| Abstract | Nepal, being an agricultural country, is greatly affected by the climate change. As most of the farmers depend on the monsoon rain for the good and timely harvest, it is quite important to know about the change in the climate beforehand. Incorrect prediction of climate leads to plenty of negative effects on the harvest as well as the economy of the farmers. In addition, Nepal has a huge potential for generation of electricity through hydro-power plants, however, till date it has not been able to utilize the full capacity of the available water bodies. In order to plan the development of the hydro-power projects in the future such that all the citizens of the country can be provided with the facility of electricity, it is necessary to accurately predict the climate change. The prediction of climate change has been made possible by development of different mathematical models, known as GCMs, by different institutes throughout the world. However, the raw values generated by the GCMs for the variables of the climate change such as precipitation and temperature have biases in it. Hence, it is essential to correct the raw GCM data in order to get the predicted value close to the actual values, i.e. observed value of the future. This study compared the performance of different bias correction methods, i.e. Quantile Mapping method and Linear Scaling method, and presented a synthesis report on the available bias correction methods for climate projection based in Tamor river basin of Nepal. The two methods were used to find the correction factors in the calibration period and the raw GCM data of the calibration period, 1990-1999 for precipitation and 1995-1999 for temperature, were corrected using the corresponding correction factors in each case. Then, the correction factors were used in the independent data set of the validation period, 2000-2004 for both precipitation and temperature, in order to insure the working capability of each bias correction method. As shown in the result and discussion of this study it was observed that different bias correction methods, Quantile Mapping and Linear Scaling in this case, have different working capabilities in different conditions. In case of bias correction of raw GCM data for precipitation, Linear Scaling method resulted in better correction, i.e. the corrected GCM data had a good correlation with the corresponding observed data, in the stations with lower elevation. In contrast, Quantile Mapping method resulted in better correction in the stations with higher elevation. However, in the case of temperature, irrespective of the elevation of the stations, the Quantile Mapping method resulted in better corrected GCM than the Linear Scaling method. Furthermore, it was observed that both Quantile mapping method and Linear Scaling method could correct the mean of the bias corrected GCM data when compared with the observed data. On the other hand, only Quantile Mapping method could correct the standard deviation of the bias corrected GCM data when compared to the observed data. These trends were observed both in the calibration and validation period. Apart from the result from the comparison of the bias correction methods, it was also observed that MRI-CGCM3 was not suitable for this study area when the Linear Method of bias correction method was used. |