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Assessing seasonal hydrological model parameters for a better real-time forecasting performance Nsubuga, R. (2021). Assessing seasonal hydrological model parameters for a better real-time forecasting performance. MSc Thesis. VUB/KU Leuven: Brussels. vi, 59 pp.
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Thesis info:
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Beschikbaar in | Auteurs |
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Documenttype: Doctoraat/Thesis/Eindwerk |
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Abstract |
The model parameters are assessed for seasonal-sensitivity, and the most seasonally-sensitive parameters are: Umax, CQOF, TIF and TG. Two methods for splitting seasons are studied:- based on (1) dates; and (2) L/Lmax; and a model is developed for each method. A model calibrated with a uniform single parameter set is also developed and used as the base-model to assess the seasonal models’ performance. A multi criteria model performance evaluation is used, which includes using goodness-of-fit statistics, hydrograph comparison and extreme value analysis. From the results, the models with seasonally-calibrated parameters show improved simulations over the single parameter set model for at least 2 of the 3 studied catchments. All the goodness-of-fit statistics showed modest improvements. The seasonal models simulate the winter peaks with relatively reduced bias, respond significantly better to the summer peaks, and show a reduction in the water balance error. For the extreme flow analysis, the seasonal models show mostly reduced bias in simulating the extreme flows. Both the date- and L/Lmax-split seasonal models alternately perform superior to each other, and they mostly show very similar behavior on average. Overall, the seasonally-calibrated models showed an interesting possibility for increasing CRR model performance. It is especially important for catchments where a single parameter set CRR model shows performance tradeoffs during the varying seasons. |
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