Abstract
Soil moisture (SM) plays a fundamental role in governing the water and energy balance at land-atmosphere interfaces and in controlling plant growth and biological interactions, which makes it a key indicator in drought identification. We compared and evaluated two types of surface SM datasets (Global Land Data Assimilation System-Noah-simulated (GLDAS-Noah); Europe Space Agency’s Climate Change Initiative (ESA CCI)) for drought analysis in China over 1979–2014. The cumulative density function (CDF) matching method was employed to fill the data gap of ESA CCI data using the GLDAS-Noah SM products. Drought characteristics of duration, severity, and frequency were appraised on a grid basis using the Standardized Soil Moisture Index (SSI). The results show that the SSI values calculated based on these two SM products are significantly correlated (p < 0.05) over most parts (70%) of China, with similar patterns of average drought duration, severity, and frequency. The duration and severity at the arid and semiarid regions (with duration over 3 months; with an average severity of −3.1) are generally higher than those over humid regions (with the duration of 2 months; with an average severity of −2.7), but both SM datasets show higher drying trends in humid regions. However, the two SM datasets exhibit large discrepancies in the spatial patterns of drought duration, severity, and frequency trends, especially in arid and cold regions. Both SM products are capable of monitoring extreme drought events reported in southwestern, southern, and northern China compared with the Standardized Precipitation Index (SPI). Overall, both data sources have the potential to be used for drought monitoring; however, caution should be paid in high altitude and latitude regions where a large discrepancy exists.
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Data Availability
The data that support the findings of this study are openly available at http://www.esasoilmoisture-cci.org, disc.gsfc.nasa.gov/datasets/, and http://data.cma.cn/.
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Code availability
The codes for calculating SPI and SSI can be accessed through R package Drought (CRAN.R-project.org/package=drought).
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This work is financially supported by the National Natural Science Foundation of China (grant numbers 51879222 and 52079111) and the China Scholarship Council (201906300059).
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Gengxi Zhang analyzed the data and wrote the manuscript. Xiaoling Su provided guidance and revised the manuscript. Olusola O. Ayantobo and Kai Feng revised the manuscript and improved grammar.
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Zhang, G., Su, X., Ayantobo, O.O. et al. Drought monitoring and evaluation using ESA CCI and GLDAS-Noah soil moisture datasets across China. Theor Appl Climatol 144, 1407–1418 (2021). https://doi.org/10.1007/s00704-021-03609-w
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DOI: https://doi.org/10.1007/s00704-021-03609-w