Response of Ecosystem Carbon–Water Fluxes to Extreme Drought in West Asia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection
2.2.1. Remote Sensing-Based Gridded Dataset of AET and GPP
2.2.2. Standardized Precipitation Evapotranspiration Index (SPEI) Data
2.2.3. Eddy Covariance (EC) Tower-Driven GPP Data
2.3. Data Analysis
2.3.1. Trending, Detrending Analysis and Standardized AET/GPP/WUE Residual Series
2.3.2. Quantification of AET, GPP, and WUE Responses to SPEI and Drought Intensification
3. Results
3.1. Spatial–Temporal Variations of Ecosystems’ AET, GPP, and WUE
3.2. In Situ AET, WUE, and GPP Response to Extreme Drought (Semi-Arid Shrubland)
3.3. Spatial–Temporal Patterns of Response sAET, sGPP, and sWUE to the SPEI
3.4. Divergent Response of sAET/sGPP/sWUE to Drought (DS)
3.5. Divergent Response of sAET/sGPP/sWUE to Drought across Different Climate and Biomes
4. Discussion
4.1. Spatia-Temporal Variability of Carbon-Water Fluxes in the Study Area
4.2. GPP, AET, and WUE and Response to the Drought
4.3. Response of GPP and WUE to Drought across Different Climate and Biomes
4.4. Uncertainties and Limitations
5. Conclusions
- (1)
- The ecosystem GPP is sensitive to drought in semi-arid ecosystems (BSh), and the GPP of croplands and shrub-covered areas recorded the highest positive significant correlations and were more sensitive to SPEI variability. The ecosystem WUE is sensitive to drought in temperate climates (Cf), followed by arid climate patterns (BW), and the WUE of bare soil and shrub-covered areas recorded the highest negative significant correlations and were more sensitive to the SPEI.
- (2)
- The ecosystem GPP and WUE declined significantly during the 2008 extreme drought (D1) in the north of Iraq and northeast of Syria, where the sWUEA of bare soil and shrub-covered areas had the highest positive response to DS years, whereas the sGPPA of croplands, grassland, and tree-covered areas had clear and high negative values with the DS group, with D1 having the highest negative values for these land cover types.
- (3)
- The ecosystem GPP and WUE in the ME are sensitive to drought disturbances and present a contrasting response regionally. The results of our study pointed out that the WUE fluctuation in arid ecosystems is mostly controlled by evaporation. In semi-arid or sub-humid environments, WUE variability is generally controlled by biological activities (i.e., assimilation).
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
- is the daily accumulated value of AET or GPP or WUE for i day and j year over the 6-month window (180 days).
- is the long-term average of the moved-accumulated 6-month values for each day of the year (DoY) over the 9-year period.
References
- IPCC. Summary for Policymakers. In Climate Change 2023: Synthesis Report Contribution of Working Groups, I.; II; III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Lee, L., Romero, J., Eds.; IPCC: Geneva, Switzerland, 2023; pp. 1–34. [Google Scholar] [CrossRef]
- Sun, J.; Bi, S.; Bashir, B.; Ge, Z.; Wu, K.; Alsalman, A.; Ayugi, B.O.; Alsafadi, K. Historical Trends and Characteristics of Meteorological Drought Based on Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index over the Past 70 Years in China (1951–2020). Sustainability 2023, 15, 10875. [Google Scholar] [CrossRef]
- Vicente-Serrano, S.M.; Quiring, S.M.; Peña-Gallardo, M.; Yuan, S.; Domínguez-Castro, F. A review of environmental droughts: Increased risk under global warming? Earth-Sci. Rev. 2020, 201, 102953. [Google Scholar] [CrossRef]
- Anderson, M.C.; Zolin, C.A.; Sentelhas, P.C.; Hain, C.R.; Semmens, K.; Yilmaz, M.T.; Gao, F.; Otkin, J.A.; Tetrault, R. The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts. Remote Sens. Environ. 2016, 174, 82–99. [Google Scholar] [CrossRef]
- Zhang, Y.; Feng, X.; Wang, X.; Fu, B. Characterizing drought in terms of changes in the precipitation–runoff relationship: A case study of the loess plateau, China. Hydrol. Earth Syst. Sci. 2018, 22, 1749–1766. [Google Scholar] [CrossRef]
