sustainability
Article Characterization and Evaluation of MODIS-Derived Crop Water Stress Index (CWSI) for Monitoring Drought from 2001 to 2017 over Inner Mongolia
Zi-Ce Ma 1, Peng Sun 1,2,3,* , Qiang Zhang 2,3,*, Yu-Qian Hu 1 and Wei Jiang 1
1 School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China; [email protected] (Z.-C.M.); [email protected] (Y.-Q.H.); [email protected] (W.J.) 2 State Key Laboratory of Surface Process and Resource Ecology, Beijing Normal University, Beijing 100875, China 3 Academy of Disaster Reduction and Emergency Management, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China * Correspondence: [email protected] (P.S.); [email protected] (Q.Z.)
Abstract: Inner Mongolia is one of the main green production bases of agricultural and animal husbandry products. Due to factors such as natural geographical location, drought occurs frequently in Inner Mongolia. Based on the MOD16 product and the method of crop water stress index (CWSI) combined with multi-year precipitation and temperature data, the spatial and temporal distribution characteristics and major influencing factors of drought in Inner Mongolia from 2001 to 2017 were analyzed. In order to provide effective scientific basis for drought control and drought resistance in Inner Mongolia for decision. The results showed that: (1) during 2001–2017, the average annual CWSI in Inner Mongolia had a strong spatial heterogeneity, which showed a trend of gradual increase from northeast to southwest. The annual average CWSI was 0.7787 and showed a fluctuating downward trend for Inner Mongolia. (2) The CWSI of every 8d during one year in Inner Mongolia showed the Citation: Ma, Z.-C.; Sun, P.; Zhang, double-peak trend, reaching its maximum of 0.9043 in the 121st day. In addition, the average CWSI Q.; Hu, Y.-Q.; Jiang, W. of every 8d was 0.6749. (3) In Inner Mongolia, the average CWSI of different land-use types showed Characterization and Evaluation of little difference and ranged from small to large: woodland (0.5954) < cropland (0.7733) < built-up MODIS-Derived Crop Water Stress land (0.8126) < grassland (0.8147) < unused land (0.8392). (4) The average correlation coefficients Index (CWSI) for Monitoring between CWSI and precipitation, temperature respectively were −0.53 and 0.18, which indicated Drought from 2001 to 2017 over Inner that CWSI was highly correlated with precipitation in Inner Mongolia. Mongolia. Sustainability 2021, 13, 916. https://doi.org/10.3390/su13020916 Keywords: drought; CWSI; land-use; temperature; precipitation; Inner Mongolia
Received: 26 December 2020 Accepted: 14 January 2021 Published: 18 January 2021 1. Introduction Publisher’s Note: MDPI stays neutral Drought is a recurring natural disaster that frequently occurs and lasts for a long with regard to jurisdictional claims in time and has an important impact on economic development and ecological environment published maps and institutional affil- construction, especially on agricultural production [1,2]. Drought is generally divided iations. into four types: meteorological drought, agricultural drought, hydrological drought and socio-economic drought [3]. At present, the main research fields are meteorological drought and agricultural drought but agricultural drought is more complex. Daily, monthly and annual weather and climate disturbances in agricultural region have a significant impact Copyright: © 2021 by the authors. on local and regional scales [4]. Licensee MDPI, Basel, Switzerland. With the development of remote sensing technology, many scholars had used satel- This article is an open access article lite land observation data to monitor the dynamic changes of various land surface [5–7]. distributed under the terms and Compared with traditional observation methods, remote sensing monitoring provides conditions of the Creative Commons continuous temporal and spatial monitoring of vegetation cover, soil moisture level and Attribution (CC BY) license (https:// drought severity [8]. Therefore, drought indices derived from remote sensing-based mea- creativecommons.org/licenses/by/ surements have been widely applied to monitor agricultural drought conditions, including 4.0/).
Sustainability 2021, 13, 916. https://doi.org/10.3390/su13020916 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 916 2 of 17
temperature vegetation drought Index (TVDI), anomaly vegetation index (AVI), Vegeta- tion condition index, VCI), crop water stress index (CWSI), perpendicular drought index (PDI), et al. [9–11]. The crop water stress index (CWSI) is based on the surface energy balance theory, which has high measurement accuracy and clear physical definition [12]. It is an important factor that is superior to other drought indices. In addition, the CWSI also considers the effects of wind speed, water vapor pressure, sunshine hours and tempera- ture [11]. However, its calculation is complex and requires the participation of conventional meteorological data and ground observation data, which limits the application of CWSI [13]. In 2011, the National Aeronautics and Space Administration (NASA) research team made significant advance in evapotranspiration (ET) retrieval from moderate MODIS data based on the Penman-Monteith (P-M) equation [14,15] and the global MOD16 data were released with high temporal-spatial resolution and free access [16]. It provides a new idea for the calculation of CWSI. At present, many scholars have validated MOD16 products, which shows that the data has a wide range of applications and high reliability. In addition, it had been widely applied in some field such as water use efficiency, monitoring of wet and dry patterns, estimating actual evapotranspiration and water deficit footprint and some scholars made significant progress [17–19]. Hence, MOD16 data has more extensive application value in drought monitoring. The Inner Mongolia is one of the major producing areas of agricultural and animal husbandry products in China. However, the frequent occurrences of droughts pose a significant threat to agricultural and animal husbandry production in the region. There- fore, in order to perform drought monitoring and research for the Inner Mongolia, the development of the appropriate drought monitoring model to analyze the spatial-temporal evolution and severity of droughts has great importance to agricultural, animal husbandry production and economic development. In this study, CWSI was used to analyze the spatial-temporal changes of drought in Inner Mongolia from 2001 to 2017 and the major influencing factors of drought in Inner Mongolia were discussed. The purpose of this study was to evaluate CWSI in the Inner Mongolia area of drought monitoring effectiveness, provide scientific basis for drought control and drought resistance decision.
2. Materials and Methods 2.1. Study Area The Inner Mongolia (37◦240 N–53◦230 N, 97◦120 E–126◦040 E) is located in the northern frontier of China. With 1,183,000 km2 in size, it is about 13% of the total area of the China. It spans the northeast, north and northwest regions of the China, adjacent to eight provinces in China and adjacent to Mongolia and Russia (Figure1). The climate in the area, which gradually transits from humid and semi-humid areas to semi-arid and arid areas from east to west, is mainly temperate continental monsoon climate, with annual precipitation between 46 mm to 489 mm. However, annual average evaporation is between 942 and 4138 mm and the degree of drought is more serious [20]. Geomorphological types are complex and diverse, mainly in plateau, with an average elevation of more than 1000 m. Due to the obvious spatial differences in climate, the vegetation types from east to West are forest, grassland and desert in turn. Because of the special geographical location, topography and climatic conditions, the ecological environment in this area is extremely fragile, grassland degradation, serious soil erosion and unbalanced distribution of water resources [21–25]. Drought is one of the most serious natural disasters in the study area. Sustainability 2021, 13, 916 3 of 17 Sustainability 2021, 13, x FOR PEER REVIEW 3 of 18
Figure 1. The spatial distribution of land use/cover in 2015 and meteorological stations across the Figure 1. The spatial distribution of land use/cover in 2015 and meteorological stations across the study area. study area.
2.2.2.2. Data Data Sources Sources and and Processing Processing TheThe data data used used in thisin this paper paper include include land land use use data, data, meteorological meteorological data, data, remote remote sensing sens- dataing and data relative and relative soil moisture soil moisture data (Table data (Table1). 1).
TableTable 1. Detailed 1. Detailed information information of data of data source source and and usage usage..
Data Content andData Content and Data Data Data UsageData Usage Data SourceData Source Characteristics Characteristics Content: Types of land Content: Types of land use Spatial resolution: 1 km use Obtaining surface coverage land use/cover Spatial resolution: 1 km Obtaining surface coverage(http://www.resdc.cn ) landTime use/cover resolution: Year information (http://www.resdc.cn) Time: 2000, 2005, 2010Time and resolution 2015 :Year information Time:2000, 2005, 2010 Content: Precipitation and temperature and 2015 Getting grid data of meteorological data Time resolution:Content month :Precipitation (http://data.cma.cn/data/) precipitation and temperature Time: From 1957 toand 2017 temperature meteorological Getting grid data of precipitation (http://data.cma.cn/data Number of stations:Time 44resolution:month data and temperature /) Content: evapotranspirationTime:From and 1957 to Evapotranspiration2017 and potential evapotranspirationNumber of stations:potential 44 evapotranspiration (https://ladsweb.modaps. Remote sensing data Spatial resolution:Content 500 m:evapotranspiratwere obtained and used to eosdis.nasa.gov) Time resolution: 8 daysion and potentialcalculate Evapotranspiration crop water stress and potential (https://ladsweb.modap Time:Remote From 2001 toevapotranspiration 2017 indexevapotranspiration (CWSI) were obtained s. sensing data Spatial resolution:500 m and used to calculate crop water Content: 20 cm relative soil eosdis.nasa.gov) moistureTime resolution:8 days stress index (CWSI) Time resolution:Time 10 days:From 2001 to 2017Used to verify the Relative soil moisture (http://data.cma.cn/data/) Time: From April to SeptemberContent:20 in cm relativeapplicability of CWSI 2003–2009 and 2011soil moisture Number of stations:Time 17resolution:10 days Relative soil Used to verify the applicability of (http://data.cma.cn/data Time:From April to moisture CWSI /) September in 2003–2009 (1) Land use data and 2011 The land useNumber data used of stations: in this study 17 are the standard grid data products with 1 km res- olution provided by Resource and Environment Science Data Center of Chinese Academy Sustainability 2021, 13, 916 4 of 17
of Sciences, including 2000, 2005, 2010 and 2015. Using ENVI and ArcGIS, the land cover data in China were cut and reclassified and classified into six first-class land types: crop- land, woodland, grassland, water body, built-up land and unused land (Figure1). The spatial resolution of the data is consistent with that of MOD16. (2) Meteorological data Meteorological data (temperature, precipitation) of 44 weather stations in Inner Mon- golia from 2001 to 2017 were provided by the China meteorological data sharing network. The basic information and the specific geographical location of 44 meteorological stations is shown in Table2 and Figure1. Based on the longitude and latitude data of weather stations in this study area, the spatial distribution of meteorological elements was realized by Kriging interpolation under the ArcGIS and the grid data of meteorological elements were obtained and the spatial resolution was consistent with MOD16 data. The time series of annual average precipitation and temperature in this study area were calculated.
