CN115758856A - Method for researching influence of landscape pattern and climate change on future water quality of drainage basin - Google Patents
Method for researching influence of landscape pattern and climate change on future water quality of drainage basin Download PDFInfo
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Abstract
The invention discloses a landscape pattern and a method for researching the influence of climate change on future water quality of a drainage basin, belonging to the field of influence of climate change on ecological environment. The optimal research scale and relevant index selection are uncertain when analyzing the influence of climate and landscape pattern change on the water quality of a future basin. Dividing the boundary and research scale of a research area by using DEM data and land utilization data, extracting surface landscape type information by using unsupervised classification, visual interpretation and on-site fixed point verification methods, constructing a prediction model of future water quality of the drainage basin, and acquiring the future water quality characteristics of the drainage basin under the condition of changing climate and landscape patterns. The method is suitable for watershed research under various scales, and the optimal research scale is selected; the method overcomes the defect of high redundancy among indexes, is beneficial to optimizing the 'source and sink' landscape pattern for reducing non-point source pollution in the flow, and has important significance on the ecological evaluation and the non-point source pollution prevention and control in the flow under the climate change condition; the application is wide.
Description
Technical Field
The invention belongs to the field of influence of climate change on ecological environment, and particularly relates to a method for researching influence of landscape patterns and climate change on future water quality of a drainage basin.
Background
Climate change has become a well-recognized fact and has affected to varying degrees the layers of the earth's system in circles of rocks, atmosphere, water and organisms. The watershed is connected with geographical units at the middle and the downstream by taking water as a ligament, and the water circulation is deeply limited by the landscape pattern of the watershed and is also inevitably influenced by local climate change, so that the water resource is changed in quality and quantity. If the ecological process of non-point source pollution is combined, the non-point source pollutants are collected into the water body along with the processes of surface runoff and the like, and different source and sink landscape composition types are required, the source and sink composition types, the space structure and the like of the landscape pattern can affect the transmission of the non-point source pollutants, and further affect the water body quality; the water body problems of eutrophication and the like are aggravated to a certain extent by uneven space-time distribution of climate factors such as future increase of air temperature, increase of extreme rainfall and the like. Therefore, when the time-space evolution characteristics of water quality are analyzed on the basis of considering the landscape pattern of the watershed, the influence of non-steady-state factors such as climate change and the like must be fully paid attention to, and the method has important practical significance for recognizing the water environment characteristics of the watershed, the non-point source pollution process and the comprehensive control management. The method for dividing the minimum hydrological response unit of the drainage basin based on the Digital Elevation Model (DEM) and the land utilization data can determine the spatial scale of research. The space heterogeneity characteristics of the watershed determine the complexity of landscape components and landscape structures, and the methods of unsupervised classification, visual interpretation and site-specific verification can improve the accuracy of extracting key elements such as watershed landscape types and landscape patterns to the maximum extent. The prediction of future landscape patterns of the drainage basin and the statistical downscaling of future climate elements can provide high-precision data support such as underlying surface information and climate information for accurately mastering the characteristics of future water quality in the drainage basin. Under the socioeconomic development conditions that agricultural pollutant discharge is pressed by intensive agriculture development and reduction is difficult to realize, the watershed needs to provide scientific theory and method support for quantification in aspects of influence of landscape composition types, landscape space structures and configuration of non-point source pollution of the watershed on water quality and the like. However, in the existing landscape pattern research on water quality, the relationship between the landscape pattern index and the ecological process is not very strict, so the landscape pattern index calculation result has no ecological significance, and the traditional landscape pattern index calculation result has no spatial difference. In addition, the potential influence of climate change on water quality and the scale effect in the relationship between the landscape pattern and the water quality index are often ignored in the existing research, and the influence of climate conditions and landscape pattern on certain water quality index or certain types of water quality index has obvious difference under different research scales. Based on this, the research result of the climate condition landscape pattern on the influence of water quality has to have a series of problems.
