CN112765808B - Ecological drought monitoring and evaluating method - Google Patents
Ecological drought monitoring and evaluating method Download PDFInfo
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Abstract
The invention discloses a method for monitoring and evaluating ecological drought, which comprises the steps of firstly collecting multi-source data of a research area, constructing an ecological drought disaster database, calculating hydrological weather and hydrological drought indexes according to the multi-source data, extracting hydrological drought characteristics, then carrying out space-time law and evolution characteristic analysis on the hydrological drought characteristics, realizing ecological drought monitoring on the research area according to analysis results, simultaneously constructing a Biome-BGC model to calculate net primary productivity of an ecosystem, and finally obtaining comprehensive ecosystem productivity of the research area by adopting a random forest model to realize ecological drought evaluation on the research area. The comprehensive ecological system production force value obtained by adopting the random forest model not only considers ecological factors, but also includes factors and human factors causing hydrological drought, so that the ecological drought is more comprehensively and accurately evaluated, and the problems of few considered factors and difficult parameter adjustment of the traditional ecological drought loss evaluation method are solved.
Description
Technical Field
The invention belongs to the technical field of ecological environment management, and particularly relates to a design of an ecological drought monitoring and evaluating method.
Background
In recent years, research directions such as monitoring and evaluation of ecological water circulation and ecological drought related to ecological civilization construction are becoming research hotspots. The research on the influence of drought on the watershed ecosystem under the influence of multiple factors is developed, so that the method has important practical significance for ecological environment treatment of the drought area prone to ecological environment, and also has important promotion and theoretical significance for promoting the research on the intercross of the subjects such as ecology, hydrology and the like.
The decision tree is an example-based inductive learning in machine learning, and adopts a top-down inductive method. The decision tree algorithm has the greatest advantages that the decision tree algorithm can be used for learning independently, a user does not need to know excessive knowledge background in the learning process, learning can be carried out only by better marking on training examples, and the decision tree algorithm belongs to supervised learning. The random forest model is a model formed by a plurality of decision trees established in a random mode, and each decision tree of the random forest is not related. The random forest model repeatedly and randomly extracts k samples from an original training sample set N in a replacement mode through a bootstrap resampling technology to generate a new training sample set, then k classification trees are generated according to the bootstrap sample set to form a random forest, classification results of new data are determined according to formed scores of voting numbers of the classification trees, the essence of the random forest model is an improvement on a decision tree algorithm, a plurality of decision trees are combined together, the establishment of each tree depends on one independently extracted sample, each tree in the forest has the same distribution, and classification errors depend on the classification capability of each tree and the correlation among the trees. The feature selection adopts a random method to split each node, then errors generated under different conditions are compared, and the number of the selected features is determined by the detected inherent estimation error, classification capability and correlation.
The existing ecological system drought damage assessment method mainly comprises qualitative or semi-quantitative evaluation such as experimental observation, remote sensing monitoring, model simulation and the like. The experimental observation method mainly utilizes a loss statistical model established by an experiment and a mathematical statistical method; the remote sensing monitoring method utilizes remote sensing data to calculate drought indexes related to various vegetations to monitor the vegetation growth; the model simulation method is used for calculating the relative evapotranspiration amount of each stage in the vegetation biological period through meteorological data such as temperature, rainfall and the like, and the main method is used for developing vegetation yield reduction assessment through constructing a water production function and a climate production potential model.
However, the existing ecosystem drought damage assessment method has corresponding defects, the experimental observation method is relatively simple, the physical influence of the drought disaster forming process on vegetation growth is ignored, the effects of soil moisture, nutrients and carbon dioxide on vegetation are not considered, the timeliness of the method is poor, and the accuracy of the estimation result is low. The model simulation method has poor description on the vegetation growth mechanism process, few determination and evaluation factors and more model parameters and is difficult to determine and adjust.
