Four-dimensional soil moisture drought event tracking and warming signal identification method

Abstract

The invention relates to the field of carbon neutralization, and discloses a four-dimensional soil moisture drought event tracking and warming signal identification method, which comprises the following steps: data acquisition; four-dimensional soil moisture drought event identification; four-dimensional soil moisture drought event characteristic quantification; artificial warming signal detection of time evolution of four-dimensional soil moisture drought characteristics in historical periods; performing four-dimensional soil moisture drought future evolution estimation; four-dimensional soil moisture drought event driving factors; the method has the advantages that future space-time evolution characteristics of four-dimensional soil moisture drought are explored, driving factors and physical mechanisms of four-dimensional soil moisture drought are analyzed, and a powerful scientific basis is provided for coping with global climate changes, guaranteeing water resource safety and formulating disaster prevention and reduction strategies.

Description

Technical Field

[0001] The present invention relates to the field of carbon neutrality, and in particular to a four-dimensional soil moisture drought event tracking and warming signal recognition method. Background Art

[0002] Soil moisture drought, characterized by soil moisture deficit, is one of the most serious natural disasters, which has adverse effects on water resources, ecosystems, plant production and crop yields. Affected by climatic factors, vegetation, topography, soil properties and land use, soil moisture changes vary with soil depth at the regional scale. Water stored in deep soil can be directly used for vegetation growth. Therefore, water deficit in deep soil causes greater damage to plants and agricultural production than surface soil. Considering that vegetation exhibits high spatial heterogeneity and strong root depth variation, understanding the changes in the vertical structure of global soil moisture drought is crucial for more accurate monitoring and assessment of its adverse effects on ecosystems. In the context of global warming, greenhouse gas emissions continue to exacerbate soil drought. At present, there is no detailed study on the changes in soil moisture drought from the perspective of continuous evolution in four dimensions (longitude, latitude, depth and time), and it is not clear whether it is affected by climate change caused by anthropogenic warming. Summary of the invention

[0003] The purpose of the present invention is to propose a four-dimensional soil moisture drought event tracking and warming signal recognition method to solve the technical problem that the spatiotemporal evolution of the vertical structure of global soil moisture drought has not been carefully studied.

[0004] The present invention provides a four-dimensional soil moisture drought event tracking and warming signal identification method, comprising the following steps:

[0005] Step S1: Data collection: Collect global climate data;

[0006] Step S2: Identification of four-dimensional soil moisture drought events: Combined with the data obtained in step S1, the 10th percentile of the climatic growing season soil moisture value of each grid point is calculated as the drought threshold, and the grid points below the threshold are identified as drought grid points. The continuous four-dimensional soil moisture drought events are identified by the Lagrangian four-dimensional tracking method.

[0007] Step S3: Quantification of four-dimensional soil moisture drought event characteristics: Combined with the four-dimensional drought events obtained in step S2, soil moisture drought event characteristics are calculated, and soil moisture drought events are divided into surface drought and deep drought;

[0008] Step S4: Detection of anthropogenic warming signals from the temporal evolution of four-dimensional soil moisture drought characteristics in the historical period; Combined with the temporal and intensity characteristics of the two types of drought events obtained in step S3, the correlation coefficient method and the optimal fingerprint method are used to detect anthropogenic warming signals from drought characteristic indicators calculated based on reanalysis data and historical climate experiments of climate models;

[0009] Step S5: Prediction of the future evolution of four-dimensional soil moisture drought; combining the time and intensity characteristics of the two types of drought events obtained in step S3, combining the historical climate experiment of the climate model and the future social and economic development path experiment, to obtain the spatiotemporal evolution characteristics of drought events in the future period; combining the global land cover data obtained in step S1, to obtain the spatiotemporal evolution characteristics of drought events under different vegetation types in the future period;

[0010] Step S6: Four-dimensional soil moisture drought event driving factors; combining the time and intensity characteristics of the two types of drought events obtained in step S3, and the near-surface temperature, precipitation and leaf area index data obtained in step S1, the two types of drought events are attributed to the three types of antecedent driving factors, and the contribution of different driving factors to drought events is quantified.

[0011] A storage medium stores instructions and data for implementing a four-dimensional soil moisture drought event tracking and warming signal recognition method.

[0012] A four-dimensional soil moisture drought event tracking and warming signal identification device comprises: a processor and the storage medium; the processor loads and executes instructions and data in the storage medium to implement a four-dimensional soil moisture drought event tracking and warming signal identification method.

[0013] The beneficial effects provided by the present invention are:

[0014] (1) The present invention provides a method for identifying four-dimensional soil moisture drought, which helps the academic community deepen its understanding of this new type of soil drought. The present invention divides four-dimensional soil drought into two different types, quantifies the spatiotemporal characteristic indicators of four-dimensional soil drought, and clarifies the changes and impacts of four-dimensional soil moisture drought in the four-dimensional spatiotemporal domain of longitude, latitude, depth, and time. The present invention quantifies the differences in the spatiotemporal evolution of two types of four-dimensional soil drought in the future period under different land cover types, providing a scientific basis for a deep understanding of the dynamic evolution process of drought events.

[0015] (2) The present invention quantitatively detects anthropogenic warming signals in the spatiotemporal evolution of four-dimensional soil moisture drought characteristics, identifies the impact of anthropogenic warming on the changes in two four-dimensional drought events, and estimates future changes in four-dimensional drought events, providing theoretical support for decision makers to make decisions on adapting to and mitigating the effects of climate change on soil moisture drought.

