Estimating the long term pattern of water streamflow with climate change adaptation - Nurul Hazimah, Mahmud (2017)
The climate changes and its affects to the world have becoming one of the issues that is important to the society. The predictions of rainfall, temperature and streamflow for future have been used in content of global climate change due to current situation where the continuous changes of world climate. This happens because of emission of carbon dioxide, CO2 that came from factory, vehicles and other factors. The increased of carbon dioxide causing the depleting of ozone layers. The studies focus in analysing prediction of rainfall, temperature and streamflow of Pahang state. The rainfall and temperature pattern can be estimate using the statistical downscaling models (SDSM) meanwhile as for stream flow, the method used was identification of unit hydrographs and component flows from rainfall, evaporation and streamflow (IHACRES). The statistical downscaling models (SDSM) is one of the SD model that interpret the predictand (local climate) – predictor (GCMs-scale) relationship using multiple regression techniques and it is allows different types of data to be transformed into standard predictor variables before being downscaled and calibrated to produce nonlinear regression models. The statistical downscaling models can reduce the standard error of estmate and increased the number of explained variance using bias correction and varianc inflation techniques. The identification of unit hydrographs and component flows from rainfall, evaporation and streamflow (IHACRES) is a hybrid conceptual-metric models where it can reduce uncertainty of involved parameter and represent more details of the internal processes.