Disclosure of Invention
The invention aims to provide a method for researching the influence of landscape patterns and climate change on future water quality of a drainage basin, which is characterized by comprising the following steps of:
step 1, dividing the spatial research scale of a watershed where the research area is located, wherein the spatial research scale comprises the boundary of the watershed, the upstream, the middle and the downstream of the watershed and a plurality of typical small watersheds;
step 2, extracting river basin landscape types and landscape pattern information by adopting unsupervised classification, visual interpretation and on-site fixed-point verification methods;
step 3, acquiring high-precision weather indexes of future air temperature, precipitation and evapotranspiration by adopting a step-by-Step Clustering Downscaling (SCD) method;
step 4, obtaining future landscape types, landscape components and landscape pattern information in the watershed by using the FLUS model;
step 5, acquiring future water quality index information of the drainage basin by utilizing a drainage basin future water quality estimation model (SCA);
step 6, researching water quality characteristics under the condition of climate and landscape pattern change, and researching the influence of climate and landscape pattern change on future water quality under multiple space-time angles;
the step 1 comprises the following steps:
(1) Filling the depression of DEM data by using a Fill function in an ArcSWAT10.3 tool;
(2) Calculating the water flow direction and the confluence accumulated amount, and determining the outlet point of a small watershed, wherein the range of the small watershed takes the watershed water outlet as a vertex and all grid units covered upstream of the small watershed as the vertex;
(3) By trying different confluence cumulant for river network extraction, the smaller the threshold area is, the denser the river network is, the larger the number of small watersheds is, and the more the spatial variation of the terrain topography of the watershed is considered when the small watersheds are extracted. Finally determining a threshold value by comparing with a water system diagram to ensure that the river network is the same as the actual situation to the maximum extent;
(4) Extracting the range of a research basin by using a Watershed tool, and determining the boundary of a research area and the boundaries of each small basin by combining a Google satellite map and the topographic features of the research basin;
(5) Finally, generating a plurality of typical small watersheds and naming the typical small watersheds in the sequence from the upstream to the downstream;
(6) On the basis of typical small watershed division, the watershed is divided into three parts, namely an upper part, a middle part and a lower part according to the landform, the climate and hydrology, the vegetation soil, the elevation characteristic and the altitude of a small watershed water outlet of a research area.
The step 2 comprises the following steps:
(1) Importing the related high-resolution remote sensing image into ArcGIS, adding and correcting a geographic space reference coordinate system, and splicing the images to obtain initial multispectral image data;
(2) Cutting the spliced image by using the boundary vector data of the research area to obtain an original remote sensing image of the research area, and interpreting by an indoor interpretation and field verification method combining unsupervised classification and visual interpretation;
(3) Establishing a preliminary landscape classification system for researching the watershed according to research purposes, on-site research data of the research watershed, digital Elevation Model (DEM) data and 'current land utilization state classification', merging through cluster analysis according to landscape type characteristics of a research area and the requirement of research data, and finally determining a logic-ordered research watershed landscape type classification standard;
(4) In order to improve the interpretation precision and the classification accuracy, verification points are respectively selected for various landscape types on the image, survey verification and correction are carried out by combining field GPS fixed point information collected on the spot to obtain the overall interpretation precision, and Kappa coefficient verification is carried out to ensure that the overall interpretation precision meets the use requirements of research;
(5) And processing and analyzing the vector data in ArcGIS10.3, extracting different landscape information according to research contents, and outputting a landscape type map of a research area by adding a legend, a north pointer, important cities and a graticule and defining a scale.