Disclosure of Invention
The invention aims to provide a method for monitoring and evaluating ecological drought, which quantitatively reveals the influence degrees and the space-time laws of different hydrological drought, determines the response relation between different types of hydrological drought evolution and an ecological system, and constructs a model for evaluating the hydrological drought evolution and the ecological system loss.
The technical scheme of the invention is as follows: a method for monitoring and evaluating ecological drought comprises the following steps:
s1, collecting multi-source data of a research area, and constructing an ecological drought disaster database by using the multi-source data.
S2, calculating hydrological weather and hydrological drought indexes of the research area according to the multi-source data, and extracting hydrological drought characteristics.
And S3, analyzing the time-space law and evolution characteristics of the hydrological drought characteristics, and monitoring ecological drought in the research area according to the analysis result.
And S4, constructing a Biome-BGC model according to the multi-source data, and calculating to obtain the net primary productivity of the ecosystem in the research area.
And S5, obtaining the comprehensive ecological system productivity of the research area by adopting a random forest model according to the hydrologic drought characteristics and the net primary productivity of the ecological system, and taking the comprehensive ecological system productivity as an evaluation value of ecological drought loss to realize ecological drought evaluation on the research area.
Furthermore, the multi-source data in the step S1 comprises natural geographic data, site hydrological data, water conservancy general survey data, remote sensing data, historical disaster situation statistical data and long-term continuous observation data of carbon-water exchange flux of a Chinese land ecosystem flux observation and research network; the natural geographic data comprises a digital elevation model, soil moisture content data, hydrogeological data and land coverage area; the site hydrological data comprise monthly rainfall data, evapotranspiration data, runoff data, temperature change data and soil water content; the water conservancy general survey data comprises the capacity and the quantity of a reservoir and main water users; the remote sensing data comprises multi-stage satellite remote sensing land utilization coverage data, remote sensing estimation evapotranspiration data and soil humidity product data; the historical disaster situation statistical data comprises drought damage data, drought areas, drought population and disaster loss of each county in the research area; the long-term continuous observation data of the carbon-water exchange flux of the Chinese land ecosystem flux observation and research network comprises data of total primary productivity of the ecosystem, total respiration of the ecosystem, net carbon exchange of the ecosystem, latent heat, sensible heat, physiological and ecological parameters of different ecosystems, perennial biomass and net primary productivity data.
Further, step S2 comprises the following substeps:
s21, dividing the research area by adopting a Thiessen polygon method according to water system distribution of the research area, taking hydrological measurement sites as the central points of the polygonal discrete data, and calculating hydrological weather and hydrological drought indexes of the research area according to precipitation, river runoff, reservoir water level and underground water level in the day-by-day hydrological weather observation data; the hydrological weather and hydrological drought indexes comprise a standardized precipitation index, a precipitation evapotranspiration index, a soil humidity index, a river flow index, an underground water level index and a reservoir storage capacity index.
S22, adopting a threshold value method to identify the hydrological drought at different time scales, and adopting a run-length theory to extract the hydrological drought characteristics; the hydrological drought characteristics include duration of drought, severity of drought, drought kurtosis, and frequency of drought.
Further, the specific method for extracting the hydrological drought characteristics by adopting the run length theory in the step S22 comprises the following steps:
a1, setting the interception level R1 to be 10 percent of the monthly rainfall from the average percent.
A2, precipitation sequence X in the month i When one or more time periods are less than R1, namely negative run occurs, judging that drought occurs in the time period, taking the length of the negative run as the duration of the drought, and taking the length of the negative run as the duration of the droughtThe area of the negative run is taken as the drought intensity, the extreme value of the negative run is taken as the drought kurtosis, wherein X i ＝{X 1 ,X 2 ,X 3 ,...,X N }。
And A3, combining the drought conditions according to the drought judgment and combination criterion.
