Small watershed flood disaster grading monitoring and early warning method based on watershed landform characteristics

Abstract

The invention discloses a small watershed flood disaster grading monitoring and early warning method based on watershed landform characteristics, and relates to the technical field of small watershed flood monitoring, and the method comprises the following steps: pre-collecting landform topographic data, meteorological hydrological data and ecological hydrological data of a small watershed; the method comprises the following steps: acquiring infiltration capacity f (t) and a slope runoff coefficient C according to acquired landform and topographic data, meteorological and hydrological data and ecological hydrological data of a small watershed, calibrating a runoff production model according to the infiltration capacity f (t) to obtain total runoff production RZ, and calculating flood wave propagation velocity v in real time; and respectively obtaining a flood risk index FRI, a landslide susceptibility index LSI, a debris flow susceptibility index DFI and a soil salinization risk index SSI. According to the method, the runoff production models of different landform types are calibrated, and the total runoff production and the flood wave propagation speed are accurately calculated, so that a scientific basis is provided for obtaining various risk indexes, the risk assessment is more fit with the actual geographical environment and hydrological characteristics of the small watershed, and the accuracy and pertinence of disaster early warning are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of small watershed flood monitoring technology, and more specifically, to a method for graded monitoring and early warning of small watershed flood disasters based on watershed geomorphological characteristics. Background Technology

[0002] As key units in the interaction between the water cycle and surface processes, small watersheds are heavily influenced by unique natural conditions in the occurrence and evolution of floods. These areas often possess complex topographical patterns and diverse ecosystems. Dramatic changes in landforms lead to high uncertainty in water convergence paths, while the integrity and stability of the ecosystem directly affect rainwater interception, infiltration, and runoff formation. Traditional disaster monitoring and early warning approaches often focus on the observation of single hydrological elements, failing to incorporate the spatial heterogeneity of watershed topography and the dynamic correlation of eco-hydrological processes into a unified analytical framework. This results in limited understanding of disaster mechanisms and makes it difficult to capture the unique patterns of rapid flood rises and falls and the rapid triggering of disaster chains within small watersheds.

[0003] With the increasing frequency of extreme weather events and the intensifying human interference in watersheds, the complexity and severity of floods in small watersheds have become more prominent. On the one hand, abnormal rainfall patterns caused by climate change have increased the frequency of short-duration heavy rainfall in small watersheds, significantly enhancing the suddenness of flood formation. On the other hand, unreasonable land use and the construction of water conservancy facilities have altered the original geomorphological features and hydrological cycle pathways, leading to more complex disaster mechanisms and more prominent chain reactions of secondary disasters. Traditional early warning methods, lacking adaptability to these new changes, often appear passive in responding to differentiated scenarios. They are unable to meet the needs of rapid disaster response in mountainous areas, nor can they address the series of problems caused by long-term water retention in plains. A systematic innovation, from theoretical framework to technical methods, is urgently needed.

[0004] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0005] To address the problems in related technologies, this invention proposes a tiered monitoring and early warning method for flood disasters in small watersheds based on watershed geomorphological characteristics, in order to overcome the aforementioned technical problems existing in existing related technologies.

[0006] The technical solution of this invention is implemented as follows:

[0007] A tiered monitoring and early warning method for flood disasters in small watersheds based on watershed geomorphological characteristics includes the following steps:

[0008] Pre-collect geomorphological, topographical, meteorological, hydrological, and eco-hydrological data for the small watershed;

[0009] Based on the collected geomorphological, meteorological, hydrological, and eco-hydrological data of the small watershed, the infiltration capacity f(t) and slope runoff coefficient C were obtained. The runoff generation model was then calibrated based on the infiltration capacity f(t) to obtain the total runoff R. Z The real-time calculation of flood wave propagation velocity v is used to obtain the flood risk index FRI, landslide susceptibility index LSI, debris flow susceptibility index DFI, and soil salinization risk index SSI, respectively.

[0010] The risk index CRI is calculated based on the flood risk index FRI, landslide susceptibility index LSI, debris flow susceptibility index DFI, and soil salinization risk index SSI. Risk levels are then classified according to the CRI values, and graded early warning strategies are implemented based on these risk levels.