The step 3 comprises the following steps:
(1) Selecting a large-scale forecasting factor, downloading and screening Climate change variables from GCM (Global simulation Model) including GFDL-CM2.0, had CM3 and NCARCCSM3 and a reanalysis data set, and carrying out default value inspection and dimension inspection on an input and output data set; constructing a matrix of input and output variables;
(2) Merging standards according to a classification principle, and classifying all samples into corresponding classes;
(3) Establishing a gradual clustering tree, namely establishing a statistical relationship between a large-scale climate forecast factor and a regional climate forecast amount;
(4) Calibrating and verifying the effectiveness of a stepwise clustering downscaling model (SCD);
(5) Generating high-precision output variable values including simulation prediction results of air temperature, precipitation and evapotranspiration according to the input variables on the basis of the step-by-step clustering tree;
the step 4 comprises the following steps:
(1) Selecting landscape pattern change driving factors including terrain factors of elevation and gradient, traffic accessibility factors and restriction conversion factors according to the actual situation of a drainage basin and the availability of data;
(2) Standardizing land utilization change driving factors, and calculating to obtain a suitability probability chart of landscape component types on each pixel;
(3) Targets of the variation quantity of each landscape component type are preset through a Markov prediction model, and the difficulty of conversion among different landscape types and the limit occurrence area of landscape type conversion are determined;
(4) Setting model parameters, including the setting of the number of times of simulation iteration targets and the size of the field, and finally realizing the simulation of the landscape pattern change of the drainage basin;
the step 5 comprises the following steps:
(1) Screening out a comprehensive forecasting index system with low redundancy and close relation with water quality indexes according to the climate data and landscape pattern data of temperature, precipitation and evapotranspiration in the historical period;
(2) Establishing a statistical relationship between a forecast index system and a water quality index;
(3) Using the observation data of the historical period to calibrate and verify a future water quality estimation model (SCA);
(4) And generating future water quality index data based on the water quality estimation model, wherein the future water quality index data comprises simulation and prediction results of ammonia nitrogen, total phosphorus and chemical oxygen demand.
The step 6 comprises the following steps:
(1) Researching future climate change of the basin, wherein future climate change characteristics including temperature, precipitation and evapotranspiration in the basin and change characteristics of extreme climate events such as high-temperature heat waves and heavy rain are researched;
(2) Selecting a multi-time-space research scale, wherein the multi-time-space research scale is used for selecting representative time scales of a dry period, a flat period and a rich period and space scales including a typical small watershed, an upstream, a middle and a downstream of a watershed and a whole watershed as the research scale of the landscape pattern according to the landscape type, the landscape pattern and the characteristics of hydrology periods in a research area;
(3) The relation analysis of the forecast index system of the climate and landscape pattern and the water quality index is used for researching the influence of the climate and landscape pattern change on the water quality in the future under the multi-space-time scale.
The invention has the beneficial effects that: the invention considers the scale effect of the ecological process and researches the influence of the landscape pattern on the water quality under the climate change and different space-time scales; aiming at the characteristic of large space-time difference of non-point source pollution, the determination method of research scale is improved, and the method is suitable for basin research under various scales, wherein the construction link of climate indexes, landscape pattern indexes and water quality indexes solves the problem of high redundancy among indexes. The method is beneficial to optimizing the 'source and sink' landscape pattern for reducing non-point source pollution in the flow and selecting the optimal research scale; meanwhile, the method has important significance for in-region ecological evaluation and non-point source pollution prevention and control under climatic change conditions. The method is widely applied to the research of the ecological process of the non-point source pollution in the drainage basin by the geographic information system and the remote sensing technology.
Drawings
FIG. 1 illustrates certain watershed boundary extraction and small watershed partitioning; wherein, (a) an elevation map; (b) a watershed river network; (c) a watershed boundary; (d) a typical small watershed;
FIG. 2 is a diagram showing the distribution of upstream, middle and downstream of a certain basin;
fig. 3 is a graphic summary of a certain watershed remote sensing data preprocessing stage, wherein, (a) a clipped watershed image map; (b) the interpreted watershed land use cover map; (c) outputting the drainage basin landscape type chart after finishing;
Detailed Description
The invention provides a method for researching influence of landscape patterns and climate change on future water quality of a drainage basin, which comprises the following steps:
step 1, dividing a spatial research scale of a river basin where a research area is located, wherein (a) an elevation map, (b) a river network of the river basin, (c) a river basin boundary, and (d) a typical small river basin;
step 2, extracting river basin landscape types and landscape pattern information by adopting an unsupervised classification, visual interpretation and field fixed point verification method;
step 3, acquiring high-precision weather indexes of future air temperature, precipitation and evapotranspiration by adopting a step-by-Step Clustering Downscaling (SCD) method;
step 4, simulating and acquiring Future landscape types, landscape components and landscape pattern information in the watershed by using a FLUS (Future Land Use Simulation) model;
step 5, acquiring future water quality index information of the drainage basin by utilizing a drainage basin future water quality estimation model (SCA);
and 6, researching the water quality characteristics under the condition of climate and landscape pattern change, and researching the influence of climate and landscape pattern change on the future water quality under multiple space-time angles.
