# Quantification of Spatiotemporal Variability of Evapotranspiration (ET) and the Contribution of Influencing Factors for Different Land Cover Types in the Yunnan Province

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Data Sources

#### 2.2.1. ET Data

#### 2.2.2. Influencing Factors for Analysis Data

#### 2.2.3. Land Cover Data

#### 2.3. Methods

#### 2.3.1. Trend Analysis

_{j}and X

_{i}represent the variable X’s values within individual pixels during years j and i, respectively. The parameter β signifies the trend of change, with β > 0 denoting an ascending trend and β < 0 indicating a descending trend.

#### 2.3.2. Significance Test

_{1}, x

_{2}, …, x

_{n}}, the test statistic S is defined as:

#### 2.3.3. Correlation Analysis

_{i}and y

_{i}are the values of the two factors x, y in year i, and x, y are the n-year averages of the two factors.

#### 2.3.4. Ridge Regression

_{i}and x

_{i}, ${z}_{y}=\frac{{y}_{i}-\overline{y}}{{\widehat{\sigma}}_{y}}$, ${z}_{xj}=\frac{{x}_{ij}-{\overline{x}}_{j}}{{\widehat{\sigma}}_{j}}$.

## 3. Results

#### 3.1. Temporal and Spatial Change of ET in Yunnan Province

#### 3.1.1. Interannual Change Feature

#### 3.1.2. Monthly Change Characteristics

#### 3.2. Correlation Analysis of ET Impact Factors

#### 3.2.1. Influence of Climate and Vegetation Factors on ET

#### 3.2.2. Correlation with Topography Factors

#### 3.3. The Relative Contribution Rate of Influencing Factors to ET

_{i}) of TEMP (°C), PRCP (mm), SLME (mm), RH (%), NDVI, WDSP (m/s), elevation, and slope and the corresponding ET values (y

_{i}), and the spatially-balanced sampling was used to obtain the sample points to construct the training sample set (x

_{i}, y

_{i}), and to use ridge regression modeling to quantitatively analyze the relative contribution of the influence factors to ET.

#### 3.3.1. Histogram of Ridge Regression Coefficients and Contributions of Each Factor on Different Grassland Types

^{2}) for both grasslands and woody savannas were above 0.6. NDVI; TEMP positively affected ET values for all three vegetation cover types, RH, SLME; DEM negatively affected ET for all three vegetation cover types; slope and WDSP were the factors that increased ET for woody savannas and decreased ET for grasslands. PRCP only had a positive effect on ET in savannas.

#### 3.3.2. Histograms of Ridge Regression Coefficients and Contributions of Each Factor on Different Land Cover Classifications

^{2}) were higher for grassland, cropland, and forest, all exceeding 0.6. The standardized regression coefficients from the ridge regression showed that only NDVI was positively correlated with ET for all land cover types, and only SLME had a negative effect on ET in grassland, while the other influencing factors had a positive effect, and the factor that had the greatest effect on ET was NDVI with a standardized regression coefficient of 0.261; only TEMP, NDVI, and WDSP had a positive effect on ET in shrub, while the others had a negative effect, and elevation had the largest negative effect on ET, with a standardized regression coefficient of −0.259; elevation in barren has a significant negative effect on ET, and NDVI and SLME have a significant positive effect on ET; TEMP, RH, and SLME had a negative effect on ET and SLME with a standardized regression coefficient of −0.391 causing a significant negative effect in cropland. The effects of TEMP and NDVI on ET in the forest were positive, with NDVI with a standardized regression coefficient of 0.283 being the main factor influencing the increase in ET in forest, and PRCP with a standardized regression coefficient of -0.24 being the main factor influencing the decrease in ET in forest. The factors that significantly affect the change in ET of wetland is NDVI and elevation, both of which have a positive effect on ET.

