CN112785031B - Method for improving drought monitoring space-time accuracy based on total water reserve loss index principle - Google Patents
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Abstract
The invention discloses a method for improving the accuracy of drought monitoring time and space based on the total water reserve loss index principle, which can realize drought monitoring, provide early warning information and furthest reduce the influence of drought on human production and natural environment. Traditional drought monitoring methods require that spatial distribution results of drought be obtained by interpolation, but in areas with complex terrain or sparse observation sites, the drought monitoring results obtained by interpolation may be inaccurate. The invention provides a method for determining drought intensity by utilizing a standardized drought index of ground measured data, and a novel total water reserve loss index method is constructed. The method provided by the invention is used for evaluating the drought in 2003-2016 in southwest China, and the evaluation result is consistent with the result reported by the government, so that the effectiveness of the method provided by the invention is verified. Compared with the traditional SPI, SPEI and SC-PDSI, the invention not only realizes the drought monitoring from point to surface, but also improves the accuracy of the drought monitoring by 37.5%, 29.2% and 50% respectively compared with the SPI, SPEI and SC-PDSI.
Description
Technical Field
The invention belongs to the technical field of intersection of satellite gravity, hydrology and the like, and particularly relates to a method for improving drought monitoring space-time accuracy based on a total water reserve loss index principle.
Background
Drought is one of the most serious natural disasters in the world, and can bring serious damage to agricultural production, social economy and ecological environment. The inter-government climate change specialization committee (The Intergovernmental Panel on CLIMATE CHANGE, IPCC) 2018 reports that human activity has led to an increase in global air temperature of about 1 ℃ since industrialization, with global warming of 1.5 ℃ being possible in 2030-2050. Drought events in many areas are expected to be exacerbated in the 21 st century due to global climate change. Therefore, effective drought monitoring is being carried out to reduce the risk of drought. The traditional drought monitoring method relies on meteorological hydrologic data observed by a meteorological station, and spatial distribution of drought is obtained through interpolation. However, in areas of complex terrain or scarce sites, the results of the interpolation drought monitoring tend to be inaccurate.
With the development of remote sensing technology, drought monitoring can get rid of dependence on traditional site observation data, and key variables related to drought can be obtained on a larger space-time scale. Gravity Recovery and climate Experiment (GRACE) Gravity satellite tasks were launched in month 3 of 2002, developed cooperatively by the national aviation administration (NASA) and the German aerospace center. Grace can acquire land water reserves change information by monitoring the change of the earth gravity field, which brings new hydrologic information sources. GRACE derived land water reserves can be divided longitudinally into five sections: surface water, groundwater, soil water, ice and snow water, and biological water, cover all land water reserves types. An important feature of drought is the lack of land water, so there is a close link between drought and land water reserves. The development of GRACE realizes dynamic monitoring of water storage information in large-scale areas, so that drought monitoring is free from traditional station measurement. Thus, GRACE has great potential in drought monitoring and has found application in regional drought surveys. Yi and Wen propose a GRACE-based hydrodrought index for monitoring drought conditions on the continental united states in 2003-2012. Thomas et al established a GRACE groundwater drought index for monitoring central valley drought in california and evaluated groundwater depletion signatures due to complex human activity and natural changes. Yu et al evaluate drought conditions in mongolian countries using GRACE water storage deficiency index, demonstrating the effectiveness of GRACE in evaluating drought severity in areas lacking hydrographic observations.
With global climate change, the frequency of drought events in southwest China has increased over the last decades. Extreme drought often results in the conditions of large-area crop harvest, water shortage of people and livestock and the like, and can cause intolerable damage to the local area. For example, the Sichuan province and Chongqing municipalities in southwest China encountered the most severe drought since 1891 in 2006 in summer, resulting in difficulties in drinking more than 1500 tens of thousands of people. In the autumn of 2009 to the spring of 2010, the southwest area suffers from 'century' extra drought, and the economic loss caused by the extra drought is equivalent to 2.13% of the total domestic production value of 2010. Therefore, the research on drought problems in southwest areas of China has important practical significance and practical value.
Disclosure of Invention
The technical solution of the invention is as follows: the method for improving the accuracy of drought monitoring time and space based on the total water reserve loss index principle is provided to overcome the defects of the prior art, so that the accuracy of the drought monitoring is improved.
In order to solve the technical problems, the invention discloses a method for improving the accuracy of drought monitoring time and space based on the total water reserve loss index principle, which comprises the following steps:
Extracting land water reserve abnormal value TSA i,j of the ith year and the jth month from GRACE data;
Calculating a total water reserve abnormal long-term average MTSA j, a maximum value MaxTSA j and a minimum value MinTSA j of the j-th month according to TSA i,j;
Calculating a total water reserve loss value TSD i,j of the ith year and the jth month according to TSA i,j、MTSAj、MaxTSAj and MinTSA j;
determining a total water reserve deficit value TSD j of month j according to the TSD i,j;
acquiring a current total water reserve loss weight p and a historical total water reserve loss weight q;
based on TSD j, p and q, the total water reserve loss index for each month is calculated.
In the method for improving the accuracy of drought monitoring space-time based on the total water storage loss index principle, the solution formula of the total water storage loss value TSD i,j of the ith year and the jth month is as follows:
in the method for improving the accuracy of drought monitoring space-time based on the total water reserve loss index principle, the solution formula of the total water reserve loss index of each month is as follows:
TSDIj＝p×TSDIj-1+q×TSDj···(2)
Wherein TSDI j-1 denotes the total water reserve deficit index of month j-1, and TSDI j denotes the total water reserve deficit index of month j.
