Improving surface roughness lengths estimation using machine learning algorithms

Surface roughness lengths, including the aerodynamic roughness length (z0m) and the thermodynamic roughness length (z0h, represented by excess resistance kB-1), are crucial parameters in the accurate simulation of surface turbulent fluxes. However, considerable uncertainties exist in physically-based surface roughness lengths models, due to insufficient knowledge of the physical mechanisms of them. In this study, we attempt to overcome this issue by establishing the data-driven surface roughness lengths models, which is based on global observations from the FLUXNET2015 dataset and Moderate Resolution Imaging Spectroradiometer (MODIS). Four machine learning algorithms, including random forest (RF), single hidden layer artificial neural network (ANN), multilayer perceptron (MLP), deep belief network (DBN) are explored. A large number of data from 45 flux tower sites (as many as 44662 daily z0m and 583484 half-hour kB-1 observations) are utilized to train and test the data-driven models. Our results show that the data-driven models surprisingly achieve significantly improved estimation of surface roughness lengths and turbulent fluxes than physical models, which indicated the model inadequacy of physical models. The RF-driven models achieve the best results. The MLP and DBN-driven models of higher complexity are slightly superior to ANN-driven models, but exhibit unstable performance. The RF and ANN accurately reproduce the unimodal function relationship between leaf area index and z0m, thus demonstrating that the machine learning methods can extract physical rules from vast numbers of observations. In contrast, the MLP and DBN fail to capture this relationship, possibly because of too complicated architecture. It implies that a suitable complexity of machine learning algorithm is critical to excavate true physical mechanism. To the best of our knowledge, this study firstly demonstrate that machine learning technique can contribute to highly accurate estimation of surface turbulent fluxes by building data-driven surface roughness lengths models.