[0011] Furthermore, the collection of the aforementioned topographic data includes: using satellite remote sensing imagery to obtain topographic information of a large area of ​​a small watershed; using a drone equipped with lidar to conduct low-altitude aerial photography of the target area to obtain data on minute topographic changes; and using ArcGIS to extract geomorphic feature parameters, including at least slope θ, catchment area A, and river channel longitudinal gradient I, based on digital elevation model data.

[0012] Furthermore, the collection of the meteorological and hydrological data includes: deploying weighing rain gauges in the target area to capture daily rainfall P. R and the cumulative rainfall P over 3 days 3R Deploy radar level gauges to monitor the actual water level H and the warning water level H0; deploy acoustic Doppler current meters to measure the actual flow rate Q and the normal flow rate Q0; deploy soil moisture sensors to monitor the relative soil moisture S.

[0013] Furthermore, the collection of the eco-hydrological data includes: obtaining vegetation coverage V through ecological remote sensing technology; and collecting soil erosion rate E, soil infiltration rate K, groundwater depth D, and groundwater discharge rate G.

[0014] Furthermore, the infiltration capacity f(t) is expressed as:

[0015] f(t) = f c +(f0-f c )e -kt ×(1+α×S)×(1-β×V);

[0016] Where f(t) is the infiltration rate at time t, f c The infiltration rate is the stable infiltration rate; f0 is the initial infiltration rate; k is the attenuation coefficient; t is the time; α is the soil moisture influence coefficient; S is the relative soil moisture; β is the vegetation cover influence coefficient; and V is the vegetation cover.

[0017] Furthermore, the slope runoff coefficient C is obtained and expressed as:

[0018] C = C0 × (1 - γ × V) × δ(θ);

[0019] Where C is the actual runoff coefficient; C0 is the bare land runoff coefficient; γ is the vegetation reduction coefficient of runoff; V is the vegetation coverage; δ(θ) is the slope influence function, where θ is the slope and when θ>>5°, δ(θ)=1+0.01×(θ-5); the greater the slope, the greater δ(θ), and the greater the runoff coefficient.

[0020] Furthermore, the total flow rate R is obtained by calibrating the flow generation model based on the infiltration capacity f(t). Z This includes the following steps:

[0021] Among them, for mountainous areas with steep slopes and low vegetation cover, where rainwater cannot infiltrate in time and forms surface runoff, represented as θ>30° and V<30%, this is calibrated as a mountainous excess-infiltration runoff model, which includes the following steps:

[0022] The hourly rainfall P(t) was obtained using a weighing rain gauge.

[0023] Calculate the actual infiltration rate:

[0024] If P(t)≤f(t), it means that all rainwater infiltrates and there is no surface runoff;

[0025] If P(t) > f(t), the actual infiltration amount is f(t), and the excess part forms surface runoff, that is: hourly surface runoff = P(t) - f(t);

[0026] The total runoff R is obtained by summing the hourly surface runoff during the rainfall period, only adding the periods where P(t) > f(t). Z , represented as:

[0027]

[0028] Specifically, for plains with gentle terrain and poor soil permeability, rainwater first replenishes the soil moisture content, and after the soil is saturated, excess rainwater forms surface runoff, i.e., runoff is generated only after the soil is full. This model is calibrated as a plain full-saturation runoff generation model, which includes the following steps:

[0029] Calculating initial soil water storage involves obtaining initial soil moisture content W0 using a soil moisture sensor and combining it with soil field capacity W. m The soil water deficit is obtained and expressed as:

[0030] D=W m -W0

[0031] Specifically, D is the maximum amount of water that the soil can still absorb. If D = 0, then the soil is saturated.

[0032] And calculate the production flow rate in stages:

[0033] If the total rainfall P Z ≤D, meaning the rainfall did not completely fill the soil moisture deficit, indicates that all rainwater infiltrated to replenish the soil moisture content, with no surface runoff, i.e., R. Z =0;

[0034] If the total rainfall P Z >D, meaning rainfall exceeds soil moisture deficit, indicates that the soil is "saturated," and excess rainwater forms surface runoff, i.e., R. Z =P Z -D.

