Identification method of ecological restoration contribution rate of ecological water replenishment in the lower reaches of inland river basins

Abstract

The present invention relates to the field of ecological restoration management methods for river basins, and specifically to a method for identifying the ecological restoration contribution rate of ecological water replenishment in the downstream of inland river basins, including obtaining multi-source public data in the study area for preprocessing and building a database; evaluating the accuracy of precipitation and potential evapotranspiration products based on the observed true values ​​of precipitation and potential evapotranspiration at meteorological stations, and obtaining accurate precipitation and potential evapotranspiration product grid data in the study area; estimating regional vegetation coverage, building a regional water balance equation, and correcting the comprehensive plant coefficient K; characterizing the regional water balance status with a hydrothermal stability index; evaluating the hydrothermal comprehensive stability of the downstream of inland rivers based on the hydrothermal stability index; screening key influencing indicators of the hydrothermal comprehensive stability in the downstream of inland rivers, and identifying the ecological restoration contribution rate of ecological water replenishment at different spatial scales of basins and pixels. The present invention can accurately determine the contribution rate of ecological water replenishment to ecological restoration.

Description

Technical Field

[0001] The present invention relates to the field of ecological restoration management methods for river basins, and in particular to a method for identifying the ecological restoration contribution rate of ecological water replenishment in the downstream of an inland river basin. Background Art

[0002] Inland river basins account for about 11.4% of the world's land area, mainly distributed in arid and semi-arid areas. With the combined effects of global warming, overexploitation of water resources, population growth, economic development and other human activities, the landscape of inland river basins that are dry in some areas has been seriously degraded, especially in the downstream desert areas. In order to curb the trend of desert landscape degradation in the downstream of inland river basins, ecological water transfer projects are implemented in some key basins to strengthen the restoration and reconstruction of the fragile ecological environment of the basin. It has been proven that ecological water transfer is one of the effective measures to redistribute water resources between the socio-economic system and the natural system. It has become a key measure to effectively solve the ecological restoration and reconstruction of desert landscapes, and plays a positive role in the local ecological environment.

[0003] However, ecological restoration is a long and protracted process. At present, ecological water transfer measures are mainly reflected in the effective improvement of environmental quality in the water transfer area. However, the ecological restoration goals on a large spatial scale have not yet been achieved, and there are still deficiencies in the research on the stability of landscape functions and structures in the restoration areas. Therefore, it is very important to accurately evaluate the contribution of ecological restoration in the lower reaches of inland rivers.

[0004] However, the definition of the stable state of ecological restoration in the lower reaches of inland rivers in the contribution assessment is greatly affected by the uncertainty of environmental factors. Although simple indicators have been used to quantify the stability of desert landscapes, the establishment of a stability index based on empirical values ​​to judge its stability cannot accurately reveal the true stability of the landscape in the lower reaches of inland rivers. As for the contribution of ecological restoration in the lower reaches of inland rivers after ecological water replenishment, due to the long-term impact of the restoration process, comprehensive consideration of surface water, groundwater, vegetation, biodiversity and other aspects is insufficient. At present, the contribution rate of ecological restoration after ecological water transfer cannot be quantified. Summary of the invention

[0005] The present invention aims to provide a method for identifying the ecological restoration contribution rate of ecological water replenishment in the downstream of an inland river basin, so as to solve the problem that the ecological restoration contribution rate cannot be quantified after ecological water transfer.

[0006] The method for identifying the ecological restoration contribution rate of ecological water replenishment in the downstream of inland river basins in this plan includes:

[0007] S1, determine the scope of the study area with the watershed as the basic unit, and obtain multi-source public data of the study area, wherein the multi-source public data includes vegetation, meteorological and hydrological data, and ecological water replenishment data;

[0008] S2, preprocessing multi-source public data, unifying the projection information and spatial resolution of different raster data, and building a database;

[0009] S3, based on the true values ​​of precipitation and potential evapotranspiration observed at all meteorological stations in the study area, the accuracy of precipitation and potential evapotranspiration products is evaluated using four evaluation indicators: correlation coefficient, root mean square error, relative deviation coefficient, and Nash-Sutcliffe model efficiency coefficient, to obtain accurate regional precipitation and potential evapotranspiration raster product data;

[0010] S4, estimates regional vegetation coverage based on the NDVI index, constructs a regional water balance equation using the water balance principle, and uses the vegetation coverage to correct the comprehensive plant coefficient K through a preset formula to improve the accuracy of estimating the actual regional evaporation ET;

[0011] S5, based on the principle of water-heat balance, uses the water-heat stability index to characterize the regional water balance, that is, using the income water / expenditure water;

[0012] S6, based on the hydrothermal stability index, the comprehensive hydrothermal stability of the lower reaches of the inland river was evaluated in terms of resistance, resilience, recovery time and variability;

[0013] S7, with the meteorological and hydrological elements of precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, land water storage change, temperature, vapor pressure deficit, and wind speed as the core, a preset method was used to screen the key influencing indicators of the comprehensive water and heat stability in the lower reaches of inland rivers;

[0014] S8, based on the comprehensive hydrothermal stability and key influencing indicators, identify the ecological restoration contribution rate of ecological water replenishment at different spatial scales of watershed and pixel.

[0015] Furthermore, in said S1, the vegetation data is derived from the Normalized Difference Vegetation Index NDVI data;

[0016] The meteorological and hydrological data include precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage change, temperature, vapor pressure deficit and wind speed. The data types of the meteorological and hydrological data include meteorological station measured data and raster data.

[0017] The ecological water replenishment data is obtained through statistical yearbooks, public data, and water conservancy authorities.

[0018] Furthermore, in S2, the raster data includes Normalized Difference Vegetation Index (NDVI) data, precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage changes, temperature, vapor pressure deficit, and wind speed.

[0019] Further, in S2, the preprocessing is: by comparing the projection information and grid pixel size of multi-source raster data of vegetation data and hydrological and meteorological data, the hydrological and meteorological data include precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage changes, temperature, vapor pressure deficit, wind speed, based on the minimum grid pixel, using a series of operations such as format conversion, cropping, projection conversion, and resampling for preprocessing.

