Transpiration of trees in a cool temperate forest on Mt. Aso, Japan: comparison of model simulation and measurements. Issue 4 (26th May 2017)

• Record Type: Journal Article
• Authors: Miyazawa, Yoshiyuki; Inoue, Akio; Maruyama, Atsushi; Otsuki, Kyoichi; Giambelluca, Thomas W.
• Is Part Of: Ecological research. Volume 32:Issue 4(2017)
• Page Start: 547
• Page End: 557
• Publication Date: 2017-05-26
• Subjects: Drought -- Leaf ecophysiological traits -- Mt. Aso -- Multilayer model -- Transpiration; Ecology -- Periodicals; Ecology -- Japan -- Periodicals; Écologie; Japon; Ecology; Japan; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); Periodicals; 577.05
• DOI: 10.1007/s11284-017-1471-2
• Languages: English
• ISSNs: 0912-3814

Abstract

Abstract: In the large caldera of Mt. Aso, Japan, artificial grasslands were converted into forests of different species due to the decline of the livestock industry in this region. These changes in species composition are thought to have changed not only the transpiration rates ( E ), but also the responses in E to variations in environmental conditions. For three introduced forest types, we monitored E using sap flux sensors and computed E with a multilayer model parameterized by independently obtained leaf‐scale ecophysiological traits. Modeled E replicated the time series of measured E reasonably well, but did not reproduce a decrease in E lasting 20 days after a short rainless period in conifer plantation. Mean leaf‐scale stomatal conductance of two deciduous broadleaved tree species was as low as that of trees under dry conditions in other studies, possibly an adaptation for the avoidance of excessive E during rainless periods. These results suggest that land use change influenced E and its response to the rainfall patterns of the region and that drought plays an important role in influencing species‐specific E characteristics in this rainy region. The multilayer model in combination with sap flux measurements was found to be a useful tool not only for the extrapolation of E, but also for the detection of the unexpected events, as long as the measured parameter values capture the species‐specific seasonality in leaf ecophysiological traits.