CN115203934A - Mountain area water-reducing downscaling method based on Logistic regression - Google Patents
Mountain area water-reducing downscaling method based on Logistic regression Download PDFInfo
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Abstract
The invention discloses a mountainous area water-reducing downscaling method based on Logistic regression, which comprises the following steps: reading TRMM3B42 satellite precipitation data and counting the daily precipitation of meteorological sites; the space-time scale of the data is uniform; building a Logistic regression downscaling model; and obtaining precipitation downscaling data with the spatial resolution of 1km based on a Logistic regression model. According to the method, meteorological station observation rainfall data are fused into the scale reduction process of satellite rainfall data, the scale reduction is carried out on the basis of a Logistic regression model by considering the relation among water vapor content, NDVI, a plurality of terrain factors and the rainfall, the precision of the scaled rainfall data and the consistency of the scaled rainfall data and the actually measured data series are greatly improved, further, the spatial rainfall with high space-time resolution is obtained, and the spatial distribution of the rainfall in the mountainous area can be more accurately reflected.
Description
Technical Field
The invention belongs to the technical field of hydrology and meteorological phenomena, and relates to a mountainous area water-reducing and downscaling method based on Logistic regression.
Background
Climate change estimation is one of the important scientific issues of general concern in international society at present. The Climate estimation based on CMIP5 mode (Coupled Model Intercom Project) led by the International Climate Change Commission (IPCC) of the United nations is the authoritative solution to the problem of the future global warming trend and amplitude. The CMIP5 comprises nearly 50 GCMs (General Circulation models) and provides reliable global large-scale data for researchers, but the accuracy of the estimation result of the CMIP5 Model is not enough for scientific research below a 200km scale. Currently, scholars in the fields of hydrology, agriculture, environmental policies, climate change and the like increasingly demand high-resolution data, and it is particularly important to develop precipitation data with high space-time resolution characteristics.
The rainfall continuously affects the global hydrothermal circulation and the production and the life of human beings, and the research on the rainfall has important significance on regional water circulation, water resource management, economic development and regional ecological environment management. At present, precipitation has high space-time variability, a traditional method is mainly used for interpolating actual-measured precipitation data of a station to obtain regional data, the regional data are influenced by representativeness of the station, the stability and precision of the data are difficult to guarantee, the effect of geographic elements on the precipitation is ignored, and further application of the interpolated data is limited, so that the space-time distribution rule of the precipitation is difficult to accurately reflect, and particularly in mountainous areas with complex terrain. The rapid development of satellite remote sensing and geographic information technology provides a new rainfall detection method, and the characteristic of large-range coverage makes up for the problem of data loss caused by sparse meteorological sites.
Nowadays, a large number of rainfall observation products based on satellite remote sensing are emerging, including rainfall products (CMORPH) developed by the american meteorological prediction center, and high-resolution rainfall products PERSIANN, GSMaP, TRMM and the like which estimate remote sensing data by using an artificial neural network algorithm, and these rainfall products provide continuous space-time rainfall information and provide powerful data support for researches on regional rainfall, hydrological simulation and the like. The TRMM satellite can provide meteorological data such as a large amount of tropical ocean rainfall, liquid water content in cloud, latent heat release and the like, but the spatial resolution of the data is relatively low, and the data precision is different along with the change of the geographical position of the region, so that the TRMM satellite is not enough to accurately depict the distribution rule of the rainfall in a complex terrain region, and cannot completely meet the precision requirement of small-scale regional research on the rainfall data. Therefore, in order to research the space-time variation characteristics of rainfall in the propulsion region, TRMM rainfall data space downscaling research is necessary.
