Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function

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 to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared 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 combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-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 and can be used to define application-specific maps of hydrologic function.

Analysis of the suitability of Polish soil texture classification for estimating soil water retention and hydraulic properties

“…The highest mean values of K s were obtained for the soils with pl, ps, pg and pyz (symbols are explained in the caption of Figure 1) texture but the highest values of the coefficient of variation (CV) were obtained for gz, gpi, pyi and pyg soil textures (from 36 to 50%). Similar results have been obtained by Groenendyk et al (2015) using the USDA soil textural triangle. As far as θ s , is concerned, the soil textural classes pyz, ipy and gpyi showed the highest mean values (0.453, 0.436 and 0.435 cm 3 •cm -3 , respectively), while the pl, gp and ps textural classes showed the lowest (0.366, 0.367 and 0.370 cm 3 •cm -3 , respectively).…”

“…Bormann (2007Bormann ( , 2008Bormann ( , 2010 has shown that some soil textural classes from the German soil texture (Ad-Hoc- AG Boden 2005) classification show a large variation in simulated soil water balances, and that this variability significantly differs between different soil texture classes. Similar results have been obtained by Groenendyk et al (2015) using the USDA (Soil Survey Division Staff 1993) soil texture classification, hence texture classification has not been primarily designed for hydrological mapping purposes (Groenendyk et al 2015).…”

Classification of Hydrological Relevant Parameters by Soil Hydraulic Behaviour

“…For these reasons, we considered the complete time series of water flux in order to use as much information as possible for the clustering. One of the patterns that was elaborated by [13] has similarity to the results of the study at hand (variant of "infiltration & drainage"). Since this variant perhaps holds the highest content of information and accounts both for behaviour of infiltration and for drainage (as storage difference), it is perhaps the most exhaustive variant.…”

“…The results of the study at hand were expected to be different to the results of [13], because there are many possible ways to simulate/observe one and the same amount of change in water storage. Two soils with different hydraulic behaviour could possibly come to the same total change in water storage-but on different pathways.…”

Automated rainfall simulator for variable rainfall on urban green areas

“…Such areas often cover more than half of the surface area in urban settings and can therefore contribute significantly to the total runoff reaching urban drainage systems. However, the amount of runoff produced largely depends on local physical properties, such as soil type, soil saturation, vegetation cover, soil compaction, and morphological properties (Gregory, ; Groenendyk, Ferré, Thorp, & Rice, ; Quinton, Edwards, & Morgan, ; Sharma, ). These factors result in a large spatial variation in runoff generation.…”