In the method for improving the accuracy of drought monitoring space-time based on the total water reserve loss index principle, the current total water reserve loss weight p and the historical total water reserve loss weight q are obtained, and the method comprises the following steps:
Fitting to obtain a loss curve of the accumulated total water reserve in the drought period according to the accumulated total water reserve in the drought period;
Acquiring the slope m and intercept b of a cumulative total water reserve loss curve in the drought period;
according to the slope m and the intercept b, the current total water reserve loss weight p and the historical total water reserve loss weight q are obtained through the following calculation:
wherein, C represents drought intensity.
In the method for improving the accuracy of drought monitoring space-time based on the total water reserve loss index principle, fitting to obtain a total water reserve loss curve accumulated in the drought period according to the accumulated total water reserve in the drought period, the method comprises the following steps:
Taking a month as a calculation period, and calculating to obtain a total water reserve loss value of each month by adopting the formula (1);
According to the calculated total water reserve loss value of each month, the accumulated water reserve loss value of the previous 1 month, the accumulated water reserve loss value of the previous 2 months and the accumulated water reserve loss value of the previous tau months are obtained;
Fitting to obtain a linear curve of the accumulated water reserve loss value and time by taking the time month as an abscissa according to the obtained accumulated water reserve loss value of the previous 1 month, the accumulated water reserve loss value of the previous 2 months, & gtand the accumulated water reserve loss value of the previous tau months;
And intercepting a curve of the set condition from a linear curve of the accumulated water reserve loss value and time obtained by fitting to serve as an accumulated total water reserve loss curve in the drought period.
In the method for improving the accuracy of drought monitoring space-time based on the total water reserve loss index principle, intercepting a curve with set conditions from a linear curve of accumulated water reserve loss values and time obtained by fitting as an accumulated total water reserve loss curve in the drought period, the method comprises the following steps:
screening the total water reserve loss value of each month to obtain a minimum monthly total water reserve loss value;
screening from a linear curve of the accumulated water reserve loss value and time obtained by fitting to obtain l critical points; wherein, the critical point is: a first negative point at which the accumulated reserve loss value changes from positive to negative;
Screening from the critical points to obtain a critical point l 0 closest to the minimum value of the month total water reserve loss;
Taking the critical point l 0 as a starting point l star of a cumulative total water reserve loss curve in the drought period;
Determining an endpoint l end of a cumulative total water reserve loss curve in the drought period; wherein, the endpoint l end satisfies: the accumulated water reserve loss value corresponding to the end point l end is smaller than the accumulated water reserve loss value corresponding to the next month;
And according to the determined starting point l star and the determined end point l end, intercepting a curve of the set condition from a linear curve of the accumulated water reserve loss value obtained by fitting and time as an accumulated total water reserve loss curve in the drought period.
In the method for improving the accuracy of drought monitoring time and space based on the total water reserve loss index principle, the value of the drought intensity C is determined by the following method:
calculating to obtain a standardized precipitation index SPI and a standardized precipitation evapotranspiration index SPEI according to weather record data of weather stations in a research area;
When-0.5 < SPI or-0.5 < SPEI, no drought is indicated, c=0;
When SPI is less than or equal to-1 and less than or equal to-0.5 or SPEI is less than or equal to-1 and less than or equal to-0.5, slight drought is represented, and C= -1;
when SPI is less than or equal to-1.5 and less than or equal to-1 or SPEI is less than or equal to-1.5, moderate drought is represented, and C= -2;
When SPI is less than or equal to-2 and less than or equal to-1.5 or SPEI is less than or equal to-2 and less than or equal to-1.5, serious drought is represented, and C= -3;
When SPI is less than or equal to-2 or SPEI is less than or equal to-2, extreme drought is represented, and C= -4.
The method for improving the accuracy of drought monitoring time and space based on the total water reserve loss index principle further comprises the following steps: and obtaining the contribution degree of each climate driving factor to drought development through a partial least squares regression model according to the calculated total water reserve loss index of each month, so that researchers can predict and prevent drought according to the contribution degree of each climate driving factor to drought development.
In the method for improving the accuracy of drought monitoring space-time based on the total water reserve loss index principle, according to the calculated total water reserve loss index of each month, the contribution degree of each climate driving factor to drought development is obtained through a partial least squares regression model, and the method comprises the following steps:
Taking the month total water reserve loss index TSDI j as a dependent variable Y, taking each climate driving factor as an independent variable X η, and constructing to obtain a partial least square regression model:
Y＝a0+a1X1+a2X2+...+aηXη+...+anXn
The partial least square regression model is a linear regression equation, a 0 represents the intercept of the linear regression equation, a 1、a2、...、aη、...、an represents the regression coefficient obtained by fitting the linear regression equation, and n=1, 2,3 and …;
And determining the contribution degree of each climate driving factor to drought development according to the a 1、a2、...、aη、...、an obtained by fitting.
In the method for improving the accuracy of drought monitoring space-time based on the total water reserve loss index principle, the climate driving factors comprise: precipitation, air temperature, ground temperature, humidity, air pressure, wind speed and transpiration.
The invention has the following advantages:
(1) Traditional drought monitoring methods require that spatial distribution results of drought be obtained by interpolation, but in areas with complex terrain or sparse observation sites, the drought monitoring results obtained by interpolation may be inaccurate. The invention discloses a method for improving the accuracy of drought monitoring time and space based on the total water reserve loss index principle, which realizes the monitoring of drought, can provide early warning information, furthest reduces the influence of drought on human production and natural environment, and has high time and space accuracy and high calculation speed.
(2) The evaluation of the parameter C in the traditional total water reserve loss index method is complex and has uncertainty, and the invention provides a method for determining the parameter C by utilizing the standardized drought index of ground measured data, thereby constructing a novel total water reserve loss index method.