[0020] Furthermore, in S2, batch processing is implemented through R code programming. The original data in *.nc format are first converted into *.tif format, and a coordinate system is assigned. The resolution is unified to 250m based on the minimum grid pixel, and the resolution, projection coordinates, and row and column numbers are kept consistent with those of NDVI, precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage changes, air temperature, vapor pressure deficit, and wind speed data. The monthly raster data set of the study area is cropped according to the boundary of the study area.

[0021] Furthermore, in S3, the precipitation and potential evapotranspiration raster data of different resolutions are uniformly interpolated to the corresponding stations of the same longitude and latitude using the bilinear interpolation method, the interpolated data are bias-corrected, and compared with the historical observation data. The accuracy of precipitation and potential evapotranspiration products is evaluated using four evaluation indicators: the correlation coefficient between the spatial interpolation data and the observation value, the root mean square error, the relative deviation coefficient, and the Nash-Sutcliffe model efficiency coefficient, to determine the optimal precipitation and potential evapotranspiration raster products in the study area. Furthermore, in S4, the calculation formula for estimating regional vegetation coverage using the pixel binary model is:

[0022]

[0023] Among them, NDVI min It is the NDVI value of the pixel in the pure bare land coverage area. Its theoretical value is close to 0. max It is the NDVI value of the pixel in the pure vegetation coverage area, and its theoretical value is close to 1;

[0024] The actual regional evapotranspiration of the non-uniform underlying surface at the regional scale is estimated using the following calculation formula:

[0025] ET = KPET;

[0026] K = f(FVC);

[0027] Where, ET is actual evapotranspiration; K is comprehensive plant coefficient; PET is potential evaporation; FVC is vegetation coverage; f(FVC) is the function of vegetation coverage;

[0028] Based on the principle of water balance, the comprehensive plant coefficient K of the region is adjusted, and the water balance model is expressed as:

[0029]

[0030] Where: i and n represent the pixel location and number of the raster data; A i It represents the area of ​​the raster data pixel; EWR represents ecological water replenishment; PRE represents precipitation; ET represents actual evapotranspiration;

[0031] Taking vegetation coverage as the link, linear, binomial, exponential and power functions were used to construct the relationship between actual evapotranspiration and comprehensive plant coefficient K, K = (average annual precipitation + ecological water replenishment) / annual potential evapotranspiration, and the comprehensive plant coefficient K was corrected according to the goodness of fit.

[0032] Further, in S6, the resistance calculation formula is:

[0033]

[0034] Where M is the value of the two sigma event sizes, the change value of ecosystem characteristics during disturbance;

[0035] The calculation formula of the restoring force is:

[0036]

[0037] Where M is the value of the size of two sigma events, the change value of ecosystem characteristics during disturbance, and T is the time required to return to normal after disturbance;

[0038] The calculation formula of the recovery time is:

[0039]

[0040] T ji = min[tT ji |W(i, t)≥0&t>T ji ];

[0041] Where, T ji It represents the time when the sigma event occurs, that is, the minimum time required for the moving window average W to become non-negative; W represents the moving window average of HSI; i represents the pixel position; t represents time; n represents the number of moving smoothing times;

[0042] The calculation formula of the variability is:

[0043]

[0044] In the formula, CV represents the coefficient of variation; σ represents the standard deviation; Indicates the average value over a period of time; HSI irepresents the value of a pixel in the i-th year; n represents the number of years in the study period;

[0045] The comprehensive stability score of the hydrothermal stability index is calculated using the comprehensive evaluation index formula:

[0046] HCSI=ResistanceP1+RecoveryP2+TP3+CVP4;

[0047] Where: HCSI is the hydrothermal comprehensive stability index; P represents the weight value of the evaluation factor.

[0048] Furthermore, in S7, the precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage change, air temperature, vapor pressure deficit, and wind speed response variables of the comprehensive stability of the hydrothermal stability index are regressed through redundancy analysis, and the key influencing indicators of the comprehensive stability are determined through the contribution rate.

[0049] Furthermore, in S8, the identification of ecological restoration contribution rate includes identification of ecological restoration contribution rate of ecological water replenishment based on regional scale in the lower reaches of inland rivers and identification of ecological restoration contribution rate of ecological water replenishment based on pixel scale in the lower reaches of inland rivers.

[0050] The beneficial effects of this program are:

[0051] Based on the hydrothermal stability index (HSI) applicable to the lower reaches of inland rivers, an innovative evaluation method for the hydrothermal comprehensive stability (HCSI) of the lower reaches of inland rivers was proposed from four aspects: resistance, resilience, recovery time, and variability. The regional ecological recovery status was judged based on the hydrothermal comprehensive stability.

[0052] On the other hand, the indicators are optimized based on the relationship between the hydrothermal comprehensive stability HCSI and key influencing indicators (precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, changes in terrestrial water storage, temperature, vapor pressure deficit, wind speed and other meteorological and hydrological elements), and an improved quantitative identification method for the ecological restoration contribution rate of ecological water replenishment at different spatial scales (regional and pixel scales) is proposed to improve the accuracy of identifying the ecological restoration contribution rate under different circumstances and provide reliable analysis and judgment indicators for ecological governance projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of an embodiment of a method for identifying the ecological restoration contribution rate of ecological water replenishment in the downstream of an inland river basin according to the present invention. DETAILED DESCRIPTION

[0054] The following is further explained in detail through specific implementation methods.

[0055] Example

[0056] Methods for identifying the ecological restoration contribution rate of ecological water replenishment in the lower reaches of inland river basins, such as Figure 1 As shown, including:

[0057] S1, determine the scope of the study area with the watershed as the basic unit, and obtain multi-source public data in the study area, including vegetation, meteorological and hydrological data, and ecological water replenishment data. The vegetation data comes from the normalized difference vegetation index NDVI data. The NDVI dataset comes from the MOD13Q1.061 product of the EARTHDATA website. Its time range is from 2000 to the present, with a time resolution of 16d and a spatial resolution of 250m. The maximum value synthesis method is used to synthesize monthly data. The GIMMS NDVI dataset is a long-term series of NDVI data obtained by NOAA's Advanced Very High Resolution Radiometer (AVHRR) sensor. The time range is 1981-2022. It is currently the NDVI data product with the longest time range, with a spatial resolution of 1 / 12° and a time resolution of 15 days. The data format is *.nc.

