Predicting micro-catchment infiltration dynamics

Authors: FOUNDS, MICHAEL - Desert Research Institute; MCGWIRE, KENNETH - Desert Research Institute; Weltz, Mark; Nouwakpo, Sayjro; VERBURG, PAUL - University Of Nevada

Submitted to: Catena
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/14/2020
Publication Date: 3/1/2020
Citation: Founds, M., McGwire, K., Weltz, M.A., Verburg, P.S. 2020. Predicting micro-catchment infiltration dynamics. Catena. 190. https://doi.org/10.1016/j.catena.
DOI: https://doi.org/10.1016/j.catena.2020.104524

Interpretive Summary: Rainfall and concentrated flow experiments were carried out on seven micro-catchments that were designed to limit soil erosion and allow for water-harvesting using the Vallerani plow. Prediction of infiltration rates within micro-catchments is necessary to design effective hillslopescale restoration projects. Continuous stage measurements and 3-D models of micro-catchments geometry were used to calculate infiltration rates from field experiments. Soil samples and Guelph permeameter measurements were collected to parameterize a predictive infiltration model. A 2-D simplified cross-section was developed to represent the micro-catchments in Hydrus 2D/3D. The antecedent rainfall, evaporation, and time-varying ponded water levels specific to each MC were recreated in Hydrus models over a 400-minute simulation. The model prediction of water velocity across the catchment boundary was averaged by depth intervals and multiplied by the surface area of each interval to calculate a volumetric flow rate. The soil at the field site had a highly variable hydraulic conductivity, both vertically within the soil profile and between sites. To account for this variability, four models of changes in conductivity with depth were evaluated. Volumetric flow rates calculated from field data were compared to model predictions while the micro-catchmentswas nearly full of water. Use of the maximum field-measured conductivity provided the least biased results, with average error between simulated and measured values across all sites of less than 1%. Model results illustrate the limitation of both particle size distribution and Guelph permeameter measurements used to predict infiltration rates in a numerical model without accounting for 1) preferential flow pathways within the soil that are not considered in the uniform flow model, 2) the small size of Guelph permeameter measurements relative to the micro-catchments, and 3) use of a 2-D model to represent 3-D flow. This modeling approach allows for further testing of hypotheses on rangeland infiltration dynamics, and development of optimal configurations of micro-catchments at sites being considered for rangeland restoration.

Technical Abstract: Rainfall and concentrated flow experiments were carried out on seven micro-catchments (MCs) that were designed to limit soil erosion and allow for water-harvesting. Prediction of infiltration rates within MCs is necessary to design effective hillslope-scale restoration projects. Continuous stage measurements and 3-D models of MC geometry were used to calculate infiltration rates from field experiments. Soil samples and Guelph permeameter (GP) measurements were collected to parameterize a predictive infiltration model in Hydrus 2D/3D. The model result of water velocity into the soil profile was averaged by depth intervals and multiplied by the corresponding MC surface area to calculate a volumetric flow rate. Four parameterizations of changes in conductivity with depth were evaluated within the model framework to determine which would best account for spatial heterogeneity. Use of the maximum field-measured conductivity provided the least biased results, with average error between simulated and measured values across all sites of less than 1%. Model results illustrate the limitations associated with particle-size distribution or GP measurements when used to predict infiltration rates in a numerical model. GP measurements with single ponded heights allowed convenient field measurement of conductivity that worked better than predictions from soil texture. The maximum of several GP samples was more representative of MC infiltration than the mean, so a higher percentile value from a distribution of MC measurements may help to account for complex infiltration processes that are not included in numerical models. This modeling approach will allow testing of process-based hypotheses about rangeland infiltration dynamics, and the development of optimal configurations of MCs at sites being considered for rangeland restoration.