A method for evaluating water source conservation function and runoff coefficient by region and type

Abstract

The application discloses a kind of regional type's water source conservation function and runoff coefficient's evaluation method, including the following steps: obtaining the optical remote sensing image of the region to be evaluated, soil data, precipitation data and evapotranspiration data, analysis processing;According to the ecological system classification data and soil zoning curve value, the runoff capacity grid of each pixel corresponding to different ecological system types different soil zoning is calculated;Obtain the runoff observation data of the region to be evaluated, combine runoff capacity grid, and the runoff observed is proportionally distributed to each pixel, and the runoff depth of each pixel is obtained;According to precipitation, evapotranspiration and runoff depth, the water source conservation amount and runoff coefficient corresponding to each pixel of the region to be evaluated are obtained.The application relates to the technical field of ecological system water source conservation, and the runoff index and runoff depth of each pixel are calculated by fusing multi-source data, so that the accuracy of evaluating the water source conservation service function and runoff coefficient of the region is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecosystem water conservation, and more particularly to a method for evaluating water conservation function and runoff coefficient in different regions and types. BACKGROUND

[0002] At present, the water balance method based on the water balance model can accurately quantify the input (rainfall) and output (evaporation and runoff) of the regional and basin scale ecosystem when evaluating the water conservation function of the region, so as to comprehensively analyze the flow and storage characteristics and process of the ecosystem water. Therefore, the water balance model has become the preferred model for evaluating the ecosystem services and functions from the perspectives of "conservation" and "runoff". In addition, the water balance model not only brings convenience for regional scale scientific research, but also can directly support the administrative work of national and regional ecological protection policies. It has become a common method for ecosystem evaluation, and even in the form of standards and norms to restrict the implementation of projects, such as forest ecosystem service evaluation, national ecological condition periodic remote sensing investigation and evaluation, and China's ecological protection red line delineation.

[0003] However, in actual work, it is very difficult to simultaneously obtain accurate runoff data in the process of applying the water balance model to carry out water conservation evaluation, which matches the data format, spatial scale and resolution. The precipitation and evapotranspiration data sets obtained by field monitoring or satellite remote sensing monitoring are relatively easy in time and space, and have similar temporal and spatial resolution; however, the flow data observed by the hydrological station is usually distributed along the river bank, and cannot be directly quantitatively related to the ecosystem, because these stations are the comprehensive reflection of the runoff of the whole basin; in addition, the difficulty of sharing the flow data observed by the hydrological station also hinders the establishment of the runoff depth spatial data set. Therefore, when using the water balance model to evaluate the water resources protection of the regional ecosystem, how to obtain relatively accurate runoff spatial distribution data has become a key technical problem. The usual way is to use the precipitation and runoff coefficient of different ecosystem types to estimate the temporal and spatial distribution of runoff depth. This method is widely used in the research of ecosystem water resources protection service evaluation.

[0004] Runoff coefficient is a key parameter in water balance models. Scientific and accurate calibration of runoff coefficient is a necessary condition to ensure the accuracy of the model and the scientific and reasonable evaluation results. The previous method for califying runoff coefficient is to establish a lookup table (LTM). However, this method has three major defects. First, due to the lack of actual hydrological observation data, the water source conservation evaluation results do not represent the true hydrological regulation capacity of the ecosystem. Second, the physical and geographical characteristics of the basin cannot be accurately described because the runoff coefficient obtained from LTM does not consider the basin morphology and physical geographical characteristics, which leads to the masking of the runoff generation capacity differences of the same ecosystem type in different basins. Third, the lack of time variation information is an important limitation because the runoff coefficient is considered to be constant, ignoring the changes in vegetation growth under the condition that the ecosystem type remains unchanged. These changes can be crucial in determining the impact of regional climate change and human activities on the ecosystem. Although LTM is widely used, there is still a lack of methods in water source conservation evaluation research that can solve its limitations and provide quantitative runoff depth.

[0005] Therefore, obtaining and spatializing runoff data, realizing the evaluation of water source conservation function and the calibration of runoff coefficient, is a problem that those skilled in the art urgently need to solve. SUMMARY

[0006] Therefore, the present application provides a method for evaluating water source conservation function and runoff coefficient by region and type, which fuses multi-source data to calculate runoff index and runoff depth of each pixel and evaluate regional water source conservation service function and runoff coefficient.

