A method for diagnosing regional moisture conditions based on evaporation stress oscillation patterns.

Abstract

This paper discloses a method for diagnosing regional moisture conditions based on evaporation stress oscillation patterns. [Solution] The method includes step 1 of data acquisition, step 2 of calculating the time series of the evaporation stress index, step 3 of calculating the rate of development of the evaporation stress index, step 4 of determining the oscillation pattern of the evaporation stress index, step 5 of calculating the frequency or duration of each oscillation pattern, and step 6 of diagnosing the evaporation stress-dominant pattern and the local moisture situation. The method of the present invention effectively solves the problem of information loss due to smoothing of long-term series in the current local moisture situation diagnosis process, and the inability to reflect the mechanism of change in stress state, and provides reliable theoretical and technical support for the diagnosis and evaluation of the evaporation stress-dominant pattern and the local moisture situation.

Description

[Technical Field]

[0001] The present invention belongs to the field of ecohydrology technology, and more particularly to a method for diagnosing regional moisture conditions based on evaporation stress oscillation patterns. [Background technology]

[0002] The evaporative stress index is defined as the ratio of actual transpiration to potential transpiration. Actual transpiration is the amount of water actually lost at the surface, including evaporation and plant transpiration, while potential transpiration is the amount of water that the surface can evaporate and plants can transpire under ideal conditions, assuming unrestricted water supply. The evaporative stress index can reflect the dynamic balance between water supply and demand. The oscillation pattern of the evaporative stress index is mainly influenced by climate disturbances and can sensitively reveal the state of regional water stress. It is widely used as an indicator in drought research and has important implications for early warning of drought. Therefore, studying the oscillation pattern of evaporative stress can reveal the main characteristics of water stress within a study area, allow for assessment of the quality of regional water conditions, and provide valuable data support and decision-making basis for regional water resource management and agricultural production, thereby improving the capacity for climate change and drought mitigation.

[0003] Regarding the determination of the moisture status of a region, most of the current common methods are based on calculating the long-term average values of relevant hydrological or meteorological variables and drought indices, and the differences lie in the selection of variables and indices, which reveal the moisture status of the region from different angles. For example, by calculating the long-term average precipitation and combining it with the dry-wet regional division criteria, the moisture conditions of the region can be diagnosed. When the long-term average precipitation exceeds 800 millimeters, the region is considered a humid region with good moisture conditions. However, such methods based on long-term average values smooth out and remove short-term abnormal changes, resulting in the loss of potential important information, so they cannot reflect seasonal and annual moisture changes and lack in-depth analysis of specific causes. Therefore, there are limitations when exploring the dominant patterns of regional moisture stress and diagnosing the moisture status. Chinese Patent with Publication Number CN117058433A discloses an eco-hydrological partitioning method based on the Gaussian mixture clustering algorithm. By selecting indicators that can reflect the characteristics of hydrology, meteorology, ecology, land use, and socio-economy in the research area, an indicator system is constructed, the indicator weights are determined by the entropy weight method, and the eco-hydrological conditions of the region are diagnosed based on the output results of the Gaussian mixture model clustering algorithm, dividing the research area into multiple eco-hydrological partitions. However, this method is still a statistical identification based on the long-term average state, and a diagnostic method based on the transition mechanism has not been proposed yet.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] The object of the present invention is to provide a method for diagnosing the moisture status of a region based on the evaporation stress oscillation pattern in order to solve the above technical problems. [Means for solving the problem]

[0006] To achieve the above objective, the present invention provides the following technical solutions. [Technical proposal of the present invention] This invention discloses a method for diagnosing regional moisture conditions based on evaporation stress oscillation patterns, the method comprising the following steps, namely: Step 1, Data Acquisition: We obtained the actual transpiration time series ETa and the potential transpiration time series ETp for the research area. Step 2: Calculation of the time series of the evaporation stress index: The evaporation stress index (ESI) time series value at time t is calculated based on equation (1). t Furthermore, we obtained the evaporation stress index time series ESI, ESI t =ETa t / ETp t (1) In the formula: ESI t This is the time series value of the evaporation stress index at time t, ETa t is the actual transpiration time series value at time t, and ETp t is the latent transpiration time series value at time t, Step 3: Calculation of the rate of development of the evaporation stress index: The rate of development of the evaporation stress index is the change in the evaporation stress index per unit time, and the calculation formula is as follows: V = ΔESI / Δt (2) In the formula, V is the rate of development of the evaporation stress index, ΔESI is the change in the evaporation stress index over a certain period, and Δt is the time resolution. Step 4: Determining the oscillation pattern of the evaporation stress index: Based on the relative magnitude of the development rate of the evaporation stress index at adjacent time points and the relative magnitude of the evaporation stress index, the oscillation pattern of the evaporation stress index is determined, and the oscillation patterns of the evaporation stress index are divided into the following six types:

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Equation

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[0007] Furthermore, the actual transpiration time series ETa and the potential transpiration time series ETp for the research area in Step 1 are obtained by downloading publicly available data from the network or by observing actual weather data.

[0008] Furthermore, the aforementioned step 6, which involves diagnosing the dominant pattern of evaporative stress and determining the local moisture situation, specifically, If the dominant pattern is S1, it indicates that the region has a strong ability to mitigate evaporative stress on a long-term average scale, and that the moisture conditions in the region are favorable. If the dominant pattern is S2, it indicates that the region has the ability to mitigate evaporative stress on a long-term average scale and that the moisture situation in the region is relatively good. If the dominant pattern is S3, it indicates that the region has the ability to mitigate evaporative stress in the short term on a long-term average scale, but evaporative stress still reversals in weight, indicating poor moisture conditions in the region. If the dominant pattern is S4, it indicates that the region has a certain capacity to mitigate evaporative stress from a weighted trend on a long-term average scale, and that the moisture conditions in the region are generally good. If the dominant pattern is S5, it indicates that the region lacks the ability to mitigate evaporation stress on a long-term average scale, evaporation stress is persistently weighted, and the moisture situation in the region is poor. A dominant pattern of S6 indicates that the region lacks the capacity to mitigate evaporation stress on a long-term average scale, leading to sustained acceleration and weighting of evaporation stress, and indicating extremely poor moisture conditions in the region. [Effects of the Invention]

[0009] The beneficial effects of the present invention are as follows: The method of the present invention can determine the primary mechanism of change exhibited by regional evaporative stress over many years, thereby analyzing whether the region has the ability to mitigate evaporative stress over long timescales, and further diagnosing the regional moisture conditions. The method of the present invention effectively solves the problem of information loss or failure to reflect the stress state change mechanism by smoothing long-term series in the current regional moisture condition diagnosis process, and provides reliable theoretical and technical support for the diagnosis and evaluation of evaporative stress-driven patterns and regional moisture conditions.

[0010] The present invention will be described in further detail below with reference to the drawings and specific embodiments. [Brief explanation of the drawing]

[0011] [Figure 1] This is a flowchart of the method of the present invention. [Figure 2] This is a schematic diagram illustrating the calculation process for the rate of development of the evaporation stress index. [Figure 3] This is a schematic diagram of the six types of oscillation patterns of the evaporation stress index. [Figure 4] This is a schematic diagram showing how the time series of the evaporation stress index is converted into a labeled pattern series. [Modes for carrying out the invention]

[0012] This invention discloses a method for diagnosing regional moisture conditions based on evaporation stress oscillation patterns, the principle of which is as follows.

[0013] The evaporative stress index is defined as the ratio of actual transpiration to potential transpiration. It directly quantizes the ratio of actual water supply to potential demand in a region, reflecting the local water supply and demand situation. By combining the numerical change in the evaporative stress index itself with the rate of change before and after adjacent time points, six types of adjacent time oscillation patterns can be obtained: accelerating relaxation, decelerating relaxation, reversal from relaxation to weighting, reversal from weighting to relaxation, decelerating weighting, and accelerating weighting. By statistically analyzing the pattern with the highest frequency and duration over a certain period, the dominant pattern of evaporative stress can be diagnosed. If the diagnosed dominant pattern is a "relaxation" pattern, it explains that the region possesses a certain capacity to mitigate evaporative stress during the transition process of evaporative stress on a long-term average scale, demonstrating that the local water situation is generally good, or conversely, poor. Therefore, the evaporative stress index oscillation pattern can be used to diagnose the local water stress situation. In contrast, traditional diagnostic methods based on long-term average values ​​may fail to reflect seasonal or annual variations, or may lack sufficient analysis of the etiology of water stress. Therefore, utilizing a leading mechanism for identifying evaporative stress based on evaporative stress oscillation patterns is advantageous for diagnosing regional water stress conditions, clarifying the main characteristics of regional water stress, and providing strong support and decision-making basis for water resource assessment and management.

