Articles | Volume 25, issue 7
https://doi.org/10.5194/hess-25-4209-2021
© Author(s) 2021. 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-25-4209-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jul 2021
Research article |
| 30 Jul 2021
| 30 Jul 2021
Comprehensive evaluation of satellite-based and reanalysis soil moisture products using in situ observations over China
Xiaolu Ling
Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization, China University of Mining and Technology, Xuzhou 221008, Jiangsu, China
School of Environment and Spatial Informatics, China University of
Mining and Technology, Xuzhou 221000, China
Ying Huang
CORRESPONDING AUTHOR
Institute for Climate and Global Change Research, School of
Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Joint International Research Laboratory of Atmospheric and Earth
System Sciences, Nanjing University, Nanjing 210023, China
Weidong Guo
Institute for Climate and Global Change Research, School of
Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Joint International Research Laboratory of Atmospheric and Earth
System Sciences, Nanjing University, Nanjing 210023, China
Yixin Wang
Institute for Climate and Global Change Research, School of
Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Chaorong Chen
Institute for Climate and Global Change Research, School of
Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Bo Qiu
Institute for Climate and Global Change Research, School of
Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Joint International Research Laboratory of Atmospheric and Earth
System Sciences, Nanjing University, Nanjing 210023, China
Institute for Climate and Global Change Research, School of
Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Joint International Research Laboratory of Atmospheric and Earth
System Sciences, Nanjing University, Nanjing 210023, China
Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization, China University of Mining and Technology, Xuzhou 221008, Jiangsu, China
School of Environment and Spatial Informatics, China University of
Mining and Technology, Xuzhou 221000, China
Yong Xue
Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization, China University of Mining and Technology, Xuzhou 221008, Jiangsu, China
School of Environment and Spatial Informatics, China University of
Mining and Technology, Xuzhou 221000, China
Jian Peng
Department of Remote Sensing, Helmholtz Centre for Environmental
Research – UFZ, Permoserstraße 15, 04318 Leipzig, Germany
Remote Sensing Centre for Earth System Research, Leipzig University, 04103 Leipzig, Germany
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Atmos. Meas. Tech., 18, 4559–4571, https://doi.org/10.5194/amt-18-4559-2025, https://doi.org/10.5194/amt-18-4559-2025, 2025
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A novel black carbon aerosol (BC) surface concentration retrieval algorithm was developed using MODIS data. The algorithm determined seasonal background aerosol model based on AERONET product, calculated the complex refractive index of the internal mixed aerosol, and combined 6SV2.1 to establish lookup tables to achieve the optimal estimation of BC fraction and column concentration. Using conversion coefficient generated by MERRA-2, column concentration was converted to surface concentration.
Le Wang, Xin Miao, Xinyun Hu, Yizhuo Li, Bo Qiu, Jun Ge, and Weidong Guo
The Cryosphere, 19, 2733–2750, https://doi.org/10.5194/tc-19-2733-2025, https://doi.org/10.5194/tc-19-2733-2025, 2025
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Snow phenology is a crucial indicator for assessing seasonal changes in snow. In this work, we find that snow phenology is significantly impacted by the datasets and methods used, and current methods often overlook the spatial and temporal variability in snow across the Northern Hemisphere. To address this, we develop a dynamic-threshold method, which contributes to better representing the seasonal changes in snow cover across the Northern Hemisphere, especially on the Tibetan Plateau.
Fan Lu, Kai Qin, Jason Blake Cohen, Qin He, Pravash Tiwari, Wei Hu, Chang Ye, Yanan Shan, Qing Xu, Shuo Wang, and Qiansi Tu
Atmos. Chem. Phys., 25, 5837–5856, https://doi.org/10.5194/acp-25-5837-2025, https://doi.org/10.5194/acp-25-5837-2025, 2025
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This work describes a field campaign and new fast emissions estimation approach to attribute methane from a large known and previously unknown coal mine in Shanxi, China. The emissions computed are shown to be larger than known oil and gas sources, indicating that methane from coal mines may play a larger role in the global methane budget. The results are found to be slightly larger than or similar to satellite observational campaigns over the same region.
Bo Zheng, Jason Blake Cohen, Lingxiao Lu, Wei Hu, Pravash Tiwari, Simone Lolli, Andrea Garzelli, Hui Su, and Kai Qin
EGUsphere, https://doi.org/10.5194/egusphere-2025-1446, https://doi.org/10.5194/egusphere-2025-1446, 2025
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This study provides TROPOMI with a new methane emission estimation method that can accurately identify emission sources. Our results generate non-negative emission datasets using objective selection and filtering methods. The results include lower minimum emission thresholds for all power grids and fewer false positives. The new method provides more robust emission quantification in the face of data uncertainty, going beyond traditional plume identification and background subtraction.
Lingxiao Lu, Jason Blake Cohen, Kai Qin, Xiaolu Li, and Qin He
Atmos. Chem. Phys., 25, 2291–2309, https://doi.org/10.5194/acp-25-2291-2025, https://doi.org/10.5194/acp-25-2291-2025, 2025
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This study applies an approach that assimilates NO2 vertical column densities from TROPOMI in a mass-conserving manner and inverts daily NOx emissions, presented over rapidly changing regions in China. Source attribution is quantified by the local thermodynamics of the combustion temperature (NOx/NO2). Emission results identify sources which do not exist in the a priori datasets, especially medium industrial sources located next to the Yangtze River.