[0007] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0008] The present application provides a method for evaluating water source conservation function and runoff coefficient by region and type, which comprises the following steps:

[0009] S1, obtaining optical remote sensing images, soil data, precipitation data and evapotranspiration data of the region to be evaluated, and analyzing and processing them to obtain ecosystem classification data, soil zoning curve values, precipitation and evapotranspiration of each pixel in the region to be evaluated;

[0010] S2, calculating the runoff index of each pixel corresponding to different soil zoning of different ecosystem types according to the ecosystem classification data and soil zoning curve values, and obtaining a runoff capacity grid;

[0011] S3, obtaining runoff observation data of the region to be evaluated, combining the runoff capacity grid, and distributing the observed runoff to each pixel in proportion to obtain the runoff depth of each pixel;

[0012] S4, obtaining water conservation and runoff coefficient of each pixel in the to-be-evaluated region according to the precipitation, evapotranspiration and runoff depth.

[0013] Further, step S1 specifically comprises:

[0014] Obtaining optical remote sensing images of the to-be-evaluated region, performing preprocessing and constructing a supervised classification model by using a random forest algorithm, interpreting the ecosystem type, and identifying the ecosystem type of each pixel;

[0015] Obtaining soil data of the to-be-evaluated region, combining a world soil database, and extracting curve values of different soil partitions of each pixel;

[0016] Obtaining precipitation data obtained by interpolating meteorological stations, and analyzing the precipitation of each pixel by using a grid statistical analysis tool;

[0017] Obtaining evapotranspiration data obtained by satellite remote sensing, and analyzing the evapotranspiration of each pixel by using a grid statistical analysis tool.

[0018] Further, the ecosystem classification data comprises different types of ecosystems and corresponding runoff coefficients; wherein the different types of ecosystems comprise forests, grasslands, wetlands, bare land, farmland and towns.

[0019] Further, step S2 specifically comprises:

[0020] S21, calculating a sub-type runoff index of each pixel corresponding to different ecosystem types according to the ecosystem classification data; and using a formula to represent as:

[0021] R it (j)=R c (j) / R c (min)

[0022] Wherein, R it (j) represents the sub-type runoff index of the ecosystem type j, R c (j) represents the runoff coefficient of the type j, and R c (min) represents the minimum value of the runoff coefficients of all types.

[0023] S22, calculating a sub-area runoff index of each pixel corresponding to different soil partitions according to the soil partition curve values; and using a formula to represent as:

[0024] R is (j,K)=CN(j,K) / CN(min)wherein, R is(j, K) represents the sub-regional runoff index of the ecosystem type j in the soil K region, CN(j, K) represents the curve value of the K region of type j, CN(min) represents the minimum value of the curve value in all ecosystem types and all soil regions;

[0025] S23, the sub-type runoff index and the sub-regional runoff index corresponding to each pixel are integrated with the same weight to obtain the integrated runoff index of the corresponding pixel; the formula is represented as:

[0026] R i (j, K) = 0.5 * R it (j) + 0.5 * R is (j, K)

[0027] Wherein, R i (j, K) represents the sub-regional, sub-type integrated runoff index of the ecosystem type j in the soil K region.

[0028] Further, step S3 specifically comprises:

[0029] S31, obtaining the runoff data observed by the import hydrological station and the export hydrological station of the to-be-evaluated region to obtain the total runoff of the to-be-evaluated region;

[0030] S32, calculating the runoff index proportion of each pixel according to the integrated runoff index value of all pixels in the runoff capacity grid;

[0031] S33, distributing the total runoff to each pixel according to the runoff index proportion to obtain the runoff depth of each pixel.

[0032] Further, the water source conservation amount corresponding to each pixel of the to-be-evaluated region is obtained based on a water balance model, and the formula is represented as:

[0033] WR = P - E - R

[0034] Wherein, WR represents the water source conservation amount corresponding to each pixel, P represents the precipitation corresponding to each pixel, E represents the evapotranspiration corresponding to each pixel, and R represents the runoff depth corresponding to each pixel.

[0035] Further, the runoff coefficient corresponding to each pixel of the to-be-evaluated region is represented by the formula:

[0036] R c = R / P

[0037] Wherein, R c represents the runoff coefficient corresponding to each pixel, P represents the precipitation corresponding to each pixel, and R represents the runoff depth corresponding to each pixel.

[0038] Compared with the prior art, the evaluation method for water source conservation function and runoff coefficient of different regions and types provided by the application has the following beneficial effects:

[0039] The application first integrates runoff observation data, soil data and ecosystem classification data, and then uses spatial analysis to integrate spatial information of different soil regions and ecosystem types at a pixel scale; the spatial and temporal distribution data of runoff depth is obtained by using data collected by hydrological stations in the basin, so as to realize the evaluation of regional water resource protection.

[0040] The application directly deduces the runoff depth spatial distribution data set consistent with the spatial scale, data format and resolution by combining the actual flow information observed by the basin hydrological station, which is consistent with the ecosystem classification; the annual water resource protection amount is calculated by using the water balance model, and the annual runoff coefficient spatial distribution data set is obtained.

