Parameter sensitivity of a watershed-scale flood forecasting model as a function of modelling time-step

Hydrology Research

Volumn 44, Issue 2, 2013, Pages 334-350

  O'Loughlin, Fiachra ^a   Bruen, Michael ^b   Wagener, Thorsten ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

Flood forecasting; Rainfall runoff modelling; Sensitivity analysis; SMARG; Time step

Indexed keywords

FLOOD FORECASTING; GLOBAL SENSITIVITY ANALYSIS; PARAMETER SENSITIVITY ANALYSIS; RAINFALL-RUNOFF MODELLING; SENSITIVITY OF PARAMETERS; SMARG; SOIL MOISTURE ACCOUNTING AND ROUTING; TIME STEP;

CATCHMENTS; FLOOD CONTROL; GROUNDWATER; MODELS; RUNOFF; SENSITIVITY ANALYSIS; SOIL MOISTURE; WATER QUALITY;

FLOODS;

ADVANCED TECHNOLOGY; CATCHMENT; FLOOD FORECASTING; FLOW MODELING; HYDROGRAPH; RAINFALL-RUNOFF MODELING; RURAL AREA; WATER QUALITY; WATERSHED;

IRELAND;

EID: 84876724761     PISSN: 00291277     EISSN: None     Source Type: Journal    
DOI: 10.2166/nh.2012.157     Document Type: Article

References (47)

