A stochastic approach for assessing the uncertainty of rainfall-runoff simulations

Water Resources Research

Volumn 40, Issue 1, 2004, Pages

  Montanari, Alberto ^a   Brath, Armando ^a  

^a UNIVERSITY OF BOLOGNA   (Italy)

Author keywords

Confidence limits; Hydrological model; Meta Gaussian; Rainfall runoff; Uncertainty

Indexed keywords

COMPUTER SIMULATION; ERROR ANALYSIS; MATHEMATICAL MODELS; RAIN; RANDOM PROCESSES; RELIABILITY; RIVERS; RUNOFF;

RAINFALL-RUNOFF SIMULATIONS; RIVER FLOWS;

WATER RESOURCES;

HYDROLOGY; MODELING; RIVER BASIN; STOCHASTIC METHOD;

HYDROLOGICAL MODELING; RAINFALL-RUNOFF MODELING; RIVER BASIN; STOCHASTICITY; UNCERTAINTY ANALYSIS;

EID: 2542555104     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2003WR002540     Document Type: Article

References (47)

1. Band, L. E. (1986), Topographic partition of watersheds with digital elevation models, Water Resour. Res., 22, 15-24.
2. Beven, K. J. (1993), Prophesy, reality and uncertainty in distributed hydrological modeling, Adv. Waler Resour., 16, 41-51.
3. Beven, K. J. (2001), How far can we go in distributed hydrological modeling, Hydrol. Earth System Sci, 5, 1-12.
4. Beven, K. J. (2002), Towards an alternative blueprint for a physically based digitally simulated hydrologic response modeling system, Hydrol. Processes, 16, 189-206.
5. Beven, K. J., and A. Binley (1992), The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Processes, 6, 279-298.
6. Blaszkova, S., and K. Beven (2002), Flood frequency estimation by continuous simulation for a catchment treated as ungauged (with uncertainty), Water Resour. Res., 38, 1-14.
7. Brath, A., and R. Rosso (1993), Adaptive calibration of a conceptual model for flash flood forecasting, Water Resour. Res., 29, 2561-2572.
8. Brath, A., A. Montanari, and G. Moretti (2002), On the use of simulation techniques for the estimation of peak river flows, paper presented at the International Conference on Flood Estimation, Int. Comm. for the Hydrol. of the Rhine Basin, Lelystad, Netherlands.
9. Brath, A., A. Montanari, and G. Moretti (2003), Assessing the effects on the flood risk of the land-use changes in the last five decades: An Italian case study, IAHS Publ., 278, 435-441.
10. Brock well, P. J., and R. A. Davis (1991), Time Series: Theory and Method, 2nd ed., Springer-Verlag, New York.
11. Cameron, D. S., K. J. Beven, J. Tawn, S. Blazkova, and P. Naden (1999), Flood frequency estimation by continuous simulation for a gauged upland catchment (with uncertainty), J. Hydrol., 219, 169-187.
12. Cameron, D. S., K. J. Beven, and P. Naden (2000), Flood frequency estimation by continuous simulation under climate change (with uncertainty), Hydrol. Earth Syst. Sci., 4, 393-405.
13. Chatfield, C. (1993), Calculating interval forecast, J. Bus. Econ. Slat., 11, 121-144.
14. Cook, R. D., and S. Weisberg (1994), An Introduction to Regression Graphics, John Wiley, Hoboken, N. J.
15. Cunge, J. A. (1969), On the subject of a flood propagation computation method (Muskingum Method), J. Hydraul. Res., 7, 205-230.
16. D'Agostino, R. B., and M. A. Stephens (1986), Goodness-Of-fit Techniques, Chapman and Hall, New York.
17. Doorembos, J., W. O. Pruitt, A. Aboukhaled, J. Damagnez, N. G. Dastane, C. van der Berg, P. E. Rijtema, O. M. Ashford, and M. Frere (1984), Guidelines for predicting crop water requirements, FAO Irrig. Drain. Pap., 24, 144 pp.
18. Duan, Q., S. Sorooshian, and V. K. Gupta (1992), Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28, 1015-1031.
19. Franchini, M. (1996), Use of a genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall runoff models, Hydrol. Sci. J., 41, 21-39.
20. Freer, J., K. J. Beven, and B. Ambroise (1996), Bayesian uncertainty in runoff prediction and the value of data: An application of the GLUE approach, Water Resour. Res., 32, 2163-2173.
21. Gardner, E. S. (1988), A simple method for computing prediction intervals for time-series forecasts, Manage. Sci., 34, 541-546.
