Karst SWAT model runoff simulation method coupled with early-stage rainfall index

Abstract

The invention relates to the field of karst basin runoff simulation calculation, and particularly discloses a karst SWAT model runoff simulation method coupled with an early-stage rainfall index, which comprises the following steps: step 1, establishing an SWAT model with karst area characteristics, and generating a land utilization map with karst characteristics and a hydrological response unit HRUs with karst and non-karst distribution characteristics; 2, modifying a source code of the SWAT model, and constructing a K-SWAT model with a karst production convergence function; 3, on the basis of the K-SWAT model, a normalized early-stage rainfall index MIWAPI is embedded, and a K-W-D-SWAT model capable of recognizing the effect of a dry and wet event on the karst production and confluence process is constructed; and 4, optimizing parameters of the SWAT model, the K-SWAT model and the K-W-D-SWAT model by adopting an SUFI-2 algorithm in the SWAT-CUP software, and evaluating the runoff simulation level of each model in the karst area. The karst SWAT model runoff simulation method coupled with the early-stage rainfall index is suitable for karst area runoff process simulation with complex climate changes, and the simulation precision and rationality of the karst area hydrological process are improved.

Description

Technical Field

[0001] The invention relates to the field of runoff simulation calculation in karst watersheds, and in particular to a karst SWAT model runoff simulation method coupled with an antecedent precipitation index. Background Art

[0002] About 25% of the world's population uses groundwater in karst areas. With the intensification of climate change and the increasing impact of human activities, as well as the unique hydrogeological structure and frequent alternation of dry and wet events in karst areas, regional hydrological processes have become increasingly complex, resulting in frequent flood disasters in karst areas, expanding scope and increasing intensity, seriously threatening water security in karst areas and the lives and property of local residents. Therefore, accurately identifying the effects of frequent alternations of dry and wet events on hydrological processes in karst areas and achieving accurate simulation of runoff in dry and wet seasons throughout the year are crucial to improving flood warning capabilities and water resources management levels in karst areas, and alleviating flood disasters and hydrological droughts. This is also the focus of research by scholars in recent years.

[0003] Since it is difficult to obtain comprehensive data on the hydrogeology and geometric characteristics of karst systems, most previous karst hydrological studies have failed to fully describe the integrity of karst hydrological systems and the differences in spatial distribution characteristics. Especially in highly complex karst areas, ignoring the integrity of karst will lead to unreasonable deviations in the water contribution of runoff systems. Secondly, due to the large extreme spatial variability of rainfall and karst distribution characteristics, the frequent alternation of dry and wet events may become a key factor affecting model performance. In seasonal rivers, models often overestimate low flows and underestimate high flows, reflecting the sensitivity of runoff systems to seasonal changes and their lack of dynamic response capabilities. Although some studies have improved model accuracy by separating simulations and reorganizing them on a dry and wet seasonal scale, they still face the problems of static parameters and temporal discontinuity of simulation elements. In addition, strongly developed karst areas are also significantly different from karst areas in terms of meteorological and hydrological characteristics, especially in terms of frequent alternation of extreme dry and wet events. Therefore, when studying hydrological processes in karst areas, it is necessary to comprehensively consider the structural characteristics of the karst system and the combined effects of climate change on hydrological processes.

[0004] Runoff simulation is a key technical means for regional flood warning and water resources management. However, for the study of runoff simulation in karst areas where dry and wet events frequently alternate, most models have not fully considered the effect of climate conditions (such as precipitation characteristics) on the change process of runoff generation and sink capacity, and the static nature of model parameters limits the dynamic response ability of runoff generation and sink process, which restricts the further development of hydrological models. Therefore, it is a key issue to be solved in the current field of hydrological simulation to develop a karst hydrological model that can accurately identify dry and wet events, enhance the dynamic response ability of the model to the spatial characteristics of the basin and climate change, and realize the accurate simulation of the hydrological process in the karst basin during the dry and wet seasons throughout the year. Summary of the invention

[0005] The present invention aims to solve at least one of the above-mentioned technical problems and provide a karst SWAT model runoff simulation method coupled with antecedent precipitation index, which is suitable for simulating runoff processes in karst areas with complex climate change. This method breaks through the time and space scale limitations of fixed parameters in traditional hydrological models and improves the simulation accuracy and rationality of hydrological processes in karst areas.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: a karst SWAT model runoff simulation method coupled with antecedent precipitation index, comprising the following steps:

[0007] Step 1: Establish a SWAT model with karst area characteristics. According to the basin boundary vector map, hydrogeological map and terrain elevation data, determine the surface and underground boundaries of the basin. Use GIS technology to superimpose the hydrogeological map, land use map and depression distribution map to generate a land use map with karst characteristics. Combined with soil data and slope data, generate hydrological response units HRUs with karst and non-karst distribution characteristics.

