Spatio-Temporal Characteristics of Drought Events and Their Effects on Vegetation: A Case Study in Southern Tibet, China

Ye, Zuxin; Cheng, Weiming; Guo, Jun; Yang, Ze-Xian; Wang, Ruibo; Wang, Nan

doi:10.3390/rs12244174

Cited by 10 publications

(4 citation statements)

References 85 publications

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“…The most widely used drought indicator is the actual precipitation, represented as a percentage variation from normal (or long-term average), although it has limited use/reliability for regional comparison due to its reliance on the mean [73]. According to Ye et al [74], the SPI reflects a deviation from the mean and is thus expressed as a normalized index in time and space in standard deviation units. The deviation from the mean is a probabilistic indicator of the severity of the wetness or drought that may be used to estimate risk.…”

Section: Discussionmentioning

confidence: 99%

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Meteorological Drought Analysis and Return Periods over North and West Africa and Linkage with El Niño–Southern Oscillation (ENSO)

et al. 2021

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Droughts are one of the world’s most destructive natural disasters. In large regions of Africa, droughts can have strong environmental and socioeconomic impacts. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. Taking North and West Africa as the study area, this study adopted multi-source data and various statistical analysis methods, such as the joint probability density function (JPDF), to study the meteorological drought and return years across a long term (1982–2018). The standardized precipitation index (SPI) was used to evaluate the large-scale spatiotemporal drought characteristics at 1–12-month timescales. The intensity, severity, and duration of drought in the study area were evaluated using SPI–12. At the same time, the JPDF was used to determine the return year and identify the intensity, duration, and severity of drought. The Mann-Kendall method was used to test the trend of SPI and annual precipitation at 1–12-month timescales. The pattern of drought occurrence and its correlation with climate factors were analyzed. The results showed that the drought magnitude (DM) of the study area was the highest in 2008–2010, 2000–2003, and 1984–1987, with the values of 5.361, 2.792, and 2.187, respectively, and the drought lasting for three years in each of the three periods. At the same time, the lowest DM was found in 1997–1998, 1993–1994, and 1991–1992, with DM values of 0.113, 0.658, and 0.727, respectively, with a duration of one year each time. It was confirmed that the probability of return to drought was higher when the duration of drought was shorter, with short droughts occurring more regularly, but not all severe droughts hit after longer time intervals. Beyond this, we discovered a direct connection between drought and the North Atlantic Oscillation Index (NAOI) over Morocco, Algeria, and the sub-Saharan countries, and some slight indications that drought is linked with the Southern Oscillation Index (SOI) over Guinea, Ghana, Sierra Leone, Mali, Cote d’Ivoire, Burkina Faso, Niger, and Nigeria.

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Section: Discussionmentioning

confidence: 99%

“…Long records provide more trustworthy statistics for the SPI. As a result of the availability of such data records, the SPI has gained traction as a potential drought indicator in recent years, allowing for comparisons across different precipitation zones [73,74].…”

Section: Discussionmentioning

confidence: 99%

Meteorological Drought Analysis and Return Periods over North and West Africa and Linkage with El Niño–Southern Oscillation (ENSO)

et al. 2021

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“…By using the "vegan" package, we performed Mantel tests to analyze the relationships between several factors, including rainfall climate, TVDI, NDVI, VSWI, VHI, TCI, and VCI. To determine the relationships between these indices, we divided the R-values and p-values of the Mantel test using the modified function [35]. Finally, to examine the link between these markers, we generated correlation heat maps.…”

Section: Partial Mantel Testmentioning

confidence: 99%

Trend Prediction of Vegetation and Drought by Informer Model Based on STL-EMD Decomposition of Ha Cai Tou Dang Water Source Area in the Maowusu Sandland

