Articles | Volume 29, issue 5
https://doi.org/10.5194/hess-29-1241-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-1241-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Critical soil moisture detection and water–energy limit shift attribution using satellite-based water and carbon fluxes over China
School of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China
Jingfeng Xiao
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
Yue Li
Department of Earth and Environmental Sciences, Indiana University Indianapolis, Indianapolis, IN 46202, USA
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Yue Li, Eugene Marais, and Lixin Wang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-245, https://doi.org/10.5194/essd-2025-245, 2025
Preprint under review for ESSD
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We investigated water availability in the hyper-arid Namib Desert by collecting long-term data on rainfall, fog, and dew inputs. Our research presents a rare, decade-long dataset of stable isotope measurements to better understand how water moves within dryland environments. This work helps scientists quantify changes in the water cycle and improve our understanding of how drylands may respond to a changing climate.
Liang Feng, Paul Palmer, Luke Smallman, Jingfeng Xiao, Paulo Cristofanelli, Ove Hermansen, John Lee, Casper Labuschagne, Simonetta Montaguti, Steffen Noe, Stephen Platt, Xinrong Ren, Martin Steinbacher, and Irene Xueref-Remy
EGUsphere, https://doi.org/10.5194/egusphere-2025-1793, https://doi.org/10.5194/egusphere-2025-1793, 2025
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2023 saw an unexpectedly high global atmospheric CO2 growth. Satellite data reveal a role for increased emissions over the tropics. Larger emissions over eastern Brazil can be explained by warmer temperatures, while changes in rainfall and soil moisture play more of a role in emission increases elsewhere in the tropics.
Xufeng Wang, Tao Che, Jingfeng Xiao, Tonghong Wang, Junlei Tan, Yang Zhang, Zhiguo Ren, Liying Geng, Haibo Wang, Ziwei Xu, Shaomin Liu, and Xin Li
Earth Syst. Sci. Data, 17, 1329–1346, https://doi.org/10.5194/essd-17-1329-2025, https://doi.org/10.5194/essd-17-1329-2025, 2025
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In this study, carbon flux and auxiliary meteorological data are post-processed to create an analysis-ready dataset for 34 sites across six ecosystems in the Heihe River basin. Overall, 18 sites have multi-year observations, while 16 were observed only during the 2012 growing season, totaling 1513 site months. This dataset can be used to explore carbon exchange, assess ecosystem responses to climate change, support upscaling studies, and evaluate carbon cycle models.
Yaotong Cai, Peng Zhu, Xing Li, Xiaoping Liu, Yuhe Chen, Qianhui Shen, Xiaocong Xu, Honghui Zhang, Sheng Nie, Cheng Wang, Jia Wang, Bingjie Li, Changjiang Wu, and Haoming Zhuang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-96, https://doi.org/10.5194/essd-2025-96, 2025
Revised manuscript under review for ESSD
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China’s forests play a crucial role in storing carbon and mitigating climate change, yet long-term, high-resolution data on their biomass have been limited. We developed a 30-m annual forest aboveground biomass dataset from 1985 to 2023 using satellite data and deep learning. Our results reveal significant biomass gains, regional variations, and the impact of forest policies. This dataset provides valuable insights for climate research, conservation planning, and sustainable forest management.
Mana Gharun, Ankit Shekhar, Jingfeng Xiao, Xing Li, and Nina Buchmann
Biogeosciences, 21, 5481–5494, https://doi.org/10.5194/bg-21-5481-2024, https://doi.org/10.5194/bg-21-5481-2024, 2024
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In 2022, Europe's forests faced unprecedented dry conditions. Our study aimed to understand how different forest types respond to extreme drought. Using meteorological data and satellite imagery, we compared 2022 with two previous extreme years, 2003 and 2018. Despite less severe drought in 2022, forests showed a 30 % greater decline in photosynthesis compared to 2018 and 60 % more than 2003. This suggests an alarming level of vulnerability of forests across Europe to more frequent droughts.
