# Divergence in Quantifying ET with Independent Methods in a Primary Karst Forest under Complex Terrain

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## Abstract

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^{−1}) and potential evapotranspiration (1030.61 mm year

^{−1}). The EC method had an energy imbalance problem, with an annual energy closure of 46% at the annual scale. The annual estimate of evapotranspiration after a 100% energy closure correction was 915.03 mm, which was higher than the reference evapotranspiration (853.26 mm), so the corrected annual estimates were considered to be unreasonable. Comparing the resistance method with the EC method, it was found that not only is the annual evapotranspiration (ET) lower in the EC method, but the sensible heat flux is also lower, indicating that the resistivity method has lower energy closure than the EC method, suggesting that this method is not suitable for use in karst forests. When comparing the PM method with the EC method, surface conductivity is the most critical parameter. As the most difficult parameter to quantify in the Penman–Monteith equation, the key influencing factor, maximum stomatal conductance, was carefully explored. In the selection of maximum stomatal conductance, the sensitivity of annual evapotranspiration to maximum stomatal conductance values was first analyzed. It was found that the sensitivity is strong before 0.018 m s

^{−1}. When g

_{smax}is 0.0025 m s

^{−1}, the annual evapotranspiration (456 mm) is equivalent to that of the EC method, and it slowly decreases after reaching 0.018 ms

^{−1}. This indicates that when g

_{smax}is 0.0025 m s

^{−1}, the annual evapotranspiration is lower or higher than the critical value of the EC method. Therefore, different maximum stomatal conductance values will result in annual evapotranspiration based on the PM method being higher or lower than the annual evapotranspiration measured by the EC method. In order to obtain a more accurate maximum stomatal conductance, the surface conductance was calculated based on the PM equation, using the maximum stomatal conductance of four key tree species in the study area. The FAO universal fixed surface conductance of 1/70 m s

^{−1}was used to constrain the calculation. The reason for this treatment is that the reference underlying surface of FAO is a uniformly flat and well-watered grassland, with a larger surface conductance than forests. The results showed that the selected maximum stomatal conductance values were all within a reasonable range, and the calculated annual evapotranspiration values were 267.28 mm, 596.42 mm, 699.59 mm and 736.90 mm, respectively. Considering the EC method as the lower limit (456.66 mm), the reference evapotranspiration as the upper limit (853.26 mm) and the specific vegetation in the study area, the estimated annual evapotranspiration of the primary forest in the Nonggang karst area of Guangxi (PM method) falls within the range of 596.42 mm to 736.90 mm, which is relatively reasonable.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Site Description

#### 2.2. Data Collection and Processing

_{2}/H

_{2}O, air temperature, atmospheric pressure, three-dimensional wind speed and sonic air temperature at a sampling frequency of 10 Hz.

#### 2.3. Methods

#### 2.3.1. Eddy Covariance Method

- $L{E}_{EC}$ the latent heat flux based on the EC method (W m
^{−2}); - ${\rho}_{v}^{\prime}$ the fluctuation in the water vapor density (kg m
^{−3}); - $\lambda $ latent heat of vaporization (J kg
^{−1}); - ${U}^{\prime}$ the fluctuation in the vertical wind speed (m s
^{−1}); - $H$ the sensible heat flux (W m
^{−2}); - $\rho $ the density of dry air (kg m
^{−3}); - ${C}_{\mathrm{p}}$ specific heat capacity of the dry air (1013 J kg
^{−1}K^{−1}); - ${T}^{\prime}$ the fluctuation in the air temperature (°C)

#### 2.3.2. Penman–Monteith Combination Equation

- Calculation Of Evapotranspiration

- $L{E}_{PM}$ the latent heat flux based on the PM method (W m
^{−2}); - $E{T}_{PM}$ evapotranspiration based on the PM method (mm time
^{−1}); - ${R}_{n}$ net radiation at the surface (W m
^{−2}); - $G$ soil heat flux (W m
^{−2}); - $\mathsf{\Delta}$ slope of the saturation vapor pressure–temperature curve (kPa °C
^{−1}); - $\lambda $ latent heat of the vaporization of water (MJ kg
^{−1}); - $\gamma $ psychrometer constant (kPa °C
^{−1}); - ${e}_{s}$ saturation vapor pressure (kPa);
- ${e}_{a}$ actual vapor pressure (kPa);
- $\left({e}_{s}-{e}_{a}\right)$ saturation vapor pressure deficit (kPa);
- ${G}_{aV}$ aerodynamic conductance for water vapor (m s
^{−1}); - ${G}_{s}$ canopy conductance (m s
^{−1}).

