Attributing the Evapotranspiration Trend in the Upper and Middle Reaches of Yellow River Basin Using Global Evapotranspiration Products
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
Parameters | Products | Spatial Resolution | Temporal Resolution | Time Duration | Reference |
---|---|---|---|---|---|
ET | GLDAS_NOAH | 0.25° | monthly | 2000–2018 | Rodell et al. [11] |
GLDAS_VIC | 1° | 2000–2018 | |||
GLDAS_CLSM | 1° | 2000–2018 | |||
GLEAM_v3.3a | 0.25° | 2000–2018 | Matens et al. [12] | ||
PML_V2 | 500 m | 8-day | 2000–2018 | Zhang et al. [9] | |
TWSA | JPL RL06_mascon | 0.25° | monthly | 2003–2018 | Wiese et al. [37] |
CSR RL06_mascon | 0.25° | monthly | 2003–2018 | Save et al. [38] | |
LAI | GLASS | 1km | 8-day | 2000–2018 | Xiao et al. [39] |
Parameters | Data Sources | Number of Sites | Temporal Resolution | Time Duration |
---|---|---|---|---|
Precipitation Temperature Wind speed Sunshine duration Relative humidity | China Meteorological Administration | 295 | daily | 2000–2018 |
Measured Runoff | Hydrological Bureau, Yellow River Water Conservancy Commission | 5 | yearly | 2000–2018 |
2.3. Method
2.3.1. Overall Methodology
2.3.2. Ensemble ET derived using Linear Weighting Method
2.3.3. Linear Slope Calculation
2.3.4. Quantitative Attribution Analysis Method for the ET Trend
3. Results
3.1. Accuracy Assessment of the Ensemble ET
3.2. Spatial-Temporal Variation in ET and the Influencing Factors
3.3. Spatial Pattern in the Sensitivity of the ET to the Influencing Factors
3.4. Impacts of the Influencing Factors on the ET Trend
4. Discussion
4.1. Implications of the Sensitivity of the ET to the Influencing Factors Derived Using the “Two-Step” Scheme
4.2. Underlying Causes of the Effects of the Influencing Factors on the ET Trend
4.3. Impact on Water Yield Change Trend at the Subregion Scale
4.4. Uncertainties
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Zhang, Y.; Leuning, R.; Hutley, L.B.; Beringer, J.; McHugh, I.; Walker, J.P. Using long-term water balances to parameterize surface conductances and calculate evaporation at 0.05° spatial resolution. Water Resour. Res. 2010, 46. [Google Scholar] [CrossRef] [Green Version]
- Liu, S.M.; Xu, Z.W.; Zhu, Z.L.; Jia, Z.Z.; Zhu, M.J. Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin, China. J. Hydrol. 2013, 487, 24–38. [Google Scholar] [CrossRef]
- Vinukollu, R.K.; Wood, E.F.; Ferguson, C.R.; Fisher, J.B. Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches. Remote Sens. Environ. 2011, 115, 801–823. [Google Scholar] [CrossRef]
- Tian, F.; Qiu, G.; Yang, Y.; Lü, Y.; Xiong, Y. Estimation of evapotranspiration and its partition based on an extended three-temperature model and MODIS products. J. Hydrol. 2013, 498, 210–220. [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]
- Ramoelo, A.; Majozi, N.; Mathieu, R.; Jovanovic, N.; Nickless, A.; Dzikiti, S. Validation of global evapotranspiration product (MOD16) using flux tower data in the African savanna, South Africa. Remote Sens. 2014, 6, 7406–7423. [Google Scholar] [CrossRef] [Green Version]
- Jung, M.; Reichstein, M.; Ciais, P.; Seneviratne, S.I.; Sheffield, J.; Goulden, M.L.; Bonan, G.; Cescatti, A.; Chen, J.; de Jeu, R.; et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 2010, 467, 951–954. [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]
- Zhang, Y.; Kong, D.; Gan, R.; Chiew, F.H.S.; McVicar, T.R.; Zhang, Q.; Yang, Y. Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017. Remote Sens. Environ. 2019, 222, 165–182. [Google Scholar] [CrossRef]
