Method and system suitable for large-scale regional mountain flood disaster risk dynamic early warning

Abstract

The application belongs to the technical field of mountain torrent early warning, and discloses a method and system suitable for large-scale regional mountain torrent disaster risk dynamic early warning, which first makes regional precipitation risk grade early warning indexes, then combines soil water content regional mountain torrent disaster risk early warning indexes, and further combines regional future precipitation and regional early-stage soil water content to make a regional future mountain torrent disaster risk grade early warning map; the regional real-time precipitation and the regional early-stage soil water content can be further combined to make a regional real-time mountain torrent risk grade early warning map. The application is suitable for large-scale regions, and can further improve the mountain torrent disaster risk prediction accuracy and provide effective data support for making plans.

Description

Technical Field

[0001] This invention relates to the field of flash flood early warning technology, specifically to a method and system for dynamic and precise early warning of flash flood disaster risks applicable to large-scale areas. Background Technology

[0002] Flash floods are a serious concern for governments worldwide due to their high mortality rate and severe damage. According to a 2007 survey by international meteorological and hydrological departments, flash floods were identified as the second leading cause of disaster after severe storms. Of the 139 countries surveyed, 105 ranked flash flood losses as the first or second leading cause of natural disaster damage. Furthermore, incomplete statistics from the WHO Emergency Data Database (EM-DAT) indicate that between 2000 and 2022, there were 638 major flash flood events globally, resulting in 20,563 deaths and direct economic losses exceeding US$60 billion. Nearly 80% of these disaster-related deaths occurred in Asia.

[0003] Despite the positive results achieved by various measures taken by humans to address flash floods, examples of their increasing severity readily available. According to the WMO Atlas of Deaths and Economic Losses Due to Weather, Climate and Water Extremes (1970-2019), the number of such events increased fivefold between 1970 and 2019. Economic losses increased even more dramatically, reaching sevenfold. Against the backdrop of global warming, with the increasing frequency of localized heavy rainfall and intensified socio-economic activities in mountainous areas, flash floods remain a weak link in flood prevention and mitigation. Especially in recent years, with the frequent occurrence of extreme precipitation and the frequent occurrence of flash flood events, flash flood forecasting and early warning have become crucial non-engineering measures in flood prevention and mitigation.

[0004] Most domestic and international research on flash flood warning technology focuses on critical rainfall analysis and calculation methods. This primarily involves statistical analysis of rainfall and flood data to study the patterns of flash floods and determine the critical rainfall for flash floods. When rainfall reaches or exceeds a certain level within a certain time scale, it reaches the warning flow and may trigger flash flood disasters. The rainfall within the corresponding time scale is the critical warning rainfall (critical rainfall) (see Liu Zhiyu, Yang Dawen, Hu Jianwei. Flash Flood Warning Method for Small and Medium Rivers Based on Dynamic Critical Rainfall and Its Application [J]. Journal of Beijing Normal University (Natural Science Edition), 2010, 45(3):317-321). However, this critical rainfall calculation method, because it does not consider the soil moisture content in the early stages, is strictly speaking a static critical rainfall method, which easily leads to missed or false warnings of flash floods. Furthermore, flash floods are characterized by high flow velocity, short forecast period, and numerous points of occurrence. Therefore, from a management and practical operational perspective, a method for dynamic flash flood disaster risk warning in large-scale areas is needed. Summary of the Invention

[0005] The purpose of this invention is to address the technical problems in existing technologies, such as the difficulty in providing large-scale, dynamic, and real-time early warning of flash flood disasters and the lack of contingency plans. This invention provides a dynamic early warning method for flash flood disaster risks applicable to large-scale areas, which can achieve dynamic and accurate prediction of flash flood disaster risks within a large area.

[0006] Another objective of this invention is to provide a dynamic early warning system for flash flood disaster risks applicable to large-scale areas for implementing the above-described methods.

[0007] To achieve the above objectives, the present invention adopts the following technical solutions.

[0008] The present invention provides a dynamic early warning method for flash flood disaster risk applicable to large-scale areas, which includes the following steps: S1 generates regional precipitation risk level early warning indicators; this step includes the following sub-steps: S11 Determine the design values ​​for extreme precipitation at different time periods and return periods in the region; this step includes the following sub-steps: S111 uses more than 15 years of hourly precipitation data from ground precipitation observation stations to determine the extreme precipitation at each observation station. S112 uses frequency calculation methods to determine the design values ​​of extreme precipitation at different time periods and return periods for each observation station; S113 uses spatial interpolation to generate design values ​​of extreme precipitation for different time periods and return periods in the region, and obtains a map of the regional extreme precipitation risk level for different time periods and return periods. S12 will create a regional precipitation risk level early warning index based on the precipitation at different times in the region according to the recurrence interval; S2 creates regional flash flood disaster risk warning indicators; the specific operation is as follows: divide the soil moisture content into several levels, and create regional flash flood disaster risk warning indicators based on the regional precipitation risk level warning indicators and the soil moisture content levels. S3 generates an early warning map of the future flash flood disaster risk level in the region; this step includes the following sub-steps: S31 determines the future precipitation in the region; S32 Determine the early soil moisture content of the area; S33 compares the region's future precipitation and previous soil moisture content with the region's flash flood disaster risk warning indicators, and displays the corresponding warning level according to the comparison results, thus obtaining a regional flash flood disaster risk level warning map.

