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Multi-variable model output statistics downscaling for the projection of spatio-temporal changes in rainfall of Borneo Island
Journal of Hydro-environment Research ( IF 2.4 ) Pub Date : 2020-05-27 , DOI: 10.1016/j.jher.2020.05.002
Zulfaqar Sa'adi , Shamsuddin Shahid , Sahar Hadi Pour , Kamal Ahmed , Eun-Sung Chung , Zaher Mundher Yaseen

A multiple variable bias correction approach has been proposed for the projection of the changes in spatial and temporal pattern of rainfall in Borneo Island due to climate change. The ensemble of General Circulation Models (GCMs) was selected through the combination of past performance and envelope approaches. The selected GCMs were downscaled using a Support Vector Machine (SVM) based multiple variable bias correction approach considering that inclusion of multiple circulation variables can provide more information on local climate and able to explicitly account for GCM-inherent error and bias. Finally, an ensemble projection was produced by using Random Forest (RF) regression to simulate the future projection. Four GCMs namely, HadGEM2-AO, HadGEM2-ES, MIROC5 and CCSM4 were found most suitable for the projection of rainfall in Borneo with associated uncertainty. The projected changes in rainfall indicate that the rainfall pattern and variation in Borneo are dominated based on the changes of monsoon season, geographical locations and terrains of the region. Overall, there is an increasing rate of rainfall projections in most parts of Borneo. However, the coastal-western region will become drier in contrast to the eastern region, although rainfall variability remains high. The highest increase in rainfall was projected in East Kalimantan in the range of 11.9% to 50.1% for 2070–2099, while the highest decrease was projected in West Kalimantan in the range of −3.7% to −13.0% for 2010–2039. The rainfall was expected to be more distributed during the Southwest monsoon while shorter Northeast monsoon with higher intensity was projected at most regions.



中文翻译:

婆罗洲岛降雨时空变化预测的多变量模型输出统计量缩减

已经提出了一种多变量偏差校正方法来预测婆罗洲岛由于气候变化而造成的降雨时空分布的变化。通用流通模型(GCM)的集合是通过结合过去的绩效和包络方法来选择的。考虑到包含多个循环变量可以提供更多有关当地气候的信息,并且能够明确考虑GCM固有的误差和偏差,因此使用基于支持向量机(SVM)的多变量偏差校正方法对选定的GCM进行了缩减。最后,通过使用随机森林(RF)回归来模拟未来的投影,产生了整体投影。四个GCM,即HadGEM2-AO,HadGEM2-ES,发现MIROC5和CCSM4最适合婆罗洲的降雨预测及相关不确定性。预计的降雨变化表明,婆罗洲的降雨模式和变化基于季风季节,地理位置和该地区地形的变化而占主导地位。总体而言,婆罗洲大部分地区的降雨预测速率都在增加。但是,与东部地区相比,沿海-西部地区将变得更干燥,尽管降雨的变异性仍然很高。预计2070-2099年东加里曼丹省的降雨增幅最高,而2010-2039年西加里曼丹省的降雨量降幅最高,增幅为-3.7%至-13.0%。

更新日期:2020-05-27
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