03032ntm a2200289 a 4500 vtls000098167 KUKTEM 20251117113324.0 161124s2016 my ab f abm 000 0 eng d THE0000237(Local) 201905141459 nazirah 201712261137 nazri 201611241648 saini UMP FKASA .A36 2016 r Bc. Ahmad Saifuddin Othman Prediction of future climate trend using stochastic weather generator / Ahmad Saifuddin Othman Kuantan, Pahang : UMP, 2016 xv, 96 p. : ill. (some col.), maps (some col.) ; 30 cm. + 1 CD-ROM Faculty of Civil Engineering and Earth Resources Project Paper (Bachelors of Civil Engineering) -- Universiti Malaysia Pahang – 2016 Bibliography : p. 87-90 The issue of climate change and its effects on various aspects of the environment has become more challenges for society. It is desirable to analyse and predict the changes of critical climatic variables, such as rainfall, temperature and potential evapotranspiration affect in the content of global climate change. This change is also affected by the increment of gas carbon dioxide (CO2) and other gases GHGs emissions. This study is focus of analyse the prediction patterns of rainfall, temperature and potential evapotranspiration of Pahang state. The rainfall pattern can be estimate the future climate change, general circulation models (GCMs) are applied. Therefore, Long Ashton Research Station Weather Generator (LARS-WG), which utilized the Stochastic Weather Generators approach, is applied in order to convert the coarse spatial resolution of the GCMs output into a fine resolution. The result show that the changes in rainfall, temperature and potential evapotranspiration can be consider is state to the trend of change in the respective by years. Therefore, the quantity of annual rainfall decreases had reached above 64%, while the distribution of temperature can increases had reached above 10% and potential evapotranspiration raise had reached above 44% increases of the end of century. In this study, have seen different results from the PRECIS and LARS-WG models though we have used the same GCMs (HadCM3) and emission scenario, which reveals the uncertainties due to the downscaling method. The LARS-WG result shows difference with PRECIS for rainfall and temperature. However the monthly rainfall prediction by LARS-WG is performed well closer to the history compare to the PRECIS. The annually LARS-WG is performance well closer with 1.19% to the history compare to the PRECIS with 32.86%. Faculty of Civil Engineering and Earth Resources Dissertations Universities and Colleges Dissertations Theses http://ecollib.ump.edu.my/id/eprint/4030 Access in library only 0 0 0 6 10000 10000 2019-09-04 0 FKASA .A36 2016 r Bc. 0000115043 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 6 10000 10000 2019-09-04 0 CD 10296 | FKASA .A36 2016 r Bc. 0000115044 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 6711 6717 VTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5000*5020*5040*5200*6100*6500*6501*8560*9992