[0009] In step S111 above, hourly precipitation observation data and the latitude, longitude, elevation, and start and end years of observation of all ground precipitation observation stations in the region are collected for more than 15 years. Through screening and quality control, extreme precipitation data of each station in different time periods of the year (including 1h, 2h, 3h, 6h, 12h, and 24h, a total of 6 time periods) are extracted. The extreme precipitation data of each station in the 6 time periods are sorted from low to high and organized into a long-sequence extreme precipitation dataset of the annual maximum precipitation of ground precipitation observation stations in different time periods (mainly 1h, 2h, 3h, 6h, 12h, and 24h, a total of 6 time periods).

[0010] In step S112 above, for each observation point, the annual maximum precipitation data for different time periods of the corresponding observation station are fitted using a frequency distribution function; then, frequency analysis is performed based on the determined frequency distribution function to determine the design values ​​of extreme precipitation for different time periods and return periods of the corresponding observation station. Here, different frequency distribution functions can be used first to fit the annual maximum precipitation data for different time periods of the corresponding observation station, and the optimal frequency distribution function can be selected based on the fitting results for subsequent frequency analysis.

[0011] In step S113 above, based on the extreme precipitation design values ​​of each observation station at different time periods and with different return periods, the precipitation design values ​​of each observation station are expanded into a regional precipitation design value distribution using spatial interpolation. Then, the regional grid size is determined according to the meteorological grid resolution, resulting in the spatial grid distribution of extreme precipitation design values ​​of different time periods and with different return periods in the predicted region. The spatial interpolation method can be Kriging interpolation, inverse distance weighted interpolation, etc.

[0012] In step S12 above, the recurrence interval includes once every 2 years to once every 5 years, once every 5 years to once every 10 years, once every 10 years to once every 20 years, once every 20 years to once every 50 years, and once every 50 years or more.

[0013] In step S2 above, the soil moisture content is increased from 0 to... WM It is divided into several levels. WM The maximum water storage capacity of a watershed is generally determined by analyzing measured rainfall and runoff data from a period of prolonged drought followed by a significant rainfall event that generates runoff across the entire watershed. The calculation formula is as follows: WM = P - R - E (1); In the formula, P The measured average rainfall in the watershed is expressed in mm. R The measured flow rate is in mm. E The measured evaporation during the rainy season is in mm.

[0014] In step S31 above, the future precipitation amount for the region can be determined based on the meteorological future precipitation grid forecast data. For example, the future short-term forecast (1h, 2h) and 24-hour precipitation grid data can be used as the future precipitation amount for the region.

[0015] In step S32 above, since there is very little measured data on soil moisture content, and even if there is, it only represents the situation at a specific point and cannot represent the complex distribution pattern of soil moisture content in the watershed, hydrological methods are used to calculate the anterior influencing rainfall based on measured rainfall data from various stations. P a This represents the soil moisture content of the watershed. The formula for its calculation is: If there was rainfall in the previous period P t-1 ,Right now P t-1 When >0, then P a,t = k ( P a,t-1 +P t-1 (2); If there was no rainfall in the previous period, that is P t-1 When =0, then P a,t = kP a,t-1 (3); In the formula, P a,t for t Rainfall in the early stages of the period, in mm; P a,t-1 The preceding rainfall amount is the amount that caused the impact of the previous period, in mm; P t-1 The rainfall amount for the previous period is in mm. k The attenuation coefficient of soil moisture content; k The value is calculated using the following formula: (4); In the formula, EM The average monthly evaporation capacity can be used. E 601 The water surface evaporation value observed by the evaporator is used as an approximation, and the unit is mm. In the above formula, the following must be true: (5); when Pa,t > WM At that time, take P a,t =WM .

[0016] Step S3 aims to predict the risk of flash floods in the next 1 hour, 2 hours, and 24 hours. At this point, the preceding rainfall calculated at the current time for each observation station is used as the preceding soil moisture content for that station. Then, using the spatial interpolation method given earlier, the preceding soil moisture content of each observation station is interpolated to a spatial resolution consistent with the spatial resolution of the regional extreme precipitation risk level map, thus creating gridded preceding soil moisture content data.