Kai Qin, Hongrui Gao, Xuancen Liu, Qin He, Pravash Tiwari, and Jason Blake Cohen
Earth Syst. Sci. Data, 16, 5287–5310, https://doi.org/10.5194/essd-16-5287-2024, https://doi.org/10.5194/essd-16-5287-2024, 2024
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Satellites have brought new opportunities for monitoring atmospheric NO2, although the results are limited by clouds and other factors, resulting in missing data. This work proposes a new process to obtain reliable data products with high coverage by reconstructing the raw data from multiple satellites. The results are validated in terms of traditional methods as well as variance maximization and demonstrate a good ability to reproduce known polluted and clean areas around the world.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
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A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Qiansi Tu, Frank Hase, Kai Qin, Jason Blake Cohen, Farahnaz Khosrawi, Xinrui Zou, Matthias Schneider, and Fan Lu
Atmos. Chem. Phys., 24, 4875–4894, https://doi.org/10.5194/acp-24-4875-2024, https://doi.org/10.5194/acp-24-4875-2024, 2024
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Four-year satellite observations of XCH4 are used to derive CH4 emissions in three regions of China’s coal-rich Shanxi province. The wind-assigned anomalies for two opposite wind directions are calculated, and the estimated emission rates are comparable to the current bottom-up inventory but lower than the CAMS and EDGAR inventories. This research enhances the understanding of emissions in Shanxi and supports climate mitigation strategies by validating emission inventories.
Kai Qin, Wei Hu, Qin He, Fan Lu, and Jason Blake Cohen
Atmos. Chem. Phys., 24, 3009–3028, https://doi.org/10.5194/acp-24-3009-2024, https://doi.org/10.5194/acp-24-3009-2024, 2024
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We compute CH4 emissions and uncertainty on a mine-by-mine basis, including underground, overground, and abandoned mines. Mine-by-mine gas and flux data and 30 min observations from a flux tower located next to a mine shaft are integrated. The observed variability and bias correction are propagated over the emissions dataset, demonstrating that daily observations may not cover the range of variability. Comparisons show both an emissions magnitude and spatial mismatch with current inventories.
Solomon H. Gebrechorkos, Jian Peng, Ellen Dyer, Diego G. Miralles, Sergio M. Vicente-Serrano, Chris Funk, Hylke E. Beck, Dagmawi T. Asfaw, Michael B. Singer, and Simon J. Dadson
Earth Syst. Sci. Data, 15, 5449–5466, https://doi.org/10.5194/essd-15-5449-2023, https://doi.org/10.5194/essd-15-5449-2023, 2023
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Drought is undeniably one of the most intricate and significant natural hazards with far-reaching consequences for the environment, economy, water resources, agriculture, and societies across the globe. In response to this challenge, we have devised high-resolution drought indices. These indices serve as invaluable indicators for assessing shifts in drought patterns and their associated impacts on a global, regional, and local level facilitating the development of tailored adaptation strategies.
Yuhang Zhang, Jintai Lin, Jhoon Kim, Hanlim Lee, Junsung Park, Hyunkee Hong, Michel Van Roozendael, Francois Hendrick, Ting Wang, Pucai Wang, Qin He, Kai Qin, Yongjoo Choi, Yugo Kanaya, Jin Xu, Pinhua Xie, Xin Tian, Sanbao Zhang, Shanshan Wang, Siyang Cheng, Xinghong Cheng, Jianzhong Ma, Thomas Wagner, Robert Spurr, Lulu Chen, Hao Kong, and Mengyao Liu
Atmos. Meas. Tech., 16, 4643–4665, https://doi.org/10.5194/amt-16-4643-2023, https://doi.org/10.5194/amt-16-4643-2023, 2023
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Our tropospheric NO2 vertical column density product with high spatiotemporal resolution is based on the Geostationary Environment Monitoring Spectrometer (GEMS) and named POMINO–GEMS. Strong hotspot signals and NO2 diurnal variations are clearly seen. Validations with multiple satellite products and ground-based, mobile car and surface measurements exhibit the overall great performance of the POMINO–GEMS product, indicating its capability for application in environmental studies.
Xiaolu Li, Jason Blake Cohen, Kai Qin, Hong Geng, Xiaohui Wu, Liling Wu, Chengli Yang, Rui Zhang, and Liqin Zhang
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Francisco José Cuesta-Valero, Hugo Beltrami, Almudena García-García, Gerhard Krinner, Moritz Langer, Andrew H. MacDougall, Jan Nitzbon, Jian Peng, Karina von Schuckmann, Sonia I. Seneviratne, Wim Thiery, Inne Vanderkelen, and Tonghua Wu
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Qiansi Tu, Frank Hase, Zihan Chen, Matthias Schneider, Omaira García, Farahnaz Khosrawi, Shuo Chen, Thomas Blumenstock, Fang Liu, Kai Qin, Jason Cohen, Qin He, Song Lin, Hongyan Jiang, and Dianjun Fang
Atmos. Meas. Tech., 16, 2237–2262, https://doi.org/10.5194/amt-16-2237-2023, https://doi.org/10.5194/amt-16-2237-2023, 2023
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Four-year TROPOMI observations are used to derive tropospheric NO2 emissions in two mega(cities) with high anthropogenic activity. Wind-assigned anomalies are calculated, and the emission rates and spatial patterns are estimated based on a machine learning algorithm. The results are in reasonable agreement with previous studies and the inventory. Our method is quite robust and can be used as a simple method to estimate the emissions of NO2 as well as other gases in other regions.