Table 2. Information of meteorological stations.
Number Name of Stations Longitude Latitude Average of Precipitation (mm) Average of Temperature (◦C) 1 Ergun City 120.11 50.15 343 −2 2 Turi 121.41 50.29 431 −4 3 Hailar 119.42 49.15 330 0 4 Xiaoergou 123.43 49.12 493 1 5 New Barag Right Banner 116.49 48.4 197 2 6 New Barag Left Banner 118.16 48.13 271 1 7 Bugt 121.92 48.77 472 0 8 Zhalantun 122.44 48 495 4 9 Arshan 119.56 47.1 441 −2 10 Sauron 121.13 46.36 462 3 11 Dong Ujimqin 116.58 45.31 231 3 12 Ejin Banner 101.07 41.95 33 10 13 Gaizihu 102.37 41.37 47 10 14 Bayan Nuoer 104.8 40.17 129 8 15 Ayouqi 101.68 39.22 123 10 16 Erenhot 111.56 43.38 124 5 17 Na Renbaolige 114.09 44.37 195 2 18 Mandula 110.08 42.32 174 6 19 Abag Banner 114.57 44.01 230 3 20 Sonid left banner 113.63 43.87 179 4 21 Zhu Rihe 112.9 42.4 200 6 22 Wulate Middle Banner 108.31 41.34 221 6 23 Damao Banner 110.26 41.42 259 5 24 Siziwang Banner 111.41 41.32 298 4 25 Huade 114 41.54 321 4 26 Baotou City 109.53 40.32 298 8 27 Hohhot 111.34 40.51 404 8 28 Tsining 113.04 41.02 354 5 29 Girantai 105.75 39.78 101 10 30 Linhe 107.22 40.44 137 9 31 Otog Banner 107.58 39.05 273 8 32 Dongsheng County 109.59 39.5 387 7 33 Azuo banner 105.67 38.83 220 9 34 Xi Ujimqin 117.36 44.35 293 2 35 Jarud Banner 120.54 44.34 346 8 36 Baarin Left Banner 119.24 43.59 341 7 37 Xilin Hot 116.07 43.57 261 3 38 Linxi County 118.02 43.38 332 5 39 Kailu County 121.17 43.36 309 7 40 Tongliao 122.16 43.36 347 8 41 Duolun County 116.28 42.11 360 3 42 Ongniud Banner 119.01 42.56 307 7 43 Chifeng 118.5 42.18 359 8 44 Baoguotu 120.42 42.2 372 8 Sustainability 2021, 13, 916 5 of 17
(3) Remote sensing data MOD16 products synthesized in 8 days from 2001 to 2017 were provided by NASA. The satellite orbital number is h25v03/h25v04/h25v05/h26v03/h26v04/h26v05/h27v04, including actual evapotranspiration (MOD16ET) and potential evapotranspiration (MOD- 16PET) data. With the help of MRT software provided by NASA, the original HDF format was transformed into GeoTiff format and the SIN projection was transformed into the WGS~1984/Geographic longitude and latitude coordinate system, which was mosaic and clipping. According to the data usage instructions provided by the website, the invalid values in the data were eliminated and the real values were restored. The ET/PET data of 8D were weighted averagely to obtain the ET and PET values of each year, month in this study area. (4) Relative soil moisture data The data set of crop growth and development in China provided by China Meteoro- logical Data Network is based on the ten-day monthly Agrometeorological data reported by China Agrometeorological Stations since 1991 (http://data.cma.cn/data/). There were 31 observatories in Inner Mongolia with sampling depths of 10, 20, 50, 70 and 100 cm and the observation time was every ten days. Due to the influence of weather, man-made, instruments and other factors, some stations lack data integrity. Therefore, based on the comprehensive consideration of data integrity, month continuity and 20 cm relative soil moisture as the drought classification standard of the National Meteorological Admin- istration, 16 stations with complete data were finally screened out, including 2003–2009 and April–September 2011 (After thawing and before freezing) 10-day 20 cm relative soil moisture data, according to the climatic conditions in this study area, the abnormal values were eliminated and the 10-day data were weighted averagely to synthesize monthly relative soil moisture data. The basic information and the specific 16 agrometeorological stations is shown in Table3 and Figure1.
Table 3. Information of agrometeorological stations.
Number Name of Stations Longitude Latitude Relative Soil Moisture CWSI 1 Ergun City 120.11 50.15 0.52 0.76 2 Zhalantun 122.44 48 0.58 0.76 3 Bayar tuhushuo 120.33 45.07 0.53 0.80 4 Tuquan 121.55 45.4 0.60 0.78 5 Bordered Yellow Banner 113.83 42.23 0.43 0.90 6 Wuchuan County 111.45 41.1 0.45 0.87 7 Chayou Middle Banner 112.62 41.27 0.45 0.87 8 Chayou Behind Banner 111.38 41.45 0.39 0.89 9 Xilingole 105.38 39.08 0.30 0.95 10 Linhe 107.22 40.44 0.56 0.83 11 Xilin Hot 116.07 43.57 0.46 0.87 12 Tongliao 122.16 43.36 0.51 0.84 13 Ongniud Banner 119.01 42.56 0.64 0.75 14 Chifeng 118.5 42.18 0.67 0.73 15 Neyman 120.65 42.85 0.56 0.81 16 Taipusi Banner 115.27 41.88 0.57 0.81
2.3. Methods 2.3.1. Crop Water Stress Index Crop water stress index (CWSI), based on the principle of surface energy balance, has high measurement accuracy and clear physical significance, which is one reason why it is superior to other drought indexes. In addition, CWSI does not need to consider that the study area has all land use types, moreover, factors such as the vegetation cover of the underlying surface, wind speed, water vapor pressure, sunshine duration and air Sustainability 2021, 13, 916 6 of 17
temperature are fully considered [25,26]. Jackson et al. studied CWSI and defined CWSI as [27,28]: ET CWSI = 1 − , (1) PET where ET is the actual evapotranspiration, PET is the potential evapotranspiration, CWSI is between 0 and 1, the larger the value is, the more arid and water-deficient the region is. Conversely, the smaller the value, the wetter it is.
2.3.2. Linear Tendency Estimation The temporal linear propensity ratio of CWSI for each pixel from 2001 to 2017 is calculated by using linear tendency estimation. The calculation formula is as follows:
n n n n ∑ (ti × CWSIi) − ∑ ti ∑ CWSIi slop = i = 0 i = 0 i = 0 , (2) n n 2 2 n ∑ ti − ∑ ti i = 0 i = 0
where slope is linear tendency rate; ti = 2001, 2002, ...... , 2017; n is the total length of the annual sequence (n = 17); CWSIi is the cumulative value of annual CWSI of the ith year; When slope > 0, with the increase of time, CWSI tends to increase, whereas CWSI tends to decrease. According to the significance test and linear tendency estimation of correlation coefficient, the change trend of CWSI was divided into seven grades (Table4).
Table 4. Classification standard of drought change trend.
|P| CWSIslope. |P| > 0.10 0.05 < |P| ≤ 0.10 0.01 < |P| ≤ 0.05 |P| < 0.01 Slope ≤ 0 Stable Slight wet wet Significantly wet Slope > 0 Stable Slight dry dry Significantly dry
2.3.3. Correlation Coefficient Method The correlation between CWSI of each pixel in Inner Mongolia and precipitation, tem- perature is calculated. The simple correlation coefficient is used to express the correlation. The formula is as follows:
n ∑ CWSIi − CWSI (xi − x) i = 0 r = s , (3) n 2 2 CWSIi − CWSI ∑ (xi − x) i = 0
where r is a simple correlation coefficient between the two variables; CWSI is the average value of CWSI from 2001 to 2017; xi is the precipitation or temperature value of the ith year; x is the average value of precipitation or temperature from 2001 to 2017; other quantities have the same meaning as before. Moreover, the proportion of area, correlation between CWSI and precipitation, temperature positively and negatively, was calculated by ENVI statistical analysis function.
