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CL montane cloud forest has a large amount of precipitation due to the orographical rainfall, northeastern monsoon systems, and winter fronts. Besides these, the frequent of occurrence also provides horizontal interception and reduces solar radiation, making soil moisture almost saturated all year round. This humid environment allows vegetation to thrive even during non-rainy seasons without experiencing water shortages. In contrast, LHC non-cloud forest has distinct seasonal variation in soil water, so water availability during the non-rainy season could have a strong impact on the local ecosystem.

We found that the photosynthetic capacity was sensitive to the meteorological factor from January to April (low soil water periods) from the analysis of observational rainfall data and vegetation indexes. Water sources from precipitation in the preceding November and December can easily influence LHC non-cloud forest, with the lag response of precipitation variations to the soil. In contrast, CL montane cloud forest seemed less susceptible to water input.

The precipitation sensitivity tests confirmed that local microclimate characteristics dominate the vegetation greenness state compared to the changes in the land type and species. Instead, stomatal conductance and soil water content play important roles in controlling gas exchange. Stomatal conductance is affected by water transport from soil to vegetation and vapor pressure deficit, which restrain each other and regulate the stomata closure (Fig. 5.1).

Our study focused on the relationship between precipitation and vegetation.

However, other climate variables, such as temperature and solar radiation, may also affect forest productivity. By considering these factors, we could better understand whether CL montane cloud forest is sensitive to energy factors in non-rainy seasons. Only then could

we know whether CL montane cloud forest is sensitive to energy factors in non-rainy seasons.

Also, the biogeochemical mechanism of photosynthesis still accounts for a crucial part that needs further investigation. It is unclear whether cloud forests will be able to function as vital carbon sinks under future climate change or if they will become vulnerable to multiple climate change factors. More idealized model simulations and observational-based data may be necessary to fully understand plant water relations in these ecosystems.

FIGURES

Figure 1.1 A schematic of Budyko diagram. The solid lines represent energy and water limits to the evaporative index, and the dashed line represents the original theoretical Budyko curve (after Budyko, 1974). (The figure is taken from Creed et al., 2014)

Figure 2.1 The location of the CL flux tower (red dot) and LHC flux tower (blue dot).

The boxes around the flux tower indicates the area of two sides in our research, approximately 5km by 5km in size.

Figure 2.2 Landtype comparison between CL and LHC. (The figure is taken from 古 (2020) and the middle Taiwan map is taken from Schulz et al. (2017))

Figure 2.3 Precipitation seasonality in CL (2008-2011) and LHC (2008-2013). Solid lines present rainfall data from flux towers; dash lines are from TReAD data.

Figure 3.1 Seasonality of three plant hydrology related variables in CL and LHC.

(a)(b) blue lines for precipitation; red lines for potential evapotranspiration (c)(d) four layers of soil moisture: dark to light gray color presents soil depth from surface to underground.

(a) (b)

(c) (d)

Figure 3.2 The comparison of seasonality vegetation indexes during different year period in CL (orange lines) and LHC (blue lines): (a) EVI data, the averaged years matches the year of valid meteorological data from the flux tower (b) long-term EVI data from 2000-2020 (c) LAI data, the averaged years matches the year of valid meteorological data from the flux tower (d) long-term LAI data from 2000-2020 The shading colors represent the variation of EVI/ LAI between first quartile and third quartile from 9 years data (left) and 20 years data (right).

(a) (b)

(c) (d)

Figure 3.3 Month to month correlation between rainfall and EVI in CL and LHC.

To the left of the dashed line present the precipitation in preceding year, while the right present current year to EVI. The blank space is VIs-leading condition, which are not to be considered.

Figure 3.4 Month to month correlation between rainfall and LAI in CL and LHC.

To the left of the dashed line present the precipitation in preceding year, while the right present current year to LAI. The blank space is vegetation-leading condition, which are not to be considered.

Figure 3.5 Scatter plot of average rainfall data in November and December and dry season vegetation indexes anomaly in CL (orange) and LHC (blue). The lines show linear regression results in each site. Both rainfall data are from in-situ flux tower.

Figure 3.6 Scatter plot of average rainfall data in November and December and dry season vegetation indexes anomaly in CL (orange) and LHC (blue). The lines show linear regression results in each site. The rainfall data in LHC are from agricultural station, and rainfall data in CL are from in-situ flux tower.

Figure 3.7 Comparison of LHC monthly rainfall from 2008 to 2016 between flux tower and agricultural station (AGR). The solid line presents linear regression between two datasets, and the p-value < 0.01.

Figure 3.8 Results of dry season transpiration and photosynthesis from experiment 1.

X-axis shows the multiple of Prec_ND, and Y-axis are the change rate for transpiration and photosynthesis compared to their CTR. (CL: orange line; LHC: blue line)

Figure 3.9 Results of four variables in dry season from from experiment 1. (a) upper 10cm soil water (b) vapor pressure deficit (c) sunlit stomatal conductance (d) shaded stomatal conductance (CL: orange line; LHC: blue line) Y-axis are the change rate for each variable compared to the CTR.