[0035] Furthermore, the real-time calculation of the flood wave propagation velocity v is expressed as:

[0036] v = k × I 0.5 ×R Z 0.67 / n;

[0037] Where k is an empirical coefficient; I is the longitudinal gradient of the river channel; and n is the Manning roughness coefficient, expressed as: n=n0×(1-0.005×V), where n0 is the basic roughness coefficient.

[0038] Furthermore, the acquisition of the Flood Risk Index (FRI), Landslide Susceptibility Index (LSI), Debris Flow Susceptibility Index (DFI), and Soil Salinization Risk Index (SSI) includes the following steps:

[0039] The Flood Risk Index (FRI) is obtained as follows:

[0040] FRI = l × (H / H0) + m × R Z +n×v;

[0041] Where H is the actual water level; H0 is the warning water level; l, m, and n are weighting coefficients;

[0042] The landslide susceptibility index (LSI) is calculated as follows:

[0043] LSI=a×θ+b×(100-V)+c×C+d×P R ;

[0044] Where θ is the slope; V is the vegetation cover; P R 1 represents daily rainfall; a, b, c, and d are weighting coefficients.

[0045] The debris flow susceptibility index (DFI) is calculated as follows:

[0046] DFI = e×C + f×E + g×A + h×P 3R ;

[0047] Where E is the soil erosion rate; A is the catchment area; P 3R The total rainfall over 3 days is represented by e, f, g, and h, which are weighting coefficients.

[0048] The time of waterlogging and waterlogging, T, is expressed as: T = H J / (K+G), where H J The depth of the water is represented by H. J =R Z ×S / 1000, where S is the area of ​​the low-lying area; K is the soil infiltration rate; and G is the groundwater discharge rate.

[0049] Based on the waterlogging retention time T, the soil salinization risk index SSI is calculated and expressed as follows:

[0050] SSI=i×T+j×(1 / K)+k×(1 / D);

[0051] Where D is the groundwater depth; i, j, and k are weighting coefficients.

[0052] Furthermore, the calculated risk index CRI is expressed as:

[0053] CRI = w1 × FRI B +w2×LSI B +w3×DFI B +w4×SSI B ;

[0054] Specifically, if 0 ≤ CRI < 0.25, it indicates low risk; if 0.25 ≤ CRI < 0.5, it indicates medium risk; if 0.5 ≤ CRI < 0.75, it indicates high risk; and if CRI ≥ 0.75, it indicates extremely high risk.

[0055] The beneficial effects of this invention are:

[0056] The present invention provides a method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics, achieving accurate monitoring and graded early warning of floods and related secondary disasters in small watersheds. This method overcomes the limitations of traditional single-element observation, incorporating multiple types of data, including geomorphological topography, meteorological hydrology, and eco-hydrology, into a unified analytical framework. It considers both static characteristics such as watershed topographic slope and catchment area, and dynamic factors such as rainfall, soil moisture, and vegetation cover, comprehensively reflecting the complex mechanisms of disaster formation. By calibrating runoff models for different geomorphological types, it accurately calculates total runoff and flood wave propagation velocity, providing a scientific basis for obtaining various risk indices. This makes risk assessment more closely aligned with the actual geographical environment and hydrological characteristics of small watersheds, effectively improving the accuracy and relevance of disaster early warning.

[0057] Meanwhile, this method constructs a Comprehensive Risk Index (CRI) and classifies risk levels, forming a hierarchical early warning strategy that enables differentiated responses based on varying risk levels. For low-risk areas, routine management can be achieved through daily information updates; for medium- and high-risk areas, timely SMS alerts and door-to-door notifications by grid workers can be initiated to prepare for potential disasters; and for extremely high-risk situations, high-level responses such as nationwide emergency alerts and mandatory evacuations can be triggered to minimize disaster losses. This hierarchical early warning model not only fully considers the disaster characteristics of different geomorphic units within a small watershed, such as mountainous and plain areas, but also enables rapid response to sudden disasters caused by extreme weather, enhancing the flexibility and timeliness of disaster response and providing strong technical support for the refined management and disaster prevention and mitigation of floods in small watersheds. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a flowchart illustrating a method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological features, according to an embodiment of the present invention. Detailed Implementation

[0060] The technical solutions of the 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. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of the present invention.