[0014] Specifically, it includes the following steps 1 to 6. That is, as shown in Figure 1, Step 1, Data Acquisition: The actual transpiration time series ETa and the latent transpiration time series ETp for the research area will be obtained. Generally, this is obtained by downloading publicly available data from the network or by observing actual meteorological data; for example, it can be obtained by downloading from the European Centre for Medium-Range Weather Forecasts network.

[0015] Step 2: Calculation of the time series of the evaporation stress index: The evaporation stress index (ESI) time series value at time t is calculated based on equation (1). t Furthermore, we obtained the evaporation stress index time series ESI, ESI t =ETa t / ETp t (1) In the formula: ESI t This is the time series value of the evaporation stress index at time t, ETa t is the actual transpiration time series value at time t, and ETp t is the latent transpiration time series value at time t, A lower evaporation stress index indicates a more severe evaporation stress situation.

[0016] Step 3: Calculation of the rate of development of the evaporation stress index: The rate of development of the evaporation stress index is the change in the evaporation stress index per unit time, and the calculation formula is as follows: V = ΔESI / Δt (2) In the formula, V is the rate of development of the evaporation stress index, ΔESI is the change in the evaporation stress index over a certain period, and Δt is the time resolution, which is shown in Figure 2.

[0017] Step 4: Determining the oscillation pattern of the evaporation stress index: Based on the relative magnitude of the development rate of the evaporation stress index at adjacent time points and the relative magnitude of the evaporation stress index, the oscillation pattern of the evaporation stress index is determined, and the oscillation patterns of the evaporation stress index are divided into the following six types:

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[0018] After determining the oscillation patterns for adjacent time points, the evaporation stress index time series ESI can be labeled, i.e., the evaporation stress index time series can be converted into a labeled pattern series, as shown in Figure 4.

[0019] Step 5: Calculate the frequency or duration of each vibration pattern: Frequency F is defined as the ratio of the number of times a particular vibration pattern appears throughout the entire study period to the total number of occurrences of all vibration patterns. Duration D is defined as the time a particular vibration pattern lasts divided by the number of times that vibration pattern appears, and represents the average duration each time a particular vibration pattern appears during the study period. Since the time resolution of the downloaded or observed data is fixed, using data with a time resolution of Δt as an example, when calculating the duration D, the effect of consecutive occurrences of vibration patterns is emphasized, and when calculating the denominator of the duration D for a labeled pattern sequence, if pattern S1 appears multiple times consecutively, it is statistically counted as one occurrence. The specific calculation process is as follows: During the research period, there are N types of vibration patterns (N ≤ 6), and pattern S i The number of times it appears during the study period is n i Assuming this is the case, frequency F i The following applies:

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[0020] Duration D i A certain vibration pattern S i This represents the average duration of each occurrence during the study period. Pattern S i The number of appearances is m i The total time of appearance in each stage is L k Let's assume that ×Δt, where L k Pattern S in the kth row i This is the number of consecutive occurrences of [the specified character].

[0021] Pattern S i Total duration T i This can also be expressed as the sum of the times of all consecutively occurring stages.

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[0022] Step 6: Diagnosis of evaporation stress-driven patterns and local moisture conditions: Throughout the study period, the dominant pattern of evaporation stress is diagnosed and the local moisture situation is determined by statistically analyzing the maximum frequency or duration of each oscillation pattern. Oscillation patterns can reflect whether the trend of change in evaporation stress is "weighted" or "mitigated." Therefore, based on the oscillation pattern with the highest frequency or longest duration, the dominant pattern of evaporation stress in the study area can be identified and the local moisture situation can be determined, as shown in Table 1. For example, if the statistical results show that the oscillation pattern with the highest frequency or longest duration is S1 (an accelerated mitigation pattern of evaporation stress), it means that the area has a strong ability to mitigate evaporation stress on a long-term average scale, indicating that the local moisture situation is good.

[0023] Table 1: Regional moisture conditions corresponding to different evaporation stress-driven patterns [Table 1]

[0024] (Example 1) This embodiment is an application example of the method described above.