Further, the drought judgment and merging criteria in step A3 are specifically:
(1) When the precipitation per month in the precipitation sequence is X 1 And satisfy X 1 <X 2 Judging that the month is a drought event; wherein X 2 Represents the amount of precipitation as X 1 The precipitation amount of the next month of the single month.
(2) When the precipitation per month in the precipitation sequence is X 1 And satisfy X 1 >X 2 It was judged that this month was not a drought event.
(3) When the time interval between two drought events is one month, if the monthly rainfall of the month is less than the average value X of the rainfall of the two adjacent months 0 Combining the two drought events into one drought event; if the monthly rainfall of the month is more than the average value X of the rainfall of two adjacent months 0 Then the two drought events need not be merged, i.e., they are still two drought events.
Further, step S3 comprises the following substeps:
and S31, performing space analysis based on ArcGIS according to the hydrological drought characteristics to obtain the hydrological drought space-time law and evolution characteristics.
And S32, analyzing the temporal-spatial regularity and evolution characteristics of the hydrological drought by adopting a time sequence to obtain the periodicity, the mutability and the trend of the occurrence of the hydrological drought.
And S33, decomposing periodic components, mutation points and trends in the runoff hydrology time sequences and the underground water level time sequences of the hydrology stations by adopting a wavelet transform method.
And S34, decomposing climate change components in hydrological time series change by adopting an extreme value modal decomposition method and an empirical mode decomposition method.
S35, carrying out statistics and comparison on the drought years and the normal years in the sub-regions divided in the step S21, selecting reservoirs with similar geographic characteristics but different functions, different reservoir capacities and different operation scheduling rules, adopting multiple regression comparison analysis on the hydrological drought characteristic difference before and after reservoir building, upstream and downstream of the reservoirs, water supply regions and non-water supply regions of the reservoirs, and quantitatively analyzing the difference influence of reservoir and reservoir group regulation on the hydrological drought characteristics according to surface water drought indexes, underground water level drought indexes and reservoir capacity indexes.
And S36, coupling the hydrologic drought space-time law and evolution characteristics with the normalized vegetation index, the normalized humidity index, the normalized dryness index and the normalized heat index by adopting a principal component transformation method to obtain a coupling model from hydrologic drought to ecological drought, and carrying out ecological drought monitoring on the researched area.
Further, step S4 includes the following substeps:
and S41, constructing a Biome-BGC model according to multi-source data.
And S42, verifying the simulation precision of the Biome-BGC model by adopting a linear regression analysis method, a root mean square error method and a significance test method, wherein the simulation precision comprises the precision of a simulation value and an observed value of the Biome-BGC model.
And S43, using meteorological data as a drive, respectively simulating the vegetation productivity changes of a study area in an arid year in which drought disasters occur and a normal year in which drought disasters do not affect by adopting a Biome-BGC model verified by simulation precision, and calculating to obtain the net primary productivity of the ecological system in the study area.
Further, the driving data adopted for constructing the Biome-BGC model in the step S41 comprise initial data, meteorological data and physiological parameter data; the initial data comprises longitude and latitude, altitude, soil layer depth, soil texture and atmospheric carbon dioxide concentration of the hydrological site; the meteorological data comprises daily precipitation, daily maximum temperature, daily minimum temperature, daily average temperature and radiation; the physiological parameter data comprises stomatal conductance, canopy specific leaf area, leaf and thin root CN ratio.
Further, the calculation formula of the net primary productivity of the ecosystem in the step S43 is:
NPP Biome-BGC ＝GPP-P plant
wherein NPP Biome-BGC Representing the net primary productivity of the ecosystem, GPP representing the total primary productivity of the ecosystem, P plant Indicating vegetation respiration.
Further, step S5 includes the following substeps:
s51, constructing a random forest model based on long-time water circulation sequence continuous data obtained by the hydrological model and the ecological process model in a simulation mode.
