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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Water Management and Conservation Research » Research » Publications at this Location » Publication #314490

Title: Hydrologic-process-based soil texture classifications for improved visualization of landscape function

item GROENENDYK, D - University Of Arizona
item FERRE, T.P - University Of Arizona
item Thorp, Kelly
item RICE, A - Colorado School Of Mines

Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2015
Publication Date: 6/29/2015
Citation: Groenendyk, D.G., Ferre, T.A., Thorp, K.R., Rice, A.K. 2015. Hydrologic-process-based soil texture classifications for improved visualization of landscape function. PLoS One. 10(6):1-17. doi: 10.1371/journal.pone.0131299.

Interpretive Summary: The USDA Soil Texture Triangle was developed in the 1930's by the Soil Conservation Service. Soils were classified using measurements of soil texture, determined by assessing the fraction of three soil particles with different sizes: sand, silt, and clay. However, with regard to the hydraulic and hydrologic function of soils, this classification approach is somewhat arbitrary. In the present study, the HYDRUS-1D simulation model, which analyzes water flow through the soil profile, was used to assess hydraulic and hydrologic properties of each soil texture on the USDA Soil Texture Triangle. Soils were then reclassified based on their hydrologic function rather than their soil texture. As compared to the original USDA Soil Texture Triangle, different patterns emerged when soils were classified using the new methodology, indicating that soil texture is a poor indicator of hydrologic response. The methodology was also used to demonstrate how soil texture maps could be re-envisioned based on hydrologic response instead of soil texture. The USDA Soil Texture Triangle is widely used for many applications, including irrigation management, flood control, construction, and trafficability. This work provides a new perspective for people in these industries to consider how soils can be classified based on their hydrologic function.

Technical Abstract: Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing bias and uncertainty in areas from slope stability to climate change. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Using a k-means clustering algorithm we developed an approach for soil classification based on hydrologic responses of soils to common meteorological conditions. Hydrologic simulations were performed using HYDRUS1D for a wide range of soils, spanning textures identified by the United States Department of Agriculture soil texture triangle. We describe one possible basis for classification: changes in water content near the ground surface during periods with and without precipitation. We compare these process-based classifications to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture, demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps using any clustering approach with georeferenced soil data from the Natural Resource Conservation Service Web Soil Survey. The spatial patterns of hydrologic response were more immediately informative and are much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function suggest that hydrologic-process-based classifications should be incorporated into environmental process models.