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Bedforms evolution in the Vistula River mouth during extreme flood event, southern Baltic Sea

Lisimenka Aliaksandr, Kubicki Adam, Kałas Maciej

Oceanologia

2022

No. 64 (1)

212--226

Results of bathymetric surveys conducted to examine changes of sand dunes geometry in the Vistula River mouth before, during and after the extreme flood event are presented. A total of 2076 dunes were analysed based on a series of bed elevation profiles obtained along the centreline of about 3.3 km length. Low-steepness dunes characterized by the mean lee-side slopes milder than β<10° are fully dominant at low flows. In contrast, at high hydrology, nearly 50% of dunes indicate β>10°. Dune height and length are substantially out of phase with progressive changes of water discharge exposing a well-pronounced anti-clockwise hysteresis. Distinct behaviour of dune dimensions reflected in increasing of dune steepness H/λ of about 3-fold and decreasing of about 4-fold were observed during rising and falling discharges, respectively. The bed roughness due to dunes presence showed changes of about 10-fold during the both of limbs and is found to be in range of about kdunes=(1/5÷3/5)Hmean. At the mesoscale region, spectra followed sufficiently by the ‘–3 power law’ for low hydrology, with steeper spectrum slopes close to ‘–4’ during moderate and high water discharges. With the development of the flood, potential of flow separation phenomena was increased of about 9-fold, from 2.2% at the flood beginning phase up to 20% at the flood peak. The obtained results could be used for the improvement of the hydraulic numerical models in sand-bed rivers to predict bedforms evolution, flow resistance and turbulence as well as water levels for proper river system management during flood events.

Suspended sediment discharge modeling during flood events using two different artificial neural network algorithms

Bouguerra Hamza, Tachi Salah-Eddine, Derdous Oussama, Bouanani Abderrazak, Khanchoul Kamel

Acta Geophysica

2019

Vol. 67, no. 6

1649--1660

This paper presents modeling of artificial neural network (ANN) to forecast the suspended sediment discharges (SSD) during flood events in two different catchments in the Seybouse basin, northeastern Algeria. This study was carried out on hourly SSD and water discharge data during flood events from a period of 31 years in the Ressoul catchment and of 28 years in the Mellah catchment. The ANNs were trained according to two different algorithms: the Levenberg–Marquardt algorithm (LM) and the Quasi-Newton algorithm (BFGS). Seven input combinations were trained for the SSD prediction. The performance results indicated that both algorithms provided satisfactory simulations according to the determination coefficient (R2) and root mean squared error (RMSE) performance criteria, with priority to the BFGS algorithm; the coefficient of determination using the LM algorithm varies between 51.0 and 90.2%, whereas using the BFGS algorithm it varies between 54.3 and 93.5% in both studied catchments, with calculated improvement for all seven developed networks with the best improvement in the Ressoul catchment presented in ANN06 with ΔR2 4.23% and ΔRMSE 1.74‰, and with the best improvement presented in ANN05 with ΔR2 6.07% and ΔRMSE 0.71‰ in the Mellah catchment. The analysis showed that the use of Quasi-Newton method performed better than the Levenberg–Marquardt in both studied areas.