English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Hydrodynamic models are important tools for simulating river water level and flow. A considerable fraction of the hydrodynamic model errors are related to parameters uncertainties. As cross sections bottom levels considerably affect water level simulation, this parameter has to be well estimated for flood studies. Automatic calibration performance and processing time depend on the search space dimension, which is related to the number of calibrated parameters. This paper shows the application of the Shuffled Complex Evolution (SCE-UA) optimization algorithm to assess the number of cross sections bottom levels used in calibration. Also was evaluated the extent of algorithm exploration regarding computational processing time and accuracy. It was tested the calibration of 2, 4, 7 and 10 cross sections bottom levels (2PAR, 4PAR, 7PAR and 10PAR calibration configurations) of a 1,100 km reach of the Madeira River. 7PAR and 10PAR representation had better fitness (lower objective function value) on cross sections used for calibration; however, the error on other cross sections (2 validation gauging stations) was higher than 2PAR and 4PAR calibration. The short number (5) of gauging stations used in calibration has limited the number of calibrated parameters to represent adequately the river level profile. Finally, this paper shows a contribution for the parsimonious selection of parameters regarding the spatial distribution of observation sites used in calibration.

Efficient number of calibrated cross sections bottom levels on a hydrodynamic model using the SCE-UA algorithm. Case study: Madeira River