Choosing an appropriate hydrological model for rainfall-runoff extremes in small catchmentsář P., Hrabalíková M., Neruda M., Neruda R., Šrejber J., Jelínková A., Bačinová H. (2015): Choosing an appropriate hydrological model for rainfall-runoff extremes in small catchments. Soil & Water Res., 10: 137-146.
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Real and scenario prognosis in engineering hydrology often involves using simulation techniques of mathematical modelling the rainfall-runoff processes in small catchments. These catchments are often up to 50 km2 in area, their character is torrential, and the type of water flow is super-critical. Many of them are ungauged. The damage in the catchments is enormous, and the length of the torrents is about 23% of the total length of small rivers in the Czech Republic. The Smědá experimental mountainous catchment (with the Bílý potok downstream gauge) in the Jizerské hory Mts. was chosen as a model area for simulating extreme rainfall-runoff processes using two different models. For the purposes of evaluating and simulating significant rainfall-runoff episodes, we chose the KINFIL physically-based 2D hydrological model, and ANN, an artificial neural network mathematical “learning” model. A neural network is a model of the non-linear functional dependence between inputs and outputs with free parameters (weights), which are created by iterative gradient learning algorithms utilizing calibration data. The two models are entirely different. They are based on different principles, but both require the same time series (rainfall-runoff) data. However, the parameters of the models are fully different, without any physical comparison. The strength of KINFIL is that there are physically clear parameters corresponding to adequate hydrological process equations, while the strength of ANN lies in the “learning procedure”. Their common property is the rule that the greater the number of measured rainfall-runoff events (pairs), the better fitted the simulation results can be expected.
Andréassian Vazken (2004): Waters and forests: from historical controversy to scientific debate. Journal of Hydrology, 291, 1-27
Beven Keith (1979): On the generalized kinematic routing method. Water Resources Research, 15, 1238-1242
Beven K.J. (2001): Rainfall-Runoff Modelling: The Primer. Chichester, John Wiley & Sons.
Čamrová L., Jílková J. (2006): Flood Damages and Tools for their Mitigation. Praha, IEEP, VŠE. (in Czech)
Kibler D.F., Woolhiser D.A. (1970): The Kinematic Cascade as a Hydrologic Model. Hydrology Paper No. 39. Fort Collins, Colorado State University.
Kirchner J.W. (2009): Catchments as simple dynamical systems: Catchment character ization, rainfall-runoff modelling, and doing hydrology backward. Water Resources Research, 45: W02429.
Kovář P. (1992): Possibilities of design floods assessment using model KINFIL. Journal of Hydrology and Hydromechanics, 40: 197–220.
Kovář P., Křovák F. (2002): Torrent Control. Praha, FLE ČZU. (in Czech)
Kovář P., Vaššová D. (2012): The KINFIL Model Manual. Praha, FŽP ČZU. (in Czech)
Kovář P., Pelikán M., Heřmanovská D., Vrana I. (2014): How to reach a compromise solution on technical and non-structural flood control measures. Soil and Water Research, 9: 143–152.
Lax Peter, Wendroff Burton (1960): Systems of conservation laws. Communications on Pure and Applied Mathematics, 13, 217-237
Lippmann R. (1987): An introduction to computing with neural nets. IEEE ASSP Magazine, 4, 4-22
Morel-Seytoux H.J., Verdin J.P. (1981): Extension of the Soil Conservation Service Rainfall-Runoff Methodology for Ungauged Watersheds. Fort Collins, Colorado State University.
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
Neruda M., Neruda R., Kudová P. (2005): Forecasting runoff with artificial neural networks. Progress in surface and subsurface water studies at plot and small basin scale. In: 10th Conf. Euromediterranean Network of Experimental and Representative Basins (ERB), Turin, Oct 13–17, 2004: 65–69.
Perrin C., Michel C., Andréassian V. (2001): Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments. Journal of Hydrology, 242, 275-301
Ponce V.M., Hawkins R.H. (1996): Runoff curve number: Has it reached maturity? Journal of Hydrologic Engineering, 1: 11–19.
Rumelhart D.E., McClelland J.L. (1986): Parallel Distributed Processing: Explorations in the Microstructure of Cognition I&II. Cambridge, MIT Press.
Singh Vijay P. (1976): A note on the step error of some finite-difference schemes used to solve kinematic wave equations. Journal of Hydrology, 30, 247-255
Singh V.P. (1996): Kinematic Wave Modelling in Water Resources: Surface Water Hydrology. New York, John Wiley&Sons.
US SCS (1986): Urban Hydrology for Small Watersheds. Technical Release 55. Washington D.C., USDA.
US SCS (1992): Soil Conservation. Program Methodology. Chapter 6.12: Runoff Curve Numbers. Washington D.C., USDA.
WMO (1984): Commission for Hydrology: Abridged Final Report of the Seventh Session. Geneva, Aug 27–Sept 7, 1984, Secretariat of the World Meteorological Organisation.
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