Shanlei Sun, Zaoying Bi, Jingfeng Xiao, Yi Liu, Ge Sun, Weimin Ju, Chunwei Liu, Mengyuan Mu, Jinjian Li, Yang Zhou, Xiaoyuan Li, Yibo Liu, and Haishan Chen
Earth Syst. Sci. Data, 15, 4849–4876, https://doi.org/10.5194/essd-15-4849-2023, https://doi.org/10.5194/essd-15-4849-2023, 2023
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Based on various existing datasets, we comprehensively considered spatiotemporal differences in land surfaces and CO2 effects on plant stomatal resistance to parameterize the Shuttleworth–Wallace model, and we generated a global 5 km ensemble mean monthly potential evapotranspiration (PET) dataset (including potential transpiration PT and soil evaporation PE) during 1982–2015. The new dataset may be used by academic communities and various agencies to conduct various studies.
Sinan Li, Li Zhang, Jingfeng Xiao, Rui Ma, Xiangjun Tian, and Min Yan
Hydrol. Earth Syst. Sci., 26, 6311–6337, https://doi.org/10.5194/hess-26-6311-2022, https://doi.org/10.5194/hess-26-6311-2022, 2022
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Accurate estimation for global GPP and ET is important in climate change studies. In this study, the GLASS LAI, SMOS, and SMAP datasets were assimilated jointly and separately in a coupled model. The results show that the performance of joint assimilation for GPP and ET is better than that of separate assimilation. The joint assimilation in water-limited regions performed better than in humid regions, and the global assimilation results had higher accuracy than other products.
Brendan Byrne, Junjie Liu, Yonghong Yi, Abhishek Chatterjee, Sourish Basu, Rui Cheng, Russell Doughty, Frédéric Chevallier, Kevin W. Bowman, Nicholas C. Parazoo, David Crisp, Xing Li, Jingfeng Xiao, Stephen Sitch, Bertrand Guenet, Feng Deng, Matthew S. Johnson, Sajeev Philip, Patrick C. McGuire, and Charles E. Miller
Biogeosciences, 19, 4779–4799, https://doi.org/10.5194/bg-19-4779-2022, https://doi.org/10.5194/bg-19-4779-2022, 2022
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Plants draw CO2 from the atmosphere during the growing season, while respiration releases CO2 to the atmosphere throughout the year, driving seasonal variations in atmospheric CO2 that can be observed by satellites, such as the Orbiting Carbon Observatory 2 (OCO-2). Using OCO-2 XCO2 data and space-based constraints on plant growth, we show that permafrost-rich northeast Eurasia has a strong seasonal release of CO2 during the autumn, hinting at an unexpectedly large respiration signal from soils.
Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu
Geosci. Model Dev., 15, 6637–6657, https://doi.org/10.5194/gmd-15-6637-2022, https://doi.org/10.5194/gmd-15-6637-2022, 2022
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Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters and we also improved the spatial pattern of carbon fluxes.
Jing Fang, Xing Li, Jingfeng Xiao, Xiaodong Yan, Bolun Li, and Feng Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-452, https://doi.org/10.5194/essd-2021-452, 2022
Revised manuscript not accepted
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The dataset provided the vegetation photosynthetic phenology instead of traditional phenology to represent plant seasonal activities. This dataset had the latest period (2001–2020) and a fine spatial resolution (0.05 degree). Our phenology metrics revealed the spatial-temporal patterns of the multiple growing seasons in the Northern Hemisphere. The dataset will facilitate various research such as developing models, evaluating phenology shifts, and monitoring climate change worldwide.
Cited articles
Akbar, R., Gianotti, D. J. S., McColl, K. A., Haghighi, E. ̧ Salvucci, G. D., and Entekhabi, D.: Estimation of Landscape Soil Water Losses from Satellite Observations of Soil Moisture, J. Hydrometeorol., 19, 871–889, https://doi.org/10.1175/JHM-D-17-0200.1, 2018.
Baldocchi, D. D., Xu, L. K., and Kiang, N.: How plant functional-type, weather, seasonal drought, and soil physical properties alter water and energy fluxes of an oak-grass savanna and an annual grassland, Agr. Forest Meteorol., 123, 13–39, https://doi.org/10.1016/j.agrformet.2003.11.006, 2004.
Bi, W. and Zhou, Y.: A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies (1992–2020), Dryad [data set], https://doi.org/10.5061/dryad.dfn2z352k, 2022.
Bi, W., He, W., Zhou, Y., Ju, W., Liu, Y., Liu, Y., Zhang, X., Wei, X., and Cheng, N.: A global 0.05 degrees dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020, Scientific Data, 9, 213 https://doi.org/10.1038/s41597-022-01309-2, 2022.