[0041] The application is closely related to the ecological condition investigation and evaluation business work, and the technical problems to be solved will directly support the scientific and refined evaluation of ecosystem service function, and improve the level and efficiency of the business work. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description only constitute the embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0043] Figure 1 A flow chart of the evaluation method for water source conservation function and runoff coefficient of different regions and types provided by the embodiment of the application.

[0044] Figure 2 An overview diagram of the evaluation method for water source conservation function and runoff coefficient of different regions and types provided by the embodiment of the application.

[0045] Figure 3 A spatial distribution diagram of the typical water source conservation area in the upper reaches of the Yellow River Basin provided by the embodiment of the application.

[0046] Figure 4 A logic diagram of the construction of the regional and type comprehensive runoff index of the typical water source conservation area in the upper reaches of the Yellow River Basin provided by the embodiment of the application.

[0047] Figure 5 A runoff depth spatial distribution diagram of the typical water source conservation area in the upper reaches of the Yellow River Basin in 2000 provided by the embodiment of the application.

[0048] Figure 6 The runoff depth spatial distribution graph of a typical water source conservation area in the upper reaches of the Yellow River Basin in 2005 is provided for the embodiment of the present application.

[0049] Figure 7 The runoff depth spatial distribution graph of a typical water source conservation area in the upper reaches of the Yellow River Basin in 2010 is provided for the embodiment of the present application.

[0050] Figure 8 The runoff depth spatial distribution graph of a typical water source conservation area in the upper reaches of the Yellow River Basin in 2015 is provided for the embodiment of the present application.

[0051] Figure 9 The runoff depth spatial distribution graph of a typical water source conservation area in the upper reaches of the Yellow River Basin in 2020 is provided for the embodiment of the present application.

[0052] Figure 10 The water source conservation amount spatial distribution graph of a typical water source conservation area in the upper reaches of the Yellow River Basin in 2000 is provided for the embodiment of the present application.

[0053] Figure 11 The water source conservation amount spatial distribution graph of a typical water source conservation area in the upper reaches of the Yellow River Basin in 2005 is provided for the embodiment of the present application.

[0054] Figure 12 The water source conservation amount spatial distribution graph of a typical water source conservation area in the upper reaches of the Yellow River Basin in 2010 is provided for the embodiment of the present application.

[0055] Figure 13 The water source conservation amount spatial distribution graph of a typical water source conservation area in the upper reaches of the Yellow River Basin in 2015 is provided for the embodiment of the present application.

[0056] Figure 14 The water source conservation amount spatial distribution graph of a typical water source conservation area in the upper reaches of the Yellow River Basin in 2020 is provided for the embodiment of the present application.

[0057] Figure 15 The runoff coefficient spatial distribution graph of a typical water source conservation area in the upper reaches of the Yellow River Basin in 2000 is provided for the embodiment of the present application.

[0058] Figure 16 The runoff coefficient spatial distribution graph of a typical water source conservation area in the upper reaches of the Yellow River Basin in 2005 is provided for the embodiment of the present application.

[0059] Figure 17 The runoff coefficient spatial distribution graph of a typical water source conservation area in the upper reaches of the Yellow River Basin in 2010 is provided for the embodiment of the present application.

[0060] Figure 18 The runoff coefficient spatial distribution graph of a typical water source conservation area in the upper reaches of the Yellow River Basin in 2015 is provided for the embodiment of the present application.

[0061] Figure 19 A runoff coefficient spatial distribution map of a typical water conservation area in the upper reaches of the Yellow River Basin in 2020 is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0063] Before introducing the method of the present application, the existing ecosystem service function evaluation method and runoff coefficient calibration method are first described.

[0064] Firstly, quantitative fusion and calibration of key parameters of models in ecological function evaluation of different scales, different regions and different types are the basis and guarantee of scientific evaluation. The result of ecosystem service function evaluation is to obtain the service function condition and change characteristics of different regions and different scales, and the evaluation parameter that is suitable for different scales and regions with good spatio-temporal continuity is an ideal way to meet this requirement. For example, the parameters such as precipitation, flooding frequency and ecosystem classification obtained based on satellite remote sensing are the continuous expression of ecological indicators in space per pixel, which can completely meet the input demand of evaluation model of different regions and different types. The parameters such as precipitation, flow and flow rate observed on the ground can be spatialized by spatial interpolation method. However, there are still some evaluation parameters that do not have or cannot realize the per-pixel spatialization requirement, such as the runoff and flow rate of a certain place based on model simulation. In addition, there are also technical problems of format, representativeness and continuity of these multi-element parameters. Therefore, the core content is to determine the comprehensive factor information such as precipitation, elevation and land feature type based on satellite remote sensing, ground observation and model simulation, comprehensively utilize various quantitative calculation methods such as spatial representativeness analysis, regional applicability analysis and multi-element regression analysis, construct the "pixel-plot-region" parameter consistency constraint method, and build the evaluation parameter data set of the test area.

