Sabine Grunwald, Ph.D. • Associate Professor of Soil and Water Science
Institute of Food and Agricultural Sciences

Sabine Grunwald’s current research is focused on carbon dynamics and modeling of terrestrial carbon at various spatial scales. The overall goal is to assess the effects of land use and climate change on soil carbon stocks, giving special attention to translating site-specific carbon pools (labile, recalcitrant and total carbon) to large landscape scales. Historic, current, and future carbon stocks are predicted using a combination of soil and remote sensors, field sampling, geostatistical, and geospatial upscaling methods.

Quantifying carbon sources/sinks and ecosystem processes that modulate the global carbon cycles are critical to identify imbalances and counteract global climate change. Soil organic carbon (SOC) patterns are dynamic in space and dependent on a multi-factorial and multi-scale system of environmental and anthropogenic drivers. Grunwald’s research is focused to identify both the critical ("tipping") points at which SOC predictions shift from linear to non-linear process behavior and the underlying causative soil and environmental factors, their variability, and spatial distribution patterns that are causing such behavior to occur. This multi-scale behavior of soil carbon across various expanding and contracting spatial scales is investigated to analyze if models are scale variant or invariant (i.e., show self-similar/fractal behavior) and identify the key environmental drivers (natural and anthropogenic factors) that modulate soil carbon patterns.

In Grunwald’s research of the Santa Fe River Watershed (SFRW), spatially-explicit relationships between soil carbon and labile, recalcitrant, and mineralizable carbon, nitrogen (N) and fractions, phosphorus (P) and fractions, and numerous environmental landscape properties (e.g. land use, spectral indices, topography, climate, and hydrology) were modeled to better understand interactions between C, N and P biogeochemical cycles and ecosystem processes. A remote sensing based land use change trajectory analysis coupled with a carbon-landscape model assessed the impact on carbon storage across the watershed.

Grunwald’s research team assessed spatial patterns of various biophysical soil properties including P, N, and metals in various aquatic (e.g. Greater Everglades) and mixed land use (e.g. SFRW) systems impacted by multiple stressors causing soil and water quality degradation. Pedometric methods (modern regression such as regression trees, geostatistics, mixed deterministic/stochastic models), geographic information systems (GIS), and remote sensing, were used to model spatio-temporal patterns. In addition, her team developed and validated a mechanistic simulation model (OntoSim) to simulate water flux and P transport using an ontology-based modeling approach.