[0017] In the specific implementation of step S33 above, the regional flash flood disaster risk warning indicators are combined with short-term meteorological forecasts (1h, 2h) and 24-hour precipitation grid forecast data, as well as the corresponding previous soil moisture content to warn of the flash flood disaster risk levels for the next 1h, 2h, and 24h, and a regional flash flood disaster risk level warning map for the next 1h, 2h, and 24h is generated. (1) Based on the grid data of the short-term forecast (1h, 2h) and the corresponding previous soil moisture content, compare the data grid by grid with the regional 1h and 2h flash flood disaster risk warning indicators, and display the warning results of the corresponding level according to the comparison results, and make a regional flash flood disaster risk level warning map for the next 1h and 2h; for example, if it exceeds a certain level indicator, use the color of the corresponding risk level to indicate it. (2) Based on the 24-hour precipitation grid forecast data and the corresponding previous soil moisture content released by the meteorological department, compare the data grid by grid with the 24-hour flash flood disaster risk warning indicators of different levels in the region, and display the warning results of the corresponding level according to the comparison results, and make a flash flood disaster risk level warning map of the region in the next 24 hours; for example, if it exceeds a certain level indicator, use the color of the corresponding risk level to indicate it.

[0018] The above-mentioned method for dynamic early warning of flash flood disaster risk applicable to large-scale areas also includes step S4, which involves creating a real-time flash flood risk level early warning map for the region. This step includes the following sub-steps: S41 determines the real-time precipitation in the region; S42 Determine the early soil moisture content of the region; S43 compares the region's real-time precipitation and previous soil moisture content with the region's flash flood disaster risk warning indicators, and displays the corresponding warning level according to the comparison results, thus obtaining a region's real-time flash flood disaster risk level warning map.

[0019] Step S41 above can determine the real-time precipitation in the region based on the measured precipitation data from various ground observation stations. For example, based on the cumulative hourly precipitation data of 1h, 2h, 3h, 6h, and 12h measured by various ground observation stations, the spatial interpolation method given above is used to interpolate the real-time precipitation at each observation station to a value with a spatial resolution consistent with the spatial resolution of the regional extreme precipitation risk level map, thus obtaining the real-time precipitation in the region.

[0020] In step S42 above, the method for determining the regional antecedent soil moisture content is the same as in step S32. This step uses the cumulative hourly precipitation data measured at each observation station for 1h, 2h, 3h, 6h, and 12h to calculate the antecedent rainfall at the corresponding time point, which is then used as the antecedent soil moisture content for that observation station. Then, using the spatial interpolation method given earlier, the antecedent soil moisture content of each observation station is interpolated to a spatial resolution consistent with the spatial resolution of the regional extreme precipitation risk level map, thus creating gridded antecedent soil moisture content data.

[0021] In the specific implementation of step S43 above, the real-time flash flood risk level information is continuously released by combining the regional flash flood disaster risk early warning indicators with the cumulative hourly regional precipitation data for 1 hour, 2 hours, 3 hours, 6 hours, and 12 hours and the corresponding previous soil moisture content. The cumulative hourly real-time precipitation data for the region obtained in step S41 (1h, 2h, 3h, 6h, 12h) is compared with the corresponding previous soil moisture content grid by grid with the regional flash flood disaster risk warning indicators for different levels of 1h, 2h, 3h, 6h, 12h. If it exceeds a certain level indicator, it is represented by the color of the corresponding risk level, and a regional real-time flash flood disaster risk level warning map for 1h, 2h, 3h, 6h, 12h is created.

[0022] This invention also provides a dynamic early warning system for flash flood disaster risks applicable to large-scale areas, comprising: The first module is used to create regional precipitation risk level early warning indicators; it includes: The regional precipitation calculation submodule is used to determine the design values ​​of extreme precipitation for different time periods and return periods in a region; this submodule includes: The first precipitation processing unit is used to determine the extreme precipitation at each observation station by using more than 15 years of hourly precipitation observation data from ground precipitation observation stations. The second precipitation processing unit is used to determine the design values ​​of extreme precipitation at different time periods and return periods for each observation station. The drawing unit is used to generate design values ​​of extreme precipitation for different time periods and return periods in a region using spatial interpolation, and to obtain a map of the regional extreme precipitation risk level for different time periods and return periods. The regional precipitation risk construction submodule is used to generate regional precipitation risk level early warning indicators based on the precipitation at different times in a region according to the recurrence interval. The second module is used to create regional flash flood disaster risk early warning indicators; The first flash flood disaster risk level early warning module is used to create a regional flash flood disaster risk level early warning map; it includes: The first acquisition unit is used to determine the future precipitation in the region; The second acquisition unit is used to determine the early soil moisture content of the region; The first comparison unit is used to compare the region's future precipitation and previous soil moisture content with the region's flash flood disaster risk warning indicators, and display the corresponding warning level according to the comparison results to obtain the region's future flash flood disaster risk level warning map.