- Elbeltagi, A.; Kumari, N.; Dharpure, J.K.; Mokhtar, A.; Alsafadi, K.; Kumar, M.; Mehdinejadiani, B.; Ramezani Etedali, H.; Brouziyne, Y.; Towfiqul Islam, A.R.M.; et al. Prediction of Combined Terrestrial Evapotranspiration Index (CTEI) over Large River Basin Based on Machine Learning Approaches. Water 2021, 13, 547. [Google Scholar] [CrossRef]
- Akyuz, D.E.; Bayazit, M.; Onoz, B. Markov chain models for hydrological drought characteristics. J. Hydrometeorol. 2012, 13, 298–309. [Google Scholar] [CrossRef]
- Xu, L.; Chen, N.; Zhang, X.; Chen, Z. An evaluation of statistical, NMME and hybrid models for drought prediction in China. J. Hydrol. 2018, 566, 235–249. [Google Scholar] [CrossRef]
- Mokhtar, A.; Elbeltagi, A.; Maroufpoor, S.; Azad, N.; He, H.; Alsafadi, K.; Gyasi-Agyei, Y.; He, W. Estimation of the Rice Water Footprint Based on Machine Learning Algorithms. Comput. Electron. Agric. 2021, 191, 106501. [Google Scholar] [CrossRef]
- Mokhtar, A.; He, H.; Alsafadi, K.; Mohammed, S.; Ayantobo, O.O.; Elbeltagi, A.; Abdelwahab, O.M.M.; Zhao, H.; Quan, Y.; Abdo, H.G.; et al. Assessment of the Effects of Spatiotemporal Characteristics of Drought on Crop Yields in Southwest China. Int. J. Climatol. 2022, 42, 3056–3075. [Google Scholar] [CrossRef]
- Lelieveld, J.; Hadjinicolaou, P.; Kostopoulou, E.; Chenoweth, J.; El Maayar, M.; Giannakopoulos, C.; Hannides, C.; Lange, M.A.; Tanarhte, M.; Tyrlis, E.; et al. Climate change and impacts in the Eastern Mediterranean and the Middle East. Clim. Chang. 2012, 114, 667–687. [Google Scholar] [CrossRef]
- Chenoweth, J.; Hadjinicolaou, P.; Bruggeman, A.; Lelieveld, J.; Levin, Z.; Lange, M.A.; Xoplaki, E.; Hadjikakou, M. Impact of climate change on the water resources of the eastern Mediterranean and Middle East region: Modeled 21st century changes and implications. Water Resour. Res. 2011, 47, W06506. [Google Scholar] [CrossRef]
- Sadeqi, A.; Irannezhad, M.; Bahmani, S.; Jelodarlu, K.A.; Varandili, S.A.; Pham, Q.B. Long-Term Variability and Trends in Snow Depth and Cover Days Throughout Iranian Mountain Ranges. Water Resour. Res. 2024, 60, e2023WR035411. [Google Scholar] [CrossRef]
- Sowers, J.; Vengosh, A.; Weinthal, E. Climate change, water resources, and the politics of adaptation in the Middle East and North Africa. Clim. Chang. 2011, 104, 599–627. [Google Scholar] [CrossRef]
- Kaniewski, D.; Van Campo, E.; Weiss, H. Drought is a recurring challenge in the Middle East. Proc. Natl. Acad. Sci. USA 2012, 109, 3862–3867. [Google Scholar] [CrossRef]
- Hameed, M.; Ahmadalipour, A.; Moradkhani, H. Drought and food security in the middle east: An analytical framework. Agric. For. Meteorol. 2020, 281, 107816. [Google Scholar] [CrossRef]
- Bozkurt, D.; Sen, O.L. Climate change impacts in the Euphrates-Tigris Basin based on different model and scenario simulations. J. Hydrol. 2013, 480, 49–161. [Google Scholar] [CrossRef]
- Ouled Belgacem, A.; Louhaichi, M. The vulnerability of native rangeland plant species to global climate change in the West Asia and North African regions. Clim. Chang. 2013, 119, 451–463. [Google Scholar] [CrossRef]
- Alsafadi, K.; Al-Ansari, N.; Mokhtar, A.; Mohammed, S.; Elbeltagi, A.; Sammen, S.S.; Bi, S. An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East. Environ. Res. Lett. 2022, 17, 014051. [Google Scholar] [CrossRef]
- Alsafadi, K.; Bi, S.; Bashir, B.; Mohammed, S.; Sammen, S.S.; Alsalman, A.; Srivastava, A.K.; El Kenawy, A. Assessment of carbon productivity trends and their resilience to drought disturbances in the middle east based on multi-decadal space-based datasets. Remote Sens. 2022, 14, 6237. [Google Scholar] [CrossRef]
- Mohammed, S.; Alsafadi, K.; Al-Awadhi, T.; Sherief, Y.; Harsanyie, E.; El Kenawy, A.M. Space and time variability of meteorological drought in Syria. Acta Geophys. 2020, 68, 1877–1898. [Google Scholar] [CrossRef]
- Deng, L.; Shangguan, Z.-P.; Sweeney, S. “Grain for green” driven land use change and carbon sequestration on the Loess Plateau, China. Sci. Rep. 2014, 4, 7039. [Google Scholar] [CrossRef] [PubMed]
- Solh, M.; van Ginkel, M. Drought preparedness and drought mitigation in the developing world׳ s drylands. Weather. Clim. Extrem. 2014, 3, 62–66. [Google Scholar] [CrossRef]