[0061] According to embodiments of the present invention, a method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics is provided.

[0062] like Figure 1 As shown, the method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics according to an embodiment of the present invention includes the following steps:

[0063] Step S1: Collect geomorphological and topographical data, meteorological and hydrological data, and eco-hydrological data of the small watershed in advance;

[0064] Among them, topographic data includes using high-resolution satellite remote sensing imagery to obtain topographic information of a large area of ​​a small watershed; using drones equipped with high-precision lidar to conduct low-altitude aerial photography of the target area to obtain minute topographic change data with centimeter-level precision; and using ArcGIS to extract geomorphic feature parameters based on ultra-high resolution digital elevation model (DEM) data, including at least: slope θ, catchment area A, and longitudinal gradient of the river channel I.

[0065] Among them, meteorological and hydrological data, in terms of target differentiation, include: weighing rain gauges to accurately capture daily rainfall P. R and the cumulative rainfall P over 3 days 3R Radar-type water level gauge to monitor the actual water level H and warning water level H0 in real time; acoustic Doppler current meter to measure the actual flow rate Q and normal flow rate Q0; soil moisture sensor to monitor the relative soil moisture S in real time.

[0066] Among them, eco-hydrological data includes: vegetation coverage V obtained through ecological remote sensing technology; and soil erosion rate E, soil infiltration rate K, groundwater depth D, and groundwater discharge rate G.

[0067] Step S2: Based on the collected geomorphological, meteorological, hydrological, and eco-hydrological data of the small watershed, obtain the infiltration capacity f(t) and the slope runoff coefficient C, and calibrate the runoff generation model based on the infiltration capacity f(t) to obtain the total runoff R. Z The real-time calculation of flood wave propagation velocity v is used to obtain the flood risk index FRI, landslide susceptibility index LSI, debris flow susceptibility index DFI, and soil salinization risk index SSI, respectively.

[0068] Among them, the calculation of infiltration capacity reflects the maximum rate at which the soil absorbs water and determines whether rainwater forms surface runoff, and is expressed as:

[0069] f(t) = f c +(f0-f c )e -kt ×(1+α×S)×(1-β×V);

[0070] Where f(t) is the infiltration rate at time t, f c The infiltration rate is the stable infiltration rate; f0 is the initial infiltration rate; k is the attenuation coefficient; t is time; α is the soil moisture influence coefficient; S is the relative soil moisture; β is the vegetation cover influence coefficient; V is the vegetation cover.

[0071] This technical solution addresses the aforementioned stable infiltration rate f. cThe initial infiltration rate f0 is not a fixed value and needs to be updated regularly according to changes in the watershed environment, such as soil improvement, vegetation restoration, and human activity disturbance. Soil samples are collected again every 1-3 years for experiments, and the parameters are given by combining the vegetation change data and NDVI index from remote sensing monitoring.

[0072] The slope runoff coefficient, which reflects the proportion of rainfall converted into surface runoff, is calculated and expressed as:

[0073] C = C0 × (1 - γ × V) × δ(θ);

[0074] Where C is the actual runoff coefficient; C0 is the bare land runoff coefficient; γ is the vegetation reduction coefficient of runoff; V is the vegetation coverage; δ(θ) is the slope influence function, where θ is the slope and when θ>>5°, δ(θ)=1+0.01×(θ-5); the greater the slope, the greater δ(θ), and the greater the runoff coefficient.

[0075] Perform calibration of the production flow model to obtain the total production flow R. Z It represents the total amount of rainfall converted into surface runoff per unit area, used to reflect the total amount of surface runoff generated within a watershed.

[0076] Among them, for mountainous areas with steep slopes and low vegetation cover, where rainwater cannot infiltrate in time and forms surface runoff, represented as θ>30° and V<30%, this is calibrated as a mountainous excess-infiltration runoff model, which includes the following steps:

[0077] The hourly rainfall P(t) was obtained using a weighing rain gauge.

