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Microbial Moisture Response Index (v3.0c, 3.1g)

SOCSPOT 3.0c and 3.1g's Microbial Moisture Response Index (MMRI) assesses microbial activity in response to soil moisture conditions, leveraging historical climate data to predict the impact on carbon assimilation and microbial response across global rangelands and croplands.

MMRI values reflect how conducive soil moisture conditions are for microbes to assimilate organic matter into stable soil carbon. Because microbial activity is the ‘engine’ for driving soil carbon sequestration, the potential to sequester carbon is influenced by conditions like soil moisture because it enhances or limits the activity of microbial communities. Soils that are too dry cannot support microbial life or activity, while soils that are waterlogged also cannot support aerobic microbial community activity. MMRI accounts for all of this, along with evolutionary strategies of microbes, in in a single value. Lower MMRI values reflect poorer moisture conditions (extremely low or high water content) while higher MMRI values reflect optimal soil moisture conditions for greater potential to sequester carbon.

To access high-resolution data for your area(s) of interest, contact our team.

High-Level Description

Within SOCSPOT 3.0c, and 3.1g, the microbial moisture response index (MMRI) is a gridded data product that captures the relative localized microbial activity in response to contemporary (5-year) soil moisture conditions. Version 3.0c extend across rangelands and croplands within the Continental US (CONUS, 'c'), while 3.1g includes both across a global extent. This product leverages understanding of how microbial communities adapt to historical climate (30-year mean annnual precipitation) to predict how locally-adapted communities respond to current soil moisture contents.

Use Cases

With MMRI, customers are enabled to:

  • Understand where soil moisture conditions are most favourable for enabling microbial activity
  • Gauge where soil moisture may be limiting carbon assimilation relative to soil temperature or pH, thereby enabling them to understand whether particular management strategies to alter soil moisture would be efficacious for increasing SOC sequestration.
  • Estimate where soil moisture values are changing most relative to historical conditions

Technical Specifications

  • Native Resolution: 100-250m
  • Spatial extent: 3.0c = CONUS, 3.1g = Global
  • Domain: Croplands and/or rangelands
  • Units: Dimensionless (0-10)
  • Inputs: 30-year historical mean annual precipitation, 8-daily soil moisture, field capacity
  • Temporal range: 2018-2022

Algorithm Theoretical Basis

MMRI leverages the concepts of microbial community adaptation theory (Maynard et al. 2019; Lustenhouwer et al. 2020; Geisen et al. 2020; Bradford et al., 2021, Evans, Allison, & Hawkes, 2022; Averill et al., 2023), which has shown that microbial communities adapt to their local climate regime (temperature and moisture availability). As such, microbial communities in areas with differing climatic regimes (e.g., Arid vs. Mediterranean) will exhibit distinct responses to the same contemporary weather conditions. MMRI captures these distinctions through the development of microbial response curves that are continuous functions of historical 30-year precipitation averages alongside contemporary soil moisture conditions. In doing so, given the same moisture conditions, MMRI captures the unique response of microbes within historically dry versus historically wet regions.

Model Assumptions & Constraints

A number of assumptions and methodological choices are important to consider when using MMRI.

  • The response index is dimensionless, as it is a measure of the relative microbial activity, not absolute.
  • We assume that 30-year precipitation records are a sufficient proxy for soil moisture conditions, as soil moisture data over such periods are unavailable. This also assumes that microbial communities would be adapted to 30-year conditions and that any nonstationarity within that window is insufficient to impact the response curve significantly.
  • We assume the same confidence as indicated by each input dataset.
  • Constraints on data availability required different long-term (30-year) climate data to be used for 3.0c and 3.1g, each with a unique 30-year time range (PRISM: 1991-2020 & WorldClim: 1960-1990). As such, the response curves will be slightly different for 3.0c and 3.1g.

Known Issues

  • No validation strategy for MMRI currently exists due to limitations on validation data.

What’s New?

Within the frameworks of SOCSPOT 3.0c and 3.1g, updates include:

  • Within SOCSPOT 3.1g, the extent has gone beyond CONUS to include global coverage. With this expansion, Soilgrids v2 replaced SoilGrids+ and WorldClim replaced PRISM.
  • From 3.0c to 3.1g, the version of Soilgrids that is used for input to field capacity changed version and resolution. Further, the long-term precipitation data was updated.
  • Across both versions, rather than using three response curves for ‘dry’, ‘moderate’ and ‘wet’ climate classes, we utilize a continuously set of response curves that change as a function of historical MAP. This improvement better reflects the continuous nature of microbial community adaptations as opposed to stark shifts along the boundaries of what is classified as dry, moderate, and wet.
  • Along with generating a single continuously shifting function for MMRI, the response curve was improved to better reflect the relative magnitude of differences in soil microbial activity response(s) to contemporary soil moisture conditions.

Data Partners, Providers, and References

  • Oregon State University : PRISM Gridded Climate Data. 30-Year Normals (1991–2020).

  • University of California, Davis : Fick, S.E., and R.J. Hijmans. 2017.
    WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas.
    International Journal of Climatology, 37(12), 4302-4315.
    DOI: 10.1002/joc.5086

  • SoilGrids 2.0 : Poggio, L., de Sousa, L.M., Batjes, N.H., et al. 2021.
    SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty.
    SOIL, 7(1), 217–240.
    DOI: 10.5194/soil-7-217-2021

  • NASA National Snow and Ice Data Center : O'Neill, P.E., Chan, S., Njoku, E.G., Jackson, T., & Bindlish, R. 2015.
    SMAP L3 Radiometer Global Daily 8 km EASE-Grid Soil Moisture. Version.
    Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center.
    DOI: 10.5067/3FS28LMYXAKG

Accessing Data

Contact our team to request high-resolution data for your area(s) of interest.