- Li, M.; Yu, H.; Meng, B.; Sun, Y.; Zhang, J.; Zhang, H.; Wu, J.; Yi, S. Drought reduces the effectiveness of ecological projects: Perspectives from the inter-annual variability of vegetation index. Ecol. Indic. 2021, 130, 108158. [Google Scholar] [CrossRef]
- Du, X.; Zhao, X.; Zhou, T.; Jiang, B.; Xu, P.; Wu, D.; Tang, B. Effects of climate factors and human activities on the ecosystem water use efficiency throughout Northern China. Remote Sens. 2019, 11, 2766. [Google Scholar] [CrossRef]
- Rezende, L.F.; de Castro, A.A.; Von Randow, C.; Ruscica, R.; Sakschewski, B.; Papastefanou, P.; Viovy, N.; Thonicke, K.; Sörensson, A.; Rammig, A.; et al. Impacts of land use change and atmospheric CO2 on gross primary productivity (GPP), evaporation, and climate in southern Amazon. J. Geophys. Res. Atmos. 2022, 127, e2021JD034608. [Google Scholar] [CrossRef]
- Ciais, P.; Reichstein, M.; Viovy, N.; Granier, A.; Ogée, J.; Allard, V.; Aubinet, M.; Buchmann, N.; Bernhofer, C.; Carrara, A.; et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 2005, 437, 529–533. [Google Scholar] [CrossRef]
- Schwalm, C.R.; Williams, C.A.; Schaefer, K.; Baldocchi, D.; Black, T.A.; Goldstein, A.H.; Law, B.E.; Oechel, W.C.; Paw, U.K.T.; Scott, R.L. Reduction in carbon uptake during turn of the century drought in western North America. Nat. Geosci. 2012, 5, 551–556. [Google Scholar] [CrossRef]
- Mokhtar, A.; He, H.; Alsafadi, K.; Mohammed, S.; He, W.; Li, Y.; Zhao, H.; Abdullahi, N.M.; Gyasi-Agyei, Y. Ecosystem Water Use Efficiency Response to Drought Over Southwest China. Ecohydrology 2022, 15, e2317. [Google Scholar] [CrossRef]
- Farquhar, G.D.; Hubick, K.T.; Condon, A.G.; Richards, R.A. Carbon Isotope Fractionation and Plant Water-Use Efficiency. In Stable Isotopes in Ecological Research; Springer: New York, NY, USA, 1989; pp. 21–40. [Google Scholar] [CrossRef]
- Ito, A.; Inatomi, M. Water-use efficiency of the terrestrial biosphere: A model analysis focusing on interactions between the global carbon and water cycles. J. Hydrometeorol. 2012, 13, 681–694. [Google Scholar] [CrossRef]
- Keenan, T.F.; Hollinger, D.Y.; Bohrer, G.; Dragoni, D.; Munger, J.W.; Schmid, H.P.; Richardson, A.D. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 2013, 499, 324–327. [Google Scholar] [CrossRef]
- Peñuelas, J.; Canadell, J.G.; Ogaya, R. Increased water-use efficiency during the 20th century did not translate into enhanced tree growth. Glob. Ecol. Biogeogr. 2011, 20, 597–608. [Google Scholar] [CrossRef]
- Tian, H.; Chen, G.; Liu, M.; Zhang, C.; Sun, G.; Lu, C.; Xu, X.; Ren, W.; Pan, S.; Chappelka, A. Model estimates of net primary productivity, evapotranspiration, and water use efficiency in the terrestrial ecosystems of the southern United States during 1895–2007. Ecol. Manag. 2010, 259, 1311–1327. [Google Scholar] [CrossRef]
- Dong, G.; Zhao, F.; Chen, J.; Qu, L.; Jiang, S.; Chen, J.; Shao, C. Divergent Forcing of Water Use Efficiency from Aridity in Two Meadows of the Mongolian Plateau. J. Hydrol. 2021, 593. [Google Scholar] [CrossRef]
- Niu, S.; Xing, X.; Zhang, Z.; Xia, J.; Zhou, X.; Song, B.; Li, L.; Wan, S. Water-use efficiency in response to climate change: From leaf to ecosystem in a temperate steppe. Glob. Chang. Biol. 2011, 17, 1073–1082. [Google Scholar] [CrossRef]
- Yang, Y.; Guan, H.; Batelaan, O.; McVicar, T.R.; Long, D.; Piao, S.; Liang, W.; Liu, B.; Jin, Z.; Simmons, C.T. Contrasting responses of water use efficiency to drought across global terrestrial ecosystems. Sci. Rep. 2016, 6, 23284. [Google Scholar] [CrossRef] [PubMed]
- Liu, Y.; Xiao, J.; Ju, W.; Zhou, Y.; Wang, S.; Wu, X. Water use efficiency of China’s terrestrial ecosystems and responses to drought. Sci. Rep. 2015, 5, 13799. [Google Scholar] [CrossRef] [PubMed]
- Huang, L.; He, B.; Han, L.; Liu, J.; Wang, H.; Chen, Z. A global examination of the response of ecosystem water-use efficiency to drought based on MODIS data. Sci. Total Environ. 2017, 601–602, 1097–1107. [Google Scholar] [CrossRef]
- Kayiranga, A.; Chen, B.; Trisurat, Y.; Ndayisaba, F.; Sun, S.; Tuankrua, V.; Wang, F.; Karamage, F.; Measho, S.; Nthangeni, W.; et al. Water Use Efficiency-Based Multiscale Assessment of Ecohydrological Resilience to Ecosystem Shifts Over the Continent of Africa During 1992–2015. J. Geophys. Res. Biogeosci. 2020, 125, e2020JG005749. [Google Scholar] [CrossRef]
