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Intercomparison of downscaling methods for daily precipitation with emphasis on wavelet-based hybrid models
Journal of Hydrology ( IF 6.4 ) Pub Date : 2021-04-26 , DOI: 10.1016/j.jhydrol.2021.126373
Yeditha Pavan Kumar , Rathinasamy Maheswaran , Ankit Agarwal , Bellie Sivakumar

Downscaling of local daily precipitation from large-scale climatic variables is required for assessing the impact of climate change on hydrology and water resources. This study proposes wavelet transform (WT)-based Feed-Forward Neural Network (FF-NN) and Nonlinear Auto Regressive with exogenous inputs Network (NARX-NN) models for downscaling daily precipitation. The models are applied to a large river basin, the Krishna River basin, in the Indian subcontinent. Several climatic variables, including geo-potential heights, wind direction, vorticity, humidity, air temperature, mean sea level pressure, meridional velocity at surface, and 500hpa and 850hpa levels, are considered based on their statistical correlations. The results are evaluated using different performance measures and the ability of the models to capture the extreme events at five selected grid points (in different locations) having varying climatic characteristics is assessed. The performance of the proposed wavelet-based models is also compared with that of four different traditional and recent downscaling methods: Multiple Linear Regression (MLR), Statistical Downscaling Model (SDSM), Genetic Programming (GP), and Artificial Neural Networks (ANNs). The results reveal that the wavelet-based neural network models (WT-FF-NN and WT-NARX-NN) are robust compared to the other methods in terms of their ability to capture the regional precipitation patterns and the extreme events. The improvement in the wavelet-based models can be attributed to their ability to unravel the hidden relationship between the predictors and precipitation. It is also observed that there is considerable increase in the correlation between precipitation and the decomposed climatic variables. All these results suggest that wavelets aid in unravelling the relationship between local precipitation and large-scale climatic variables and improving the overall performance of the downscaling models.



中文翻译:

日降水量降尺度方法的比较,重点是基于小波的混合模型

为了评估气候变化对水文和水资源的影响,需要从大规模的气候变量中减少局部日降水量。这项研究提出了基于小波变换(WT)的前馈神经网络(FF-NN)和带有外来输入网络的非线性自回归网络(NARX-NN)模型来缩减每日降水量。这些模型被应用于印度次大陆的一个大型流域,即克里希纳流域。根据它们的统计相关性,考虑了几个气候变量,包括地势高度,风向,涡度,湿度,空气温度,平均海平面压力,地表子午速度以及500hpa和850hpa的水平。使用不同的性能指标评估结果,并评估模型在具有不同气候特征的五个选定网格点(在不同位置)捕获极端事件的能力。还将提出的基于小波的模型的性能与四种不同的传统和最近的降尺度方法的性能进行了比较:多重线性回归(MLR),统计降尺度模型(SDSM),遗传规划(GP)和人工神经网络(ANN) 。结果表明,与其他方法相比,基于小波的神经网络模型(WT-FF-NN和WT-NARX-NN)在捕获区域降水模式和极端事件方面具有较强的鲁棒性。基于小波的模型的改进可以归因于其解开预测变量和降水之间隐藏关系的能力。还可以观察到,降水与分解的气候变量之间的相关性大大增加。所有这些结果表明,小波有助于揭示局部降水与大规模气候变量之间的关系,并有助于改善降尺度模型的整体性能。

更新日期:2021-05-08
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