[0016] (3) The present invention analyzes the contribution of driving factors to four-dimensional soil moisture drought events, clarifies the contribution of different driving factors to four-dimensional soil moisture drought in history and future periods, quantifies the dominant driving factors of two types of four-dimensional soil moisture drought, and provides theoretical support for the attribution of four-dimensional soil moisture drought. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of the method of the present invention;

[0018] Figure 2 This is an example map of surface drought and deep drought defined using the Lagrangian four-dimensional tracking framework;

[0019] Figure 3 It is the spatial distribution map of the centroids of global surface drought and deep drought in historical periods;

[0020] Figure 4 The duration and intensity of global surface drought and deep drought have changed over the historical period;

[0021] Figure 5 It is a probability density map of the duration and intensity trend of global surface drought and deep drought in historical period;

[0022] Figure 6 It is the spatial and temporal evolution of the duration of global surface drought and deep drought events in the future period;

[0023] Figure 7 is the spatiotemporal evolution of the intensity of global surface drought and deep drought events in the future period;

[0024] Figure 8 It is the difference in duration and intensity of global deep drought and surface drought under different land cover;

[0025] Fig. 9 is the contribution of different driving factors to the duration of surface drought and deep drought events;

[0026] Fig.10 is the contribution of different driving factors to the intensity of surface drought and deep drought events;

[0027] Fig.11 are the time series of the three driving factors during the growing season under different scenarios;

[0028] Fig.12 It is a working schematic diagram of the hardware device of the present invention. DETAILED DESCRIPTION

[0029] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0030] Before formally describing the present invention, the scheme of the present invention is first generally described for easy understanding.

[0031] Please refer to Figure 1 , Figure 1 It is a schematic diagram of the process of the present invention.

[0032] The present invention provides a four-dimensional soil moisture drought event tracking and warming signal identification method, comprising the following steps:

[0033] Step S1: Data collection: Collect global climate data;

[0034] Specifically, the present invention collects soil moisture, near-surface air temperature and precipitation data from the fifth-generation land surface reanalysis data set (ERA5-Land) of the European Center for Medium-Range Weather Forecasts, the global atmosphere and land surface reanalysis product (CRA-Land) of the China Meteorological Administration and the Global Land Data Assimilation System Model (GLDAS-Noah); collects leaf area index data from the global long-term leaf area index GLOBMAP data; collects precipitation data from the Global Soil Moisture Project (GSWP-3); collects soil moisture, near-surface air temperature, precipitation and leaf area index data from the sixth phase of the International Coupled Comparison Program (CMIP6); collects global land cover data from the Moderate Resolution Imaging Spectroradiometer Land Cover Climate Simulation Grid (MCD12C1);

[0035] Step S2: Identification of four-dimensional soil moisture drought events: Combined with the data obtained in step S1, the 10th percentile of the climatic growing season soil moisture value of each grid point is calculated as the drought threshold, and the grid points below the threshold are identified as drought grid points. The continuous four-dimensional soil moisture drought events are identified by the Lagrangian four-dimensional tracking method.

[0036] It should be noted that in step S2, the soil moisture data is preprocessed: according to the GSWP-3 precipitation data, the grid points in Antarctica, Greenland and desert areas with annual precipitation less than 100 mm are removed; the soil moisture value of the growing season (May to September in the northern hemisphere and November to March in the southern hemisphere) is used; the unit of soil moisture value is changed from mass unit (kg·m -2 ) to volume units (m 3 ·m 3 ); linearly interpolate multi-layer soil moisture values ​​to a common vertical profile (ranging from 0-7, 7-28, 28-100, and 100-289 cm); transform different time scale datasets to daily scale; use second-order conservative remapping to interpolate all datasets to 1°×1° resolution; use 11-day smoothing average to eliminate abnormal series fluctuations;

[0037] The Lagrangian method was used to simulate the spatiotemporal evolution of soil moisture drought in four dimensions, as follows: for each grid point in a single layer, when the soil moisture value was lower than the 10th percentile of the growing season in the climatological period (1981-2010), the grid point was defined as a drought grid point; adjacent drought grid points in a single layer were merged into two-dimensional spatially continuous drought patches; for each time step, all adjacent two-dimensional drought patches in 26 adjacent regions (9 in the upper layer, 8 in this layer excluding the central grid point, and 9 in the lower layer) were connected into an independent continuous three-dimensional drought patch; any spatially continuous three-dimensional drought patches were merged into a single four-dimensional (longitude-latitude-depth-time) drought event if the overlap rate in consecutive time steps was greater than 50%; and the four-dimensional drought event lasted at least 3 days and had a projected area greater than 100,000 square kilometers.

