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008 171227s2017 my da f am 000 0 eng d
020 _aTHE0000814(Local)
039 9 _a201905271150
_bnazirah
_y201712271511
_zsaini
040 _aUMP
090 _aFKASA .Z87 2017 r Bc.
100 0 _aZuraida Hasbullah
245 1 0 _aApplication of bias correction in climate prediction /
_cZuraida Hasbullah
260 _aKuantan, Pahang :
_bUMP,
_c2017
300 _axii, 74 p. :
_bill. (some col.), charts ;
_c30 cm. +
_e1 CD-ROM
500 _aFaculty of Civil Engineering and Earth Resources
502 _aProject Paper (Bachelors of Civil Engineering) -- Universiti Malaysia Pahang – 2017
504 _aBibliography : p. 51
520 3 _aThe issue of climate change and its effects on many aspects of the environment become more challenges for society. The emission and concentration of carbon dioxide and greenhouse gases give impact to the increase in temperature, and thus leading to global warming. It is important and desirable to analyze and predict the changes of climatic variables especially for rainfall. However, the accuracy in the climate simulation is becomes significant to ensure the reliability of the projection results. Thus, the bias correction (BC) methods were suggested to imply to treat the gaps between observed and simulated results. This study is focus on analysis the prediction patterns of rainfall in Lubuk Paku and Temerloh in Pahang state based on the historical rainfall. The rainfall pattern can be estimate the future climate change, general circulation models (GCMs) are applied. Therefore, Statistical Downscaling Model (SDSM) is applied in order to convert the coarse spatial resolution of the GCMs output into a fine resolution. However, there are biases in SDSM result and GCMs result. Therefore, two bias correction methods which are Linear Scaling (LS) and Local Intensity Scaling (LOCI) are applied to reduce the bias of those model and the performance of those methods are being compared.
610 2 0 _aFaculty of Civil Engineering and Earth Resources
_xDissertations
650 0 _aUniversities and Colleges
_xDissertations
650 0 _aTheses
999 _aVIRTUA40
_c7543
_d7549
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5000*5020*5040*5200*6100*6500*6501*9992