Cookies

We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Durham University

Department of Geography

Staff Profile

Publication details for Professor Alexander Densmore

Sanyal, J., Carbonneau, P. & Densmore, A.L. Hydraulic routing of extreme floods in a large ungauged river and the estimation of associated uncertainties: a case study of the Damodar River, India. Natural Hazards. 2013;66:1153-1177.

Author(s) from Durham

Abstract

Many developing countries are very vulnerable to flood risk since they are located in climatic zones characterised by extreme precipitation events, such as cyclones and heavy monsoon rainfall. Adequate flood mitigation requires a routing mechanism that can predict the dynamics of flood waves as they travel from source to flood-prone areas, and thus allow for early warning and adequate flood defences. A number of cutting edge hydrodynamic models have been developed in industrialised countries that can predict the advance of flood waves efficiently. These models are not readily applicable to flood prediction in developing countries in Asia, Africa and Latin America, however, due to lack of data, particularly terrain and hydrological data. This paper explores the adaptations and adjustments that are essential to employ hydrodynamic models like LISFLOOD-FP to route very high-magnitude floods by utilising freely available Shuttle Radar Topographic Mission digital elevation model, available topographical maps and sparse network of river gauging stations. A 110 km reach of the lower Damodar River in eastern India was taken as the study area since it suffers from chronic floods caused by water release from upstream dams during intense monsoon storm events. The uncertainty in model outputs, which is likely to increase with coarse data inputs, was quantified in a generalised likelihood uncertainty estimation framework to demonstrate the level of confidence that one can have on such flood routing approaches. Validation results with an extreme flood event of 2009 reveal an encouraging index of agreement of 0.77 with observed records, while most of the observed time series records of a 2007 major flood were found to be within 95 % upper and lower uncertainty bounds of the modelled outcomes.