New methods for studying the soil-plant-atmosphere continuum with stable isotope data

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Stable isotopes are essential tools for tracing water and nutrient fluxes in terrestrial ecosystems.  In recent years, studies of the soil-plant-atmosphere continuum have yielded impressive volumes of stable isotope tracer data at previously unattainable precision and spatiotemporal resolution.  These emerging data sets facilitate new methods of analysis that promise new insights into transport, storage and mixing.  For decades, end-member mixing analysis (EMMA) has been the standard workhorse for interpreting tracer data, but new methods can overcome some of its limitations and facilitate new inferences into ecosystem processes.    At the catchment scale, for example, end-member mixing has been widely used to quantify streamflow as a mixture of isotopically distinct sources, but knowing where streamwater comes from is not the same as knowing where precipitation goes, for which one needs end-member splitting (Kirchner and Allen, 2020) instead.  End-member splitting allows summer and winter precipitation to be partitioned between evapotranspiration, summer streamflow, and winter streamflow, without direct measurements of evapotranspiration water fluxes or their isotopic composition.    At the hillslope and plot scale, end-member mixing has been widely used to quantify the relative contributions of isotopically distinct sources to soils and xylem waters.  If any end-members are missing or their tracer distributions overlap, however, conventional mixing models become unusable.  These constraints can be overcome by exploiting the information contained in tracer time-series using ensemble end-member mixing analysis (EEMMA; Kirchner, 2023).  EEMMA can potentially quantify many sources using a single tracer, even if their mean concentrations are indistinguishable. EEMMA can also quantify source contributions when some sources are unknown, and even infer the tracer time series of a missing source.    These new methods will be demonstrated using benchmark data, and proof-of-concept applications will be presented.