[0078] Calculate the actual infiltration rate:

[0079] If P(t)≤f(t), it means that all rainwater infiltrates and there is no surface runoff;

[0080] If P(t) > f(t), the actual infiltration amount is f(t), and the excess part forms surface runoff, that is: hourly surface runoff = P(t) - f(t);

[0081] The total runoff R is obtained by summing the hourly surface runoff during the rainfall period, only adding the periods where P(t) > f(t). Z , represented as:

[0082]

[0083] Specifically, for plains with gentle terrain and poor soil permeability, rainwater first replenishes the soil moisture content, and after the soil is saturated, excess rainwater forms surface runoff, i.e., runoff is generated only after the soil is full. This model is calibrated as a plain full-saturation runoff generation model, which includes the following steps:

[0084] Calculating initial soil water storage involves obtaining initial soil moisture content W0 using a soil moisture sensor and combining it with soil field capacity W. m The soil water deficit is obtained and expressed as:

[0085] D=W m -W0

[0086] Specifically, D is the maximum amount of water that the soil can still absorb. If D = 0, then the soil is saturated.

[0087] Calculate the production flow rate in stages:

[0088] If the total rainfall P Z ≤D, meaning the rainfall did not completely fill the soil moisture deficit, indicates that all rainwater infiltrated to replenish the soil moisture content, with no surface runoff, i.e., R. Z =0;

[0089] If the total rainfall P Z >D, meaning rainfall exceeds soil moisture deficit, indicates that the soil is "saturated," and excess rainwater forms surface runoff, i.e., R. Z =P Z -D;

[0090] The propagation speed v of a flood wave is calculated as follows:

[0091] v = k × I 0.5 ×R Z 0.67 / n;

[0092] Where k is an empirical coefficient; I is the longitudinal gradient of the river channel; and n is the Manning roughness coefficient, expressed as: n=n0×(1-0.005×V), where n0 is the basic roughness coefficient;

[0093] The Flood Risk Index (FRI) is obtained as follows:

[0094] FRI = l × (H / H0) + m × R Z +n×v;

[0095] Where H is the actual water level; H0 is the warning water level; l, m, and n are weighting coefficients;

[0096] The landslide susceptibility index (LSI) is calculated as follows:

[0097] LSI=a×θ+b×(100-V)+c×C+d×P R ;

[0098] Where θ is the slope; V is the vegetation cover; P R 1 represents daily rainfall; a, b, c, and d are weighting coefficients.

[0099] The debris flow susceptibility index (DFI) is calculated as follows:

[0100] DFI = e×C + f×E + g×A + h×P 3R ;

[0101] Where E is the soil erosion rate; A is the catchment area; P 3R The total rainfall over 3 days is represented by e, f, g, and h, which are weighting coefficients.

[0102] The time of waterlogging and waterlogging, T, is expressed as: T = H J / (K+G), where H J The depth of the water is represented by H. J =R Z ×S / 1000, where S is the area of ​​the low-lying area; K is the soil infiltration rate; and G is the groundwater discharge rate.

[0103] Based on the waterlogging retention time T, the soil salinization risk index SSI is calculated and expressed as follows:

[0104] SSI=i×T+j×(1 / K)+k×(1 / D);

[0105] Where D is the groundwater depth; i, j, and k are weighting coefficients;

[0106] Step S3: Calculate the risk index CRI based on the flood risk index FRI, landslide susceptibility index LSI, debris flow susceptibility index DFI, and soil salinization risk index SSI. Divide the risk levels according to the CRI values ​​and implement graded early warning strategies based on the risk levels.

[0107] Obtaining the Risk Index (CRI) includes the following steps:

[0108] By pre-standardizing and unifying the units of measurement using the min-max method to eliminate the influence of numerical differences on the weights, it can be expressed as:

[0109]

[0110] Among them, I Y These are the original values ​​for FRI, LSI, DFI, and SSI; I min =0, I max =100; after standardization, we get: FRI B LSI B DFI B SSI B ;

[0111] Based on the dominant disaster type in the small watershed, the weights of each index are determined by the analytic hierarchy process (AHP) or the proportion of historical disaster losses. This includes setting weight coefficients: w1 as the FRI weight, w2 as the LSI weight, w3 as the DFI weight, and w4 as the SSI weight, satisfying w1+w2+w3+w4=1.