## 4. Discussion

#### 4.1. Impact of Climate and Vegetation Greening on Ecohydrological Processes

#### 4.2. Analysis of ET Differences under Different Land Cover Types

#### 4.3. Uncertainties and Future Improvements

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Sellers, P.J.; Dickinson, R.E.; Randall, D.A.; Betts, A.K.; Hall, F.G.; Berry, J.A.; Collatz, G.J.; Denning, A.S.; Mooney, H.A.; Nobre, C.A.; et al. Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science
**1997**, 275, 502–509. [Google Scholar] [CrossRef] [PubMed] - Trenberth, K.E.; Smith, L.; Qian, T.; Dai, A.; Fasullo, J. Estimates of the global water budget and its annual cycle using observational and model data. J. Hydrometeorol.
**2007**, 8, 758–769. [Google Scholar] [CrossRef] - Wang, K.; Dickinson, R.E. A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Rev. Geophys.
**2012**, 50. [Google Scholar] [CrossRef] - Kool, D.; Agam, N.; Lazarovitch, N.; Heitman, J.L.; Sauer, T.J.; Ben-Gal, A. A review of approaches for evapotranspiration partitioning. Agric. For. Meteorol.
**2014**, 184, 56–70. [Google Scholar] [CrossRef] - Wang, Z.; Zhan, C.; Ning, L.; Guo, H. Evaluation of global terrestrial evapotranspiration in CMIP6 models. Theor. Appl. Climatol.
**2021**, 143, 521–531. [Google Scholar] [CrossRef] - Zhang, L.; Dawes, W.R.; Walker, G.R. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resour. Res.
**2001**, 37, 701–708. [Google Scholar] [CrossRef] - Montaldo, N.; Oren, R. Changing Seasonal Rainfall Distribution With Climate Directs Contrasting Impacts at Evapotranspiration and Water Yield in the Western Mediterranean Region. Earths Future
**2018**, 6, 841–856. [Google Scholar] [CrossRef] - Zhang, D.; Liu, X.; Zhang, L.; Zhang, Q.; Gan, R.; Li, X. Attribution of Evapotranspiration Changes in Humid Regions of China from 1982 to 2016. J. Geophys. Res.-Atmos.
**2020**, 125, e2020JD032404. [Google Scholar] [CrossRef] - Hansen, J.; Ruedy, R.; Sato, M.; Lo, K. Global surface temperature change. Rev. Geophys.
**2010**, 48, RG4004. [Google Scholar] [CrossRef] - Li, X.; He, Y.; Zeng, Z.; Lian, X.; Wang, X.; Du, M.; Jia, G.; Li, Y.; Ma, Y.; Tang, Y.; et al. Spatiotemporal pattern of terrestrial evapotranspiration in China during the past thirty years. Agric. For. Meteorol.
**2018**, 259, 131–140. [Google Scholar] [CrossRef] - Chen, C.; Park, T.; Wang, X.; Piao, S.; Xu, B.; Chaturvedi, R.K.; Fuchs, R.; Brovkin, V.; Ciais, P.; Fensholt, R.; et al. China and India lead in greening of the world through land-use management. Nat. Sustain.
**2019**, 2, 122–129. [Google Scholar] [CrossRef] [PubMed] - Zhao, F.; Wu, Y.; Yao, Y.; Sun, K.; Zhang, X.; Winowiecki, L.; Vagen, T.-G.; Xue, J.; Qiu, L.; Sun, P.; et al. Predicting the climate change impacts on water-carbon coupling cycles for a loess hilly-gully watershed. J. Hydrol.
**2020**, 581, 124388. [Google Scholar] [CrossRef] - Friedl, M.A. Relationships among remotely sensed data, surface energy balance, and area-averaged fluxes over partially vegetated land surfaces. J. Appl. Meteorol.
**1996**, 35, 2091–2103. [Google Scholar] [CrossRef] - Lettenmaier, D.P.; Famiglietti, J.S. Hydrology—Water from on high. Nature
**2006**, 444, 562–563. [Google Scholar] [CrossRef] - Lei, H.; Yang, D. Interannual and seasonal variability in evapotranspiration and energy partitioning over an irrigated cropland in the North China Plain. Agric. For. Meteorol.
**2010**, 150, 581–589. [Google Scholar] [CrossRef] - Leuning, R.; Zhang, Y.Q.; Rajaud, A.; Cleugh, H.; Tu, K. A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation. Water Resour. Res.