[0038] Step S3: Quantification of four-dimensional soil moisture drought event characteristics: Combined with the four-dimensional drought events obtained in step S2, soil moisture drought event characteristics are calculated, and soil moisture drought events are divided into surface drought and deep drought;

[0039] It should be noted that in step S3, the temporal and spatial characteristics of the four-dimensional soil moisture drought event are duration and intensity, respectively. The duration is the duration of the four-dimensional soil moisture drought event, and the intensity is the weighted average of the grid area and depth of the soil moisture deficit (threshold minus soil water value) of all grid points in the four-dimensional drought event, as follows:

[0040]

[0041] Where i is the grid point, t is the time, D is the duration, l is the soil layer, d is the soil depth, s is the grid area, thrse is the drought threshold, sm is the soil water value, and cluster is the drought event;

[0042] These four-dimensional drought events were divided into three types of drought: surface drought with a heavy top inverted iceberg structure, defined as the total duration of the surface soil moisture deficit area being greater than the deep soil moisture deficit area for more than 60%; deep drought with a heavy bottom iceberg structure, defined as the total duration of the deep soil moisture deficit area being greater than the surface soil moisture deficit area for more than 60%; when neither of the two situations occurs, it is a columnar drought structure. In the classification process, the top two layers (0-7cm and 7-28cm) were selected as the surface soil, and the third layer (28-100cm) was selected as the deep soil.

[0043] Step S4: Detection of anthropogenic warming signals from the temporal evolution of four-dimensional soil moisture drought characteristics in the historical period; Combined with the temporal and intensity characteristics of the two types of drought events obtained in step S3, the correlation coefficient method and the optimal fingerprint method are used to detect anthropogenic warming signals from drought characteristic indicators calculated based on reanalysis data and historical climate experiments of climate models;

[0044] It should be noted that in step S4, the detection of anthropogenic warming signals of four-dimensional soil moisture drought events is performed based on the correlation coefficient method, and the Spearman correlation coefficient between the changes in global surface and deep drought characteristic sequences based on reanalysis (average of three reanalysis data sets) and CMIP6 historical experiment simulation (multi-model average) is calculated; the CMIP6 pre-industrial simulation experiment is divided into multiple number blocks of the same years as the study period (40 years), and random sampling is performed 2000 times to generate a large number of samples, and the Spearman correlation coefficient between them and the multi-model average of the historical experiment simulation is calculated; it is analyzed whether the correlation coefficient of the drought event characteristic sequence between the reanalysis data and the historical experiment simulation exceeds the 95th or 99th percentile of the correlation coefficient set between the multiple number blocks of the historical simulation experiment and the pre-industrial simulation experiment; if the correlation between the reanalysis and the historical simulation experiment is greater than the 95th or 99th percentile of the correlation between the historical simulation experiment and the 2000 repeated 40-year pre-industrial simulation experiment blocks, it indicates that there is an external forcing signal in the changes in surface and deep drought characteristics, which can be attributed to anthropogenic warming;

[0045] The detection of anthropogenic warming signals of four-dimensional soil moisture drought events based on the optimal fingerprint method is calculated as follows:

[0046] Y=(X-α)β+ε

[0047] Among them, Y is the global average characteristic change of surface and deep drought based on reanalysis, x is the global average characteristic change of surface and deep drought simulated by the model, α is the sampling uncertainty, β is the scale factor, and ε is the internal climate variability estimated by the pre-industrial simulation experiment; to reduce the noise caused by interannual changes, the characteristic series of surface and deep drought are averaged at intervals of three consecutive years; if the scale factor and its 90% confidence interval are both greater than zero, the external forcing signal is considered to be detectable at the 5% significance level, that is, the anthropogenic warming signal can be detected.

[0048] Step S5: Prediction of the future evolution of four-dimensional soil moisture drought; combining the time and intensity characteristics of the two types of drought events obtained in step S3, combining the historical climate experiment of the climate model and the future social and economic development path experiment, to obtain the spatiotemporal evolution characteristics of drought events in the future period; combining the global land cover data obtained in step S1, to obtain the spatiotemporal evolution characteristics of drought events under different vegetation types in the future period;

[0049] It should be noted that in step S5, the CMIP6 historical simulation experiment and the representative concentration pathways and shared socioeconomic pathways (SSPs) of future scenarios, including the SSP245 and SSP585 scenarios, are used to calculate the spatiotemporal evolution of global surface and deep drought characteristics in the future period;

[0050] Combined with the land cover dataset, the changes in surface and deep drought characteristics of different vegetation types were estimated. Deep-rooted vegetation cover categories (i.e., forest, shrub, and savanna) were selected, and the area-weighted average of the differences in deep and surface drought characteristics (duration and intensity) in different vegetation areas was calculated.

[0051] Step S6: Four-dimensional soil moisture drought event driving factors; combining the time and intensity characteristics of the two types of drought events obtained in step S3, and the near-surface temperature, precipitation and leaf area index data obtained in step S1, the two types of drought events are attributed to the three types of antecedent driving factors, and the contribution of different driving factors to drought events is quantified.

[0052] It should be noted that in step S6, the surface and deep drought events are attributed to different driving factors, such as extreme high temperature (TEM), precipitation deficit (PRE) and vegetation greening (high leaf area index; LAI), and the average values ​​of the three variables in the 30 days before the drought event are calculated, and the severity index (SI) is calculated as follows:

[0053] SI v =(v i,j -μ j ) / σ j

[0054] Where i represents year, j represents month, v represents driving factor, μ and σ represent the climate mean and standard deviation of monthly variables in the climate state (historical period: 1981-2010, future period: 2061-2100), respectively;

[0055] S62: Define extreme heat as SI TEM >0.8, precipitation deficit is defined as SI PRE <-0.8, vegetation greening is defined as SI LAI >0.8, if a drought event and an extreme event that occurred in the previous 30 days occurred at the same grid point, the drought event at that grid point was attributed to this type of driving factor, and the normalized contribution of different driving factors to the history and intensity of the two types of drought events was calculated.