- $\mathsf{\Delta}$ slope of saturation vapor pressure curve at air temperature ${T}_{a}$ (kPa °C
^{−1}); - ${T}_{a}$ mean air temperature (°C).

- $\gamma $ psychrometric constant (kPa °C
^{−1}); - $P$ atmospheric pressure (kPa);
- $\lambda $ latent heat of vaporization (MJ kg
^{−1}); - ${c}_{p}$ air specific heat at constant pressure, 1.013 × 10
^{−3}(MJ kg^{−1}°C^{−1}); - $\epsilon $ ratio of molecular weight of water vapor/dry air ($\mathsf{\epsilon}\text{}=0.622$).

- Calculating the vapor dynamic conductivity

- ${G}_{\mathrm{aM}}$ aerodynamic conductance for momentum (m s
^{−1}); - $u$ the mean wind speed at the reference height (m s
^{−1}); - ${u}_{*}$ friction velocity at the reference height (m s
^{−1}).

^{−1}), $\zeta $ is the fraction of height as the height of the canopy top, $\varphi \left(\zeta \right)$ is the vertically normalized light absorption profile such that ${\int}_{0}^{1}\varphi \left(\zeta \right)\mathrm{d}\zeta =1$ and $\alpha =4.39-3.97{\mathrm{e}}^{-0.258\times \mathrm{LAI}}$ is the extinction coefficient for the assumed exponential wind profile.

- where:
- ${r}_{\mathrm{b}}$ boundary layer resistance to water vapor transport (m s
^{−1}); - ${G}_{\mathrm{b}}$ boundary layer conductance (m s
^{−1}); - $Sc$ Schmidt number for water vapor (0.67);
- $Pr$ Prandtl number for air (0.71);
- $\mathrm{LAI}$ leaf area index (m
^{2}m^{−2}); - $L$ characteristic leaf dimension (0.1 m);
- $u$ wind speed at the top of the canopy (m s
^{−1}); - $\zeta $ height as a fraction of canopy top height;
- $\varphi \left(\zeta \right)$ vertical profile of light absorption normalized such that ${\int}_{0}^{1}\varphi \left(\zeta \right)\mathrm{d}\zeta =1$;
- $\alpha $ extinction coefficient for the assumed exponential wind profile, $\alpha =4.39-3.97{\mathrm{e}}^{-0.258\times \mathrm{LAI}}$.

- ${G}_{\mathrm{aV}}$ aerodynamic conductance for water vapor (m s
^{−1}); - ${G}_{\mathrm{aM}}$ aerodynamic conductance for momentum (m s
^{−1}); - ${G}_{\mathrm{b}}$ boundary layer conductance (m s
^{−1}).

- Calculation of Canopy Conductance ${G}_{\mathrm{c}}$

- ${G}_{c\_L}$ leaf stomatal conductance (m s
^{−1}); - $\left({e}_{s}-{e}_{a}\right)$ saturation vapor pressure deficit (kPa);
- ${g}_{smax}$ the maximum stomatal conductance of leaves at the top of the canopy;
- ${D}_{50}$ the humidity deficit at which stomatal conductance is half its maximum value, ${D}_{50}$= 0.7 kpa;
- ${Q}_{50}$ the visible radiation flux when stomatal conductance is half its maximum value, ${Q}_{50}$= 30 W m
^{−2}; - ${K}_{Q}$ the extinction coefficient for shortwave radiation, ${K}_{Q}$= 0.6;
- ${K}_{A}$ the extinction coefficient for available energy, ${K}_{A}$= 0.6;
- PAR the flux density of visible radiation at the top of the canopy (approximately half of incoming solar radiation);

- LAI the leaf area index

- $L{E}_{EC}$ the latent heat flux (W m
^{−2}); - ${R}_{n}$ net radiation at the surface (W m
^{−2}); - $G$ soil heat flux (W m
^{−2}); - Δ slope of the saturation vapor pressure–temperature curve (kPa K
^{−1}); - $\gamma $ psychrometer constant (kPa K
^{−1}); - ${e}_{s}$ saturation vapor pressure (kPa);
- ${e}_{a}$ actual vapor pressure (kPa);
- $\left({e}_{s}-{e}_{a}\right)$ saturation vapor pressure deficit (kPa);
- ${G}_{aV}$ aerodynamic conductance for water vapor (m s
^{−1}); - ${G}_{s\text{}EC}$ surface conductance (m s
^{−1}).