Karina von Schuckmann, Audrey Minière, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michaël Ablain, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
Earth Syst. Sci. Data, 15, 1675–1709, https://doi.org/10.5194/essd-15-1675-2023, https://doi.org/10.5194/essd-15-1675-2023, 2023
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Earth's climate is out of energy balance, and this study quantifies how much heat has consequently accumulated over the past decades (ocean: 89 %, land: 6 %, cryosphere: 4 %, atmosphere: 1 %). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris Agreement’s Global Stocktake.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
EGUsphere, https://doi.org/10.5194/egusphere-2022-1111, https://doi.org/10.5194/egusphere-2022-1111, 2022
Preprint archived
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A process-based plant Carbon (C)-Nitrogen (N) interface coupling framework has been developed, which mainly focuses on the plant resistance and N limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem-biogeochemical model and testing results show a general improvement in simulating plant properties with this framework.
Zexia Duan, Zhiqiu Gao, Qing Xu, Shaohui Zhou, Kai Qin, and Yuanjian Yang
Earth Syst. Sci. Data, 14, 4153–4169, https://doi.org/10.5194/essd-14-4153-2022, https://doi.org/10.5194/essd-14-4153-2022, 2022
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Land–atmosphere interactions over the Yangtze River Delta (YRD) in China are becoming more varied and complex, as the area is experiencing rapid land use changes. In this paper, we describe a dataset of microclimate and eddy covariance variables at four sites in the YRD. This dataset has potential use cases in multiple research fields, such as boundary layer parametrization schemes, evaluation of remote sensing algorithms, and development of climate models in typical East Asian monsoon regions.
Shijie Li, Guojie Wang, Chenxia Zhu, Jiao Lu, Waheed Ullah, Daniel Fiifi Tawia Hagan, Giri Kattel, and Jian Peng
Hydrol. Earth Syst. Sci., 26, 3691–3707, https://doi.org/10.5194/hess-26-3691-2022, https://doi.org/10.5194/hess-26-3691-2022, 2022
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We found that the precipitation variability dominantly controls global evapotranspiration (ET) in dry climates, while the net radiation has substantial control over ET in the tropical regions, and vapor pressure deficit (VPD) impacts ET trends in boreal mid-latitude climate. The critical role of VPD in controlling ET trends is particularly emphasized due to its influence in controlling the carbon–water–energy cycle.
Stephanie G. Stettz, Nicholas C. Parazoo, A. Anthony Bloom, Peter D. Blanken, David R. Bowling, Sean P. Burns, Cédric Bacour, Fabienne Maignan, Brett Raczka, Alexander J. Norton, Ian Baker, Mathew Williams, Mingjie Shi, Yongguang Zhang, and Bo Qiu
Biogeosciences, 19, 541–558, https://doi.org/10.5194/bg-19-541-2022, https://doi.org/10.5194/bg-19-541-2022, 2022
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Uncertainty in the response of photosynthesis to temperature poses a major challenge to predicting the response of forests to climate change. In this paper, we study how photosynthesis in a mountainous evergreen forest is limited by temperature. This study highlights that cold temperature is a key factor that controls spring photosynthesis. Including the cold-temperature limitation in an ecosystem model improved its ability to simulate spring photosynthesis.
Jiao Lu, Guojie Wang, Tiexi Chen, Shijie Li, Daniel Fiifi Tawia Hagan, Giri Kattel, Jian Peng, Tong Jiang, and Buda Su
Earth Syst. Sci. Data, 13, 5879–5898, https://doi.org/10.5194/essd-13-5879-2021, https://doi.org/10.5194/essd-13-5879-2021, 2021
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This study has combined three existing land evaporation (ET) products to obtain a single framework of a long-term (1980–2017) daily ET product at a spatial resolution of 0.25° to define the global proxy ET with lower uncertainties. The merged product is the best at capturing dynamics over different locations and times among all data sets. The merged product performed well over a range of vegetation cover scenarios and also captured the trend of land evaporation over different areas well.
Mengyuan Mu, Martin G. De Kauwe, Anna M. Ukkola, Andy J. Pitman, Weidong Guo, Sanaa Hobeichi, and Peter R. Briggs
Earth Syst. Dynam., 12, 919–938, https://doi.org/10.5194/esd-12-919-2021, https://doi.org/10.5194/esd-12-919-2021, 2021
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Groundwater can buffer the impacts of drought and heatwaves on ecosystems, which is often neglected in model studies. Using a land surface model with groundwater, we explained how groundwater sustains transpiration and eases heat pressure on plants in heatwaves during multi-year droughts. Our results showed the groundwater’s influences diminish as drought extends and are regulated by plant physiology. We suggest neglecting groundwater in models may overstate projected future heatwave intensity.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Meng-Zhuo Zhang, Zhongfeng Xu, Ying Han, and Weidong Guo
Geosci. Model Dev., 14, 3079–3094, https://doi.org/10.5194/gmd-14-3079-2021, https://doi.org/10.5194/gmd-14-3079-2021, 2021
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The Multivariable Integrated Evaluation Tool (MVIETool) is a simple-to-use and straightforward tool designed for evaluation and intercomparison of climate models in terms of vector fields or multiple fields. The tool incorporates some new improvements in vector field evaluation (VFE) and multivariable integrated evaluation (MVIE) methods, which are introduced in this paper.
