| Popis: |
The impact of climate change on water resources is usually assessed at the local scale. However, regional climate models (RCM) are known to exhibit systematic biases in precipitation. Hence, RCM simulations need to be post-processed in order to produce reliable estimators of local scale climate. A popular post-processing approach is quantile mapping (QM), which is designed to adjust the distribution of modeled data, such that it matches observed climatologies. However, the diversity of suggested QM methods renders the selection of optimal techniques difficult and hence there is a need for clarification. In this paper, QM methods are reviewed and classified into: (1) distribution derived transformations, (2) parametric transformations and (3) nonparametric transformations; each differing with respect to their underlying assumptions. A real world application, using observations of 82 precipitation stations in Norway, showed that nonparametric transformations have the highest skill in systematically reducing biases in RCM precipitation. |
