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1 Santos, C. A. G.; Neto, R. M. B.; do Nascimento, T. V. M.; da Silva, R. M.; Mishra, M.; Frade, T. G. 2021. Geospatial drought severity analysis based on PERSIANN-CDR- [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record] estimated rainfall data for Odisha State in India (1983–2018). Science of the Total Environment, 750:141258. [doi: https://doi.org/10.1016/j.scitotenv.2020.141258]
Drought ; Extreme weather events ; Climatic data ; Rainfall patterns ; Precipitation ; Climate change ; Vulnerability ; Temperature ; Satellite observation ; Neural networks ; Spatial distribution ; Coastal area / India / Odisha
(Location: IWMI HQ Call no: e-copy only Record No: H050146)
(4.66 MB)
Studying the behavior of drought and its short-, medium- and long-term features throughout a region is very important for the creation of adequate public policies and actions aimed at the economic and social development of the region. Furthermore, the frequency and intensity of weather-related natural hazards (rainfall, heatwaves and droughts) are increasing every year, and these extreme weather-related events are potent threats worldwide, particularly in developing countries, such as India. Thus, this paper aims to evaluate the drought behavior in the Odisha region of India (1983–2018) by using the standardized precipitation index (SPI) and the new drought severity classification (DS). PERSIANN-CDR-estimated rainfall data were used to provide 271 time series, which were equally spaced at intervals of 0.25°, over Odisha state. The accuracy of these time series was evaluated with rain gauge-measured data at multiple time scales, and it was observed that the PERSIANN-CDR-estimated rainfall data effectively captured the pattern of rainfall over Odisha state. It was noted that almost half of the mean annual rainfall was concentrated in July and August. On addition, northeastern Odisha and areas near the coast were the rainiest regions. Furthermore, the drought pattern was evaluated based on nine distinct four-year periods (SPI-48), and the results indicated that there was high spatiotemporal variability in drought occurrence among those periods; e.g., in the last four years, extreme drought events occurred throughout the state. For the DS severity index analysis, it was noted that the values tended to be more significant with the increase in the drought time scale. For short-term droughts, DS values were less significant throughout the region, whereas for the medium-term droughts, there was an increase in the DS values in all regions of Odisha, especially in the north-central region. For long-term droughts, the values were more significant throughout the region, especially in the areas with the highest rainfall levels. Finally, the PERSIANN-CDR data should also be analyzed in other regions of India, and the obtained results are useful for the identification of droughts throughout the region and for the management of water resources and can be replicated in any part of the world.
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