Mapping the global depth to bedrock for land surface modeling

Shangguan Wei, Tom Hengl, Jorge Mendes de Jesus, Hua Yuan, Yongjiu Dai*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

55 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)65-88
JournalJournal of Advances in Modeling Earth Systems
Volume9
Issue number1
DOIs
Publication statusPublished - 2017

Keywords

  • depth to bedrock
  • earth system model
  • land surface model
  • machine learning
  • spatial prediction model

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