## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Land Surface Properties
#### 3.3.2 Soil Depth  
While earth-observing satellite data have been widely used in modeling land processes (e.g., global land cover [210]), in situ measurements as constrained by satellite observations are needed to estimate other variables. One such variable is the Depth To Bedrock (DTB), which is essential for accurate land surface modeling of the energy, water, carbon cycle, and dynamic vegetation. In general, a lower boundary condition is needed to solve the governing equation for vadose zone soil moisture. The presence of bedrock is a player in soil hydrology as can change the water flow and storage [211]. Earlier land models assume a free drainage bottom condition (i.e., without considering groundwater), while more recent models include an unconfined aquifer which implicitly assumes a globally constant bedrock depth. However, no conditions are satisfactory without a DTB estimate [212].  
Ref. [213] has recently developed the world's first global 1 km soil and alluvial thickness dataset (Figure 18) by combining geomorphologic theory with the best available data for topography (using satellite elevation measurements), climate, ecosystem [210], and geology as input. This dataset utilised different approaches to separately estimate soil and alluvial thickness for upland hillslopes, upland valley bottoms, and lowlands, as the character of soil depth is fundamentally different for these units: upland hillslopes have relatively shallow soils (\(\sim\)1 m), while valleys and lowlands have relatively deeper soils/alluvium (10 s of meters or more) (Figure 18). These three units are distinguished at the 90 m pixel scale using a valley network extraction algorithm as well as criteria related to geologic age. On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U.S. and Europe and on lowlands using DTB observations from groundwater wells in the U.S. This dataset is publicly available at: [[https://daac.ornl.gov/SOILS/guides/Global_Soil_Regolith_Sediment.html](https://daac.ornl.gov/SOILS/guides/Global_Soil_Regolith_Sediment.html)]([https://daac.ornl.gov/SOILS/guides/Global_Soil_Regolith_Sediment.html](https://daac.ornl.gov/SOILS/guides/Global_Soil_Regolith_Sediment.html)). A similar product based on global spatial ensemble prediction models with various input datasets is also available [214].  
Figure 17: Global map of land orography and ocean+lakes bathymetry in m (above and below sea level or lake shore respectively) at T1279 resolution ( 9 km) as used in the ECMWF—IFS operational system.  
The impact of this DTB dataset on land surface modeling has been demonstrated [215] implementing variable DTB in the Community Land Model (CLM4.5) with 0.9\({}^{\circ}\)latitude x 1.25\({}^{\circ}\)longitude grid boxes. The greatest changes in the simulation with variable DTB are to baseflow, with the annual minimum generally occurring earlier in the year comparing to fixed soil depth simulations. Smaller changes are seen in latent heat flux and surface runoff primarily as a result of an increase in the annual cycle amplitude. These changes are related to soil moisture changes that are most substantial in locations with shallow bedrock. Total water storage anomalies are not strongly affected over most river basins because they tend to contain deep soils. These anomalies are substantially different for river basins in more mountainous terrain. Additionally, the annual cycle in soil temperature is partially affected by including realistic soil thicknesses resulting from changes in the vertical profile of heat capacity and thermal conductivity. However, the largest changes to soil temperature are introduced by the soil moisture changes in the variable soil thickness simulation.