[0008] Step 2: Modify the SWAT model source code and construct a K-SWAT model with karst flow generation and confluence functions. The K-SWAT model includes three major systems: surface karst zone, karst matrix and karst pipeline.

[0009] Step 3: On the basis of the K-SWAT model, the normalized antecedent precipitation index MIWAPI is embedded to construct a karst KWD-SWAT model with a dry-wet effect identification mechanism, so as to accurately identify the effects of dry-wet events on the karst runoff generation and convergence process, and realize the simulation of the basin response in the dry and wet seasons throughout the year;

[0010] Step 4: Use the SUFI-2 algorithm in the SWAT-CUP software to optimize the parameters of the SWAT model, K-SWAT model, and KWD-SWAT model, and use the determination coefficient R 2 , Nash efficiency coefficient NSE, percentage deviation PBIAS and Kling-Gupta efficiency coefficient KGE are used to evaluate the runoff simulation level of each model in karst areas.

[0011] Preferably, the water balance equation of the epikarst zone system is:

[0012]

[0013] The water balance equation of the karst matrix system is:

[0014]

[0015] The water balance equation of the karst pipeline system is:

[0016]

[0017] In the formula, E H is the surface karst zone water level; P r，i is the recharge of the surface karst zone on the i-th day; Q sec The secondary spring flow is triggered by the surface karst zone; Q EH is the delayed outflow; Q sepbtm,i Q is the amount of recharge from the surface karst zone to the matrix on the i-th day; F,i is the recharge amount that seeps into the karst pipeline on the ith day; E E,T is the evaporation of the surface karst zone system; M is the water level of the matrix system; Q s，i is the diffusion recharge from the surface karst zone to the karst matrix system on the i-th day; Q sc is the amount of water exchanged from the pipeline to the matrix system; Q SM is the delayed outflow of the matrix system; E T，1 is the evaporation amount of the matrix system; Q FC is the rapid outflow of the pipeline; E T，2 is the evaporation capacity of the piping system.

[0018] Preferably, the description formula of the dry and wet events is as follows:

[0019] Based on the weighted average values ​​of the previous precipitation index WAPI and IWAPI, regional dry and wet events are identified. The calculation formulas of WAPI and IWAPI are:

[0020]

[0021]

[0022] The above formula is normalized and IWAPI is scaled to [-1,1] to indicate the intensity of drought and flood:

[0023]

[0024] Based on MIWAPI, the description parameters of flow generation and convergence capacity are adjusted. The specific adjustment method is shown in the following formula:

[0025]

[0026] In the formula, P a is the average annual rainfall; if the model parameters have a promoting effect on the runoff generation and sink process, ω = 1, otherwise it is -1, Kopt is the number of days of early precipitation impact; N opt is the decay coefficient; i is the simulation time; j represents the HRU number; Parm origin Original parameters assigned to the SUFI-2 algorithm

[0027] Preferably, in step 3, the SCS-CN equation for calculating surface runoff in the SWAT model is used to dynamically adjust the initial interception rate λ in the SCS-CN model for calculating surface runoff, and the calculation formula is:

[0028]

[0029] I a =λS (26)

[0030] In the formula, Q surf is surface runoff; P day is the precipitation on that day; I a is the initial loss; S is the maximum interception; λ is the initial loss rate, which is defined as 0.2 in the SWAT model.

[0031] Preferably, in step 4, the determination coefficient R is used 2 , Nash efficiency coefficient NSE, deviation percentage PBIAS and Kling-Gupta efficiency coefficient KGE are used to evaluate the runoff simulation level of each model in the karst area. The calculation formula is:

[0032]

[0033]

[0034] Among them, Q0, Q s are observed runoff and simulated runoff, respectively. represents the average values ​​of runoff observation and simulation, n is the length of the sequence, r is the correlation coefficient, α is the ratio of the standard deviation of simulated runoff to observed runoff, β is the ratio of the average simulated runoff to observed runoff; m is the ranking of the samples in descending order, and P is the frequency of flow exceeding a certain intensity during the observation period.

[0035] Preferably, in step 4, the flow duration curve FDC is used to further evaluate the runoff simulation level of each model in the karst area.