[0056] As an implementation case, the present invention further describes the present invention by taking the global scope from 1981 to 2020 as an example. The implementation case is used to illustrate the present invention, but this case is not used to limit the scope of application of the present invention. It is also applicable to different regions and other time periods.

[0057] The implementation flow chart of the four-dimensional soil moisture drought event tracking and warming signal identification of the method of the present invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0058] (1) Collection of test data;

[0059] In this implementation case, the hourly soil moisture content (unit: m2) with a spatial resolution of 0.1°×0.1° from 1981 to 2020 was collected from the fifth generation land surface reanalysis dataset (ERA5-Land) of the European Centre for Medium-Range Weather Forecasts. 3 ·m -3 ), depths of 0-7cm, 7-28cm, 28-100cm and 100-289cm; and near-surface air temperature and precipitation data;

[0060] The 3-hour soil moisture data (unit: m2) with a spatial resolution of 34 km × 34 km from the China Meteorological Administration's Global Atmosphere and Land Surface Reanalysis product (CRA-Land) were collected. 3 ·m -3 ), depths of 0-10cm, 10-40cm, 40-100cm and 100-200cm; and near-surface air temperature and precipitation data;

[0061] The 3-hour soil moisture content (in kg·m2) at a spatial resolution of 34 km × 34 km was collected from the Global Land Surface Data Assimilation System (GLDAS-Noah) version 2.0 for 1981-2014 and version 2.1 for 2000-2020. -2 ), depths of 0-10cm, 10-40cm, 40-100cm and 100-200cm; and near-surface air temperature and precipitation data;

[0062] The 8km×8km leaf area index data of the global long-term leaf area index GLOBMAP data were collected with a temporal resolution of half a month during 1981-2000 and a temporal resolution of 8 days during 2001-2020;

[0063] Collect precipitation data from the Global Soil Moisture Project (GSWP-3); collect global land cover data with a spatial resolution of 0.05°×0.05° from 2001 to 2022 from the Moderate Resolution Imaging Spectroradiometer Land Cover Climate Simulation Grid (MCD12C1);

[0064] Experimental data from the sixth phase of the International Coupled Comparison Program (CMIP6) were also collected. The experimental scenarios included historical climate model experiments, future socioeconomic development path simulation experiments, and pre-industrial simulation experiments. The variables were soil moisture content, near-surface temperature, precipitation, and leaf area index. The time series were 1981-2020 and 2021-2100, as shown in Table 1 below.

[0065]

[0066] (2) Four-dimensional soil moisture drought event identification;

[0067] This implementation case is based on the global scope. Based on the three-layer soil water value preprocessing of three global reanalysis datasets from 1981 to 2020, the 10% quantile threshold of soil water value in the growing season of each grid point climate state (1981-2010) is calculated, and the grid points below this threshold are identified as drought grid points. The growing season is defined as May to September in the northern hemisphere and November to March of the following year in the southern hemisphere. The four-dimensional continuous drought events are obtained through the Lagrangian method.

[0068] First, the soil moisture data were preprocessed: according to the GSWP-3 precipitation data, the grid points in Antarctica, Greenland and desert areas with annual precipitation less than 100 mm were removed; the soil moisture values ​​in the growing season (May to September in the northern hemisphere and November to March in the southern hemisphere) were used; the units of soil moisture values ​​were changed from mass units (kg·m -2 ) to volume units (m 3 ·m 3 ); linearly interpolate multi-layer soil moisture values ​​to a common vertical profile (ranging from 0-7, 7-28, 28-100, and 100-289 cm); transform different time scale datasets to daily scale; use second-order conservative remapping to interpolate all datasets to 1°×1° resolution; use 11-day smoothing average to eliminate abnormal series fluctuations;

[0069] The Lagrangian method is used to simulate the spatiotemporal evolution of soil moisture drought in four dimensions, as follows: for each grid point in a single layer, when the soil moisture value is lower than the 10th percentile of the growing season in the climatological period (1981-2010), the grid point is defined as a drought grid point; adjacent drought grid points in a single layer are merged into two-dimensional spatially continuous drought patches; for each time step, all adjacent two-dimensional drought patches in 26 adjacent regions (9 in the upper layer, 8 in this layer excluding the central grid point, and 9 in the lower layer) are connected into an independent continuous three-dimensional drought patch; any spatially continuous three-dimensional drought patches are merged into a single four-dimensional (longitude-latitude-depth-time) drought event if the overlap rate in consecutive time steps is greater than 50%; in this implementation case, the four-dimensional drought event lasts at least 3 days and the projected area is greater than 100,000 square kilometers.

[0070] In this implementation case, the schematic diagram of deep drought and surface drought is determined by using the Lagrangian four-dimensional framework as shown in the following figure: Figure 2 As shown, during the local growing season of 2012, a deep drought event occurred in Central Asia, and the drought area in the deep soil was larger than that in the surface soil for 62% of the time; during the local growing season of 2017, a surface drought event in Australia showed that the drought area in the surface soil was larger than that in the deep soil for 86% of the time.