Bolton, D.: The computation of equivalent potential temperature, Mon. Weather Rev., 108, 1046–1053, https://doi.org/10.1175/2008MWR2593.1, 1980.
Case, M. F., Nippert, J. B., Holdo, R. M., and Staver, A. C.: Root-niche separation between savanna trees and grasses is greater on sandier soils, J. Ecol., 108, 2298–2308, https://doi.org/10.1111/1365-2745.13475, 2020.
Cheng, M.: Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 1, Version v3, Zenodo [data set], https://doi.org/10.5281/zenodo.10803216, 2024a.
Cheng, M.: Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 2, Version v2, Zenodo [data set], https://doi.org/10.5281/zenodo.10803553, 2024b.
Cheng, M., Jiao, X., Li, B., Yu, X., Shao, M., and Jin, X.: Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation, Earth Syst. Sci. Data, 13, 3995–4017, https://doi.org/10.5194/essd-13-3995-2021, 2021.
Denissen, J. M. C., Teuling, A. J., Reichstein, M., and Orth, R.: Critical Soil Moisture Derived from Satellite Observations Over Europe, J. Geophys. Res.-Atmos., 125, e2019JD031672, https://doi.org/10.1029/2019JD031672, 2020.
Dong, J., Akbar, R., Feldman, A. F., Gianotti, D. S., and Entekhabi, D.: Land Surfaces at the Tipping-Point for Water and Energy Balance Coupling, Water Resour. Res., 59, e2022WR032472, https://doi.org/10.1029/2022wr032472, 2023.
Duan, S. Q., Findell, K. I., and Fueglistaler, S. A.: Coherent Mechanistic Patterns of Tropical Land Hydroclimate Changes, Geophys. Res. Lett., 50, e2022GL102285, https://doi.org/10.1029/2022gl102285, 2023.
Feldman, A. F., Gianotti, D. J. S., Trigo, I. F., Salvucci, G. D., and Entekhabi, D.: Satellite-Based Assessment of Land Surface Energy Partitioning-Soil Moisture Relationships and Effects of Confounding Variables, Water Resour. Res., 55, 10657–10677, https://doi.org/10.1029/2019WR025874, 2019.
Feldman, A. F., Short Gianotti, D. J., Trigo, I. F., Salvucci, G. D., and Entekhabi, D.: Observed Landscape Responsiveness to Climate Forcing, Water Resour. Res., 58, e2021WR030316, https://doi.org/10.1029/2021WR030316, 2022.
Fu, Z., Ciais, P., Feldman, A. F., Gentine, P., Makowski, D., Prentice, I. C., Stoy, P. C., Bastos, A., and Wigneron, J.-P.: Critical soil moisture thresholds of plant water stress in terrestrial ecosystems, Science Advances, 8, eabq7827, https://doi.org/10.1126/sciadv.abq7827, 2022a.
Fu, Z., Ciais, P., Makowski, D., Bastos, A., Stoy, P. C., Ibrom, A., Knohl, A., Migliavacca, M., Cuntz, M., Sigut, L., Peichl, M., Loustau, D., El-Madany, T. S., Buchmann, N., Gharun, M., Janssens, I., Markwitz, C., Gruenwald, T., Rebmann, C., Molder, M., Varlagin, A., Mammarella, I., Kolari, P., Bernhofer, C., Heliasz, M., Vincke, C., Pitacco, A., Cremonese, E., Foltynova, L., and Wigneron, J.-P.: Uncovering the critical soil moisture thresholds of plant water stress for European ecosystems, Glob. Change Biol., 28, 2111–2123, https://doi.org/10.1111/gcb.16050, 2022b.
Fu, Z., Ciais, P., Wigneron, J. P., Gentine, P., Feldman, A. F., Makowski, D., Viovy, N., Kemanian, A. R., Goll, D. S., Stoy, P. C., Prentice, I. C., Yakir, D., Liu, L., Ma, H., Li, X., Huang, Y., Yu, K., Zhu, P., Li, X., Zhu, Z., Lian, J., and Smith, W. K.: Global critical soil moisture thresholds of plant water stress, Nat. Commun., 15, 4826–4826, https://doi.org/10.1038/s41467-024-49244-7, 2024.
Gallego-Elvira, B., Taylor, C. M., Harris, P. P., Ghent, D., Veal, K. L., and Folwell, S. S.: Global observational diagnosis of soil moisture control on the land surface energy balance, Geophys. Res. Lett., 43, 2623–2631, https://doi.org/10.1002/2016GL068178, 2016.
