The influence of the calibration metric on design flood estimation using continuous simulation

References

• Ball, J.E., 2013. Estimation of design floods using continuous simulation. Floodplain Management Association National Conference, Tweed Heads, NSW, Australia.
   Google Scholar
• Ball, J.E. and Cu, T.P., 2014. Daily rainfall disaggregation for a monsoon catchment in Vietnam. Hydrology & Water Resources Symposium. Perth: Engineers Australia, 501–508.
   Google Scholar
• Bevan, K. and Binley, A., 1992. The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes, 6, 279–298. doi: 10.1002/hyp.3360060305
   Web of Science ®Google Scholar
• Calver, A. and Lamb, R., 1995. Flood frequency estimation using continuous rainfall-runoff modelling. Physics and Chemistry of the Earth, 20, 479–483. doi: 10.1016/S0079-1946(96)00010-9
   Google Scholar
• Cameron, D., et al., 1999. Flood frequency estimation by continuous simulation (with likelihood based uncertainty estimation). Hydrology and Earth System Sciences, 4, 23–34. doi: 10.5194/hess-4-23-2000
   Web of Science ®Google Scholar
• Choi, K.S. and Ball, J.E., 2002. Parameter estimation for urban runoff modelling. Urban Water, 4, 31–41. doi: 10.1016/S1462-0758(01)00072-3
   Google Scholar
• Cohn, T.A., et al., 2013. A generalized Grubbs-Beck test statistic for detecting multiple potentially influential low outliers in flood series. Water Resources Research, 49, 5047–5058. doi: 10.1002/wrcr.20392
   Web of Science ®Google Scholar
• Criss, R.E. and Winston, W.E., 2008. Do Nash values have value? Discussion and alternate proposals. Hydrological Processes, 22, 2723–2725. doi: 10.1002/hyp.7072
   Web of Science ®Google Scholar
• Das, T., et al., 2008. Comparison of conceptual model performance using different representations of spatial variability. Journal of Hydrology, 356, 106–118. doi: 10.1016/j.jhydrol.2008.04.008
   Web of Science ®Google Scholar
• Garrick, M., Cunnane, C., and Nash, J.E., 1978. A criterion of efficiency for rainfall-runoff model. Journal of Hydrology, 36, 375–381. doi: 10.1016/0022-1694(78)90155-5
   Web of Science ®Google Scholar
• Grubbs, F.E. and Beck, G., 1972. Extension of sample sizes and percentage points for significance tests of outlying observations. Technometrics, 14 (4), 847–854. doi: 10.1080/00401706.1972.10488981
   Web of Science ®Google Scholar
• Gupta, H.V., et al., 2009. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modeling. Journal of Hydrology, 377, 80–91. doi: 10.1016/j.jhydrol.2009.08.003
   Web of Science ®Google Scholar
• Krause, P., Boyle, D.P., and Base, F., 2005. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences, 5, 89–97. doi: 10.5194/adgeo-5-89-2005
   Google Scholar
• Kuczera, G. and Parent, E., 1998. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the metropolis algorithm. Journal of Hydrology, 211, 69–85. doi: 10.1016/S0022-1694(98)00198-X
   Web of Science ®Google Scholar
• Kuczera, G. and Franks, S., 2016. At-site flood frequency analysis. In: J. Ball, M. Babister, R. Nathan, W. Weeks, E. Weinmann, M. Retallick and I. Testoni, eds. Australian rainfall and runoff: a guide to flood estimation. Commonwealth of Australia, Chap. 2, Book 3. Available from: http://arr.ga.gov.au/.
   Google Scholar
• Lee, P., 1989. Bayesian statistics: an introduction. New York: Charles Griffin.
   Google Scholar
• Legates, D.R. and Mccabe, G.J., 1999. Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35, 233–241. doi: 10.1029/1998WR900018
   Web of Science ®Google Scholar
• McMillan, H. and Clark, M., 2009. Rainfall-runoff model calibration using informal likelihood measures within a Markov chain Monte Carlo sampling scheme. Water Resources Research, 45. doi:10.1029/2008WR007288
   Web of Science ®Google Scholar
• Podger, G., 2004. Rainfall runoff library user manual. Melbourne: CRC for Catchment Hydrology.
   Google Scholar
• Refsgaard, J.C. and Knudsen, J., 1996. Operational validation and intercomparison of different types of hydrological models. Water Resources Research, 32, 2189–2202. doi: 10.1029/96WR00896
   Web of Science ®Google Scholar
• Seibert, J., 2001. On the need for benchmarks in hydrological modelling. Hydrological Processes, 15, 1063–1064. doi: 10.1002/hyp.446
   Web of Science ®Google Scholar
• Smithers, J.C., 2012. Methods for design flood estimation in South Africa. Water SA, 38, 633–646. doi: 10.4314/wsa.v38i4.19
   Web of Science ®Google Scholar
• US Army Corps of Engineers, 2010. Hydrologic modeling system HEC – HMS, user’s manual. Davis, CA: Hydrologic Engineering Center.
   Google Scholar
• US Interagency Advisory Committee on Water Data, 1982. Guidelines for determining flood flow frequency, bulletin 17-B of the hydrology subcommittee. Reston, VA: US Geological Survey, Office of Water Data Coordination.
   Google Scholar
• VKT KTTV&MT, 2010. Đánh giá tác động của biến đổi khí hậu lên tài nguyên nước và các biện pháp thích ứng – Lưu vực sông Ba. Hanoi: Trung tâm nghiên cứu thủy văn và môi trường, Viện khoa học Khí Tượng Thủy văn & Môi trường.
   Google Scholar
• Westerberg, I.K., et al., 2011. Calibration of hydrological models using flow-duration curves. Hydrology and Earth System Sciences, 15, 2205–2227. doi: 10.5194/hess-15-2205-2011
   Web of Science ®Google Scholar