An artificial directional rain enhancement vegetation restoration and effect evaluation method

Abstract

The present application belongs to the technical field of vegetation restoration, and provides a method for artificial directional rain enhancement vegetation restoration and effect evaluation, comprising: degraded research area selection, wind corridor construction, rain enhancement landscape smoke furnace fixation, comparison area selection, rainfall catalysis, rainfall data collection, predicted rainfall calculation, artificial rain enhancement effect evaluation, and vegetation restoration effect evaluation. The present application realizes vegetation restoration of vegetation degradation area under directional rain enhancement technology through degraded research area selection, wind corridor construction, rain enhancement landscape smoke furnace fixation, and rainfall catalysis, and improves vegetation restoration efficiency. Through stable artificial rain enhancement test, comparison analysis of biodiversity index and vegetation coverage of target area and comparison area is carried out, and accurate evaluation of ecological restoration benefit under artificial directional rain enhancement is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vegetation restoration, in particular to a method for artificial directional rain enhancement and vegetation restoration and effect evaluation. BACKGROUND

[0002] Climate change is another important reason for accelerating the degradation of the ecosystem and the loss of biodiversity. With the change of global temperature, the actual evaporation loss increases, and the runoff depth and water storage decrease relatively. The influence of climate change on regional hydrology is an important factor in the problem of climate impact. In the region of vegetation degradation caused by climate drought, the primary task is to change the local water and heat conditions. While reasonably utilizing existing water resources, new ways of increasing water are explored to accelerate the realization of vegetation ecological restoration and the sustainable development of the ecological system. For the target ecological restoration area of vegetation degradation, the air cloud water resources are developed and utilized, and the artificial directional rain enhancement technology is used to reduce drought. Artificial directional rain enhancement is to change the cloud precipitation physical process by using the microphysical instability of natural clouds and adopting artificial catalysis method, so as to achieve the important purposes of increasing precipitation, alleviating the problem of water shortage in the vegetation degradation area, and accelerating vegetation restoration. At present, artificial rain enhancement operation is mainly applied to agricultural production disaster relief in the dry season in the arid region. The main operation tools include artificial rain enhancement, ground high-voltage guns, rocket artificial rain enhancement operations, and landscape stove rain enhancement operations.

[0003] However, in the aspect of ecological restoration, the research on artificial rain enhancement is relatively weak, mainly in the following aspects: the response relationship between vegetation degradation and rainfall is not clear, there is a lack of scientific model between artificial rain enhancement and vegetation restoration and related indexes of artificial rain enhancement operation; the operation technical means such as detection and catalysis are backward; there is a lack of long-term stable artificial rain enhancement test with scientific design, and the evaluation of ecological benefits of vegetation restoration after artificial rain enhancement is not strict enough. SUMMARY

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present application is to provide an artificial directional rain enhancement vegetation restoration and effect evaluation method, which realizes more efficient vegetation restoration and more strict vegetation restoration effect evaluation.

[0005] To achieve the above purpose, the present application provides the following scheme:

[0006] An artificial directional rain enhancement vegetation restoration and effect evaluation method comprises the following steps:

[0007] The vegetation degradation index of the target research area is calculated by using remote sensing inversion NPP and climate NPP, and the area corresponding to the vegetation degradation index of the preset severe degradation range is selected to obtain a degradation research area;

[0008] A wind corridor is constructed according to the preset site condition and the wind speed and direction of the vegetation growing season high altitude, and a rain enhancement landscape stove is arranged on the upper part of the windward slope corresponding to the upwind direction of the wind corridor in the degradation research area.

[0009] A rectangular area is defined based on the ignition length, echo speed, and duration of the rain-enhancing landscape smoke generator, and the rain-enhancing landscape smoke generator is fixed at the middle position of the short side of the rectangular area.

[0010] According to the preset control selection requirements, a contrast area is selected on the degradation study area to serve as a contrast to the rectangular area;

[0011] When the preset operating conditions are met, AgI smoke is released through the rain-enhancing landscape smoke generator to obtain the operating cloud, and the operating cloud is used to catalyze rainfall in the rectangular area;

[0012] Rainfall data was obtained by collecting rainfall data in the rectangular area before and after catalysis and in the control area.

[0013] The WRF model was used to predict the rainfall in the rectangular area without catalysis, and the predicted rainfall was obtained.