- Kobayashi, S.; Ota, Y.; Harada, Y.; Ebita, A.; Moriya, M.; Onoda, H.; Onogi, K.; Kamahori, H.; Kobayashi, C.; Endo, H.; et al. The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteorol. Soc. Jpn. Ser. II 2015, 93, 5–48. [Google Scholar] [CrossRef] [Green Version]
- Rodell, M.; Houser, P.; Jambor, U.; Gottschalck, J.; Mitchell, K.; Meng, C.-J.; Arsenault, K.; Cosgrove, B.; Radakovich, J.; Bosilovich, M. The global land data assimilation system. Bull. Am. Meteorol. Soc. 2004, 85, 381–394. [Google Scholar] [CrossRef] [Green Version]
- Martens, B.; Miralles, D.G.; Lievens, H.; van der Schalie, R.; de Jeu, R.A.M.; Fernández-Prieto, D.; Beck, H.E.; Dorigo, W.A.; Verhoest, N.E.C. GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci. Model. Dev. 2017, 10, 1903–1925. [Google Scholar] [CrossRef] [Green Version]
- Liu, F.; Chen, S.; Peng, J.; Chen, G. Temporal variability of water discharge and sediment load of the Yellow River into the sea during 1950–2008. J. Geogr. Sci. 2011, 21, 1047–1061. [Google Scholar] [CrossRef]
- Yin, Y.; Tang, Q.; Liu, X.; Zhang, X. Water scarcity under various socio-economic pathways and its potential effects on food production in the Yellow River basin. Hydrol. Earth Syst. Sci. 2017, 21, 791–804. [Google Scholar] [CrossRef] [Green Version]
- Chen, Y.; Wang, K.; Lin, Y.; Shi, W.; Song, Y.; He, X. Balancing green and grain trade. Nat. Geosci. 2015, 8, 739–741. [Google Scholar] [CrossRef]
- Hua, F.; Wang, X.; Zheng, X.; Fisher, B.; Wang, L.; Zhu, J.; Tang, Y.; Yu, D.W.; Wilcove, D.S. Opportunities for biodiversity gains under the world’s largest reforestation programme. Nat. Commun. 2016, 7, 12717. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, W.; Zhang, Y.; Tang, Q. Impact assessment of climate change and human activities on streamflow signatures in the Yellow River Basin using the Budyko hypothesis and derived differential equation. J. Hydrol. 2020, 591, 125460. [Google Scholar] [CrossRef]
- Jia, X.; Shao, M.A.; Zhu, Y.; Luo, Y. Soil moisture decline due to afforestation across the Loess Plateau, China. J. Hydrol. 2017, 546, 113–122. [Google Scholar] [CrossRef]
- Cai, X.; Rosegrant, M.W. Optional water development strategies for the Yellow River Basin: Balancing agricultural and ecological water demands. Water Resour. Res. 2004, 40, 1–11. [Google Scholar] [CrossRef] [Green Version]
- Gao, G.; Chen, D.; Xu, C.-Y.; Simelton, E. Trend of estimated actual evapotranspiration over China during 1960–2002. J. Geophys. Res. Atmos. 2007, 112. [Google Scholar] [CrossRef] [Green Version]
- Goyal, R.K. Sensitivity of evapotranspiration to global warming: A case study of arid zone of Rajasthan (India). Agric. Water Manag. 2004, 69, 1–11. [Google Scholar] [CrossRef]
- Li, T.; Xia, J.; Zhang, L.; She, D.; Wang, G.; Cheng, L. An improved complementary relationship for estimating evapotranspiration attributed to climate change and revegetation in the Loess Plateau, China. J. Hydrol. 2021, 592, 125516. [Google Scholar] [CrossRef]
- Zha, T.; Li, C.; Kellomäki, S.; Peltola, H.; Wang, K.-Y.; Zhang, Y. Controls of evapotranspiration and CO2 fluxes from Scots pine by surface conductance and abiotic factors. PLoS ONE 2013, 8, e69027. [Google Scholar] [CrossRef] [Green Version]
- Burn, D.H.; Hesch, N.M. Trends in evaporation for the Canadian Prairies. J. Hydrol. 2007, 336, 61–73. [Google Scholar] [CrossRef]
