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Regional Drought Assessment over India using SPEI under Climate ChangeAuthor: Tinku Nellibilli Date: 2019-11-21 Report no: IIIT/TH/2019/120 Advisor:Shaik Rehana AbstractAmong the extreme climate events, droughts are the most widespread and slowly developing atmo-spheric hazards which remain for a long duration affecting natural resources, environment, and people.Drought is a temporary deviation from normal weather conditions. It corresponds to the failure of spa-tial and temporal precipitation and water availability and therefore consequent impact on agriculture,ecosystem and socioeconomic activities of human being. Understanding the drought characteristics in the context of climate change is critical for water resources management in water stressed countries such as India. The frequency of severe and widespread multi-year droughts has increased in India during the recent decades due to the erratic summer monsoon and increase in air temperature and thereby creating a huge damage to crops and society. India has experienced 23 large-scale droughts starting from 1891 to 2009 and the frequency of droughts is increasing. In this context, several drought indices have been developed, which evaluate the deviation of climate variables in a given year from the normal conditions. These drought indices serve as monitoring tools and operational indicators for regional water resources management. The Standardized Precipitation and Evapotranspiration Index (SPEI) became one of the popular drought indices in the context of increasing temperatures under global warming conditions in recent periods. The SPEI is estimated by fitting a probability distribution to the difference between Precipita- tion (P) and Potential Evapotranspiration (PET) (P-PET), which represents the climatic water balance. SPEI has been widely used in the drought characterization for the Indian context but none of the drought studies analyzed the applicability of SPEI in the Indian context. So, in this study, the applicability of the SPEI over India has been evaluated and modified to account for the spatio-temporal heterogeneity of rainfall and seasonality present due to the monsoon rainfall variability. The choice of an inappropriate probability distribution may lead to bias in the index values leading to distorted drought severity. Further, application of SPEI with a single probability distribution all over India may not be a meaningful index due to the large spatial heterogeneity in the rainfall patterns at various temporal scales of Indian monsoon rainfall. So, the ability of a group of candidate probability distributions over seven meteorological homogeneous zones of India has been evaluated. The results suggested that the Log-Logistic distribution which was used in the original formulation of SPEI was not the best distribution to fit the water balance series for the Indian context. Pearson type III distribution for shorter time scales (3 and 6 months) and GEV distribution for longer time scales (12 and 24 months) have been identified as the best distributions for fitting SPEI for Indian case study. India being a country associated with strong seasonal rainfall (80% rain in June-September) it is important for any drought index to represent the seasonality in the index values. The study analyzed the strength of SPEI in capturing the seasonality in the drought assessment. The results of the study revealed that SPEI in its original formulation was unable to capture the seasonal aspect of the rainfall in the Indian context. The study makes use water balance deficits instead of water balance values itself to capture the seasonality of Indian monsoon rainfall in the drought estimation. Furthermore, suitability of Actual Evapotranspiration (AET), which can account for both water and energy based evaporative demands, in drought characterization using SPEI is unexplored for Indian context. In this study we have divided India into water and energy limited zones and compared the drought characteristics with PET and AET. The research findings of the study reveal that use of AET in the drought categorisation provides more insight towards the drought assessment for water-limited zones. The proposed modified SPEI has been identified as a better drought index in characterisation of various drought characteristics with better agreement with remote sensing-based drought indices, which accounts for the energy budget, canopy transpiration, soil evaporation and open water evaporation of a region. The global land surface in extreme drought is predicted to increase from 1-3% for the present day to 30% by the 2090s (International Panel on Climate Change (IPCC), Assessment Report 4). More intense droughts and increased precipitation variability lead to increased stresses to water, agriculture and economic activities (IPCC AR5 WG2 Ch26 Exec. Summary). The severity of droughts has been reported to be increasing in many parts of the Indian sub-continental basins under climate change. The present study used the projections of precipitation and temperatures based on General Circulation Models (GCMs) outputs to study the climate change impacts on various characteristics of drought using the improvised SPEI for Indian landmass. The climate change projections of precipitation and temperatures were studied using statistical machine learning based downscaling models from General Circu- lation Models (GCMs) and dynamical downscaling model outputs from Regional Circulation Models (RCMs). The dynamically downscaled RCM outputs were not able to represent the inter annual variability of rainfall over India. The poor and non-uniform performance of each RCM model projections of precipitation over India, limited the use of such projections in the drought impact assessment over India. The study adopted and modified a statistical downscaling model based on machine learning al- gorithms such as K-means clustering, Classification And Regression Trees (CART), Support Vector Regression (SVR) for the development of climate change projections of precipitation and temperatures using GCM outputs. The machine learning algorithm based statistical downscaled GCM precipitation and temperature estimates were able to capture the variation in observed rainfall and temperature with the performance measure, Nash-Sutcliffe Efficiency (NSE) score around 0.7 for most of the country. The statistical downscaled projections of precipitation and temperatures were further used for the drought impact assessment all over India for the current and future scenarios. An alarming rise in the number of drought years were predicted over India for the future scenarios. Considerable increase of drought frequencies over the Western and North zones of India for the period of 2019 to 2045. Also, consid- erable increase of drought years was predicted over Western Ghats, which may result in the drying of the most of the South Indian river systems. Majority of India is expected to face high intensity droughts for the period of 2019-2045 with the exception of South and North-east zones. Significant increase in the drought severity was observed along the Krishna and Godavari delta in the years starting from 2045. The research findings of the study emphasize that climate change is likely to intensify the Indian droughts, hence development of adaptive responses is crucial for the food security of the country. Full thesis: pdf Centre for Earthquake Engineering |
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