Hydrological modelling of temporally-varying catchments: Facets of change and the value of information

A. Efstratiadis, I. Nalbantis, and D. Koutsoyiannis, Hydrological modelling of temporally-varying catchments: Facets of change and the value of information, Hydrological Sciences Journal, 60 (7-8), 1438–1461, doi:10.1080/02626667.2014.982123, 2015.

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[English]

River basins are by definition temporally varying systems: changes are apparent at every temporal scale, in terms of changing meteorological inputs and catchment characteristics, respectively due to inherently uncertain natural processes and anthropogenic interventions. In an operational context, the ultimate goal of hydrological modelling is predicting responses of the basin under conditions that are similar or different from those observed in the past. Since water management studies require that anthropogenic effects are considered known and a long hypothetical period is simulated, the combined use of stochastic models, for generating the inputs, and deterministic models that also represent the human interventions in modified basins, is found to be a powerful approach for providing realistic and statistically consistent simulations (in terms of product moments and correlations, at multiple time scales, and long-term persistence). The proposed framework is investigated on the Ferson Creek basin (USA) that exhibits significantly growing urbanization during the last 30 years. Alternative deterministic modelling options include a lumped water balance model with one time-varying parameter and a semi-distributed scheme based on the concept of hydrological response units. Model inputs and errors are respectively represented through linear and non-linear stochastic models. The resulting nonlinear stochastic framework maximizes the exploitation of the existing information, by taking advantage of the calibration protocol used in this issue.

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Works that cite this document: View on Google Scholar or ResearchGate

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Tagged under: Determinism vs. stochasticity, Hydrological models, Stochastics, Uncertainty