Disclosure of Invention
Precipitation is influenced by a plurality of factors in the formation process, wherein the geographical position and the topographic pattern directly determine the amount of moisture vapor obtained; when the water vapor content in the air is supersaturated, cloud rain can be formed, and the air cooling process is influenced by various topographic factors such as altitude, gradient, slope direction, topographic relief, topographic breadth, surface roughness and the like; plant transpiration can increase the water vapor content in the air, and the vegetation coverage condition can also reflect the abundant degree of regional rainfall, so most TRMM rainfall data downscaling related researches mainly use a vegetation index NDVI as a single climate factor of a downscaling model, but the response of vegetation to rainfall has certain hysteresis. Therefore, in order to obtain TRMM precipitation products with high spatial resolution, the invention provides a mountainous area precipitation downscaling method based on Logistic regression, which selects longitude and latitude, DEM, gradient, slope direction, terrain undulation, terrain breadth and surface roughness as downscaling factors of the terrain, and constructs a downscaling space model fused with multi-source data by combining climate factors such as water vapor index and NDVI, so that regional TRMM precipitation downscaling data with higher spatial resolution can be obtained, which is beneficial to obtaining continuous spatial precipitation of areas with few precipitation actual measurement sites, and can provide reliable data sources for water resource research, agricultural drought and flood monitoring, ecological environment management and the like of a research area.
The patent provides a mountain area precipitation downscaling method based on Logistic regression, which comprises the following steps:
reading satellite precipitation data and counting the daily precipitation of meteorological sites;
step 3, establishing a Logistic regression downscaling model, and obtaining downscaling data with the spatial resolution of 1km based on the Logistic regression model;
in the step 3, the method specifically comprises the following steps:
step 31, determining an independent variable and a dependent variable of the regression and reduction scale model, taking actually measured precipitation data of a station as the dependent variable, and taking DEM data, gradient data, slope data, water vapor content data, NDVI data, longitude data, latitude data, topographic relief degree data, topographic opening width data, surface roughness data and TRMM precipitation data of 0.25-degree spatial resolution with uniform space-time scale as the independent variables;
step 32, resampling the DEM data of the research area to obtain DEM data with 1km spatial resolution, and calculating according to the DEM data with 1km spatial resolution to obtain slope data, terrain waviness data, terrain breadth data and surface roughness data with 1km spatial resolution;
step 33, resampling NDVI data and water vapor content data of the research area to obtain NDVI data and water vapor content data with a spatial resolution of 1 km;
step 34, completely consistent spatial grids of gradient data, slope direction data, topographic relief degree data, topographic breadth data, surface roughness data, NDVI data water vapor content data and TRMM rainfall data of 1km spatial resolution, calculating the geometric center of the downscale grid by selecting one grid data, and calculating the longitude data and the latitude data of each grid under the spatial resolution;
step 35, establishing a Logistic regression relationship between the daily precipitation data and the independent variables by using a Logistic regression method, wherein the Logistic regression can perform continuous and discrete independent variable analysis, the independent variables are not required to conform to normal distribution, and the problem of interdependence between factors can be solved well, and the calculation formula is as follows:
in the formula:
y unit mm is measured data of station precipitation of ground actual measurement;
grid data of respective variables under the spatial resolution of 0.25 DEG, a, b, \8230, k is an independent variable Logistic regression coefficient, and gamma is a constant.
Step 36, substituting the slope data, the NDVI data, the water vapor content data, the longitude data, the latitude data, the topographic relief degree data, the topographic surface roughness data and the TRMM rainfall data of the 1km spatial resolution into a Logistic regression downscaling model to obtain the daily downscaling water loss, wherein the calculation formula is as follows:
in the formula: p unit mm is TRMM downscaling precipitation data;
raster data at a spatial resolution of 1km for each variable.
Further, the step 1 specifically includes the following steps:
step 13, settling the daily precipitation monitored by the meteorological stations in the research area;
further, the step 2 specifically includes the following steps:
step 21, obtaining NDVI data of a research area according to MODIS MOD13Q1 product data, and obtaining water vapor content data of the research area according to MODIS MOD05 product data;
step 22, resampling the DEM data of the research area to obtain DEM data with 0.25-degree spatial resolution, and calculating data such as gradient, slope direction, terrain relief degree, terrain breadth and surface roughness on each pixel of the research area according to the DEM data with 0.25-degree spatial resolution;
step 23, resampling the NDVI data and the water vapor content data of the research area to respectively obtain NDVI data and water vapor content data with the spatial resolution of 0.25 degrees;
and 24, calculating the geometric center point of the 0.25-degree spatial resolution grid after the time-space scales of TRMM precipitation data, the DEM with the spatial resolution of 0.25 degrees, water vapor content data, NDVI data, gradient data, slope direction data, topographic relief degree data, topographic breadth data and surface roughness data are unified, and obtaining longitude data and latitude data of each grid.