S52, taking the social water circulation factor change index and the hydrologic drought characteristics as independent variables of a random forest model, taking the net primary productivity of the ecological system as dependent variables of the random forest model, and performing coupling simulation through the random forest model to obtain the comprehensive ecological system productivity of the research area; the social water circulation factor change indexes comprise a vegetation state index, a temperature state index, a TRMM index and an effective soil water holding capacity.
And S53, taking the productivity of the comprehensive ecosystem as an evaluation value of ecological drought loss to realize ecological drought evaluation on the research area.
The invention has the beneficial effects that:
(1) The comprehensive ecological system production force value obtained by adopting the random forest model not only considers ecological factors, but also includes factors and human factors causing hydrological drought, and the evaluation on the ecological drought is more comprehensive and accurate.
(2) The invention adopts a random forest model to optimize the traditional model simulation method and solves the problems of few considered factors and difficult parameter adjustment of the traditional ecological drought loss evaluation method.
Drawings
Fig. 1 is a flowchart of a method for monitoring and evaluating ecological drought according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It is to be understood that the embodiments shown and described in the drawings are merely exemplary and are intended to illustrate the principles and spirit of the invention, not to limit the scope of the invention.
The embodiment of the invention provides a method for monitoring and evaluating ecological drought, which comprises the following steps S1-S5 as shown in figure 1:
s1, collecting multi-source data of a research area, and constructing an ecological drought disaster database by using the multi-source data.
In the embodiment of the invention, the multi-source data comprises natural geographic data, site hydrological data, water conservancy general survey data, remote sensing data, historical disaster statistical data and long-term continuous observation data of carbon-water exchange flux of a Chinese land ecosystem flux observation and research network.
The natural geographic data comprises a digital elevation model, soil moisture content data, hydrogeological data and land coverage area.
The site hydrological data comprises monthly rainfall data, evapotranspiration data, runoff data, temperature change data and soil water content.
The water conservancy general survey data comprises the capacity and the quantity of a reservoir and main water users.
The remote sensing data comprises multi-stage satellite remote sensing land utilization coverage data, remote sensing estimation evapotranspiration data and soil humidity product data.
The historical disaster situation statistical data comprises drought damage data, drought areas, drought population and disaster loss of each county in the research area.
The long-term continuous observation data of the carbon-water exchange flux of the Chinese land ecosystem flux observation and research network comprises data of total primary productivity of the ecosystem, total respiration of the ecosystem, net carbon exchange of the ecosystem, latent heat, sensible heat, physiological and ecological parameters of different ecosystems, perennial biomass and net primary productivity data.
And S2, calculating the hydrological weather and hydrological drought indexes of the research area according to the multi-source data, and extracting the hydrological drought characteristics.
Step S2 includes the following substeps S21-S22:
s21, dividing the research area by adopting a Thiessen polygon method according to water system distribution of the research area, taking hydrological measurement sites as the central points of the polygon discrete data, and calculating hydrological weather and hydrological drought indexes of the research area according to precipitation, river runoff, reservoir water level and underground water level in the day-by-day hydrological weather observation data.
In the embodiment of the invention, the hydrological weather and hydrological drought indexes comprise a standardized precipitation index, a precipitation evapotranspiration index, a soil humidity index, a river channel flow index, an underground water level index and a reservoir storage capacity index.
And S22, performing hydrological drought identification of different time scales by adopting a threshold value method, and extracting hydrological drought characteristics by adopting a run length theory.
In embodiments of the invention, the hydrologic drought characteristics include duration, severity, kurtosis, and frequency of drought.
The run-length theory refers to that the events which occur before and after one type of continuously occurring events are another type of events, and natural phenomena such as alternate occurrence of drought and waterlogging and the like exist. The specific method for extracting the hydrological drought characteristics by adopting the run-length theory in the step S22 comprises the following steps:
a1, setting the interception level R1 to be 10 percent of the monthly rainfall from the average percent.