2.3.4. Drought Suitability Evaluation and Classification Standard China Meteorological Administration takes the relative soil moisture at 20 cm as the grading standard for drought [29]. Therefore, scholars tested the accuracy of drought monitoring by analyzing the correlation between CWSI and relative soil moisture at 20 cm. While the degree of drought was divided by the linear regression equation between CWSI and relative soil moisture at 20 cm [8,11]. Sustainability 2021, 13, 916 7 of 17
3. Results 3.1. Drought Monitoring Results Test In order to ensure the accuracy of CWSI drought monitoring results in this study area, the CWSI calculated based on MOD16 products was tested. The results showed that relative soil moisture at 20 cm can effectively reflect the accuracy of CWSI drought detection. Therefore, CWSI is used as a drought monitoring index. Figure2 shows the correlation between monthly CWSI and relative soil moisture at 20 cm in 16 stations from 2003 to 2009 and from April to September 2011. The results showed that the correlation coefficient between CWSI and relative soil moisture at 20 cm Sustainability 2021, 13, x FOR PEER REVIEW 8 of 18 is −0.86, which indicated that the CWSI calculated by MOD16 has good validation accuracy and applicability in Inner Mongolia and can be used for drought monitoring in this area.
FigureFigure 2.2.The The correlationcorrelation betweenbetween cropcrop waterwater stress index and r relativeelative soil moisture from from April April to to SeptemberSeptember inin 2003–20092003–2009 and and 2011. 2011.
InIn addition,addition, thethe resultsresults show show thatthat the the linear linear regression regression equation equation between between CWSI CWSI and and relativerelative soilsoil moisturemoisture (RSM)(RSM) at 2020 c cmm is y = =−0.0063x−0.0063x + 1.1505, + 1.1505, where where y is y CWSI is CWSI and and X is X soil is soilrelative relative humidity. humidity. Finally, Finally, the theclassification classification standard standard for drought for drought is determined. is determined. The Theclas- classificationsification is as is shown as shown in Table in Table 5. 5.
TableTable 5. 5.Classification Classification standard standard for for drought. drought.
DegreeDegree RSM RSMCWSI CWSIDegree Degree of D ofrought Drought 1 1RSM > 0.60 RSM > 0.600~0.77 0~0.77No drought No drought 2 20.50 < RS 0.50M <≤ RSM0.60 ≤ 0.600.77~0.84 0.77~0.84 Mild Milddrought drought ≤ 3 30.40 < RS 0.40M <≤ RSM0.50 0.500.84~0.90 0.84~0.90 Moderate Moderate drought drought 4 0.30 < RSM ≤ 0.40 0.90~0.96 Severe drought 4 50.30 < RSM RSM ≤ 0.40≤ 0.30 0.90~0.96 0.96~1Severe Extreme drought drought 5 RSM ≤ 0.30 0.96~1 Extreme drought 3.2. Interannual Spatio-Temporal Distribution Characteristics of CWSI 3.2. Interannual Spatio-Temporal Distribution Characteristics of CWSI According to the interannual variation of evapotranspiration and crop water shortage According to the interannual variation of evapotranspiration and crop water short- index in Inner Mongolia (Figure3), ET and PET fluctuated little. Among them, the range of ETage fluctuation index in Inner is 211–282 Mongolia mm, (Figure the average 3), ET ET and for manyPET fluctuated years is 246 little. mm. Among In addition, them, thethe rangerange of of PET ET fluctuationfluctuation isis 1024–1210211–282 mm, mm, the the average average ET PET for for many many years years is is 24 11106 mm. mm. In The ad- rangedition, of the the range difference of PET between fluctuation PET andis 102 ET4– is1210 742–981 mm, mmthe average and the averagePET for differencemany years for is many1110 mm. years The is 864 range mm. of ETthe is difference increasing between slowly, PETPET isand decreasing ET is 742 slowly,–981 mm DET and is the decreasing average obviously,difference CWSIfor many is decreasing years is 86 obviously,4 mm. ET indicating is increasing that slowly, the difference PET is decreasing between PET slowly, and ETDET is increasingis decreasing slowly. obviously, The trend CWSI of is drought decreasing in Mongolia obviously, has indicating slowed down. that the The difference average CWSIbetween for PET many and years ET is is increasing 0.7787. The slowly. maximum The trend value of appears drought in in 2001 Mongolia (0.8189) has and slowed the minimumdown. The value average in 2016 CWSI (0.7243). for many As ayears whole, is 0.7787. Inner Mongolia The maximum is in a mildvalue state appears of drought. in 2001 (0.8189) and the minimum value in 2016 (0.7243). As a whole, Inner Mongolia is in a mild state of drought. Sustainability 2021, 13, 916 8 of 17 SustainabilitySustainability 2021 2021, 13, ,13 x ,FOR x FOR PEER PEER REVIEW REVIEW 9 of9 of18 18
FigureFigureFigure 3. 3. 3.AnnualAnnual Annual variations variations variations of of ofevapotranspiration evapotranspiration evapotranspiration ( (ET),ET (ET), potential ), potential evapotranspiration evapotranspiration (PET)(PE (PET) andTand) and crop cropwatercrop water stresswater stress indexstress index (CWSI)index (CWSI) (CWSI) in Inner in inInner Mongolia Inner Mongolia Mongolia during during during 2001–2017. 200 2001–12017–2017. .
StatisticalStatisticalStatistical results results results of of ofCWSI CWSICWSI of of ofInner InnerInner Mongolia MongoliaMongolia cities cities (Figure (Figure 4) 4 4show)) showshow that that the the CWSI CWSI CWSI of of eachofeach each League League League City City Cityin inInner inInner Inner Mongolia Mongolia Mongolia region region region shows shows shows a downwarda downward a downward trend trend trendas as a wholea as whole a wholefrom from 2001from2001 to 2001 to2017. 2017. to The 2017.The average average The values average values of of valuesCWSI CWSI of of ofeach CWSIeach city city of are eachare quite quite city different different are quite for for many different many years. years. for ThemanyThe order order years. from from The small small order to tolarge fromlarge is isHulunbeier small Hulunbeier to large (0.6007), (0.6007), is Hulunbeier < Hinggan < Hinggan (0.6007) League League <(0.6837), Hinggan(0.6837), < Chifeng < League Chifeng City(0.6837)City (0.7684), (0.7684), < Chifeng < Tongliao< Tongliao City City (0.7684) City (0.7698), (0.7698), < Tongliao < Hohhot< Hohhot City City (0.7698)City (0.8286), (0.8286), < Hohhot < Xilin< Xilin Gol City Gol League (0.8286) League (0.829 <(0.829 Xilin6),6),
FigureFigureFigure 4. 4. 4.AnnualAnnual Annual variations variations variations of of ofCWS CWSI CWSI of ofI ofcities cities cities in in inInner Inner Inner Mongolia Mongolia Mongolia during during during 200 2001–2017. 2001–12017–2017. . From 2001 to 2017, the spatial distribution of ET, PET and CWSI on the surface of FromFrom 2001 2001 to to2017, 2017, the the spatial spatial distribution distribution of ofET, ET, PET PET and and CWSI CWSI on on the the surface surface of of Inner Mongolia has obvious differences (Figure5a–c). ET shows a strip-like change in InnerInner Mongolia Mongolia has has obvious obvious differences differences (Figure (Figure 5a 5–ac–).c ET). ET shows shows a stripa strip-like-like change change in inthe the the east, the middle and the West on the whole, while PET and CWSI shows a trend of east,east, the the middle middle and and the the West West on on the the whole, whole, while while PET PET and and CWSI CWSI shows shows a trenda trend of ofchange change change in the west, the middle and the East on the whole. The trend of CWSI and ET is in inthe the west, west, the the middle middle and and the the East East on on the the whole. whole. The The trend trend of ofCWSI CWSI and and ET ET is isopposite, opposite, opposite, which is consistent with that of PET. The annual average ET fluctuation ranges whichwhich is isconsistent consistent with with that that of ofPET. PET. The The annual annual average average ET ET fluctuation fluctuation ranges ranges from from 39 39 from 39 mm to 677 mm, PET fluctuation ranges from 375 mm to 1874 mm and CWSI mmmm to to677 677 mm, mm, PET PET fluctuation fluctuation ranges ranges from from 375 375 mm mm to to187 1874 mm4 mm and and CWSI CWSI fluctuation fluctuation fluctuation ranges from 0.3312 to 0.9991. The ET in the Great Hinggan Mountains and the rangesranges from from 0.3312 0.3312 to to 0.9991. 0.9991. The The ET ET in in the the Great Great Hinggan Hinggan Mountains Mountains and and the the forest forest- - forest-steppe ecotone in eastern Inner Mongolia are significantly higher than those in other steppesteppe ecotone ecotone in ineastern eastern Inner Inner Mongolia Mongolia are are significantly significantly higher higher than than those those in inother other re- re- regions, while the PET and CWSI values are significantly lower than those in other regions gions,gions, while while the the PET PET and and CWSI CWSI val valuesues are are significantly significantly lower lower than than those those in inother other regions regions Sustainability 2021, 13, x FOR PEER REVIEW 10 of 18 Sustainability 2021, 13, 916 9 of 17
andand the the annual annual average average CWSI CWSI values values are below are 0.7725 below and 0.7725 there isand no drought.there is no The drought. ETs The ETs ofof Hunshandake Hunshandake sandy sandy land atland the southernat the southern end of Xilinguole end of Xilinguole grassland in grassland central Inner in central Inner MongoliaMongolia and and Yinshan Yinshan Mountain Mountain range inrange Hetao in plain Hetao of the plain Yellow of the River Yellow are relatively River are relatively low,low, while while the PET the and PET CWSI and are CWSI relatively are high relatively and the multi-yearhigh and average the multi CWSI-year is above average CWSI is 0.8985, which is characterized by severe drought and extreme drought. above 0.8985, which is characterized by severe drought and extreme drought.