[0023] The first production module, the second production module, and the first flash flood disaster risk level early warning module and their constituent units shall be operated in accordance with steps S1-S3 given in the aforementioned early warning method.

[0024] The aforementioned early warning system also includes a second flash flood disaster risk level early warning module, used to generate real-time regional flash flood risk level early warning maps; it includes: The third acquisition unit is used to determine the real-time precipitation in the region; The fourth acquisition unit is used to determine the early soil moisture content of the region; The second comparison unit compares the region's real-time precipitation and previous soil moisture content with the region's flash flood disaster risk warning indicators, and displays the corresponding warning level according to the comparison results to obtain the region's real-time flash flood disaster risk level warning map.

[0025] The aforementioned second flash flood disaster risk level early warning module and its constituent units shall perform operations in accordance with step S4 given in the aforementioned early warning method.

[0026] Compared with existing technologies, the method and system for dynamic early warning of flash flood disaster risks in large-scale areas provided by this invention have the following superior effects: (1) The method for dynamic early warning of flash flood disaster risk in large-scale areas provided by the present invention firstly creates regional precipitation risk level early warning indicators, and then combines future forecast information and previous soil moisture content to dynamically warn of future flash flood disaster risk, which can further improve the prediction accuracy. (2) The method for dynamic early warning of mountain torrent disaster risk in large-scale areas provided by the present invention firstly creates regional precipitation risk level early warning indicators, and then combines real-time ground precipitation monitoring data and previous soil moisture content to achieve rolling real-time early warning of mountain torrent disaster risk, further improving the prediction accuracy and providing effective data support for the formulation of contingency plans.

[0027] Figure 1 This is a schematic diagram of the dynamic early warning method for flash flood disaster risk applicable to large-scale areas provided in Example 1; Figure 2 A regional flash flood disaster risk level warning map for the next 24 hours, created for Example 1; Figure 3 This is a schematic diagram of the process for a dynamic early warning method for flash flood disaster risk applicable to large-scale areas, as provided in Example 2. Implementation

[0028] The technical solutions of various embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0029] This embodiment provides a method for dynamic early warning of flash flood disaster risk applicable to large-scale areas, such as... Figure 1 As shown, it includes the following steps: S1 generates regional precipitation risk level early warning indicators; S2 generates regional flash flood disaster risk warning indicators; S3 generates an early warning map of the future flash flood disaster risk level in the region.

[0030] The purpose of step S1 above is to determine the extreme precipitation risk level map for different time periods and return periods in the region, thereby achieving refined early warning of flash flood disaster risk. Step S1 includes the following sub-steps: S11 Determine the design values ​​for extreme precipitation at different time periods and return periods in the region; specifically, this includes the following steps: S111 uses over 15 years of hourly precipitation data from ground precipitation observation stations to determine the extreme precipitation at each station.

[0031] In this embodiment, hourly precipitation observation data and the latitude, longitude, elevation, and start and end years of observation of all ground precipitation observation stations in the region are collected for more than 15 years. Through screening and quality control, extreme precipitation data for 1h, 2h, 3h, 6h, 12h and 24h of each year are extracted for each station. The extreme precipitation data for each of the six time periods of each station are sorted from low to high and organized into a long-sequence extreme precipitation dataset of the annual maximum precipitation for the six time periods of 1h, 2h, 3h, 6h, 12h and 24h of each year.

[0032] S112 uses frequency calculation methods to determine the design values ​​of extreme precipitation at different time periods and return periods for each observation station.

[0033] For a given observation station, different frequency distribution functions (including generalized logistic distribution GLO, generalized extreme value distribution GEV, generalized normal distribution GNO, Pearson type III distribution PE3, generalized Paluto distribution GPA, and Wakeby distribution WAK) are first used to fit the annual maximum precipitation data of the corresponding observation station for different time periods. Based on the fitting results, the best frequency distribution function is selected for subsequent frequency analysis.

[0034] Frequency analysis was performed on the corresponding observation stations based on the determined frequency distribution function to determine the design values ​​of extreme precipitation for different time periods (1h, 2h, 3h, 6h, 12h, 24h) with different return periods (2-year, 5-year, 10-year, 20-year, 50-year). Table 1 shows the design values ​​of extreme precipitation for different return periods at multiple observation stations over one hour.

