Implementing and Evaluating Variable Soil Thickness in the Community Land Model, Version 4.5 (CLM4.5)

Abstract

One of the recognized weaknesses of land surface models as used in weather and climate models is the assumption of constant soil thickness because of the lack of global estimates of bedrock depth. Using a 30-arc-s global dataset for the thickness of relatively porous, unconsolidated sediments over bedrock, spatial variation in soil thickness is included here in version 4.5 of the Community Land Model (CLM4.5). The number of soil layers for each grid cell is determined from the average soil depth for each 0.98 latitude 3 1.258 longitude grid cell. The greatest changes in the simulation with variable soil thickness are to baseflow, with the annual minimum generally occurring earlier. 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 (TWS) anomalies are not strongly affected over most river basins since most basins contain mostly deep soils, but TWS anomalies are substantially different for a river basin with 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. This implementation of variable soil thickness represents a step forward in land surface model development.

Hillslope Hydrology in Global Change Research and Earth System Modeling

“…Agricultural soil surveys provided particle size information to compute hydraulic properties via pedotransfer functions (e.g., Clapp & Hornberger, ; van Genuchten, ; although these functions, based on midlatitude soils, cannot represent the deeply weathered tropical clay which “holds on to moisture like clay but drains like sand”; Tomasella et al, ). Such shallow and freely drained model soil columns have been shown to hold insufficient storage for continued ET in the dry season in the Amazon and other regions of the world with a strongly seasonal climate (e.g., Baker et al, ; Brunke et al, ; Fan et al, ; Kleidon & Heimann, ; Kuppel et al, ; Miguez‐Macho & Fan, ; Milly & Shmakin, ; Nepstad et al, ), prompting several inverse‐modeling studies to estimate the “effective” root zone depth necessary to support satellite‐observed leaf area, based on seasonal precipitation and atmospheric ET demand (e.g., Fan et al, ; Kleidon & Heimann, ; Wang‐Erlandsson et al, ; Yang et al, ). These inverse estimates place integrated (atmosphere, vegetation, and soil) plant water constraints on the necessary model soil depth, but the persisting assumption that the land drains freely requires unrealistically deep soil column where the water table is within the 2‐ to 3‐m soil column and plant rooting depth is restricted by the shallow water table (Figure ), such as in wetlands and river valleys.…”

Direct observations of rock moisture, a hidden component of the hydrologic cycle

“…Hydrologic models, including those used in LSMs, could alternatively model rock moisture storage as deep soil moisture in variably thick soils (48). There is no relationship, however, between soil thickness and the thickness of weathered bedrock hosting rock moisture, and the two material types likely require different model treatment.…”

Soil Moisture Memory and Its Effect on the Surface Water and Heat Fluxes on Seasonal and Interannual Time Scales

“…So, in this paper, the Community Land Model 4.5 (CLM4.5) is used to study the SMM. The CLM4.5 has been extensively evaluated, and it has the good performance in simulating water and heat flux, soil temperature, soil moisture, and so forth (Brunke et al, 2016;Di et al, 2015;Guo et al, 2018;Jia et al, 2015;Lawrence et al, 2011;Li et al, 2018;Liu & Mishra, 2017;Song et al, 2014;Wang et al, 2016;Xie et al, 2017).…”