CN113095621B - Agricultural drought monitoring method based on meteorological time lag of soil moisture - Google Patents
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Abstract
The invention relates to an agricultural drought monitoring method based on soil moisture to meteorological time lag, which comprises the following steps: preparing data of a research area, calculating evapotranspiration data by using a Peneman formula, interpolating precipitation and evapotranspiration, resampling vegetation health indexes, and ensuring that monthly average data in the research area have the same spatial resolution. Estimating the lag time of soil moisture to precipitation and soil moisture to evapotranspiration by means of a cross wavelet analysis method; and sequentially calculating the relative values of rainfall, evapotranspiration and soil moisture relative to the historical overall level, and calculating the agricultural drought index of the research area by combining the time lag condition. The method is subjected to correlation analysis with the traditional drought index, and the reliability of the method is proved in the aspects of time, space and drought area of crops. The invention constructs a brand-new agricultural drought index to realize more accurate agricultural drought identification, monitoring and disaster loss evaluation, and can provide important scientific basis for natural disaster monitoring evaluation and disaster prevention and reduction decision making.
Description
Technical Field
The invention belongs to the application technology of a spatial information technology in the field of hydraulic engineering, and particularly relates to an agricultural drought monitoring method based on soil moisture to meteorological time lag.
Background
Agricultural drought is a phenomenon of crop yield reduction due to insufficient soil moisture, which can restrict grain production and cause a global grain safety problem. Drought indices are widely used to identify and monitor drought conditions in order to quantitatively assess the intensity, duration and spatial extent of drought and are currently becoming effective methods for monitoring drought events.
Traditional drought indexes are widely applied to global drought research, such as the Pelmer drought index, the standardized rainfall index and the standardized rainfall evapotranspiration index. The indexes are designed for specific applications such as weather drought, only single weather factors of rainfall and evapotranspiration are considered, the indexes are insufficient in reflecting the influence of an agricultural drought mechanism and drought on crops, and due to the influence of residual moisture in soil, the lag response of vegetation to insufficient rainfall makes the agricultural drought possibly lag behind the traditional drought index. A large number of research achievements are made on the aspect of comprehensively considering soil moisture, hydrology and meteorological factors to construct an agricultural drought index, for example, the comprehensive drought index is constructed on the basis of precipitation, runoff and soil moisture factors, and the weight of each factor is determined by an entropy weight method; or based on the inverse relation between the surface temperature and the vegetation condition, then considering the response of the vegetation to the soil moisture, setting the soil moisture as a multiplier factor, and establishing the soil moisture agricultural drought index. The possible hysteresis relationships between the multiple factors still need to be explored in depth.
The drought can be divided into various categories according to the application field, the essential reason causing the drought is insufficient rainfall, the direct reason causing the agricultural drought is insufficient soil moisture, the precipitation amount is one of important sources of the soil moisture, meanwhile, the soil moisture has important feedback effects on evapotranspiration and the precipitation by influencing the distribution of latent heat flux and sensible heat flux, the precipitation serves as an input factor of the soil moisture, the evapotranspiration serves as an output factor of the soil moisture, and therefore, complex response and feedback relations exist among the soil moisture, the precipitation and the evapotranspiration. Precipitation, evapotranspiration and soil moisture are potential factors which must be considered when building an agricultural drought index and monitoring agricultural drought.
In combination with the above studies, many scholars have studied the use of drought index for agricultural drought monitoring. The method comprises the steps of constructing an agricultural drought index containing soil moisture, precipitation and evapotranspiration factors and considering time lag effect so as to realize more accurate agricultural drought identification, monitoring and disaster loss assessment, and has important application value for natural disaster monitoring and assessment and disaster prevention and reduction decision making.
Disclosure of Invention
The invention aims to provide an agricultural drought monitoring method based on time lag of soil moisture to weather, which constructs a brand-new agricultural drought index based on soil moisture, precipitation and evapotranspiration factors and considering the time lag effect of the soil moisture to the precipitation and evapotranspiration factors, and realizes the monitoring of agricultural drought.