[0014] The absolute and relative effects of artificial rain enhancement are calculated based on the rainfall data and the predicted rainfall to obtain the evaluation results of the artificial rain enhancement effect.

[0015] The biodiversity index and vegetation cover of the rectangular area and the control area before and after catalysis are calculated and compared to obtain the evaluation results of vegetation restoration effect.

[0016] Preferably, the expression for the vegetation degradation index is: Wherein, VDI is the vegetation degradation index; R P For the climate NPP; R a The remote sensing inversion NPP is described.

[0017] Preferably, the severe degradation ranges from 0.6 to 1.

[0018] Preferably, the length of the shorter side of the rectangular region is the ignition length; the length of the longer side of the rectangular region is the product of the echo velocity and the duration.

[0019] Preferably, the operating conditions include: weather conditions, steering airflow direction conditions, echo parameter conditions, cloud top temperature conditions, precipitation intensity conditions, and water vapor pressure conditions; the weather conditions include: frontal precipitation, Mongolian cold vortex, and upper-level northwesterly airflow.

[0020] Preferably, the expression for the absolute effect is: E = y - y0; where E is the calculated value of the absolute effect; y is the rainfall in the rectangular area after catalysis; and y0 is the predicted rainfall.

[0021] Preferably, the expression for the biodiversity index is:

[0022] BI=H×0.3+S×0.25+C×0.15+P×0.15+A×0.15;

[0023] Wherein, BI is the calculated value of the biodiversity index; H is the vegetation landscape diversity index; S is the species diversity index; C is the nature reserve index; P is the national protected plant species diversity index; and A is the national protected animal species diversity index.

[0024] Preferably, the expression for the vegetation cover is:

[0025] NDVI = (NIR - R) / (NIR + R);

[0026] Wherein, NDVI is the vegetation cover; NIR is the near-infrared reflectance; and R is the red light reflectance.

[0027] Preferably, the expression for the relative effect is: R = (y - y0) / y0 * 100%; where R is the calculated value of the relative effect; y is the precipitation after the rain enhancement operation; and y0 is the natural precipitation without the rain enhancement operation.

[0028] The present invention discloses the following technical effects:

[0029] This invention provides a method for targeted artificial rain enhancement for vegetation restoration and effect evaluation. By selecting degraded study areas, constructing wind corridors, fixing rain enhancement landscape smoke generators, and using rainfall catalysis, it solves the problem of poor vegetation restoration effects in existing methods and achieves targeted rain enhancement for vegetation degradation areas. By using rainfall catalysis, predictive rainfall calculation, artificial rain enhancement effect evaluation, and vegetation restoration effect evaluation, it solves the problem of inaccurate vegetation restoration evaluation in existing methods and achieves multi-dimensional evaluation of restoration effects on rainfall and biodiversity. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of the artificial targeted rain enhancement vegetation restoration and effect evaluation process provided in an embodiment of the present invention;

[0032] Figure 2 A flowchart for artificial targeted rain enhancement, vegetation restoration, and effect evaluation provided in an embodiment of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] The purpose of this invention is to provide a method for artificial targeted rain enhancement for vegetation restoration and effect evaluation, so as to achieve more efficient vegetation restoration and more rigorous evaluation of vegetation restoration effect.

[0035] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] Figure 1 This is a schematic diagram of the artificial targeted rain enhancement vegetation restoration and effect evaluation process provided in an embodiment of the present invention, such as... Figure 1 As shown, this invention provides a method for artificial targeted rain enhancement to restore vegetation and evaluate its effectiveness, including:

[0037] Step 100: Calculate the vegetation degradation index of the target study area using remote sensing inversion NPP and climate NPP, and select the area corresponding to the vegetation degradation index of the preset severely degraded range to obtain the degradation study area.

[0038] Step 200: Construct a wind corridor based on the preset site conditions and the wind speed and direction of the upper-level winds during the vegetation growing season, and set up a rain-enhancing landscape smoke generator on the upper part of the windward slope corresponding to the wind corridor in the degraded study area.

