Author: Sireesha Naidu.G
Date: 2020-06-24
Report no: IIIT/TH/2020/52
Advisor:Shaik Rehana

Abstract

Global warming, changes in precipitation levels have led us to keep an eye on water resources and to adopt new management strategies for their sustainability. Prediction of projected climatological variables accounting for greenhouse gases in the atmosphere, accurate projections of hydroclimatological variables under climate change is crucial for making adaptive measures and mitigation policies. An analysis of the changes in energy and water balance of a hydrologic system is required to establish these strategies and check their efficiency. Also an analysis of extreme variations in climate will give us a better picture as how a hydrologic system is interlinked with these events. Droughts are one such extreme events which can cause huge damage to ecosystems and society, if sufficient steps are not taken to assess and monitor them. Various meteorological drought indices have evolved to characterize the drought at larger scales. Meteorological drought indicators with precipitation and Potential Evapotranspiration (PET) can incorporate atmospheric water demand based on energy available in drought characterization, which are ideal for energy-limited regions, however, for water-limited regions where Actual Evapotranspiration (AET) is the major hydrological variable, therefore drought is defined by water availability rather than energy. Furthermore, at river basin scales meteorological drought assessments may not be enough to understand the water availability to the crops and other environmental aspects of the river systems. The present thesis aimed to develop a hydrometeorological drought prediction index by considering precipitation, evapotranspiration and runoff. The study mainly emphasized to understand the effect of various meteorological and hydrological variables on drought and how the inclusion of AET can outperform a drought index based solely on PET and P at a river basin scale. In this context, the study proposed hydrologically calibrated Actual Evapotranspiration (AET) considering precipitation, potential evapotranspiration and runoff. The hydrologically calibrated AET was further used in the formulation of Standardized Precipitation Evapotranspiration Index (SPEI) to develop hydrometeorological drought index, Standardized Precipitation Actual Evapotranspiration Index (SPAEI). The proposed drought index SPAEI imposes the effect of precipitation, Potential Evapotranspiration, Actual Evapotranspiration using operational meteorological data sets of precipitation and temperatures and surface runoff. The performance of PET and AET based drought indices was compared using historical droughts in terms of severity, areal extent, frequency and duration. The proposed AET based drought indices have effectively captured the historical drought years over the Krishna river basin. Inclusion of AET in the drought characterization along with precipitation and PET can drive the highly intensified drought events vi vii determined by SPEI into moderate and less frequent droughts with short durations. As SPAEI is more reasonable in reflecting surface water-energy balance it enables better characterization of meteorological and hydrological droughts at regional scales. Further, the study assessed the climate signals on drought assessment with Regional Circulation Model (RCM) outputs and Global Circulation Model(GCM) outputs. Meteorological climate projections for the future were obtained using statistical downscaling which involves establishing a relationship between predictands(local climate variables) and predictors(global coarse scale projections). This relationship is established using a set of statistical and machine learning techniques such as Quantile Bias Correction, K-means Clustering, Classification and Regression Trees(CART) and Support Vector Regression. The study observed that compared to RCM climate projections, GCM based statistical downscaling models have performed well in capturing the historical observations, indicating reliable predictions based on GCM based observations at river basin scales. Although, statistical downscaling models has proved to be reliable projections for climate change impact assessment studies, the RCM data sets provides ease of quick implementation with less computational effort. Therefore, the study suggested the use of RCM projections for preliminary understanding and GCM based projections for a detailed climate change impact assessment study. The projected drought characteristics based on GCM analysis has observed that while the intensity and duration of the drought has been found to be slightly increasing, the frequency and the drought areal extent have been increasing alarmingly over the Krishna river basin. With the GCMs, it has been estimated that there is a net increase of 25%-31% in the drought areal extent, an increase of 50%-90% in drought frequencies in Krishna river basin from the current to future periods. The climate change impact assessment on drought characteristics based on RCM and GCM outputs can provide insights for the river basin water management and decision-making policies.

Full thesis: pdf