A CN value correction method considering soil moisture changes applicable to the Pearl River Basin

Abstract

The present invention proposes a CN value correction method that takes soil moisture changes into consideration and is applicable to the Pearl River Basin, including: calculating the soil curve number of the normal wetness of the sub-basin based on the soil and land use data of the Pearl River Basin, substituting it as the initial value into several groups of typhoon-runoff simulations for calibration, obtaining the calibrated CN value, and then calculating the CN correction coefficient under each typhoon; obtaining the soil moisture rise value 5 days before each typhoon based on the remote sensing inversion data of soil moisture content; constructing a regression relationship model between the CN correction coefficient and the soil moisture rise value; based on the regression relationship model, calculating the CN correction coefficient for the typhoon in the verification period and verifying its accuracy, and comparing it with the original SCS method. The present invention is a method that can improve the simulation accuracy of the model under different hydrological conditions.

Description

Technical Field

[0001] The invention belongs to the field of hydrological models, and in particular relates to a CN value correction method for considering soil moisture changes applicable to the Pearl River Basin. Background Art

[0002] In hydrological simulation, the SCS model is often nested in other hydrological models as a runoff module for simulating surface runoff. It introduces the runoff curve number CN to generalize the runoff capacity of the basin. The traditional SCS-CN method is based on an empirical model obtained under natural conditions in the United States. Direct use in the study area of ​​my country will produce large errors. In addition, when simulating rainfall-runoff in different scenarios, due to differences in underlying surface conditions, the simulation accuracy is difficult to guarantee and parameter correction is required. Therefore, it is necessary to select a characteristic value that can reflect the actual simulation scenario to improve the SCS model. At present, most studies have optimized it from the perspective of previous rainfall, but there is still a certain error with the actual soil moisture. Summary of the invention

[0003] The purpose of the present invention is to propose a CN value correction method that takes soil moisture changes into consideration and is applicable to the Pearl River Basin, so as to improve the simulation accuracy of the model under different hydrological conditions.

[0004] To achieve the above object, the present invention provides a CN value correction method taking into account soil moisture changes applicable to the Pearl River Basin, comprising:

[0005] Calibrate several sets of typhoon data in the Pearl River Basin to obtain the CN correction coefficient α for each typhoon i ;

[0006] Based on the soil moisture remote sensing inversion data, the soil moisture rise value W before each typhoon is obtained. i ;

[0007] Construct the CN correction coefficient α i and the soil moisture rise value W i The regression relationship model between α-W;

[0008] Based on the regression relationship model α-W, the CN correction coefficient of the typhoon in the verification period is calculated and the accuracy is verified.

[0009] Optionally, calibrate several sets of typhoon data in the Pearl River Basin to obtain the CN correction coefficient α for each typhoon i include:

[0010] Based on the soil type data and land classification data of the Pearl River Basin, an initial value CN2 of the runoff curve number under normal soil moisture level is obtained;

[0011] Based on the rainfall time series data and daily runoff data of the Pearl River Basin, the typhoons in the rated group were simulated to obtain the runoff curve data of each typhoon.

[0012] Based on the initial value CN2 of the runoff curve number under normal soil moisture and the runoff curve number determined by each typhoon Get the CN correction coefficient α of each typhoon i .

[0013] Optionally, the initial value CN2 of the runoff curve number under normal soil moisture is based on the soil type data and land classification data of the Pearl River Basin, and is obtained by querying the CN index table.

[0014] Optionally, the CN correction coefficient α i for:

[0015]

[0016] Among them, α i is the CN correction coefficient for the ith typhoon simulation, is the runoff curve number calibrated by the ith typhoon simulation, and CN2 is the initial value of the runoff curve number under normal soil moisture level.

[0017] Optionally, the regression relationship model α-W is:

[0018] α=f(W)=C1×W+C2

[0019] Among them, α=f(W) represents the linear regression relationship between the CN correction coefficient and the soil moisture rise value, and C1 and C2 are constant terms.

