Evaluating the contribution of multi-model combination to streamflow hindcasting by empirical and conceptual models
Journal
Hydrological Sciences Journal
Journal Volume
62
Journal Issue
9
Pages
1456-1468
Date Issued
2017
Author(s)
Abstract
The contribution of multi-model combination to daily streamflow hindcasting was evaluated through the HBV (Hydrologiska Byr?ns Vattenbalansavdelning) and RNN (recurrent neural networks) models with 100 ensemble members generated with different initial conditions for both. In the calibration phase, the analysis showed that the HBV and RNN models with 20 members have better accuracy and require less calibration time. The combination of two models, however, did not provide significant improvements when 80 more members were added in the combination. In the validation phase, the results indicated that both HBV and RNN models with 20 members not only accurately produce reliable and stable streamflow hindcasting, but also effectively simulate the timing and the value of peak flows. From the consistency of calibration and validation results, the study provides an important contribution, namely, that ensemble size is not sensitive to the type of hydrological model in terms of streamflow hindcasting. ? 2017 IAHS.
SDGs
Other Subjects
Hydrology; Neural networks; Recurrent neural networks; Stream flow; Calibration and validations; Calibration time; Ensemble averaging; Ensemble members; Hydrological modeling; Initial conditions; Multi-model combination; Validation phase; Calibration; artificial neural network; conceptual framework; empirical analysis; ensemble forecasting; hindcasting; hydrological modeling; peak flow; streamflow; Hepatitis B virus
Type
journal article