Ewan Pinnington, Javier Amezcua, Elizabeth Cooper, Simon Dadson, Rich Ellis, Jian Peng, Emma Robinson, Ross Morrison, Simon Osborne, and Tristan Quaife
Hydrol. Earth Syst. Sci., 25, 1617–1641, https://doi.org/10.5194/hess-25-1617-2021, https://doi.org/10.5194/hess-25-1617-2021, 2021
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Land surface models are important tools for translating meteorological forecasts and reanalyses into real-world impacts at the Earth's surface. We show that the hydrological predictions, in particular soil moisture, of these models can be improved by combining them with satellite observations from the NASA SMAP mission to update uncertain parameters. We find a 22 % reduction in error at a network of in situ soil moisture sensors after combining model predictions with satellite observations.
Wenkai Li, Shuzhen Hu, Pang-Chi Hsu, Weidong Guo, and Jiangfeng Wei
The Cryosphere, 14, 3565–3579, https://doi.org/10.5194/tc-14-3565-2020, https://doi.org/10.5194/tc-14-3565-2020, 2020
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Understanding the forecasting skills of the subseasonal-to-seasonal (S2S) model on Tibetan Plateau snow cover (TPSC) is the first step to applying the S2S model to hydrological forecasts over the Tibetan Plateau. This study conducted a multimodel comparison of the TPSC prediction skill to learn about their performance in capturing TPSC variability. S2S models can skillfully forecast TPSC within a lead time of 2 weeks but show limited skill beyond 3 weeks. Systematic biases of TPSC were found.
Cited articles
AghaKouchak, A., Farahmand, A., Melton, F. S., Teixeira, J., Anderson, M.
C., Wardlow, B. D., and Hain, C. R.: Remote sensing of drought: progress,
challenges and opportunities, Rev. Geophys., 53, 452–480,
https://doi.org/10.1002/2014rg000456, 2015.
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.
Albergel, C., Rosnay, P., Gruhier, C., Munoz-Sabater, J., Hasenauer, S., Isaksen, L., Kerr, Y., and Wagner, W.: Evaluation of remotely sensed and
modelled soil moisture products using global ground-based in situ observations, Remote Sens. Environ., 118, 215–226,
https://doi.org/10.1016/j.rse.2011.11.017, 2012.
Albergel, C., Dutra, E., Munier, S., Calvet, J. C., Munoz-Sabater, J., de
Rosnay, P., and Balsamo, G.: ERA-5 and ERA-interim driven ISBA land surface
model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22,
3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018.
An, R., Zhang, L., Wang, Z., Quaye-Ballard, J. A., You, J., Shen, X., Gao, W., Huang, L., Zhao, Y., and Ke, Z.: Validation of the ESA CCI soil moisture product in China, Int. J. Appl. Earth Obs. Geoinf., 48, 28–36,
https://doi.org/10.1016/j.jag.2015.09.009, 2016.
Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. A., and Kustas,
W. P.: A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology, J. Geophys. Res., 112, D11112,
https://doi.org/10.1029/2006jd007507, 2007.
Balenzano, A., Mattia, F., Satalino, G., and Davidson, M. W. J.: Dense temporal series of C- and L-band SAR data for soil moisture retrieval over
agricultural crops, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 4, 439–450, https://doi.org/10.1109/jstars.2010.2052916, 2011.
Bárdossy, A. and Lehmann, W.: Spatial distribution of soil moisture in a small catchment. Part 1: geostatistical analysis, J. Hydrol., 206, 1–15,
https://doi.org/10.1016/s0022-1694(97)00152-2, 1998.
Bastiaanssen, W. G. M., Molden, D. J., and Makin, I. W.: Remote sensing for
irrigated agriculture: examples from research and possible applications, Agric. Water Manage., 46, 137–155, https://doi.org/10.1016/s0378-3774(00)00080-9, 2000.
Beck, H. E., Pan, M., Miralles, D. G., Reichle, R. H., Dorigo, W. A., Hahn, S., Sheffield, J., Karthikeyan, L., Balsamo, G., Parinussa, R. M., van Dijk, A. I. J. M., Du, J., Kimball, J. S., Vergopolan, N., and Wood, E. F.: Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors, Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, 2021.
Berg, A. A., Famiglietti, J. S., Walker, J. P., and Houser, P. R.: Impact of
bias correction to reanalysis products on simulations of North American soil
moisture and hydrological fluxes, J. Geophys. Res., 108, 4490, https://doi.org/10.1029/2002jd003334, 2003.
Berrisford, P., Dee, D., Poli, P., Brugge, R., Fielding, K., Fuentes, M., Kallberg, P., Kobayashi, S., Uppala, S., and Simmons, A.: The ERA-Interim archive, version 2.0, ERA report series, 1. Technical Report, ECMWF, Shinfield Park, Reading, 23 pp., 2011.
Bogena, H. R., Huisman, J. A., Oberdörster, C., and Vereecken, H.: Evaluation of a low-cost soil water content sensor for wireless network
applications, J. Hydrol., 344, 32–42, https://doi.org/10.1016/j.jhydrol.2007.06.032, 2007.
Busch, F. A., Niemann, J. D., and Coleman, M.: Evaluation of an empirical
orthogonal function-based method to downscale soil moisture patterns based
on topographical attributes, Hydrol. Process., 26, 2696–2709,
https://doi.org/10.1002/hyp.8363, 2012.
Chakravorty, A., Chahar, B. R., Sharma, O. P., and Dhanya, C. T.: A regional
scale performance evaluation of SMOS and ESA-CCI soil moisture products over
India with simulated soil moisture from MERRA-Land, Remote Sens. Environ.,
186, 514–527, https://doi.org/10.1016/j.rse.2016.09.011, 2016.