**2008**, 44, 581–589. [Google Scholar] [CrossRef] - Zhao, F.; Wu, Y.; Qiu, L.; Sivakumar, B.; Zhang, F.; Sun, Y.; Sun, L.; Li, Q.; Voinov, A. Spatiotemporal features of the hydro-biogeochemical cycles in a typical loess gully watershed. Ecol. Indic.
**2018**, 91, 542–554. [Google Scholar] [CrossRef] - Weizhou, W.; Zhonghui, Z.; Wenxing, K.; Dalun, T.; Wenhua, X.; Wende, Y.A.N. Characteristics of latent heat flux over Cunninghamia lanceolata plantations in Huitong county. J. Cent. South Univ. For. Technol.
**2011**, 31, 192–197. [Google Scholar] - Wang, W.; Liao, Y.; Wen, X.; Guo, Q. Dynamics of CO
_{2}fluxes and environmental responses in the rain-fed winter wheat ecosystem of the Loess Plateau, China. Sci. Total Environ.**2013**, 461, 10–18. [Google Scholar] [CrossRef] - Liang, W.; Zhang, W.; Jin, Z.; Yan, J.; Lu, Y.; Li, S.; Yu, Q. Rapid Urbanization and Agricultural Intensification Increase Regional Evaporative Water Consumption of the Loess Plateau. J. Geophys. Res.-Atmos.
**2020**, 125, e2020JD033380. [Google Scholar] [CrossRef] - Su, Z. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci.
**2002**, 6, 85–99. [Google Scholar] [CrossRef] - Bastiaanssen, W.G.M.; Menenti, M.; Feddes, R.A.; Holtslag, A.A.M. A remote sensing surface energy balance algorithm for land (SEBAL)—1. Formulation. J. Hydrol.
**1998**, 212, 198–212. [Google Scholar] [CrossRef] - Mu, Q.; Heinsch, F.A.; Zhao, M.; Running, S.W. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ.
**2007**, 111, 519–536. [Google Scholar] [CrossRef] - Mu, Q.; Zhao, M.; Running, S.W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ.
**2011**, 115, 1781–1800. [Google Scholar] [CrossRef] - Priestley, C.H.B.; Taylor, R.J. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather Rev.
**1972**, 100, 81–92. [Google Scholar] [CrossRef] - Fisher, J.B.; Tu, K.P.; Baldocchi, D.D. Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sens. Environ.
**2008**, 112, 901–919. [Google Scholar] [CrossRef] - Cheng, M.; Jiao, X.; Jin, X.; Li, B.; Liu, K.; Shi, L. Satellite time series data reveal interannual and seasonal spatiotemporal evapotranspiration patterns in China in response to effect factors. Agric. Water Manag.
**2021**, 255, 107046. [Google Scholar] [CrossRef] - Groisman, P.Y.; Karl, T.R.; Easterling, D.R.; Knight, R.W.; Jamason, P.F.; Hennessy, K.J.; Suppiah, R.; Page, C.M.; Wibig, J.; Fortuniak, K.; et al. Changes in the probability of heavy precipitation: Important indicators of climatic change. Clim. Chang.
**1999**, 42, 243–283. [Google Scholar] [CrossRef] - Huntington, T.G. Evidence for intensification of the global water cycle: Review and synthesis. J. Hydrol.
**2006**, 319, 83–95. [Google Scholar] [CrossRef] - Acharjee, T.K.; van Halsema, G.; Ludwig, F.; Hellegers, P. Declining trends of water requirements of dry season Boro rice in the north-west Bangladesh. Agric. Water Manag.
**2017**, 180, 148–159. [Google Scholar] [CrossRef] - Fu, J.; Gong, Y.; Zheng, W.; Zou, J.; Zhang, M.; Zhang, Z.; Qin, J.; Liu, J.; Quan, B. Spatial-temporal variations of terrestrial evapotranspiration across China from 2000 to 2019. Sci. Total Environ.
**2022**, 825, 153951. [Google Scholar] [CrossRef] - Yang, L.; Feng, Q.; Adamowski, J.F.; Alizadeh, M.R.; Yin, Z.; Wen, X.; Zhu, M. The role of climate change and vegetation greening on the variation of terrestrial evapotranspiration in northwest China’s Qilian Mountains. Sci. Total Environ.
**2021**, 759, 143532. [Google Scholar] [CrossRef] [PubMed] - Shao, R.; Zhang, B.; Su, T.; Biao, L.; Cheng, L.; Xue, Y.; Yang, W. Estimating the Increase in Regional Evaporative Water Consumption as a Result of Vegetation Restoration Over the Loess Plateau, China. J. Geophys. Res.