Method for analyzing space-time evolution characteristics and development trend of basin precipitable water

Abstract

The invention relates to the technical field of precipitation analysis, and discloses a basin precipitation space-time evolution characteristic and development trend analysis method, which comprises the following steps of: selecting precipitation data and foundation laser radar water vapor data of a plurality of ground meteorological stations in a target basin, and performing monthly correlation analysis on the precipitation data and precipitation data parameters respectively; and analyzing the variation trend of the four-season precipitable water in the recent N years along with the years, analyzing the evolution rule of the four-season precipitable water in the recent N years along with the years by utilizing a Mangkendall test method, and analyzing the evolution trend of the precipitable water in the recent N years by utilizing an empirical orthogonal function analysis method. According to the invention, through analyzing the annual, latitude and longitude and seasonal spatial-temporal distribution of the precipitable water amount of the target drainage basin in recent 20 years, the spatial-temporal distribution evolution characteristics of the precipitable water amount of the target drainage basin in recent 20 years and the long-time sequence development change trend of the target drainage basin can be obtained; therefore, important reference can be provided for the climate and natural disaster early warning of the target drainage basin.

Description

Technical Field

[0001] The present invention relates to the field of precipitation analysis, and in particular to a method for analyzing the spatiotemporal evolution characteristics and development trends of precipitation in a river basin. Background Art

[0002] Water vapor is the most active component of the atmosphere and a key indicator of weather and climate transitions. Precipitable water (PW) refers to the total amount of water vapor contained in a column of air per unit area. It is of great significance to the study of radiative forcing, the water cycle, climate, and weather.

[0003] A river basin refers to the catchment area of ​​a river or water system bounded by a watershed, such as the Yellow River, Yangtze River, and Huai River basins. Precipitation within a river basin is typically unevenly distributed in time and space. With global warming, extreme climate events are becoming more frequent, severely impacting agricultural production and endangering public health. For example, an increase in extreme rainstorms can easily trigger floods, inundating farmland and villages, leading to reduced grain production, casualties, and property losses. Therefore, studying the changing patterns of precipitation in a river basin is crucial for climate and natural disaster warnings.

[0004] Therefore, in response to the above problems, the present invention designs an analysis method for the spatiotemporal evolution characteristics and development trends of precipitation in a river basin. This method can obtain the spatiotemporal variation patterns and future trends of precipitation in any river basin in recent years under the influence of global climate change, thereby providing an important basis for future flood disaster prevention in the river basin. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a method for analyzing the spatiotemporal evolution characteristics and development trends of precipitation in a watershed.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A method for analyzing the spatiotemporal evolution characteristics and development trends of precipitation in a watershed comprises the following steps:

[0008] Step 1: Obtain the precipitation data parameters of the target basin in the last N years;

[0009] Step 2: Select precipitation data and ground-based lidar water vapor data from multiple ground meteorological stations in the target basin, and perform monthly correlation analysis with the precipitable water data parameters;

[0010] Step 3: Based on the monthly correlation analysis of the precipitable water data parameters in step 2, analyze the spatial distribution of the average precipitable water in the last N years, the distribution of the average precipitable water with longitude and latitude, the spatial distribution of the average precipitable water in four seasons, and the annual development trend of the precipitable water;

[0011] Step 4: Analyze the annual variation trend of seasonal precipitation in the last N years and use the Mann-Kendall test method to analyze the annual evolution pattern of seasonal precipitation.

[0012] Step 5: Use the empirical orthogonal function analysis method to analyze the evolution trend of precipitation in the last N years to obtain the spatiotemporal evolution characteristics and development trend of precipitation in the target basin.

[0013] Specifically, the precipitable water data parameters are obtained through the Modis satellite data product MYD08-M3.

[0014] Specifically, the month-by-month correlation analysis adopts Pearson correlation analysis.

[0015] Specifically, the spatial distribution of the average precipitation in step 3 is obtained by performing N-year average calculation on the precipitation data and visualizing the data using programming software to obtain a spatial distribution map of the average precipitation in the target basin in the last N years.

[0016] Specifically, the distribution of the average precipitation amount with longitude and latitude in step 3 is obtained by calculating and drawing a curve chart showing the average precipitation amount over the past 20 years changing with longitude and latitude.