[0065] Second, the runoff coefficient is the ratio of runoff to rainfall in a specific time period, and is influenced by topography, basin morphology, vegetation, and soil type. It is a comprehensive index that reflects the interaction between natural geographical factors and the relationship of runoff. Small-scale runoff coefficient surveys are mainly concerned with municipal construction, flood control projects, drought resistance, soil erosion mechanisms, and small watershed drainage irrigation. Using remote sensing data, it is relatively easy to develop a spatial distribution grid dataset of ecosystem types. Combining experimental observations and ecological hydrological model research can help obtain the runoff coefficients of various ecosystem types. Therefore, by matching the ecosystem type values and the corresponding runoff coefficients to create a lookup table, the lookup table method (LTM) can generate a spatial distribution grid dataset of runoff coefficients. This technique has been implemented and widely used in national and regional water resources protection and management. Several case studies and methodological studies have demonstrated its effectiveness. Although there are differences between the lookup tables used in these studies, there are no fundamental differences or improvements in essence. The differences mainly come from the differences in literature reviews and different research purposes between researchers.

[0066] The method of the present application is described below.

[0067] The embodiment of the present application discloses a method for evaluating water conservation function and runoff coefficient of different regions and different types, referring to Figure 1 As shown in the figure, the method comprises the following steps:

[0068] S1, optical remote sensing image, soil data, precipitation data and evapotranspiration data of the region to be evaluated are obtained, and are analyzed and processed; the corresponding ecosystem classification data, soil zoning curve value, precipitation and evapotranspiration of each pixel of the region to be evaluated are obtained;

[0069] S2, according to the ecosystem classification data and the soil zoning curve value, the runoff index of each pixel corresponding to different soil zoning of different ecosystem types is calculated, and the runoff capacity grid is obtained;

[0070] S3, the runoff observation data of the region to be evaluated are obtained, and the observed runoff is distributed to each pixel in proportion in combination with the runoff capacity grid, and the runoff depth of each pixel is obtained;

[0071] S4, according to the precipitation, evapotranspiration and runoff depth, the water conservation capacity and the runoff coefficient corresponding to each pixel of the region to be evaluated are obtained.

[0072] The embodiment of the present application refers to Figure 2As shown, first, the sub-regional and sub-type parameter table construction is carried out; through collecting and arranging the data materials of field investigation, literature research and the like, the runoff coefficient table corresponding to the classification data of different ecological systems of the to-be-evaluated region and the curve number (CN) value and runoff index table corresponding to different soil partitions are determined; according to the optical remote sensing image, soil data, precipitation data and evapotranspiration data of the to-be-evaluated region, the classification data of the ecological system, the soil partition, the precipitation and the evapotranspiration corresponding to each pixel of the to-be-evaluated region are determined; secondly, the sub-regional and sub-type runoff capacity grid construction is carried out; the runoff capacity grid corresponding to different ecological system types and different soil partitions of each pixel is determined in combination with the above parameter table, the ecological system classification grid data and the soil partition grid data; next, the hydrological data acquisition and quantification and distribution are carried out; according to the runoff observation data of the to-be-evaluated region, the runoff observed by the hydrological station is distributed to each pixel in combination with the above runoff capacity grid, and the runoff depth grid spatial distribution is determined; finally, the water source conservation amount calculation and the runoff coefficient space-time data set generation are carried out; according to the precipitation, the evapotranspiration and the runoff depth of the to-be-evaluated region, the water source conservation amount corresponding to each pixel of the to-be-evaluated region and the runoff coefficient corresponding to each pixel are determined.

[0073] The present application is oriented to the national and regional ecological condition investigation and evaluation business work, is aimed at the ecological system service function evaluation model parameter, fuses multi-source data (satellite remote sensing inversion, ground station observation, model simulation and the like), constructs the sub-regional and sub-type rating technique of the key parameter (such as runoff coefficient and the like) in water source conservation evaluation model, and evaluates the water source conservation service function of region.

[0074] The present embodiment takes the typical water source conservation area (including 6 sub-basins) in the upper reaches of the Yellow River as an example, refers to Figure 3 As shown, the region takes Jima hydrological station as the entrance and Lanzhou hydrological station as the exit, and both are national stations. The region is located at the junction of the Qinghai-Tibet Plateau and the Loess Plateau, with an average elevation of more than 3400 meters, characterized by meadow and natural wetland, and is an important water source conservation area of the Yellow River Basin, including the Huangshui River, the Baihe River, the Heihe River and the Taohe River and the like. The main industry in the basin is animal husbandry, and the density of industrial and irrigated agriculture is relatively low.