[0039] Step 300: Delineate a rectangular area based on the ignition length, echo speed, and duration of the rain-enhancing landscape smoke generator, and fix the rain-enhancing landscape smoke generator in the middle of the short side of the rectangular area;

[0040] Step 400: According to the preset control selection requirements, select a contrast area on the degradation study area that serves as a contrast to the rectangular area;

[0041] Step 500: When the preset operating conditions are met, AgI smoke is released through the rain enhancement landscape smoke generator to obtain the operating cloud, and the operating cloud is used to catalyze rainfall in the rectangular area;

[0042] Step 600: Collect rainfall data in the rectangular area before and after catalysis and in the control area to obtain rainfall data;

[0043] Step 700: Use the WRF model to predict the rainfall in the rectangular area without catalysis, and obtain the predicted rainfall;

[0044] Step 800: Calculate the absolute and relative effects of artificial rain enhancement based on rainfall data and predicted rainfall to obtain the evaluation results of artificial rain enhancement effects;

[0045] Step 900: Calculate and compare the biodiversity index and vegetation cover of the rectangular area before and after catalysis and the control area to obtain the evaluation results of vegetation restoration effect.

[0046] Specifically, the expression for the vegetation degradation index is: Wherein, VDI is the vegetation degradation index; R P For climate NPP; R a This is for remote sensing inversion of NPP.

[0047] Preferably, the severe degradation ranges from 0.6 to 1.

[0048] Specifically, the shorter side of the rectangular region is the ignition length; the longer side of the rectangular region is the product of the echo velocity and the duration.

[0049] Furthermore, the operational conditions include: weather conditions, steering airflow direction conditions, echo parameter conditions, cloud top temperature conditions, precipitation intensity conditions, and water vapor pressure conditions; weather conditions include: frontal precipitation, Mongolian cold vortex, and upper-level northwesterly airflow.

[0050] Specifically, the expression for the absolute effect is: E = y - y0; where E is the calculated value of the absolute effect; y is the rainfall in the rectangular area after catalysis; and y0 is the predicted rainfall.

[0051] Preferably, the expression for the biodiversity index is:

[0052] BI=H×0.3+S×0.25+C×0.15+P×0.15+A×0.15;

[0053] Wherein, BI is the calculated value of the biodiversity index; H is the vegetation landscape diversity index; S is the species diversity index; C is the nature reserve index; P is the national protected plant species diversity index; and A is the national protected animal species diversity index.

[0054] Specifically, the expression for vegetation cover is:

[0055] NDVI = (NIR - R) / (NIR + R);

[0056] Wherein, NDVI is the vegetation cover; NIR is the near-infrared reflectance; and R is the red light reflectance.

[0057] Furthermore, the expression for the relative effect is: R = (y - y0) / y0 * 100%; where R is the calculated value of the relative effect; y is the precipitation after the rain enhancement operation; and y0 is the natural precipitation without the rain enhancement operation.

[0058] refer to Figure 2 The vegetation restoration and evaluation process in this embodiment is described as follows:

[0059] 1) Diagnose vegetation degradation and select severely degraded areas as target areas for artificial targeted rain enhancement vegetation restoration technology;

[0060] 2) Based on site conditions and according to the wind speed and direction of the upper-level winds during the vegetation growing season, construct wind corridors and select areas located upwind of the vegetation-degraded areas to place landscape smoke generators in higher-altitude areas.

[0061] 3) Determine the scope of influence based on the ignition length, echo velocity, and duration of the landscape smoke generator, and calibrate the long side of the rectangle; the short side of the rectangle is the length of the particulate matter dispersed after the landscape smoke generator is ignited, and the long side of the rectangle is determined by using the product of the radar detection echo movement velocity and the catalytic reaction time. Within the determined target rectangular area, a grid of 1KM*1KM is drawn, and automatic rain gauges are placed.

[0062] 4) Without the influence of artificial catalysis operations, a control area is selected based on the statistically observed prevailing upper-level wind direction, either upwind of the artificial catalysis operation or perpendicular to the wind direction. The terrain and area of ​​the control area are similar to the target area, and the weather systems and precipitation types affected by the two areas during and outside the operation period are similar. A 1km x 1km grid is drawn within the control area, and automatic rain gauges are installed.