- Hao, X.; Ma, H.; Hua, D.; Qin, J.; Zhang, Y. Response of ecosystem water use efficiency to climate change in the Tianshan Mountains, Central Asia. Environ. Monit. Assess. 2019, 191. [Google Scholar] [CrossRef]
- Peters, W.; Van Der Velde, I.R.; Van Schaik, E.; Miller, J.B.; Ciais, P.; Duarte, H.F.; Schaefer, K. Increased water-use efficiency and reduced CO2 uptake by plants during droughts at a continental scale. Nat. Geosci. 2018, 11, 744–748. [Google Scholar] [CrossRef]
- Ahmadi, B.; Ahmadalipour, A.; Tootle, G.; Moradkhani, H. Remote sensing of water use efficiency and terrestrial drought recovery across the Contiguous United States. Remote Sens. 2019, 11, 731. [Google Scholar] [CrossRef]
- Boese, S.; Jung, M.; Carvalhais, N.; Teuling, A.J.; Reichstein, M. Carbon–water flux coupling under progressive drought. Biogeosciences 2019, 16, 2557–2572. [Google Scholar] [CrossRef]
- Wang, M.; Ding, Z.; Wu, C.; Song, L.; Ma, M.; Yu, P.; Tang, X. Divergent responses of ecosystem water-use efficiency to extreme seasonal droughts in Southwest China. Sci. Total Environ. 2020, 760, 143427. [Google Scholar] [CrossRef]
- Huang, M.; Zhai, P.; Piao, S. Divergent responses of ecosystem water use efficiency to drought timing over Northern Eurasia. Environ. Res. Lett. 2021, 16, 045016. [Google Scholar] [CrossRef]
- Ma, D.; Yu, Y.; Hui, Y.; Kannenberg, S.A. Compensatory response of ecosystem carbon-water cycling following severe drought in Southwestern China. Sci. Total Environ. 2023, 899, 165718. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Z.; Zhang, L.; Xu, H.; Creed, I.F.; Blanco, J.A.; Wei, X.; Sun, G.; Asbjornsen, H.; Bishop, K. Forest water-use efficiency: Effects of climate change and management on the coupling of carbon and water processes. For. Ecol. Manag. 2023, 534, 120853. [Google Scholar] [CrossRef]
- Below, R.; Grover-Kopec, E.; Dilley, M. Documenting drought-related Disasters: A global reassessment. J. Environ. Dev. 2007, 16, 328–344. [Google Scholar] [CrossRef]
- Barlow, M.; Zaitchik, B.; Paz, S.; Black, E.; Evans, J.; Hoell, A. A review of drought in the Middle East and southwest Asia. J. Clim. 2016, 29, 8547–8574. [Google Scholar] [CrossRef]
- FAO. FAO Global Land Cover (GLC-SHARE) Beta-Release 1.0 Database; Food and Agriculture Organization of the United Nations: Rome, Italy, 2014; pp. 1–39. [Google Scholar]
- Beck, H.E.; Zimmermann, N.E.; McVicar, T.R.; Vergopolan, N.; Berg, A.; Wood, E.F. Present and future köppen-geiger climate classification maps at 1-km resolution. Sci. Data 2018, 5, 180214. [Google Scholar] [CrossRef]
- Miralles, D.G.; Holmes, T.R.H.; De Jeu, R.A.M.; Gash, J.H.; Meesters, A.G.C.A.; Dolman, A.J. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 2011, 15, 453–469. [Google Scholar] [CrossRef]
- 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]
- Martens, B.; de Jeu, R.A.M.; Verhoest, N.E.C.; Schuurmans, H.; Kleijer, J.; Miralles, D.G. Towards estimating land evaporation at field scales using GLEAM. Remote Sens. 2018, 10, 1720. [Google Scholar] [CrossRef]
- Michel, D.; Jiménez, C.; Miralles, D.G.; Jung, M.; Hirschi, M.; Ershadi, A.; Martens, B.; McCabe, M.F.; Fisher, J.B.; Mu, Q.; et al. The WACMOS-ET project—Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms. Hydrol. Earth Syst. Sci. 2016, 20, 803–822. [Google Scholar] [CrossRef]
- Miralles, D.G.; Jiménez, C.; Jung, M.; Michel, D.; Ershadi, A.; Mccabe, M.F.; Hirschi, M.; Martens, B.; Dolman, A.J.; Fisher, J.B.; et al. The WACMOS-ET project—Part 2: Evaluation of global terrestrial evaporation data sets. Hydrol. Earth Syst. Sci. 2016, 20, 823–842. [Google Scholar] [CrossRef]
- Yuan, W.; Liu, S.; Zhou, G.; Zhou, G.; Tieszen, L.L.; Baldocchi, D.; Bernhofer, C.; Gholz, H.; Goldstein, A.H.; Goulden, M.L.; et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agric. For. Meteorol. 2007, 143, 189–207. [Google Scholar] [CrossRef]
- Zheng, Y.; Shen, R.; Wang, Y.; Li, X.; Liu, S.; Liang, S.; Chen, J.M.; Ju, W.; Zhang, L.; Yuan, W. Improved estimate of global gross primary production for reproducing its long-Term variation, 1982–2017. Earth Syst. Sci. Data 2020, 12, 2725–2746. [Google Scholar] [CrossRef]
