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04 Jan 2021
04 Jan 2021
Abstract. Soil moisture (SM) plays a critical role in the water and energy cycles of the earth system; consequently, a long-term SM product with high quality is urgently needed. In this study, five SM products, including one microwave remote sensing product [European Space Agency's Climate Change Initiative (ESA CCI)] and four reanalysis datasets [European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERAI), National Centers for Environmental Prediction (NCEP), the Twentieth Century Reanalysis Project from National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5)], are systematically evaluated using in situ measurements during 1981–2013 in four climate regions at different timescales over mainland China. The results show that ESA CCI is closest to the observations in terms of both the spatial distributions and magnitude of the monthly SM. All reanalysis products tend to overestimate soil moisture in all regions but have higher correlations than the remote sensing product except in Northwest China. The largest inconsistency is found in southern Northeast China, with a relative RMSE value larger than 0.1. However, none of the products can well reproduce the trends of interannual anomalies. The largest relative bias of 44.6 % is found for the ERAI SM product under severe drought conditions, and the lowest relative biases of 4.7 % and 9.5 % are found for the ESA CCI SM product under severe drought conditions and the NCEP SM product under normal conditions, respectively. As decomposing mean square errors in all the products suggests that the bias terms are the dominant contribution, the ESA CCI SM product is a good option for long-term hydrometeorological applications in mainland China. ERA5 is also a promising product, which is attributed to the incorporation of more observations. This long-term intercomparison study provides clues for SM product enhancement and further hydrological applications.
Xiaolu Ling et al.
Status: open (until 01 Mar 2021)
Soil moisture, as one of the essential climate variables, has attracted more and more attention from climate research. However, there is still a long way to go for the recently widely used soil moisture products, including reanalyses based on models and retrievals from remotely sensed data, to be comparable with observations. To further develop and properly use them, it is necessary to compare with in situ observations to reveal their uncertainties. In this manuscript, the five satellite-based and reanalysis soil moisture products were evaluated in China with in situ observations for top soil layer (0-10 cm). By now the manuscript still needs to further discuss the uncertainties of in situ observations of soil moisture data, the influence of sparse data samples, and thus the unfair to compare grid products using point-scale measurements. In particular, the author pointed out that the bias term controlled the deviations of soil moisture products from the observed values. This partly stems from the spatial mismatches in the comparisons of the soil moisture measured at a point with model grid means. So, it requires more discussion about its implications. In addition, the method part needs to provide more details, for example, how the monthly means were estimated using 3-sample observations per month.
Specific comments and suggestions:
This interesting analysis used in-situ observations in China to evaluate several reanalysis- and RS-based SM products. While it is a nice self-contained study with seemingly comprehensive analyses, I found the study lacking sufficient physical explanations supporting several findings of their analyses. Also, some figures are not very well presented and need to be updated. Therefore, I’d suggest the authors go through moderate revisions before this paper can be publishable. Below are some suggestions to improve the paper:
Insufficient explanations/supports:
Figure presentation problems:
Minor:
Overall comment:
Xiaolu Ling et al.
Xiaolu Ling et al.
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