#### 2.3.3. The Resistance Method

- $\rho $ mean air density at constant pressure (kg m
^{−3}); - ${C}_{p}$ air-specific heat at constant pressure (MJ kg
^{−1}°C^{−1}); - ${e}_{s}$ saturation vapor pressure (kPa);
- ${e}_{a}$ actual vapor pressure (kPa);
- (${e}_{s}$ − ${e}_{a}$) saturation vapor pressure deficit (kPa);
- ${G}_{aV}$ aerodynamic resistance (m s
^{−1}); - ${T}_{s}$ the canopy temperature (K);
- ${T}_{a}$ the air temperature (°C).

- E upward long-wave radiation (W ${\mathrm{m}}^{-2}$);
- ε the emissivity of the object, which is a value between 0 and 1 and is determined by the surface properties of the object;
- σ the Stefan–Boltzmann constant, σ = 5.67 × ${10}^{-8}$ W/(${\mathrm{m}}^{2}$·${\mathrm{K}}^{4}$);
- $T$ the canopy temperature (K).

_{n}-G), air temperature (T

_{a}), air vapor pressure deficit (VPD) and wind speed or air diffusivity (G

_{a})) and a physiological factor (crop conductance (G

_{c})). According to Brown and Norman (1973) [49], the dependence of LE on these parameters can be expressed by the following equation, and the specific data processing flow chart is shown in Figure 4:

- $\rho $ mean air density at constant pressure (kg m
^{−3}); - ${C}_{p}$ air-specific heat at constant pressure (MJ kg
^{−1}°C^{−1}); - ${e}_{s}$ saturation vapor pressure (kPa);
- ${e}_{a}$ actual vapor pressure (kPa);
- $\left({e}_{s}-{e}_{a}\right)$ saturation vapor pressure deficit (kPa);
- $\gamma $ psychrometric constant (kPa °C
^{−1}); - ${G}_{c}$ canopy resistance (m s
^{−1}); - ${G}_{aV}$ aerodynamic resistance (m s
^{−1}).

#### 2.3.4. Potential Evapotranspiration

_{c}) set to nearly infinity:

- $\lambda $ latent heat of vaporization of water (MJ kg
^{−1}); - $PE$ potential evapotranspiration (mm day
^{−1}); - ${R}_{n}$ net radiation at the crop surface (MJ m
^{−2}day^{−1}); - $G$ soil heat flux (MJ m
^{−2}day^{−1}); - $\rho $ mean air density at constant pressure (kg m
^{−3}); - ${c}_{p}$ air-specific heat at constant pressure (MJ kg
^{−1}°C^{−1}); - ${e}_{s}$ saturation vapor pressure (kPa);
- ${e}_{a}$ actual vapor pressure (kPa);
- $\left({e}_{s}-{e}_{a}\right)$ saturation vapor pressure deficit (kPa);
- $\mathsf{\Delta}$ slope vapor pressure curve (kPa °C
^{−1}); - $\gamma $ psychrometric constant (kPa °C
^{−1}); - ${G}_{c}$ canopy resistance (m s
^{−1}); - ${G}_{aV}$ aerodynamic resistance (m s
^{−1}).

#### 2.3.5. Reference Evapotranspiration FAO56

_{o}) for a reference surface. This method defines the reference surface based on the following assumptions: “a crop height of 0.12 m, a constant surface resistance of 70 s ${\mathrm{m}}^{-1}$ and an albedo of 0.23” [52].

- $E{T}_{o}$ reference evapotranspiration (mm day
^{−1}); - ${R}_{n}$ net radiation at the crop surface (MJ m
^{−2}day^{−1}); - $G$ soil heat flux density (MJ m
^{−2}day^{−1}); - ${T}_{a}$ mean daily air temperature at 2 m height (m s
^{−1}); - ${u}_{2}$ wind speed at 2 m height (m s
^{−1}); - ${e}_{s}$ saturation vapor pressure (kPa);
- ${e}_{a}$ actual vapor pressure (kPa);
- ${e}_{s}-{e}_{a}$ saturation vapor pressure deficit (kPa);
- Δ slope vapor pressure curve (kPa °C
^{−1}); - γ psychrometric constant (kPa °C
^{−1}).

