ABSTRACT: Climate change assessment at a local scale requires downscaling of general circulation models (GCMs) using various approaches. In this study, statistical downscaling using established machine learning techniques is compared with the proposed extreme gradient boosting decision tree (EXGBDT) technique. The Cauvery river basin in southern peninsular India, which is known for its frequent droughts and floods, was considered in this study. The ACCESS 1.0 CMIP5 historical GCM simulation was used for downscaling the local climate with the help of daily observation data from 35 stations located in the study zone. An intercomparison of model performance in predicting daily weather variables such as precipitation and average, maximum, and minimum temperatures over the upper, middle, and lower Cauvery river basin was performed. The findings show that mean-variance is around 15% and bias is negligible for the proposed EXGBDT model, which is better than other models under consideration. The NSE and R² values range from 0.75-0.85 for both training and testing periods. The intercomparison of monthly mean values of observed and downscaled data for different sub-basins and parameters suggests higher model efficiency. The lower variance observed in the comparison of CLIMDEX indices suggests that the EXGBDT model performance is better in representing the local climatic condition.

中文翻译：

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统计缩减模型的比对：一个大型流域的案例研究

摘要：在地方范围内进行气候变化评估需要使用各种方法来缩小通用循环模型（GCM）的规模。在这项研究中，将使用已建立的机器学习技术的统计缩减与拟议的极限梯度提升决策树（EXGBDT）技术进行了比较。这项研究考虑了印度南部半岛的Cauvery流域，该流域以频繁的干旱和洪水闻名。利用ACCESS 1.0 CMIP5历史GCM模拟，借助研究区域35个站点的每日观测数据，对当地气候进行了缩减。对模型性能进行了比较，以预测日常天气变量，例如Cauvery流域的上，中和下流地区的降水以及平均，最高和最低温度。结果表明，对于建议的EXGBDT模型，均值方差约为15％，偏差可以忽略不计，这比正在考虑的其他模型要好。NSE和R^2个值的范围从0.75-0.85用于训练和测试周期。不同子流域和参数的观测数据和缩减数据的月平均值之间的比较表明模型效率更高。在CLIMDEX指数的比较中观察到的较低方差表明，EXGBDT模型的性能在表示局部气候条件方面表现更好。