A2, precipitation sequence X in the month i When one or more time periods are less than R1, namely negative run occurs, judging that drought occurs in the time period, taking the length of the negative run as the duration of drought, the area of the negative run as the intensity of drought, and the extreme value of the negative run as the kurtosis, wherein X is i ＝{X 1 ,X 2 ,X 3 ,...,X N }。
And A3, combining the drought conditions according to the drought judgment and combination criterion.
In the practical analysis, the drought conditions are complex, so that sometimes drought needs to be merged, and the drought judgment and merging criteria in the embodiment of the invention are as follows:
(1) When the precipitation per month in the precipitation sequence is X 1 And satisfy X 1 <X 2 Judging that the month is a drought event; wherein X 2 Represents the amount of precipitation as X 1 The precipitation amount of the next month of the single month.
(2) When the precipitation per month in the precipitation sequence is X 1 And is made ofSatisfy X 1 >X 2 It was judged that this month was not a drought event.
(3) When the time interval between two drought events is one month, if the monthly precipitation of the month is less than the average value X of the precipitation of two adjacent months 0 Combining the two drought events into one drought event; if the monthly rainfall of the month is more than the average value X of the rainfall of two adjacent months 0 Then the two drought events need not be merged, i.e., they are still two drought events.
And S3, analyzing the time-space law and evolution characteristics of the hydrological drought characteristics, and monitoring ecological drought in the research area according to the analysis result.
Step S3 includes the following substeps S31-S36:
and S31, performing space analysis based on ArcGIS according to the hydrological drought characteristics to obtain the hydrological drought space-time law and evolution characteristics.
And S32, analyzing the spatial-temporal law and evolution characteristics of the hydrological drought by adopting a time sequence to obtain the periodicity, the mutability and the trend of the hydrological drought.
And S33, decomposing periodic components, mutation points and trends in the runoff hydrological time sequence and the groundwater level time sequence of each hydrological station by adopting a Wavelet Transform (Wavelet Transform).
And S34, decomposing climate change components in the hydrological time series change by using an extreme value modal decomposition method (ESMD) and an empirical mode decomposition method (EEMD).
S35, carrying out statistics and comparison on the drought years and the normal years in the sub-regions divided in the step S21, selecting reservoirs with similar geographic characteristics but different functions, different reservoir capacities and different operation scheduling rules, adopting multiple regression comparison analysis on the hydrological drought characteristic difference before and after reservoir building, upstream and downstream of the reservoirs, water supply regions and non-water supply regions of the reservoirs, and quantitatively analyzing the difference influence of reservoir and reservoir group regulation on the hydrological drought characteristics according to surface water drought indexes, underground water level drought indexes and reservoir capacity indexes.
And S36, coupling the hydrologic drought space-time law and evolution characteristics with the normalized vegetation index, the normalized humidity index, the normalized dryness index and the normalized heat index by adopting a principal component transformation method to obtain a coupling model from hydrologic drought to ecological drought, and carrying out ecological drought monitoring on the researched area.
In the embodiment of the invention, as the occurrence of the ecological drought has certain hysteresis compared with the hydrographic drought, a coupling model from the hydrographic drought to the ecological drought needs to be constructed, and the ecological drought is monitored by combining meteorological data and ecological environment indexes.
And S4, constructing a Biome-BGC model according to the multi-source data, and calculating to obtain the net primary productivity of the ecosystem in the research area.
Step S4 includes the following substeps S41-S43:
s41, constructing a Biome-BGC model according to multi-source data.
In the embodiment of the invention, because the most serious influence of drought on the ecological environment is vegetation degradation caused by vegetation productivity reduction, the Net Primary Productivity (NPP) of a land ecosystem is used as an evaluation index of the drought response of the ecosystem loss. In order to quantify the influence degree of drought of different levels on the loss of an ecological system in a research area, an ecological process model Biome-BGC model of a historical drought period is adopted for simulation and disclosure.