[0036] The beneficial effect is that compared with the prior art, the karst SWAT model runoff simulation method coupled with the previous precipitation index of the present invention has the following advantages:

[0037] 1. The runoff simulation method of the karst SWAT model coupled with the previous precipitation index of the present invention breaks through the time and space scale limitations of the fixed parameters in the traditional hydrological model, improves the simulation level and rationality of the hydrological process in the karst area, and is suitable for the simulation of the runoff process in the karst area with complex climate change. By embedding the karst system into the SWAT model, the K-SWAT model is proposed, and the normalized previous precipitation index MIWAPI is introduced, and the KWD-SWAT model is further proposed. Compared with the traditional SWAT model, the K-SWAT and KWD-SWAT models not only significantly improve the accuracy of runoff simulation in the karst area, but also can more realistically reflect the runoff generation and convergence process in the karst area;

[0038] 2. The KWD-SWAT model proposed in the present invention realizes the dynamic update of parameters on the temporal and spatial scales by coupling the normalized previous precipitation index MIWAPI, so that its runoff generation and convergence process responds to the conversion of dry and wet effects in real time, breaking through the limitation of the single temporal and spatial scale of parameters in traditional hydrological models;

[0039] 3. The KWD-SWAT model proposed in this invention significantly improves the runoff simulation accuracy during wet and dry periods, solving the problem of SWAT underestimating high flows and K-SWAT overestimating low flows;

[0040] 4. The KWD-SWAT model proposed in this invention is driven by climate conditions and is theoretically applicable to research areas with different hydrological conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The specific embodiments of the present invention are further described in detail below with reference to the accompanying drawings, wherein:

[0042] Figure 1 It is a flow chart of a karst SWAT model runoff simulation method coupled with antecedent precipitation index according to the present invention;

[0043] Figure 2 A schematic diagram of the Diaojiang River Basin in a specific embodiment of the present invention;

[0044] Figure 3 It is the multi-year monthly average rainfall and runoff process of the two catchment areas of Hekou Station and Malong Station;

[0045] Figure 4 The runoff simulation results of the three models SWAT, K-SWAT and KWD-SWAT at the Hekou Station and Malong Station;

[0046] Figure 5 FDC results of runoff simulation at the Hekou Station and Malung Station for the SWAT, K-SWAT and KWD-SWAT models. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] It should be noted that when a component is referred to as being "fixed to" another component, it may be directly on the other component or there may be a central component. When a component is considered to be "connected to" another component, it may be directly connected to the other component or there may be a central component at the same time. When a component is considered to be "set on" another component, it may be directly set on the other component or there may be a central component at the same time. When a component is referred to as being "set in the middle", it does not only mean being set in the middle, as long as it is not set at both ends within the range defined by the middle. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0050] like Figure 1 As shown, the present application discloses a karst SWAT model runoff simulation method coupled with antecedent precipitation index, comprising the following steps:

[0051] Step 1: Establish a SWAT model with karst area characteristics. According to the basin boundary vector map, hydrogeological map and terrain elevation data, determine the surface and underground boundaries of the basin. Use GIS technology to superimpose the hydrogeological map, land use map and depression distribution map to generate a land use map with karst characteristics. Combined with soil data and slope data, generate hydrological response units HRUs with karst and non-karst distribution characteristics.

[0052] Step 2: Modify the SWAT model source code and construct a K-SWAT model with karst flow generation and confluence functions. The K-SWAT model includes three major systems: surface karst zone, karst matrix and karst pipeline.

[0053] Step 3: On the basis of the K-SWAT model, the normalized antecedent precipitation index MIWAPI is embedded to construct a karst KWD-SWAT model with a dry-wet effect identification mechanism, so as to accurately identify the effects of dry-wet events on the karst runoff generation and convergence process, and realize the simulation of the basin response in the dry and wet seasons throughout the year;

[0054] Step 4: Use the SUFI-2 algorithm in the SWAT-CUP software to optimize the parameters of the SWAT model, K-SWAT model, and KWD-SWAT model, and use the determination coefficient R 2 , Nash efficiency coefficient NSE, percentage deviation PBIAS and Kling-Gupta efficiency coefficient KGE are used to evaluate the runoff simulation level of each model in karst areas.

[0055] like Figure 2 As shown, the present invention takes the typical karst basin of Diaojiang in southwest China as an example for specific description, and includes the following steps:

[0056] Step 1. Construct a SWAT model project of karst distribution characteristics in the Diaojiang River Basin. Determine the surface and underground boundaries of the basin based on the basin boundary vector map, hydrogeological map, and terrain elevation data. Use GIS technology to superimpose hydrogeological maps, land use maps, and depression distribution maps to generate a land use map with karst distribution characteristics, and superimpose soil data and slope data (divided into four levels: 0–15°, 15–30°, 30–45°, and >45°). A combined threshold of 15% is used to generate 579 hydrological response units (HRUs) for the Diaojiang River Basin, including 441 karst HRUs and 138 non-karst HRUs.