- Shen, M.; Piao, S.; Jeong, S.-J.; Zhou, L.; Zeng, Z.; Ciais, P.; Chen, D.; Huang, M.; Jin, C.-S.; Li, L.Z.X.; et al. Evaporative cooling over the Tibetan Plateau induced by vegetation growth. Proc. Natl. Acad. Sci. USA 2015, 112, 9299. [Google Scholar] [CrossRef] [Green Version]
- Shao, R.; Zhang, B.; Su, T.; Long, B.; 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]
- Qiu, L.; Wu, Y.; Shi, Z.; Chen, Y.; Zhao, F. Quantifying the responses of evapotranspiration and its components to vegetation restoration and climate change on the Loess plateau of China. Remote Sens. 2021, 13, 2358. [Google Scholar] [CrossRef]
- Lv, X.; Zuo, Z.; Sun, J.; Ni, Y.; Wang, Z. Climatic and human-related indicators and their implications for evapotranspiration management in a watershed of Loess plateau, China. Ecol. Indic. 2019, 101, 143–149. [Google Scholar] [CrossRef]
- Zhang, M.; Yuan, X. Crucial role of natural processes in detecting human influence on evapotranspiration by multisource data analysis. J. Hydrol. 2020, 580, 124350. [Google Scholar] [CrossRef]
- Li, G.; Zhang, F.; Jing, Y.; Liu, Y.; Sun, G. Response of evapotranspiration to changes in land use and land cover and climate in China during 2001–2013. Sci. Total Environ. 2017, 596–597, 256–265. [Google Scholar] [CrossRef] [PubMed]
- Yang, L.; Feng, Q.; Adamowski, J.F.; Alizadeh, M.R.; 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. 2020, 759, 143532. [Google Scholar] [CrossRef]
- Jiang, Z.-Y.; Yang, Z.-G.; Zhang, S.-Y.; Liao, C.-M.; Hu, Z.-M.; Cao, R.-C.; Wu, H.-W. Revealing the spatio-temporal variability of evapotranspiration and its components based on an improved Shuttleworth-Wallace model in the Yellow River Basin. J. Environ. Manag. 2020, 262, 110310. [Google Scholar] [CrossRef]
- Pei, T.; Wu, X.; Li, X.; Zhang, Y.; Shi, F.; Ma, Y.; Wang, P.; Zhang, C. Seasonal divergence in the sensitivity of evapotranspiration to climate and vegetation growth in the Yellow River Basin, China. J. Geophys. Res. Biogeosci. 2017, 122, 103–118. [Google Scholar] [CrossRef]
- Zhou, L.; Tucker, C.J.; Kaufmann, R.K.; Slayback, D.; Shabanov, N.V.; Myneni, R.B. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J. Geophys. Res. Atmos. 2001, 106, 20069–20083. [Google Scholar] [CrossRef]
- Li, J.; Peng, S.; Li, Z. Detecting and attributing vegetation changes on China s Loess Plateau. Agric. For. Meteorol. 2017, 247, 260–270. [Google Scholar] [CrossRef]
- Yuan, W.; Zheng, Y.; Piao, S.; Ciais, P.; Lombardozzi, D.; Wang, Y.; Ryu, Y.; Chen, G.; Dong, W.; Hu, Z.; et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 2019, 5, eaax1396. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wiese, D.N.; Landerer, F.W.; Watkins, M.M. Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution. Water Resour. Res. 2016, 52, 7490–7502. [Google Scholar] [CrossRef]
- Save, H.; Bettadpur, S.; Tapley, B.D. High-resolution CSR GRACE RL05 mascons. J. Geophys. Res. Solid Earth 2016, 121, 7547–7569. [Google Scholar] [CrossRef]
- Xiao, Z.; Liang, S.; Wang, J.; Chen, P.; Yin, X.; Zhang, L.; Song, J. Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Trans. Geosci. Remote Sens. 2014, 52, 209–223. [Google Scholar] [CrossRef]
- Wan, Z.; Zhang, K.; Xue, X.; Hong, Z.; Hong, Y.; Gourley, J.J. Water balance-based actual evapotranspiration reconstruction from ground and satellite observations over the conterminous United States. Water Resour. Res. 2015, 51, 6485–6499. [Google Scholar] [CrossRef]
