Data analysis and model building for understanding catchment processes:
the case study of the Thur catchment
Abstract
The development of semi-distributed hydrological models that reflect the
dominant processes controlling streamflow spatial variability is a
challenging task. In small, well-instrumented headwater catchments the
model can be built taking advantage of knowledge derived from extensive
fieldwork activities; that is, however, not possible in much larger
catchments where, usually, these models are actually needed. To address
this problem, we propose a new methodology where we analyze the
correlations between hydrological signatures, catchments
characteristics, and climatic indices to get insights about the
hydrological functioning of the catchment and to guide the decisions
involved in the development of a semi-distributed model. The methodology
is tested in the Thur catchment (Switzerland, 1702 km2); in a first
stage we show how to identify catchment characteristics and climatic
indices that control streamflow variability; in a second stage, we use
these findings to develop a set of model experiments aimed at
determining an appropriate model representation for the catchment.
Results show that only models that account for the influencing factors
indicated by the correlation analysis are able to represent correctly
the observed streamflow signatures, confirming our understanding of the
processes happening in the catchment.