[0112] Specifically, a purely mountainous watershed, i.e., one dominated by landslides / debris flows, is represented as:

[0113] w1=0.2, w2=0.35, w3=0.35, w4=0.1;

[0114] A purely plain watershed, characterized primarily by waterlogging and salinization, is represented as follows:

[0115] w1=0.3, w2=0.1, w3=0.1, w4=0.5;

[0116] Mountain-plain transition watershed, represented as:

[0117] w1=0.3, w2=0.2, w3=0.2, w4=0.3;

[0118] Calculate the risk index CRI, expressed as

[0119] CRI = w1 × FRI B +w2×LSI B +w3×DFI B +w4×SSI B ;

[0120] Based on the CRI value, risks are divided into four levels: low, medium, high, and extremely high. A tiered early warning strategy is then implemented based on the risk level, as detailed below:

[0121] In this context, if 0 ≤ CRI < 0.25 (i.e., CRI < 0.25) and there is no high risk for any of the sub-hazard types, it indicates low risk. This will be reported via community app push notifications and daily village broadcasts. For example: the current comprehensive watershed risk index is 0.2, mainly mild waterlogging (FRI = 20), with no landslide / mudslide risk. Farmers are advised to pay attention to weather forecasts; no special measures are required.

[0122] If 0.25 ≤ CRI < 0.5, meaning the CRI is between 0.25 and 0.5, the dominant risk is considered medium, indicating a medium risk level. Responses include SMS alerts, announcements on township government platforms, and door-to-door notifications by grid workers (specifically for households in high-risk areas). For example: The current comprehensive risk index for the watershed is 0.35, with the dominant risk being plain flooding (FRI = 45, weight 0.3), and a waterlogging duration of approximately 5 days. Recommendations: Clear drainage ditches in low-lying farmland in advance; closely monitor rainfall over the next 24 hours and suspend outdoor work.

[0123] If 0.5 ≤ CRI < 0.75, meaning the CRI is between 0.5 and 0.75, a high-risk sub-hazard exists and is classified as high-risk. Emergency broadcasts will be continuously broadcast, mobile phone pop-ups will be displayed, and village emergency teams will provide on-site command. For example: the current comprehensive risk index of the watershed is 0.6, with the dominant risks being debris flow (DFI = 70, weight 0.35) and flood (FRI = 60, weight 0.3). Emergency measures: Residents in mountainous areas are prohibited from entering valleys; farmers in low-lying plains areas should move agricultural machinery to higher ground; village emergency teams should be on standby.

[0124] If the CRI is ≥ 0.75, indicating multiple extremely high-risk sub-hazards, it is classified as extremely high risk. Emergency measures will be implemented, including nationwide push notifications (including SMS messages for non-smartphones), drone announcements, gong / siren warnings, and coordination with the county-level emergency command center. For example: The current comprehensive risk index of the watershed is 0.85, with the dominant risks being landslides (LSI = 90, weight 0.35) and floods (FRI = 80, weight 0.3). A disaster may occur within one hour! Mandatory evacuation notice: All residents on the west slope must immediately evacuate along the eastward ridge to the town's middle school resettlement point; power to low-lying areas in the plains should be cut off, and all personnel should be transferred to buildings with three or more stories; rescue teams have already departed, do not return to the danger zone.

[0125] In summary, by employing the above-described technical solution of the present invention, the following effects can be achieved:

[0126] The present invention provides a method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics, achieving accurate monitoring and graded early warning of floods and related secondary disasters in small watersheds. This method overcomes the limitations of traditional single-element observation, incorporating multiple types of data, including geomorphological topography, meteorological hydrology, and eco-hydrology, into a unified analytical framework. It considers both static characteristics such as watershed topographic slope and catchment area, and dynamic factors such as rainfall, soil moisture, and vegetation cover, comprehensively reflecting the complex mechanisms of disaster formation. By calibrating runoff models for different geomorphological types, it accurately calculates total runoff and flood wave propagation velocity, providing a scientific basis for obtaining various risk indices. This makes risk assessment more closely aligned with the actual geographical environment and hydrological characteristics of small watersheds, effectively improving the accuracy and relevance of disaster early warning.