[0058] Meteorological and hydrological data include precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage changes, temperature, vapor pressure deficit and wind speed. The data types of meteorological and hydrological data include meteorological station measured data and raster data. The measured data of precipitation and potential evapotranspiration in the meteorological station measured data are mainly from the China Surface Climate Data Daily Dataset (V3.0) of the National Meteorological Science Data Center. The core data used in this study are mainly 20-20 precipitation, daily small evaporation, and daily large evaporation. This dataset has been strictly quality controlled and manually verified, and the data accuracy is close to 100%, which is highly reliable. Raster data of precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage changes, temperature, vapor pressure deficit, and wind speed data. The precipitation grid data mainly come from 15 sets of precipitation products that are currently widely used, such as Qu precipitation products, CMFD precipitation products, CRU TS V4.07 precipitation products, TRMM precipitation products, GPCP precipitation products, IMERG precipitation products, PERSIANN-CDR precipitation products, CHIRPS V2.0 precipitation products, ERA5-Land precipitation products, FLDAS precipitation products, GLDAS precipitation products, CRA40LAND precipitation products, Peng precipitation products, TerraClimate precipitation products, and MSWEP precipitation products. The detailed information of each precipitation product is shown in Table 1.

[0059] Table 1 Basic information of 15 precipitation products

[0060] Serial number Data production type Product Name Time span Spatial resolution Temporal resolution 1 Spatial interpolation Qu 1960-01-2020-12 1km moon 2 Spatial interpolation CMFD 1979-01-2018-12 0.1° moon 3 Spatial interpolation CRU TS V4.07 1901-01-2022-12 0.1° moon 4 Satellite inversion TRMM 2000-01-2019-12 0.25° moon 5 Satellite inversion GPCP 1979-01-present 2.5° moon 6 Satellite inversion IMERG 2000-01-present 0.1° moon 7 Satellite inversion PERSIANN CDR 1983-01-present 0.25° day 8 Satellite inversion CHIRPS V2.0 1981-01-present 0.1° moon 9 Reanalysis ERA5 LAND 1950-01-present 0.1° moon 10 Reanalysis FLDAS Noah 1982-01-present 0.1° moon 11 Reanalysis GLDAS Noah V2.1 2000-01-present 0.25° moon 12 Reanalysis CRA40LAND 1979-01-2021-12 0.25° moon 13 Reanalysis Peng 1901-01-2022-12 1km moon 14 Reanalysis TerraClimate 1958-01-2022-12 4km moon 15 Reanalysis MSWEP 1979-01-present 0.1° moon

[0061] This embodiment uses six sets of potential evapotranspiration grid products, namely Peng, ERA5-land, CRA40LAND, CRU TS V4.07, GLDAS, and TerraClimate.

[0062] The actual evapotranspiration data are mainly obtained through the revised comprehensive plant coefficient K, combined with precipitation and potential evapotranspiration estimation.

[0063] The change in terrestrial water reserves is mainly obtained through the satellite gravity-based China regional terrestrial water and groundwater reserves change product (2002-2022) of the National Tibetan Plateau Science Data Center.

[0064] The vapor pressure deficit is mainly obtained through the first 1 km high-resolution atmospheric humidity index dataset in China from the National Tibetan Plateau Data Center (https: / / www.tpdc.ac.cn / home).

[0065] Hydrometeorological data such as soil water storage, air temperature, and wind speed are mainly obtained through the National Meteorological Science Data Center.

[0066] The ecological water replenishment data is obtained through publicly released data. The ecological water replenishment data is mainly obtained through data released by public data websites such as the National Glacier, Frozen Soil and Desert Science Data Center, local water conservancy authorities, statistical yearbooks, etc.

[0067] S2, preprocessing multi-source public data, unifying the projection information and spatial resolution of different raster data, and building a database. The raster data includes NDVI data, precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage changes, temperature, vapor pressure deficit, and wind speed.

[0068] The preprocessing is: by comparing the projection information and pixel size of multi-source raster data of vegetation data and hydrological and meteorological data, the hydrological and meteorological data include precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage changes, temperature, vapor pressure deficit, wind speed, based on the minimum raster pixel, using a series of operations such as format conversion, clipping, projection conversion, and resampling for preprocessing.

[0069] Data preprocessing can be achieved through batch processing through R code programming. First, the original data in *.nc format are converted into *.tif format, and a coordinate system is assigned. The resolution is unified to 250m based on the minimum grid pixel to ensure consistency with the resolution, projection coordinates, and row and column numbers of NDVI, precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage changes, temperature, vapor pressure deficit, wind speed and other data. The monthly raster dataset of the study area is cropped according to the boundary of the study area.

[0070] Reconstruct the long-term NDVI data series and use the variance matching method to reconstruct a homogenized NDVI time series covering the preset year range. The core idea of ​​the variance matching method is to adjust one of the data sets so that the mean and standard deviation of the data in the overlapping period of the two time series are consistent, thereby ensuring that the variances of the two sections before and after the reconstructed sequence are comparable. The calculation formula is:

[0071]

[0072]

[0073] In the formula, MODIS i represents the MODIS NDVI dataset for the i-th year, MODIS and GIMMS represent the average values ​​of MODIS NDVI and GIMMS NDVI 3g within the preset year range, respectively, and S MODIS and S GIMMS They represent the standard deviations of MODIS NDVI and GIMMS NDVI 3g for the preset year range, Indicates the average value for a preset range of years.

[0074] Reconstructing the potential evapotranspiration data of long time series, according to the industry standard "Surface Meteorological Observation Specification Part 10: Evapotranspiration Observation" issued by the China Meteorological Administration, evaporation pan evaporation is mainly measured by two methods: small evaporator and large evaporator. Considering that the synchronous observation data of the two evaporation pans at a single meteorological station are relatively small, we refer to Ye Linyuan, Lu Han, Qin Shujing, et al. Evolution of evaporation pan evaporation and actual evaporation in the Yangtze River Basin from 1960 to 2019 [J]. Advances in Water Science, 2022, 33(05): 718-729. The relevant research in this article obtains the conversion coefficients of different evaporation pans in the Heihe River Basin through linear regression, thereby obtaining consistent evaporation pan evaporation in the region, that is, potential evaporation.