[0057] Step 2: Secondary development of the SWAT model was carried out based on the Visual studio 2019 compiler software. The development content mainly included integrating the karst structure into the SWAT model source code, and adding the identification and judgment function of karst HRU and non-karst HRU. Finally, the developed source code was recompiled and the executable file was regenerated, that is, the K-SWAT model with karst flow generation and convergence function. The K-SWAT model includes three major systems: surface karst zone, karst matrix and karst pipeline. The SWAT model source code was modified to construct a K-SWAT model with karst flow generation and convergence function. The K-SWAT model includes three major systems: surface karst zone, karst matrix and karst pipeline:

[0058] a) Epikarst zone system

[0059] The recharge of the surface karst zone comes from the direct infiltration recharge at the bottom of the soil profile or the exposed surface, and its water balance equation can be expressed as:

[0060]

[0061] In the formula, E H is the surface karst zone water level, mm; P r,i is the water recharge of the surface karst zone on the i-th day, mm; Q sec Q is the flow rate of secondary springs triggered by the surface karst zone, mm; EH is the delayed outflow of the surface karst zone, mm; Q sepbtm，i is the amount of recharge from the surface karst zone to the matrix on the i-th day, mm; Q F，i is the recharge amount from the surface karst zone to the karst pipeline on the i-th day, mm; E E，T is the evaporation amount, mm.

[0062] Considering the time lag effect of soil or bare surface recharge to the surface karst zone, an exponential decay weight function is used to express it:

[0063]

[0064] Where, P prec，i is the amount of water supplied from the bottom of the soil profile or the exposed surface on the i-th day, mm; δ is the time required for water to leave the bottom of the soil profile or the exposed surface and reach the surface karst zone, day.

[0065] In epikarst reservoirs, nonlinear hysteresis transfer functions can be used to describe delayed outflow and to characterize the hydrological connectivity of the karst zone through reservoir water level changes. eh :

[0066]

[0067] In the formula, ε eh is the hydrological connectivity of the epikarst zone; E H，1 is the threshold water depth for delayed outflow in the epikarst zone, mm; E H，2 is the threshold water depth for secondary flow to occur in epikarst springs, mm.

[0068] Combined with the hydrological connectivity of the karst zone ε eh Calculation of nonlinear time-delay outflow Q in epikarst zone EH :

[0069]

[0070] In the formula, k eh is the outflow coefficient of delayed outflow in the surface karst zone; α eh is the exponential coefficient of delayed outflow in the epikarst zone.

[0071] When the water level E H Reach a certain threshold water level E H,2 , secondary flow Q will be generated from the surface karst springs sec , which can be described as:

[0072]

[0073] In the formula, k sec Outflow coefficient of secondary flow triggered by epikarst zone.

[0074] For the water recharge process from the surface karst zone to the matrix system, the exponential decay weight function of the SWAT model applied to the groundwater response model is used to describe the hysteresis effect of the karst matrix receiving water recharge from the surface karst zone:

[0075]

[0076] In the formula, δ s It indicates the time required for water to leave the bottom of the epikarst zone and reach the karst aquifer zone, day.

[0077] The process of water infiltration from the surface karst zone to the pipeline reservoir is a linear and rapid replenishment, and this module calculation is only enabled in the area of ​​underground rivers:

[0078] Q F,i =k f ×(max[E H -E H,1 ,0]

[0079] In the formula, k f It is the flow coefficient transferred from the surface karst zone to the pipeline.

[0080] b) Karst matrix system and karst pipeline system

[0081] For the karst matrix system, the water balance formula is:

[0082]

[0083] Where, M is the water level of the matrix reservoir, mm; Q s,i is the diffusion recharge from the surface karst zone to the karst reservoir on the i-th day, mm; Q sc is the amount of exchange water transported from the pipeline to the matrix reservoir, mm; Q SM is the delayed outflow of the matrix reservoir, i.e., slow flow, mm; E T，1 is the evaporation of the matrix reservoir, mm.

[0084] The delay of water supply between the surface karst zone and the karst matrix. This application uses an exponential decay weight function to describe the karst matrix receiving the surface karst zone infiltration water Q sepbtm，i The hysteresis effect:

[0085]

[0086] In the formula, δSM Delay coefficient of recharge of karst aquifer matrix by epikarst zone, day.