The technical scheme adopted by the invention is that the agricultural drought monitoring method based on the time lag of soil moisture to weather comprises the following steps,
preparing research area data, including long-time-sequence soil moisture data, precipitation data, evapotranspiration data, vegetation health index data and ground climate data, wherein the ground climate data comprises sunshine time, average temperature, average highest temperature, average lowest temperature, average wind speed, average water pressure and average relative humidity;
(21) calculation of lag time
(22) The rainfall data and the evapotranspiration data are normalized to eliminate the influence of different data absolute values and promote the uniformity of the drought standard;
(23) defining an agricultural drought index as CADIiThe calculation formula is as follows:
wherein the SMCIiDefined as the soil moisture condition, the soil moisture condition is used as a multiplier factor and the early-stage climate condition DjMultiplying to obtain an agricultural drought index, wherein the difference value between i and j represents the time that the soil moisture lags behind precipitation and evapotranspiration; PCIjAnd ECIjDefining precipitation conditions and evapotranspiration conditions, which respectively represent the relative values of precipitation and evapotranspiration with respect to historical overall levels;
SMCIirepresenting the relative value of soil moisture with respect to the historical overall level, SMiRepresenting the absolute value of soil moisture at time i, SMmax、SMminRespectively representing the maximum value and the minimum value of soil moisture in the historical period;
finally, the CADI in equation (4) is usediThe values were normalized between 0 and 1 and then multiplied by 8 to facilitate selection of a drought threshold for drought grading;
Further, in the step 1, evaporation and emission data are calculated by using a Peneman formula and ground climate data, spatial interpolation operation is carried out on precipitation data and evaporation and emission data, resampling operation is carried out on vegetation health index data, corresponding grid data in a research area range are obtained, the spatial resolution of soil moisture data, precipitation data, evaporation and emission data and vegetation health index data is guaranteed to be consistent, and the time resolution is a monthly average value.
Further, the specific implementation manner of step (21) is as follows;
and performing cross wavelet transformation of soil moisture to precipitation and soil moisture to evapotranspiration by using a cross wavelet analysis method to obtain a cross wavelet energy spectrum and a phase relation of the high-energy accumulation area of the two corresponding relations, and calculating corresponding lag time.
Further, the specific implementation manner of the step (22) is as follows,
defining the climatic conditions as DjThe expression indicates the early-stage climate condition of the research area, and the larger the value is, the drier is;
wherein PCIjAnd ECIjDefining precipitation conditions and evapotranspiration conditions, which respectively represent the relative values of precipitation and evapotranspiration with respect to historical overall levels; pjAnd EjRespectively representing the absolute values of precipitation and evapotranspiration in period j, Pmax，Emax，Pmin，EminRespectively representing the maximum value and the minimum value of the precipitation and the evapotranspiration in the historical period; calculated according to the formula, PCIjAnd ECIjAre all in the range of 0 to 1, PCIjLarger values indicate less precipitation, ECIjSmaller values indicate more evapotranspiration.
Further, in step 3, the precipitation data and the evapotranspiration data obtained in step 1 are used for calculating a standardized precipitation index SPI and a standardized precipitation evapotranspiration index SPEI of each month scale, and correlation analysis is carried out on the CADI result, the SPI, the SPEI and the vegetation health index.
Further, the threshold partitioning of CADI drought levels was set as: and (3) normal: 0 to 0.2; mild drought: 0.2-0.4; moderate drought: 0.4-0.6; severe drought: 0.6 to 1; extreme drought: is greater than 1.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) the drought index which is easy to calculate is constructed to identify, monitor and represent the agricultural drought phenomenon, and the drought index has important application values for drought monitoring evaluation and disaster prevention and reduction decision making;
(2) in the implementation process, a plurality of influence factors are comprehensively considered, and the lag response time of soil moisture in different areas to precipitation and evapotranspiration can be reflected in space and time, so that the method is more comprehensive than the prior art;
(3) the method is more sensitive to the air agriculture drought in capture time and space, and is more suitable for agriculture drought monitoring than the prior art;
(4) the method has obvious drought monitoring effect in the embodiment, and is beneficial to assisting in making a decision for enhancing disaster prevention and reduction in the crop growth period.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a study area of an embodiment of the present invention;
FIG. 3 illustrates a time lag relationship according to an embodiment of the present invention;
FIG. 4 shows CADI calculation results for an embodiment of the present invention, (a) semi-arid area, (b) semi-moist area, (c) moist area;
FIG. 5 illustrates the monitoring advantages of embodiments of the present invention in summer drought;
FIG. 6 shows the spatial monitoring effect of an embodiment of the present invention, (a)1981-2018 CADI mean (b) 2016-8 CADI (c) 2016-8 CADI mean (d) 2016-spring CADI mean (e) 2016-summer CADI mean (f) 2016-autumn CADI mean;
fig. 7 shows the agricultural drought monitoring effect of the embodiment of the present invention, (a) sichuan province, (b) Chongqing city, (c) Hubei province, (d) Hunan province, and (e) Jiangxi province.