The Biome-BGC model simulates carbon circulation and water circulation of an ecosystem mainly based on algorithms such as photosynthesis, respiration and transpiration. The Biome-BGC model itself provides default physiological ecological parameters based on extensive literature studies and currently validated evaluation of physiological parameters. And carrying out adaptability adjustment, correction and simulation effect verification on model parameters by using data such as vegetation, weather, flux observation data, soil moisture content and the like in the research area, and carrying out parameter sensitivity analysis on the constructed model by adopting a sensitivity coefficient.
In the embodiment of the invention, the driving data adopted for constructing the Biome-BGC model comprises initial data, meteorological data and physiological parameter data.
Wherein the initial data comprises latitude and longitude of the hydrological site, altitude, soil depth, soil texture and atmospheric carbon dioxide concentration.
The meteorological data includes daily precipitation, daily maximum temperature, daily minimum temperature, daily average temperature, and radiation.
The physiological parameter data comprises stomatal conductance, canopy specific leaf area, leaf and thin root CN ratio.
And S42, verifying the simulation precision of the Biome-BGC model by adopting a linear regression analysis method, a root mean square error method and a significance test method, wherein the simulation precision comprises the precision of a simulation value and an observed value of the Biome-BGC model.
S43, driving by using meteorological data, respectively simulating the vegetation productivity changes of a study area in an arid year in which drought disasters occur and a normal year in which drought disasters are not affected by the drought disasters by adopting a Biome-BGC model verified by simulation precision, and calculating to obtain the net primary productivity of the ecological system in the study area, wherein the calculation formula is as follows:
NPP Biome-BGC ＝GPP-P plant
wherein NPP Biome-BGC Representing the net primary productivity of the ecosystem, GPP representing the total primary productivity of the ecosystem, P plant Indicating vegetation respiration.
The average productivity of perennial vegetation in the normal year of the research area is used as a comparison standard, the productivity of the vegetation in the drought year with different drought levels is compared with the normal year statistics, and the influence degree of the drought with different levels on the loss of the ecological system in the research area can be obtained from the statistical angle.
And S5, obtaining the comprehensive ecological system productivity of the research area by adopting a random forest model according to the hydrological drought characteristics and the net primary productivity of the ecological system, and taking the comprehensive ecological system productivity as an evaluation value of ecological drought loss to realize the ecological drought evaluation of the research area.
Step S5 includes the following substeps S51-S53:
s51, constructing a random forest model based on long-time water circulation sequence continuous data obtained by simulating a hydrological model and an ecological process model, and revealing a multi-process coupling relation between hydrological drought evolution and ecological system loss.
And S52, taking the social water circulation factor change index and the hydrologic drought characteristics as independent variables of the random forest model, taking the net primary productivity of the ecological system as dependent variables of the random forest model, and performing coupling simulation through the random forest model to obtain the comprehensive ecological system productivity of the research area.
In the embodiment of the invention, the social water circulation factor change indexes not only comprise indexes reflecting rainfall, soil water stress, vegetation growth conditions and the like, but also indexes reflecting soil water holding capacity, land cover types, landform types and the like, for example, the change indexes comprise a vegetation state index (VCI), a temperature state index (TCI), a TRMM index and an effective soil water holding capacity (AWC).
And S53, taking the productivity of the comprehensive ecological system as an evaluation value of ecological drought loss to realize ecological drought evaluation on the research area.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art, having the benefit of this disclosure, may effect numerous modifications thereto and changes may be made without departing from the scope of the invention in its aspects.