The invention discloses a mountainous area water-reducing downscaling method based on Logistic regression, which comprises the following steps: reading TRMM3B42 satellite precipitation data and counting the daily precipitation of meteorological sites; the space-time scale of the data is uniform; building a Logistic regression downscaling model; and obtaining the precipitation downscaling data with the spatial resolution of 1km based on a Logistic regression model. According to the method, meteorological station observation rainfall data are fused into the scale reduction process of satellite rainfall data, the scale reduction is carried out on the basis of a Logistic regression model by considering the relation among water vapor content, NDVI, a plurality of terrain factors and the rainfall, the precision of the scaled rainfall data and the consistency of the scaled rainfall data and the actually measured data series are greatly improved, further, the spatial rainfall with high space-time resolution is obtained, and the spatial distribution of the rainfall in the mountainous area can be more accurately reflected.
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FIG. 1 is a schematic view of the research flow of the present invention;
FIG. 2 is a schematic diagram of a research area boundary, meteorological station distribution, and DEM of the present invention;
FIG. 3 is a schematic diagram of the TRMM satellite original precipitation, TRMM down-scale precipitation products and site interpolation precipitation variation extracted by the present invention;
fig. 4 is a schematic diagram of the spatial distribution of TRMM satellite raw precipitation, TRMM downscaling precipitation products and site interpolation contrast extracted by the present invention.
Detailed Description
In order to facilitate understanding for those skilled in the art, the present invention is further described below with reference to the following embodiments and the accompanying drawings.
The Wudu area is located in the southeast of Gansu province and is located in the midstream region of Carling river tributary white Longjiang, 32-33-42 'N across north latitude, 104-34-105-38' E across east longitude, 100.8km in north and south, 76.2km in east-west width and 4683km in total area 2 . The area is affected by the warm and humid subtropical monsoon climate, and the climate conditions of the area greatly change with different altitudes and terrain positions due to the characteristics of mountainous terrain (the valley, the deep mountain and the high mountain). The climatic characteristics are as follows: rainfall is mostly concentrated in summer and autumn, and is drier in spring and winter. The average precipitation for many years reaches 400-900mm, and 75-85% of precipitation is concentrated in 5-9 months.
As shown in fig. 1, the schematic diagrams of the distribution and DEM of the boundary of the research area, the meteorological station and the DEM of the mountainous area based on Logistic regression provided by the present invention are shown in fig. 2, the schematic diagrams of the change of the original precipitation of the TRMM satellite, the reduced precipitation product of the TRMM and the interpolated precipitation of the site extracted by the present invention are shown in fig. 3, and the schematic diagrams of the comparison space distribution of the original precipitation of the TRMM satellite, the reduced precipitation product of the TRMM and the interpolated precipitation of the site extracted by the present invention are shown in fig. 4, and the present invention comprises the following steps:
step 1, reading TRMM3B42 satellite precipitation data and counting the daily precipitation of meteorological sites, specifically:
step 13, counting the extracted satellite rainfall information pixel by pixel to obtain TRMM rainfall data of each pixel every day;
step 14, settling the daily precipitation monitored by the weather station in the research area of the buffer area;
step 21, writing codes and manufacturing batch processing MODIS MOD13Q1 product data through an MODIS reproduction Tool, realizing operations such as rapid projection conversion, wave band extraction and mosaic, and finally cutting by utilizing boundary data of a research area to obtain NDVI data of the research area;
step 22, opening an MCTK plug-in ENVI software to process MODIS MOD05 data to obtain water vapor content distribution data of a research area;