FigureFigure 5. The 5. The spatial spatial distribution distrib ofution ET and of PETET and in annual PET in scale annual from 2001scale to from 2017. 2001 to 2017.
TheThe variation variation trend trend of CWSI of CWSI is opposite is opposite with that with of ET that but itof is ET same but as it PET. is same This as PET. This cancan be be explained explained by relatedby related theory theory analysis analysis that evapotranspiration that evapotranspiration is complementary is complementary and and the definition of CWSI [30]. When the underlying surface is wet enough, ET is the definition of CWSI [30]. When the underlying surface is wet enough, ET is equal to equal to PET and CWSI = 0. When the water content is insufficient, the ET decreases andPET the and interaction CWSI between= 0. When the the land water surface content and the is atmosphereinsufficient, results the ET in thedecreases increase and the inter- ofaction PET and between the increase the land of CWSI; surface when and there the isatmosphere no water supply, results there in the is basically increase no of PET and the evapotranspirationincrease of CWSI; and w chamaephytehen there is no will water wither supply, or die and there CWSI is basically equals to no 1. evapotranspiration In the easternand chamaephyte part of Inner Mongolia, will wither the vegetation or die and coverage CWSI and equals precipitation to 1. In are the higher eastern in the part of Inner DaMongolia, Hinggan Mountains the vegetation and the coverage interlaced and areas precipitation of woodland are and higher grassland. in the The Da lower Hinggan Moun- average annual temperature makes the ET value and PET value larger, so the CWSI is smallertains inand this the area. interlaced The Hunshandak areas of woodland sandy land and at the grassland. southern endThe of lower the Xilin average Gol annual tem- Prairieperature in the makes central partthe ET and value the Yinshan and PET Mountains value inlarger, the Hetao so the plain CWSI of the is Yellow smaller River in this area. The hasHunshandak higher solar radiation, sandy land longer at sunshinethe southern hours, end less precipitation,of the Xilin higherGol Prairie temperature in the central part andand insufficient the Yinshan water Mountains supply on the in underlying the Hetao surface, plain of resulting the Yellow in a lower River ET has value higher and solar radia- a largertion, longer PET value, sunshine so CWSI hours, is larger less here. precipitation, higher temperature and insufficient water The change trends of ET, PET and CWSI from 2001 to 2017 were calculated by For- supply on the underlying surface, resulting in a lower ET value and a larger PET value, mula (2), the results are shown in Figure6a–c. According to Table4, the change trend of CWSIso CWSI is divided is larger and the here. results are shown in Figure6d. The Figure6a shows that from 2001 to 2017,The change the annual trends spatial of change ET, PET rate ofand ET CWSI ranged from − 200131.39 to to 37.922017 mm/a,were calculated with by for- anmula average (2) annual, the results change are rate shown of 3.75 mm/a. in Fig Amongure 6a– thec. According annual rate ofto change, Table.4, 55.84% the change of trend of theCWSI area shows is divided a significant and the change. results Among are shown them, the in area Figure. with6 ad negative. The Figure change 6 trenda shows that from only2001 accounts to 2017, for the 1.62% annual and the spatial area with change a positive rate changeof ET ranged trend is asfrom high −3 as1.39 98.38% to 37.92 and mm/a, with an average annual change rate of 3.75 mm/a. Among the annual rate of change, 55.84% of the area shows a significant change. Among them, the area with a negative change trend only accounts for 1.62% and the area with a positive change trend is as high as 98.38% and the area larger than 8 mm/a accounts for 7.23%, mainly distributed in the woodland and grassland ecotone and the area with relatively dense farmland (Figure 1). Most of the areas in Hulunbeir failed to pass the significance test, which indicates that the trend of change is obscure. Sustainability 2021, 13, 916 10 of 17
the area larger than 8 mm/a accounts for 7.23%, mainly distributed in the woodland and grassland ecotone and the area with relatively dense farmland (Figure1). Most of the areas Sustainability 2021, 13, x FOR PEER inREVIEW Hulunbeir failed to pass the significance test, which indicates that the trend of change 11 of 18
is obscure.
FigureFigure 6. The6. The change change trend trend of annual of annual ET, PET ET, and PET CWSI and in InnerCWSI Mongolia in Inner during Mongolia 2001~2017. during 2001~2017.
TheThe annual annual change change rate of rate PET of space PET is space between is −between104.08 and −1 86.1104.08 mm/a, and 86.1 the1 annual mm/a, the annual changechange rate rate is − is3.95 −3 mm/a.95 mm/a and thereand there is a significant is a significant change in change the area in of the 33.08% area of of the 33.08% of the annual rate of change. The area with positive change trend only accounts for 1.99%. The annual rate of change. The area with positive change trend only accounts for 1.99%. The area with a negative change trend is as high as 98.01% and the area smaller than −10 mm/a accountsarea with for 85.57%.a negative It is change mainly distributed trend is as in high the low as 98.01% hilly area and in the the southeast area smaller of Xilin than −10 mm/a Golaccounts League andfor the85.57%. meadow It is steppe mainly area distributed in the east, while in the most low other hilly areas area do in not the pass southeast the of Xilin significanceGol League test and and the changemeadow trend steppe is obscure area (Figure in the6 b).east, while most other areas do not pass theThe significance annual change test and rate ofthe CWSI change space trend is between is obscure−0.0296 (Figure and 0.0195,6b). the annual − changeThe rate isannual0.0047 change and there rate is a of significant CWSI space change is in between the area of −0 79.81%.0296 of and the annual0.0195, the annual rate of change. The area with positive change trend only accounts for 0.08%. The area with achange negative rate change is −0 trend.0047 is and as high there as 99.92%is a significant and the area change smaller in the than area 0.0089 of accounts79.81% of the annual forrate 11.65%. of chan Thege. range The of area variation with graduallypositive change decreases trend from only east to accounts west and for the 0.08%. drought The area with mitigationa negative trend change is obvious trend in theis as meadow high as steppe 99.92% in the and east the of area Xilin smaller Gol League than but 0.0089 not in accounts for the11.65 Yinshan%. The Mountain range range of variation and Hunshandak gradually sandy decreases land (Figure from6c–d). east to west and the drought mitigation trend is obvious in the meadow steppe in the east of Xilin Gol League but not 3.3. Spatial-Temporal Distribution Characteristics of CWSI in the Year in the Yinshan Mountain range and Hunshandak sandy land (Figure 6c–d). Figure7 shows the annual average change of ET, PET and CWSI in 8 d Mongolia from 2001 to 2017 and as shown in the graph, we can see that the yearly variation of ET in Inner Mongolia3.3. Spatial area-T isemporal a single Distribution peak curve and Characteristics the maximum of occursCWSI onin the day Year 217 (at summer) (14.77 mm);Figure the 7 yearly show changes the ofannual PET also average shows achange single peak of ET, pattern PET but and during CWSI day in 145 8 d Mongolia tofrom 217 (late 2001 spring to 2017 to summer), and as shown the PET in fluctuates the graph, slightly we can and see the that large the value yearly appeared variation of ET in on the 145th day (late spring) (49.75 mm). For specific explanation is that CWSI shows no Inner Mongolia area is a single peak curve and the maximum occurs on day 217(at sum- drought on day 1 to 72 (winter, early spring), day 169 to 248 (summer, early autumn) and daymer)(14.77 289 to 365 (366mm) days ; the a year)yearly (late change autumn, of winter), PET also which shows may bea single caused bypeak the pattern lowest but during day 145 to 217 (late spring to summer), the PET fluctuates slightly and the large value appeared on the 145th day (late spring) (49.75 mm). For specific explanation is that CWSI shows no drought on day 1 to72 (winter, early spring), day 169 to 248 (summer, early autumn) and day 289 to 365 (366 days a year) (late autumn, winter), which may be caused by the lowest temperature and the lesser potential evapotranspiration on day 1 to 72(win- ter, early spring) and day 289 to 365 (366 days a year) (late autumn, winter). But in the period of day 169 to 248, the temperature is higher, the evapotranspiration is stronger and the precipitation is abundant, so the surface water can be replenished in time. CWSI had different degrees of drought on 73 to 168 and day 249 to 288 and on day 113 to 144, it appears to be severe drought. The main reason may be that it is in the spring snow melt period, the temperature rises and the appropriate temperature and moisture are used for Sustainability 2021, 13, 916 11 of 17
Sustainability 2021, 13, x FOR PEER REVIEW 12 of 18
temperature and the lesser potential evapotranspiration on day 1 to 72 (winter, early spring) and day 289 to 365 (366 days a year) (late autumn, winter). But in the period of day 169 to 248, the temperature is higher, the evapotranspiration is stronger and the precipitation Sustainability 2021, 13, x FOR PEER REVIEWisforage abundant, returning so the surface to green, water so can there be replenished is no enough in time. temperature CWSI had different and moisture degrees for12 of evapotran- 18
ofspiration drought on[31 73– to33 168]. While and day during 249 to 288day and 249 on to day 288, 113 it to is 144, in itthe appears autumn to be vegetation severe withering drought.period, The the main temperature reason may be begins that it is to in thedecrease spring snow and melt the period, evapotranspi the temperatureration of vegetation risesweakens and the [34]. appropriate Therefore, temperature in this andperiod, moisture the areoverall used forsituation forage returning of Inner to Mongolia green, is drought. forageso there returning is no enough to green, temperature so there and is no moisture enough for temperature evapotranspiration and moisture [31–33]. for While evapotran- spirationduring day [31 249–33 to]. 288,While it is during in the autumn day 249 vegetation to 288, it withering is in the period,autumn the vegetation temperature withering period,begins to the decrease temperature and the evapotranspiration begins to decrease of vegetation and the evapotranspi weakens [34].ration Therefore, of vegetation in weakensthis period, [34]. the Therefore, overall situation in this of period, Inner Mongolia the overall is drought. situation of Inner Mongolia is drought.
