Date of Award


Degree Name

Master of Science


Civil Engineering

First Advisor

Kalra, Ajay


Regional assessments of droughts are limited and meticulous assessment of droughts over larger spatial scales are often not substantial. Understanding drought variability on a regional scale is crucial for enhancing resiliency and adaptive ability of water supply and distribution systems. Moreover, it can be essential for appraising the dynamics and predictability of droughts based on regional climate across various spatial and temporal scales. The drought analysis of the past was carried out with the development of a high-resolution dataset (1km×1km) for three drought-prone regions of India between 1950 and 2016. In the study the monthly values of self-calibrating Palmer Drought Severity Index (scPDSI), incorporating Penman–Monteith (PM) approximation, which is physically based on potential evapotranspiration. Climate data were statistically downscaled using the delta downscaling method and was formulated to form a timeline for characterizing major drought events that occurred in the past. The downscaled climate data were validated with the station observations. Major severe drought events that occurred between 1950 and 2016 were identified and studied with greater emphasis to the drought situation in smaller spatial extent such as districts, villages or localities. A timeline of drought events within the period of study was also prepared to have an understanding of the severity of drought in all three regions.Likewise, the future drought durations are projected for droughts of different levels of severity and assessed in the same regions of India. Coupled Model Intercomparison Project Phase 6 (CMIP6) simulated precipitation and climate data were used for near‐future (2015–2044) for different shared socio-economic pathways (SSPs). scPDSI, was used again based on its fairness in identifying drought conditions which accounts for the temperature as well. Gridded rainfall and temperature data of spatial resolution of 1km were used to bias correct the multi-model ensemble (MME) mean of 7 Global Climatic Models (GCMs) from CMIP6 project. Equidistant quantile-based mapping was adopted to remove the bias in the rainfall and temperature data and were corrected at the monthly scale. The downscaled climate data exhibited good statistical agreement with station data with correlation coefficient (R) ranging from 0.83 to 0.93 for both precipitation and temperature. Drought analysis indicated several major incidences over the analysis time period considered in this work, which truly adheres to the droughts recorded in qualitative reports of meteorological institutions in those regions. The drought study of the past helped to understand the situation in local levels and understand the necessities that can be opted for the future by proper management of water resources. While the outcome of the future prediction of drought duration suggests multiple severe to extreme drought events in all three study areas of appreciable durations mostly during the mid-2030s under the SSP2-4.5 scenario. The severe drought durations under the SSP2-4.5 scenario were found to be ranging around 25 to 30 months in 30 years period of the near future. The high-resolution drought index proved to be key to assess the drought situation for both the past and the future in three different drought-prone regions of India.




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