Zheng,

Hou,

et al. 2024

Agronomy

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To accurately forecast the future development trend of vegetation in dry areas, it is crucial to continuously monitor phenology, vegetation health indices, and vegetation drought indices over an extended period. This is because drought caused by high temperatures significantly affects vegetation. This study thoroughly investigated the spatial and temporal variations in phenological characteristics and vegetation health indices in the abdominal part of Maowusu Sandland in China over the past 20 years. Additionally, it established a linear correlation between vegetation health and temperature indices in the arid zone. To address the issue of predicting long-term trends in vegetation drought changes, we have developed a method that combines the Informer deep learning model with seasonal and Seasonal Trend decomposition using Loess (STL) and empirical mode decomposition (EMD). Additionally, we have utilized the linearly correlated indices of vegetation health and meteorological data spanning 20 years to predict the Normalized Difference Vegetation Index (NDVI) and Temperature Vegetation Dryness Index (TVDI). The study’s findings indicate that over the 20-year observation period, there was an upward trend in NDVI, accompanied by a decrease in both the frequency and severity of droughts. Additionally, the STL-EMD-Informer model successfully predicted the mean absolute percentage error (MAPE = 1.16%) of the future trend in vegetation drought changes for the next decade. This suggests that the overall health of vegetation is expected to continue improving during that time. This work examined the plant growth circumstances in dry locations from several angles and developed a complete analytical method for predicting long-term droughts. The findings provide a strong scientific basis for ecological conservation and vegetation management in arid regions.

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“…The low and negative correlations of some areas may be caused by the complex topographical features and atmospheric conditions. Some studies [68] have shown that the precipitation difference in the Qinghai-Tibet Plateau is the main reason for the north Atlantic oscillation, and the north Atlantic Oscillation and Pacific interannual oscillations has a negative correlation, enhancing the La Niña phenomenon that influenced Indian monsoons. These convective systems and active water vapor transport jets are not alone in creating delays in precipitation and dryness over southern Tibet due to increased water vapor from the Bay of Bengal.…”

Section: Regional Climate Characteristicsmentioning

confidence: 99%

Responses of the Remote Sensing Drought Index with Soil Information to Meteorological and Agricultural Droughts in Southeastern Tibet

Wang

Xiong

et al. 2022

Remote Sensing

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The Temperature–Vegetation–Precipitation–Drought Index (TVPDI) has a good performance in drought monitoring in China. However, different regions have different responses to droughts due to terrain differences. In southeastern Tibet, the drought monitoring capacity of some drought indices without soil information has to be assessed on account of the poor sensitivity between temperature and soil humidity. Therefore, soil moisture was added to calculate a new drought index based on TVPDI in southeastern Tibet, named the Temperature–Vegetation–Soil-Moisture–Precipitation–Drought Index (TVMPDI). Then, the TVMPDI was validated by using the Standardized Precipitation Evapotranspiration Index (SPEI) and other remote sensing drought indices, including the Vegetation Health Index (VHI) and Scale Drought Conditions Index (SDCI), during the growing seasons of 2003–2018. The Standardized Precipitation Index (SPI) and SPEI were used to represent meteorological drought and Gross Primary Productivity (GPP) was used to represent agricultural drought. The relation between TVMPDI and these drought indices was compared. Finally, the time trends of TVMPDI were also analyzed. The relation coefficients of TVMPDI and SPEI were above 0.5. The correlations between TVMPDI and drought indices, including the Vegetation Health Index (VHI) and Scale Drought Conditions Index (SDCI), also had a good performance. The correlation between the meteorological drought indices (SPI and SPEI) and TVMPDI were not as good as for the TVPDI, but the temporal correlation between the TVMPDI and GPP was greater than that between the TVPDI and GPP. This indicates that the TVMPDI is more suitable for monitoring agricultural drought than the TVPDI. In addition, historical drought monitoring had values that were consistent with those of the actual situation. The trend of the TVMPDI showed that drought in the study area was alleviated from 2003 to 2018. Furthermore, GPP was negatively correlated with SPEI (r = −0.4) and positively correlated with Soil Moisture (SM) drought index (TVMPDI, SMCI) (r = 0.4) in the eastern part of the study area, which suggests that SM, rather than precipitation, could promote the growth of vegetation in the region. A correct understanding of the role of soil information in drought comprehensive indices may monitor meteorological drought and agricultural drought more accurately.

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Spatio-Temporal Characteristics of Drought Events and Their Effects on Vegetation: A Case Study in Southern Tibet, China

Cited by 10 publications

References 85 publications

Meteorological Drought Analysis and Return Periods over North and West Africa and Linkage with El Niño–Southern Oscillation (ENSO)

Meteorological Drought Analysis and Return Periods over North and West Africa and Linkage with El Niño–Southern Oscillation (ENSO)

Trend Prediction of Vegetation and Drought by Informer Model Based on STL-EMD Decomposition of Ha Cai Tou Dang Water Source Area in the Maowusu Sandland

Responses of the Remote Sensing Drought Index with Soil Information to Meteorological and Agricultural Droughts in Southeastern Tibet

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