- Yao, Y.; Liang, S.; Xie, X.; Cheng, J.; Jia, K.; Li, Y.; Liu, R. Estimation of the terrestrial water budget over northern China by merging multiple datasets. J. Hydrol. 2014, 519, 50–68. [Google Scholar] [CrossRef]
- Xu, S.; Yu, Z.; Yang, C.; Ji, X.; Zhang, K. Trends in evapotranspiration and their responses to climate change and vegetation greening over the upper reaches of the Yellow River Basin. Agric. For. Meteorol. 2018, 263, 118–129. [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] [PubMed]
- Liu, Q.; Yang, Z. Quantitative estimation of the impact of climate change on actual evapotranspiration in the Yellow River Basin, China. J. Hydrol. 2010, 395, 226–234. [Google Scholar] [CrossRef]
- Villarreal, S.; Vargas, R.; Yepez, E.A.; Acosta, J.S.; Castro, A.; Escoto-Rodriguez, M.; Lopez, E.; Martínez-Osuna, J.; Rodriguez, J.C.; Smith, S.V.; et al. Contrasting precipitation seasonality influences evapotranspiration dynamics in water-limited shrublands. J. Geophys. Res. Biogeosci. 2016, 121, 494–508. [Google Scholar] [CrossRef] [Green Version]
- Feng, K.; Siu, Y.L.; Guan, D.; Hubacek, K. Assessing regional virtual water flows and water footprints in the Yellow River Basin, China: A consumption based approach. Appl. Geogr. 2012, 32, 691–701. [Google Scholar] [CrossRef]
- Heitman, J.L.; Horton, R.; Sauer, T.J.; DeSutter, T.M. Sensible heat observations reveal soil-water evaporation dynamics. J. Hydrometeorol. 2008, 9, 165–171. [Google Scholar] [CrossRef] [Green Version]
- Cai, X. Water stress, water transfer and social equity in Northern China—Implications for policy reforms. J. Environ. Manag. 2008, 87, 14–25. [Google Scholar] [CrossRef]
- Deng, C.; Zhang, B.; Cheng, L.; Hu, L.; Chen, F. Vegetation dynamics and their effects on surface water-energy balance over the Three-North Region of China. Agric. For. Meteorol. 2019, 275, 79–90. [Google Scholar] [CrossRef]
- Williams, I.N.; Torn, M.S. Vegetation controls on surface heat flux partitioning, and land-atmosphere coupling. Geophys. Res. Lett. 2015, 42, 9416–9424. [Google Scholar] [CrossRef]
- He, Z.; Jia, G.; Liu, Z.; Zhang, Z.; Yu, X.; Xiao, P. Field studies on the influence of rainfall intensity, vegetation cover and slope length on soil moisture infiltration on typical watersheds of the Loess Plateau, China. Hydrol. Processes 2020, 34, 4904–4919. [Google Scholar] [CrossRef]
- Piao, S.; Fang, J.; Zhou, L.; Ciais, P.; Zhu, B. Variations in satellite-derived phenology in China’s temperate vegetation. Glob. Chang. Biol. 2006, 12, 672–685. [Google Scholar] [CrossRef]
- Wu, X.; Liu, H. Consistent shifts in spring vegetation green-up date across temperate biomes in China, 1982–2006. Glob. Chang. Biol. 2013, 19, 870–880. [Google Scholar] [CrossRef]
- Wei, W.; Feng, X.; Yang, L.; Chen, L.; Feng, T.; Chen, D. The effects of terracing and vegetation on soil moisture retention in a dry hilly catchment in China. Sci. Total Environ. 2019, 647, 1323–1332. [Google Scholar] [CrossRef] [PubMed]
- Zhang, H.; Wei, W.; Chen, L.; Wang, L. Effects of terracing on soil water and canopy transpiration of Pinus tabulaeformis in the Loess Plateau of China. Ecol. Eng. 2017, 102, 557–564. [Google Scholar] [CrossRef] [Green Version]
- Cao, W.; Sheng, Y.; Wu, J.; Peng, E. Differential response to rainfall of soil moisture infiltration in permafrost and seasonally frozen ground in Kangqiong small basin on the Qinghai-Tibet Plateau. Hydrol. Sci. J. 2021, 66, 525–543. [Google Scholar] [CrossRef]