[0063] 5) Precipitation Forecasting and Impact Assessment of Landscape Smoke Furnaces on Precipitation. A WRF model was used to simulate precipitation using historical meteorological data and perform regression analysis with actual rainfall data to determine the precipitation forecast correction coefficient. Simultaneously, two sets of data were collected: one set of precipitation observations and forecasts during the landscape smoke furnace operation period; the other set of precipitation observations and forecasts during the non-operation period. By comparing these two sets of data, as well as comparing data from the target area and the control area, the impact of landscape smoke furnace operation on precipitation was estimated.

[0064] 6) Based on the natural geographical background characteristics of the artificial rain enhancement area, characteristic data of the artificial rain enhancement effect are obtained from remote sensing information sources, artificial rain enhancement operation data, surface meteorological and vegetation observation data, and evaluation factors are determined to form a criterion layer. Then, evaluation indicators are selected according to each evaluation factor to form an indicator layer. An artificial rain enhancement effect index model is constructed. Weights are assigned to the indicators and they are substituted into the index model to evaluate the artificial rain enhancement effect.

[0065] Furthermore, the method for determining the target area:

[0066] 1) Select areas with severely degraded vegetation according to the vegetation degradation level (refer to Table 1); as the target areas;

[0067] The actual productivity (NPP) is represented by remote sensing inversion, and the potential productivity (NPP) is represented by climate model calculations. The relationship between the two is used to construct the vegetation degradation index (VDI). Its value ranges from 0 to 1.

[0068] Table 1

[0069]

[0070] 2) Based on the site conditions and according to the wind speed and direction of the upper-level winds during the vegetation growing season, construct a wind corridor, select the upwind direction of the wind corridor located in the vegetation degradation area, and place the rain-enhancing landscape smoke generator on the upper part of the windward slope of the mountain.

[0071] 3) Based on 1) and 2), a rectangular area is defined. The rain enhancement landscape smoke generator is located in the middle of the short side of the rectangular area. The range of influence is determined according to the smoke generator ignition length, echo velocity and duration. The long side of the rectangle is then checked. The length of the short side of the rectangle is the length of the particulate matter dispersed after the landscape smoke generator is ignited. The long side of the rectangle is determined by the product of the radar detection echo movement speed and the catalytic action time.

[0072] Alternatively, the selection of the comparison region usually needs to meet the following requirements:

[0073] 1) Unaffected by artificial catalysis operations. Based on the statistically observed prevailing wind direction at high altitudes during the experiment, the control area should be selected on the upwind side of the artificial catalysis operation or on the side perpendicular to the wind direction.

[0074] 2) The terrain and area are similar to the target area to eliminate the difference in precipitation caused by the terrain.

[0075] 3) The area of ​​the comparison area should be the same as that of the target area.

[0076] 4) Both areas should have a relatively dense number of precipitation observation points (designed interval of 1000 meters).

[0077] Preferably, the atmospheric precipitation prediction technique is based on the WRF model. The Weather Research and Foreeast (WRF) model is a new generation of mesoscale numerical weather prediction model jointly developed by the meteorological community, with broad application prospects in weather forecasting, atmospheric chemistry, and regional climate. In this embodiment, the model uses Arakawa-C coordinates in the horizontal direction and shape-following coordinates (referred to as mass coordinates) in the vertical direction. It uses a third-order accuracy Rung-Kutta time integration scheme with a time step of 90 seconds. The vertical direction uses an 11-coordinate system divided into 27 layers, with the top layer pressure at 50 hPa. The model simulation employs a double-nested approach, with the simulation region centered on the vegetation degradation zone. The horizontal grid spacing is 27 km and 9 km, corresponding to 128x96 grid points respectively. The initial background field of 130x910 uses 1°*1° NCEP global reanalysis data, and the boundary is changed every 6 hours. The integration time is 24 hours with a time step of 90 seconds, and simulation results are output every hour. Considering that this embodiment mainly simulates precipitation processes, the microphysics scheme and cumulus convection parameterization scheme, which have a significant impact on precipitation, were selected for analysis. To better compare and analyze the impact of the precipitation scheme on the simulation results, the other physical process schemes were chosen identically: RRTM longwave radiation scheme, Dudhia shortwave radiation scheme, Noah and Surface model road surface process scheme, YSU boundary layer parameterization scheme, and Monin-Obukhovifi surface layer scheme. Data sources: The topographic data used were from the WRF official website (http: / / www.Wrfmodel.org); the measured precipitation data used were from routine observation data of a certain country's meteorological bureau, and this data was used as the true values ​​for evaluating the model's simulated precipitation.