-Atmos.
**2019**, 124, 11783–11802. [Google Scholar] [CrossRef] - Bai, M.; Mo, X.; Liu, S.; Hu, S. Contributions of climate change and vegetation greening to evapotranspiration trend in a typical hilly-gully basin on the Loess Plateau, China. Sci. Total Environ.
**2019**, 657, 325–339. [Google Scholar] [CrossRef] - Lin, S.; Wang, G.; Hu, Z.; Huang, K.; Sun, X.; Sun, J.; Luo, M.; Xiao, X. Dynamics of Evapotranspiration and Variations in Different Land-Cover Regions over the Tibetan Plateau during 1961–2014. J. Hydrometeorol.
**2021**, 22, 955–969. [Google Scholar] [CrossRef] - Theil, H. A rank-invariant method of linear and polynomial regression analysis. Indag. Math.
**1950**, 12, 173. [Google Scholar] - Sen, P.K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc.
**1968**, 63, 1379–1389. [Google Scholar] [CrossRef] - Mann, H.B. Nonparametric tests against trend. Econom. J. Econom. Soc.
**1945**, 13, 245–259. [Google Scholar] [CrossRef] - Kendall, M.G. Rank Correlation Methods; Griffin: London, UK, 1948. [Google Scholar]
- Partal, T.; Kahya, E. Trend analysis in Turkish precipitation data. Hydrol. Process. Int. J.
**2006**, 20, 2011–2026. [Google Scholar] [CrossRef] - Feng, Y.; Cui, N.; Zhao, L.; Gong, D.; Zhang, K. Spatiotemporal variation of reference evapotranspiration during 1954–2013 in Southwest China. Quat. Int.
**2017**, 441, 129–139. [Google Scholar] [CrossRef] - Hoerl, A.E.; Kennard, R.W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics
**1970**, 12, 55–67. [Google Scholar] [CrossRef] - Suzuki, R.; Masuda, K.; Dye, D.G. Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVIs of northern Asia. Remote Sens. Environ.
**2007**, 106, 387–398. [Google Scholar] [CrossRef] - Mao, D.; Wang, Z.; Luo, L.; Ren, C. Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China. Int. J. Appl. Earth Obs. Geoinf.
**2012**, 18, 528–536. [Google Scholar] [CrossRef] - Suzuki, R.; Masuda, K. Interannual covariability found in evapotranspiration and satellite-derived vegetation indices over northern Asia. J. Meteorol. Soc. Jpn.
**2004**, 82, 1233–1241. [Google Scholar] [CrossRef] - Chen, J.; Zhang, J.; Peng, J.; Zou, L.; Fan, Y.; Yang, F.; Hu, Z. Alp-valley and elevation effects on the reference evapotranspiration and the dominant climate controls in Red River Basin, China: Insights from geographical differentiation. J. Hydrol.
**2023**, 620, 129397. [Google Scholar] [CrossRef] - Ma, Y.-J.; Li, X.-Y.; Liu, L.; Yang, X.-F.; Wu, X.-C.; Wang, P.; Lin, H.; Zhang, G.-H.; Miao, C.-Y. Evapotranspiration and its dominant controls along an elevation gradient in the Qinghai Lake watershed, northeast Qinghai-Tibet Plateau. J. Hydrol.
**2019**, 575, 257–268. [Google Scholar] [CrossRef] - Xiang, K.; Li, Y.; Horton, R.; Feng, H. Similarity and difference of potential evapotranspiration and reference crop evapotranspiration—A review. Agric. Water Manag.
**2020**, 232, 106043. [Google Scholar] [CrossRef] - Lu, J.; Wang, G.; Li, S.; Feng, A.; Zhan, M.; Jiang, T.; Su, B.; Wang, Y. Projected Land Evaporation and Its Response to Vegetation Greening Over China Under Multiple Scenarios in the CMIP6 Models. J. Geophys. Res.-Biogeosci.
**2021**, 126, e2021JG006327. [Google Scholar] [CrossRef] - Batra, N.; Islam, S.; Venturini, V.; Bisht, G.; Jiang, L. Estimation and comparison of evapotranspiration from MODIS and AVHRR sensors for clear sky days over the Southern Great Plains. Remote Sens. Environ.
