# Spatiotemporal Variations in Vegetation Canopy Interception in China Based on a Revised Gash Model
## 5 Conclusions  
This study investigated the spatial and temporal variations in canopy interception by applying a revised Gash model to remote sensing and meteorological data collected in China from 2000 to 2018. The impacts of the main environmental variables on interception were also explored. The results showed that the revised Gash model had a good performance in simulating canopy interception at the regional scale compared with the PML_V2 dataset product and the in situ measurements. The average annual canopy interception was 166.55 \(\pm\) 160.61 mm during the 2000-2018 period, showing a significant decreasing pattern from Southeastern to Northwestern China. The ratio of canopy interception to precipitation presented similar spatial patterns, with the average value of 22.30 \(\pm\) 11.45%. At the temporal scale, the mean annual canopy interception over the last two decades increased significantly, at a rate of 1.79 mm yr\({}^{-1}\) (\(p\) < 0.01), and the increasing trend was more pronounced in the 2000-2009 period, with a rate of 3.34 mm yr\({}^{-1}\) (\(p\) < 0.01). Except for the deciduous broadleaved forest and temperate desert, most vegetation types have experienced a significant increasing trend (\(p\) < 0.01) of canopy interception. The variations of precipitation, NDVI, temperature, relative humidity, and solar radiation explained 82% of the variations in canopy interception of rainwater. Among these variables, precipitation, temperature, and NDVI predominantly controlled the vegetation canopy interception in China during the last two decades. Overall, the present study successfully applied the revised Gash model at the national scale and provided direct evidence of the spatial and temporal variations of canopy interception in China. The results obtained could greatly improve our understanding of the regional ecohydrological processes and contribute to the development of comprehensive global hydrological models.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/f13091404/s1](https://www.mdpi.com/article/10.3390/f13091404/s1)]([https://www.mdpi.com/article/10.3390/f13091404/s1](https://www.mdpi.com/article/10.3390/f13091404/s1)), Figure S1: Interannual variations of canopy interception of eight vegetation types in different seasons from 2000 to 2018; Table S1: Partial correlation coefficients between interception and the environmental factors; File S1: A list of selected papers from which the data were extracted from field studies for validation.  
Conceptualization, Z.-Y.J. and W.H.; methodology, Z.-Y.J., W.H., C.-M.L. and Y.J.; software, W.H., Y.J., Y.Y., J.-H.P. and Y.-D.Z.; validation, Z.-Y.J., C.-M.L. and G.-L.H.; data curation, Y.Y. and [PERSON]-H.P.; writing--original draft preparation, Z.-Y.J., W.H. and Y.J.; writing--review and editing, Z.-Y.J., W.H. and Y.J.; visualization, Y.Y. and S.-Y.Z.; supervision, Z.-Y.J.; project administration, Z.-Y.J.; funding acquisition, Z.-Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No. 41901027) and the Natural Science Foundation of Guangdong Province, China (Grant No. 2021A1515012208).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank [PERSON] and [PERSON] for assistance with model development and data analysis and are grateful for the support of the Pearl River Delta Forest Ecosystem Research Station. We also appreciate the handing editor and the anonymous reviewers for their valuable comments to improve this manuscript.  
The authors declare no conflict of interest.