[0075] S3, based on the true values ​​of precipitation and potential evapotranspiration observed at all meteorological stations in the study area, uses four evaluation indicators, namely correlation coefficient, root mean square error, relative deviation coefficient, and Nash-Sutcliffe model efficiency coefficient, to evaluate the accuracy of precipitation and potential evapotranspiration products, obtain accurate precipitation and potential evapotranspiration product raster data, and obtain the most accurate precipitation and potential evapotranspiration products in the region.

[0076] The bilinear interpolation method was used to uniformly interpolate the precipitation and potential evapotranspiration raster data of different resolutions to the corresponding stations at the same longitude and latitude. The interpolated data were bias-corrected and compared with the historical observation data. The accuracy of precipitation and potential evapotranspiration products was evaluated using four evaluation indicators, namely the correlation coefficient between simulation values ​​and observation values, root mean square error, relative deviation coefficient, and Nash-Sutcliffe model efficiency coefficient, to determine the optimal precipitation and potential evapotranspiration raster products in the study area.

[0077] The measured data of meteorological stations are used as the basis for evaluating the "true value". Four evaluation indicators, including correlation coefficient (CC), root mean square error (RMSE), relative bias coefficient (PBIAS), and Nash-Sutcliffe model efficiency coefficient (NSE), are mainly used to evaluate the accuracy, reliability and applicability of precipitation and potential evapotranspiration product data in the study area. The specific calculation formula is as follows:

[0078]

[0079] In the formula, S i represents the evaluation value of the i-th precipitation and potential evapotranspiration product data; O i represents the true value of the measured data of the ith meteorological station; n represents the number of evaluation data samples; Represent the average values ​​of sample evaluation data and actual observation data respectively.

[0080] Among the above parameters, CC is mainly used to analyze the degree of correlation and consistency between raster products and measured data. The value range is [-1,1]. Positive and negative data indicate the direction of correlation. The closer the value is to 1, the stronger the correlation. RMSE describes the error between raster product data and measured data. The value range is non-negative real numbers. The closer the value is to 0, the higher the accuracy and the better the applicability of the raster product data. PBIAS is used to describe the relative deviation between raster product data and measured data. The positive and negative represent the direction of error and deviation, respectively indicating that the evaluation data is overestimated or underestimated than the measured value. The closer the value is to 0, the higher the accuracy and the better the applicability of the raster product data. NSE is usually used to verify the quality of hydrological model simulation results. The whole method is mainly used to analyze the reliability of raster product data compared with measured data. The NSE value range is from negative infinity to 1, and is divided into the following three cases: if NSE is close to 1, it means that the data quality of the raster product data is good and the credibility is high; if NSE is close to 0, it means that the evaluation value of the raster product data is close to the average level of the observed real data, and the overall result is relatively credible, but the error is relatively large; if NSE is much less than 0, it means that the raster product data is unreliable.

[0081] S4, estimates regional vegetation coverage based on vegetation data, constructs a regional water balance equation using the water balance principle, and uses the vegetation coverage to correct the comprehensive plant coefficient K through a preset formula. The preset formula is the Penman-Monteith formula of the Food and Agriculture Organization of the United Nations, which improves the accuracy of the estimation of the actual regional evaporation ET.

[0082] The calculation formula for estimating regional vegetation coverage through the pixel binary model is:

[0083]

[0084] Among them, NDVI min It is the NDVI value of the pixel in the pure bare land coverage area. Its theoretical value is close to 0. max It is the NDVI value of the pixel in the pure vegetation coverage area, and its theoretical value is close to 1.

[0085] This embodiment selects 5% confidence level as the threshold range of NDVI of the study area, that is, when the cumulative probability is close to 5% and 95%, the NDVI of the pure bare land coverage area is obtained respectively. min , NDVI of pure vegetation coverage area max In this embodiment, ArcGIS pro software is used to obtain the NDVI distribution value of the remote sensing image of the study area and obtain its probability distribution data.

[0086] The existing technology uses the method of estimating crop evapotranspiration under standard conditions to determine the actual evapotranspiration through optimization. The calculation formula is:

[0087] ET c =K c ET o ;

[0088] Where: ET c is crop evapotranspiration; K c is the crop coefficient; ET o The reference crop evapotranspiration is calculated using the Penman-Monteith formula of the Food and Agriculture Organization of the United Nations. This method is considered to be the only standard method for calculating reference crop evapotranspiration using standard meteorological stations. The calculation formula is as follows:

[0089]

[0090] Where ET0 is the reference crop evapotranspiration, in mm / day; E n is the net solar radiation on the crop surface, in MJ / m2·day; G is the soil heat flux, in MJ / m2, day; T is the daily average humidity at a height of 2 meters, in ℃; u2 is the wind speed at a height of 2 meters, in m / s; e sis the saturated water vapor pressure, in kPa; e a is the actual water vapor pressure, in kPa; e s -e a is the saturated water vapor pressure difference, in kPa; Δ is the slope of the water vapor pressure curve; γ is the hygrometer constant, in kPa / ℃.

[0091] The existing method needs to determine the ET of the crop surface through the crop coefficient under standard conditions, and the crop coefficient is related to , while the ET of the crop surface under non-standard conditions is adjusted by correcting the crop coefficient. Therefore, this embodiment estimates the actual regional evapotranspiration of the non-uniform underlying surface at the regional scale, and the calculation formula is:

[0092] ET = KPET;

[0093] K = f(FVC);

[0094] Where, ET is actual evapotranspiration; K is comprehensive plant coefficient; PET is potential evaporation; FVC is vegetation coverage; f(FVC) is the function of vegetation coverage;

[0095] Based on the principle of water balance, it means that in any region of the earth, the difference between the amount of water received and the amount of water spent in a certain period of time is equal to the water storage variable in the region, and the water storage variable in the long term remains almost unchanged. Taking the lower reaches of inland rivers as an example, the amount of water received TAWS mainly comes from the total ecological water replenishment EWR (Ecological water replenishment) and the total precipitation PRE; the amount of water spent TAWE is mainly used for evapotranspiration. Based on the principle of water balance, the comprehensive plant coefficient K of the region is adjusted, and the water balance model is expressed as:

[0096]

[0097] Where: i and n represent the pixel position and number of the raster data; A i It represents the area of ​​the raster data pixel; EWR represents ecological water replenishment; PRE represents precipitation; ET represents actual evapotranspiration.

