Issues in generating stochastic observables for hydrological models

Beven, K. (2021) Issues in generating stochastic observables for hydrological models. Hydrological Processes, 35 (6): e14203. ISSN 0885-6087

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Abstract

This paper provides a historical review and critique of stochastic generating models for hydrological observables, from early generation of monthly discharge series, through flood frequency estimation by continuous simulation, to current weather generators. There are a number of issues that arise in such models, from uncertainties in the observational data on which such models must be based, to the potential persistence effects in hydroclimatic systems, the proper representation of tail behaviour in the underlying distributions, and the interpretation of future scenarios.

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Journal Article
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Hydrological Processes
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Funding details: Natural Environment Research Council, NERC, NE/R004722/1 Funding text 1: Work on this paper has been supported by the NERC Q‐NFM project led by Dr. Nick Chappell (grant no. NE/R004722/1). The paper has greatly benefitted from an excellent review and the recent work of Demetris Koutsoyiannis for which I am most grateful. References: Benson, M.A., Thoughts on the design of design floods (1973) Floods and droughts, Proceedings of the 2nd international symposium in hydrology, pp. 27-33. , Water Resour. Publ; Bertoni, G., Daemen, J., Peeters, M., Van Assche, G., Sponge-based pseudo-random number generators (2010) International workshop on cryptographic hardware and embedded systems, pp. 33-47. , Springer; Betson, R.P., What is watershed runoff (1964) Journal of Geophysical Research, 69, pp. 1541-1552; Beven, K.J., Hillslope runoff processes and flood frequency characteristics (1986) Hillslope processes, pp. 187-202. , A. D. 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/dk/atira/pure/subjectarea/asjc/2300/2312
Subjects:
?? flood controlfloodsfrequency estimationstochastic systemscontinuous simulationflood frequency estimationgenerating modelshistorical reviewhydrological modelsobservational dataunderlying distributionweather generatorstochastic modelswater science and techn ??
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158113
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