Figure 7. Changes of 8d of ET, PET and CWSI in Inner Mongolia from 2001 to 2017.
FigureFigure 7.In ChangesChanges Inner ofMongolia, of 8d 8d of of ET, ET, PET PETCWSI and and CWSI fluctuates CWSI in Inner in Inner Mongolia obviously Mongolia from 2001infrom the to 2001 2017. year to 2017.(Figure 8). In the eastern and central regions (except Baotou city), the changes to the Bayannur city and the whole In InnerInner Mongolia, Mongolia, CWSI CWSI fluctuates fluctuates obviously obviously in the year in the (Figure year8). (F Inigure the eastern 8). In andthe eastern centralregion regions are basically (except Baotou the same. city), the However, changes to the the BayannurCWSI in cityBaotou and the city whole and region western region (ex- and central regions (except Baotou city), the changes to the Bayannur city and the whole arecept basically Bayannur the same. city) However, shows trapezoidal the CWSI in Baotoucurves citywith and no western obvious region bottom (except value, which in- regionBayannurcrease are rapidly city)basically shows in theday trapezoidal same. 1 to However,112, curves stabilize with the no CWSI between obvious in Baotou bottom 0.8648 city value, and and which 0.9801 western increase in region day 11 (ex-3–280 and cept Bayannur city) shows trapezoidal curves with no obvious bottom value, which in- rapidlydecrease in day rapidly 1 to 112, in stabilizeday 281 between–365 (366 0.8648 days and a 0.9801 year). in This day 113–280 may be and due decrease to the lower precipi- creaserapidly rapidly in day 281–365 in day (366 1 to days 112, a year).stabilize This between may be due 0.8648 to the and lower 0.9801 precipitation, in day higher 113–280 and decreasetemperature,tation, higherrapidly weaker temperature, in actualday 28 evapotranspiration1–365 weaker (366 days actual a and year). evapotranspir stronger This potentialmay beation evapotranspirationdue and to the stronger lower precipi- potential evap- tation,inotranspiration these higher areas temperature, during in these the rainy areas weaker season, during actual in turn,the evapotranspir rainy the value season, ofation CWSI in and turn, does stronger the not value fluctuate potential of CWSI evap- does not otranspirationsignificantlyfluctuate significantly at 113–280in these days. areas at 11 during3–280 the days. rainy season, in turn, the value of CWSI does not fluctuate significantly at 113–280 days.
FigureFigureFigure 8. 8.ChangesChanges Changes of of 8d 8dof average average8d average CWSI CWSI of CWSI citiesof cities inof Inner cities in Inner Mongolia in InnerMongolia from Mongolia 2001from to 2001 2017. from to 20172001. to 2017.
3.4.3.4. Characteristics Characteristics of CWSIof CWSI Changes Changes in Different in Different Land UseLand Types Use Types TheThe overall overall difference difference in CWSI in CWSI of different of different land useland types use istypes small is (Figure small (Figure9). The 9). The annualannual average average of ofCWSI CWSI is from is from small small to large: to large: woodland woodland (0.5954) (0.5954) < cropland < cropland (0.7733) (0.7733)< < builtbuilt-up-up land land (0.8126) (0.8126) < grassland < grassland (0.8147) (0.8147) < unused < unused land (0.8392); land (0.8392); the annual the meanannual values mean values of ET for different land use types were: woodland (352.03 mm) > cropland (266.84 mm), > of ET for different land use types were: woodland (352.03 mm) > cropland (266.84 mm), > built-up land (231.22 mm) > grassland (214.55 mm) and > unused land (192.07 mm).The annualbuilt- upaverage land of(231.2 PET 2is minm) the > order grassland of woodland (214.5 5(870.0 mm)1 mandm) >< grasslandunused land (1157.6 (192.01 mm)7 m m).The < annualcropland average (1176.8 8of m PETm) < is unused in the landorder (1194.5 of woodland8 mm) < built (870.0-up1 landmm) (1233.7 < grassland0 mm). (1157.61 mm) < cropland (1176.88 mm) < unused land (1194.58 mm) < built-up land (1233.70 mm). Sustainability 2021, 13, 916 12 of 17
3.4. Characteristics of CWSI Changes in Different Land Use Types The overall difference in CWSI of different land use types is small (Figure9). The annual average of CWSI is from small to large: woodland (0.5954) < cropland (0.7733) < built-up land (0.8126) < grassland (0.8147) < unused land (0.8392); the annual mean values of ET for different land use types were: woodland (352.03 mm) > cropland (266.84 mm) > built-up land (231.22 mm) > grassland (214.55 mm) and > unused land (192.07 mm).The Sustainability 2021, 13, x FOR PEER REVIEWannual average of PET is in the order of woodland (870.01 mm) < grassland (1157.6113 mm) of 18
< cropland (1176.88 mm) < unused land (1194.58 mm) < built-up land (1233.70 mm).
Figure 9. Annual variations of ET, PET and CWSI of different land use types. Figure 9. Annual variations of ET, PET and CWSI of different land use types.
SurfaceSurface droughtdrought is is mainlymainly relatedrelated toto landland useuse type,type, geographicalgeographical locationlocation andand climate.climate. ForFor InnerInner Mongolia,Mongolia, most most of of thethe woodlandwoodland are are locatedlocated in in thethe DaDa HingganHinggan Mountains,Mountains, with with abundantabundant rainfall,rainfall, lowerlower temperaturetemperature andand betterbetter waterwater conservationconservation ability.ability. The The ET ET value value isis higherhigher andand PETPET valuevalue isis lowerlower here,here, thusthus thethe CWSICWSI valuevalue isis lower,lower, which which shows shows that that thisthis areaarea hashas thethe strongeststrongest droughtdrought resistanceresistance capabilitycapability andand thethe lowestlowest riskrisk ofof drought.drought. CroplandCropland plantsplants artificialartificial vegetationvegetation andand hashas irrigationirrigation waterwater supply,supply, plus plus the the ET ET value value is is higher,higher, so so the the CWSI CWSI value value is is low. low. The The vegetation vegetation cover cover of of built-up built-up land land is is less less and and because because ofof thethe heatheat islandisland effecteffect andand canyoncanyon effect,effect, thethe temperaturetemperature herehere isis higherhigher thanthan that that ofof surroundingsurrounding area area and and strong strong wind wind is is easier easier to to happen happen in in partial partial urban urban town town and and the the largest larg- PETest PET value value indicates indicates that that the riskthe risk of drought of drought in built-up in built land-up land is high; is high; because because the grassland the grass- areland small, are small, short andshort dense, and dense, evapotranspiration evapotranspiration makes itmakes consume it consume more water, more while water, unused while landunused is mostly land is desertmostly and desert sandy and land,sandy vegetation land, vegetation coverage coverage is low, is precipitation low, precipitation is rare is andrareevapotranspiration and evapotranspiration results results in faster in faster water water loss, loss, thus thus the riskthe risk of drought of drought is higher is higher in grasslandin grassland and and unused unused land. land.