[0127] Meanwhile, this method constructs a Comprehensive Risk Index (CRI) and classifies risk levels, forming a hierarchical early warning strategy that enables differentiated responses based on varying risk levels. For low-risk areas, routine management can be achieved through daily information updates; for medium- and high-risk areas, timely SMS alerts and door-to-door notifications by grid workers can be initiated to prepare for potential disasters; and for extremely high-risk situations, high-level responses such as nationwide emergency alerts and mandatory evacuations can be triggered to minimize disaster losses. This hierarchical early warning model not only fully considers the disaster characteristics of different geomorphic units within a small watershed, such as mountainous and plain areas, but also enables rapid response to sudden disasters caused by extreme weather, enhancing the flexibility and timeliness of disaster response and providing strong technical support for the refined management and disaster prevention and mitigation of floods in small watersheds.

[0128] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Those skilled in the art, upon considering the disclosure in the specification and embodiments, will readily conceive of other embodiments of this disclosure. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0129] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics, characterized in that, Includes the following steps: Pre-collect geomorphological, topographical, meteorological, hydrological, and eco-hydrological data for the small watershed; Based on the collected geomorphological, meteorological, hydrological, and eco-hydrological data of the small watershed, the infiltration capacity f(t) and slope runoff coefficient C were obtained. The runoff generation model was then calibrated based on the infiltration capacity f(t) to obtain the total runoff R. Z The real-time calculation of flood wave propagation velocity v is used to obtain the flood risk index FRI, landslide susceptibility index LSI, debris flow susceptibility index DFI, and soil salinization risk index SSI, respectively. The risk index CRI is calculated based on the flood risk index FRI, landslide susceptibility index LSI, debris flow susceptibility index DFI, and soil salinization risk index SSI. Risk levels are then classified according to the CRI values, and graded early warning strategies are implemented based on the risk levels.

2. The method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics according to claim 1, characterized in that, The collection of the aforementioned topographic data includes: using satellite remote sensing imagery to obtain topographic information of a large area of ​​a small watershed; using a drone equipped with lidar to conduct low-altitude aerial photography of the target area to obtain data on minute topographic changes; and using ArcGIS to extract geomorphic feature parameters, including at least slope θ, catchment area A, and river channel longitudinal gradient I, based on digital elevation model data.

3. The method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics according to claim 2, characterized in that, The collection of the meteorological and hydrological data includes: deploying weighing rain gauges in the target area to capture daily rainfall P. R and the cumulative rainfall P over 3 days 3R Deploy radar level gauges to monitor the actual water level H and the warning water level H0; deploy acoustic Doppler current meters to measure the actual flow rate Q and the normal flow rate Q0; deploy soil moisture sensors to monitor the relative soil moisture S.

4. The method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics according to claim 3, characterized in that, The collection of the eco-hydrological data includes: obtaining vegetation cover V through ecological remote sensing technology; and collecting soil erosion rate E, soil infiltration rate K, groundwater depth D, and groundwater discharge rate G.

5. The method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics according to claim 4, characterized in that, The infiltration capacity f(t) is expressed as: f(t)=f c +(f0-f c )e -kt ×(1+α×S)×(1-β×V); Where f(t) is the infiltration rate at time t, f c The infiltration rate is the stable infiltration rate; f0 is the initial infiltration rate; k is the attenuation coefficient; t is the time; α is the soil moisture influence coefficient; S is the relative soil moisture; β is the vegetation cover influence coefficient; and V is the vegetation cover.

6. The method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics according to claim 5, characterized in that, The slope runoff coefficient C is obtained and expressed as: C = C0 × (1 - γ × V) × δ(θ); Where C is the actual runoff coefficient; C0 is the bare land runoff coefficient; γ is the vegetation reduction coefficient of runoff; V is the vegetation coverage; δ(θ) is the slope influence function, where θ is the slope and when θ>5°, δ(θ)=1+0.01×(θ-5); the greater the slope, the greater δ(θ), and the greater the runoff coefficient.

