Improving global and catchment estimates of runoff through computationally-intelligent ensemble approaches Applications of intelligent multi-model combination, cross-scale model comparisons, ensemble analyses, and new model parameterisations
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- @PhdThesis{Zaherpour:thesis,
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author = "Jamal Zaherpour",
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title = "Improving global and catchment estimates of runoff
through computationally-intelligent ensemble approaches
Applications of intelligent multi-model combination,
cross-scale model comparisons, ensemble analyses, and
new model parameterisations",
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year = "2018",
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month = dec # "~14",
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school = "University of Nottingham",
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address = "UK",
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keywords = "genetic algorithms, genetic programming",
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bibsource = "OAI-PMH server at eprints.nottingham.ac.uk",
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language = "en",
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oai = "oai:eprints.nottingham.ac.uk:55340",
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type = "NonPeerReviewed",
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URL = "http://eprints.nottingham.ac.uk/55340/",
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broken = "http://eprints.nottingham.ac.uk/55340/1/JZaherpour-(4206914)Final-.pdf",
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URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.785833",
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abstract = "Water related problems (scarcity, availability and
hazards) together form one of the three major crises
(the two other are food and energy) for today and the
future across the globe (World Economic Forum, 2016,
Schewe et al., 2014a, Hanasaki et al., 2013, Rockstrom
et al., 2009). Water crises are widespread and
heterogeneous around the world and climate change and
socioeconomic drivers are expected to accelerate these
problems (Veldkamp, 2017). To deal with the above
concerns, mitigation and adaptation strategies are
developed at different scales (global, regional and
local). Developing these strategies, as well as
selecting the most appropriate one to the problem of
interest, should ideally benefit from the highest
possible accuracy in estimates of the hydrological
cycle and water resources. More reliable decisions can
in turn be made by applying tools and techniques that
enhance decision makers perception of the hydrological
cycle, particularly extreme events i.e. droughts and
floods. These tools should also facilitate insights
into the cycle: ideally by reference to hydrological
indicators, spatially (globally and locally) and
temporally (present day and future). Global and
catchment scale hydrological models (GHMs and CHMs)
have been used as such tools that along with advances
in data acquisition, analytical techniques and
computation power offer powerful tools for modelling
natural processes and provide useful insights into the
hydrological cycle. GHMs have a shorter history of
emergence and application than CHMs. GHMs have been
developed and applied from 1986 in recognition of the
fact that hydrological processes and water resources
are global phenomena and should be treated at global
scale (Bierkens, 2015). A GHM is a pragmatic trade-off
between a faithful representation of the diversity of
hydrological processes found across the worlds
catchments, and a generalised and simplified
representation of hydrological processes that can
support multi-decadal, generalised hydrological
simulations at global scales. Compared to hydrological
models designed for catchment-scale simulations (Arnold
et al., 1993; Krysanova et al., 1998; Lindstrom et al.,
2010), GHMs employ coarser spatial discretisation and
model the global land surface in a single
instantiation. The global scope of GHMs, limited
availability and quality of observed discharge data
across the global domain and their use of spatially
generalised parameters make them more difficult to
calibrate than catchment hydrological models. Whilst
examples of calibrated GHMs do exist (Muller Schmied et
al., 2016), the majority of GHMs are uncalibrated
(Gosling et al., 2016; Hattermann et al., 2017). This
lack of calibration, coupled with the diversity of
simplifications employed in the hydrological process
representations, means that there can be large
inconsistency in the skill, bias and uncertainty of an
individual GHM at different locations, as well as large
inconsistencies between different GHMs at any given
location (van Huijgevoort et al., 2013). This spatial
inconsistency means that GHMs risk becoming a jungle of
models (Kundzewicz, 1986) in which it can be difficult
to determine where a particular GHM output is likely to
be capable of delivering optimal hydrological
simulations. It also makes it dangerous to assume that
any individual GHM will be an adequate basis for making
projections at any given location, even if the models
ability to replicate observed data in particular
catchments is enhanced through the acquisition of
higher quality input data or efforts to improve process
representations (Liu et al., 2007). To an extent, these
arguments are also applicable to CHMs because whilst
they have been shown to generally perform better than
GHMs in model evaluation studies, ensembles of such
models still result in an uncertainty range when the
models are run with identical inputs (Hattermann et
al., 2017; Hattermann et al., 2018). To minimise the
challenge of varying outputs from different models,
several model inter-comparison projects (MIPs) have
been undertaken around the world (Henderson-Sellers et
al., 1995, Entin et al., 1999, Guo and Dirmeyer, 2006,
Koster et al., 2006, Harding et al., 2011). These
projects usually use standard modelling baselines to
deal with discrepancies between model outputs. This
results in higher consistency in the climate forcings
input to the models (where applicable), their process
representations (e.g. the simulation of human impacts
such as water abstractions), and the temporal and
spatial resolutions of their simulations. This way,
model outputs are directly comparable to each other,
which supports diagnostic inter-comparisons between
them (Bierkens, 2015). One of the largest, ongoing MIPs
(whose data are used in this thesis) is the
Inter-Sectoral Impact Model Inter-comparison Project
(ISIMIP) (Schellnhuber et al., 2014, Warszawski et al.,
2014). ISIMIP is a community-driven effort by more than
130 modelling groups, that covers different sectors
including water (both global and catchment hydrological
modelling communities). Outputs from ISIMIP are widely
used in different projects, such as reports of the
International Panel on Climate Change (IPCC)
http://www.ipcc.ch/. MIPs including ISIMIP provide a
unique opportunity to access data from different models
and to assess their relative performance. It also
facilitates continuous model improvement via the
inclusion of new schemes (e.g. human impacts such as
dams, reservoirs and water abstractions) accompanied by
dozens of models, as well as communication between
modelling groups working in the same or different
sectors. Nonetheless, they do not fully address the
challenge of spatial inconsistencies between models, as
well as the question of what ensemble representative to
select for use when trying to improve the reliability
of decision-making. There remain other shortcomings or
unexplored aspects within MIPs (particularly ISIMIP as
this research focus MIP), hence areas of further
research and potential improvement in model evaluation
and application which will be addressed later in this
introduction. The question of how to address the
challenges of spatial inconsistency in hydrological
models has been a feature of catchment-scale model
research for several decades. In answering it,
catchment modellers have recognised that reliance on a
single, inconsistent model is inherently risky and
should be avoided (Marshall et al., 2006; Shamseldin et
al., 1997). Instead, they have developed ways to take
advantage of the diversity of outputs (Clemen, 1989)
generated by different models by using optimised
mathematical combination methods to deliver a combined
output that performs better than the individual models
from which it was created (Hagedorn et al., 2005). This
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