A Processes‐Based Dynamic Root Growth Model Integrated Into the Ecosystem Model

Abstract

Plant roots play a critical role in regulating the uptake of soil water and nutrients. Shifts in root growth and biomass distribution resulting from climate change can impact ecosystem water and carbon cycling. Such interactions between root growth and ecosystem functioning have not been integrated into current terrestrial ecosystem models. Here a three-dimensional dynamic root model (DyRoot) was developed and implemented into the Integrated Biosphere Simulator (IBIS) ecosystem model for simulating root growth, architecture (i.e., fine and coarse roots), and distribution driven by soil water and nitrogen availabilities. Field root biomass observations were used for model calibration and validation. Results showed that DyRoot was able to simulate the root biomass distribution across five forest ecosystem types with good performance (R 2 = 0.79, P < 0.01). The validations of root distribution in three forest chronosequences suggested that DyRoot captured the changes in rooting depth with the increase in stand age. The sensitivity of simulated root distribution to soil water availability was compared against root biomass observations from two precipitation reduction experiments. Results indicated that DyRoot reasonably represented the decrease of root biomass in topsoil layers in response to the deficits of soil moisture simulated by (IBIS). The DyRoot model algorithm in this study has great implications for representing an optimized root distribution in ecosystem models, which can improve model performance by simulating the feedbacks of root evolution in response to climate change. Furthermore, allowing explicit root architecture in the DyRoot model also sheds light on depicting the responses of root traits to environmental changes.

Root Foraging Alters Global Patterns of Ecosystem Legacy From Climate Perturbations

“…Future work would benefit from the inclusion of more holistic root dynamic including dynamic morphology, more complexity to the turnover time of root carbon pools (Matamala et al., 2003), dynamic allocation schemes between aboveground and belowground pools Lu et al. (2019) as well as the addition of deeper root pools (Fan et al., 2017) and hydraulic redistribution. As the representation of root processes becomes more complex in ESMs and a wider observational network becomes available, testing the effects of these dynamics on ecosystem recovery will enable field‐testable hypotheses about the role of belowground root structure as a source of ecosystem legacy on land surface‐atmosphere coupling.…”

“…So while the analysis did not yield a clear prescriptive rule for when or where to include dynamic roots in ESMs, the results suggest that studies focusing on recovery from disturbance or effects of extreme events should utilize dynamic roots to generate a spectrum of recovery timescales particular in the context of the compounding effects of increasingly frequent stress events (Szejner, 2020). Future work would benefit from the inclusion of more holistic root dynamic including dynamic morphology, more complexity to the turnover time of root carbon pools (Matamala et al, 2003), dynamic allocation schemes between aboveground and belowground pools Lu et al (2019) as well as the addition of deeper root pools (Fan et al, 2017) and hydraulic redistribution. As the representation of root processes becomes more complex in ESMs and a wider observational network becomes available, testing the effects of these dynamics on ecosystem recovery will enable field-testable hypotheses about the role of belowground root structure as a source of ecosystem legacy on land surface-atmosphere coupling.…”

The Primacy of Temporal Dynamics in Driving Spatial Self-organization of Soil Redox Patterns

“…H BO2 is half-saturation constant to regulate the effect of O 2L on root respiration (mol m -3 ). Root growth could be influenced by an array of factors, such as soil water content, nutrients and oxygen availability, and mechanical impedance (81, 82). We describe root growth G B (g m -3 d -1 ) as follows: where k 0 (d -1 ) is the root growth rate constant, and k g (d -1 ) is that part of root growth affected by oxygen.…”

Development of a Process‐Based N2O Emission Model for Natural Forest and Grassland Ecosystems

“…IBIS is a comprehensive model that simulates biophysical, physiological, biogeochemical and ecological processes in the terrestrial biosphere (Foley et al., 1996; Kucharik et al., 2000). Several previous studies have examined the performance of IBIS for representing carbon cycle processes (Liu et al., 2014; Lu et al., 2019; Song et al., 2020; Yuan et al., 2014). Therefore, only N model (MicN) over forest and grassland ecosystems is introduced in this study.…”