sediment one- and two-dimensional hydrodynamic model, and determine the input and output data of each model; the input data of the immediate short-term numerical precipitation forecast model are radar quantitative estimation precipitation and satellite inversion precipitation data, and the output data are short-term forecast precipitation data; the input data of the nonlinear runoff generation and confluence model are site observed precipitation and meteorological data, and the output data are channel flow process data; the input data of the snowmelt generation and confluence model are site observed precipitation and meteorological data, and the output data are channel flow and snowmelt process data; the input data of the non-uniform flow mountain torrent and sediment one- and two-dimensional hydrodynamic model are site observed precipitation and channel measurement data, and the output data are channel flow, water level, sediment load and flood depth process data; The input and output interfaces and model structures of each established model were standardized to build a full-type flash flood model library; according to the determined flash flood type zoning, the corresponding calculation model was selected from the full-type flash flood model library, and the calculation model was combined according to the order of precipitation-runoff-flood evolution in the small watershed to establish a flash flood prediction model for each flash flood type zoning; then, based on the output data of the flash flood prediction model of each zoning, the maximum values ​​were extracted from the channel water level process, flow process, sediment load process and inundation depth process respectively to determine the predicted peak water level, peak flow, sediment load peak value and maximum inundation depth of each zoning, and the channel snowmelt process was summed to determine the total predicted snowmelt of each zoning; according to the established watershed topological relationship of each flash flood type zoning, the upstream type zoning model and the downstream type zoning model of each zoning flash flood prediction model were determined to build a multi-type flash flood prediction integrated model for the study area; Step 4: Determine the optimal parameter set and evaluation indicators for the integrated model: Based on the established catchment topology of each flash flood type zone, calibrate the flash flood prediction model for each flash flood type zone one by one in the order of upstream first and downstream, and determine the optimal parameter set for the integrated model of multi-type flash flood prediction; The weighted comprehensive index is used to evaluate the accuracy of the integrated model for multi-type flash flood prediction and forecasting and the flash flood prediction and forecasting model for each sub-region. The optimal value is 1, and the calculation formula is: Where g is the weighted comprehensive index of the integrated model for multi-type flash flood forecasting; m is the number of flash flood type zones in the study area; i is the i-th flash flood type zone with station observation data and flood trace point data in the study area, 1≤i≤m; g i and α i are the weighted comprehensive index and its weight of the i-th partition, RMSE i is the root mean square error of precipitation in the ith partition, P o,j,i and P s,j,i are the observed precipitation at the station at the jth moment in the ith subarea and the short-term forecast precipitation output by the model; M i is the sequence length of the precipitation data observed at the station in the ith partition; |Re i | is the absolute value of the relative error of flood peak flow, flood peak water level, sediment load peak, snowmelt, or flood depth in the ith subarea. Q o,p,i is the peak flow or peak water level or peak sediment load or total snowmelt or flood mark elevation of the ith zone determined by the site observation data; Q s,p,i The model predicts the peak flow or peak water level or peak sediment load or total snowmelt or maximum flood depth for the i-th zone; NSE i is the Nash efficiency coefficient of the flow process line, the water level process line, the sediment load process line, or the snowmelt process line of the i-th partition, Q o,j,i is the observed flow, water level, sediment load, or snowmelt at the jth moment in the i-th partition; Q s,j,i The model predicts the flow or water level or sediment load or snowmelt for the jth moment in the i-th partition; is the mean flow, water level, sediment load or snowmelt observed at the station in the ith zone; N i is the length of the sequence of observed flow, water level, sediment load or snowmelt at the station in the i-th sub-area; γ i is the precipitation assessment index weight of the i-th partition; β i is the weight of the evaluation indicator for flow, water level, sediment load, snowmelt or flood depth of the ith subarea; Step 5. Forecast the flash flood process for each flash flood type zone and outlet: Based on the multi-source precipitation data, meteorological data, and channel measurement data collected in step 1, drive the multi-type flash flood prediction and forecasting integrated model to forecast the short-term precipitation, channel flow, water level, sediment load, snowmelt, and flooding depth for each flash flood type zone and outlet in the study area.

2. A flash flood prediction method suitable for different cause mechanisms according to claim 1, characterized in that: The water level, flow, sediment load and snowmelt process data in step 1 are all site observation data; the meteorological data include site observation temperature, soil moisture, water surface evaporation, sunshine hours, and wind speed data; the channel measurement data include cross-section, longitudinal section, and bridge and culvert measurement data; the basic geographic information data include DEM of no less than 1:50,000, land use type vector data, and soil texture type vector data; the set area threshold is 50km 2 The set catchment area threshold is 200km 2 .

3. A flash flood prediction method suitable for different cause mechanisms according to claim 1, characterized in that: The specific process of establishing the watershed topological relationship of each basin unit in step 1 is: take the basin unit where the river source is located as the source basin, and determine the river into which the river at the outlet of the source basin flows according to the watershed topological relationship of the water system. The basin unit where the river is located is the downstream basin of the source basin, and repeat the above process until there is no river into which the river at the outlet of the basin unit flows.