[0035] Table 1. Design values ​​of extreme precipitation with different return periods within 1 hour

[0036] S113 uses spatial interpolation to generate design values ​​for extreme precipitation at different time periods and return periods in the region, thus obtaining a map of the regional extreme precipitation risk level at different time periods and return periods.

[0037] Based on the extreme precipitation design values ​​of different time periods and return periods at each observation station, the precipitation design values ​​of each observation station are extended into a regional precipitation design value distribution using spatial interpolation. Then, the regional grid size is determined according to the meteorological grid resolution to obtain the spatial grid distribution of extreme precipitation design values ​​of different time periods and return periods in the predicted area. The spatial interpolation method used in this embodiment is Kriging interpolation. For specific operation, please refer to Goovaerts P. Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall[J]. Journal of Hydrology, 2000,228(1):113-129.

[0038] The design values ​​of extreme precipitation with different return periods in different time periods obtained through step S11 can reflect the spatial distribution of extreme precipitation in the region as accurately as possible.

[0039] S12 will create regional precipitation risk level early warning indicators based on the recurrence interval of precipitation at different times in the region.

[0040] Based on the design values ​​of extreme precipitation with different return periods in different time periods in the region, this embodiment sets the precipitation in different time periods in the region into early warning indicators for regional precipitation risk levels according to the following categories: once every 2 years to once every 5 years (inclusive), once every 5 years to once every 10 years (inclusive), once every 10 years to once every 20 years (inclusive), once every 20 years to once every 50 years (inclusive), and once every 50 years or more.

[0041] The purpose of step S2 above is to create a regional flash flood disaster risk early warning indicator.

[0042] The specific operation involves dividing soil moisture content into several levels and creating regional flash flood risk warning indicators based on regional precipitation risk level warning indicators and soil moisture content levels.

[0043] In this embodiment, the quartile method is used to divide the soil moisture content from 0 to... WM The risk levels are divided into four categories (S1, S2, S3, S4), where S1 represents drought, S2 represents moderate drought, S3 represents moderate wetness, and S4 represents wetness. Based on the grid-based extreme precipitation risk level map, a regional flash flood disaster risk warning index for different time periods is constructed, comprising four levels: Level IV (at risk), Level III (relatively high risk), Level II (high risk), and Level I (very high risk), represented by blue, yellow, orange, and red respectively, as shown in Table 2.

[0044] Table 2 Risk levels of extreme precipitation with different return periods

[0045] WM The maximum water storage capacity of a watershed is generally determined by analyzing measured rainfall and runoff data from a period of prolonged drought followed by a significant rainfall event that generates runoff across the entire watershed. The calculation formula is as follows: WM = P - R - E (1); In the formula, P The measured average rainfall in the watershed is expressed in mm. R The measured flow rate is in mm. E The measured evaporation during the rainy season is in mm.

[0046] The purpose of step S3 above is to create a regional early warning map of future flash flood disaster risk levels. This step includes the following sub-steps: S31 determines the future precipitation in the region.

[0047] Future precipitation can be estimated based on gridded precipitation forecasts. For example, short-term forecasts (1h, 2h) and 24-hour precipitation gridded data can be used to estimate future precipitation in a region.

[0048] S32 determines the early soil moisture content of the area.

[0049] Because measured data on soil moisture content are scarce, and even when available, they only represent point-specific conditions and cannot reflect the complex distribution patterns of soil moisture content throughout the watershed, hydrological methods are used to calculate anterior rainfall influencing factors based on measured rainfall data from various stations. P a This represents the soil moisture content of the watershed. The formula for its calculation is: If there was rainfall in the previous period P t-1 ,Right now P t-1 When >0, then P a,t = k ( P a,t-1 +P t-1 (2); If there was no rainfall in the previous period, that is P t-1 When =0, then P a,t = kP a,t-1 (3); In the formula, P a,t for t Rainfall in the early stages of the period, in mm; P a,t-1 The preceding rainfall amount is the amount that caused the impact of the previous period, in mm; P t-1 The rainfall amount for the previous period is in mm. k The attenuation coefficient of soil moisture content; k The value is calculated using the following formula: (4); In the formula, EM The average monthly evaporation capacity can be used. E 601 The water surface evaporation value observed by the evaporator is used as an approximation, and the unit is mm. In the above formula, the following must be true: (5); when P a,t > WM At that time, take P a,t =WM .

[0050] The purpose of this embodiment is to predict the risk of flash floods in the next 1 hour, 2 hours, and 24 hours. The preceding rainfall calculated at the current time for each observation station is used as the preceding soil moisture content for that station. Then, using the spatial interpolation method described earlier, the preceding soil moisture content of each observation station is interpolated to a spatial resolution consistent with the spatial resolution of the regional extreme precipitation risk level map, thus creating gridded preceding soil moisture content data.

