Trend Identification in Multiscalar SPI in Uttarakhand, India

Climate change on a global scale profoundly influences water resources, which causes increased droughts, floods, heat waves, and forest fires, and consequently adverse impacts on the survival and sustainability of humanity. This study emphasizes temporal multi-scalar Standardized Precipitation Index (SPI) trend and magnitude detection at 13 meteorological stations situated in Uttarakhand state (India) by using Mann-Kendall (MK), Sen’s innovative trend analysis (SITA), and Thiel-Sen slope estimator (TSSE) techniques at 5% significance level. The multi-scalar SPI (i.e., SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, SPI-24) was computed at study stations using monthly records of 54 years (1962-2015). Results of comparison revealed that the SITA method detected a significant positive (negative) trend over 4 (9) stations for SPI-1, and 4 (8) stations for SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The MK method examined the significant positive (negative) trend over 1 (3) stations for SPI-1, 3 (4) stations for SPI-3, 4 (5) stations for SPI-6, 5 (6) stations for SPI-9 and SPI-12, and 4 (7) stations for SPI-24. The trend magnitude attained at the studied stations (mm/SPI scale) ranges from -0.0010 to 0.0004 for SPI-1, -0.0015 to 0.0009 for SPI-3, -0.0019 to 0.0013 for SPI-6, -0.0024 to 0.0017 for SPI-9, -0.0027 to 0.0021 for SPI-12, and -0.0035 to 0.0031 for SPI-24. In general terms, the positive (negative) trends were found in multiscalar SPI, resulting in more attention toward drought and wet conditions for the studied stations (extreme, severe, moderate). Furthermore, the results showed the supremacy of the SITA method by investigating the unknown tend that could not be examined by the MK method in multi-scalar SPI data series over the study area. The results of this research can be useful to formulate the mitigation strategies for sustainable development of water resources under climate change over the study region.