#### 2.4. Energy Balance Calculation

^{−2}), which is the ultimate source of energy required for ecosystem physiological processes. When the ecosystem gains energy, ${R}_{n}$ is positive, and when the energy is released from the ecosystem, ${R}_{n}$ is negative; $G$ is the heat flux into the soil matrix, $S$ is the storage of heat between the sonic anemometer and the soil surface and $Q$ is the sum of all additional energy sources and sinks (mainly referring to the energy consumed by ecosystem vegetation photosynthesis). In general, $S$ and $Q$ are small terms that are ignored. Therefore, the flux balance can be simplified as:

_{n}is the net radiation of the ecosystem canopy, LE is the latent heat flux (W·m

^{−2}), H is the sensible heat flux (W·m

^{−2}), G is the soil heat flux (W·m

^{−2}) and $L{E}_{cor}$ is the corrected latent heat flux (W·m

^{−2}).

## 3. Results

#### 3.1. Comparison of Independent Methods for Estimating Evapotranspiration

^{−1}) and potential evapotranspiration (1030.61 mm year

^{−1}).

#### 3.1.1. Energy Closure Analysis (EC Method)

_{n}-G) was 0.46, the intercept was 9.32 and the correlation coefficient was 0.67. The calculation results in Section 2 showed that the turbulent flux-based energy closure at the site was low, which was below the reasonable range value previously reported (55~80%). To address this issue, we conducted an energy closure correction assuming that both latent and sensible heat fluxes were consistently underestimated (Twine et al., 2000) and adjusted the Bowen ratio to quantify the systematic underestimation of turbulent fluxes. This method has been supported by airborne measurements [59].

#### 3.1.2. Eddy Covariance Method

#### 3.1.3. Comparison between Eddy Covariance and Resistance Methods

#### 3.1.4. Comparing the Eddy Covariance and PM Method

_{smax}values are 0.004 m s

^{−1}, 0.006 m s

^{−1}and 0.007 m s

^{−1}. On the other hand, when g

_{smax}is 0.001 m s

^{−1}, the PM method produces a lower estimate than the one measured by the eddy covariance method. Among them, 0.001 m s

^{−1}, 0.004 m s

^{−1}and 0.007 m s

^{−1}are the maximum stomatal conductance values of the key tree species in the forest canopy (namely, Prunus salicina, Osmunda japonica and Ficus altissima, respectively) determined by the LI-6400XT portable photosynthesis system in the field [60], and 0.006 m s

^{−1}is the average value of the maximum single-leaf stomatal conductance (g

_{smax}) dataset in a tropical rainforest from Kelliher (1995) [61]. All the maximum leaf stomatal conductance values are within the range of 0.0029 m s

^{−1}to 0.0093 m s

^{−1}, which is the maximum stomatal conductance range of tropical forest leaves mentioned in the study of Kelliher and Leuning et al. [61].

_{smax}) parameter. The surface conductance measured by the eddy covariance method does not have the same sampling problem, as the flux represents the evaporation of all vegetation in a large number of counter-flowing air currents.

_{smax}is less than 0.02 m s

^{−1}, and evapotranspiration reaches a steady state of around 1000 mm. We also found that when g

_{smax}is 0.0025 m s

^{−1}, the annual evapotranspiration estimated by the two independent methods tends to converge (456 mm). When g

_{smax}is less than 0.0025 m s

^{−1}, the evapotranspiration estimated by the PM method is lower than that measured by the eddy covariance method, and vice versa. When the maximum leaf stomatal conductance (g

_{smax}= 0.018 m s

^{−1}) cited in the estimation of evapotranspiration in the Southwest Karst region is introduced, the annual evapotranspiration reaches 901.86 mm.

#### 3.2. Surface Conductance Analysis

## 4. Discussion

^{−1}) for a temperate mixed forest [66], while Pauwels et al. (2006) obtained consistent results for a wet grassland by using a fixed surface conductance of 1/70 m s

^{−1}for the PM method [65]. The careful selection of maximum stomatal conductance can greatly increase the credibility of PM method evapotranspiration estimates.