- Yuan, W.; Liu, S.; Yu, G.; Bonnefond, J.M.; Chen, J.; Davis, K.; Desai, A.R.; Goldstein, A.H.; Gianelle, D.; Rossi, F.; et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sens. Environ. 2010, 114, 1416–1431. [Google Scholar] [CrossRef]
- Yuan, W.; Cai, W.; Xia, J.; Chen, J.; Liu, S.; Dong, W.; Merbold, L.; Law, B.; Arain, A.; Beringer, J.; et al. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agric. For. Meteorol. 2014, 192–193, 108–120. [Google Scholar] [CrossRef]
- Yuan, W.; Chen, Y.; Xia, J.; Dong, W.; Magliulo, V.; Moors, E.; Olesen, J.E.; Zhang, H. Estimating crop yield using a satellite-based light use efficiency model. Ecol. Indic. 2015, 60, 702–709. [Google Scholar] [CrossRef]
- Li, X.; Liang, S.; Yu, G.; Yuan, W.; Cheng, X.; Xia, J.; Zhao, T.; Feng, J.; Ma, Z.; Ma, M.; et al. Estimation of gross primary production over the terrestrial ecosystems in China. Ecol. Modell. 2013, 261–262, 80–92. [Google Scholar] [CrossRef]
- Jia, W.; Liu, M.; Wang, D.; He, H.; Shi, P.; Li, Y.; Wang, Y. Uncertainty in simulating regional gross primary productivity from satellite-based models over northern China grassland. Ecol. Indic. 2018, 88, 134–143. [Google Scholar] [CrossRef]
- Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I.; Angulo, M.; El Kenawy, A. A new global 0.5° gridded dataset (1901–2006) of a multiscalar drought index: Comparison with current drought index datasets based on the palmer drought severity index. J. Hydrometeorol. 2010, 11, 1033–1043. [Google Scholar] [CrossRef]
- Beguería, S.; Vicente-Serrano, S.M.; Angulo-Martínez, M. A multiscalar global drought dataset: The SPEI base: A new gridded product for the analysis of drought variability and impacts. Bull. Am. Meteorol. Soc. 2010, 91, 1351–1356. [Google Scholar] [CrossRef]
- Beguería, S.; Vicente-Serrano, S.M.; Reig, F.; Latorre, B. Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 2014, 34, 3001–3023. [Google Scholar] [CrossRef]
- Harris, I.; Osborn, T.J.; Jones, P.; Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 2020, 7, 109. [Google Scholar] [CrossRef] [PubMed]
- Baldocchi, D. Measuring fluxes of trace gases and energy between ecosystems and the atmosphere—The state and future of the eddy covariance method. Glob. Chang. Biol. 2014, 20, 3600–3609. [Google Scholar] [CrossRef] [PubMed]
- Grünzweig, J.M.; Gelfand, I.; Fried, Y.; Yakir, D. Biogeochemical factors contributing to enhanced carbon storage following afforestation of a semi-arid shrubland. Biogeosciences 2007, 4, 891–904. [Google Scholar] [CrossRef]
- Rotenberg, E.; Yakir, D. Distinct patterns of changes in surface energy budget associated with forestation in the semiarid region. Glob. Chang. Biol. 2011, 17, 1536–1548. [Google Scholar] [CrossRef]
- Rotenberg, E.; Qubaja, R.; Preisler, Y.; Yakir, D.; Tatarinov, F. Carbon and energy balance of dry mediterranean pine forests: A case study. In Pines and Their Mixed Forest Ecosystems in the Mediterranean Basin; Ne’eman, G., Osem, Y., Eds.; Managing Forest Ecosystems; Springer: Cham, Switzerland, 2021; Volume 38. [Google Scholar]
- Hu, Z.; Yu, G.; Fu, Y.; Sun, X.; Li, Y.; Shi, P.; Wang, Y.; Zheng, Z. Effects of vegetation control on ecosystem water use efficiency within and among four grassland ecosystems in China. Glob. Chang. Biol. 2008, 14, 1609–1619. [Google Scholar] [CrossRef]
- Beer, C.; Ciais, P.; Reichstein, M.; Baldocchi, D.; Law, B.E.; Papale, D.; Soussana, J.-F.; Ammann, C.; Buchmann, N.; Frank, D.; et al. Temporal and among-site variability of inherent water use efficiency at the ecosystem level. Glob. Biogeochem. Cycles 2009, 23, GB2018. [Google Scholar] [CrossRef]
- Huang, M.; Piao, S.; Sun, Y.; Ciais, P.; Cheng, L.; Mao, J.; Poulter, B.; Shi, X.; Zeng, Z.; Wang, Y. Change in terrestrial ecosystem water-use efficiency over the last three decades. Glob. Chang. Biol. 2015, 21, 2366–2378. [Google Scholar] [CrossRef] [PubMed]
- Sen, P.K. Estimates of the regression coefficient based on Kendall’s Tau. J. Am. Stat. Assoc. 1968, 63, 1379–1389. [Google Scholar] [CrossRef]
- Theil, H. A rank-invariant method of linear and polynomial regression analysis. In Henri Theil’s Contributions to Economics and Econometrics. Advanced Studies in Theoretical and Applied Econometrics; Raj, B., Koerts, J., Eds.; Springer: Dordrecht, The Netherlands, 1992; Volume 23, pp. 345–381. [Google Scholar]
