Process-Based Modeling of Infiltration, Soil Loss, and Dissolved Solids on Saline and Sodic Soils

Abstract

Abstract. The Colorado River is a central socio-economic resource of the western U.S. but is vulnerable to excessive salt load. To improve knowledge of the surface processes controlling salt loading, a series of rainfall simulation experiments were conducted in saline rangelands of the upper Colorado River basin (UCRB). In this study, data from these rainfall simulation experiments were used to develop predictive equations for the process-based Rangeland Hydrology and Erosion Model (RHEM). Runoff and soil loss prediction performances were assessed with the Nash-Sutcliffe efficiency (NSE), the coefficient of determination (R2), and the percent bias (PBIAS). Calibration on 36 individual plots, randomly selected to cover each treatment, yielded improved runoff prediction (NSE = 0.73, R2 = 0.74, and PBIAS = 6.93%) compared to the non-calibrated RHEM parameter estimation equation (NSE = 0.65, R2 = 0.68, and PBIAS = 32.03%) when a refined ground cover coefficient was used to estimate the effective hydraulic conductivity (Ke). Soil loss prediction with the calibration data was also improved compared to the non-calibrated parameter estimation equation (NSE = 0.94, R2 = 0.94, and PBIAS = 4.25% vs. NSE = 0.81, R2 = 0.85, and PBIAS = 6.47%) when the soil sodium adsorption ratio (SAR) was included in estimation of the sheet and splash erosion parameter (Kss). Improvements in runoff and soil loss predictions with the calibration data were maintained with an independent set of 36 plots from the original rainfall simulation dataset not used for calibration. Overall, soil sodicity was an important consideration in the performance of the newly developed Kss parameterization equation in this study. Performance on sodic soils (SAR = 15) gained the most from the inclusion of SAR in the Kss estimation. Salt load was linearly related to soil loss (R2 = 0.94), and this linear model performed well in estimating runoff salt load from RHEM-predicted soil loss. These new developments will provide a physically based modeling scheme for land managers for predicting rainfall-driven soil and salt load to surface waters of the UCRB. Keywords: Erosion modeling, Hydrology, Rangeland, Salinity prediction, Soil erosion, Water quality.