[0078] Furthermore, the technology of targeted artificial rain enhancement primarily utilizes ground-based AgI smoke generators to burn AgI smoke sticks, relying on updrafts to carry artificial ice nuclei (AgI) into the target cloud, influencing the cloud's microphysical processes and achieving the goal of artificial rain enhancement. Understanding and mastering the correct catalytic conditions and methods is crucial for artificial rain enhancement. Monitoring and identification techniques are also essential. Artificial rain enhancement is only effective when catalyzed at the appropriate location and time within the cloud. For example, in precipitation stratiform clouds, natural precipitation development is generally quite sufficient, and the presence of supercooled water is often considered a sign of suitable conditions for artificial rain enhancement operations. However, this water only exists in certain parts of the cloud and for certain periods. Therefore, real-time monitoring of different cloud systems, obtaining multiple pieces of information reflecting operational conditions, and real-time identification and control of favorable operational opportunities and locations are key technologies for reducing errors and omissions in operations and improving the effectiveness of artificial rain enhancement.

[0079] Preferably, the operating conditions are selected as follows:

[0080] 1) Weather conditions. There are three types of weather conditions for operations: frontal precipitation, Mongolian cold vortex, and upper-level northwesterly airflow.

[0081] 2) Direction of guiding airflow. The 500hPa upper-level wind direction is the guiding wind direction for the operational cloud. In this embodiment, it is specified that the operation is suitable when the 500hPa wind direction is the wind direction required for the vegetation restoration period.

[0082] 3) Echo parameters of the operational cloud. The echo parameters of the operational cloud were provided by the Doppler radar of the meteorological bureau of the study area. It was specified that the echo reached 35 dBz and the echo top height was greater than 7 km, which was suitable for landscape smoke furnace ignition.

[0083] 4) Specific criteria include: cloud top temperature of -25℃ to -5℃, cloud system with precipitation intensity greater than 0.1mm / h, and atmospheric water vapor pressure in the cloud-bearing area greater than the saturated water vapor pressure on the ice surface. These three points are essential criteria for the potential conditions of implementing rain enhancement operations.

[0084] Optionally, the catalytic time and metering are determined as follows: The AgI smoke generator can simultaneously hold 20 AgI smoke sticks, each 0.8m long, containing 100g of AgI, and can burn for 20 minutes. The AgI smoke generator control module can determine the operating time of the landscape smoke generator based on factors such as the size, intensity, and movement speed of the cloud being processed, using radar echoes in a radar chart. The radar echo display shows the system's movement direction and speed. Simultaneously, one or more smoke sticks can be ignited, or multiple smoke sticks can be set to be ignited at different times.

[0085] Preferably, this embodiment evaluates the effect of artificial rain enhancement technology on vegetation restoration by assessing changes in rainfall and plant growth before and after the technology during a growing season.

[0086] Furthermore, the quantitative assessment of the effects of artificial rain enhancement usually adopts a statistical method based on regional historical regression. The basic idea is to select a suitable target area and control area, fit the correlation between rainfall between the two areas based on historical data of the two areas, and then calculate the estimated rainfall of the target area from the rainfall of the control area during the operation period. By comparing it with the actual rainfall of the target area, the increased precipitation caused by artificial rain enhancement can be obtained.

[0087] 1) The statistical analysis method uses precipitation as the statistical variable to determine whether precipitation in the operation area increases before and after the rain enhancement. The operation period and the non-operation period should be separated. The target area is designed to install one automatic rain gauge per square kilometer, and the actual ground rainfall in the target area and the control area is collected in the operation area and the non-operation period respectively.

[0088] 2) The numerical simulation method is a mathematical model gradually established based on the cloud-precipitation formation theory. By forecasting the precipitation from natural clouds and comparing it with the measured precipitation after artificial operations, the rain enhancement effect can be determined. The rainfall distribution predicted by the WRF model is used.

[0089] 3) The regression method is to consider the basic elements such as the weather system of the artificial precipitation enhancement operation, the spatio-temporal distribution of the artificial precipitation enhancement operation, the aging of catalyst diffusion and catalysis, and the spatio-temporal fluctuations of natural precipitation during statistical tests. Finally, a correlation analysis method between the operation target area and the comparison area is established, the rainfall amounts in the two areas under historically similar weather conditions are analyzed, and correction parameters are constructed to more accurately evaluate the effect of artificial precipitation enhancement.

