EGU2020-21957, updated on 12 Jun 2020
https://doi.org/10.5194/egusphere-egu2020-21957
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Root architecture and hydrologic fluctuations explain spatiotemporal soil aggregation patterns

Teamrat Ghezzehei1 and Dani Or2,3
Teamrat Ghezzehei and Dani Or
  • 1University of California, Merced, School of Natural Sciences, Life and Environmental Sciences Department, Merced, United States of America.
  • 2Department of Environmental Science, Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Zurich, Switzerland.
  • 3Desert Research Institute, Reno, Nevada, USA.

Soil aggregation is a dynamic state involving numerous biophysical interactions that cannot be deduced from snapshots of soil aggregate sizes nor the state of bulk soil organic carbon (SOC) alone. Hydrophysical and  biogeochemical functions of soil aggregation are directly linked with dynamic nature of soil aggregation. At the local scale, aggregates are formed and around particulate organic debris and they evolve as undifferentiated biogeochemical hotspots. The rate of evolution varies with the life-stage of each hotspot (the remaining reserve of C and nutrients within the hotspot) as well as the physical environmental conditions (wetness and temperature). Thus, the macroscopic patterns of hotspot (aggregate) distributions reflect the interplay between the spatial/temporal patterns of C inputs and fluctuations of physical environmental conditions. Here, we show a modeling analysis of how these aggregation patterns vary across ranges of climatic and vegetation (root architecture) conditions. We utilize a model that considers the dynamic lifecycle of ensembles of multigenerational aggregates originating from polydisperse C inputs.

How to cite: Ghezzehei, T. and Or, D.: Root architecture and hydrologic fluctuations explain spatiotemporal soil aggregation patterns, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21957, https://doi.org/10.5194/egusphere-egu2020-21957, 2020

Comments on the presentation

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Presentation version 2 – uploaded on 05 May 2020
  • CC1: Comment on EGU2020-21957, Paul Hallett, 05 May 2020

    Very nice work Teamrat.  How are you accounting for EPS impacts in the model and is there is a consideration of POM vs. macrostructure location when modelling mineralisation rates?  Thanks.  Paul Hallett

    • AC1: Reply to CC1, Teamrat Ghezzehei, 05 May 2020

      Thank you, Paul. EPS is considered as a glue that binds particles together. The strength of the bond depends on the local concentration. We define aggregates as regions where the strength of the bonds exceeds the disruptive force of flowing water (wet sieving). 

    • AC2: Reply to CC1, Teamrat Ghezzehei, 05 May 2020

      The reverse feedback of the macrostructure on POM mineralization is not implemented here. The life-cycle of individual hotspots is simulated without considering the existence of neighbors. Water content and aeration can vary arbitrarily as needed.

  • AC3: Comment on EGU2020-21957, Dani Or, 05 May 2020

    can you comment on how crop rotation with different root architectures (and rhizobiome) might work here...

    • AC4: Reply to AC3, Teamrat Ghezzehei, 05 May 2020

      Year to year change in root architecture is straightforward to implement. This requires to define the statistical distribution of each cohort of POMS at the start of their life-cycle. The change in rhizobiome slightly more complicated. If the nature of the rhizobiome is known a priori, it is possible to implement that by making the rate constants (mineralization rate, respiration rate, EPS production rate etc) time-dependent. But realistically, these changes should not be prescribed. The rhizobiome should evolve as the nature of the soil structure changes.

  • CC2: Comment on EGU2020-21957, Horst H. Gerke, 05 May 2020

    Dear Teamrat and Dani,

    very nice approach especially for surface-near soil structures in grassland soils! Besides POM how much influence would you think have the wet-dry cycles?

    • AC6: Reply to CC2, Teamrat Ghezzehei, 05 May 2020

      Thank you!

      That is a good question. We do not account for the effect of wet-dry cycles on aggregation per se. We consider the influences of wet/dry cycles on biological rates and diffusion rates. More than that it should play a direct role, for example by mobilizing and depositing EPS or any other soluble cementing. agents to contact regions. We have looked at that a few years ago with Ammara Alabalsmeh () but have not considered such effects yet.

      • AC7: Reply to AC6, Dani Or, 05 May 2020

        However, the model considers the entire range of water contents on hot spot dynamics  - but not in this spatially explicit application that is already quick complex... perhpas in the future.

  • AC5: Comment on EGU2020-21957, Teamrat Ghezzehei, 05 May 2020

    Thank you!

    That is a good question. We do not account for the effect of wet-dry cycles on aggregation per se. We consider the influences of wet/dry cycles on biological rates and diffusion rates. More than that it should play a direct role, for example by mobilizing and depositing EPS or any other soluble cementing. agents to contact regions. We have looked at that a few years ago with Ammara Alabalsmeh () but have not considered such effects yet.

     

  • CC3: Comment on EGU2020-21957, Amandine Erktan, 05 May 2020

    Dear Teamrat and Dani. Thank you very much for this very nice work. Does your model may as well inform us about the changes in pore space characteristics and how the pore space is affected by root architecture and hydrologic fluctuations?

    • AC8: Reply to CC3, Dani Or, 05 May 2020

      Hi Amandine,

      The water retention properties are updated with EPS production and hyphae - but not details of particle displacement... In other words, some effects are considered and also the spatial extent of the biological influence around a POM core, but not explicitly pore scale alterations...

  • CC4: Comment on EGU2020-21957, Aaron Thompson, 05 May 2020

    Very nice work, I am excited to see the paper when it comes out. Can you clarify Example 5 for me. It looks like you are saying that the model predicts more total aggregates when you start with smaller POM initial aggregates. Is this correct?

    • AC9: Reply to CC4, Teamrat Ghezzehei, 05 May 2020

      Hi Aaron,

      The macro-aggregates (aggregates of aggregates) are larger when the POMs are finer. But, whether the aggregates are stable (survive sieving) we cannot say. For the individual hotspots around individual, we can estimate the size of the stable aggregates, because the geometry is well defined and allows force balance calculation. 

Presentation version 1 – uploaded on 05 May 2020 , no comments