<p>Indian summer monsoon rainfall (ISMR) contributes 80% of the annual rainfall in South Asian countries and is strongly influenced by El Ni&#241;o Southern Oscillation (ENSO) phenomenon. The most recent El Ni&#241;o event during 2015-2016, with an Oceanic Ni&#241;o Index (ONI) of 2.6 had enormous effects on rainfall and water resources in South Asia. Thus, agricultural output, economy, water resources and societal well-being of South Asian Region heavily rely on the impacts of ENSO on the variability of ISMR. Godavari River Basin (GRB) is the second largest river basin in India with an annual runoff yield around 81 Gm<sup>3</sup> and thus important in terms of agriculture and food security. Uncertainty of the ENSO-monsoon relationship and its implications on rainfall variations in GRB necessitates the importance of modelling the impacts of ENSO on changing rainfall, hydrology and agricultural productivity. Hence, this study focused on the understanding of the variations in hydrological processes under the scenario of pre, ongoing and post El Ni&#241;o events in the Godavari GRB using the Variable Infiltration Capacity (VIC) model. Accurate estimation of VIC parameters is pivotal to produce reasonable simulations of catchment responses under the impact of El Ni&#241;o events. In this regard, a framework with enhanced calibration methods is presented for estimating VIC parameters (Baseflow and runoff both). Using this framework, we identified the most critical parameters, which could represent the landscape and climatic characteristics of GRB in response to El Ni&#241;o events. The proposed framework also reduced the number of parameters needing to be calibrated and hence increased computational efficiency. The parameters identified strengthened the accuracy of VIC simulations to examine the relative changes in hydrological processes with respect to magnitude of ONI. Improved understanding of impact pathways of El Ni&#241;o in the GRB can help water resource managers to reduce El Ni&#241;o-induced vulnerabilities and to better prepare in meeting the irrigation water supply and power demands under different El Ni&#241;o conditions.</p>
The paper is a review article on the basics of uncertainty, necessity of its quantification and a comparative study of various methods of uncertainty estimation. The paper primarily focusses on uncertainty estimation of soil hydraulic parameters as of their pivotal importance in groundwater flow and transport simulations, soil moisture modelling techniques etc. The deterministic and probabilistic approaches of uncertainty quantification are studied and an understanding of uncertainty based on field scale measurements, empirical methods and pedotransfer functions is established. A comparative analysis of the basic methods of uncertainty analysis Monte Carlo, Bayesian, FORM/SORM and GLUE is done and the preferential use based on the importance is suggested. Bayesian approach was most suitable for evaluating parametric uncertainty, Monte Carlo was one of the most powerful tools but computationally expensive, FORM was applicable to both numerical and analytical solutions but didn’t guarantee a global convergence and GLUE was conceptually simple but gave only a statistical measure.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.