- Mann, H.B. Mann Nonparametric Test against Trend. Econometrica 1945, 13, 245–259. [Google Scholar] [CrossRef]
- Kendall, M.G. Rank Correlation Methods, 4th ed.; Charles Griffin: London, UK, 1975. [Google Scholar]
- Hamed, K.H.; Rao, A.R. A Modified Mann-Kendall Trend Test for Autocorrelated Data. J. Hydrol. 1998, 204, 182–196. [Google Scholar] [CrossRef]
- Qu, S.; Wang, L.; Lin, A.; Yu, D.; Yuan, M.; Li, C. Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the Yangtze River Basin, China. Ecol. Indic. 2020, 108, 105724. [Google Scholar] [CrossRef]
- Jiang, H.; Xu, X.; Guan, M.; Wang, L.; Huang, Y.; Jiang, Y. Determining the contributions of climate change and human activities to vegetation dynamics in agro-pastural transitional zone of northern China from 2000 to 2015. Sci. Total Environ. 2020, 718, 134871. [Google Scholar] [CrossRef]
- Ge, W.; Deng, L.; Wang, F.; Han, J. Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016. Sci. Total Environ. 2021, 773, 145648. [Google Scholar] [CrossRef] [PubMed]
- Jiang, Y.; Guo, J.; Peng, Q.; Guan, Y.; Zhang, Y.; Zhang, R. The effects of climate factors and human activities on net primary productivity in Xinjiang. Int. J. Biometeorol. 2020, 64, 765–777. [Google Scholar] [CrossRef] [PubMed]
- Li, Y.; Yao, N.; Chau, H.W. Influences of removing linear and nonlinear trends from climatic variables on temporal variations of annual reference crop evapotranspiration in Xinjiang, China. Sci. Total Environ. 2017, 592, 680–692. [Google Scholar] [CrossRef]
- Liu, H.; Jia, J.; Lin, Z.; Wang, Z.; Gong, H. Relationship between net primary production and climate change in different vegetation zones based on EEMD detrending—A case study of Northwest China. Ecol. Indic. 2021, 122, 107276. [Google Scholar] [CrossRef]
- Zhang, L.; Ren, X.; Wang, J.; He, H.; Wang, S.; Wang, M.; Piao, S.; Yan, H.; Ju, W.; Gu, F. Interannual variability of terrestrial net ecosystem productivity over China: Regional contributions and climate attribution. Environ. Res. Lett. 2019, 14, 014003. [Google Scholar] [CrossRef]
- Alsafadi, K.; Mohammed, S.A.; Ayugi, B.; Sharaf, M.; Harsányi, E. Spatial–Temporal Evolution of Drought Characteristics over Hungary between 1961 and 2010. Pure Appl. Geophys. 2020, 177, 3961–3978. [Google Scholar] [CrossRef]
- Mokhtar, A.; Jalali, M.; He, H.; Al-Ansari, N.; Elbeltagi, A.; Alsafadi, K.; Abdo, H.G.; Sammen, S.S.; Gyasi-Agyei, Y.; Rodrigo-Comino, J. Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms. IEEE Access 2021, 9, 65503–65523. [Google Scholar] [CrossRef]
- Lloyd-Hughes, B. A spatio-temporal structure-based approach to drought characterisation. Int. J. Climatol. 2012, 32, 406–418. [Google Scholar] [CrossRef]
- Knapp, P.A.; Soulé, P.T. Increasing Water-Use Efficiency and Age-Specific Growth Responses of Old-Growth Ponderosa Pine Trees in the Northern Rockies. Glob. Chang. Biol. 2011, 17, 631–641. [Google Scholar] [CrossRef]
- Wang, Q.; Yang, Y.; Liu, Y.; Tong, L.; Zhang, Q.P.; Li, J. Assessing the impacts of drought on grassland net primary production at the global scale. Sci. Rep. 2019, 9, 14041. [Google Scholar] [CrossRef]
- Fu, Z.; Ciais, P.; Bastos, A.; Stoy, P.C.; Yang, H.; Green, J.K.; Wang, B.; Yu, K.; Huang, Y.; Knohl, A.; et al. Sensitivity of gross primary productivity to climatic drivers during the summer drought of 2018 in Europe. Philos. Trans. R. Soc. B 2020, 375, 20190747. [Google Scholar] [CrossRef]
- Zhao, J.; Xu, T.; Xiao, J.; Liu, S.; Mao, K.; Song, L.; Yao, Y.; He, X.; Feng, H. Responses of water use efficiency to drought in southwest China. Remote Sens. 2020, 12, 199. [Google Scholar] [CrossRef]
- Hao, Y.; Choi, M. Recovery of Ecosystem Carbon and Water Fluxes after Drought in China. J. Hydrol. 2023, 622, 129766. [Google Scholar] [CrossRef]
- Liu, Y.; Ding, Z.; Chen, Y.; Yan, F.; Yu, P.; Man, W.; Liu, M.; Li, H.; Tang, X. Restored vegetation is more resistant to extreme drought events than natural vegetation in Southwest China. Sci. Total Environ. 2023, 866, 161250. [Google Scholar] [CrossRef]
