Prediction of design flood discharge by statistical downscaling and General Circulation Models

- We develop a model for predicting monthly peak discharge.
- Hydrometeorological data is driven by downscaling.
- NCEP and CGCM weather data is modeled by LGP.
- Future climate scenarios are developed.
- Most effective parameters are proposed for PMF prediction.

SummaryThe global warming and the climate change have caused an observed change in the hydrological data; therefore, forecasters need re-calculated scenarios in many situations. Downscaling, which is reduction of time and space dimensions in climate models, will most probably be the future of climate change research. However, it may not be possible to redesign an existing dam but at least precaution parameters can be taken for the worse scenarios of flood in the downstream of the dam location. The purpose of this study is to develop a new approach for predicting the peak monthly discharges from statistical downscaling using linear genetic programming (LGP). Attempts were made to evaluate the impacts of the global warming and climate change on determining of the flood discharge by considering different scenarios of General Circulation Models. Reasonable results were achieved in downscaling the peak monthly discharges directly from daily surface weather variables (NCEP and CGCM3) without involving any rainfall-runoff models.