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  • Data assimilation for improved discharge estimates with the wflow_sbm model: a case study of the Overijsselse Vecht river (The Netherlands) : master thesis = Asimilacija podatkov za izboljšane ocene razrešnice z wflow_sbm modelom: študija primera reke Overijsselse Vecht (Nizozemska)
    Koronaci, Kristina
    Extreme hydrological events have become more frequent, as evidenced by the European floods of July 2021, which affected the southern provinces of the Netherlands. The need for improved discharge ... predictions to be used in operational water management to avoid potential adverse effects of flooding has encouraged researchers to employ several ways to improve hydrological model estimates, including data assimilation. This thesis explores the data assimilation effects in the discharge predictions of the wflow_sbm distributed hydrological model of the Vecht river basin. Additionally, effects on other hydrological states and fluxes like subsurface flow, saturated water depth, and soil moisture were explored spatially. This work presents a methodology for applying data assimilation in a model where water is routed from the surface and subsurface. In contrast, previous studies used a model in which water is routed only via surface water. Ensemble Kalman Filter is used to update the model’s discharge predictions by assimilating external discharge observations. This methodology also explores how the data assimilation effect is influenced by the uncertainty characterization considered in the assimilation framework and other factors like the length of the assimilation window and the number of assimilation locations. A preliminary study of the rainfall data is performed to determine the uncertainties of the chosen rainfall product. A benchmark simulation scenario is then selected after the review of deterministic and ensemble model predictions. Finally, data assimilation experiments are developed after discussing the characterization of the uncertainty model. The results of the model output analysis indicate that streamflow assimilation typically has a positive effect on improving model discharge estimations. Additionally, the Ensemble Kalman Filter update effectively captures the system’s spatial state dynamics for subsurface states and fluxes, such as saturated water depth, soil moisture, etc. Two alternative experimental setups with different assimilation intervals and numbers of assimilated observations are examined concerning how this effect varies over other flow gauge locations. As demonstrated by both experiments, longer assimilation times give better results, with the assimilation effect significantly improving in the final timesteps of the assimilation frame. Furthermore, it is concluded that assimilation of observations near the outlet and interior gauges will improve discharge predictions, whereas assimilation of observations only near the outlet will only improve discharge predictions at a number of stations, typically those that are closer to the assimilation location and those where the wflow sbm model exhibits the same trend as the assimilation station. An uncertainty factor of 2.5 for the precipitation error and 0.1 for the observation error yielded the best results for both experiments. However, this study has several limitations, including assumptions of a perfect model and initial conditions; the way the precipitation and observations error model was derived. As a result, the model gives unrealistic discharge predictions when compensating for the neglected errors. Additionally, a limited number of experiments due to the extensive computational times, attributed to the combination of the OpenDA tool with the distributed model, and the algorithm choice, does not allow the DA impact on the discharge predictions to be judged accurately. Therefore, the final section of this study provides recommendations for future research, suggesting additional experiments with longer assimilation windows; analysis of the spatial correlation structure of precipitation, the use of more statistically reliable techniques to assess the precipitation uncertainties; consideration of the model parameter and initial conditions uncertainty; etc.
    Type of material - master's thesis ; adult, serious
    Publication and manufacture - Ljubljana : [K. Koronaci], 2022
    Language - english
    COBISS.SI-ID - 124507139

    Link(s):

    Repository of the University of Ljubljana – RUL

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