The optimal research scale and the relevant index selection are uncertain when the influence of climate and landscape pattern change on the water quality of a future basin is analyzed. Dividing the boundary and research scale of a research area by using DEM data and land utilization data, extracting surface landscape type information by using unsupervised classification, visual interpretation and site-specific verification methods, respectively acquiring future high-precision climate data and landscape pattern information of a basin based on historical-period climate and landscape pattern data, constructing a prediction model of future water quality of the basin on the basis of calculating historical-period landscape pattern indexes and correlation coefficients of climate indexes and water quality indexes, and acquiring future water quality data of the basin to research future water quality characteristics of the basin under the condition of climate and landscape pattern change.
Example 1
A method for researching influence of landscape patterns and climate change on future water quality of a drainage basin comprises the following steps:
step 1, as shown in fig. 1, a drainage basin where a certain secondary branch is located is taken as an example. Dividing the spatial research scale of the watershed, wherein the spatial research scale comprises the boundary of the watershed (shown in figure 1 c), a plurality of typical small watersheds (shown in figure 1 d) and the upstream, middle and downstream watersheds (shown in figure 2);
specifically, the watershed space research scale dividing method provided by the invention can comprise the following steps:
(1) The Fill function in the arcswat10.3 tool is used to Fill in the hole in DEM data as shown in fig. 1 a;
(2) Calculating the water flow direction and the confluence accumulation amount, and determining an outlet point of a small watershed, wherein the range of the small watershed takes a watershed water outlet as a vertex and all grid units covered upstream of the small watershed;
(3) By trying different confluence cumulant for river network extraction, the smaller the threshold area is, the denser the river network is, the larger the number of small watersheds is, and the more the spatial variation of the terrain topography of the watershed is considered when the small watersheds are extracted. Finally determining a threshold value by comparing with the water system diagram to ensure that the river network is closest to the actual situation, as shown in fig. 1 b;
(4) Extracting the scope of the research Watershed by using a Watershed tool, and determining the boundary of the research area and the boundaries of each small Watershed by combining a Google satellite map and the topographic features of the Watershed where the research is carried out, as shown in figure 1 cd;
(5) Finally, generating a plurality of typical small watersheds, as shown in FIG. 1d, and naming the small watersheds in the order from upstream to downstream;
(6) On the basis of typical small watershed division, dividing a watershed into an upper part, a middle part and a lower part according to landforms, climate and hydrology, vegetation soil, elevation features and the altitude of a small watershed water outlet, wherein the three parts are shown in figure 2;
step 2, the unsupervised classification, visual interpretation and site-specific verification method is used for extracting river basin landscape types and landscape pattern information;
specifically, the method for extracting information of the watershed landscape type and landscape pattern using the present invention may include the steps of:
(1) Introducing the related high-resolution remote sensing image into ArcGIS, and performing addition and correction of a geographic space reference coordinate system and image splicing processing to obtain initial multispectral image data;
(2) Cutting the spliced image by using the boundary vector data of the research area shown in fig. 1c to obtain an original remote sensing image of the research basin shown in fig. 3a, interpreting by an indoor interpretation and field verification method combining unsupervised classification and visual interpretation, wherein the interpreted remote sensing image is shown in fig. 3 b;
(3) Constructing a preliminary landscape classification system for researching the watershed according to research purposes, field research data of the research watershed, DEM data, current land utilization state classification (GB/T21010-2018) and the like, merging through clustering analysis according to landscape type characteristics of a research area and the requirement of research data, and finally determining a logic-sequenced landscape type classification standard of the research watershed, wherein the system comprises the following steps: cultivated land, woodland, grassland, canal, pond, construction land and unused land;
(4) In order to improve the interpretation precision and the classification accuracy, verification points are respectively selected for various landscape types on the image, investigation verification and correction are carried out by combining with field GPS fixed-point information collected on the spot, the overall interpretation precision is obtained, and Kappa coefficient verification is carried out so as to enable the interpretation precision and the classification accuracy to meet the use requirements of research;
