Projecting Seasonal Streamflow from Climate Forecast Downscaling by Genetic Programming
Date Issued
2005
Date
2005
Author(s)
Hsieh, Ming-Sheng
DOI
zh-TW
Abstract
Extremely hydrologic events create severe loss in agriculture, industry and lives nowadays. The purpose of this thesis is to forecast the climate and prevent the damage. Based on General Circulation Model-ECHAM4.5, this thesis is using linear regression and genetic programming as the downscaling model and mean absolute error to evaluate the downscaling method. By comparing these two methods, we find linear regression is more efficient in temperature and genetic programming is a better method for precipitation. For precipitation, we make twelve different math formulas for each month. Furthermore, a weather generator can be used to produce daily data based on downscaled monthly statistics. The GWLF model is applied to simulate streamflow, and the validation result indicates it can simulate San-Shia River effectively. Daily data producing by weather generator as the input to the GWLF model to provide seasonal streamflows. In conclusion, this thesis is choosing three downscaled climate forecast data to generate streamflow from the GWLF model. The observed streamflow is within the ranges from min to 25% percentile of simulated streamflow. The result of simulating the trend for extremely events is positive. Over all, this downscaling model is effective in predicting streamflow.
Subjects
遺傳規劃法
genetic programming
SDGs
Type
thesis
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