[0017] Specifically, the spatial distribution of the average precipitation amount in four seasons described in step 3 is obtained by respectively calculating the spatial distribution maps of the precipitation amount in spring, summer, autumn and winter averaged over the last N years, wherein March to May of the current year is defined as spring, June to August of the current year is defined as summer, September to November of the current year is defined as autumn, and December of the current year to February of the following year is defined as winter.

[0018] Specifically, the annual development trend of the precipitation amount in step 3 is obtained by calculating the annual average precipitation amount curve of the last N years, and performing linear fitting on the curve in different time periods.

[0019] Specifically, the evolution trend of the precipitable water in the last N years described in step five is obtained by drawing the first three mode diagrams and PC line diagram of the precipitable water in the target basin in the last N years through the empirical orthogonal function analysis method.

[0020] Compared with the existing technology, the beneficial technical effect of the present invention is: by analyzing the annual, longitude and latitude, and seasonal spatiotemporal distribution of the target basin's precipitation in the last N years, the spatiotemporal distribution evolution characteristics of the target basin's precipitation in the last N years can be obtained, and by using the Mann-Kendall test method and the empirical orthogonal function analysis method, the development and change trend of the long-term series of the target basin can be obtained, which can provide an important reference for the climate and natural disaster early warning of the target basin. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1Flowchart of the method for analyzing the spatiotemporal evolution characteristics and development trends of precipitation in a watershed according to the present invention;

[0022] Figure 2 2003 to 2022 is a 20-year average annual precipitation spatial distribution map of the Huaihe River Basin in the embodiment of the present invention;

[0023] Figure 3 2003 to 2022 is a distribution diagram of the 20-year average annual precipitation in the Huaihe River Basin with longitude (a) and latitude (b) in an embodiment of the present invention;

[0024] Figure 4 20-year average spatial distribution map of spring (a), summer (b), autumn (c), and winter (d) precipitation in the Huaihe River Basin from 2003 to 2022 in the embodiment of the present invention;

[0025] Figure 5 2 is a graph showing the annual precipitation amount in the Huaihe River Basin from 2003 to 2022 in an embodiment of the present invention;

[0026] Figure 6 This is a line graph of the precipitation amount in spring (a), summer (b), autumn (c), and winter (d) in the Huaihe River Basin from 2003 to 2022 in an embodiment of the present invention;

[0027] Figure 7 This is a line graph of the Mann-Kendall (MK) analysis results of the precipitable water in the Huaihe River Basin in spring (a), summer (b), autumn (c), and winter (d) from 2003 to 2022 in an embodiment of the present invention.

[0028] Figure 8 These are the first three modal distribution diagrams of the empirical orthogonal function (EOF) analysis of the Huaihe River Basin from 2003 to 2022 in the embodiment of the present invention.

[0029] Figure 9 This is a PC line graph of the first three modes of the empirical orthogonal function (EOF) analysis of the Huaihe River Basin from 2003 to 2022 in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] A preferred embodiment of the present invention will be described in detail below with reference to the accompanying drawings.

[0031] A method for analyzing the spatiotemporal evolution characteristics and development trends of precipitation in a watershed comprises the following steps:

[0032] Step 1: Obtain the precipitation data parameters of the target basin for the past 20 years;

[0033] Step 2: Select precipitation data and ground-based lidar water vapor data from multiple ground meteorological stations in the target basin, and perform monthly correlation analysis with the precipitable water data parameters;

[0034] Step 3: Based on the monthly correlation analysis of the precipitable water data parameters in step 2, analyze the spatial distribution of the average precipitable water over the past 20 years, the distribution of the average precipitable water along longitude and latitude, the spatial distribution of the average precipitable water over four seasons, and the annual development trend of the precipitable water;

[0035] Step 4: Analyze the annual variation trend of seasonal precipitation over the past 20 years and use the Mann-Kendall test to analyze the annual evolution pattern of seasonal precipitation.

[0036] Step 5: Use the empirical orthogonal function analysis method to analyze the evolution trend of precipitation in the past 20 years and obtain the spatiotemporal evolution characteristics and development trend of precipitation in the target basin.

[0037] In this embodiment, the precipitable water data parameter is obtained over the last 20 years. This is because precipitation typically exhibits certain cyclical variations, and 20 years of data can help identify these cyclical characteristics. Furthermore, a 20-year span is long enough to clearly demonstrate climate change trends. Alternatively, a 10-year, 15-year, or 25-year span can be selected.