7. The method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics according to claim 6, characterized in that, The total flow rate R is obtained by calibrating the flow model based on the infiltration capacity f(t). Z , Includes the following steps: Among them, for mountainous areas with steep slopes and low vegetation cover, where rainwater cannot infiltrate in time and forms surface runoff, represented as θ>30° and V<30%, this is calibrated as a mountainous excess-infiltration runoff model, which includes the following steps: The hourly rainfall P(t) was obtained using a weighing rain gauge. Calculate the actual infiltration rate: If P(t)≤f(t), it means that all rainwater infiltrates and there is no surface runoff; If P(t) > f(t), the actual infiltration amount is f(t), and the excess part forms surface runoff, that is: hourly surface runoff = P(t) - f(t); The total runoff R is obtained by summing the hourly surface runoff during the rainfall period, only adding the periods where P(t) > f(t). Z , represented as: Specifically, for plains with gentle terrain and poor soil permeability, rainwater first replenishes the soil moisture content, and after the soil is saturated, excess rainwater forms surface runoff, i.e., runoff is generated only after the soil is full. This model is calibrated as a plain full-saturation runoff generation model, which includes the following steps: Calculating initial soil water storage involves obtaining initial soil moisture content W0 using a soil moisture sensor and combining it with soil field capacity W. m The soil water deficit is obtained and expressed as: D=W m -W0 Specifically, D is the maximum amount of water that the soil can still absorb. If D = 0, then the soil is saturated. And calculate the production flow rate in stages: If the total rainfall P Z ≤D, meaning the rainfall did not completely fill the soil moisture deficit, indicates that all rainwater infiltrated to replenish the soil moisture content, with no surface runoff, i.e., R. Z =0; If the total rainfall P Z >D, meaning rainfall exceeds soil moisture deficit, indicates that the soil is "saturated," and excess rainwater forms surface runoff, i.e., R. Z =P Z -D.

8. The method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics according to claim 7, characterized in that, The real-time calculation of the flood wave propagation velocity v is expressed as: v=k×I 0.5 ×R Z 0.67 / n; Where k is an empirical coefficient; I is the longitudinal gradient of the river channel; and n is the Manning roughness coefficient, expressed as: n=n0×(1-0.005×V), where n0 is the basic roughness coefficient.

9. The method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics according to claim 8, characterized in that, The data obtained include the Flood Risk Index (FRI), Landslide Susceptibility Index (LSI), Debris Flow Susceptibility Index (DFI), and Soil Salinization Risk Index (SSI). Includes the following steps: The Flood Risk Index (FRI) is obtained as follows: FRI=l×(H / H0)+m×R Z +n×v; Where H is the actual water level; H0 is the warning water level; l, m, and n are weighting coefficients; The landslide susceptibility index (LSI) is calculated as follows: LSI=a×θ+b×(100-V)+c×C+d×P R ; Where θ is the slope; V is the vegetation cover; P R 1 represents daily rainfall; a, b, c, and d are weighting coefficients. The debris flow susceptibility index (DFI) is calculated as follows: DFI=e×C+f×E+g×A+h×P 3R ; Where E is the soil erosion rate; A is the catchment area; P 3R The total rainfall over 3 days is represented by e, f, g, and h, which are weighting coefficients. The time of waterlogging and waterlogging, T, is expressed as: T = H J / (K+G), where H J The depth of the water is represented by H. J =R Z ×S / 1000, where S is the area of ​​the low-lying area; K is the soil infiltration rate; and G is the groundwater discharge rate. Based on the waterlogging retention time T, the soil salinization risk index SSI is calculated and expressed as follows: SSI=i×T+j×(1 / K)+k×(1 / D); Where D is the groundwater depth; i, j, and k are weighting coefficients.

10. The method for graded monitoring and early warning of flood disasters in small watersheds based on watershed geomorphological characteristics according to claim 9, characterized in that, The calculated risk index CRI is expressed as follows: CRI=w1×FRI B +w2×LSI B +w3×DFI B +w4×SSI B ; Specifically, if 0 ≤ CRI < 0.25, it indicates low risk; if 0.25 ≤ CRI < 0.5, it indicates medium risk; if 0.5 ≤ CRI < 0.75, it indicates high risk; and if CRI ≥ 0.75, it indicates extremely high risk.

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