Abstract
Soil moisture (SM) is critical for various hydro-meteorological applications. Land surface models (LSMs) can produce global spatio-temporal continuous SM estimates. Recently, NASA and ECMWF released GLDAS-2.1 and ERA5-Land datasets, respectively, which contain newly produced LSM-based global SM products, and these have not been thoroughly evaluated in China. To better understand the two products, we decomposed them into SM climatology (i.e., mean seasonal cycle) and SM anomaly (i.e., short-term variability) components and evaluated them separately in China. In particular, the evaluation was conducted considering ground-based SM observations obtained from 1411 stations and two remotely sensed SM products. The following key results were obtained: (a) In the SM climatology evaluation, ERA5-Land showed a larger bias in (semi-) humid areas (0.06 m3/m3 on an average), while GLDAS-2.1 was generally unbiased. GLDAS-2.1 showed higher temporal precision (temporal mean R = 0.47 [-]) than ERA5-Land (temporal mean R = 0.17 [-]) in northern arid areas, while ERA5-Land exhibited better performance (temporal mean R = 0.64 [-]) than GLDAS-2.1 (temporal mean R = 0.34 [-]) in southern humid areas. (b) For the SM anomaly evaluation, ERA5-Land and GLDAS-2.1 performed similarly, and ERA5-Land (temporal mean R = 0.45 [-]) marginally outperformed GLDAS-2.1 (temporal mean R = 0.40 [-]). (c) For the raw SM, GLDAS-2.1 and ERA5-Land had higher temporal precision in the northern and southern areas, respectively, which are mostly determined by their SM climatology. Our findings highlight the important role of SM climatology and provide an important reference for improving the aforementioned SM products.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The GLDAS-2.1 SM product is available in https://disc.gsfc.nasa.gov/. The ERA5-Land SM data can be found at https://cds.climate.copernicus.eu/cdsapp#!/home. The ASCAT SM index data is obtained from https://rs.geo.tuwien.ac.at/products/. The SMAP SM product used in this study is available at https://nsidc.org/data/smap/smap-data.html. The in-situ SM are obtained from the Ministry of Water Resources Information Center, but the data are not publicly available due to restrictions of their data license.
References
Basheer AA (2018) Chemical chiral pollution: Impact on the society and science and need of the regulations in the 21(st) century. Chirality 30:402–406. https://doi.org/10.1002/chir.22808
Basheer AA (2018b) New generation nano-adsorbents for the removal of emerging contaminants in water. J Mol Liq 261:583–593. https://doi.org/10.1016/j.molliq.2018.04.021
Basheer AA, Ali I (2018) Stereoselective uptake and degradation of (+/-)-o,p-DDD pesticide stereomers in water-sediment system. Chirality 30:1088–1095. https://doi.org/10.1002/chir.22989
Beck HE et al (2017) Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrol Earth Syst Sci 21:6201–6217. https://doi.org/10.5194/hess-21-6201-2017
Cammalleri C, Vogt JV, Bisselink B, de Roo A (2017) Comparing soil moisture anomalies from multiple independent sources over different regions across the globe . Hydrol Earth Syst Sci 21:6329–6343. https://doi.org/10.5194/hess-21-6329-2017
Chakravorty A, Chahar BR, Sharma OP, Dhanya CT (2016) 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
Chan SK et al (2016) Assessment of the SMAP passive soil moisture product. IEEE Trans Geosci Remote Sens 54:4994–5007. https://doi.org/10.1109/tgrs.2016.2561938
Chen F et al (2017) Application of triple collocation in ground-based validation of soil moisture active/passive (SMAP) level 2 data products. IEEE J Sel Top Appl Earth Obs Remote Sens 10:489–502. https://doi.org/10.1109/jstars.2016.2569998
Chen N et al (2020) Drought propagation in Northern China Plain: A comparative analysis of GLDAS and MERRA-2 datasets. J Hydrol 588. https://doi.org/10.1016/j.jhydrol.2020.125026
Colliander A et al (2017) Validation of SMAP surface soil moisture products with core validation sites. Remote Sens Environ 191:215–231. https://doi.org/10.1016/j.rse.2017.01.021
Dong JZ et al (2020) Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation. Remote Sens Environ 242:14. https://doi.org/10.1016/j.rse.2020.111756
Dorigo WA, Scipal K, Parinussa RM, Liu YY, Wagner W, de Jeu RAM, Naeimi V (2010) 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
Entekhabi D et al (2010) The Soil Moisture Active Passive (SMAP) Mission. Proc IEEE 98:704–716. https://doi.org/10.1109/jproc.2010.2043918
FAO/IIASA/ISRIC/ISSCAS/JRC (2008) Harmonized world soil database (version 1.0) International Institute for Applied Systems Analysis (IIASA), Laxenburg
Grove B (2019) Improved water allocation under limited water supplies using integrated soil-moisture balance calculations and nonlinear programming. Water Resour Manag 33:423–437. https://doi.org/10.1007/s11269-018-2110-6
Hagan DFT, Parinussa RM, Wang G, Draper CS (2020) An evaluation of soil moisture anomalies from global model-based datasets over the People’s Republic of China. Water 12. https://doi.org/10.3390/w12010117
Hans H et al (2019) Global reanalysis: goodbye ERA-Interim, hello ERA5. https://doi.org/10.21957/vf291hehd7