[0075] Firstly, the construction of the sub-regional and sub-type parameter table is carried out.

[0076] In the present embodiment, satellite remote sensing images are used for preprocessing through radiation correction, atmospheric correction and geometric fine correction, a supervised classification model is constructed by using a random forest algorithm, ecological system type interpretation is carried out, and the ecological system type of each pixel in the research region is identified.

[0077] The present embodiment eliminates the influence of sensor characteristics and atmospheric scattering on image brightness values through radiation correction, making images obtained at different times or by different sensors comparable. Atmospheric correction is then used to further remove the effects caused by atmospheric absorption and scattering to restore the true reflectance of the earth's surface. Finally, geometric refinement is performed to adjust the spatial position errors of the image through ground control points (GCPs) or high-precision digital elevation models (DEMs), ensuring that pixels accurately correspond to actual geographic coordinates.

[0078] After preprocessing, the present embodiment extracts features from the image that help distinguish different ecosystem types. These features include raw band values, vegetation indices (such as NDVI), texture features, topographic factors, and the like.

[0079] Before performing supervised classification, the present embodiment collects a certain number of samples of known categories as a training set. These samples can be obtained through field surveys, historical data, or other reliable sources. Then the random forest algorithm is applied to improve prediction accuracy by constructing a large number of decision trees and voting on their results. In this step, the prepared training samples and extracted features described above will be input into the random forest algorithm to train the model. The model parameters of the present embodiment (such as the number of trees, node splitting criteria, etc.) can be adjusted and optimized according to specific circumstances.

[0080] The trained random forest model is used to classify the entire study area, identifying the ecosystem type to which each pixel belongs. The classification results are then verified and evaluated, with commonly used evaluation indicators including overall accuracy, Kappa coefficient, and the like, and a thematic map may be produced to display the classification results.

[0081] The CN values associated with different soil zones are extracted using the World Soil Database version 1.2 (HWSD). Through meta-analysis methods, relevant field investigation data, literature, and other sources are used to collect runoff coefficient values for various ecosystem types.

[0082] The present embodiment demonstrates the runoff coefficients and runoff indices for different ecosystem types, as shown in Table 1:

[0083] Table 1

[0084] Ecosystem type Runoff coefficient Rc (%) Runoff index Rit Broadleaf forest 3.33 1.55 Coniferous forest 2.15 1.00 Mixed forest 2.4 1.12 Sparse forest 16.02 7.45 Broadleaf shrubland 3.58 1.67 Coniferous shrubland 3.41 1.59 Sparse shrubland 16.02 7.45 Meadow 9.11 4.24 Grassland 12.34 5.74 Grassland 10.18 4.73 Sparse grassland 16.02 7.45 Marsh 0 0 Lake 0 0 River 0 0 Farmland 49.69 23.11 Garden 4.62 2.15 Residential area 90 41.86 Urban green space 7.91 3.68 Industrial and transport land 73.33 34.11 Desert 30 13.95 Glacier / perennial snow 0 0

[0085] The present embodiment also demonstrates CN values and runoff indices for different soil zones, as shown in Table 2:

[0086] Ecosystem type CNA CNB CNC CND Ris(A) Ris(B) Ris(C) Ris_(D) Broadleaf forest 36 62 75 81 1.00 1.72 2.08 2.25 Coniferous forest 37 62 75 81 1.00 1.68 2.03 2.19 Mixed forest 38 62 75 81 1.00 1.63 1.97 2.13 Sparse forest 72 82 83 87 1.00 1.14 1.15 1.21 Broadleaf shrubland 45 65 75 80 1.00 1.44 1.67 1.78 Coniferous shrubland 49 69 79 84 1.00 1.41 1.61 1.71 Sparse shrubland 72 82 83 87 1.00 1.14 1.15 1.21 Meadow 49 69 79 84 1.00 1.41 1.61 1.71 Grassland 49 69 79 84 1.00 1.41 1.61 1.71 Grassland 49 69 79 84 1.00 1.41 1.61 1.71 Sparse grassland 72 82 83 87 1.00 1.14 1.15 1.21 Marsh 0 0 0 0 0 0 0 0 Lake 0 0 0 0 0 0 0 0 River 0 0 0 0 0 0 0 0 Farmland 67 78 85 89 1.00 1.16 1.27 1.33 Garden 52 69 79 84 1.00 1.33 1.52 1.62 Residential area 80 85 90 95 1.00 1.06 1.13 1.19 Urban green space 52 70 79 84 1.00 1.35 1.52 1.62 Industrial and transport land 80 85 90 95 1.00 1.06 1.13 1.19 Desert 72 82 83 87 1.00 1.14 1.15 1.21 Glacier / perennial snow 0 0 0 0 0 0 0 0

[0087] For precipitation, long-term precipitation records from various weather stations need to be collected first; using spatial interpolation methods in GIS software (such as inverse distance weighting (IDW), Kriging, etc.), based on the location of weather stations and their recorded precipitation, the precipitation at locations without weather stations in the study area is estimated. The interpolation process will generate a precipitation raster layer covering the entire study area, where each pixel represents the estimated precipitation at that location. Raster statistical analysis: In the GIS environment, using raster statistical analysis tools, the precipitation value of each pixel can be directly obtained. When further analyzing (for example, calculating the average precipitation, total precipitation, etc.), different raster operation functions can be applied according to specific needs.