[0098] Taking vegetation coverage as the link, linear, binomial, exponential and power functions were used to construct the relationship between actual evapotranspiration and comprehensive plant coefficient K, K = (average annual precipitation + ecological water replenishment) / annual potential evapotranspiration, and the comprehensive plant coefficient K was corrected according to the goodness of fit.

[0099] According to the optimal comprehensive plant coefficient K, the actual evapotranspiration estimation method is used to estimate the results, thereby effectively improving the estimation accuracy of the actual evapotranspiration of the region.

[0100] S5, based on the principle of hydrothermal balance, the hydrothermal stability index HSI (Hydrothermal stability index) is used to characterize the regional water balance status.

[0101] Water and heat balance is a necessary condition for maintaining landscape stability, especially in desert areas with scarce water resources. Water and heat conditions have become an important or even the only factor affecting the stability of desert landscapes. In arid areas, water balance has always been the core scientific issue facing ecological environment construction, and it determines the sustainability and stability of vegetation-soil systems. Climate change and human activities have greatly interfered with the succession process of natural ecosystems, and have also had a significant impact on the stability of ecosystems, thereby directly or indirectly affecting the regional landscape pattern. Therefore, the hydrothermal stability index HSI characterizes the regional water balance status. The regional water balance status adopts the income water / expenditure water, and the calculation formula is:

[0102]

[0103] Where HSI represents the hydrothermal stability index; TAWS represents the total amount of regional water income; TAWE represents the total amount of regional water expenditure.

[0104] S6, based on the hydrothermal stability index HSI, the hydrothermal comprehensive stability of the lower reaches of the inland river is evaluated from the perspectives of resistance, resilience, recovery time and variability, that is, the hydrothermal comprehensive stability index HCSI is obtained.

[0105] Ecosystem stability is a multidimensional and complex concept. It is the ability of ecosystem structure and function to resist disturbance, restore to the original state or maintain long-term stability. Therefore, the perspectives and indicators used to evaluate ecosystem stability are not consistent. At present, stability evaluation is often affected by the uncertainty of obtaining environmental factors. Most time series of ecosystem characteristics are affected by the uncertainty and noise of changing data characteristics, which makes the comparison of ecosystem stability indicators between different regions complicated. Data quality will affect the accuracy of ecosystem stability. At the same time, the complexity of ecological landscape stability often requires considering multiple indicators in stability evaluation, which undoubtedly brings certain difficulties to the evaluation work. Although some researchers have used simple indicators to quantify the stability of desert landscapes, establishing a stability index to judge its stability based on empirical values ​​cannot accurately reveal the true stability of desert landscapes.

[0106] The hydrothermal stability HSI is measured from four aspects: resistance, resilience, recovery time, and variability, so as to quantitatively measure the stability of the downstream ecosystem of inland rivers through multidimensional state assessment from the perspective of water balance.

[0107] Resistance quantifies the direct impact of external disturbances on the ecosystem, and represents the ability of the ecosystem to maintain its original function and structure under external disturbances. The resistance calculation formula is:

[0108]

[0109] Where M is the value of the two sigma event sizes, the change in ecosystem characteristics during disturbance.

[0110] Resilience is the ability of an ecosystem to recover from a degraded state to its initial state after external disturbance is eliminated. The calculation formula for resilience is:

[0111]

[0112] Where M is the value of the two sigma event sizes, the change in ecosystem characteristics during disturbance, and T is the time required to return to normal after disturbance.

[0113] Recovery time represents the time required for an ecosystem to recover from external disturbance. The recovery time calculation formula is:

[0114]

[0115] T ji = min[tT ji |W(i, t)≥0&t>T ji ];

[0116] Where, T ji It represents the time when the sigma event occurs, that is, the minimum time required for the moving window average W to become non-negative; W represents the moving window average of HSI; i represents the pixel position; t represents time; and n represents the number of moving smoothing times.

[0117] Variability represents the change of ecosystems over time under external disturbances. The smaller the variability, the greater the temporal similarity of the spatial distribution patterns of ecosystem functions and structures. The calculation formula for variability is:

[0118]

[0119] In the formula, CV represents the coefficient of variation; σ represents the standard deviation; Indicates the average value over a period of time; HSI i It represents the value of a pixel in the i-th year; n represents the number of years in the study period.

[0120] Comprehensive evaluation analysis methods are usually used together with the index system. The most common ones are the analytic hierarchy process (AHP). AHP treats a complex multi-objective decision-making problem as a system, decomposes the goal into multiple sub-goals or criteria, and then decomposes it into several levels of multiple indicators. By constructing a matrix through normalized assignment, the single hierarchical ranking and total ranking of the hierarchy are calculated, which is used as a systematic method for multi-indicator optimization decision-making. In practical applications, the comprehensive evaluation method is often used in combination with the Delphi method, entropy weight method, fuzzy mathematics method, etc. This embodiment uses the comprehensive evaluation index formula to calculate the comprehensive stability score of the hydrothermal stability index, which is:

[0121] HCSI=ResistanceP1+RecoveryP2+TP3+CVP4;

[0122] In the formula: HCSI is the hydrothermal comprehensive stability index; Resistance is resistance; Recovery is recovery; T is recovery time; CV is variability; P represents the weight value of the evaluation factor.

[0123] S7, with the meteorological and hydrological elements of precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, land water storage change, air temperature, vapor pressure deficit, and wind speed as the core, a preset method was used to screen the key influencing indicators of the hydrothermal comprehensive stability HCSI in the lower reaches of inland rivers.

[0124] The preset methods include redundancy analysis (RDA). Redundancy analysis (RDA) is a PCA analysis of the fitted value matrix of the multivariate linear regression between the response variable matrix and the explanatory variable matrix, and is also an extension of multi-response regression analysis. RDA is a ranking method that combines linear regression analysis with principal component analysis. The purpose is to find a series of linear combinations of explanatory variables that can best explain the variation of the response variable matrix, that is, the impact of the environment on the sample. Therefore, RDA is a ranking constrained by the explanatory variables. RDA is a method that combines regression and principal component analysis (PCA). It is a direct extension of multivariate regression analysis and is used for modeling multivariate response data. RDA is an extremely powerful tool in the hands of ecologists. Calculations are performed using the open source R language package "Vegan".

