Stefan Wirtz Vom Fachbereich VI (Geographie/Geowissenschaften ...

Experimentelle Rinnenerosionsforschung vs. Modellkonzepte – Quantifizierung der hydraulischen und erosiven Wirksamkeit von Rinnen
Santisteban et al., 2005, Helming et al., 1999, Parsons et al., 1999, Parsons and Wainwright,
2006, Rejman and Brodowski, 2005). The aim of these studies was:
- to observe rill network formation (Bruno et al., 2008, Mancilla et al., 2005),
- to define the initial conditions for rilling (Bruno et al., 2008, Bryan et al., 1998,
Govers and Poesen, 1988, Slattery and Bryan, 1992, Torri et al., 1987),
- to study the development of rill head morphology (Bruno et al., 2008, Brunton and
Bryan, 2000),
- to estimate the main hydraulic variables like cross-section area, wetted perimeter,
hydraulic radius, mean velocity and shear stress for calculating other hydraulic
parameters which could not be measured or estimated (Abrahams and Li, 1996, Bruno
et al., 2008, Foster et al., 1984, Gilley et al., 1990, Giménez et al., 2004, Govers, 1992
b), to validate existing models (Huang et al., 1996),
- to measure the transmission losses in rills (Parsons et al., 1999) or
- to propose mathematical models for estimating soil loss due to rill erosion (Bruno et
al., 2008, Foster, 1982, Nearing et al., 1989) or for rill initiation (Parsons and
Wainwright, 2006).
Field data are as close to reality as possible. But observations can be collected from a long
term plot measurement or only by scientists who are in the right place at the right time. This is
not always possible, so field experiments are used to trigger the processes to be observed
when the measurement team is on site. However, both observations and experiments have
certain disadvantages: (i) Measurement techniques may disturb the processes being observed,
(ii) the time scale of human observations is shorter than that of the process under study, (iii)
some processes cannot easily be measured and (iv) some processes are apparently chaotic and
their spatial and temporal variations are difficult to capture (Favis-Mortlock and de Boer,
2003, Kleinhans et al., 2010).
Nevertheless, simplified experimental setups are ruled by the same natural laws as the
processes found in nature. Thus, experimental observations can be considered as a simplified
but valid representation of the reality (Paola et al., 2009). A model is always simpler than the
thing which it aims to represent (Favis-Mortlock et al., 2001).
Mathematical models describe reality in terms of mathematical equations. Physical laws are
often simplified to allow numerical solutions, but in many cases it is not clear which laws
apply and to what extent simplification is possible. Model parameters are always the result of
simplifications. Values of some parameters are not well known, so the model has to be
calibrated. Here, the phenomenon of equifinality is a problem: A wide range of parameters
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