[0087] Characterize the outflow of the matrix reservoir using a linear storage-discharge relationship:

[0088]

[0089] In the formula, δ M Matrix outflow delay factor, day.

[0090] Considering the water exchange Q between the matrix and the pipeline reservoir SC , defined as the system water level difference function:

[0091]

[0092] In the formula, k sc is the exchange coefficient between the matrix and the channel; α SC is the exchange index between the matrix and the pipeline;

[0093] C is the water level of the pipeline reservoir, mm.

[0094] For the karst pipe system, the water balance is as follows:

[0095]

[0096] In the formula, Q FC is the rapid outflow of the pipeline, mm; E T，2 is the evaporation of the pipeline reservoir, mm.

[0097] Unlike the matrix system, the rapid outflow process of the pipe system is represented by an exponential function:

[0098]

[0099] In the formula, k FC is the outflow coefficient of pipe flow; α FC is the specific flow coefficient of pipeline flow; C H is the threshold water depth of the pipeline, mm.

[0100] Step 3: On the basis of the K-SWAT model, the normalized previous precipitation index MIWAPI is embedded into the K-SWAT model source code to integrate the dry-wet effect feedback mechanism. The developed source code is compiled using Visual studio 2019 software, and the executable file is regenerated to build a karst KWD-SWAT model with a dry-wet effect identification mechanism to accurately identify the effects of dry-wet events on the karst runoff process and realize the basin response simulation in the dry and wet seasons throughout the year. Specifically, the weighted average values ​​of the previous precipitation index WAPI and IWAPI are used to identify regional dry-wet events. The calculation formulas of WAPI and IWAPI are as follows:

[0101]

[0102] The above formula is normalized to reduce IWAPI to [-1,1] to indicate the intensity of drought and flood:

[0103]

[0104] Based on MIWAPI, the flow generation and convergence capacity description parameters are adjusted as shown in the following formula:

[0105]

[0106] In the formula, P a is the average annual rainfall, the range of IWAPI is [-1,1], if the model parameter has a promoting effect on the runoff generation and sink process, ω=1, otherwise it is -1, Kopt is the previous impact days, day; N opt Decay coefficient; i represents the simulation time, day; j represents the HRU number; Parm origin Original parameters assigned to the SUFI-2 algorithm.

[0107] In addition, the initial interception rate λ (which has an inhibitory effect on the runoff generation and sink process) in the SCS-CN equation used by the SWAT model to calculate surface runoff was dynamically adjusted. The core formula of SCS-CN is expressed as follows:

[0108]

[0109] I a =λS

[0110] In the formula, Q surf is surface runoff, mm; P day is the daily precipitation, mm; I a is the initial loss, mm; S is the maximum interception, mm; λ is the initial loss rate, which is defined as 0.2 in the SWAT model.

[0111] Step 4: Use the SUFI-2 algorithm in the SWAT-CUP software to optimize the parameters of the original SWAT model, K-SWAT model, and KWD-SWAT model. 2 , Nash efficiency coefficient NSE, deviation percentage PBIAS and Kling-Gupta efficiency coefficient KGE, as well as flow duration curve FDC further evaluate the simulation performance of the model in simulating flow levels of different magnitudes (runoff in wet and dry seasons). The relevant calculation formula is as follows:

[0112]

[0113] Among them, Q0, Q s are observed runoff and simulated runoff, respectively. They represent the average values ​​of runoff observation and simulation respectively, n is the length of the sequence, r is the correlation coefficient, α is the ratio of the standard deviation of simulated runoff to observed runoff, β is the ratio of the average simulated runoff to observed runoff; m is the rank of the samples in descending order, and P is the frequency of flow exceeding a certain intensity during the observation period.

[0114] To verify the applicability of the method, the present invention takes the Diaojiang River Basin, a typical karst basin in southwestern China, as an example, uses the rainfall station, meteorological station and underlying surface topography data in the basin to build a model, and selects the runoff observation data of the estuary station and the Malung station as the verification object of the model. The three versions of the SWAT model are set as follows: 2007 as the warm-up period, 2008-2015 as the calibration period, and 2016-2020 as the verification period. The sensitive parameters of the three models were calibrated using the SUFI-2 algorithm, and the coefficient of determination R 2 , Nash efficiency coefficient NSE, deviation percentage PBIAS and Kling-Gupta efficiency coefficient KGE were used to evaluate the model accuracy. At the same time, the simulation period was divided into two periods: the flood and dry periods. The performance of various indicators of the simulation results of each model in different hydrological stages and the flow continuity curve FDC results were compared.