_{smax}is less than 0.018 m s

^{−1}, the different values of maximum stomatal conductance have a significant impact on it, indicating that a more conservative selection of maximum stomatal conductance is required to obtain relatively accurate estimates of annual transpiration. First, we evaluated the canopy conductance obtained by the EC method and the PM method based on the fixed value of the surface conductance of reference evapotranspiration, and the results showed that both were lower than 1/70 m s

^{−1}, indicating that the value of maximum stomatal conductance is credible. Therefore, we obtained the range of maximum stomatal conductance within the allowable limits. Studies have shown that there are significant differences in stomatal conductance among different vegetation types [2]. Jiang et al. used the Breathing Earth System Simulator (BESS) model to calculate ET, and the results showed that distinguishing between C3 and C4 plants in the model improved accuracy compared to not distinguishing between vegetation types [3]. To obtain relatively accurate parameters of canopy conductance, we believe that selecting the maximum leaf stomatal conductance of a key tree species in the forest where the eddy covariance station is located can provide a relatively accurate estimate of annual transpiration. Although the stomatal characteristic data are based on literature research, the actual sampling points of the data are located within the study site area, without temporal and spatial differences, which can provide relatively accurate results. Therefore, in this study, we tend to believe that 0.006 ${\mathrm{m}\text{}\mathrm{s}}^{-1}$ as the maximum single-leaf stomatal conductance (g

_{smax}) for PML method applications is relatively reasonable. However, the following issue still exists: in complex vegetation canopies, some tree crowns may extend beyond the general horizontal of the canopy, and this variability may cause problems when sampling vegetation to obtain representative stomatal conductance. To use stomatal conductance measurements to estimate transpiration, representative samples are needed in both the horizontal and vertical directions [67]. The maximum leaf stomatal conductance of a single tree species used in this study cannot represent the comprehensive characteristics of all species. It is critical to find the maximum leaf stomatal conductance that can represent all species, and this issue may need to be further explored in future studies.

_{smax}= 0.004 ${\mathrm{m}\text{}\mathrm{s}}^{-1}$), 699.59 mm (g

_{smax}= 0.006 ${\mathrm{m}\text{}\mathrm{s}}^{-1}$) and 736.90 mm (g

_{smax}= 0.0047 ${\mathrm{m}\text{}\mathrm{s}}^{-1}$), respectively, and is more reliable than the estimates obtained by the other independent methods.

## 5. Conclusions

_{smax}= 0.006 ${\mathrm{m}\text{}\mathrm{s}}^{-1}$). All three methods underestimated annual evapotranspiration compared to reference evapotranspiration (853.26 mm year

^{−1}) and potential evapotranspiration (1030.61 mm year

^{−1}), which increased the credibility of the results.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

Symbol | Definition | Units |

$L{E}_{EC}$ | latent heat flux based on the EC method | W m^{−2} |

${\rho}_{v}^{\prime}$ | fluctuation in the water vapor density | kg m^{−3} |

$\lambda $ | latent heat of vaporization | J kg^{−1} |

${U}^{\prime}$ | fluctuation in the vertical wind speed | m s^{−1} |

$H$ | sensible heat flux | W m^{−2} |

$\rho $ | density of dry air | kg m^{−3} |

${C}_{\mathrm{p}}$ | specific heat capacity of the dry air, 1.013 × 10^{−3} | MJ kg^{−1} K ^{−1} |

${T}^{\prime}$ | fluctuation in the air temperature | ℃ |

$L{E}_{PM}$ | latent heat flux based on the PM method | W m^{−2} |

${R}_{n}$ | net radiation at the surface | W m^{−2} |

$G$ | soil heat flux | W m^{−2} |

$\mathsf{\Delta}$ | slope of the saturation vapor pressure–temperature curve | kPa °C^{−1} |

$\lambda $ | latent heat of vaporization of water | MJ kg^{−1} |

$\gamma $ | psychrometer constant | kPa °C^{−1} |

${e}_{s}$ | saturation vapor pressure | kPa |

${e}_{a}$ | actual vapor pressure | kPa |

$\left({e}_{s}-{e}_{a}\right)$ | saturation vapor pressure deficit | kPa |

${G}_{aV}$ | aerodynamic conductance for water vapor | m s^{−1} |

${G}_{c}$ | canopy conductance | m s^{−1} |

$P$ | atmospheric pressure | kPa |

$\epsilon $ | ratio of molecular weight of water vapor/dry air, $\mathsf{\epsilon}\text{}=0.622$ | dimensionless |