22. Hosking, J. R. M., and J. R. Wallis (1988), The effect of intersite dependence on regional flood frequency analysis, Water Resour. Res., 24, 588-600.
23. Kelly, K. S., and R. Krzysztofowicz (1997), A bivariate meta-Gaussian density for use in hydrology, Stochastic Hydrol. Hydraul., 11, 17-31.
24. Kelly, K. S., and R. Krzysztofowicz (2000), Precipitation uncertainty processor for probabilistic river stage forecasting, Water Resour. Res., 36, 2643-2653.
25. Kottegoda, N. T. (1980), Stochastic Water Resources Technology, McMillan, New York.
26. Kizysztofowicz, R. (1999). Bayesian theory of probabilistic forecasting via deterministic hydrologic model, Water Resour. Res., 35, 2739-2750.
27. Krzysztofowicz, R. (2001), The case for probabilistic forecasting in hydrology, J. Hydrol., 249, 2-9.
28. Krzysztofowicz, R. (2002), Bayesian system for probabilistic river stage forecasting, J. Hydrol., 268, 16-40.
29. Krzysztofowicz. R., and H. D. Herr (2001), Hydrologic uncertainty processor for probabilistic river stage forecasting: Precipitation-dependent model. J. Hydrol., 249, 46-68.
30. Krzysztofowicz, R.. and K. S. Kelly (2000), Hydrologic uncertainty processor for probabilistic river stage forecasting, Water Resour. Res., 36, 3265-3277.
31. Kuczera, G. (1994), NLFIT: A Bayesian nonlinear regression program suite, users manual, Dep. of Civ. Eng. and Surv., Univ. of Newcastle, Callaghan, Australia.
32. Kuczera. G.. and E. Parent (1998), Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm, J. Hydrol., 211, 69-85.
33. Loukas, A., L. Vasiliades, and N. R. Dalezios (2002), Climatic impacts on the runoff generation processes in British Columbia, Canada, Hydrol. Earth Syst. Sci., 6, 211-228.
34. Metropolis. N. (1987), The beginning of the Monte Carlo method, Los Alamos Sci., 15, 125-130.
35. Montanari, A., R. Rosso, and M. S. Taqqu (1997), Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation and simulation, Water Resour. Res., 33, 1035-1044.
36. Montgomery, D. L., and E. Foufoula-Georgiou (1993), Channel network source representation using digital elevation models, Water Resour. Res., 29, 3925-3934.
37. Moran, P. A. P. (1970), Simulation and evaluation of complex water system operations, Water Resour. Res., 6, 1737-1742.
38. Nash, J. E., and J. V. Sutcliffe (1970), River flow forecasting through conceptual models I: A discussion of principles, J. Hydrol., 10, 282-290.
39. Orlandini, S., A. Perotti, G. Sfondrini, and A. Bianchi (1999), On the storm flow response of upland Alpine catchments, Hydrol. Processes, 13, 549-562.
40. Singh, V. P., and D. A. Woolhiser (2002), Mathematical modeling of watershed hydrology, J. Hydrol Eng., 7, 270-292.
41. Soil Conservation Service (1972), National Engineering Handbook, sect. 4, Hydrology, U.S. Dep. of Agric., Washington, D. C.
42. Stedinger, J. R., R. M. Vogel, and E. Foufoula-Georgiou (1993), Frequency analysis of extreme events, in Handbook of Hydrology, edited by D. R. Maidment, pp. 18.1-18.66, McGraw-Hill, New York.
43. Thiemann, M., M. Trosset, H. Gupta, and S. Sorooshian (2001), Bayesian recursive parameter estimation for hydrologic models, Water Resour. Res., 37, 2521-2536.
44. Vrugt, J. A., H. V. Gupta, W. Bouten, and S. Sorooshian (2003), A shuffled complex evolution Metropolis algorithm for optimization and uncertainty assessment of hydrological model parameters, Water Resour. Res., 39(6), 1201, doi:10.1029/2002WR001642.
45. Wallis, K. F. (1974), Discussion of "Experience with forecasting univariate time-series and the combination of forecasts" by P. Newbold and C. W. J. Granger, J. R. Stat. Soc., Sci: A, 137, 155-156.
46. Yar, M., and C. Chatfield (1990), Prediction intervals for Holt-Winters forecasting procedure, Int. J. Forecast., 6, 1-11.
47. Zhao, R. J., Y. L. Zhuang, L. R. Fang, X. R. Liu, and Q. S. Zhang (1980), The Xinanjiang model, IAHS Publ., 129, 351-356.