[0115] Diaojiang River is a first-level tributary of the Hongshui River in the Xijiang River system of the Pearl River Basin. It is 229 km long and has an altitude range of 80–1148 m. The terrain is undulating, with a maximum height difference of 200–500 m. The basin controls an area of ​​approximately 3,601 km. 2 . The region has a subtropical monsoon climate, with an average annual temperature of 15.6–22.9°C. Affected by tropical cyclones, the Diaojiang River Basin has abundant precipitation, but due to the complex terrain and the development of karst landforms, the temporal and spatial distribution of precipitation is extremely uneven. The rainfall from May to September each year accounts for 70% of the total annual rainfall, which is prone to floods and waterlogging disasters. The rainfall in the remaining months only accounts for 30%, which is prone to drought and water shortage problems. The average annual precipitation in the basin is 1400–1700 mm, floods occur frequently, and the average annual runoff is as high as 2.43 billion m3 The Diaojiang River Basin is as follows: Figure 2 shown.

[0116] The basic data for constructing the SWAT model project in the present invention include DEM terrain elevation, land use type, soil type, hydrogeological map, precipitation, maximum and minimum temperature, relative humidity, average wind speed, sunshine duration and measured runoff data. The relevant information of the data is shown in Table 1.

[0117] Table 1 Data attributes used in the present invention

[0118]

[0119] The present invention uses the daily precipitation data of 14 rain gauges in the Diaojiang River Basin, and the daily maximum and minimum temperatures, relative humidity, sunshine duration and average wind speed data of 6 meteorological stations, with a time span of 2007-2020, to drive the three hydrological models of SWAT, K-SWAT and KWD-SWAT for runoff simulation. At the same time, the three models of SWAT, K-SWAT and KWD-SWAT are calibrated and verified using the daily runoff data of Hekou Station and Malong Station from 2008 to 2020. The data of the rain gauge and hydrological station are derived from the "China Hydrological Yearbook", and the data of the meteorological station are obtained from the China Meteorological Data Network. The details of the relevant data are shown in Table 2.

[0120] Table 2 Hydrological and meteorological station information in the study area

[0121]

[0122] Parameters are crucial to the runoff simulation results of the model, so parameter calibration is required. In this study, the SUFI-2 algorithm was used to calibrate the parameters of the three models: SWAT, K-SWAT, and KWD-SWAT. Table 3 lists the initial ranges of sensitive parameters of the three models.

[0123] Table 3 Description and range of model calibration parameters

[0124]

[0125] (R_) represents the relative change of the parameter, where the current value is multiplied by 1 plus a factor; (*) represents a positive effect on runoff generation, i.e. w = 1, and (**) represents w = -1.

[0126] Table 4 lists the optimal values ​​and sensitivity ranking results of the three models SWAT, K-SWAT, and KWD-SWAT using the SUFI-2 algorithm for parameter calibration at the Hekou Station and the Malong Station. For SWAT, the zoning calibration results of the two stations show that CH_N2 is the most sensitive parameter, followed by CN2, CH_K2, ALPHA_BNK, GWQMN, and GWQMN. The main sensitive parameters of K-SWAT are α_sc, h_(M,0), δ_s, GW_DELAY, E_(H,2), and GW_REVAP, highlighting the significant impact of karst structure on hydrological processes. For KWD-SWAT considering dry and wet events, CN2 is one of the main sensitive parameters of the Hekou Station, indicating that there is a certain proportion of non-karst areas in the catchment area controlled by this station, and the surface structure still plays an important role in the runoff generation process. As the most sensitive parameter of the Malong Station, N_opt further confirms the significance of drought and flood events in the karst area.

[0127] Table 4 Optimal parameter values ​​of the three models for controlling the catchment area at two hydrological stations

[0128]

[0129]

[0130] Table 5 and Figure 4 The runoff simulation results of SWAT, K-SWAT, and KWD-SWAT at the Hekou Station and the Malong Station are shown. The runoff simulation accuracy of the original SWAT model at the two stations is low, especially at the Malong Station. 2 and NSE are both lower than 0.60. The simulation performance of K-SWAT considering the karst system structure is significantly improved: R 2 and NSE are both higher than 0.70, which are 0.10–0.27 and 0.11–0.18 higher than the original SWAT, respectively. However, the PBIAS and KGE results show that the K-SWAT model has a serious overestimation of runoff, especially at the Malong station with rich karst distribution (PBIAS during the calibration period: -45.1%, validation period: -49.2%; KGE during the calibration period: 0.54, validation period: 0.50). This deviation may be due to the frequent alternation of dry and wet events in the karst area, which makes it difficult for the K-SWAT model to coordinate runoff simulation during the wet and dry seasons. In contrast, the KWD-SWAT model incorporating the MIWAPI index significantly improved the runoff simulation performance at both hydrological stations, outperforming the SWAT and K-SWAT models in both the calibration and validation periods, and the reduction in PBIAS indicates that KWD-SWAT effectively reduces the overestimation of low flows by K-SWAT. In addition, the KWD-SWAT model showed a significant improvement in the PBIAS and KGE indicators at the Malong station, indicating that dry and wet events in karst areas are more frequent than in non-karst areas. Figure 4 The runoff simulation results shown in the figure show that K-SWAT and KWD-SWAT have a stronger ability to capture flood events, which can reflect the rapid recharge process of underground pipelines in karst areas, and the simulation accuracy is significantly better than the original SWAT. Most importantly, KWD-SWAT can better demonstrate the ability to simulate hydrological processes that take into account drought and flood events by considering the impact of dry and wet events on the runoff generation and sink process.

