Liselot ArkesteijnDelft University of Technology | TU
Liselot Arkesteijn
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11
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Publications
Publications (11)
Recent analysis of equilibrium and quasi‐equilibrium channel geometry in engineered (fixed‐width) rivers has successfully shown that two temporal scales can be distinguished, with quasi‐static (long‐term) and dynamic (short‐term) components. This distinction is based on the fact that channel slope cannot keep pace with short‐term fluctuations of th...
The active layer model (Hirano, 1971) is frequently used for modeling mixed‐size sediment river morphodynamic processes. It assumes that all the dynamics of the bed surface are captured by a homogeneous top layer that interacts with the flow. Although successful in reproducing a wide range of phenomena, it has two problems: (1) It may become mathem...
An engineered alluvial river (i.e., a fixed‐width channel) has constrained planform but is free to adjust channel slope and bed surface texture. These features are subject to controls: the hydrograph, sediment flux, and downstream base level. If the controls are sustained (or change slowly relative to the timescale of channel response), the channel...
When the water discharge, sediment supply, and base level vary around stable values, an alluvial river evolves toward a mean equilibrium or graded state with small fluctuations around this mean state (i.e. a dynamic or statistical equilibrium state). Here we present analytical relations describing the mean equilibrium geometry of an alluvial river...
This paper shows that instability of hydrological system
representation in response to different pieces of information and
associated prediction uncertainty is a function of model
complexity. After demonstrating the connection between unstable
model representation and model complexity, complexity is analyzed in
a step by step manner. This is done m...
This paper presents evidence that model prediction uncertainty does not necessarily rise with parameter dimensionality (the number of parameters). Here by prediction we mean future simulation of a variable of interest conditioned on certain future values of input variables. We utilize a relationship between prediction uncertainty, sample size 5 and...
Knowledge of hydrological model complexity can aid selection of an
optimal prediction model out of a set of available models. Optimal model
selection is formalized as selection of the least complex model out of a
subset of models that have lower empirical risk. This may be considered
equivalent to minimizing an upper bound on prediction error, defi...
Higher dimensionality of hydrologic model parameters is often, in an
ad-hoc manner, associated with higher model complexity and prediction
uncertainty. We establish a formal relationship between hydrological
model complexity and prediction uncertainty, where we show that higher
model complexity leads to higher prediction uncertainty. We build on
th...