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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@PhdThesis{Hashmi:thesis,

• 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 change.",
• 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. Melville",

}

Genetic Programming entries for Muhammad Z Hashmi

Citations