[0020] Optionally, based on the regression relationship model α-W, calculating the CN correction coefficient of the typhoon in the verification period and verifying the accuracy includes:

[0021] Typhoons within the validation period were selected, and the initial value CN2 of the runoff curve number under normal soil moisture level of each sub-basin was obtained based on soil type and land use classification data;

[0022] Based on the initial value CN2 of the runoff curve number under the normal soil moisture level of each sub-basin, combined with the regression relationship model α-W, the CN correction value α is obtained i , substitute it into the runoff generation module to simulate the runoff, and evaluate the accuracy based on the measured runoff;

[0023] The CN values ​​under different soil moisture levels were calculated according to the preset original SCS-CN formula. Different CN values ​​were used for runoff simulation and compared with the simulation results of CN correction values.

[0024] Optionally, the preset original SCS-CN formula is:

[0025]

[0026] Among them, CN1, CN2 and CN3 are the runoff curve numbers under three soil wetness levels: AMCI drought, AMCII normal and AMCIII wet.

[0027] Optionally, calculating the initial value of the runoff curve number under normal soil moisture level includes:

[0028] Establish land use grouping data and hydrological soil grouping data within the basin based on soil and land classification data;

[0029] Based on the land use grouping data and hydrological soil grouping data, the runoff curve data CN2(i) under normal soil moisture level of each cell in the basin is calculated with reference to the preset runoff curve number table (CN index table), where i is the cell number;

[0030] The grid data in the basin is converted to points, and then Thiessen polygons are established for the points to obtain the area weight P of the sub-basin occupied by each grid data. i ;

[0031] The area weight is area-weighted to obtain the composite value CN2(j) of the runoff curve number under normal soil moisture level of each sub-basin, and the composite value CN2(j) of the runoff curve number is used as the initial value of the runoff curve number, j is the sub-basin number, that is, the sub-basin CN2(j) is calculated according to the cell CN2(i).

[0032] The present invention has the following beneficial effects:

[0033] The present invention calibrates several groups of typhoon data in the Pearl River Basin to obtain the CN correction coefficient under each typhoon; and establishes a regression relationship model in combination with the soil moisture rise value before the typhoon, and then selects the typhoon in the verification period to verify the CN correction coefficient. The CN correction coefficient proposed by the present invention can improve the simulation accuracy of the model under different hydrological conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0035] Figure 1 Schematic diagram of a CN value correction method taking soil moisture changes into account in the Pearl River Basin according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0037] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] This embodiment proposes a method for correcting model parameters by introducing soil moisture remote sensing inversion data to improve the simulation accuracy of the model under different hydrological conditions. Multiple sets of typhoon data are calibrated for the Dongjiang and Beijiang basins of the Pearl River Basin to obtain the CN correction coefficient α under each typhoon; based on the soil moisture remote sensing inversion data, the soil moisture rise value W 5 days before each typhoon is extracted; the regression relationship α-W between the CN correction coefficient and the soil moisture rise value W of the Dongjiang and Beijiang rivers is determined by a fitting method; the fitted α-W regression relationship is applied to 4 sets of typhoon rainfall-runoff simulations to verify its simulation accuracy.

[0039] like Figure 1 As shown, the CN value correction method considering soil moisture changes applicable to the Pearl River Basin proposed in this embodiment includes:

[0040] Calibrate several sets of typhoon data in the Pearl River Basin to obtain the CN correction coefficient for each typhoon;

[0041] Specifically, in this embodiment, based on the existing soil type data and land classification data, the initial value of the runoff curve number under normal soil moisture level is calculated;

[0042] The methods for calculating the initial value of the runoff curve number include:

[0043] Based on soil and land classification data, the surface cover and hydrological soil grouping in the basin are established, and finally the runoff curve number distribution in the basin is calculated by combining the runoff curve number table. The weight of each grid data is obtained by establishing Thiessen polygons for the grid points, and the composite value of the runoff curve number of each basin is obtained by the area weighted method, which is used as the initial value for the subsequent calibration process.

[0044] Based on the existing rainfall time series data and daily runoff data, each typhoon is calibrated to obtain the calibrated runoff curve number of each typhoon;

[0045] Based on the initial value and rated value of the runoff curve number of each typhoon, the rated runoff curve number correction coefficient of each typhoon is obtained.