(3) According to the invention, the novel total water reserve loss index method based on GRACE is utilized to evaluate the drought in 2003-2016 in the southwest area, and the result shows that 7 drought events are experienced in the southwest area during the research period, wherein the most serious drought occurs in 2009-2010 and is consistent with the result reported by the government, so that the effectiveness of developing drought monitoring research based on the novel total water reserve loss index method is verified.
(4) Compared with the traditional standardized precipitation index SPI, the standardized precipitation evapotranspiration index SPEI and the self-calibrated Parmer drought intensity index SC-PDSI, the method not only realizes the drought monitoring from point to surface, but also improves the accuracy of the drought monitoring by 37.5%, 29.2% and 50% compared with the SPI, the SPEI and the SC-PDSI respectively.
Drawings
FIG. 1 is a flow chart of drought monitoring and assessment according to an embodiment of the present invention;
FIG. 2 is a GRACE derived land water reserve anomaly time series;
fig. 3 (a) is a diagram showing the total water reserve loss in southwest 2003-2016; FIG. 3 (b) is a diagram showing the total accumulated water reserve loss in southwest 2003-2016;
FIG. 4 is a schematic diagram of the proportion of weather stations with different drought severity in southwest regions from 9 months in 2009 to 4 months in 2010;
FIG. 5 is a graph showing novel total water reserve loss index in southwest 2003-2016;
FIG. 6 is a diagram of a comparison between TSDI and SPI, SPEI, and SC-PDSI;
FIG. 7 is a graph showing correlation coefficients between drought indices;
FIG. 8 is a schematic diagram of variable importance results of partial least squares regression analysis;
FIG. 9 is a graphical representation of the percentage of areas in southwest areas of drought of varying degrees detected by TSDI;
Fig. 10 is a schematic diagram of the annual trend of humidity, precipitation and transpiration and the annual trend of TSDI.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention disclosed herein will be described in further detail with reference to the accompanying drawings.
In this embodiment, the method for improving the accuracy of drought monitoring space-time based on the total water reserve loss index principle includes:
And step 1, extracting land water reserve abnormal value TSA i,j of the ith year and the jth month from GRACE data.
Step 2, calculating the MTSA j, the maximum MaxTSA j and the minimum MinTSA j of the abnormal long-term average of the total water reserves in the j-th month according to the TSA i,j.
Step 3, calculating the total water reserve loss value TSD i,j of the ith month of the ith year according to TSA i,j、MTSAj、MaxTSAj and TSA MinTSA j.
In this embodiment, drought events may occur when the dry state of the area continues for a certain period of time, and the total water reserve deficit index is used to describe the long-term dry and wet state of the area; the total water reserve loss index is calculated by first calculating the total water reserve loss value using the land water reserve anomaly value. The calculation formula of the total water reserve loss TSD i,j in the ith month is as follows:
Step 4, determining a total water reserve loss value TSD j of the j month according to the TSD i,j.
And 5, acquiring a current total water reserve loss weight p and a historical total water reserve loss weight q.
In this embodiment, the current total water reserve deficit weight p and the historical total water reserve deficit weight q may be determined from a drought period cumulative total water reserve deficit curve (map): fitting to obtain a loss curve of the accumulated total water reserve in the drought period according to the accumulated total water reserve in the drought period; acquiring the slope m and intercept b of a cumulative total water reserve loss curve in the drought period; and according to the slope m and the intercept b, calculating to obtain the current total water reserve loss weight p and the historical total water reserve loss weight q through the following formula (3).
Wherein, C represents drought intensity.
Preferably, the method for obtaining the accumulated total water reserve loss curve in the drought period can be as follows: taking a month as a calculation period, and obtaining a total water reserve loss value of each month according to the formula (1); according to the calculated total water reserve loss value of each month, the accumulated water reserve loss value of the previous 1 month, the accumulated water reserve loss value of the previous 2 months and the accumulated water reserve loss value of the previous tau months are obtained; fitting to obtain a linear curve of the accumulated water reserve loss value and time by taking the time month as an abscissa according to the obtained accumulated water reserve loss value of the previous 1 month, the accumulated water reserve loss value of the previous 2 months, & gtand the accumulated water reserve loss value of the previous tau months; and intercepting a curve of the set condition from a linear curve of the accumulated water reserve loss value and time obtained by fitting to serve as an accumulated total water reserve loss curve in the drought period.
Further, the method for obtaining the accumulated total water reserve loss curve in the drought period can be as follows: screening the total water reserve loss value of each month to obtain a minimum monthly total water reserve loss value; screening from a linear curve of the accumulated water reserve loss value and time obtained by fitting to obtain l critical points; screening from the critical points to obtain a critical point l 0 closest to the minimum value of the month total water reserve loss; taking the critical point l 0 as a starting point l star of a cumulative total water reserve loss curve in the drought period; determining an endpoint l end of a cumulative total water reserve loss curve in the drought period; and according to the determined starting point l star and the determined end point l end, intercepting a curve of the set condition from a linear curve of the accumulated water reserve loss value obtained by fitting and time as an accumulated total water reserve loss curve in the drought period. Wherein, the critical point is: a first negative point at which the accumulated reserve loss value changes from positive to negative; the endpoint l end satisfies: the accumulated water reserve deficit value corresponding to endpoint l end is less than the accumulated water reserve deficit value corresponding to the next month.
