Mapping the global depth to bedrock for land surface modeling

By Shangguan Wei, Tomislav Hengl, Jorge Mendes de Jesus, Hua Yuan, Yongjiu Dai. 2017

314 citations recorded by the source corpus.

Depth to bedrock serves as the lower boundary of land surface models, which controls hydrologic and biogeochemical processes. This paper presents a framework for global estimation of depth to bedrock (DTB). Observations were extracted from a global compilation of soil profile data (ca. 1,30,000 locations) and borehole data (ca. 1.6 million locations). Additional pseudo‐observations generated by expert knowledge were added to fill in large sampling gaps. The model training points were then overlaid on a stack of 155 covariates including DEM‐based hydrological and morphological derivatives, lithologic units, MODIS surface reflectance bands and vegetation indices derived from the MODIS land products. Global spatial prediction models were developed using random forest and Gradient Boosting Tree algorithms. The final predictions were generated at the spatial resolution of 250 m as an ensemble prediction of the two independently fitted models. The 10–fold cross‐validation shows that the models explain 59% for absolute DTB and 34% for censored DTB (depths deep than 200 cm are predicted as 200 cm). The model for occurrence of R horizon (bedrock) within 200 cm does a good job. Visual comparisons of predictions in the study areas where more detailed maps of depth to bedrock exist show that there is a general match with spatial patterns from similar local studies. Limitation of the data set and extrapolation in data spare areas should not be ignored in applications. To improve accuracy of spatial prediction, more borehole drilling logs will need to be added to supplement the existing training points in under‐represented areas.

Subfields: Environmental Engineering, Soil Science, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/64e35b998977552a69a8adc51510225d5f3b1e4f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Y. Bar-On
2. Rob Phillips
3. Ron Milo
4. Marc F. P. Bierkens
5. Yoshihide Wada

In Soil Science

1. Y. Bar-On
2. Rob Phillips
3. Ron Milo

In Water Science and Technology

1. Marc F. P. Bierkens
2. Yoshihide Wada

Citations in the corpus

• The evolution of process-based hydrologic models: Historical challenges and the collective quest for physical realism. (2017, unknown): “, 2014), soil characteristics and regolith thickness (Pelletier et al., 2016; Shangguan et al., 2016; Hengl et al., 2017), and global thickness of the upper aquifers (de Graaf et al.”
• Non-renewable groundwater use and groundwater depletion: a review (2019, background): “…(1997), Richey et al (2015a), Gleeson et al (2015), DeGraaf (2016), see table 5 Global hydrogeological datasets Richts et al 2011, Gleeson et al (2011), Hartmann andMoosdorf (2012), Gleeson et al (2014), DeGraaf et al (2015), Pelletier et al 2016, Shangguan et al 2017, Limberger et al 34 Environ.”
• The biomass distribution on Earth (2018, background): “Applying this value across the entire terrestrial surface and assuming a mean soil depth of 10 meters (276), we get to an estimate of ~10(29)-10(30) virions in soil globally.”