[0051] S33 compares the region's future precipitation and previous soil moisture content with the region's flash flood disaster risk warning indicators, and displays the corresponding warning level according to the comparison results, thus obtaining a regional flash flood disaster risk level warning map.

[0052] In the specific implementation method, the regional flash flood disaster risk warning indicators are combined with short-term meteorological forecasts (1h, 2h) and 24-hour precipitation grid forecast data, as well as previous soil moisture content data, to warn of the flash flood disaster risk level for the next 1h, 2h, and 24h, and a regional flash flood disaster risk level warning map for the next 1h, 2h, and 24h is produced: (1) Based on the grid data of the short-term forecast (1h, 2h) and the corresponding previous soil moisture content, compare the data grid by grid with the regional 1h and 2h flash flood disaster risk warning indicators, and display the warning results of the corresponding level according to the comparison results, and make a regional flash flood disaster risk level warning map for the next 1h and 2h; for example, if it exceeds a certain level indicator, use the color of the corresponding risk level to indicate it. (2) Based on the 24-hour precipitation grid forecast data and the corresponding previous soil moisture content released by the meteorological department, compare the data grid by grid with the 24-hour flash flood disaster risk warning indicators of different levels in the region, and display the warning results of the corresponding level according to the comparison results, and make a flash flood disaster risk level warning map of the region in the next 24 hours; for example, if it exceeds a certain level indicator, use the color of the corresponding risk level to indicate it.

[0053] Figure 2 This paper presents a 24-hour flash flood disaster risk level warning map of Sichuan Province, generated using the method described in this embodiment. The map clearly shows the flash flood risk level, enabling dynamic early warning of future flash flood disaster risks with high prediction accuracy.

[0054] Example 2 The dynamic early warning method for flash flood disaster risk applicable to large-scale areas provided in this embodiment is a further improvement based on Embodiment 1, such as... Figure 3 As shown, it also includes step S4, creating a real-time flash flood risk level early warning map for the region, which specifically includes the following sub-steps: S41 determines the real-time precipitation in the region.

[0055] Real-time precipitation in a region can be determined based on measured precipitation data from various ground observation stations. For example, based on the cumulative hourly precipitation data of 1h, 2h, 3h, 6h, and 12h measured by various ground observation stations, the spatial interpolation method given earlier is used to interpolate the real-time precipitation at each observation station to a value with a spatial resolution consistent with the spatial resolution of the regional extreme precipitation risk level map, thus obtaining the real-time precipitation in the region.

[0056] S42 determines the early soil moisture content of the area.

[0057] The method for determining the regional soil moisture content in the early stage is the same as in step S32.

[0058] This step uses the cumulative hourly precipitation data of 1h, 2h, 3h, 6h, and 12h measured at each observation station to calculate the preceding impact rainfall at the corresponding time as the preceding soil moisture content of the corresponding observation station. Then, using the spatial interpolation method given earlier, the preceding soil moisture content of each observation station is interpolated to a spatial resolution consistent with the spatial resolution of the regional extreme precipitation risk level map, and grid-based preceding soil moisture content data is produced.

[0059] S43 compares the region's real-time precipitation and previous soil moisture content with the region's flash flood disaster risk warning indicators, and displays the corresponding warning level according to the comparison results, thus obtaining a regional flash flood disaster risk level warning map.

[0060] In the specific implementation method, the regional flash flood disaster risk early warning indicators are combined with the cumulative hourly regional real-time precipitation data for 1 hour, 2 hours, 3 hours, 6 hours, and 12 hours, as well as the corresponding previous soil moisture content, to continuously release real-time flash flood disaster risk level information: The cumulative hourly real-time precipitation data for the region obtained in step S41 (1h, 2h, 3h, 6h, 12h) is compared with the corresponding previous soil moisture content grid by grid with the regional flash flood disaster risk warning indicators for different levels of 1h, 2h, 3h, 6h, 12h. If it exceeds a certain level indicator, it is represented by the color of the corresponding risk level, and a regional real-time flash flood disaster risk level warning map for 1h, 2h, 3h, 6h, 12h is created.