Further, the value of drought intensity C may be determined by: calculating to obtain a standardized precipitation index SPI and a standardized precipitation evapotranspiration index SPEI according to weather record data of weather stations in a research area; when-0.5 < SPI or-0.5 < SPEI, no drought is indicated, c=0; when SPI is less than or equal to-1 and less than or equal to-0.5 or SPEI is less than or equal to-1 and less than or equal to-0.5, slight drought is represented, and C= -1; when SPI is less than or equal to-1.5 and less than or equal to-1 or SPEI is less than or equal to-1.5, moderate drought is represented, and C= -2; when SPI is less than or equal to-2 and less than or equal to-1.5 or SPEI is less than or equal to-2 and less than or equal to-1.5, serious drought is represented, and C= -3; when SPI is less than or equal to-2 or SPEI is less than or equal to-2, extreme drought is represented, and C= -4. The method for determining the drought intensity C is improved, and the SPI and the SPEI obtained by calculation of weather record data of a weather station in a research area jointly determine the value of the drought intensity C. The total water reserve loss index calculated by the method can be applied in the global scope, is not limited to areas with rich research data, and has profound significance for global drought monitoring.
And 6, calculating the total water reserve loss index of each month according to TSD j, p and q.
In this embodiment, drought is a long term process, and the current month of drought is not only related to the current total water reserve loss value, but also related to the previous drought conditions. Therefore, the total water reserve loss index can be calculated according to the previous drought condition and the current total water reserve loss. Wherein, the calculation formula of the total water reserve loss index of each month is as follows:
TSDIj＝p×TSDIj-1+q×TSDj···(2)
Wherein TSDI j-1 denotes the total water reserve deficit index of month j-1, and TSDI j denotes the total water reserve deficit index of month j.
And 7, obtaining the contribution degree of each climate driving factor to drought development through a partial least squares regression model according to the calculated total water reserve loss index of each month, so that researchers can predict and prevent drought according to the contribution degree of each climate driving factor to drought development.
In this embodiment, the importance of the predicted variable obtained by the partial least squares regression model can quantitatively explain the influence of the independent variable on the dependent variable. It is generally recognized that independent variables with variable importance greater than 0.8 are significant for interpreting dependent variables. In the present invention, the independent variable of partial least squares regression is a climate driving factor (including precipitation, air temperature, ground temperature, humidity, air pressure, wind speed, evapotranspiration, etc.), the dependent variable is a drought index, and the importance of the variable is used to determine which climate forcing factor has the greatest contribution to the development of drought. Specifically, a partial least squares regression model can be constructed by taking a month total water reserve loss index TSDI j as a dependent variable Y and each climate driving factor as an independent variable X η:
Y＝a0+a1X1+a2X2+...+aηXη+...+anXn
The partial least square regression model is a linear regression equation, a 0 represents the intercept of the linear regression equation, a 1、a2、...、aη、...、an represents the regression coefficient obtained by fitting the linear regression equation, so that the contribution degree of each climate driving factor to drought development can be determined according to a 1、a2、...、aη、...、an obtained by fitting. Wherein n=1, 2,3 …. According to the invention, the independent variable with the greatest influence on the dependent variable is found out by adopting the partial least square regression model and extracting and screening the components of the data, so that the correlation problem between the independent variables in the common least square regression model is solved.
In summary, unlike previous studies, the present invention utilizes a novel total water reserve loss index method (Total Storage Deficit Index, TSDI) based on GRACE data to evaluate drought conditions in southwest 2003-2016 and to reveal the relationship between GRACE identified drought and climate driving factors. The important parameter C in the novel total water reserve loss index method is used as a reference value for drought evaluation, namely, a certain drought grade is designated in advance, and other drought events are evaluated by taking the reference value as a reference. The traditional total water reserve loss index method depends on the previous research result to determine the parameter C, so that the method is difficult to apply in areas lacking drought research data. The method utilizes the standardized drought index based on ground measured data to determine the parameter C, expands the application range of the total water reserve loss index, and has important significance for global drought monitoring and evaluation work.
Based on the above embodiments, the method for improving the accuracy of drought monitoring based on the total water reserve loss index principle (i.e., the novel total water reserve loss index method TSDI) according to the present invention is compared with other existing methods.
As shown in fig. 1, the novel total water reserve loss index method TSDI proposed by the present invention is as follows: first, a new total water reserve loss index calculated based on GRACE data evaluates drought in southwest areas. Second, the new water reserve loss index and normalized drought index were validated, and TSDI was compared to a global dataset of normalized precipitation index SPI (Standardize Precipitation Index, SPI), normalized precipitation evapotranspiration index SPEI (Standardize Precipitation Evapotranspiration Index, SPEI), and Self-calibrated pamer drought intensity index (Self-Calibrating Palmer Drought Severity Index, SC-PDSI) from the university of eastern australia climate research center (CLIMATIC RESEARCH Unit, CRU). And finally, establishing a novel relation between the total water reserve loss index and meteorological data through a partial least square regression model, and analyzing main reasons for drought in southwest areas.
Wherein:
SPI is Mckee et al, and represents weather drought index of the probability of precipitation in a certain period based on long-term precipitation record. Because of simple calculation and reliable results, the index is one of the most widely used drought indexes at present. The invention calculates SPI of three time scales of 3 months, 6 months and 12 months respectively by adopting month precipitation data of 138 weather stations and 51 years in a research area. Calculating SPI requires first fitting long-term precipitation records to Gamma probability density function, then converting to cumulative probability function, and finally dividing drought grade by normalized precipitation cumulative frequency distribution.
Wherein P represents the cumulative probability of precipitation exceeding a threshold, c 0、c1、c2、d1、d2 and d 3 represent fixed coefficients, and the values are 2.515517, 0.802853, 0.010328, 1.432788, 0.189269 and 0.001308 respectively.
SPEI is a normalized drought index developed by Vicente-Serrano taking into account both precipitation and temperature effects. The SPEI calculation method is the same as SPI except that the cumulative distribution function is fitted to the precipitation minus the value of potential evapotranspiration. In the invention, the SPEI is calculated by taking the monthly rainfall and air temperature data of the meteorological site in the research area as input quantity. Both SPI and SPEI calculated three time scales of 3 months, 6 months and 12 months.
