Geography Reference
In-Depth Information
network into account. Also, the length of the flood
record was taken into account by the KUD (kriging of
uncertain data) approach (Merz and Blöschl,
2005
)
where the local kriging variance of the moments is esti-
mated as a function of record length. Stations with short
records can therefore be profitably used in the regional-
isation. A number of additional controls were considered
by adjusting the MAF:
lower, as catchments are much more orographically shel-
tered. The smallest specific flood discharges occur in the
lowlands of eastern Austria. The small values are related
both to much smaller rainfall inputs than in other parts of
Austria and to relatively dry catchment conditions towards
the Slovak and Hungarian border. As an example of the
results,
Figure 11.38
shows the normalised specific 100-
year flood (MAF
*
) for the Danube region in Upper Austria.
There is a high variability in the specific flood discharges of
the tributaries. The tributaries from the North (e.g., Naarn,
Klambach, Sarmingbach) show much lower specific dis-
charges, while the tributaries from the south exhibit higher
specific discharges than the Danube River. The top-kriging
estimates on the Danube are similar to the measurements on
the river and do not change much along the reach. In the top-
kriging procedure the estimates on the main rivers are not
much affected by the measurement of small tributaries, as
top-kriging takes catchment area and the nested structure of
the river network into account. However, estimates using
other distance-based methods, such as ordinary kriging,
differ substantially as they are too much influenced by the
measurements at small tributaries along the main river.
To assess the performance of estimating the floods in
ungauged basins, jack-knife estimates were calculated. For
each stream gauge, the local flood data were withheld, the
T-year floods were estimated from flood data of the neigh-
bouring catchments only, and finally the flood estimates
were compared with the local flood data. The results of the
jack-knifing are given in Tables
11.8
and
11.9
as a function
of catchment area and mean annual precipitation. The
relative root mean square error (RRMSE) for the classes
selected ranges between 20 and 65%. There is a clear trend
for the RRMSE to decrease with catchment size and to
decrease with mean annual precipitation. The smallest
catchments show some bias, but for the other classes, the
biases are relatively small.
MAF
¼
A
β
α
−β
FARL
−γ
ln
ð
MAF
F
P
Þ
Specific flood discharges tend to decrease with catchment
area and this effect was accounted for by scaling MAF by
catchment area A,where
α
is the reference catchment area of
100 km
2
and
ranged between 0.25 and 0.40 depending on
the flood process types (large for regions where flash floods
prevail, small where frontal systems prevail). To account for
the retention of reservoirs and lakes, the Flood Attenuation
by Reservoirs and Lakes index, FARL (IH,
1999
), was
calculated for each catchment and again used to scale
MAF. InAustria the floods are closely related tomean annual
rainfall as an index of catchment soil moisture and river
morphology (Merz and Blöschl,
2009b
). To account for this
effect, MAF within regions were correlated against mean
annual catchment rainfall and the residual between the
regression line and the local value was expressed as a factor
F
P
, which was used to scale MAF. The three flood moments
(MAF
*
, C
V
, C
S
) were then regionalised to ungauged catch-
ments by top-kriging. For each ungauged catchment, the
equation above was inverted to estimate MAF from the
top-kriging estimates of MAF
*
.TheT-year floods were then
estimated from the flood moments using the generalised
extreme value (GEV) distribution for all nodes of the stream
network. To account for local particularities of catchments,
the estimates of the automatic regionalisation approach were
used as a starting point for a manual adjustment similar to
that for the local flood data. In this step, hydrogeology, soil
type, land use, geomorphology and hydraulic structures such
as retention basins were accounted for. The methods are
β
Discussion
The HORA project was the first nation-wide estimation of
flood frequencies in Austria. The scale of the project
-
-
10 586 basins
required a strategy that had the capability
of estimating the T-year flood for a large number of
ungauged basins with an accuracy that was acceptable to
the local water authorities. This goal was addressed by a
combination of automatic methods and manual assess-
ments by hydrologists. Experience from this study indi-
cated that this strategy is indeed feasible. The combined
approach in the project proved to be very efficient.
Traditionally, estimation of the T-year flood in a
regional context has been based on flood peak samples
only. The expanded (temporal, spatial and causal) infor-
mation used in this study was extremely useful
Results
The highest specific flood discharges occur at the northern
fringe of the Alps. Topographic enhancement effects often
result in high and persistent rainfalls. Due to the high pre-
event soil moisture and high rainfall rates, large runoff rates
occur regularly. In addition, the soils and the Flysch geol-
ogy contribute to large discharges. At the southern fringe of
the Alps, some of the largest floods in these catchments
have resulted from high intensity precipitation associated
with the advection of moist air from the Mediterranean. In
the inner part of the high Alps, specific discharges are much
for
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