- Cooley, S.S.; Fisher, J.B.; Goldsmith, G.R. Convergence in water use efficiency within plant functional types across contrasting climates. Nat. Plants 2022, 8, 341–345. [Google Scholar] [CrossRef] [PubMed]
- Sun, S.; Song, Z.; Wu, X.; Wang, T.; Wu, Y.; Du, W.; Che, T.; Huang, C.; Zhang, X.; Ping, B.; et al. Spatio-temporal variations in water use efficiency and its drivers in China over the last three decades. Ecol. Indic. 2018, 94, 292–304. [Google Scholar] [CrossRef]
- Guo, L.; Sun, F.; Liu, W.; Zhang, Y.; Wang, H.; Cui, H.; Wang, H.; Zhang, J.; Du, B. Response of ecosystem water use efficiency to drought over China during 1982-2015: Spatiotemporal variability and resilience. Forests 2019, 10, 598. [Google Scholar] [CrossRef]
- Poppe Terán, C.; Naz, B.S.; Graf, A.; Qu, Y.; Hendricks Franssen, H.J.; Baatz, R.; Ciais, P.; Vereecken, H. Rising water-use efficiency in European grasslands is driven by increased primary production. Commun. Earth Environ. 2023, 4, 95. [Google Scholar] [CrossRef]
- Chen, L.; Keski-Saari, S.; Kontunen-Soppela, S.; Zhu, X.; Zhou, X.; Hänninen, H.; Pumpanen, J.; Mola-Yudego, B.; Wu, D.; Berninger, F. Immediate and carry-over effects of late-spring frost and growing season drought on forest gross primary productivity capacity in the northern hemisphere. Glob. Chang. Biol. 2023, 29, 3924–3940. [Google Scholar] [CrossRef] [PubMed]
- Liu, Y.; Zhou, Y.; Ju, W.; Wang, S.; Wu, X.; He, M.; Zhu, G. Impacts of droughts on carbon sequestration by China’s terrestrial ecosystems from 2000 to 2011. Biogeosciences 2014, 11, 2583. [Google Scholar] [CrossRef]
- Kang, M.; Hao, Y.; Choi, M. The effects of flash drought on the terrestrial ecosystem in Korea. J. Hydrol. 2023, 624, 129874. [Google Scholar] [CrossRef]
- Reichstein, M.; Ciais, P.; Papale, D.; Valentini, R.; Running, S.; Viovy, N.; Cramer, W.; Granier, A.; Ogée, J.; Allard, V.; et al. Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: A joint flux tower, remote sensing and modelling analysis. Glob. Chang. Biol. 2007, 13, 634–651. [Google Scholar] [CrossRef]
- He, S.; Zhang, Y.; Ma, N.; Tian, J.; Kong, D.; Liu, C. A daily and 500 m coupled evapotranspiration and gross primary production product across China during 2000–2020. Earth Syst. Sci. Data 2022, 14, 5463–5488. [Google Scholar] [CrossRef]
- Zhang, Y.; Wang, Y.; Zhang, M.; Ma, K. Climate change threats to protected plants of China: An evaluation based on species distribution modeling. Chin. Sci. Bull. 2014, 59, 4652–4659. [Google Scholar] [CrossRef]
- Tang, X.; Li, H.; Ma, M.; Yao, L.; Peichl, M.; Arain, A.; Xu, X.; Goulden, M. How do disturbances and climate effects on carbon and water fluxes differ between multi-aged and even-aged coniferous forests? Sci. Total Environ. 2017, 599–600, 1583–1597. [Google Scholar] [CrossRef] [PubMed]
- Law, B.E.; Falge, E.; Gu, L.; Baldocchi, D.D.; Bakwin, P.; Berbigier, P.; Davis, K.; Dolman, A.J.; Falk, M.; Fuentes, J.D.; et al. Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation. Agric. For. Meteorol. 2002, 113, 97–120. [Google Scholar] [CrossRef]
- Ponton, S.; Flanagan, L.B.; Alstad, K.P.; Johnson, B.G.; Morgenstern, K.; Kljun, N.; Black, T.A.; Barr, A.G. Comparison of ecosystem water-use efficiency among Douglas-fir forest, aspen forest and grassland using eddy covariance and carbon isotope techniques. Glob. Chang. Biol. 2006, 12, 294–310. [Google Scholar] [CrossRef]
- Zhao, J.; Feng, H.; Xu, T.; Xiao, J.; Guerrieri, R.; Liu, S.; Wu, X.; He, X.; He, X. Physiological and environmental control on ecosystem water use efficiency in response to drought across the northern hemisphere. Sci. Total Environ. 2021, 758, 143599. [Google Scholar] [CrossRef] [PubMed]
- Zhou, S.; Yu, B.; Huang, Y.; Wang, G. The Effect of vapor pressure deficit on water use efficiency at the subdaily time scale. Geophys. Res. Lett. 2014, 14, 41–5005. [Google Scholar] [CrossRef]
- Zou, J.; Ding, J.; Welp, M.; Huang, S.; Liu, B. Assessing the response of ecosystem water use efficiency to drought during and after drought events across central Asia. Sensors 2020, 20, 581. [Google Scholar] [CrossRef] [PubMed]
- Yu, Z.; Wang, J.; Liu, S.; Rentch, J.S.; Sun, P.; Lu, C. Global gross primary productivity and water use efficiency changes under drought stress. Environ. Res. Lett. 2017, 12, 014016. [Google Scholar] [CrossRef]