Claims (8)
1. A method for monitoring and evaluating ecological drought is characterized by comprising the following steps:
s1, collecting multi-source data of a research area, and constructing an ecological drought disaster database by using the multi-source data;
s2, calculating hydrological weather and hydrological drought indexes of the research area according to the multi-source data, and extracting hydrological drought characteristics;
s3, analyzing the time-space law and evolution characteristics of the hydrological drought characteristics, and realizing ecological drought monitoring on the research area according to the analysis result;
s4, constructing a Biome-BGC model according to multi-source data, and calculating to obtain the net primary productivity of the ecological system in the research area;
s5, obtaining comprehensive ecological system productivity of the research area by adopting a random forest model according to the hydrologic drought characteristics and the net primary productivity of the ecological system, and taking the comprehensive ecological system productivity as an evaluation value of ecological drought loss to realize ecological drought evaluation on the research area;
the step S2 comprises the following sub-steps:
s21, dividing the research area by adopting a Thiessen polygon method according to water system distribution of the research area, taking hydrological measurement sites as the central points of the polygonal discrete data, and calculating hydrological weather and hydrological drought indexes of the research area according to precipitation, river runoff, reservoir water level and underground water level in the day-by-day hydrological weather observation data; the hydrological weather and hydrological drought indexes comprise a standardized precipitation index, a precipitation evapotranspiration index, a soil humidity index, a river channel flow index, an underground water level index and a reservoir storage capacity index;
s22, hydrological drought recognition of different time scales is carried out by adopting a threshold value method, and hydrological drought characteristics are extracted by adopting a run length theory; the hydrologic drought characteristics include duration, severity, kurtosis, and frequency of drought;
the step S3 comprises the following sub-steps:
s31, performing space analysis based on ArcGIS according to the hydrological drought characteristics to obtain a hydrological drought space-time law and evolution characteristics;
s32, analyzing the temporal-spatial regularity and evolution characteristics of the hydrological drought by adopting a time sequence to obtain the periodicity, the mutability and the trend of the occurrence of the hydrological drought;
s33, decomposing periodic components, mutation points and trends in runoff hydrological time sequences and underground water level time sequences of each hydrological station by adopting a wavelet transform method;
s34, decomposing climate change components in hydrological time series change by adopting an extreme value modal decomposition method and an empirical mode decomposition method;
s35, carrying out statistics and comparison on the drought years and the normal years in the sub-regions divided in the step S21, selecting reservoirs with similar geographic characteristics but different functions, different reservoir capacities and different operation scheduling rules, adopting multiple regression comparison analysis on the hydrological drought characteristic difference before and after reservoir building, upstream and downstream of the reservoirs, water supply regions and non-water supply regions of the reservoirs, and quantitatively analyzing the difference influence of reservoir and reservoir group regulation on the hydrological drought characteristics according to surface water drought indexes, underground water level drought indexes and reservoir capacity indexes;
and S36, coupling the hydrologic drought space-time law and evolution characteristics with the normalized vegetation index, the normalized humidity index, the normalized dryness index and the normalized heat index by adopting a principal component transformation method to obtain a coupling model from hydrologic drought to ecological drought, and carrying out ecological drought monitoring on the researched area.
2. The ecological drought monitoring and evaluating method according to claim 1, wherein the multi-source data in step S1 comprises natural geographic data, site hydrological data, water conservancy general survey data, remote sensing data, historical disaster statistical data and long-term continuous observation data of carbon-water exchange flux of a national land ecosystem flux observation and research network;
the natural geographic data comprise a digital elevation model, soil moisture content data, hydrogeological data and land coverage area;
the station hydrological data comprise monthly rainfall data, evapotranspiration data, runoff data, temperature change data and soil water content;
the water conservancy general survey data comprises the capacity and the quantity of a reservoir and main water users;
the remote sensing data comprises multi-phase satellite remote sensing land use coverage data, remote sensing estimation evapotranspiration data and soil humidity product data;
the historical disaster statistical data comprises drought loss data, drought areas, drought population and disaster loss of each county in a research area;
the long-term continuous observation data of the carbon-water exchange flux of the Chinese land ecosystem flux observation and research network comprises data of total primary productivity of the ecosystem, total respiration of the ecosystem, clean carbon exchange of the ecosystem, latent heat, sensible heat, physiological and ecological parameters of different ecosystems, perennial biomass and clean primary productivity data.