Detailed Description
The technical scheme of the invention is detailed below based on specific embodiments, and the flow is shown in fig. 1:
Preparing research area data including long-time-sequence soil moisture data, precipitation data, vegetation health index data, ground climate data, (including sunshine time (h/day), average temperature (DEG C), average maximum temperature (DEG C), average minimum temperature (DEG C), average wind speed (m/s), average water air pressure (hPa) and average relative humidity (%), and vegetation health index data. And calculating evapotranspiration data by using a Peneman formula and ground climate data, performing spatial interpolation operation on precipitation and evapotranspiration, and performing resampling operation on the vegetation health index to obtain corresponding grid data in a research area range, so that the spatial resolutions of the soil moisture data, the precipitation data, the evapotranspiration data and the vegetation health index data are consistent, and the time resolution is a monthly average value.
And 2, constructing and calculating an agricultural drought index.
(1) And calculating the lag time.
And performing cross wavelet transformation of soil moisture to precipitation and soil moisture to evapotranspiration by using a cross wavelet analysis method to obtain a cross wavelet energy spectrum and a phase relation of the high-energy accumulation area of the two corresponding relations, and calculating corresponding lag time.
(2) Defining the climatic conditions as DjAnd represents the early-stage climate condition of the research area, and the larger the value of the early-stage climate condition is, the drier the value is.
Wherein PCIjAnd ECIjDefined as precipitation conditions and evapotranspiration conditions, representing the relative values of precipitation and evapotranspiration, respectively, with respect to the historical overall level. PjAnd EjRespectively representing the absolute values of precipitation and evapotranspiration in period j, Pmax，Emax，Pmin，EminRespectively representing the maximum value and the minimum value of the rainfall and the evapotranspiration in the historical period. In the step, precipitation and evapotranspiration are normalized to eliminate the influence of different data absolute values and promote the uniformity of the drought standard. Calculated according to the formula, PCIjAnd ECIjAre all in the range of 0 to 1, PCIjLarger values indicate less precipitation, ECIjSmaller values indicate more evapotranspiration.
(3) Defining an agricultural drought index as CADIi。
Wherein the SMCIiDefined as the soil moisture condition, the soil moisture condition is used as a multiplier factor and the early-stage climate condition DjMultiplying to obtain the agricultural drought index. The difference between i and j indicates the time that the soil moisture lags behind precipitation and transpiration.
SMCIiIs calculated and PCIj、ECIjSimilarly, the relative value representing soil moisture relative to historical overall levels, SMiRepresenting the absolute value of soil moisture at time i, SMmax、SMminRespectively representing the maximum value and the minimum value of soil moisture in the historical period. SMCIiSoil moisture values were normalized to between 1 (dry condition) and 0 (wet condition). Soil moisture conditions SMCIiAs a multiplier factor, will be boosted (CADI)iNear 1) drought conditions in pre-climatic conditions, or reduced (CADI)iNear 0) drought conditions in the pre-climate. Finally, the CADI in equation (4) is usediValues were normalized between 0 and 1 and then multiplied by 8 to facilitate selection of drought thresholds for drought grading.
And 3, performing agricultural drought monitoring performance.
(1) The precipitation data and evapotranspiration data obtained in step 1 can be used for calculating the standardized precipitation indexes (SPI-1, 3 and 12) at the 1, 3 and 12 month scale and the standardized precipitation evapotranspiration indexes (SPEI-1, 3 and 12) at the 1, 3 and 12 month scale. To verify the applicability and analyze the superiority of CADI, the CADI results were correlated with SPI-1, 3, 12, SPEI-1, 3, 12, vegetation health index, and then subjected to thresholding for different drought levels. The superiority of CADI in monitoring agricultural drought was analyzed based on historical drought events.