[0038] In this embodiment, the precipitation data parameters in step 1 are obtained through the MODIS satellite data product MYD08-M3. The satellite data product used to obtain the precipitation data parameters may also be other series, such as MOD05_L2.

[0039] The monthly correlation analysis in step 2 uses Pearson correlation analysis to verify the accuracy and reliability of the precipitable water data parameters.

[0040] In this example, the spatial distribution of average precipitation in step 3 is determined by averaging the 20-year precipitation data and visualizing the data using programming software to obtain a spatial distribution map of the target basin's average precipitation over the past 20 years. By averaging the 20-year precipitation data for the target basin, the impact of short-term fluctuations and outliers can be effectively eliminated, more accurately reflecting the long-term average precipitation status of the basin.

[0041] In order to further study the spatial distribution trend of precipitation in the target basin in the east-west and south-north directions, the distribution of average precipitation with longitude and latitude in step three was obtained by calculating and drawing a curve chart of the average precipitation with longitude and latitude in the past 20 years.

[0042] The spatial distribution of average precipitation in four seasons in step 3 is obtained by calculating the spatial distribution maps of precipitation in spring, summer, autumn and winter averaged over the past 20 years, where March to May of the current year is defined as spring, June to August of the current year is defined as summer, September to November of the current year is defined as autumn, and December of the current year to February of the following year is defined as winter.

[0043] To study the long-term trends in the target basin's precipitable water content, the annual trends in precipitable water content (PW) in Step 3 were determined by calculating the average annual PW curve for the past 20 years and performing a linear fit on the curve for different time periods. The resulting linear equation can be used to make preliminary predictions of future PW values. If past PW data exhibit a clear linear trend, it can be assumed to be consistent for some time to come. The fitted equation can then be used to extrapolate and predict future PW values.

[0044] In step 4, we calculated and obtained line graphs of the annual changes in precipitation in the target basin for the past 20 years in spring, summer, autumn, and winter. Using the Mann-Kendall test, we generated a line graph of the MK value over the years, which we used to detect trends in time series data. This seasonal line graph can visually demonstrate the changing trends in precipitation in the target basin in spring, summer, autumn, and winter, allowing us to promptly identify unusual changes in precipitation in specific seasons within certain years. Based on these seasonal trends, we can rationally plan agricultural production, water resource allocation, and other activities.

[0045] In this embodiment, the evolution trend of the precipitation amount in the past 20 years in step five is obtained by drawing the first three modal diagrams and PC line diagrams of the precipitation amount in the target basin in the past 20 years through the empirical orthogonal function analysis method. In this embodiment, the main spatial variation mode in the data, that is, the main spatial structure of the data, can be extracted by the empirical orthogonal function analysis method, and the time variation trend of the data can be obtained by combining the time coefficient PC. The modal diagram can be used to determine the areas that play a key role in the change of precipitation amount, and the correlation between the precipitation amounts in different regions can also be intuitively analyzed. Combining the main spatial variation mode with the PC line diagram shows the change of the time coefficient corresponding to each mode over time, reflecting the evolution trend of precipitation amount on different time scales. It can clearly see the strong and weak changes in precipitation amount in each mode in different years, thereby making certain predictions and trend analysis on the changes in precipitation amount in the future.

[0046] This application takes the Huaihe River Basin as an example for detailed explanation, and selects data from 2003-2022 for analysis.

[0047] In step 1, 28 ground meteorological stations were selected in Shou County, Zhengzhou, Bozhou, Rizhao, etc. The selected ground-based lidar was located at Shou County Station. The spatial distribution map of the average precipitation in the Huaihe River Basin from 2003 to 2022 is shown in the figure below: Figure 2 As shown in Figure 2, we can conclude that the spatial distribution pattern of precipitation in the southern part of the Huaihe River Basin, especially in the Dabie Mountains, is significantly greater than that in the northern part.

