Bruno Oliveira, Rodrigo Maia
Thursday 2 july 2015
11:15 - 11:30h at Oceania (level 0)
Themes: (T) Water engineering, (ST) Computational methods
Parallel session: 11E. Engineering - Computational
In nearly all studies involving the consideration of river flow and its variability (e.g. on Water Resource Management, Environmental Protection), the selection/definition of the flow’s time series is of great importance. Commonly these time series suffer from either being too short or from having too many missing values for the desired applications, thereby limiting its interest, relevance and applicability. In the present paper we study the possibility of generating a large number of flow time series by using a non-parametric methodology, or, conversely, of creating a long, realistic, flow series from a comparatively small amount of observed data. Overall, this procedure involves the separation of the available observed time series into its multiple components (i.e. periodicity, random variation, events, etc.), the assessment of their respective relevance and the disentanglement of their variation, as well as the generation/sampling of the time series’ own values. The series of stream flow values to be generated must perform a complete reproduction of the variability and sequencing of the real, known, flow sequences, without having to reproduce the same exact values. Comparatively, existing tools mostly focus on flow prediction and are often limited to the reproduction of the simpler statistical characteristics (mean, standard deviation, etc.) or to the use of auxiliary variables, locations or flow auto-correlation (Runoff Models, Stream Flow Downscaling, Artificial Neural Networks, etc.) [1, 2] The tool here proposed brings significant benefits to a multiplicity of techniques and models, not only for the completion of missing data and the extension of available data, but also in order to extend the applicability of any and all stochastic based applications which perform event (e.g. flash flood) simulation or hysteresis related effects. References  Kim, B. S., Kim, H. S., & Seoh, B. H. (2004). Streamflow simulation and skewness preservation based on the bootstrapped stochastic models. Stochastic Environmental Research and Risk Assessment, 18(6), 386-400.  Ahmed, J. A., & Sarma, A. K. (2007). Artificial neural network model for synthetic streamflow generation. Water resources management, 21(6), 1015-1029.