[0061] Example 3 This embodiment provides an early warning system for implementing the dynamic early warning method for flash flood disaster risk applicable to large-scale areas in Embodiment 1, which includes: The first module is used to create regional precipitation risk level early warning indicators; it includes: The regional precipitation calculation submodule is used to determine the design values ​​of extreme precipitation for different time periods and return periods in a region; this submodule includes: The first precipitation processing unit is used to determine the extreme precipitation at each observation station by using more than 15 years of hourly precipitation observation data from ground precipitation observation stations. The second precipitation processing unit is used to determine the design values ​​of extreme precipitation at different time periods and return periods for each observation station. The drawing unit is used to generate design values ​​of extreme precipitation for different time periods and return periods in a region using spatial interpolation, and to obtain a map of the regional extreme precipitation risk level for different time periods and return periods. The regional precipitation risk construction submodule is used to generate regional precipitation risk level early warning indicators based on the precipitation at different times in a region according to the recurrence interval. The second module is used to create regional flash flood disaster risk early warning indicators; The first flash flood disaster risk level early warning module is used to create a regional flash flood disaster risk level early warning map; it includes: The first acquisition unit is used to determine the future precipitation in the region; The second acquisition unit is used to determine the early soil moisture content of the region; The first comparison unit is used to compare the region's future precipitation and previous soil moisture content with the region's flash flood disaster risk warning indicators, and display the corresponding warning level according to the comparison results to obtain the region's future flash flood disaster risk level warning map.

[0062] The first production module, the second production module, and the first flash flood disaster risk level early warning module and their constituent units shall be operated in accordance with steps S1-S3 given in the early warning method of Embodiment 1.

[0063] Example 4 This embodiment is a further improvement on embodiment 3. The provided dynamic early warning system for flash flood disaster risk applicable to large-scale areas also includes a second flash flood disaster risk level early warning module, used to generate a real-time regional flash flood risk level early warning map; it includes: The third acquisition unit is used to determine the real-time precipitation in the region; The fourth acquisition unit is used to determine the early soil moisture content of the region; The second comparison unit compares the region's real-time precipitation and previous soil moisture content with the region's flash flood disaster risk warning indicators, and displays the corresponding warning level according to the comparison results to obtain the region's real-time flash flood disaster risk level warning map.

[0064] The second flash flood disaster risk level early warning module and its constituent units shall perform operations in accordance with step S4 given in the early warning method of Embodiment 2.

[0065] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for dynamic early warning of flash flood disaster risk applicable to large-scale areas, characterized in that, Includes the following steps: S1 generates regional precipitation risk level early warning indicators; this step includes the following sub-steps: S11 Determine the design values ​​for extreme precipitation at different time periods and return periods in the region; this step includes the following sub-steps: S111 uses hourly precipitation data from ground precipitation observation stations for more than 15 years to determine the extreme precipitation at each station; it extracts extreme precipitation data for different time periods each year for each station, including 1h, 2h, 3h, 6h, 12h, and 24h, for a total of 6 time periods. The extreme precipitation data for each station in the 6 time periods are sorted from low to high and organized into a long-sequence extreme precipitation dataset of the annual maximum precipitation for different time periods of the ground precipitation observation stations. S112 uses frequency calculation methods to determine the design values ​​of extreme precipitation for different time periods and return periods at each observation station. Specifically, for each observation point, the annual maximum precipitation data for different time periods at the corresponding observation station are fitted using a frequency distribution function. Then, frequency analysis is performed based on the determined frequency distribution function to determine the design values ​​of extreme precipitation for different time periods and return periods at the corresponding observation station. S113 uses spatial interpolation to generate design values ​​for extreme precipitation at different time periods and return periods in a region, resulting in a regional extreme precipitation risk level map for different time periods and return periods. Specifically, based on the design values ​​for extreme precipitation at different time periods and return periods of each observation station, the station precipitation design values ​​are extended to a regional precipitation design value distribution using spatial interpolation. Then, the regional grid size is determined according to the meteorological grid resolution to obtain the spatial grid distribution of the design values ​​for extreme precipitation at different time periods and return periods in the predicted region. S12 will generate regional precipitation risk level early warning indicators based on the recurrence intervals of precipitation at different times in the region; the recurrence intervals include 2-year to 5-year, 5-year to 10-year, 10-year to 20-year, 20-year to 50-year and more than 50-year. S2. Create regional flash flood disaster risk warning indicators; the specific operation is as follows: use the quartile method to divide the soil moisture content from 0 to WM into 4 levels S1, S2, S3, S4, where S1 represents drought, S2 represents relatively drought, S3 represents relatively wet, and S4 represents wet; based on the regional precipitation risk level warning indicators and the soil moisture content level, create regional flash flood disaster risk warning indicators. ； S3 generates a regional flash flood level early warning map; this step includes the following sub-steps: S31 determines the future precipitation in the region; S32 Determine the early soil moisture content of the area; S33 compares the region's future precipitation and previous soil moisture content with the region's flash flood disaster risk warning indicators, and displays the corresponding warning level according to the comparison results to obtain the region's future flash flood disaster risk level warning map; specifically, it combines the region's flash flood disaster risk warning indicators with the meteorological short-term forecast data for 1-hour, 2-hour, and 24-hour precipitation grid forecasts and the corresponding previous soil moisture content to warn the flash flood disaster risk level for the next 1-hour, 2-hour, and 24-hour periods, and produces the region's future 1-hour, 2-hour, and 24-hour flash flood disaster risk level warning maps.