The pamer drought intensity index PDSI starts from a water balance equation, comprehensively considers the influences of precipitation, temperature, soil moisture and other hydrologic parameters, and can quantify the long-time drought characteristics of specific time and area. PDSI is limited by fixed climate weight factors and duration factors such that the index is not spatially comparable in different climate zones. SC-PDSI is improved by Wells and the like on the basis of PDSI, and the climate data dynamic calculated value of each area is used for replacing the traditional constant, thereby being beneficial to drought assessment of different areas. The invention adopts SC-PDSI global data set of CRU (https:// crudata. Uea. Ac. Uk/CRU/data/drought /), the spatial resolution is 0.5 degrees, and the time span is 2003-2016 years.
The novel total water reserve loss index method TSDI provided by the invention is verified.
Study area overview
The invention selects southwest areas of China as the verification and application areas of the novel total water reserve loss index. The southwest area of China is between 97 DEG and 112 DEG E and 21 DEG and 35 DEG N, and the area is about 1360000km 2. The area is composed of Chongqing city and four provinces of Sichuan, yunnan, guizhou and Guangxi. There were 138 weather stations in the study area and the eastern site density was significantly higher than the western. The area is mainly grasslands and woodlands, and the agricultural land is mainly concentrated in Sichuan basin. The southwest area is located in subtropical monsoon climate areas, and the annual average air temperature and the precipitation are respectively-16 ℃ and-1100 mm. The water resources in southwest area are rich, great rivers such as Yangtze river, zhujiang river, and Langjiang river are in the interior, but the space-time distribution of the precipitation is very uneven, 85% of the precipitation is concentrated in 4-10 months (rainy season) each year, and the tendency is reduced from southeast to northwest. Due to the influence of climate change, the southwest area in recent years often suffers drought, which causes large-area grain yield reduction and water shortage of population.
GRACE data
The GRACE data provides the basis for understanding land water circulation, ice cap and glacier mass balance, sea level changes and subsea pressure changes, and their links to global climate change. Point quality solutions are recently developed methods of processing GRACE data to describe the earth's gravitational field. Compared to traditional spherical harmonic solutions, point quality solutions reduce signal leakage errors between sea and land, and do not require additional decorrelation filtering to remove north-south banding errors. The CSRRL point quality method product (http:// www2.CSR. Utexas. Edu/GRACE/RL06 masons. Html) was the most recently introduced GRACE product by the osbeck space research Center (University of Texas at Austin, center for SPACE RESEARCH, UTCSR) of the university of texas, usa, and the CSR point quality method solution with a spatial resolution of 0.25 ° covered 1 month to 2016 12 months in 2003, with 17 months of data missing (6 months in 2003, 1 month in 2011, 6 months in 2011, 5 months in 2012, 10 months in 2012, 3 months in 2013, 8 months in 2013, 9 months in 2014, 2 months in 2014, 7 months in 2014, 12 months in 2014, 6 months in 2015, 10 months in 2015, 11 months in 2015, 4 months in 2016, 9 months in 2016, 10 months in 2016). The missing values in the GRACE data are approximated by linear interpolation.
Meteorological data
The meteorological data of the ground meteorological station is used to calculate SPI and SPEI and to investigate TSDI the link with climate driving factors. Daily weather data comes from the national weather information center (http:// data. Cma. Cn) of the Chinese weather office, including precipitation, air temperature, ground temperature, humidity, air pressure and wind speed. And accumulating daily precipitation data to obtain a monthly precipitation record, and averaging other daily data to obtain a monthly record. In order to ensure that the time length of the meteorological data is as complete as possible, the meteorological station built after 1966 is eliminated. The study area is provided with 138 weather stations, the time span is 1966-2016, and the space distribution is uniform.
In addition to the meteorological data of the ground meteorological stations, the evapotranspiration data obtained by the remote sensing technology are also used for evaluating the driving effect on drought. The evapotranspiration data were obtained from a medium resolution imaging spectrometer (Moderate Resolution Imaging Spectroradiometer, MODIS) (https:// MODIS. Gsfc. Nasa. Gov /). This data time resolution has been synthesized from 8 days to a month scale to be compatible with other data.
Novel drought event for total water reserve loss index monitoring
Insufficient land water reserves often indicate drought events. Fig. 2 shows time-series changes and overall trends of land water reserves abnormality in southwest areas 2003 to 2016. The land water reserves in southwest areas are abnormal during the research period, show obvious seasonal variation, fall to the bottom of the paddy in 3-5 months and rise to the peak value in 8-10 months each year. According to the land water reserves anomaly trend line in fig. 3, the land water reserves anomaly generally has an upward trend during 2003-2016, and the increase rate is 4.67mm/yr. But during 2009-2010, abnormal land water reserves were abnormally low in southwest, and water reserves were abnormally up to-100.18 mm at the lowest value during the study period in month 3 of 2010, indicating that serious drought events may occur during 2009-2010.
Fig. 3 (a) shows that the total water reserve loss in the study area during 9-4 months 2009-2010 persists to a negative value, with a minimum of-29.83%, indicating that the study area is in a dry state. Assuming that the drought event is initiated at 9 months 2009, the cumulative total water reserve deficit from 9 months 2009 to 4 months 2010 can be calculated from the total water reserve deficit, as indicated by the dotted line in (b) of fig. 3. During this period, the accumulated total water reserve deficit gradually decreases, reaches a minimum value at month 4 of 2010, and then slowly rises. And (3) performing linear fitting by using the accumulated total water reserve loss from 9 months in 2009 to 4 months in 2010 to obtain a best fit line, and obtaining parameters required by the formula (3). The solid line in fig. 3 (b) is the best fit line, and the slope and intercept are m= -22.90 and b=2.93, respectively.
