Watershed Scale Climate Change Projections for Use in Hydrologic Studies: Exploring New Dimensions

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Muhammad Zia ur Rahman Hashmi",
  title =        "Watershed Scale Climate Change Projections for Use in
                 Hydrologic Studies: Exploring New Dimensions",
  school =       "The University of Auckland",
  year =         "2012",
  address =      "New Zealand",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  URL =          "http://hdl.handle.net/2292/10876",
  URL =          "https://researchspace.auckland.ac.nz/bitstream/handle/2292/10876/02whole.pdf",
  size =         "288 pages",
  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
  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.",
  notes =        "Supervisors Asaad Y. Shamseldin and Bruce W.

Genetic Programming entries for Muhammad Z Hashmi