- Zhang, B.; He, C.; Burnham, M.; Zhang, L. Evaluating the coupling effects of climate aridity and vegetation restoration on soil erosion over the Loess Plateau in China. Sci. Total Environ. 2016, 539, 436–449. [Google Scholar] [CrossRef] [PubMed]
- Yan, W.; Deng, L.; Zhong, Y.; Shangguan, Z. The characters of dry soil layer on the Loess Plateau in China and their influencing factors. PLoS ONE 2015, 10, e0134902. [Google Scholar]
- Blazquez, A.; Meyssignac, B.; Lemoine, J.; Berthier, E.; Ribes, A.; Cazenave, A. Exploring the uncertainty in GRACE estimates of the mass redistributions at the Earth surface: Implications for the global water and sea level budgets. Geophys. J. Int. 2018, 215, 415–430. [Google Scholar] [CrossRef]
Impacts of Influencing Factors | PCPN | Temp | SD | VPD | WS | LAI | Resi. | |
---|---|---|---|---|---|---|---|---|
Increasing role on ET trend | The amount of impact(mm/yr) | 0.68 | 0.64 | 0.19 | 0.48 | 0.38 | 2.77 | 1.82 |
The percentage of the area(%) | 75 | 50 | 40 | 52 | 40 | 90 | 87 | |
Decreasing role on ET trend | The amount of impact (mm/yr) | –0.23 | –1 | –0.36 | –0.41 | –0.55 | –0.35 | –1.87 |
The percentage of the area(%) | 25 | 50 | 60 | 48 | 60 | 10 | 13 |
“Two-Step”-“with Trend” (mm/yr) | |||||||
---|---|---|---|---|---|---|---|
Subregion | PCPN | Temp | SD | VPD | WS | LAI | Resi. |
Source area | 0.070 | 0.222 | –0.086 | –0.051 | 0.028 | –0.006 | –0.177 |
Tangnaihai—Qingtongxia | 0.158 | –1.263 | –0.202 | –0.407 | –0.243 | 0.092 | 1.765 |
Qingtongxia—Toudaoguai | 0.214 | –0.262 | –0.314 | 0.008 | –0.284 | –0.200 | 0.838 |
Toudaoguai—Longmen | 0.170 | 0.009 | 0.022 | –0.039 | –0.023 | –0.747 | 0.408 |
Longmen—Huayuankou | 0.020 | –0.883 | –0.167 | –0.164 | –0.367 | 0.425 | 1.136 |
The upper and middle reaches | –0.174 | –0.521 | –0.143 | –0.144 | –0.211 | –0.018 | 0.863 |
“Two-Step”-“Detrended” (mm/yr) | |||||||
Subregion | PCPN | Temp | SD | VPD | WS | LAI | Resi. |
Source area | 0.024 | 0.199 | –0.016 | –0.009 | 0.122 | –0.046 | –0.273 |
Tangnaihai—Qingtongxia | 0.068 | –0.033 | –0.015 | 0.055 | –0.010 | 1.091 | –1.156 |
Qingtongxia—Toudaoguai | 0.015 | –0.051 | –0.007 | 0.035 | –0.030 | 0.056 | –0.218 |
Toudaoguai—Longmen | 0.078 | 0.270 | 0.044 | –0.018 | 0.037 | –0.692 | –0.018 |
Longmen—Huayuankou | 0.050 | –0.001 | –0.019 | –0.060 | 0.048 | 1.703 | –1.722 |
The upper and middle reaches | 0.038 | 0.058 | –0.004 | –0.008 | 0.021 | 0.616 | –0.822 |
Subregions | Variation Rate of the Water Supply Services (mm/yr) |
---|---|
Upper and middle reaches of the YRB | −1.67 |
Source area | 3.07 |
Tangnaihai–Qingtongxia | −3.47 |
Qingtongxia–Toudaoguai | −1.34 |
Toudaoguai–Longmen | −1.01 |
Longmen–Huayuankou | −3.90 |
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Wang, Z.; Cui, Z.; He, T.; Tang, Q.; Xiao, P.; Zhang, P.; Wang, L. Attributing the Evapotranspiration Trend in the Upper and Middle Reaches of Yellow River Basin Using Global Evapotranspiration Products. Remote Sens. 2022, 14, 175. https://doi.org/10.3390/rs14010175
Wang Z, Cui Z, He T, Tang Q, Xiao P, Zhang P, Wang L. Attributing the Evapotranspiration Trend in the Upper and Middle Reaches of Yellow River Basin Using Global Evapotranspiration Products. Remote Sensing. 2022; 14(1):175. https://doi.org/10.3390/rs14010175
Chicago/Turabian StyleWang, Zhihui, Zepeng Cui, Tian He, Qiuhong Tang, Peiqing Xiao, Pan Zhang, and Lingling Wang. 2022. "Attributing the Evapotranspiration Trend in the Upper and Middle Reaches of Yellow River Basin Using Global Evapotranspiration Products" Remote Sensing 14, no. 1: 175. https://doi.org/10.3390/rs14010175