[0046] Based on the soil moisture remote sensing inversion data, the soil moisture rise value before each typhoon is obtained;

[0047] Construct a regression relationship model between the CN correction coefficient and the soil moisture rise value;

[0048] Based on the regression relationship model, the CN correction coefficient was verified.

[0049] Furthermore, based on the regression relationship model, the CN correction coefficient is verified including:

[0050] Typhoons within the validation period were selected, and the initial values ​​of the runoff curve numbers under normal soil moisture levels of each sub-basin were obtained based on soil type and land use classification data;

[0051] Based on the initial value of the runoff curve number under normal soil moisture level of each sub-basin, combined with the regression relationship model, the CN correction value is obtained to evaluate the runoff simulation accuracy;

[0052] The CN values ​​under different soil moisture levels were calculated according to the preset formula, and different CN values ​​were used for runoff simulation and compared with the simulation results of CN correction values.

[0053] The implementation steps of this embodiment are described in detail below;

[0054] Step 1: Determine the initial value CN2 of the basin runoff curve number;

[0055] Based on the existing soil type data and land classification data, the initial values ​​of the runoff curve number CN2 in the Beijiang and Dongjiang basins under normal soil moisture level (AMCII) were calculated respectively;

[0056]

[0057] CN1, CN2 and CN3 are the runoff curve numbers under three soil wetness levels: AMCI drought, AMCII normal and AMCIII wet.

[0058] The parameter CN is a quantitative indicator of the underlying surface conditions in the SCS model. The U.S. Soil and Water Conservation Service has produced an estimate of the curve number (CN table) for different soil hydrological groups (HSGs), cover types, land cultivation types, hydrological conditions, antecedent runoff conditions (ARCs), and different water areas in the basin.

[0059] Based on soil and land classification data, surface cover and hydrological soil grouping were established, and finally CN2 data in the basin was calculated by combining CN table. Thiessen polygons were established to obtain the weight of each grid data, and the CN2 composite value of each sub-basin was obtained by area weighting method.

[0060]

[0061] Where i is the grid point number in the sub-basin, j is the sub-basin number, and P is the area ratio of the grid point to the sub-basin. The CN2(j) of the sub-basin is obtained by compound calculation of the cell CN2(i), which is called the composite value.

[0062] The subsequent model calibration requires initial values, so the composite value of the runoff curve number CN2(j) is used as the initial value CN2 of the runoff curve number of each sub-basin for calibration.

[0063] Step 2: Runoff curve rate values ​​for each typhoon

[0064] Based on the existing GPM rainfall time series data and daily runoff data, the typhoons in the calibration group are simulated to obtain the calibration parameters of each typhoon.

[0065] Step 3: Correction coefficient α of runoff curve number under each typhoon i ;

[0066] Based on the initial value CN2 and rated value of each typhoon Get the CN2 correction coefficient α of each typhoon i ;

[0067]

[0068] i is the serial number of each typhoon, is the runoff curve number after calibration.

[0069] Step 4: The initial soil moisture rise value W under each typhoon i ;

[0070] Based on the soil moisture remote sensing inversion data, the soil moisture rise value W within 5 days before each typhoon was obtained. i ;

[0071] Step 5: Fitting the relationship between CN2 correction coefficient and soil water content rise value α-W;

[0072] Correction coefficient α based on typhoons in the basin i and soil moisture rise value W i , establish the regression relationship α-W between the two;

[0073] α=f(W)=C1×W+C2(5)

[0074] Among them, α=f(W) is the linear regression relationship between the CN correction coefficient and the soil moisture rise value.

[0075] Step 6: CN2 correction method verification

[0076] Typhoons during the validation period were selected, and the CN2 values ​​of each sub-basin were obtained based on soil type and land use classification data. The CN correction value was obtained by combining the α-W fitting relationship to evaluate the runoff simulation accuracy. Finally, the CN values ​​under three soil wetness levels were calculated according to formula (1), and the three values ​​were used for runoff simulation and compared with the simulation results of the CN correction value.