${G}_{\mathrm{aM}}$ | aerodynamic conductance for momentum | m s^{−1} |

$u$ | mean wind speed at reference height | m s^{−1} |

${u}_{*}$ | friction velocity at reference height | m s^{−1} |

${r}_{\mathrm{b}}$ | boundary layer resistance to water vapor transport | s m^{−1} |

$Sc$ | Schmidt number for water vapor, 0.67 | dimensionless |

$Pr$ | Prandtl number for air, 0.71 | dimensionless |

$\mathrm{LAI}$ | leaf area index | dimensionless |

$L$ | characteristic leaf dimension, 0.1 m | m |

$\zeta $ | height as a fraction of canopy top height | dimensionless |

$\varphi \left(\zeta \right)$ | vertical profile of light absorption normalized such that ${\int}_{0}^{1}\varphi \left(\zeta \right)\mathrm{d}\zeta =1$ | dimensionless |

$\alpha $ | extinction coefficient for the assumed exponential wind profile, $\alpha =4.39-3.97{\mathrm{e}}^{-0.258\times \mathrm{LAI}}$ | dimensionless |

$E{T}_{o}$ | reference evapotranspiration | mm day^{−1} |

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**Figure 1.**Location of the study area. The image in the upper left corner was quoted from Google Maps Online (https://www.google.com). Carbonate rock outcrop data were accessed from https://www.fos.auckland.ac.nz/ourresearch/karst/index.html (accessed on 14 February 2023) (Ford and Williams 2007).

**Figure 3.**Data flow of evapotranspiration estimation for the PM method. The white text box is the instrument and equipment, the light gray box is the process data and the dark gray box is the result.

**Figure 4.**Data flow of evapotranspiration estimation for the resistant method. The white text box is the instrument and equipment, the light gray box is the process data and the dark gray box is the result.

**Figure 5.**Comparison among evapotranspiration from different methods for the period 1 January 2019 to 30 June 2020, using cumulative daily average value. The vertical dashed line represents the cumulative evapotranspiration for 2019.

**Figure 6.**Analysis of the energy balance closure using 30 min average fluxes measured by the eddy covariance system.

**Figure 7.**Comparison among evapotranspiration based on different energy closure, for the period (1 January 2019 to 30 June 2020), using cumulative daily average value, The vertical dashed line represents the cumulative evapotranspiration for 2019.

**Figure 8.**Half-hour average of sensible heat flux under different emissivity values; 0.9, 0.95 and 1.0 are the emissivity values, respectively.

**Figure 9.**Comparison between the resistance model approximation and EC-based sensible heat flux (Hs) for the period 1 January 2019 to 30 June 2020, using the half-hourly average value.

**Figure 10.**Variation in daily sensible heat flux (Hs) for resistance-based and EC-based methods (1 January 2019 to 30 June 2020).

**Figure 11.**Comparison among evapotranspiration based on different g

_{smax}values for the period January 9 to June 20, using the cumulative daily average value. The vertical dashed line represents the cumulative evapotranspiration for 2019.

**Figure 13.**Sensitivity analysis of annual evapotranspiration on maximum stomatal conductance (g

_{smax}, for single leaves).

**Figure 14.**Comparing monthly mean diurnal variation for surface conductance inverted from latent heat flux based on the EC method and modified by the PML model; g

_{smax}= 0.0025 m s

^{−1}; the shaded area represents the rainy season.

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**MDPI and ACS Style**

Li, Q.; Liu, W.; Zheng, L.; Liu, S.; Zhang, A.; Wang, P.; Jin, Y.; Liu, Q.; Song, B.
Divergence in Quantifying ET with Independent Methods in a Primary Karst Forest under Complex Terrain. *Water* **2023**, *15*, 1823.
https://doi.org/10.3390/w15101823

**AMA Style**

Li Q, Liu W, Zheng L, Liu S, Zhang A, Wang P, Jin Y, Liu Q, Song B.
Divergence in Quantifying ET with Independent Methods in a Primary Karst Forest under Complex Terrain. *Water*. 2023; 15(10):1823.
https://doi.org/10.3390/w15101823

**Chicago/Turabian Style**

Li, Qingyun, Wenjie Liu, Lu Zheng, Shengyuan Liu, Ang Zhang, Peng Wang, Yan Jin, Qian Liu, and Bo Song.
2023. "Divergence in Quantifying ET with Independent Methods in a Primary Karst Forest under Complex Terrain" *Water* 15, no. 10: 1823.
https://doi.org/10.3390/w15101823