Process realism: flow paths and storage - Runoff Prediction in Ungauged Basins: Synthesis across Processes, Places and Scales - page 59

N K

Neuhausen Klammbach

30 30

20 20

10 10

0 0

0 500 1000 1500 2000 2500 0 500 1000 1500 2000 2500

Runoff Löhnersbach (l/s) Runoff Löhnersbach (l/s)

Figure 4.5. Percentage runoff contribution to the Löhnersbach from the left tributary (N, Neuhausen) and the right tributary (K, Klammbach)
plotted against runoff at the total catchment outlet. Simultaneous runoff measurements during 1993 - 7. From Kirnbauer et al.( 2005 ).

large storage capacity and deep flow paths of this subcatch-
ment. The much larger storage and the longer pathways are
related to the higher degree of weathering and the dip angle
of the bedrock fractures, being normal to the topographic
slope. It is clear, therefore, that even within small catch-
ments, the relative contributions of surface/near surface
flow paths versus deeper flow paths can vary significantly
and detailed spatial spot gauging can assist in understand-
ing these differences (comparative hydrology at a small
scale).
A number of studies have reported on regression ana-
lyses performed between runoff response characteristics
and catchment characteristics to identify the main controls.
For example, Sayama et al.( 2011 ) found the maximum
volume of storage change to be positively correlated with
catchment slope, which they explained by lower subsur-
face connectivity in steeper catchments. At a larger scale,
Gaál et al. (2012) performed a regional analysis of flood
event time scales based on the concept of comparative
hydrology. In one of their regions, the catchment form
had adapted to flashy floods (due to convective storms)
and produced an efficient drainage network. This enhanced
understanding of surface flow paths and the flashiness of
the flood response. In another region, a tortuous drainage
network had evolved, which led to deeper flow paths
and more dampened flood response. Both systems were a
manifestation of the co-evolution of landform and hydro-
logical processes within the constraints of the geology in
the regions. In a similar study in the USA, albeit focusing
on longer time scales, Schaller and Fan ( 2009 ) related the
subsurface losses and gains of catchments estimated from
the water balance (using runoff data, rainfall data and
evaporation estimates) to climate and geological factors.
Figure 4.6 , taken from Schaller and Fan, shows the sub-
surface losses and gains over the Colorado River and the

coastal basins in Texas. It shows that most of the upstream
catchments in the north-west lose water into the subsurface
while some of the coastal catchments gain water from the
subsurface. This is because of the regional topographic
gradients. However, there is a cluster of catchments situ-
ated over the Balcones fault zone (see Figure 4.6 ) where
the highly permeable carbonate rocks force groundwater
upward to the land surface. Numerous springs exist along
the Balcones fault zone, often where faulting has placed
highly permeable units adjacent to impermeable units. In
this case, the elevation gradient from the west to the east is
punctuated by a geological singularity, which significantly
alters groundwater flow paths.

4.3.2 Inference from tracers
Learning from temporal patterns of tracers in one
catchment
Tracer data can yield integral fingerprints of activated flow
paths and storage at different spatial and temporal scales
(Leibundgut et al., 2009 ). It is important to recognise that
tracers give information on the movement of particles
(related to the hydraulic conductivity of the soil medium)
while runoff tends to give information on the propagation
of pressure (related to the compressibility of the medium).
The concentration of tracers therefore scales differently
from pressure in the advection dispersion equation that is
often used to interpret the results. It is therefore important
not to confuse the two. One option is artificial tracers,
which are markers added to the system to trace the move-
ment of water, and these can include chloride, bromide,
and various dyes including fluorescent ones. Alternatively,
the occurrence and variability of tracers naturally occurring
in the environment can be exploited, such as the chemical
and isotopic composition of water. Tracers exploit