Model Development for Seasonal Forecasting of Hydro Lake Inflows in the Upper Waitaki Basin, New Zealand
Purdie, J. M. (2007). Model Development for Seasonal Forecasting of Hydro Lake Inflows in the Upper Waitaki Basin, New Zealand (Thesis, Doctor of Philosophy (PhD)). The University of Waikato, Hamilton, New Zealand. Retrieved from http://hdl.handle.net/10289/2643
Permanent Research Commons link: http://hdl.handle.net/10289/2643
Approximately 60% of New Zealand's electricity is produced from hydro generation. The Waitaki River catchment is located in the centre of the South Island of New Zealand, and produces 35-40% of New Zealand's electricity. Low inflow years in 1992 and 2001 resulted in the threat of power blackouts, and a national demand for electricity that is currently growing at 2 to 5% a year gives strong justification for better management of the hydro resource. Improved seasonal rainfall and inflow forecasts will result in the better management of the water used in hydro generation on a seasonal basis. Seasonal rainfall forecasting has been the focus of much international research in recent years, but seasonal inflow forecasting is in its relative infancy. Researchers have stated that key directions for both fields are to decrease the spatial scale of forecast products, and to tailor forecast products to end user needs, so as to provide more relevant and targeted forecasts, which will hopefully decrease the enormous socio-economic costs of climate fluctuations. This study calibrated several season ahead lake inflow and rainfall forecast models for the Waitaki river catchment, using statistical techniques to quantify relationships between land-ocean-atmosphere state variables and seasonally lagged inflows and rainfall. Techniques included principal components analysis and multiple linear regression, with cross-validation techniques applied to estimate model error. Many of both the continuous and discrete format models calibrated in this study predict anomalously wet and dry seasons better than random chance, and better than the long term mean as a predictor. 95% confidence limits around most model predictions in this study offer significant skill when compared with the range of all probable inflows (based on the 80 year recording history in the catchment). Models predicting winter Lake Pukaki inflows are those with the strongest predictive relationships in this study. Spring and summer predictions were generally less skilful than those for winter and autumn. Inflows could be predicted with some skill in winter and summer, but not rainfall, and rainfall could be predicted with some skill in autumn and spring, but not inflows. Models predicting inflows and rainfall for different seasons in this study use very different sets of predictor variables to accomplish their seasonal predictability. This may be related to the significant seasonal snow storage in the catchment, so that other factors such as temperature and the number of north-westerly storms may have a large part to play in the magnitude of inflows. Similarly, predicting the same dependent variable but for different seasons led to different contributing variables, leading to the conclusion that different wider physical causative mechanisms are behind the predictability in different seasons, and that they too should be studied separately in any future research. SST5 (sea surface temperature to the north of New Zealand) was found to have more relevance than any other predictor in predicting Waitaki river inflows and rainfall in any season. The models calibrated with SOI and IPO included as predictor variables were almost invariably worse in their predictive skill than those without, and the list of the most important predictor variables in all models did not include equatorial sea surface temperatures, sea level pressures, or 700hpa geopotential height variables. The conclusion from these findings is that equatorial ocean-atmosphere state variables do not have significant relationships with season ahead inflows and rainfall in the South Island of New Zealand. Seasonal climate forecasting on single catchment scale, and focussed to end user needs, is possible with some skill, at least in the South Island of New Zealand.
The University of Waikato
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