Modelling Catchment-Scale Responses to Climate Change - Climate Change Impacts on Freshwater Ecosystems - page 248

cool- and warm-water species (e.g. Mohseni et al . 2003). Nevertheless, compared
with other taxa for which the impacts of climate change could be very detrimental
(e.g. Thomas et al . 2004, 2006), this assessment for French stream fish species
was rather positive, as most fish were predicted to expand their distributional
area rather than reduce it. This may result from the scarcity of cold-water species
in French fish assemblages compared to cool- and warm-water species which
have a larger range of tolerance.
Dynamic models
A fuller understanding of the potential effects of climate change on aquatic
ecosystems can be obtained using dynamic, mechanistic models. Dynamic models
allow model components to change with time: mechanistic models attempt to
capture all the important processes as connected systems of equations. Dynamic,
mechanistic models should be able to represent the transient changes in the pollutant
stores in catchments and represent the response of biogeochemical cycles to altered
precipitation and temperature inputs. Thus, they are eminently suited to modelling
climate change effects. The disadvantages of these models are well known (e.g.
Chapra 1997): they can require large amounts of data, which may not be available,
they require a full knowledge of the important processes and how they are
connected, they are difficult and time-consuming to write and programme and
uncertainty is a major issue if a model aims for complete understanding of a system
(see below). Nevertheless, they represent the best hope for accurate predictions of
climate change effects, and building and improving them is a priority for research.
The most widely used dynamic models in the Euro-limpacs project have been
the INCA models. The INCA (INtegrated CAtchment) models are dynamic
computer models that predict aspects of water quantity and quality in rivers and
catchments (Whitehead et al . 1998a, b; Wade et al . 2002a). They are designed to
represent the factors and processes controlling flow and water quality dynamics
in both the land and the in-stream components of river catchments, whilst
minimizing data requirements and model structural complexity (Whitehead et al .
1998a, b). INCA can produce daily estimates of discharge, stream water
concentrations and fluxes over a period of many years at any point along a river's
main channel. The model provides a number of tools to aid understanding of the
system, and statistics to allow comparison with observed data can be generated.
The original INCA-N model was developed and used to model nitrogen in
catchments (Wade et al . 2002a, Wade 2006). The INCA framework has now
been extended to phosphorus (INCA-P, Wade et al . 2002b, c), particulates (INCA-
SED, Jarritt & Lawrence 2007), dissolved organic carbon (INCA-C, Futter et al .
2007a, 2008), mercury (INCA-Hg, Futter et al . 2007b) and macrophyte and
epiphytic algal dynamics (Wade et al . 2002b; Whitehead et al . 2008). A notable
example is the application of the INCA-N model to the Garonne river system in
France (Tisseuil et al . 2008), at 62,700 km 2 the largest river basin modelled so far.
The spatial and temporal dynamics in the stream water nitrate concentrations
were described and related to variations in climate, land management and effluent
point sources using multivariate statistics. In conjunction with the hydrological