Parameter C is jointly determined by SPI and SPEI. SPI and SPEI values were calculated on a 6 month time scale (SPI-6 and SPEI-6) using a 51 year meteorological record in the study area. SPI and SPEI data from 9 months 2009 to 4 months 2010 are used to estimate parameter C. As shown in FIG. 4, during this period, SPI-6 results showed that 92.75% of the sites suffered drought, while SPEI-6, which takes into account the temperature effects, showed 97.83% of the sites suffered drought. Both drought indices showed that nearly half of the sites suffered extreme drought (D4). The results for SPI-6 indicate that 20.29% of sites are exposed to severe drought (D3), while the results for SPEI-6 more show that 28.26% of sites are exposed to severe drought (D3). Therefore, considering the results of two drought indexes in the southwest region from 9 months 2009 to 4 months 2010, the parameter C during this period was defined as severe drought, and the C value was-3. The best fit line for the total water reserve deficit accumulated during the period 9 months 2009 to 4 months 2010 is defined as the upper limit of severe drought. The horizontal zero line in (b) of fig. 3 is defined as normal, and two dotted lines at equal intervals are inserted in the interval from normal to severe drought, representing the upper limits of mild and moderate drought, respectively, i.e., the dotted lines labeled "-1" and "-2" in (b) of fig. 3. A fourth line is added at equal intervals below the best fit line, representing the upper limit of extreme drought, i.e., the dashed line labeled "-4 in fig. 3 (b).
From the values of parameters m, b and C, the parameter p can be calculated to be-0.15 and q to be 0.15. By substituting the values of p and q into equation (2), TSDI of the study area is obtained:
TSDIj＝-0.15×TSDIj-1+0.15×TSDj···(8)
wherein, initial TSDI 1 is obtained by multiplying TSD 1 by 2%.
As shown in FIG. 5, TSDI in southwest 2003-2016 fluctuates between-4.56 and 5.41. When TSDI values are less than-1 for three consecutive months and above, this period is defined as a drought event. A total of 7 drought events were identified in 2003-2016. By calculating the best fit line for the accumulation TSDI of each drought event, the drought extent for each drought event can be obtained. Drought in southwest is mainly slight and moderate, and is concentrated in winter and spring each year.
Novel comparison of total Water storage loss index with other Standard drought index
The most serious drought monitored by the novel total water reserve loss index method occurs in the period 2009.09-2010.04, and the average value of TSDI months in southwest area is-2.68, -2.80, -4.00, -2.65, -2.64, -3.65, -3.95 and-1.99 respectively. The most severe drought event occurred in month 11 of 2009 with TSDI averages-4.00, reaching extreme drought levels (D4). Severe drought events (D3) occurred in month 2 2010 and month 3 2010, TSDI had average values of-3.65 and-3.95, respectively. Drought relief was mild drought at 4 months 2010 with TSDI mean-1.99.
After calculation of TSDI time series, the present invention compares TDSI results with SPI, SPEI, SC-PDSI. As shown in FIG. 6, TSDI are consistent with SPI, SPEI, and SC-PDSI, having similar peaks and valleys. Of these, SPI-6 and SPEI-6 and TSDI are the best consistent, with large differences in SPI-3 and SPEI-3 and TSDI, whereas SPI-12 and SPEI-12 are too smooth compared to TSDI (FIGS. 6 (a) and (b)). After 2010, SC-PDSI was different from TSDI, but the general trend was consistent (fig. 6 (c)). All drought indices have three major valleys in 2006, 2009-2010 and 2011.
As can be seen from fig. 7, there is a significant correlation (95% confidence interval) between TSDI and other drought indices. TSDI has the highest correlation with SPI-6 and the lowest correlation with SPEI-3. SPI and SPI have higher correlation on same time scale, and SC-PDSI is strongest with SPI and SPEI on 12 month's time scale. Overall, there is a significant correlation between the three standardized drought indicators and TSDI, indicating that TSDI can effectively monitor drought in southwest regions. Meanwhile, drought information extracted from Chinese water and drought disaster gazette is used as a standard, and the drought monitoring accuracy of the indexes is compared. On drought monitoring accuracy, TSDI was improved by 37.5%, 29.2% and 50% over SPI, SPEI and SC-PDSI, respectively. Especially on short-term drought, TSDI's drought monitoring results are more accurate.
During drought, yunnan suffers from severe and extreme drought, while Chongqing suffers less from drought. At the worst drought of 11 months in 2009, the average TSDI values of Sichuan, chongqing, guizhou, guangxi and Yunnan were-3.22, -1.22, -2.85, -4.34 and-5.98, respectively.
TSDI is compared with the SC-PDSI in time, the spatial distribution is also compared. For continuous drought in 2009-2010, TSDI better shows the process of drought development from Yunnan province to Guangxi Zhuang nationality in Guizhou province, which is consistent with the records of Chinese water and drought disaster gazette, while SC-PDSI underestimates drought of Yunnan province and Guizhou province in the early period of drought and overestimates drought of Guangxi Zhuang nationality in the late period of drought. In this drought event, TSDI showed more intuitive drought range and severity changes than SC-PDSI.
Partial least squares regression model results
TSDI in southwest was simulated using partial least squares regression, and the contribution of each climate driving factor to drought progression was evaluated. Precipitation reflects the moisture input condition of an area, air temperature, ground temperature and wind speed influence moisture loss through evapotranspiration, and humidity reflects the air drying degree of the area. As shown in fig. 8, the magnitude of the influence of each climate driving factor on drought development can be quantitatively evaluated through the variable importance results of partial least squares regression analysis. When a variable has a variable importance value greater than 0.8, it is considered significant for interpreting the dependent variable. Fig. 8 shows that variables with values of variable importance greater than 0.8 are humidity (1.57), precipitation (1.44) and evapotranspiration (0.96). The dominant factors affecting drought during 2003-2016 in southwest are lower humidity, insufficient precipitation and excessive transpiration.
