Evaluating the Effectiveness of Phase Difference in Early Drought Detection

Nawai Habib; Abu Talha Manzoor; Sawaid Abbas; Syed Muhammad Irteza

Authors

Nawai Habib Smart Sensing for Climate and Development, Centre for Geographic Information System, University of the Punjab, Lahore, Pakistan
Abu Talha Manzoor Smart Sensing for Climate and Development, Centre for Geographic Information System, University of the Punjab, Lahore, Pakistan
Sawaid Abbas Smart Sensing for Climate and Development, Centre for Geographic Information System, University of the Punjab, Lahore, Pakistan
Syed Muhammad Irteza Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong

Keywords:

Lag Time, NDVI, Rainfall, Drought, Thar dessert

Abstract

Introduction. This research work focuses on how various phase relationships can enhance our understanding of the effects of drought on moisture deficiency in desert ecosystems, an extensive and damaging environmental phenomenon that affects natural ecosystems, economies, health, agriculture, and society.

Novelty Statement. The primary objective of this research is to inspect the lag time variance between fixed and dynamic lag windows correlated with NDVI, aiming to devise an optimal methodology for drought analysis in this region.

Material and Methods. Leveraging remote sensing data, this study delves into the complex drought dynamics of the Thar Desert, employing a comprehensive analysis of 22 years of CHIRPS rainfall time series data and MODIS NDVI (Normalized Difference Vegetation Index) product. This study performed a cross-correlation of rainfall and NDVI, comparing the lag time difference between fixed lag windows (16, 32, 48, 64 days) and dynamic lag windows (ranging from 4 to 64 days with incremental steps) against 22 years of NDVI data of MODIS.

Results and Discussions. The preliminary results showed that dynamic lag windows of 4, 8, 12, 16, …, and 64 days exhibit the highest correlation with NDVI, with a lag time of 40 days showing maximum correlation. These findings suggest that dynamic lag windows capture the temporal variability of drought impact on vegetation more effectively compared to fixed lag windows in the Thar Desert. The same work was done with a sub-dynamic lag window ranging in between the highly correlated lag episodes of dynamic and fix windows respectively i.e.,40 days and 48 days, concluding that a lag phase of 42 days exhibits the highest correlation with vegetation more effectively. Furthermore, the study unveils a significant drought event in 2002, showcasing the sensitivity of the dynamic lag approach in detecting extreme drought occurrences.

Concluding Remarks. This research not only advances drought analysis methodologies in arid regions but also underscores the imperative for future investigations to explore the generalizability of dynamic lag windows across diverse regions and evaluate their predictive capacity in forecasting drought-induced vegetation changes.

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