Characterization of Surface Roughness and Computation of Depression Storage

Surface runoff and infiltration are two essential hydrologic processes. Microtopography is one of the key factors affecting these processes. Thus, quantification of surface microtopography is critical to hydrologic modeling. In this study, high-resolution digital elevation models (DEMs) were acquired by using an instantaneous-profile laser scanner. In addition, some hypothetical surfaces were created. Six soil surfaces with different roughness were selected and three data processing methods were used in the computation of random roughness (RR) index for characterizing surface microtopography. Varying sloping surfaces were created by rotating the original ones and a coordinate conversion program was used to generate new corresponding DEM data. Maximum depression storage (MDS) was estimated by using two RR index-based methods. Particularly, a puddle delineation (PD) program was also applied to precisely compute the MDS for each surface and the results were compared with those from the RR index-based methods. Finally, the performance and applicability of the three RR index-based methods were evaluated and some improvements were suggested.