Development of observation-based global multilayer soil moisture products for 1970 to 2016

Yaoping Wang,Yongjiu Dai,Xiaoying Shi,Mingzhou Jin,Stan D Wullschleger,Jiafu Mao,Forrest M Hoffman

doi:10.5194/essd-13-4385-2021

Abstract

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5∘ resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from −0.044 to 0.033 m3 m−3, root mean square errors from 0.076 to 0.104 m3 m−3, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

Highlights

High-quality global soil moisture (SM) datasets benefit many applications, such as understanding drought changes and ecosystem dynamics (Green et al, 2019; Kumar et al, 2019), studying land–atmosphere feedbacks (Li et al, 2020a), benchmarking model capabilities (Loew et al, 2013), and initializing weather and climate forecast systems (SospedraAlfonso and Merryfield, 2018)
The bias values of individual merged products were similar; the root mean squared error (RMSE) and Corr values of the ORS-based merged products (Mean ORS, optimal linear combination (OLC) ORS, emergent constraint (EC) ORS) were better than the EC ALL product, and the RMSE and Corr values of the EC ALL product were better than the CMIP5or CMIP6-based merged products (EC CMIP5, EC CMIP6, EC CMIP5+6) (Fig. 2)
The hybrid products underestimated the SM in the 0–10 cm layer of the evergreen broadleaf forests, the deciduous needleleaf forests, and the deeper soil layers of many other land cover types (Fig. S5a–d)

Summary

Introduction

High-quality global soil moisture (SM) datasets benefit many applications, such as understanding drought changes and ecosystem dynamics (Green et al, 2019; Kumar et al, 2019), studying land–atmosphere feedbacks (Li et al, 2020a), benchmarking model capabilities (Loew et al, 2013), and initializing weather and climate forecast systems (SospedraAlfonso and Merryfield, 2018). The majority of SM products fall into five categories: in situ measurements, satellite observations, offline land surface model (LSM) simulations, reanalysis, and Earth system model (ESM) simulations. The SM in LSM simulations usually spans multiple soil layers and has no spatial or temporal gaps, which is convenient for regional and global analysis (Gu et al, 2019); LSM simulations may contain considerable errors because of inadequacies in the model physics, parameterization, and drivers (Andresen et al, 2020). Reanalysis datasets assimilate observations into LSMs or coupled forecast systems that have LSMs as a component and are gap-free. The meteorological variables, especially precipitation, simulated by the atmosphere model of the coupled reanalysis system may be biased, leading to inaccurate SM estimates by the intrinsic LSM component (Balsamo et al, 2015). ESMs, share the same uncertainty sources for the SM estimates as the LSMs; the SM in ESM simulations has internal variability-related uncertainties induced by unrealistic initialization from the preindustrial conditions rather than the real world (Eyring et al, 2016; Taylor et al, 2012)

Methods

Results

Conclusion