Biological Systems Engineering


Date of this Version

Summer 7-26-2012


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Environmental Engineering, Under the Supervision of Professor Dean E. Eisenhauer Lincoln, Nebraska: August, 2012

Copyright (c) 2012 Michael B. McKinney


Surface depression storage (Ds) is the volume of precipitation excess which is stored by surface microrelief features of soils. The volume of water stored in surface depressions when precipitation rate exceeds infiltration rate reduces the amount of runoff generated. Because Ds is dependent on soil microrelief, land slope, and crop residue, tillage and management practices can have a considerable impact on the magnitude of this value.

When modeling irrigation systems and surface hydrology, depression storage is often treated as a static abstraction, meaning that maximum storage volume must be filled before runoff occurs. However, several researchers have documented that runoff begins before depressions fill maximally. To investigate this process, Plaster of Paris casts of 12 soil surfaces were collected from plots that are part of a tillage study which includes plow, disk, and no-till treatments. The plaster surfaces were subjected to 100 mm/hr of simulated rain on a bed with adjustable slope. Dynamic filling of surface depressions was analyzed by measuring depression storage at points before maximum depression storage was achieved. An empirical relationship relating depressional storage to potential precipitation excess is proposed, and suggests that the dynamic nature of depressional storage may be predicted if the maximum depressional storage and excess precipitation hyetograph are known.

The effect of dynamic depression filling on runoff generation from a hillslope was simulated in the HEC-HMS hydrologic model. This was accomplished by manipulating Green and Ampt infiltration parameters to generate precipitation excess predicted by the empirical dynamic depression storage equation. The results show that dynamic depression filling leads to significant changes in both the rate and timing of runoff generation compared to the static depression storage filling assumption.

Advisor: Dean E. Eisenhauer