Linkage of Large Scale Ocean-Atmospheric Phenomena on Hydroclimatic Extremes in India

Civil Engineering
Project type: 
Sponsored Projects
2019 - 2021
Principal Investigator: 
Dr. Sarmistha Singh
Project Number: 
Sponsoring Agency: 
SERB - Start-Up Research Grant (SRG)
Total Budget: 
Inter-annual (El Nino Southern Oscillation (ENSO)), decadal and multidecadal (Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), etc.) ocean atmospheric (OA) oscillations affect different components of the hydrologic cycle and are responsible for extreme hydrologic events such as floods and droughts around the world. Studies have reported that variability in Indian Summer Monsoon (ISM) are linked to ENSO phases. However, ENSO impacts are also modulated by decadal/multi-decadal OA cycles and might play a major role in weakening or intensification of the ISM. Therefore, it is important to study and quantify the “interaction/coupled” impacts of ENSO and other OA cycles (such as EQUINOO, PDO, AMO and NAO) on hydro-climatic components over the Indian subcontinent which is lacking in the present literature. Even though, few studies have analysed the individual impacts of OA cycles on ISM at a regional scale, almost no literature exists on interactions of ENSO with decadal/multi-decadal OA cycles on ISM and hydrologic components (such as streamflow). Also, incorporating OA cycle information towards forecasting of water resources availability and early drought detection systems is an active area of research around the world. However, such forecasting feasibility studies are yet to be explored in the Indian context. Therefore, the proposed study is divided into two parts: (1) quantify the individual and coupled tele- connections of OA cycles on ISM and the streamflow levels (2) evaluate the feasibility of incorporating OA information in forecasting of ISM and streamflow levels in India. Two powerful non-parametric (linear mixed effect and linear quantile mixed effect) models will be used to identify the OA phases responsible for modulation of ENSO and quantify their coupled impact on ISM and streamflow levels. Moreover, machine learning techniques will be employed to develop a forecast framework and evaluate the feasibility of forecasting ISM and streamflow levels using OA information in India. The outcome of this study will identify the phase combinations that are responsible for extreme events and will be helpful towards development of early detection systems. For example, if it is evident that a particular phase combination of PDO and ENSO are responsible for severe droughts and water shortages, that information can be used by the state or region to declare "drought watch" and plan accordingly when such phase combinations occur in future. This information can be used to inform farmers and stakeholder regarding the impendingclimate conditions that will help them plan ahead of time and take critical decisions regarding the agricultural planting dates or choose crops that have a low water footprint. These information can be readily used by state agencies and local NGOs in the planning and management of reservoir operation and water resources, and mitigation of extreme events at a regional scale.