Abstract:
Drought is serious and complex natural phenomena that can affect life on the planet in several ways. Droughts can be divided into hydrological (dry condition in rivers and groundwater), meteorological (no or less rainfall) and agricultural (low moisture content in soil) and each type of drought was historically assessed through several indices developed by using their most related variables. However, by the consideration of different variables belongs to different drought indices is momentous to develop a composite drought index (CDI) which incorporates all possible variables belongs to an individual type of droughts. The main objective of the study was to build a modified CDI by considering four variables i.e., precipitation, land surface temperature (LST), Normalized Difference Vegetation Index (NDVI) and evapotranspiration, subsequently validated with standard precipitation index (SPI). Cholistan Desert, Pakistan, was selected for the development of CDI. The satellite products of MODIS TERRA i.e. NDVI, LST and observed precipitation and temperature data obtained from Pakistan Meteorological department (PMD) during selected years 2001, 2005, 2010 and 2015, were utilized. Primarily, the offered CDI was established on the similarities found through the weighted measure (entropy weighted Euclidian distance) and the variance from the potential dried and wettest conditions of the designated study area (Cholistan Desert). The results obtained from developed CDI values indicate that 2001 was the driest year and November is the driest month during whole selected data series (2001, 2005, 2010 and 2015). Further, the obtained results show that the monthly based CDI monthly has a significant correlation with the monthly SPI with least correlation coefficient of 0.90, however, the correlation of CDI with six (6) monthly
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SPI was not significant. Overall, results showed that the CDI countered to the variability occurred in the discrete attributes of the drought. It also noticed that the established CDI unbiased, flexible and substantially sound index that can potentially overcome several uncertainties in climate data and conditions of the study region.
