Bias correction for non-extreme and extreme values for precipitation

Date

2023-4-24

Author

Körpınar, Ahmet

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Regional climate models are crucial in climate change impact analysis. Short-term and long-term effects of climate change need to be investigated to plan necessary mitigation measures and to lower the impacts. Regional climate models allow analysis of the effects of climate change in smaller scales such as regions and nations and consequently leads to the development of more effective management strategies. One of the most commonly used products of regional climate models is precipitation predictions. For flood risk analysis, especilly extreme precipitations are crucial. However, raw data obtained from the regional climate model have errors. To obtain reliable predictions, the data should be bias corrected first. The basic principle of bias correction is to reduce the bias in raw data. Bias correction is also region-specific due to climate conditions of the area. In this study, three alternatives for a commonly used bias correction method, the Distribution Based Scaling method, are proposed. Alternatives proposed in this study differ from the original method by division point of data and fitted distributions to extreme part of the data. Performance assesment for these methods are done for 53 meteorological stations located at different regions of Turkey, and the most effective methods are identified. Performances of alternative methods proposed in this study did not provide significant improvements compared to the original method. Future changes in extreme precipitation according to bias corrected RCM outputs are investigated spatially as well.

Subject Keywords

Daily precipitation, Bias correction, Extreme values, Distribution based scaling, Climate change

URI

https://hdl.handle.net/11511/103068

Collections

Graduate School of Natural and Applied Sciences, Thesis