Identifying major drivers of daily streamflow from large-scale atmospheric circulation with machine learning
نویسندگان
چکیده
Previous studies linking large-scale atmospheric circulation and river flow with traditional machine learning techniques have predominantly explored monthly, seasonal or annual streamflow modelling for applications in direct downscaling hydrological climate-impact studies. This paper identifies major drivers of daily from using two reanalysis datasets six catchments Norway representing various Köppen-Geiger climate types flood-generating processes. A nested loop roughly pruned random forests is used feature extraction, demonstrating the potential automated retrieval physically consistent interpretable input variables. Random forest (RF), support vector (SVM) regression multilayer perceptron (MLP) neural networks are compared to multiple-linear assess role model complexity utilizing identified reconstruct streamflow. The models were trained on 31 years aggregated data distinct moving windows each catchment, reflecting catchment-specific forcing-response relationships between atmosphere rivers. results show that accuracy improves some extent complexity. In all but smallest, rainfall-driven most complex model, MLP, gives a Nash-Sutcliffe Efficiency (NSE) ranging 0.71 0.81 testing spanning five years. poorer performance by smallest catchment discussed relation characteristics, sub-grid topography local variability. intra-model differences also viewed consistency automatically retrieved selections datasets. study provides benchmark future development deep variables Norway.
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ژورنال
عنوان ژورنال: Journal of Hydrology
سال: 2021
ISSN: ['2589-9155']
DOI: https://doi.org/10.1016/j.jhydrol.2021.126086