[0131] Table 5 Comparison of runoff calibration and validation performance of three models in Diaojiang River Basin

[0132]

[0133]

[0134] based on Figure 3 The multi-year average monthly rainfall and runoff change process is shown, with May to September as the wet season and October to April of the following year as the dry season. Table 6 shows the runoff simulation results of the three models SWAT, K-SWAT, and KWD-SWAT during the wet and dry seasons. The model performance comparison is as follows: During the wet season, the original SWAT performed poorly and did not meet the basic requirements of simulation accuracy, while K-SWAT showed significant improvement, R 2 The KWD-SWAT model performed better than the previous two in all aspects, with the best performance in KGE, which exceeded 0.70, and in R 2 , NSE, and PBIAS have all been improved to varying degrees, effectively alleviating the misestimation of high flows in the flood season by SWAT and K-SWAT. In the dry season, the simulation capability of the original SWAT is not outstanding, especially the PBIAS during the verification period at the estuary station is large (-32.18%), and low flows are overestimated. Similarly, the performance of K-SWAT in the dry season is not as good as that in the flood season, and indicators in all aspects are at a poor level. In contrast, the performance of the KWD-SWAT model has improved, with KGE higher than 0.60 and PIBIAS lower than ±20%, demonstrating better simulation capabilities. At the same time, the runoff simulation performance of extreme dry and wet events is evaluated based on the flow duration curve (FDC) results of the three models at the estuary station and Malung station. Figure 5 Overall, the KWD-SWAT model results during the entire simulation period fit the FDC of the measured runoff best, and KWD-SWAT showed the best ability in dealing with high flow (0–0.1 flow exceedance probability) and low flow events (0.9–1.0 flow exceedance probability). Figure 5It is obvious that SWAT and K-SWAT have defects in simulating different flow levels at the two stations, indicating that this type of model has weak coordination ability in runoff simulation during the entire period and is not suitable for the region. In high-flow events, the model performance ranking is: KWD-SWAT>K-SWAT>SWAT. The flood peak simulation level of SWAT is not as good as that of K-SWAT and KWD-SWAT, and the gap widens further with the increase in the proportion of karst area. In low-flow events, KWD-SWAT also performs best, while K-SWAT performs relatively poorly in areas with more intense karst development (Malong Station). In general, although the K-SWAT model takes into account the karst structure, it still has deficiencies in simulating different flow levels in karst areas where dry and wet events frequently alternate, reflecting the high dynamics and complex variability of the runoff generation and convergence process in karst areas. This result shows that it is difficult to fully capture the hydrological characteristics of karst areas only by parameterizing the karst structure, especially when the dry and wet alternation is significant. KWD-SWAT fully considers these factors, making it the best in simulating the details of the hydrological process.

[0135] Table 6 Comparison of runoff simulation performance of three models during dry and wet events in Diaojiang River Basin

[0136]

[0137] In summary, the runoff simulation method of the karst SWAT model coupled with the previous precipitation index proposed in the present invention has greatly improved the runoff simulation accuracy compared with the traditional hydrological model. The present invention takes into account the effects of karst structure and dry and wet events on hydrological processes, and can meet the high dynamics and variability of runoff generation and convergence processes in karst areas. It has great practical significance for conducting regional runoff simulation research with climate change and complex underlying surfaces, and has good application prospects.

[0138] The above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Any modification or equivalent substitution that does not depart from the spirit and scope of the present invention shall be included in the scope of the technical solution of the present invention.

