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dc.contributor.authorMpelasoka, Freddie Simon
dc.date.accessioned2011-02-23T22:23:55Z
dc.date.availableNO_RESTRICTIONen_US
dc.date.available2011-02-23T22:23:55Z
dc.date.issued2000
dc.identifier.urihttp://hdl.handle.net/10179/2145
dc.description.abstractThe derivation of local scale climate information from experiments of coarse- resolution general climate models (GCM) can be addressed with variety of 'downscaling techniques. 'Downscaling' refers to attempts to address the scale mismatch between information from the GCMs and that at which impacts occur. Methods for downscaling range from simple interpolation of climate model outputs to the use of regional climate models nested within larger-scale simulations. Some methods use statistical representations and interpolations; some use dynamic approaches. All of these methods depend on the quality of the initial simulation. Downscaling models fitted to present climatological records are generally referred to as empirical approaches. In a semi-dynamical approach, regional free atmospheric circulation indices simulated by a GCM were employed in this study to derive local climate variables from cross-scale relationships. The relationships were captured from historical records of simultaneously observed local variables and regional-scale circulation indices. Subsequent climate change scenarios were used in impact case studies of two New Zealand catchments' response and water resources. The assessment of climate change impacts requires data at the spatial and temporal resolution at which impacts occur. The outputs of the current GCMs cannot be used directly in the development of specific climate change scenarios due to their coarse resolution although semi-empirical downscaling of GCM outputs to desired scales may offer an immediate solution by relating GCM outputs to single-site climate elements. Artificial neural network (ANN) and multivariate statistics (MST) models were adapted to derive the changes to a number of New Zealand site precipitation and temperature characteristics from free atmosphere circulation indices in a comparative study of their potential in downscaling outputs of GCM transient experiments. Both downscaling models capture similar general patterns from free atmosphere circulation indices. Subsequently the ANN model was used to derive changes of mean monthly precipitation and temperature characteristics from circulation variables projected in a transient climate change experiment performed by the Hadley Centre coupled ocean-atmosphere global climate model (HadCM2). HadCM2 validated well with respect to the National Centers for Environment Prediction reanalysis for its 'present climate' simulation. The predicted changes in seasonal mean sea level pressure fields over the 'New Zealand' region include an intensified anticyclonic belt coupled with negative pressure tendencies to the southwest, which is expected to squeeze stronger westerly winds over southern and central New Zealand. Monthly mean precipitation and temperature time series for 18 points on a 0.25°latitude x 0.25°longitude grid over New Zealand were derived from the circulation indices. The indices were defined by anomalies (with respect to 1961-1990) of mean sea level pressure, zonal and meridional mean sea-level pressure gradients, atmospheric geopotential thickness between 850-700 hPa pressure surfaces, and wind speeds at 10 m above the surface over New Zealand for the period 1980- 2099. Temperature and precipitation characteristics were examined for four decades (1980-2009, 2010-2039, 2040-2069 and 2070-2099), and changes projected with respect to the pseudo-present tri-decade (1980-2009). An average temperature increase of 0.3-0.4°C per tri-decade is projected. Precipitation distribution was modelled using the Gamma probability function and the precipitation characteristics determined by the 'scale' and 'shape' parameters of the Gamma function. Precipitation is predicted to decrease over the north of North Island while marked precipitation increases are projected over the western, central and southwestern areas of the country. Changes in coefficients of variation of monthly precipitation exhibited both increases and decreases in interannual variability of precipitation over the region. Interannual variability in monthly precipitation increases to 1.2-2.2 and decreases to 0.5-0.9 times the pseudo-present coefficients of variations of monthly precipitation by 2070-2099 are projected. The tri-decade to tri-decade changes however, show no trend and this may be attributed to high frequency variations in monthly precipitation. A water balance model was adapted to assess the impacts of changes in precipitation and temperature in two case studies of catchment response. Time series of monthly flows were simulated for each tri-decade. Data for each tri-decade were modelled using a lognormal distribution to generate a 3000-year data set, which was used in a risk analysis to determine the reliability, resiliency and vulnerability of the two water resource systems (hydro power and irrigation schemes). For both of these water resource systems, the changes in operational risk-descriptors with respect to the pseudo-present tri-decade, are within limits in which adjustments can be made, taking into account that traditional design criteria incorporate considerable buffering capacity for extreme events.en_US
dc.language.isoenen_US
dc.publisherMassey Universityen_US
dc.rightsThe Authoren_US
dc.subjectClimate changeen_US
dc.subjectPrecipitation forecastingen_US
dc.subjectHydrological forecastingen_US
dc.subjectWater resource developmenten_US
dc.subjectNew Zealanden_US
dc.subject.otherFields of Research::260000 Earth Sciences::260500 Hydrologyen_US
dc.titleGCM-derived climate change scenarios and their impacts on New Zealand water resources : this thesis is presented in partial fulfilment of the requirements of the degree of Doctor of Philosophy in Science at Massey University, Palmerston North, New Zealanden_US
dc.typeThesisen_US
thesis.degree.disciplineScienceen_US
thesis.degree.grantorMassey Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophy (Ph.D.)en_US


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