1. Bastidas, L. A., Gupta, H. V., Sorooshian, S., Shuttleworth, W. J. & Yang, Z. L. 1999 Sensitivity analysis of a land surface scheme using multicriteria methods. Journal of Geophysical Research 104 (D16), 19481-19490.
2. Bates, B. C, Kundzewics, Z. W., Wu, S. & Palutikof, J. P. 2008 Climate Change and Water. Technical Paper of the Intergovernmental Panel on Climate Change, IPCC Secretariat, Genève.
3. Bell, V. A. & Moore, R. J. 1998 A grid-based distributed flood forecasting model for use with weather radar data: Part 2. Case studies. Hydrology and Earth System Sciences 2, 283-298.
4. Blanchard, S., Papadopoulo, T., Benar, C.-G, Voges, N., Clerc, M., Benali, H., Warnking, J., David, O. & Wendling, F. 2011 Relationship between flow and metabolism in BOLD signals: insights from biophysical models. Brain Topography 24, 40-53.
5. Cibin, R, Sudheer, K. P. & Chaubey, I. 2010 Sensitivity and identifiability of stream flow generation parameters of the SWAT model. Hydrological Processes 24, 1133-1148.
6. Clarke, M. P. & Kavetski, D. 2010 Ancient numerical daemons of conceptual hydrological modelling: 1. Fidelity and efficiency of time stepping schemes. Water Resources Research 46, W10510.
7. Davis, M. J., Skodje, R T & Tomlin, A. S. 2011 Global sensitivity of chemical-kinetic reaction mechanisms: construction and deconstruction of the probability density function. Journal of Physical Chemistry A 115, 1556-1578.
8. Demaria, E. M., Nijssen, B. & Wagener, T 2007 Monte Carlo sensitivity analysis of land surface parameters using the variable infiltration capacity model. Journal of Geophysical Research 112, D11113.
9. Dunne, S., Hanafin, J., Lynch, P., McGrath, R, Nishimura, E., Nolan, P., Venkata Ratnam, J., Semmler, T, Sweeney, C, Varghese, S. & Wang, S. 2008 Ireland in a Warmer World: Scientific Predictions of the Irish Climate in the Twenty-First Century. Report. Met Eireann, Dublin, Ireland.
10. Fenicia, F, Savenije, H. H. G, Matgen, P. & Pfister, L. 2008 Understanding catchment behavior through stepwise model concept improvement. Water Resources Research 44, W01402.
11. Hossain, F., Anagnostou, E. N, Dinku, T & Borga, M. 2004 Hydrological model sensitivity to parameter and radar rainfall estimation uncertainty. Hydrological Processes 18, 3277-3291.
12. Kavetski, D. & Clarke, M. P. 2010 Ancient numerical daemons of conceptual hydrological modelling: 2. Impact of time stepping schemes on model analysis and prediction. Water Resources Research 46, W10511.
13. Khan, H. 1986 Conceptual Modelling of rainfall-runoff systems. M. Eng. Thesis. National University of Ireland, Galway.
14. Li, G, Wang, W., Rabitz, H., Wang, S. & Jaffé, P. 2002 Global uncertainty assessments by high dimensional model representations (HDMR). Chemical Engineering Science 57, 4445-4460.
15. Liang, G C 1992 A Note on the Revised SMAR Model. Workshop memorandum. Department of Engineering Hydrology, National University College of Ireland, Galway (Unpublished).
16. Littlewood, I. G & Croke, F. W. 2008 Data time-step dependency of conceptual rainfall-streamflow model parameters. Hydrological Sciences Journal 53, 685-695.
17. Littlewood, I. G 2007 Rainfall-streamflow models for ungauged basins: uncertainty due to modelling time-step. In: Proceedings Eleventh Biennial Conference of the EuroMediterranean Network of Experimental Basins (ERB), Luxembourg, 20-23 September 2006 (L Pfister & L Hoffmann, eds). UNESCO Technical Documents in Hydrology, Series no. 81. UNESCO, Geneva, pp. 149-155.
18. McIntyre, N. & Al-Qurashi, A. 2009 Performance of ten rainfall-runoff models applied to an arid catchment in Oman. Environmental Modelling & Software 24, 726-738.
19. Min, S.-K., Zhang, X., Zwiers, F. W. & Hegeri, G. C. 2011 Human contribution to more-intense precipitation extremes. Nature 470, 378-381.
20. Moreda, F., Koren, V., Zhang, Z., Reed, S. & Smith, M. 2006 Parameterization of distributed hydrological models: learning from the experiences of lumped modelling. Journal of Hydrology 320, 218-237.
21. Nash, J. E. & Sutcliffe, J. V. 1970 River flow forecasting through conceptual models, Part I-A discussion of principles. Journal of Hydrology 10, 282-290.
22. O'Connell, P. E., Nash, J. E. & Farrell, J. P. 1970 River Flow forecasting through conceptual models. Part 2: The Brosna catchment at Febrane. Journal of Hydrology 10, 317-329.
23. Pall, P., Aina, T., Stone, D. A., Stott, P. A., Nozawa, T., Hilberts, A. G. J., Lobmann, D. & Allen, M. R 2011 Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature 470, 382-385.
24. Pechlivanidis, I. G, McIntyre, N. R & Wheater, H. S. 2010 Calibration of the semi-distributed PDM rainfall-runoff model in the Upper Lee catchment, UK. Journal of Hydrology 386, 198-209.
25. Rabitz, H., Alis, Ö. F., Shorter, J. & Shim, K. 1999 Efficient input-output model representations. Computer Physics Communications 117, 11-20.
26. Refsgaard, J. C. & Knudsen, J. 1996 Operation validation and intercomparison of different types of hydrological models. Water Resources Research 32, 2189-2202.
27. Saltelli, A. 2000 Fortune and future of sensitivity analysis. In: Sensitivity Analysis (A. Saltelli, K. Chan & E. M. Scott, eds). Wiley, Chichester, pp. 421-426.
28. Saltelli, A. 2002 Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications 145, 280-297.
29. Schoups, G, Vrugt, J. A., Fenicia, F. & van de Giesen, N. C. 2010 Corruption of accuracy and efficiency of Markov chain Monte Carlo simulation by inaccurate numerical implementation of conceptual hydrologic models. Water Resources Research 46, pW10530.
30. Sieber, A. & Uhlenbrook, S. 2005 Sensitivity analysis of a distributed catchment model to verify the model structure. Journal of Hydrology 310, 216-235.
31. Skodje, R T., Tomlin, A. S., Klippenstein, S. J., Harding, L. B. & Davis, M. J. 2010 Theoretical validation of chemical kinetic mechanisms: combustion of methanol. Journal of Physical Chemistry A 114, 8286-8301.
32. Sobol, I. M. 1993 Sensitivity analysis for non-linear mathematical models. Mathematical Modeling & Computational Experiment 1, 407-414.
33. Sobol, I. M. 2001 Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation 55, 271-280.
34. Spear, R. C. & Hornberger, G M. 1981A technical note on the SPS energy analysis of Herendeen et al. Space Solar Power Review 2, 305-306.
35. Tan, B. Q. & O'Connor, K. M. 1996 Application of an empirical infiltration equation in the SMAR conceptual model. Journal of Hydrology 185, 275-295.
36. Tang, Y., Reed, P., van Werkhoven, K. & Wagener, T 2007a Advancing the identification and evaluation of distributed rainfall-runoff models using global sensitivity analysis. Water Resources Research 43, W06415.
37. Tang, Y., Reed, P., Wagener, T & van Werkhoven, K. 2007b Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation. Hydrology and Earth System Sciences 11, 793-817.
38. Uhlenbrook, S., Seibert, J., Leibundgut, C. & Rodhe, A. 1999 Prediction uncertainty of conceptual rainfall-runoff models caused by problems in identifying model parameters and structure. Hydrological Sciences 44, 779-797.
39. USEPA 2010 Better Assessment Science Integrating point and Nonpoint Sources (BASINS). User Manual, Version 3.0. EPA-823-C-01-001, Office of Water, Washington, DC.
40. van Werkhoven, K., Wagener, T, Reed, P. & Tang, Y 2008 Characterization of watershed model behaviour across a hydroclimatic gradient. Water Resources Research 44, W01429.
41. van Werkhoven, K., Wagener, T, Reed, P. & Tang, Y 2009 Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models. Advances in Water Resources 32, 1154-1169.
42. Wagener, T, van Werkhoven, K., Reed, P. & Tang, Y 2009 Multiobjective sensitivity analysis to understand the information content in streamflow observations for distributed watershed modelling. Water Resources Research 45, W02501.
43. Yatheendradas, S., Wagener, T, Gupta, H, Unkrich, C, Goodrich, D., Schaffner, M. & Stewart, A. 2008 Understanding uncertainty in distributed flash flood forecasting for semiarid regions. Water Resources Research 44, W05S19.
44. Younger, P. M., Freer, J. E. & Beven, K. J. 2009 Detecting the effects of spatial variability of rainfall on hydrological modelling within an uncertainty analysis framework. Hydrological Processes 23, 1988-2003.
45. Ziehn, T & Tomlin, A. S. 2008a Global sensitivity analysis of a 3D street canyon model-part I: the development of high dimensional model representations. Atmospheric Environment 42, 1857-1873.
46. Ziehn, T & Tomlin, A. S. 2008b A global sensitivity study of sulphur chemistry in a premixed methane flame model using HDMR International Journal of Chemical Kinetics 40, 742-753.
47. Ziehn, T, Dixon, N. S. & Tomlin, A. S. 2009 The effects of parametric uncertainties in simulations of a reactive plume using a Lagrangian stochastic model. Atmospheric Environment 43, 5978-5988.