**2006**, 103, 1–15. [Google Scholar] [CrossRef] - Liu, Y.; Lian, J.; Luo, Z.; Chen, H. Spatiotemporal variations in evapotranspiration and transpiration fraction following changes in climate and vegetation in a karst basin of southwest China. J. Hydrol.
**2022**, 612, 128216. [Google Scholar] [CrossRef] - Niu, Z.; He, H.; Zhu, G.; Ren, X.; Zhang, L.; Zhang, K.; Yu, G.; Ge, R.; Li, P.; Zeng, N. An increasing trend in the ratio of transpiration to total terrestrial evapotranspiration in China from 1982 to 2015 caused by greening and warming. Agric. For. Meteorol.
**2019**, 279, 107701. [Google Scholar] [CrossRef] - Goulden, M.L.; Bales, R.C. Mountain runoff vulnerability to increased evapotranspiration with vegetation expansion. Proc. Natl. Acad. Sci. USA
**2014**, 111, 14071–14075. [Google Scholar] [CrossRef] [PubMed] - Ye, L.; Cheng, L.; Liu, P.; Liu, D.; Zhang, L.; Qin, S.; Xia, J. Management of vegetative land for more water yield under future climate conditions in the over-utilized water resources regions: A case study in the Xiong’an New area. J. Hydrol.
**2021**, 600, 126563. [Google Scholar] [CrossRef] - Sala, O.; Lauenroth, W.; Golluscio, R. 11 Plant functional types in temperate semi-arid regions. Plant Funct. Types Their Relev. Ecosyst. Prop. Glob. Chang.
**1997**, 1, 217. [Google Scholar] - Prevey, J.S.; Germino, M.J.; Huntly, N.J.; Inouye, R.S. Exotic plants increase and native plants decrease with loss of foundation species in sagebrush steppe. Plant Ecol.
**2010**, 207, 39–51. [Google Scholar] [CrossRef] - Rodriguez-Iturbe, I.; Porporato, A.; Ridolfi, L.; Isham, V.; Cox, D.R. Probabilistic modelling of water balance at a point: The role of climate, soil and vegetation. Proc. R. Soc. A Math. Phys. Eng. Sci.
**1999**, 455, 3789–3805. [Google Scholar] [CrossRef] - Breshears, D.D.; Cobb, N.S.; Rich, P.M.; Price, K.P.; Allen, C.D.; Balice, R.G.; Romme, W.H.; Kastens, J.H.; Floyd, M.L.; Belnap, J.; et al. Regional vegetation die-off in response to global-change-type drought. Proc. Natl. Acad. Sci. USA
**2005**, 102, 15144–15148. [Google Scholar] [CrossRef] - Zemp, D.C.; Schleussner, C.F.; Barbosa, H.M.J.; Rammig, A. Deforestation effects on Amazon forest resilience. Geophys. Res. Lett.
**2017**, 44, 6182–6190. [Google Scholar] [CrossRef] - Jia, G.; Epstein, H.; Walker, D. Controls over intra-seasonal dynamics of AVHRR NDVI for the Arctic tundra in northern Alaska. Int. J. Remote Sens.
**2004**, 25, 1547–1564. [Google Scholar] [CrossRef] - Xue, B.; Wang, G.; Xiao, J.; Helman, D.; Sun, W.; Wang, J.; Liu, T. Global convergence but regional disparity in the hydrological resilience of ecosystems and watersheds to drought. J. Hydrol.
**2020**, 591, 125589. [Google Scholar] [CrossRef] - Li, X.; Liang, S.; Yuan, W.; Yu, G.; Cheng, X.; Chen, Y.; Zhao, T.; Feng, J.; Ma, Z.; Ma, M. Estimation of evapotranspiration over the terrestrial ecosystems in China. Ecohydrology
**2014**, 7, 139–149. [Google Scholar] [CrossRef] - Liu, J.; Jia, B.; Xie, Z.; Shi, C. Ensemble simulation of land evapotranspiration in China based on a multi-forcing and multi-model approach. Adv. Atmos. Sci.
**2016**, 33, 673–684. [Google Scholar] [CrossRef] - Ji, Y.; Tang, Q.; Yan, L.; Wu, S.; Yan, L.; Tan, D.; Chen, J.; Chen, Q. Spatiotemporal Variations and Influencing Factors of Terrestrial Evapotranspiration and Its Components during Different Impoundment Periods in the Three Gorges Reservoir Area. Water
**2021**, 13, 2111. [Google Scholar] [CrossRef] - Luo, Y.; Gao, P.; Mu, X. Influence of Meteorological Factors on the Potential Evapotranspiration in Yanhe River Basin, China. Water
**2021**, 13, 1222. [Google Scholar] [CrossRef]

