Soil thickness,intended as depth to bedrock,is a key input parameter for many environmental models.Nevertheless,it is often difficult to obtain a reliable spatially exhaustive soil thickness map in widearea applicatio...Soil thickness,intended as depth to bedrock,is a key input parameter for many environmental models.Nevertheless,it is often difficult to obtain a reliable spatially exhaustive soil thickness map in widearea applications,and existing prediction models have been extensively applied only to test sites with shallow soil depths.This study addresses this limitation by showing the results of an application to a section of Wanzhou County(Three Gorges Reservoir Area,China),where soil thickness varies from 0 to40 m.Two different approaches were used to derive soil thickness maps:a modified version of the geomorphologically indexed soil thickness(GIST)model,purposely customized to better account for the peculiar setting of the test site,and a regression performed with a machine learning algorithm,i.e.,the random forest,combined with the geomorphological parameters of GIST(GIST-RF).Additionally,the errors of the two models were quantified,and validation with geophysical data was carried out.The results showed that the GIST model could not fully contend with the high spatial variability of soil thickness in the study area:the mean absolute error was 10.68 m with the root-mean-square error(RMSE)of 12.61 m,and the frequency distribution residuals showed a tendency toward underestimation.In contrast,GIST-RF returned a better performance with the mean absolute error of 3.52 m and RMSE of 4.56 m.The derived soil thickness map could be considered a critical fundamental input parameter for further analyses.展开更多
基金support for this work:National Natural Science Foundation of China(Grant Nos.41877525,61971037 and 31727901)Chongqing Key Laboratory of Geological Environment Monitoring and Disaster Early-warning in Three Gorges Reservoir Area(No.MP2020B0301)。
文摘Soil thickness,intended as depth to bedrock,is a key input parameter for many environmental models.Nevertheless,it is often difficult to obtain a reliable spatially exhaustive soil thickness map in widearea applications,and existing prediction models have been extensively applied only to test sites with shallow soil depths.This study addresses this limitation by showing the results of an application to a section of Wanzhou County(Three Gorges Reservoir Area,China),where soil thickness varies from 0 to40 m.Two different approaches were used to derive soil thickness maps:a modified version of the geomorphologically indexed soil thickness(GIST)model,purposely customized to better account for the peculiar setting of the test site,and a regression performed with a machine learning algorithm,i.e.,the random forest,combined with the geomorphological parameters of GIST(GIST-RF).Additionally,the errors of the two models were quantified,and validation with geophysical data was carried out.The results showed that the GIST model could not fully contend with the high spatial variability of soil thickness in the study area:the mean absolute error was 10.68 m with the root-mean-square error(RMSE)of 12.61 m,and the frequency distribution residuals showed a tendency toward underestimation.In contrast,GIST-RF returned a better performance with the mean absolute error of 3.52 m and RMSE of 4.56 m.The derived soil thickness map could be considered a critical fundamental input parameter for further analyses.