step 23, resampling DEM Data of the research area by using a sample tool of a Data Management Tools toolbox of an ArcGIS10.6 platform to obtain DEM Data with 0.25-degree spatial resolution, and calculating Data such as gradient, slope direction, topographic relief, topographic breadth and surface roughness on each pixel of the Wudu area according to the DEM Data with 0.25-degree spatial resolution;
step 24, resampling NDVI Data and water vapor content Data of the research area by using a sample tool of a Data Management Tools toolbox of the ArcGIS10.6 platform to respectively obtain NDVI Data and water vapor content Data with 0.25-degree spatial resolution;
Step 3, building a Logistic regression downscaling model, and obtaining the downscaling data with the spatial resolution of 1km based on the Logistic regression model, wherein the method specifically comprises the following steps:
step 31, determining an independent variable and a dependent variable of the regression and reduction scale model, taking actually measured precipitation data of a station as the dependent variable, and taking DEM data, gradient data, slope data, water vapor content data, NDVI data, longitude data, latitude data, topographic relief degree data, topographic opening width data, surface roughness data and TRMM precipitation data of 0.25-degree spatial resolution with uniform space-time scale as the independent variables;
step 32, resampling the DEM Data of the research area by using a sample tool of a Data Management Tools toolbox of an ArcGIS10.6 platform to obtain DEM Data with 1km Spatial resolution, and calculating gradient Data, slope Data, topographic relief Data, topographic development Data and surface roughness Data with 1km Spatial resolution by using a Spatial analysis Tools tool of the ArcGIS10.6 platform according to the DEM Data with 1km Spatial resolution;
step 33, resampling NDVI data and water vapor content data of the research area by using a Resample tool under an ArcGIS10.6 platform Spatial Analysis toolbox to obtain NDVI data and water vapor content data with a Spatial resolution of 1 km;
step 34,1km spatial resolution gradient data, slope data, terrain waviness data, surface roughness data, NDVI data water vapor content data and TRMM rainfall data spatial grids are completely consistent, a Raster To Point tool under an ArcGIS10.6 platform Conversion Tools toolbox is used for selecting one grid data To calculate To obtain a geometric center of a downscaled grid, and longitude data and latitude data of each grid under the spatial resolution are calculated;
step 35, establishing a Logistic regression relationship between the daily precipitation data and the independent variables by using a Logistic regression method, wherein the Logistic regression can perform continuous and discrete independent variable analysis, the independent variables are not required to conform to normal distribution, and the problem of interdependence between factors can be solved well, and the calculation formula is as follows:
in the formula: y unit mm is measured precipitation data of ground actual measurement stations;
grid data of respective variables at a spatial resolution of 0.25 DEG are represented by a, b, \8230, k is an independent variable Logistic regression coefficient, and gamma is a constant.
Step 36, substituting the gradient data, the slope direction data, the NDVI data, the water vapor content data, the longitude data, the latitude data, the topographic relief degree data, the topographic opening degree data, the surface roughness data and the TRMM rainfall data of the 1km spatial resolution into a Logistic regression downscaling model to obtain the daily downscaling water quantity after downscaling, wherein the calculation formula is as follows:
in the formula: p is unit mm, which is TRMM downscaling precipitation data;
raster data at a spatial resolution of 1km for each variable.
And step 37, comparing and analyzing the downscaled TRMM precipitation data with precipitation data obtained by interpolating the station precipitation data by using an IDW interpolation method by using the verification and observation station distribution data, and finding that the downscaled TRMM precipitation data has better precision.
And step 38, using an Extract by Mask tool under an ArcGIS10.6 platform Spatial analysis tool box, and cutting the TRMM downscaling data of the large area range to obtain the TRMM downscaling data of the research area based on the boundary data of the Wudu small area.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.