- Gao, D.; Wang, S.; Wang, L.; Li, Z.; Pan, N.; Liu, Y.; Fu, B. Enhanced coupling of light use efficiency and water use efficiency in arid and semi-arid environments. Ecohydrology 2022, 15, e2391. [Google Scholar] [CrossRef]
- Nie, C.; Huang, Y.; Zhang, S.; Yang, Y.; Zhou, S.; Lin, C.; Wang, G. Effects of soil water content on forest ecosystem water use efficiency through changes in transpiration/evapotranspiration ratio. Agric. For. Meteorol. 2021, 308, 108605. [Google Scholar] [CrossRef]
- Fathi-Taperasht, A.; Shafizadeh-Moghadam, H.; Kouchakzadeh, M. MODIS-based evaluation of agricultural drought, water use efficiency and post-drought in Iran; considering the influence of heterogeneous climatic regions. J. Clean. Prod. 2022, 374, 133836. [Google Scholar] [CrossRef]
- Chen, Y.; Li, J.; Ju, W.; Ruan, H.; Qin, Z.; Huang, Y.; Propastin, P. Quantitative assessments of water-use efficiency in temperate Eurasian steppe along an aridity gradient. PLoS ONE 2017, 12, e0179875. [Google Scholar] [CrossRef] [PubMed]
- Lu, X.; Zhuang, Q. Evaluating evapotranspiration and water-use efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data. Remote Sens. Environ. 2010, 114, 1924–1939. [Google Scholar] [CrossRef]
- Xu, H.-j.; Wang, X.-p.; Zhao, C.-y.; Zhang, X.-x. Responses of ecosystem water use efficiency to meteorological drought under different biomes and drought magnitudes in northern China. Agric. For. Meteorol. 2019, 278, 107660. [Google Scholar] [CrossRef]
- Malone, S.L.; Tulbure, M.G.; Pérez-Luque, A.J.; Assal, T.J.; Bremer, L.L.; Drucker, D.P.; Hillis, V.; Varela, S.; Goulden, M.L. Drought resistance across California ecosystems: Evaluating changes in carbon dynamics using satellite imagery. Ecosphere 2016, 27, e01561. [Google Scholar] [CrossRef]
- Zhou, L.; Wang, S.; Chi, Y.; Ju, W.; Huang, K.; Mickler, R.A.; Yu, Q. Changes in the carbon and water fluxes of subtropical forest ecosystems in south-western china related to drought. Water 2018, 10, 821. [Google Scholar] [CrossRef]
- Zhang, M.; Yuan, X. Rapid reduction in ecosystem productivity caused by flash droughts based on decade-long FLUXNET observations. Hydrol. Earth Syst. Sci. 2020, 24, 5579–5593. [Google Scholar] [CrossRef]
- Elfarkh, J.; Johansen, K.; El Hajj, M.M.; Almashharawi, S.K.; McCabe, M.F. Evapotranspiration, gross primary productivity and water use efficiency over a high-density olive orchard using ground and satellite based data. Agric. Water Manag. 2023, 287, 108423. [Google Scholar] [CrossRef]
- Chen, Y.; Feng, X.; Fu, B.; Wu, X.; Gao, Z. Improved global maps of the optimum growth temperature, maximum light use efficiency, and gross primary production for vegetation. J. Geophys. Res. Biogeosci. 2021, 126, e2020JG005651. [Google Scholar] [CrossRef]
- Zhou, S.; Yu, B.; Schwalm, C.R.; Ciais, P.; Zhang, Y.; Fisher, J.B.; Michalak, A.M.; Wang, W.; Poulter, B.; Huntzinger, D.N.; et al. Response of Water Use Efficiency to Global Environmental Change Based on Output from Terrestrial Biosphere Models. Glob. Biogeochem. Cycles 2017, 31, 1639–1655. [Google Scholar] [CrossRef]
- Zhang, M.; Yuan, X.; Otkin, J.A. Remote sensing of the impact of flash drought events on terrestrial carbon dynamics over China. Carbon. Balance Manag. 2020, 15, 20. [Google Scholar] [CrossRef]
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. |
© 2024 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
Alsafadi, K.; Bashir, B.; Mohammed, S.; Abdo, H.G.; Mokhtar, A.; Alsalman, A.; Cao, W. Response of Ecosystem Carbon–Water Fluxes to Extreme Drought in West Asia. Remote Sens. 2024, 16, 1179. https://doi.org/10.3390/rs16071179
Alsafadi K, Bashir B, Mohammed S, Abdo HG, Mokhtar A, Alsalman A, Cao W. Response of Ecosystem Carbon–Water Fluxes to Extreme Drought in West Asia. Remote Sensing. 2024; 16(7):1179. https://doi.org/10.3390/rs16071179
Chicago/Turabian StyleAlsafadi, Karam, Bashar Bashir, Safwan Mohammed, Hazem Ghassan Abdo, Ali Mokhtar, Abdullah Alsalman, and Wenzhi Cao. 2024. "Response of Ecosystem Carbon–Water Fluxes to Extreme Drought in West Asia" Remote Sensing 16, no. 7: 1179. https://doi.org/10.3390/rs16071179