A review of the global soil property maps for Earth system models

Abstract

Abstract. Soil is an important regulator of Earth system processes, but remains one of the least well-described data layers in Earth system models (ESMs). We reviewed global soil property maps from the perspective of ESMs, including soil physical and chemical and biological properties, which can also offer insights to soil data developers and users. These soil datasets provide model inputs, initial variables, and benchmark datasets. For modelling use, the dataset should be geographically continuous and scalable and have uncertainty estimates. The popular soil datasets used in ESMs are often based on limited soil profiles and coarse-resolution soil-type maps with various uncertainty sources. Updated and comprehensive soil information needs to be incorporated into ESMs. New generation soil datasets derived through digital soil mapping with abundant, harmonized, and quality-controlled soil observations and environmental covariates are preferred to those derived through the linkage method (i.e. taxotransfer rule-based method) for ESMs. SoilGrids has the highest accuracy and resolution among the global soil datasets, while other recently developed datasets offer useful compensation. Because there is no universal pedotransfer function, an ensemble of them may be more suitable for providing derived soil properties to ESMs. Aggregation and upscaling of soil data are needed for model use, but can be avoided by using a subgrid method in ESMs at the expense of increases in model complexity. Producing soil property maps in a time series still remains challenging. The uncertainties in soil data need to be estimated and incorporated into ESMs.

Importance of dry deposition parameterization choice in global simulations of surface ozone

“…More multi-seasonal observations are especially needed over seasonally dry ecosystems where the role of hydroclimate in deposition parameterizations needs to be evaluated. Recently, standard micrometeorological measurements have been used to derive g s and stomatal deposition of O 3 over North America and Europe (Ducker et al, 2018), highlighting the potential of using global networks of micrometeorological observation (e.g. FLUXNET; Baldocchi et al, 2001) to benchmark and calibrate g s of dry deposition parameterizations, which could at least increase the spatiotemporal representativeness, if not the absolute accuracy, of dry deposition parameterizations, since it would be difficult to constrain non-stomatal sinks with this method.…”

CHLSOC: the Chilean Soil Organic Carbon database, a multi-institutional collaborative effort

“…Access to spatially explicit, consistent and reliable soil data is essential to model and map the status of soil resources globally to an increasingly detailed resolution in order to respond and assess global issues (Arrouays et al, 2014;FAO, 2015;Hengl et al, 2014;Omuto et al, 2013). Furthermore, soil datasets are one of the most important inputs for Earth system models (ESMs) to address, for example, the importance of terrestrial sinks and sources of greenhouse gases (Dai et al, 2019;Luo et al, 2016). At the same time, soils in ESMs are one of the largest sources of uncertainty (Dai et al, 2019).…”

“…Furthermore, soil datasets are one of the most important inputs for Earth system models (ESMs) to address, for example, the importance of terrestrial sinks and sources of greenhouse gases (Dai et al, 2019;Luo et al, 2016). At the same time, soils in ESMs are one of the largest sources of uncertainty (Dai et al, 2019). Hence, in recent years, there has been a growing effort to improve access to and quality of soil datasets, a key goal of the Global Soil Partnership Pillar 4 Implementation Plan sponsored by the Food and Agriculture Organization of the United Nations (Batjes et al, 2017;Omuto et al, 2013).…”

How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?

“…Combining local and global predictions could be a promising possibility, as well as the creation of SoilGrids at higher spatial resolution based on state-of-the-art remote sensing products that are relevant for soil mapping [27]. Currently, SoilGrids show the highest accuracy and resolution among the soil datasets covering the world [58], and was therefore our information source of choice.…”