[0088] For evapotranspiration, select appropriate satellite data sources (such as MODIS, Landsat, etc.) to obtain multispectral images containing parameters such as surface temperature, vegetation index, reflectivity, etc., which are key inputs for estimating evapotranspiration. Calculate evapotranspiration using specific models (such as SEBAL, Surface Energy Balance Algorithm for Land; Penman-Monteith equation, etc.) based on the above satellite data to calculate evapotranspiration. These models take into account factors such as solar radiation, air temperature, humidity, wind speed, etc. to estimate the energy balance and latent heat flux of the surface, thereby obtaining evapotranspiration. After model calculation, a raster layer representing the evapotranspiration of each pixel is also generated. Raster statistical analysis, similar to processing precipitation data, in GIS, using raster statistical analysis tools, can easily extract the evapotranspiration of each pixel. In addition, this embodiment can also perform more complex analysis, such as comparing the evapotranspiration trend in different time periods, evaluating the relationship between evapotranspiration and precipitation, etc.

[0089] Secondly, construct a regional and type-specific runoff capacity raster.

[0090] This embodiment matches the above runoff coefficient table based on various ecosystem types to the ecosystem classification raster of the area to be evaluated, obtains the runoff coefficient of each pixel, and then calculates the type-specific runoff index of each pixel.

[0091] The calculation formula is as follows:

[0092] R it (j)=R c (j) / R c (min)where R it (j) represents the type-specific runoff index of ecosystem type j, R c (j) represents the runoff coefficient of type j, and R c (min) represents the minimum value of the runoff coefficient in all types.

[0093] The table of curve number (CN) value information based on soil zoning is matched to the soil zoning grid of the region to be evaluated to obtain the CN value of each pixel, and then the sub-regional runoff index of each pixel is calculated.

[0094] The calculation formula is as follows:

[0095] R is (j,K) = CN(j,K) / CN(min)

[0096] Wherein, R is (j,K) represents the sub-regional runoff index of the ecosystem type j in the soil K region, CN(j,K) represents the curve number value of type j in the K region, and CN(min) represents the minimum curve number value of all ecosystem types and all soil regions.

[0097] The "sub-regional and type-specific" runoff index grid dataset is integrated using equal weight to represent the runoff generation capacity of each pixel.

[0098] The formula is as follows:

[0099] R i (j,K) = 0.5 x R it (j) + 0.5 x R is (j,K)

[0100] Wherein, R i (j,K) represents the sub-regional and type-specific comprehensive runoff index of the ecosystem type j in the soil K region.

[0101] The logic diagram of the sub-regional and type-specific comprehensive runoff index constructed in this embodiment is shown in Figure 4 The table containing the runoff coefficient and curve number (CN) value information of various ecosystem types is combined with the ecosystem classification to construct the type-specific runoff index and the sub-regional runoff index spatial data, respectively. The "sub-regional and type-specific" runoff index grid dataset is integrated using equal weight to represent the runoff generation capacity of each pixel.

[0102] Figure 4 The construction of sub-regional and type-specific runoff capacity grid and the acquisition and quantification of hydrological data are shown. Among them, yellow and white A represents sand, loam or sandy loam, yellow B represents loam, silty loam or silt, pink C represents sandy clay loam, sky blue D represents clay loam, silty clay loam, sandy clay, silty clay or clay; dark green represents forest, light green represents grassland, gray blue represents wetland, white represents bare land, pink represents farmland, and red represents town.

[0103] Figure 4Figures 1 and 2 in the table show the runoff coefficient table corresponding to the different ecosystem classification data of the to-be-evaluated area determined by combining data research, and the ecosystem classification raster data obtained by remote sensing interpretation, which determines the runoff index raster corresponding to different ecosystem types of each pixel.

[0104] Figure 4 Figures 3 and 4 in the table show the table based on the curve number (CN) value information of the soil zoning, and the CN value is matched to the soil zoning raster of the to-be-evaluated area by combining the soil zoning raster data, and finally the runoff index of each pixel in the soil zoning is obtained.