4. A flash flood prediction method suitable for different cause mechanisms according to claim 1, characterized in that: The specific process of analyzing the main controlling factors of the formation, development and disaster-causing processes of flash floods in each river basin as described in step 2 is as follows: using multivariate data mining technology, analyzing the contribution rates of the single factors of peak water level, peak flow, peak sediment load, and total snowmelt of each flash flood disaster in each river basin unit to the corresponding disaster data of the number of deaths / missing persons, number of collapsed houses, direct economic losses, maximum flood mark elevation, and maximum inundated area, as well as the contribution rates of multiple basin combination factors of each flash flood disaster in each river basin unit to the corresponding disaster data, including the combination of peak flow and total snowmelt, the combination of peak flow, peak water level and peak sediment load, and the combination of total snowmelt, peak water level and peak sediment load; if the contribution rate of a single basin factor exceeds 0.5, then the single basin factor is the main controlling factor of the formation, development and disaster-causing processes of flash floods in each river basin; if the contribution rate of the basin combination factor is greater than the sum of the contribution rates of the single factors, then the basin combination factor is the main controlling factor of the flash flood in the basin; The contribution rate calculation formula is: In the formula, CR(X i ,Y i ) is the contribution rate of the factor X of the i-th watershed unit to the disaster data Y, 1≤i≤bsn, bsn is the number of watershed units in the study area; X i is the factor matrix of the ith watershed unit, X i ∈X bsn×c×dx , dx is the number of factors, if it is a single factor, dx = 1, if it is a combination factor, dx is the number of combination factors, c is the number of games; Y i ∈Y bsn×c×dy , dy is the number of indicators of disaster data, dy = 5; σ(·) 2 is the overall variance.

5. The method for predicting and forecasting flash floods suitable for different cause mechanisms according to claim 1, characterized in that: The specific process of establishing the catchment topological relationship of each flash flood type partition described in step 2 is as follows: starting from the source basin, analyze the catchment order from the upstream basin to the downstream basin. If the flash flood type partition of a basin unit is recorded as partition A and the flash flood type partition of its downstream basin unit is recorded as partition B, then determine the downstream type partition of partition A as partition B, and the upstream type partition of partition B as partition A; if the two are consistent, then merge partition A and partition B into a virtual partition, and continue searching downstream basin units until a downstream basin unit that is inconsistent with its flash flood type partition is found; Traverse all watershed units until the watershed unit at the exit of the study area, and finally determine the upstream type zone and downstream type zone of each flash flood type zone.

6. A flash flood prediction method suitable for different cause mechanisms according to claim 1, characterized in that: The specific process of standardizing the input and output interfaces and model structures of each established model described in step 3 is as follows: standardizing the site-scale input data according to the data format of site code, site longitude, site latitude, time step, site-observed precipitation, meteorology, snowmelt, and sediment load; standardizing the radar quantitative estimation precipitation data and satellite inversion precipitation data according to the MICAPS Class 4 grid data format; standardizing the channel measurement data according to the data format of section number, longitude, latitude, and elevation; standardizing the output short-term forecast precipitation data according to the MICAPS Class 4 grid data format; standardizing the output channel flow process data according to the data format of model time step, current time step, model code, channel code, flow, water level, snowmelt, sediment load, and inundation depth; standardizing the model file according to basic information, parameter information, and model information; using a unified input and output interface to form a dynamic integration framework for various models, and performing model registration, deployment, packaging, release, and call.

7. A flash flood prediction method suitable for different cause mechanisms according to claim 1, characterized in that: The specific process of constructing the integrated model for multi-type flash flood prediction and forecasting in the study area described in step 3 is as follows: for flash flood type partitions with multiple upstream type partitions, the submerged water depth process of all upstream basin partitions is taken as the upper boundary of the flash flood type partition, and the sum of the model calculation results of all upstream type partitions except the submerged water depth is taken as the channel input value of the flash flood type partition; for flash flood type partitions with multiple downstream type partitions, the submerged water depth process of the flash flood type partition is taken as the lower boundary of all downstream type partitions, and the channel output values ​​of the flash flood type partition except the submerged water depth are determined according to the channel water cross-sectional area weight of the downstream type partition.

8. The method for predicting and forecasting flash floods suitable for different cause mechanisms according to claim 1, characterized in that: The specific process of determining the optimal parameter set of the integrated model for multi-type flash flood prediction and forecasting described in step 4 is: using the flow process data collected in step 1 to optimize the parameters of the flash flood prediction and forecasting model for the rainstorm type flash flood zoning, using the flow and snowmelt process data to optimize the parameters of the flash flood prediction and forecasting model for the snowmelt type and rain-snow composite flash flood zoning, using the flow, water level and sediment load process data and flood mark data to optimize the parameters of the flash flood prediction and forecasting model for the water-sand type flash flood zoning, using the flow, water level, snowmelt and sediment load process data and flood mark data to optimize the parameters of the flash flood prediction and forecasting model for the snow-sand composite flash flood zoning, and determine the model optimal parameters of the corresponding zoning respectively; based on the determined model optimal parameters of each zoning, use the parameter regionalization analysis method to determine the model parameters of the flash flood zoning that lacks station observation data and flood mark data; and determine the optimal parameter set of the integrated model with the flash flood type zoning as the unit.

9. A flash flood prediction method suitable for different cause mechanisms according to claim 8, characterized in that: The parameter regionalization analysis method is a spatial proximity method, an attribute similarity method, a parameter regression method or a classification and regression method.

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