[0125] Taking the lower reaches of inland rivers as an example, multiple response variables (precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage changes, temperature, vapor pressure deficit, wind speed, etc.) of the explanatory variable (hydrothermal comprehensive stability index HCSI) were analyzed through redundancy analysis. The key influencing indicators of hydrothermal comprehensive stability were determined through their contribution rates.

[0126] S8, based on the comprehensive hydrothermal stability and key influencing indicators, identify the ecological restoration contribution rate of ecological water replenishment at different spatial scales of region and pixel. The identification of ecological restoration contribution rate includes the identification of ecological restoration contribution rate of ecological water replenishment at the regional scale in the lower reaches of inland rivers and the identification of ecological restoration contribution rate of ecological water replenishment at the pixel scale in the lower reaches of inland rivers.

[0127] (1) When the amount of ecological water replenishment is regional statistical data, the ecological restoration contribution rate of ecological water replenishment at the regional scale in the lower reaches of inland rivers is identified, and the ecological restoration contribution rate of ecological water replenishment at the regional scale is estimated using regression analysis.

[0128] Multivariate regression analysis is often used to study the relationship between geographical factors and determine their quantitative contributions. By establishing the correlation between the hydrothermal comprehensive stability index HCSI and key hydroclimatic factors (precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage changes, temperature, vapor pressure deficit, wind speed) or human factor indicators (ecological water replenishment, etc.), the relative role of each factor can be analyzed. The calculation formula is:

[0129] HCSI=A1X1+A2X2+...+A n X n ;

[0130] The above equation is a standardized regression equation, where HCSI is the target variable, X is the predictor variable, and A is the regression coefficient (=1, 2, 3, ..., n), which are precipitation, actual evapotranspiration, soil water storage, land water storage change, temperature, vapor pressure deficit, and ecological water replenishment. This embodiment adopts a multivariate second-order least squares regression model to establish a relationship with the hydrothermal comprehensive stability index HCSI as the target variable and all influencing variables with a contribution rate greater than 90% as predictor variables. According to the relationship established after data correction, the contribution of ecological water replenishment to the hydrothermal comprehensive stability index HCSI is calculated as follows:

[0131]

[0132] Among them, ρ EWS It is the relative contribution (%) of ecological water replenishment at the regional scale in the lower reaches of inland rivers.

[0133] (2) When the ecological water replenishment statistical data is converted into pixel scale, the residual trend analysis is used to identify the ecological restoration contribution rate of ecological water replenishment at the pixel scale in the lower reaches of inland rivers.

[0134] The residual trend analysis method can avoid the problem of confusing climate and human activity factors in conventional regression analysis. Based on the correlation between the hydrothermal comprehensive stability index HCSI and climate factors, the model is used to simulate the difference between the change of the hydrothermal comprehensive stability index HCSI_climate without human interference and the actual change of the hydrothermal comprehensive stability index HCSI, which represents the impact of human activity ecological water replenishment on the hydrothermal comprehensive stability index HCSI_EWS. The relative effects of climate change and ecological water replenishment on the hydrothermal comprehensive stability index HCSI are analyzed through the residual trend changes.

[0135] Taking the ecosystem in the lower reaches of inland rivers as an example, the changes in the recovery of its ecosystem are mainly affected by two factors: human activities (ecological water replenishment, etc.) and climate factors (precipitation PRE, actual evapotranspiration AET, temperature TEMP, vapor pressure deficit VPD, etc.). By using the relationship between HCSI, HCSI_climate, and HCSI_EWS, the contribution rate of human activities (ecological water replenishment, etc.) and climate factors in the lower reaches of inland rivers to the hydrothermal comprehensive stability index HCSI was determined from the pixel scale through the residual analysis method, thereby effectively judging the contribution of regional ecological restoration.

[0136] ① Construct the HCSI_climate model of the hydrothermal comprehensive stability index under the climate impact scenario. Take the lower reaches of the inland river as the hydrothermal comprehensive stability index HCSI_climate dependent variable, take all influencing variables with a contribution rate greater than 90% as predictor variables, construct a binary linear regression model, and calculate the parameters of the regression model:

[0137] HCSI_climate=B1PRE+B2AET+B3TEMP+B4VPD+ε;

[0138] Where B is the regression parameter; ε is the model residual; PRE is precipitation; AET is actual evapotranspiration; TEMP is temperature; and VPD is vapor pressure deficit.

[0139] Based on the measured data such as precipitation PRE, actual evapotranspiration AET, temperature TEMP, vapor pressure deficit VPD and the above model parameters, the comprehensive water and heat stability index HCSI_climate under the regional climate impact scenario is obtained.

[0140] ②Construct the HCSI_EWS model of the comprehensive hydrothermal stability index under the scenario of ecological water replenishment. The HCSI_EWS model of the comprehensive hydrothermal stability index under the scenario of ecological water replenishment is the difference between the actual observed value of the comprehensive hydrothermal stability index HCSI and the comprehensive hydrothermal stability index HCSI_climate under the climate impact scenario, which is used to express the impact of ecological water replenishment caused by human activities on the comprehensive hydrothermal stability of the region. The calculation formula is:

[0141] HCSI_EWS=HCSI-HCSI_climate.

[0142] ③ Calculate the changing trends of the comprehensive hydrothermal stability index HCSI under the scenarios of ecological water replenishment and comprehensive climate impact, the comprehensive hydrothermal stability index HCSI_EWS under the scenario of ecological water replenishment, and the comprehensive hydrothermal stability index HCSI_climate under the scenario of climate impact.

[0143] Theil-Sen Median trend method is a very robust nonparametric statistical trend calculation method that can effectively reflect the degree of change and trend of factors. It has no specific requirements for data distribution type, does not require data to follow a certain distribution, and has strong resistance to data errors. It is now widely used in meteorology, hydrology, ecology and other fields. The Theil-Sen Median trend calculation formula is:

[0144]

[0145] In the formula, median represents the median function; x j and x i They represent the data of the jth and ith time series respectively. When Slope is greater than 0, it indicates that the element is increasing, otherwise it is decreasing.