[0105] Figure 4 Figure 5 in the table is based on the type-specific runoff index in Figure 2 and the regional-specific runoff index in Figure 4, and the “regional-specific and type-specific” comprehensive runoff index raster data set is obtained by using equal weight.

[0106] Figure 4 Figure 6 in the table is combined with the above-mentioned comprehensive runoff capacity raster, and the runoff observed by the hydrological station is distributed to each pixel to determine the spatial distribution of the runoff depth raster.

[0107] Next, the hydrological data acquisition and distribution are performed.

[0108] According to the runoff observed by the hydrological station, the spatial distribution of the runoff depth of each pixel is generated from the runoff observation data of the hydrological station by combining the above-mentioned runoff index spatial raster data. The total runoff in the basin is calculated according to the following formula:

[0109] ΔR = R(outlet) - R(inlet)

[0110] In the formula, ΔR represents the total runoff in the to-be-evaluated basin, R(outlet) represents the measured runoff of the hydrological station at the outlet of the basin, and R(inlet) represents the measured runoff of the hydrological station at the inlet of the basin.

[0111] According to the comprehensive runoff index of each pixel in the to-be-evaluated area, the runoff proportion of each pixel in the entire to-be-evaluated area is converted, and the total runoff in the basin is matched to each pixel in proportion to obtain the runoff depth of each pixel.

[0112] In this embodiment, the runoff observed by the hydrological station is distributed, and the spatial distribution of the runoff depth of the study area 2000-2020 is generated year by year in the comprehensive runoff index spatial raster data. This method only needs ecosystem classification and soil survey data, so that the resolution of the runoff depth spatial data is higher, reaching 30 meters. In contrast, the traditional method relies on precipitation data input, mainly data obtained from limited meteorological stations (resolution of 1000 meters), resulting in relatively low spatial resolution of runoff depth. For reference Figure 5 - Figure 9Figure 6 shows the spatial distribution of runoff depth in the typical water conservation area in the upper reaches of the Yellow River in 2000, 2005, 2010, 2015 and 2020, respectively.

[0113] Finally, the water conservation amount calculation and the generation of the runoff coefficient spatio-temporal dataset are performed.

[0114] Regarding the water conservation amount calculation, based on the runoff depth obtained above, combined with the precipitation obtained by meteorological station interpolation and the evapotranspiration product dataset obtained by satellite remote sensing, the spatial distribution dataset of the water conservation amount is calculated according to the principle of the water balance model.

[0115] The calculation formula is as follows:

[0116] WR = P - E - R

[0117] In the formula, WR is the water conservation amount, P is the precipitation, E is the evaporation, and R is the runoff depth.

[0118] In this embodiment, the spatial distribution dataset of the water conservation amount in the study area from 2000 to 2020 is calculated year by year according to the principle of the water balance model, combined with the precipitation obtained by meteorological station interpolation and the evapotranspiration product dataset obtained by satellite remote sensing. The results obtained by this method and the traditional method both show similar interannual variation trends, but this method is less affected by precipitation, mainly due to its interannual fluctuation characteristics. Referring to Figure 6, the spatial distribution of the water conservation amount in the typical water conservation area in the upper reaches of the Yellow River in 2000, 2005, 2010, 2015 and 2020 is shown. Figure 10 - Figure 14

[0119] Regarding the generation of the runoff coefficient spatio-temporal dataset, the runoff coefficient grid dataset is calculated from the runoff depth and precipitation datasets.

[0120] The calculation formula is as follows:

[0121] R c = R / P

[0122] In the formula, R c is the runoff coefficient, P is the precipitation, and R is the runoff depth.

[0123] In this embodiment, the runoff coefficient grid dataset in the study area from 2000 to 2020 is calculated year by year from the runoff depth and precipitation datasets. Compared with the traditional method, the runoff coefficient obtained by this method inherits the accuracy of the ecosystem type data resolution, can be obtained from high-resolution satellite remote sensing, and can thus finely describe the spatial pattern of surface runoff. Compared with the previous static runoff coefficient value, the spatial distribution of the annual runoff coefficient is obtained by this method. Referring to Figure 7, the spatial distribution of the runoff coefficient in the typical water conservation area in the upper reaches of the Yellow River in 2000, 2005, 2010, 2015 and 2020 is shown. Figure 15 - Figure 19 ​As shown, respectively, the runoff coefficient spatial distribution of the typical water conservation area in the upper reaches of the Yellow River in 2000, 2005, 2010, 2015 and 2020 is displayed.

[0124] The embodiment of the present application closely surrounds the ecological condition investigation and evaluation business work, directly supports the scientific and refined evaluation of the ecosystem service function, can more accurately evaluate the water conservation function, and provides a more scientific basis for the ecological system protection and management. A large amount of data is automatically processed, and high-resolution runoff depth and water conservation amount spatial distribution data are generated, so as to improve the efficiency of the business work and improve the level and efficiency of the business work.