[0025] This embodiment selects the Chinese region as the research area. In recent years, the regional leaf area index (LAI) has risen significantly by 7.7%, and in response to significant changes in the terrestrial environment and the impact of human activities, significant changes have occurred in China's hydrological, vegetation, and climatic conditions. As a result, the assessment of regional moisture conditions must fully consider variability and transition mechanisms. This invention aims to improve the problems that arise when long-term series are smoothed during the moisture condition diagnosis process, such as information loss or failure to reflect the mechanisms of stress changes.

[0026] Monthly time-resolution time series of actual and latent transpiration for the research area from 1950 to 2020 were downloaded. The data was obtained from publicly available sources through the European Centre for Medium-Range Weather Forecasts network. The downloaded data was in grid format, and the accuracy of the moisture status assessment was verified by applying the method of the present invention to each grid.

[0027] Based on the downloaded time series of actual and potential transpiration, a time series of evaporative stress indices for each grid in the China region is calculated, and the values ​​in this series fluctuate continuously between 0.70 and 0.75.

[0028] By calculating the difference between the time series of the evaporation stress index and dividing by the time resolution, i.e., one month, a monthly series of the rate of development of the evaporation stress index is obtained, and the values ​​are between -0.61 and 0.73.

[0029] Based on the steps above, a time series of the evaporation stress index and a series of the evaporation stress index development rate are obtained for each grid. The magnitude of the evaporation stress index values ​​and the evaporation stress index development rate values ​​at adjacent time points are compared to determine the oscillation pattern of the evaporation stress index, and a labeled oscillation pattern series is obtained.

[0030] The frequency of each vibration pattern occurring in the study area between 1950 and 2020 was calculated, the vibration pattern with the highest frequency occurring in each grid was statistically analyzed, and spatial diffusion was performed. From the statistical results, it was found that in approximately 79.6% of the study area between 1950 and 2020, pattern S3 (i.e., evaporation stress reverses from relaxation to weighting) was the vibration pattern with the highest frequency, and such dominant patterns are mainly distributed in northern areas with poor moisture conditions. In approximately 19.0% of the study area, pattern S4 (i.e., evaporation stress reverses from weighting to relaxation) was the vibration pattern with the highest frequency, and it is mainly distributed in southern areas or densely forested areas with relatively good moisture conditions.

[0031] The above examples demonstrate the effectiveness and accuracy of the method of the present invention when diagnosing the dominant pattern of evaporative stress and the regional moisture conditions.

[0032] Finally, it should be noted that the above description is intended to illustrate the proposed technical aspects of the present invention and is not limiting. While the present invention has been described in detail with reference to preferred arrangements, as those skilled in the art will understand, the proposed technical aspects of the present invention can be modified or replaced with equivalents without departing from the spirit and scope of the proposed technical aspects.

