Geography Reference
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assessment of the predictive uncertainty across many real
catchments based on cross-validation. The Level 1 and
Level 2 assessments in this topic may provide guidance
on the magnitude of uncertainty to be expected in different
climates and catchment situations. Both kinds of uncer-
tainty analyses may assist
vegetation catena, and runoff signatures such as the
Budyko curves. The aim is not to predict runoff by these
comparative analyses but to learn from the differences
between catchments to gain understanding of co-
evolutionary processes. This is the Darwinian approach,
which stands apart from regionalisation approaches in that
the focus is on generalised understanding and not on
predictions.
in the choice of prediction
method.
13.1.4 Data availability and predictions
The predictive context varies vastly around the world due
to differences in processes, data availability, modellers
13.2.3 Newtonian
Darwinian synthesis
The Newtonian approach builds on local field observa-
tions and detailed process modelling to identify direct
causality and causality chains. The synthesis of the
Newtonian and Darwinian approaches will lead to a
new understanding, benefiting from their complemen-
tary strengths: learning from process cascades and using
such models to help interpret regional and global pat-
terns; and learning from comparative studies that shed
light on the co-evolution of real catchments. New model
concepts can be built on the basis of the organising
principles yet to be discovered through the Newtonian
and Darwinian synthesis such as vegetation optimality,
minimum energy expenditure and maximum entropy
production.
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experience and modelling purpose. There is therefore no
single best method for all situations. In contrast, the
particular circumstances one finds can be exploited to
develop creative methods for runoff predictions using
proxy data. A hierarchy of data collection approaches
from global to regional to local may maximise the infor-
mation gained from the available data sources. However,
installing a stream gauge is always the best option. Runoff
prediction performance is strongly related to runoff data
availability and performance is lower in data-poor
regions. Concerted efforts should therefore be made to
increase the number and quality of stream gauges in data-
poor regions.
13.2 Advancing hydrological science globally
via PUB
13.2.1 Viewing catchments as complex systems
Catchments are complex adaptive systems that are the
result of the co-evolution of climate, soils, topography
and vegetation. They consist of many parts that are closely
interconnected, the cause
13.2.4 The globe is our laboratory
The new approach requires assembling and processing
patterns of all kinds, guided by the Newtonian
Darwinian
synthesis, with a view to generating new understanding in
hydrology. The synthesis involves a synthesis of concepts,
models and data from various sources and disciplines. The
efforts should be underpinned by a new uncertainty frame-
work that combines aspects of error propagation (Newton-
ian) and cross-validation performance between regions
(Darwinian), which accounts for the uncertainty arising
from the different trajectories of co-evolution they may
have followed. This data-based approach takes the view
that the entire world is our laboratory, with catchments
serving as nature
-
effect relationships span many
time and space scales, and processes and process inter-
actions cannot be worked out easily. The process view of
catchments should therefore be extended to include the
interactions and feedbacks between water flow processes
and geomorphological (erosion, sedimentation), pedo-
logical (pedogenesis), ecological and biogeochemical pro-
cesses. Emergent patterns of interest in the landscape that
are legacies of their co-evolution include runoff and water
quality signatures, soil catenas, vegetation patterns, and the
stream network structure.
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s experiments.
13.3 Organising the hydrology community to
advance science and predictions
13.3.1 Capacity building
New educational concepts are needed, including ways to
treat catchments as complex systems and to promote the
practice of comparative hydrology. In addition to teaching
students basic knowledge about individual processes, we
need to train them in the use of signatures, interpretation of
the differences between different places, and viewing
13.2.2 Comparative hydrology to detect co-evolution
patterns
Since complex systems are hard to analyse we suggest
comparing landscapes across places in terms of their co-
evolutionary patterns (river network structure, landforms,
vegetation
patterns,
hydraulic
geometry,
soil
and
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