3.5.3.5. CorrelationCorrelation betweenbetween CWSICWSI andand ClimateClimate FactorsFactors TheThe spatialspatial distributiondistribution ofof meteorologicalmeteorological elementselements inin InnerInner MongoliaMongolia isis asas follows:follows: TheThe precipitationprecipitation gradually decreases from from northeast northeast to to southwest southwest and and the the high high value value re- regiongion appears appears in in the the Da Da Hinggan Hinggan Mountains. Mountains. The precipitation graduallygradually decreaseddecreased fromfrom northeastnortheast toto southwestsouthwestand and thethehigh high valuevalueregion region appeared appeared in in the the Da DaHinggan Hinggan Mountains Mountains (Figure(Figure 10 10).). Correlation Correlation coefficient coefficient analysis analysis is usedis used to analyzeto analyze the the spatial spatial distribution distribution of the of correlationthe correlation between between the annual the annual average average CWSI CWSI value value and the and annual the annual average average precipitation precipi- andtation the and annual the meanannual temperature mean temperature on the pixel on the scale pixel from scale 2001 from to 2017, 2001 as to shown 2017, inas Figureshown 11 in ThroughFigure 11 statistical Through analysis, statistical the analysis, average correlationthe average coefficient correlation r of coefficient CWSI with r of precipitation CWSI with − andprecipitation temperature and is temperature0.53 and 0.18, is −0 respectively..53 and 0.18, respectively. Comparatively, Comparatively, the correlation the between correla- CWSI and precipitation is stronger. tion between CWSI and precipitation is stronger.
Figure 10. Spatial distributions of annual average precipitation and temperature in Inner Mongolia during 2001~2017. Sustainability 2021, 13, x FOR PEER REVIEW 13 of 18
Figure 9. Annual variations of ET, PET and CWSI of different land use types.
Surface drought is mainly related to land use type, geographical location and climate. For Inner Mongolia, most of the woodland are located in the Da Hinggan Mountains, with abundant rainfall, lower temperature and better water conservation ability. The ET value is higher and PET value is lower here, thus the CWSI value is lower, which shows that this area has the strongest drought resistance capability and the lowest risk of drought. Cropland plants artificial vegetation and has irrigation water supply, plus the ET value is higher, so the CWSI value is low. The vegetation cover of built-up land is less and because of the heat island effect and canyon effect, the temperature here is higher than that of surrounding area and strong wind is easier to happen in partial urban town and the larg- est PET value indicates that the risk of drought in built-up land is high; because the grass- land are small, short and dense, evapotranspiration makes it consume more water, while unused land is mostly desert and sandy land, vegetation coverage is low, precipitation is rare and evapotranspiration results in faster water loss, thus the risk of drought is higher in grassland and unused land.
3.5. Correlation between CWSI and Climate Factors
Sustainability 2021, 13, x FOR PEER REVIEWThe spatial distribution of meteorological elements in Inner Mongolia is as follows:14 of 18 The precipitation gradually decreases from northeast to southwest and the high value re- gion appears in the Da Hinggan Mountains. The precipitation gradually decreased from northeast to southwest and the high value region appeared in the Da Hinggan Mountains The Correlation between CWSI and precipitation, temperature from 2001 to 2017 are (Figure 10). Correlation coefficient analysis is used to analyze the spatial distribution of calculated by Formula (3). The results are shown in Figure 11. Statistical results shows that the correlation between the annual average CWSI value and the annual average precipi- there is a negative correlation between CWSI and precipitation as a whole, which indicat- tation and the annual mean temperature on the pixel scale from 2001 to 2017, as shown in ing that CWSI decreases with the increase of precipitation; the statistical results show that Figure 11 Through statistical analysis, the average correlation coefficient r of CWSI with Sustainability 2021, 13, 916 the area with negative correlation is 96.58% and the area of 65.95% is significantly13 of 17 negative precipitation and temperature is −0.53 and 0.18, respectively. Comparatively, the correla- correlation at the significant level of 0.05, which is concentrated in the steppe area; And tion between CWSI and precipitation is stronger. only 0.02% of the area is positively correlated, that is mainly because CWSI is affected by vegetation coverage, temperature, sunshine hours, solar radiation, slope, elevation, wind speed and other factors. There is a positive correlation between CWSI and temperature, which indicating that CWSI increases with the increase of temperature. Among them, the area with positive correlation accounted for 82.04% of the total area, while at the signifi- cant level of 0.05 it only accounted for 5.65% of the total area. It is concentrated in Chen Barhu Banner, Chifeng City, Ulanchab City, Hohhot City and Baotou City. It shows that these regions are greatly affected by temperature. According to the analysis above, CWSI is closely related to rainfall and temperature, the increase of precipitation makes the rela- tive soil moisture increase and the water supply is sufficient. In addition, the lower tem- perature reduces evapotranspiration and decreases the rate of water loss, which is bene- ficial to the growth of vegetation. Therefore, in the regions with more precipitation and Figure 10. Spatial distributions of annual average precipitation and temperature in Inner Mongolia Figurelower 10. temperature,Spatial distributions the ofvegetation annual average cover precipitation degree andis higher, temperature the inCWSI Inner Mongoliavalue is smaller and duringduring 2001~2017. 2001~2017. the drought degree is less.
FigureFigure 11. 11.Correlation Correlation coefficients coefficients of CWSI of and CWSI precipitation, and precipitation, temperature temperature in Inner Mongolia in Inner during Mongolia dur- 2001~2017.ing 2001~2017.
4. DiscussionThe Correlation between CWSI and precipitation, temperature from 2001 to 2017 are calculated by Formula (3). The results are shown in Figure 11. Statistical results shows that there isThe a negative existing correlation research between on the CWSI temporal and precipitation and spatial as avariation whole, which of drought indicating in Inner Mon- thatgolia CWSI is decreasesmainly focused with the increase on the of long precipitation;-term meteorological the statistical results observation show that data, the such as the areaanalysis with negative of the characteristics correlation is 96.58% and causes and the of area temporal of 65.95% and is significantlyspatial changes negative of SPEI and SPI. correlationBased on atthe the Penmen significant-Monteith level of 0.05, formula which and is concentrated meteorological in the data, steppe Zhang area; And Xuting and oth- onlyers 0.02%analyzed of the the area temporal is positively and correlated, spatial variation that is mainly characteristics because CWSI of is drought affected by in Inner Mon- vegetation coverage, temperature, sunshine hours, solar radiation, slope, elevation, wind speedgolia and from other 1960 factors. to 2015 There by iscalculating a positive correlationSPEI in different between time CWSI scales and temperature, combined with various whichstatistical indicating methods that CWSI, and increases the relationship with the increase between of temperature. drought characteristics Among them, the and Pacific In- areaterdecadal with positive Oscillation correlation Index accounted is discussed for 82.04% [34]. of the An total Qiang area, and while others at the analyzed significant the temporal leveland of spatial 0.05 it onlyvariation accounted characteristics for 5.65% of theof totaldrought area. Itin is Inner concentrated Mongolia in Chen from Barhu 1958 to 2019 by Banner,calculating Chifeng SPEI City, in Ulanchab different City, time Hohhot scales City and and combined Baotou City. with It showsthe influence that these of continuous regions are greatly affected by temperature. According to the analysis above, CWSI is drought, the comprehensive evaluation index of drought intensity was put forward [35]. closely related to rainfall and temperature, the increase of precipitation makes the relative soilLiu moisture Jiyao and increase others and used the water monthly supply observational is sufficient. In addition,meteorology the lower data, temperature combined with linear reducestrend analysis evapotranspiration and SPI analysis and decreases to study the the rate spatial of water and loss, temporal which is variations beneficial to of drought in theInner growth Mongolia of vegetation. from 1961 Therefore, to 2013 in the[36]. regions These with studies more illustrate precipitation in anddetail lower the variation of temperature,drought in theInner vegetation Mongolia cover on degree a certain is higher, scale and the CWSI have valueimportant is smaller practical and the significance in droughtdrought degree monitoring is less. and decision making but they may be confined to the “point” scale or the lack of large-scale and long-term continuous observation data, therefore they lack a general understanding of the drought situation in the whole research area. In this paper, based on the analysis of the temporal and spatial variation characteristics of ET, PET, Sustainability 2021, 13, 916 14 of 17