(5) Processing and analyzing the vector data in ArcGIS10.3, extracting different landscape information according to research contents, and outputting a landscape type map of a research area as shown in FIG. 3c by adding legends, north pointers, important cities, graticules, defined scales and the like, wherein the main landscape types comprise: cultivated land, woodland, grassland, canal, pond, construction land and unused land;
step 3, the step-by-step clustering downscaling method (SCD) is used for obtaining high-precision weather indexes such as future air temperature, precipitation, evapotranspiration and the like;
specifically, the stepwise clustering downscaling method using the present invention may include the following steps:
(1) Selecting a large-scale forecasting factor, downloading and screening Climate change variables from GCM (Global simulation Model) including GFDL-CM2.0, had CM3 and NCARCCSM3 and a reanalysis data set, and carrying out default value inspection and dimension inspection of an input and output data set; constructing a matrix of input and output variables;
(2) Merging standards according to a classification principle, and classifying all samples into corresponding classes;
(3) Establishing a gradual clustering tree, namely establishing a statistical relationship between a large-scale climate forecast factor and a regional climate forecast amount;
(4) Calibrating and verifying the effectiveness of a stepwise clustering downscaling model (SCD);
(5) Generating high-precision output variable values including simulation prediction results of air temperature, precipitation and evapotranspiration according to the input variables on the basis of the step-by-step clustering tree; step 4, the FLUS model is used for acquiring future landscape pattern information of the river basin, including landscape types, landscape components and landscape pattern information in the river basin;
specifically, the method for acquiring future landscape pattern information of the drainage basin, provided by the invention, can comprise the following steps:
(1) Selecting landscape pattern change driving factors including terrain factors of elevation and gradient, traffic accessibility factors and limiting conversion factors according to the actual conditions of the drainage basin and the availability of data;
(2) Standardizing land use change driving factors, and calculating to obtain a suitability probability chart of landscape component types on each pixel;
(3) Targets of the change quantity of each landscape component type are preset through a Markov prediction model, and the difficulty of conversion among different landscape types and the limited occurrence area of landscape type conversion are determined;
(4) Setting model parameters, including the setting of the number of times of simulation iteration targets and the size of the field, and finally realizing the simulation of the landscape pattern change of the drainage basin;
step 5, the estimation model (SCA) of future water quality of the drainage basin is used for acquiring the future water quality index information of the drainage basin;
specifically, the estimation model of the future water quality of the watershed, provided by the invention, can comprise the following steps:
(1) Screening out a comprehensive forecasting index system with low redundancy and close relation with water quality indexes according to the climate data and landscape pattern data of temperature, precipitation and evapotranspiration in the historical period;
(2) Establishing a statistical relationship between a forecast index system and a water quality index;
(3) Using the observation data of the historical period to calibrate and verify the estimation model of the future water quality;
(4) Generating future water quality index data based on a water quality estimation model, wherein the future water quality index data comprises simulation prediction results of ammonia nitrogen, total phosphorus and chemical oxygen demand;
step 6, researching the water quality characteristics under the condition of changing the climate and landscape pattern, wherein the research is used for researching the influence of the climate and landscape pattern change on the future water quality under multiple space-time angles;
specifically, the water quality characteristic study under the condition of changing the climate and landscape patterns by using the invention can comprise the following steps:
(1) Researching future climate change of the basin, wherein the future climate change characteristics of the basin, including temperature, precipitation and evapotranspiration, and the change characteristics of extreme climate events such as high-temperature heat waves and heavy rain are researched;
(2) Selecting a multi-time-space research scale, wherein the multi-time-space research scale is used for selecting representative time scales of a dry period, a flat period and a rich period and space scales including a typical small watershed, an upstream, a middle and a downstream of a watershed and a whole watershed as the research scale of the landscape pattern according to the landscape type, the landscape pattern and the characteristics of hydrology periods in a research area;
(3) The relation analysis of the forecast index system of the climate and landscape pattern and the water quality index is used for researching the influence of the climate and landscape pattern change on the water quality in the future under the multi-space-time scale.