Jing W, Song J, Zhao X (2018) Evaluation of multiple satellite-based soil moisture products over continental US Based on situ measurements. Water Resour Manag 32:3233–3246. https://doi.org/10.1007/s11269-018-1989-2
Kim H et al (2018) Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products . Remote Sens Environ 204:260–275. https://doi.org/10.1016/j.rse.2017.10.026
Liang X, Jiang L, Pan Y, Shi C, Liu Z, Zhou Z (2020) A 10-Yr global land surface reanalysis interim dataset (CRA-Interim/Land): implementation and preliminary evaluation. J Meteorol Res 34:101–116. https://doi.org/10.1007/s13351-020-9083-0
Liu YY et al (2012) 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
McColl KA, Vogelzang J, Konings AG, Entekhabi D, Piles M, Stoffelen A (2014) Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target. Geophys Res Lett 41:6229–6236. https://doi.org/10.1002/2014gl061322
Miralles DG, Crow WT, Cosh MH (2010) Estimating spatial sampling errors in coarse-scale soil moisture estimates derived from point-scale observations. J Hydrometeorol 11:1423–1429. https://doi.org/10.1175/2010jhm1285.1
Mousa BG, Shu H (2020) Spatial evaluation and assimilation of SMAP, SMOS, and ASCAT satellite soil moisture products over africa using statistical techniques. Earth Space Sci 7:16. https://doi.org/10.1029/2019ea000841
Nearing G, Yatheendradas S, Crow W, Zhan XW, Liu JC, Chen F (2018) The efficiency of data assimilation. Water Resour Res 54:6374–6392. https://doi.org/10.1029/2017wr020991
Nguyen HH, Kim H, Choi M (2017) Evaluation of the soil water content using cosmic-ray neutron probe in a heterogeneous monsoon climate-dominated region . Adv Water Resour 108:125–138. https://doi.org/10.1016/j.advwatres.2017.07.020
Ouyang Y, Feng G, Leininger TD, Read J, Jenkins JN (2018) Pond and Irrigation Model (PIM): a tool for simultaneously evaluating pond water availability and crop irrigation demand. Water Resour Manag 32:2969–2983. https://doi.org/10.1007/s11269-018-1967-8
Pei W, Fu Q, Liu D, Li TX, Cheng K, Cui S (2019) A novel method for agricultural drought risk assessment. Water Resour Manag 33:2033–2047. https://doi.org/10.1007/s11269-019-02225-8
Rodell M et al (2004) The global land data assimilation system. Bull Am Meteorol Soc 85:381-381+. https://doi.org/10.1175/bams-85-3-381
Saxton KE, Rawls WJ (2006) Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci Soc Am J 70:1569–1578. https://doi.org/10.2136/sssaj2005.0117
Stoffelen A (1998) Toward the true near-surface wind speed: Error modeling and calibration using triple collocation. J Geophys Res Oceans 103:7755–7766. https://doi.org/10.1029/97jc03180
Wagner W, Lemoine G, Rott H (1999) A method for estimating soil moisture from ERS scatterometer and soil data . Remote Sens Environ 70:191–207. https://doi.org/10.1016/s0034-4257(99)00036-x
Wu ZY, Lu GH, Wen L, Lin CA (2011) Reconstructing and analyzing China’s fifty-nine year (1951–2009) drought history using hydrological model simulation. Hydrol Earth Syst Sci 15:2881–2894. https://doi.org/10.5194/hess-15-2881-2011
Yang K et al (2016) Land surface model calibration through microwave data assimilation for improving soil moisture simulations. J Hydrol 533:266–276. https://doi.org/10.1016/j.jhydrol.2015.12.018
Yilmaz MT, Crow WT, Anderson MC, Hain C (2012) An objective methodology for merging satellite- and model-based soil moisture products. Water Resour Res 48. https://doi.org/10.1029/2011wr011682
Zhou J, Wu Z, Crow WT, Dong J, He H (2020) Improving spatial patterns prior to land surface data assimilation via model calibration using SMAP Surface Soil Moisture Data. Water Resour Res 56. https://doi.org/10.1029/2020wr027770
Zhuo L, Han D, Dai Q, Islam T, Srivastava PK (2015) Appraisal of NLDAS-2 multi-model simulated soil moistures for hydrological modelling. Water Resour Manag 29:3503–3517. https://doi.org/10.1007/s11269-015-1011-1
Acknowledgements
This work is supported by the National Key R&D Program of China (Grants No. 2017YFC1502403); the National Natural Science Foundation of China (grants No. 51779071); the National Key R&D Program of China (Grants No. 2018YFC 0407701); the Fundamental Research Funds for the Central Universities (grants No. 2019B10214).
Author information
Authors and Affiliations
Contributions
Conceptualization: Zhiyong Wu, Huihui Feng; Methodology: Huihui Feng, Jianhong Zhou; Funding acquisition: Zhiyong Wu, Hai He; Writing – original draft preparation: Zhiyong Wu, Huihui Feng; Writing – review and editig: Jianhong Zhou, Hai He, Yuliang Zhang. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Ethical Approval
Not applicable.
Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Code Availability
Some code generated during the study are available from the corresponding author by request.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wu, Z., Feng, H., He, H. et al. Evaluation of Soil Moisture Climatology and Anomaly Components Derived From ERA5-Land and GLDAS-2.1 in China. Water Resour Manage 35, 629–643 (2021). https://doi.org/10.1007/s11269-020-02743-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-020-02743-w