2. The method for dynamic early warning of flash flood disaster risk applicable to large-scale areas according to claim 1, characterized in that, In step S2, the soil moisture content is divided into several levels from 0 to WM, where WM is the maximum water storage capacity of the watershed; the calculation formula is as follows: WM=PRE(1) In the formula, P is the measured average rainfall of the watershed, in mm; R represents the measured flow rate in mm; E represents the measured evaporation during the rainy season, in mm.

3. The method for dynamic early warning of flash flood disaster risk applicable to large-scale areas according to claim 1, characterized in that, In step S32, the amount of rainfall P that affected the preceding period is calculated. a The soil moisture content represents the watershed; its calculation formula is: If there was rainfall in the previous period P t-1 That is, P t-1 When >0, then P a,t =k(P a,t-1 +P t-1 )(2) If there was no rainfall in the previous period, that is When =0, then P a,t =kP a,t-1 (3) In the formula, P a,t The amount of rainfall preceding the t-period event is expressed in mm. P a,t-1 The preceding period's rainfall, in mm; P t-1 This represents the rainfall in the previous period, in mm. k is the attenuation coefficient of soil moisture content; The value of k is calculated using the following formula: （4） In the formula, EM represents the monthly average evaporation capacity, which can be expressed as E 601 The water surface evaporation value observed by the evaporator is used as an approximation, and the unit is mm. In the above formula, the following must be true: （5） When P a,t When >WM, take P. a,t =WM; The preceding impact rainfall calculated at the current time for each observation station is taken as the preceding soil moisture content of the corresponding observation station. Then, the spatial interpolation method is used to interpolate the preceding soil moisture content of each observation station to a spatial resolution consistent with the spatial resolution of the regional extreme precipitation risk level map, and grid-based preceding soil moisture content data is produced.

4. The method for dynamic early warning of flash flood disaster risk applicable to large-scale areas according to claim 1, characterized in that, It also includes step S4, which involves creating a real-time flash flood risk level early warning map for the region, specifically including the following sub-steps: S41 determines the real-time precipitation in the region; S42 Determine the early soil moisture content of the region; S43 compares the region's real-time precipitation and previous soil moisture content with the region's flash flood disaster risk warning indicators, and displays the corresponding warning level according to the comparison results, thus obtaining a region's real-time flash flood disaster risk level warning map.

5. An early warning system for implementing the dynamic early warning method for flash flood disaster risk applicable to large-scale areas as described in any one of claims 1 to 4, characterized in that, include: The first module is used to create regional precipitation risk level early warning indicators; It includes: The regional precipitation calculation submodule is used to determine the design values ​​of extreme precipitation for different time periods and return periods in a region; this submodule includes: The first precipitation processing unit is used to determine the extreme precipitation at each observation station by using more than 15 years of hourly precipitation observation data from ground precipitation observation stations. The second precipitation processing unit is used to determine the design values ​​of extreme precipitation at different time periods and return periods for each observation station. The drawing unit is used to generate design values ​​of extreme precipitation for different time periods and return periods in a region using spatial interpolation, and to obtain a map of the regional extreme precipitation risk level for different time periods and return periods. The regional precipitation risk construction submodule is used to generate regional precipitation risk level early warning indicators based on the precipitation at different times in a region according to the recurrence interval. The second module is used to create regional flash flood disaster risk early warning indicators; The first flash flood disaster risk level early warning module is used to create a regional flash flood disaster risk level early warning map; it includes: The first acquisition unit is used to determine the future precipitation in the region; The second acquisition unit is used to determine the early soil moisture content of the region; The first comparison unit is used to compare the region's future precipitation and previous soil moisture content with the region's flash flood disaster risk warning indicators, and display the corresponding warning level according to the comparison results to obtain the region's future flash flood disaster risk level warning map.

6. The early warning system according to claim 5, characterized in that, It also includes a second flash flood disaster risk level early warning module, used to create a real-time flash flood risk level early warning map for the region; It includes The third acquisition unit is used to determine the real-time precipitation in the region; The fourth acquisition unit is used to determine the early soil moisture content of the region; The second comparison unit compares the region's real-time precipitation and previous soil moisture content with the region's flash flood disaster risk warning indicators, and displays the corresponding warning level according to the comparison results to obtain the region's real-time flash flood disaster risk level warning map.

Images