Geography Reference
In-Depth Information
prediction in ungauged basins around the world. Know-
ledge accumulates in this way.
In the era of the Anthropocene (Crutzen,
2002
; Sivapa-
lan et al.,
2012
), we also need to collect data relating to
human impacts, not just land cover changes, but also the
volume of water abstractions for household use, irrigation
and industrial use as well as return flows after water
treatment. There is a lot of work needed to integrate these
diverse data sources to generate patterns and meta-data
sources, and new prediction methods that exploit these
new data
12.4 Synthesis and the science community
12.4.1 Accumulation of knowledge in the
hydrological sciences
What the new framework needs: models of various kinds,
data of the right kind
While in the past the focus has been on individual studies,
comparative hydrology is all about learning from patterns
in data on a regional to global scale. Therefore, the advent
of comparative hydrology introduces a brand new focus
and emphasis on data, but on a global scale. Excellent
examples already exist where comparative studies have
contributed much to our understanding; substantial data
sets have been an important basis in each case. The Model
Parameter Estimation Experiment data set (MOPEX,
Schaake et al.,
2006
; Duan et al.,
2006
) has been used by
numerous researchers around the world for benchmarking
studies (e.g., Hydrologic Synthesis special section in Water
Resources Research, see Sivapalan et al.,
2011b
). Simi-
larly, although spatially more limited, the data set used in
the Distributed Model Inter-comparison Project (DMIP)
has been very valuable for understanding the strengths
and limitations of distributed rainfall
-
Darwinian uncertainty framework to assess the resulting
uncertainties. This has to be formalised so that it becomes a
valuable framework for the next phase of PUB (e.g.,
looking at catchments in colour, i.e., going away from
lumped black-box models).
sources
including the
joint Newtonian
From data to information, knowledge and understanding
Gupta et al.(
2008
) pointed out that
'
data
'
are not the same
'
'
thing as
. They noted that information is
obtained by viewing data in context through perceptual
and conceptual filtering. There may be multiple plausible
contexts, and the most relevant context is generally given
by an underlying theory. It is clear, therefore, that amass-
ing large data sets, as needed for comparative hydrology,
will not by itself render information. From the perspective
of the science of hydrology as a whole it would be good to
also go beyond representing only
information
runoff models
(Smith et al.,
2004b
,
2012
). Global atmospheric data sets
(van der Ent and Savenije,
2011
) and regional flux tower
data sets (Williams et al.,
2012
) have revealed interesting
patterns of land
-
'
information for a par-
ticular catchment
. Ackoff (
1989
) and Bellinger et al.
(
2004
) defined data, information, knowledge and under-
standing along the following lines (partly modified):
'
atmosphere interactions, and have led to
improved understanding of the hydrological cycle at a
range of scales.
The combined Darwinian
-
Data represent a fact or statement of event without
relation to other things, e.g.,
'
It is raining.
'
Information embodies the understanding of a relation-
ship of some sort, possibly cause and effect, e.g.,
Newtonian framework pro-
posed here requires not just traditional rainfall
-
The
temperature dropped 15 degrees and then it started
raining.
'
runoff data
(and other hydrological data, e.g., soil moisture, evapor-
ation, snow, tracers), but also other kinds of data relating to
all co-evolved entities (e.g., vegetation, topography, soil
catena, drainage structure), not just in one catchment but in
a large population of catchments around the world, along
chosen natural (e.g., climatic) and anthropogenic (e.g.,
urban to rural to agricultural) gradients. This forces us to
think more broadly about new data sources. High-
resolution satellite data are one important data source rele-
vant to hydrological studies aimed at exploring spatial
connections, learning from landscape organisation, and
exploiting the natural co-evolution and self-organisation
of landscape features. At the other end of the spectrum,
local data may be just as important for shedding light on
catchment processes. This includes soft data and expert
judgement of local hydrologists. Based on reading the
landscape, geomorphological features of the landscape
can provide useful insights into landscape co-evolution
and self-organisation.
-
'
Knowledge represents a pattern that connects and gen-
erally provides some predictability as to what will
happen next, e.g.,
If the humidity is very high and the
temperature drops substantially the atmosphere is often
unlikely to be able to hold the moisture so it rains.
'
'
Understanding involves an element of extrapolation and
is able to synthesise new knowledge from the previously
held knowledge, e.g.,
'
-
humidity rela-
tionship can be employed to predict or even speculate
whether there will be more rain in a warmer climate.
The temperature
'
Clearly, we need techniques to exploit information from
individual catchment studies, as well as the compilation of
all studies from around the world. However, as a commu-
nity collectively we need to go beyond that, and find
systematic ways to (i) generate knowledge in terms of the
patterns that connect across the multitude of studies and
thereby provide a higher level of predictability as to what
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