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models (more than nine transferred parameters). There is a
tendency to apply simpler models in arid and mixed arid
and humid catchments, while in humid and cold regions
more complex models have been used.
To what extent does runoff prediction performance depend
on climate and catchment characteristics?
The assessment of NSE predictive performance with
respect to the four climate and catchment characteristics
is presented in Figure 10.38 . The top panel shows a very
clear pattern of decreasing performance with aridity index
for catchments with an aridity larger than 0.6. The per-
formance in the humid catchments is generally above 0.6,
while it decreases to 0.5 or less in more arid catchments. It
appears that in humid catchments the rainfall
Main findings of Level 1 assessment
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In humid and cold regions the performance of predict-
ing daily runoff hydrographs in ungauged basins
tends to be better than in arid regions.
runoff pro-
cesses are more linear, the hydrological states tend to be less
variable and the controls on runoff are spatially less vari-
able, so a better performance would be expected. For the
regional calibration method there is little dependency of the
performance on aridity, but these studies are from Germany
and Austria, where the catchments are never very arid.
The relationships between performance and air tem-
perature, and performance and elevation are more com-
plex and depend on the region used for the assessment.
There is a clear decrease of performance with increasing
elevation in France (Oudin et al., 2008 ) and Australia
( Zhang et al., 2008d ) and a clear increase of performance
with increasing elevation in Austria (Parajka et al., 2005 ).
These differences are due to the different dependencies of
aridity with elevation ( Figure 10.39 ). While in Austria the
aridity is less than 0.5 in catchments above 900 m a.s.l.
and strongly decreases with increasing elevation, in
France the aridity index exceeds 0.75 and actually
increases with elevation. In Australia the aridity index is
always larger than in the other regions. This pattern is
consistent for all regionalisation approaches except
regional calibration, which is for studies in Germany
and Austria where the aridity range is smaller. The pattern
for air temperature is similar, with a clear tendency of
decreasing performance with increasing temperature in
Austria and the opposite in France. Interestingly, the
model averaging method has a low median and large
scatter of performance in colder catchments, which may
be due to snow processes. Similarly, as for other charac-
teristics, the regional calibration is less sensitive to air
temperature than the other methods.
The results show a very clear increase of the perform-
ance with catchment scale for all approaches and essen-
tially all regions. The median performance is around 0.60
in small catchments (0
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All regionalisation methods analysed (spatial proxim-
ity, similarity, model averaging, parameter regres-
sion and regional calibration) show a similar
performance with considerable scatter within each
method. There is a tendency towards a somewhat
lower performance of regressions than other methods
in those studies that apply different methods in the
same region.
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Studies with few catchments and studies with a large
number of catchments tend to exhibit better perform-
ance than studies with an intermediate number of
catchments. For studies with a large number of
catchments (dense stream gauge network) there is a
tendency for spatial proximity and geostatistics to
perform better than regression or regionalisation
based on the simple averaging of model parameters.
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There is no clear dependence of the performance on
the number of model parameters regionalised.
10.5.2 Level 2 assessment
The Level 1 synthesis of existing studies ( Table A10.1 )
clearly showed that many studies only report summary
statistics of regionalisation performance and/or catchment
characteristics, which hampers detailed attribution of the
performance and inter-study comparison of results. The
objective of the Level 2 synthesis is to examine and explain
the performance of the regionalisation methods in greater
detail. Nine study authors out of the Level 1 assessment
provided detailed information about climate and catchment
characteristics in a consistent way and reported the region-
alisation performance for each catchment ( Table A10.2 ).
This data set combines data from 1832 catchments, five
groups of regionalisation methods and four catchment char-
acteristics. The regionalisation methods are spatial proxim-
ity, similarity, model averaging, parameter regression and
regional calibration. The catchment characteristics are arid-
ity (potential evaporation by mean annual precipitation),
mean annual air temperature, mean elevation and catchment
area. For comparison with the other runoff signatures in
Chapter 12 , the median NSE of daily runoff were calculated
for all methods in each study separately. The 25% and 75%
quantiles of these NSE are 0.66 and 0.71, respectively.
300 km 2 ), and increases to around
0.80 for larger catchments. Also, the variability in perform-
ance between the catchments decreases with catchment
scale, i.e., the large catchments never give a very low
performance. An exception is a slight increase of perform-
ance variability for the spatial proximity method in the
largest catchments in Australia and France, but this is only
for a small group of catchments. Overall, this very clear
pattern of an increase of the performance with catchment
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