Gentine, P., Green, J. K., Guerin, M., Humphrey, V., Seneviratne, S. I., Zhang, Y., and Zhou, S.: Coupling between the terrestrial carbon and water cycles-a review, Environ. Res. Lett., 14, 083003, https://doi.org/10.1088/1748-9326/ab22d6, 2019.
Global Ecology Group Data Repository: http://globalecology.unh.edu/data/GOSIF-GPP.html, last access: 27 February 2025.
Good, S. P., Noone, D., and Bowen, G.: Hydrologic connectivity constrains partitioning of global terrestrial water fluxes, Science, 349, 175–177, https://doi.org/10.1126/science.aaa5931, 2015.
Grossiord, C., Buckley, T. N., Cernusak, L. A., Novick, K. A., Poulter, B., Siegwolf, R. T. W., Sperry, J. S., and McDowell, N. G.: Plant responses to rising vapor pressure deficit, New Phytol., 226, 1550–1566, https://doi.org/10.1111/nph.16485, 2020.
Haghighi, E., Gianotti, D. J. S., Akbar, R., Salvucci, G. D., and Entekhabi, D.: Soil and Atmospheric Controls on the Land Surface Energy Balance: A Generalized Framework for Distinguishing Moisture-Limited and Energy-Limited Evaporation Regimes, Water Resour. Res., 54, 1831–1851, https://doi.org/10.1002/2017WR021729, 2018.
He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: The first high-resolution meteorological forcing dataset for land process studies over China, Scientific Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y, 2020.
He, S., Zhang, Y., Ma, N., Tian, J., Kong, D., and Liu, C.: A daily and 500 m coupled evapotranspiration and gross primary production product across China during 2000–2020, Earth Syst. Sci. Data, 14, 5463–5488, https://doi.org/10.5194/essd-14-5463-2022, 2022.
Herman, M. R., Nejadhashemi, A. P., Abouali, M., Hernandez-Suarez, J. S., Daneshvar, F., Zhang, Z., Anderson, M. C., Sadeghi, A. M., Hain, C. R., and Sharifi, A.: Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability, J. Hydrol., 556, 39–49, https://doi.org/10.1016/j.jhydrol.2017.11.009, 2018.
Homaee, A., Feddes, R. A., and Dirksen, C.: Simulation of root water uptake II. Non-uniform transient water stress using different reduction functions, Agr. Water Manage., 57, 111–126, https://doi.org/10.1016/S0378-3774(02)00071-9, 2002.
Hsu, H. and Dirmeyer, P. A.: Soil moisture-evaporation coupling shifts into new gears under increasing CO2, Nat. Commun., 14, 1162, https://doi.org/10.1038/s41467-023-36794-5, 2023a.
Hsu, H. and Dirmeyer, P. A.: Uncertainty in Projected Critical Soil Moisture Values in CMIP6 Affects the Interpretation of a More Moisture-Limited World, Earths Future, 11, e2023EF003511, https://doi.org/10.1029/2023ef003511, 2023b.
Karthikeyan, L., Chawla, I., Mishra, A. K.: A review of remote sensing applications in agriculture for food security: Crop growth and yield, irrigation, and crop losses, J. Hydrol., 586: 124905, https://doi.org/10.1016/j.jhydrol.2020.124905, 2020.
Kendall, M. G.: Rank Correlation Methods, Hafner, https://doi.org/10.1017/S0020268100013019, 160 pp., 1948.
Konings, A. G. and Gentine, P.: Global variations in ecosystem-scale isohydricity, Glob. Change Biol., 23, 891–905, https://doi.org/10.1111/gcb.13389, 2017.
Koster, R. D., Guo, Z., Yang, R., Dirmeyer, P. A., Mitchell, K., and Puma, M. J.: On the Nature of Soil Moisture in Land Surface Models, J. Climate, 22, 4322–4335, https://doi.org/10.1175/2009JCLI2832.1, 2009.
Laio, F., Porporato, A., Ridolfi, L., and Rodriguez-Iturbe, I.: Plants in water-controlled ecosystems: active role in hydrologic processes and response to water stress – II. Probabilistic soil moisture dynamics, Adv. Water Resour., 24, 707–723, https://doi.org/10.1016/S0309-1708(01)00005-7, 2001.
