Watershed Scale Climate Change Projections for Use in Hydrologic Studies: Exploring New Dimensions
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @PhdThesis{Hashmi:thesis,
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author = "Muhammad Zia ur Rahman Hashmi",
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title = "Watershed Scale Climate Change Projections for Use in
Hydrologic Studies: Exploring New Dimensions",
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school = "The University of Auckland",
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year = "2012",
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address = "New Zealand",
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month = jan,
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keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
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URL = "http://hdl.handle.net/2292/10876",
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URL = "https://researchspace.auckland.ac.nz/bitstream/handle/2292/10876/02whole.pdf",
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size = "288 pages",
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abstract = "Global Circulation Models (GCMs) are considered the
most reliable source to provide the necessary data for
climate change studies. At present, there is a wide
variety of GCMs, which can be used for future
projections of climate change using different emission
scenarios. However, for assessing the hydrological
impacts of climate change at the watershed and the
regional scale, the GCM outputs cannot be used directly
due to the mismatch in the spatial resolution between
the GCMs and hydrological models. In order to use the
output of a GCM for conducting hydrological impact
studies, downscaling is used to convert the coarse
spatial resolution of the GCM output into a fine
resolution. In broad terms, downscaling techniques can
be classified as dynamical downscaling and statistical
downscaling. Statistical downscaling approaches are
further classified into three broad categories, namely:
(1) weather typing; (2) weather generators; and (3)
multiple regression-based. For the assessment of
hydrologic impacts of climate change at the watershed
scale, statistical downscaling is usually preferred
over dynamical downscaling as station scale information
required for such studies may not be directly obtained
through dynamical downscaling. Among the variables
commonly downscaled, precipitation downscaling is still
quite challenging, which has been recognised by many
recent studies. Moreover, statistical downscaling
methods are usually considered to be not very effective
for simulation of precipitation, especially extreme
precipitation events. On the other hand, the frequency
and intensity of extreme precipitation events are very
likely to be impacted by envisaged climate change in
most parts of the world, thus posing the risk of
increased floods and droughts. In this situation,
hydrologists should only rely on those statistical
downscaling tools that are equally efficient for
simulating mean precipitation as well as extreme
precipitation events. There is a wide variety of
statistical downscaling methods available under the
three categories mentioned above, and each method has
its strengths and weaknesses. Therefore, no single
method has been developed which is considered universal
for all kinds of conditions and all variables. In this
situation there is a need for multi-model downscaling
studies to produce probabilistic climate change
projections rather than a point estimate of a projected
change.",
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abstract = "In order to address some of the key issues in the
field of statistical downscaling research, this thesis
study includes the evaluation of two well established
and popular downscaling models, i.e. the Statistical
DownScaling Model (SDSM) and Long Ashton Research
Station Weather Generator (LARS-WG), in terms of their
ability to downscale precipitation, with its mean and
extreme characteristics, for the Clutha River watershed
in New Zealand. It also presents the development of a
novel statistical downscaling tool using Gene
Expression Programming (GEP) and compares its
performance with the SDSM-a widely used tool of similar
nature. The GEP downscaling model proves to be a
simpler and more efficient solution for precipitation
downscaling than the SDSM model. Also, a major part of
this study comprises of an evaluation of all the three
downscaling models i.e. the SDSM, the LARS-WG and the
GEP, in terms of their ability to simulate and
downscale the frequency of extreme precipitation
events, by fitting a Generalised Extreme Value (GEV)
distribution to the annual maximum data obtained from
the three models. Out of the three models, the GEP
model appears to be the least efficient in simulating
the frequency of extreme precipitation events while the
other two models show reasonable capability in this
regard. Furthermore, the research conducted for this
thesis explores the development of a novel
probabilistic multi-model ensemble of the three
downscaling models, involved in the thesis study, using
a Bayesian statistical framework and presents
probabilistic projections of precipitation change for
the Clutha watershed. In this way, the thesis
endeavoured to contribute in the ongoing research
related to statistical downscaling by addressing some
of the key modern day issues highlighted by other
leading researchers.",
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notes = "Supervisors Asaad Y. Shamseldin and Bruce W.
Melville",
- }
Genetic Programming entries for
Muhammad Z Hashmi
Citations