[0146] Theil-Sen Median trend method was used to analyze the changing trends of the comprehensive hydrothermal stability index HCSI under the scenario of ecological water replenishment and comprehensive climate impact, the comprehensive hydrothermal stability index HCSI_EWS under the scenario of ecological water replenishment, and the comprehensive stability hydrothermal stability index HCSI_climate under the scenario of climate impact.

[0147] Among them, when Slope(HCSI_EWS)>0, it indicates that the ecological water replenishment activities have a positive impact on the comprehensive hydrothermal stability, that is, the regional hydrothermal comprehensive stability is in a recovery state; when Slope(HCSI_EWS)<0, it indicates that the ecological water replenishment activities have a negative reverse impact on the comprehensive hydrothermal stability, that is, the regional hydrothermal comprehensive stability is in a degraded state; when Slope(HCSI_EWS)=0, it indicates that the ecological water replenishment activities have a weak impact on the comprehensive hydrothermal stability, that is, the regional hydrothermal comprehensive stability is in a basically stable state.

[0148] ④ The relative contribution of ecological water replenishment and climate change to the comprehensive hydrothermal stability of long-term series is calculated by combining scenario simulation and evaluation methods, specifically:

[0149] When the hydrothermal comprehensive stability index HCSI trend under the scenario of ecological water replenishment and climate comprehensive impact is a significant improvement trend, that is, when Slope (HCSI)>0:

[0150] If it is scenario 1 where both factors are positive driving factors, the predicted value and residual are judged. When Slope(HCSI_EWS)>0 and Slope(HCSI_climate)>0, the relative contribution rate of climate change as a driving factor is calculated as follows: The calculation formula for the relative contribution rate of ecological water replenishment as a driving factor is: It indicates that climate change and ecological water replenishment jointly promote the restoration of regional water-heat comprehensive stability;

[0151] If it is scenario 2 where one factor is a positive driving factor, the predicted value and residual are judged. When Slope(HCSI_EWS)<0 and Slope(HCSI_climate)>0, the relative contribution rate of climate change as a driving factor is 100%, and the relative contribution rate of ecological water replenishment as a driving factor is 0, indicating that the comprehensive water-heat stability of the region dominated by climate change has been restored;

[0152] If it is scenario 3 where another factor is a positive driving factor, the predicted value and residual are judged. When Slope(HCSI_EWS)>0 and Slope(HCSI_climate)<0, the relative contribution rate of climate change as a driving factor is 0, and the relative contribution rate of ecological water replenishment as a driving factor is 100%, indicating that the comprehensive water-heat stability of the area dominated by ecological water replenishment has been restored;

[0153] When the hydrothermal comprehensive stability index HCSI under the scenario of ecological water replenishment and climate comprehensive impact shows a significant downward trend, that is, SlopeHCSI) < 0:

[0154] If it is scenario 4 where both factors are negative driving factors, the predicted value and residual are judged. When Slope(HCSI_EWS)<0 and Slope(HCSI_climate)<0, the relative contribution rate of climate change as a driving factor is calculated as follows: The calculation formula for the relative contribution rate of ecological water replenishment as a driving factor is: It indicates that climate change and ecological water replenishment have jointly led to the degradation of regional hydrothermal integrated stability;

[0155] If it is scenario 5 where one factor is a negative driving factor, the predicted value and residual are judged. When Slope(HCSI_EWS)>0 and Slope(HCSI_climate)<0, the relative contribution rate of climate change as a driving factor is 100%, and the relative contribution rate of ecological water replenishment as a driving factor is 0, indicating that climate change dominates the degradation of the comprehensive water and heat stability of the region;

[0156] In case of scenario 6 where the other factor is a negative driving factor, the predicted value and residual are judged. When Slope(HCSI_EWS)<0 and Slope(HCSI_climate)>0, the relative contribution rate of climate change as the driving factor is 0, and the relative contribution rate of ecological water replenishment as the driving factor is 100%, indicating that the comprehensive hydrothermal stability of the area dominated by ecological water replenishment is degraded.

[0157] Compared with the existing methods, this embodiment, based on the principle of hydrothermal balance, will apply the hydrothermal stability index applicable to the lower reaches of inland rivers, and propose an evaluation method for the comprehensive hydrothermal stability in the lower reaches of inland rivers from four aspects: resistance, resilience, recovery time, and variability. It optimizes indicators based on the relationship between the comprehensive hydrothermal stability and key influencing indicators (precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, changes in terrestrial water storage, air temperature, vapor pressure deficit, wind speed and other meteorological and hydrological elements), and proposes an improved quantitative identification method for the ecological restoration contribution rate of ecological water replenishment at different spatial scales (regional and pixel scales). The method is not affected by the uncertainty of environmental factors, and comprehensively considers the influence of various factors of the ecological environment. The system is more complete and accurate, which improves the accuracy of quantitative identification of the ecological restoration contribution rate under different circumstances, and provides reliable analysis, evaluation and judgment indicators for ecological governance projects.