Chauhan, N. S., Miller, S., and Ardanuy, P.: Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach,
Int. J. Remote Sens., 24, 4599–4622, https://doi.org/10.1080/0143116031000156837, 2003.
Chen, Y. and Yuan, H.: Evaluation of nine sub-daily soil moisture model products over China using high-resolution in situ observations, J. Hydrol.,
588, 125054, https://doi.org/10.1016/j.jhydrol.2020.125054, 2020.
Choi, M. and Hur, Y.: A microwave-optical/infrared disaggregation for improving spatial representation of soil moisture using AMSR-E and MODIS
products, Remote Sens. Environ., 124, 259–269, https://doi.org/10.1016/j.rse.2012.05.009, 2012.
Climate research unit: Drought indices, available at: https://crudata.uea.ac.uk/cru/data/drought/#global/, last access: 12 January 2020.
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J.,
Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P.,
Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman,
P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D., and Worley, S. J.: The twentieth century reanalysis
project, Q. J. Roy. Meteorol. Soc., 137, 1–28, https://doi.org/10.1002/qj.776, 2011.
Crow, W. and Van den Berg, M.: An improved approach for estimating observation and model error parameters in soil moisture data assimilation,
Water Resour. Res., 46, W12519, https://doi.org/10.1029/2010WR009402, 2010.
Crow, W. T. and Ryu, D.: A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals, Hydrol.
Earth Syst. Sci., 13, 1–16, https://doi.org/10.5194/hess-13-1-2009, 2009.
Crow, W. T. and Wood, E. F.: Multi-scale dynamics of soil moisture variability observed during SGP'97, Geophys. Res. Lett., 26, 3485–3488,
https://doi.org/10.1029/1999gl010880, 1999.
Crow, W. T., Berg, A. A., Cosh, M. H., Loew, A., Mohanty, B. P., Panciera, R., de Rosnay, P., Ryu, D., and Walker, J. P.: Upscaling sparse ground-based
soil moisture observations for the validation of coarse-resolution satellite
soil moisture products, Rev. Geophys., 50, RG2002, https://doi.org/10.1029/2011rg000372, 2012.
C3S: ERA5: fifth generation of ECMWF atmospheric reanalyses of the global
climate, available at: https://cds.climate.copernicus.eu/cdsapp#!/home (last access: 12 July 2018), 2017.
Dai, A., Trenberth, K. E., and Qian, T.: A global dataset of palmer drought
severity index for 1870–2002: relationship with soil moisture and effects of surface warming, J. Hydrometeorol., 5, 1117–1130, https://doi.org/10.1175/jhm-386.1, 2004.
Das, N. N., Entekhabi, D., and Njoku, E. G.: An algorithm for merging SMAP
radiometer and radar data for high-resolution soil-moisture retrieval, IEEE
T. Geosci. Remote, 49, 1504–1512, https://doi.org/10.1109/tgrs.2010.2089526, 2011.
Decker, M., Brunke, M. A., Wang, Z., Sakaguchi, K., Zeng, X., and Bosilovich, M. G.: Evaluation of the reanalysis products from GSFC, NCEP, and ECMWF using flux tower observations, J. Climate, 25, 1916–1944, https://doi.org/10.1175/jcli-d-11-00004.1, 2012.
de Jeu, R. A. M., Wagner, W., Holmes, T. R. H., Dolman, A. J., van de Giesen, N. C., and Friesen, J.: Global soil moisture patterns observed by space borne microwave radiometers and scatterometers, Surv. Geophys., 29, 399–420, https://doi.org/10.1007/s10712-008-9044-0, 2008.
Deng, Y., Wang, S., Bai, X., Wu, L., Cao, Y., Li, H., Wang, M., Li, C., Yang, Y., Hu, Z., Tian, S., and Lu, Q.: Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations, Hydrol. Process., 34, 836–851, https://doi.org/10.1002/hyp.13636, 2020.
Dirmeyer, P. A.: The terrestrial segment of soil moisture-climate coupling,
Geophys. Res. Lett., 38, L16702, https://doi.org/10.1029/2011gl048268, 2011.
Dobriyal, P., Qureshi, A., Badola, R., and Hussain, S. A.: A review of the
methods available for estimating soil moisture and its implications for water resource management, J. Hydrol., 458–459, 110–117, https://doi.org/10.1016/j.jhydrol.2012.06.021, 2012.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L.,
Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI soil moisture for improved earth system understanding: state-of-the art and future directions, Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017.
Dorigo, W., Wagner, W., Gruber, A., Scanlon, T., Hahn, S., Kidd, R., Paulik,
C., Reimer, C., van der Schalie, R., and de Jeu, R.: ESA soil moisture
climate change initiative (soil_moisture_cci): version 04.2 data collection, Cent. Environ. Data Anal., https://doi.org/10.5285/3a8a94c3fa464d68b6d70df291afd457, 2018.
Dorigo, W. A., Scipal, K., Parinussa, R. M., Liu, Y. Y., Wagner, W., de Jeu,
R. A. M., and Naeimi, V.: Error characterisation of global active and
passive microwave soil moisture datasets, Hydrol. Earth Syst. Sci., 14,
2605–2616, https://doi.org/10.5194/hess-14-2605-2010, 2010.
Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A.,
Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and
Jackson, T.: The international soil moisture network: a data hosting facility for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15, 1675–1698, https://doi.org/10.5194/hess-15-1675-2011, 2011.