Li, C., Yang, H., Yang, W., Liu, Z., Jia, Y., Li, S., and Yang, D.: CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data, Version 4.0, Zenodo, https://doi.org/10.5281/zenodo.6283239, 2021.
Li, C., Yang, H., Yang, W., Liu, Z., Jia, Y., Li, S., and Yang, D.: CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-456, 2022.
Li, F., Xiao, J., Chen, J., Ballantyne, A., Jin, K., Li, B., Abraha, M., and John, R.: Global water use efficiency saturation due to increased vapor pressure deficit, Science, 381, 672–677, https://doi.org/10.1126/science.adf5041, 2023.
Li, Q., Shi, G., Shangguan, W., Nourani, V., Li, J., Li, L., Huang, F., Zhang, Y., Wang, C., Wang, D., Qiu, J., Lu, X., and Dai, Y.: A 1 km daily soil moisture dataset over China using in situ measurement and machine learning, Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022, 2022.
Li, X. and Xiao, J.: Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2, Remote Sens.-Basel, 11, 2563, https://doi.org/10.3390/rs11212563, 2019.
Li, X., Ryu, Y., Xiao, J., Dechant, B., Liu, J., Li, B., Jeong, S., and Gentine, P.: New-generation geostationary satellite reveals widespread midday depression in dryland photosynthesis during 2020 western US heatwave, Science Advances, 9, eadi0775, https://doi.org/10.1126/sciadv.adi0775, 2023.
Liu, W., Mo, X., Liu, S., Lin, Z., and Lv, C.: Attributing the changes of grass growth, water consumed and water use efficiency over the Tibetan Plateau, J. Hydrol., 598, 126464, https://doi.org/10.1016/j.jhydrol.2021.126464, 2021.
Liu, Y., Mo, X., Hu, S., Chen, X., and Liu, S.: Attribution analyses of evapotranspiration and gross primary productivity changes in Ziya-Daqing basins, China during 2001–2015, Theor. Appl. Climatol., 139, 1175–1189, https://doi.org/10.1007/s00704-019-03004-6, 2020.
Liu, Y. Y., Dorigo, W. A., Parinussa, R. M., de Jeu, R. A. M., Wagner, W., McCabe, M. F., Evans, J. P., and van Dijk, A. I. J. M.: Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sens. Environ., 123, 280–297, https://doi.org/10.1016/j.rse.2012.03.014, 2012.
Mann, H. B.: Non-parametric test against trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945.
McHugh, M. L.: The Chi-square test of independence, Biochem. Medica, 23, 143–149, https://doi.org/10.11613/bm.2013.018, 2013.
Nash, J. E., Sutcliffe, J. V.: River flow forecasting through conceptual models part I – a discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Porporato, A., D'Odorico, P., Laio, F., Ridolfi, L., and Rodriguez-Iturbe, I.: Ecohydrology of water-controlled ecosystems, Adv. Water Resour., 25, 1335–1348, https://doi.org/10.1016/S0309-1708(02)00058-1, 2002.
Rodriguez-Iturbe, I.: Ecohydrology: A hydrologic perspective of climate-soil-vegetation dynamics, Water Resour. Res., 36, 3–9, https://doi.org/10.1029/1999WR900210, 2000.
Schwarz, G.: Estimating the Dimension of a Model, Ann. Stat., 6, 461–464, https://doi.org/10.1214/aos/1176344136, 1978.
Schwingshackl, C., Hirschi, M., and Seneviratne, S. I.: Quantifying Spatiotemporal Variations of Soil Moisture Control on Surface Energy Balance and Near-Surface Air Temperature, J. Climate, 30, 7105–7124, https://doi.org/10.1175/JCLI-D-16-0727.1, 2017.
Seneviratne, S. I., Luethi, D., Litschi, M., and Schaer, C.: Land–atmosphere coupling and climate change in Europe, Nature, 443, 205–209, https://doi.org/10.1038/nature05095, 2006.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil moisture-climate interactions in a changing climate: A review, Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Teuling, A. J., Uijlenhoet, R., van den Hurk, B., and Seneviratne, S. I.: Parameter Sensitivity in LSMs: An Analysis Using Stochastic Soil Moisture Models and ELDAS Soil Parameters, J. Hydrometeorol., 10, 751–765, https://doi.org/10.1175/2008JHM1033.1, 2009.
