Promise and pitfalls of modeling grassland soil moisture in a free-air CO2 enrichment experiment (BioCON) using the SHAW model

Abstract

Free-air carbon dioxide (CO2) enrichment (FACE) experiments provide an opportunity to test models of heat and water flow under novel, controlled situations and eventually allow use of these models for hypothesis evaluation. This study assesses whether the United States Department of Agriculture SHAW (Simultaneous Heat and Water) numerical model of vertical one-dimensional soil water flow across the soil-plant-atmosphere continuum is able to adequately represent and explain the effects of increasing atmospheric CO2 on soil moisture dynamics in temperate grasslands. Observations in a FACE experiment, the BioCON (Biodiversity, CO2, and Nitrogen) experiment, in Minnesota, USA, were compared with results of vertical soil moisture distribution. Three scenarios represented by different plots were assessed: bare, vegetated with ambient CO2, and similarly vegetated with high CO2. From the simulations, the bare plot soil was generally the wettest, followed by a drier high-CO2 vegetated plot, and the ambient CO2 plot was the driest. The SHAW simulations adequately reproduced the expected behavior and showed that vegetation and atmospheric CO2 concentration significantly affected soil moisture dynamics. The differences in modeled soil moisture amongst the plots were largely due to transpiration, which was low with high CO2. However, the modeled soil moisture only modestly reproduced the observations. Thus, while SHAW is able to replicate and help broadly explain soil moisture dynamics in a FACE experiment, its application for point- and time-specific simulations of soil moisture needs further scrutiny. The typical design of a FACE experiment makes the experimental observations challenging to model with a one-dimensional distributed model. In addition, FACE instrumentation and monitoring will need improvement in order to be a useful platform for robust model testing. Only after this can we recommend that models such as SHAW are adequate for process interpretation of datasets from FACE experiments or for hypothesis testing.