4. Discussion The existing research on the temporal and spatial variation of drought in Inner Mongo- lia is mainly focused on the long-term meteorological observation data, such as the analysis of the characteristics and causes of temporal and spatial changes of SPEI and SPI. Based on the Penmen-Monteith formula and meteorological data, Zhang Xuting and others analyzed the temporal and spatial variation characteristics of drought in Inner Mongolia from 1960 to 2015 by calculating SPEI in different time scales combined with various statistical methods, and the relationship between drought characteristics and Pacific Interdecadal Oscillation Index is discussed [34]. An Qiang and others analyzed the temporal and spatial variation characteristics of drought in Inner Mongolia from 1958 to 2019 by calculating SPEI in different time scales and combined with the influence of continuous drought, the comprehensive evaluation index of drought intensity was put forward [35]. Liu Jiyao and others used monthly observational meteorology data, combined with linear trend analysis and SPI analysis to study the spatial and temporal variations of drought in Inner Mongolia from 1961 to 2013 [36]. These studies illustrate in detail the variation of drought in Inner Mongolia on a certain scale and have important practical significance in drought monitoring and decision making but they may be confined to the “point” scale or the lack of large-scale and long-term continuous observation data, therefore they lack a general understanding of the drought situation in the whole research area. In this paper, based on the analysis of the temporal and spatial variation characteristics of ET, PET, CWSI, we use high-resolution remote sensing products and meteorological observation data of continuous time period to further discuss the variation trend of CWSI over the years, which reflects the spatial and temporal distribution of drought in the region. The results can reflect more information of surface drought characteristics. Moreover, it can provide a new method for large scale inhomogeneous drought monitor. Compared to meteorological data, MOD16 data has a shorter time scale, which is only after 2000. What’s more, due to the influence of cloud, shadow and vegetation cover on MOD16 data, some pixels have no value phenomenon, especially in winter, which is difficulty to study the drought in winter. The relationship between ET, PET and CWSI in different land use types is discussed in this paper: forest land ET is the largest, PET is the smallest and CWSI is the smallest, which shows that the ability of forest land to resist drought is the strongest and it is of great significance to regional water conservation. The author has reclassified the land use data of 1 km resolution provided by the Chinese Academy of Sciences and the 30 m resolution land use data provided by Tsinghua University according to the unified standard and the CWSI extracted from different land use types were compared separately (Figure 12). It can be seen from the figure that the results obtained from the two data sources are basically the same and the average absolute error is only −0.0034, which indicates that the reliability of CWSI extracted from 1 km land use data provided by the Chinese Academy of Sciences is higher; this paper may underestimate the drought resistance of woodland and construction land, the possibility of drought risk on bare land and overestimate the drought resistance of cropland and grassland. This paper discusses the correlation between CWSI and precipitation and temperature and CWSI in Inner Mongolia has a significant correlation with precipitation, indicating that precipitation has a greater impact on CWSI, which is consistent with Park and others view that precipitation is the main cause of drought [37]. However, the process of CWSI change is very complex, which is influenced by many meteorological factors, such as altitude, slope, slope direction and vegetation coverage and the influence mechanism of these factors on different regions is different [38]. Therefore, according to the actual situation of the surface of the study area, in order to strengthen the ability of drought prevention and drought resistance and improve the regional climate environment, the focus of further research is the contribution of different factors to CWSI. Sustainability 2021, 13, x FOR PEER REVIEW 15 of 18
CWSI, we use high-resolution remote sensing products and meteorological observation data of continuous time period to further discuss the variation trend of CWSI over the years, which reflects the spatial and temporal distribution of drought in the region. The results can reflect more information of surface drought characteristics. Moreover, it can provide a new method for large scale inhomogeneous drought monitor. Compared to meteorological data, MOD16 data has a shorter time scale, which is only after 2000. What’s more, due to the influence of cloud, shadow and vegetation cover on MOD16 data, some pixels have no value phenomenon, especially in winter, which is difficulty to study the drought in winter. The relationship between ET, PET and CWSI in different land use types is discussed in this paper: forest land ET is the largest, PET is the smallest and CWSI is the smallest, which shows that the ability of forest land to resist drought is the strongest and it is of great significance to regional water conservation. The author has reclassified the land use data of 1 km resolution provided by the Chinese Academy of Sciences and the 30 m reso- lution land use data provided by Tsinghua University according to the unified standard and the CWSI extracted from different land use types were compared separately (Figure 12). It can be seen from the figure that the results obtained from the two data sources are basically the same and the average absolute error is only −0.0034, which indi- cates that the reliability of CWSI extracted from 1 km land use data provided by the Chi- Sustainability 2021, 13, 916 15 of 17 nese Academy of Sciences is higher; this paper may underestimate the drought resistance of woodland and construction land, the possibility of drought risk on bare land and over- estimate the drought resistance of cropland and grassland.
FigureFigure 12. Comparison 12. Comparison of CWSI of of CWSI 1 km of resolution 1 km resolution and 30 and m resolution 30 m resolution land cover land data cover in data 2015. in 2015.
This5. Conclusions paper discusses the correlation between CWSI and precipitation and tempera- ture and CWSIIn this in paper,Inner MOD16Mongolia data has are a significant used to calculate correlation crop waterwith precipitation, shortage index indicat- (CWSI) and ing thatthe precipitation spatial and temporal has a greater variation impact characteristics on CWSI, which of meteorological is consistent factorswith Park are analyzed,and otherspossible view that reasons precipitation of droughts is the were main discussed cause of and drought explored. [37]. TheHowever, correlation the process between ofCWSI CWSIand change the measuredis very complex, relative which soil moisture is influenced data (p by< 0.many 05, r meteorological = −0.86) was negative. factors, Itsuch is shown as altitude,that the slope, CWSI slope calculated direction by MOD16and vegetation products coverage can be used and forthe drought influence monitoring mechanism in Inner of theseMongolia. factors on The different conclusions regions are asis follows:different [38]. Therefore, according to the actual situation(1) ofDroughts the surface in 2001–2017 of the study showed area, a in significant order to decreasingstrengthentrend the ability (p < 0.05) of drought at the interan- prevention nualand drought scale in terms resistance of CWSI and monitoring improve the and reg theional study climate area showedenvironment, mild drought. the fo- The cus of furthereight research days variation is the contribution characteristics of different of CWSI factors indicates to CWSI. that severe droughts occurred mainly during 113 to 144 days, this time are also the key period of natural forage re- 5. Conclusionsgreening period. Therefore, frequent droughts will affect grassland productivity. It is In thissuggested paper, MOD16 that the data local are government used to calculate should crop pay morewater attention shortage to index drought (CWSI) resistance and the spatialduring and the temporal regreening variation period. characteristics of meteorological factors are ana- lyzed,(2) possibleThe reasons annual changeof droughts of droughts were discussed indicates and that explored. droughts The occurred correlation mainly between in Ulanchab CWSI and theCity, measured Ordos City, relative Baotou soil City, moisture Bayan data Nur, (p Alxa< 0. 05, League r = −0.86) and was Wuhai negative. City. WhileIt is the drought intensity of Hulunbeier, Hinggan League, Chifeng City and Tongliao City were relatively weak. Spatially, more crops can be observed mainly in northern Ordos, southern Baotou and southern Ulanchab. These areas are also the key regions of severe drought and moderate drought. Frequent droughts will definitely affect crop yields. It is suggested that the local government should strengthen the use of irrigation water in crop growth period to prevent the economic losses caused by drought. (3) The annual average of droughts in different land use/cover types indicates that woodland (0.5954) < cropland (0.7733) < built-up land (0.8126) < grassland (0.8147) < unused land (0.8392). Hence, the ability of woodland to resist drought is the strongest and the possibility of drought in unused land is the highest. Therefore, the possibility of drought in Inner Mongolia can be reduced through afforestation, closing hillsides for afforestation. (4) The average correlation coefficients between CWSI and precipitation, temperature are −0.53 and 0.18. This indicates that CWSI is more sensitive to precipitation. Spatially, droughts of grassland and cropland will definitely be more sensitive to precipitation, while droughts of woodland is little or no affected by precipitation. Droughts of Chen Barhu Banner, Chifeng City, Ulanchab City, Hohhot City and Baotou City will definitely be more sensitive to temperature, while droughts of other areas is little or no affected by temperature. Therefore, precipitation has a certain indicative effect on drought monitoring of grassland and crops in the study area, while temperature also has a certain indicative effect on drought monitoring of Chen Barhu banner, Chifeng City, Ulanchab City, Hohhot City and Baotou city. Sustainability 2021, 13, 916 16 of 17
The results of this study may provide some practical reference for the aim to prevent drought and maintain grassland productivity in a warming climate.
Author Contributions: Conceptualization, Z.-C.M., P.S. and Q.Z.; methodology, Z.-C.M., P.S. and Q.Z. and W.J. and Y.-Q.H.; validation, Z.-C.M., P.S. and Q.Z.; formal analysis, Z.-C.M., P.S. and Q.Z. and W.J.; writing—original draft preparation, Z.-C.M., P.S. and Q.Z. and W.J. and Y.-Q.H.; writing—review and editing, Z.-C.M., P.S. and Q.Z. and W.J.; visualization, Z.-C.M., P.S. and Q.Z. and W.J.; supervision, Z.-C.M., P.S. and Q.Z.; funding acquisition, P.S. and Q.Z. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by China National Key R&D Program, grant number: 2019YFA- 0606900”; National Natural Science Foundation of China, grant number: 41601023; The University Synergy Innovation Program of Anhui Province: GXXT 2019047; Natural Science Foundation of Anhui province, grant number: 1808085QD117; Nature Science Foundation of Anhui province of China, grant number: 1908085QD165; The Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, grant number:20I04; The key research and development project of Wuhu city, grant number:2020ms1-3; Action project of academician helping Chuzhou, grant number: 2019 1/S002. Institutional Review Board Statement: Not applicable for studies not involving humans or animals. Informed Consent Statement: Not applicable for studies not involving humans. Data Availability Statement: The data used to support the findings of this study are available from the corresponding author upon request. Conflicts of Interest: The authors declare no conflict of interest.
