Mapping the global depth to bedrock for land surface modeling. (22nd January 2017)

• Record Type: Journal Article
• Authors: Shangguan, Wei; Hengl, Tomislav; Mendes de Jesus, Jorge; Yuan, Hua; Dai, Yongjiu
• Is Part Of: Journal of advances in modeling earth systems. Volume 9:Number 1(2017)
• Page Start: 65
• Page End: 88
• Publication Date: 2017-01-22
• Subjects: depth to bedrock -- land surface model -- earth system model -- machine learning -- spatial prediction model; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1002/2016MS000686
• Languages: English
• ISSNs: 1942-2466

Abstract

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. Key Points: Observations from soil and geological surveys are combined for developing global spatial prediction models of depth to bedrock Machine learning explains 59% of variation in spatial distribution of depth to bedrock for interpolation but much less for extrapolation The framework proposed can be used to gradually improve accuracy by adding more ground observations