Claims (3)
1. A mountainous area precipitation downscaling method based on Logistic regression is characterized by comprising the following steps:
reading satellite precipitation data and counting daily precipitation of meteorological sites;
step 2, unifying the space-time scales of all data, including TRMM, longitude, latitude, DEM, gradient, slope direction, NDVI, water vapor content, topographic relief degree, topographic breadth and surface roughness;
step 3, building a Logistic regression downscaling model, and obtaining downscaling data with spatial resolution of 1km based on the Logistic regression model;
the step 3 specifically comprises the following steps:
step 31, determining an independent variable and a dependent variable of the regression and reduction scale model, taking actually measured precipitation data of a station as the dependent variable, and taking DEM data, gradient data, slope data, water vapor content data, NDVI data, longitude data, latitude data, topographic relief degree data, topographic opening width data, surface roughness data and TRMM precipitation data of 0.25-degree spatial resolution with uniform space-time scale as the independent variables;
step 32, resampling the DEM data of the research area to obtain DEM data with 1km spatial resolution, and calculating gradient data, slope data, topographic relief data, topographic breadth data and surface roughness data with 1km spatial resolution according to the DEM data with 1km spatial resolution;
step 33, resampling NDVI data and water vapor content data of the research area to obtain NDVI data and water vapor content data with a spatial resolution of 1 km;
and 34, spatial grids of gradient data, slope direction data, terrain relief degree data, terrain breadth data, surface roughness data, NDVI data, water vapor content data and TRMM rainfall data of the spatial resolution are completely consistent. Optionally calculating the data of one grid to obtain the geometric center of the downscaled grid, and calculating to obtain longitude data and latitude data of each grid under the spatial resolution;
step 35, establishing a Logistic regression relationship between the daily precipitation data and the independent variables by using a Logistic regression method, wherein the Logistic regression can perform continuous and discrete independent variable analysis, the independent variables are not required to conform to normal distribution, and the problem of interdependence between factors can be solved well, and the calculation formula is as follows:
in the formula: y unit mm is measured precipitation data of ground actual measurement stations;
grid data of respective variables under the spatial resolution of 0.25 DEG, a, b, \8230, k is an independent variable Logistic regression coefficient, and gamma is a constant.
Step 36, substituting the slope data, the NDVI data, the water vapor content data, the longitude data, the latitude data, the topographic relief degree data, the surface roughness data and the TRMM rainfall data with the spatial resolution of 1km into a Logistic regression downscaling model to obtain the daily downscaling water loss, wherein the calculation formula is as follows:
in the formula: p is unit mm, which is TRMM downscaling precipitation data;
raster data at spatial resolution 1km for each variable.
2. The mountainous area water-reducing and scale-reducing method based on Logistic regression as claimed in claim 1, wherein the step 1 specifically comprises the following steps:
step 11, according to the vector boundary of the research area, expanding 0.25 degrees outwards along the outer boundary of the research area to establish a buffer area, reading TRMM precipitation information in the range of the buffer area, and obtaining TRMM precipitation data with the research area time interval day and the spatial resolution of 0.25 degrees;
step 12, counting the extracted satellite rainfall information pixel by pixel to obtain TRMM rainfall data of each pixel every day;
and step 13, settling the daily precipitation monitored by the meteorological station in the research area.
3. The mountainous area water-reducing downscaling method based on Logistic regression according to claim 1, wherein the step 2 specifically comprises the following steps:
step 21, obtaining NDVI data of a research area according to MODISMOD13Q1 product data, and obtaining water vapor content data of the research area according to MODISMOD05 product data;
step 22, resampling the DEM data of the research area to obtain DEM data with 0.25-degree spatial resolution, and calculating data such as gradient, slope direction, topographic relief, topographic breadth and surface roughness of each pixel of the research area according to the DEM data with 0.25-degree spatial resolution;
step 23, resampling NDVI data and water vapor content data of the research area to respectively obtain NDVI data and water vapor content data with a spatial resolution of 0.25 degrees;
and step 24, calculating the geometric center point of the 0.25-degree spatial resolution grids after the TRMM rainfall data, the DEM with the spatial resolution of 0.25 degrees, the water vapor content data, the NDVI data, the gradient data, the slope direction data, the topographic relief degree data, the topographic widening degree data and the surface roughness data are unified, and obtaining longitude data and latitude data of each grid.
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| CN116089832A (en) * | 2022-12-29 | 2023-05-09 | 清华大学 | Method and device for reducing ground water reserves of gravity satellites and computer equipment |
| CN116151474A (en) * | 2022-12-08 | 2023-05-23 | 四川省气象探测数据中心 | Precipitation product downscaling method integrating multisource data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116151474A (en) * | 2022-12-08 | 2023-05-23 | 四川省气象探测数据中心 | Precipitation product downscaling method integrating multisource data |
| CN116089832A (en) * | 2022-12-29 | 2023-05-09 | 清华大学 | Method and device for reducing ground water reserves of gravity satellites and computer equipment |
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