(a)

(c)

(b)

(d)

Figure 3.10 Results of four variables in dry season from experiment 1. (a) saturated vapor pressure (b) air vapor pressure (c) transpiration beta factor (d) CO2 partial pressure (CL: orange line; LHC: blue line) Y-axes are the change rate for each variable compared to the CTR.

(a)

(c)

(b)

(d)

Figure 3.11 Results of dry season transpiration and photosynthesis from experiment 1 and 2. X-axis shows the multiple of Prec_ND, and Y-axis are the change rate for transpiration and photosynthesis compared to their CTR. (CL: orange line; LHC: blue line; CLatm_LHCsurf: red line; LHCatm_CLsurf: dodgerblue line)

Figure 3.12 Results of dry season transpiration and photosynthesis from experiment 1 to 3. X-axis shows the multiple of Prec_ND, and Y-axis are the change rate for transpiration and photosynthesis compared to their CTR. (CL: orange line; LHC: blue line;

CLatm_LHCsurf: red line; LHCatm_CLsurf: dodgerblue line; CLclm_LHCsurf: brown line; LHCclm_CLsurf: lightblue line)

Figure 3.13 Vapor pressure deficit seasonality calculated by observational flux tower data.

Orange line for CL and blue line for LHC. Left picture are the was calculated by total time steps, while right picture only include daytime from 8a.m. to 5p.m. The shading colors represent the variation of VPD between first quartile and third quartile from 4 years in CL and 5 years in LHC.

Figure 4.1 Month to month correlation between TReAD rainfall (P) and EVI (up) and LAI (down) in CL and LHC. To the left of the dashed line present the precipitation in preceding year, while the right present current year. The blank space is vegetation-leading condition, which are not to be considered.

Figure 4.2 Month to month correlation between TReAD surface temperature (T) and EVI (up) and LAI (down) in CL and LHC. The blank space is vegetation-leading condition, which are not to be considered.

Figure 4.3 Month to month correlation between TReAD net radiation (Rn) and EVI (up) and LAI (down) in CL and LHC. The blank space is vegetation-leading condition, which are not to be considered.

Figure 4.4 Monthly normalized potential evapotranspiration (PET) and actual evapotranspiration (AET) by precipitation in CL and LHC. Red circles present the calculation from January to April. Dash gray line represent energy and water limited.

Figure 5.1 Schematic of low soil water period plant-water relation in tropical non-cloud forests, all the parameters derived from stomatal conductance formula in Community Land Model.

TABLES

Table 2.1 Experiment Design in CLM model simulation.

Experiment

Number Name of

experiments Land surface condition Atmospheric forcing

1 CL 100% evergreen needleleaf tree,

annual mean LAI = 4.3, coefficient of maximum allowed dew = 0.2533

CL 2008~2011 half hourly observational data

LHC 4.64% evergreen needleleaf

tree,

57.9% evergreen broadleaf tree, 2.04% deciduous broadleaf tree, 35.22% C3 grass,

annual mean LAI = 3.95

LHC 2009~2013 half hourly observational data

2 LHCatm_CLsurf 100% evergreen needleleaf tree, annual mean LAI = 4.3,

coefficient of maximum allowed dew = 0.2533

LHC 2009~2013 half hourly observational data

CLatm_LHCsurf 4.64% evergreen needleleaf tree,

57.9% evergreen broadleaf tree, 2.04% deciduous broadleaf tree, 35.22% C3 grass,

annual mean LAI = 3.95

CL 2008~2011 half hourly observational data

3 LHCclm_CLsurf 100% evergreen needleleaf tree, annual mean LAI = 4.3,

coefficient of maximum allowed dew = 0.2533

LHC 2009~2011 half hourly observational data

Nov. to Dec. Precipitation:

CL 2009~2011 half hourly observational rainfall data.

CLclm_LHCsurf 4.64% evergreen needleleaf tree,

57.9% evergreen broadleaf tree, 2.04% deciduous broadleaf tree, 35.22% C3 grass,

annual mean LAI = 3.95

CL 2009~2011 half hourly observational data

Nov. to Dec. Precipitation:

LHC 2009~2011 half hourly observational rainfall data.

Table 2.2 Daily average of multiple of November and December precipitation (Prec_ND) in CL and LHC.

Multiple value CL (mm/day) LHC (mm/day)

0.1 0.76 0.22

0.2 1.53 0.45

0.3 2.29 0.67

0.4 3.05 0.9

0.5 3.82 1.12

0.6 4.58 1.35

0.7 5.34 1.57

0.8 6.11 1.79

0.9 6.87 2.02

Control Run (CTR) 7.63 2.24

1.1 8.39 2.47

1.2 9.16 2.69

1.3 9.92 2.91

1.4 10.68 3.14

1.5 11.45 3.36

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