Inclusion of bedrock vadose zone in dynamic global vegetation models is key for simulating vegetation structure and functioning

Abstract

Abstract. Across many upland environments, soils are thin and plant roots extend into fractured and weathered bedrock where moisture and nutrients can be obtained. Root water extraction from unsaturated weathered bedrock is widespread and, in many environments, can explain gradients in vegetation community composition, transpiration, and plant sensitivity to climate. Despite increasing recognition of its importance, the "rock moisture" reservoir is rarely incorporated into vegetation and Earth system models. Here, we address this weakness in a widely used dynamic global vegetation model (DGVM, LPJ-GUESS). First, we use a water flux-tracking deficit approach to more accurately parameterize plant-accessible water storage capacity across the contiguous United States, which critically includes the water in bedrock below depths typically prescribed by soils databases. Secondly, we exploit field-based knowledge of contrasting plant-available water storage capacity in weathered bedrock across two bedrock types in the Northern California Coast Ranges as a detailed case-study. For the case study in Northern California, climate and soil water storage capacity are similar at the two study areas, but the site with thick weathered bedrock and ample rock moisture supports a mixed evergreen temperate broadleaf-needleleaf forest whereas the site with thin weathered bedrock and limited rock moisture supports an oak savanna. The distinct biomes, seasonality and magnitude of transpiration and primary productivity, and baseflow magnitudes only emerge from the DGVM when a new and simple subsurface storage structure and hydrology scheme is parameterized with storage capacities extending beyond the soil into the bedrock. Across the contiguous United States, the updated hydrology and subsurface storage improve annual evapotranspiration estimates as compared to satellite-derived products, particularly in seasonally dry regions. Specifically, the updated hydrology and subsurface storage allow for enhanced evapotranspiration through the dry season that better matches actual evapotranspiration patterns. While we made changes to both the subsurface water storage capacity and the hydrology, the most important impacts on model performance derive from changes to the subsurface water storage capacity. Our findings highlight the importance of rock moisture in explaining and predicting vegetation structure and function, particularly in seasonally dry climates. These findings motivate efforts to better incorporate the rock moisture reservoir into vegetation, climate, and landscape evolution models.

Root zone in the Earth system

“…Catchment and global scale studies have revealed that ecosystems tune to a drought period with 10-40-year return periods depending on the type of ecosystem (evergreen, deciduous, grassland, etc.). Successful applications have been made by (Lapides et al, 2023).…”

“…Comparing the reductionist with the holistic approach, it is interesting that although the holistic method is much simpler, its performance is even better than the most complex reductionist approach in runoff modeling (Mao and Liu, 2019;Wang et al, 2021), especially in ungauged basins (Hrachowitz et al, 2013), and evaporation flux simulations by land surface models (van Oorschot et al, 2021). The holistic method has also been used in the widely used dynamic vegetation model, i.e., LPJ-GUESS, by the inclusion of bedrock vadose zone, to improve its representation of storage and hydrology (Lapides et al, 2023). Considering plant's water use from bedrock improved evaporation estimation, especially in seasonally dry regions.…”

Geologic Controls on Apparent Root‐Zone Storage Capacity

“…Nevertheless, storage capacity calculated via deficit‐style approaches has many theoretical and pragmatic advantages. S R results in improved hydrological model performance when used as an input parameter (Lapides et al, 2023; Wang‐Erlandsson et al., 2016) and can explain continental‐scale patterns in water partitioning (Cheng et al., 2022) and storage dynamics (Trautmann et al., 2022); deficit calculations have also proven essential in the accurate prediction of snowmelt contributions to streamflow following droughts (Lapides et al., 2022). Importantly, deficit‐calculated S R does not require a priori assumptions regarding porosity or rooting profiles, and distributed hydrologic flux datasets make it feasible to estimate S R at large spatial scales in cloud‐based analysis platforms like Google Earth Engine.…”