Spatiotemporal variations in sap flow in a larch plantation: sampling size for stand scale estimates
Zebin Liu, Songping Yu, Lihong Xu, Yanhui Wang, Mengfei Wang, Pengtao Yu
Spatiotemporal variations in sap flow in a larch plantation: sampling size for stand scale estimates
The sap flow method is widely used to estimate forest transpiration. However, at the individual tree level it has spatiotemporal variations due to the impacts of environmental conditions and spatial relationships among trees. Therefore, an in-depth understanding of the coupling effects of these factors is important for designing sap flow measurement methods and performing accurate assessments of stand scale transpiration. This study is based on observations of sap flux density (SFd) of nine sample trees with different Hegyi’s competition indices (HCIs), soil moisture, and meteorological conditions in a pure plantation of Larix gmelinii var. principis-rupprechtii during the 2021 growing season (May to September). A multifactorial model of sap flow was developed and possible errors in the stand scale sap flow estimates associated with sample sizes were determined using model-based predictions of sap flow. Temporal variations are controlled by vapour pressure deficit (VPD), solar radiation (R), and soil moisture, and these relationships can be described by polynomial or saturated exponential functions. Spatial (individual) differences were influenced by the HCI, as shown by the decaying power function. A simple SFd model at the individual tree level was developed to describe the synergistic influences of VPD, R, soil moisture, and HCI. The coefficient of variations (CV) of the sap flow estimates gradually stabilized when the sample size was > 10; at least six sample trees were needed if the CV was within 10%. This study improves understanding of the mechanisms of spatiotemporal variations in sap flow at the individual tree level and provides a new methodology for determining the optimal sample size for sap flow measurements.
[1] |
Arieska PK, Herdiani N (2018) Margin of Error between Simple Random Sampling and Stratified Sampling. In: Proceeding of the 1st International Conference Technopreneur and Education 2018, Nov 14, 2018, Surabaya, Indonesia. 1(1), pp. 408–412.
|
[2] |
|
[3] |
|
[4] |
|
[5] |
|
[6] |
|
[7] |
|
[8] |
|
[9] |
|
[10] |
|
[11] |
|
[12] |
|
[13] |
|
[14] |
|
[15] |
|
[16] |
|
[17] |
|
[18] |
|
[19] |
|
[20] |
|
[21] |
|
[22] |
|
[23] |
|
[24] |
|
[25] |
|
[26] |
|
[27] |
|
[28] |
|
[29] |
|
[30] |
|
[31] |
|
[32] |
|
[33] |
|
[34] |
|
[35] |
|
[36] |
|
[37] |
|
[38] |
|
[39] |
|
[40] |
|
[41] |
|
[42] |
|
[43] |
|
[44] |
|
[45] |
|
[46] |
|
[47] |
|
[48] |
Turner R (2019) Deldir: Delaunay Triangulation and Dirichlet (Voronoi) Tessellation. R package version 0.2–9. Available online at: https://CRAN.R-project.org/package=deldir
|
[49] |
|
[50] |
|
[51] |
|
[52] |
|
[53] |
|
[54] |
|
[55] |
|
/
〈 | 〉 |