Claims

1. A method for diagnosing regional moisture conditions based on evaporation stress oscillation patterns, the method comprising the following steps, namely: Step 1, Data Acquisition: We obtained the actual transpiration time series ETa and the potential transpiration time series ETp for the research area. Step 2: Calculation of the time series of the evaporation stress index: The time series value of the evaporation stress index ESI at time t is calculated based on equation (1). ｔ Furthermore, we obtained the time-series ESI of the evaporation stress index. ESI ｔ =ETa ｔ / ETp ｔ (1) In the formula: ESI ｔ is the time series value of the evaporation stress index at time t, and ETa ｔ is the actual transpiration time series value at time t, and WTp ｔ is the latent transpiration time series value at time t, Step 3: Calculation of the rate of development of the evaporation stress index: The rate of development of the evaporation stress index is the change in the evaporation stress index per unit time, and the calculation formula is as follows: V=ΔESI / Δt (2) In the formula: V is the rate of development of the evaporation stress index, ΔESI is the change in the evaporation stress index over a certain period, and Δt is the change over time. Step 4: Determining the oscillation pattern of the evaporation stress index: Based on the relative magnitude of the development rate of the evaporation stress index at adjacent time points and the relative magnitude of the evaporation stress index, the oscillation pattern of the evaporation stress index is determined, and the oscillation patterns of the evaporation stress index are divided into the following six types: [Math 1] where: S represents the pattern number, including six types of vibration patterns S1 to S6, V ｔ represents the velocity in the process of change from ESI ｔ－１ to ESI ｔ ; V ｔ＋１ represents the velocity in the process of change from ESI ｔ to ESI ｔ＋１ ; Pattern S1 represents the accelerating relaxation of evaporation stress, where V ｔ＋１ > V ｔ The increase in velocity represents acceleration, and ESI ｔ＋１ > ESI ｔ > ESI ｔ－１ The increase in the evaporation stress index represents the relaxation of evaporation stress. Similarly, pattern S2 represents the decelerating relaxation of evaporation stress, pattern S3 represents the reversal of evaporation stress from relaxation to aggravation, pattern S4 represents the reversal of evaporation stress from aggravation to relaxation, pattern S5 represents the decelerating aggravation of evaporation stress, and pattern S6 represents the accelerating aggravation of evaporation stress. After determining the oscillation patterns for adjacent time points, the evaporation stress index time series ESI is labeled, that is, the evaporation stress index time series is converted into a labeled pattern series. Step 5, Calculate the frequency or duration of each vibration pattern: Frequency F is defined as the ratio of the number of times a particular vibration pattern appears throughout the entire study period to the total number of occurrences of all vibration patterns. Duration D is defined as the time a particular vibration pattern lasts divided by the number of times that vibration pattern appears, representing the average duration each time a particular vibration pattern appears during the study period. The time resolution of this data is Δt. When calculating the duration D, the influence of consecutive occurrences of vibration patterns is emphasized. When calculating the denominator of the duration D for a labeled pattern sequence, if pattern S1 appears multiple times consecutively, it is statistically counted as one occurrence. The specific calculation process is as follows: During the research period, there are N types of vibration patterns, N ≤ 6, and pattern S ｉ The number of times it appears during the study period is n ｉ Assuming this is the case, the frequency F ｉ The following applies: [Math 2] Here, [Math 3] This represents the total number of occurrences of all patterns. Duration D ｉ A certain vibration pattern S ｉ This represents the average duration of each occurrence during the study period, and pattern S ｉ The number of appearances is m ｉ The total appearance time for each stage is L. ｋ Let's assume that ×Δt, where L ｋ Pattern S at the kth stage ｉ This is the number of consecutive occurrences, and Δt is the time resolution of the data. Pattern S ｉ Total duration T ｉ It is expressed as the sum of the times of all consecutively occurring stages, [Math 4] Pattern S ｉ Duration D ｉ The total duration T of the pattern ｉ The number of steps that continue m ｉ It is expressed as the value obtained by dividing by, D ｉ =T ｉ / m ｉ (6) That is, pattern S ｉ When a character appears consecutively in a row and its duration is calculated, the duration of that row is counted as only one instance in the statistics. Step 6: Diagnosis of evaporation stress-dominant patterns and local moisture conditions: The maximum frequency or duration of each oscillation pattern throughout the entire study period is statistically analyzed to diagnose the dominant pattern of evaporative stress and to determine the local moisture situation. A method for diagnosing regional moisture conditions based on evaporation stress oscillation patterns, characterized by including the above steps 1 to 6.

2. In Step 1, the actual transpiration time series ETa and the potential transpiration time series ETP for the research area are obtained by downloading publicly available data from the network or by observing actual weather data. A method for diagnosing regional moisture conditions based on the evaporation stress oscillation pattern described in feature 1.

3. Step 6 involves diagnosing the dominant pattern of evaporative stress and further determining the local moisture situation. Specifically, this step involves: When the dominant pattern is S1, it indicates that the region has a strong ability to mitigate evaporative stress on a long-term average scale, and that the moisture conditions in the region are favorable. If the dominant pattern is S2, it indicates that the region has the capacity to mitigate evaporative stress on a long-term average scale, and that the moisture conditions in the region are relatively good. If the dominant pattern is S3, it indicates that the region has the ability to mitigate evaporative stress in the short term on a long-term average scale, but evaporative stress still reversals in weight, indicating poor moisture conditions in the region. If the dominant pattern is S4, it indicates that the region has a certain capacity to mitigate evaporative stress based on the weighted trend on a long-term average scale, and that the moisture conditions in the region are generally good. If the dominant pattern is S5, it indicates that the region lacks the capacity to mitigate evaporation stress on a long-term average scale, evaporation stress is persistently weighted, and the moisture situation in the region is poor. If the dominant pattern is S6, it indicates that the region is not fully able to mitigate evaporation stress on a long-term average scale, evaporation stress is persistently accelerating and aggravating, and the moisture situation in the region is extremely poor. A method for diagnosing regional moisture conditions based on the evaporation stress oscillation pattern described in feature 1.

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