[0071] (3) Quantification of four-dimensional spatial and temporal characteristics of soil moisture drought;

[0072] This implementation case uses three reanalysis data and the model simulation data of the sixth phase of the International Coupled Comparison Program CMIP6 to quantify the spatiotemporal characteristics of four-dimensional soil moisture drought. The temporal and spatial characteristics of four-dimensional soil moisture drought events are duration and intensity, respectively. Duration is the duration of the four-dimensional soil moisture drought event, and intensity is the weighted average of the grid area and depth of the soil moisture deficit (threshold minus soil water value) of all grid points in the four-dimensional drought event, as follows:

[0073]

[0074] Where i is the grid point, t is the time, D is the duration, l is the soil layer, d is the soil depth, s is the grid area, thrse is the drought threshold, sm is the soil water value, and cluster is the drought event;

[0075] These four-dimensional drought events were divided into three types of drought: surface drought with a heavy top inverted iceberg structure, defined as the total duration of the surface soil moisture deficit area being greater than the deep soil moisture deficit area for more than 60%; deep drought with a heavy bottom iceberg structure, defined as the total duration of the deep soil moisture deficit area being greater than the surface soil moisture deficit area for more than 60%; when neither of the two situations occurs, it is a columnar drought structure. In the classification process, the top two layers (0-7cm and 7-28cm) were selected as the surface soil, and the third layer (28-100cm) was selected as the deep soil.

[0076] During the period 1981-2020, Figure 3As shown, 6065 four-dimensional continuous drought events were identified and divided into surface drought and deep drought according to whether they presented a heavy top inverted iceberg structure or a heavy bottom iceberg structure. Among them, the surface soil moisture deficit area of ​​surface drought is larger, and the deep drought area is smaller, similar to the shape of an inverted iceberg, while the deep soil moisture deficit area of ​​deep drought is larger than that of surface drought, similar to the shape of an iceberg. A total of 3557 surface droughts occurred worldwide, accounting for 58.7% of the total drought events, while deep droughts occurred 1655 times, accounting for 27.3% of the total drought events. Deep droughts are more persistent and intense than surface droughts, especially in the Amazon Basin, Africa, North America, East Asia and high latitudes in the Northern Hemisphere.

[0077] (4) Detection and attribution of four-dimensional soil moisture drought indicators;

[0078] This implementation case calculates the duration and intensity of global surface and deep droughts each year based on the reanalysis (average of three reanalysis data sets) and the historical simulation of the CMIP6 phase 6 of the International Coupled Comparison Program (multi-model average), and obtains the indicator series of global surface and deep drought duration and intensity ( Figure 4 The duration and intensity of surface droughts showed a significant (p < 0.01) upward trend from 1981 to 2020, while the growth rate of deep droughts was much higher than that of surface droughts. The trends in duration and intensity of surface and deep droughts based on reanalysis data exceeded the range of 2.5% to 97.5% of the trends of 2000 overlapping 40-year periods based on pre-industrial control simulations ( Figure 5 ), which indicates that the increase in characteristic indicators of surface and deep drought cannot be fully explained by internal climate variability. When considering human influence, the CMIP6 historical simulation experiment can well capture the upward trend of characteristic indicators of these two types of drought, as well as the faster growth rate of deep drought.

[0079] Two detection and attribution methods (correlation coefficient method and optimal fingerprint method) are used to quantitatively evaluate the impact of anthropogenic warming on the changes of two types of four-dimensional drought events around the world. Based on the correlation coefficient method, the Spearman correlation coefficients between the changes in the duration and intensity characteristics of annual global surface and deep droughts based on reanalysis (average of three reanalysis data sets) and the simulations of the CMIP6 historical experiment of the sixth phase of the International Coupled Comparison Program (multi-model average) are calculated. At the same time, the Spearman correlation coefficients between the multi-model average of the CMIP6 historical simulation experiment and 2000 overlapping 40-year time blocks simulated by the pre-industrial simulation experiment for the two types of drought indicator series are calculated. Figure 4). In this implementation case, the time series of the two characteristic indicators of surface drought under the historical simulation test conditions are significantly correlated with the time series based on the reanalysis dataset (p<0.05). The correlation between deep drought between the historical simulation test and the reanalysis is greater than the 99th percentile of all correlations between the historical simulation test and the pre-industrial simulation test ( Figure 4 ), confirming that anthropogenic emissions lead to increased drought across the entire vertical soil profile.

[0080] The regularized optimal fingerprint method is used to verify the results based on the correlation coefficient method and quantify the differences between the reanalysis and historical simulation experiments. The specific calculation is as follows:

[0081] Y=(X-α)β+ε

[0082] Where Y is the global average characteristic change of surface and deep drought based on reanalysis, X is the global average characteristic change of surface and deep drought simulated by the model, α is the sampling uncertainty, β is the scale factor, and ε is the internal climate variability estimated by the pre-industrial simulation experiment; to reduce the noise caused by interannual changes, the characteristic series of surface and deep drought are averaged at intervals of three consecutive years; if the scale factor and its 90% confidence interval are both greater than zero, the external forcing signal is considered to be detectable at the 5% significance level, that is, the anthropogenic warming signal can be detected.

[0083] like Figure 4 As shown in the right column, the influence of historical external forcing can be detected in the increase in the duration and intensity of both types of drought events, which verifies the results based on the correlation coefficient, that is, at least at a 90% (5% to 95%) confidence level, anthropogenic emissions have caused the temporal changes in the characteristics of the two types of drought. Except for the duration of surface drought, the scale factor values ​​of the two types of drought characteristics are close to 1, indicating that the model can accurately reproduce the changes in most drought characteristics based on the reanalysis.