Claims (7)
1. A method for researching influence of landscape patterns and climate change on future water quality of a drainage basin is characterized by comprising the following steps:
step 1, dividing the spatial research scale of a watershed where the research area is located, wherein the spatial research scale comprises the boundary of the watershed, the upstream, the middle and the downstream of the watershed and a plurality of typical small watersheds;
step 2, extracting river basin landscape types and landscape pattern information by adopting an unsupervised classification, visual interpretation and field fixed point verification method;
step 3, acquiring high-precision weather indexes of future air temperature, precipitation and evapotranspiration by adopting a step-by-Step Clustering Downscaling (SCD) method;
step 4, obtaining Future landscape types, landscape components and landscape pattern information in the watershed by using a FLUS (Future Land Use organization) model;
step 5, acquiring future water quality index information of the drainage basin by using an estimation model (SCA) of future water quality of the drainage basin;
and 6, researching the water quality characteristics under the condition of climate and landscape pattern change, and researching the influence of climate and landscape pattern change on the future water quality under multiple space-time angles.
2. The method for researching the influence of landscape patterns and climate changes on future water quality of a watershed according to claim 1, wherein the step 1 comprises the following steps:
(1) Filling the depression of DEM data by using Fill function in an ArcSWAT10.3 tool;
(2) Calculating the water flow direction and the confluence accumulation amount, and determining an outlet point of a small watershed, wherein the range of the small watershed takes a watershed water outlet as a vertex and all grid units covered upstream of the small watershed;
(3) By trying different confluence cumulant for river network extraction, the smaller the threshold area is, the denser the river network is, the larger the number of small watersheds is, and the more the spatial variation of the terrain topography of the watershed is considered when the small watersheds are extracted. Finally determining a threshold value by comparing with a water system diagram to ensure that the river network is the same as the actual situation to the maximum extent;
(4) Extracting the scope of a research basin by using a Watershed tool, and determining the boundary of a research area and the boundaries of small basins by combining a Google satellite map and the topographic features of the basin where the research is located;
(5) Finally, generating a plurality of typical small watersheds and naming the typical small watersheds in the sequence from the upstream to the downstream;
(6) On the basis of typical small watershed division, the watershed is divided into three parts, namely an upstream part, a middle part and a downstream part, according to the landform, the climate and hydrology, the vegetation soil, the elevation characteristics and the altitude of a small watershed water outlet of a research area.
3. The method for researching the influence of landscape patterns and climate changes on future water quality of a watershed according to claim 1, wherein the step 2 comprises the following steps:
(1) Introducing the related high-resolution remote sensing image into ArcGIS, and performing addition and correction of a geographic space reference coordinate system and image splicing processing to obtain initial multispectral image data;
(2) Cutting the spliced image by using the boundary vector data of the research area to obtain an original remote sensing image of the research area, and interpreting by an indoor interpretation and field verification method combining unsupervised classification and visual interpretation;
(3) Establishing a preliminary landscape classification system for researching the watershed according to research purposes, on-site research data of the research watershed, digital Elevation Model (DEM) data and 'current land utilization state classification', merging through cluster analysis according to landscape type characteristics of a research area and the requirement of research data, and finally determining a logic-ordered research watershed landscape type classification standard;
(4) In order to improve the interpretation precision and the classification accuracy, verification points are respectively selected for various landscape types on the image, investigation verification and correction are carried out by combining with field GPS fixed-point information collected on the spot, the overall interpretation precision is obtained, and Kappa coefficient verification is carried out so as to enable the interpretation precision and the classification accuracy to meet the use requirements of research;
(5) And processing and analyzing the vector data in ArcGIS10.3, extracting different landscape information according to research contents, and outputting a landscape type map of a research area by adding a legend, a north pointer, an important city, a graticule and a defined scale.