(2) The CADI is applied to agricultural drought monitoring in time and space angles of a research area, and is compared with historical crop drought area data, so that the reliability of the CADI in agricultural drought monitoring is proved.
The steps of the present invention will be described in detail below with reference to specific embodiments.
Research area data were prepared including long-term soil moisture data (from active and passive microwave synthetic remote sensing soil moisture data sets released by the european space agency), precipitation data and ground climate data (including sunshine time (h/day), average temperature (c), average maximum temperature (c), average minimum temperature (c), average wind speed (m/s), average water pressure (hPa) and average relative humidity (%) (from the chinese meteorological data network), vegetation health index data (from the National Oceanic and Atmospheric Administration satellite applications and research center). Calculating evapotranspiration by using a Peneman formula and ground climate data, performing spatial interpolation operation on precipitation data and evapotranspiration data, and performing resampling operation on vegetation health index data to obtain corresponding grid data in a research area range, so that the spatial resolution of soil moisture, precipitation, evapotranspiration and vegetation health indexes is ensured to be 0.25 degrees multiplied by 0.25 degrees, the time resolution is a monthly average value, and the time span is 1 month in 1981 to 12 months in 2018.
The study area is the Yangtze river basin, see FIG. 2, and the data respectively have 2540 grid data in the study area. The Yangtze river basin is divided into 11 sub-basins according to the tributaries. The Yangtze river basin has complex climatic conditions and uneven time-space distribution, and is a semi-arid area, a semi-humid area and a humid area from the northwest to the southeast in sequence; the annual average precipitation from west to east is about 300-2400mm, and the annual average temperature is about 4-24 ℃; the precipitation is concentrated in summer, and the drought is prone to occur from the middle of 7 months to the middle of 8 months. The Yangtze river basin has six main crop production areas, which are main commercial food bases in China.
And 2, constructing and calculating an agricultural drought index.
(1) And estimating the lag time of soil moisture to precipitation and soil moisture to evapotranspiration by means of a cross wavelet transform method. Cross wavelet analysis is an effective tool developed by Friehe et al (1993) for exploring the correlation between two time series data, is a new technology combining cross spectrum analysis and wavelet transformation, and can better check the relation between two time series in the time-frequency domain. The code for performing the cross-wavelet analysis in the present invention is from Grinsted et al (2004). 24 meteorological sites which are uniformly distributed are selected in the Yangtze river basin for experiments, and the cross wavelet energy spectrum of soil moisture to precipitation and soil moisture to evapotranspiration and the phase relation of a high-energy gathering area are obtained by utilizing cross wavelet transformation. The results show that the cross wavelet energy spectrum, the phase relationship of the high energy concentration region have spatial heterogeneity and spatial concentration effect. In a drier area in the northwest of the Yangtze river basin, the cross wavelet energy spectrums of the soil moisture to precipitation and the soil moisture to evapotranspiration are gathered in a common period of 4-8 months, and the phase relation is that the precipitation and the evapotranspiration both lead the soil moisture 1/8 period, namely the soil moisture lags behind the precipitation and the evapotranspiration for about 0.5-1 month. In the middle and east wet areas of the Yangtze river basin, the cross wavelet energy spectrums of soil moisture to precipitation and soil moisture to evapotranspiration are gathered in a common period of 8-16 months, the phase relation is that the precipitation and evapotranspiration both lead the soil moisture 7/8 period, namely the soil moisture lags behind the precipitation and evapotranspiration by about 7-14 months, and the time lag of 2 months is selected in consideration of the short-term influence of the precipitation and evapotranspiration on the soil moisture to the soil moisture in one year. The time lag spatial distribution of soil moisture to precipitation and soil moisture to evapotranspiration is shown in figure 3.
(2) And (3) sequentially calculating the relative values of precipitation, evapotranspiration and soil moisture relative to the historical overall level according to the formulas (2), (3), (1) and (5), and calculating the agricultural drought index CADI of the Yangtze river basin from 1 month to 2018 month in 1981 by respectively taking the time lag condition in the step 2(1) and the time lag condition i lags behind j1 month and 2 months. In order to embody the advantage of the method of the invention in comparison with the method of monitoring agricultural drought by using soil moisture, precipitation and evapotranspiration independently, 3 regions of interest are selected in the semi-arid region, the semi-moist region and the moist region respectively to perform CADI, soil moisture, precipitation and evapotranspiration time sequence visualization, as shown in FIG. 4. The CADI value has obvious fluctuation range between 0 and 1, and combines soil water data with small numerical variation range and meteorological factors, thereby having obvious seasonal variation characteristics.
