Analysis of Drought Characteristics in Xilingol Grassland of Northern China Based on SPEI and Its Impact on Vegetation

Abstract

This research is based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI) which represent the drought and vegetation condition on land. Take the linear regression method and Pearson correlation analysis to study the spatial and temporal evolution of SPEI and NDVI and the drought effect on vegetation. The results show that (1) during 1961–2015, SPEI values at different time scales showed a downward trend; SPEI-12 has a mutation in 1997 and the SPEI value significantly decreased after this year. (2) During 2000–2015, the annual growing season SPEI has an obvious upward trend in time and the apparent wetting spatially. (3) In the recent 16 years, the growing season NDVI showed an upward trend and more than 80% of the total area’s vegetation increased in Xilingol. (4) Vegetation coverage in Xilingol grew better in humid years and opposite in arid years. SPEI and NDVI had a significant positive correlation; 98% of the region showed positive correlation, indicating that meteorological drought affects vegetation growth more in arid and semiarid region. (5) The effect of drought on vegetation has lag effect, and the responses of different grassland types to different scales of drought were different.

The impact of drought on vegetation conditions within the Damqu River Basin, Yangtze River Source Region, China

“…Where n is the number of the years in a region that involved droughts based on SPI or SPEI (SPI or SPEI ≤ −0.50) [ 16 , 27 ], N is the number of the years during 1988 to 2015, and i is the code for a region.…”

“…Climate data for the period between 1971 and 2010 reveals that the TRSR can nevertheless be characterized by significant regional differences; although the climate of northern and eastern regions has been predominantly warm and humid since the 1990s, southern and western areas have remained mainly warm and dry [ 14 ]. It is also the case that global warming scenarios predict that droughts are likely to become more frequent and severe in the future [ 15 ]; these events negatively impact the growth of grasslands, reducing their productivity [ 16 ], and are a major reason for land degradation [ 17 – 20 ]. Droughts also represent a considerable threat to the sustainable development of animal husbandry economic systems, especially on grasslands [ 16 ].…”

“…It is also the case that global warming scenarios predict that droughts are likely to become more frequent and severe in the future [ 15 ]; these events negatively impact the growth of grasslands, reducing their productivity [ 16 ], and are a major reason for land degradation [ 17 – 20 ]. Droughts also represent a considerable threat to the sustainable development of animal husbandry economic systems, especially on grasslands [ 16 ]. This is relevant because one previous field study carried out within the Damqu River Basin between 2015 and 2016 showed that southwestern region of the TRSR is susceptible to aridification [ 21 ].…”

“…The American Meteorological Society classified drought-related phenomena into four types, meteorological, agricultural, hydrological, and socioeconomical [ 22 ]. Researchers often apply a variety of indices in this context to characterize complex drought phenomena, the most common of which include the standardized precipitation index (SPI) [ 23 ], the standardized precipitation evapotranspiration index (SPEI) [ 10 , 16 , 24 – 28 ], the Palmer drought severity index (PDSI) [ 29 , 30 ], the integrated drought condition index (IDCI) [ 31 ], the standardized vegetation index (SVI) [ 32 ], the optimized meteorological drought index (OMDI), and the optimized vegetation drought index (OVDI) [ 33 ]. The first two of these, the SPI and SPEI, have the advantage of incorporating multiple time scales (i.e., one month, three months, six months, 12 months, 24 months, and 36 months [ 24 ]) and are therefore more suitable than their counterparts for assessing the sensitivity of vegetation communities to water deficits [ 34 ].…”

“…The first two of these, the SPI and SPEI, have the advantage of incorporating multiple time scales (i.e., one month, three months, six months, 12 months, 24 months, and 36 months [ 24 ]) and are therefore more suitable than their counterparts for assessing the sensitivity of vegetation communities to water deficits [ 34 ]. In addition, the SPEI also evaluates the positive influence of precipitation and the negative influence of evapotranspiration on drought intensity [ 16 , 24 ] while at the same time remaining sensitive to the negative influence of temperature in the same way as the SPI and PDSI [ 16 ].…”