[0090] Specifically, the operation unit is defined as the precipitation amount in 24 hours, that is, the daily rainfall amount is used as the statistical variable. Let y be the precipitation amount after the artificial precipitation enhancement operation, and y0 be the natural precipitation amount when the artificial precipitation enhancement operation is not carried out. Then the effect of the artificial precipitation enhancement can be expressed as:

[0091] E = y - y0

[0092] R = (y - y0) / y0 * 100%

[0093] Thus, three situations occur. When y > y0, it indicates a positive effect; when y < y0, it indicates a negative effect; when y = y0, it indicates no effect. Since the two quantities y and y0 cannot be measured simultaneously. After the artificial precipitation enhancement operation, only y can be measured, while y0 cannot be measured; conversely, y0 can be measured, while y cannot be measured. The method for estimating y0 adopts numerical simulation methods and regression analysis methods to make a timed, located, and quantitative forecast for a single precipitation process to meet the accuracy requirements of effect testing. At the same time, the regional comparison method is adopted, the rainfall amounts in the comparison area and the target area are respectively statistically analyzed, and then the rainfall amounts in the comparison area and the target area are compared. Finally, the effect and benefit of the artificial precipitation enhancement operation are calculated.

[0094] Furthermore, the method for evaluating the vegetation restoration effect. Two indicators, namely the biodiversity index and the vegetation coverage, are selected to comparatively analyze the changes in the indicator values between the target area and the comparison area and analyze the restoration effect.

[0095] Specifically, the biodiversity index. It is the weighted sum of 5 evaluation indicators, such as the vegetation landscape diversity index, the species diversity index based on ecosystem types, the nature reserve index, the national protected plant species diversity index, and the national protected animal species diversity index. The weights of each evaluation indicator and the establishment of the weights of the sub-indicators involved in calculating a certain index are determined by the expert consultation method. This guide recommends the following weight settings:

[0096] Biodiversity index (BI) = vegetation landscape diversity index (H) × 0.3 + species diversity index (S) × 0.25 + nature reserve index (C) × 0.15 + national protected plant species diversity index (P) × 0.15 + national protected animal species diversity index (A) × 0.15;

[0097] 1) Vegetation landscape diversity index (H)

[0098] The Shannon-Weiner diversity index is calculated using the following expression:

[0099]

[0100] In the formula, H is the Shannon-Weiner diversity index of the vegetation landscape of the region, and P i H represents the proportion of the area occupied by vegetation landscape type (vegetation community) i, where n is the number of vegetation communities. The larger the H value, the greater the vegetation landscape diversity and the richer the ecosystem.

[0101] 2) Species diversity index (S)

[0102] Species diversity index (S) = SI coniferous forest × 0.08 + SI mixed coniferous and broad-leaved forest × 0.2 + SI broad-leaved forest × 0.4 + SI bamboo forest × 0.05 + SI shrubland × 0.05 + SI grassland × 0.03 + SI meadow × 0.03 + SI sparse vegetation × 0.02 + SI wetland × 0.1 + SI desert ecosystem × 0.01 + SI agricultural ecosystem × 0.03;

[0103] 3) Nature Reserve Index (C)

[0104] Based on the normalized area of ​​various types and levels of nature reserves and wildlife-type nature reserves in the region, the nature reserve index is calculated:

[0105] Nature Reserve Index (C) = CI National Nature Reserves × 0.6 + CI Provincial Nature Reserves × 0.3 + CI Municipal and County Nature Reserves × 0.1;

[0106] 4) National Protected Plant Diversity Index (P)

[0107] The species and quantity of nationally protected plants within the statistical area were collected, and after normalization, the biodiversity index of nationally protected plants and animals was calculated.

[0108] National protected plant species diversity index (P) = PI (national first-class protected plants) × 0.65 + PI (national second-class protected plants) × 0.35;

[0109] 5) National Protected Animal Species Diversity Index (A)

[0110] The species and numbers of nationally protected animals within the statistical area were collected, and after normalization, the species diversity indices of nationally protected plants and animals were calculated.