[0048] The curve of the average precipitation amount in the Huaihe River Basin from 2003 to 2022 changing with longitude and latitude is as follows: Figure 3 As shown in (b), it can be concluded that the precipitation in the Huaihe River Basin shows a distribution pattern of decreasing from south to north;

[0049] The spatial distribution of average precipitation in spring, summer, autumn and winter in the Huaihe River Basin from 2003 to 2022 is shown in the figure below: Figure 4 As shown, it can be concluded that the precipitation in the Huaihe River Basin is obviously concentrated in summer, and the precipitation shows a four-season distribution pattern of summer > autumn > spring > winter;

[0050] The average annual precipitation curve of the Huaihe River Basin from 2003 to 2022 is as follows Figure 5 As shown, it can be concluded that the precipitation in the Huaihe River Basin showed an overall oscillating growth trend from 2003 to 2022, as well as the development trends in different time periods;

[0051] The line graph of the annual changes in precipitation in spring, summer, autumn and winter in the Huaihe River Basin from 2003 to 2022 is as follows: Figure 6 As shown in the figure, the line graph of MK value changes with the year is as follows Figure 7 As shown, the changing trend of seasonal precipitation in different years can be analyzed;

[0052] The first three modes of precipitation in the Huaihe River Basin from 2003 to 2022 are shown in the figure below: Figure 8 As shown, the time coefficient PC line chart is as follows Figure 9 As shown in Figure 2, the combined analysis of the two figures can reveal the main spatial modes and long-term variation trends of precipitation in the Huaihe River Basin.

[0053] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. It is intended that all variations within the meaning and range of equivalents of the claims be embraced herein, and any reference signs in the claims should not be construed as limiting the claims to which they relate.

[0054] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for analyzing the spatiotemporal evolution characteristics and development trends of precipitation in a river basin, characterized by: The following steps are involved: Step 1: Obtain the precipitation data parameters of the target basin in the last N years; Step 2: Select precipitation data and ground-based lidar water vapor data from multiple ground meteorological stations in the target basin, and perform monthly correlation analysis with the precipitable water data parameters; Step 3: Based on the monthly correlation analysis of the precipitable water data parameters in step 2, analyze the spatial distribution of the average precipitable water in the last N years, the distribution of the average precipitable water with longitude and latitude, the spatial distribution of the average precipitable water in four seasons, and the annual development trend of the precipitable water; Step 4: Analyze the annual variation trend of seasonal precipitation in the last N years and use the Mann-Kendall test method to analyze the annual evolution pattern of seasonal precipitation. Step 5: Use the empirical orthogonal function analysis method to analyze the evolution trend of precipitation in the last N years to obtain the spatiotemporal evolution characteristics and development trend of precipitation in the target basin.

2. The method for analyzing the spatiotemporal evolution characteristics and development trends of precipitation in a river basin according to claim 1 is characterized by: The precipitable water data parameters are obtained through the MODIS satellite data product MYD08-M3.

3. The method for analyzing the spatiotemporal evolution characteristics and development trends of precipitation in a river basin according to claim 1 is characterized by: The month-by-month correlation analysis was performed using Pearson correlation analysis.

4. The method for analyzing the spatiotemporal evolution characteristics and development trends of precipitation in a river basin according to claim 1 is characterized by: The spatial distribution of the average precipitation in step 3 is obtained by performing N-year average calculation on the precipitation data and visualizing the data using programming software to obtain a spatial distribution map of the average precipitation in the target basin in the last N years.

5. The method for analyzing the spatiotemporal evolution characteristics and development trends of precipitation in a river basin according to claim 1 is characterized by: The distribution of the average precipitation amount with longitude and latitude in step 3 is obtained by calculating and drawing a curve chart showing the average precipitation amount of the last N years changing with longitude and latitude.

6. The method for analyzing the spatiotemporal evolution characteristics and development trends of precipitation in a river basin according to claim 1, characterized in that: The four-seasonal spatial distribution of the average precipitation amount described in step 3 is obtained by calculating the spatial distribution maps of the average precipitation amount in spring, summer, autumn and winter in the last N years, respectively, where March to May of the current year is defined as spring, June to August of the current year is defined as summer, September to November of the current year is defined as autumn, and December of the current year to February of the following year is defined as winter.

7. The method for analyzing the spatiotemporal evolution characteristics and development trends of precipitation in a river basin according to claim 1 is characterized by: The annual development trend of the precipitation amount in step 3 is obtained by calculating the average precipitation amount curve of the last N years, and performing linear fitting on the curve in different time periods.

8. The method for analyzing the spatiotemporal evolution characteristics and development trends of precipitation in a river basin according to claim 1, characterized in that: The evolution trend of the precipitation amount in the last N years described in step 5 is obtained by drawing the first three mode diagrams and PC line diagram of the precipitation amount in the target basin in the last N years through the empirical orthogonal function analysis method.

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