Claims

1. A karst SWAT model runoff simulation method coupled with antecedent precipitation index, characterized in that: The following steps are involved: Step 1: Establish a SWAT model with karst area characteristics. According to the basin boundary vector map, hydrogeological map and terrain elevation data, determine the surface and underground boundaries of the basin. Use GIS technology to superimpose the hydrogeological map, land use map and depression distribution map to generate a land use map with karst characteristics. Combined with soil data and slope data, generate hydrological response units HRUs with karst and non-karst distribution characteristics. Step 2: Modify the SWAT model source code and construct a K-SWAT model with karst flow generation and confluence functions. The K-SWAT model includes three major systems: surface karst zone, karst matrix and karst pipeline. Step 3: On the basis of the K-SWAT model, the normalized antecedent precipitation index MIWAPI is embedded to construct a karst KWD-SWAT model with a dry-wet effect identification mechanism, so as to accurately identify the effects of dry-wet events on the karst runoff generation and convergence process, and realize the simulation of the basin response in the dry and wet seasons throughout the year; Step 4: Use the SUFI-2 algorithm in the SWAT-CUP software to optimize the parameters of the SWAT model, K-SWAT model, and KWD-SWAT model, and use the determination coefficient R 2 , Nash efficiency coefficient NSE, percentage deviation PBIAS and Kling-Gupta efficiency coefficient KGE are used to evaluate the runoff simulation level of each model in karst areas.

2. The karst SWAT model runoff simulation method coupled with antecedent precipitation index according to claim 1 is characterized in that: The water balance equation of the epikarst zone system is: The water balance equation of the karst matrix system is: The water balance equation of the karst pipeline system is: In the formula, E H is the surface karst zone water level; P r,i is the recharge of the surface karst zone on the i-th day; Q sec The secondary spring flow is triggered by the surface karst zone; Q EH is the delayed outflow; Q sepbtm,i is the amount of recharge from the surface karst zone to the matrix on the i-th day; Q F,i is the recharge amount that seeps into the karst pipeline on the ith day; E E,T is the evaporation of the surface karst zone system; M is the water level of the matrix system; Q s,i is the diffusion recharge from the surface karst zone to the karst matrix system on the i-th day; Q sc is the amount of water exchanged from the pipeline to the matrix system; Q SM is the delayed outflow of the matrix system; E T,1 is the evaporation amount of the matrix system; Q FC is the rapid outflow of the pipeline; E T,2 is the evaporation capacity of the piping system.

3. The karst SWAT model runoff simulation method coupled with antecedent precipitation index according to claim 1 is characterized in that: The description formula of the dry and wet events is as follows: Based on the weighted average values ​​of the previous precipitation index WAPI and IWAPI, regional dry and wet events are identified. The calculation formulas of WAPI and IWAPI are: The above formula is normalized and IWAPI is scaled to [-1,1] to indicate the intensity of drought and flood: Based on MIWAPI, the description parameters of flow generation and convergence capacity are adjusted. The specific adjustment method is shown in the following formula: In the formula, P a is the average annual rainfall; if the model parameters have a promoting effect on the runoff generation and sink process, ω = 1, otherwise it is -1, Kopt is the number of days of early precipitation impact; N opt is the decay coefficient; i is the simulation time; j represents the HRU number; Parm origin Original parameters assigned to the SUFI-2 algorithm.

4. The karst SWAT model runoff simulation method coupled with antecedent precipitation index according to claim 1 is characterized in that: In the step 3, the initial interception rate λ in the SCS-CN equation used to calculate surface runoff in the SWAT model is dynamically adjusted, and the calculation formula is: I a =λS (26) In the formula, Q surf is surface runoff; P day is the precipitation on that day; I a is the initial loss; S is the maximum interception; λ is the initial loss rate, which is defined as 0.2 in the SWAT model.

5. The karst SWAT model runoff simulation method coupled with antecedent precipitation index according to claim 1 is characterized in that: In step 4, the determination coefficient R 2 , Nash efficiency coefficient NSE, deviation percentage PBIAS and Kling-Gupta efficiency coefficient KGE are used to evaluate the runoff simulation level of each model in the karst area. The calculation formula is: Among them, Q0, Q s are observed runoff and simulated runoff, respectively. They represent the average values ​​of runoff observation and simulation respectively, n is the length of the sequence, r is the correlation coefficient, α is the ratio of the standard deviation of simulated runoff to observed runoff, β is the ratio of the average simulated runoff to observed runoff; m is the rank of the samples in descending order, and P is the frequency of flow exceeding a certain intensity during the observation period.

6. The method for simulating runoff using a karst SWAT model coupled with antecedent precipitation index according to claim 5, characterized in that: In the step 4, the flow duration curve FDC is used to further evaluate the runoff simulation level of each model in the karst area.

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