Discussion of the invention
Uncertainty of GRACE-based land water reserve anomalies
The point quality method product has been applied to drought monitoring in some areas in the past researches, for example Sun et al, and the drought serious state of the Yangtze river basin of the middle country in 2003-2015 is evaluated based on CSR RL05 point quality method products. The comparison result of CSR RL05 point quality method and CSR RL06 point quality method in southwest region shows that the two products have higher correlation (the correlation coefficient is 0.98). Thus, CSR RL06 point quality method products can be used for drought characterization. In addition, uncertainty in CSR RL06 point quality method products in southwest regions was also assessed.
The invention adopts Root Mean Square (RMS) of residual components obtained by STL decomposition to evaluate uncertainty of CSR RL06 point quality method products in southwest regions. The spatial distribution of RMS of the southwest regional land water reserve anomaly time series residuals indicates that the northwest region is less uncertainty than the southern region. This is substantially consistent with Scanlon et al, product evaluation on a global scale of RMS results based on CSR RL05 point quality method.
Drought severity assessment
The southwest region of china has suffered from more frequent extreme disasters in the last decades, affected by global climate change. In the first 10 years of the 21 st century, southwest China suffered from multiple severe drought. Zhang and Zhou also confirmed in a retrospective analysis of drought that extreme drought events occurred in southwest regions during both 2006 and 2009-2010. This suggests TSDI that it is effective to estimate the severity of extreme drought. Notably, since TSDI overestimates the length of drought in 2006, the estimated overall drought level in southwest 2006 is only moderate drought, with a lower overall drought level than is practical. This may be due to deviations caused by insufficient time series of GRACE data.
An important measure of drought events is severity and coverage. The monitoring of extreme drought in southwest 2009-2010 based on GRACE TSDI was mainly focused in northwest of the autonomous areas of the province of yunnan, the province of noble and the guangxi. This is substantially consistent with Zhao et al giving the spatial distribution of this extreme drought event based on GRACE data. TSDI clearly shows the most severe areas of this drought event and the spatial distribution detected is substantially consistent with other studies and government reports. Therefore TSDI can effectively detect the spatial distribution of drought severity.
Figure 9 shows the percentage of area that is subject to drought of varying severity per month, which can help to better distinguish between different periods of drought severity in southwest regions. The southwest region in 2003-2011 suffered from an average of 51% drought area for all severity, whereas 2012-2016 were only 25%. This shows that the southwest region suffers from drought in 2003-2011 much more than 2012-2016. The most affected period is 2009-2010 southwest winter-spring drought. On average, 33% of the southwest regions suffered extreme drought (D4), 13% suffered severe drought (D3), 14% suffered moderate drought (D2), 19% suffered slight drought (D1), and only 21% did not suffer drought in the regions 9 to 4 months 2010.
Partial least squares regression model result analysis
The invention establishes the connection between the climate forcing factor and the drought by using the partial least square regression model, and analyzes the importance of the climate forcing factor on the drought by using the variable importance based on the partial least square regression model. The invention uses variable importance to determine climate forcing factors which have substantial influence on drought in southwest China, namely humidity, precipitation and evapotranspiration. Wherein the variable importance values of humidity and precipitation are far greater than the evapotranspiration, indicating that the main factors affecting drought are lower humidity and precipitation. In addition, the invention extracts the annual trend of TSDI and the climate forcing factor by using the STL method and reflects the relation between the TSDI and the climate forcing factor. From fig. 10, the annual trend of humidity, precipitation and evapotranspiration is consistent with that of TSDI, which indicates that drought and these three climate forcing factors have important relations in southwest China. However, there is a significant time lag between the climate forcing factor and the drought index TSDI, which may lead to uncertainty in evaluating the relationship between them.
Advantages and limitations of GRACE data
Compared to traditional drought monitoring methods based on site observations, GRACE satellites provide additional information about drought. Grace satellites can detect changes in reserves from the surface to deep groundwater, but traditional hydrologic and meteorological stations have difficulty capturing the changing state of deep groundwater. Thus, GRACE data can help more effectively characterize the intrinsic mechanisms of drought formation.
According to the invention, the GRACE point quality method solution is used as an input parameter to calculate the drought index, and compared with the spherical harmonic function solution, the point quality method solution reduces additional post-treatment, and a more accurate result can be obtained. However, GRACE data is mainly used to evaluate impoundment change information for large watershed (> 200000km 2), which limits its application in small areas. Furthermore, studies have shown that a long time series (at least 30 years) of GRACE data is required to accurately determine whether regional water reserves are truly in an inadequate state. The GRACE Follow-On gravity satellite launched in month 5 of 2018 will be able to continue to provide global land water reserve change data. The GRACE data of the future long-time sequence can improve the accuracy of global drought monitoring and provide sufficient data for researching the cause and development of drought.
Although the present invention has been described in terms of the preferred embodiments, it is not intended to be limited to the embodiments, and any person skilled in the art can make any possible variations and modifications to the technical solution of the present invention by using the methods and technical matters disclosed above without departing from the spirit and scope of the present invention, so any simple modifications, equivalent variations and modifications to the embodiments described above according to the technical matters of the present invention are within the scope of the technical matters of the present invention.
What is not described in detail in the present specification belongs to the known technology of those skilled in the art.