[0077] CN 修正 =CN2×f(W)(6)

[0078] This embodiment is a method for correcting the runoff module parameter CN based on the SCS model for different rainfall scenarios by introducing soil moisture remote sensing inversion data and establishing a regression relationship between the parameter and the underlying surface characteristic value.

[0079] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A CN value correction method considering soil moisture changes applicable to the Pearl River Basin, characterized in that: include: Calibrate several sets of typhoon data in the Pearl River Basin to obtain the CN correction coefficient α for each typhoon i ;include: Based on the soil type data and land classification data of the Pearl River Basin, an initial value CN2 of the runoff curve number under normal soil moisture level is obtained; Based on the rainfall time series data and daily runoff data of the Pearl River Basin, the typhoons in the rated group were simulated to obtain the runoff curve data of each typhoon. Based on the initial value CN2 of the runoff curve number under normal soil moisture and the runoff curve number determined by each typhoon Get the CN correction coefficient α of each typhoon i ; Based on the soil moisture remote sensing inversion data, the soil moisture rise value W before each typhoon is obtained. i ; Construct the CN correction coefficient α i and the soil moisture rise value W i The regression relationship model between α-W; The regression relationship model α-W is: α=f(W)=C1×W+C2 Among them, α = f(W) represents the linear regression relationship between the CN correction coefficient and the soil moisture rise value, and C1 and C2 are constant terms; Based on the regression relationship model α-W, the CN correction coefficient of the typhoon in the verification period is calculated and the accuracy is verified; including: Typhoons within the validation period were selected, and the initial value CN2 of the runoff curve number under normal soil moisture level of each sub-basin was obtained based on soil type and land use classification data; Based on the initial value CN2 of the runoff curve number under the normal soil moisture level of each sub-basin, combined with the regression relationship model α-W, the CN correction value α is obtained i , substitute it into the runoff generation module to simulate the runoff, and evaluate the accuracy based on the measured runoff; The CN values ​​under different soil moisture levels were calculated according to the preset original SCS-CN formula. Different CN values ​​were used for runoff simulation and compared with the simulation results of CN correction values.

2. The CN value correction method considering soil moisture changes applicable to the Pearl River Basin according to claim 1 is characterized in that: The initial value CN2 of the runoff curve number under the normal soil moisture level is the initial value of the runoff curve number under the normal soil moisture level obtained by querying the CN index table based on the soil type data and land classification data of the Pearl River Basin.

3. The CN value correction method considering soil moisture changes applicable to the Pearl River Basin according to claim 1 is characterized in that: The CN correction factor α i for: Among them, α i is the CN correction coefficient for the ith typhoon simulation, is the runoff curve number calibrated by the ith typhoon simulation, and CN2 is the initial value of the runoff curve number under normal soil moisture level.

4. The CN value correction method considering soil moisture changes applicable to the Pearl River Basin according to claim 1 is characterized in that: The preset original SCS-CN formula is: Among them, CN1, CN2 and CN3 are the runoff curve numbers under three soil wetness levels: AMCI drought, AMCII normal and AMCIII wet.

5. The CN value correction method considering soil moisture changes applicable to the Pearl River Basin according to claim 2 is characterized in that: Calculation of the initial value CN2 of the runoff curve number under normal soil moisture level includes: Establish land use grouping data and hydrological soil grouping data within the basin based on soil and land classification data; Based on the land use grouping data and hydrological soil grouping data, combined with the preset runoff curve table, the runoff curve data CN2(i) under the normal soil moisture level of each cell in the basin is calculated, where i is the cell number; The grid data in the basin is converted to points, and then Thiessen polygons are established for the points to obtain the area weight P of the sub-basin occupied by each grid data. i ; The area weight is area-weighted to obtain the composite value CN2(j) of the runoff curve number under normal soil moisture level of each sub-basin, and the composite value CN2(j) of the runoff curve number is used as the initial value of the runoff curve number, j is the sub-basin number, that is, the sub-basin CN2(j) is calculated according to the cell CN2(i).

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