Dorigo, W. A., Gruber, A., De Jeu, R. A. M., Wagner, W., Stacke, T., Loew, A., Albergel, C., Brocca, L., Chung, D., Parinussa, R. M., and Kidd, R.:
Evaluation of the ESA CCI soil moisture product using ground-based observations, Remote Sens. Environ., 162, 380–395, https://doi.org/10.1016/j.rse.2014.07.023, 2015.
ECMWF: European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis Interim (ERA-Interim) Model Data, NCAS British Atmospheric Data Centre, Oxford, UK, 2009.
ECMWF: ERA5 Catalogue, available at: https://apps.ecmwf.int/data-catalogues/era5/?class=ea, last access: 27 November 2017.
ECMWF: ERA Interim, Daily, available at:
https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/, last access: 5 June 2018.
Engman, E. T.: Applications of microwave remote sensing of soil moisture for
water resources and agriculture, Remote Sens. Environ., 35, 213–226,
https://doi.org/10.1016/0034-4257(91)90013-v, 1991.
Entekhabi, D., Yueh, S., O'Neill, P., Kellogg, K., Allen, A., Bindlish, R., Brown, M., Chan, S., Colliander, A., Crow, W., Das, N., De Lannoy, G., Dunbar, R., Edelstein, W., Entin, J., Escobar, V., Goodman, S., Jackson, T., Jai, B., Johnson, J., Kim, E., Kim, S., Kimball, J., Koster, R., Leon, A., McDonald, K., Moghaddam, M., Mohammed, P., Moran, S., Njoku, E., Piepmeier, J., Reichle, R., Rogez, F., Shi, J., Spencer, M., Thurman, S., Tsang, L., Van Zyl, J., Weiss, B., and West, R.: SMAP Handbook Soil Moisture Active Passive, Mapping Soil Moisture Freeze/Thaw From Space, Nat. Aeronaut. Space Admin., Jet Propul. Lab., Pasadena, California, 180 pp., 2014.
ESA: v05.2 release: ESA CCI SM now including SMAP data!, available at:
https://www.esa-soilmoisture-cci.org/ (last access: 7 September 2018), 2018.
GCOS: Essential Climate Variables-Land, available at:
https://public.wmo.int/en/programmes/global-climate-observing-system/essential-climate-variables
(last access: 12 July 2019), 2010.
González-Zamora, Á., Sánchez, N., Pablos, M., and
Martínez-Fernández, J.: CCI soil moisture assessment with SMOS soil
moisture and in situ data under different environmental conditions and
spatial scales in Spain, Remote Sens. Environ., 225, 469–482,
https://doi.org/10.1016/j.rse.2018.02.010, 2019.
Gottschalck, J., Meng, J., Rodell, M., and Houser, P.: Analysis of multiple
precipitation products and preliminary assessment of their impact on Global
Land Data Assimilation System land surface states, J. Hydrometeorol., 6,
573–598, 2005.
Gruber, A., Dorigo, W. A., Crow, W., and Wagner, W.: Triple collocation-based merging of satellite soil moisture retrievals, IEEE T. Geosci. Remote, 55, 6780–6792, https://doi.org/10.1109/tgrs.2017.2734070, 2017.
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.:
Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739,
https://doi.org/10.5194/essd-11-717-2019, 2019.
Gupta, H., Kling, H., Yilmaz, K., and Martinez, G. F.: Decomposition of the
mean squared error and NSE performance criteria: Implications for improved
hydrological modeling, J. Hydrol., 377, 80–91, 2009.
Hagan, D. F. T., Parinussa, R. M., Wang, G., and Draper, C. S.: An evaluation of soil moisture anomalies from global model-based datasets over the people's republic of China, Water, 12, 117, https://doi.org/10.3390/w12010117, 2020.
Harmsen, E. W., Norman, L. M., Nicole, J. S., and Gonzalez, J. E.: Seasonal climate change impacts on evapotranspiration, precipitation deficit and crop yield in Puerto Rico, Agr. Water Manage., 96, 1085–1095, 2009.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., Munoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Holm, E., Janiskova, M., Keeley, S., Laloyaux, P., Lopez, P., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thepaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Ikonen, J., Smolander, T., Rautiainen, K., Cohen, J., Lemmetyinen, J., Salminen, M., and Pulliainen, J.: Spatially distributed evaluation of ESA
CCI soil moisture products in a northern boreal forest environment, Geosciences, 8, 51, https://doi.org/10.3390/geosciences8020051, 2018.
ISMN: Welcome to the International Soil Moisture Network, available at: https://ismn.geo.tuwien.ac.at/en/, last access: 15 June 2020.
Jackson, T., Cosh, M., Bindlish, R., Starks, P., Bosch, D., Seyfried, M.,
Goodrich, D., Moran, S., and Du, J.: Validation of Advanced Microwave Scanning Radiometer soil moisture products, IEEE T. Geosci. Remote, 48, 4256–4272, https://doi.org/10.1109/TGRS.2010.2051035, 2010.
Jackson, T., Bindlish, R., Cosh, M., Zhao, T., Starks, P., Bosch, D., Seyfried, M., Moran, M., Goodrich, D., Kerr, Y., and Leroux, D.: Validation of Soil Moisture and Ocean Salinity (SMOS) soil moisture over watershed networks in the U.S., IEEE T. Geosci. Remote, 50, 1530–1543, https://doi.org/10.1109/TGRS.2011.2168533, 2012.
Jasper, K., Calanca, P., and Fuhrer, J.: Changes in summertime soil water patterns in complex terrain due to climatic change, J. Hydrol., 327, 550–563, 2006.
