Future temperature for drought prediction in Bukit Merah, Perak by using SDSM modelling
Climate change is a worldwide phenomenon that can cause many sudden changes, especially to water resources. Malaysia has experienced warming and rainfall abnormalities, especially in the last two decades, and therefore estimates of climate change in Malaysia receive a lot of attention. Global clim...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/12099/1/P16850_69f5c571dbc931ee61c4ca25d7e8ae91.pdf%2017.pdf http://eprints.uthm.edu.my/12099/ https://doi.org/10.1063/5.0199148 |
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| Summary: | Climate change is a worldwide phenomenon that can cause many sudden changes, especially to water resources.
Malaysia has experienced warming and rainfall abnormalities, especially in the last two decades, and therefore estimates
of climate change in Malaysia receive a lot of attention. Global climate research is increasingly focused on severe
temperature changes due to severe climatic phenomena like droughts and heat waves globally. This research aims to
forecast maximum and minimum temperatures in Bukit Merah, Perak, for the years 2020-2050 and 2050-2080. This project
predicted the magnitude of drought over the next 60 years, and the data collected is aid hydrologic modelling in the Bukit
Merah, Perak. The findings analysed and addressed to estimate the future drought that may occur in the next 60 years.
SDSM has been widely used for downscaling climate variables such as precipitation, rainfall, and temperature among
statistical downscaling methods. Statistical downscaling provides local scale statistics, which is useful in climate change
analysis. It involves the use of past weather data for a longer time to collect large-scale variables. Therefore, it was
necessary to utilize the Root mean Square error (RSME) and the coefficient R2 to evaluate the performance of historical
and simulated data from the model during the calibration and validation periods. The coefficient of determination (R2)
during calibration and validation for maximum temperature were 0.89 and 0.67, while for minimum temperature, the value
for calibration and validation is 0.83 and 0.85. Therefore, the drought forecasting is an early warning system that the most
crucial stages for drought management that will arise in the future |
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