3. The ecological drought monitoring and evaluation method according to claim 1, wherein the concrete method for extracting the hydrological drought characteristics by adopting the run-length theory in the step S22 is as follows:
a1, setting an interception level R1 to be 10% of the monthly rainfall from the flat percentage;
a2, precipitation sequence X in the month i When one or more time periods are less than R1, namely negative run occurs, judging that drought occurs in the time period, taking the length of the negative run as the duration of the drought, the area of the negative run as the intensity of the drought, and the extreme value of the negative run as the kurtosis, wherein X is i ＝{X 1 ,X 2 ,X 3 ,...,X N }；
And A3, merging the drought conditions according to the drought judgment and merging criteria.
4. The ecological drought monitoring and evaluating method according to claim 3, wherein the drought judging and merging criteria in step A3 are specifically:
(1) When the precipitation per month in the precipitation sequence is X 1 And satisfy X 1 <X 2 Judging that the month is a drought event; wherein X 2 Represents the amount of precipitation as X 1 The precipitation amount of the next month of the single month;
(2) When the precipitation per month in the precipitation sequence is X 1 And satisfy X 1 >X 2 Judging that the month is not a drought event;
(3) When the time interval between two drought events is one month, if the monthly rainfall of the month is less than the average value X of the rainfall of the two adjacent months 0 Combining the two drought events into one drought event; if the monthly rainfall of the month is more than the average value X of the rainfall of two adjacent months 0 Then the two drought events need not be merged, i.e., they are still two drought events.
5. The ecological drought monitoring and evaluation method according to claim 1, wherein the step S4 comprises the following substeps:
s41, constructing a Biome-BGC model according to multi-source data;
s42, verifying the simulation precision of the Biome-BGC model by adopting a linear regression analysis method, a root mean square error method and a significance test method, wherein the simulation precision comprises the precision of a simulation value and an observed value of the Biome-BGC model;
and S43, using meteorological data as a drive, respectively simulating the vegetation productivity changes of a study area in an arid year in which drought disasters occur and a normal year in which drought disasters do not affect by adopting a Biome-BGC model verified by simulation precision, and calculating to obtain the net primary productivity of the ecological system in the study area.
6. The ecological drought monitoring and evaluating method according to claim 5, wherein the driving data adopted for constructing the Biome-BGC model in the step S41 comprises initial data, meteorological data and physiological parameter data;
the initial data comprises longitude and latitude, altitude, soil layer depth, soil texture and atmospheric carbon dioxide concentration of the hydrological site;
the meteorological data comprises daily precipitation, daily maximum temperature, daily minimum temperature, daily average temperature and radiation;
the physiological parameter data comprise stomatal conductance, canopy specific leaf area, leaf and thin root CN ratio.
7. The method for monitoring and evaluating ecological drought according to claim 5, wherein the calculation formula of the net primary productivity of the ecosystem in the step S43 is as follows:
NPP Biome-BGC ＝GPP-P plant
wherein NPP Biome-BGC Representing the net primary productivity of the ecosystem, GPP representing the total primary productivity of the ecosystem, P plant Indicating vegetation respiration.
8. The ecological drought monitoring and evaluation method according to claim 1, wherein the step S5 comprises the following substeps:
s51, constructing a random forest model based on long-time water circulation sequence continuous data obtained by the hydrological model and the ecological process model;
s52, taking the social water circulation factor change index and the hydrologic drought characteristics as independent variables of a random forest model, taking the net primary productivity of the ecological system as dependent variables of the random forest model, and performing coupling simulation through the random forest model to obtain the comprehensive ecological system productivity of the research area; the social water circulation factor change indexes comprise a vegetation state index, a temperature state index, a TRMM index and an effective soil water holding capacity;
and S53, taking the productivity of the comprehensive ecological system as an evaluation value of ecological drought loss to realize ecological drought evaluation on the research area.
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