And 3, performing agricultural drought monitoring performance.
(1) The invention is used for analyzing the correlation with the traditional drought index.
To evaluate the effect of CADI, the present example calculated the normalized precipitation indices SPI-1, 3, 12 and the normalized precipitation evapotranspiration indices SPEI-1, 3, 12, and calculated pearson's correlation coefficients between CADI and the above indices and the vegetation health index in the semiarid, semihumid and humid regions, with the maximum correlation coefficients being-0.51, -0.39, -0.50(p-value <0.01) in the three regions in order, since the larger the CADI value, the more arid the smaller the contrast index value, and therefore with a clear negative correlation. The correlation coefficient of different climate areas is comprehensively considered, the correlation between CADI and SPEI-3 is strongest, therefore, SPEI-3 is selected as a reference index for determining CADI drought threshold standard in the embodiment. CADI drought thresholds were set as: and (3) normal: 0 to 0.2; mild drought: 0.2-0.4; moderate drought: 0.4-0.6; severe drought: 0.6 to 1; extreme drought: is greater than 1.
(2) The invention has the performance in time, space and drought-stricken area of crops.
According to the record of the annual book of Chinese meteorological disasters, during 2007 to 2009, rainfall in the south China from 7 to 8 in the middle of the month is obviously less than the average level of the history, and the occurring drought covers the southern parts of Guizhou province, Hunan province and Jiangxi province in the Yangtze river basin; according to the records of drought areas of crops in China countryside annual book, the drought areas of crops in Hunan province and Jiangxi provinces are remarkably increased in 2007 and 2009, while most of crops in Yangtze river basin grow and mature in summer, so that the embodiment takes 2007 and 2009 as an example, and proves the advantages of CADI in the aspect of monitoring the drought, as shown in FIG. 5. FIG. 5 shows a trend and scatter plot of the time evolution of CADI and SPEI-3 values. FIG. 5(b, d, f) shows that CADI and SPEI-3 in the scatter plot have good negative correlation; FIG. 5(a, c, e) illustrates that both CADI and SPEI-3 show significant seasonality over time; the black rectangles in fig. 5(a, c, e) correspond to drought occurring between 7 and 8 months in 2007, 2008 and 2009. Compared with SPEI-3, CADI can not only identify spring and winter drought, but also identify summer drought more effectively, which is especially important for sowing and growing of summer and autumn early rice, cotton, late rice, winter wheat and rape in Yangtze river valley.
In order to embody the monitoring performance of the invention on agricultural drought space of Yangtze river basin, the example visualizes CADI in the Yangtze river basin from 1981 to 2018, CADI in 2016 in 8 months, and CADI in 2016 in spring, summer, autumn and winter, as shown in FIG. 6. FIG. 6(a) shows that, in the past 38 years, regions of severe drought include the northeast of the Wujiang river basin, the midstream of the Yangtze river, and the north and southwest of the Poyang lake basin; FIG. 6(c-f) shows that summer and autumn drought in the Yangtze river basin was more severe than winter and spring drought in 2016, especially in the northwest region of the Jinsha river basin, Minjiang river basin, Tuojiang river basin and Jialin river basin, mid-way to, upstream of the Yangtze river basin and autumn drought in the Poyang lake basin; according to fig. 6(b), in 2016, spatially continuous drought occurred in the Minjiang, Tuojiang and Jialin river basins, which is consistent with the recorded meteorological drought spatial range in the annual book of Chinese meteorological disasters.
In this embodiment, according to "national countryside statistics yearbook" of china, historical data of drought areas of crops in the provinces of the four provinces, Chongqing city, Hubei province, Hunan province and Jiangxi province are counted, and fig. 7 shows average values of CADI years and time evolution trends of drought areas of crops in five provinces and cities in the Yangtze river basin from 1984 to 2018. The results show that the average CADI annual value of the five administrative regions has similar evolution trend with the area value of the drought-stricken crops. The CADI value of the administrative district which is greatly influenced by drought is also larger. The trend also shows the reliability of the invention in application to drought monitoring in agriculture in Yangtze river basin.