References 1. Tfwalaa, C.M.; van Rensburg, L.D.; Schall, R. Drought dynamics and interannual rainfall variability on the Ghaap plateau, South Africa, 1918–2014. Phys. Chem. Earth 2018.[CrossRef] 2. Gao, X.; Zhao, Q.; Zhao, X. Temporal and spatial evolution of the standardized precipitation evapotranspiration index (SPEI) in the Loess Plateau under climate change from 2001 to 2050. Sci. Total Environ. 2017, 595, 191–200. [CrossRef][PubMed] 3. Wilhite, D.A.; Glantzl, M.H. Understanding the drought phenomenon: The role of definitions. Water Int. 1985, 10, 111–120. [CrossRef] 4. Zambreski, Z.T.; Lin, X.; Aiken, R.M.; Kluitenberg, G.J.; Pielke, R.A. Identification of hydroclimate subregions for seasonal drought monitoring in the U.S. Great Plains. J. Hydrol. 2018, 567, 370–381. [CrossRef] 5. Anyamba, A.; Tucker, C.J. Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. J. Arid Environ. 2005, 63.[CrossRef] 6. Xiang, Q.; Yin, R.; Xu, J.; Deng, X. Modeling the Driving Forces of the Land Use and Land Cover Changes Along the Upper Yangtze River. Environ. Manag. 2010, 45, 454–465. [CrossRef] 7. Ranagalage, M.; Estoque, R.C.; Zhang, X.; Murayama, Y. Spatial Changes of Urban Heat Island Formation in the Colombo District, Sri Lanka: Implications for Sustainability Planning. Sustainability 2018, 10, 1367. [CrossRef] 8. Bai, J.; Yuan, Y.; Di, L. Comparison between TVDI and CWSI for drought monitoring in the Guanzhong Plain, China. Sci. Direct 2017, 16, 389–397. [CrossRef] 9. Zormand, S.; Jafari, R.; Koupaei, S.S. Assessment of PDI, MPDI and TVDI drought indices derived from MODIS Aqua/Terra Level 1B data in natural lands. Nat. Hazards 2017, 86, 757–777. [CrossRef] 10. Yu, Y.; Wang, J.; Li, X. Monitoring the Occurrence of Drought in Central Yunnan Province Based on MODIS Data. J. Irrig. Drain. 2018, 37, 91–98. (In Chinese) 11. Tian, G.; Wu, Y.; Liang, Y. Drought monitoring and timeliness based on evapotranspiration model. Arid Land Geogr. 2016, 39, 721–729. (In Chinese) 12. Ru, C.; Hu, X.; Wang, W.; Ran, H.; Song, T.; Guo, Y. Evaluation of the Crop Water Stress Index as an Indicator for the Diagnosis of Grapevine Water Deficiency in Greenhouses. Horticulturae 2020, 6, 86. [CrossRef] 13. Wang, X.; Guo, N. Some Research Advances and Methods on Drought Monitoring by Remote Sensing. Arid Meteorol. 2003, 18, 76–81. (In Chinese) 14. Mu, Q.Z.; Heinsch, F.A.; Zhao, M.S. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 2007, 111, 519–536. [CrossRef] 15. Mu, Q.Z.; Zhao, M.S.; Running, S.W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 2011, 115, 1781–1800. [CrossRef] 16. Wu, G.; Liu, Y.; Zhao, X.; Ye, C. Spatio-temporal variations of evapotranspiration in Poyang Lake Basin using MOD16 products. Geogr. Res. 2013, 32, 617–627. (In Chinese) Sustainability 2021, 13, 916 17 of 17
17. Liaqat, U.W.; Choi, M. Accuracy comparison of remotely sensed evapotranspiration products and their associated water stress footprints under different land cover types in Korean peninsula. J. Clean. Prod. 2016, 155, 93–104. [CrossRef] 18. Zhang, T.; Peng, J.; Liang, W.; Yang, Y.; Liu, Y. Spatial–temporal patterns of water use efficiency and climate controls in China’s Loess Plateau during 2000–2010. Sci. Total Environ. 2016, 565, 105–122. [CrossRef] 19. Zolotokrylin, A.N.; Titkova, T.B.; Brito-Castillo, L. Wet and dry patterns associated with ENSO events in the Sonoran Desert from, 2000–2015. J. Arid Environ. 2016, 134, 21–32. [CrossRef] 20. Li, H.; Ma, L.; Liu, T. Change and relationship of temperature and precipitation in Inner Mongolia during 1951–2014. J. Glaciol. Geocryol. 2017, 39, 1098–1112. (In Chinese) 21. Pei, Z.; Fang, S.; Wang, L.; Yang, W. Comparative Analysis of Drought Indicated by the SPI and SPEI at Various Timescales in Inner Mongolia, China. Water 2020, 12, 1925. [CrossRef] 22. Elaine, W.; Suren, K. Environmental Sustainability of Agriculture Stressed by Changing Extremes of Drought and Excess Moisture: A Conceptual Review. Sustainability 2017, 9, 970. [CrossRef] 23. Zheng, Y.; Liu, H.; Liu, D.; Zhuo, Y.; Qing, H.; Wen, L.; Wang, L. Spatial Distribution Pattern and Dynamic Change of Wetlands in Inner Mongolia. Environ. Sci. Technol. 2016, 39, 1–6. (In Chinese) 24. Miao, B.L.; Liang, C.Z.; Han, F.; Liang, M.; Zhang, Z. Responses of phenology to climate change over the major grassland types. Acta Ecol. Sin. 2016, 36, 7689–7701. (In Chinese) 25. Liu, W.; Sun, F.; Sun, S.; Guo, L.; Wang, H.; Cui, H. Multi-scale assessment of eco-hydrological resilience to drought in China over the last three decades. Sci. Total Environ. 2019, 672, 201–211. [CrossRef][PubMed] 26. Wang, Z.; Wang, F.; Zhang, Y. Spatio-temporal Distribution Characteristics and Influencing Factors of Drought in Anhui Province Based on CWSI. J. Nat. Resour. 2018, 33, 853–866. (In Chinese) 27. Jackson, R.D.; Idso, S.B.; Reginato, R.J.; Pinter, P.J., Jr. Canopy temperature as a crop water stress indicator. Water Resour. Res. 1981, 17, 1133–1138. [CrossRef] 28. Jackson, R.; Kustas, W.; Choudhury, B. A reexamination of the crop water stress index. Irrig. Sci. 1988, 9, 309–317. [CrossRef] 29. Zhang, Q.; Zou, X.K.; Xiao, F.J. The Grade of Meteorological Drought; GB/T 20481-2006; China Standard Press: Beijing, China, 2006. (In Chinese) 30. Wufu, A.; Rusuli, Y.; Kadeer, R.; Jiang, H. Spatio-temporal distribution and evolution trend of evapotranspiration in Xinjiang based on MOD16 data. Geogr. Res. 2017, 36, 1245–1256. (In Chinese) 31. Zhang, W.; Bao, G.; Bao, Y. Vegetation SOS dynamic monitoring in Inner Mongolia from 1982 to 2013 and Its responses to climatic changes. China Agric. Inf. 2018, 30, 63–75. (In Chinese) 32. Bao, G.; Bao, Y.; Tuya, A. Spatio-temporal Dynamics of Vegetation Phenology in the Mongolian Plateau during 1982~2011. Remote Sens. Technol. Appl. 2017, 32, 866–874. (In Chinese) 33. Li, J.; Yang, X.; Xu, B.; Cao, Y.; Qin, Z.; Jin, Y.; Zhao, L. Snow Monitoring Using MODIS and AMSR-E in Six Main Pastoral Areas of China. Sci. Geogr. Sin. 2011, 31, 1097–1104. (In Chinese) 34. Zhang, X.; Pan, X.; Xu, L.; Wei, P.; Yin, Z.; Shao, C. Analysis of spatio-temporal distribution of drought characteristics based on SPEI in Inner Mongolia during 1960–2015. Trans. Chin. Soc. Agric. Eng. 2017, 33, 190–199. (In Chinese) 35. An, Q.; He, H.; Nie, Q.; Cui, Y.; Gao, J.; Wei, C.; You, J. Spatial and Temporal Variations of Drought in Inner Mongolia, China. Water 2020, 12, 1715. [CrossRef] 36. Liu, J.; Chun, X.; Liang, W. Dryness Temporal-Spatial Distribution Based on Standardized Precipitation Index in Inner Mongolia. J. Inn. Mong. Norm. Univ. (Nat. Sci. Ed.) 2016, 45, 533–539. (In Chinese) 37. Park, J.; Lim, Y.J.; Kim, B.J.; Sung, J.H. Appraisal of drought characteristics of representative drought indices using meteorological variables. KSCE J. Civ. Eng. 2017.[CrossRef] 38. Peña-Gallardo, M.; Vicente-Serrano, S.M.; Hannaford, J.; Lorenzo-Lacruz, J.; Svoboda, M.; Domínguez-Castro, F.; El Kenawy, A. Complex influences of meteorological drought time-scales on hydrological droughts in natural basins of the contiguous Unites States. J. Hydrol. 2019, 568, 611–625. [CrossRef]