[0111] National protected animal species diversity index (A) = AI National Class I protected animals × 0.65 + AI National Class II protected animals × 0.35;

[0112] Optionally, data sources include: the number and area proportion of regional vegetation communities: obtained based on operational statistics from local forestry (forestry inventory), environmental protection (ecological protection), and agricultural departments, supplemented by field surveys and remote sensing image interpretation. The area of ​​various ecosystem types in the region: obtained based on operational statistics from local land (land use data), forestry (forest stand survey), environmental protection, and agricultural departments, supplemented by field surveys and remote sensing image interpretation. The area of ​​various types of protected areas within the region: provided by the superior authorities of the corresponding types of protected areas. The species and numbers of nationally protected plants and animals within the region: derived from the nationally protected wild animal and plant lists provided by the local forestry department.

[0113] Furthermore, vegetation cover. The Normalized Difference Vegetation Index (NDVI) is the most commonly used vegetation index; therefore, this paper selects this index to establish a vegetation cover estimation model. The NDVI calculation formula is as follows:

[0114] NDVI = (NIR - R) / (NIR + R)

[0115] Preferably, the rain-enhancing landscape smoke generator consists of a smoke pipe, a smoke generator, an ignition control system, and a power supply system. By setting the ignition time and number of smoke pipes, the smoke pipes are ignited normally at the selected time, burning the catalyst. The catalyst is released into the air through the smoke generator and lifted into the clouds by the rising airflow, thus achieving the effect of increasing rain (snow).

[0116] Preferably, the technical implementation principle is as follows:

[0117] 1) Targeted principle: Artificial targeted rain enhancement technology is used to target vegetation degradation areas. Based on the local vegetation degradation water demand and the spatiotemporal distribution characteristics of cloud water resources, as well as the potential of artificial rain enhancement, vegetation degradation experimental areas that are conducive to artificial rain enhancement are selected, and regional vegetation restoration is carried out in a targeted manner.

[0118] 2) Comparison principle: Based on the site conditions and cloud and water resources of the vegetation restoration target area, the location of the landscape smoke generator should be reasonably set, and the comparison area should be reasonably delineated according to radar, wind speed, wind direction, etc., so that it is not affected by artificial rain enhancement and can be compared with the rainfall in the target area, so as to provide scientific and effective data for the evaluation of the benefits of artificial targeted rain enhancement for vegetation restoration.

[0119] 3) Operability principle: Fully consider the cloud and water resources in the area where artificial targeted rain enhancement vegetation restoration technology is implemented, reasonably formulate field monitoring plans for automatic weather stations or automatic rain gauges in the target area and the comparison area, and on this basis, formulate an easy-to-operate evaluation index system for vegetation restoration effect under artificial rain enhancement technology. The selection of evaluation indicators and their parameters should fully consider the quantification, measurability and availability of data, and the evaluation method should be highly operable in application.

[0120] 4) Demonstration principle: Vegetation restoration based on artificial measures is a long-term and arduous task. Artificial targeted rain enhancement vegetation restoration technology based on ecological and meteorological principles can accelerate the vegetation restoration process and improve restoration efficiency. Therefore, the fundamental purpose of this embodiment guide is to monitor the implementation effect of this technology in the field for a long time, continuously adjust the implementation plan based on the results to make it scientific and operable, and to be able to demonstrate and promote its application.

[0121] The beneficial effects of this invention are as follows:

[0122] This invention achieves targeted rain enhancement for vegetation degradation by selecting degraded research areas, constructing wind corridors, fixing rain enhancement landscape smoke generators, and catalyzing rainfall, thereby improving vegetation restoration efficiency. Through rainfall catalysis, predicted rainfall calculation, evaluation of artificial rain enhancement effects, and evaluation of vegetation restoration effects, it provides a long-term and stable rain enhancement experiment, enabling an accurate assessment of the ecological benefits of targeted artificial rain enhancement.