Claims (7)
1. A method for improving drought monitoring space-time accuracy based on a total water reserve loss index principle, comprising the steps of:
Extracting land water reserve abnormal value TSA i,j of the ith year and the jth month from GRACE data;
Calculating a total water reserve abnormal long-term average MTSA j, a maximum value MaxTSA j and a minimum value MinTSA j of the j-th month according to TSA i,j;
Calculating a total water reserve loss value TSD i,j of the ith year and the jth month according to TSA i,j、MTSAj、MaxTSAj and MinTSA j;
determining a total water reserve deficit value TSD j of month j according to the TSD i,j;
acquiring a current total water reserve loss weight p and a historical total water reserve loss weight q;
according to TSD j, p and q, calculating to obtain a total water reserve loss index of each month;
The solution formula for the total water reserve deficit TSD i,j for month j of the i-th year is as follows:
The solution formula of the total water reserve loss index of each month is as follows:
TSDIj＝p×TSDIj-1+q×TSDj···(2)
Wherein TSDI j-1 represents the total water reserve deficit index of month j-1, TSDI j represents the total water reserve deficit index of month j;
acquiring a current total water reserve loss weight p and a historical total water reserve loss weight q, including:
Fitting to obtain a loss curve of the accumulated total water reserve in the drought period according to the accumulated total water reserve in the drought period;
Acquiring the slope m and intercept b of a cumulative total water reserve loss curve in the drought period;
according to the slope m and the intercept b, the current total water reserve loss weight p and the historical total water reserve loss weight q are obtained through the following calculation:
wherein, C represents drought intensity.
2. The method for improving the accuracy of drought monitoring space-time based on the total water reserve loss index principle according to claim 1, wherein fitting the accumulated total water reserve loss curve during the drought period comprises:
Taking a month as a calculation period, and calculating to obtain a total water reserve loss value of each month by adopting the formula (1);
According to the calculated total water reserve loss value of each month, the accumulated water reserve loss value of the previous 1 month, the accumulated water reserve loss value of the previous 2 months and the accumulated water reserve loss value of the previous tau months are obtained;
Fitting to obtain a linear curve of the accumulated water reserve loss value and time by taking the time month as an abscissa according to the obtained accumulated water reserve loss value of the previous 1 month, the accumulated water reserve loss value of the previous 2 months, & gtand the accumulated water reserve loss value of the previous tau months;
And intercepting a curve of the set condition from a linear curve of the accumulated water reserve loss value and time obtained by fitting to serve as an accumulated total water reserve loss curve in the drought period.
3. The method for improving accuracy of drought monitoring space-time based on the total water storage loss index principle according to claim 2, wherein the step of intercepting a curve of a set condition from a linear curve of a cumulative water storage loss value obtained by fitting and time as a cumulative total water storage loss curve in a drought period comprises the steps of:
screening the total water reserve loss value of each month to obtain a minimum monthly total water reserve loss value;
screening from a linear curve of the accumulated water reserve loss value and time obtained by fitting to obtain l critical points; wherein, the critical point is: a first negative point at which the accumulated reserve loss value changes from positive to negative;
Screening from the critical points to obtain a critical point l 0 closest to the minimum value of the month total water reserve loss;
Taking the critical point l 0 as a starting point l star of a cumulative total water reserve loss curve in the drought period;
Determining an endpoint l end of a cumulative total water reserve loss curve in the drought period; wherein, the endpoint l end satisfies: the accumulated water reserve loss value corresponding to the end point l end is smaller than the accumulated water reserve loss value corresponding to the next month;
And according to the determined starting point l star and the determined end point l end, intercepting a curve of the set condition from a linear curve of the accumulated water reserve loss value obtained by fitting and time as an accumulated total water reserve loss curve in the drought period.
4. The method for improving drought monitoring space-time accuracy based on the total water reserve loss index principle according to claim 1, wherein the value of drought intensity C is determined by:
calculating to obtain a standardized precipitation index SPI and a standardized precipitation evapotranspiration index SPEI according to weather record data of weather stations in a research area;
When-0.5 < SPI or-0.5 < SPEI, no drought is indicated, c=0;
When SPI is less than or equal to-1 and less than or equal to-0.5 or SPEI is less than or equal to-1 and less than or equal to-0.5, slight drought is represented, and C= -1;
when SPI is less than or equal to-1.5 and less than or equal to-1 or SPEI is less than or equal to-1.5, moderate drought is represented, and C= -2;
When SPI is less than or equal to-2 and less than or equal to-1.5 or SPEI is less than or equal to-2 and less than or equal to-1.5, serious drought is represented, and C= -3;
When SPI is less than or equal to-2 or SPEI is less than or equal to-2, extreme drought is represented, and C= -4.
5. The method for improving drought monitoring spatiotemporal accuracy based on the total water reserve loss index principle of claim 1, further comprising: and obtaining the contribution degree of each climate driving factor to drought development through a partial least squares regression model according to the calculated total water reserve loss index of each month, so that researchers can predict and prevent drought according to the contribution degree of each climate driving factor to drought development.
6. The method for improving the accuracy of drought monitoring space-time based on the total water storage loss index principle according to claim 5, wherein the contribution degree of each climate driving factor to drought development is obtained through a partial least squares regression model according to the calculated total water storage loss index of each month, comprising:
Taking the month total water reserve loss index TSDI j as a dependent variable Y, taking each climate driving factor as an independent variable X η, and constructing to obtain a partial least square regression model:
Y＝a0+a1X1+a2X2+...+aηXη+...+anXn
The partial least square regression model is a linear regression equation, a 0 represents the intercept of the linear regression equation, a 1、a2、…、aη、…、an represents the regression coefficient obtained by fitting the linear regression equation, and n=1, 2,3 and …;
And determining the contribution degree of each climate driving factor to drought development according to the a 1、a2、…、aη、…、an obtained by fitting.
7. The method for improving drought monitoring spatiotemporal accuracy based on the total water reserve loss index principle of claim 6, characterized by climate driving factors comprising: precipitation, air temperature, ground temperature, humidity, air pressure, wind speed and transpiration.
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