[0084] This implementation case shows that anthropogenic climate change is very likely (90%) to have caused an increase in the duration and intensity of global surface and deep droughts between 1981 and 2020. The strong external signal (mainly anthropogenic emissions) in the change of deep drought timing further indicates that the anthropogenic impact on global soil moisture droughts has penetrated deeper soil layers below the surface.

[0085] (5) Prediction of future evolution of four-dimensional soil moisture drought;

[0086] In this implementation case, the CMIP6 historical simulation experiments and representative concentration pathways and shared socioeconomic pathways (SSPs) of future scenarios, including the SSP245 and SSP585 scenarios, are used to estimate the spatiotemporal evolution of global surface and deep drought characteristics in the future period; Figure 6As shown in the figure, under the SSP245 scenario, compared with the historical period (1981-2020), the projected increase in surface drought duration in the future period (2061-2100) will mainly occur in the middle and high latitudes of the Northern Hemisphere (above 30°N), and the intensity of surface drought in these regions is expected to increase by 335.9% ( Figure 7 In contrast, more persistent (+154.9%) and more intense (+477.3%) deep droughts are projected over global land, particularly in the Amazon Basin, southern Africa, Australia, southeastern North America, the Middle East, and mid- and high-latitudes of the Northern Hemisphere ( Figure 6 and Figure 7 ). In the soil vertical profile, the shallower the soil depth, the greater the increase in the two characteristics of surface drought; while the deeper the soil depth, the greater the increase in deep drought. This indicates that under climate warming, the duration and intensity of deep (surface) drought are expected to intensify from the bottom (top). Under the SSP585 scenario, the increase in the above-mentioned surface drought and deep drought is greater.

[0087] Figure 6 and Figure 7 The temporal variation trends of the characteristics of the two types of drought events also show a significant upward trend. Under the SSP245 scenario, the duration of surface drought is expected to increase by 1.3 days per decade, while the intensity will increase by 5.4×10 -4 m 3 ·m -3 , the two characteristic increases of deep drought (duration: 2.4 days per decade; intensity: 6.5×10- -4 m 3 ·m -3 ) is greater than surface drought. Under the influence of increased anthropogenic emissions, the increase in surface and deep drought under the SSP585 scenario is further amplified by 1.6 to 2.0 times. This indicates that global deep drought will last longer and be more intense, and the risk of intensified drought in the deep soil will also be greater.

[0088] Combined with the land cover dataset, the changes in surface and deep drought characteristics of different vegetation types were estimated. The area-weighted average of the differences in deep and surface drought characteristics (duration and intensity) in different vegetation areas was calculated by selecting deep-rooted vegetation cover categories (i.e., forest, shrub, and savanna). Figure 8 As shown, for ecosystems known to contain deep-rooted species, such as forests, shrubs, and savannas, the difference in duration and intensity of both deep and surface droughts showed a significant upward trend between 1981 and 2100, and this increase in difference was also found in the Amazon basin.

[0089] (6) Physical drivers of surface and deep drought;

[0090] This implementation case attributes surface and deep drought events to different driving factors, such as extreme heat (TEM), precipitation deficit (PRE), and vegetation greening (high leaf area index; LAI), calculates the average of the three variables 30 days before the drought event, and calculates its severity index (SI), which is calculated as follows:

[0091] SI v =(v i,j -μ j ) / σ j

[0092] Where i represents year, j represents month, v represents driving factor, μ and σ represent the climate mean and standard deviation of monthly variables in the climate state (historical period: 1981-2010, future period: 2061-2100), respectively;

[0093] Defining extreme heat as SI TEM >0.8, precipitation deficit is defined as SI PRE <-0.8, vegetation greening is defined as SI LAI >0.8, if a drought event and an extreme event occurring in the previous 30 days occur at the same grid point, the drought event at that grid point is attributed to this type of driving factor, and the normalized contribution of different driving factors to the duration and intensity of the two types of drought events is calculated.

[0094] like Fig. 9 and Fig.10 As shown, according to the three reanalysis datasets from 1981 to 2020, the antecedent precipitation deficit accounted for 45% and 44% of the total duration and intensity of global surface drought, respectively, and dominated 69% and 61% of the global surface drought evolution, respectively. This shows that global surface drought is highly sensitive to precipitation changes. The main driving factors of deep drought have obvious spatial heterogeneity. In arid areas, antecedent extreme high temperatures are the main driving factors of deep drought, especially in eastern North America, Europe and the Middle East, accounting for 52% of the total duration and intensity. These regions are hotspots of soil moisture-air temperature coupling.

[0095] In humid regions, such as the tropics and eastern Asia, the total duration and intensity of deep droughts are mainly attributed to the simultaneous occurrence of antecedent precipitation deficits (40% and 42%) and extreme heat (43% and 44%). In global vegetation greening hotspots, such as the high latitudes of the Northern Hemisphere, the role of antecedent vegetation greening cannot be ignored, with the first three driving factors each contributing 33% ( Fig. 9 The i and Fig.10 i) in the text box.

[0096] like Fig.11As shown in the data, under the scenario of continued anthropogenic emissions, the main driving factors of drought in different climate zones will change little during 2061-2100. It is expected that the number of extreme high temperature days will increase in eastern North America, Europe and the Middle East, the simultaneous occurrence of precipitation deficits and extreme high temperatures will become more frequent in tropical and East Asia, and vegetation greening will become more persistent in high latitudes of the Northern Hemisphere. Anthropogenic climate change will intensify these driving processes, further exacerbating deep droughts.

