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Schilling, Oliver

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Schilling, Oliver
Affiliation principale
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https://libra.unine.ch/handle/123456789/807

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  • Publication
    Accès libre
    Simulating Flood‐Induced Riverbed Transience Using Unmanned Aerial Vehicles, Physically Based Hydrological Modeling, and the Ensemble Kalman Filter
    (2018-11)
    Tang, Qi
    ;
    Schilling, Oliver 
    ;
    Kurtz, W.
    ;
    Brunner, Philip 
    ;
    Vereecken, H.
    ;
    Hendricks Franssen, Harrie-Jan
    Abstract Flood events can change the riverbed topography as well as the riverbed texture and structure, which in turn can influence the riverbed hydraulic conductivity (Krb) and river-aquifer exchange fluxes. A major flood event occurred in the Emme River in Switzerland in 2014, with major implications for the riverbed structure. The event was simulated with the fully integrated hydrological model HydroGeoSphere. The aim was to investigate the effect of the spatial and temporal variability of riverbed topography and Krb on predictions of hydraulic states and fluxes and to test whether data assimilation (DA) based on the ensemble Kalman filter (EnKF) can better reproduce flood-induced changes to hydraulic states and parameters with the help of riverbed topography changes recorded with an unmanned aerial vehicle (UAV) and through-water photogrammetry. The performance of DA was assessed by evaluating the reproduction of the hydraulic states for the year 2015. While the prediction of surface water discharge was not affected much by the changes in riverbed topography and in Krb, using the UAV-derived postflood instead of the preflood riverbed topography reduced the root-mean-square error of predicted heads (RMSE [h]) by 24%. If, in addition to using the postflood riverbed topography, also Krb and aquifer hydraulic conductivity (Kaq) were updated through DA after the flood, the RMSE (h) was reduced by 55%. We demonstrate how updating of Krb and Kaq based on EnKF and UAV-based observations of riverbed topography transience after a major flood event strongly improve predictions of postflood hydraulic states.
  • Publication
    Accès libre
    The influence of riverbed heterogeneity patterns on river-aquifer exchange fluxes under different connection regimes
    (2017-9)
    Tang, Qi
    ;
    Kurtz, W.
    ;
    Schilling, Oliver 
    ;
    Brunner, Philip 
    ;
    Vereecken, H.
    ;
    Hendricks Franssen, Harrie-Jan
    Riverbed hydraulic conductivity (K) is a critical parameter for the prediction of exchange fluxes between a river and an aquifer. In this study, the role of heterogeneity patterns was explored using the fully integrated hydrological model HydroGeoSphere simulating complex, variably saturated subsurface flow. A synthetic 3-D river-aquifer reference model was constructed with a heterogeneous riverbed using nonmulti-Gaussian patterns in the form of meandering channels. Data assimilation was used to test the ability of different riverbed K patterns to reproduce hydraulic heads, riverbed K and river-aquifer exchange fluxes. Both fully saturated as well as variably saturated conditions underneath the riverbed were tested. The data assimilation experiments with the ensemble Kalman filter (EnKF) were carried out for four types of geostatistical models of riverbed K fields: (i) spatially homogeneous, (ii) heterogeneous with multiGaussian distribution, (iii) heterogeneous with non-multi-Gaussian distribution (channelized structures) and (iv) heterogeneous with non-multi-Gaussian distribution (elliptic structures). For all data assimilation experiments, state variables and riverbed K were updated by assimilating hydraulic heads. For saturated conditions, heterogeneous geostatistical models allowed a better characterization of net exchange fluxes than a homogeneous approximation. Among the three heterogeneous models, the performance of non-multi-Gaussian models was superior to the performance of the multi-Gaussian model, but the two tested non-multi-Gaussian models showed only small differences in performance from one another. For the variably saturated conditions both the multi-Gaussian model and the homogeneous model performed clearly worse than the two non-multi-Gaussian models. The two non-multi-Gaussian models did not show much difference in performance. This clearly shows that characterizing heterogeneity of riverbed K is important. Moreover, particularly under variably saturated flow conditions the mean and the variance of riverbed K do not provide enough information for exchange flux characterization and additional histogram information of riverbed K provides crucial information for the reproduction of exchange fluxes.
  • Publication
    Accès libre
    Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources
    (2017-7-1)
    Kropf, Peter 
    ;
    Kurtz, Wolfgang
    ;
    Lapin, Andrei 
    ;
    Tang, Qi 
    ;
    Schilling, Oliver 
    ;
    Schiller, Eryk 
    ;
    Braun, Torsten
    ;
    Hunkeler, Daniel 
    ;
    Vereecken, Harry
    ;
    Sudicky, Edward
    ;
    Franssen, Harrie-Jan Hendricks
    ;
    Brunner, Philip 
    Online data acquisition, data assimilation and integrated hydrological modelling have become more and more important in hydrological science. In this study, we explore cloud computing for integrating field data acquisition and stochastic, physically-based hydrological modelling in a data assimilation and optimisation framework as a service to water resources management. For this purpose, we developed an ensemble Kalman filter-based data assimilation system for the fully-coupled, physically-based hydrological model HydroGeoSphere, which is able to run in a cloud computing environment. A synthetic data assimilation experiment based on the widely used tilted V-catchment problem showed that the computational overhead for the application of the data assimilation platform in a cloud computing environment is minimal, which makes it well-suited for practical water management problems. Advantages of the cloud-based implementation comprise the independence from computational infrastructure and the straightforward integration of cloud-based observation databases with the modelling and data assimilation platform.