[0158] The above is only an embodiment of the present invention, and the common knowledge such as the known specific structure and characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, several deformations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. The method for identifying the ecological restoration contribution rate of ecological water replenishment in the lower reaches of inland river basins is characterized by: include: S1, determine the scope of the study area with the watershed as the basic unit, and obtain multi-source public data of the study area, wherein the multi-source public data includes vegetation, meteorological and hydrological data, and ecological water replenishment data; S2, preprocessing multi-source public data, unifying the projection information and spatial resolution of different raster data, and building a database; S3, based on the true values ​​of precipitation and potential evapotranspiration observed at all meteorological stations in the study area, the accuracy of precipitation and potential evapotranspiration products is evaluated using four evaluation indicators: correlation coefficient, root mean square error, relative deviation coefficient, and Nash-Sutcliffe model efficiency coefficient, to obtain accurate regional precipitation and potential evapotranspiration product raster data; S4, estimates regional vegetation coverage based on the NDVI index, constructs a regional water balance equation using the water balance principle, and uses the vegetation coverage to correct the comprehensive plant coefficient K through a preset formula to improve the accuracy of estimating the actual regional evaporation ET; S5, based on the principle of water-heat balance, uses the water-heat stability index to characterize the regional water balance, that is, using the income water / expenditure water; S6, based on the hydrothermal stability index, the comprehensive hydrothermal stability of the lower reaches of the inland river was evaluated in terms of resistance, resilience, recovery time and variability; The resistance calculation formula is: Where M is the value of the two sigma event sizes, the change value of ecosystem characteristics during disturbance; The calculation formula of the restoring force is: Where M is the value of the size of two sigma events, the change value of ecosystem characteristics during disturbance, and T is the time required to return to normal after disturbance; The calculation formula of the recovery time is: T ji =min[t-T ji |W(i,t)≥0&t>T ji ]; Where, T ji It represents the time when the sigma event occurs, that is, the minimum time required for the moving window average W to become non-negative; W represents the moving window average of the thermal stability index HSI; i represents the pixel position; t represents the time; n represents the number of moving smoothing times; The calculation formula of the variability is: In the formula, CV represents the coefficient of variation; σ represents the standard deviation; Indicates the average value over a period of time; HSI i represents the value of a pixel in the i-th year; n represents the number of years in the study period; The comprehensive stability score of the hydrothermal stability index is calculated using the comprehensive evaluation index formula: HCSI=ResistanceP1+RecoveryP2+TP3+CVP4; Where: HCSI is the hydrothermal comprehensive stability index; P represents the weight value of the evaluation factor; S7, with the meteorological and hydrological elements of precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, land water storage change, temperature, vapor pressure deficit, and wind speed as the core, a preset method was used to screen the key influencing indicators of the comprehensive water and heat stability in the lower reaches of inland rivers; S8. Based on the comprehensive hydrothermal stability and key influencing indicators, the ecological restoration contribution rate of ecological water replenishment at different spatial scales of watershed and pixel is identified, and the ecological restoration contribution rate identification includes the ecological restoration contribution rate identification based on the regional scale of ecological water replenishment in the lower reaches of inland rivers and the ecological restoration contribution rate identification based on the pixel scale of ecological water replenishment in the lower reaches of inland rivers.

2. The method for identifying the ecological restoration contribution rate of the downstream ecological water replenishment of an inland river basin according to claim 1 is characterized by: In S1, the vegetation data is derived from the Normalized Difference Vegetation Index (NDVI) data; The meteorological and hydrological data include precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage change, temperature, vapor pressure deficit and wind speed. The data types of the meteorological and hydrological data include meteorological station measured data and raster data. The ecological water replenishment data is obtained through statistical yearbooks, public data, and water conservancy authorities.

3. The method for identifying the ecological restoration contribution rate of the downstream ecological water replenishment of an inland river basin according to claim 2 is characterized by: In S2, the grid data includes Normalized Difference Vegetation Index (NDVI) data, precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage change, temperature, vapor pressure deficit, and wind speed.

4. The method for identifying the ecological restoration contribution rate of the downstream ecological water replenishment of an inland river basin according to claim 3 is characterized by: In S2, the preprocessing is: by comparing the projection information and grid pixel size of multi-source raster data of vegetation data and hydrological and meteorological data, the hydrological and meteorological data include precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage changes, temperature, vapor pressure deficit, wind speed, based on the minimum grid pixel, using format conversion, clipping, projection conversion, resampling series operations for preprocessing.

5. The method for identifying the ecological restoration contribution rate of the downstream ecological water replenishment of an inland river basin according to claim 4 is characterized by: In S2, batch processing is implemented through R code programming. The original data in *.nc format are first converted into *.tif format, and a coordinate system is assigned. The resolution is unified to 250m based on the minimum grid pixel, and the resolution, projection coordinates, and row and column numbers are kept consistent with those of NDVI, precipitation, potential evapotranspiration, actual evapotranspiration, soil water storage, terrestrial water storage changes, temperature, vapor pressure deficit, and wind speed data. The monthly raster dataset of the study area is cropped according to the boundary of the study area.

6. The method for identifying the ecological restoration contribution rate of the downstream ecological water replenishment of an inland river basin according to claim 5 is characterized by: In S3, the precipitation and potential evapotranspiration raster data of different resolutions are uniformly interpolated to the corresponding stations of the same longitude and latitude using the bilinear interpolation method, the interpolated data are bias-corrected and compared with the historical observation data of the meteorological station, and the accuracy of precipitation and potential evapotranspiration products is evaluated using four evaluation indicators, namely the correlation coefficient between the spatial interpolation data and the observation value, the root mean square error, the relative deviation coefficient, and the Nash-Sutcliffe model efficiency coefficient, to determine the optimal precipitation and potential evapotranspiration raster products in the study area.

7. The method for identifying the ecological restoration contribution rate of the downstream ecological water replenishment of an inland river basin according to claim 5 is characterized by: In S4, the calculation formula for estimating regional vegetation coverage by using the pixel binary model is: Among them, NDVI min It is the NDVI value of the pixel in the pure bare land coverage area. Its theoretical value is close to 0. max It is the NDVI value of the pixel in the pure vegetation coverage area, and its theoretical value is close to 1; The actual regional evapotranspiration of the non-uniform underlying surface at the regional scale is estimated using the following calculation formula: ET = KPET; K = f(FVC); Where, ET is actual evapotranspiration; K is comprehensive plant coefficient; PET is potential evaporation; FVC is vegetation coverage; f(FVC) is the function of vegetation coverage; Based on the principle of water balance, the comprehensive plant coefficient K of the region is adjusted, and the water balance model is expressed as: Where: i and n represent the pixel location and number of the raster data; A i It represents the area of ​​the raster data pixel; EWR represents ecological water replenishment; PRE represents precipitation; ET represents actual evapotranspiration; Taking vegetation coverage as the link, linear, binomial, exponential and power functions were used to construct the relationship between actual evapotranspiration and comprehensive plant coefficient K, K = (average annual precipitation + ecological water replenishment) / annual potential evapotranspiration, and the comprehensive plant coefficient K was corrected according to the goodness of fit.

8. The method for identifying the ecological restoration contribution rate of the downstream ecological water replenishment of an inland river basin according to claim 5 is characterized by: In S7, the hydrothermal comprehensive stability index HCSI and the influencing factor of vapor pressure deficit are analyzed through redundancy analysis, and the key influencing indicators of the hydrothermal comprehensive stability index HCSI are determined through the contribution rate.

Images