4. The method for researching the influence of landscape patterns and climate change on the future water quality of a drainage basin according to claim 1, wherein the step 3 comprises the following steps:
(1) Selecting a large-scale forecasting factor, downloading and screening Climate change variables from GCM (Global simulation Model) including GFDL-CM2.0, had CM3 and NCARCCSM3 and a reanalysis data set, and carrying out default value inspection and dimension inspection of an input and output data set; constructing a matrix of input and output variables;
(2) Merging standards according to a classification principle, and classifying all samples into corresponding classes;
(3) Establishing a gradual clustering tree, namely establishing a statistical relationship between a large-scale climate forecast factor and a regional climate forecast amount;
(4) Calibrating and verifying the effectiveness of a stepwise clustering downscaling model (SCD);
(5) And generating high-precision output variable values including simulation prediction results of air temperature, precipitation and evapotranspiration according to the input variables on the basis of the step-by-step clustering tree.
5. The method for researching the influence of landscape patterns and climate change on the future water quality of a watershed as claimed in claim 1, wherein the step 4 comprises the following steps:
(1) Selecting landscape pattern change driving factors including terrain factors of elevation and gradient, traffic accessibility factors and limiting conversion factors according to the actual conditions of the drainage basin and the availability of data;
(2) Standardizing land use change driving factors, and calculating to obtain a suitability probability chart of landscape component types on each pixel;
(3) Targets of the change quantity of each landscape component type are preset through a Markov prediction model, and the difficulty of conversion among different landscape types and the limited occurrence area of landscape type conversion are determined;
(4) And setting model parameters, including the number of times of simulation iteration targets and the setting of the field size, and finally realizing the simulation of the landscape pattern change of the watershed.
6. The method for researching the influence of landscape patterns and climate changes on future water quality of a watershed according to claim 1, wherein the step 5 comprises:
(1) Screening out a comprehensive forecasting index system with low redundancy and close relation with water quality indexes according to the climate data and landscape pattern data of temperature, precipitation and evapotranspiration in the historical period;
(2) Establishing a statistical relationship between a forecast index system and a water quality index;
(3) Using the observation data of the historical period to calibrate and verify a future water quality estimation model (SCA);
(4) And generating future water quality index data based on the water quality estimation model, wherein the future water quality index data comprises simulation prediction results of ammonia nitrogen, total phosphorus and chemical oxygen demand.
7. The method for researching the influence of landscape patterns and climate change on the future water quality of a watershed as claimed in claim 1, wherein the step 6 comprises:
(1) Researching future climate change of the basin, wherein future climate change characteristics including temperature, precipitation and evapotranspiration in the basin and change characteristics of extreme climate events such as high-temperature heat waves and heavy rain are researched;
(2) Selecting a multi-time-space research scale, wherein the multi-time-space research scale is used for selecting representative time scales of a dry period, a flat period and a rich period and space scales including a typical small watershed, an upstream, a middle and a downstream of a watershed and a whole watershed as the research scale of the landscape pattern according to the landscape type, the landscape pattern and the characteristics of hydrology periods in a research area;
(3) The relation analysis of the forecasting index system of the climate and landscape pattern and the water quality index is used for researching the influence of the climate and landscape pattern change on the water quality in the future under multiple space-time scales.
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| CN117933572A (en) * | 2024-03-20 | 2024-04-26 | 四川飞洁科技发展有限公司 | Water quality prediction method and related device |
| CN118094395A (en) * | 2024-04-22 | 2024-05-28 | 安徽农业大学 | Surface temperature estimation method, system and computer equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117933572A (en) * | 2024-03-20 | 2024-04-26 | 四川飞洁科技发展有限公司 | Water quality prediction method and related device |
| CN117933572B (en) * | 2024-03-20 | 2024-06-11 | 四川飞洁科技发展有限公司 | Water quality prediction method and related device |
| CN118094395A (en) * | 2024-04-22 | 2024-05-28 | 安徽农业大学 | Surface temperature estimation method, system and computer equipment |
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