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S. K., Hnilo, J. J., Fiorino, M., and Potter, G. L.: NCEP-DOE AMIP-II reanalysis (R-2), B. Am. Meteorol. Soc., 83, 1631–1643, https://doi.org/10.1175/BAMS-83-11-1631, 2002.
Kim, J. E. and Hong, S. Y.: Impact of soil moisture anomalies on summer rainfall over east Asia: a regional climate model study, J. Climate, 20,
5732–5743, https://doi.org/10.1175/2006jcli1358.1, 2007.
Komma, J., Blöschl, G., and Reszler, C.: Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting, J. Hydrol., 357,
228–242, https://doi.org/10.1016/j.jhydrol.2008.05.020, 2008.
Lai, X., Wen, J., Ceng, S. X., Song, H. Q., Tian, H., Shi, X. K., He, Y., and Huang, X.: Numerical simulation and evaluation study of soil moisture over China by using CLM4.0 model, J. Atmos. Sci., 38, 499–512, 2014.
Li, H. Y., Robock, A., Liu, S., Mo, X., and Viterbo, P.: Evaluation of reanalysis soil moisture simulations using updated Chinese soil moisture
observations, J. Hydrometeorol., 6, 180–193, https://doi.org/10.1175/jhm416.1, 2009.
Li, M., Wu, P., and Ma, Z.: A comprehensive evaluation of soil moisture and
soil temperature from third-generation atmospheric and land reanalysis data
sets, Int. J. Climatol., 40, 5744–5766, https://doi.org/10.1002/joc.6549, 2020.
Li, Y., Li, Y., Yuan, X., Zhang, L., and Sha, S.: Evaluation of model-based
soil moisture drought monitoring over three key regions in China, J. Appl.
Meteorol. Clim., 57, 1989–2004, https://doi.org/10.1175/jamc-d-17-0118.1, 2018.
Liu, L., Zhang, R., and Zuo, Z.: Intercomparison of spring soil moisture among multiple reanalysis data sets over eastern China, J. Geophys. Res., 119, 54–64, https://doi.org/10.1002/2013jd020940, 2014.
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.
Luo, L., Tang, W., Lin, Z., and Wood, E.: Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China, Clim. Dynam., 41, 2213–2230, 2013.
Ma, H. , Zeng, J. , Chen, N. , Zhang, X. , and Wang, W.: Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: a comprehensive assessment using global ground-based observations, Remote Sens. Environ., 231, 111215, https://doi.org/10.1016/j.rse.2019.111215, 2019.
Ma, S., Zhu, K., Li, M., and Ma, Z.: A comparative study of multi-source soil moisture data for China's regions, Clim. Environ. Res., 21, 121–133, 2016.
Mall, R., Gupta, A., Singh, R., Singh, R., and Rathore, L.: Water resources and climate change: an Indian perspective, Curr. Sci., 90, 1610–1626, 2006.
Markewitz, D., Devine, S., Davidson, E. A., Brando, P., and Nepstad, D. C.:
Soil moisture depletion under simulated drought in the Amazon: impacts on
deep root uptake, New Phytol., 187, 592–607, https://doi.org/10.1111/j.1469-8137.2010.03391.x, 2010.
McColl, K. A., Wang, W., Peng, B., Akbar, R., Gianotti, D. J. S., Lu, H., Pan, M., and Entekhabi, D.: Global characterization of surface soil moisture
drydowns, Geophys. Res. Lett., 44, 3682–3690, https://doi.org/10.1002/2017gl072819, 2017.
Murphy, A.: Skill scores based on the mean square error and their relationships to the correlation coefficient, Mon. Weather Rev., 116, 2417–2424, 1988.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through. Part I. A
conceptual models discussion of principles, J. Hydrol., 10, 282–290, 1970.
Nie, S., Luo, Y., and Zhu, J.: Trends and scales of observed soil moisture
variation in China, Adv. Atmos. Sci., 25, 43–58, 2008.
NMIC: Data set of crop growth and soil moisture in China (AGME_AB2_CHN_TEN): http://data.cma.cn/data/cdcdetail/dataCode/AGME_AB2_CHN_TEN.html (last access: 27 November 2017), 2006.
NorBiasto, D., Borga, M., Esposti, S. D., Gaume, E., and Anquetin, S.: Flash
flood warning based on rainfall thresholds and soil moisture conditions: an
assessment for gauged and ungauged basins, J. Hydrol., 362, 274–290,
https://doi.org/10.1016/j.jhydrol.2008.08.023, 2008.
Ochsner, T. E., Cosh, M. H., Cuenca, R. H., Dorigo, W. A., Draper, C. S.,
Hagimoto, Y., Kerr, Y. H., Larson, K. M., Njoku, E. G., Small, E. E., and Zreda, M.: State of the art in large-scale soil moisture monitoring, Soil Sci. Soc. Am. J., 77, 1888–1919, https://doi.org/10.2136/sssaj2013.03.0093, 2013.
Pasik, A., Scanlon, T., Dorigo, W., de Jeu, R. A. M., Hahn, S., van der
Schalie, R., Wagner, W., Kidd, R., Gruber, A., Moesinger, L., and Preimesberger, W.: ESA Climate Change Initiative Plus – Soil Moisture: Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version v05.2, Earth Observation Data Centre for Water Resources Monitoring (EODC) GmbH, TU Wien, VanderSat, CESBIO and ETH, Zürich, 71 pp., 2020.
Peng, J. and Loew, A.: Recent advances in soil moisture estimation from remote sensing, Water, 9, 530, https://doi.org/10.3390/w9070530, 2017.
