DSpace Collection:http://hdl.handle.net/2440/9082015-08-02T00:14:54Z2015-08-02T00:14:54ZAustralian Rainfall and Runoff: Revision project 18: Coincidence of fluvial flooding events and coastal water levels in estuarine areas (Stage 3 Report)Zheng, F.Westra, S.P.Leonard, M.http://hdl.handle.net/2440/932502015-07-31T03:21:26Z2013-12-31T13:30:00ZTitle: Australian Rainfall and Runoff: Revision project 18: Coincidence of fluvial flooding events and coastal water levels in estuarine areas (Stage 3 Report)
Author: Zheng, F.; Westra, S.P.; Leonard, M.2013-12-31T13:30:00ZInfluential point detection diagnostics in the context of hydrological model calibrationWright, D.P.Thyer, M.Westra, S.http://hdl.handle.net/2440/932112015-07-30T00:03:55Z2014-12-31T13:30:00ZTitle: Influential point detection diagnostics in the context of hydrological model calibration
Author: Wright, D.P.; Thyer, M.; Westra, S.
Abstract: Abstract not available2014-12-31T13:30:00ZApplication of the design variable method to estimate coastal flood riskZheng, F.Leonard, M.Westra, S.http://hdl.handle.net/2440/932062015-07-29T23:57:28Z2014-12-31T13:30:00ZTitle: Application of the design variable method to estimate coastal flood risk
Author: Zheng, F.; Leonard, M.; Westra, S.2014-12-31T13:30:00ZEfficient joint probability analysis of flood riskZheng, F.Leonard, M.Westra, S.http://hdl.handle.net/2440/931642015-07-30T23:45:53Z2014-12-31T13:30:00ZTitle: Efficient joint probability analysis of flood risk
Author: Zheng, F.; Leonard, M.; Westra, S.
Abstract: Flood attributes such as the water level may depend on multiple forcing variables that arise from common meteorological conditions. To correctly estimate flood risk in these situations, it is necessary to account for the joint probability distribution of all the relevant forcing variables. An example of a joint probability approach is the design variable method, which focuses on the extremes of the forcing variables, and approximates the hydraulic response to forcing variables with a water level table. In practice, however, application of the design variable method is limited, even for the bivariate case, partly because of the high computational cost of the hydrologic/hydraulic simulations. We develop methods to minimise the computational cost and assess the appropriate extent and resolution of the water level table in a bivariate context. Flood risk is then evaluated as a bivariate integral, which we implement as an equivalent line integral. The line integral is two orders of magnitude quicker and therefore beneficial to settings that require multiple evaluations of the flood risk (e.g., optimisation studies or uncertainty analyses). The proposed method is illustrated using a coastal case study in which floods are caused by extreme rainfall and storm tide. An open-source R package has been developed to facilitate the uptake of joint probability methods among researchers and practitioners.2014-12-31T13:30:00Z