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Long-term Sensitivity Analysis of Palmer Drought Severity Index (PDSI) Through Uncertainty and Error Estimation from Plant Productivity and Biophysical Parameters
* 1 , 2 , 3
1  Department of Geography, Visva-Bharati (A Central University); Santiniketan; India
2  Department of Ecology and Environmental Protection; Poznan University of Life Sciences; Piatkowska 94; 60-649 Poznan; Poland
3  Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES) de la Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas; Perú


The Palmer Drought Severity Index (PDSI) is the most effective and well-acknowledged drought severity index that particularly determines the long-term drought conditions over the forest and other terrestrial ecosystems. The PDSI is based on demand and supply concept of the water balance model, taking consideration not only precipitation deficit but also includes local temperature and soil moisture anomalies. However, the sensitivity of PDSI has not been explored yet based on productivity (i.e. Gross Primary Productivity or GPP), biophysical parameters (i.e. biomass- Leaf-area Index or LAI, Enhanced vegetation index or EVI; greenness content- Normalized difference vegetation index or NDVI) and solar radiation (i.e. fraction of absorbed solar radiation or fAPAR) over a humid-subtropical forest ecosystem. In this study, the sensitivity of PDSI has been analyzed through uncertainty and error estimation from long term (2015-2019) MODIS GPP and reflectance data using Google Earth Engine (GEE). The study was carried out over the humid-subtropical forest region of Arunachal Pradesh, India, the state that is enriched with the second largest forest cover. Most of the existing studies on drought severity showed a high sensitivity of GPP or NDVI during the drought period. However, in this study, it was experimentally observed that EVI was the most sensitive parameter to PDSI in a long-term observation based on low uncertainty and error. Besides, EVI had a strong agreement with PDSI compared to GPP, NDVI, LAI, and fAPAR where Pearson’s r was ranging from -0.87 to -0.63 except 2015. The estimated uncertainty (RMSE) and error (SE) between PDSI and EVI were also very low ranging from 1% to 2% and 0.07-0.12 respectively compared to the other parameters. So, based on the long-term analysis from this study, it is stated that EVI is the simple, effective, and most complementary indicator for assessing PDSI over forest regions of the tropical ecosystem. This study showed that EVI might be a promising tool for effectively evaluating long-term drought impacts on the forest ecosystem that indicates the actual water deficit induced stress conditions.

Keywords: Palmer Drought Severity Index (PDSI); Enhanced Vegetation Index (EVI); Forest; Tropical Ecosystem; Google Earth Engine; India