[0097] See also Fig.12 , Fig.12 4 is a schematic diagram of the working of the hardware device of an embodiment of the present invention, wherein the hardware device specifically comprises: a four-dimensional soil moisture drought event tracking and warming signal identification device 401, a processor 402 and a storage medium 403.

[0098] A four-dimensional soil moisture drought event tracking and warming signal identification device 401: The four-dimensional soil moisture drought event tracking and warming signal identification device 401 implements the four-dimensional soil moisture drought event tracking and warming signal identification method.

[0099] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the four-dimensional soil moisture drought event tracking and warming signal recognition method.

[0100] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the four-dimensional soil moisture drought event tracking and warming signal recognition method.

[0101] The beneficial effects of the present invention are:

[0102] (1) The present invention provides a method for identifying four-dimensional soil moisture drought, which helps the academic community deepen its understanding of this new type of soil drought. The present invention divides four-dimensional soil drought into two different types, quantifies the spatiotemporal characteristic indicators of four-dimensional soil drought, and clarifies the changes and impacts of four-dimensional soil moisture drought in the four-dimensional spatiotemporal domain of longitude, latitude, depth, and time. The present invention quantifies the differences in the spatiotemporal evolution of two types of four-dimensional soil drought in the future period under different land cover types, providing a scientific basis for a deep understanding of the dynamic evolution process of drought events.

[0103] (2) The present invention quantitatively detects anthropogenic warming signals in the spatiotemporal evolution of four-dimensional soil moisture drought characteristics, identifies the impact of anthropogenic warming on the changes in two four-dimensional drought events, and estimates future changes in four-dimensional drought events, providing theoretical support for decision makers to make decisions on adapting to and mitigating the effects of climate change on soil moisture drought.

[0104] (3) The present invention analyzes the contribution of driving factors to four-dimensional soil moisture drought events, clarifies the contribution of different driving factors to four-dimensional soil moisture drought in history and future periods, quantifies the dominant driving factors of two types of four-dimensional soil moisture drought, and provides theoretical support for the attribution of four-dimensional soil moisture drought.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A four-dimensional soil moisture drought event tracking and warming signal identification method, characterized in that: The method comprises the following steps: Step S1: Data collection: Collect global climate data; Step S2: Identification of four-dimensional soil moisture drought events: Combined with the data obtained in step S1, the 10th percentile of the climatic growing season soil moisture value of each grid point is calculated as the drought threshold, and the grid points below the threshold are identified as drought grid points. The continuous four-dimensional soil moisture drought events are identified by the Lagrangian four-dimensional tracking method. Step S3: Quantification of four-dimensional soil moisture drought event characteristics: Combined with the four-dimensional drought events obtained in step S2, soil moisture drought event characteristics are calculated, and soil moisture drought events are divided into surface drought and deep drought; Step S4: Detection of anthropogenic warming signals from the temporal evolution of four-dimensional soil moisture drought characteristics in the historical period; Combined with the temporal and intensity characteristics of the two types of drought events obtained in step S3, the correlation coefficient method and the optimal fingerprint method are used to detect anthropogenic warming signals from drought characteristic indicators calculated based on reanalysis data and historical climate experiments of climate models; Step S5: Prediction of the future evolution of four-dimensional soil moisture drought; combining the time and intensity characteristics of the two types of drought events obtained in step S3, combining the historical climate experiment of the climate model and the future social and economic development path experiment, to obtain the spatiotemporal evolution characteristics of drought events in the future period; Combined with the global land cover data obtained in step S1, the spatiotemporal evolution characteristics of drought events under different vegetation types in the future period are obtained; Step S6: Four-dimensional soil moisture drought event driving factors; combining the time and intensity characteristics of the two types of drought events obtained in step S3, and the near-surface temperature, precipitation and leaf area index data obtained in step S1, the two types of drought events are attributed to the three types of antecedent driving factors, and the contribution of different driving factors to drought events is quantified.

2. A four-dimensional soil moisture drought event tracking and warming signal identification method as claimed in claim 1, characterized in that: The global climate data in step S1 include: ERA5-Land dataset of the Weather Forecast Center, CRA-Land data of global atmosphere and land surface reanalysis product of the Meteorological Administration, soil moisture, near-surface temperature and precipitation data of the Global Land Data Assimilation System GLDAS-Noah, leaf area index data of the global long-term leaf area index GLOBMAP data, precipitation data collected from the Global Soil Moisture Project GSWP-3, soil moisture, near-surface temperature, precipitation and leaf area index data of the sixth phase of the International Coupled Comparison Program CMIP6, and global land cover data collected from the Moderate Resolution Imaging Spectroradiometer Land Cover Climate Simulation Grid MCD12C1.

