Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping

Authors: Owens, Phillip; MANCINI, MARCELO - UNIVERSITY OF ARKANSAS; Winzeler, Hans - Edwin; Read, Quentin; SUN, NING - U.S. DEPARTMENT OF ENERGY; BLACKSTOCK, JOSHUA; Libohova, Zamir

Submitted to: Geoderma
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/8/2024
Publication Date: 5/8/2024
Citation: Owens, P.R., Mancini, M., Winzeler, H.E., Read, Q.D., Sun, N., Blackstock, J.M., Libohova, Z. 2024. Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping. Geoderma. https://doi.org/10.1016/j.geoderma.2024.116911.
DOI: https://doi.org/10.1016/j.geoderma.2024.116911

Interpretive Summary: Detailed high resolution maps of soils and properties are needed for precision agriculture. Current soil maps are static and are derived from topographic characteristics combined with field observations and laboratory analysis of soil properties. The more advanced methods for mapping soils like Digital Soil Mapping (DSM) platforms uses also topographic characteristics to produce high resolution soil maps but that are still static. Soils and properties are influenced by water movement, which is dynamic, but both conventional and DSM mapping uses terrain characteristics to characterize water movement and how it relates to soil mapping. We applied a distributed hydrological soil vegetation model to characterize soil water dynamics (soil moisture, and water table depth) and used them to map functional soil map units not only for the surface but for subsurface soils and over time. This approach creates dynamic functional soil map units that will help farms especially with limited resources to sustainable management of natural resources while saving cost and the environment.

Technical Abstract: Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed under pasture containing two USDA NRCS Soil Survey mapping units; however, only one soil was used to evaluate the effects of topography on water redistribution. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped within SSURGO map units. If a credible conceptual connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrological models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.