[0123] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0124] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for artificial targeted rain enhancement to restore vegetation and evaluate its effectiveness, characterized in that, include: The vegetation degradation index of the target study area is calculated using remote sensing inversion NPP and climate NPP, and the area corresponding to the vegetation degradation index of the preset severe degradation range is selected to obtain the degradation study area. A wind corridor is constructed based on the preset site conditions and the high-altitude wind speed and direction during the vegetation growing season, and a rain-enhancing landscape smoke generator is set on the upper part of the windward slope corresponding to the wind direction of the wind corridor in the degradation study area. A rectangular area is defined based on the ignition length, echo speed, and duration of the rain-enhancing landscape smoke generator, and the rain-enhancing landscape smoke generator is fixed at the middle position of the short side of the rectangular area. According to the preset control selection requirements, a contrast area is selected on the degradation study area to serve as a contrast to the rectangular area; When the preset operating conditions are met, AgI smoke is released through the rain-enhancing landscape smoke generator to obtain the operating cloud, and the operating cloud is used to catalyze rainfall in the rectangular area; Rainfall data was obtained by collecting rainfall data in the rectangular area before and after catalysis and in the control area. The WRF model was used to predict the rainfall in the rectangular area without catalysis, and the predicted rainfall was obtained. The absolute and relative effects of artificial rain enhancement are calculated based on the rainfall data and the predicted rainfall to obtain the evaluation results of the artificial rain enhancement effect. The biodiversity index and vegetation cover of the rectangular area and the control area before and after catalysis are calculated and compared to obtain the vegetation restoration effect evaluation results. The length of the shorter side of the rectangular region is the ignition length; The length of the longer side of the rectangular region is the product of the echo velocity and the duration; The operational conditions include: weather conditions, steering airflow direction conditions, echo parameter conditions, cloud top temperature conditions, precipitation intensity conditions, and water vapor pressure conditions. The weather conditions include: frontal precipitation, Mongolian cold vortex, and upper-level northwesterly airflow. The 500hPa upper-level wind direction serves as the steering wind direction for the operational cloud. The echo parameters of the operational cloud are provided by the Doppler radar of the meteorological bureau in the study area. It is stipulated that the landscape smoke furnace will be ignited when the echo reaches 35dBz and the echo top height is greater than 7km. Specific criteria include: cloud top temperature of -25℃ to -5℃, precipitation intensity of the cloud system greater than 0.1mm / h, and atmospheric water vapor pressure in the cloud-spreading area greater than the saturated water vapor pressure of the ice surface. The expression for the biodiversity index is: BI=H×0.3+S×0.25+C×0.15+P×0.15+A×0.15; Wherein, BI is the calculated value of the biodiversity index; H is the vegetation landscape diversity index; S is the species diversity index; C is the nature reserve index; P is the national protected plant species diversity index; and A is the national protected animal species diversity index. The expression for the species diversity index is: Species diversity index S = coniferous forest × 0.08 + mixed coniferous and broad-leaved forest × 0.2 + broad-leaved forest × 0.4 + bamboo forest × 0.05 + shrubland × 0.05 + grassland × 0.03 + meadow × 0.03 + sparse vegetation × 0.02 + wetland × 0.1 + SI desert ecosystem × 0.01 + agricultural ecosystem × 0.

03.

2. The method for artificial targeted rain enhancement for vegetation restoration and effect evaluation according to claim 1, characterized in that, The expression for the vegetation degradation index is: Wherein, VDI is the vegetation degradation index; R P For the climate NPP; R a The remote sensing inversion NPP is described.

3. The method for artificial targeted rain enhancement for vegetation restoration and effect evaluation according to claim 1, characterized in that, The severe degradation ranges from 0.6 to 1.

4. The method for artificial targeted rain enhancement for vegetation restoration and effect evaluation according to claim 1, characterized in that, The expression for the absolute effect is: E = y - y0; where E is the calculated value of the absolute effect; y is the rainfall in the rectangular area after catalysis; and y0 is the predicted rainfall.

5. The method for artificial targeted rain enhancement for vegetation restoration and effect evaluation according to claim 1, characterized in that, The expression for the vegetation cover is: NDVI = (NIR - R) / (NIR + R); Wherein, NDVI is the vegetation cover; NIR is the near-infrared reflectance; and R is the red light reflectance.

6. The method for artificial targeted rain enhancement for vegetation restoration and effect evaluation according to claim 4, characterized in that, The expression for the relative effect is: R = (y - y0) / y0 * 100%; where R is the calculated value of the relative effect; y is the precipitation after the rain enhancement operation; and y0 is the natural precipitation without the rain enhancement operation.

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