Claims (3)
1. An agricultural drought monitoring method based on soil moisture to meteorological time lag is characterized in that: comprises the following steps of (a) carrying out,
step 1, preprocessing data;
preparing research area data, including long-time-sequence soil moisture data, precipitation data, evapotranspiration data, vegetation health index data and ground climate data, wherein the ground climate data comprises sunshine time, average temperature, average highest temperature, average lowest temperature, average wind speed, average water pressure and average relative humidity;
calculating evapotranspiration data by using a Peneman formula and ground climate data, performing spatial interpolation operation on precipitation data and evapotranspiration data, and performing resampling operation on vegetation health index data to obtain corresponding grid data in a research area range, so that the spatial resolutions of soil moisture data, precipitation data, evapotranspiration data and vegetation health index data are consistent;
step 2, constructing and calculating an agricultural drought index, which comprises the following substeps;
(21) calculating the lag time, wherein the specific implementation mode is as follows;
performing cross wavelet transformation of soil moisture to precipitation and soil moisture to evapotranspiration by means of a cross wavelet analysis method to obtain a cross wavelet energy spectrum of the soil moisture to the precipitation and a phase relation of a high-energy accumulation area, and a phase relation of the soil moisture to the evapotranspiration and the phase relation of the high-energy accumulation area, and calculating corresponding lag time;
(22) the rainfall data and the evapotranspiration data are normalized to eliminate the influence of different data absolute values and promote the uniformity of the drought standard;
the specific implementation of step (22) is as follows,
defining the climatic conditions as DjThe expression indicates the early-stage climate condition of the research area, and the larger the value is, the drier is;
wherein PCIjAnd ECIjDefining precipitation conditions and evapotranspiration conditions, which respectively represent the relative values of precipitation and evapotranspiration with respect to historical overall levels; pjAnd EjRespectively representing the absolute values of precipitation and evapotranspiration in period j, Pmax，Emax，Pmin，EminRespectively representing the maximum value and the minimum value of the precipitation and the evapotranspiration in the historical period; calculated according to the formula, PCIjAnd ECIjAre all in the range of 0 to 1, PCIjLarger values indicate less precipitation, ECIjSmaller values indicate more evapotranspiration;
(23) defining an agricultural drought index as CADIiThe calculation formula is as follows:
wherein the SMCIiDefined as the soil moisture condition, the soil moisture condition is used as a multiplier factor and the early-stage climate condition DjMultiplying to obtain agricultureDrought index, the difference between i and j representing the time that soil moisture lags behind precipitation and evapotranspiration; PCIjAnd ECIjDefining precipitation conditions and evapotranspiration conditions, which respectively represent the relative values of precipitation and evapotranspiration with respect to historical overall levels;
SMCIirepresenting the relative value of soil moisture with respect to the historical overall level, SMiRepresenting the absolute value of soil moisture at time i, SMmax、SMminRespectively representing the maximum value and the minimum value of soil moisture in the historical period;
finally, the CADI in equation (1) is usediValues are normalized between 0 and 1, then multiplied by 8 to give CADI'iTo facilitate selection of a drought threshold for drought grading;
step 3, CADI'iPerforming correlation analysis with the traditional drought index, and then performing CADI'iCarrying out threshold division of different drought grades, and finally carrying out CADI'iThe method is applied to agricultural drought monitoring in time and space angles of a research area, and is compared with historical data of the drought areas of crops.
2. The agricultural drought monitoring method based on soil moisture to meteorological time lag as claimed in claim 1, wherein the method comprises the following steps: in step 3, the precipitation data and the evapotranspiration data obtained in step 1 are used for calculating the standardized precipitation index SPI and the standardized precipitation evapotranspiration index SPEI of each month scale, and CADI 'is used'iCorrelation analysis was performed with SPI, SPEI, vegetation health index.
3. The agricultural drought monitoring method based on soil moisture to meteorological time lag as claimed in claim 1, wherein the method comprises the following steps: the threshold partitioning of drought levels was set as: and (3) normal: 0 to 0.2; mild drought: 0.2-0.4; moderate drought: 0.4-0.6; severe drought: 0.6 to 1; extreme drought: is greater than 1.
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