Tumber-Davila, S. J., Schenk, H. J., Du, E., and Jackson, R. B.: Plant sizes and shapes above and belowground and their interactions with climate, New Phytol., 235, 1032–1056, https://doi.org/10.1111/nph.18031, 2022.
van Doorn, J., Ly, A., Marsman, M., and Wagenmakers, E.-J.: Bayesian Inference for Kendall's Rank Correlation Coefficient, Am. Stat., 72, 303–308, https://doi.org/10.1080/00031305.2016.1264998, 2018.
Xiao, J.: Satellite evidence for significant biophysical consequences of the “Grain for Green” Program on the Loess Plateau in China, J. Geophys. Res.-Biogeo., 119, 2261–2275, https://doi.org/10.1002/2014JG002820, 2014.
Yang, K., He, J., Tang, W., Qin, J., and Cheng, C. C. K.: On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau, Agr. Forest Meteorol., 150, 38–46, https://doi.org/10.1016/j.agrformet.2009.08.004, 2010.
Yao, Y., Liang, S., Cheng, J., Liu, S., Fisher, J. B., Zhang, X., Jia, K., Zhao, X., Qing, Q., Zhao, B., Han, S., Zhou, G., Zhou, G., Li, Y., and Zhao, S.: MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley–Taylor algorithm, Agr. Forest Meteorol., 171, 187–202, https://doi.org/10.1016/j.agrformet.2012.11.016, 2013.
Yao, Y., Liang, S., Li, X., Hong, Y., Fisher, J. B., Zhang, N., Chen, J., Cheng, J., Zhao, S., Zhang, X., Jiang, B., Sun, L., Jia, K., Wang, K., Chen, Y., Mu, Q., and Feng, F.: Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations, J. Geophys. Res.-Atmos., 119, 4521–4545, https://doi.org/10.1002/2013JD020864, 2014.
Yuan, W., Liu, S., Zhou, G., Zhou, G., Tieszen, L. L., Baldocchi, D., Bernhofer, C., Gholz, H., Goldstein, A. H., Goulden, M. L., Hollinger, D. Y., Hu, Y., Law, B. E., Stoy, P. C., Vesala, T., Wofsy, S. C., and AmeriFlux, C.: Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes, Agr. Forest Meteorol., 143, 189–207, https://doi.org/10.1016/j.agrformet.2006.12.001, 2007.
Yuan, W., Cai, W., Xia, J., Chen, J., Liu, S., Dong, W., Merbold, L., Law, B., Arain, A., Beringer, J., Bernhofer, C., Black, A., Blanken, P. D., Cescatti, A., Chen, Y., Francois, L., Gianelle, D., Janssens, I. A., Jung, M., Kato, T., Kiely, G., Liu, D., Marcolla, B., Montagnani, L., Raschi, A., Roupsard, O., Varlagin, A., and Wohlfahrt, G.: Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the La Thuile database, Agr. Forest Meteorol., 192, 108–120, https://doi.org/10.1016/j.agrformet.2014.03.007, 2014.
Zhang, P., Jeong, J.-H., Yoon, J.-H., Kim, H., Wang, S. Y. S., Linderholm, H. W., Fang, K., Wu, X., and Chen, D.: Abrupt shift to hotter and drier climate over inner East Asia beyond the tipping point, Science, 370, 1095–1099, https://doi.org/10.1126/science.abb3368, 2020.
Zhang, Y., Kong, D., Gan, R., Chiew, F. H. S., McVicar, T. R., Zhang, Q., and Yang, Y.: Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017, Remote Sens. Environ., 222, 165–182, https://doi.org/10.1016/j.rse.2018.12.031, 2019.
Zhu, W., Wang, Y., and Jia, S.: A remote sensing-based method for daily evapotranspiration mapping and partitioning in a poorly gauged basin with arid ecosystems in the Qinghai-Tibet Plateau, J. Hydrol., 616, 128807, https://doi.org/10.1016/j.jhydrol.2022.128807, 2023.
Short summary
This work demonstrates that multi-source satellite-based water and carbon fluxes can capture critical soil moisture at a large spatial scale. In particular, grassland and clay with critical soil moisture higher than average soil moisture may be in a state of water limitation for long periods. Increased water demand could expose western grassland to more vulnerability.
This work demonstrates that multi-source satellite-based water and carbon fluxes can capture...