| Even in physically based distributed hydrological models, various remaining
parameters must be estimated for each sub-catchment. This can involve
tremendous effort, especially when the number of sub-catchments is large and
the applied hydrological model is computationally expensive. Automatic
parameter estimation tools can significantly facilitate the calibration
process. Hence, we combined the nonlinear parameter estimation tool PEST with
the distributed hydrological model WaSiM. PEST is based on the
Gauss-Marquardt-Levenberg method, a gradient-based nonlinear parameter
estimation algorithm. WaSiM is a fully distributed hydrological model using
physically based algorithms for most of the process descriptions.
WaSiM was applied to the alpine/prealpine Ammer River catchment (southern Germany,
710 km2 in a 100×100 m2 horizontal resolution. The catchment is
heterogeneous in terms of geology, pedology and land use and shows a complex
orography (the difference of elevation is around 1600 m). Using the
developed PEST-WaSiM interface, the hydrological model was calibrated by comparing
simulated and observed runoff at eight gauges for the hydrologic year 1997
and validated for the hydrologic year 1993. For each sub-catchment four
parameters had to be calibrated: the recession constants of direct runoff
and interflow, the drainage density, and the hydraulic conductivity of the
uppermost aquifer. Additionally, five snowmelt specific parameters were
adjusted for the entire catchment. Altogether, 37 parameters had to be
calibrated. Additional a priori information (e.g. from flood hydrograph analysis)
narrowed the parameter space of the solutions and improved the
non-uniqueness of the fitted values. A reasonable quality of fit was
achieved. Discrepancies between modelled and observed runoff were also due
to the small number of meteorological stations and corresponding
interpolation artefacts in the orographically complex terrain. Application
of a 2-dimensional numerical groundwater model partly yielded a slight decrease of
overall model performance when compared to a simple conceptual groundwater
approach. Increased model complexity therefore did not yield in general
increased model performance.
A detailed covariance analysis was performed allowing to derive confidence
bounds for all estimated parameters. The correlation between the estimated
parameters was in most cases negligible, showing that parameters were
estimated independently from each other. |