3. A four-dimensional soil moisture drought event tracking and warming signal identification method as claimed in claim 1, characterized in that: Step S2 is specifically as follows: S21: Soil moisture data preprocessing: Based on the GSWP-3 precipitation data, remove the grid points in the Arctic and Antarctic regions and desert areas with annual precipitation less than 100 mm; use the soil water value of the growing season; convert the soil water value unit from mass unit to volume unit; linearly interpolate the soil moisture values ​​of different data layers to a common vertical profile; convert different time scale data sets to daily scale; Second-order conservative remapping was used to interpolate all data sets to a resolution of 1°×1° to ensure consistent temporal and spatial resolutions; an 11-day smoothing average was used to exclude abnormal series fluctuations; S22: Use the Lagrangian tracing method to simulate the spatiotemporal evolution of soil moisture drought in four dimensions as follows: For each grid point in a single layer, when the soil water value is lower than the 10th percentile of the growing season in the climatic period, the grid point is defined as a drought grid point; the adjacent drought grid points in the single layer are merged into two-dimensional spatially continuous drought patches; for each time step, all adjacent two-dimensional drought patches in 26 adjacent areas are connected into an independent three-dimensional continuous drought patch; any spatially continuous three-dimensional drought patches are merged into a single four-dimensional drought event if the overlap rate in continuous time steps is greater than the preset percentage; the four-dimensional drought event lasts for at least the preset number of days and the projected area is greater than the preset value.

4. A four-dimensional soil moisture drought event tracking and warming signal identification method as claimed in claim 1, characterized in that: Step S3 is as follows: S31: The temporal and spatial characteristics of the four-dimensional soil moisture drought event are duration and intensity, respectively. The duration is the duration of the four-dimensional soil moisture drought event, and the intensity is the weighted average of the grid area and depth of the soil moisture deficit of all grid points in the four-dimensional drought event, as follows: Where i is the grid point, t is time, D is duration, l is soil layer, d is soil depth, s is grid point area, thres is drought threshold, sm is soil water value, and cluster is drought event; S32: The four-dimensional drought events are divided into three types of drought: surface drought with a heavy top inverted iceberg structure, which is defined as the total duration of the surface soil moisture deficit area being greater than the deep soil moisture deficit area for a period of more than a preset percentage; deep drought with a heavy bottom iceberg structure, which is defined as the total duration of the deep soil moisture deficit area being greater than the surface soil moisture deficit area for a period of more than a preset percentage; when neither of the two situations occurs, it is a columnar drought structure; S33: During the division process, the top two layers were selected as the surface soil and the third layer as the deep soil.

5. A four-dimensional soil moisture drought event tracking and warming signal identification method as claimed in claim 1, characterized in that: Step S4 is specifically as follows: S41: Detect and attribute anthropogenic climate change to four-dimensional soil moisture drought events based on the correlation coefficient method, calculate the Spearman correlation coefficient between the changes in the global surface drought and deep drought characteristic sequences based on reanalysis data and CMIP6 historical experiments; divide the CMIP6 pre-industrial simulation experiment into multiple blocks of the same years as the study period, and calculate the Spearman correlation coefficient between it and the multi-model average of the historical experiment simulation; analyze whether the correlation coefficient between the drought event characteristic sequence simulated by the reanalysis data and the historical experiment exceeds the 95th or 99th percentile of the correlation coefficient set between multiple blocks of the historical simulation experiment and the pre-industrial simulation experiment; S42: Detection and attribution of anthropogenic climate change for four-dimensional soil moisture drought events based on the optimal fingerprint method. The specific calculation is as follows: Y=(X-α)β+ε where Y is the change in the global mean characteristics of surface and deep drought based on reanalysis, x is the change in the global mean characteristics of surface and deep drought simulated by the model, α is the sampling uncertainty, β is the scale factor, and ε is the internal climate variability estimated from the pre-industrial simulation experiment.

6. A four-dimensional soil moisture drought event tracking and warming signal identification method as claimed in claim 1, characterized in that: Step S5 is specifically as follows: S51: Use the CMIP6 historical simulation experiments and representative concentration pathways and shared socioeconomic pathways (SSPs) of future scenarios, including the SSP245 and SSP585 scenarios, to calculate the spatiotemporal evolution of global surface and deep drought characteristics in the future period; S52: Combined with the land cover dataset, estimate the changes in surface drought and deep drought characteristics of different vegetation types, select deep-rooted vegetation cover categories, and calculate the area-weighted average of the differences in deep drought and surface drought characteristics in different vegetation areas.

7. A four-dimensional soil moisture drought event tracking and warming signal identification method as claimed in claim 1, characterized in that: Step S6 is as follows: S61: Surface and deep drought events are attributed to three different driving factors, including extreme high temperature TEM, precipitation deficit PRE and high leaf area index LAI. The average values ​​of the three variables in the 30 days before the drought event are calculated, and the severity index SI is calculated as follows: ARE YOU v =(v i,j -μ j ) / σ j Where i represents year, j represents month, v represents driving factor, μ and σ represent the climatological mean and standard deviation of monthly variables of climatological state, respectively; S62: Define extreme heat as SI TEM >Default value, precipitation deficit is defined as SI PRE <Default value, vegetation greening is defined as SI LAI >Default value: if a drought event and an extreme event that occurred in the previous 30 days occur at the same grid point, the drought event at that grid point is attributed to this type of driving factor, and the contribution of different driving factors to the duration and intensity of the two types of drought events is calculated.

8. A storage medium, characterized in that: The storage medium stores instructions and data for implementing a four-dimensional soil moisture drought event tracking and warming signal identification method as described in any one of claims 1 to 7.

9. A four-dimensional soil moisture drought event tracking and warming signal identification device, characterized in that: include: Processor and storage medium; the processor loads and executes instructions and data in the storage medium to implement a four-dimensional soil moisture drought event tracking and warming signal recognition method as described in any one of claims 1 to 7.

Images