Spatial variations in throughfall in a Moso bamboo forest: sampling design for the estimates of stand-scale throughfall
Version of Record online: 28 SEP 2009
Copyright © 2009 John Wiley & Sons, Ltd.
Volume 24, Issue 3, pages 253–259, 30 January 2010
How to Cite
Shinohara, Y., Onozawa, Y., Chiwa, M., Kume, T., Komatsu, H. and Otsuki, K. (2010), Spatial variations in throughfall in a Moso bamboo forest: sampling design for the estimates of stand-scale throughfall. Hydrol. Process., 24: 253–259. doi: 10.1002/hyp.7473
- Issue online: 18 JAN 2010
- Version of Record online: 28 SEP 2009
- Manuscript Accepted: 27 JUL 2009
- Manuscript Received: 18 DEC 2008
- Japanese Ministry of Education, Culture, Sports, Science and Technology
- bamboo forest;
- interception loss;
- Monte Carlo sampling;
- spatial variation;
We investigated the spatial and seasonal variations in throughfall (Tf) in relation to spatial and seasonal variations in canopy structure and gross rainfall (Rf) and assessed the impacts of the variations in Tf on stand-scale Tf estimates. We observed the canopy structure expressed as the leaf area index (LAI) once a month and Tf once a week in 25 grids placed in a Moso bamboo (Phyllostachys pubescens) forest for 1 year. The mean LAI and spatial variation in LAI did have some seasonal variations. The spatial variations in Tf reduced with increasing Rf, and the relationship between the spatial variation and the Rf held throughout the year. These results indicate that the seasonal change in LAI had little impact on spatial variations in Tf, and that Rf is a critical factor determining the spatial variations in Tf at the study site. We evaluated potential errors in stand-scale Tf estimates on the basis of measured Tf data using Monte Carlo sampling. The results showed that the error decreases greatly with increasing sample size when the sample size was less than ∼8, whereas it was near stable when the sample size was 8 or more, regardless of Rf. A sample size of eight results in less than 10% error for Tf estimates based on Student's t-value analysis and would be satisfactory for interception loss estimates when considering errors included in Rf data. Copyright © 2009 John Wiley & Sons, Ltd.