Peng, J., Niesel, J., Loew, A., Zhang, S., and Wang, J.: Evaluation of
satellite and reanalysis soil moisture products over southwest China using
ground-based measurements, Remote Sens., 7, 15729–15747, https://doi.org/10.3390/rs71115729, 2015.
Peng, J., Loew, A., Merlin, O., and Verhoest, N. E. C.: A review of spatial
downscaling of satellite remotely sensed soil moisture, Rev. Geophys., 55,
341–366, https://doi.org/10.1002/2016RG000543, 2017.
Petropoulos, G. P., Ireland, G., and Barrett, B.: Surface soil moisture
retrievals from remote sensing: current status, products & future trends,
Phys. Chem. Earth Pt. A/B/C, 83–84, 36–56, https://doi.org/10.1016/j.pce.2015.02.009, 2015.
Qiu, B., Xue, Y., Fisher, J. B., Guo, W., Berry, J. A., and Zhang, Y.: Satellite chlorophyll fluorescence and soil moisture observations lead to
advances in the predictive understanding of global terrestrial coupled
carbon-water cycles, Global Biogeochem. Cy., 32, 360–375, https://doi.org/10.1002/2017gb005744, 2018.
Robock, A., Vinnikov, K. Y., Srinivasan, G., Entin, J. K., Hollinger, S. E.,
Speranskaya, N. A., Liu, S., and Namkhai, A.: The global soil moisture data
bank, B. Am. Meteorol. Soc., 81, 1281–1299,
https://doi.org/10.1175/1520-0477(2000)081<1281:tgsmdb>2.3.co;2, 2000.
Schellekens, J., Dutra, E., Martínez-de la Torre, A., Balsamo, G., van Dijk, A., Weiland, F. S., Minvielle, M., Calvet, J.-C., Decharme, B.,
Eisner, S., Fink, G., Flörke, M., Peßenteiner, S., van Beek, R.,
Polcher, J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo, W., and Weedon, G. P.: A global water resources ensemble of hydrological models: the
eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data, 9, 389–413,
https://doi.org/10.5194/essd-9-389-2017, 2017.
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.
Sevanto, S., McDowell, N. G., Dickman, L. T., Pangle, R., and Pockman, W. T.: How do trees die? A test of the hydraulic failure and carbon starvation hypotheses, Plant Cell Environ., 37, 153–161, https://doi.org/10.1111/pce.12141, 2014.
Shangguan, W., Dai, Y., Liu, B., Ye, A., and Yuan, H.: A soil particle-size distribution dataset for regional land and climate modelling in China, Geoderma, 171–172, 85–91, https://doi.org/10.1016/j.geoderma.2011.01.013, 2012.
Shrivastava, S., Kar, S. C., and Sharma, A. R.: Soil moisture variations in
remotely sensed and reanalysis datasets during weak monsoon conditions over
central India and central Myanmar, Theor. Appl. Climatol., 129, 305–320,
https://doi.org/10.1007/s00704-016-1792-z, 2017.
Taylor, C. M., De Jeu, R. A. M., Guichard, F., Harris, P. P., and Dorigo, W.
A.: Afternoon rain more likely over drier soils, Nature, 489, 282–286, 2012.
Wells, N., Goddard, S., and Hayes, M. J.: A self-calibrating palmer drought
severity index, J. Climate, 17, 2335–2351,
https://doi.org/10.1175/1520-0442(2004)017<2335:aspdsi>2.0.co;2, 2004.
Western, A. W. and Blöschl, G.: On the spatial scaling of soil moisture, J. Hydrol., 217, 203–224, https://doi.org/10.1016/s0022-1694(98)00232-7, 1999.
Wu, J. and Gao, X. J.: A gridded daily observation dataset over China region
and comparison with the other datasets, Chinese J. Geophys., 56, 1102–1111, https://doi.org/10.6038/cjg20130406, 2013.
Yun, Y., Liu, C., Luo, Y., Liang, X., Huang, L., Chen, F., and Rasmmusen, R.: Convection-permitting regional climate simulation of warm-season precipitation over Eastern China, Clim. Dynam., 54, 1469–1489, https://doi.org/10.1007/s00382-019-05070-y, 2020.
Zeng, J., Li, Z., Chen, Q., Bi, H., Qiu, J., and Zou, P.: Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan plateau using in-situ observations, Remote Sens. Environ., 163, 91–110, 2015.
Zhang, W., Zhou, T., and Zhi, H.: Numerical test of soil moisture affecting
summer climate in China, J. Meteorol., 70, 78–90, https://doi.org/10.1007/s11783-011-0280-z, 2012.
Zhu, Z., Shi, C., Zhang, T., and Wang, J.: Applicability analysis of four
reanalysis soil moisture datasets in China, Plateau Meteorol., 37, 240–252,
https://doi.org/10.7522/j.issn.1000-0534.2017.00033, 2018.
Zuo, Z. and Zhang, R.: Soil Moisture and its Impact on the East Asian Summer Monsoon, American Geophysical Union, Washington, DC, 2016.
Short summary
Soil moisture (SM) plays a critical role in the water and energy cycles of the Earth system, for which a long-term SM product with high quality is urgently needed. In situ observations are generally treated as the true value to systematically evaluate five SM products, including one remote sensing product and four reanalysis data sets during 1981–2013. This long-term intercomparison study provides clues for SM product enhancement and further hydrological applications.
Soil moisture (SM) plays a critical role in the water and energy cycles of the Earth system, for...
