Quantification of Soil Random Roughness and Surface Depression Storage: Methods, Applicability, and Limitations

Abstract

Soil surface microtopography controls surface runoff, infiltration, and other hydrologic processes by storing water in depressions, changing flow directions and accumulations, and even altering the entire drainage system. Studies have been conducted to characterize surface microrelief and quantify surface depression storage. Various indices have been proposed to quantify soil surface roughness, and some indices have been further used to estimate maximum depression storage (MDS). Random roughness (RR) has been one of the widely used indices for estimating MDS over the past four decades. However, there have been some confusions and controversies on the data processing procedures and computation methods associated with the RR index-based approach. The focus of this study is on evaluating the performance and applicability of the RR index-based approach that incorporates four different data processing procedures for a series of microtopographic surfaces. It is demonstrated that selecting proper data processing procedures is critical and helpful to improve the estimation of RR and MDS. Particularly, removal of oriented roughness (slope and tillage marks) is a necessary data processing procedure. For a real soil surface, the relationship of MDS and slope is nonlinear and the changing pattern of MDS with RR varies, depending on the surface microtopographic conditions. In addition, the mean upslope depression (MUD) method is applied to estimate MDS for tillage surfaces. It has been found that the RR index-based approach is better suited for gentle sloping surfaces, while the MUD index method is able to provide improved estimation of MDS for steeper slopes.