Ecological Processes (Oct 2023)

Time lag effect of vegetation response to seasonal precipitation in the Mara River Basin

  • Shouming Feng,
  • Zhenke Zhang,
  • Shuhe Zhao,
  • Xinya Guo,
  • Wanyi Zhu,
  • Priyanko Das

DOI
https://doi.org/10.1186/s13717-023-00461-w
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 15

Abstract

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Abstract Background Mara River Basin is an ecologically fragile area in East Africa, with a pattern of alternating wet and dry seasons shaped by periodic precipitation. Considering the regional biological traits and climatic change, the vegetation's response to seasonal variation is complicated and frequently characterized by time lags. This study analyzed the variation of the Normalized Difference Vegetation Index (NDVI) and investigated its time lag to precipitation at the monthly scale. NDVI characteristic peaks were proposed from the perspective of seasonal mechanisms and were quantified to assess the lag effect. Results The results showed that the Anomaly Vegetation Index could identify low precipitation in 2006, 2009, and 2017. The NDVI showed an increasing trend in 75% of areas of the basin, while showed a decreased significance in 3.5% of areas, mainly in savannas. As to the time lag, the 1-month lag effect dominated most months, and the spatiotemporal disparities were noticeable. Another method considering the alternations of wet and dry seasons found that the time lag was approximately 30 days. Based on the time distribution of NDVI characteristic peaks, the average time lag was 35.5 days and increased with the range of seasons. Conclusions The findings confirmed an increasing trend of NDVI in most regions from 2001 to 2020, while the trends were most obvious in the downstream related to human activities. The results could reflect the time lag of NDVI response to precipitation, and the 1-month lag effect dominated in most months with spatial heterogeneity. Four NDVI characteristic peaks were found to be efficient indicators to assess the seasonal characteristics and had a great potential to quantify vegetation variation.

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