Continuous simulation for computing design hydrographs for water structures

The contribution discusses the problems with modelling design floods for water structures. The statistical extrapolations of observed flood series of e.g. 80 years “only” to the annual exceedance probability AEP = 0.01 is difficult due to the large variability in extreme values. For large dams, however, the AEP = 0.001 or 0.0001 is required. Most of the uncertainties in hydrological modelling are epistemic (uncertainties in model structure, model parameters, inputs, calibration data, in measurements) and moreover some measurements can be disinformative. With powerful computers it is now possible to produce very long series (100 to100 thousand years in hourly timestep) using precipitation and temperatures computed with a weather model. Within the framework of the Generalised Likelihood Uncertainty Estimation (GLUE) many (thousands) of such continuous simulations are produced and compared to the observed historical data. According to Keith Beven's “Manifesto for the equifinality thesis” the differences between modelled and observed values should not be larger than some limits of acceptability based on what is known about errors in the input and output observations used for model evaluation (e.g. for flow the current metering data are used). The unacceptable realisations are rejected. We have been working with the frequency version of TOPMODEL in various versions according to the unique characteristics of each catchment. Design hydrographs for water structures are then extracted from the acceptable realisations. The continuous simulation with uncertainty estimation seems nowadays the most promising method of computing design hydrographs for important water structures, even if issues associated with epistemic uncertainty of model assumptions remain.

[1]  Martyn P. Clark,et al.  Hydrological field data from a modeller's perspective: Part 1. Diagnostic tests for model structure , 2011 .

[2]  P. Arnaud,et al.  Uncertainties of extreme rainfall quantiles estimated by a stochastic rainfall model and by a generalized Pareto distribution / Incertitudes des quantiles extrêmes de pluie estimés par un modèle stochastique d'averses et par une loi de Pareto généralisée , 2009 .

[3]  R. T. Clarke,et al.  Hydrological prediction in a non-stationary world , 2007 .

[4]  Göran Lindström,et al.  Spillway design floods in Sweden: I. New guidelines , 1992 .

[5]  L. Vasiliades,et al.  Flood producing mechanisms identification in southern British Columbia, Canada , 2000 .

[6]  Keith Beven,et al.  Concepts of Information Content and Likelihood in Parameter Calibration for Hydrological Simulation Models , 2014 .

[7]  Keith Beven,et al.  Flood frequency estimation by continuous simulation for a gauged upland catchment (with uncertainty) , 1999 .

[8]  Keith Beven,et al.  Testing the distributed water table predictions of TOPMODEL (allowing for uncertainty in model calibration): The death of TOPMODEL? , 2002 .

[9]  Florian Pappenberger,et al.  Impacts of uncertain river flow data on rainfall‐runoff model calibration and discharge predictions , 2010 .

[10]  Zbynek Sokol,et al.  An estimation of the probable maximum precipitation for river basins in the Czech Republic , 2005 .

[11]  Rob Lamb,et al.  Confidence intervals for a spatially generalized, continuous simulation flood frequency model for Great Britain , 2004 .

[12]  K Beven,et al.  On the concept of model structural error. , 2005, Water science and technology : a journal of the International Association on Water Pollution Research.

[13]  Keith Beven,et al.  On the colour and spin of epistemic error (and what we might do about it) , 2011 .

[14]  K. Beven,et al.  A limits of acceptability approach to model evaluation and uncertainty estimation in flood frequency estimation by continuous simulation: Skalka catchment, Czech Republic , 2009 .

[15]  Liam Graham,et al.  A Procedure for Estimating Loss of Life Caused by Dam Failure , 1999 .

[16]  Keith Beven,et al.  Model and data limitations:The sources and implications of epistemic uncertainty , 2013 .

[17]  Keith Beven,et al.  Towards the use of catchment geomorphology in flood frequency predictions , 1987 .

[18]  Keith Beven,et al.  An evaluation of three stochastic rainfall models. , 2000 .

[19]  Keith Beven,et al.  Runoff Production and Flood Frequency in Catchments of Order n: An Alternative Approach , 1986 .

[20]  Dmitri Kavetski,et al.  Rainfall uncertainty in hydrological modelling: An evaluation of multiplicative error models , 2011 .

[21]  K. Beven.  Rainfall-Runoff Modelling: The Primer , 2012 .

[22]  M. Lang,et al.  Statistical analysis of extreme events in a non-stationary context via a Bayesian framework: case study with peak-over-threshold data , 2006 .

[23]  Patrick Willems,et al.  A time series tool to support the multi-criteria performance evaluation of rainfall-runoff models , 2009, Environ. Model. Softw..

[24]  Keith Beven,et al.  So how much of your error is epistemic? Lessons from Japan and Italy , 2013 .

[25]  Keith Beven,et al.  Uniqueness of place and process representations in hydrological modelling , 2000 .

[26]  Keith Beven,et al.  Flood frequency estimation by continuous simulation of subcatchment rainfalls and discharges with the aim of improving dam safety assessment in a large basin in the Czech Republic , 2004 .

[27]  N. Kottegoda,et al.  Stochastic water resources technology , 1980 .

[28]  Patrick Arnaud,et al.  Coupled rainfall model and discharge model for flood frequency estimation , 2002 .

[29]  Keith Beven,et al.  Flood frequency prediction for data limited catchments in the Czech Republic using a stochastic rainfall model and TOPMODEL , 1997 .

[30]  M. Woo,et al.  Prediction of annual floods generated by mixed processes , 1982 .

[31]  Keith Beven,et al.  A manifesto for the equifinality thesis , 2006 .

[32]  Jiri Cajthaml,et al.  Comparison of saturated areas mapping methods in the Jizera Mountains, Czech Republic , 2014 .

[33]  P. S. Eagleson.  Dynamics of flood frequency , 1972 .

[34]  Keith Beven,et al.  Flood frequency estimation by continuous simulation for a catchment treated as ungauged (with uncertainty) , 2002 .

[35]  P. Taylor.  Fooled by Randomness , 2012 .

[36]  Frequency version of TOPMODEL as a tool for assessing the impact of climate variability on flow sources and flood peaks. , 1995 .

[37]  Comparative analysis of statistical and catchment modelling approaches to river flood frequency estimation , 2009 .

[38]  Keith Beven,et al.  On red herrings and real herrings: disinformation and information in hydrological inference , 2011 .

[39]  Keith Beven,et al.  Vegetation pattern as an indicator of saturated areas in a Czech headwater catchment , 2014 .

[40]  Keith Beven,et al.  On constraining TOPMODEL hydrograph simulations using partial saturated area information , 2002 .

[41]  Keith Beven,et al.  Uncertainty in flood estimation , 2009 .