[0125] The method of the present application can provide a scientific basis for ecological protection red line demarcation, water resource management, land use planning and the like, and support the formulation and implementation of national and regional ecological protection policies.

[0126] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts of each embodiment can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0127] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating water source conservation function and runoff coefficient by sub-area and sub-type, characterized in that, The method comprises the following steps: S1, obtaining optical remote sensing images, soil data, precipitation data and evapotranspiration data of the to-be-evaluated region for analysis and processing; obtaining the ecological system classification data corresponding to each pixel of the to-be-evaluated region, the soil partition curve value, the precipitation and the evapotranspiration; S2, calculating the runoff index of each pixel corresponding to different soil partitions of different ecological system types according to the ecological system classification data and the soil partition curve value, and obtaining a runoff capacity grid; S3, obtaining runoff observation data of the to-be-evaluated region, combining the runoff capacity grid, and proportionally distributing the observed runoff to each pixel to obtain the runoff depth of each pixel; S4, obtaining the water source conservation and the runoff coefficient corresponding to each pixel of the to-be-evaluated region according to the precipitation, the evapotranspiration and the runoff depth; Specifically, step S2 comprises: S21, calculating the sub-type runoff index of each pixel corresponding to different ecological system types according to the ecological system classification data; and the formula is as follows: R it (j) = R c (j) / R c (min) where R it (j) denotes the sub-type runoff index, R c (j) denotes the runoff coefficient of type j, R c (min) denotes the minimum value of the runoff coefficients among all types; S22, calculating the sub-area runoff index of each pixel corresponding to different soil partitions according to the soil partition curve value; and the formula is as follows: R is (j, K) = CN(j, K) / CN(min) where R is (j, K) denotes the subregional runoff index for ecosystem type j in soil K region, CN(j, K) denotes the curve number value for type j in K region, and CN(min) denotes the minimum value of the curve number value among all ecosystem types and all soil regions. S23, synthesizing the sub-type runoff index and the sub-area runoff index corresponding to each pixel by using the same weight to obtain the comprehensive runoff index of the corresponding pixel; and the formula is as follows: R i (j, K) = 0.5 x R it (j) + 0.5 x R is (j, K) wherein R i (j, K) denotes the subregional, subtypical, integrated runoff index of the ecosystem type j in the soil K region.

2. The method for evaluating water source conservation function and runoff coefficient by sub-region and sub-type according to claim 1, wherein, Specifically, step S1 comprises: obtaining optical remote sensing images of the to-be-evaluated region, pre-processing the images, constructing a supervised classification model by using a random forest algorithm, interpreting the ecological system types, and identifying the ecological system types of each pixel; obtaining soil data of the to-be-evaluated region, combining a world soil database, and extracting the curve value of different soil partitions of each pixel; obtaining precipitation data obtained by interpolating meteorological stations, and analyzing the precipitation of each pixel by using a grid statistical analysis tool; obtaining evapotranspiration data obtained by satellite remote sensing, and analyzing the evapotranspiration of each pixel by using a grid statistical analysis tool.

3. The method for evaluating water source conservation function and runoff coefficient by sub-region and sub-type according to claim 2, characterized in that, The ecological system classification data comprises different types of ecological systems and corresponding runoff coefficients; wherein the different types of ecological systems comprise forests, grasslands, wetlands, bare lands, farmlands and towns.

4. The method for evaluating water source conservation function and runoff coefficient by sub-region and sub-type according to claim 1, wherein, Specifically, step S3 comprises: S31, obtaining runoff data observed by an import hydrological station and an export hydrological station of the to-be-evaluated region to obtain the total runoff of the to-be-evaluated region; S32, calculating the runoff index proportion of each pixel according to the comprehensive runoff index value of all pixels in the runoff capacity grid; S33, distributing the total runoff to each pixel according to the runoff index proportion to obtain the runoff depth of each pixel.

5. The method for evaluating water source conservation function and runoff coefficient by subarea and subcategory according to claim 4, wherein, The water source conservation corresponding to each pixel of the to-be-evaluated region is obtained based on a water balance model, and the formula is as follows: WR = P - E - R wherein, WR represents the water source conservation corresponding to each pixel, P represents the precipitation corresponding to each pixel, E represents the evapotranspiration corresponding to each pixel, and R represents the runoff depth corresponding to each pixel.

6. The method for evaluating water source conservation function and runoff coefficient by sub-region and sub-type according to claim 5, wherein, The runoff coefficient corresponding to each pixel of the to-be-evaluated region is as follows: R c = R / P wherein R c represents the runoff coefficient corresponding to each pixel, P represents the precipitation corresponding to each pixel, and R represents the runoff depth corresponding to each pixel.

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