**Figure 1.**Geographic location of the Yunnan Province in China: (

**a**) map of the Yunnan Province; (

**b**) spatial distribution of land cover types in the Yunnan Province.

**Figure 7.**(

**a**) Spatial distribution of elevation in the Yunnan Province. (

**b**) Mean evapotranspiration from different elevations under different surface cover types.

**Figure 8.**(

**a**) Spatial distribution of slopes in the Yunnan Province; (

**b**) Mean evapotranspiration at different slopes under different surface cover types.

Factors | R^{2} | TEMP | PRCP | WDSP | RH | NDVI | SLME | Elevation | Slope | |
---|---|---|---|---|---|---|---|---|---|---|

Landcover | ||||||||||

Grasslands | 0.62 | 0.113 | −0.221 * | −0.211 * | −0.056 | 0.196 | −0.065 | −0.183 | −0.03 | |

Savannas | 0.49 | 0.274 * | 0.139 | −0.145 | −0.199 | 0.403 | −0.105 | −0.127 | −0.056 | |

Woody savannas | 0.77 | 0.119 | −0.283 * | 0.025 | −0.043 | 0.366 * | −0.451 * | −0.007 | 0.209 |

Factors | R^{2} | TEMP | PRCP | WDSP | RH | NDVI | SLME | Elevation | Slope | |
---|---|---|---|---|---|---|---|---|---|---|

Landcover | ||||||||||

grassland | 0.61 | 0.18 * | 0.203 * | 0.055 | 0.079 | 0.261 * | −0.015 | 0.119 | 0.134 | |

shrub | 0.48 | 0.028 | −0.047 | −0.096 | −0.237 * | 0.054 | −0.011 | −0.259 * | 0.104 | |

barren | 0.38 | −0.022 | −0.03 | 0.102 | −0.124 | 0.362 * | 0.214 * | −0.218 * | −0.188 | |

cropland | 0.77 | −0.124 * | 0.138 * | 0.061 | −0.17 * | 0.033 | −0.391 * | 0.179 * | 0.014 | |

forest | 0.74 | 0.18 * | −0.24 * | −0.052 | −0.028 | 0.283 * | −0.01 | −0.217 * | −0.081 | |

wetland | 0.41 | 0.008 | 0.082 | 0.183 | 0.178 | 0.307 * | −0.141 | 0.218 * | −0.058 |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Su, W.; Shao, H.; Xian, W.; Xie, Z.; Zhang, C.; Yang, H.
Quantification of Spatiotemporal Variability of Evapotranspiration (ET) and the Contribution of Influencing Factors for Different Land Cover Types in the Yunnan Province. *Water* **2023**, *15*, 3309.
https://doi.org/10.3390/w15183309

**AMA Style**

Su W, Shao H, Xian W, Xie Z, Zhang C, Yang H.
Quantification of Spatiotemporal Variability of Evapotranspiration (ET) and the Contribution of Influencing Factors for Different Land Cover Types in the Yunnan Province. *Water*. 2023; 15(18):3309.
https://doi.org/10.3390/w15183309

**Chicago/Turabian Style**

Su, Wei, Huaiyong Shao, Wei Xian, Zhanglin Xie, Cunbo Zhang, and Huilin Yang.
2023. "Quantification of Spatiotemporal Variability of Evapotranspiration (ET) and the Contribution of Influencing Factors for Different Land Cover Types in the Yunnan Province" *Water* 15, no. 18: 3309.
https://doi.org/10.3390/w15183309